Clinical Technologies: Concepts, Methodologies, Tools and Applications (3 Volume Set)

Clinical Technologies:

Concepts, Methodologies, Tools and Applications Information Resources Management Association USA

Volume I

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Kristin Klinger Julia Mosemann Lindsay Johnston Erika Carter Chris Wozniak Jamie Snavely Devvin Earnest Deanna Zombro Michael Brehm, Keith Glazewski, Natalie Pronio, Jennifer Romanchak, Milan Vracarich, Jr., Deanna Zombro

Nick Newcomer

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Library of Congress Cataloging-in-Publication Data Clinical technologies : concepts, methodologies, tools and applications / Information Resources Management Association, editor. p. cm. Summary: “This multi-volume book delves into the many applications of information technology ranging from digitizing patient records to highperformance computing, to medical imaging and diagnostic technologies, and much more”-- Provided by publisher. Includes bibliographical references and index. ISBN 978-1-60960-561-2 (hardcover) -- ISBN 978-1-60960-562-9 (ebook) 1. Medical technology. I. Information Resources Management Association. R855.3.C55 2011 610--dc22 2011011100 British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book set is original material. The views expressed in this book are those of the authors, but not necessarily of the publisher.

Editor-in-Chief

Mehdi Khosrow-Pour, DBA Editor-in-Chief Contemporary Research in Information Science and Technology, Book Series

Associate Editors Steve Clarke University of Hull, UK Murray E. Jennex San Diego State University, USA Annie Becker Florida Institute of Technology USA Ari-Veikko Anttiroiko University of Tampere, Finland

Editorial Advisory Board Sherif Kamel American University in Cairo, Egypt In Lee Western Illinois University, USA Jerzy Kisielnicki Warsaw University, Poland Keng Siau University of Nebraska-Lincoln, USA Amar Gupta Arizona University, USA Craig van Slyke University of Central Florida, USA John Wang Montclair State University, USA Vishanth Weerakkody Brunel University, UK

Additional Research Collections found in the “Contemporary Research in Information Science and Technology” Book Series Data Mining and Warehousing: Concepts, Methodologies, Tools, and Applications John Wang, Montclair University, USA • 6-volume set • ISBN 978-1-60566-056-1 Electronic Business: Concepts, Methodologies, Tools, and Applications In Lee, Western Illinois University • 4-volume set • ISBN 978-1-59904-943-4 Electronic Commerce: Concepts, Methodologies, Tools, and Applications S. Ann Becker, Florida Institute of Technology, USA • 4-volume set • ISBN 978-1-59904-943-4 Electronic Government: Concepts, Methodologies, Tools, and Applications Ari-Veikko Anttiroiko, University of Tampere, Finland • 6-volume set • ISBN 978-1-59904-947-2 Knowledge Management: Concepts, Methodologies, Tools, and Applications Murray E. Jennex, San Diego State University, USA • 6-volume set • ISBN 978-1-59904-933-5 Information Communication Technologies: Concepts, Methodologies, Tools, and Applications Craig Van Slyke, University of Central Florida, USA • 6-volume set • ISBN 978-1-59904-949-6 Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications Vijayan Sugumaran, Oakland University, USA • 4-volume set • ISBN 978-1-59904-941-0 Information Security and Ethics: Concepts, Methodologies, Tools, and Applications Hamid Nemati, The University of North Carolina at Greensboro, USA • 6-volume set • ISBN 978-1-59904-937-3 Medical Informatics: Concepts, Methodologies, Tools, and Applications Joseph Tan, Wayne State University, USA • 4-volume set • ISBN 978-1-60566-050-9 Mobile Computing: Concepts, Methodologies, Tools, and Applications David Taniar, Monash University, Australia • 6-volume set • ISBN 978-1-60566-054-7 Multimedia Technologies: Concepts, Methodologies, Tools, and Applications Syed Mahbubur Rahman, Minnesota State University, Mankato, USA • 3-volume set • ISBN 978-1-60566-054-7 Virtual Technologies: Concepts, Methodologies, Tools, and Applications Jerzy Kisielnicki, Warsaw University, Poland • 3-volume set • ISBN 978-1-59904-955-7

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List of Contributors

Aarts, Jos \ Erasmus University, Rotterdam, The Netherlands ........................................................... 25 Abeyratne, Udantha R. \ The University of Queensland, Australia ................................................. 295 Abraham, Chon \ College of William and Mary, USA ...................................................................... 108 Adang, Eddy M. M. \ Radboud University Nijmegen Medical Center, The Netherlands . ................... 1 Adler-Milstein, Julia \ Harvard University, USA . .......................................................................... 1075 Aggarwal, Vikram \ Johns Hopkins University, USA ....................................................................... 917 Ahmad, Yousuf J. \ Mercy Health Partners, USA ............................................................................. 132 Aittokallio, Tero \ University of Turku, Finland . .............................................................................. 422 Albert, Asunción \ University Hospital Dr. Peset/University of Valencia, Spain ........................... 1263 Alexander, Suraj M. \ University of Louisville, USA . .................................................................... 1393 Al-Jumaily, Adel A. \ University of Technology, Australia ............................................................... 965 Anderson, James G. \ Purdue University, USA . ............................................................................. 1491 Anderson, Kent \ University of California, Davis, USA ................................................................. 2094 Anselma, Luca \ Università di Torino, Torino, Italy ...................................................................... 1721 Antoniotti, Nina \ Marshfield Clinic Telehealth Network, USA ...................................................... 1843 Apweiler, Rolf \ European Bioinformatics Institute, UK . ............................................................... 1360 Arias, Pablo \ University of A Coruña, Spain . .................................................................................. 853 Armstrong, Alice J. \ The George Washington University, USA . ..................................................... 877 Astray, Loxo Lueiro \ University of Vigo, Spain . ............................................................................. 703 Atanasov, Asen \ Medical University Hospital “St. George”, Bulgaria ........................................... 172 Bali, R. K. \ Coventry University, UK . ............................................................................................ 1592 Barba, Pierluigi \ University of Pisa, Italy . ...................................................................................... 792 Battin, Malcolm \ National Women’s Health, Auckland City Hospital, New Zealand .................... 1215 Baxi, Hassan \ University of California, Davis, USA ...................................................................... 2094 Becker, Shirley Ann \ Florida Institute of Technology, USA .......................................................... 2047 Becker, Clemens \ Robert Bosch Gesellschaft für medizinische Forschung, Germany .................... 801 Beckers, Stefan \ University Hospital Aachen, Germany, . ............................................................... 974 Bergkvist, Sofi \ ACCESS Health Initiative, India .......................................................................... 1438 Bissett, Andy \ Sheffield Hallam University, UK ............................................................................. 1922 Biswas, Rakesh \ Manipal University, Malaysia & People’s College of Medical Sciences, India ....................................................................................................................... 1030, 1996, 2153 Borycki, Elizabeth M. \ University of Victoria, Canada .................................................................. 532 Botsivaly, M. \ Technological Education Institute of Athens, Greece . ............................................ 1674 Bottrighi, Alessio \ Università del Piemonte Orientale , Alessandria, Italy ................................... 1721 Bouchard, Stéphane \ University of Quebec in Outaouais, Canada .............................................. 2073

Bourantas, Christos V. \ Michailideion Cardiology Center, Greece & University of Hull, UK ..................................................................................................................................... 2114 Bowers, Clint \ University of Central Florida, Orlando, USA ........................................................ 2126 Boye, Niels \ Aalborg University, Denmark ..................................................................................... 1105 Bradford, John \ University of Ottawa, Canada . ........................................................................... 2073 Bratan, Tanja \ Brunel University, UK . .......................................................................................... 1047 Brimhall, Bradley B. \ University of New Mexico, USA . ............................................................... 1812 Brodziak, Tadeusz \ P3 Communications GmbH, Germany . ........................................................... 974 Bromage, Adrian \ Coventry University, UK ...................................................................................... 93 Burdick, Anne \ University of Miami Miller School of Medicine, USA .......................................... 1843 Byrd, Terry A. \ Auburn University, USA . ...................................................................................... 1461 Byrne, Elaine \ University of Pretoria, South Africa . ....................................................................... 444 Camon, Evelyn \ European Bioinformatics Institute, UK ............................................................... 1360 Camphausen, Kevin \ National Cancer Institute, USA .................................................................... 885 Cannon-Bowers, Jan \ University of Central Florida, Orlando, USA ........................................... 2126 Capilla, Rafael \ Universidad Rey Juan Carlos, Spain ..................................................................... 633 Carmona-Villada, Hans \ Instituto de Epilepsia y Parkinson del Eje Cafetero – Neurocentro, Colombia .................................................................................................................................... 1191 Cassim, M. \ Ritsumeikan Asia Pacific University, Japan ............................................................... 1770 Castellanos-Domínguez, Germán \ Universidad Nacional de Colombia, Colombia .................... 1191 Chartier, Sylvain \ University of Ottawa, Canada .......................................................................... 2073 Chatterjee, Aniruddha \ Johns Hopkins University, USA ................................................................ 917 Chatzizisis, Yiannis \ Aristotle University of Thessaloniki, Greece .................................................. 359 Chen, Dongqing \ University of Louisville, USA . ........................................................................... 1340 Chen, Hsiao-Hwa \ National Cheng Kung University, Taiwan ......................................................... 210 Cheung, Dickson \ Johns Hopkins Bayview Medical Center, USA ................................................... 917 Cho, Yoonju \ Johns Hopkins University, USA . ................................................................................ 917 Chorbev, Ivan \ Ss. Cyril and Methodius University, Republic of Macedonia ................................. 486 Citrin, Deborah \ National Cancer Institute, USA ............................................................................ 885 Clarke, Malcolm \ Brunel University, UK . ..................................................................................... 1047 Claster, William \ Ritsumeikan Asia Pacific University, Japan ...................................................... 1017 Colomo-Palacios, Ricardo \ Universidad Carlos III de Madrid, Spain ........................................... 995 Couto, Francisco M. \ Universidade de Lisboa, Portugal .............................................................. 1360 Covvey, H. Dominic \ University of Waterloo, Canada .................................................................. 1403 Cudeiro, Javier \ University of A Coruña, Spain .............................................................................. 853 Culhane, AedínC. \ Harvard School of Public Health, USA . ........................................................... 877 Curra, Alberto \ University of A Coruña, Spain ............................................................................... 368 Dadich, Ann \ University of Western Sydney, Australia .................................................................. 1759 Dahl, Yngve \ Telenor Research & Innovation, Norway ................................................................. 1171 Dale, Katherine \ Worcestershire Royal Hospital, UK . .................................................................. 1592 Daniel, Christel \ Université René Descartes, France .................................................................... 1235 Daskalaki, Andriani \ Max Planck Institute for Molecular Genetics, Germany .............................. 866 De Rossi, Danilo \ University of Pisa, Italy ....................................................................................... 792 del Río, Alfonso \ Universidad Rey Juan Carlos, Spain . .................................................................. 633 Delgado-Trejos, Edilson \ Instituto Tecnológico Metropolitano ITM, Colombia ........................... 1191 Dembowski, James \ Texas Tech University, Health Sciences Center USA .................................... 1554 Denzinger, Jörg \ University of Calgary, Canada ........................................................................... 1419

Di Giacomo, Paola \ University of Udine, Italy ................................................................................ 572 Díaz, Gloria \ National University of Colombia, Colombia .............................................................. 325 Dimmer, Emily \ European Bioinformatics Institute, UK ............................................................... 1360 Djebbari, Amira \ National Research Council Canada, Canada ..................................................... 877 Doyle, D. John \ Case Western Reserve University, USA & Cleveland Clinic Foundation, USA . ............................................................................................................................................. 190 Dryden, Gerald W. \ University of Louisville, USA ........................................................................ 1340 Duquenoy, Penny \ Middlesex University, London, UK . ................................................................ 1831 Efthimiadis, Efthimis N. \ University of Washington, USA ............................................................ 1121 Eldabi, Tillal \ Brunel University, UK ............................................................................................. 1738 Ellaway, Rachel \ Northern Ontario School of Medicine, Canada ................................................. 2153 Ergas, Henry \ Concept Economics, Australia . .................................................................................. 25 Espinosa, Nelson \ University of A Coruña, Spain ............................................................................ 853 Exarchos, Themis P. \ University of Ioannina, Greece ..................................................................... 412 Falk, Robert L. \ Jewish Hospital & St. Mary’s Healthcare, USA .................................................. 1340 Farag, Aly A. \ University of Louisville, USA .................................................................................. 1340 Farrell, Maureen \ University of Ballarat, Australia ...................................................................... 1504 Faxvaag, Arild \ Norwegian University of Science and Technology, Norway ................................ 1171 Fedoroff, Paul \ University of Ottawa, Canada .............................................................................. 2073 Feng, Dagan \ BMIT Research Group, The University of Sydney, Australia & Hong Kong Polytechnic University, Hong Kong ............................................................................................. 766 Fernández-Peruchena, Carlos \ University of Seville, Spain . ....................................................... 1284 Fielden, Kay \ UNITEC New Zealand, New Zealand ...................................................................... 1922 Fischermann, Harold \ University Hospital Aachen, Germany ....................................................... 974 Fitzgerald, Janna Anneke \ University of Western Sydney, Australia ............................................ 1759 Fotiadis, Dimitrios I. \ University of Ioannina, Greece, Michaelideion Cardiology Center, Greece & Biomedical Research Institute, Greece ........................................................................ 412 Fotiadis, Dimitrios I. \ University of Ioannina, Greece, Biomedical Research InstituteFORTH, Greece, & Michaelideion Cardiology Center, Greece ........................................ 305, 2114 Frenzel, Nadja \ University Hospital Aachen, Germany ................................................................... 974 Fursse, Joanna \ Brunel University, UK . ........................................................................................ 1047 Ganesh, A. U. Jai \ Sri Sathya Sai Information Technology Center, India . .................................... 1030 Gao, Bo \ Sichuan University, China ............................................................................................... 2191 García-Crespo, Ángel \ Universidad Carlos III de Madrid, Spain . ................................................. 995 Gasmelseid, Tagelsir Mohamed \ King Faisal University, Saudi Arabia ........................................ 250 Ghiassi, M. \ Santa Clara University, USA . ...................................................................................... 263 Ghotbi, Nader \ Ritsumeikan Asia Pacific University, Japan ......................................................... 1017 Giannakeas, Nikolaos \ University of Ioannina, Greece & National and Kapodistrian University of Athens, Greece .............................................................................................. 412, 2029 Giannoglou, George D. \ Aristotle University of Thessaloniki, Greece ............................................ 359 Gibson, Candace J. \ The University of Western Ontario, Canada ...................................... 1403, 1934 Gillies, Alan \ UCLAN, UK ................................................................................................................ 508 Godson, Nina \ Coventry University, UK ............................................................................................ 93 Goletsis, Yorgos \ University of Ioannina, Greece ............................................................................ 412 Gómez-Berbís, Juan M. \ Universidad Carlos III de Madrid, Spain ............................................... 995 González, Rubén Romero \ University of Vigo, Spain ..................................................................... 703 González Moreno, Juan Carlos \ University of Vigo, Spain ............................................................ 703

Gregory, Pete \ Coventry University, UK ........................................................................................ 1592 Gundlapalli, Adi V. \ University of Utah School of Medicine, USA . .............................................. 1470 Halazonetis, Demetrios J. \ National and Kapodistrian University of Athens, Greece . .................. 926 Hammer, Barbara \ Technical University of Clausthal, Germany ................................................... 478 Hammond, Kenric W. \ VA Puget Sound Health Care System, USA . ............................................ 1121 Hannan, Terry \ Australian College of Health Informatics, Australia ............................................... 25 Hartel, Pieter \ University of Twente, The Netherlands .................................................................... 391 Hayes, Richard L. \ University of South Alabama, USA . ............................................................... 1656 Hernández, Jesús Bernardino Alonso \ University of Las Palmas de Gran Canaria, Spain . ...... 1008 Hiner, Julie \ University of Calgary, Canada .................................................................................. 1419 Ho, Kendall \ University of British Columbia, Canada .................................................................... 147 Houliston, Bryan \ Auckland University of Technology, New Zealand . ........................................... 825 Ibraimi, Luan \ University of Twente, The Netherlands ................................................................... 391 Inoue, Yukiko \ University of Guam, Guam .................................................................................... 1530 Janß, A. \ RWTH Aachen University, Aachen, Germany ................................................................... 554 Jasemian, Yousef \ Engineering College of Aarhus, Denmark ......................................................... 717 Jean-Jules, Joachim \ Université de Sherbrooke, Canada ............................................................. 1962 Jiménez, Nicolás Victor \ University Hospital Dr. Peset/University of Valencia, Spain ................ 1263 Johnson, Roy D. \ University of Pretoria, South Africa .................................................................... 444 Joksimoski, Boban \ European University, Republic of Macedonia . ............................................... 486 Jones, Peter \ NHS Community Mental Health Nursing Older Adults, UK ...................................... 451 Jones, Erin \ University Health Network, Canada .............................................................................. 50 Jones, Russell \ Chorleywood Health Centre, UK ........................................................................... 1047 Jonker, Willem \ University of Twente, The Netherlands . ................................................................ 391 Kaan, Terry \ National University of Singapore, Singapore ........................................................... 1853 Kabene, Stefane M. \ University of Western Ontario, Canada & Ecole des Hautes Etudes en Sante Publique (EHESP), France ........................................................................................ 13, 1934 Kannry, Joseph L. \ Mount Sinai Medical Center, USA ................................................................. 1600 Karagiannis, G. E. \ Royal Brompton & Harefield NHS Trust, UK ................................................ 1093 Kastania, Anastasia N. \ Biomedical Research Foundation of the Academy of Athens, Greece & Athens University of Economics and Business, Greece ............................................... 118 Kasthuri, A. S. \ AFMC, India . ....................................................................................................... 1996 Kerstein, Robert B. \ Tufts University School of Dental Medicine, USA ......................................... 895 Khetrapal, Neha \ University of Bielefeld, Germany ........................................................................ 779 Khushaba, Rami N. \ University of Technology, Australia ............................................................... 965 Kim, Jeongeun \ Seoul National University, Korea ........................................................................ 2054 Kirsch, Harald \ European Bioinformatics Institute, UK ............................................................... 1360 Knight, David \ Mater Mother’s Hospital, Brisbane, Australia ...................................................... 1215 Kompatsiaris, Ioannis \ Informatics and Telematics Institue, Centre for Research and Technology Hellas, Greece . ......................................................................................................... 359 Kossida, Sophia \ Biomedical Research Foundation of the Academy of Athens, Greece ................. 118 Koul, Rajinder \ Texas Tech University, Health Sciences Center, USA .......................................... 1554 Koutsourakis, K. \ Technological Education Institute of Athens, Greece . ..................................... 1674 Krupinski, Elizabeth A. \ University of Arizona, USA ................................................................... 1843 Kuruvilla, Abey \ University of Wisconsin Parkside, USA ............................................................. 1393 Kuschel, Carl \ The Royal Women’s Hospital, Melbourne, Australia ............................................. 1215 Kushniruk, Andre W. \ University of Victoria, Canada ................................................................... 532

Kyriacou, Eftyvoulos \ Frederick University, Cyprus ...................................................................... 949 LaBrunda, Michelle \ Cabrini Medical Center, USA . .................................................................... 2183 Lagares-Lemos, Ángel \ Universidad Carlos III de Madrid, Spain ................................................. 995 Lagares-Lemos, Miguel \ Universidad Carlos III de Madrid, Spain ............................................... 995 Lauer, W. \ RWTH Aachen University, Aachen, Germany . ............................................................... 554 Leduc, Raymond W. \ University of Western Ontario, Canada . .................................................... 1934 Lee, Edwin Wen Huo \ Kuala Lumpur, Malaysia & Intel Innovation Center, Malaysia ...... 1030, 1996 Lee, Vivian \ European Bioinformatics Institute, UK ...................................................................... 1360 Lefkowitz, Jerry B. \ Weill Cornell College of Medicine, USA ...................................................... 1812 Li, Guang \ National Cacer Institute, USA ........................................................................................ 885 Liao, Guojie \ Sichuan University, China ........................................................................................ 2191 Ligomenides, Panos A. \ Academy of Athens, Greece . ..................................................................... 314 Linden, Ariel \ Linden Consulting Group & Oregon Health & Science University, USA . ............. 1075 Llobet, Holly \ Cabrini Medical Center, USA . ................................................................................ 2183 Llobet, Paul \ Cabrini Medical Center, USA ................................................................................... 2183 Logeswaran, Rajasvaran \ Multimedia University, Malaysia .......................................................... 744 López, M. Gloria \ University of A Coruña, Spain . .......................................................................... 368 Lum, Martin \ Department of Human Services, Australia . ............................................................ 1759 Macredie, Robert D. \ Brunel University, UK ................................................................................ 1738 Maliapen, Mahendran \ University of Sydney, Australia, National University of Singapore, Singapore and UCLAN, UK ......................................................................................................... 508 Manning, B. R. M. \ University of Westminster, UK ....................................................................... 1093 Manzanera, Antoine \ ENSTA-ParisTech, France ............................................................................ 325 Martin, Carmel M. \ Northern Ontario School of Medicine, Canada & Trinity College Dublin, Ireland ....................................................................................................... 1030, 1996, 2153 Martz, Jr., William Benjamin \ Northern Kentucky University, USA . ............................................ 132 Mato, Virginia \ University of A Coruña, Spain . .............................................................................. 368 May, Arnd T. \ University Hospital Aachen, Germany ..................................................................... 974 McDonald, Claudia L. \ Texas A&M University-Corpus Christi, USA .................................. 620, 2126 Melby, Line \ Norwegian University of Science and Technology, Norway ..................................... 1171 Mezaris, Vasileios \ Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece .............................................................................................................................. 359 Michalis, Lambros K. \ University of Ioannina, Greece & Michaelideion Cardiology Center, Greece ................................................................................................................................ 305, 2114 Miller, Thomas W. \ University of Connecticut, USA ..................................................................... 1566 Miller, Kenneth L. \ Youngstown State University, USA ................................................................. 1637 Miller, Robert W. \ National Cancer Institute, USA ......................................................................... 885 Miller, Susan M. \ Kent State University, USA . .............................................................................. 1637 Molino, Gianpaolo \ AOU San Giovanni Battista, Torino, Italy ..................................................... 1721 Montani, Stefania \ Università del Piemonte Orientale, Alessandria, Italy ................................... 1721 Moumtzoglou, Anastasius \ Hellenic Society for Quality & Safety in Healthcare, European Society for Quality in Healthcare Executive Board Member, Greece ............................................ 73 Mu, Ling \ West China Hospital, China . ......................................................................................... 2191 Mueller, Boris \ Memorial Sloan-Kettering Cancer Center, USA ..................................................... 885 Mukherji, Kamalika \ Hertfordshire Partnership NHS Foundation Trust, UK ............................. 1996 Mychalczak, Borys \ Memorial Sloan-Kettering Cancer Center, USA ............................................. 885 Na, In-Sik \ University Hospital Aachen, Germany .......................................................................... 974

Naka, Katerina K. \ Michailideion Cardiology Center, Greece ..................................................... 2114 Neofytou, Marios \ University of Cyprus, Cyprus . ........................................................................... 949 Noble, Robert \ The Robert Gordon University, Scotland . ............................................................. 1327 Nøhr, Christian \ Aalborg University, Denmark ............................................................................. 1105 Nóvoa, Francisco J. \ University of A Coruña, Spain ....................................................................... 368 Nytrø, Øystein \ Norwegian University of Science and Technology, Norway . ............................... 1171 Oladapo, Olufemi T. \ Olabisi Onabanjo University Teaching Hospital, Nigeria ......................... 1153 Orozco-Gutiérrez, Álvaro \ Universidad Tecnológica de Pereira, Colombia ................................ 1191 Ostermann, Herwig \ University for Health Sciences, Medical Informatics and Technology, Austria ........................................................................................................................................ 2035 Pain, Den \ Massey University, New Zealand .................................................................................. 1922 Paolucci, Francesco \ The Australian National University, Australia ................................................ 25 Papadogiorgaki, Maria \ Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece . ......................................................................................................... 359 Parise, Salvatore \ Babson College, USA . ...................................................................................... 1142 Parry, Gareth \ Horsmans Place Partnership, UK ......................................................................... 1518 Parry, Emma \ The University of Auckland, New Zealand ............................................................. 1581 Parsons, Thomas D. \ University of Southern California, USA ........................................................ 655 Pasupathy, Kalyan Sunder \ University of Missouri, USA ............................................................ 1684 Pattichis, Marios \ University of New Mexico, USA ......................................................................... 949 Pattichis, Constantinos \ University of Cyprus, Cyprus ................................................................... 949 Paul, Ray J. \ Brunel University, UK . ............................................................................................. 1738 Pekam, F. Chuembou \ RWTH Aachen University, Aachen, Germany . ........................................... 554 Pernefeldt, Hanna \ ACCESS Health Initiative, India .................................................................... 1438 Petroudi, Dimitra \ National and Kapodistrian University of Athens, Greece ............................... 2029 Pfeiffer, Klaus \ Robert Bosch Gesellschaft für medizinische Forschung, Germany ........................ 801 Piana, Michele \ Universita’ di Verona, Italy . ................................................................................... 353 Pongracz, Ferenc \ Albadent Inc., Hungary .................................................................................... 2143 Portas, Cesar Parguiñas \ University of Vigo, Spain . ...................................................................... 703 Prado-Velasco, Manuel \ University of Seville, Spain .................................................................... 1284 Price, Morgan \ University of Victoria, Canada, & University of British Columbia, Canada ....... 1874 Protogerakis, Michael \ RWTH Aachen University, Germany . ........................................................ 974 Proulx, Jean \ University of Montreal, Canada . ............................................................................. 2073 Quackenbush, John \ Harvard School of Public Health, USA ......................................................... 877 Quinn, Tom \ Coventry University, UK ........................................................................................... 1592 Radermacher, K. \ RWTH Aachen University, Aachen, Germany .................................................... 554 Raghavan, Vijay V. \ Northern Kentucky University, USA ............................................................... 132 Rapoport, Benjamin I. \ Massachusetts Institute of Technology, USA & Harvard Medical School, USA . ................................................................................................................................ 581 Rebholz-Schuhmann, Dietrich \ European Bioinformatics Institute, UK ..................................... 1360 Reid, Jonathan H. \ University of Utah School of Medicine, USA & Utah Department of Health, USA . .............................................................................................................................. 1470 Reis, Shmuel \ Technion- Israel Institute of Technology, Israel ........................................................ 160 Renaud, Patrice \ University of Quebec in Outaouais / Institut Philippe-Pinel de Montréal, Canada ....................................................................................................................................... 2073 Ribera, John \ Utah State University, USA ....................................................................................... 693 Ries, Nola M. \ University of Alberta, Canada, & University of Victoria, Canada ........................ 1948

Roberts, Jean M. \ University of Central Lancashire, UK . ............................................................ 1623 Rodríguez, Patricia Henríquez \ University of Las Palmas de Gran Canaria, Spain . ................. 1008 Rojo, Marcial García \ Hospital General de Ciudad Real, Spain .................................................. 1235 Root, Jan \ Utah Health Information Network, USA ....................................................................... 1470 Rossaint, Rolf \ University Hospital Aachen, Germany .................................................................... 974 Rouleau, Joanne L. \ University of Montreal, Canad ..................................................................... 2073 Ruiz-Fernández, Daniel \ University of Alicante, Spain ................................................................ 1782 Sahba, Farhang \ Medical Imaging Analyst, Canada ....................................................................... 377 Sánchez, José Antonio \ Universidad Politécnica de Madrid, Spain ............................................... 633 Sánchez Chao, Castor \ University of Vigo, Spain ........................................................................... 703 Sarpeshkar, Rahul \ Massachusetts Institute of Technology, USA . .................................................. 581 Schleif, Frank-M. \ University of Leipzig, Germany ........................................................................ 478 Schneiders, Marie-Thérèse \ RWTH Aachen University, Germany ................................................. 974 Scilingo, Enzo Pasquale \ University of Pisa, Italy . ......................................................................... 792 Seland, Gry \ Norwegian University of Science and Technology, Norway ..................................... 1171 Serrano, Antonio J. \ University of Valencia, Spain ....................................................................... 1263 Shachak, Aviv \ University of Toronto, Canada ................................................................................ 160 Shanmuganathan, Subana \ Auckland University of Technology, New Zealand ........................... 1017 Shibl, Rania \ University of the Sunshine Coast, Australia ............................................................. 1922 Silva, Mário J. \ Universidade de Lisboa, Portugal . ...................................................................... 1360 Skorning, Max \ University Hospital Aachen, Germany .................................................................. 974 Smith, Kevin \ National Digital Research Centre, Ireland ................................................... 1030, 2153 Sneed, Wanda \ Tarleton State University, USA .............................................................................. 2013 Soar, Jeffrey \ University of Southern Queensland, Australia . ....................................................... 1539 Song, Yulin \ Memorial Sloan-Kettering Cancer Center, USA .......................................................... 885 Sørby, Inger Dybdahl \ Norwegian University of Science and Technology, Norway ..................... 1171 Soria, Emilio \ University of Valencia, Spain .................................................................................. 1263 Soriano-Payá, Antonio \ University of Alicante, Spain .................................................................. 1782 Spera, C. \ Zipidy, Inc., USA .............................................................................................................. 263 Spyropoulos, B. \ Technological Education Institute of Athens, Greece ......................................... 1674 Spyrou, George M. \ Academy of Athens, Greece . ........................................................................... 314 Stamatopoulos, V. G. \ Biomedical Research Foundation of the Academy of Athens, Greece & Technological Educational Institute of Chalkida, Greece ......................................... 1093 Staudinger, Bettina \ University for Health Sciences, Medical Informatics and Technology, Austria ........................................................................................................................................ 2035 Staudinger, Roland \ University for Health Sciences, Medical Informatics and Technology, Austria ........................................................................................................................................ 2035 Stefurak, James “Tres” \ University of South Alabama, USA ........................................................ 1656 Sturmberg, Joachim \ Monash University and The University of Newcastle, Australia . ... 1030, 1996, 2153 Surry, Daniel W. \ University of South Alabama, USA ................................................................... 1656 Swennen, Maartje H. J. \ University Medical Centre Utrecht, The Netherlands . ......................... 1900 Tang, Qiang \ University of Twente, The Netherlands . ..................................................................... 391 Tanos, Vasilios \ Aretaeion Hospital, Nicosia, Cyprus ...................................................................... 949 Tantbirojn, Daranee \ Universtiy of Minnesota, USA .................................................................... 1374 Terenziani, Paolo \ Università del Piemonte Orientale, Alessandria, Italy .................................... 1721 Tesconi, Mario \ University of Pisa, Italy . ........................................................................................ 792

Thrasher, Evelyn H. \ University of Massachusetts Dartmouth, USA ............................................ 1461 Tong, Carrison K. S. \ Pamela Youde Nethersole Eastern Hospital, Hong Kong, China .............. 2173 Topps, David \ Northern Ontario School of Medicine, Canada ...................................................... 2153 Torchio, Mauro \ AOU San Giovanni Battista, Torino, Italy .......................................................... 1721 Toussaint, Pieter \ Norwegian University of Science and Technology, Norway ............................. 1171 Tsipouras, Markos G. \ University of Ioannina, Greece .......................................................... 305, 412 Tzavaras, A. \ Technological Education Institute of Athens, Greece .............................................. 1674 Übeyli, Elif Derya \ TOBB Ekonomi ve Teknoloji Üniversitesi, Turkey ............................................ 676 Umakanth, Shashikiran \ Manipal University, Malaysia .................................................... 1030, 1996 Urowitz, Sara \ University Health Network, Canada .......................................................................... 50 Valero, Miguel Ángel \ Universidad Politécnica de Madrid, Spain ................................................. 633 Versluis, Antheunis \ University of Minnesota, USA ...................................................................... 1374 Villablanca, Amparo C. \ University of California, Davis, USA .................................................... 2094 Villeneuve, Alain O. \ Université de Sherbrooke, Canada .............................................................. 1962 Villmann, Thomas \ University of Leipzig, Germany ....................................................................... 478 Vladzymyrskyy, Anton V. \ Association for Ukrainian Telemedicine and eHealth Development & Donetsk R&D Institute of Traumatology and Orthopedics, Ukraine . ....................................... 1062 Wadhwani, Arun Kumar \ MITS, India ........................................................................................... 467 Wadhwani, Sulochana \ MITS, India . .............................................................................................. 467 Walczak, Steven \ University of Colorado at Denver, USA ............................................................ 1812 Wang, Xiu Ying \ BMIT Research Group, The University of Sydney, Australia ............................... 766 Webbe, Frank \ Florida Institute of Technology, USA .................................................................... 2047 Wei, Wei \ West China Hospital, China ........................................................................................... 2191 Weir, Charlene R. \ George W. Allen VA Medical Center, USA ...................................................... 1121 White, Raymond \ Sunderland University, UK . ............................................................................. 1327 Whitehouse, Diane \ The Castlegate Consultancy, Malton, UK ..................................................... 1831 Wickramasinghe, Nilmini \ RMIT University, Melbourne, Australia ............................................ 1706 Wiljer, David \ University Health Network, Canada . ......................................................................... 50 Wilson, E. Vance \ The University of Toledo, USA . ........................................................................ 1800 Wolfe, Melody \ University of Western Ontario, Canada . .................................................................. 13 Wong, Eric T. T. \ The Hong Kong Polytechnic University, Hong Kong, China ............................ 2173 Wood, Jennifer A. \ VA Texas Valley Coastal Bend Health Care System, USA .............................. 1566 Xiao, Yang \ The University of Alabama, USA .................................................................................. 210 Xie, Shuyan \ The University of Alabama, USA ................................................................................ 210 Xu, Wu \ Utah Department of Health, USA . ................................................................................... 1470 Yergens, Dean \ University of Manitoba and University of Calgary, Canada ................................ 1419 Zhang, Zhiqiang \ Sichuan University, China ................................................................................ 2191 Zijlstra, Wiebren \ University Medical Center Groningen, The Netherlands .................................. 801

Contents

Volume I Section I. Fundamental Concepts and Theories This section serves as the groundwork for this comprehensive reference book by addressing central theories essential to the understanding of clinical technologies. Chapters found within these pages provide a tremendous framework in which to position clinical technologies within the field of information science and technology. Insight regarding the critical integration of global measures into clinical technologies is addressed, while crucial stumbling blocks of this field are explored. In the chapters comprising this introductory section, the reader can learn and choose from a compendium of expert research on the elemental theories underscoring the clinical technologies discipline. Chapter 1.1. The Implementation of Innovative Technologies in Healthcare: Barriers and Strategies............................................................................................................................. 1 Eddy M.M. Adang, Radboud University Nijmegen Medical Center, The Netherlands Chapter 1.2. Risks and Benefits of Technology in Health Care............................................................. 13 Stefane M. Kabene, University of Western Ontario, Canada Melody Wolfe, University of Western Ontario, Canada Chapter 1.3. The Effectiveness of Health Informatics........................................................................... 25 Francesco Paolucci, The Australian National University, Australia Henry Ergas, Concept Economics, Australia Terry Hannan, Australian College of Health Informatics, Australia Jos Aarts, Erasmus University, Rotterdam, The Netherlands Chapter 1.4. Personal Health Information in the Age of Ubiquitous Health......................................... 50 David Wiljer, University Health Network, Canada Sara Urowitz, University Health Network, Canada Erin Jones, University Health Network, Canada

Chapter 1.5. E-Health as the Realm of Healthcare Quality: A Mental Image of the Future.................. 73 Anastasius Moumtzoglou, Hellenic Society for Quality & Safety in Healthcare, European Society for Quality in Healthcare Executive Board Member, Greece Chapter 1.6. The Use of Personal Digital Assistants in Nursing Education.......................................... 93 Nina Godson, Coventry University, UK Adrian Bromage, Coventry University, UK Chapter 1.7. Reforming Nursing with Information Systems and Technology..................................... 108 Chon Abraham, College of William and Mary, USA Chapter 1.8. Quality Issues in Personalized E-Health, Mobile Health and E-Health Grids................ 118 Anastasia N. Kastania, Biomedical Research Foundation of the Academy of Athens, Greece & Athens University of Economics and Business, Greece Sophia Kossida, Biomedical Research Foundation of the Academy of Athens, Greece Chapter 1.9. Adoption of Electronic Health Records: A Study of CIO Perceptions............................ 132 Yousuf J. Ahmad, Mercy Health Partners, USA Vijay V. Raghavan, Northern Kentucky University, USA William Benjamin Martz Jr., Northern Kentucky University, USA Chapter 1.10. Technology Enabled Knowledge Translation: Using Information and Communications Technologies to Accelerate Evidence Based Health Practices................................ 147 Kendall Ho, University of British Columbia, Canada Chapter 1.11. The Computer-Assisted Patient Consultation: Promises and Challenges..................... 160 Aviv Shachak, University of Toronto, Canada Shmuel Reis, Technion- Israel Institute of Technology, Israel Chapter 1.12. Quality and Reliability Aspects in Evidence Based E-Medicine.................................. 172 Asen Atanasov, Medical University Hospital “St. George”, Bulgaria Chapter 1.13. E-Medical Education: An Overview............................................................................. 190 D. John Doyle, Case Western Reserve University, USA & Cleveland Clinic Foundation, USA Chapter 1.14. Nursing Home............................................................................................................... 210 Shuyan Xie, The University of Alabama, USA Yang Xiao, The University of Alabama, USA Hsiao-Hwa Chen, National Cheng Kung University, Taiwan

Section II. Development and Design Methodologies This section provides exhaustive coverage of conceptual architecture frameworks to endow with the reader a broad understanding of the promising technological developments within the field of clinical technologies. Research fundamentals imperative to the understanding of developmental processes within clinical technologies are offered. From broad surveys to specific discussions and case studies on electronic tools, the research found within this section spans the discipline while offering detailed, specific discussions. From basic designs to abstract development, these chapters serve to expand the reaches of development and design technologies within the clinical technologies community. Chapter 2.1. Improving Clinical Practice through Mobile Medical Informatics................................. 250 Tagelsir Mohamed Gasmelseid, King Faisal University, Saudi Arabia Chapter 2.2. A Web-Enabled, Mobile Intelligent Information Technology Architecture for On-Demand and Mass Customized Markets....................................................................................... 263 M. Ghiassi, Santa Clara University, USA C. Spera, Zipidy, Inc., USA Chapter 2.3. A Framework for Information Processing in the Diagnosis of Sleep Apnea.................. 295 Udantha R. Abeyratne, The University of Queensland, Australia Chapter 2.4. Computer-Aided Diagnosis of Cardiac Arrhythmias...................................................... 305 Markos G. Tsipouras, University of Ioannina, Greece Dimitrios I. Fotiadis, University of Ioannina, Greece, Biomedical Research Institute FORTH, Greece, & Michaelideion Cardiology Center, Greece Lambros K. Michalis, University of Ioannina, Greece & Michaelideion Cardiology Center, Greece Chapter 2.5. Computer Aided Risk Estimation of Breast Cancer: The “Hipprocrates-mst” Project........................................................................................................... 314 George M. Spyrou, Academy of Athens, Greece Panos A. Ligomenides, Academy of Athens, Greece Chapter 2.6. Automatic Analysis of Microscopic Images in Hematological Cytology Applications......................................................................................................................... 325 Gloria Díaz, National University of Colombia, Colombia Antoine Manzanera, ENSTA-ParisTech, France Chapter 2.7. Computational Methods in Biomedical Imaging............................................................ 353 Michele Piana, Universita’ di Verona, Italy

Chapter 2.8. Visual Medical Information Analysis.............................................................................. 359 Maria Papadogiorgaki, Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece Vasileios Mezaris, Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece Yiannis Chatzizisis, Aristotle University of Thessaloniki, Greece George D. Giannoglou, Aristotle University of Thessaloniki, Greece Ioannis Kompatsiaris, Informatics and Telematics Institue, Centre for Research and Technology Hellas, Greece Chapter 2.9. Angiographic Images Segmentation Techniques............................................................ 368 Francisco J. Nóvoa, University of A Coruña, Spain Alberto Curra, University of A Coruña, Spain M. Gloria López, University of A Coruña, Spain Virginia Mato, University of A Coruña, Spain Chapter 2.10. Segmentation Methods in Ultrasound Images.............................................................. 377 Farhang Sahba, Medical Imaging Analyst, Canada Chapter 2.11. Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme to Securely Manage Personal Health Records......................................................................................... 391 Luan Ibraimi, University of Twente, The Netherlands Qiang Tang, University of Twente, The Netherlands Pieter Hartel, University of Twente, The Netherlands Willem Jonker, University of Twente, The Netherlands Chapter 2.12. Integration of Clinical and Genomic Data for Decision Support in Cancer................. 412 Yorgos Goletsis, University of Ioannina, Greece Themis P. Exarchos, University of Ioannina, Greece Nikolaos Giannakeas, University of Ioannina, Greece Markos G. Tsipouras, University of Ioannina, Greece Dimitrios I. Fotiadis, University of Ioannina, Greece, Michaelideion Cardiology Center, Greece & Biomedical Research Institute, Greece Chapter 2.13. Module Finding Approaches for Protein Interaction Networks.................................... 422 Tero Aittokallio, University of Turku, Finland Chapter 2.14. Networks of Action for Anti Retroviral Treatment Information Systems..................... 444 Elaine Byrne, University of Pretoria, South Africa Roy D. Johnson, University of Pretoria, South Africa Chapter 2.15. Socio-Technical Structures, 4Ps and Hodges’ Model................................................... 451 Peter Jones, NHS Community Mental Health Nursing Older Adults, UK

Chapter 2.16. Techniques for Decomposition of EMG Signals........................................................... 467 Arun Kumar Wadhwani, MITS, India Sulochana Wadhwani, MITS, India Chapter 2.17. Prototype Based Classification in Bioinformatics......................................................... 478 Frank-M. Schleif, University of Leipzig, Germany Thomas Villmann, University of Leipzig, Germany Barbara Hammer, Technical University of Clausthal, Germany Chapter 2.18. An Integrated System for E-Medicine: E-Health, Telemedicine and Medical Expert Systems..................................................................................................................................... 486 Ivan Chorbev, Ss. Cyril and Methodius University, Republic of Macedonia Boban Joksimoski, European University, Republic of Macedonia Chapter 2.19. The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy Simulation Analysis................................................................................................ 508 Mahendran Maliapen, University of Sydney, Australia, National University of Singapore, Singapore and UCLAN, UK Alan Gillies, UCLAN, UK Chapter 2.20. Use of Clinical Simulations to Evaluate the Impact of Health Information Systems and Ubiquitous Computing Devices Upon Health Professional Work.................................. 532 Elizabeth M. Borycki, University of Victoria, Canada Andre W. Kushniruk, University of Victoria, Canada Chapter 2.21. Using New Model-Based Techniques for the User Interface Design of Medical Devices and Systems............................................................................................................. 554 A. Janß, RWTH Aachen University, Aachen, Germany W. Lauer, RWTH Aachen University, Aachen, Germany F. Chuembou Pekam, RWTH Aachen University, Aachen, Germany K. Radermacher, RWTH Aachen University, Aachen, Germany Chapter 2.22. The European Perspective of E-Health and a Framework for its Economic Evaluation........................................................................................................................... 572 Paola Di Giacomo, University of Udine, Italy Chapter 2.23. A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders.............................................................................................................. 581 Benjamin I. Rapoport, Massachusetts Institute of Technology, USA & Harvard Medical School, USA Rahul Sarpeshkar, Massachusetts Institute of Technology, USA

Section III. Tools and Technologies This section presents an extensive treatment of various tools and technologies existing in the field of clinical technologies practitioners and academics alike must rely on to develop new techniques. These chapters enlighten readers about fundamental research on the many methods used to facilitate and enhance the integration of this worldwide phenomenon by exploring software and hardware developments and their applications—an increasingly pertinent research arena. It is through these rigorously researched chapters that the reader is provided with countless examples of the up-and-coming tools and technologies emerging from the field of clinical technologies. Chapter 3.1. Avatars and Diagnosis: Delivering Medical Curricula in Virtual Space......................... 620 Claudia L. McDonald, Texas A&M University-Corpus Christi, USA Chapter 3.2. Agile Patient Care with Distributed M-Health Applications........................................... 633 Rafael Capilla, Universidad Rey Juan Carlos, Spain Alfonso del Río, Universidad Rey Juan Carlos, Spain Miguel Ángel Valero, Universidad Politécnica de Madrid, Spain José Antonio Sánchez, Universidad Politécnica de Madrid, Spain Chapter 3.3. Affect-Sensitive Virtual Standardized Patient Interface System..................................... 655 Thomas D. Parsons, University of Southern California, USA Chapter 3.4. Telemedicine and Biotelemetry for E-Health Systems: Theory and Applications.......... 676 Elif Derya Übeyli, TOBB Ekonomi ve Teknoloji Üniversitesi, Turkey Chapter 3.5. Tele-Audiology in the United States: Past, Present, and Future..................................... 693 John Ribera, Utah State University, USA Chapter 3.6. SIe-Health, e-Health Information System....................................................................... 703 Juan Carlos González Moreno, University of Vigo, Spain Loxo Lueiro Astray, University of Vigo, Spain Rubén Romero González, University of Vigo, Spain Cesar Parguiñas Portas, University of Vigo, Spain Castor Sánchez Chao, University of Vigo, Spain Chapter 3.7. Sensing of Vital Signs and Transmission Using Wireless Networks.............................. 717 Yousef Jasemian, Engineering College of Aarhus, Denmark

Volume II Chapter 3.8. Neural Networks in Medicine: Improving Difficult Automated Detection of Cancer in the Bile Ducts...................................................................................................................... 744 Rajasvaran Logeswaran, Multimedia University, Malaysia

Chapter 3.9. Image Registration for Biomedical Information Integration........................................... 766 Xiu Ying Wang, BMIT Research Group, The University of Sydney, Australia Dagan Feng, BMIT Research Group, The University of Sydney, Australia & Hong Kong Polytechnic University, Hong Kong Chapter 3.10. Cognition Meets Assistive Technology: Insights from Load Theory of Selective Attention............................................................................................................................... 779 Neha Khetrapal, University of Bielefeld, Germany Chapter 3.11. Wearable Kinesthetic System for Joint Knee Flexion-Extension Monitoring in Gait Analysis........................................................................................................................................ 792 Mario Tesconi, University of Pisa, Italy Enzo Pasquale Scilingo, University of Pisa, Italy Pierluigi Barba, University of Pisa, Italy Danilo De Rossi, University of Pisa, Italy Chapter 3.12. Wearable Systems for Monitoring Mobility Related Activities: From Technology to Application for Healthcare Services................................................................... 801 Wiebren Zijlstra, University Medical Center Groningen, The Netherlands Clemens Becker, Robert Bosch Gesellschaft für medizinische Forschung, Germany Klaus Pfeiffer, Robert Bosch Gesellschaft für medizinische Forschung, Germany Chapter 3.13. RFID in Hospitals and Factors Restricting Adoption.................................................... 825 Bryan Houliston, Auckland University of Technology, New Zealand Chapter 3.14. Transcranial Magnetic Stimulation (TMS) as a Tool for Neurorehabilitation in Parkinson’s Disease............................................................................................................................. 853 Pablo Arias, University of A Coruña, Spain Nelson Espinosa, University of A Coruña, Spain Javier Cudeiro, University of A Coruña, Spain Chapter 3.15. Modeling of Porphyrin Metabolism with PyBioS......................................................... 866 Andriani Daskalaki, Max Planck Institute for Molecular Genetics, Germany Chapter 3.16. AI Methods for Analyzing Microarray Data................................................................. 877 Amira Djebbari, National Research Council Canada, Canada Aedín C. Culhane, Harvard School of Public Health, USA Alice J. Armstrong, The George Washington University, USA John Quackenbush, Harvard School of Public Health, USA

Chapter 3.17. 3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization........................................................................................................... 885 Guang Li, National Cacer Institute, USA Deborah Citrin, National Cancer Institute, USA Robert W. Miller, National Cancer Institute, USA Kevin Camphausen, National Cancer Institute, USA Boris Mueller, Memorial Sloan-Kettering Cancer Center, USA Borys Mychalczak, Memorial Sloan-Kettering Cancer Center, USA Yulin Song, Memorial Sloan-Kettering Cancer Center, USA Chapter 3.18. Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters.............................................................................................................. 895 Robert B. Kerstein, Tufts University School of Dental Medicine, USA Chapter 3.19. Ultrasound Guided Noninvasive Measurement of Central Venous Pressure................ 917 Vikram Aggarwal, Johns Hopkins University, USA Aniruddha Chatterjee, Johns Hopkins University, USA Yoonju Cho, Johns Hopkins University, USA Dickson Cheung, Johns Hopkins Bayview Medical Center, USA Chapter 3.20. Software Support for Advanced Cephalometric Analysis in Orthodontics................... 926 Demetrios J. Halazonetis, National and Kapodistrian University of Athens, Greece Chapter 3.21. Quantitative Analysis of Hysteroscopy Imaging in Gynecological Cancer.................. 949 Marios Neofytou, University of Cyprus, Cyprus Constantinos Pattichis, University of Cyprus, Cyprus Vasilios Tanos, Aretaeion Hospital, Nicosia, Cyprus Marios Pattichis, University of New Mexico, USA Eftyvoulos Kyriacou, Frederick University, Cyprus Chapter 3.22. Myoelectric Control of Prosthetic Devices for Rehabilitation...................................... 965 Rami N. Khushaba, University of Technology, Australia Adel A. Al-Jumaily, University of Technology, Australia Chapter 3.23. Med-on-@ix: Real-Time Tele-Consultation in Emergency Medical Services Promising or Unnecessary?.................................................................................................................. 974 In-Sik Na, University Hospital Aachen, Germany Max Skorning, University Hospital Aachen, Germany Arnd T. May, University Hospital Aachen, Germany Marie-Thérèse Schneiders, RWTH Aachen University, Germany Michael Protogerakis, RWTH Aachen University, Germany Stefan Beckers, University Hospital Aachen, Germany, Harold Fischermann, University Hospital Aachen, Germany Nadja Frenzel, University Hospital Aachen, Germany Tadeusz Brodziak, P3 Communications GmbH, Germany Rolf Rossaint, University Hospital Aachen, Germany

Chapter 3.24. DISMON: Using Social Web and Semantic Technologies to Monitor Diseases in Limited Environments......................................................................................................................... 995 Ángel Lagares-Lemos, Universidad Carlos III de Madrid, Spain Miguel Lagares-Lemos, Universidad Carlos III de Madrid, Spain Ricardo Colomo-Palacios, Universidad Carlos III de Madrid, Spain Ángel García-Crespo, Universidad Carlos III de Madrid, Spain Juan M. Gómez-Berbís, Universidad Carlos III de Madrid, Spain Section IV. Utilization and Application This section discusses a variety of applications and opportunities available that can be considered by practitioners in developing viable and effective clinical technologies programs and processes. This section includes over 20 chapters reviewing certain utilizations and applications of clinical technologies, such as artificial intelligence and social network analysis in healthcare. Further chapters show case studies from around the world, and the applications and utilities of clinical technologies. The wide ranging nature of subject matter in this section manages to be both intriguing and highly educational. Chapter 4.1. Speech-Based Clinical Diagnostic Systems.................................................................. 1008 Jesús Bernardino Alonso Hernández, University of Las Palmas de Gran Canaria, Spain Patricia Henríquez Rodríguez, University of Las Palmas de Gran Canaria, Spain Chapter 4.2. The Use of Artificial Intelligence Systems for Support of Medical Decision-Making................................................................................................................................ 1017 William Claster, Ritsumeikan Asia Pacific University, Japan Nader Ghotbi, Ritsumeikan Asia Pacific University, Japan Subana Shanmuganathan, Auckland University of Technology, New Zealand Chapter 4.3. Persistent Clinical Encounters in User Driven E-Health Care...................................... 1030 Rakesh Biswas, Manipal University, Malaysia Joachim Sturmberg, Monash University, Australia Carmel M. Martin, Northern Ontario School of Medicine, Canada A. U. Jai Ganesh, Sri Sathya Sai Information Technology Center, India Shashikiran Umakanth, Manipal University, Malaysia Edwin Wen Huo Lee, Kuala Lumpur, Malaysia Kevin Smith, National Digital Research Centre, Ireland Chapter 4.4. Remote Patient Monitoring in Residential Care Homes: Using Wireless and Broadband Networks......................................................................................................................... 1047 Tanja Bratan, Brunel University, UK Malcolm Clarke, Brunel University, UK Joanna Fursse, Brunel University, UK Russell Jones, Chorleywood Health Centre, UK Chapter 4.5. Telemedicine Consultations in Daily Clinical Practice: Systems, Organisation, Efficiency.................................................................................................................... 1062 Anton V. Vladzymyrskyy, Association for Ukrainian Telemedicine and eHealth Development & Donetsk R&D Institute of Traumatology and Orthopedics, Ukraine

Chapter 4.6. The Use and Evaluation of IT in Chronic Disease Management.................................. 1075 Julia Adler-Milstein, Harvard University, USA Ariel Linden, Linden Consulting Group & Oregon Health & Science University, USA Chapter 4.7. Safe Implementation of Research into Healthcare Practice through a Care Process Pathways Based Adaptation of Electronic Patient Records.................................................. 1093 V. G. Stamatopoulos, Biomedical Research Foundation of the Academy of Athens, Greece & Technological Educational Institute of Chalkida, Greece G. E. Karagiannis, Royal Brompton & Harefield NHS Trust, UK B. R. M. Manning, University of Westminster, UK Chapter 4.8. Towards Computer Supported Clinical Activity: A Roadmap Based on Empirical Knowledge and Some Theoretical Reflections................................................................. 1105 Christian Nøhr, Aalborg University, Denmark Niels Boye, Aalborg University, Denmark Chapter 4.9. Nursing Documentation in a Mature EHR System....................................................... 1121 Kenric W. Hammond, VA Puget Sound Health Care System, USA Charlene R. Weir, George W. Allen VA Medical Center, USA Efthimis N. Efthimiadis, University of Washington, USA Chapter 4.10. Applying Social Network Analysis in a Healthcare Setting........................................ 1142 Salvatore Parise, Babson College, USA Chapter 4.11. The Graphic Display of Labor Events......................................................................... 1153 Olufemi T. Oladapo, Olabisi Onabanjo University Teaching Hospital, Nigeria Chapter 4.12. The MOBEL Project: Experiences from Applying User-Centered Methods for Designing Mobile ICT for Hospitals................................................................................................. 1171 Inger Dybdahl Sørby, Norwegian University of Science and Technology, Norway Line Melby, Norwegian University of Science and Technology, Norway Yngve Dahl, Telenor Research & Innovation, Norway Gry Seland, Norwegian University of Science and Technology, Norway Pieter Toussaint, Norwegian University of Science and Technology, Norway Øystein Nytrø, Norwegian University of Science and Technology, Norway Arild Faxvaag, Norwegian University of Science and Technology, Norway Chapter 4.13. Processing and Communication Techniques for Applications in Parkinson Disease Treatment.............................................................................................................................. 1191 Álvaro Orozco-Gutiérrez, Universidad Tecnológica de Pereira, Colombia Edilson Delgado-Trejos, Instituto Tecnológico Metropolitano ITM, Colombia Hans Carmona-Villada, Instituto de Epilepsia y Parkinson del Eje Cafetero – Neurocentro, Colombia Germán Castellanos-Domínguez, Universidad Nacional de Colombia, Colombia

Chapter 4.14. Informatics Applications in Neonatology................................................................... 1215 Malcolm Battin, National Women’s Health, Auckland City Hospital, New Zealand David Knight, Mater Mother’s Hospital, Brisbane, Australia Carl Kuschel, The Royal Women’s Hospital, Melbourne, Australia Chapter 4.15. Digital Pathology and Virtual Microscopy Integration in E-Health Records............. 1235 Marcial García Rojo, Hospital General de Ciudad Real, Spain Christel Daniel, Université René Descartes, France Chapter 4.16. Clinical Decision Support System to Prevent Toxicity in Patients Treated with Digoxin......................................................................................................................... 1263 Asunción Albert, University Hospital Dr. Peset/University of Valencia, Spain Antonio J. Serrano, University of Valencia, Spain Emilio Soria, University of Valencia, Spain Nicolás Victor Jiménez, University Hospital Dr. Peset/University of Valencia, Spain Chapter 4.17. An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering Founded on a Non-Speech Etiologic Paradigm........................................................... 1284 Manuel Prado-Velasco, University of Seville, Spain Carlos Fernández-Peruchena, University of Seville, Spain Chapter 4.18. Reporting Clinical Gait Analysis Data........................................................................ 1327 Raymond White, Sunderland University, UK Robert Noble, The Robert Gordon University, Scotland Chapter 4.19. Variational Approach Based Image Pre- Processing Techniques for Virtual Colonoscopy.......................................................................................................................... 1340 Dongqing Chen, University of Louisville, USA Aly A. Farag, University of Louisville, USA Robert L. Falk, Jewish Hospital & St. Mary’s Healthcare, USA Gerald W. Dryden, University of Louisville, USA Chapter 4.20. Verification of Uncurated Protein Annotations........................................................... 1360 Francisco M. Couto, Universidade de Lisboa, Portugal Mário J. Silva, Universidade de Lisboa, Portugal Vivian Lee, European Bioinformatics Institute, UK Emily Dimmer, European Bioinformatics Institute, UK Evelyn Camon, European Bioinformatics Institute, UK Rolf Apweiler, European Bioinformatics Institute, UK Harald Kirsch, European Bioinformatics Institute, UK Dietrich Rebholz-Schuhmann, European Bioinformatics Institute, UK Chapter 4.21. Relationship Between Shrinkage and Stress............................................................... 1374 Antheunis Versluis, University of Minnesota, USA Daranee Tantbirojn, Universtiy of Minnesota, USA

Chapter 4.22. Predicting Ambulance Diversion................................................................................ 1393 Abey Kuruvilla, University of Wisconsin Parkside, USA Suraj M. Alexander, University of Louisville, USA Section V. Organizational and Social Implications This section includes a spacious range of inquiry and research pertaining to the behavioral, emotional, social and organizational impact of clinical technologies around the world. From demystifying human resources to privacy in mammography, this section compels the humanities, education, and IT scholar all. Section 5 also focuses on hesitance in some hospital members’ integration with clinical technologies, and methods therein. With more than 10 chapters, the discussions on hand in this section detail current and suggest future research into the integration of global clinical technologies as well as implementation of ethical considerations for all organizations. Overall, these chapters present a detailed investigation of the complex relationship between individuals, organizations and clinical technologies. Chapter 5.1. Demystifying E-Health Human Resources................................................................... 1403 Candace J. Gibson, The University of Western Ontario, Canada H. Dominic Covvey, University of Waterloo, Canada Chapter 5.2. Multi-Agent Systems in Developing Countries............................................................ 1419 Dean Yergens, University of Manitoba and University of Calgary, Canada Julie Hiner, University of Calgary, Canada Jörg Denzinger, University of Calgary, Canada Chapter 5.3. Primary Care through a Public-Private Partnership: Health Management and Research Institute............................................................................................................................... 1438 Sofi Bergkvist, ACCESS Health Initiative, India Hanna Pernefeldt, ACCESS Health Initiative, India Chapter 5.4. Strategic Fit in the Healtcare IDS................................................................................. 1461 Evelyn H. Thrasher, University of Massachusetts Dartmouth, USA Terry A. Byrd, Auburn University, USA Chapter 5.5. Regional and Community Health Information Exchange in the United States............. 1470 Adi V. Gundlapalli, University of Utah School of Medicine, USA Jonathan H. Reid, University of Utah School of Medicine, USA & Utah Department of Health, USA Jan Root, Utah Health Information Network, USA Wu Xu, Utah Department of Health, USA

Volume III Chapter 5.6. Regional Patient Safety Initiatives: The Missing Element of Organizational Change....................................................................................................................... 1491 James G. Anderson, Purdue University, USA Chapter 5.7. Use of Handheld Computers in Nursing Education...................................................... 1504 Maureen Farrell, University of Ballarat, Australia Chapter 5.8. Women’s Health Informatics in the Primary Care Setting............................................ 1518 Gareth Parry, Horsmans Place Partnership, UK Chapter 5.9. Assistive Technology for Individuals with Disabilities................................................. 1530 Yukiko Inoue, University of Guam, Guam Chapter 5.10. Ageing, Chronic Disease, Technology, and Smart Homes: An Australian Perspective.................................................................................................................. 1539 Jeffrey Soar, University of Southern Queensland, Australia Chapter 5.11. Synthetic Speech Perception in Individuals with Intellectual and Communicative Disabilities............................................................................................................... 1554 Rajinder Koul, Texas Tech University, Health Sciences Center, USA James Dembowski, Texas Tech University, Health Sciences Center USA Chapter 5.12. Telepractice: A 21st Century Model of Health Care Delivery.................................... 1566 Thomas W. Miller, University of Connecticut, USA Jennifer A. Wood, VA Texas Valley Coastal Bend Health Care System, USA Chapter 5.13. The Electronic Health Record to Support Women’s Health........................................ 1581 Emma Parry, The University of Auckland, New Zealand Chapter 5.14. Managing Paramedic Knowledge for Treatment of Acute Myocardial Infarction........................................................................................................................ 1592 Tom Quinn, Coventry University, UK R. K. Bali, Coventry University, UK Katherine Dale, Worcestershire Royal Hospital, UK Pete Gregory, Coventry University, UK

Section VI. Managerial Impact This section presents contemporary coverage of the social implications of clinical technologies, more specifically related to the corporate and managerial utilization of information sharing technologies and applications, and how these technologies can be facilitated within organizations. Section 6 is especially helpful as an addition to the organizational and behavioral studies of section 5, with diverse and novel developments in the managerial and human resources areas of clinical technologies. Typically, though the fields of industry and education are not always considered co-dependent, section 6 provides looks into how clinical technologies and the business workplace help each other. The interrelationship of such issues as operationalizing, supervision, and diagnosis management are discussed. In all, the chapters in this section offer specific perspectives on how managerial perspectives and developments in clinical technologies inform each other to create more meaningful user experiences. Chapter 6.1. Operationalizing the Science: Integrating Clinical Informatics into the Daily Operations of the Medical Center............................................................................................ 1600 Joseph L. Kannry, Mount Sinai Medical Center, USA Chapter 6.2. Current Challenges in Empowering Clinicians to Utilise Technology......................... 1623 Jean M. Roberts, University of Central Lancashire, UK Chapter 6.3. Challenges and Solutions in the Delivery of Clinical Cybersupervision...................... 1637 Kenneth L. Miller, Youngstown State University, USA Susan M. Miller, Kent State University, USA Chapter 6.4. Technology in the Supervision of Mental Health Professionals: Ethical, Interpersonal, and Epistemological Implications................................................................. 1656 James “Tres” Stefurak, University of South Alabama, USA Daniel W. Surry, University of South Alabama, USA Richard L. Hayes, University of South Alabama, USA Chapter 6.5. Optimization of Medical Supervision, Management, and Reimbursement of Contemporary Homecare................................................................................................................... 1674 B. Spyropoulos, Technological Education Institute of Athens, Greece M. Botsivaly, Technological Education Institute of Athens, Greece A. Tzavaras, Technological Education Institute of Athens, Greece K. Koutsourakis, Technological Education Institute of Athens, Greece Chapter 6.6. Systems Engineering and Health Informatics: Context, Content, and Implementation........................................................................................................................... 1684 Kalyan Sunder Pasupathy, University of Missouri, USA Chapter 6.7. Critical Factors for the Creation of Learning Healthcare Organizations...................... 1706 Nilmini Wickramasinghe, RMIT University, Melbourne, Australia

Chapter 6.8. Supporting Knowledge-Based Decision Making in the Medical Context: The GLARE Approach...................................................................................................................... 1721 Luca Anselma, Università di Torino, Torino, Italy Alessio Bottrighi, Università del Piemonte Orientale , Alessandria, Italy Gianpaolo Molino, AOU San Giovanni Battista, Torino, Italy Stefania Montani, Università del Piemonte Orientale, Alessandria, Italy Paolo Terenziani, Università del Piemonte Orientale, Alessandria, Italy Mauro Torchio, AOU San Giovanni Battista, Torino, Italy Chapter 6.9. Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials................................................................................................................ 1738 Tillal Eldabi, Brunel University, UK Robert D. Macredie, Brunel University, UK Ray J. Paul, Brunel University, UK Chapter 6.10. How Can Human Technology Improve the Scheduling of Unplanned Surgical Cases?.................................................................................................................................. 1759 Janna Anneke Fitzgerald, University of Western Sydney, Australia Martin Lum, Department of Human Services, Australia Ann Dadich, University of Western Sydney, Australia Chapter 6.11. TACMIS: A Total Access Care and Medical Information System.............................. 1770 M. Cassim, Ritsumeikan Asia Pacific University, Japan Chapter 6.12. A Distributed Approach of a Clinical Decision Support System Based on Cooperation........................................................................................................................ 1782 Daniel Ruiz-Fernández, University of Alicante, Spain Antonio Soriano-Payá, University of Alicante, Spain Chapter 6.13. Applying Personal Health Informatics to Create Effective Patient-Centered E-Health................................................................................................................. 1800 E. Vance Wilson, The University of Toledo, USA Chapter 6.14. Diagnostic Cost Reduction Using Artificial Neural Networks: The Case of Pulmonary Embolism........................................................................................................................ 1812 Steven Walczak, University of Colorado at Denver, USA Bradley B. Brimhall, University of New Mexico, USA Jerry B. Lefkowitz, Weill Cornell College of Medicine, USA

Section VII. Critical Issues Section 7 details some of the most crucial developments in the critical issues surrounding clinical technologies. Importantly, this refers to critical thinking or critical theory surrounding the topic, rather than vital affairs or new trends that may be found in section 8. Instead, the section discusses some of the latest developments in ethics, law, and social implications in clinical technology development. Within the chapters, the reader is presented with an in-depth analysis of the most current and relevant issues within this growing field of study. Chapter 7.1. eHealth and Ethics: Theory, Teaching, and Practice..................................................... 1831 Diane Whitehouse, The Castlegate Consultancy, Malton, UK Penny Duquenoy, Middlesex University, London, UK Chapter 7.2. Standards and Guidelines Development in the American Telemedicine Association......................................................................................................................................... 1843 Elizabeth A. Krupinski, University of Arizona, USA Nina Antoniotti, Marshfield Clinic Telehealth Network, USA Anne Burdick, University of Miami Miller School of Medicine, USA Chapter 7.3. The Regulation of Genetic Testing and the Protection of Genetic and Medical Information in Singapore................................................................................................................... 1853 Terry Kaan, National University of Singapore, Singapore Chapter 7.4. A Bio-Psycho-Social Review of Usability Methods and their Applications in Healthcare................................................................................................................. 1874 Morgan Price, University of Victoria, Canada, & University of British Columbia, Canada Chapter 7.5. The Gap between What is Knowable and What We Do in Clinical Practice................ 1900 Maartje H.J. Swennen, University Medical Centre Utrecht, The Netherlands Chapter 7.6. Trust and Clinical Information Systems........................................................................ 1922 Rania Shibl, University of the Sunshine Coast, Australia Kay Fielden, UNITEC New Zealand, New Zealand Andy Bissett, Sheffield Hallam University, UK Den Pain, Massey University, New Zealand Chapter 7.7. Data Security in Electronic Health Records.................................................................. 1934 Stefane M. Kabene, Ecole des Hautes Etudes en Sante Publique (EHESP), France Raymond W. Leduc, University of Western Ontario, Canada Candace J Gibson, University of Western Ontario, Canada Chapter 7.8. Legal Issues in Health Information and Electronic Health Records............................. 1948 Nola M. Ries, University of Alberta, Canada, & University of Victoria, Canada

Chapter 7.9. Integrating Telehealth into the Organization’s Work System........................................ 1962 Joachim Jean-Jules, Université de Sherbrooke, Canada Alain O. Villeneuve, Université de Sherbrooke, Canada Chapter 7.10. Social Cognitive Ontology and User Driven Healthcare............................................ 1996 Rakesh Biswas, Manipal University, Malaysia Carmel M. Martin, Northern Ontario School of Medicine, Canada Joachim Sturmberg, Monash University, Australia Kamalika Mukherji, Hertfordshire Partnership NHS Foundation Trust, UK Edwin Wen Huo Lee, Intel Innovation Center, Malaysia Shashikiran Umakanth, Manipal University, Malaysia A. S. Kasthuri, AFMC, India Chapter 7.11. A Treatise on Rural Public Health Nursing................................................................. 2013 Wanda Sneed, Tarleton State University, USA Section VIII. Emerging Trends The final section explores the latest trends and developments, and suggests future research potential within the field of clinical technologies while exploring uncharted areas of study for the advancement of the discipline. The section advances through medical imaging techniques, diagnostics, virtual reality, and more new technologies by means of describing some of the latest trends in clinical research and development. These and several other emerging trends and suggestions for future research can be found within the final section of this exhaustive multi-volume set. Chapter 8.1. New Technologies in Hospital Information Systems.................................................... 2029 Dimitra Petroudi, National and Kapodistrian University of Athens, Greece Nikolaos Giannakakis, National and Kapodistrian University of Athens, Greece Chapter 8.2. IT-Based Virtual Medical Centres and Structures......................................................... 2035 Bettina Staudinger, University for Health Sciences, Medical Informatics and Technology, Austria Herwig Ostermann, University for Health Sciences, Medical Informatics and Technology, Austria Roland Staudinger, University for Health Sciences, Medical Informatics and Technology, Austria Chapter 8.3. Emerging Technologies for Aging in Place................................................................... 2047 Shirley Ann Becker, Florida Institute of Technology, USA Frank Webbe, Florida Institute of Technology, USA Chapter 8.4. The Development and Implementation of Patient Safety Information Systems (PSIS).................................................................................................................................. 2054 Jeongeun Kim, Seoul National University, Korea

Chapter 8.5. The Use of Virtual Reality in Clinical Psychology Research: Focusing on Approach and Avoidance Behaviors.................................................................................................. 2073 Patrice Renaud, University of Quebec in Outaouais / Institut Philippe-Pinel de Montréal, Canada Sylvain Chartier, University of Ottawa, Canada Paul Fedoroff, University of Ottawa, Canada John Bradford, University of Ottawa, Canada Joanne L. Rouleau, University of Montreal, Canad Jean Proulx, University of Montreal, Canada Stéphane Bouchard, University of Quebec in Outaouais, Canada Chapter 8.6. Novel Data Interface for Evaluating Cardiovascular Outcomes in Women................. 2094 Amparo C. Villablanca, University of California, Davis, USA Hassan Baxi, University of California, Davis, USA Kent Anderson, University of California, Davis, USA Chapter 8.7. New Developments in Intracoronary Ultrasound Processing....................................... 2114 Christos V. Bourantas, Michailideion Cardiology Center, Greece & University of Hull, UK Katerina K. Naka, Michailideion Cardiology Center, Greece Dimitrios I. Fotiadis, Michailideion Cardiology Center, Greece Lampros K. Michalis, Michailideion Cardiology Center, Greece Chapter 8.8. Pulse!!: Designing Medical Learning in Virtual Reality............................................... 2126 Claudia L. McDonald, Texas A&M University-Corpus Christi, USA Jan Cannon-Bowers, University of Central Florida, Orlando, USA Clint Bowers, University of Central Florida, Orlando, USA Chapter 8.9. Visualization and Modelling in Dental Implantology................................................... 2143 Ferenc Pongracz, Albadent Inc., Hungary Chapter 8.10. Patient Journey Record Systems (PaJR) for Preventing Ambulatory Care Sensitive Conditions: A Developmental Framework......................................................................... 2153 Carmel M. Martin, Trinity College Dublin, Ireland Rakesh Biswas, People’s College of Medical Sciences, India Joachim Sturmberg, Monash University and The University of Newcastle, Australia David Topps, Northern Ontario School of Medicine, Canada Rachel Ellaway, Northern Ontario School of Medicine, Canada Kevin Smith, National Digital Research Centre, Ireland Chapter 8.11. Picture Archiving and Communication System for Public Healthcare....................... 2173 Carrison K. S. Tong, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China Eric T. T. Wong, The Hong Kong Polytechnic University, Hong Kong, China

Chapter 8.12. Imaging Advances of the Cardiopulmonary System................................................... 2183 Holly Llobet, Cabrini Medical Center, USA Paul Llobet, Cabrini Medical Center, USA Michelle LaBrunda, Cabrini Medical Center, USA Chapter 8.13. The Study of Transesophageal Oxygen Saturation Monitoring.................................. 2191 Zhiqiang Zhang, Sichuan University, China Bo Gao, Sichuan University, China Guojie Liao, Sichuan University, China Ling Mu, West China Hospital, China Wei Wei, West China Hospital, China

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Preface

Clinical technologies integrate the fields of Information Technology and Systems with healthcare, clinical design methodologies, and medicine. The constantly changing landscape of clinical technologies makes it challenging for experts and practitioners to stay informed of the field’s most up-to-date research. That is why Information Science Reference is pleased to offer this three-volume reference collection that will empower students, researchers, and academicians with a strong understanding of critical issues within clinical technologies by providing both extensive and detailed perspectives on cutting-edge theories and developments. This reference serves as a single, comprehensive reference source on conceptual, methodological, technical, and managerial issues, as well as providing insight into emerging trends and future opportunities within the discipline. Clinical Technologies: Concepts, Methodologies, Tools, and Applications is organized into eight distinct sections that provide wide-ranging coverage of important topics. The sections are: (1) Fundamental Concepts and Theories, (2) Development and Design Methodologies, (3) Tools and Technologies, (4) Utilization and Application, (5) Organizational and Social Implications, (6) Managerial Impact, (7) Critical Issues, and (8) Emerging Trends. Section 1, Fundamental Concepts and Theories, serves as a foundation for this extensive reference tool by addressing crucial theories essential to the understanding of clinical technologies. Chapters such as Risks and Benefits of Technology in Health Care, by Stefane Kabene and Melody Wolfe, introduce some of the fundamental issues involved with integrating technology into the field of healthcare. Other chapters introduce electronic health records, such as Adoption of Electronic Health Records, by Yousuf J. Ahmad, Vijay V. Raghavan, and William Benjamin Martz Jr., detailing the future of health records as they move forward in an increasingly electronic era. Other topics include medical (E-Medical Education by D. John Doyle) and nursing education (The use of Personal Digital Assistants in Nursing Education by Adrian Bromage and Nina Godson), and how e-learning has begun to take hold as a technological advancement allowing for off-site, online education. Overall, the first section is a fantastic introduction to some of the concepts and theories that will be addressed as the collection continues. Section 2, Development and Design Methodologies, presents in-depth coverage of the conceptual design and architecture of clinical technologies, focusing on aspects including the Balanced Scorecard framework, computer-aided diagnostics, biomedical imaging, and much more. Chapters in these areas include The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy Simulation Analysis by Mahendran Maliapen and Alan Gillies, detailing the potential for improving simulation analysis in healthcare policy, useful for tacticians and hospital designers interested in system dynamics and the Balanced Scorecard framework. Likewise, An Integrated System for E-Medicine (E-Health, Telemedicine and Medical Expert Systems) by Ivan Chorbev and Boban Joksimoski, is an included chapter, chosen for this reference book for its general breadth and articulation of the latest in design methodology within the clinical setting. Also included in this section are chapters on designing new medical vocabulary, and the alignment of clinical and general semantics. Together, section 2

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comprises a comprehensive collection of the most recent publication in the design and development methodologies in clinical technologies. Section 3, Tools and Technologies, presents extensive coverage of the various tools and technologies used in the development and implementation of clinical technologies, including some of the newest applications to healthcare and bioinformatics. This comprehensive section includes such chapters that detail new methods for diagnosis and treatment of genetic, chronic, infectious, and other types of diseases and conditions. Such chapters include Transcranial Magnetic Stimulation (TMS) as a Tool for Neurorehabilitation in Parkinson’s Disease by Javier Cudeiro, Nelson Espinosa, and Pablo Arias, and Quantitative Analysis of Hysteroscopy Imaging in Gynecological Cancer by Marios Neofytou, Constantinos Pattichis, Vasilios Tanos, Marios Pattichis, and Eftyvoulos Kyriacou. The section also specifically focuses on imaging technologies, with emphasis in chapters such as 3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization by Guang Li, Deborah Citrin, Robert W. Miller, Kevin Camphausen, Boris Mueller, Borys Mychalczak, and Yulin Song, as well as Image Registration for Biomedical Information Integration by Xiu Ying Wang and Dagan Feng. Section 3 concludes with a fascinating look at some recent developments in robot-assisted surgery. Section 4, Utilization and Applications, describes how clinical technology has been utilized, and offers insight on important lessons for its continued use and evolution. Due to the breadth of this section’s subject matter, section 4 contains the widest range of topics, including chapters such as The Use of Artificial Intelligence Systems for Support of Medical Decision-Making by William Claster, Nader Ghotbi, and Subana Shanmuganathan, and Applying Social Network Analysis in a Healthcare Setting by Salvatore Parise. As broad as the applications of clinical technology are, chapters within this section are pointed and precise, detailing case studies and lessons learned from integrating Information Technology with clinical systems. One intriguing example of this comes in the final chapter of the section, Predicting Ambulance Diversion by Abey Kuruvilla and Suraj M. Alexander, a detailed look at statistical and predictive analysis of one vital part of emergency care. Section 5, Organizational and Social Implications, includes chapters discussing the organizational and social impact of clinical technologies. Chapters are expository (Demystifying eHealth Human Resources by Candace J. Gibson and H. Dominic Covvey), pioneering, and based in research (Multi-Agent Systems in Developing Countries by Dean Yergens, Julie Hiner, and Joerg Denzinger). Overall, these chapters present a detailed investigation of the complex relationship between individuals, organizations and clinical technologies. Section 6, Managerial Impact, presents focused coverage of clinical technology as it relates to improvements and considerations in the workplace. In all, the chapters in this section offer specific perspectives on how managerial perspectives and developments in clinical technologies inform each other to create more meaningful user experiences. Typically, though the fields of industry and healthcare are not always considered co-dependent, section 6 provides looks into how clinical technologies and the business workplace help each other. Examples include Operationalizing the Science by Joseph L. Kannry and Technology in the Supervision of Mental Health Professionals by Daniel W. Surry, James R. Stefurak, and Richard L. Hayes. Section 7, Critical Issues, addresses some of the latest academic theory related to clinical technologies. Importantly, this refers to critical thinking or critical theory surrounding the topic, rather than vital affairs or new trends, which may be found in section 8. Instead, this section discusses some of the latest developments in ethics, law, and social implications in clinical technology development. Chapters include: eHealth and Ethics by Penny Duquenoy and Diane Whitehouse, Legal Issues in Health Information and Electronic Health Records by Nola M. Ries, and A Bio-Psycho-Social Review of Usability Methods and their Applications in Healthcare by Morgan Price.

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Section 8, Emerging Trends, highlights areas for future research within the field of clinical technologies, while exploring new avenues for the advancement of the discipline. Beginning this section is New Technologies in Hospital Information Systems by Dimitra Petroudi and Nikolaos Giannakakis, which gives a summary view of some of the newest technological developments in hospital communications and health records. The section advances through medical imaging techniques, diagnostics, virtual reality, and more new technologies by means of describing some of the latest trends in clinical research and development. The book concludes with The Study of Transesophageal Oxygen Saturation Monitoring by Bo Gao, Guojie Liao, Ling Mu, Wei Wei, and Zhiqiang Zhang, a chapter from some of the most updated publication on digital clinical technology. Although the primary organization of the contents in this multi-volume work is based on its eight sections, offering a progression of coverage of the important concepts, methodologies, technologies, applications, social issues, and emerging trends, the reader can also identify specific contents by utilizing the extensive indexing system listed at the end of each volume. Furthermore, to ensure that the scholar, researcher, and educator have access to the entire contents of this multi volume set, as well as additional coverage that could not be included in the print version of this publication, the publisher will provide unlimited multi-user electronic access to the online aggregated database of this collection for the life of the edition, free of charge when a library purchases a print copy. This aggregated database provides far more contents than what can be included in the print version, in addition to continual updates. This unlimited access, coupled with the continuous updates to the database, ensures that the most current research is accessible to knowledge seekers. As a comprehensive collection of research on the latest findings related to using technology to providing various services, Clinical Technologies: Concepts, Methodologies, Tools and Applications, provides researchers, administrators and all audiences with a complete understanding of the development of applications and concepts in clinical technologies. Given the vast number of issues concerning usage, failure, success, policies, strategies, and applications of clinical technologies in healthcare organizations, Clinical Technologies: Concepts, Methodologies, Tools and Applications addresses the demand for a resource that encompasses the most pertinent research in clinical technologies development, deployment, and impact.

Section I

Fundamental Concepts and Theories This section serves as the groundwork for this comprehensive reference book by addressing central theories essential to the understanding of clinical technologies. Chapters found within these pages provide a tremendous framework in which to position clinical technologies within the field of information science and technology. Insight regarding the critical integration of global measures into clinical technologies is addressed, while crucial stumbling blocks of this field are explored. In the chapters comprising this introductory section, the reader can learn and choose from a compendium of expert research on the elemental theories underscoring the clinical technologies discipline.

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Chapter 1.1

The Implementation of Innovative Technologies in Healthcare: Barriers and Strategies

Eddy M. M. Adang Radboud University Nijmegen Medical Center, The Netherlands

ABSTRACT Proven cost-effectiveness of innovative technologies is more and more a necessary condition for implementation in clinical practice. But proven cost-effectiveness itself does not guarantee successful implementation of an innovation. A reason for this could be the potential discrepancy between efficiency on the long run, on which cost-effectiveness is based, and efficiency on the DOI: 10.4018/978-1-60960-561-2.ch101

short run. In economics, long run and short run efficiency are discussed in the context of economies of scale. This chapter addresses the usefulness of cost-effectiveness for decision making considering the potential discrepancy between long run and short run efficiency of innovative technologies in healthcare, the potential consequences for implementation in daily clinical practice, explores diseconomies of scale in Dutch hospitals, and makes suggestions for what strategies might help to overcome hurdles to implement innovations due to that short run-long run efficiency discrepancy.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

The Implementation of Innovative Technologies in Healthcare

COST-EFFECTIVENESS AND IMPLEMENTATION Cost-effectiveness analysis as part of the evaluation of medical innovations has become mainstream in several European countries as in Canada and Australia. For example in the UK, the National Institute of Clinical Excellence (NICE) uses costeffectiveness outcome, expressed as cost per quality adjusted life year gained, as a criterion for coverage recommendations to the National Health Service (Weinstein, 2008). In the Netherlands the Dutch Health Insurance Board (CVZ) uses the cost-effectiveness criterion in their advise to the minister about the inclusion of expensive intramural pharmaceuticals in the benefit package. Unlike the reimbursement authorities in Canada and Australia, and in many countries in Europe, in the US Medicare officials do not formally consider cost-effectiveness when determining the coverage of new medical interventions (Neumann, 2005). In general one would assume that if the evidence on therapeutic value and cost-effectiveness of innovations in health care is convincing, implementation in clinical practice is warranted. However, a characteristic of the health care sector is the somewhat fuzzy priority setting about implementation and the numerous potential conflicts between the stakeholders in the health system. Also, behavioral factors in individual health professionals, such as clinical inertia and persistent routine behaviors, may inhibit change. Therefore implementation of evidence-based innovations and guidelines does not follow automatically, as there might be barriers for change at different levels that need to be solved. Specific strategies targeting increasing speed and level of adoption of innovations can be launched, such as ‘tailored’ strategies directed at the medical profession and patients (for example, providing information, education, training, communication etc.). However, in a situation with convincing cost-effectiveness evidence and a high willingness to implement by the medical profession, implementation of

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a technology or guideline into clinical practice might stagnate because there are negative consequences for specific stakeholders. The fact that economic evaluation might oversimplify complex health care decisions and disregards the multistakeholder issue has been noted before (Davies et al., 1994; Drummond et al., 2005; Eddama & Coast, 2008; Gold et al., 1996; Hoffmann & Graf von der Schulenburg, 2000; Johannesson, 1995; Lessard, 2007). This chapter argues that implementation of technologies, despite proven cost-effectiveness evidence, might be hampered due to specific economic barriers related to disincentives to implement by management of care providers. Key in the argumentation about successful implementation directed to technological change is the state of equilibrium in production: long run versus short run. Aletras (1999) concludes from his empirical work on estimating long-run and short-run cost functions in a sample of Greek NHS general hospitals that the use of long-run cost functions should be avoided since it might seriously mislead policymakers. Consequently, according to Aletras (1999), evidence on economies should presumably place lower validity weight on estimates derived from long-run as opposed to short-run cost functions. A necessary condition for achieving technical efficiency (an assumption underlying long-term cost-effectiveness) is that all inputs can be set at their cost-minimizing levels (Smet, 2002). However, inputs such as capital but also personnel with fixed labor contracts are difficult to adjust quickly to respond to changing output (levels) and will therefore not be set at their cost-minimizing level, given the output produced. This might have consequences for the management of health care providers who are often accountable for short run results like for example a balanced yearly budget or are restricted by tight financial frameworks. In health care systems where budgetary exceeds are sanctioned by a discount in the budget for next year (for example in the Netherlands) achieving

The Implementation of Innovative Technologies in Healthcare

budget becomes so important that those who perceive great pressure to meet budget, may be less inclined to partake of any activity that may cause temporarily inefficiencies in their environment (Adang, in press). Kallapur and Eldenburg (2005) found that if hospitals, when faced with increasing uncertainty, have a choice between technologies for a given activity, technologies with high variable and low fixed costs become more attractive relative to those with low variable and high fixed costs. The aim of this chapter is to discuss the usefulness of cost-effectiveness analysis in the context of implementation of innovations in health care. To come up with implementation strategies that are able to create a short run equilibrium that equals the long run equilibrium. This chapter is structured as follows: first the long run – short run discrepancy in efficiency is presented using an innovation in the treatment of psoriasis. Then the short-long run efficiency is discussed in an economic conceptual perspective focusing on returns to scale. Further, to get a quantitative impression whether diseconomies of scale are present in health care the prevalence of variable returns to scale is investigated in a sample of 33 Dutch hospitals (Adang & Wensing, 2008). Finally, using an example in diagnostics, implementation strategies that are able to overcome short run diseconomies are illustrated.

LONG RUN EFFICIENCY AND SHORT RUN LOSSES An earlier study about a new combined outpatient and home-treatment of psoriasis technology showed that 89% of the anticipated savings, based on the outcomes of a cost-effectiveness analysis, could not be achieved in the short run when implementing this technology due to inflexibility of production factors labor and infrastructure (Adang et al., 2005; Hartman et al., 2002). Consequently this meant that only €694 of the anticipated €6058

savings per patient could be freed and more than €5000 per patient could not be re-invested in the outpatient and home-treatment in the short run. This was considered a serious obstacle for health care management to implement the technology. On the other hand, the government and the medical profession strongly advocated the new technology as it was found convincingly cost-effective (Hartman et al., 2002). This resulted in the situation that both psoriasis treatment modalities existed next to each other. The “economic” reasoning behind this could be summarized as that the opportunity costs of using the inpatient treatment modality on the short run was very low. Hospital beds and personnel on the dermatology ward were fixed factors of production in the short run with little or no alternative use. At the end of the day both psoriasis treatment modalities co-existed in the same organization but were used at less than optimal capacity. Obviously information about an innovation’s cost-effectiveness provides not all economic information necessary for decision making about successful implementation. The evaluation of costs and benefits of new technologies and implementation of technologies is generally discussed in the context of Welfare Economics where welfare losses on the short run are considered ‘sunk’ (Adang, 2008; Drummond et al., 2005; Gold et al., 1996; Katz & Rosen, 1991). This might be true for stakeholders that decide on a long enough planning horizon where all production factors are flexible. In fact this convexity assumption is traditionally invoked in economics. In mathematical terms, in Euclidean space, an object is convex if for every pair of points within the object, every point on the straight line segment that joins them is also within the object. However, the convexity assumption is not without criticism. Farrel (1959) points to indivisibilities and economies of scale as sources of non-convexities. Allais (1977) confirms Farrel’s arguments where he rejects global convexity but favors local convexity. More recently for example Tone and Sahoo (2003) and Briec et al., (2004) point

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The Implementation of Innovative Technologies in Healthcare

to non-convexity behavior of inputs. In a global convexity context, technologies implemented by health care providers are assumed to be infinitely divisible and production factors are supposed to be operating in a constant returns to scale region. Local convexity constraints this reasoning to a specific range. Global convexity is often not the case on the short run (Adang et al., 2005). Some factors of production are costly to adjust in the short run, and this induces short-run decreasing returns to scale and an upward sloping short run marginal cost curve. Short run inefficiencies might cause disincentives for health care management to implement innovations. According to Smith and Street (2005) in the short run many factors (like for example differences in efficiency and organizational priorities) are outside the control of the organization. On the long run this potentially can change. Implementation strategies directed to solve the short run – long run discrepancy can be exogenous and endogenous. Exogenous means the solution lays outside the care provider, for example changing the health system (and budgetary system) in a way that is consistent with decision making based on long run efficiency. This chapter focuses on endogenous implementation strategies meaning strategies that can be employed by the care provider itself. Therefore to optimally implement innovations that in the long run are found cost-effective, implementation strategies need to be directed at minimizing welfare losses, or inefficiencies, on the short run. To build effective implementation strategies information about potential short run diseconomies is necessary.

THE ECONOMICS BEHIND SHORT AND LONG RUN EFFICIENCY The discussion about long run and short run cost and efficiency is in economic literature discussed as part of “theory of the firm” (Katz & Rosen, 1991). The relationship between short run and

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long run efficiency is associated with economies of scale and scope (Baumol, Panzar, Willig, 1988; Katz & Rosen, 1991). Returns to scale is a long run concept that reflects the degree to which a proportional increase in all inputs increases output. For example, one can consider scale economies to the degree that costs change in relation to changes in number of diagnostic performances. Cost-effectiveness analysis assumes constant returns to scale (Adang,2008; Drummond et al., 2005; Gold et al., 1996). This occurs when a proportional increase in all inputs results in the same proportional increase in output. This assumes that all production factors are functioning at optimal capacity. The case of the outpatient treatment of psoriasis innovation clearly shows that, at least on the short run, both treatment of psoriasis modalities do not function at optimal capacity and suffer from inefficiencies. Cremieux et al., (2005) argue that hospital unit costs may increase in response to increases in demand despite significant flexibility in inpatient care because other services such as laboratory exams may adjust poorly. Elbasha and Messonnier (2004) argue that technologies that are administered in health care settings often violate the assumption of constant returns to scale (costs increase linear with increasing production) that underlies cost-effectiveness analysis (CEA). These authors refer to various publications that illustrate the violation of the constant returns to scale assumption. For example, studies about nursing homes have revealed a mixture of findings, ranging from economies to diseconomies of scale (Dor, 1989; Gertler & Walman, 1992; Kass, 1987; Vitaliano & Toren, 1994). In fact, violation of the constant returns to scale assumption seems prevalent in health care. The constant returns to scale assumption is only appropriate when all health care programs or technologies are operating at an optimal scale. Imperfect competition, budgetary constraints, technology shifts, fixed contracts, etc. may cause a health care program to be not operating at optimal scale (Adang et al., 2005; Roberts, Frutos and Ciavarella, 1999).

The Implementation of Innovative Technologies in Healthcare

However, Kass (1987) presents empirical findings that show that economies of scale were not substantial in home health care. In this setting, according to Kass (1987), the ratio fixed to total costs was 5.6%, as labor (about 95%) could be considered variable costs and therefore moved linearly with output. On the other hand, Roberts, Frutos and Ciavarella (1999) found that about 84% of hospital costs were fixed. All this illustrates that diseconomies of scale are an important potential cause of inefficiency in the organization. Summarizing, diseconomies of scale refer to the relationship of average costs with volume of production. Diseconomies of scale arise when marginal costs of production get, with increasing volume of production, higher than average cost. This may be the result of a variety of factors: returns to scale, behavior of overheads, indivisibility of factors of production, nature of contracts between different stakeholders and organizational governance (Tone & Sahoo, 2003). Diseconomies of scope refer to the multipurpose use of capital investments. Diseconomies of scope are conceptually similar to diseconomies of scale. Where diseconomies of scale refer to changes in the output of a single technology, diseconomies of scope refer to changes in the number of different types of technologies (Adang, 2008). Being such a major source in explaining the potential long run – short run efficiency discrepancy raises the question whether (dis)economies of scale are prevalent in health care.

THE PREVALENCE OF DISECONOMIES OF SCALE IN DUTCH HOSPITALS To investigate the prevalence of returns to scale in health care a sample of 33 Dutch hospitals was researched (Adang & Wensing, 2008). The data came from annual reports of Dutch hospitals over the year 2003 (Adang, 2005). To create insight in whether these hospitals are working in the area of

constant returns to scale, increasing returns to scale or decreasing returns to scale, data envelopment analysis (DEA) was applied. In fact there are two principal approaches to analyzing the presence of returns to scale in organizations: parametric (corrected ordinary least squares and stochastic production frontiers) and non parametric (DEA). According to Coelli (1998) in the non-profit service sector, where random influences are less of an issue, multiple-output production is important, prices are difficult to define and behavioral assumptions, such as cost minimization or profit maximization, are difficult to justify, the DEA approach may often be the optimal choice. DEA is a non-parametric approach originally developed in the field of operations research for evaluating the performance of a set of peer entities using linear programming techniques (Charnes, Cooper and Rhodes, 1978). Farrel (1957) proposed a piece-wise-linear convex hull approach to efficient frontier estimation. Linear programming methods are used to construct a non-parametric piece-wise frontier over the data. Efficiency measures are then calculated relative to this frontier (Coelli, 2004). Charnes, Cooper and Rhodes (1978) developed a DEA model that assumed constant returns to scale (CRS). However, as is described earlier in this chapter the constant returns to scale assumption will be rejected if organizations are not operating at an optimal scale. Banker, Charnes and Cooper (1984) suggested an extension of the constant returns to scale DEA model to account for variable returns to scale (VRS). The analysis regarding the prevalence of (dis) economies of scale in hospitals begins with the determination of the hospital production function. The inputs of the hospital production function were: personnel costs, feeding and hotel costs, general costs, patient related costs and maintenance and energy costs. The outputs were: number of day-care, number of hospital days, and number of first outpatient visits. This production function is surely not complete, however the inputs and

5

The Implementation of Innovative Technologies in Healthcare

outputs are well acceptable and generally used, given a hospital environment. Inputs and outputs were on a yearly basis. The basic constant returns to scale DEA model is represented as follows: Min s.t .

Figure 1. Constant & Variable returns to scale

θ n

∑λ x

≤ θx i 0

i = 1, 2,..., m (inputs )

∑ λj yrj ≥ yr 0

r = 1, 2,..., s(outputs )

j =1 n

j

ij



j =1

λj ≥ 0

λj is the weight given to hospital j in its efforts to dominate hospital 0 and θ is the efficiency of hospital 0. Therefore, λ and θ are the variables to solve from the model. Figure 1 displays two production frontiers, one under constant returns to scale and one under variable returns to scale. Scale efficiencies are found by comparing efficiency on the variable returns to scale frontier to efficiency on the constant returns to scale frontier (Coelli, 2004). The variable returns to scale model adds the convexity constraint

n

∑λ j =1

j

= 1 to the constant returns

to scale DEA model. The convexity constraint ensures that an inefficient hospital is only benchmarked against hospitals of a similar size. This approach forms a convex hull of intersecting planes which envelope the data points more tightly than the constant returns to scale model. To explore returns to scale both DEA models were run using an input-orientation (Zhu, 2003). I, II, III, IV, V and VI are the regions associated with Table 1. Solving the VRS model (Table 1) shows that variable returns to scale are prevalent in Dutch hospitals. Results show that 55% of the hospitals were operating under decreasing returns to scale, 33% under constant returns to scale and 12% under increasing returns to scale. Based on these results it seems appropriate to assume that (dis)

6

economies of scale are prevalent in the Dutch hospital environment and consequently a potential concern for implementation of innovations.

SOLUTIONS FOR REDUCING SHORT RUN DISECONOMIES: CHOOSING THE RIGHT IMPLEMENTATION STRATEGIES Recently it was found that in patients with suspicion for prostate cancer diagnosis using a MRI device (with a specific contrast agent) is costeffective (the point estimate showed dominance: saving money and gaining quality adjusted life years) compared to diagnosis with a CT device (Hovels et al., 2008). From a long run perspective it is therefore clinically and economically sound to substitute CT for MRI in this particular patient group. Now let’s assume that prior to substitution both modalities CT and MRI were functioning at long run equilibrium i.e., optimal capacity (at constant returns to scale). To deal with the extra demand for MRI it is necessary to increase capacity by investing in an extra MRI device. On the short run output (number of MRIs) will increase less than proportionate with the input (production factors) (see Figure 2). This results in disecono-

The Implementation of Innovative Technologies in Healthcare

Table 1. Returns to scale in Dutch hospitals

Hospital location in the Netherlands

Input-oriented

Input-oriented

VRS

CRS

Input-oriented

RTS Region

Efficiency

Efficiency

1

Maastricht (UMC)

Region III

0,64782

0,48635

2,04154

Decreasing

2

Amsterdam (UMC)

Region III

0,56774

0,31385

3,02658

Decreasing

∑λ

RTS

3

Amsterdam (UMC)

Region III

0,48221

0,40303

1,94916

Decreasing

4

Groningen (UMC)

Region III

1,00000

0,50571

3,26753

Decreasing

5

Utrecht (UMC)

Region III

0,58401

0,33767

3,91163

Decreasing

6

Nijmegen (UMC)

Region III

0,58175

0,38371

2,37224

Decreasing

7

Leiden (UMC)

Region III

0,51136

0,44677

1,94743

Decreasing

8

Rotterdam (UMC)

Region III

1,00000

0,33021

3,31326

Decreasing

9

Deventer

Region III

0,85615

0,79930

1,32166

Decreasing

Tilburg

Region III

1,00000

0,77854

2,05430

Decreasing

10 11

Amsterdam

Region III

0,86786

0,69859

2,18329

Decreasing

12

Sittard

Region III

0,95313

0,85709

1,49226

Decreasing

13

Nieuwegein

Region III

0,89809

0,71196

1,66093

Decreasing

14

Ede

Region III

1,00000

0,99875

1,88144

Decreasing

15

Dordrecht

Region II

1,00000

1,00000

1,00000

Constant

16

Den Haag

Region III

1,00000

0,85115

1,65932

Decreasing

17

Delft

Region II

1,00000

1,00000

1,00000

Constant

18

Alkmaar

Region III

1,00000

0,78603

2,07834

Decreasing

19

Drachten

Region II

1,00000

1,00000

1,00000

Constant

20

Sneek

Region II

1,00000

1,00000

1,00000

Constant

21

Rotterdam

Region II

1,00000

1,00000

1,00000

Constant

22

Hilversum

Region VI

0,85072

0,85036

0,98041

Increasing

23

Winschoten

Region II

1,00000

1,00000

1,00000

Constant

24

Roosendaal

Region II

1,00000

1,00000

1,00000

Constant

25

Den Haag

Region I

0,97634

0,97023

0,92509

Increasing

26

Almere

Region II

1,00000

1,00000

1,00000

Constant

27

Lelystad

Region I

0,97577

0,95121

0,91390

Increasing

28

Utrecht

Region II

1,00000

1,00000

1,00000

Constant

29

Den Helder

Region I

0,97726

0,92168

0,74411

Increasing

30

Hoogeveen

Region II

1,00000

1,00000

1,00000

Constant

31

Rotterdam

Region III

1,00000

0,83530

2,55243

Decreasing

32

Enschede

Region III

1,00000

0,76230

2,45027

Decreasing

33

Zwolle

Region II

1,00000

1,00000

1,00000

Constant

RTS region means returns to scale region (see Figure 1). VRS means variable returns to scale CRS, IRS, DRS mean respectively constant, increasing and decreasing returns to scale:

7

The Implementation of Innovative Technologies in Healthcare

Figure 2. Returns to scale

mies of scale on the short run, assuming that on the long run constant returns to scale is restored. Implementation of the MRI modality has also consequences for the CT modality. To successfully implement MRI there is a need to establish substitution between both modalities. So an increase in demand for MRI comes with a decrease in demand for CT in the short run. This demand shift causes to reduce the multipurpose function of the CT modality and causes, on the short run, diseconomies of scope (see Figure 3). How do these phenomenon translate into barriers of implementation? This can be illustrated with the following numerical example. Table 2 shows the hypothetical production process on a yearly basis of the CT and MRI modality for a Figure 3. Returns to scope

8

particular organization. Let’s assume that fixed costs of production constitute of depreciation costs of the CT and MRI device respectively. Variable costs constitute of personnel and material cost like contrast agents. In the short run the health care provider has an incentive (marginal costs decrease) to meet the extra demand by investing in the variable input. However, in Table 2 the assumption is made that the health care provider is functioning at full capacity and in order to meet the extra demand for MRI needs to invest in an extra MRI device. Also assume that the health care provider negotiated earlier an internal transfer price or with the insurance companies a tariff of €75 for a CT image and €165 for an MRI image. Initially profitable performances need now to be compensated elsewhere in the health care organization to meet a balanced budget at the end of the year. The management which is accountable for a balanced budget at the end of the year has, under these circumstances, a disincentive to implement the MRI modality. This disincentive is due to inflexibility of the production factor MRI and CT capacity. In general innovations cause shifts in the demand for services. Implementation strategies consequently should be directed at increasing flexibility of production factors. The first step in the development of a framework for implementation strategies directed at increasing flexibility in production factors is the identification of fixed factors of production. Adang et al., 2005 developed a simple checklist specifically for the health care context to detect the proportion of fixed costs in the total production costs. Next, the fixed factors of production need to be disentangled into specific factors, like for example housing, capacity and personnel. Next a strategy directed at increasing flexibility of the specific production factor(s) should be developed. To illustrate this approach it will be applied to the diagnostic modality case. Employing the checklist to the diagnostic case shows that diagnostic capacity (both MRI and CT) is the major

The Implementation of Innovative Technologies in Healthcare

Table 2. Illustration of hurdle to implementation Production*

Fixed costs

Variable costs

CostPrice

New production*

New Fixed costs

Variable costs

CostPrice

CT

10000

200000

50

70

8000

200000

50

75

MRI

3500

350000

60

160

5500

700000

60

187

Cost in € *Production in number performances per year

inflexible factor. Now a tailored strategy directed at making diagnostic capacity more flexible needs to be developed. Making capacity more flexible urges for capacity planning. Depending on whether the production process is operating under decreasing returns to scale or increasing returns to scale a capacity planning strategy needs to be developed. Under increasing returns to scale capacity planning should be directed to increase production with the same inputs. Under decreasing returns to scale outsourcing production to meet extra demand can be a way of capacity planning without changing the size of operations in the organization. This means that the organization that is facing these inefficiencies needs to search for organizations that have excess MRI capacity available and are in need for extra capacity CT. Consequently this organization faces search costs that result in an increase in marginal cost. A regional market for diagnostic capacity could limit search costs and increase the adoption of innovations by limiting or even eliminating short run diseconomies. Such a market is not viable if all organizations in that particular market are operating at optimal capacity (i.e., constant returns to scale, meaning the most productive scale size). This means that some organizations need to operate under increasing returns to scale whereas others operate under decreasing returns to scale. These latter organizations produce above the optimal scale of operations and would improve their efficiency by downsizing or outsourcing, whereas for the organizations operating at increasing returns to scale it is efficient to increase production.

A minor inflexible factor of production (semifixed) was personnel. An MRI image takes more time than a CT image. An implementation strategy directed at personnel, being the inflexible production factor, could be for example teleradiology. According to Wachter the technical and logistic hurdles of remote teleradiology have been overcome (Wachter, 2006). This implementation strategy follows theoretically the same principles as in the capacity example. Other implementation strategies aiming at increasing flexibility of the labor force are ‘learning’ and ’making labor contracts more flexible’ (Alonso-Borrego, 1998).

RECOMMENDATIONS AND DISCUSSION In general firms’ ability to adjust to demand shifts due to innovations will determine fluctuations in the average cost of the firm. In a competitive market, a firms’ inability to cope with shifting demand will result in exit or major restructurings. In a non-market industry, like health care, similar rigidities may persist without exit or serious corrections, unless health care management identifies their sources and apply appropriate remedies (Cremieux et al., 2005). Implementation of cost-effective technologies (pharmaceuticals, technological devices, practice guidelines, etc.) in clinical practice is therefore a crucial process in healthcare, as in all industries. Health technology assessment has focused on long-term efficiency at societal level, ignoring short term inefficiencies for a specific hospital or

9

The Implementation of Innovative Technologies in Healthcare

other health care provider (Adang & Wensing, 2008). Cost-effectiveness analysis would be more informative if it extended the long run perspective with an additional short run perspective (Adang, 2008). Such an extended cost-effectiveness analysis provides not only insight in the cost-effectiveness of an innovation but also in the hurdles that need to be taken to implement the innovation. This leads to less co-existence of alternative treatment modalities, a higher adoption rate of the cost-effective modality and ultimately it leads to an increase in hospital value. Research on implementation processes in healthcare has focused mostly on perceived barriers for behavioral change and on educational interventions targeted at health professionals. This chapter aimed to contribute to both bodies of research. Key to solving this long run – short run efficiency discrepancies are implementation strategies directed towards making fixed factors of production more or entirely flexible. If all factors of production are entirely flexible the contrast between long run and short run efficiency will no longer exist and is then paradoxical. This chapter has been largely argumentative. There is little empirical evidence in support that the implementation strategies directed to increasing flexibility of factors of production lead to higher level and faster adoption compared to usual implementation strategies. Empirical research on this topic is important to quantify the impact of changes in production factors, and to examine potential side effects.

REFERENCES Adang, E. M. (2005). Efficiency of hospitals [in Dutch] [ESB]. Economic Statistical Bulletin, 4451, 40–41. Adang, E. M., & Voordijk, L., Wilt van der, G.J., & Ament, A. (2005). Cost-effectiveness analysis in relation to budgetary constraints and reallocative restrictions. Health Policy (Amsterdam), 74, 146–156. doi:10.1016/j.healthpol.2004.12.015 10

Adang, E. M. M. (2008). Economic evaluation in healthcare should include a short run perspective. The European Journal of Health Economics, 9, 381–384. doi:10.1007/s10198-007-0093-y Adang, E. M. M., & Wensing, M. (2008). Economic barriers to implementation of innovations in healthcare: Is the long run-short run efficiency discrepancy a paradox? Health Policy (Amsterdam), 2-3, 256–262. Aletras, A. (1999). Comparison of hospital scale effects in short run, long run cost functions. Health Economics, 8, 521–530. doi:10.1002/(SICI)10991050(199909)8:6<521::AID-HEC462>3.0.CO;2G Allais, M. (1977). Theories of general economic equilibrium and maximum efficiency. In G. Schwodiauer (Ed.), Equilibrium and Disequilibrium in Economic Theory (pp.129-201). Dordrecht: Reidel. Alonso-Borrego, C. (1998). Demand for labour inputs and adjustment costs: Evidence from Spanish manufacturing firms. Labour Economics, 5, 475–497. doi:10.1016/S0927-5371(98)00011-6 Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092. doi:10.1287/mnsc.30.9.1078 Baumol, W., Panzar, J. C., & Willig, R. D. (1988). Contestable markets and the theory of industry structure. San Diego: Harcourt Brace Jovanovich. Briec, W., Kerstens, K., & VandenEeckhaut, P. (2004). Nonconvex technologies and cost functions: Definitions, duality, and nonparametric tests of convexity. Journal of Economics, 81, 155–192. doi:10.1007/s00712-003-0620-y Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444. doi:10.1016/0377-2217(78)90138-8

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Coelli, T., Prasada Rao, D. S., & Battese, G. E. (2004). An introduction to efficiency and productivity analysis. Norwell, MA: Kluwer Academic Publishers.

Gertler, P., & Walman, D. (1992). Quality-adjusted cost functions and policy evaluation in nursing home industry. The Journal of Political Economy, 100, 1232–1256. doi:10.1086/261859

Cremieux, P. Y., Ouellette, P., & Rimbaud, F. (2005). Hospital cost flexibility in the presence of many outputs: A public-private comparison. Health Care Management Science, 8, 111–120. doi:10.1007/s10729-005-0394-6

Gold, M. R., Siegel, J. E., Russel, L. B., et al. (1996). Cost-effectiveness in health and medicine. New York: Oxford University Press.

Davies, L., Coyle, D., & Drummond, M., & The EC Network. (1994). Current status of economic appraisal of health technology in the European community: Report of the network. Social Science & Medicine, 38, 1601–1607. doi:10.1016/02779536(94)90060-4 Dor, A. (1989). The costs of medicare patients in nursing homes in the United States: A multiple output anapysis. Journal of Health Economics, 8, 253–270. doi:10.1016/0167-6296(89)90021-0 Drummond, M. F., Sculpher, M. J., Torrance, G. W., O’Brien, B., & Stoddart, G. L. (2005). Methods for the economic evaluation of health care programmes. Oxford, UK: Oxford University Press. Eddama, O., & Coast, J. (2008). A systematic review of the use of economic evaluation in local decision-making. Health Policy (Amsterdam), 86, 129–141. doi:10.1016/j.healthpol.2007.11.010 Elbasha, E. H., & Messonnier, M. L. (2004). Cost-effectiveness analysis and healthcare resource allocation: Decision rules under variable returns to scale. Health Economics, 13, 21–35. doi:10.1002/hec.793 Farrel, M. (1959). The convexity assumption in the theory of competitive markets. The Journal of Political Economy, 67, 377–391. doi:10.1086/258197 Farrel, M. J. (1975). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 3, 253–290.

Hartman, M., Prins, M., & Swinkels, O. Q. J. (2002). Cost-effectiveness analysis of a psoriasis care instruction programme with dithranol compared with UVB phototherapy and inpatient dithranol treatment. The British Journal of Dermatology, 147, 538–544. doi:10.1046/j.13652133.2002.04920.x Hoffmann, C., & Graf von der Schulenburg, J. M. (2000). The influence of economic evaluation studies on decision making: A European survey. The EUROMET group. Health Policy (Amsterdam), 52, 179–192. doi:10.1016/S01688510(00)00076-2 Hovels, A., Heesakkers, R. A. M., & Adang, E. M. M. (in press). Cost-effectiveness analysis of MRI with a lymph node specific contrast agent for the detection of lymph node metastases in patients with prostate cancer and intermediate or high risk of lymph node metastases. Radiology. Johannesson, M. (1995). Economic evaluation of health and policymaking. Health Policy (Amsterdam), 33, 179–190. doi:10.1016/01688510(95)00716-6 Kallapur, S., & Eldenburg, L. (2005). Uncertainty, real options, and cost behavior: Evidence from Washington state hospitals. Journal of Accounting Research, 43, 735–752. doi:10.1111/j.1475679X.2005.00188.x Kass, D. (1987). Economies of scale, scope in the provision of home health services. Journal of Health Economics, 6, 129–146. doi:10.1016/01676296(87)90003-8

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Katz, M. L., & Rosen, H. S. Microeconomics. Boston: Irwin. Lessard, C. (2007). Complexity and reflexivity: Two important issues for economic evaluation in healthcare. Social Science & Medicine, 64, 1754– 1765. doi:10.1016/j.socscimed.2006.12.006 Neumann, P. J., Rosen, A. B., & Weinstein, M. C. (2005). Medicare and cost-effectiveness analysis. The New England Journal of Medicine, 353(14), 1516–1522. doi:10.1056/NEJMsb050564 Roberts, R., Frutos, P., & Ciavarella, G. (1999). The distribution of variable vs. fixed costs of hospital care. Journal of the American Medical Association, 281, 644–649. doi:10.1001/ jama.281.7.644 Smet, M. (2002). Cost characteristics of hospitals. Social Science & Medicine, 55, 895–906. doi:10.1016/S0277-9536(01)00237-4 Smith, P. C., & Street, A. (2005). Measuring the efficiency of public services: The limits of analysis. Journal of the Royal Statistical Society, 168, 401–417. doi:10.1111/j.1467-985X.2005.00355.x Tone, K., & Sahoo, B. K. (2003). Scale, indivisibilities, and production function in data envelopment analysis. International Journal of Production Economics, 84, 165–192. doi:10.1016/ S0925-5273(02)00412-7 Vitaliano, D., & Toren, M. (1994). Cost and efficiency in nursing homes: A stochastic frontier approach. Journal of Health Economics, 13, 281–300. doi:10.1016/0167-6296(94)90028-0 Wachter, R. M. (2006) International teleradiology. N Engl J Med., 16, 354(7), 662-663.

Weinstein, M. C. (2008). How much are Americans willing to pay for a quality adjusted life year? Medical Care, 46, 343–345. doi:10.1097/ MLR.0b013e31816a7144 Zhu, J. (2003). Quantitative models for performance evaluation and benchmarking. New York: Springer Science +Business Media, Inc.

KEY TERMS AND DEFINITIONS Cost-Effectiveness Analysis: Tests the null hypothesis that the mean cost-effectiveness of one health care innovation is different from the cost-effectiveness of some competing alternative often common practice. Diseconomies of Scale: When average costs rise with the output level. Efficiency: How well an organization is performing with regard to optimizing its output for given inputs or its inputs for given output. Implementation: The way an objective is realized, here refers to an innovation adopted by the specific stakeholder(s). Long Run: A period where all factors of production are variable. Returns to Scale: An economic term that describes what happens if the scale of production changes. Short Run: A period where at least one of the factors of production is fixed. Technology in Health Care: A broad concept that deals with innovations in pharmaceuticals, diagnostics, disease management, guideline development and so on.

This work was previously published in Handbook of Research on Information Technology Management and Clinical Data Administration in Healthcare, edited by Ashish N. Dwivedi, pp. 321-332, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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13

Chapter 1.2

Risks and Benefits of Technology in Health Care Stefane M. Kabene University of Western Ontario, Canada Melody Wolfe University of Western Ontario, Canada

ABSTRACT The integration of technology into health care has created both advantages and disadvantages for patients, providers, and healthcare systems alike. This chapter examines the risks and benefits of technology in health care, with particular focus on electronic health records (EHRs), the availability of health information online, and how technology affects relationships within the healthcare setting. Overall, it seems the benefits of technology in health care outweigh the risks; however, it is imperative that proper measures are taken to ensure successful implementation and DOI: 10.4018/978-1-60960-561-2.ch102

integration. Accuracy, validity, confidentiality, and privacy of health data and health information are key issues that must be addressed for successful implementation of technology.

ELECTRONIC HEALTH RECORDS Technological advances in information and communication technologies (ICT) and computing have made way for the implementation of electronic health records (EHRs), the comprehensive compilation of health care provided to an individual over their lifetime —an exciting and impressive accomplishment. Despite the vast possibilities and efficiencies that EHRs can potentially

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Risks and Benefits of Technology in Health Care

offer, their implementation into existing healthcare systems poses some potentially deterring and serious risks, such as confidentiality breaches, identity theft, and technological breakdowns and incompatibilities. Therefore, electronic records should be not hastily integrated into healthcare systems without proper precautions.

Advantages Electronic records offer many advantages over conventional paper-based methods of recording patient data. The comprehensiveness of EHRs can help to bridge the geographic and temporal gaps that exist when several clinicians who are geographically dispersed treat the same patient. It is extremely important that all clinicians are aware of past and current medical histories when one patient is treated by several healthcare providers (Mandl, Szolovits, & Kohane, 2001). Since paperbased records are location specific, information contained in one record may differ substantially from records kept in another area or by another provider. When various specialists treat the same patient, patient communication is often hindered, as it can be extremely difficult and time consuming to share patient records between providers using conventional methods (for example, by phone, fax or mail, or physically transporting the record from location to location). Electronic health records, however, enable comprehensive databases of information to be viewed and used by authorized users when they need it and where they need it. Greater efficiency in accessibility of patient information is thus made possible by the use of electronic records. Accessibility allows for a faster transfer of medical history in a medical emergency or when visiting a new doctor, and also allows researchers and public health authorities—with the permission and consent of the patient—to efficiently collect and analyze updated patient data. Such access is imperative in emergency situations, and also allows public health officials to easily

14

conduct outbreak and incident investigations that may help control epidemics and pandemics, such as SARS, Listeriosis, or new strains of influenza. Accessibility also enables health care providers to reduce costs associated with duplicating tests, since providers have access to already performed test results (Myers, Frieden, Bherwani, & Henning, 2008). Additionally, clerical activities such as appointment reminders and notification of laboratory results can be handled electronically, resulting in greater efficiency and reduced human error. EHRs can also be equipped with authentication systems, a major guard against security breaches. Patients may be especially wary of having their personal health information part of a comprehensive database because they are unsure as to who will have access to their medical records. Authentication systems allow for the imposition of various security levels, providing greater control over access to personal information such as immunization records and diagnostic test results. Conversely, paper-based medical records allow healthcare staff to access any part of a patient’s medical records. By applying authentication and role-based access to EHRs, personnel such as secretaries and clerical staff will only have access to necessary information (such as that needed for scheduling appointments or providing reminders of scheduled visits) (Myers et al., 2008). In case of an emergency, however, it is possible to develop policies that allow medical professionals to override the protection barriers and gain immediate access to all medical information (Mandl et al., 2001). An additional security feature is accountability, which enables the system to track input sources and record changes. Accountability systems provide an audit trail that can help to eliminate security breaches and, at the very least, track user activities to ensure their appropriateness, authorization, and ethicality (Myers et al., 2008). Despite the impressive advantages EHRs offer, one must recognize the trade off that exists between accessibility and confidentiality. As noted by Rind et al. (1997, p. 138) “It is not always

Risks and Benefits of Technology in Health Care

possible to achieve both perfect confidentiality as well as perfect access to patient information, whether information is computerized or handwritten.” Confidentiality, among other issues, must be considered in order to utilize the EHR system to its fullest potential.

Disadvantages One potential deterrent to full implementation of the electronic health record is compatibility and interoperability across different health information systems. Electronic health records require a standardized system and technology to promote transfer, input and compilation of data from multiple sources; unfortunately, these standards are not easily achieved due to the complexity of linking disparate and often older legacy systems and the incumbent costs of doing so. Thus, despite the potential of EHRs to increase communication between practitioners, and practitioners and patients, the various already existing computer systems pose a major road block. Until a standard model for secure data transmission and linkage is efficiently in place, EHRs will remain fragmented and inconsistent. Of course, the most serious danger in widespread EHR implementation is the potential for security breaches. Electronically-bound information always comes with the risk of being exposed to inappropriate parties; “No matter how sophisticated security systems become, people will always manage to defeat them” (Mandl et al., 2001, p. 285). Due to the wealth of information contained in the EHR, they are an obvious target to for hacking and identity theft. Additionally, because electronic theft can occur in a variety of contexts—including the comfort of one’s home— electronic theft may be harder to track or detect and require less physical effort and planning. In contrast, physically stealing medical records from an office is difficult to accomplish successfully, since most medical offices implement effective security measures.

Aside from security breaches, there are core problems associated with using computers to house mass amounts of important and often sensitive information. Technological issues arise throughout the lifetime of a computer system. Not only are computer systems created and updated rapidly, there are few systems that last the entire lifespan of a person. Such technological issues raise questions regarding the safety of transferring medical information from system to system, as any breakdown may cause record loss, or pose a potential security breach (Mandl et al., 2001). The reaction of patients to confidentiality and privacy issues pose a major concern as well. As previously mentioned, patients have expressed concerns about who is seeing their medical information and for what purpose it is being used. Concerns also arise when patients are unable to see their own records, but secondary sources can view these records in an unregulated manner. Consequently, it has been noted that “they might fail to disclose important medical data or even avoid seeking medical care because of concern over denial of insurance, loss of employment or housing, or stigmatisation and embarrassment” (Mandl et al., 2001, p. 284). The failure to disclose important medical information can affect patient health and increase the risk of misdiagnosis. Since privacy is important for most patients when it comes to their medical records and health history, one option is to allow patients the right to decide who can examine and alter specific parts of their records. Although this seems like a plausible solution, its complexity can create problems that may lead to inferior care and uninformed practitioners (e.g., if certain portions of the history are restricted). Thus, granting patients the right to monitor their own privacy is not a solution in and of itself.

Discussion It is very likely that EHRs will be the key to linking disparate pieces of a patient’s medical record into a unified database. The comprehensiveness

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Risks and Benefits of Technology in Health Care

and accessibility of EHRs will mend the gap in provider-provider communication that currently exists due to temporal and geographic differences in paper-based records. However, in order for these possibilities to actualize, proper precautions must be taken, and specific policies enforced. Authentication and audit trails of EHRs allow various health care professionals different levels of access to patient records, as well as a mechanism to track the use and changes of each patient’s file. Aside from these security enhancing features, various preventative actions should be taken in order to eliminate the risks associated with EHR use. When an electronic record system is put in place, staff and healthcare providers must use the system properly to obtain beneficial outcomes. Accordingly, education and training are also necessary components in enabling individuals to understand and maximize EHR system abilities. As well, a “change agent,” or expert on electronic record systems, could help successful implementation and adoption of digital medical records. This change manager would be in charge of training staff, monitoring security and confidentiality, and enforcing policy. In addition to educating employees, several preventative measures should be embedded into EHR systems; for example, passwords allowing for authentication and limiting the access of each person on an individual, role-based basis. Since different information is needed by various personnel, these passwords would allow doctors to access a wider range of information than, for instance, a secretary. Encryption, or the process of making the data unreadable unless the user possesses suitable authorization, is another preventative measure which hinders hackers from obtaining confidential information. Furthermore, it should be noted that already existing policies aid the privacy of an individual’s electronic health record. In Canada, federal privacy legislation under the Personal Information Protection and Electronic Documents Act (PIPEDA) ensures privacy via rules for the collection, use, and disclosure of

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personal information (Taylor, 2003). In using EHR systems, health care providers must ensure that they comply with the PIPED Act in order to eliminate the risks of confidentiality and privacy breaches. With regards to the issue of authentication, access to a patients’ medical record for reasons incongruent with the individual’s health care should be a decision that only the patient themselves can make. Although public health and government research could significantly benefit from access to patient medical records, due to issues of privacy and confidentiality, it is advised that every patient should possess the right to deny access to their medical records for public use. Additionally, patients who choose to provide personal health information to government and public health associations should have the ability to limit what information is exposed. A technology such as “time keys” could be utilized in alignment with electronic records in allowing patients to give an organization access to their records only for a specified amount of time (Mandl et al., 2001). Since patients visit a number of healthcare service providers over the course of their life, each provider has a different computer system that encodes and stores patient data according to different levels and standards—which poses a great challenge when attempting to unify all patient information into a single database. It has not yet been fully understood how one system will capture all data from various physicians into a standard computer form or interface that shares the information with other computers (i.e. similar to the web browser and access to disparate web pages) or whether only a minimum data set will be captured in a central repository. In the future, universal coding and standardization of data must be implemented in order to manage fragmented pieces of patient data. Due to vast language barriers and numerous medical terminologies, universal coding is a challenge; however, it is hopeful that future advancements in technology can develop

Risks and Benefits of Technology in Health Care

systems able to appropriately incorporate EHRs and shared data repositories (Mandl et al., 2001).

ONLINE MEDICAL INFORMATION Demographics Fifty two million adults have used the Internet to access health or medical information through an estimated 20, 000 to 100,000 health related websites (Diaz et al., 2002). In general, 62% of Internet users go online for medical advice, and most of these Internet users are women between the ages of 50 and 64 (Rice, 2006). Searching for health information increases with education, as well as with years of Internet experience and access to a broadband connection (Rice, 2006, p 10). In a survey conducted by Diaz et al., (2002) 54% of respondents revealed that they used the Internet for health information, and a higher proportion of patients who used the Internet were college educated with an annual household income greater than $50 000.

Advantages Technological advancement—in particular, the use of Internet research for medical information—has a major influence on health care for both patients and providers alike. Because the Internet provides such accessible health information, individuals are often encouraged to seek out information regarding their own health, as well as about the health of others. Additionally, medical information on the Internet allows individuals to become exposed to a wide array of health information and become involved in their own personal health (Rice, 2006). In North America, the use of Internet research is an increasingly popular strategy for obtaining diagnostic information. In October 2002, 73 million American adults used the Internet to find health care information, and by November of 2004, this number had risen to 95 million (Rice, 2006).

There are many advantages associated with the use of the Internet for medical research, such as easy access to a wide array of information, personalized information that is uncontaminated by medical jargon, and anonymity while searching for health information (Rice, 2006). Additionally, online websites and support groups provide information, support, acceptance, and a sense of understanding to patients and their loved ones (Rice, 2006). Since the Internet allows for immediate access to an incredible amount of information directed at both health care providers and patients, it provides a sense of privacy, convenience, and perspective (Cotton & Gupta, 2004). The Internet also allows users to ask awkward, sensitive, or detailed questions without the risk of facing judgment, scrutiny, or stigma (Cotton & Gupta, 2004). Consequently, doctors are beginning to see a new type of patient: a patient with sharp intelligence and curiosity who knows how to utilize and benefit from information available online (Ferguson, 2000). Perhaps the most significant benefit online medical information offers is its ability to prevent long hospital wait-times for minor issues. Patients suffering from trivial matters who are simply seeking reassurance from doctors can go online and seek information without going to the hospital. Additionally, patients can gain medical knowledge and insight that may teach them to distinguish between symptoms that seem minor, but are actually serious. The Internet allows for patients to consult various perspectives on illness and treatments rather than visiting several doctors for multiple opinions. For example, if a patient is given a type of medicine to treat a thyroid problem, they can go online and research it, which enables them to gain perspective on their own medical issue. Since online medical information is free, it is fairly accessible to patients in tough financial positions. This is especially advantageous for countries such as the United States, in which private health care is extremely expensive. Online health information provides citizens of the United States with immediate information

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about health problems; such knowledge may help citizens decide whether it is necessary to visit a doctor or hospital.

Disadvantages Although there are many advantages associated with the ability to research health-related information online, such as increased patient medical knowledge, it is imperative to consider the various disadvantages associated with hastily researching medical information on anonymous databases. For example, accurate information on credible websites may be hidden behind complicated medical language, and the quality and authenticity of available information is often questionable. At times, the vast amount of online information can be confusing, making it difficult for a patient to filter out what is important. Studies of postsurgery patients revealed that 83% had difficulties understanding online health information, and one third felt that the retrieved information was overwhelming (Rice, 2006). A major problem occurs when patients try to diagnose and treat a potentially serious medical condition without consulting a doctor. Research indicates that 11% of those using the Internet for medical information revealed that they did not discuss this information with their doctor (Diaz et al., 2002). According to Berland et al. (2001), less than one quarter of search engines’ first pages of links lead to relevant content; therefore, accessing health information using search engines and simple search terms is not sufficient. Additionally, Internet usage is often hindered by navigational challenges due to poor design features such as disorganization, technical language, and lack of permanent websites (Cline & Haynes, 2001). Support groups may also be problematic as they may distribute misleading information. Considering support information is often based on personal experience, it may lack the knowledgeable and experienced perspective developed by health care professionals who are trained to distinguish

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among resources, determine information accuracy, and examine the quality of information (Cotton & Gupta, 2004). As most of the information available online lacks peer review, it may be misleading and inaccurate (Rice, 2006). Researchers consistently identify problems with the quality of online health information (Rice, 2006), and very few sites pass through traditional editorial processes or display sources of authorship or origin (Diaz et al., 2002). Much of this uncontrolled information commonly strays from recognized safety standards, is not updated regularly, and presents limited advice on avoiding dangerous drug interactions (Rice, 2006). Despite the existence of many reliable medical and health related websites, several reviews have demonstrated that patients may encounter potentially misleading or inaccurate information when navigating the Internet in search of them. Some health seekers even fear that confidentiality breaches will track their Internet searches and unfavorably reveal their private information to insurance companies and employers (Cotton & Gupta, 2004). Unfortunately, there exists a digital divide or gap between those who can access online resources and those who cannot. Internet health information remains inaccessible to large and specific parts of the population (lower income, those in rural communities with limited Internet access), and physically impaired and disabled persons are often at a disadvantage in a networked society (Rice, 2006). Additionally, those individuals who often cannot access the tools to seek health information online are usually those with preventable health problems or without health insurance (Rice, 2006). Sadly, it seems as though individuals who would benefit the most from online medical information are the least likely to acquire it.

Discussion As noted, there are a number of advantages and disadvantages associated with researching medi-

Risks and Benefits of Technology in Health Care

cal information online. The advantages include access to a wide array of information, personalized information that is easy to understand, and anonymous help. Help is provided by support groups, and allows for the maintenance of privacy and anonymity. The Internet also allows patients to remain current on information regarding their own health or the health of others. However, some disadvantages include medical language barriers on legitimate websites, unequal Internet access, self diagnosis, and inaccurate information (Rice, 2006). Support groups may rely too heavily on personal experience, and consequently distribute misleading information (Culver, Gerr, & Frumkin, 1997). The risk in researching medical knowledge online is that much available information lacks peer review and accuracy. Confidentiality of information searching online is also a risk to consider. With all issues in consideration, it is apparent that the benefits of researching medical information, if used with the proper discretion, can have an invaluable impact on the patient. As indicated in the demographics summarized above, it is evident that the majority of individuals currently researching medical information are well educated. Thus most individuals/patients who seek information online have the ability to understand the information, know the disadvantages of online data, and can utilize the online information to help address their own medical needs. The Internet also allows patients to seek medical advice immediately, easily, and at their convenience. Because Internet usage is on the rise, it is anticipated that the digital divide will narrow in scope, and more individuals will gain access to the information available on the Internet. Individuals who have not previously been able to take advantage of online information will have the opportunity to do so. However, not all individuals who gain access to online health information have the understanding to apply it; therefore, medical information on the Internet should be correct and user friendly. It is evident that there is already an increasing trend in search engines providing more valid, accurate

information. For example, “Google” has a more advanced search tool “Google Scholar,” which provides scholarly articles published by credible sources. Additionally, many medical websites inform patients of serious symptoms by stating that if certain symptoms are present, patients should seek medical attention immediately. If online medical information is credible and user-friendly, it can be extremely beneficial to patients and providers. Doctors are forced to stay up to date and well informed about all advances in medicine, and by using online medical information, can do so with ease. Patients become well versed in their medical issues, and can provide their doctors with insightful questions. Additionally, patients expect doctors to know about the information available online and answer their questions; they have the opportunity to print articles, bring information directly to their physicians, or email their doctor for a professional opinion. However, if the information online does not maintain a certain level of accuracy and validity, negative implications may occur. Overall, the Internet should be used as a supplementary tool. Professional opinions should always be used as the primary source.

TECHNOLOGY AND RELATIONSHIPS Technological progressions impact all relationships within healthcare, including those between patients and healthcare providers, patients and other patients, as well as those between various healthcare providers, including doctors, nurses, and specialists. In other words, the presence of information technology in health care has had a profound effect on relationship dynamics within the health care system. The relationships that exist between physicians and patients are important and central to the quality and efficiency of patient care—information technology (IT) affects both aspects of this relationship significantly. With the

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Risks and Benefits of Technology in Health Care

emergence and growing prevalence of technology in health care, patients have been able take a more active role with regards to their health. Through increased access to information, the possibility of faster communication through e-mail beginning to emerge, and the existence of online patient portals, the presence of IT has had many positive effects on the patient-physician relationship (Wald, Dube, & Anthony, 2007).

Advantages The empowerment patients acquire by having access to their medical records along with online health researching enables them to intelligently discuss health issues with their clinician. In doing so, patients become a de facto part of the healthcare team. When properly counseled and mentored by healthcare professionals, individuals can potentially become true “clinical assistants” in their own health; they have more knowledge of their own problems and changes that enable clearer, more effective, and more efficient patientprovider communication. Technology can also aid patient decision making. For example, giving patients a visual “map” of the treatment options has been shown to help patients remember treatment options and their differential impacts with more accuracy, enabling patients to make faster, more appropriate decisions that increase the overall chances of a positive outcome (Panton et al. 2008). Along with the increase in online health resources, electronic communication via e-mail between patients and doctors is also increasing (Ferguson, 2002). For example, Weiner and Biondich (2006) found that 95% of surveyed patients who used e-mail communication (about 16.5% of the survey group) felt it was more efficient than the telephone, and that e-mail proved especially useful for patients with “sensitive issues” (Houston, Sands, Jenckes, & Ford, 2004; Weiner & Biondich, 2006). The patient-patient relationship is also facilitated in a positive manner by technology, and is

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often accomplished through online patient helpers, who oversee medical communication between patients. Online patient helpers are individuals who provide help for other patients via the Internet (Ferguson, 2000). For example, blogs and online support groups provide an environment in which helpers can serve as a valuable resource by assisting patients with medical information, encouraging discussion, and facilitating support. It should be noted that these online resources are best used to supplement the patient-physician relationship by encouraging patient-patient relations, and should by no means be the sole source of health information for any patient (Ferguson, 2000).

Disadvantages While the provision of online health information can strengthen the patient-physician relationship, it can also hinder it. Since much of the information online is not credible, physicians may be required to spend unnecessary amounts of time separating fact from fiction with their patients (Dugdale, Epstein & Pantilat, 1999). Consequently, patients are putting pressure on physicians to help sort through and clarify their online findings—which is a timely and effortful endeavor for many physicians (Ferguson, 2002). Implementation of electronic health records (EHRs), and the increased reliance on technological devices in general, may also hinder the patient-physician relationship, as time-consuming, and potentially distracting, data entry can take valuable time away from patient-provider relations. The increasing use of IT in health care may be so time consuming that it actually diminishes the patient-physician relationship (Weiner & Biondich, 2006). Although patient helpers encourage and facilitate patient-patient relationships, many patients do not have the formal training to differentiate between credible and non-credible information. Without this knowledge, some patients put too much reliance on online helpers, which can jeopardize both their health and the patient-physician

Risks and Benefits of Technology in Health Care

relationship (Ferguson, 2000). Because some online sources are certainly not credible, and many patients are insufficiently equipped to differentiate between accurate and bogus sources, many doctors discourage the use of such information.

Discussion The increasing presence of IT in health has profound effects on patient-physician and patientpatient relationships. With the surplus of health information available online, patients are able to actively participate in the patient-physician relationship and provoke discussion that serves in the best case to help patients make more informed decisions when choosing medical options. The growing acceptance of e-mail communication between patients and health professionals is giving patients greater access to medical advice. Also, online patient helpers give patients a portal for communicating to others in cyberspace that increases knowledge, promotes coping, and forms patient-patient relationships. However, if online medical information is used irresponsibly, it can have detrimental effects on one’s health, and the patient-physician relationship. The presence of IT can waste time, and increase patient acceptance of non-credible information. If one substitutes online information for professional, clinical advice, the patient-physician relationship deteriorates. Negligence to seek out physician care can result in unfortunate health consequences. Overall, if these disadvantages are lessened or alleviated, than the presence of IT on the patient-physician relationship will be beneficial. The increasing prevalence of e-mail communication between patients and doctors allows for physicians to be more accessible to their patients and vice versa. If used correctly, e-mail communication allows the physician-patient relationship to be more efficient—an important factor given the time pressures placed on today’s physicians. Rapid communication also allows

patients who are experiencing extremely stressful medical issues to be in immediate contact with their physician. E-mail also helps to reduce the apprehension of a face-to-face interaction that is often experienced by many patients, and allows doctors to answer general inquiries that do not require face-to-face contact. This may make booking a doctor’s appointment easier for those who really need it (Wald et al., 2007). However, the inappropriate use of online patient-provider communication can be wasteful. It may lead to e-mail overload; patients may ask very similar questions that physicians would have to answer repeatedly. As well, patients may take advantage of such constant contact and abuse communication privileges (Weiner & Biondich, 2006). Technology has enabled the emergence of patient-patient relationships, and these relationships can offer patients much needed emotional and psychological support when dealing with various illnesses (Mandl et al., 2001). Although patient-patient relationships can be very beneficial, individuals should be cautious before entering a relationship for anything more than support. If a patient were taking specific medical advice from their patient-patient relationship, it should be discussed with their physician, as the credibility of the average Internet user to be giving medical advice is most likely questionable. It is also important to keep in mind that diseases can affect patients in many different ways, so a patient giving advice may not know what is best for someone else (Culver et al., 1997). Sometimes patients feel more comfortable conversing with patient helpers as opposed to doctors because there is a mutual understanding: each individual has experienced the same illness. Online patient-patient relationships can be especially helpful for patients who are dealing with sensitive issues, such as AIDS. Given the social stigma surrounding the disease, being able to do anonymous research and talk to others in the same situation can help to reduce the embarrassment that infected individuals may

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feel. Patients can offer personal experiences about the disease and give advice (Wald et al., 2007). It should be mentioned that individuals seeking medical advice online may feel the need for faceto-face communication in person from a professional as opposed to a cyber friend—especially when seeking treatment.

CONCLUSION In order to combat issues threatening the successful integration of technology in health care, certain steps must be taken. One of the most important considerations when implementing electronic health records is the provision of proper training to all employees. For example, secretaries in medical offices must be extremely knowledgeable in all PIPEDA legislation, and must demonstrate proper system use to preserve privacy and confidentiality of patient information. Firewalls and other prevention measures must be developed and updated frequently to ensure that the records are private, confidential, and secure. A number of important implementations should be undertaken if the availability of online medical information is to be used to its fullest potential. First, governments and credible medical institutions should combine efforts in creating a large, easily understandable database of medical information accessible to laypersons (e.g. the National Library of Medicine’s Medline Plus web site, see http://www.nlm.nih.gov/medlineplus/). Such a database will provide credible information that is current and understandable from a reliable source. Second, doctors need to play a role in the information put forth on the Internet by researching it themselves and posting correct articles and information for their patients. Last, there should be a stamp or certification that ensures health information posted on various websites is legitimate (e.g. the HONcode certification now provided voluntarily by the Health on the Net

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Foundation, see http://www.hon.ch/). As well, more stringent efforts to monitor non-credible information should be taken. A potential way of reducing harmful, non-credible online information is to implement legislation that restricts the release of medical information from those without formal training or licensure to certain groups (i.e. registered support groups). In addressing healthcare relationships affected by technology, it is important to consider the potential for patient e-mails to affect physician stress levels and time management; email overflow puts pressure on physicians. The creation of a “frequently asked questions” page would help solve the issue of redundant e-mails, whereas the establishment of guidelines for users may help reduce e-mail abuse. Additionally, doctors should inform patients about the potential harms online patient helpers may present. Patients should be aware that much information provided by online helpers is subjective, and may not be credible or valuable in their particular case. As well, specific patient-helper sites should make their purpose known to all visitors. Technology undoubtedly has the potential to benefit healthcare; the IT advances discussed above will improve the efficiency, accuracy, and effectiveness of health care systems. Although the benefits are shadowed by risks, EHRs allow for increased and more efficient patient care; researching medical information online, if used properly, can provide a wealth of invaluable information for patients; and technology has allowed patient-physician and patient-patient relationships to become more effective. If the negative effects on these relationships can be mitigated via proper precautions and responsibility, the benefits will be maximized. Although the medical system is able to function without advancements in technology, technology allows for innovation and efficiencies that can greatly improve and revamp the way health care systems work.

Risks and Benefits of Technology in Health Care

REFERENCES Berland, G. K., Elliott, M. N., Morales, L. S., Algazy, J. I., Kravitz, R. L., & Broder, M. S. (2001). Health information on the Internet: Accessibility, quality, and readability in English and Spanish. Journal of the American Medical Association, 285, 2612–2621. doi:10.1001/jama.285.20.2612 Cline, R. J. W., & Haynes, K. M. (2001). Consumer health information seeking on the Internet: The state of the art. Health Education Research, 16, 671–692. doi:10.1093/her/16.6.671

Houston, T. K., Sands, D. Z., Jenckes, M. W., & Ford, D. E. (2004). Experiences of patients who were early adopters of electronic communication with their physician: satisfaction, benefits, and concerns. The American Journal of Managed Care, 10, 601–608. Mandl, D. K., Szolovits, P., & Kohane, S. I. (2001). Public standards and patients’ control: How to keep electronic medical records accessible but private. British Medical Journal, 322, 283–287. doi:10.1136/bmj.322.7281.283

Cotton, S. R., & Gupta, S. S. (2004). Characteristics of online and offline health information seekers and factors that discriminate between them. Social Science & Medicine, 59, 1795–1806. doi:10.1016/j.socscimed.2004.02.020

Myers, J., Frieden, T. R., Bherwani, K. M., & Henning, K. J. (2008). Ethics in public health research: Privacy and public health at risk: Public health confidentiality in the digital age. American Journal of Public Health, 98, 793–800. doi:10.2105/ AJPH.2006.107706

Culver, J. D., Gerr, F., & Frumkin, H. (1997). Medical information on the internet: A study of an electronic bulletin board. Journal of General Internal Medicine, 12(8), 466–470. doi:10.1046/ j.1525-1497.1997.00084.x

Panton, R. L., Downie, R., Truong, T., Mackeen, L., Kabene, S., & Yi, Q. L. (2008). A visual approach to providing prognostic information to parents of children with retinoblastoma. PsychoOncology, 18(3), 300–304. doi:10.1002/pon.1397

Diaz, J. A., Griffith, R. A., Ng, J. J., Reinert, S. E., Friedmann, P. D., & Moulton, A. W. (2002). Patients’ use of the Internet for medical information. Journal of General Internal Medicine, 17, 180–185. doi:10.1046/j.1525-1497.2002.10603.x

Rice, R. E. (2006). Influences, usage and outcomes of Internet health information searching: Multivariate results from the pew surveys. International Journal of Medical Informatics, 75, 8–28. doi:10.1016/j.ijmedinf.2005.07.032

Dugdale, D. C., Epstein, R., & Pantilat, S. Z. (1999). Time and the patient-physician relationship. Journal of General Internal Medicine, 14(1), 34–40. doi:10.1046/j.1525-1497.1999.00263.x

Rind, D. M., Kohane, I. S., Szolovits, P., Safran, C., Chueh, H. C., & Barnett, G. O. (1997). Maintaining the confidentiality of medical records shared over the Internet and the World Wide Web. Annals of Internal Medicine, 127, 138–141.

Ferguson, T. (2000). Online patient-helpers and physicians working together: A new partnership for high quality health care. British Medical Journal, 321, 1129–1132. doi:10.1136/bmj.321.7269.1129 Ferguson, T. (2002). From patients to end users. British Medical Journal, 324, 555–556. doi:10.1136/bmj.324.7337.555

Smith, R. (2000). Getting closer to patients and their families. British Medical Journal, 321, Retrieved from http://www.bmj.com.proxy1.lib. uwo.ca:2048/cgi/reprint/321/7275/0. Taylor, S. (2003). Protecting privacy in Canada’s private sector. Information Management Journal, 37, 33–39.

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Wald, H. S., Dube, C. E., & Anthony, D. C. (2007). Untangling the web – the impact of Internet use on health care and the physician-patient relationship. Patient Education and Counseling, 68(3), 218–224. doi:10.1016/j.pec.2007.05.016

Weiner, M., & Biondich, P. (2006). The influence of information technology on patient-physician relationships. Journal of General Internal Medicine, 21(Suppl 1), S35–S39. doi:10.1111/j.15251497.2006.00307.x

This work was previously published in Healthcare and the Effect of Technology: Developments, Challenges and Advancements, edited by Stéfane M. Kabene, pp. 60-71, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 1.3

The Effectiveness of Health Informatics Francesco Paolucci The Australian National University, Australia Henry Ergas Concept Economics, Australia Terry Hannan Australian College of Health Informatics, Australia Jos Aarts Erasmus University, Rotterdam, The Netherlands

ABSTRACT Health care is complex and there are few sectors that can compare to it in complexity and in the need for almost instantaneous information management and access to knowledge resources during clinical decision-making. There is substantial evidence available of the actual, and potential, benefits of e-health tools that use computerized clinical decision support systems (CDSS) as a means for improving health care delivery. CDSS and associated DOI: 10.4018/978-1-60960-561-2.ch103

technologies will not only lead to an improvement in health care but will also change the nature of what we call electronic health records (EHR) The technologies that “define” the EHR will change the nature of how we deliver care in the future. Significant challenges relating to the evaluation of these health information management systems relate to demonstrating their ongoing cost-benefit, cost-effectiveness, and effects on the quality of care and patient outcomes. However, health information technology is still mainly about the effectiveness of processes and process outcomes, and the technology is still not mature, which may

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

The Effectiveness of Health Informatics

lead to unintended consequences, but it remains promising and unavoidable in the long run.

INTRODUCTION The Institute of Medicine (IOM) report, To Err is Human: Building a Safer Health System provides a landmark review of the functionality of modern health care delivery in the information and technology revolutions (Kohn, Corrigan & Donaldson, 2000). It concludes that health care is error-prone and costly, as a result of factors that include persistent major errors and delays in diagnosis and diagnostic accuracy, under/ over-use of resources (e.g. excessive ordering or unnecessary laboratory tests), or inappropriate use of resources (e.g. use of outmoded tests or therapies) (Kohn, Corrigan & Donaldson, 2000). Health care is complex and there are few sectors that can compare to it in complexity as well as in the need for almost instantaneous information management and access to knowledge resources during clinical decision-making. An example of a comparable system of complex decision-making can be seen in air travel and is highlighted in the report on the factors contributing to the Tenerife air disaster on Sunday 17th, 1977. In the final summary on this disaster, Weick (1990) makes the following comments that could also be used to describe health care decision-making: The Tenerife air disaster, in which a KLM 747 and a PanAm 747 collided with a loss of 583 lives, is examined as a prototype of system vulnerability to crisis. It is concluded that the combination of interruption of important routines among interdependent systems, interdependencies that become tighter, a loss of cognitive efficiency due to autonomic arousal, and a loss of communication accuracy due to increased hierarchical distortion, created a configuration that encouraged the occurrence and rapid diffusion of multiple small errors (Weick, 1990, pp. 593).

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This major air disaster led to significant changes in air travel and reforms in the regulatory framework that have resulted in a higher level of safety and quality in this industry. If we compare the changes that occurred in aviation to improvements in health care delivery following the To Err Is Human report (Kohn, Corrigan & Donaldson, 2000), which focused on documenting deaths due to medical errors in the U.S. health care system, then the changes have not been as significant. A number of studies have found evidence of a lack of improvement in health care delivery in the U.S. despite major public and private investments in technology. In 2005, Leape and Berwick (2005) reviewed the U.S. health delivery system five years after the IOM report was released. They found significant deficiencies and faults in nearly all aspects of health care delivery. For example, in patient diagnoses there remained significant errors in accuracy and delays. In attempts to evaluate a given diagnosis there were failures to employ appropriate tests (underuse), the continued use of outmoded tests or therapies (inappropriate use), and the failure to act on the results of tests or monitoring (ignoring medical alerts and reminders). In treatment protocols they reported significant errors in operations, surgical procedures, and tests. They also found evidence in the administration of medication, the continued administration of the wrong drugs, doses, and medications given to patients with a known allergy to the drug (also in Evans et al., 1998). Bates et al.(1994; 1997) and Rothschild et al., (2002) found the persistence of a high incidence of adverse drug events (ADE) and their associated costs during care delivery, and demonstrated a close relationship between the incidence of preventable ADE, costs and medical malpractice claims. Preventive care is also considered to be a significant area of health care where costs savings and better health outcomes can be delivered. Fries (1994) has estimated that healthcare cost savings of up to 70% can be achieved through the implementation of more effective preventive care measures. Other

The Effectiveness of Health Informatics

areas of healthcare information management that continue to impair healthcare delivery include persistent failures in communication, equipment failure, and other information systems failures. It is well known that human beings in all lines of work make errors, and available data show that the health care sector is complex and error-prone resulting in substantial harm (Leape & Berwick, 2005). We also know that current and emerging technologies have the potential to provide significant improvements in healthcare delivery systems. Similar trends have occurred in aviation, which has also provided a guide as to where the focus of change should lie (Coiera, 2003). In this chapter, the following questions are addressed: •

•

Can information technology and health information management tools improve the health care process, quality and outcomes, while containing costs? How do we assess the cost-effectiveness of health information technology?

This chapter will provide a historical perspective with examples of how information technologies, designed around computerised Clinical Decision Support Systems (CDSS), can facilitate the more accurate measurement of the healthcare delivery process, and have provided reproducible solutions for cost savings, improved patient outcomes, and better quality of care.

BACKGROUND: HISTORICAL PERSPECTIVE The importance of clinical decision making (CDM) and its effects on outcomes in care have been well documented since the 1970s (Dick, Steen & Detmer, 1997; Kohn, Corrigan & Donaldson, 2000; Osheroff et al., 2006). The care process is now understood to function across complex environments involving the patient,

primary care, prevention, in-hospital care, and sub-specialisation care. The information management interrelationships extend beyond the direct care process to research, epidemiology, planning and management, health insurance, and medical indemnity. In 1976, McDonald discussed the key limitations of CDM in complex health information-rich environments, in particular by showing the failure of CDM to meet pre-defined standards of care, and concluded that computerised (electronic) decision support (CDSS) was an essential “augmenting tool” for CDM (McDonald, 1976). McDonald’s research became a stimulus to the ongoing research in this domain of health care and revealed not only the benefits of CDSS in health care, but also how we can now begin to “measure the care processes” and evaluate what we do much more effectively. In 2008 the importance of CDSS in health care was reconfirmed in a policy document prepared by the American Medical Informatics Association (AMIA) entitled A Roadmap for National Action on Clinical Decision Support (Osheroff et al., 2006). In the Executive Summary, the functions of CDSS in a modern health care system are clearly defined: Clinical decision support provides clinicians, staff, patients or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care. It encompasses a variety of tools and interventions such as computerized alerts and reminders, clinical guidelines, order sets, patient data reports and dashboards, documentation templates, diagnostic support, and clinical workflow tools. Clinical decision support has been effective in improving outcomes at some health care institutions and practice sites by making needed medical knowledge readily available to knowledge users (Osheroff et al., 2006, p.4). and the summary concludes:

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The Effectiveness of Health Informatics

Achieving desirable levels of patient safety, care quality, patient centeredness, and cost-effectiveness requires that the health system optimize its performance through consistent, systematic, and comprehensive application of available healthrelated knowledge – that is, through appropriate use of clinical decision support (Osheroff et al.,2006, p.4). The knowledge we have from more than 25 years of research in clinical information management systems allows us to conclude that “information is care” (Leao, 2007). In the words of Tierney et al. (2007, p. 373), “although health care is considered a service profession, most of what clinicians do is manage information.” A number of studies support the centrality of CDSS in improving CMD and the overall health care delivery system. From the 1950s to 1970s the technology supporting medical laboratory procedures was evolving. There was a dramatic escalation in the number of procedures performed with a minimal change in the number of technical personnel to support the care process. During this time the number of personnel in hospitals numbered in the 100s or 1000s, yet the number of procedures was rising to the millions each year (Speicher, 1983). Even though the average cost of many of these laboratory procedures at that time (e.g. chest X-rays, full blood analysis) was less than $20 US, the overall decision making process was already very costly. The results of the study by Speicher (1983) provide supportive evidence to the earlier study by Johns and Blum (1979) that linked CDM to resource utilisation and ongoing data generation in clinical environments and found that within a set of four nursing units where the annual expenditure was $44 million U.S., there were, on average, 2.2 million clinical decisions per year, 6,000 per day, and six per patient per day. A further example that linked CDM to compliance with care, clinical outcomes, and resource utilisation is that of immunization rates. In 1993 Gardner and Schaffner showed that vaccination

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rates for common illnesses such as influenza, pneumococcus, hepatitis B, and tetanus-diphtheria ranged from 10 to 40%. These are diseases where the vaccines have a clinical effectiveness ranging from 60 to 99% (Gardner & Schaffner, 1993). There are several cost and quality implications of these vaccination rates. From a quality perspective Gardner and Schaffner were able to correlate low vaccination rates with preventable deaths. A similar study by Tang, LaRosa, Newcomb, and Gorden (1999) demonstrated similar effects of immunisation rates on clinical outcomes. Further historical evidence for the close relationship between CDM and outcomes of health care has been documented for a variety of parameters that measure healthcare processes. These include: the failure to comply with pre-defined standards of care, adverse drug event (ADE) detection, preventive healthcare procedures, health insurance claims management, and data acquisition for research (Bates et al., 1994; James, 1989; Tierney et al., 2007). To these factors can be added the significant cost inflation associated with the attempts to manage health care predominantly as a business or administrative organizational model. James (1989) analysed a range of common clinical scenarios for procedures such as cholecystectomy, prosthetic hip replacement, and transurethral resection of the prostate (TURP). He found a wide variation, not only in the care process amongst groups of clinicians within different health care institutions for standardised conditions, but also within each individual practitioner’s activities. He also found that low quality leads directly to higher costs; he defines these costs that arise from an initial process failure and the resulting low quality output, as “quality waste” (i.e., resources that are consumed, in the form of scrap or repairs, when a unit of output fails to meet quality expectations - in clinical care this can represent death or short and long term morbidity). James also emphasises the importance of documentation in determining the quality of care. He states that fundamental elements for quality improve-

The Effectiveness of Health Informatics

ment are to eliminate inappropriate variation and document continuous improvement (i.e., measure what we do). This is not possible using essentially paper-based record systems for decision support. It has also been shown in the Harvard Medical Practice Study looking at negligence in medical care that paper-based record systems actually hide decision-making errors that promote poor clinical outcomes (Brennan et al., 1991). ADE remains a significant reason for poor, and preventable, patient outcomes. In 1998, Cook, Woods, and Miller documented that 50% of ADEs are preventable and that they represent the highest incidence of medical deaths compared to motor vehicle accidents, breast cancer, and AIDS. These preventable events represent a cost of $17 to $29 billion U.S. per year. Bates et al. (1994) also demonstrated the costly nature of ADEs at the Brigham and Women’s Hospital. All ADEs at that hospital cost $5.6 million U.S. and of these, preventable ADEs represented $2.6 million U.S. These figures excluded costs of injuries to patients, malpractice costs, and the costs of admissions due to ADE (Bates et al., 1994). The close relationship between ADEs and malpractice claims (outcomes) was demonstrated by Rothschild et al. (2002) and Studdert et al. (2006). These studies show that many of the added costs of these events were related to litigation and administrative costs and approximately 50% of the events were preventable. Another factor contributing to the current status of health care delivery is the ability of clinicians to comply with pre-defined standards of care. For the three decades from 1979 to 1990, several studies demonstrated that the overall rate of what is done in routine medical practice that is based on published scientific research remained steady at between 10 to 20% (Ferguson, 1991; Williamson, Goldschmidt, & Jillson, 1979). In 2003 a RAND Corporation review revealed that, on average, patients received recommended care in only 54.9% of instances. While this reflects an improvement in compliance with care standards,

it also indicates that around 45% of patients do not receive standardised care (Farley, 2005). A final example that discusses the core principles of CDSS implementation is the Academic Model for Prevention and Treatment of HIV (AMPATH) in resource-poor Kenya (Tierney et al., 2007). This project saw the successful implementation of health information technologies based on electronic medical record (EMR) functionalities in a resource poor nation. The successful partnership now sees this EMR as the largest e-health system for developing nations and is implemented in more than 23 countries. To obtain an extensive review of successes, difficulties, and an understanding of the complexity of information management in health, as well as how solutions can be found, we refer to the 25 year review of electronic medical record systems that focuses on CDSS in North America and Europe in the full issue of the International Journal of Medical Informatics in 1999 (see Editorial by Safran, 1999, pp. 155-156).

CLINICAL INFORMATION SYSTEMS AND CLINICAL DECISION SUPPORT SYSTEMS (CDSS) IN THE 21ST CENTURY: THE IMPACT ON HEALTH CARE QUALITY, COSTS, AND OUTCOMES The statement “information (management) is care” (Tierney et al., 2007, p. 374) emphasises one of the core principals of health care, that is, everything a provider does with a patient involves the flow of information (e.g. clinical history, physical examination, orders for tests, instructions for care, follow-up). This process involves the collection, management, and reporting of data in readable formats to the provider, thereby facilitating the care process (Tierney et al., 2007). Coiera (2003) also provides a clear description of where “clinical informaticians” fit into the care process:

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Informaticians should understand that our first contribution is to see healthcare as a complex system, full of information flows and feedback loops, and we also should understand that our role is to help others ‘see’ the system, and re-conceive it in new ways (Coiera, 2003, p. xxii). Any clinical decision support system(s) (CDSS) provides clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care (Coiera, 2003; Osheroff et al., 2006). CDSS encompass a variety of tools and interventions, such as computerized alerts and reminders, clinical guidelines, order sets, patient data reports and dashboards, documentation templates, diagnostic support, and clinical workflow tools. The following four key functions have been defined for CDSS (Perreault & Metzger, 1999): (1) Administrative: These systems provide support for clinical coding and documentation, authorization of procedures, and referrals. (2) Managing clinical complexity and details: Keeping patients on research and chemotherapy protocols; tracking orders, referrals follow-up and preventive care. That is, complying with pre-defined standards of care. (3) Cost control: This involves activities such as the monitoring of medication orders and avoiding duplicate or unnecessary tests. (4) Decision support: These are complex information management systems that support clinical diagnosis and treatment plan processes; and that promote use of best practices, condition-specific guidelines, and population-based disease management. Based on the existing evidence it is accepted that health information management systems centred on CDSS provide the most significant opportunity to improve health care delivery and management

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(Brennan et al., 2007). One could wonder why their use is not universal (Ford, Menachemi, Peterson & Huerta, 2009). A major barrier to CDSS implementation is clinician involvement in the development of the information management tools. We know that age, gender, and computer literacy are not significant factors in the use of computers in health care (Sands, 1999; Slack, 2001). Uptake of health information technologies is related to the efficiency and useability of the information management tools that CDSS deliver through the total e-health system. The current focus to provide solutions to the problem of clinician involvement relates to Computerized Provider Order Entry (CPOE) (Brennan et al., 2007). It is believed that CPOE will facilitate safe, effective care for patients by insuring that clinical care directions are communicated in a timely, accurate, and complete manner. The integration of clinical decision support functions with CPOE systems provides functionality that incorporates contemporary knowledge and best practice recommendations into the clinical management process. Additionally, by ensuring the quality, accuracy, and relevance of the decision logic integrated within CPOE systems, a guaranteed method for creating safe and effective practice is ensured. Brennan emphasises that CPOE is not a technology, rather it is a design (or redesign) of clinical processes that integrates technology to optimize provider ordering of medications, laboratory tests, procedures, etc. CPOE is distinguished by the requirement that the provider is the primary user. It is not the “electronification” of the paper record system in existing formats. In summary the evidence to date indicates that the beneficial effects of health information technology on quality, efficiency, and costs of care can be found in three major areas (Chaudhry et al., 2006): • • •

increased adherence to guideline-based care, enhanced surveillance and monitoring, and decreased medication errors.

The Effectiveness of Health Informatics

A recent overview of health information technology studies by Orszag (2008) from the US Congressional Budget Office (CBO) suggests, albeit with some qualifications, that a more comprehensive list of potential benefits from the use of HIT would include the following: •

•

•

•

•

Eliminating paper medical records – however, the CBO notes that these savings might not apply in very small practices that have low but relatively fixed costs related to medical records; Avoiding duplicated or inappropriate diagnostic tests – according to the CBO, some studies (e.g. Bates et al., 1998; Bates et al., 1994) suggest that electronic health records with a notice of redundancy could reduce the number of laboratory tests by about 6%; Reducing the use of radiological services though the CBO notes that the evidence on this is weak. While studies (e.g. Harpole, Khorasani, Fiskio, Kuperman & Bates, 1997) show that HIT may ease the job of monitoring the use of radiological services, there is little evidence that it helps control costs; Promoting the cost-effective use of prescription drugs, particularly through decision support software and computerized provider order entry which prompts providers to use generic alternatives, lowercost therapies, and cost-effective drug management programs (Mullett, Evans, Christenson & Dean, 2001); Improving the productivity of nurses and physicians – This has to be qualified, as one study found that when HIT was in use, nurses in hospitals saw reductions in the time required to document the delivery of care, but physicians saw increases in documentation time (Poissant, Pereira, Tamblyn, & Kawasumi, 2005). However, the CBO notes that the latter effect may re-

•

•

flect a short-run learning phase for doctors. Few studies have measured effects on physicians’ efficiency in outpatient settings, and those that have show mixed results (Pizziferri et al., 2005). Reducing the length of hospital stays – HIT may reduce the average length of a hospital stay by speeding up certain hospital functions and avoiding costly errors (Mekhjian et al., 2002); General improvements in the quality of care through avoiding adverse drug events1; expanding exchanges of health care information thus reducing duplication of diagnostic procedures, preventing medical errors and reducing administrative costs; expanding the practice of evidencebased medicine2; and generating data for research on comparative effectiveness and cost-effectiveness of treatments. This benefit is also consistent with the results of an older literature review which found that HIT increased adherence to guideline- or protocol-based care (Chaudry et al., 2006). This increased quality of care would also be manifest in an associated decrease in malpractice claims, another prediction which is confirmed by a recent study (Virapongse et al., 2008).

Given the need for establishing the existing and ongoing benefits from investments in clinical information management technologies, it has been demonstrated that there are long-term financial benefits, in the form of an acceptable return on investment (ROI), in computerised provider order entry systems (Kaushal et al., 2006). Despite these established benefits there remain many barriers to the widespread successful implementation of CDSS. Most of these are not technical. They relate to the design of information management tools and their acceptance by clinicians who have a long history of autonomy in health care (Beresford, 2008).

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The Effectiveness of Health Informatics

By contrast, the costs associated with implementation of HIT are: •

• • •

the initial fixed cost of the hardware, software, and technical assistance necessary to install the system; licensing fees; the expense of maintaining the system; and the “opportunity cost” of the time that health care providers devote to learning how to use the new system and how to adjust their work practices accordingly Orszag (2008).

On the costs of installation or implementation, the CBO notes that these may vary widely among physicians and among hospitals, depending on the size and complexity of those providers’ operations and the extent to which a system’s users wish to perform their work electronically. For instance, smaller practices will pay more per physician than larger practices to implement an HIT (Orszag, 2008). The estimation of these costs will also be complicated by the differences in the types and available features of the systems being sold and differences in the characteristics of the practices that are adopting them. The CBO notes that existing studies of costs have tended to make the mistake of not including estimates of indirect costs, such as the opportunity costs of time which providers dedicate to learn the new system and to adopt it in their work routines (Orszag, 2008). The initial opportunity costs in terms of learning time and adapting the operations of the practice around the implemented system can turn out to be quite significant, with one survey of health IT adoption finding that reported productivity in a practice may drop between 10 to 15 per cent for several months after implementation (Gans, Kralewski, Hammons & Dowd, 2005). One study of a sample of 14 small physicians’ offices implementing an HIT estimated the average drop in revenue from loss of productivity at about $7,500 per physi-

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cian over a year (Miller, West, Brown, Sim & Ganchoff, 2005).

PUTTING HEALTH INFORMATION TECHNOLOGY (HIT) TO USE Health information technology (HIT) systems are basically a repository for patient data. The physician is able to retrieve information, often in a clinically meaningful way that may not necessarily have been entered by himself/herself in the electronic health record (EHR). The information might have been acquired and created, for instance, during the patient’s course in the healthcare organization. Increasingly, EHR systems are connected to regional health information networks enabling access to patient data in disparate systems, such as primary care.

Overcoming the Limitations of Paper-based Records The electronic patient record has been introduced to overcome perceived limitations encountered with the use of the paper-based medical record and to allow planning that goes beyond a static view of events. Some of the limitations of the paper-based patient record that can be overcome include: •

Accessibility. Often the record is not accessible when it is needed. It may be stored elsewhere, or another professional may be seeking to use it concurrently. Electronic records are accessible independent of place and time, and can be rapidly retrieved using a patient identifier. It is exactly this that is most valued by clinicians. However, access is usually constrained because of data protection and privacy. Authorizations and passwords are required to allow a clinician to review patient information. Making information available both from within the

The Effectiveness of Health Informatics

•

•

•

hospital and from ambulatory systems is a key goal of most national efforts to implement HITs. Completeness. Not all patient data are written in the record. This can pose problems when other professionals reading the record try to make sense of a patient problem or when a doctor tries to recall what she has done after seeing the information again. Forcing the user to enter data in all fields can improve the completeness of a patient record. Readability. Handwriting is often hard to read. On a medication list, numbers and units of dosages can be misinterpreted and require attention of a pharmacist checking the prescription or a nurse translating and transcribing the order or trying to prepare the medication to be administered. Entering the data digitally, and even structuring the fields, can enhance readability. Analysis. Information written in the record is generally not suited for quantitative analysis. Test results may be entered in pre-structured forms and even plotted on a graph, which may reveal a trend, but comparison with baseline data is painstakingly difficult. Auditing past records to identify and analyze patterns in a group of patients is very labor intensive and time consuming. Digitized data are exceptionally suited for computer analysis.

Some Reasons for Limited Diffusion of Health Information Technology & Electronic Health Records If the advantages are so obvious, why then is the electronic record not widely in use and why haven’t electronic records replaced paper records? It is often argued that physicians resist innovation and do not like to give up familiar tools. The wide adoption of advanced technology in health care, and certainly in emergency medicine, de-

fies this argument. Paper-based records have proved to be durable tools for medical practice and information technology specialists have only recently become aware of this (Clarke, Hartswood, Procter, Rouncefield & Slack, 2003). As a cognitive artefact, the physician can examine the paper record easily. The layout and structure can guide the physician to find the most relevant information and ignore other items. Often the use of tabs, coloured paper, tables, and flowcharts facilitates navigation through a paper-based record. Within a short period of time a complete mental picture of the patient can be created. In contrast, using a computer, a user can be forced to page through a large number of screens in order to find the needed piece of information. The paper-based record also allows the physician to judge the quality of the information. Handwriting or a signature can show who entered the information and inform the physician about the trustworthiness of the information. The absence of information does not necessarily imply that the record is incomplete; it often means that a particular item was considered not relevant for the case at hand (Berg, 1998). For example, if the patient has no known history of heart problems and is in good physical condition, blood pressure recordings or an electrocardiogram (ECG) may be missing from the record. This is not to say that there are no compelling arguments to adopt electronic records; there is ample evidence that the efficiency and quality of care benefits from their use (Dick, Steen & Detmer, 1997). However, one must look carefully at the role of paper-based records in medical practice and avoid simply translating it into an electronic system.

Particular Features of Electronic Health Records That Offer Advantages over Paper Records The two powerful and distinctive advantages that electronic records have over a paper record are

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The Effectiveness of Health Informatics

summarized in the concepts of accumulating and coordinating (Berg, 1999). Accumulating refers to the fact that an EHR system allows for powerful analysis of data. For example, lab test outcome data can be accumulated to show time-dependent trends. When grouped for a large number of patients, the same data can be subjected to statistical analysis to reveal patterns. Combined with data from other sources, information can be used for planning, billing, and quality assessment. A most powerful application is the combination of patient data with decision support techniques that enable the physician to make decisions about patient care founded on accepted clinical rules stored in a database and patient data that is as complete as possible. The other concept of coordination provides the opportunity to plan and coordinate activities of healthcare professionals by allowing concurrent access to the electronic record. Access is no longer dependent on the physical location of the record, but possible from every computer that is connected to the system.

Standardization and Integration of Technologies Many conditions have to be met in order to successfully implement HIT and the EHR, most importantly standardization of the underlying technologies and of the content and meaning of healthcare data. This may seem obvious, but it has been shown that the lack of standardization is a major impediment to the introduction of electronic records in medical practice. Technological standards are required to link systems in networks. The wide diffusion of the Internet would have been impossible without underlying standards for communicating (e.g. messages, texts, and graphics) and establishing links and pointers to other sources of information. Yet, this achievement was not possible without negotiating and consultation about what and how to standardize. Often proprietary rights and the perceived need to protect markets stand in the way.

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In health care, standardization is much harder since it is not only about the underpinning rationale and scientific evidence for data standards, but also about the diverse social and cultural values within and across geographic communities that influence choice of data standards. We will not elaborate on the standards relevant for health care; they are explained elsewhere. Typical examples include TCP/IP that determines how information is communicated on the Internet, HL7 that determines how health care information can be represented and communicated between diverse applications in health care, and SNOMED CT that describes how medical concepts are defined and represented. Standardization can be effected through national and international bodies with legislative power, such as ASTM (originally American Society for Testing and Materials), ISO (International Standards Organization) and CEN (Comité Européen de Normalisation). Also standardization can be achieved through market power, e.g., Microsoft’s Windows operating system accounts for about 90% of the personal computer market and can therefore be construed as a de facto standard for personal computer operating systems.

Implementing Health Information Technology The traditional approach to implementing health information technology has been top-down. An important factor in this respect was the perception that health information technology is an expensive resource that usually exceeded the financial capabilities of individual physicians and physician groups. However, the advent of personal computers to some degree altered this situation, particularly in primary care. In countries such as the Netherlands, the United Kingdom and Australia, up to 90% of GPs have adopted electronic health record systems (Jha, Doolan, Grandt, Scott & Bates, 2008). However, adoption, in many cases, was helped by financial incentives given by their governments.

The Effectiveness of Health Informatics

A key characteristic of implementing information systems is that organizational changes are an integral part of implementation. Unfortunately, however, the changes are not always for the better, and more often than not, the performance of organizations is worse after a system has been installed than before. The natural tendency is then to conclude that the system was somehow badly designed. In 1975, when the design and operation of information systems were considered primarily technical activities, Henry C. Lucas, Jr. wrote about failing systems. However, all our experience suggests that the primary cause for system failure has been organizational behavior problems (Lucas, 1975). Thirty years of research has increased our understanding of information systems in organizational contexts; yet, the record in terms of developing and implementing successful systems is still dismal (Ewusi-Mensah, 2003). HIT systems are particularly hard to implement because not only do they affect health care organizations as a whole, but also the work of health professionals who pride themselves on their professional autonomy. Implementing HIT is a social process (Aarts, Doorewaard & Berg, 2004). But the relevant organizational changes are not easily predictable. This creates a predicament for an implementer. S/he might like to design a system according to blueprints and to plan systematically its deployment. But Ciborra (2002) advises making organizational improvisation part of the implementation process, to allow prospective users to tinker with the system and let them find ways of working that fit them best, to plan for the unexpected and value emerging practices, and to give up strict control (Ciborra, 2002).

Adverse Effects of Health Information Technology Recent studies reveal that putting HIT to use, whatever its many advantages may be associated with unexpected outcomes. A study by Koppel et

al. (2005) of one CPOE system documented 22 different error-enhancing aspects of that system. Another study reported a doubling of infant mortality after the introduction of a CPOE system, probably resulting from increased time to enter orders, reduced communication among nurses and doctors, and the loss of advance information previously radioed in from the transfer team before patients arrived at the hospital (Han et al., 2005). Nebeker, Hoffman, Weir, Bennett, and Hurdle (2005), likewise, found high rates of ADEs in the highly computerized Veterans Administration system. Shulman, Singer, Goldstone, and Bellingan (2005) found that, compared to paperbased systems, CPOE was associated with fewer inconsequential errors, but also with more serious errors. Ash, Berg, and Coiera (2004); Campbell, Sittig, Ash, Guappone, and Dykstra (2006); and Aarts, Ash, and Berg (2007) have found unintended consequences from CPOE systems to be the rule, rather than the exception. Nemeth and Cook (2005, p. 262), noting these systems’ interactivity and complexity, add: “If [human error] exists, error is a consequence of interaction with IT systems. The core issue is to understand healthcare work and workers”. And although “healthcare work seems to flow smoothly,” the reality is “messy.”

METHODS OF ASSESSING COST-EFFECTIVENESS There have not been many rigorous studies of cost effectiveness of e-health measures in the literature. The most recent literature review of studies in this field concluded that there remained a “paucity of meaningful data on the cost-benefit calculation of actual IT implementation” (Goldzweig, Towfigh, Maglione & Shekelle, 2009, p. 292). The studies which this literature review collected, after a comprehensive selection process, can be broken into four different approaches. One approach looks at the experience of a few large organizations that had implemented

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The Effectiveness of Health Informatics

multifunctional, interoperable electronic health records (EHRs), computerized physician order entry (CPOE), decision-support systems, and other functions. However, these studies cannot be described as appropriate cost effectiveness or cost benefit analyses since they only evaluated the impacts in terms of clinical performance/ quality improvement or potential benefit in terms of patient safety measures, but did not attempt to quantify the costs of these technologies or to then derive a net benefit calculation using some common measure (for instance, some imputed welfare gain in terms of dollars). Among the individual studies in this category: •

•

•

•

36

Three studies looked at a quality improvement project related to blood product administration that used automated alert technology associated with CPOE; electronic clinical reminders related to coronary artery disease and diabetes mellitus; and patient-specific e-mail to providers regarding cholesterol levels, and found small improvements in quality of care. (Lester, Grant, Barnett & Chueh, 2006; Rothschild et al., 2007; Sequist et al., 2005) Roumie et al. (2006) evaluated the impact of electronic provider alerts and found they provided a modest, non–statistically significant improvement over provider education alone as measured in terms of improvements in blood pressure control. Dexter, Perkins, Maharry, Jones, and McDonald (2004) compared the impact on rates of influenza and pneumococcal vaccinations of computer generated standing orders for nurses versus computerized physician reminders and found that immunization rates were significantly higher with the nurse standing order. Murray et al. (2004) and Tierney et al. (2005) evaluated computer-generated treatment suggestions for hypertension, and for asthma and chronic obstructive

•

pulmonary disease (COPD), and it was found that neither of these technologies resulted in improvements in care. Potts, Barr, Gregory, Wright, and Patel (2004), Butler et al., (2006) and Ozdas et al., (2006) evaluated the potential benefits of CPOE. These studies arrived at mixed results but generally found improvements in patient safety with the introduction of CPOE, as well as modest improvements in quality of care when CPOE was tailored to the management of patients with acute myocardial infarction.

Insofar as there was a basis for comparison implicit in these studies, this involved comparing the refinement in existing systems, addition of new applications or enhancement of existing functionalities, against the resulting improvements. The general finding was one of modest or even no benefits from the new applications or changed functionalities (Goldzweig et al., 2009, p. 285). A second approach found in the literature review was to look specifically at the experiences of commercial practices implementing commercially available or developed electronic health records. Among the individual studies in this category: •

•

Garrido, Jamieson, Zhou, Wiesenthal, and Liang (2005) compared outcomes before and after the implementation of a homegrown EHR at commercial hospitals and found that the number of ambulatory visits and radiology studies decreased after implementation, while telephone contacts nearly doubled. On the other hand, limited measures of quality (immunizations and cancer screening) did not change. O’Neill and Klepack, (2007) assessed the effect of implementing a commercial EHR in a rural family practice, looking specifically at financial impacts rather than quality of care measures. They found that average monthly revenue increased 11 per

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•

cent in the first year and 20 per cent in the second year, and the charge-capture ratio increased 65 to 70 per cent, because of better billing practices. Asaro, Sheldahl, and Char (2006); Del Beccaro, Jeffries, Eisenberg, and Harry (2006); Feldstein et al. (2006); Galanter, Polikaitis, and DiDomenico (2004); Han et al. (2005); Palen, Raebel, Lyons, and Magid (2006); Smith et al. (2006); Steele et al. (2005); and Toth-Pal, Nilsson, and Furhoff (2004) studied the effect of adding new functionalities to existing EHRs. Some of these studies found modest benefits, some found no benefits, and a few found marked benefits.

A third group of approaches involved evaluations of stand- alone applications such as health systems that link patients with their care providers and are intended to improve the management of chronic diseases; computer/video decision aids for use by patients and providers; text messaging systems for appointment reminders; electronic devices for use by patients to improve care; and patient-directed applications for use outside traditional settings. The main findings here were also mixed, with some studies showing no or only modest effects, and many more studies providing insufficient descriptions to reach strong conclusions. Among the individual studies in this category: •

•

McMahon et al. (2005) compared Webbased care management with usual care for patients with diabetes. Intervention patients had a statistically significant, modest improvement in their results for 2 out of 3 clinical measures. Cavanagh et al. (2006), Grime (2004), and Proudfoot et al. (2004) evaluated an interactive, multimedia, computerized cognitive behavioral therapy package. In two of the studies, statistically significant im-

•

provements of modest size were found for patients in the intervention groups compared to usual care, although the differences were no longer significant at three or six months. Cintron, Phillips, and Hamel (2006); Jacobi et al. (2007); and Wagner, Knaevelsrud, and Maercker (2006) looked at Internet applications that could be accessed directly by the patient with three involving randomized trials. Clinical improvements were found.

Another set of studies covered by the literature review that are not directly relevant to the issue of cost effectiveness or even general effectiveness of HIT, but nonetheless have implications for the likely costs of implementing HIT, looked at barriers to HIT adoption. One of these studies, which involved a survey of US paediatric practices found that the main barriers to HIT adoption were resistance from physicians (77 per cent of practices without an EHR reported this barrier), system downtime (72 per cent), increase in physicians’ time (64 per cent), providers having inadequate computer skills (60 per cent), cost (94 per cent), and an inability to find an EHR that met the practice’s requirements (81 per cent) (Kemper, Uren & Clark, 2006). A survey of the Connecticut State Medical Society Independent Practice Association found that the most commonly stated barrier was cost (72 per cent) and other barriers were time necessary to train staff (40 per cent), lack of proficiency among staff (26 per cent), and lack of an IT culture within the office (18 per cent) (Mattocks et al., 2007). One methodological problem with demonstrating that there are particular positive associations between clinical outcomes and use of HIT is that these associations are not necessarily causal hospitals that have more HIT tend to have greater resources and better performance. However, one study that at least attempted to control for these confounders still found a statistically significant

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relationship. Amarasingham, Platinga, DienerWest, Gaskin, and Powe (2009) looked at the relationship between HIT and both costs and clinical outcomes in hospitals in Texas. A particular focus in this study was whether increased automation of hospital information was associated with decreased mortality, complication rates and costs, and length of stay. They found strong relationships between the presence of several technologies and complication and mortality rates and lower costs. For instance, use of order entry was associated with decreases in mortality rate for patients with myocardial infarction and coronary artery bypass surgery. Use of decision support software was associated with a decrease in the risk of complications. Automated notes were associated with a decrease in the risk of fatal hospitalizations. The researchers controlled for the fact that hospitals that have more HIT tend to have more resources and still found that the relationships persisted, though there were also some instances in which relationships in the opposite direction were found. For example, electronic documentation was associated with a 35% increase in the risk of complications in patients with heart failure, though this may have been because it was easier to find these events due to better documentation (Bates, 2009). In short, there is a dearth of appropriate cost effectiveness studies and of useful data for conducting such studies. Although the review by Goldzweig et al. (2009, pp. 290-291) concluded on the basis of the individual studies surveyed that “there is some empirical evidence to support the positive economic value of an EHR;” they also found that the projections of large cost savings in previous literature assumed levels of health IT adoption and interoperability that had not been achieved anywhere. A less comprehensive survey by the US Congressional Budget Office (Orszag, 2008) focused on two prominent studies that attempted to quantify the benefits of HITs. One was a study by the RAND Institute (Girosi, Meili, & Scoville, 2005)

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and the other a study by the Center for Information Technology Leadership (CITL) (Pan 2004). Both these studies had estimated annual net savings to the US health care sector of about $80 billion (in 2005 dollars) relative to total spending for health care of about $2 trillion per year, though they identified different sources of those savings. The RAND research had quantified savings that the use of health IT could generate by reducing costs in physicians’ practices and hospitals. In contrast, the CITL study narrowed the focus to savings from achieving full interoperability of health IT, while excluding potential improvements in efficiency within practices and hospitals. The approach adopted by both these studies did have in common the use of various extrapolations and these were a source of criticism by the CBO that found that they were inappropriate by the standards of a rigorous cost effectiveness analysis. In particular, according to the Orszag (2008), the RAND study had the following flaws: •

•

•

•

It assumed “appropriate changes in health care” from HIT rather than likely changes taking into account present-day payment incentives that would constrain the effective utilization of HIT. It drew solely on empirical studies from the literature that found positive effects for the implementation of health IT systems, thus creating a possible bias in the results. It ignored ways in which some cost reductions would be mitigated by cost shifting in other areas. Some of its assumptions about savings from eliminating or reducing the use of paper medical records were unrealistic for small practices.

Similarly, the CITL study came in for criticism by the CBO for, in particular, not fully considering the impact of financial incentives in the analysis, estimating savings against a baseline of little or

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no information technology use, and using overoptimistic assumptions. While the CBO identified the numerous ways in which two prominent studies of HIT may have overestimated the benefits of HIT, its list of benefits does suggest that there are some areas in which the long-term benefits of HIT may be underestimated insofar as the scale of use has not reached a critical mass. In particular, we would conjecture that the possible improvements in quality of care through the expansion of health care information and generation of data for research may presuppose a base of participating health care providers and institutions that have implemented HIT and are able to share data over their networks. It is possible that these benefits may not be significant until use of HIT is diffused over a greater percentage of health care providers and institutions. In other words, some of the benefits to be derived from health IT increase in value as the network of those using the technology expands, i.e. as other providers also purchase health IT systems. This phenomenon is known to economists as network effects and is not necessarily restricted to benefits arising from exchange of information for research purposes - providers who can perform functions electronically, such as sending and receiving medical records or ordering laboratory and imaging procedures, also gain when other providers develop similar electronic capabilities. For example, the cost to a general practitioner of sending medical data to a consulting specialist is potentially lower with an HIT system, but only so long as the consulting specialist has an interoperable system that can receive the data electronically. As a general matter, economists distinguish between direct and indirect network effects, where the former refer to “technological” externalities while the latter refer to “pecuniary” externalities. The former involve situations where use of a technology by agent A directly affects the value agent B derives from that technology (for instance, through increased inter-operability). In

the latter, the effects are mediated through the price system, so adoption by agent A reduces the cost of the technology (for instance, through the achievement of greater economies of scale) and hence yields a benefit to agent B. Generally, it is assumed that the price system will take account of pecuniary externalities (although this is not always correct), but the technological externalities can drive a wedge between private and social costs at the margin. When that occurs, it is crucial that cost-benefit studies appropriately distinguish between private and social costs and benefits; this is not generally the case with the studies of HIT deployment that we have reviewed. At the same time, when network effects are significant, there will typically be multiple equilibria; for example, private costs and benefits may be equalised at one, low level of adoption (with low net benefits), and at another, high level of adoption (with potentially higher net benefits). At the low level equilibrium, no individual non-adopter will face a private net gain from adopting. For example, in a telephone system with few customers, the marginal subscriber gains little by joining the network. A cost-benefit evaluation conducted at that low level of adoption will therefore find that the private benefits of adoption are less than the private costs. However, were adoption levels increased, joining the telephone system would allow the marginal user to communicate with a greater base of subscribers, so that the benefits of subscription (especially taking account of the gains made by those receiving the calls) exceed the private costs. These issues of differences between private and social costs and benefits and of multiple equilibria can create biases. For example, what may seem like low benefits relative to costs may reflect an initial low level equilibrium and an associated coordination failure. In other words, if instead of looking at a decision by say, an individual practitioner or medical practice to adopt HIT, one evaluated the costs and benefits of adding a large number of practitioners or medical practices to the

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installed base of HIT, the balance of the costs and benefits might differ. It is pertinent that according to a new analysis of HIT deployment in seven industrialized countries, US deployment lags well behind other countries (Davis, Doty, Shea, & Stramekis, 2009). Electronic medical records usage ranged from nearly all physicians in the Netherlands to 23 per cent in Canada and 28 per cent in the US. Incidentally, the same study also found that physicians with greater IT capacity were more likely to report feeling well-prepared to manage patients with chronic diseases. Insofar as the bulk of HIT studies have been from the US, the benefits documented from use of HIT in these studies may not be representative of benefits in countries with a higher deployment of HIT. However, this is not a hypothesis we are in a position to test. One barrier to the achievement of the full magnitude of network effects that would maximize the benefits of using HIT may be legal restrictions. A recent US study found that privacy regulations impose costs that deter the diffusion of EMR technology (Miller & Tucker, 2009). These regulations may inhibit adoption by restricting the ability of hospitals to exchange patient information with each other, which may be particularly important for patients with chronic conditions who wish to see a new specialist, or emergency room patients whose records are stored elsewhere. The study calculated that the inhibition of network benefits from privacy regulations reduced hospital adoption of EMR by 25 per cent. It is clear from the various literature reviews discussed so far, first, that there have been very few studies that have attempted to meet the rigorous standards of a cost effectiveness analysis, and second, that there are numerous pitfalls in conducting such analyses owing to the use of various assumptions and extrapolations in quantifying benefits or costs. Another issue which remains to be addressed with greater rigour in the literature is how to quantify the likely benefits of HIT taking

40

into account various projections of network effects associated with different uptake rates.

CONCLUSION The picture that emerges from our overview has several dimensions. First of all, it is evident that little is known about the overall impact of health IT on the outcomes of health care. In this chapter we have reported a number of studies that showed positive outcomes on, for example, the reduction of adverse drug events, better resource utilization, and improved adherence to clinical guidelines. These studies are well bounded in scope and size, and are mainly about the effectiveness of processes. However, the findings do not always unequivocally point to positive outcomes. Reminders and alerts are an essential feature of decision support in computerized physician order entry systems. They warn users of potentially dangerously interacting medications. A systematic review showed that they are suppressed frequently, and this may prevent detection of ADEs and thus compromise patient safety (van der Sijs, Aarts, Vulto, & Berg, 2006). Yet, the positive outcomes are often extrapolated to a larger population to make a case for the wide scale implementation of health IT. Equally, implementing health IT often entails significant organizational change, both at the level of practicing medicine and the structure of health care organizations. However, a study of organizational change in Australian hospitals by Braithwaite, Westbrook, Hindle, Iedema, and Black (2006) provides a sobering reminder that there can be a large gap between expectations and reality. The introduction of clinical directorates, in which clinical departments, wards, and units that best fitted conceptually were joined together, was seen to increase the effectiveness of health delivery. Using diachronic data the authors found that the introduction of clinical directorates had no effect. Unfortunately, we have no way of fully knowing the effectiveness of health IT unless it has

The Effectiveness of Health Informatics

been widely adopted and diachronic data become available for analysis. There is growing awareness that health IT is far from mature. In a report to the Office of the National Coordinator of Health Information Technology (ONCHIT), the American Medical Informatics Association writes that some current clinical decision support systems often disrupt clinical workflow in a manner that interferes with efficient delivery (Osheroff et al., 2006). In our overview we already mentioned the adverse effects of health IT, indicating how difficult implementation in practice is. In another study Koppel, Wetterneck, Telles, and Karsh (2008) also found that bar-coded medication administration systems did not reduce dispensing errors substantially because they induced workaround to mitigate unintended effects. The main causes seem to be the technology itself and the generally poor understanding of how technology affects work practices, let alone how it can improve them by introducing notions of patient-centered care and a working collaborative of different providers. A telling example is how computerized provider order entry systems are designed and implemented on the model of an individual physician prescribing medication, instead of a collaborative model involving physicians, pharmacists, and nurses who are all involved in providing medication to patients (Niazkhani, Pirnejad, Berg, & Aarts, 2009). Often expectations are overblown. In a Dutch hospital the implementers of a CPOE system expected that physicians would use the system, because the system that was being replaced was also about electronic order entry (Aarts et al., 2004). They did not realize that physicians were not at all accustomed to electronic order entry, and that the system requires a doctor to send electronic notes, but doctors don’t send notes as other people do that for them. Implementation should begin by asking the question ‘what organizational problem is going to be solved,’ and what can be done to engage problem owners. There is also a serious lack of organizational learning when a

system is designed and implemented (Edmondson, Winslow, Bohmer, & Pisano, 2003). This may be due to the fact that implementation teams are often dissolved after the project is considered finished, leading to a change in personnel who actually use the system. To conclude, we find ourselves in a double bind. The effectiveness of health IT is anecdotal, and increasingly unintended consequences are being reported (Ash et al., 2004). Health IT is far from mature. Hard work is needed to get safe and reliable systems to work in practice. Yet, it is a dictum that without IT, health care will grind to a halt. We have become dependent on health IT, and yet its overall contribution to health care is hard to quantify. It is comparable to the productivity paradox that some economists pointed to in the early 1990s. Society has become dependent and intertwined with information technology, and yet its contribution didn’t seem to show up in the productivity figures (Brynjolfsson, 1993; Landauer, 1995). While some of the positive macroeconomic impacts did become clearer over time, the situation with respect to health IT is still at the earlier stage. This leaves a need to identify proxies for health IT effectiveness and better capture longitudinal and diachronic data to assess its impact on process and outcome.

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Linder, J. A., Ma, J., Bates, D. W., Middleton, B., & Stafford, R. S. (2007). Electronic health record use and the quality of ambulatory care in the United States. Archives of Internal Medicine, 167(13), 1400–1405. doi:10.1001/archinte.167.13.1400 Lucas, H. C. Jr. (1975). Why information systems fail. New York: Columbia University Press. Mattocks, K., Lalime, K., Tate, J. P., Giannotti, T. E., Carr, K., & Carrabba, A. (2007). The state of physician office-based health information technology in Connecticut: current use, barriers and future plans. Connecticut Medicine, 71(1), 27–31. McDonald, C. J. (1976). Protocol-based computer reminders, the quality of care and the nonperfectability of man. The New England Journal of Medicine, 295(24), 1351–1355.

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McGlynn, E. A., Ash, S. M., Adams, J., Keesey, J., Hicks, J., DeCristoforo, A., & Kerr, E. A. (2003). The quality of health care delivered to adults in the United States. The New England Journal of Medicine, 348, 2635–2645. doi:10.1056/ NEJMsa022615 McMahon, G. T., Gomes, H. E., Hickson Hohne, S., Hu, T. M., Levine, B. A., & Conlin, P. R. (2005). Web-based care management in patients with poorly controlled diabetes. Diabetes Care, 28(7), 1624–1629. doi:10.2337/diacare.28.7.1624 Mekhjian, H. S., Kumar, R. R., Kuehn, L., Bentley, T. D., Teater, P., & Thomas, A. (2002). Immediate benefits realized following implementation of physician order entry at an academic medical center. Journal of the American Medical Informatics Association, 9(5), 529–539. doi:10.1197/ jamia.M1038 Miller, A. R., & Tucker, C. (2009). Privacy protection and technology diffusion: the case of electronic medical records. Management Science, 55(7), 1077–1093. doi:10.1287/mnsc.1090.1014 Miller, R. H., West, C., Brown, T. M., Sim, I., & Ganchoff, C. (2005). The value of electronic health records in solo or small group practices. Health Affairs (Project Hope), 24(5), 1127–1137. doi:10.1377/hlthaff.24.5.1127 Mullett, C. J., Evans, R. S., Christenson, J. C., & Dean, J. M. (2001). Development and impact of a computerized pediatric antiinfective decision support program. Pediatrics, 108(4), E75. doi:10.1542/peds.108.4.e75 Murray, M. D., Harris, L. E., Overhage, J. M., Zhou, X. H., Eckert, G. J., & Smith, F. E. (2004). Failure of computerized treatment suggestions to improve health outcomes of outpatients with uncomplicated hypertension: results of a randomized controlled trial. Pharmacotherapy, 24(3), 324–337. doi:10.1592/phco.24.4.324.33173

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Ozdas, A., Speroff, T., Waitman, L. R., Ozbolt, J., Butler, J., & Miller, R. A. (2006). Integrating “best of care” protocols into clinicians’ workflow via care provider order entry: impact on qualityof-care indicators for acute myocardial infarction. Journal of the American Medical Informatics Association, 13(2), 188–196. doi:10.1197/jamia. M1656 Palen, T. E., Raebel, M., Lyons, E., & Magid, D. M. (2006). Evaluation of laboratory monitoring alerts within a computerized physician order entry system for medication orders. The American Journal of Managed Care, 12(7), 389–395. Pan, W.T. (2004, November 18 - 19). Health information technology 2004: improving chronic disease care in California. California HealthCare Foundation. San Francisco, CA: SBC Park. Perreault, L., & Metzger, J. (1993). A pragmatic framework for understanding clinical decision support. Journal of Healthcare Information Management, 13(2), 5–21. Pizziferri, L., Kittler, A. F., Volk, L. A., Honour, M. M., Gupta, S., & Wang, S. (2005). Primary care physician time utilization before and after implementation of an electronic health record: a timemotion study. Journal of Biomedical Informatics, 38(3), 176–188. doi:10.1016/j.jbi.2004.11.009 Poissant, L., Pereira, J., Tamblyn, R., & Kawasumi, Y. (2005). The impact of electronic health records on time efficiency of physicians and nurses: a systematic review. Journal of the American Medical Informatics Association, 12(5), 505–516. doi:10.1197/jamia.M1700 Potts, A. L., Barr, F. E., Gregory, D. F., Wright, L., & Patel, N. R. (2004). Computerized physician order entry and medication errors in a pediatric critical care unit. Pediatrics, 113(Pt 1), 59–63. doi:10.1542/peds.113.1.59

Proudfoot, J., Ryden, C., Everitt, B., Shapiro, D. A., Goldberg, D., & Mann, A. (2004). Clinical efficacy of computerised cognitive-behavioural therapy for anxiety and depression in primary care: randomised controlled trial. The British Journal of Psychiatry, 185, 46–54. doi:10.1192/bjp.185.1.46 Rothschild, J. M., Federico, F. A., Gandhi, T. K., Kaushal, R., Williams, D. H., & Bates, D. W. (2002). Analysis of medication-related malpractice claims: causes, preventability, and costs. Archives of Internal Medicine, 162(21), 2414–2420. doi:10.1001/archinte.162.21.2414 Rothschild, J. M., McGurk, S., Honour, M., Lu, L., McClendon, A. A., & Srivastava, P. (2007). Assessment of education and computerized decision support interventions for improving transfusion practice. Transfusion, 47(2), 228–239. doi:10.1111/j.1537-2995.2007.01093.x Roumie, C. L., Elasy, T. A., Greevy, R., Griffin, M. R., Liu, X., & Stone, W. J. (2006). Improving blood pressure control through provider education, provider alerts, and patient education: a cluster randomized trial. Annals of Internal Medicine, 145(3), 165–175. Safran, C. (1999). Editorial. International Journal of Medical Informatics, 54(3), 155–156. doi:10.1016/S1386-5056(99)00003-9 Sands, D. Z. (1999). Electronic patient-centered communication: managing risks, managing opportunities, managing care. The American Journal of Managed Care, 5(12), 1569–1571. Sequist, T. D., Gandhi, T. K., Karson, A. S., Fiskio, J. M., Bugbee, D., & Sperling, M. (2005). A randomized trial of electronic clinical reminders to improve quality of care for diabetes and coronary artery disease. Journal of the American Medical Informatics Association, 12(4), 431–437. doi:10.1197/jamia.M1788

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ENDNOTES 1.



2.



between 50 per cent and over 90 per cent (Evans et al., 1998; Potts, Barr, Gregory, Wright, & Patel, 2004). A review of studies on clinical decision support found that most such functions improved the performance of practitioners – see Garg et al., 2005. On the other hand, other research finds no evidence of an increase in physicians’ adherence to evidence-based standards of treatment for a wide variety of conditions – see for instance Crosson et al., 2007, and Linder, Ma, Bates, Middleton, & Stafford, 2007.

Some studies suggest potential reductions in error rates from the use of health IT of

This work was previously published in Healthcare and the Effect of Technology: Developments, Challenges and Advancements, edited by Stéfane M. Kabene, pp. 13-37, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 1.4

Personal Health Information in the Age of Ubiquitous Health David Wiljer University Health Network, Canada Sara Urowitz University Health Network, Canada Erin Jones University Health Network, Canada

ABSTRACT We have long passed through the information age into an information perfusion in health care, and new strategies for managing it are emerging. The ubiquity of health information has transformed the clinician, the public, and the patient, forever changing the landscape of health care in the shift toward consumerism and the notion of the empowered patient. This chapter explores essential issues of ubiquitous health information (UHI), beginning with its origins in the explosion of health information and the advent of new technologies. Challenges of UHI include privacy issues, change management, and the lack of basic infrastructure. DOI: 10.4018/978-1-60960-561-2.ch104

However, benefits for patients include improvements in access to information, communication with providers, prescription renewals, medication tracking, and the ability to self-manage their conditions. Benefits at the organizational level include increased patient satisfaction, continuity of care, changes in costing models and improved standardization of care as organizations streamline processes to address this change in clinical practice.

INTRODUCTION In health care the “information age” has long passed and we have entered into the era of information perfusion. The ubiquity of health information

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Personal Health Information in the Age of Ubiquitous Health

has transformed the clinician, the public, and the patient. As technology progresses and we see exciting and innovative strategies for managing it emerge, ubiquitous health information (UHI) has brought on a tectonic shift that will forever change the landscape of health care. This chapter explores the essential issues of UHI: the debates and controversies, the risks and benefits, and efforts that must be made to manage them. To begin, we trace the origins of UHI – the rise and explosion of several genres of health information in conjunction with the evolution of new technologies. We look at the definition and components of UHI, the types of health information that are available, and the methods for their exchange. We also explore the rise of consumerism and the notion of empowered patients within the context of ubiquitous health information. The chapter also examines the role of UHI in the changing landscape of health care. We investigate the growing number of social, economic, cultural, ethical and legal issues, as technologies allow for the dissemination and exchange of personal health information. The impact of the perfusion of information on the public and patient is explored, as well as its impact on health professionals and the type of care delivered, and the new and alternative environments in which it is carried out. The benefits and the risks of UHI are discussed, including the educational, clinical and research opportunities. And finally, we offer a consideration of future research directions, and potential frameworks for the evaluation and assessment of UHI.

BACKGROUND Ubiquitous Heath Information Today: Towards a Definition The perfusion of health information today can be overwhelming. Health information is now everywhere, all the time. We see messages about

our health from television, radio, newspapers, the Internet, billboards and advertising. Health services and health products are an enormous industry, and controlling the messages is an ongoing battle between multiple interest groups that is being waged at the expense of those individuals who need it most – the public and the patients. Media and web sites are becoming battlegrounds over major health issues of the twenty-first century, ranging from circumcision and vaccinations to novel and alternative treatments. The social and financial stakes are high, and information flows at a rate that is almost incomprehensible. As new strategies for navigating this sea of information develop, so our ability to generate more information increases, contributing to this state of UHI. The emergence of new technologies, and the changing expectations of health care consumers have each influenced the explosion of UHI, both in their own right, and through an intricate relationship to each other. It is evident that healthcare has experienced a shift toward consumerism and the notion of the empowered patient. Patients and the public are no longer satisfied with the status quo and a growing wave of public and patient expectation is mounting (Ball, Costin, & Lehmann, 2008; Hassol et al., 2004; Leonard, Casselman, & Wiljer, 2008; Leonard & Wiljer, 2007; Pyper, Amery, Watson, & Crook, 2004; Wiljer et al., 2006). The traditional health care system that was characterized by the physician driven model, an insular system of practice that neither had the means nor the inclination to share expert knowledge, is now a thing of the past. Today, patients are acknowledged as expert consumers. Health care providers work in partnership with their patients to make shared decisions, and there is the growing global trend of adopting legislation to ensure that patients are able to access, review, and amend their medical record (Beardwood & Kerr, 2005; Blechner & Butera, 2002; Chasteen, Murphy, Forrey, & Heid, 2003; Dietzel, 2002; France & Gaunt, 1994; Harman, 2005; Ishikawa et al., 2004; Jones, 2003; Kluge,

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1993; Mathews, 1998; Mitchell, 1998; Pyper, Amery, Watson, Crook, & Thomas, 2002). As a result, the information asymmetry that once existed between health care professionals and the health care consumer is now significantly diminished and UHI is pervasive. At the same time, technology is advancing. The role of information and communications technologies (ICTs) in health care is growing, and evidence of this trend is all around us. Research has shown that the number of MEDLINE citations for “Web-based therapies” showed a 12-fold increase between 1996 and 2003 (Wyatt & Sullivan, 2005). There are virtual health networks and electronic health records (EHRs) being used to coordinate the delivery of services, and knowledge management ICTs used to establish care protocols, scheduling, and information directories. ICTs are also increasingly seen as a key element of consumer-based health education, and the delivery of evidence-based clinical protocols (Branko, Lovell, & Basilakis, 2003). Health care teams are finding increases in efficiency and efficacy when new technologies help to manage patient data and provide a method of coordinating team members’ interactions (Wiecha & Pollard, 2004). There are many potential benefits to be realized by effective use of UHI. Patient benefits include better access to health information, increased ability to self-manage chronic health conditions, increased medication tracking, safer prescription renewals, and improved connections for patients and providers (Ball, Smith, & Bakalar, 2007; Tang, Ash, Bates, Overhage, & Sands, 2006). Potential benefits at the professional level include increased patient satisfaction, continuity of care, changes in costing models and improved standardization of care as organizations streamline processes and information to address this change in clinical practice (Ball et al., 2007; Tang et al., 2006). There are still a number of barriers to overcome, including privacy and security issues, change management issues, and the lack of basic infrastructure such as EHRs (Tang et al., 2006).

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UHI has transformed the entire concept of health, and we are seeing rapid changes in the health delivery system. One prominent idea is the prospect of chronic disease management and self-managed care, facilitated through UHI. This ambition has inspired a growing interest in harnessing the power of EHRs beyond the point of care delivery. Health care organizations are also realizing the potential benefits of patient accessible health records (PAEHRs), including improving the patient experience, supporting patients with chronic conditions, improving transparency, increasing referral rates, and ensuring the continuity of care beyond the hospital walls.

THE HISTORY OF UHI: HOW DID WE GET HERE? Health Care Consumerism The role of the patient has been impacted by societal influences, starting with the Civil Rights movement and continuing through the 1980’s and beyond. Expectations are changing, and patients are no longer seen to be passive recipients of health care information, but rather are considered to be health care consumers who actively seek out, and more recently, create knowledge (Cresci, Morrell, & Echt, 2004; Urowitz & Deber, 2008).

Shared Decision Making As consumers of health care, people are more invested in actively participating in their care and making informed health care choices. This is commonly referred to as shared decision making (Charles, Gafni, & Whelan, 1997; Deber, Kraetschmer, Urowitz & Sharpe, 2005; Deber, Kraetschmer, Urowitz & Sharpe 2007). Effective shared decision-making requires that people are able to access and understand large amounts of complex medical and health related information. Once, this would have been a daunt-

Personal Health Information in the Age of Ubiquitous Health

ing task for most individuals. It would require trips to specialized libraries, an understanding of professional textbooks, and synthesizing large amounts of information from both traditional and non-traditional sources. Now, the pervasiveness of technology-supported health information has simplified shared decision-making. Video, CD-ROMs and the Internet have brought health information more into the public domain, making expert information available to the masses. Decision aids are one possibility for supporting health care consumers through this process. Decision aids provide health information to help people understand their options, and to consider the personal importance of possible benefits and harms (O’Connor et al., 2002). Like in prostate cancer, they are most helpful when there is more than one medically reasonable option. Information in decision aids was traditionally delivered through pamphlets and more recently through videos, CDs and Internet based programs. For instance, the Ottawa Health Research institute offers a decision aids page (http://decisionaid.ohri. ca) that allows the public to search their inventory of decision aids, or download a generic decision strategy sheet, which can be applied to the process of making any health decision. Shared decision making has been greatly facilitated by the introduction of the Internet, which has enabled health care consumers to independently seek out information that ranges from the layperson’s to the expert’s level. The National Library of Medicine in the United States has a section of their web page (www.nlm.nih.gov) designated “especially for the public” where users can find current and accurate health information for patients, families and friends.

The Chronic Care Model and Chronic Disease Management The welcoming of patients’ participation in their own care, combined with the abundance of health related information available through the Internet,

is continuing to drive a shift in the delivery of health care and increasing the demand for accessible, timely and relevant health information. We are moving away from the clinician driven model of care toward a patient centred model, in which empowered patients are encouraged to play an active role, not only in the decision making process, but in their own care management. This model is particularly relevant to the growing segment of the population living with long-term chronic illnesses that require ongoing monitoring and treatment. The aging population and advances in modern medicine have resulted in larger numbers of people living longer and experiencing more chronic illnesses, many dealing with multiple conditions at the same time (Orchard, Green, Sullivan, Greenberg, & Mai, 2008; Ralston et al., 2007). Whereas a disease such as cancer once had a high probability of mortality, people are now living longer, disease-free lives, even after a diagnosis of this once fatal condition (Hewitt & Ganz, 2006). The Chronic Care Model (CCM) and the paradigm of chronic disease management (CDM) are responses to the changing health care needs of the population. The Chronic Care Model suggests that optimal functional and clinical outcomes can be achieved in a system that supports productive interactions between informed clients, which includes patients, their families, and knowledgeable providers, who are working as part of a team with a client-centered focus (Wagner, 1998). CDM espouses the importance of people who are living with chronic illness taking responsibility for effectively managing their illness. People need to see themselves as active partners in their care with a keen understanding of what is best for them and their bodies, who can make behaviour changes through goal setting, but also know when to ask for help. Adopting this approach to disease management can result in overall improvements in long-term health (Lorig, Sobel, Ritter, Laurent, & Hobbs, 2001). There are 5 steps to effective CDM 1) Identification of a problem 2) Goal setting 3) Planning

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ahead and considering obstacles to achieving goals 4) Determining confidence level for achieving the goal, and 5) Following up and staying connected with the health care team. Applying these 5 steps is an effective approach to changing behaviours. Since behaviour changes can be difficult to effect, access to UHI can facilitate knowledge sharing, education, and an understanding of the condition which are all factors in ensuring success (Wyatt & Sullivan, 2005). CDM is most effective when coupled with personal empowerment interventions. These interventions might consist of education in improving individual decision-making, disease complication management, efficient use of health services, improved health behaviors, or life coping skills (Paterson, 2001; Wallerstein, 2006). Empowerment interventions aim to increase the capacity of individuals to make choices and transform those choices into desired actions and outcomes (Wallerstein, 2006). The empowerment facets of CDM include the promotion of new knowledge and skills, within a system that reinforces active participation (Lorig et al., 2001; Wallerstein, 2006), resulting in better health outcomes and improvements in quality of life for the chronically ill (Wallerstein, 2006).

information becomes key to maintaining good continuity of care, and now UHI is enabling the provision of this sort of care in a less fragmented manner (Anderson & Knickman, 2001). In fact, the emerging goal for information technologies managing UHI is to be unobtrusive, seamlessly working in the background to provide users with value-added services (Pallapa & Das, 2007). As we approach this goal, technology is being more and more commonly applied to the home monitoring of vital signs for patients with chronic disease, and telemedicine is beginning to replace some home nursing visits (Branko et al., 2003). For example, remote patient monitoring for home haemodialysis utilized a system that that acquired, transmitted, stored and processed patient vital signs that were monitored according to algorithms that triggered alarms if intervention was required. In addition, the system included an IP-based pan-tilt-zoom video camera for clinical staff to observe remotely if the patient desired (Cafazzo, K.J, Easty, Rossos, & Chan, 2008). The hope is that UHI, coupled with the right technology, will be responsive to patient demand; ensuring that information can be personalized for the individual seeking it, and that services can be delivered at the time and the place they are needed (Wyatt & Sullivan, 2005).

UHI and the Management of Chronic Illness

Exchanging Health Information

The growth of technology and the appearance of UHI have had an impact on the rise of CDM, especially in terms of the increasing capacity to share information. Often, patients with chronic illnesses are seeing many physicians and specialists, who will all need access to their information. For example, a diabetes patient may be seeing his or her family doctor along with any number of specialists, including nephrologists, podiatrists, and ophthalmologists. All of these specialists may be prescribing various medications, ordering tests or making additional referrals to still other health care providers. Timely transmission of this

The notion of technology assisted exchange of health information is not a new idea. It has been over four decades since physicians first started using interactive video as a way of providing and supporting health care when distances separated participants. This was one of the earliest forms of telemedicine, or the use of electronic information and communication technologies for the provision of health care. Most commonly telemedicine was restricted to the sub-specialities (Field & Grigsby, 2002), but as Internet technology progresses, and the growth of health consumerism increases, our expectations and information practices in health

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care are changing. Innovative means of communication that were once limited to sub-speciality health care providers are becoming more common place. Traditional ways of providing information are still in use in many health care institutions, but health care is slowly beginning to harness the power of the Internet to store, manage, exchange, and share information. More recently, the collaborative properties of Web 2.0 applications are making information sharing possible for a wide range of participants, including patients, providers, caregivers and researchers through the use of collaborative, adaptive, and interactive technologies (CAIT) that (1) facilitate collaboration among users in traditional or novel ways, (2) support adaptation of form, function, and content according to user needs or preferences, and (3) enable users to interact with the technology via mechanisms of explicit interaction (O’Grady et al., 2009). Currently, there are already some guidelines in place for providing patients with information. Many jurisdictions have sophisticated legal requirements that protect not only the privacy of patients, but also their right to access this information in a timely and accessible manner (Wiljer & Carter, In Press). There are several obstacles to meeting these requirements using the traditional methods of delivering patient information; that is, locating a patient’s file – either paper or electronic – and producing a hard copy of the information. There are often limited resources to dedicate to providing this information. Nurses and administrative staff already face a time crunch in dealing with the volume of their work. In addition, certain jurisdictions suggest that an explanation of medical terms be given along with the information (Wiljer & Carter, In Press). This is often not feasible for the same reasons: time constraints on overloaded staff, and a lack of infrastructure for providing the information. In some cases there may be pre-existing pamphlets or glossaries available; however, these resources contain very generic content which may or may

not address the specific information the patient is looking for. The traditional methods of delivering information have always faced these barriers, but as information continues to proliferate, new strategies and, correspondingly, new challenges are coming to the fore.

Electronic Health Records (EHRs) The pervasiveness of the Internet supports new methods for accessing patient information. Electronic Health Records (EHRs) are now increasingly being used to record and manage patient data. An EHR can be defined in its simplest form as a computerized version of a traditional health record. Detailed definitions can be more complex, however, as an EHR may contain patient’s full medical record, or may be used only for recording certain aspects of care, such as lab results (Urowitz et al., 2008; Tang et al., 2006). EHRs can facilitate the sharing of information between health care providers and with in a health care system. This sharing of information is dependent upon the existence of interoperable systems. One promising approach is through the use of health level 7 (HL7), a message-based communication system implemented by an asynchronous common communication infrastructure between facilities with different EHRs (Berler, Pavlopoulos, & Koutsouris, 2004). HL7 is now commonly used as the standard interface for the exchange of health information. The benefits of an EHR include that it can be made widely accessible, and that it can facilitate the integration of care by allowing all members of a particular patient’s care team to access his or her information at need (Urowitz et al., 2008; Tang et al., 2006). However, the use of EHRs is still in its early stages in many health systems. A Canadian study in 2008 determined that over half of the nation’s hospitals (54.2%) had some sort of EHR, but that only a very few had records that were predominantly electronic (Urowitz et al., 2008). In Japan, a similar study determined

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that most hospitals (92%) used computerized administrative systems, but few (21%) used electronic “official documents”. Barriers to adoption became evident when professionals participating in the study reported that they expected efficiency and quality improvements from their EHR, but worried that the proposed system might be too complicated, threatening to slow down workflow or compromise patient privacy (Ishikawa et al., 2007). These types of barriers are of great interest, since having data in an electronic format is key to facilitating access to information.

Personal Health Records (PHRs) The development of storing data in electronic formats opens up further opportunities to integrate information. As a compliment to EHRs, which increase accessibility for professionals, there are many proponents for the implementation of Personal Health Records (PHRs). Markle Foundation’s Connecting For Health group, a public-private collaborative for interoperable health information structures, defined PHR as “An electronic application through which individuals can access, manage and share their health information, and that of others for whom they are authorized, in a private, secure and confidential environment” (Markle, 2003). There is a broad spectrum of approaches to implementing a PHR. At its simplest, a PHR may allow a patient to enter, store, and access his or her own information in a stand-alone application. In a more integrated model, the PHR could allow patients to access the information stored in their provider’s EHR, or even request appointments, renew prescriptions or communicate with their doctors online (Tang et al., 2006). PHRs can also provide a platform for communication between patients and health care providers. For example, Partners Group in Massachusetts supports the Patient Gateway. Patient Gateway allows patients to access an extract of their medical record and facilitates online communication through secure

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messaging with medical practices (Kittler et al., 2004). The actual benefits of PHRs are being researched and documented. In a study by Wuerdeman et al (2005) it was found that medical records are often incomplete, missing data from certain test results, for example. Patient-reported data became very useful in these situations, as practitioners who need comprehensive information must often ask the patient if they have had a certain test, or what the results were. While patients’ ability to report specific numeric test results did prove less accurate than information found in the medical record, patients were more reliable in reporting the absence or presence of a test and whether or not it fell in the normal range. In addition, patients were able to report problems that may not have appeared in the symptom list of the official record, such as depression (Wuerdeman et al., 2005).

Sharing Personal Information and Web 2.0 Patients uploading their own information would not only enable the sharing of information with their doctors, but also with one another. Traditionally, patients who wished to share information about their illness joined support groups or found other ways of meeting face-to-face. This is changing with the spread of Web 2.0 culture. Web 2.0 is a somewhat nebulous concept that is coming to be defined as a general spirit of open sharing and collaboration (Giustini, 2006). Generally, the idea is that the web can act as more than a unidirectional method of publishing information. Information can travel back and forth, and users are able to both access what they need as well as contribute what they have to share. Wikipedia, an information resource that functions as an encyclopaedia that anyone can contribute to, is a well-known example of the Web 2.0 philosophy. The proliferation of these Web 2.0 technologies is adding a new exponential dimension to UHI.

Personal Health Information in the Age of Ubiquitous Health

We are seeing the use of Web 2.0 technologies in health care, especially wikis and blogs (a contraction of web logs). A wiki is a website that allows multiple users to enter information, acting as a repository of information from different resources, as well as a mode of discussion about the information added. For example, Healthocrates. com is a health care wiki that offers free membership and encourages anyone and everyone to join and become a collaborator. Healthocrates claims to be home to over 8,500 articles, adding 500 to 1,000 new items each month. “Articles” include all types of media, from video and images, to discussion forums and case reports. A blog is another type of website, containing regular entries, much like an online diary. Blogs allow their authors to write articles, or even post personal videos, as well as have discussions with readers in the form of posted comments in a miniature forum, which generally appears below the day’s entry. Patients and clinicians alike have been known to blog. One well known example is Ves Dimov’s Clinical Cases and Images, which offers a reliable “one stop shopping” location for health news and research findings taken from the web daily (Giustini, 2006). Specialized and reliable sources like these can be helpful in reducing the time patients and professionals spend wading through ubiquitous health information on the web. The introduction of UHI, and collaborative technologies such as blogs and wikis is contributing to a new phenomenon of the democratization of health information. Information that was once firmly in the strong hold of the hierarchical health system is much more freely exchanged.

Types of Health Information Even as our abilities to store, exchange, and seek out information change, the information itself is changing. As technology increases, our ability to manage large amounts of information, and as the value of different kinds of information is becoming recognized, we are seeing information that

is increasingly specific and personalized joining stores of information that were once predominantly generic. Leonard et al. have argued that there is an identifiable information paradigm for patients who are actively managing their health, especially those patients managing chronic conditions (Leonard, Wiljer, & Urowitz, 2008). The paradigm encompasses several distinct information types, including general health information, and personal health information and experiential health information.

General Health Information General health information is very pervasive. A visit to a family physician can result in large amounts of information about a wide variety of health issues, from screening programs for a wide array of diseases to lifestyle choices and concerns. General health information covers a wide spectrum, and is usually basic information about a health issue (i.e. obesity), a condition, (i.e. diabetes), or medications, (i.e. diuretics or beta blockers). This information is intended to provide a starting point to give a layperson a basic understanding of a particular aspect of health. The literature shows that many patients are still most likely to seek information directly from physicians (Rutten, Arorab, Bakosc, Azizb, & Rowland, 2005). However, information is not always clear during a clinical encounter, which is often short and overwhelming, with large quantities of information being exchanged. Traditionally, patients left their physician’s office with the essential health information that they have heard from the physician or nurse reinforced by a pamphlet or a brochure. However, new technologies now provide important alternative sources of information. The Internet has changed the dynamic of general health information. The notion of searching for information, or turning to “Dr. Google”, has been commonplace for many individuals. The pervasiveness of general health information is astounding; for example,

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type “diabetes” into Google and in a tenth of a second, 96,800,000 hits are returned, “asthma” 30,900,000, “cancer” 232,000,000. At that rate, if you looked at each page for a second, it would take 7 years to view all of the information on cancer. The amount of health information on the Internet alone is beyond ubiquitous and will only continue to grow at an astounding rate. There are, of course, many different types of general information available on the Internet, and they vary greatly in quality and reliability. Many sites offer static content that is updated at regular or irregular intervals. Other sites have been developed to be more dynamic, offering tools and resources to help patients find information much more specific to their particular circumstances. For example, a palliative care site, www.CaringtotheEnd.ca, allows patients and family members to complete questionnaires based on a series of questions and then provides specific information to meet the needs of the user (Figure 1). Several technologies have also been developed to enhance the exchange of general health information between patients and clinicians. For example, a patient education prescription system, known as PEPTalk, (Figure 2,3) has been developed, that allows physicians to prescribe general health information to their patients (Atack, Luke, & Chien, 2008).

Personal Health Information It is evident that general health information has become ubiquitous, and certainly the advent of new technologies has improved access to it. Nonetheless, this of type information is of a general nature and does not provide patients with access to the information that is directly relevant to their particular situation. One of the promises of new technologies is to provide people with access to their own information. As an example, this type of access has clearly revolutionized the banking industry with the advent of online banking. Yet the

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Figure 1. Caring to the end of life

Figure 2. PEPTalk - clinician view

Figure 3. PEPTalk – patient view

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health care industry, overall, has been much more reluctant to embrace these types of technologies. Over the last decade, however, there has been a growing interest in utilizing new technologies to improve access to personal health information. This type of information generally includes an individual’s physical or mental health and family history, documentation of provided care, a care or service plan as well as administrative data (Cavoukian, 2004). For the moment, personal health information has the potential to be accessible anywhere the user is, but the promise has not yet been fully realized. In 2005, a collaboration began in Canada between Shared Information Management System (SIMS) Partnership and the Weekend to End Breast Cancer (WEBC) Survivorship Program began. The project was a an online portal called InfoWell, designed specifically to empower cancer patients to be active partners in their care, to participate in self-management activities and to find the support and resources they need in dealing with long-term consequences of their illness and treatment. Built using a commercially available patient portal as a foundation and customized by SIMS system engineers, InfoWell provides general health information as well as personal health information, which encompasses patientgenerated information including a patient profile, medication lists, and treatment history (Figure 4).

InfoWell also allows patients to view elements of their own EHR (Figure 5) (Leonard, Wiljer, & Urowitz, 2008).

Experiential Health Information From an early age, many of us glean valuable health information from our parents, siblings, teachers, and physicians. We learn important life lessons about things like diet, nutrition, exercise, and personal hygiene. Some of this “experiential” information is based on scientific evidence, and some of it based on family folklore, but it will all have a large impact on our lifestyle choices. It is amazing how a story or experience that a family member has when we are young can influence the day-to-day decisions that we make. The ubiquity of health experience and health information informs the very essence of who we are, whether we are always conscious of it or not. There is a specific type of experiential information that is of particular interest to most patients: the experiences and feelings of others with the same or similar conditions. Online communities are on the rise as a new way of finding and sharing this kind of information; 84% of Internet users participate in online groups, and 30% of these participants are members of medical or health related groups (Johnson & Ambrose, 2006). There are several Figure 5. InfoWell, my health – access to EHR

Figure 4. InfoWell – patient-generated PHR

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reasons people go looking for experiential information. There are information needs that can be met, such as to better understand their diagnosis, or to become more informed about its treatments. However, there are also social needs that can be fulfilled by online communities, such as finding support, helping others in the same situation, or to feel less alone or afraid (Preece, 2000). To help fulfill these needs among cancer patients, a large cancer centre in Canada developed CaringVoices. ca (Figure 6), an online community that offers support for people living with cancer and their caregivers. CaringVoices.ca provides access to current educational resources, peer support, and advice and education from health care and community experts. There is also a people-matching function that allows members of the community to connect and share experiential information. A real-time chat function enables social networking between patients, as well as discussions hosted by health care professionals, volunteer cancer survivors, and staff from community cancer agencies. There are several benefits to patients’ participation in online communities. A major cause of non-compliance with a care plan is a lack of understanding of the treatment. Online communities offer patients a place to share information and foster a greater understanding of the process; they also offer a chance to connect for patients who

are separated by distance or time. This is especially helpful to those who live in rural areas or may be homebound due to illness. The holistic approach to most online communities might also help to ease the burden on the health care system of patients who might otherwise over utilize the health care system. There are potential research benefits, as well. Observing interactions between large groups of patients provides an unobtrusive method of gathering information. Patterns in discussions may emerge, producing useful information about topics like adverse drug reactions, or clustering of disease types within age groups or geographical locations (Johnson & Ambrose, 2006). The use of online communities can also have drawbacks. Especially viewed from a standpoint of ubiquitous health information, there is concern that patients may be unable to find reliable resources, and may even be mislead by inaccurate information. As a response to this, there are many communities that now offer question-and-answer periods with a real physician. However, it has been argued that these too are insufficient, as the doctors leading these sessions do not treat the patient, and have no information as to their particular medical background (Preece, 2000). As such, it is important that patients have reliable means of way-finding through such vast expanses of ubiquitous information.

Figure 6. Caring voices

BENEFITS AND RISKS: WHERE ARE WE NOW? We have traced the history of UHI and gained some insight into the different circumstance and events that have led to the vast amounts of information that surround us today. In our exploration, we have touched on the idea that one of the major benefits of growth in both technology and levels of information, is the potential to personalize. With the management of large amounts of personal information, however, also comes risk. It

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is important to critically examine both the risks and benefits that face us today in order to ensure that in the future we can properly take advantage of the opportunities as well as manage our risks.

Benefits of Personal Health Information Once personal information has been captured, the benefits that can be realized from utilizing it correctly are substantial. Personal information is more and more commonly found in electronic format. This development opens up possibilities for technologies that can improve access and transportability of information, and in turn, help patients to engage as members of their own care team.

Access to Information As the role of the modern patient continues to shift toward the idea of the empowered consumer, there is an increasing demand for access to, and control over, personal information. Leonard, Wiljer and Urowitz (Leonard, Wiljer, & Urowitz, 2008), estimate that 40- 45% of the population in the United States and Canada are chronic illness patients with a strong desire for information on their condition. Members of this demographic group are knowledgeable about the health care system and eager to bypass the painstaking traditional methods of getting access to information. Wagner estimated that 40% of the population accounts for 70-80% of health care spending globally (Wagner, 1998). These numbers suggest that there are huge potential financial savings to be made by helping patients to manage their own health (Leonard et al., 2008). It has been shown, however, that improving access to information can introduce benefits beyond cost reduction. In 2003 an award-winning project entitled Getting Results focused on the potential benefits of providing electronic access to laboratory results for haematology patients who

spend long periods of time waiting in the hospital for these results. Patients and professionals alike saw benefits from the approach. Staff identified potential benefits such as improved workflow, decreased workload and a lower occurrence of unnecessary hospital visits (Wiljer et al., 2006).

Transportability Another benefit of the electronic management of personal health information is that it becomes transportable. Information that is more easily transported, is also more easily shared. Some independent or “untethered” ways of storing data are quite physically transportable: smart card technology, USB drives, or CDs, for example (Tang et al., 2006). Information that is “tethered” to a network, or stored online, can also be easy to share (Tang et al., 2006). The communication benefits of this can be seen in all types health care relationships; patient to provider, provider to provider, and patient to patient. Information that can be passed freely between patient and care provider changes care from episodic visits to a continuous process, and could dramatically reduce the time taken to address medical problems (Tang et al., 2006). In the relationship between separate providers, interoperable systems could virtually eliminate the need for paper and make important patient records instantly available in any facility involved in treatment, resulting in efficiencies and quality improvements in care (Gloth, Coleman, Philips, & Zorowitz, 2005). Additional benefits are seen when information can be passed easily from patient to patient. With the advent of Web 2.0 technology, large numbers of people can now unite and collaborate (Deshpande & Jadad, 2006). Patients are able to find one another and create communities that allow them to feel less alone or anxious, offer each other support, and increase their understanding of the information they collect at a physician visit.

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Self-Managed Care Once information is personal (that is, specific to a given individual), and can be easily accessed by that individual as well as shared with care providers that need it, another avenue of patient empowerment opens up, through the possibility of self-managed care. Self-managed care involves the patient or non-professional caregiver (usually a close friend or family member) taking an active role in the patient’s care. The degree to which a patient is involved can vary substantially from patient to patient: one patient may feel very comfortable in taking an active role, navigating the system and ensuring that they receive the care that is right for them. Another patient may want to participate in their care, but have much more involvement from their health care team. The management of more clinical aspects of care, or self-care requires patients to have very specific knowledge and some training around a medical task. The type of self-care is, of course, often very specific to the type of disease that is being managed: for diabetes, it may be monitoring blood levels and taking insulin as required; for heart disease, it may be monitoring daily weight and controlling diet and intake of sodium; for breast cancer, it may monitoring activities to avoid infections and performing self-massage to control lymphedema. Advents in technology such as wearable monitoring technologies have made these tasks easier for people. As early as 1999, one set of researches have reported on a subminiature implantable device to provide remote monitoring of glucose levels and the ability to transmit the glucose concentration data to a corresponding receiver and computer (Beach, Kuster, & Moussy, 1999). Now monitoring devices connected to specialized computer modems are used to reliably measure and transmit physiological parameters including blood pressure, heart rate, blood glucose level, and pulse oximetry data (Field & Grigsby, 2002). Technologies such as portable monitoring devices, wearable sensors and even smart shirts

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can all be used for electronically support self-care (Tan, 2005).

RISKS OF UBIQUITOUS HEALTH INFORMATION We have largely focused on the benefits and potential of UHI, but there are also risks to ubiquitous health information that can threaten an individual or their family, an organization or a community. UHI has the potential to lead to the loss of privacy for individuals, to security threats to organizations, and to the loss of social justice for communities and society. All of these risks have real implications and the use of electronic media pose risks at a much larger magnitude than paper. There have occasionally been errors in the safe disposal of paper records that lead to sensitive data appearing in inappropriate places, or the risk of losing a medical file, but the potential for wide scale breaches is much larger when dealing with electronic data. It is important therefore, to understand the risks and implications of the directions UHI is taking, in order to create protocols, policies and procedures that will ensure that as the amount of health information grows, the risks are mitigated as much as possible. In discussing the protection of information, it is useful to distinguish between the terms security, privacy, and confidentiality. Security, in all its contexts, refers to the notion of protection. In the case of sensitive information, it is the information itself that must be protected, by extension protecting the owner of that information. Security then, refers to the systems or means we use to ensure that access to protected information is appropriate. Privacy, in turn, deals with denying access that would not be appropriate, and determining policies for what information should be protected, and who should have access. Lastly, confidentiality refers to defining those situations where some access to personal data is appropriate (Wiljer & Cater, In press). Any person who is accessing data

Personal Health Information in the Age of Ubiquitous Health

that they do not own is being held in confidence, and it is understood that they will use their access only in the manner and purpose that is intended (Schoenburg, 2005).

Risks to the Individual: Privacy The greatest potential risks to the individual are around privacy issues. Awareness of these risks is increasing, and Internet users are beginning to protect their anonymity online. Members of the online community at PatientsLikeMe.com, who visit with the express purpose of sharing their information still tend to choose user names and avatars that represent them while keeping their identities anonymous. This may be especially desirable for patients living with illnesses such as HIV or mood disorders. With respect to receiving care, the notion of privacy relates to the fiduciary relationship between the patient and individual members of their health care team. The majority of privacy issues relate to data in health records such as family history, diagnostic tests, treatment and medication records and clinical notes. In most circumstances the patient and provider relationship is a private one, nonetheless, there are complex ethical issues that, at times, can impact on an individual’s right to privacy. Most jurisdictions have legislation that protect the rights of patients to privacy in general or specifically with respect to information pertaining to their health information. In the United States for example, the Health Insurance Portability and Accountability Act (HIPAA) protects personal health data.

Risks to Organizations: Security UHI also has the potential to cause security risks for organizations. The potential for wide scale breaches is much larger when dealing with electronic data, than the risks involved with paper files; errors in the safe disposal of files or the risk of losing a medical record. It is because of

this that is it so important to create protocols, policies and procedures that mitigate this risk as much as possible. Data must be protected in three major domains: the server side, where information is stored; the network, through which it travels; and the client side, the place where the information is received and used (Schoenburg, 2005). Each of these domains will require different actions to protect the data. It is important to allot equal attention to creation of protocols for each domain, since a vulnerability at any point could be the area where a potential breach would occur. Yet it is also important to find a balance between security and the need to construct usable systems. Applications that are not easily understood and used, or that are not effective in the delivery of care invite “workaround” solutions from frustrated staff who have found more efficient ways to operate. Oftentimes, workaround solutions are indeed more efficient, but they may circumvent the security protocols that are in place, creating new vulnerabilities for information to leak through.

Risks to the Community: Social Justice and Equity In addition to the issues affecting every day operations in health care, UHI has social implications on quite a broad scale. A survey in the United States (Brodie et al., 2000) shows that the Internet provides health information to large numbers of lower income, less-educated, and minority citizens. Nearly one-third of adults under age sixty are seeking such information online. And yet, there is still progress to be made. A digital divide still exists in terms of computer ownership. Lower income African Americans in the United States are far less likely than lower income whites to own a computer at home (Brodie et al., 2000) Once this barrier has been overcome, however, this study suggests that use of the internet to find health care information among computer-owners is similar across income, education, race, and age.

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It is clear that online health information is key to closing these gaps and improving equity in access to information. When it comes to personal health information, however, there is a delicate balance between the desire for accessibility and the need for security. Breaches in security could result in the loss of social justice for entire communities. Delicate information that is not well protected can lead to financial threats, misinformation, fraudulent information, and even identify theft (Setness, 2003).

your health” is a very generic piece of information. It applies to you whether you are male or female, 6 or 60, African American or Caucasian. One could argue it should be sufficient to influence behaviours. Yet, the United Nations predicts over 7.1 million tonnes of tobacco will be consumed in 2010 (“FAO Newsroom,” 2004). Even with 6.7 billion people around the globe (“World Factbook,” 2009), the number is staggering. So, the question arises whether or not a simple generic piece of information is sufficient to influence the behaviour of individuals.

OPPORTUNITIES

Opportunities for the Clinic: Personal Health Programs

The ubiquity of information is coming to be synonymous with increased accessibility, transportability, and capacity for detail. All of these important elements of information translate to health care opportunities on several levels. In education, personal data is enabling information to be tailored to the individual who needs it, making it more meaningful, and thereby improving learning. On the clinical level, personalized health and wellness programs are on the rise, offering the public the chance to engage as managers of their own health matters. And finally, in research, the opportunities for data mining the wealth of surrounding information are beginning to be realized, not only by health care professionals, but by patients who are eager for in-depth learning about their condition.

Opportunities in Education: Tailored Education Health information is everywhere, but it can be delivered in a number of different ways, depending on who the target audience is and what the goals are for disseminating the information. The same basic kernel of information can evolve into several different key messages, when we are informed about who will be receiving it. For example, “don’t smoke because it is harmful to

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As developments like EHRs and Web 2.0 technologies allow us to make information more transportable, shareable, and specific to a particular individual, health care can become more and more personalized. Personal information gathered from health care professionals, and from patients themselves, can be combined with standard care plans, transforming them into personal health programs. An example of this type of synergistic effort can be found in employee wellness programs in the United States, where a partnership between CapMed and Staywell Health Management offers opportunities for managing personal health. CapMed offers PHR solutions that allow patients to monitor and manage their health information, while Staywell is a health assessment and disease management organization, creating wellness programs for employers. By adding the power of the personal information PHRs can capture, wellness programs can become personalized to a particular individual. Members will be able to access individually relevant patient education resources and set up health alerts and reminders. Additional health management tools are also available, including a drug interaction checker, data importer to collect information from remote health care systems, and authorizations that allow family

Personal Health Information in the Age of Ubiquitous Health

members and caregivers to view all or part of the data a patient is storing (“CapMed and Staywell Health Managment Offer PHR/Wellness Program Presentations at AHIP 2008,” 2008).

Opportunities in Research: Data Mining It is difficult to consider the concept of ubiquitous health information in depth, without touching on the prospect of public health and surveillance. Public health is an issue that is currently booming, due to the events of the past decade. The anthrax threats that occurred after September 11th, and natural disasters such as hurricane Katrina and the Asian tsunami have raised awareness of the need for strengthened public health infrastructures (Kukafka, 2007). Spending has been increased for public health informatics in the United States, and research is showing that syndromes surveillance systems, which operate by collecting existing information by mining ubiquitous data are now capable of detecting some types of disease outbreaks quite rapidly. This trend of public health being increasingly tied to developments in informatics and the proliferation of health information and geographical information systems (GIS) (Tan, 2005) appears to be continuing as we move forward into the future. Prior to September 11th, 2001 a PubMed search for articles on “public health informatics” yielded only six results. In 2007, this same search resulted in over 600 articles (Kukafka, 2007). The public health benefits of data mining go beyond prevention. There is also the potential for identifying areas for improvement in quality of care. As an example, a New York state initiative in 1979 began collecting data on all hospitalized patients (Setness, 2003). Ten years later, a report card was published giving comparisons of cardiac bypass surgery deaths, by hospital and surgeon. The statistics were a shock for staff at hospitals that did not score well in the report. This discovery

led to the revamping of several cardiac surgery programs throughout the state. Improvements to the system are not the only type of public health benefit; there are benefits to be seen on the level of the individual as well. Patients being treated for a wide spectrum of conditions are finding benefits from this capacity for data mining UHI. PatientsLikeMe.com, is a website open to the public, that offers patients being treated for a variety of illnesses the chance to form communities. The home page offers the visitor three options that sum up the motivations for seeking health information online: Share your experiences, find patients like you, and learn from others. The site also hosts message boards with postings on information on treatments and symptoms, although it is not clearly documented where the information originates from. When they join, patients create their own PHR, by filling in information fields and posting their own data about their symptoms, treatments and outcomes. Of particular interest is the Research section of the site. The research team at PatientsLikeMe have harnessed the self-reported information from the PHR aspects of the site. The data in these personal profiles is compiled and displayed graphically to the community in the form of symptom and treatment reports. Members can view these reports to gain a broader understanding of what an entire population of patients like themselves are experiencing. The site allows members to discuss these issues in forums, contact each other through private messaging, and post comments on each other’s personal profiles (Frost, 2008). Having such robust information has proved very valuable to patients. Being able to locate others with very specific experiences allows members to target information searching and sharing. Users could find the right person to ask a specific question to, and know who was likely to benefit most from hearing their own experiences. It became easier for patients to form relationships that were stronger and more edifying; when they

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were communicating with people they had much in common with (Frost, 2008).

FUTURE RESEARCH DIRECTIONS The field of UHI is burgeoning. It is difficult to know which areas will really blossom as integral parts of the health care system. Certainly, the domain of EHRs and PHRs seems to be firmly taking root with the emergence of industry giants such as Google and Microsoft staking a claim to this virtual space. In other areas of social networking and information exchange, organizations such as PatientsLikeMe are transforming the paradigm for health information sharing in ways that are still not evident. What is clear is that there are many new and intriguing opportunities in this area. There are opportunities to implement solutions and services that will contribute to the empowerment of patients and their families and provide them with new opportunities to be active participants in control of their health care. Leadership in the health system is required to affect the required changes. Finally, research is essential to produce new evidence and new knowledge so that effective change can be made. Research is required into many, aspects of ubiquitous health information and this research needs to build on the growing literature in health informatics and information and communication technologies.

Benefits Evaluation and Research on UHI Ubiquitous Heath Information poses several challenges to current models of the evaluation of health information. Many evaluation and research approaches of online resources and tools assume a relatively static, homogenous model. From this model, there are a series of assumptions made about the reliability and quality of the online resource. The very notion of ubiquitous information, however, suggests that this type of information is

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not only everywhere, but it is, at the same time, highly dynamic in nature. The current evaluation techniques, for the most part, depend on measuring a constant or static condition and, therefore, in the realm of ubiquitous health information, many of the current evaluation endpoints for online information must themselves be challenged and examined. Evaluation and research models for online health information are based on the assumption that there is a “single” content provider. This may be one author, a group of authors, or an organization that can be identified as the source of content. Based on this assumption, a user of the information can employ a series of endpoints or parameters to assess the nature of the site. For example, the reliability of the site may be assessed based on the credibility of the source of the content. One may look at the credentials of the source, any conflicts of interest, publication record, and so on. However, if there are multiple authors who have no other association than being contributors to a collaborative site, such as a wiki or community of practice, it is much more difficult to assess the “global” reliability of the content on a site. Each “fragment” of authored content must be assessed individually. Making matters somewhat more complex, it is not always easy to immediately identify the “voice” of an individual. For example, wiki technologies are designed to integrate the work of many authors into a single message within a set framework. Although many wiki sites track individual contributions, these unique contributions or content fragments are not always readily apparent to the reader. In the case of a wiki, assessing the reliability of the information may be more about assessing the ability of a wiki community to self-correct and self-monitor the quality of the information on its site than assessing the credentials or credibility of individuals generating the content. Difficulties also arise when applying current assessment tools to the quality of an online resource. In assessing the usability of the site, there are often

Personal Health Information in the Age of Ubiquitous Health

several distinct technologies stitched together in what are often referred to as “mashups”. In assessing the usability of the platform or information communication technology, a new dimension of the evaluation may be required to assess not only the ease of use of the technology, but also the “integration” of information being aggregated from a number of sources. New models of evaluation are required that consider the exchange and flow of information. These new models need to expand the parameters for evaluation and assessment. For example, the strength of an online community can be assessed by looking at the number of users, frequency of posting and quantity of collaboration. A shift in focus is also required to investigate health systems improvements that are linked to UHI. These improvements may include continuity of care, the quality of information provided, fewer duplicated tests and better surveillance. In addition, psychosocial outcomes need to be included, such as reductions in distress, anxiety and depression. Levels of patient empowerment, activation and participation also must be considered to incorporate adherence to guidelines and care plans, as well as improved self-efficacy and self-management.

CONCLUSION Starting with general health information and the advent of the Internet, advances in technology and changes in the expectations of health care consumers have brought on a massive shift in the treatment of information in health care. We have moved from a focus on protecting and securing data to understanding how to provide equitable access to information in a safe and useful format. Health information is truly all around us, in many forms. The Age of the Internet has brought us dynamic social networking technologies and the spread of experiential health information, from

video stories to the real-time exchange of health experiences. Slowly, health care is beginning to enjoy the benefits of UHI, especially the potential to personalize care. Patients are seeing improvements in access to health information and communication with their providers, and beginning to take an active role in their care through easier prescription renewals, medication tracking, and the power of Internet technologies to empower those with chronic illness to self-manage their conditions. At the organizational level, we are starting to see increased patient satisfaction, better continuity of care, reductions in cost and improved standardization of care. However, it is also important to properly manage the risks and opportunities that come with ubiquitous health information. We must remember the challenges, so that we may create policies that effectively manage the risks of privacy and security issues, and strive to overcome the barriers of change management and the lack of basic infrastructure in many areas. If we are to take full advantage of our opportunities and make sense of the “everywhere, all the time” presence of health information, researchers must create a roadmap for utilizing emerging technologies. This new map must plot a pathway that will ensure that these new, disruptive and transformative technologies will result in more effective and efficient health systems, in which patient outcomes and quality of life are dramatically improved.

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ADDITIONAL READING Andersen, J., & Aydin, C. (Eds.). (2005). Evaluating the Organizational Impact of Healthcare Information Systems. New York: Springer Science+Business Media Inc. Brender, J. (2006). Evaluation Methods for Health Informatics. San Diego: Elsevier. Bruslilovsky, P., Kobsa, A., & Nejdl, W. (Eds.). (2007). The Adaptive Web: Methods and Strategies of Web Personalization. Pittsburgh: Springer. Committee on Quality of Health Care in America. (2001). Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press. Europe, W. H. O. (2006). What is the evidence on effectiveness of empowerment to improve health? Retreived March 13, 2009 from http://www.euro. who.int/Document/E88086.pdf Gerteis, M., Edgman-Levitan, S., & Daley, J. Delblanco, T. (Eds.) (1993). Through the Patient’s Eyes. San Francisco: Jossey-Bass. Johnston Roberts, K. (2001). Patient empowerment in the United States: a critical commentary. Health Expectations, 2(2), 82–92. doi:10.1046/ j.1369-6513.1999.00048.x Kemper, D., & Mettler, M. (2002). Information Therapy: Prescribed Information as a Reimbursable Medical Service. Boise, ID: Healthwise.

Kreuter, M., Farrell, D., Olevitch, L., & Brennan, L. (Eds.). (2000). Tailoring Health Messages: Customizing Communication with Computer Technology. New Jersey: Lawrence Earlbaum Associates Inc. Leonard, K. J., Wiljer, D., & Casselman, M. (2008). An Innovative Information Paradigm for Consumers with Chronic Conditions: The value proposition. The Journal on Information Technology in Healthcare. Lewis, D., Eysenbach, G., Kukafka, R., Stavri, P. Z., & Jimison, H. (Eds.). (2005). Consumer Health Informatics. New York: Springer Science+Business Media Inc. Picker, N. C. R. Eight Dimensions of PatientCentred Care. Retreived March 13, 2009 from http://www.nrcpicker.com/Measurement/Understanding%20PCC/Pages/DimensionsofPatientCenteredCare.aspx Preece, J. (2000). Online Communities: Desigining Usability, Supporting Sociability. Chichester, England: Wiley & Sons Ltd. Sands, D. Z. (2008). Failed Connections: Why Connecting Humans Is as Important as Connecting Computers. Medscape Journal of Medicine, 10(11), 262. Sands, D. Z. (2008). ePatients: Engaging Patients in Their Own Care. Medscape Journal of Medicine, 10(1), 19. Tan, J. (2005). E-Health Care Information Systems: An Introduction for Students and Professionals. San Francisco: Jossey-Bass. Wagner, E. H. (¾¾¾). Chronic disease management: What will it take to improve care for chronic illness? Effective Clinical Practice, 1, 2–4.

This work was previously published in Ubiquitous Health and Medical Informatics: The Ubiquity 2.0 Trend and Beyond, edited by Sabah Mohammed and Jinan Fiaidhi, pp. 166-189, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 1.5

E-Health as the Realm of Healthcare Quality: A Mental Image of the Future

Anastasius Moumtzoglou Hellenic Society for Quality & Safety in Healthcare, European Society for Quality in Healthcare Executive Board Member, Greece

ABSTRACT E-health has widely revolutionized medicine, creating subspecialties that include medical image technology, computer aided surgery, and minimal invasive interventions. New diagnostic approaches, treatment, prevention of diseases, and rehabilitation seem to speed up the continual pattern of innovation, clinical implementation and evaluation up to industrial commercializa-

tion. The advancement of e-health in healthcare derives large quality and patient safety benefits. Advances in genomics, proteomics, and pharmaceuticals introduce new methods for unraveling the complex biochemical processes inside cells. Data mining detects patterns in data samples, and molecular imaging unites molecular biology and in vivo imaging. At the same time, the field of microminiaturization enables biotechnologists to start packing their bulky sensing tools and medical simulation bridges the learning divide

DOI: 10.4018/978-1-60960-561-2.ch105

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

E-Health as the Realm of Healthcare Quality

by representing certain key characteristics of a physical system.

INTRODUCTION There is a worldwide increase in the use of health information technology (IT), which holds promise of health system breakthroughs. Emerging information and communication technologies promise groundbreaking solutions for healthcare problems. Moreover, a global e-health consensus framework is beginning to take shape in IT discourse, which includes stakeholders, policy, funding, coordination, standards and interoperability (Gerber, 2009). However, in an unprecedented technological innovation, many aspects of health care systems require careful consideration due to error and inefficiency although e-health bridges clinical and nonclinical sectors. The endorsement of information technology in the health sector is spreading slowly. Few companies focus on population-oriented e-health tools partly because of perceptions about the viability and extent of the market segment. Moreover, developers of e-health resources are a highly diverse group with differing skills and resources while a common problem for developers is finding the balance between investment and outcome. According to recent surveys, one of the most severe restraining factors for the proliferation of e-health is the lack of security measures. Therefore, a large number of individuals are not willing to engage in e-health (Katsikas et al., 2008). E-health presents risks to patient health data that involve not only technology and appropriate protocols but also laws, regulations and professional security cultures. Furthermore, breaches of network security and international viruses have elevated the public awareness of online data and computer security. Although the overwhelming majority of security breaches do not directly involve health-related data, the notion that online data are exposed to security threats is widespread. Moreover, as we

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understand the clinical implications of the genetic components of disease, we expect a remarkable increase in the genetic information of clinical records. As a result, there is likely to be considerable pressure in favor of specific laws protecting genetic privacy (Magnusson, 2002). Therefore, secure e-health requires not only national standardization of professional training and protocols but also global interoperability of regulations and laws. Professional health information organizations must take the lead in professional certification, security protocols and applicable codes of ethics on a global basis ((Kluge, 2007; Moor & Claerhoutb, 2004). On the other hand, clinicians have moved toward the Internet within the last few years while purchasers seek higher quality and lower costs. The Internet offers an unprecedented opportunity to integrate various health-related sectors while some Internet-related trends and technologies will have a substantial impact on the design, content, functionality, dissemination, and use of future e-health tools. Moreover, quality assurance and improvement are key issues for the e-health sector while strategic planning could provide an insightful view of the impacts (Asoh et al., 2008). However, the accessibility and confidentiality of electronic resources does not guarantee quality access (West and E. A. Miller, 2006) and the current quality assurance strategies do not address the dynamic nature of e-health technologies. Furthermore, the contribution of various socioeconomic factors to ‘the digital divide’, which refers to the difference in computer and Internet access between population groups segmented by various parameters, is controversial. However, recent data suggests that the digital divide may be closing in some aspects, due to access to PCs, and the Internet. Access, however, is only one facet of the digital divide, as health literacy (Bass, 2005), and relevant content are also key elements. Moreover, there are overlapping and gaps in e-health content due to the uncoordinated and essentially independent efforts. Current market

E-Health as the Realm of Healthcare Quality

forces are driving e-health development in clinical care support, and health care transactions, but they do not provide population health-related functions. Therefore, increased information exchange and collaboration among developers may result in efficient use of resources. The challenge is to foster collaborative e-health development in the context of fair competition. Greater collaboration presents new communication challenges, which include a standardized communication and information flow (Lorence & R. Churchill, 2008), which will enhance: • • • •

social support cognitive functioning clinical decision-making cost-containment

Many observers believe that a picture of interoperable clinical, laboratory, and public health information systems, will provide unprecedented opportunities for improving individual and community health care (Godman, 2008; Lorence & Sivaramakrishnan, 2008; Toledo et al., 2006). Interoperable electronic medical records (EMRs) contain the potential to improve efficiency and reduce cost (James, 2005). Moreover, semantic interoperability is essential for sound clinical care (Goodenough, 2009) although electronic prescribing does not increase steadily (Friedman et al., 2009). The lack of integration in health care also carries over into the online world. Therefore, universal data exchange requires a translating software and the development of data exchange standards (Friedman et al., 2009). There is also need to integrate the various features and functions of e-health tools to provide a seamless continuum of care, which might include: • • • •

health data processing of transactions electronic health records clinical health information systems

• •

disease management programs behavior modification and promotion

health

Finally, as the complexity and amount of genetic information expand, new e-health tools, which support clinicians and consumers decisionmaking in genetics will be in considerable demand. Moreover, once nanotechnology applications become available, we have to establish nanoethics as a new sub-discipline of applied ethics (Godman, 2008). Overall, although we have to understand care in a renewed dimension (Nord, 2007), we might consider e-health as a method to improve the health status of the population. The promise of applying emerging e-health technologies to improve health care is substantial. However, it is crucial to build partnerships among healthcare providers, organizations, and associations to ensure its continued development (Harrison & Lee, 2006). Thus, the perspective of the chapter is to examine the prospects of e-health to become the realm of healthcare quality.

BACKGROUND The term e-health has evolved through prolonged periods of time, which include the era of discovery (1989-1999), acceptance (1999-2009), and deployment (2009-). Each period has distinctive features, and critical applications. For example, in the age of discovery, we denounced traditional approaches to realize during the era of acceptance that we need a collaboration model. Key application areas of this period of time include electronic medical records (including patient records, clinical management systems, digital imaging & archiving systems, e-prescribing, e-booking), telemedicine and telecare services, health information networks, decision support tools, Internet-based technologies and services. E-health also covers virtual reality, robotics, multimedia, digital imaging, computer

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E-Health as the Realm of Healthcare Quality

assisted surgery, wearable and portable monitoring systems, health portals. Finally, the distinctive features of the era of deployment might involve community care, evidence based medicine, collaborative care, and self-management. Furthermore, centrifugal or centripetal mobility will contribute to a new service integrating e-health and quality. However, the term e-health is a common neologism, which lacks precise definition (Oh et al., 2005). The European Commission defines e-health as ‘the use of modern information and communication technologies to meet needs of citizens, patients, healthcare professionals, healthcare providers, as well as policy makers’ while the World Health Organization offers a more precise definition. Specifically, e-health is ‘the cost-effective and secure use of information and communications technologies in support of health and health-related fields, including health-care services, health surveillance, health literature, and health education, knowledge and research’. Eysenbach, in the most frequently cited definition, defines e-health as ‘an emerging field in the intersection of medical informatics, public health and business, referring to health services and information delivered or enhanced through the Internet and related technologies. In a broader sense, the term characterizes not only a technical development, but also a state-of-mind, a way of thinking, an attitude, and a commitment for networked, global thinking, to improve health care locally, regionally, and worldwide by using information and communication technology. Finally, Pagliari et al., (2005) in a highly detailed analysis provide a definition, which covers human and organizational factors. E-health is not the only sector, which lacks a clear definition. A literature review reveals a multitude of definitions of quality of care (Banta, 2001). The World Health Organization has defined the quality of health systems as ‘the level of attainment of a health system’s intrinsic goals for health improvement and responsiveness to the legitimate expectations of the population’

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(WHO, 2000). W. Edward Deming argued that ‘a product or service possesses quality if it helps somebody and enjoys a good and sustainable market’, Juran (1974) stated that ‘quality is fitness for use’, and Crosby (1979) ‘quality means conformance to requirements’. Still, the most widely cited definition of healthcare quality was formulated by the Institute of Medicine (IOM) in 1990. According to the IOM, quality consists of the ‘degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge’. Overall, the meaning of quality is complex, elusive, and uncertain (Burhans, 2007), and its meaning is an optimal balance between possibilities and the framework of norms and values (Harteloh, 2004). Nevertheless, the concept of healthcare quality involves the standardization and national endorsement of performance measures, the evaluation of outcomes, reporting for accountability, and technological innovation (Smith, 2007). Furthermore, we might distinguish three levels of quality, which include conformance quality, requirements quality, and quality of kind. Even so, the present and future healthcare systems demonstrate a quality chasm, formed by a large number of different factors. Medical science and advanced technology have advanced at an unprecedented rate but have also contributed to a growing complexity. Faced with such rapid changes, the healthcare system has fallen considerably short in its ability to refine theoretical knowledge into professional practice and to use modern technology. The healthcare needs have changed quite dramatically as well. Individuals are living longer, due to fundamental advances in medical science. Subsequently, the aging population translates into an exponential growth in the prevalence of chronic conditions. In addition, today’s complex health system overly deals with acute, episodic care needs and cannot consistently deliver today’s science and technology. It is less prepared to respond to the tremendous advances

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that surely will appear in the near future. Healthcare delivery cannot face the various challenges and is overly complicated and uncoordinated. Therefore, complex processes waste resources and lead to excessive loss of useful information. Eventually, healthcare organizations, hospitals, and physicians operate without the added value of information. As a result, state-of-the-art care requires a fundamental and extensive redesign. The health system should avoid injuries, provide responsive services based on scientific knowledge, reduce waits and delays, avoid waste, and eliminate differences in quality. Overall, according to the Institute of Medicine ‘Crossing the Quality Chasm: A New Health System for the 21st Century’ report, the health system should be safe, effective, patientcentered, timely, efficient, and equitable (IOM, 2001). To achieve these improvement aims, we should adopt ten simple statements (IOM, 2001): • • • • • • • • • •

patients should receive care or access to care whenever they need it the entire system should meet the most frequent types of needs and preferences empower the patient allow information flow freely and share knowledge decision-making is evidence-based patient safety is a system property transparency is necessary anticipate needs decrease waste enhance cooperation

Furthermore, redesigning the healthcare delivery system involves changing the structure and administrative processes of healthcare organizations. Such changes need to appear in four key areas (IOM, 2001): • •

application of evidence to healthcare delivery information technology

• •

alignment of payment policies with quality improvement preparation of the future workforce

Finally, we should not underestimate the importance of adequately preparing the future workforce to undergo the transition into a new healthcare system. There are three contrasting approaches, which we might use (IOM, 2001): • • •

redesign the educational approach (Hasman et al., 2006) change the way we accredit or regulate health professionals define and ensure accountability among health professionals and professional organizations

Conclusively, the Institute of Medicine’s ‘Crossing the Quality Chasm: A New Health System for the 21st Century’ report, implies: •

•

•

•

• •

to improve physician-patient interaction beyond encounter-oriented care (Leong et al., 2005) to introduce a partnership approach to clinical decision-making (Coulter & Ellins, 2006) to improve health literacy and encourage full disclosure of adverse events (Gallagher et al., 2007) to revolutionize patient safety by providing clinicians with clinical decision support and by involving patients (Coulter & Ellins, 2006) to raise public accountability by measuring the quality of care at the provider level to improve patient care on the basis of ongoing evaluation, lifetime learning, systems-based practice and interpersonal skills of clinicians

Although groundbreaking, IOM’s approach raises several limitations:

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•

•

• •

the evidence on health literacy demonstrates that there are substantial gaps in what we know about how to raise its standards (Coulter & Ellins, 2006) the implementation of innovations, which improve clinical decision-making and promote greater patient involvement, has yet to occur (Coulter & Ellins, 2006; Billings 2004; Graham et al., 2003) safety improvement, through patient involvement, should be enhanced patient feedback, provider choice, complaints, and advocacy systems should be carefully assessed

E-health holds enormous potential for transforming the quality aspect of healthcare by supporting the delivery of care, improving transparency and accountability, aiding evidence-based practice and error reduction, improving diagnostic accuracy and treatment appropriateness, facilitating patient empowerment, and improving costefficiency.

THE QUALITY PERSPECTIVE OF E-HEALTH E-health has its roots in physics, engineering, informatics, mathematics and chemistry and links with biosciences, especially biomedical physics, biomedical engineering, biomedical computing and medicine. Therefore, the improvement in science and technology accelerate its tremendous growth the last few decades. Furthermore, e-health has widely revolutionized medicine, creating subspecialties that include medical image technology, computer aided surgery, and minimal invasive interventions. New diagnostic approaches, treatment, prevention of diseases, and rehabilitation seem to speed up the continual pattern of innovation, clinical implementation and evaluation up to industrial commercialization. Some of the current technologies are the electronic patient

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record, a routine for a few countries, the patient data card, health professional card, e-prescription, e-reporting, networking and tele-consulting. BioMEMOS, imaging technology, minimally invasive surgery, and computer-assisted diagnosis, therapy and treatment monitoring, e-health/telemedicine/ networking, and medical engineering for regenerative medicine are the fields of e-health technologies, which have emerged in recent years. For example, tissue engineering combines specific medical disciplines with cell and molecular biology, material sciences, physics and engineering aiming at developing methods for regenerating, repairing or replacing human tissue. Specifically, it uses in situ stem cell control focusing specifically on stem cells of the central nervous system for neurogeneration (Nusslin, 2006). Finally, beyond the boundaries of the medical field, at least in the highly industrialized countries, e-health has a substantial impact on the economy, the whole society and the ethical system. The advancement of e-health in healthcare derives large quality and patient safety benefits. Health information technology holds enormous potential to improve outcomes, and efficiency within the health care system. Electronic health records, decision support systems, computerized physician order entry systems, computerized adverse events systems, automatic alerts, national or regional incident or event reporting systems improve administrative efficiency, adherence to guideline-based care, and reduce medication errors. Electronic health records, which store information electronically, provide the opportunity to find patients with certain conditions, and look for specific issues (Honigman et al., 2001a; Honigman et al., 2001b).The IOM (2001) argued that an electronic based patient record system would be the single action that would most improve patient safety. Coiera et al., (2006) argued that the use of decision support systems, comprehensive solutions often incorporated in a variety of e-health applications, can improve patient outcomes and make clinical services more effective.

E-Health as the Realm of Healthcare Quality

Tierney et al (2003) found that the intervention had no effect on physicians’ adherence to care suggestions while Kawamoto et al (2005), in a meta-analysis of seventy studies, concluded that decision support systems significantly improve clinical practice. Computerized physician order entry systems, a process whereby physicians file electronically instructions to individuals responsible for patient care, improve the quality of care by increasing clinician compliance. It is an outstanding application (Sittig & Stead, 1994), which reduces preventable adverse drug events (Kaushal & Bates, 2003). Computerized adverse event systems have shown a notable increase in the number of reported adverse drug events, and automatic alerts reduce the time until treatment of patients with critical laboratory tests (2001). Many studies have shown that prevention guidelines and the reminder computerization improve adherence (Balas et al., 2000) while reminders are noteworthy in the care of chronic conditions, which constitute a large quotient of expenditures (Lobach & Hammond, 1994). Furthermore, national or regional incident or event reporting systems, which collect data from local sources, lead to remarkable changes at the community and national level (Runciman, 2002). Finally, a particularly compelling reason for promoting the adoption of e-health is its potential to improve the processes of health care. E-Health improves the likelihood that the processes will be successful; strengthens the distribution of evidence-based decision support to providers, narrows the gaps between evidence and practice, and might lead to exceptional savings in administrative costs. On the other hand, electronic clinical knowledge support systems have decreased barriers to answering clinical questions (Bonis et al., 2008), and organizational learning at the system level fosters improvement (Rivard et al., 2006). However, e-health is not just a technology but a complex technological and relational process (Metaxiotis et al., 2004). Therefore, the Internet, a platform

available to remote areas, constitutes a radical change, which empowers patients. Patient-centered care is an emerging approach, which the Picker Institute defines as informing and involving patients, eliciting and respecting their preferences; responding quickly, effectively and safely to patients’ needs and wishes; ensuring that patients are treated in an ennobled and caring manner; delivering well coordinated and integrated care. Patient empowerment, which means that patients play a more active role in partnership with health professionals, and relates to strategies for education and e-health information (Pallesen et al., 2006), is increasingly being seen as a essential component of a modern patient-centered health system. Kilbridge (2002) suggested that technologies, which allow access to general and specific health care information, technologies capable to conduct data entry and tracking of personal self-management, empower patients. These technologies included Personal Health Records, Patient Access to Hospital Information Systems, Patient Access to General Health Information, Electronic Medical Records (EMRs), Pre-Visit Intake, Inter-Hospital Data Sharing, Information for Physicians to Manage Patient Populations, Patient-Physician Electronic Messaging, and Patient Access to Tailored Medical Information, Online Data Entry and Tracking, Online Scheduling, Computer-Assisted Telephone Triage and Assistance, and Online Access to Provider Performance Data. Moreover, handheld devices increasingly allow expansion of desktop systems and capture vital signs or administer medications while much of the quality improvement revolves around refinement of processes. It is worthwhile mentioning that telemedicine, a branch of e-health that uses communication networks and it is deployed to overcome uneven distribution and lack of infrastructural and human resources, provides a category by itself (Sood et al., 2007). Finally, the sciences of chaos and complexity are generating considerable interest within the domains of healthcare quality and patient safety

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(Benson, 2005), and clinical narratives has the potential to improve healthcare quality and safety (Brown et al., 2008; Baldwin, 2008). Still, there is a lack of interoperability, which makes the provision of clinical decision support and the extracting of pertinent information vastly more complicated (Schiff & Rucker, 1998; Schiff et al., 2008), and risks to patient health data (Kluge, 2007).

reliability and quality of health data and tort-based liability while recommendations for legal reform include (Hodge et al., 1999):

Issues, Controversies, Problems

Technological barriers to e-health stem from the evolutionary nature of these systems but also rapid obsolescence, and the lack of standards or criteria for interoperability. Without the technical specifications that enable interoperability, data exchange between providers who use different e-health systems is severely limited. Moreover, security and confidentiality in information technology represent a serious matter (Anderson, 2007). Finally, implementation and dissemination issues are decipherable. The effects of e-health tools on patient behavior, the patient-clinician relationship, the legal and ethical implications of using health information technologies and clinical decision support systems are unclear. Furthermore, potential health inequalities resulting from the digital divide have to restrain within bounds. Overall, key questions include clinical decision support, refinement of guidelines for local implementation, implementation and dissemination of clinical information systems, patient involvement and the role of the Internet. On the other hand, the variation in the quality strategies is the effect of different levels of political commitment and/or available financial resources (Legido-Quigley, 2008; Lombarts, et al., 2009; Spencer & Walshe, 2008; Spencer & Walshe, 2009; Sunol et al., 2009). The most eminent drivers of policy have been governments, professional organizations, scientific societies, and media. Governments are the key players in developing and implementing quality improvement policies, and setting quality standards and targets. However, the lack of quality incentives, lack of funding for quality improvement and absence of professional

Notwithstanding the e-health potential for contributing to improved processes of health care, greater efficiency and enhanced understanding of clinically-effective care, financial and other barriers persist that result in comparatively low penetration rates of e-health. These barriers include acquisition and implementation costs, the lack of interoperability standards, skepticism about the business case for investment, uncertainty about system longevity, and psychological barriers related to uncertainty and change. E-health systems are expensive and providers face problems making the investment. Even if they can handle the initial acquisition and implementation costs, still must remain confident that an e-health system will improve efficiency or cover its upfront and ongoing costs to make the investment. In addition, some providers’ reluctance to adopt e-health may be a rational response to skewed financial incentives since the health care system provides little financial incentive for quality improvement (Blumenthal & Glaser, 2007). Perhaps the most controversial barrier to adoption of quality-related information technology is the lack of incentives for change. Therefore, non-governmental groups might play an extremely pivotal role in developing incentives, and regulatory agencies can ask for valid, standardized, and more easily gathered quality measurement data. Additional barriers to the spread of e-health include legal and regulatory concerns and technological issues (Davenport, 2007). Legal challenges include privacy of identifiable health information,

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• • • •

sensitivity of health information privacy safeguards patient empowerment data and security protection

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training and education in quality improvement are the key barriers to progress. We might also argue that funding and financing cause conflict of interest. Furthermore, in the current economic crisis, governments are less inclined to invest in the development of quality strategies as they will not provide adequate financial return in the short or medium terms. Additionally, the existence of a statutory legal requirement to implement quality improvement strategies for healthcare systems and organizations is an overriding motivation for supporting progress in the development of quality improvement initiatives. Nevertheless, patient and service user organizations have the least impact on the development of quality improvement strategy. The preponderance of governments and health professions makes difficult for patient and user groups to set the limits of quality improvement policies and strategies. This means that the quality improvement activities reflect a professional and provider perspective. Another issue concerns evaluation and implementation strategies. Little information is available about the effects of the strategies. As a consequence, investing in quality strategies appears expensive and inefficient, and education and training in quality improvement constitutes a barrier to progress. Therefore, there is a role for quality improvement training in education and institutional development of healthcare professionals. Finally, quality improvement requires strong, engaged and informed professional leadership, which develops if healthcare professionals have access to appropriate training in healthcare quality improvement. However, such training is usually not available, the development of clinical guidelines, accreditation schemes, auditing of standards, and quality management are optional, and evaluation of quality improvement is not available to patients.

SOLUTIONS AND RECOMMENDATIONS The integration of e-health into the existing work flow is increasingly difficult due to the complexity of the clinical setting. Consequently, strong leadership within the clinical setting is essential in the successful implementation. Leadership should work in conjunction with the staff in order to mitigate any apprehensions (Iakovidis, 1998; Burton et al., 2004). With this internal backing and commitment, healthcare professionals will become involved and integrate e-health into their daily practice. Moreover, there is a critical relationship between organizational and technological change (Berg, 2001), which providers should understand prior to implementation because they constitute the driving force behind changes within the clinical setting. Conclusively, the successful implementation of e-health requires a thoughtful integration of routine procedures and information technology elements. E-health integration is a process, which is determined by the uniqueness of each setting (Berg, 2001) and social and human variables. Immediate policy changes, which promote e-health adoption, might include standards for interoperability, privacy protection, clinical effectiveness research, and amelioration of parallel working by software developers and health services researchers (Pagliari, 2007). Quality, access and efficiency are the general key issues for the success of e-health (Vitacca et al., 2009). However, a realistic definition of the dimensions of quality care is insufficient to accomplish the goal of continuous improvement. Therefore, healthcare quality improvement is a work in progress. Nonetheless, we might argue that quality improvement relates with leadership, measurement, reliability, practitioner skills and the marketplace. Kotter (1996) provides an eight-stage process to cope with change, which is necessary for the effective leadership. The eight processes include:

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• • • • • • • •

establish a sense of urgency set up the guiding coalition develop a vision and strategy spread the change perspective empower broad-based events trigger short-term wins consolidate gains and provide more change harbor new approaches in the culture

Change acceleration is also the issue of the ADKAR psychological model, which rates on a scale of 1 to 5 the following elements (Hiatt & Creasey, 2003): • • • • •

awareness of the need to change desire to participate and continue change understanding of how to change means to implement change support to keep change in place

Quality measurement involves the outcome or the process (Auerbach, 2009). However, process measurement is the commonest because we can easily measure changes in processes (Davis & Barber, 2004) that in the patient health status. There is also resistance although change has spawned a number of trends in the assessment of doctors’ competence (Norcini & Talati, 2009), when we recommend practicing medicine in a predictable and reliable way. Reliability revolves around command and control, risk appreciation, quality, metrics, and reward (Rochlin et al., 1987). However, a highly reliable organization must include mechanisms, which support flexibility, constrained improvisation, and cognition management (Bigley & Roberts, 2001). The problem in creating reliable processes is reducing variability, which interprets into high-quality decision making and high-quality performance of the practitioners. Finally, taking into account that quality is a vital component of healthcare business model, we have to understand the role it plays in the market. So far, quality has a tough time demonstrating its business case because of the complexity of care

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and the difficulty in capturing the actual fixed and variable cost of patient treatment (Leatherman et al., 2003; Sachdeva & Jain, 2009).

FUTURE RESEARCH DIRECTIONS To substantiate the argument that e-health is the future realm of healthcare quality, we identify emerging scientific fields, and assess their impact on quality and patient safety. •

Biobanks, formally known as biological resource centers (BRCs), are depositories of ‘biological standards’. Haga & Beskow (2008) estimated that the U.S. store more than 270 million tissue samples, with a growth rate of approximately 20 million samples annually. There are many types of BRC, which differ in their functional role and the kinds of material they hold. Public and private entities have developed them in order to provide researchers the opportunity to explore collections of human biospecimen. However, we believe that all BRCs have the potential to unravel the causes of complex diseases, as well as the interaction between biological and environmental factors.

Biobanking is an emerging discipline (Riegman et al., 2008), which follows the continuing expansion of new techniques and scientific goals. Overall, it constitutes a device, which facilitates the understanding of the genetic basis of disease (Ormond et al., 2009) and holds taxonomic strains (Day et al., 2008). However, its establishment and maintenance is a skill-rich activity, which requires careful attention to the implementation of preservation technologies. Moreover, biobanking represents a challenge to informed consent (Ormond et al., 2009) while only appropriate quality assurance ensures that recovered cultures and other bio-

E-Health as the Realm of Healthcare Quality

logical materials behave in the same way as the originally isolated culture. •

•

A biochip is a series of microarrays arranged on a solid substrate which permits to perform various tests at the same time (Fan et al., 2009; Cady, 2009). It replaces the typical reaction platform and produces a patient profile, which we use in disease screening, diagnosis and monitoring disease progression. The development of biochips is a considerable thrust of the rapidly growing biotechnology industry, which encompasses a diverse range of research efforts. Advances in genomics, proteomics, and pharmaceuticals introduce new methods for unraveling the complex biochemical processes inside cells. At the same time, the field of microminiaturization enables biotechnologists to start packing their bulky sensing tools. These biochips are essentially miniaturized laboratories, which allow researchers to produce hundreds or thousands of simultaneous biochemical reactions, and rapidly screen large numbers of biological analyses. Reproducibility and standardization of biochip processes is, therefore, essential to ensure quality of results and provide the best tool for the elucidation of complex relationships between different proteins in detrimental conditions (Molloy et al., 2005). As traditional clinical investigation evolves, different needs have emerged, which require data integration. Data mining is the process of extracting patterns from data. It supports workflow analysis (Lang et al., 2007) and saves time and energy leading to less hassle for clinicians. While data mining can be used to detect patterns in data samples, it is crucial to understand that non-representative samples may cause non-indicative results. Therefore, we sometimes use the term in

a negative sense. As a result, in order to avoid confusion, we recently mention the negative perception of data mining as data dredging and data snooping. Some people believe that data mining is ethically neutral. However, the way we might use data mining can raise questions regarding privacy, legality, and ethics. Specifically, data mining may compromise confidentiality and privacy obligations through data aggregation. Data mining is rarely applied to healthcare, although it can facilitate the understanding of patient healthcare preferences (Liu et al., 2009). However, as we gather more data, data mining is becoming an increasingly indispensable tool in mining a wide selection of health records (Norén et al., 2008). Moreover, we might complement it with semantics-based reasoning in the management of medicines. Finally, data mining can support quality assurance (Jones, 2009), simplify the automation of data retrieval, facilitate physician quality improvement (Johnstone et al., 2008), and accurately capture patient outcomes if combined with simulation (Harper, 2005). Recently, there is interest in switching to algorithms and database development for microarray data mining (Cordero et al., 2008). •

Disease modeling, the mathematical representation of a clinical condition, summarizes the knowledge of disease epidemiology, and requires computational modeling, which follows two different approaches. The bottom-up approach accentuates complex intracellular molecular models while the top-down modeling strategy identifies key features of the disease.

However, despite ongoing efforts, there are complex issues regarding the use of computational modeling. A key question concerns the handling of model uncertainty since the selection of a computational model has to take into account the additional

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interactions and components beyond those of a discursive model. Therefore, a successful strategy is the identification of minimal models or search for parameter dimensions that are indispensable for the model performance. However, we cannot select competing structures on the basis of their relative fitness. As a result, a high priority for future disease modeling is model selection and hierarchical modeling (Tegnér et al., 2009). •

Genomics, the study of the genomes of living entities, encompasses considerable efforts to determine the complete DNA sequence of organisms through fine-scale genetic mapping efforts. It also includes studies of intragenomic phenomena and interactions between loci and alleles within the genome. However, it does not deal with the functions of individual genes, a primary focus of molecular biology or genetics. Research of individual genes does not fall into the meaning of genomics unless the purpose of the research is to clarify its impact on the networks of the genome.

Technology development has played a vital role in structural genomics (Terwilliger et al., 2009). Nowadays, we can quantify the difficulties of determining a pattern of a single protein. Moreover, the systems approach, which the post-genomics follow, interprets into a greater responsibility for artificial intelligence and robotics. Overall, many disciplines turn on the issue of automating the different stages in post-genomic research with a view to developing high-dimensional data of high quality (Laghaee et al., 2005). •

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Molecular Imaging unites molecular biology and in vivo imaging while enabling the visualization of the cellular function and the follow-up of the molecular process (Weissleder & Mahmood, 2001). It differs from conventional imaging in that we use biomarkers to image specific reference

points. Biomarkers and their surroundings interact chemically altering the image according to molecular changes, which occur within the point of interest. This method is markedly different from previous methods of imaging which typically image differences in qualities. The accomplishment to image sheer molecular changes opens up an impressive number of exciting possibilities for medical attention. These include the optimization of preclinical and clinical tests of new medication and early detection and treatment of disease. Molecular imaging imparts a greater degree of fairness to quantitative tests, and numerous potentialities to the diagnosis of cancer, neurological and cardiovascular diseases. Therefore, it has a substantial economic impact due to earlier and more accurate diagnosis. •

Nanotechnology is the study of the atomic and molecular matter. Although, we might think that it only develops materials of the size of 100 nanometers or smaller, nanotechnology is extremely diverse. It encompasses the extensions of conventional device physics, different approaches based upon molecular self-assembly, and new materials with nanoscale dimensions.

Nanomedicine, the medical practice of nanotechnology, encompasses the use of nanomaterials and nanoelectronic biosensors and seeks to provide a valuable collection of research and tools (Wagner et al., 2006; Freitas, 2005a). New applications in the pharmaceutical industry include advanced drug delivery systems, alternative therapies, and in vivo imaging. Moreover, molecular nanotechnology, a preliminary subfield of nanotechnology, deals with the engineering of molecular assemblers. So far, it is highly speculative seeking to predict what inventions nanotechnology might produce. However, we already know that we will need nanocomputers to lead molecular assemblers and

E-Health as the Realm of Healthcare Quality

expect nanorobots to join the medical armamentarium (Cavalcanti et al., 2008; Freitas, 2005b). Finally, neuro-electronic interfacing, a visionary project dealing with the creation of nanodevices, will connect computers to the nervous system while nanonephrology will play a role in the management of patients with kidney disease. However, advances in nanonephrology require nano-scale information on the cellular molecular mechanism involved in kidney processes. There has been much discussion in which reasons are advanced for and against nanotechnology. However, nanotechnology has the inherent capacity to provide many different materials in medicine, electronics, and energy production. In contrast, it raises concerns about its ecological effects of nanomaterials, and their potential effects on international economics (Allhoff & Lin, 2008). These concerns have led to a dialogue among advocacy groups and governments on whether particular regulation of nanotechnology is warranted. •

Ontology is the philosophical study of the nature of an entity, which also examines the main categories of reality. As a part of the metaphysics philosophy, ontology deals with questions concerning the entity existence and their grouping according to similarities and differences. However, in computer science, ontology is a formal representation of a set of concepts and their relationships.

Ontologies have become a mainstream issue in biomedical research (Pesquita et al., 2009) since we can explain biological entities by using annotations. This type of comparability, which we call semantic similarity, assesses the length of connectedness between two entities using annotations similarity. The implementation of semantic similarity to biomedical ontologies is new. Nevertheless, semantic similarity is a valuable tool for the validation of gene clustering

results, molecular interactions, and disease gene prioritization (Pesquita et al., 2009). However, the capacity to assure the quality of ontologies or evaluate their eligibility is limited (Rogers, 2006). Therefore, we need a combination of existing methodologies and tools to support a comprehensive quality assurance scheme. However, an ontology of superlative quality is not verifiable and might not be useful since a ‘perfect’ ontology, which complies with all current philosophical theories, might be too complex (Rogers, 2006). •

Proteomics, a term coined in 1997 to make an analogy with genomics, is the comprehensive study of proteins, their structures and functions (Anderson & Anderson, 1998; Blackstock & Weir, 1999). The term proteome, a combination of protein and genome (Wilkins et al., 1996) defines the full complement of proteins (Wilkins et al., 1996), and encompasses the modifications made to a single set of proteins. Therefore, proteome varies with time rendering proteomics, the next step in the study of biological systems, as more complicated than genomics.

One of the most promising developments from the study of human genes and proteins is the discovery of potential new drugs. This relies on the identification of proteins associated with a disease, and involves computer software which uses proteins as targets for new drugs. For example, virtual ligand screening is a computer technique which attempts to fit millions of small molecules to the three-dimensional structure of a protein. The computer assigns a rank of quality matching to various sites in the protein, enhancing or disabling the role of the protein in the cell. In the last ten years, the field of proteomics has expanded at a rapid rate. Nevertheless, it appears that we have underestimated the level of stringency required in proteomic data generation

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and analysis. As a result, several published findings require additional evidence (Wilkins et al., 2006). However, clinical proteomics certify that the majority of errors occur in the preanalytical phase. Therefore, before introducing complex proteomic analysis into the clinical setting, we need standardization of the preanalytical phase. •

Modern health systems have focused their attention on micro issues, which include the minimization of diagnostic, treatment, and medication errors. However, safety culture, a macro issue, requires our attention. Safety culture is a sub-set of the organizational culture (Olive et al., 2006). Although an overriding concern in recent years, we need additional work to identify and soundly measure the key dimensions of patient safety culture (Ginsburg et al., 2009; Singer et al., 2008) and understand its relationship with the leadership.

A key finding of research is that efforts to improve culture may not succeed if hospital managers perceive patient safety differently from frontline workers. Research shows the pivotal role of managers in the promotion of employees’ safe behavior, both through their attitudes, and by developing a safety management system (Fernandez-Muniz et al., 2007). Senior managers perceive patient safety climate more positively than non senior managers while it has proved difficult to engage frontline staff with the concept (Hellings et al., 2007). Therefore, the agenda should move from rhetoric to converting the concept into observable behavior. A prerequisite for the realization of this perspective is the collection, analysis, and dissemination of information deriving from incidents and near misses as well as the adoption of the reporting, just, flexible and learning cultures (Ruchlin et al., 2004).

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•

As patients become increasingly concerned about safety, clinical medicine focuses more on quality (Simmons & Wagner, 2009) than on bedside teaching and education. Educators react to these challenges by restructuring curricula, developing smallgroup sessions, and increasing self-directed education and independent research (Okuda et al., 2009). Nevertheless, there is a disconnect between the classroom and the clinical setting, resulting in medical students feeling that they are inadequately trained.

Medical simulation bridges the learning divide by representing certain key characteristics of a physical system. Historically, the first medical simulators were unsophisticated models of human patients (Meller, 1997). Later on, active models seemed to imitate living anatomy and more recently interactive models respond to actions taken by a student or physician. These are two dimensional computer programs, which constitute a textbook, and demonstrate the edge of allowing a student to fix errors. Quality improvement, patient safety, and the actual assessment of clinical skills have impelled medical simulation into the clinical arena (Carroll & Messenger, 2008). Still, there is convincing evidence that simulation training improves provider self-efficacy and effectiveness (Nishisaki et al., 2007) and increases patient safety. Finally, the process of iterative learning creates a much stronger learning environment and computer simulators are an ideal tool for evaluation of students’ clinical skills (Murphy et al., 2007).. On the contrary, there is no evidence that simulation training improves patient outcome. Therefore, we need ongoing academic research in order to evaluate the teaching effectiveness of simulation, and determine its impact on quality of care, patient safety (Cherry & Ali, 2008), and retention of knowledge. Even so, there is a plau-

E-Health as the Realm of Healthcare Quality

sible belief that medical simulation is the training device of the future.

CONCLUSION E-health, which already supports outstanding advances in healthcare quality, has the potential to become its realm, if we make a representation of the cognitive and social factors related to its design and use and associate them with the clinical practice.

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ADDITIONAL READING

Tegnér, J. N., Compte, A., Auffray, C., An, G., Cedersund, G., & Clermont, G. (2009). Computational disease modeling - fact or fiction? BMC Systems Biology, 3, 56. doi:10.1186/1752-0509-3-56 Terwilliger, T. C., Stuart, D., & Yokoyama, S. (2009). Lessons from structural genomics. Annual Review of Biophysics, 38, 371–383. doi:10.1146/ annurev.biophys.050708.133740 Vitacca, M., Mazzu, M., & Scalvini, S. (2009). Socio-technical and organizational challenges to wider e-Health implementation. Chronic Respiratory Disease, 6(2), 91–97. doi:10.1177/1479972309102805 Wagner, V., Dullaart, A., Bock, A. K., & Zweck, A. (2006). The emerging nanomedicine landscape. Nature Biotechnology, 24(10), 1211–1217. doi:10.1038/nbt1006-1211 Weissleder, R., & Mahmood, U. (2001). Molecular imaging. Radiology, 219, 316–333. Wilkins, M., Pasquali, C., Appel, R., Ou, K., Golaz, O., & Sanchez, J. C. (1996). From Proteins to Proteomes: Large Scale Protein Identification by Two-Dimensional Electrophoresis and Arnino Acid Analysis. Nature Biotechnology, 14(1), 61–65. doi:10.1038/nbt0196-61

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This work was previously published in E-Health Systems Quality and Reliability: Models and Standards, edited by Anastasius Moumtzoglou and Anastasia Kastania, pp. 291-310, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 1.6

The Use of Personal Digital Assistants in Nursing Education Nina Godson Coventry University, UK Adrian Bromage Coventry University, UK

ABSTRACT The use of Personal Digital Assistants (PDAs) and smartphones (combined mobile telephone and PDA) in Nurse Education is a relatively new development, in its infancy. The use of mobile technologies by health care professionals is increasing, and it seems likely to accelerate, as mobile information and communication technologies become more ubiquitous in wider society. The chapter reports on a small-scale feasibility study to evaluate the practicalities of supporting student nurses on their first clinical placements with PDAs that have been pre-loaded with reusable e-learning DOI: 10.4018/978-1-60960-561-2.ch106

objects. The student nurses generally found the PDA easy to use and carry on their person, valued the availability of the reusable e-learning object on their clinical placements and called for more of them to be made available to learners.

INTRODUCTION Clinical Nurse Tutors face the challenge of managing large numbers of students in clinical skills sessions, when demonstrating and practising skills acquisition, including interprofessional working. This involves maintaining accurate and fair assessments of individual performance that may later serve as evidence of safe practice for their

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

The Use of Personal Digital Assistants in Nursing Education

clinical placements. There needs to be a means of continuing this supportive learning environment, in the transition from simulation to reality, to ensure the bridge from theory to practice is strong. Ideally, to enhance learning and support development of decision-making skills, students should have access to information at the moment and the physical location where it is needed. In relation to this, a major aim of pre registration nursing programs is to assist students to attain competencies for practice, for example, in the use of the tools and technology associated with nursing. Healthcare professionals need to continually update knowledge and skills, in order to enhance their clinical practice and professional development. Similarly, student nurses in any speciality must get into the habit of continually becoming familiar with the latest practice recommendations, such as guidelines for preventing the spread of infection and standards from regulatory agencies. Using the world wide web can help meet these needs, given that anyone can publish web-pages that are then available instantly across the globe. Information published on line offers advantages compared to text books and journal articles, which are often outdated by the time they reach publication and distribution. However, up to 2003, connection to the internet was typically on either a desktop or laptop personal computer. Since that time, pocket-sized personal digital assistants (PDAs), also known as palmtop computers, with wireless access to the World Wide Web have become available. The first PDA, the Apple Newton MessagePad, was launched in 1993. In 1996, Nokia introduced the first mobile telephone with full PDA functionality (a genre known as ‘smartphones’). However, early PDAs relied on wired connection to networked desktop or laptop PCs, until the launch in 2003 of the Hewlett-Packard iPAQ H5450. This was the first PDA featuring built-in Wi-Fi for connecting to wireless computer networks and the World Wide Web. In 2009, the majority of PDAs sold are Wi-Fi enabled smartphones, for

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example, the RIM Blackberry, the Apple iPhone and the Nokia N-Series. It is argued in this chapter that the Personal digital assistants (PDAs) and smartphones can enable nurse educators can incorporate into the curriculum opportunities for students to develop critical approaches, and also introduce information technology as a tool in clinical decision making. This will serve as a foundation for using mobile computing technologies later in their careers. This chapter describes a small-scale feasibility study in which the practicalities of using smartphones to support the undergraduate nursing curriculum programme at a UK University were evaluated. The students who participated were drawn from all branches of nursing, and were in their first year of nurse training.

BACKGROUND In the mid-1990’s the World Wide Web radically changed the possibilities of our information landscape, and simultaneously became available to users of pocket-sized hand held computing devices such as Personal Digital Assistants (PDAs) (Murphy 2005). Even so, the use of PDAs in nursing was rare until comparatively recently. The Cumulative Index to Nursing and Allied Health Literature (CINAHL) has had a subject heading for PDAs (“Computers, Hand-Held”) since 1997, while the National Library of Medicine first used the term “Computers, Handheld” in the Medical Subject Headings (MeSH) in 2003. Discussions in the early articles focused on theories of informatics and technology. More recent articles indicate that PDAs are in wider use (De Groote, 2004), for example in nursing practice and student education. Interestingly, the Medical professions have written most of the literature on PDA use in health care, and it is clear that as a group they are very interested in this. Members of other health care disciplines also demonstrate interest in using PDAs (De Groote, 2004). The

The Use of Personal Digital Assistants in Nursing Education

integration of personal digital assistants (PDAs) into health care delivery continues to grow; and health professionals are adopting PDAs faster than the general public (Stolworthy et al., 2000). However, there has been little evidence that PDAs improve patient care (Fischer, 2003). PDA use by healthcare professionals tends to be in the context of on everyday routine. Many use their PDAs as a diary or address book, rather than as a knowledge base (Criswell & Parchman, 2002). Considering these facts, it might be argued that the use of PDA s has not been fully understood or its full potential recognised by health care professionals. However, the PDA can be considered a new learning tool, offering the potential to keep healthcare professionals up to date with developments in evidence based practice, and having potential to help nurse educators meet students’ educational needs, as will be seen. There is emerging evidence of PDA use in graduate nurse education as well as undergraduate nursing programs (Thomas et al., 2001). There have been accounts of how various institutions have used PDAs, for example, Huffstutler (2002). While university libraries have been known to provide information and support to students who use PDAs, many health care programs making use of PDAs seek help from institutional information technology department instead (Young, 2002). The incorporation of information and communication technologies into nursing practice and education by student nurses and staff is potentially challenging, particularly on their clinical work placements. To meet the healthcare needs of today’s society, student nurses are expected to demonstrate compassion when providing patient care. However, reduced time for patient contact may have eroded their ability to do so. It has become clear that increasing staff workloads have increased while staffing levels have decreased in the clinical setting (Aston & Molassiotis, 2003). Simultaneously, nursing curricula are forever evolving, introducing new ways of learning and new learning technologies, partly in response to

the challenge of accommodating increasingly large student cohorts. When student nurses go on clinical placements, teaching in the ward area is guided by both time and space limits. In light of nursing shortages, teaching that takes place at the patient’s bedside is not always possible, and it is becoming more difficult to take students away from the ward area to train them. Thus the students’ educational needs are not always reliably met within the current context of patient care settings. Nursing educators can usefully integrate technology as a means of support to student’s learning and to prepare the new generation of nurses as independent learners. Pre-registration nursing programmes aim to assist undergraduate students to attain competencies for practice, including interprofessional working, and the earlier these skills are introduced the better. Nurse tutors need to constantly review areas where information and training gaps exist, to highlight where nurse mentors at clinical placements and university-based tutors can play a larger part in training and preparation, both before and during students’ clinical placements. In the case described here, a need was identified for first-year undergraduate students on their first clinical placements to have access to learning materials and resources at the moment they are needed, to enhance learning and support development of practical clinical skills (Huffstutler, 2002). In the feasibility study reported in this chapter, tutors responded to these pressures by developing a way to meet students’ individual learning needs and styles.

The use of PDAs by Student Nurses The evidence discussed in the preceding section of increasing adoption of PDAs by healthcare professionals suggests that PDAs can be considered as if any other piece of healthcare equipment. It is arguable that nurse educators should begin to introduce PDAs to student nurses, given the importance of providing opportunities for students to become familiar with PDAs as a tool for clinical skills

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development. Both tutors and students are more likely to learn to use PDAs effectively through consistent use in both clinical and classroom settings. However, it was seen in the preceding section that there is currently limited literature addressing use of PDAs in the nursing curriculum, and it appears that their educational potential in clinical settings is relatively unexplored. The preparation of student nurses will always involve the development of clinical skills at the patient bedside, therefore quick and easy access to information is needed, not only in the skills laboratory but also in clinical practice. PDAs can operate as if electronic text books to help student nurses manage the information they need to learn. In the past, when students had queries about practical skills, they would turn to textbooks for answers, often having to find relevant volumes. This can be very time consuming, when there are so many clinical skills to learn, not to mention the problem of carrying heavy books to clinical placements. A PDA can enable student nurses to retrieve information in the clinical area quickly and easily, conserving precious learning time (Koeniger-Donohue, 2008). PDAs can provide nurses with portable access to extensive reference materials as well as other organizational resources and time saving benefits. This can be in the form of reusable elearning objects accessible from a central point by an entire cohort of students. The experience of learning from a PDA potentially offers students an opportunity to revisit at will any practical skill and reflect on their interpretation and understanding of it, so knowledge is accumulated and transferred into an individual’s own understanding (White et al., 2005). This way of learning alongside other supportive mechanisms promises an enriching clinical experience for the student. This is especially relevant when mentors are busy with heavy workloads and staffing has decreased due to cut backs in the economy (Yonge et al., 2002), and ward areas are as a result struggling to meet the

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educational needs of student on clinical placements. The ultimate goal of nurse educators is to develop competent and passionate student learners, who feel supported in transition from academia to clinical practice. PDAs can be a powerful tool that ‘holds the hand’ of trainee nurses when reassurance is needed. This could help reduce students’ stress, which has been found to lead them to miss work shifts or abandon their studies (Timmins & Kaliszer, 2002). However, handheld devices must fit into the ward routine, and not be perceived as extra work, or as interfering with the nurse-patient interaction or nurse mentor relationships. The question of the practicalities of PDAs on clinical placement required investigation before tutors at the university in which the case study reported in this chapter took place could decide whether to invest time, effort and money in their adoption as learning tools.

Feasibility Study: Knowledge in the Palm of your Hands In 2008, a small-scale feasibility study was undertaken at Coventry University to examine the fundamental practicalities and issues surrounding the use of hand held devices (PDAs) by first year student nurses on their first clinical placement. It was thought that by creating reusable learning objects covering relevant skills and competencies and loading them onto a PDA, student nurses from any branch of nursing can practice the skills and competencies in the clinical room, were their mentor unavailable. The student nurses can gain confidence and requisite background knowledge prior to the hands-on portion of their learning, saving the mentor time to work toward mastering these skills. The student nurse can then move to the patient bedside to gain the crucial supervised practice required to successfully and safely perform clinical skills. While the learning object used in the study was not explicitly designed to promote

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interprofessional learning, this was appropriate, give the aims of the feasibility study to explore the fundamental practicalities of smartphones on clinical placements. It is worth briefly outlining the nature of the learning object, however, and considerations that were taken into account to conduct a fair trial of the PDA. The reusable e-learning object was designed to make the learning experience personally relevant and meaningful, related to unambiguous skills that students had previously learned in the skills laboratories run in face-to-face university-based teaching. This was so the nature of its content did not distort the students’ smartphone use by being perceived as irrelevant or too challenging in its content matter. It comprised a “teachable moment” that was developed by nurse educators at the University into a reusable e-learning object that depicts a generic clinical skill for all branches of nursing, a hand-washing technique known as the ‘Ayliff procedure’ (Ayliffe, 1992). The resulting reusable e-learning object features a set of film clips that demonstrate and explain the Ayliff procedure (Gould, 2008), and was loaded on to each smartphone, enabling the participants to take it into their clinical practice placements. The issue of using smartphones in a clinical area was discussed with an infection control nurse. It was decided that students should not use smartphones at patients’ bedside and only in a study area. Thus it was explained to the participants that as an infection control precaution, smartphones were only to be used in the study room in the clinical area, and never at patients’ bedsides. Ethical approval for the study was sought from the University ethics committee, and was duly granted.

Methodology PDA Choice

considered carefully. Having a standard device was crucial, and one that is simple to use by all. Basically, there are two operating systems systems: the ‘Palm’ System and the ‘Pocket PC’ System. This study used a smartphone running the palm operating system. It was found by the course tutors that this operating system has the advantage of simplicity and ease of use. Smartphones are becoming as popular as ordinary mobile phones, which most students already possess, thus it was thought that the students would be more likely to adapt to a smartphone rather than a pure PDA. It is worth noting that there is also a learning curve for the tutors involved. It was important that the research team had access to the log in information, so they themselves could not be ‘locked out’of the smartphones. Preparing the smartphones for use is time consuming, in that reusable e-learning resources must be loaded onto each one. However once loaded the data are stored permanently.

Participants The sample comprised eighteen female students volunteers from a cohort of 215 students, as the researcher only had funding for twenty smartphones. The students were from the first year of a pre-registration nursing program that ran in January 2008, covering all four branches of nursing; adult, mental health, child and young persons, and learning disabilities. They were about to go on their first clinical placement, and they were to visit different types of placements, hospital based and community. It was considered important that the participants expressed an interest in the use of such technology, as should any technical hurdles become apparent while the students are on placement, they would have to solve them with only telephone or email support from the researchers.

To ensure that the PDA has a long service life, given the capital investment necessary for their purchase, the best choice of product needs to be

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Ethical Briefing of Participants The project was introduced to the students during one of their initial lectures, and a participant ethical briefing sheet was distributed. The briefing explained that each participant would be asked to complete both a pre- and post-placement questionnaire to evaluate the use of the smartphone, that they had the right to withdraw from the study at any time, and that their responses would be anonymised and pooled, then statistically evaluated and published. Students read the information and asked any questions, and those who were interested signed up to participate. A further date was arranged for those who signed up to meet with the researchers and learn how to use the smartphone and discuss the project further, after which they signed the necessary ‘consent to participate’ forms.

Training for Smartphone Use The smartphone is a tool that a student must learn to use effectively, just as they would a medical tool such as a tympanic thermometer or sphygmomanometer. Clinical Skills are developed through consistent practice in the clinical skills laboratory, and similar techniques can be used to familiarise individuals with a smartphone. To these ends, the participants were invited to a workshop on how to use the smartphone. This session provided hands on experience, in a structured supportive environment. Each student was briefed about how to use the smartphone and provided with an instruction leaflet, and was then shown how to log into the device. They were briefed that after their twelve-week placement had finished, they were to return the smartphone to the researchers. It was also explained that as an infection control precaution, the devices were only to be used in the study room in the clinical area, and never at patients’ bedsides. Getting started with a PDA is like learning any other new skill. A positive attitude, practice and willingness to make mistakes will help to over-

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come initial unfamiliarity. The students were thus asked to keep the smartphone in their coat pocket or work bag while in placement, so as to have the reusable learning objects available whenever they felt they needed to review the handwashing technique. The students were reminded that they were not alone. A contact number for the researchers was provided, in case the participants needed technical support. They were also made aware that they were effectively in a networking group with other students in the placement using the same device.

Pre-Placement Questionnaire The students next completed a pre clinical placement questionnaire. Its purpose was to gather information about their general familiarity with information and communication technologies, and any learning resources they had used so far in their training for the hand washing technique. It was found that the majority of participants owned a mobile phone. Furthermore, nine of the eighteen participants posessed PDAs of their own, and were familiar with the function and layout of these devices. All students seemed keen to participate in the project, and did not appear worried about using the smartphones. The main learning resources that the students had used prior to clinical placement were clinical skills laboratories, online learning, handouts/ leaflets, Objective Structured Clinical Examinations and Clinical skills tutors. Objective structured clinical examinations are used as a form of assessment of clinical skills at the beginning of the nursing curriculum. Students found clinical skills laboratories and open laboratories the most effective way of learning the hand washing technique. The majority of participants found pictorial presentation, continual practice of the skill and memory aides the most effective ways of remembering the eight-step hand washing technique. Some felt that they needed reminders

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of these steps, not only in the skills laboratories but in clinical practice placements. The participants felt that, pre-placement, they were able to demonstrate the correct hand washing technique, and would practice further to become proficient at this clinical skill. The participants were then asked to comment upon the least effective method of learning. The majority stated that the least effective was learning from textbooks and from previous practical experience before entering the nursing course. It could be argued that textbooks offer abstract experience, not ‘hands on’ concrete experience, and that previous care experience may not have taught the most up-todate technique. Finally the students were given the option to state their Objective Structured Clinical Examination (OSCE) score on the pre- placement questionnaire. All OSCEs include the assessment of hand washing. All of the participants in the sample indicated that they had passed the year one OSCE, however, some chose not to record their actual score on the pre-placement questionnaire.

Post-Placement Questionnaire Each participant then took their smartphone into their first clinical placement, and twelve weeks later returned from their placements to the University for the theoretical component of their course. At this point, each participant completed a post- placement evaluation questionnaire, so that their thoughts and experiences on using the smartphones while on their placements could be evaluated. The questionnaire comprised eighteen likert scale questions and answer options in three ‘blocks’ of questions. The first block covered the usability of the smartphone. The second covered the educational effectiveness of the reusable e-learning object. The final block covering the participants’ feelings about the value of using the learning objects and smartphone on placement. The data of interest in this account are the

first and third question blocks, which dealt with the usability of the smartphone, rather than the block covering the reusable e-learning object itself. This is partly because the latter was not explicitly designed to promote interprofessional learning, but mainly because this chapter seeks to examine the practicalities of mobile learning on clinical placements. The scores on each question for each participant were entered onto a MS-Excel spreadsheet, and the number of participants choosing each response option for each question were summed and then entered onto bar charts that summarised the overall results for each question. The measure of central tendency is the modal response, the response option chosen by the greatest number of participants. The findings are presented below.

Q1: Overall Ease of Use Thirteen of the eighteen participants rated the overall usability of the smartphone ‘good’ ‘very good’ or ‘excellent’. The central tendency, the mode, was six participants who agreed with the latter (see Figure 1).

Q2: Smartphone Portability Thirteen of the eighteen participants rated the portability of the smartphone ‘good’ ‘very good’ or ‘excellent’. The central tendency, the mode, was six participants who agreed that the smartphone was ‘very good’ in this respect (see Figure 2)

Q3: Comprehensibility of the Smartphone ‘User Guide’ Thirteen of the eighteen participants rated the user guide to the smartphone that was provided by the researcher to the participants as ‘good’ ‘very good’ or ‘excellent’. The central tendency, the mode, was eight participants who agreed that the user guide was ‘very good’ in this respect (see Figure 3).

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Figure 1. Overall ease of use

Figure 2. Smartphone portability

Figure 3. Comprehensibility of the smartphone ‘user guide’

Q4: Device Interface Usability

Q5: Smartphone Battery Life

Fourteen of the eighteen participants rated the ease-of-use of the smartphone user interface as ‘good’ ‘very good’ or ‘excellent’. The central tendency, the mode, was six participants who agreed that the smartphone was ‘good’ in this respect (see Figure 4).

Ten of the eighteen participants rated battery life (the time between recharges) of the smartphone ‘satisfactory’ or ‘good’. The central tendency, the mode, was seven participants who agreed with the latter (see Figure 5).

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Figure 4. Device interface easy to manipulate

Figure 5. Smartphone recharging frequency

Q6: Smartphone Usefulness in the ‘Clinical Area’ Ten of the eighteen participants rated the usefulness of the smartphone in the ‘clinical area’ as ‘satisfactory’ or ‘very good’. The central tendency, the mode, was six participants who agreed with the latter (see Figure 6).

Questions eight to twelve focus on the learning object itself. As has already been discussed, it is felt more appropriate to focus on the delivery technology and the practicalities of its use. This is in part because the learning object was intended for cross-professional use, rather than one which explicitly promotes interprofessional learn-

Figure 6. Smartphone use in the ‘clinical area

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ing. For these reasons, the next question to be examined is number 13.

Q13: Reflection on Learning All 18 participants tended to agree that using the smartphone on clinical placement enabled them to reflect on clinical skills, with no ratings of ‘unsatisfactory’ given. The central tendency, the mode, was six participants rating the smartphone as ‘excellent’ in this respect (see Figure 7).

Q14: Smartphone Enhancement of Learning Fourteen of the eighteen participants rated the way the smartphone enhanced independent learning as ‘satisfactory’, ‘very good’ or ‘excellent’. The central tendency, the mode, was eight participants agreeing with the latter (see Figure 8).

Q15: Smartphone Facilitates Flexibility of Learning Place and Rime Fourteen of the eighteen participants rated the ability of the smartphone to facilitate flexibility of learning place and time as ‘satisfactory’ to ‘excellent’. The central tendency, the mode, was nine participants agreeing with the latter (see Figure 9).

Q16: Mobile Learning Accommodates Individual Learning Atyles Thirteen of the eighteen participants rated the ability of mobile learning to accommodate individual learning styles as ‘satisfactory’ to ‘excellent’. The central tendency, the mode, was five participants who agreed that mobile learning was ‘very good’ in this respect (see Figure 10).

Figure 7. Reflection on learning materials

Figure 8. Smartphone enhancement of independent learning

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Figure 9. Smartphone facilitates flexibility of learning place and time

Figure 10. Mobile learning accommodates individual learning styles

Q17: Mobile Learning Sustains Individual Learner Motivation

Q18: Willingness to Engage in Mobile Learning Again

Thirteen of the eighteen participants rated the ability of mobile learning to sustain their motivation to learn as ‘satisfactory’ to ‘excellent’. The central tendency, the mode, was seven participants who agreed that mobile learning was ‘excellent’ in this respect (see Figure 11).

Fourteen of the eighteen participants rated their willingness to engage in mbile learning again as ‘satisfactory’ to ‘excellent’. The central tendency, the mode, was nine participants who agreed that mobile learning was ‘excellent’ in this respect (see Figure 12).

Figure 11. Mobile learning sustains learner motivation

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Figure 12. Willingness to engage in mobile learning again

It is interesting to note that many students made comments that indicated they wanted more smartphone-based learning and learning objects, as the selected quotes below clearly demonstrate: “happy to do more learning supported by a mobile device in the future” “It would have been good to have all the clinical skills sessions on a device, the Aseptic technique especially”

DISCUSSION The small-scale feasibility study reported here provides evidence that smartphones pre-loaded with reusable e-learning objects are a useful and practical means by which to support students nurses on their first clinical placements. Opportunities do not always arise in the clinical area to practice certain skills, this study strongly suggests that the smartphone can act as a back up mechanism. It thus seems reasonable to conclude that nurse academics and hospital trusts can use such mobile information and communication technologies to take a more proactive approach to bridge the theory-practice gap. The students in the study felt that a smartphone pre-loaded with reusable e-learning objects facilitated independent learning. It seems plausible to imagine that their use may facilitate the phenomenon whereby students retain a higher percentage of knowledge when they learn at their own pace 104

(Nelson 2003). In addition, Students who for whatever reason had no opportunity to attend the clinical skills sessions offered will not lose out. There are however organizational and technical barriers to the large-scale adoption of PDAs or smartphones, and they will now be discussed briefly.

Change Management Moving towards supporting collaborative learning with PDAs and smartphones requires consideration of not only the student’s learning needs, but also those of their tutors and mentors, as well as the circumstances of their clinical placement. It must be remembered that clinical Staff are not always technical experts, and they are primarily concerned with providing patient care under tight time constraints. Imposed changes in work habits may be met with resistance from staff and students. It is important that these barriers are considered before any introduction of handheld devices in the clinical area. One possible barrier to student nurses’ use of PDAs is lack of knowledge of how to use them. While the majority of the participants in the feasibility study reported in this chapter owned a mobile telephone, not all individuals have grown up with mobile technology, for example, students returning to study aged over forty years. It could also be argued that the nursing culture has not encouraged nurses toward information and communication technology use either. Furthermore, training that enables students to use new technology takes time; time that is taken away from patients.

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For PDAs to be successfully integrated into their education, it should be explained to student nurses why they should be using them, what they can offer, which models to buy, and how to operate their PDA applications. The value of introducing mobile computing devices to students early in their education has been recognised for some time (Thomas et al., 2001). Nurses are skilled in information management, routinely collecting and organizing data. Tutors can explain that by using a PDA that has relevant electronic resources uploaded onto it, nurses will find answers in their hands, not at the nurses’ station. It can be explained that PDAs can bring benefits such as increased productivity, reduced errors, better patient care, and increased worker satisfaction (Huffstutler et al., 2002). All nurses need to network with other nurses to share information, to assist in research, provide and receive mentoring, emotional support, and friendship. PDAs can play a role in many of these areas, thus their adoption can be justified in these terms. PDAs can also be a means by which to help health care staff stay abreast of new information about clinical skills and support mentors in their teaching role. It follows that the information on the PDA will need continually updating, as evidence based research changes as fast as technology. However, it is possible that, nurses can support PDA development by creating their own learning resources. The creation of learning objects that meet the needs of nursing students on placement and reflects and supports previous learning in clinical skills laboratories can itself be incorporated into the curriculum as a reflective learning exercise.

FUTURE DEVELOPMENTS The feasibility study reported in this chapter involved a small sample of eighteen students, due to only twenty PDA s being available. While sufficient to demonstrate the feasibility of PDAs on clinical

placement, however, it is too few for a thorough evaluation to be made and firm conclusions to be reached. Furthermore, the participants put themselves forward on the basis that they had an interest in using information and communications technologies. It is quite possible that those who chose to participate were eager to have access to as much help as possible, whereas those who did not may have been too anxious about using new technology. Further action research can shed light on these questions, which has implications for the possibilities of scaling-up the use of PDAs in nurse education. The next developmental step would be to develop and evaluate a core online repository of PDA-ready reusable e-learning objects to satisfy the needs of students on placement. Of particular interest would be the value of this approach as a means to support the development of interprofessional working in students on placement. This would require a set of reusable e-learning objects that explicitly promote and explore interprofessionalism, rather than a genric skills object that is essentially cross-professional rather than interprofessional in nature. On the other hand, cross-professional e-learning objects can form the basis of interprofessional learning if used as discussion triggers between students from different professional groups, for example, nurses, medics, occupational therapists, dieticians and social workers. The inherent potential for communication through smartphones with Wi-Fi internet capability opens interesting possibilities in this regard. For example, online discussion boards are already available to the students of the university where the feasibility study was conducted through the interprofessional learning module that all healthcare students, regardless of their professional pathway, must undertake.

CONCLUSION Student and practicing nurses are likely become more familiar with handheld technology in their

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daily lives as these technologies become more ubiquitous in society, and such devices are likely to become more prevalent in the workplace. In the near future, it is thus likely that nurses will be increasingly open to the use of handheld technology to enhance nursing research and nursing practice. PDAs open the possibilities for effectively delivering educational material that is always available at the point of need, regardless of an individual’s physical location, as long as its use does not compromise infection control. The clinical mentor is supported during busy times, as students can use this as an intermediate measure. Learning can take place at any time and place, with one or multiple users, providing great flexibility and ensuring work coverage. The results of the small feasibility study described in this chapter indicate that most of the students participating in the study were comfortable with this approach to mobile learning, finding it useful and calling for greater use of PDAs. The findings demonstrate that this approach is a feasible way to enable universities to meet students’ individual needs to be on-demand while they are on clinical placement.

ACKNOWLEDGMENT The feasibility study that is reported in this chapter was funded as a small research project by the Centre of InterProfessional e-learning (CIPeL).

REFERENCES Aston, L., & Molassiotis, A. (2003). Supervising and supporting student nurses in clinical placements: the peer support initiative. Nurse Education Today, 3(3), 202–210. doi:10.1016/ S0260-6917(02)00215-0

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Ayliffe, C. A. J., Lowbury, E. J. L., Geddes, A. M., & Williams, J. D. (1992). Control of Hospital Infection: A practical handbook (3rd ed.). London: Chapman, and Hall Medical. Criswell, D. F., & Parchman, M. L. (2002). Handheld computer use in U.S. family practice residency programs. Journal of the American Medical Informatics Association, 9(1), 80–86. De Groote, S. (2004). The use of personal digital assistants in the health sciences: results of a survey. Medical Library association, 92(3), 341-349. Fischer, S., Stewart, T. E., Mehta, S., Wax, R., & Lapinsky, S. E. (2003). Handheld computing in medicine. Journal of the American Medical Informatics Association, 10(2), 139–149. doi:10.1197/ jamia.M1180 Gould, D., & Drey, N. (2008). Hand hygiene technique. Nursing Standard, 22(34), 42–46. Huffstutler, S., Wyait, T. H., & Wright, C. R. (2002). The use of handheld technology in nursing education. Nurse Educator, 27(6), 271–275. doi:10.1097/00006223-200211000-00008 Koeniger-Donohue, R. (2008). Hand held computers in nurse education: a PDA pilot project. The Journal of Nursing Education, 47(2). doi:10.3928/01484834-20080201-01 Murphy, E. A. (2005). Handheld computers software for school nurses. The Journal of School Nursing, 21(6), 56–360. doi:10.1177/10598405 050210061101 Nelson, E. (2003). A Practical Solution for Training and Tracking in Patient-care Settings. Nursing Administration Quarterly, 27(I), 29. Stolworthy, Y. & Suszka-Hildebrandt, S. (2000). Mobile information technology at the point-ofcare: the grass roots origin of mobile computing in nursing. PDA Cortex 2000 [serial online].

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Thotnas, B. A., Coppola, J. F., & Feldman, H. (2001). Adopting handheld computers for community-based curriculum: Case study. The Journal of the New York State Nurses’ Association, 32(1), 3–6. Timmins, F., & Kaliszer, M. (2002). Aspects of nurse education programmes that frequently cause stress to nursing students -- fact-finding sample survey. Nurse Education Today, 22(3), 203–211. doi:10.1054/nedt.2001.0698

Yonge, O., Krahn, H., Trojan, L., Reid, D., & Haase, M. (2002). Being a preceptor is stressful! Journal for Nurses in Staff Development, 18(1), 22–27. doi:10.1097/00124645-200201000-00005 Young, P. M., Leung, R. M., Ho, L. M., & McGhee, S. M. (2001). An evaluation of the use of hand-held computers for bedside nursing care. International Journal of Medical Informatics, 62(2-3), 189–193. doi:10.1016/S1386-5056(01)00163-0

White, A., Allen, P., Goodwin, L., Breckinridge, D., Dowell, J., & Garvy, R. (2005). Infusing PDA technology into nursing education. Nurse Educator, 30(4), 150–154. doi:10.1097/00006223200507000-00006 This work was previously published in Interprofessional E-Learning and Collaborative Work: Practices and Technologies, edited by Adrian Bromage, Lynn Clouder, Jill Thistlethwaite and Frances Gordon, pp. 336-351, copyright 2010 by Information Science Reference (an imprint of IGI Global).

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Chapter 1.7

Reforming Nursing with Information Systems and Technology Chon Abraham College of William and Mary, USA

INTRODUCTION Much of healthcare improvement via technology initiatives addresses gaining physician by-in (Reinertsen, Pugh & Bisognano, 2005) and does not adequately address engaging nurses, despite the fact that nurses serve as the front-line caregivers and are a primary user group (Wiley-Patton & Malloy, 2004). However, the tide is changing, and visibility of nurses as information gatherers and processors in the patient care process is increasing (Romano, 2006). Nurses perform the majority of

the data-oriented tasks involved in patient care and would benefit most from having access to information at the point of care (Bove, 2006). RADM Romano, Chief Professional Officer of Nursing and advisor to the U.S. Surgeon General concerning public health, recently addressed the American Informatics Nursing Informatics Association and stated, “This is the year of the nurse. The technologies that have the means to improve the efficiencies in patient care are in the hands of the nurses.” Nurses need to embrace technology in everyday work or continue suffering the consequences of antiquated methods of computing

DOI: 10.4018/978-1-60960-561-2.ch107

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that take us away from where we work—at the point of care (Abbott, 2006). Healthcare can benefit greatly from use of a diverse set of technologies as facilitators of change to improve quality and safety of patient care by decreasing errors made because of the lack of faster, more comprehensive, and more accessible patient documentation at the point of care (IOM, 2000). Healthcare institutions abroad share similar sentiments according to international healthcare reports by the International Council of Nurses (Buchan & Calman, 2004). In light of these concerns and a severe U.S. nursing shortage (i.e., an estimated 400,000 shortage by 2020) (Bass, 2002), institutions are beginning to consider employing point-of-care IT as a means to promote patient safety, decrease medical errors, and improve working conditions for overtaxed nurses (TelecomWeb, 2005). This chapter provides an overview of nursing reform and novel ubiquitous computing integrating voice, hands-free, and mobile devices being researched or used to make nursing more efficient and promote the following: •

• •

•

•

A decrease in laborious documentation aspects (i.e., decrease problems with access to information at the point of care, written errors or legibility issues precluding comprehension of medical regimens). An increase in recruitment and retention rates. Improvement in communication in which nurses are required to consolidate and process information during care. Promotion of involvement in systems analysis and design of information systems to increase the likelihood of technology acceptance. Baseline and continuing education in both professional training and on-the-job training to allay technology aversion and build computer self-efficacy.

•

•

•

Development of standards for electronic documentation of patient interaction and processes for nursing that can be codified. Identification of role changes in the nursing community because of the use of information systems and technologies. More rigorous research concerning information research in the nursing community.

BACKGROUND This chapter provides an overview of tactics to reform nursing sponsored by leading nursing healthcare information systems research in professional organizations such as the American Nurses Informatics Association (ANIA), Association of Medical Informatics (AMIA), and the International Council on Nursing (ICN). The sociotechnical systems framework (STS) (Bostrom & Heinen, 1977) will be employed to categorize tasks, technologies, people involved, roles, and structure of aspects of the reform. STS emphasizes workplace interactions with various technologies and is espoused as a realistic view of organizations and a way to change them (Bostrom & Heinen, 1977). STS concerns the social system that is comprised of contributions of people (i.e., attitudes, skills, and values), the relationships among people and their roles in the organization, reward systems, and authority structures (Bostrom & Heinen, 1977). STS is also an intervention strategy in which technology implementations intervene within a work system to improve task accomplishment, productivity, and work quality of life, and to prompt supportive organizational structural changes. Innovative technologies may improve task efficiency and task effectiveness by automating or reengineering antiquated/manual processes, changing people’s roles, and making organizational structures (Bostrom & Heinen, 1977). A graphical depiction of STS is displayed in Figure 1.

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Figure 1. STS (Bostrom & Heinen, 1977)

DESCRIBING NURSING REFORM USING AN STS FRAMEWORK STS is used as a frame to describe elements addressed in the nursing reform initiative, such as tasks, technology, people involved, changes in roles, and structure in conjunction with their interplay, outputs, and goals.

Task The tasks in this reform are indicative of the pervasive problems plaguing nursing and healthcare in general. There is a critical nursing shortage crisis in the United States alone in which more than 90,000 nursing position vacancies were reported in 2004, and nearly 400,000 are expected by 2020, which has grave impacts on quality of care (JCAHO, 2004). Similar statistics reported by the International Council of Nursing (ICN) also highlights the great supply and demand disparities. Much of this is attributed to the problems recruiting new nurses and retaining seasoned ones. Nursing is an aging workforce in which the following is reported:

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• • • •

One in five registered nurses plan to leave the profession. 81% say morale is extremely low. 64% do not have enough time to spend with the patient. 60% note the manual paperwork burden to document information about patient care tasks is “a necessary evil.”

The last percentage indicates the manual paperwork burden, which is one problem that can be directly addressed via application of novel information systems (IS) and information technology (IT). Information-oriented patient care tasks for which nurses are primarily responsible entail the following: •

•

Triage: The process of taking vital statistics, documenting impending problems, and making initial assessments to categorize criticality of the emergent condition. Charting: The process of monitoring the patient and recording vital statistics, effectiveness of medical intervention, and overall patient wellness, which is an ongoing process across nurse shifts.

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•

•

Throughput flow: The process of managing when, where, and how patients are moved about in a patient care system across units within the hospital for labs, procedures, and so forth. Medication administration: The process of validating medication regimens with prescriptions, delivering the medication, and documenting the effectiveness.

Virtually 98,000 people die each year due to medical errors in hospitals (Institute of Medicine, 2000), and many of these are likely due to antiquated methods of information management and delivery during these fundamental patient care tasks. These errors can also be attributed to the inability of personnel relying on the information documented by nurses during these tasks to actually access it in a timely fashion. In essence, not having access to pertinent information when and where needed cripples the abilities of healthcare professionals such as nurses from validating medical information and diagnosing properly. So the primary task for reform becomes automating manual charting and equipping nurses, who are highly mobile in their work environment and move from patient to patient, with devices that enable easy and unobtrusive means of accessing and documenting needed information during patient care.

Technology The reform in this industry recognizes that the paperwork burden is one where technology can be used as a facilitator of change or as part of the intervention strategy to promote change. Therefore, the goal is to promote productivity in which nurses are able to decrease errors associated with manual charting and reduce charting time via easy non-time-consuming data input and output capabilities. These improvements enable a nurse to spend more time with patients, which is a stated desire among nurses (Abraham, Watson, Boudreau

& Goodhue, 2004). AMIA and ANIA encourage partnerships among vendors, academic researchers, and nurses in practice to discern technologies that are vehicles for promoting their workflow efficiencies. Figure 2 provides an overview of these technology categories. One aspect of technology that is specific to the healthcare industry and not akin to the average mobile business environment is the need to protect patients and caregivers from the transfer of infectious diseases or bacteria introduced by mobile devices. The typical mobile device is assigned to one nurse who carries it throughout his or her shift. Therefore, the nurse uses the same device/ technologies across every medical intervention in which the likelihood of bacterial transfer is high. Thus, antimicrobial protective coatings or materials on healthcare mobile devices are of utmost importance in order to avoid transfer of bacteria from one patient environment to another, or from one nurse to another as the devices turn over during a shift change.

People Nursing in general is a highly caring and nurturing profession (Patistea, 1999). Nurses have chosen their profession based on their individual desires to care for others and promote the sanctity of human life. Nurses, for example, who are satisfied in their jobs are characterized by high levels of affective empathy (i.e., the ability to be moved by other people’s feelings) and service-mindedness (i.e., a strong tendency to let other people be central and dominant) (Sand, 2003). In addition, they display a high degree of sensitivity to nonverbal communication and a strong need for affirmation, support, and dominance (Sand, 2003). The traits prioritize patient care tasks for servicing nurses. For example if a patient is exhibiting physiological problems associated with anxiety or is in pain, the primary task becomes physical care of the patient. Additionally, since nurses do display a strong need for affirmation from their patients,

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Figure 2. Technology overview

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peers, and superiors, the technologies need not interfere with the desire to establish mutual trust with the patient and the need for nurses to exhibit confidence in use of the technology, especially in the presence of the patient. The clinical nursing population can be divided into two demographics: A and B. Demographic A is an average age of 40, has work experience of 15 or more years, lacks professional computer training that is a systemic problem recognized by servicing IS personnel and nursing managers (Bass, 2002), and has low computer selfefficacy (Abraham et al., 2004). Demographic B is characterized by an average age of 25, has less than seven years experience, was trained under a “new” regimen that introduced him or her to PC-based documentation, and exhibits medium to high computer self-efficacy (Abraham et al., 2004). Both demographics use traditional PCs for other tasks, such as monitoring vital statistics or maintaining an apparatus for intravenous drug administration. However, information systems for electronic charting or mobile devices that provide access to these information systems are revered initially as foreign to this population (Abraham, forthcoming). The majority of nurses in Demographic A do not type effectively but regard narrative oriented data entry as problematic (Abraham et al., forthcoming). Additionally, the personality traits of nurses influence how they accept and use technology (Abraham et al., 2004). In essence, intended utilization for mobile technologies is for the nurse to use at the point of care and, most likely, in the presence of the patient. Therefore, the technology should not impede nurses from being able to maintain eye contact nor interfere with the interaction with their patients. Additionally, healthcare workplaces are very tumultuous, hectic, and characterized by a great deal of uncertainty, which contributes to stressful work environments. Thus, the technologies need to be designed in a manner that are easy enough not to excite anxiety among the nurse user group that causes nurses to fumble with the

technology, display a lack of confidence, or inhibit nurse/patient interaction (e.g., having to pay more attention to the computer because of ill-designed interfaces) (Abraham, forthcoming). Nurses feel that their lack of confidence concerning operating the technology is interpreted by patients as incompetence not only with the technology but with performing their required patient care tasks (Abraham et al., 2004). Not only does the technology need to support the way nurses work, but it also needs to support the way nurses think about servicing their patients. Also, adequate computer training, including how to most appropriately use the mobile technologies as vehicles to support the patient/nurse relationship, is of utmost importance. Physical care of the patient will always overcome the need to document the intervention until the hands-on care has been performed. However, providing nurses with easy unobtrusive access to medical information when and where needed enables nurses to make better well-informed decisions.

Interplay Between Task, Technology, and People in Healthcare Environments Mobile technologies make new dimensions salient that are driving novel interplay between the task, technology, and people in healthcare environments. For example, traits of the nurse concerning the need to attain affirmation or appear competent in front of the patient while operating the technology is novel since prior to mobile computing the act of operating the computer most likely was done away from the point of care (i.e., at the nurses’ station). When computing using technologies that were tethered, nurses were less likely to exhibit as much anxiety about using the system in front of the patient (Abraham, forthcoming). The interplay among task, technology, and people in healthcare environments has become more pronounced because of changes in computing methods that

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enable access to information when and where needed via wireless technologies. Nursing reform addresses initiatives in the development of technologies not only to better support the way nurses actually work, but also to develop information systems based on a unified nursing language known as the International Classification for Nursing Practice (ICNP). It is an arduous task to implement compositional terminology for the nursing practice since nurses affectionately note that each individual “nurses” differently (i.e., they label common patient care functions differently) (ICN, 2006). The goal of the ICNP is to provide a global infrastructure informing healthcare practice and policy to improve patient care via codifying and retaining tacit, experiential knowledge to inform the practice and promote easier cross-training, especially in light of the aging nurse workforce and the problems with recruitment and retention (ICN, 2006). In light of these globally recognized nursing shortages, persons with either formalized nursing training or interest in the field from foreign countries have been recruited and trained to fill many vacant positions (Bass, 2002). ICNP hopes to streamline the learning curve in order for these foreign nurses to become acclimated to the nursing profession. Additionally, as people relocate, which is increasingly the case, their patient information should move or be automatically accessible despite the physical location of the patient (Council on Systemic Interoperability, 2006). However, the limiting factor is the lack of systemic interoperability within the United States and globally to make this idea a reality. ICNP focus is to ensure that at least care providers speak a common language, which is documented in the patient’s medical record that patients transport when they relocate or acquire services from a multitude of different healthcare providers. Therefore, ICNP and systemic interoperability projects are addressing the interplay between fulfilling the task with suitable information systems and technologies to support the people who are both task doers and

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task recipients in this field in order to promote better quality of care—the ultimate goal.

Roles and Structure These novel technologies and initiatives in improving the communication among nurses and about what they actually provide as value-added effort helps to better define and highlight the crucial role of nurses. By virtue of these technologies being placed in the hands of nurses, it is indicative of the technology being a facilitator of change to realize the role of the nurse as instrumental to the patient care process since they do perform the bulk of documentation for any medical intervention. Contributing to realization of the criticality of their role is the nurse’s ability to recognize in an easier and more timely fashion the potential errors in the prescribed regimen, such as medication prescriptions that may have been prescribed erroneously or entered into the system mistakenly by a physician but validated by the pharmacist. In this sense, having access to IT and IS empowers nurses to at least bring to light problems they discern regarding the prescribed regimen for a patient. Typically, nurses that are assigned to inhospital patients interface with their patients more frequently than any other medical personnel; they are very familiar with their patient’s regimen and have the astuteness to recognize when there may be a potential error in the prescribed regimen. For example, the servicing nurse may discern allergic reactions to the prescribed medication or an ineffectiveness of the medication requiring a physician’s order to be clarified or modified. Having access to information coupled with efficient means of communicating with other servicing medical personnel such as a patient’s physician allows the nurse to discern the validity of the regimen or prescription in question. In essence, having access to information when and where needed and having the ability to communicate effectively because of the aforesaid novel technologies enables the nurse to appear more competent and thus gain

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more respect for his or her level of knowledge and critical role in the ability of the patient to be serviced properly. As far as structural impacts, nurses contend that they have been somewhat neglected and overlooked as principal components in the care process, which contributes to poor morale and self-efficacy issues (Abraham et al., 2004; Bass, 2002). This role of the nurse as being critical in patient throughput needs to be adequately articulated to the population, management, and associated physicians (Abraham et al., 2004). With the dire status of the industry and the grave projections of the impact on patient care, more and more healthcare organization administrators recognize the need to establish a vision of change for nurses, articulate how they fit into the care process, and explain why it is important for nurses to accept the technologies that enable efficiencies. Bringing physicians into the fold to internalize and articulate this message will also undoubtedly promote acceptance of the technology among the nursing population since they have a strong need for external affirmation. The vision needs to manifest into useful but nonobtrusive IT and IS training for nurses in order to improve computer self-efficacy and increase the likelihood that the technology will be used as intended to produce desired efficiencies. Nurses commented that the majority of the physicians they work with do not appreciate their services, which contribute to the nurses rejecting many technologies that management presents to them because they feel that the technologies are being thrust upon them in an effort to make the physicians’ jobs less demanding and not for improving nursing work conditions (Abraham et al., 2004). The technology can be the main facilitator to empower nurses as the principal information managers in patient care. One area of future research is to explore more thoroughly how technology is facilitating a change in the authoritative structure between physicians and nurses. The hope is for physicians to see nurses as more competent and capable because of the

ability to more readily address questions of them made possible with the use of IT and IS (Abraham et al., 2004).

FUTURE TRENDS A future trend is the exploration of nursing decision support systems comparable to those developed for physicians, but with a focus on nursing-related patient care tasks that have elements of diagnoses. Another area for future research is the incorporation of project management skills and facilitating IS and IT into nursing curricula in order to aid nurse managers and clinical nurses with a means for balancing patient loads with fewer nurse resources.

CONCLUSION The primary contribution of this chapter is to promote visibility of the needs of the nursing community regarding electronic information exchange, manipulation, and management that can be addressed in healthcare information systems research. Typically, IS research focus groups are geared toward physicians; however, nurses are responsible for the bulk of documentation for each medical intervention in each step of the patient care system. Physicians rely heavily on the information supplied by nurses concerning the historical documentation of charting patients’ ailments, effectiveness of prescribed regimens, and anomalies in their condition (Abraham et al., 2004). Focus has been placed on using technology to streamline processes and automate as much as possible to avoid human documentation error. However, healthcare IS research has frequently underexplored nurses or not recognized them as the principal personnel who supply the information to the system, whether the system is manual or automated. IS researchers can help to uncover nuances in the nature of nursing and design IT and IS to better support the way nurses work in

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hopes of helping our society achieve its goal of improving the quality of patient care.

REFERENCES Abbott, P. (2006). Innovations beyond the lab: Putting information into the hands of those who care. In Proceedings of the 2006 American Nursing Informatics Association Conference, Nashville, Tennessee. Abraham, C., Watson, R. T., & Boudreau, M. C. (forthcoming). Ubiquitous access: Patient care on the Frontlines. Communications of the ACM. Abraham, C., Watson, R. T., Boudreau, M. C., & Goodhue, D. (2004) Patient care and safety at the frontlines: Nurses’ experiences with wireless computing. Publication Series for IBM Center for Healthcare Management American Nurses Informatics Association. (2006). Description for Bayada Award for technological innovation in nursing education and practice. Retrieved from http://www.ania.org/Bayada.htm Bass, J. (2002). It’s not just nurses anymore: Survey of three other health occupations finds patient safety at risk due to understaffing. Retrieved from http://www.aft.org/press/2002/041102.html Bostrom, R., & Heinen, J. (1977). MIS problems and failures: A socio-technical perspective. Part I: The causes. MIS Quarterly, 1(3), 17–32. doi:10.2307/248710 Bove, L. (2006). Implementing a system with clinical transformation in mind. In Proceedings of the 2006 American Nursing Informatics Association Conference, Nashville, Tennessee. Buchan, J., & Calman, L. (2004). The global shortage of registered nurses: An overview of issues and actions. International Council of Nurses. Retrieved from http://www.icn.ch/global/shortage.pdf

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Council on Systemic Interoperability. (2006). Interviews from practitioners speaking about ending the document game in U.S. healthcare. Retrieved from http://endingthedocumentgame. gov/video.html Institute of Medicine (IOM). (2000). To err is human: Building a safer health system. The National Academic Press. Retrieved from http://www.nap. edu/books/0309068371/html/ International Council of Nurses. (2006). International classification for nursing practice (ICNP) fact sheet. Retrieved from http://www.icn.ch/ icnp.htm Joint Commission on Accreditation of Healthcare Organizations (JCAHO). (2004). Healthcare at the crossroads: Strategies for addressing the evolving nursing crisis. Retrieved from http://www. jointcommission.org/NR/rdonlyres/5C138711ED76-4D6F-909F-B06E0309F36D/0/health_ care_at_the_crossroads.pdf Patistea, E. (1999). Nurses’ perceptions of caring as documented in theory and research. Journal of Clinical Nursing, 8(5), 487–495. doi:10.1046/ j.1365-2702.1999.00269.x Reinertsen, J., Pugh, M., & Bisognano, M. (2005). Seven leadership leverage points for organization-level improvement in healthcare. Retrieved from http://www.delmarvafoundation.org/html/ culture_club/docs/Seven%20Leadership%20 Leverage%20Points.pdf Romano, C. (2006). Improving the health of the nation: The promise of health information technology. In Proceedings of the 2006 American Nursing Informatics Association Conference, Nashville, Tennessee. Sand, A. (2003). Nurses’ personalities, nursesrelated qualities and work satisfaction: A ten year perspective. Journal of Clinical Nursing, 12, 177–187.

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TelecomWeb. (2005). U.S. healthcare providers seen growing IT budgets by over 10 percent. Retrieved from http://www.telecomweb.com/ news/1086273449.htm Wiley-Patton, S., & Malloy, A. (2004). Understanding healthcare professionals’ adoption and use of IT. In Proceedings of the Tenth Americas Conference on Information Systems, New York.

KEY TERMS AND DEFINITIONS Charting: The process of monitoring the patient and recording vital statistics, effectiveness of medical intervention, and overall patient wellness, an ongoing process across nursing shifts. Electronic Medical or Health Records: Electronic version of the manual chart that outlines historical medical information pertaining to one individual. Medication Administration: The process of validating medication regimens with prescriptions, delivering the medication, and documenting the effectiveness. Mobile or Wireless Computing: The use of mobile, untethered devices to access information

stored in information systems or communicate via wireless networks. Nursing: Encompasses autonomous and collaborative care of individuals of all ages, families, groups, and communities, sick or well and in all settings. Nursing includes the promotion of health, prevention of illness, and the care of ill, disabled, and dying people. Advocacy, promotion of a safe environment, research, participation in shaping health policies and patient and health systems management, and education are also key nursing roles. Systemic Interoperability: Comprehensive network of privacy-protected systems of electronic personal health information developed around the healthcare consumer to facilitate wellness and the safe delivery of optimal healthcare. Throughput Flow: The process of managing when, where, and how patients are moved about in the patient care system across units within the hospital for labs, procedures, and so forth. Triage: The process of taking vital statistics, documenting impending problems, and making initial assessments to categorize criticality of the emergent condition.

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, pp. 1137-1145, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 1.8

Quality Issues in Personalized E-Health, Mobile Health and E-Health Grids Anastasia N. Kastania Biomedical Research Foundation of the Academy of Athens, Greece & Athens University of Economics and Business, Greece Sophia Kossida Biomedical Research Foundation of the Academy of Athens, Greece

ABSTRACT The electronic healthcare in the modern society has the possibility of converting the practice of delivery of health care. Currently, chaos of information is characterizing the public health care, which leads to inferior decision-making, increasing expenses and even loss of lives. Technological progress in the sensors, integrated circuits, and the wireless communications have allowed designing low cost, microscopic, light, and smart sensors. These smart sensors are able to feel, transport one

or more vital signals, and they can be incorporated in wireless personal or body networks for remote health monitoring. Sensor networks promise to drive innovation in health care allowing cheap, continuous, mobile and personalized health management of electronic health records with the Internet. The e-health applications imply an exciting set of requirements for Grid middleware and provide a rigorous testing ground for Grid. In the chapter, the authors present an overview of the current technological achievements in the electronic healthcare world combined with an outline of the quality dimensions in healthcare.

DOI: 10.4018/978-1-60960-561-2.ch108

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Quality Issues in Personalized E-Health, Mobile Health and E-Health Grids

INTRODUCTION

BACKGROUND

E-health offers an optimistic vision of how computer and communication technology can help and improve healthcare provision at a distance. Health care provision is an environment that has presented remarkable improvement in the computing technology. For example, mobile e-health includes information and telecommunications technologies, which provide health care to patients that are at a distance from the supplier, and provide reinforcing tools for mobile health care. Recently, health care has embraced the mobile technology in electronic applications (Panteli et al., 2007). Various initiatives (public and private) have examined the different mobile applications of electronic health focusing on the mobility of doctors, the mobility of the patients, up to the Web based data access (Germanakos et al., 2005). Moreover, personalized electronic health care provision through autonomous and adaptive Web applications is noteworthy (American College of Physicians, 2008). The growth of high bandwidth wireless networks, such as GPRS and UMTS, combined with miniaturized sensors and the computers will create applications and new services that will change the daily life of citizens. The citizens will be able to get medical advice from a distance but will also be in a position to send, from any place, complete and accurate vital signs measurements (Van Halteren et al., 2004). However, grid technologies and standards, currently examined in health care, will be adopted if they prove that they face all the valid concerns of security and follow the ethical guidelines (Stell, et al., 2007). In this chapter, an overview of the existing personalized, mobile and Grid applications in electronic healthcare is presented. Further, quality aspects, which have successfully been applied in traditional health care quality assessment, are presented. A future challenge is to examine how these quality aspects can be successfully applied in electronic healthcare.

To realize the potential of electronic health, future electronic health should be able to support patient information management and medical decision support in an open and mobile medical environment. Such an environment will be strong in knowledge, sensitive and flexible in the needs of patients, and will allow collaboration of virtual teams that work in different geographic areas. Context-specific services to each individual are defined as personalized services. These are provided by agents usually with autonomy playing the role of personal assistant (Panayiotou & Samaras, 2004; Delicato et al., 2001). Personalization is a technique used to explore both interaction and information. It allows doctors to search personalized information about the health state of each patient (Nikolidakis, et al., 2008). Since the number and the use of wireless connection and portable devices are increasing, the complexity of designing and setting up adaptive e-health systems is also increasing. The current improvement in physiologic sensors allows realizing future mobile computing environments that can impressively strengthen health care that is provided to the community, and individuals with chronic problems. Finally, there is interest from the academic and industrial world to assess the challenge of e-health automation management of mobile Grid infrastructures (Lupu et al., 2008).

PERSONALIZED E-HEALTH The current tendency towards ubiquitous computing, the new sensor technologies, the powerful mobile devices and the wearable computers support different types of personalized electronic health applications. Telemonitoring applications have adopted this new technology to improve the quality of care and the quality of treatment for the sick and the elderly using questions and actions based on user preferences. The personalized application

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collects user activities and begins interpreting them to act according to user’s wishes. The study of user activities also allows individualization of content. Reproduction of user experience, emotions, sentiments, thoughts, behavior and their actions is also performed (Lugmayr et al., 2009). The textile products and computers are combined to create a recent model in the individualized mobile data processing. To create a programmable device, as part of clothes, design and implementation of a ‘wearable’ motherboard architecture is needed to embody clothing items, hardware and software. This type of information processing should not only provide high bandwidth but should also have the capability of seeing, feeling, thinking, and acting (Park et al., 2002). A picture of remote health management supports the use of an individual cell phone, as a

mediator that transfers multiple data streams from multiple wearable sensors in a response infrastructure (Mohomed et al., 2008). Autonomous and adaptive Web applications encourage individual ‘participation’ in the medical decisionmaking and self-management (American College of Physicians, 2008). The personalization is based on adapting information content that is returned as an answer to a user request based on who is the end user (Figure 1). Internet-based applications allow individuals to shape the application based on their individual needs because patients require timely access to high quality personalized health care (Nijland et al., 2009). Using websites of health risk calculation, the users of the Internet can receive personalized predictions for their health (Harle et al., 2009). Life assistance protocols, determined by medical experts, are based on

Figure 1. Current aspects involved in personalized e-health

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evidence-based medicine and have been proved acceptable practice that is applied in one population group. Developing a life assistance protocol in different user groups will provide, in the long run, enormous volumes of information on health risk, and documentation on different healthcare paths (Meneu et al., 2009). Moreover, it will allow creating diverse and personalized snapshots and adaptations of the life assistance protocol (Meneu, et al., 2009). The electronic health record (EHR) used in health administration reports and in clinical decision-making is an electronic record of patient history produced from personal health monitoring (Dong et al., 2009). Case-based reasoning has been used for the individualized production and delivery of health information to extract a personalized health record from the individual health profile (Abidi, 2002). Transport-independent personal-health data and protocol standards are defined by the ISO 11073/IEEE 1073 (known also as X73) for interoperability with the different sensors (Yusof et al., 2006; de Toledo et al., 2006). Finally, an emerging opportunity for artificial intelligence researchers is to develop technologies that would check continuously ‘healthy’ in their houses and constantly direct them to healthy behavior (Intille et al., 2002). The use of intelligent techniques to analyze user satisfaction ensures the medical accuracy of the dynamically created patient empowerment form (Abidi et al., 2001). Overall, the term context-awareness refers to the ability to obtain context information that describes the user environment and adjusts according to this context. Such an adaptation clearly leads to individualized services, but the sensitive information handling causes various privacy concerns (Chorianopoulos, 2008). Context-awareness is a promising model for flow control. The context is any information that characterizes the environment and the state of the user (for example setting and time) and any other component relative to the interaction between the user and the service (Wac et al., 2006). The personal user data and preferences

are the main ‘exploitation points’ for finding ways to add value to user experience. Therefore, essential areas of future e-health research should be personalization, the exchange of secure messages and the mobile services incorporating genetic information in electronic health applications (Carlsson & Walden, 2002). This will improve the medical practice, research and the convergence between the bioinformatics and the medical informatics (Oliveira et al., 2003). Integrating enormous sums of genetic information in the clinical setting will ‘inspire’ modern clinical practices where the clinical diagnosis and treatment will be supported by information on molecular level. Gene therapy provides the opportunity of hereditary illnesses minimization while molecular medicine requires an increasing exchange of knowledge between the clinicians and the biologists. The information that is available in biology and in medicine is so impressive that requires specific tools for information management. However, tools are needed to effectively connect the genetic and clinical information, electronic health applications and existing genetic databases. The usage of evolutionary algorithms opens many prospects for treatment optimization to combine biological and medical traits in the decision support.

Mobile Health In the mobile health world, we conduct clinical data collection, healthcare information delivery, direct provision of care and real time monitoring of vital signs using mobile devices and other wireless devices. Moreover, in order to ensure that we deliver care of high quality at any time and space, access to patient records is necessary as well as additional information from distant sources such as consultation with experts and direct contact with databases. The mobile, wireless technology can offer this support but there are few medical devices on the market, which can be adapted and personalized according to patient needs (Riva,

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2003; Joshi, 2000). Finally, classifying medical devices is critical because their effects can fluctuate from no threat to life threat. On the other hand, we might integrate electronic health services in cell phones and connect biosensors with cell phones (Han et al., 2007). The necessary information is organized to establish an ontology for mobile electronic health services (Han, et al., 2007). Furthermore, mobile health applications introduce new mobile services in health care based on the technologies 2.5 (GPRS) and 3G (UMTS). This can be achieved by integrating sensors and activators to continuously measure and transmit vital signs, with sound and video, to suppliers of health care in a Wireless Body Area Network (BAN) (Konstantas et al., 2002). These sensors and the activators are improving the life of patients while introducing new services in disease prevention and diagnostics, remote support, recording of normal conditions and the clinical research. The Wireless Body Area Network will help the fast and reliable implementation of distant-aid services for sending reliable information from the place of accidents. Finally, the ubiquitous computing is a promising model for creating information systems. Databases improve data management for the patients, public health, drugstores and the workforce (Milenković et al., 2006). The field of mobile healthcare requires continuous planning, solutions, decision-making and intelligent support technology that will reduce both the number of faults and their range. In our wireless world, the mobile devices use various networking installations. The existing context aware applications are benefited from the user context (for example positioning information), nevertheless, few question the network quality of service (Wac, 2005; Broens et al., 2007; Hanak et al., 2007). However, the services of electronic health share several distinctive features concerning the structure of services, the component services and the data requirements. Therefore, the ideal is to

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develop electronic health services in a common platform using standard features and services. Finally, various fundamental questions can affect the successful implementation of a mobile electronic health application. However, portable wireless applications at the point of care, based on information and connected with the clinical data, can reduce these problems. Interoperability of the existing applications of health care and/or databases is essential. The mobile technology of health care has the solution on not only supporting health care but also on easier management of mobile patients. The mobile applications can reduce faults and delays in creating accessible digital data (Archer, 2005).

GRID COMPUTING Grid computing is a term that involves a ‘hot’ model of distributed computing. This model embraces various architectures and forms but is restricted by detailed operational and behavior rules. Its precise definition is a difficult task because of the model nature and the large number of its different implementations. As a result, there are several misunderstandings of what the Grid is and what architectures can be called that way. Therefore, a clarification of the meaning of the Grid is needed. The computational Grid is practically a network of various types of heterogeneous computational engines and resources. Its size can vary from a small collection of stand-alone computers to a global network of thousands of interconnected units sharing distributed resources and working in parallel to establish a dominant global supercomputer. It involves computing, storage, application, coordination, and sharing. This architecture could be implemented at a local area network or the Internet to provide services to individuals or organizations all over the world. A Grid should include mechanisms for dividing a specific task into a subset of smaller ones,

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scheduling and dynamic resource provisioning in order to perform parallel computation and merging the results. Overall, there are three basic principles that ought to be followed for a system to be called Computational Grid: •

•

•

The shared resources should not have centralized management, and there should be a successful coordination and service provision to users within different domains and geographical locations. A Grid should use standard, open and general-purpose protocols and interfaces for all its operations, such as authentication, resource sharing, and internode communication to avoid being an application-specific system. It should provide a competent service to the users about the collection of services, response time, computational performance and resource utilization to produce better resource utilization and performance rates than stand-alone units.

Grid computing is often confused with utility computing, cluster computing or P2P computing. Utility computing involves trade on-demand supply of various resources that are charged as services. Utility computing, although it is limited to the service providers’ network, resembles the Grid or can follow the Grids specifications. Cluster computing is a technology similar to the Grid computing but not quite the same. Clusters usually involve interconnected processors inside the restricted area networks. It is likely to be distributed and manage different resources, but they almost always require centralized control, which is the essential feature that distinguishes them from the Grid. Peer-to-peer applications, on the other hand, have remarkably similar infrastructure with the Grid since they use grid-like services for manipulating files. The main difference is based on the fact that P2P focus on services to be delivered to as many users as possible and these

services are detailed and restrained while the Grid model is set on other priorities such as resource integration, performance, reliability, quality of service and security. Grid computing is considered as the computing of the future. There is already an enormous amount of research around it, and new Grid applications are constantly emerging or take advantage of it. It already has a broad social value since it can be used by medical, biological and scientific research centers to handle resources and time-consuming calculations and tasks. However, there are issues under investigation for the Grid since it is a system of multiple objectives, a lot of which are difficult to be met. There is also a lot of research on the Quality of Service of the Grid, the performance, the effective coordination of users and resources and the serious issue of security at various levels and areas with attention to the remote computation and data integrity. Grid architecture makes extensive use of the Internet and the remote and distributed resources to create a global supercomputer that can perform complex tasks. It embraces the best features of many other distributed models, such as utility, cluster or P2P computing but its capacity and persistence to generalizing protocols, lack of centralized control, quality of service and failure tolerance make it unique and most probable ‘partner’ of the Semantic Web, which is the future of the Web. Its social and scientific contribution is incredible, and is expected to succeed in the future as its security, resource coordination and quality of service techniques will overcome the drawbacks of the present. Furthermore, the questions of data management in the Grids become more and more serious. From the side of data management, the Grid allows many replicated data objects, which allow a high-level of availability, reliability and performance to assure the user and application needs. A breakdown of communication happens when (Voicu et al., 2009): (1) a message is degraded during the communication between two regions (2) a message is lost because of dysfunction of

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the network connection or (3) two regions cannot communicate because a network path is unavailable. In the first two cases, network protocols are used to maintain reliability (Voicu et al., 2009). Incorporating mobile devices in the Grid can lead to uncertainty and to under performance because of the inherent restrictions of mobile devices and the wireless communication connections. For the future generation of ubiquitous Grids, we have to find ways to rectify the restrictions that are innate in these devices and combine them in the Grid, so that the available resources are strengthened and the field of provided services is extended (Isaiadis & Getov, 2005). Tools and environments are available for simulation, data mining, knowledge discovery and collaborative work. There are various requirements for the Grid resource management strategies and its components (resource allocation, reservation management, job scheduling) since they should be in a position to react to failure of network connections (Volckaert et al., 2004). Health Grids are computing environments of distributed resources in which diverse and scattered biomedical data are accessed, and knowledge discovery and computations are performed. The growth of the information technology allows the biomedical researchers to capitalize in ubiquitous and transparent distributed systems and in a broad collection of tools for resource distribution (computation, storage, data, replication, security, semantic interoperability, and distribution of software as services) (Kratz et al., 2007). Electronic health records in collaboration with the Grid technologies and the increasing interdisciplinary collaborations offer the opportunity to participate in advanced scientific and prestigious medical discoveries. Medical Informatics based on a Grid can be extended to support the entire spectrum of health care: screening, diagnosis, treatment planning, epidemiology and public health. Health Grid was the first that determined the need for a particular middleware layer, between the global grid infrastructure, the middleware and the medical

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or health applications (Breton et al., 2006). The Grid technology can support healthcare services integration but is usually unavailable at the point of care (for example in the house of the patient) (Koufi et al., 2008). The Share Project is a European Union financed research to establish a road map for future health grid research, giving priority to the opportunities, the obstacles and the likely difficulties (Olive et al., 2007a; Olive et al., 2007b). The primary technical road map has produced a series of technologies, models and essential growth points, development of a reference implementation for grid services, and has concluded with expanding a knowledge grid for medical research. In the Share Project, to create knowledge from data requires complex data integration, data mining and image processing applications and includes the use of artificial intelligence techniques to build relations between data from different sources and frameworks (Olive et al., 2007a; Olive et al., 2007b). Even if certain key pharmaceutical companies have established powerful Grid extensions for drug discovery, the specific skepticism remains for the value of Grid Computing in the sector. Therefore, in the Health Grid road map, particular attention should be given to the security and the choice of standards for the HealthGrid operating system and technologies. The security is an issue in all the technical levels: the networks should provide the protocols for secure data transfer, the Grid infrastructure should support secure mechanisms for Access, Authentication and Authorization, and the establishments should provide mechanisms for secure data storage (Breton et al., 2006). Computer systems should be autonomous (Sterritt & Hinchey, 2005a), where autonomicity implies self-management that often becomes visible in the frameworks of self-configuring, self-healing, self-optimizing, self-protecting and self-awareness. The autonomous computation is intended to improve the widespread utilization and computation possibilities of systems to assist the computer users. Since for most of the users access

Quality Issues in Personalized E-Health, Mobile Health and E-Health Grids

to computation is by personal devices, research on autonomous computation focuses on this field (Sterritt & Bantz, 2004). The autonomous computing and other initiatives on the self-management systems are a powerful new vision for constructing complex computer systems. They provide the ability of complexity control by implementing self-governance (autonomy) and self-management (autonomicity) (Sterritt & Hinchey, 2005b). The principal emerging strategies to achieve this requirement are the ‘autonomic networks’ and the ‘autonomic communications’. The autonomicity vision is incorporated into the existing efforts that aim to the advanced automation including artificial intelligence research. This implies (Sterritt & Hinchey, 2005b) that all the processes should be designed effectively with autonomicity and self-managing capabilities. Adding semantics in the Grid using semantic services and the support of self-organization will allow creating more flexible and heterogeneous Grids (Beckstein et al., 2006). Proposals for the support of reliable electronic health business systems deal with adopting of the autonomic Grid computing and the Services-Oriented Architecture (SOA) (Omar & Taleb-Bendiab, 2006). This improvement provides the benefit of high quality of services and on-demand health monitoring (ubiquitously and on-line) (Omar & Taleb-Bendiab, 2006). Overall, the wireless Grid allows the user mobility to share underused available resources in a network that is required for e-health applications. The applications of wireless Grid contain (Benedict et al., 2008) patient monitoring, multimedia, wireless devices, etc. Incorporating mobile devices in Grid technologies and server applications can provide (Ozturk & Altilar, 2007) the potential to control the state of supercomputers with mobile devices and allow applications to access valuable data ubiquitously (for example, the production of positioning information from GPS mobile devices). Moreover, integrating mobile, wireless devices in the Grid has recently (Jan et al., 2009) drawn the attention of the research community since this

initiative has led to new challenges. Mobile ad hoc grid is a new approach of Grid computing (Jan et al., 2009) where appealing real scenarios can be applied. Finally, Knowledge Grid is an intelligent and realistic environment of Internet applications that allow operation from people or virtual roles to capture, publish and share explicit knowledge resources (Anya, et al., 2009). However, handling situations that result from emergencies requires semantic information about the environment and one flexible self-organizing information technology infrastructure to provide services that can be used (Beckstein et al., 2006). For example, intelligent services can achieve personalized and proactive management of individual patients (Omar et al., 2006).

Quality Dimensions in Healthcare During the last decades extensive efforts were focused on improvement of the provided products and services and quality improvement theories were adopted (Deming, 2000; Juran, 1988). The meaning of quality and the quality management strategies are much more complex in the health sector, compared to the industrial sector, where initially the theories for the quality were applied (Deming, 2000; Juran, 1988). Avedis Donabedian defined the following three dimensions of quality in healthcare: (1) structure, (2) process and (3) outcome (Donabedian, 1980a; Donabedian, 1980b). Given these quality dimensions, Donabedian formulated the following definition for quality in health care: this nature of health care that is expected to maximize the well-being of the patient, considering the balance of benefits but also losses that follow the health care process (Donabedian, 1980a; Donabedian, 1980b). In Figure 2, the essential quality dimensions both for products and services, as these have been defined by distinguished researchers are presented (Donabedian, 1982; Donabedian, 1983; Donabedian, 1985; Donabedian, 1991; Donabedian, 2001; Garvin, 1988; Maxwell, 1984; Maxwell,

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Figure 2. Quality dimensions as defined by Donabedian, Maxwell and Garvin

1992). Quality assessment and measurement in traditional health care systems is feasible through these dimensions.

FUTURE RESEARCH DIRECTIONS The Semantic Grid applies Semantic Web technologies to raising the technical challenges we have to overcome. With the available data flow and handling, these issues are of utmost importance. Nowadays, when we think about amounts of data produced by simulations, experiments and sensors it becomes apparent that we have to automate the discovery. Therefore, we need automatic data management. This, in turn, calls for automatic annotation of data with metadata describing attractive features of both of data storage and management. Lastly, we have to try to move towards automatic knowledge management.

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Producing data are one issue; preserving data is a separate issue. This process involves automated, semi-automated and manual annotation and data cleaning. Overall, there are many technical challenges to be resolved to ensure the information created today can undergo changes in storage media, devices and digital formats. Furthermore, we should focus on utilizing the previously presented quality dimensions in healthcare by developing mathematical models, which will be useful for the quality assessment of public e-health systems and services.

CONCLUSION E-health is an ambitious idea. It expects to set up a novel and powerful middleware that encourages a novel way of providing ubiquitous, personalized, intelligent and proactive health care services at

Quality Issues in Personalized E-Health, Mobile Health and E-Health Grids

a distance. The three promising visions embrace the three corners of the ‘Semantic-PervasiveGrid Triangle’. Exploring all these three visions together requires working across at least three communities. The proponents of this ‘holistic’ approach argue that only through exploring the combined world (the entire triangle) we will make a comprehensive infrastructure for the vision of ambient intelligence and e-Medicine. If crowned with success, the e-health approach will unite different communities creating reliable, virtual organizations, which tackle new challenges, utilize a wide range of distributed resources for improving the quality of care and quality of life. Issues of quality assurance in e-health should consider the dimensions already used for quality assessment in the traditional health care.

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Sterritt, R., & Hinchey, M. (2005b). Why Computer-Based Systems Should be Autonomic. Paper presented at the Proceedings of the 12th IEEE International Conference and Workshops on Engineering of Computer-Based Systems. Van Halteren, A., Konstantas, D., Bults, R., Wac, K., Dokovsky, N., & Koprinkov, G. (2004). MobiHealth: Ambulant Patient Monitoring Over Next Generation Public Wireless Networks. In Demiris, G. (Ed.), E-Health: Current Status and Future Trends (Vol. 106). Amsterdam: IOS Press. Voicu, L. C., Schuldt, H., Breitbart, Y., & Schek, H. J. (2009). Replicated Data Management in the Grid: The Re:GRIDiT Approach. Paper presented at the 1st ACM workshop on Data grids for eScience. Volckaert, B., Thysebaert, P., De Leenheer, M., De Turck, F., Dhoedt, B., & Demeester, P. (2004). Grid computing: the next network challenge! Journal of the Communications Network, 3, 159–165.

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Wac, K. (2005). Towards QoS-awareness of Context-aware Mobile Applications and Services. Paper presented at the On the Move to Meaningful Internet Systems 2005: OTM Workshops, Ph.D. Student Symposium.

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Wac, K., van Halteren, A., & Konstantas, D. (2006). QoS-Predictions Service: Infrastructural Support for Proactive QoS- and Context-Aware Mobile Services (Position Paper) On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops. Berlin: Springer Berlin / Heidelberg.

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Yusof, M. M., Paul, R. J., & Stergioulas, L. K. (2006). Towards a Framework for Health Information Systems Evaluation. Paper presented at the 39th Annual Hawaii International Conference on System Sciences.

This work was previously published in E-Health Systems Quality and Reliability: Models and Standards, edited by Anastasius Moumtzoglou and Anastasia Kastania, pp. 278-290, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 1.9

Adoption of Electronic Health Records: A Study of CIO Perceptions Yousuf J. Ahmad Mercy Health Partners, USA Vijay V. Raghavan Northern Kentucky University, USA William Benjamin Martz Jr. Northern Kentucky University, USA

ABSTRACT Adoption of Electronic Health Records (EHR) can provide an impetus to a greater degree of overall adoption of Information Technology (IT) in many healthcare organizations. In this study, using a Delphi technique, input from 40 CIO responses is analyzed to provide an insight into acceptance and adoption of EHRs at an enterprise level. Many useful findings emerged from the study. First, a majority of the participants believed DOI: 10.4018/978-1-60960-561-2.ch109

that about 40-49 percent of the providers will be using EHRs by the year 2014, thus highlighting the need for studying EHR diffusion in hospitals. As predictors of successful implementations, physicians’ leadership and attitude was ranked as the most important factor. Another significant determinant of success was the business model of the physicians—whether they are affiliated with hospitals or working independently. This factor highlights the need to employ different strategies to encourage adoption of EHRs among these distinct groups. All findings are discussed, including their implications for IT diffusion in healthcare.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Adoption of Electronic Health Records

INTRODUCTION In spite of many widespread uses of information technology, its potential to help in delivering quality healthcare has not yet been realized. A centralized database of patient data that can be easily accessed by healthcare providers is not yet a reality. Companies do offer proprietary solutions but often these systems cannot interact with each other. Adoption and diffusion rates for technologies such as computerized physician order entry (CPOE) have been very low (Poon, Blumenthal, & Jaggi, 2004). We seek to understand the rationale behind the rather slow adoption rate for electronic health records (EHRs). For the purposes of this study, an electronic health record refers to an individual patient’s medical record in a digital format; a real-time patient health record with access to evidence-based decision support tools that can be used to aid clinicians in decision-making. EHRs can also automate and streamline a clinician’s workflow ensuring that all clinical information is clearly communicated across individuals and groups that use the patient data. They can also prevent delays in responding to individuals needing urgent care (Chau & Hu, 2002). One of the complexities with EHRs is that its database activities (create, read, update or delete) can originate from many diverse sources: at a physician’s office during patient visits, pharmacy counters or at hospitals when patients receive care from the hospital. In physician practices, adoption of EHR is often an individual or a small-group decision. While the adoption of technology in general at small-group levels has been studied previously (Baxley & Campbell, 2008), the current study focuses specifically on the institutional level adoption of electronic health record systems. We know from prior studies that organizational adoption of software product lines hinges on the adopter making business, technical, organizational, procedural, financial, and personnel changes (Clements, Jones, McGregor, & Northrop, 2006). Studies evaluating healthcare information systems from an enterprise

perspective have identified potential conflict areas and have proposed conceptual models to facilitate implementation of healthcare Information systems (Connell & Young, 2007). Meetings have been called, retreats have been held, and policy recommendations ranging from financial incentives to promoting through educational, marketing, and supporting activities have been proposed (Middleton, Hammond, Brennan, & Cooper, 2005). And yet, implementing EHR systems remains the least understood process in the area of healthcare information systems. CIOs of healthcare institutions comprise a primary stakeholder group that is on the forefront of implementing organization-wide EHRs. It seems unlikely that the implementation of an organization-wide EHR would be undertaken without the participation and concurrence of the organization’s CIO. Hence we pursue a study of CIO perceptions of critical issues surrounding organization adoption of EHRs.

AREAS OF INVESTIGATION This section segments the areas investigated in our Delphi study under three broad categories: issues of adoption, barriers to adoption, and finally determinants of EHR adoption success. Under issues of adoption a variety of aspects of EHR adoption are examined. Barriers to Adoption and Determinants of success are of special significance as judged from many prior studies (Baxley & Campbell, 2008; Cooper, 2005; Gans, Kralewski, Hammons, & Dowd, 2005; Lowes & Spikol, 2008; Withrow, 2008) and we treat them separately.

Issues of EHR Adoption There is no disagreement about the powerful ways in which IT can help the creation and management of EHRs and consequently improve the quality of healthcare. Computerized order entry can ensure that medication and laboratory orders are accurate

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and legible. A system as envisioned can also allow logical checks for duplication or redundancy, thereby eliminating both risk and waste from the healthcare delivery process. Managing the results from a laboratory order is another key area where digitization and increased availability can provide significant benefits. The ideal system enables instant communication of laboratory results as well as archival of past results for immediate comparisons. Management of chronic diseases, reducing variations in care delivery, and disease registries are other outcomes enabled by EHRs that can provide significant benefits to both organizations and to patients. In spite of these potential benefits, an organization’s successful diffusion and adoption of EHRs depends on how it manages its way through a variety of nontechnical issues. Diffusion of EHRs: Varying estimates of the extent of diffusion of EHRs are reported. While most agree that EHR diffusion is low at present, its anticipated diffusion rate into healthcare systems needs to be understood more clearly. A high level of diffusion is not guaranteed. For example, while 60% of primary practitioners in UK use some form of EHR, most private practitioners in the UK still manage their patient information manually. Even those physicians who have adopted EHRs are not actively using functionalities that are necessary to improve health care quality and patient safety (Simon et al., 2008). The US has more complexity and therefore may have more root causes associated with lack of diffusion than the UK; these include such issues as US not being a single payer system, lack of governmental standards and mandates, fragmented nature of US healthcare, and lack of government ownership. Scope: Organization-wide EHR implementation projects, in general, have a very wide scope as they are designed as a blanket to help cover a variety of private and public health issues. EHRs include not only clinical information systems but also scheduling, billing, and other functionalities, and these broader functionalities are critical for the fiscal viability of the New Model of Family

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Medicine (Newton et al., 2006). Size not only adds complexity, but also problems related to manageability. An understanding of what is really expected of EHRs will help projects focus on those issues deemed to have highest priority. Governance: Governance issues have remained an obstacle as institutions that have implement EHRs try to obtain high quality data for quality initiatives (Jain & Hayden, 2008). Governance of a state-wide or nationwide EHR must be properly defined. Who is going to be responsible for the implementation, monitoring of compliance, and ownership of the EHR? Who will be the ultimate custodian of data and who will be able to police? Experts agree (see for example, Collins, 2004; Bakker, 2004) that the information governance issues of the EHR have been the hardest part of maintaining an EHR. Although all of these are important issues, we focus especially on the question of dealing with privacy and security aspects of implementing an EHR. Incentivizing the providers: It has been suggested that policies should be designed to provide incentives to help practices improve the quality of their care by using EHRs (Miller et al., 2005; Vijayan, 2005). Incentivizing the providers of healthcare, especially physicians, is going to be very difficult. The state or the federal government would have to reimburse providers who are participating in this EHR better. There can be no significant penalty, perceived or real, associated with adopting EHRs. This will be the most challenging limitation since physicians are inherently risk averse, and therefore any level of risk that they would have to undertake would need to be managed and defrayed by the state. The government, including the Center for Medicare and Medicaid Services (CMS), payers and the employers need to work together not only on the criteria for evaluating physician performance, but also on the rewards. The incentive would have to be motivating and probably not just financial in nature. The state or federal government must decide on the outcomes it wishes to achieve. From

Adoption of Electronic Health Records

these outcomes, a proper set of incentives can be created. For example, not only should there be incentives for physicians to practice medicine using an EHR, but a proportionate incentive to see patients with an EHR in rural areas. Public health: Public Health: Information Technology can be used to improve the quality of healthcare for the general population. Examples include, but are not limited to, incident reporting, disease surveillance, transfer of epidemiology maps, maintenance of disease registries, and delivery of alerts and other information to clinicians and health workers (Institute of Medicine, 2001). Aims of the Institute of Medicine: The relationship between electronic health records and the quality of healthcare warrants further exploration. The Institute of Medicine has laid out the following six aims for healthcare: safety, timeliness, effectiveness, efficiency, equity, and patient-centeredness. Healthcare consumption is a multi-criteria decision process and a rank ordering of these factors by content experts will help us understand which one of the factors is important in promoting EHR adoption. Measurement: Measuring the benefits of the EHR is difficult, especially since in certain areas we do not have any baseline metrics with which to compare. For this project, we elicited CIO opinions on important metrics for measuring the adoption of EHRs. Barriers to adoption: Although the use of the EHR today is still low, there is an increased interest in the application of this technology and a sense of urgency is felt even at the federal level. Our study explores the perspective of CIOs of health care organizations and more especially those who are directly involved in EHR implementations. We build on some of the barriers already identified to explore how CIOs feel about these factors as possible barriers to adoption. For example, cost, resistance by physicians (Berner, Detmer, & Simborg, 2005), and vendor and product immaturity have been identified as barriers to implementing CPOEs (Poon et al., 2004). EHRs are more com-

plex systems with a broader scope and we borrow some of these barriers for the study including cost, cultural barriers, lack of standards and interoperability, complexity of medicine, and workflow changes (Ash & Bates, 2005). Determinants of success: A study by Menachemi, Perkins, Durme, and Brooks (2006) presents some evidence to support the claim that physicians of younger age are more likely to adopt EHR systems and male physicians are more likely to adopt EHRs. The same study also suggests that physician specialty may have some influence on EHR adoption. Family physicians are more likely to use robust features such as allergy and medication lists, diagnosis, problem lists, patient scheduling and educational materials, preventive services reminders and access to reference material. There is also some evidence to suggest that hospital-based practices and practices that teach medical students or residents were more likely to have an EHR (Simon et al., 2007). We decided to label this aspect the business model of the physicians. Medical training and state leadership in promoting EHRs were other factors included in the current study as determinants of success. The impact of governmental activity on EHR adoption has been explored in prior studies (Ford et al., 2009). Summary: Healthcare information systems necessarily span a wide area of literature and research. Although several of these have been mentioned and drive this research study, we are left with two broad research questions: • •

What are the factors that facilitate adoption of EHRs? Will EHRs deliver the administrative and clinical benefits that they are being touted for?

RESEARCH METHOD CIOs of healthcare institutions comprise a primary group that is on the forefront of implementing

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EHRs. Their participation and backing are necessary to study any issue surrounding EHR. We employed a Delphi method survey to elicit feedback on areas surrounding the research questions. The value of the Delphi method is in the ideas it generates, both those that build consensus and ones that do not. Also, it encourages respondents to consider group opinions especially on those aspects of the study about which the respondents may be unsure. Delphi is particularly appropriate for subjective information where measurable data is not available (Dalkey & Helmer, 1963). Delphi is a method of choice to help achieve convergence or consensus on various aspects of an emerging topic. It achieves this consensus by providing for repeated responses to the same questions after a controlled feedback (Nevo & Chan, 2007; Hayes, 2007). Our study was conducted with the support of the College of Healthcare Information Management Executives (CHIME) and a total of 40 health care CIOs participated, 20 in each round. A total of 4 of these survey had missing responses for some questions and were not fully usable. The profile of the subset of CHIME subjects that participated in the study was similar to the organization as a whole. Three demographic characteristics of age, gender and operating budget of the IT organization as reported in Table 1 shows that the respondent characteristics were representative of the organization as a whole. While all questions were objective in nature, they solicited free-text commenting by the respondents to better explain their position(s). Although we have labeled it as a 3-round Delphi survey, in the pure traditional definition it is a 2-round Delphi with an informal intermediate round wherein two respondents who used the free-form response to suggest additional items to be included in the survey were contacted to discuss these items as well as items that were not be clear to them. Clarifications were provided before the third round on all items that were deemed not clearly understood. One of the authors who is himself a CIO of a healthcare organization made subjective judgments on items that might not have

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been understood clearly, based on this feedback. Such enhancements helped improve the quality of the responses in the final round and do not detract from the spirit of the Delphi technique. This means that complete data were available for the first and final round with an informal second round used to improve the questions surveyed for the final round.

ANALYSIS AND RESULTS We employed three different types of non-parametric tests: (1) the Wilcoxon sign-ranked test was used to compare each pair of scores to understand the differences between the initial and final round responses of respondents, (2) the Mann-Whitney test was used; where the scores are not treated pair wise, but ranked as though they were all from a

Table 1. Demographic characteristics Characteristic Gender

N(40)a 14 Females 22 Males

Age Group Less than or equal to 30 years

0

31-39

6

40-49

5

50-59

23

60 years or Older

2

IT Operating Budget Less than $1 million

4

$1.1 million to 10 million

7

$10.1 million to 20 million

11

$20.1 million to 30 million

4

$30.1 million to 40 million

2

$40.1 million to 50 million

4

More than 50 million

4

4 Missing data

a

Adoption of Electronic Health Records

single list, then sum of the ranks for each subgroup list is calculated, For the Mann-Whitney test, if there is no real difference between conditions, the ranks of the subgroups will remain roughly equal. The data from the initial and final rounds were compared for significance, and (3) the KruskalWallis, which is similar to Mann-Whitney, but used for statistical significance when there were three or more conditions used for questions that contained answers with three or more categories. Considering the small sample size that is typical of Delphi studies and the other assumptions necessary for proper use of parametric statistics, we chose to employ these non-parametric methods.

Issues of Adoption Diffusion of EHRs: Four different aspects of diffusion of EHRs were surveyed: current level of diffusion in healthcare organizations, current level of diffusion in physician offices, expected level of diffusion in about five years, and finally the reason for differences in the level of diffusion compared to UK where it is expected to be about 60%. Four commonly given reasons for this variation are: differences in single payer system, lack of governmental standards and mandates, fragmented nature of US healthcare, lack of government ownership. The current penetration of EHR in health organizations were generally estimated to be less than 20% with 17 responses estimating it to be less than 9%. For physician practices, the responses were similar. The variations between the two rounds of the Delphi study were insignificant. Expected future penetration by the year 2014 was estimated to be between 40% and 59%. The expected future penetration in both rounds was estimated to be between 40% and 59% with the average increasing significantly from 2.80 in the first round to 3.39 in the second round where the percentages were split in quintiles and coded from 1 through 5 (n=38, df=4, Pearson’s _2=.047). This is surprisingly low considering that some

expect that there might be a presidential mandate for EHR in the U.S. by the year 2014. Finally, the reason for EHR utilization in the USA being low compared to UK was explored. This was an open-ended question in the first round and using these responses it was made a forced-choice response in the final round. Four respondents believed it was due to the differences in single-payer system but a majority of them believed it was due to the lack of mandates and standards. Fragmented nature of healthcare received 5 votes and the lack of government ownership received 3 votes. Considering a fairly even distribution of the responses, we believe that all these remain competing reasons for facilitating EHR adoption. Scope. Given the breadth of promises for EHRs, what components are most important to its users? Majority of the opinion both in round 1 and round 3 centered around improving communication between care givers. Reducing adverse drug events (ADEs), reducing avoidable deaths, and improving physician/hospital integration were other choices. In the final round, physician hospital integration received five votes. Although this aspect of EHR adoption, physician-hospital integration, is increasingly mentioned by the practitioners, it is not clearly defined in the academic literature nor was it elaborated in our survey questionnaire. Considering the number of votes it received, this may be an area of EHR that might increase its adoption at least among the hospitals. There was another category available for the respondents and two respondents chose this in the final round. Convergence to the majority opinion was statistically significant (p=0.096). Governance: Although there are multiple facets of governance, we focused mainly on the privacy concerns since this is often cited as one of the reasons for lack of EHR adoption. Some believe that the privacy protections in Healthcare Insurance Portability and Accountability Act (HIPAA) may be insufficient with the advent of Health Information Exchanges (Walker, 2008).

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The question was phrased as: Do you believe the privacy and confidentiality of patient records are compromised by going from paper to electronic? The responses were elicited based on a five point Likert Scale ranging from Strongly Agree (1.00) to Strongly Disagree (5.00). Of the 35 valid responses received for this question, the mean was 3.74 with a standard deviation of 0.561. The frequencies of responses were Neutral 11, Disagree 22, and Strongly Disagree 2, making up a total of 35. This means that although privacy and confidentiality of patient records is cited as a reason for non-adoption, the CIOs believe that digitization of patient records does not adversely affect privacy and confidentiality. The Wicoxon signed rank test of the difference between the first and final round responses is significant with a p value of .034. Interestingly, this finding is counter to the popular discussion. Incentivizing the providers: The question of funding was addressed indirectly through the choices provided for the question: What will make private practice physicians purchase EHR on their own? Among the choices were: demand from their patients, pressure (mandate) from the governments, improved reimbursement from payers, realization of the return on investment, someone else paying for it and improved quality. The changes in responses from the initial to the final round indicate that improved reimbursement from payers would be a significant driver (p=0.096). These are conditions for which healthcare providers had traditionally been paid for, but starting in 2008, they will not be paid for and for certain cases healthcare providers will not be allowed to bill the payer or the patient for these cases. A statistically significant number of respondents agreed with the majority opinion of very likely, when asked whether engaging patients in the treatment of their own care improves their health outcomes (p=0.043). Given the pressures to earn the same income and with stagnating volume, it might be natural to think that unless physicians can get financial assistance in the form of payer

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reimbursement, there really is not much incentive for them to switch from paper-based systems to EHRs. Public health: A clarification on what exactly is public health was made during the final round as two of the respondents did not know how inclusively to define. A definition on public health as well as distinguishing the work that public health professionals do from privatized medicine or clinical professionals was provided. In the final round, public health was defined as the science of protecting and improving the health of communities through education, promotion of healthy lifestyle, and research for disease and injury prevention. Additionally, public health professionals try to prevent problems from happening or recurring through implementing educational programs, developing policies, administering services, and conducting research, in contrast to clinical professionals such as doctors and nurses, who focus primarily on treating individuals after they become sick or injured. These definitions were taken from the Association of Schools of Public Health. The clarification not withstanding we saw that respondents had a statistically significant change in their response, especially on immunizations (p=0.010). Responses were elicited on six areas of public health and a seventh choice for free form responses was provided. Biosurveillance/Bioterrorism, detection of an epidemic, drug recall, natural disaster relief, mandatory disease reporting, and immunizations were the identified areas of public health. Respondents were asked to identify whether EHR could help facilitate the public health efforts in each of these areas. Frequencies of responses in both the initial and final rounds are provided in Figure 1. Improving the quality of healthcare: The CIOs were asked to rate the six aims for healthcare as proposed by the institute of medicine (safety, timeliness, effectiveness, efficiency, equity, and patient-centeredness) along with compliance and cost. The rating was done on Likert scale ranging

Adoption of Electronic Health Records

Figure 1. Potential use of EHRs in public health

from not at all important (1) to extremely important (5). The rating for all the components were above 3 indicating that the CIOs believed that all aspects of quality of healthcare delivery will be improved by the adoption of EHRs. The difference is rating between the first and the final round was not significant on any of these components indicating the absence of any influence of Delphi study on the perception of CIOs. The mean ratings for each of these items are given in table 2. Measurement: CIO impressions on return on investment (ROI) metrics for EHRs were elicited in five dimensions: reduction in transcription costs, reduction in paper costs, faster patient throughput, and patient satisfaction. Response was positive in all these dimensions indicating that it is important to measure EHR benefits in Table 2. Components of healthcare quality Quality Component

Initial Round

Final Round

Safety

4.15

4.33

Timeliness

4.15

3.89

Quality

3.84

3.83

Compliance

3.75

3.56

Effectiveness

3.85

3.78

Efficiency

3.85

4.00

Patient Focused

3.40

3.50

Cost

3.75

3.86

all these areas. There was no significant change between rounds. Barriers to adoption: Barriers to adoption identified in the study were: cost, cultural barriers, lack of standards and interoperability, complexity of medicine, and workflow changes for caregivers. Another item was included to capture the rest of the barriers not mentioned here. While the aggregate response did not change significantly, we did see that categorically, there was a statistically significant change (p = 0.034) between rounds. Respondents picked workflow changes for caregivers based on the majority opinion, which is still not abundantly mentioned in the literature. The Delphi technique clearly had an influence on how the group responded to this question as workflow was relayed to them to be the majority opinion from the first round. The frequency of responses is noted in Figure 2. Determinants of success: Age, specialty, gender, and medical training (such as school attended) of the physicians along with the business model (whether employed by hospital or working independently), leadership of the state in which the physician practices, and physician leadership and attitude were studied as possible determinants of successful adoption of EHRs. Physician leadership and attitude was included only the final round after feedback from the second round. Once

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Figure 2. Barriers to EHR adoption

included, physician leadership received the most votes followed by business model of the physicians. Interestingly, traditional significant factors such as the physician’s age fared much less favorably. The votes for various items in both the rounds of Delphi are provided in Figure 3.

SUMMARY OF FINDINGS, LIMITATIONS AND FUTURE DIRECTIONS In summary, we see that the Delphi method did have an impact on the experts’ opinion between the initial and final rounds, but there were certain areas where the impact was not as expected or Figure 3. Determinants of success

140

did not conform to group opinion. A group of experts conforming on their opinion(s) does not mean that they are correct. Analysis across the gender, age category, or budget size of the survey respondents did not reveal any changes of statistical significance. We see that CIOs perspectives did change over time in other areas. The sharing and reiteration characteristics of the Delphi technique are suspected to be the cause. We learned some of the factors that will make EHRs successful. As there was no difference between rounds and the emphasis remains high, one can conclude that Business model of the physician, whether they are employed by the hospital or independent, and physician leadership and attitude are key factors that determine

Adoption of Electronic Health Records

adoption of EHRs. The results affirmed findings from the prior studies that EHRs are expected to improve safety, timeliness, quality, compliance, effectiveness, efficiency, patient-focus, and cost of healthcare. The Delphi method helped the majority opinion influence the respondents when asked about the critical barriers, which included cost, culture, complexity of medicine, and workflow changes for caregivers. While not found explicitly in the literature, workflow changes for caregivers was the majority opinion in the end. A majority of the respondents agreed that EHRs help place the patient at the center of care, which is what the literature reflects. Additionally, EHRs are believed to help bridge the chasm between inpatient and outpatient care. Not only are patients more engaged when an EHRs is being used, the experts concurred with the literature that their health outcomes are going to be better as a result of being engaged in their own care. However, the experts believe that only 40-59% of the providers will be using an EHR by 2014. This is concerning given that there is a presidential mandate to implement EHRS by 2014. Most of the respondents did not see privacy and security of the patient being compromised by the utilization of an EHR. EHRs do support public health efforts of biosurveillance, detection of an epidemic, such as, Avian Flu or SARS, natural disaster relief, mandatory disease reporting and immunizations. The results were influenced by the majority opinion as well as by giving the respondents an official definition of public health and how public health professionals differed from clinical professionals in private medicine. In looking at tangible metrics around financial savings, all the responses agreed that EHR utilization does result in the reduction of transcription and paper cost, increased patient throughput, and higher patient satisfaction. Despite the unanimously agreeing on the potential savings that a physician might observe, the survey findings revealed that unless there is higher reimbursement

from payers or unless someone else is going to defray the costs, the private physician sees no reason to start using an EHR today. The experts mirrored the statistic mentioned in the literature on the percentage of U.S. adults that receive the recommended care and if EHRs will help improve this number. Their final response, after being influenced by the majority opinion as a product of the Delphi technique, was 50% as opposite to 55% percent as stated in the literature. This was indeed very encouraging to see, given the rising costs of healthcare. This study identified many determinants in the successful deployment of an EHR and it does affirm that EHRs can help deliver clinical and administrative savings as trumpeted by the literature. The study is particularly pertinent as the panelists were experts who are in the forefront of EHR implementation. One of the interesting findings is the identification of changes to the workflow of a physician as being the biggest barrier to EHR implementation. Cost of an EHR was a close second. It was disturbing to observe how little respondents knew about the role of EHRs in public health, especially in its most broad definition. It was not until an explicit definition of public health as the science of protecting and improving the health of communities through education, promotion of healthy lifestyles, and research for disease and injury prevention in the third round that respondents understood the distinction between the fields of medicine and public health. However, once defined, the experts did agree that there were several public health efforts that EHRs can help extend. Another new finding, not generally discussed in the literature, is that the business model of the physician is identified as a predictor of success in the dissemination of EHRs. It makes a significant difference if a physician is employed (salaried) or if he or she owns the practice because it is difficult to swallow the cost of an EHR by a physician owner, especially given all the incremental cuts in reimbursement. While still difficult, it is

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Table 3. Summary of findings Aspect Measured

Determinants of EHR Success

Critical Barriers to Implementing EHR

Initial Round

Final Round

Improved Workflows

12

15

+

Cost affordable

12

10

-

Lack of Standards

11

4

-

5

16

+

19

14

-

Complexity of Medicine Business model of the physician

Physician Specific Factors

Factors Influencing Purchase of an EHR By Private Practice Physicians

Aims of the Institute of Medicine

Change Direction

Physician leadership/attitude

0

14

+

Physician age

8

1

-

Physician gender

3

0

-

Physician specialty

6

2

-

State government’s leadership

3

2

-

Medical school attended

4

3

-

Improve payer reimbursement

No

Yes

Yes

Improve Quality

Yes

No

No

Pressure from government

No

Yes

No

Demand from patients

No

Yes

Yes

Improve safety

4.15

4.33

+

Improve timeliness

4.15

3.89

-

Improve efficiency

3.84

3.83

=

Improve effectiveness

3.85

3.78

-

Improve patient-centeredness

3.4

3.5

+

3.75

3.86

+

Reduce adverse drug events

5

1

-

EHR as an Enabler to Achieving

Improve communication between caregivers

4

10

+

Higher Quality c

Reduce avoidable deaths

3

1

-

Improve regulatory compliance

Improve physician-hospital integration

Public Health

c

5

2

-

Biosurveillance/bioterrorism

11

5

-

Detection of an epidemic

12

7

-

Drug Recall

14

11

-

Natural disaster relief

11

2

-

Mandatory disease reporting

16

16

=

Immunizations

Return on Investment

b

0

12

+

Transcription cost

4.06

4.11

+

Paper cost

3.76

3.83

+

Storage cost

4.12

4

-

Patient throughput

4.12

3.78

-

Patient satisfaction

3.82

3.94

+

Likert scale ranging from not important to extremely important

a

Likert scale ranging from Strongly Disagree to Strongly Agree

b

Checkboxes - number of respondents (frequencies)

c

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comparatively palatable when the cost of an EHR does not directly hit the pocket of the physician – as usually in the case of employed physicians. The majority of the opinion supported the literature that EHRs help improve communication between care givers; however, another new finding surfaced in this area - that EHRs also assist with the physician-hospital integration. This is intriguing to observe given that the Stark laws have most recently been relaxed to allow hospitals to defray the cost of an EHR for an affiliated physician. Age, specialty, and gender of the physician were not seen as factors of significance impacting the adoption of EHRs even though the literature suggests that older physicians find it hard to learn and adapt to using an EHR in their everyday practice. The expert respondents did agree that EHRs enabled healthcare organizations to achieve the six aims of the IOM - for care to be safe, timely, efficient, effective, equitable, and patient-centered. There are several areas within healthcare and public health that can leverage the findings from this study and build upon it. The results of the survey show that physician-hospital integration is an important area that can benefit from the deployment of an EHR. Physician-hospital integration has had a checkered history and one can further research and leverage the benefits of an EHR toward improving physician-hospital relationship(s). Future research can study how an EHR can help address the market forces that are converging around the need for more physicianhospital integration. Some of these forces that can be addressed are unsustainable growth in health care costs due to structural issues, such as aging population and a failure of the managed care paradigm, the move by the federal government and commercial payers to value-based reimbursement, significant shifts in provider demographics, including the nursing shortage and the noticeable shift toward quality of life requirements by younger physicians. Another area for potential research is what key features and functionality of an EHR contribute

the most toward delivering high quality and safe patient care. Does Computerized Physician Order Entry (CPOE) save more lives than a bar-coded medication administration system that checks the 5-rights of drug delivery (right patient, right drug, right time, right dose, and right route)? On the public health side exclusively, one can build upon this research to find out how an EHR can contribute toward meeting basic public health requirements. For example, can an EHR automatically report on all reportable diseases as mandated by the respective State Health Departments? How can an EHR bring public health and private medicine closer so that the common goals of the two somewhat disparate areas are met? A second significant factor in EHR success found in this study is physician leadership. Interestingly, physician leadership was a significant factor as a determinant of EHR success, yet where does a physician go to in order to learn these leadership skills? Which schools and their respective curricula didactically teach physicians leadership skills? Similarly, one may wonder, how important is physician leadership in other areas of health delivery including public health? Given today’s financial pressures on healthcare providers, it is very important to know how changes in the regulatory world will impact utilization of an EHR and how hospitals can assist private physicians in purchasing such technologies without ending up in violation of a Stark or Antikickback stature. The impact of the relaxation of the Stark Law, effective October of 2007, may have a positive impact on the deployment of EHRs into the physician practices. This is because hospitals can now discount EHR software, up to 85%, for the physician with the intent that patient care is going to improve. Given the overwhelmingly supported return-on-investment metrics, such as transcription and paper cost, faster patient throughput, and increased patient satisfaction, one can argue why physicians or hospitals are not investing in EHR technology already. Until there is an incentive to do so, such as higher reimbursement (or perhaps

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Adoption of Electronic Health Records

diminished reimbursement for lack of not using an EHR), physicians do not want to proactively make such an investment. We presently see that government and private payers are exerting downward pressure on the reimbursements for nosocomial infections and for conditions that are present on admission. These are cases for which health care providers have traditionally billed for and have received payment. In the future, providers cannot even bill for certain cases, let alone, expect payment. In addition, with the wave of consumerism and transparency of price, quality, and cost, there is a doubt on how much of an impact these pressures will have on healthcare to have an EHR as a cost of doing business.

CONCLUSION The nature of this study is exploratory and studies EHR adoption at an enterprise level. Before building richer conceptual models on EHR adoption, this preliminary study confirms as well as adds to factors determining successful adoption of EHR. The findings of the study have authenticity since the panel of experts is the CIOs of healthcare organizations that academic researchers have not surveyed thus far. Although the physicians are the ultimate adopters of EHR, healthcare organizations and hospitals have an important role to play in EHR adoption. Hence this study using CIOs as an expert panel sheds light on the perspective of an important constituency on the adoption of EHRs.

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Withrow, S. C. (2008). Why can’t physicians interoperate? barriers to adoption of EHRs. Healthcare Financial Management, 62(2), 90–96.

Walker, T. (2008). Report calls for more EHR privacy. Managed Healthcare Executive, 18(3), 9–9.

This work was previously published in International Journal of Healthcare Delivery Reform Initiatives (IJHDRI), Volume 2, Issue 1, edited by Matthew W. Guah, pp. 18-42, copyright 2010by IGI Publishing (an imprint of IGI Global).

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Chapter 1.10

Technology Enabled Knowledge Translation:

Using Information and Communications Technologies to Accelerate Evidence Based Health Practices Kendall Ho University of British Columbia, Canada

ABSTRACT Because of the rapid growth of health evidence and knowledge generated through research, and growing complexity of the health system, clinical care gaps increasingly widen where best practices based on latest evidence are not routinely integrated into everyday health service delivery. Therefore, there is a strong need to inculcate knowledge translation strategies into our health system so as to promote seamless incorporation of

new knowledge into routine service delivery and education to promote positive change in individuals and the health system towards eliminating the clinical care gaps. E-health, the use of information and communication technologies (ICT) in health which encompasses telehealth, health informatics, and e-learning, can play a prominently supportive role. This chapter examines the opportunities and challenges of technology enabled knowledge translation (TEKT) using ICT to accelerate knowledge translation in today’s health system with two

DOI: 10.4018/978-1-60960-561-2.ch110

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case studies for illustration. Future TEKT research and evaluation directions are also articulated.

KNOWLEDGE TRANSLATION: INTRODUCTION The tenet of modern healthcare practice is evidence-based, established from knowledge generated through medical research and proven practice patterns. Evidence-based practice takes time to evolve. It is estimated that incorporating advances advocated by current research into routine, everyday medical practice takes one to two decades or more (Haynes, 1998; Sussman, Valente, Rohrbach et al., 2006). The causes of this apparent lag time of translating evidence into routine health practice are multifactorial, including but not restricted to: explosion of research and generation of resultant evidence, ineffective continuing education for health professionals to propagate the knowledge, lack of adoption of the knowledge by health professionals after exposure and education, the complexity of health management strategies that commonly demand more than simple changes in treatment approaches, reduction in healthcare resources, a lack of mutual understanding and dialogue between researchers that generated the research and health practitioners and health policy makers who need to translate the research into routine practices, and the practitioners’ and policy makers’ own beliefs and experiences that influence how knowledge will ultimately be utilized in clinical situations and quality assurance initiatives. As a result, a clinical care gap occurs when the best evidence is not routinely applied in clinical practice (Davis, 2006; Grol & Grimshaw, 2003).

Definition Canadian Institutes of Health Research (CIHR), one of the three members of the Canadian Research Tri-council and the guiding force in Canadian Health Research, defines knowledge translation

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as “the exchange, synthesis, and ethically-sound application of knowledge, within a complex set of interactions among researchers and users, to accelerate the capture of the benefits of research for Canadians through improved health, more effective services and products, and a strengthened healthcare system” (CIHR, 2007). The Social Sciences and Humanities Research Council of Canada (SSHRC, 2006), another member of the Tri-council with focus on humanities research, defines knowledge mobilization as “moving knowledge into active service for the broadest possible common good.” SSHRC further contextually defines knowledge to be “…understood to mean any or all of (1) findings from specific social sciences and humanities research, (2) the accumulated knowledge and experience of social sciences and humanities researchers, and (3) the accumulated knowledge and experience of stakeholders concerned with social, cultural, economic and related issues” (SSHRC, 2006). Both definitions speak to the central principle of the need for not only discovering new knowledge through research, but also utilizing the resultant knowledge effectively and routinely in order to fully realize the benefits of the body of research. For the rest of this chapter, knowledge translation (KT) will be used to denote this core concept of effective knowledge application.

Strategic Considerations Strategically, effective and sustainable KT requires synchronized efforts at several levels towards a common vision of evidence based practice (Berwick, 2003; Katzenbach & Smith, 2005; Senge, 1994): the personal level where individuals influence their own behaviors towards change, the team level where groups of individuals work together collaboratively and cooperatively to drive towards group-based change, and the system level where health organizations and policy making bodies evolve and innovate on policies and establish

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organizational patterns and cultures to motivate members towards change. Driving forces to motivate change at the individual level include: • •

•

•

Helping individuals to arrive at their own willingness and commitment to change Recognizing explicitly the contributions that individuals would make in carrying out the change Providing individuals with appropriate compensation, either monetary or otherwise, in making the change Sharing successful results with and giving feedback to the individuals after practice change has been instituted

Key factors to promote effective change at the team level include: • •

•

•

•

Jointly owning a shared vision towards an important goal Having effective overall leadership of the team, and also distributive leadership of the various individuals in the team and corresponding power and responsibility to drive change Sharing mutual trust with and accountability to each other in carrying out the necessary work Having an effective conflict resolution mechanism to bring differences; respectfully to the table for understanding, discussion, and resolution Achieving and celebrating success together

Important change management levers at the system’s level include: • •

Creating and adjusting fair and appropriate recognition and reward systems Bringing understanding to the impact of change in healthcare service delivery pat-

•

•

•

tern towards the social, economic, and population health context Cultivating the spirit of innovation to motivate individuals in the system to generate better evidence and pathways against current standards and practice patterns Promoting transfer of functions amongst individuals in the health system as effective division of labor and recognition of increasing competence through expansion of responsibilities Implementing routine system’s level reflection for continuous quality improvement and iterative modifications towards excellence

Success in sustainable KT requires not only transformation at the various levels, but also the harmonization of efforts in the totality of all these levels towards the common vision. For example, the April 2003 Institute of Medicine report advocates five core competencies that the health professionals of the 21st century need to possess (Institute of Medicine, 2003): delivering patient-centered care; working as part of interdisciplinary teams; practicing evidence based medicine; focusing on quality improvement; and using information technology. As an illustration, let us examine the core competency of interdisciplinary team establishment. In order to translate the concept of interdisciplinary team into routine health practice, having research that demonstrates examples of successful team based practice is not enough to cause lasting change in practice patterns. These successful examples, or documented knowledge, need to be vivified in health professionals’ own practice context so they and their own teams can visualize how they can model after these successful examples to replicate success (Ho et al., 2006). This type of education is necessary but not sufficient either; innovative policy translation by the policy makers to promote team based practice, patient and health consumer demand or preference for same, health

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system redesign and implementation by health administrators, and appropriate accreditation for the educational system to promote the values and transform the curriculum for health professional trainees should all occur in synchrony in order to bring about lasting change, that is, sustainable knowledge translation. Finally, it is important to note that knowledge translation is not a straightforward and linear approach, but rather a complex and adaptive process based on common vision, solid principles, shared commitment at different levels, and human ingenuity in flexible adaptation.

Effective KT in Health It is desirable and achievable to accelerate knowledge translation in health to expeditiously reap the benefits health research to realize optimal evidence-based care for patients. Known and tested KT pathways, such as the Model of Improvement developed by Associates in Process Improvement (API, 2007) and endorsed by The Institute for Healthcare Improvement (IHI, 2007), can lead to successful KT in health system transformation through the following sequence of steps: • • • • • •

Setting aims to decide what accomplishments are to be achieved Establishing measure in order to assess positive change Selecting the key changes that will result in improvement Testing changes through the plan-dostudy-act cycle Implementing changes after testing Spreading changes to other organizations

Each of these steps require not only systems based mind-shift, but also individuals in the systems changing their personal behaviors. Therefore, in the health system context, health research can and ideally should be synchronized with individual practice, education, and policy setting environ-

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ments so as to accelerate KT towards expeditious evidence based healthcare delivery.

ICT IN HEALTH Modern information and communication technologies (ICT), including computers, personal digital assistants, cellular phones, and an ever expanding list of imaginative electronic communication devices, are making unprecedented and innovative impact on healthcare service access, delivery, education and research (Ho, Chockalingam, Best, & Walsh, 2003). The rapid growth of affordable, interoperable ICTs that can facilitate seamless data communication and increase connectivity to the Internet are breaking down geographic and temporal barriers in accessing information, service, and communication. These advances are transforming the ways regional, national and global health services, surveillance, and education are being delivered. Some of the clear advantages of using ICT in health service delivery and education, or commonly referred to as e-Health, include but not limited to (Health Canada, 2006; Miller & Sim, 2004; Shortliffe, 1999): •

•

•

Anywhere, anytime access to accurate and searchable health information for knowledge and clinical case exchange, such as the use of ePocrates to help health professionals in rapidly accessing drug information to promote safe medication prescribing Large capacity for information storage and organization for health surveillance, such as the World Health Organization’s international health surveillance system to monitor infectious disease outbreaks in real time Ease of synchronous and asynchronous communication between health professionals in different geographic areas for health

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service delivery, knowledge exchange or consultations

Defining E-Health When first introduced, the term “e-health” was used to signify health service delivery and activities on the Internet. Today, this term has been adopted to become an over-arching term to denote generally any use of ICT in healthcare (U Calgary Health Telematics Unit, 2005; Health Canada 2006), from health service delivery to health data storage and analysis to health education through ICT use. E-health can be conceptually visualized to be supported by three distinct but inter-related pillars: telehealth, health informatics, and e-learning (Figure 1). Telehealth commonly refers to “…the use of ICT to delivery health services, expertise, and information over distance” (U Calgary Health Telematics Unit, 2005). Whereas in the past, telehealth focused on the use of the videoconferencing medium as a distinguishing feature, today in many circles the emphasis is placed on the service delivery aspect of the definition. Therefore, telehealth can be either video-based through a closed network such as ISDN or fibreoptic videoconferencing, or Web-based through the use of the multimedia capabilities of the Internet. Telehealth can also be delivered either synchronous-

ly where communication between individuals occur in real time, or asynchronously in a “store and forward” fashion where one individual can send the information and expecting a response from others in a delayed fashion (Ho et al., 2004). Ample examples of telehealth can be found in the literature ranging from tele-psychiatry to teledermatology, tele-ophthalmology, emergency medicine, nursing, physiotherapy, and usage by other health disciplines. Health informatics (HI) have many definitions by different institutions, as documented on the University of Iowa Health Informatics Web site (U Iowa Health Informatics, 2005). One such typical definition of HI from Columbia University is “…the scientific field that deals with the storage, retrieval, sharing and optimal use of biomedical information, data, and knowledge for problem solving and decision making. It touches on all basic and applied fields in biomedical science and is closely tied to modern information technologies, notably in the areas of computing and communication.” The emphasis on HI, then, is on the storage and utilization of information captured through ICT. Excellent examples of the application of HI in practice are electronic health records in clinics and hospitals in a regional scale, public health on line disease surveillance systems nationally (Public Health Agency of Canada, 2006) or the World Health Organization epidemic and

Figure 1. E-health components

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pandemic alert and response (EPR) system (World Health Organization, 2007). E-learning is commonly defined as “the use of electronic technology to deliver education and training applications, monitor learner performance, and report learner progress” (Sales, 2002). The distinguishing aspect of e-learning is the focus on the acquisition, utilization and evaluation of the knowledge captured by and synthesized through ICT. Examples of e-learning in health and their utility in changing health professionals’ practice is well documented in the literature. A recent example of a randomized control trial demonstrating the equivalence of e-learning compared to face to face workshops in helping learners in knowledge retention, with a statistical significance favoring e-learning in helping learners to actually change their behaviours (e.g., Fordis, 2005). While telehealth, health informatics, and elearning have their own distinguishing features and pillars of pursuits, they also synergistically interact to offer maximal benefits to the health system as a whole. On the contrary, each pillar on its own can only have limited impact on the health system. For example, trans-geographic telehealth without capturing outcomes through health informatics and e-learning to teach and propagate the service delivery module might only lead to temporary adoption of telehealth without sustaining effects to the health system as a whole. Similarly, health data capturing would not be complete without considering the contextual elements of health service delivery and the accumulated practice knowledge to date in the communities from which the data was generated. It is also obvious that e-learning will be dependent upon accurate data and best practices models in health service delivery. Therefore, the exciting challenge and opportunity of e-health is indeed in the seamless and comprehensive and complimentary utilization of data, service and knowledge to drive the prospective transformation of health practices that are based on evidence, knowledge,

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Figure 2. Optimizing TEKT

and the needs of the health consumers and the communities (Figure 2).

TECHNOLOGY ENABLED KNOWLEDGE TRANSLATION E-health is rapidly gaining momentum worldwide as a vital part of the healthcare system in and amongst nations. For example, National Health Services in the United Kingdom, in Australia, and Infoway in Canada are actively facilitating the establishment of infrastructure and implementation strategies in e-health to promote its entrenchment in health practices. Collaboration amongst different agencies and organizations is also evident in establishing emerging e-health networks in other countries such as the Africa Health Infoway (World Health Organization, 2006). Recognizing the potential, the UN General assembly categorically stated in its Millennium declaration that “… the benefits of new technologies, especially information and communication technologies … (should be made) available to all” (United Nations, 2000). When considering e-health in practice, one needs to question not only “how do we augment or replace existing services with ICT,” but also “how can ICT be innovatively used to best serve the health system that may not have any

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precedence?” This question can further be asked in two ways: “How can ICT be used to make current health service and education pathways more efficient, accessible, or higher quality?” and the companion question “How can ICT be used to provide unprecedented health service and educational models that were not possible in the past?” These questions guide us towards both adoption and innovation in e-health, and also properly consider ICT as serving the agenda of the health system, rather than using the latest and greatest technologies regardless of whether or not the usage actually improves health services and education.

TEKT Defined Technology enabled knowledge translation (TEKT) is defined as the use of ICT to accelerate the incorporation of evidence and knowledge into routine health practices (Ho et al., 2003). By definition, TEKT is not only about using ICT to achieve one type of purpose, such as telehealth for service delivery or health informatics for data storage and analysis. Rather, TEKT strategies synchronize and coordinate data, service, and knowledge capturing and utilization to synergistically cause a system’s level change so as to translate evidence and health knowledge into routine practice and policy establishment. ICT can play a pivotal role in the health system for knowledge synthesis, evidence-based decisionmaking, building shared capacity for knowledge exchange, and minimization of duplication of decision support systems. Various Web-based data and information repositories networked together can facilitate just-in-time clinical consultations, and support access to the latest management information on diseases, treatments, and medications. Instant sharing and exchange of knowledge by healthcare providers facilitated in these Internet portals as discussion groups or informal e-mail exchange can play an important role in team building. Remote access to centralized data

repositories, such as electronic medical records via the Internet as well as intelligent information retrieval functionality and data pattern recognition are just some examples of the ways in which technologies can save time, eliminate laborious tasks, and interconnect to capture, disseminate, and help translate knowledge into practice in ways previously not possible.

Case Studies in TEKT The following section highlights two case studies in TEKT to provide a qualitative illumination on best practices in TEKT. However, this discussion is not meant to be comprehensive or exhaustive, but rather illustrations to highlight and celebrate the innovation and ingenuity of the applications of ICT to facilitate TEKT and improved health outcome as they meet the challenges and needs of the healthcare system with existing and emerging technological solutions. SARS (Srinivasan, McDonald, Jernigan et al., 2004; Marshall, Rachlis, & Chen, 2005; Wenzel, Bearman, & Edmond, 2005) From late 2002 to 2003, the world was gripped by the emergence of a deadly and, up till then, unknown health threat sudden acute respiratory syndrome (SARS). This rapidly spreading infection with associated high mortality made its notorious impact worldwide into early 2003. In particular the South East Asian countries were most severely affected, with transmission to other continents due to mobility of infected populations. The eventual toll of SARS was recorded to be 8,098 cases worldwide, with 774 deaths. During that period of outbreak, the many unknown features of SARS needed to be rapidly disseminated to health professionals and administrators around the world. Also, tracking of the spread of the infection, and patterns of spread to understand the modes of transmission and contact persons involved were paramount to bring effective control of this outbreak. SARS related infection control policies, criteria of diagnosis,

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education of health professionals and the general public, quality assurance activities were all vital information that were rapidly disseminated and exchanged through the use of ICT worldwide. Data repositories and electronic systems, together with Web-based information dissemination and consultations, were vital aspects of global SARS management and decision support for health professionals worldwide. The urgent and intensive efforts to disseminate information, sharing of best practices in infection control methods, and careful preparation of unaffected counties were key lessons learned, and ICT playing key roles in TEKT were pivotal in these activities. Also, as a result of SARS, great attention is paid in different countries and globally on disease monitoring and surveillance systems to prepare for future expected and unexpected outbreaks such as influenza, avian flu, or other diseases. Medication Safety Surveillance (Bell et al., 2004; Graham, Campen, Hui et al., 2005; Leape & Berwick, 2005; Topol, 2004) Non-steroidal anti-inflammatory drugs (NSAIDs) is a class of medications effective for pain management in patients with arthritis. However, NSAIDs are known to have substantial side effects in the gastroenteral system in causing ulcers and erosions. A new class of COX-II NSAIDs were introduced, the first one of which were rofecoxib (Vioxx®). Initial trials seemed to affirm that COX-II NSAIDs were effective for pain management and had lower gastrointestinal side effects compared to traditional NSAIDs. However, subsequent analysis of the data, with additional studies done by other groups, suggested the potential of increased cardiovascular side effects including myocardial infarctions and deaths. Kaiser Permanente, an American Health Management Organization, wanted to clarify this controversy. It had an electronic health record system (HER) where, amongst a variety of health and administrative records, every doctor would track patient visits over time, medication prescriptions, and side effects. Using the Cali-

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fornia database of this EHR system, 2,302,029 person-years follow-up over a three year period where patients were exposed to rofecoxib were analyzed. The researchers found that there were 8,143 cases of serious coronary heart disease that occurred, with 2,210 cases (27.1 percent) where the patients died. This represented a more than three times risk (Odd ratio as high as 3.58) for the use of rofecoxib compared to another NSAID agent. This data, together with other studies, led to the company that made rofecoxib voluntarily withdrew the medication from the market in September 2004, and the Food and Drug Administration (FDA) officially recommending its withdraw in February 2005. A very significant development in this medication surveillance was that, once Kaiser Permanente detected the significant cardiovascular side effect risk of rofecoxib, this information was passed onto physicians practicing in Kaiser, leading to a dramatic drop of rofecoxib prescription rate of four percent compared to the national average in United States of 40 percent, well before the medication was withdrawn from the market place. This case demonstrates the power of EHR in not only being able to rapidly and prospectively track medication side effects so as to increase safety, but also disseminating the evidence to practitioners to influence practice outcome rapidly.

CHALLENGES AND OPPORTUNITIES IN TEKT Barriers to ICT Uptake Despite the potential and real benefits of ICT use in health, health professionals’ uptake of ICT has been slow. Factors that impede the adoption of ICT tools include cost, lack of informatics platform standards, physicians’lack of time or technological literacy, the need for a cultural shift to embrace these necessary changes in medicine, and a lack

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proaches to evidence based knowledge translation or create unprecedented models Understanding the human-technology interface how ICT can be configured to optimize the utilization and practice of these tools in healthcare contexts Understanding and assisting in evidence based e-health policy making, as policy innovation is essential to guide the optimal use of ICT in the system’s level Integrating ICT into teams and communities where human to human interactions and collaborations can be enhanced through ICT facilitation Cost effectiveness and return on investment evaluation as to how ICT can lead to increased capacity of the health system, cost avoidance or savings in health service delivery, or improved access and quality Building capacity in TEKT research over time

of integration of various ICT methodologies into a cohesive deployment strategy. For example, in a recent survey supported by The Commonwealth Fund (Schoen, Osborn, Huynh et al., 2006), the authors found that there was a wide variation of ICT uptake by primary care physicians amongst these seven countries, from as low as 23 percent to as high as 98 percent uptake in electronic patient medical records. This variation in electronic patient record uptake directly correlated with and underpinned issues related to quality and efficiency broached in this survey, such as coordination of care of patients, multifunctional capacity including automated alerts and reminders, or information sharing amongst interprofessional team members. Of note, both United States and Canada were lagging significantly behind the other five countries surveyed in terms of ICT uptake in practice. These variations were in large part due to the underlying policy choices of the different countries. Therefore, in consideration as to how best to overcome barriers to technology uptake, it is important to not only focus on health professionals to increase their attitudes, knowledge and skills in ICT use, but also place emphasis on policy innovation to motivate the health systems towards quality and the adoption of ICT in support of this important vision of care.

In order to accelerate the discipline of TEKT, it is important that efforts in this research and innovation be harmonized to enable cross study comparisons and standardized documentation of best practices. In this line of thinking, establishing a research evaluation framework towards TEKT would be an ideal approach (Ho et al., 2004).

FUTURE RESEARCH DIRECTIONS

CONCLUSION

As TEKT is an emerging field with a rapidly evolving environment, there is ample opportunity for research, innovation, and evaluation. Examples of dimensions of TEKT research could include but not restricted to:

ICT has tremendous potential to improve healthcare service delivery, and TEKT can help accelerate ICT adoption and change management to reap the corresponding benefits. Excellent literature based and practice based examples of TEKT have shone some best practice examples, and more innovative and effective models in the future are sure to come with the continuing improvement of technologies and practices. As a result, the practice of and research in TEKT are both timely

• •

Documentation of best practices in TEKT to date Innovative demonstrations of ICT enabled models of knowledge translation, where ICT is used to either augment current ap-

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and urgently needed to help achieve excellence in healthcare delivery.

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Graham, D. J., Campen, D., Hui, R., Spence, M., Cheetham, C., & Levy, G. (2005). Risk of acute myocardial infarction and sudden cardiac death in patients treated with cyclo-oxygenzse 2 selective and non-selective non-steroidal antiinflammatory drugs: Nested case-control study. Lancet, 365(9458), 475–481. Grol, R., & Grimshaw, J. (2003). From best evidence to best practice: Effective implementation of change in patients’ care. Lancet, 362, 1225–1230. doi:10.1016/S0140-6736(03)14546-1 Haynes, R. (1998). Using informatics principles and tools to harness research evidence for patient care: Evidence-based informatics. Medinfo, 9(Pt 1Suppl), 33–36. Health Canada. (2006). eHealth. Retrieved on May 4, 2007, from http://www.hc-sc.gc.ca/hcssss/ehealth-esante/index_e.html Ho, K., Bloch, R., Gondocz, T., Laprise, R., Perrier, L., & Ryan, D. (2004). Technology-enabled knowledge translation: Frameworks to promote research and practice. The Journal of Continuing Education in the Health Professions, 24(2), 90–99. doi:10.1002/chp.1340240206 Ho, K., Borduas, F., Frank, B., Hall, P., HandsfieldJones, R., Hardwick, D., et al. (2006). Facilitating the integration of interprofessional education into quality healthcare: strategic roles of academic institutions. Report to Health Canada Interprofessional Education for Collaborative Patient Centred Practice. October 2006. Ho, K., Chockalingam, A., Best, A., & Walsh, G. (2003). Technology-enabled knowledge translation: Building a framework for collaboration. Canadian Medical Association Journal, 168(6), 710–711.

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Ho, K., Karlinsky, H., Jarvis-Selinger, S., & May, J. (2004). Videoconferencing for Telehealth: unexpected challenges and unprecedented opportunities. British Columbia Medical Journal, 46(6), 285–289. Institute for Healthcare Improvement. (2007). How to improve: improvement methods. Retrieved on June 7, 2007 from http://www.ihi.org/ IHI/Topics/Improvement/ImprovementMethods/ HowToImprove/ Institute of Medicine of the National Academies. (2003). Health professions education: a bridge to quality. Institute of Medicine April 8, 2003 Report. Retrieved June 9, 2007, from http://www.iom.edu/ CMS/3809/4634/5914.aspx Katzenbach, J. R., & Smith, D. K. (2005). reprint). The discipline of teams. Harvard Business Review, 83(7), 162–171. Leape, L. L., & Berwick, D. M.(n.d.). Five years after ‘to err is human’: What have we learned? Journal of the American Medical Association, 293(19), 2384–2390. doi:10.1001/ jama.293.19.2384 Marshall, A. H., Rachlis, A., & Chen, J. (2005). Severe acute respiratory syndrome: responses of the healthcare system to a global epidemic. Current Opinion in Otolaryngology & Head & Neck Surgery, 13(3), 161–164. doi:10.1097/01. moo.0000162260.42115.b5 Miller, R. H., & Sim, I. (2004). Physicians’ use of electronic medical records: Barriers and solutions. Health Affairs, 23(2), 116–126. doi:10.1377/ hlthaff.23.2.116 Public Health Agency of Canada. (2006). Disease surveillance on-line. Retrieved June 7, 2007, from http://www.phac-aspc.gc.ca/dsol-smed/ Sales, G. C. (2002). A quick guide to e-learning. Andover, MN: Expert Publishing.

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University of Calgary Health Telematics Unit. (2005). Glossary of telhealth related terms, acronyms and abbreviations. Retrieved June 7, 2007, from http://www.fp.ucalgary.ca/telehealth/ Glossary.htm University of Iowa Health Informatics. (2005). What is Health Informatics? Retrieved June 7, 2007 from http://www2.uiowa.edu/hinfo/academics/what_is_hi.html Wenzel, R. P., Bearman, G., & Edmond, M. B. (2005). Lessons from severe acute respiratory syndrome (SARS): Implications for infection control. Archives of Medical Research, 36(6), 610–616. doi:10.1016/j.arcmed.2005.03.040 World Health Organization. (2006). WHO Partnership—The Africa Health Infoway (AHI). Retrieved June 7, 2007 from http://www.research4development.info/projectsAndProgrammes. asp?ProjectID=60416 World Health Organization. (2007). Epidemic and pandemic alert and response (EPR). Retrieved June 7, 2007 from http://www.who.int/csr/en/

ADDITIONAL READING Booz, A. Hamilton (2005). Pan-Canadian electronic Health record: Quantitative and qualitative benefits. Canada Health Infoway’s 10-year investment strategy costing. March 2005 Report. Retrieved June 7, 2007 from http://www.infowayinforoute.ca/Admin/Upload/Dev/Document/ VOL1_CHI%20Quantitative%20&%20Qualitative%20Benefits.pdf Bower, A. G. (2005). The diffusion and value of healthcare information technology. A RAND Report. Accessible at http://www.rand.org/pubs/ monographs/MG272-1/

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Canadian Institutes of Health Research. (2007). Knowledge Translation Strategy 2004-2009: Innovation in Action. Retrieved June 9, 2007, from http://www.cihr-irsc.gc.ca/e/26574.html#defining eHealth ERA Report. (2007). eHealth priorities and strategies in European countries. European Commission Information Society and Media. Retrieved June 9, 2007, from http://ec.europa. eu/information_society/activities/health/docs/ policy/ehealth-era-full-report.pdf Fonkych, K., & Taylor, R. (2005). The state and pattern of health information technology adoption. A RAND report. Accessible at http://www.rand. org/pubs/monographs/MG409/ Ho, K., Bloch, R., Gondocz, T., Laprise, R., Perrier, L., & Ryan, D. (2004). Technology-enabled knowledge translation: Frameworks to promote research and practice. The Journal of Continuing Education in the Health Professions, 24(2), 90–99. doi:10.1002/chp.1340240206 Ho, K., Chockalingam, A., Best, A., & Walsh, G. (2003). Technology-enabled knowledge translation: Building a framework for collaboration. Canadian Medical Association Journal, 168(6), 710–711. Institute for Healthcare Improvement. (2007). How to improve: Improvement methods. Cambridge, MA. Retrieved on June 7, 2007, from http://www.ihi.org/IHI/Topics/Improvement/ ImprovementMethods/HowToImprove/ Schoen, C., Osborn, R., Huynh, P. T., Doty, M., Peugh, J., & Zapert, K. (2006). On the front line of care: primary care doctors’ office systems, experiences, and views in seven countries. Health Affairs 25(6), w555-w571. Retrieved June 9, 2007, from http://content.healthaffairs.org/cgi/content/ abstract/hlthaff.25.w555?ijkey=3YyH7yDwrJSo c&keytype=ref&siteid=healthaff

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Stroetmann, K. A., Jones, T., Dobrev, A., & Stroetmann, V. N. (2006). eHealth is worth it: The economic benefits of implemented eHealth solutions at ten European sites. Awww.ehealth-impact. orgreport. Retrieved June 7, 2007 from http:// ec.europa.eu/information_society/activities/ health/docs/publications/ehealthimpactsept2006. pdf

Sussman, S., Valente, T. W., Rohrbach, L. A., Skara, S., & Pentz, M. A. (2006). Translation in the health professions: Converting science into action. Evaluation & the Health Professions, 29(1), 7–32. doi:10.1177/0163278705284441

This work was previously published in Human, Social, and Organizational Aspects of Health Information Systems, edited by Andre W. Kushniruk and Elizabeth M. Borycki, pp. 301-313, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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The Computer-Assisted Patient Consultation: Promises and Challenges Aviv Shachak University of Toronto, Canada Shmuel Reis Technion- Israel Institute of Technology, Israel

ABSTRACT The implementation of electronic health records (EHRs) holds the promise to improve patient safety and quality of care, as well as opening new ways to educate patients and engage them in their own care. On the other hand, EHR use also changes clinicians’ workflow, introduces new types of errors, and can distract the doctor’s attention from the patient. The purpose of this chapter is to explore these issues from a

micro-level perspective, focusing on the patient consultation. The chapter shows the fine balance between beneficial and unfavorable impacts of using the EHR during consultations on patient safety and patient-centered care. It demonstrates how the same features that contribute to greater efficiency may cause potential risk to the patient, and points to some of the strategies, best practices, and enabling factors that may be used to leverage the benefits of the EHR. In particular, the authors point to the role that medical education should play in preparing practitioners for the challenges

DOI: 10.4018/978-1-60960-561-2.ch111

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of the new, computerized, environment of 21st century medicine.

INTRODUCTION Mrs. Jones is a new patient to the practice who has come in for a certificate that the local gym requires for enrollment. She is 50 years old, married and mother of two, who works as a high school teacher. Dr. Smith introduces himself, welcoming her to the practice. He asks for her USB memory stick, and her cumulative electronic health record (EHR) pops-up on his screen, indicating no significant health concerns. Her family tree (genogram) is also generated and is displayed. Dr. Smith asks “anything else?” and she answers that she would like to be informed about needed health promotion advice. Her family history contains the existence of heart disease (coronary artery disease) and breast cancer. Her life style is generally healthy. Dr. Smith performs a focused physical examination, and hooks her up to the multi-task physiological monitor that gives, within 90 seconds, a reading of her blood pressure and pulse, electrocardiogram, and lung function (spirometry) - all are normal. Since she had laboratory tests three months ago as part of a woman’s health program she follows, Dr Smith shares with her the results of her cholesterol test (hypercholesterolemia or a high cholesterol level). The EHR automatically generates the recommended screening and health promotion recommendations for her age and risk status, which he explains, and then goes over some patient education materials that appear on the screen that he shares with her. Finally, she asks his opinion about the future consequences of her ten year old infection of the abdomen’s inner lining (peritonitis) and a subsequent surgical procedure to explore it (laparotomy). The doctor does not recall any, but he sends a query through the Inforetriever (a program for updated sound medical information retrieval) on his desktop. When the evidence-based answer arrives 15 sec-

onds later, he is able to share it with her. All the generated materials are beamed to her cell phone and emailed to her, together with the certificate. Throughout the encounter Dr. Smith has been applying the patient-doctor–computer communication skills he had trained in six months ago at the national simulation center. The subsequent results of her age and risk appropriate screening arrive at his desktop (and hers) automatically within the next week. Dr. Smith interprets the results for her and sends them over the encrypted office email. Mrs. Jones also shares her exercise and diet program data electronically, and he monitors those. Three months later, a new cholesterol screen indicates that she has much improved. A week later Dr. Smith receives the annual report of his performance: the clinical quality indicators and patient safety monitoring show another 5 point improvement. The patient satisfaction survey is at its usual high. When Dr. Smith sits to plan his next year needs-based goals for his continuous learning plan he chooses tropical and poverty medicine. The bonus he will receive will enable him to finally choose the medical relief to Africa he has been hoping to accomplish for some years now. To some readers, the above scenario may seem imaginary. However, the technologies which enable it have already been, or are being, developed. As this scenario demonstrates, the application of information and communication technology (ICT) in health care holds the promise to improve quality of care, as well as opening new ways to educate patients and engage them in their own care. However, there are also many challenges involved. In this chapter, we will review the present literature on the benefits of the computerized consultation, as well as the challenges and problems associated with it. We will discuss the fine line between benefits and risks of using the EHR during consultations, and demonstrate how the same features that contribute to efficiency may pose a risk or interfere with patient centeredness. Finally, we will discuss some of the strategies, best

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practices, and enabling factors that could enhance realization of the vision. In particular, we will point to the role that medical education should play in preparing practitioners for the challenges of the new, computerized, environment of 21st century medicine.

BACKGROUND The potential and actual outcomes of ICT in health care are often discussed at the systems level. In particular, impacts on quality of care and patient safety of ICTs such as the EHR, computerized provider order entry (CPOE) and clinical decision support systems (CDSS) have been examined. Table 1 describes these and some other commonly used clinical information systems. Although the majority of studies have emerged from a small number of large health care organizations such as the Department of Veterans Affairs (DVA) or

Kaiser Permanente in the US, a recent systematic review has concluded that health information systems can indeed improve quality of care and patient safety. Preventive care was the primary domain of quality improvement reported. The main benefits were increased adherence to guidelines, enhanced surveillance, and monitoring, and decreased medication errors. The major efficiency improvement was decreased utilization of care (Chaudhry et al., 2006). However, a growing number of studies - especially of CPOE systems - have drawn attention to some of the unintended consequences of ICT in health care that may be beneficial or adverse. Such unintended consequences include more or new work for clinicians, unfavorable changes in workflow, high system demands, problems related to paper persistence, untoward changes in communication patterns, negative emotions, new kinds of errors, unexpected changes in the power structure, and overdependence on the technology

Table 1. Types of commonly used clinical information systems. Type of system

Description

Electronic Medical Record (EMR)

An electronic record of health-related information on an individual that can be created, gathered, managed, and consulted by authorized clinicians and staff within one health care organization. The National Alliance for Health Information Technology (NAHIT), 2008

Electronic Health Record (EHR)

An electronic record of health-related information on an individual that conforms to nationally recognized interoperability standards and that can be created, managed, and consulted by authorized clinicians and staff across more than one health care organization. The National Alliance for Health Information Technology (NAHIT), 2008). The terms EMR and EHR are sometimes used interchangeably.

Personal Health Record (PHR)

An electronic record of health-related information on an individual that conforms to nationally recognized interoperability standards and that can be drawn from multiple sources while being managed, shared, and controlled by the individual. The National Alliance for Health Information Technology (NAHIT), 2008.

Computerized Provider Order Entry (CPOE)

A system which allows providers to post and communicate medical orders and their application. CPOE can facilitate patient safety and quality of care by means of eliminating illegible handwriting and use of predefined, evidence-based, order sets for specific medical conditions.

Clinical Decision Support Systems (CDSS)

Computerized systems which are designed to assist health care professionals in making clinical decisions (e.g. about diagnosis, treatment or care management). CDSS may be characterized by their intended function, the mode by which they offer advice, style of consultation and the underlying decision making process (Musen, Shahar & Shortliffe, 2006)

Picture Archiving and Communication System (PACS)

Computerized information system which is used to acquire, store, retrieve and display digital images; in particular diagnostic images such as x-rays, Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) images.

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(Campbell, Guappone, Sittig, Dykstra, & Ash, 2009; Campbell, Sittig, Ash, Guappone, & Dykstra, 2006). As system-level outcomes of information systems are thought to emerge from the combination of multiple individual impacts (DeLone & McLean, 1992; 2003), through the rest of this chapter we will explore the impact of ICT in health care at a micro-level, focusing on the patient consultation. In particular we argue that there is a fine balance between benefits and risks of using EHR during consultation, which is greatly influenced by cognitive factors, and that the computer has become a third actor in the clinical encounter and thus influences patient-doctor relationships.

THE COMPUTER ENABLED (OR DISABLED) CONSULTATION: AN IN-DEPTH LOOK Efficiency vs. Risk One of the most serious reported unintended consequences of CPOE systems is the generation of new kind of errors. It has been termed “e-iatrogenesis” (Weiner, Kfuri, Chan, & Fowles, 2007). A deeper look at how these new types of errors arise, demonstrates that the line between greater efficiency and error is often very thin, and that sometimes the same features and user characteristics that make work more efficient, and presumably safer, are those which generate computer-related errors. For example, in the EHR there are often dropdown lists, e.g. for patients’ names, diagnoses, or medications. Structuring data this way is very useful in that it enables data analysis for health management, administration, and research purposes. “Point and click” also eliminates the need to type in data, which is time consuming and may divert the doctor’s attention from the patient. Finally, these lists may serve as a quick reminder and thus minimize memory load (Shachak, Hadas-Dayagi,

Ziv, & Reis, 2009). However, the structured format of entering data into the EMR is very different from the narrative structure of the traditional patient record. It has been demonstrated that moving to an EMR system affected physicians’ information gathering and reasoning processes, and could lead to potential loss of information (Patel, Kushniruk, Yang, & Yale, 2000). Furthermore, experienced users perform selection from lists very quickly, in a semi-automatic manner. This automaticity, the term used in cognitive science to describe skilled performance that requires little or no conscious attention (Wheatley & Wegner, 2001), makes it very easy to accidentally select the wrong item. Two of the most commonly reported errors with a primary care electronic medical record (EMR) system—selecting the wrong medication and adding to the wrong patient’s chart—were associated, fully or in part, with selection from lists (Shachak et al., 2009). Similar errors were also reported with hospital CPOE systems, suggesting this is a universal problem (Campbell et al., 2009). Other examples are the use of copy-paste and user- (or system-) generated templates. The computer allows quick copying and pasting of data, e.g. from a previous to the present visit, or from one patient’s chart to another. Undoubtedly, copy-paste and templates make an efficient use of computers; however, they raise ethical questions and concerns about data quality (Thielke, Hammond, & Helbig, 2007). Moreover, copying and pasting poses the risk of inducing errors by accidental failure to update data which should be modified. In a cross-sectional survey held at two medical centers that use computerized documentation systems physicians’ overall attitude toward copying and pasting was positive. However, they noted that inconsistencies and outdated information were more common in copied notes (O’Donnell et al., 2009). Thielke et al. (2007) have reported a steep rate of copy-paste examination data in EHR, including notes copied from another author or from a document older than six months, which they defined as high risk.

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Similarly, templates may be inserted into the patient chart using keyboard shortcuts or a mouse click. Templates make work more efficient as they eliminate the need for extensive typing. Furthermore, if comprehensive templates are prepared they may serve as checklists that help clinicians make sure they have collected all required information, performed the necessary tests, or provided recommended treatment. However, the use of templates, too, can become semi-automatic. As in copy-paste, it is very easy to approve a template without correcting wrong data - “clicking enter, enter, enter is a prescription for error” as one of the physician participants in our study described it (Shachak et al., 2009). Finally, alerts of drug contraindications can be beneficial or detrimental. Clearly, notifying clinicians that the patient is allergic to the medication they had just prescribed, or that it interacts with another drug the patient takes, is important. A number of studies have attributed significant improvements in medication safety to the embedding of alerts in CPOE systems; these improvements include reduced numbers of medication errors and adverse drug events (Bates et al., 1999; Eslami, Keizer, & Abu-Hanna, 2007; Tamblyn et al., 2003). However, others have reported alert override rates greater than 80% in various systems and settings (Hsieh et al., 2004; Weingart et al., 2003). Why are alerts being overridden? Prescribers provide various reasons such as poor quality and high volumes of alerts, patients already taking the drug in question, and the monitoring of patients (Grizzle et al., 2007; Hsieh et al., 2004; Lapane, Waring, Schneider, Dube, & Quilliam, 2008; van der Sijs, Aarts, Vulto, & Berg, 2006; Weingart et al., 2003). A recent study also suggests that some specified reasons for alert override typed into a CPOE system were, in fact, failed attempts to communicate important medical information which no one has read (Chused, Kuperman, & Stetson, 2008). In addition to causing interference or increasing cognitive load, we propose that poor quality and too high a volume of alerts may result

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in formation of an automatic behavior to instantly shut down alerts, similar to the way users attempt to immediately close pop-up ads on websites (McCoy, Galleta, Everard, & Polak, 2004). This automatic behavior—or “alert fatigue” as van der Sijs et al. (2006) describe it—may accidentally result in overriding crucial alerts. How can the risk-benefit balance shift to the benefits side? A key component in systems improvement is usability and human factors engineering. User interfaces need to be designed and evaluated with an understanding of these cognitive elements, in an attempt to reduce effort on one hand and prevent automaticity-related errors on the other. Examples include drop-down lists with greater distance between items, or highlighting items on mouse-over just before selection. Alerts acceptance can be improved by tiering alerts on the basis of their severity, and designating only critical alerts as interruptive (Paterno et al., 2009; Shah et al., 2005). To some extent, the system itself can detect and prevent errors. For example, one of the systems we had checked accepted an input that a three year old patient had been smoking for 15 years. When a doctor makes this error, it is likely s/he is adding to the wrong chart. Such errors are preventable by better system design. Another key factor is education or training. Our findings suggest that experts are aware of these potential errors and, therefore, may be more careful when performing risky actions (Shachak et al., 2009). Education that goes beyond the technical aspects of using the system to a broader view of the benefits, risks, and principles of high-quality use is required. Simulation-based training, in particular, may be highly effective.

The Computer as a Third Player in the Patient-Doctor Encounter In our initial scenario, the computer is an active actor in the consultation. It triggers some of the discussion and information exchange, and acts as a colleague or advisor. Indeed, in computerized

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settings, the consultation cannot be viewed as dyadic patient-doctor interaction anymore. Rather, it is now triadic relationships of the patient, the doctor, and computer (Pearce, Dwan, Arnold, Phillips, & Trumble, 2009). The computer influences the consultation in various ways, beginning with the physical location of the monitor, keyboard, mouse, and other ancillary devices such as printers. The space these hardware occupy, their location, and orientation have significant influence on the consultation. For example, large fixed monitors may narrow the doctor’s or the patient’s personal space and interfere with eye contact. Even when it does not, the monitor in some settings is oriented towards the doctor only, which excludes the patient from the doctor’s interaction with the computer. For some patients, this may be disturbing (Frankel et al., 2005; Pearce, Walker, & O’Shea, 2008). However, the other option of a monitor that can be viewed by both doctor and patient is also not optimal for everyone. In our opening scenario, both Dr. Smith and Mrs. Jones seem to be comfortable with technology, so the computer becomes a useful tool to facilitate their discussion, share information, and help with decision making. However, this is not the case for all doctors and patients. As Pearce and others have demonstrated, whether the monitor’s position promotes information sharing or becomes distracting depends on both the doctor’s and patient’s styles (Pearce, Trumble, Arnold, Dwan, & Phillips, 2008). Perhaps an optimal solution is the use of flat LCD monitors on mobile arms, or using mobile hand-held devices, which allow greater flexibility. A positive influence of the EMR is greater information exchange (Shachak & Reis, 2009). EMR use was positively related to biomedical discussion, including questions about therapeutic regimen, exchange of information about medications, and patient disclosure of health information to the physician. Physicians who used an EMR were able to accomplish information-related tasks such as checking and verifying patient history,

encouraging patients to ask questions and ensuring completeness at the end of a visit to a greater extent than physicians who used paper records (Arar, Wen, McGrath, Steinbach, & Pugh, 2005; Hsu et al., 2005; Kuo, Mullen, McQueen, Swank, & Rogers, 2007; Makoul, Curry, & Tang, 2001; Margalit, Roter, Dunevant, Larson, & Reis, 2006). On the negative side, it is very hard for physicians to divide attention between the patient and the EMR. It has been demonstrated, that even within the first minute of the consultation, much of the interaction is driven by the computer, not by the patient’s agenda (Pearce et al., 2008). Physicians often walked straight to the monitor after only a short greeting. The average physician screen gaze was 24% to 55% of the visit time and it was inversely related to their engagement in psychosocial question asking and emotional responsiveness (Margalit et al., 2006; Shachak et al., 2009). The computer often caused physicians to lose rapport with patients; e.g. physicians typed in data or screen gazed while talking to patients or while the patient was talking. Our literature review also suggests that the computer’s potential to assist in patient education is underutilized (Shachak & Reis, 2009). How can we make the ideal future scenario come true, and overcome the negative influences of the computer on patient centeredness? Computer skills are important enablers. Blind typing, navigation skills, the use of templates and keyboard shortcuts, and the ability to organize and search for information were associated with physicians’ ability to effectively communicate with patients in computerized settings (Frankel et al., 2005; Shachak et al., 2009; Shachak & Reis, 2009). Similarly, physicians’ styles as well as their basic communication skills are highly important. Both Ventres et al. and Booth et al. identified relatively similar styles and suggested the computer’s impact on patient centeredness depended on them (Booth, Robinson, & Kohannejad, 2004; Ventres, Kooienga, Marlin, Vuckovic, & Stewart, 2005; Ventres et al., 2006). We have recently provided

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a unified classification of these styles (Shachak & Reis, 2009). The three styles in this unified classification are: 1. Informational-ignoring style. This style is characterized by extensive information gathering, that is often facilitated by the EMR, and focus on details of information. Physicians with this style often lost rapport with patients while engaged with the EMR, e.g. they frequently talked while gazing at the monitor, hardly faced the patients, and often left them idle while engaging with the computer. 2. Controlling-managerial style, which is characterized by separating computer use from communication. Physicians with this style alternated their attention between the patient and the computer in defined intervals or stages of the encounter. While with the patient, they turned away from the computer and vice versa. Switches of attention were often indicated by these physicians using non-verbal cues such as turning body or gaze. 3. Interpersonal style. This style is characterized by its focus on the patient. Physicians of this style oriented themselves to the patient even when using the EMR, did not usually talk while using the computer, and utilized the computer to share and review information together with the patient. They spent less time on data entry and usually refrained from using the computer in the beginning of the encounter. Along the same lines, Frankel et al. (2005) have suggested that the computer facilitates both negative and positive baseline communication skills. Some of the physicians we observed were able to compensate for their lack of computer mastery by excellent communication skills (Shachak et al., 2009). Additionally, strategies and best practices may be employed. The major strategy we have

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identified is dividing the encounter into patientand computer-focused stages that are clearly distinctive and indicated by body language and focus of gaze. Another is keeping the patients engaged by sharing the screen with them or reading out loud while typing (Shachak et al., 2009).

FUTURE RESEARCH DIRECTIONS As pointed out, education is important for enhancing safer use of computers during consultation. Education at all levels, from medical school to residency to continuing medical education, is also essential for acquiring and continuously improving computer and communication skills and learning effective strategies for integrating the computer into the patient-doctor relationships. Dr. Smith in our scenario was trained in using the EHR at the national simulation center. The training he received went well beyond the technical aspects of using the software, which are usually the scope of clinical information systems implementations. Rather, he learned how to avoid EHR-induced errors, accommodate for his own and various patient styles, employ strategies and best practices for patient-doctor-computer communication, and make optimal use of the computer for patient education and shared decision making. The development and evaluation of such educational interventions is one area for future research. Ten tips for physicians on how to incorporate the EMR into consultation had been suggested by Ventres, Kooienga, and Marlin (2006), and have been recently modified by us (Shachak & Reis, 2009). These tips include suggestions regarding the computer skills required of the physician, encounter management practices, and ways to engage the patient. However, as far as we can detect from the literature, educational modules of patient-doctor communication have not yet included the computer. As of February, 2008, we were able to find only two examples of instructional modules supporting the introduction

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of computers to clinical care settings (Institute for Healthcare Communication, 2008; Simpson, Robinson, Fletcher, & Wilson, 2005). We are currently in the process of developing and evaluating a simulation-based training intervention aimed at qualifying Family Medicine residents in incorporating the use of EMRs into consultations. A number of scenarios have been developed for training participants to identify the potential pitfalls, learn various strategies and skills for using an EMR during consultations, and employ them flexibly depending on the situation and the patient and physician styles. Technology also opens new ways to incorporate the computer into a seamless clinical encounter. Present systems rely heavily on text, but we can envision a multimedia EHR which combines image, text, sound, and video with technologies such as voice and handwriting recognition, automatic transcription, natural language processing, and automated tagging. These technologies can minimize the interference of data entry with communication, maintain a significant narrative component of the record in rich media that may reduce ambiguity (Daft, Lengel, & Trevino, 1987) and, therefore, support reasoning and decision making, while still enabling fast retrieval and data structuring for management and analysis. Multiple challenges are involved in the development of such multimedia EHRs including spoken and medical natural language processing, assigning metadata to videotaped medical interviews, language standardization, and semantic information retrieval. Furthermore, even if these technological challenges are overcome, numerous factors such as legal issues, cost, usability, time required to review information, and potential users’ perceptions of the system—especially concerns about privacy and confidentiality—may still hinder adoption of multimedia-EHRs. All of these challenges open multiple directions for future research. In our scenario Dr. Smith is conscious of the service the data he generates render for the

benefit of the larger population, and welcomes feedback on his data quality. However, we have not discussed the relationships between micro and macro levels, and particularly how the way that physicians document influences data quality and the use of data for clinical management, administration, and research. More research is required to better understand the micro-macro relationships. Finally, Web 2.0 applications such as RSS feeds, social networking, blogs, wikis, and Twitter open new ways of communication and knowledge exchange among patients and doctors. These applications can add a component of virtual consultation into the clinical practice. They also facilitate many-to-many instead of, or in addition to, the traditional one-to-one communication (Hawn, 2009; Jain, 2009; Kamel Boulos & Wheeler, 2007). In this chapter we have focused on the impact of integrating ICT into the traditional patient-doctor consultation. The effect of Web 2.0 applications on medical practice and consultations in particular, remains to be seen. As far as we can detect, there is a dearth in rigorous research with this focus. Most of the literature to date consists of opinions and personal reports from the field.

LIMITATIONS As the scope of this chapter is the medical consultation, the majority of research discussed emerged from ambulatory or outpatient settings. Acute care presents different challenges and employs much different workflow processes, though some of the challenges discussed here also apply (e.g. new types of errors—Campbell et al., 2009).

CONCLUSION The micro level perspective provided in this chapter reveals some of the processes that underlie the impacts of ICT on the health care system. Throughout this chapter we have demonstrated

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the thin line between beneficial and adverse consequences of using ICT during consultation. Human-centered design, medical education, as well as developing the technologies toward the multimedia-EHR have the potential to improve the individual-level impact of the computerized consultation.

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This work was previously published in Healthcare and the Effect of Technology: Developments, Challenges and Advancements, edited by Stéfane M. Kabene, pp. 72-83, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Quality and Reliability Aspects in Evidence Based E-Medicine Asen Atanasov Medical University Hospital “St. George”, Bulgaria

ABSTRACT This chapter is a brief survey on some e-medicine resources and international definitions focused on the three main subjects of the healthcare quality – the patient, the costs and the evidence for quality. The patients can find in e-medicine everything that they need, but often without data on the supporting evidence. The medical professionals can learn where to find e-information on cost, quality and patient safety, and, more importantly, how to distinguish claims from evidence by applying the principles of evidence based medicine. The goal is to spread and popularize the knowledge in this field with an emphasis on how one can DOI: 10.4018/978-1-60960-561-2.ch112

find, assess and utilize the best present evidence for more effective healthcare. The sites discussed below could assist in the retrieval of information about methods for obtaining evidence along with the ways of measuring evidence strength and limitations. These sites also provide information on implementing the ultimate evidence-based product – clinical guidelines for better medical practice and health service.

INTRODUCTION The international consensus document (CLSI, HS1-A2, 2004) emphasized that the top in the hierarchy of healthcare quality is the undivided of complete customer satisfaction at minimal cost

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and highest quality. The customer, who might be unsatisfied by healthcare quality or other reasons, may prefer to search the Internet for some alternative sources of health information. Looking for a solution to her/his own problem amongst hundreds of thousands of Webb sites, she/he might find exactly what is needed in “minimal cost” but sometimes with unknown quality. The health professionals can also find scientific basis for continuous education along with answers of almost all practical problems of their patients. However, information about the evidence strength and quality is not always available. This chapter shortly considers the three main subjects of total quality in healthcare - customer, cost, and evidence for quality, in the context of e-medicine. The goal is more people to obtain knowledge in this field and use properly the best present evidence. The patient can find in emedicine everything that she/he needs, but often without data on the supporting evidence. The medical professionals can be assisted to decide what kind of e-resources they are interested in and how they can learn more about evidence in medicine. The information concerning the achievement of basic medical science, specific regulations, laws, accreditation and healthcare audits remains beyond the scope of the chapter.

BACKGROUND A lot of e-medical information can be easily obtained through Internet and Wikipedia. Its usefulness, however, depends on the users’ willingness, behavior, knowledge and skills to distinguish a claim from actual evidence, and to users’ ability to use properly e-medical information. Although for some regions the access to e-resources might be a problem, Bratislava Declaration clearly outlined as a priority the validity and quality of electronic health information, education and training (UEMS, 2007).

THE CUSTOMER Most of us have been, are, or will be patients. The patient, a suffering human being, becomes “customer”, one of the numerous external and internal customers of the healthcare system (other patients, suppliers, institutions, factories, agencies, hospitals, doctors, nurses, pharmacists, technicians, and all other staff engaged in healthcare). As a customer, the patient is told that his/her welfare is paramount for the healthcare system. Thus, the patient-customer expects the best quality of help or, in other words, service. Being a customer and receiving service, the patient obtains the opportunity to actively assist the medical staff regarding his/her personal health. The result, however, is that the customer evaluates the health service rather than his/her health behavior. However, customer’s “satisfaction” with the quality of health service might be far away from the “evidence for quality”. Satisfaction is very subjective and cannot be objectively measured hence it is not the best end point for healthcare evaluation. Most patients today are well informed, but some prefer illusions in place of reality. People are not always able to make a clear distinction between personal satisfaction and healthy life style. The best health strategy for the society is not be the best approach for a single person. Any healthcare system needs money and can be easily destroyed by growing expectations of uncertain nature in an environment of limited and often badly managed resources. An organized group of active, even aggressive, patients might politically impose disproportional distribution of funds that otherwise might be spent more effectively for the advantage of more patients. Attractive new technologies, diagnostic instruments, tools and devices, new curative approaches and therapeutic drugs are often subject of commercial rather than medical interest. In such a complicated situation, the healthcare customers are looking at the Internet for health information because they want to obtain dependable

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service, communicate online with their physicians, talk for their health problems, receive interpretation for their laboratory or instrumental records and medications, or arrange an appointment. They are ready to browse pay-for-performance programs in which recognized physicians are committed to providing quality care and service. Such e-Medicine and e-Prescribing sources need support for delivering high quality and efficient healthcare focused on the physician as the core of the network. Improvement of the customer’s experience with a physician’s knowledge contains the providers’ medical costs. The providers of medical advice directly use the technology and should be accountable for patient care. Moreover,, the providers should guarantee service and system quality together with the quality of information for the ultimate consumer of e-healthcare – the patient (LeRouge, & al, 2004). Another cash service is online clinical conferencing. It offers consultations on many clinical cases. The patient tells his/her medical history and answers clinical questionnaires, including data of physical examination, laboratory findings, medication, and other relevant topics. The patient receives third party comments, and expert opinion about diagnosis, medication, or other questions in sites like [http://www.thirdspace.org], [Clinical Conferences Online.mht], and [e-medicine.com] which are continuously updated. Web-based health services, such as [WebMD], also provide health information, including symptom checklists, pharmacy information, blogs of physicians with specific topics, and a place to store personal medical information. [WebMD], the Magazine is a patient-directed publication distributed bimonthly to physician waiting rooms in the USA. Similar health-related sites include [MedicineNet] - an online media publishing company; [Medscape] - a professional portal with 30 medical specialty areas which offers up-to-date information for physicians and other healthcare professionals; [RxList] – providing detailed pharmaceutical information on generic and name-brand drugs.

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The web site formerly known as [eMedicine.com], created for physicians and healthcare professionals, is now [eMedicineHealth] - a consumer site offering similar information to that of [WebMD]. Most are last modified February 2009. Another useful site is MedHelp [http://www. medhelp.org/]. It delivers the opportunity for online discussion on different healthcare topics by a partnership with medical professionals from hospitals and medical research institutions. The site offers several forums of contacts - “Ask an Expert” for users’ questions, “Health Communities” for questions, comments, responses and support from other users, and “International Forums” on topics including addiction, allergy, cancer, cosmetic surgery, dental, and various other topics. The site Med-e-Tel [http://www.medetel.lu/ index.php] comprises e-health, Telemedicine and Health ICT as tools for service to medical and nurse practitioners, patients, healthcare institutions and governments. The site offers broad information on markets, various research and experience, and appears to be a gathering place, for education, networking and business aimed at a worldwide audience with diverse professional backgrounds. Another site destined for professionals is eMedicine - a separate medical website that provides regular clinical challenges comprising of a short case history accompanied by a visual cue in the form of a radiograph, ECG or photo, along with the clinical resolution for each case. The customer can click on one of the cases listed in the index, after which will leave the Global Family Doctor website and go to the eMedicine website [http://www.globalfamilydoctor.com/education/ emedicine/eMedicine.asp].↜eMedicine offers also a series of clinical problems in an interactive format with CME credit available after completion. E-medicine related sites are continuously updated and offer enormous information useful for everybody. For example, if one is concerned by healthy life styles, he or she may find information about which are the benefits of physical exercise, how to avoid undesirable events, how to incorpo-

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rate exercise in customer lifestyle, how to succeed with weight control, what to do for a healthy pregnancy and post-natal care. Another example is the Journal Bandolier Extra, [www.ebandolier. com]. The top ten eMedicine Case Studies can be seen in recently updated [http://www.who.is/ whois-net/ip-address/emedcine.net/]. One analysis of American Sociological Association [http://www.asanet.org] showed that the users should be capable of evaluating the quality of e-health information since the information might be inaccurate or out of date. The users also have to distinguish between commercial advertising and appropriate health information, and recognize potential conflicts of interest. The abundance of health information permits customers to assume more responsibility for their own health care, and, therefore, patients should be included in developing standardized quality assurance systems for online health information [http://www.emedicine. com/med/]. An important support of the patient needs is so cold Bratislava Declaration on e-Medicine. It was published by the Council of European Union of Medical Specialists (UEMS) on October 13th 2007. [www.uems.net]. The Declaration establishes that there is existing potential for improvement of e-medicine in the context of quality and the manner in which the patient’ care is provided. The priority of quality over the cost-efficiencies is advocated, and the electronic development, recording, transfer, and storage of medical data are accepted as useful and inevitable. The Declaration also recognizes the need of support for (1) further progress in the accessibility of medical information; (2) developing higher standards in medical qualification and specialisation; (3) promotion of improvements in the well-being and healthcare of persons. The Council of EUMS also emphasizes that the misuse of e-Medicine could damage persons, communities and countries, and can become a risk to the security of data, patient confidentiality, the medical ethics, and the law. “The principles of a patient’s privacy and confi-

dentiality must be respected, and only patients have the right voluntarily to decide to have their data held in storage” – the Declaration says. In the context of the electronic recording, transfer and storage of medical information, the Council will make an effort, through registration and validation procedures, to implement, promote, develop and control future development of the fields •

• • •

respect for the security and privacy of persons, and the rights and laws governing these; respect for medical ethical principles; the validity of electronic health information; the quality of electronic medical education and training (UEMS, 2007).

Patients’ values and preferences are important and might differ markedly from those of physicians.

COSTS It is always useful to keep cost in mind, especially in times of financial crisis. An analysis of the first generation e-health companies showed that some of them lost billions in value during the early 2000 and failed to build a profitable business. Four most important factors in predicting the success or failure of an Internet healthcare company are identified - compelling value, unambiguous revenue model, competitive barriers, and organizational structure for cost control. Companies that make certain that they meet all or most of these factors, will have a better chance for success with a unique and valuable product and disciplined spending. Three factors introduce more challenges (Itagaki, & al, 2002). Some companies have a more impressive array of e-health properties that allow them to offer a bulk of services, earn enough revenue, and continue operating successfully. However, today’s economic situation introduces a great

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deal of uncertainty into their future. The success depends on the stability of users’ interest and how long their cash reserves will last. Few industries can sustain the rapid economic downturn. However, despite the people less income, some medical supply industries continue to grow. An example is Total e-Medical - a physician-supervised supplier of diabetic testing materials, durable medical equipment, respiratory care, and arthritis and pain management products. The company realizes unprecedented growth of revenue, hired new employees in the past months, and intends to continue its workforce expansion [www.totalemedical.com], last updated 13.02.2009. On February 16, 2009 it was announced that as much as $21 billion in the economic stimulus package is designated for IT for medical records in the USA. The funds will help health care providers to implement such IT systems, which are expensive and difficult to deploy. About $3 billion will be directed to help health care providers buy IT systems while an additional $18 billion would be covering for additional Medicare and Medicaid payments to health care providers who use technology to improve patient care. Hospitals could receive up to $1.5 million while physicians would qualify for about $40,000 over several years. The hope is that the potential for e-health to improve medical care remains excellent. This is important because the improvement can produce savings for healthcare and profits for enterprising start-ups. The prospect that any person might have his/her personal e-medical record would be a realistic goal (Kolbasuk, & McGree, 2009). The e-medicine needs governmental support to improve the delivery of healthcare services to the elderly and poor customers. It is estimated that e-medicine or telemedicine can reduce about 60% of the on-site care cost by using e-mail, faxes and telephone consultations to link patients with health-care providers. It also improves access to health-delivery services in rural areas with shortages of doctors and hospitals. Instead, some

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insurance agencies reimburse only “face-to-face consultations” – an expensive type of communication between patient and the doctor. Telemedicine was proven especially effective in Norway where even the installation of costly equipment turned out to be cheaper than flying doctors into remote regions [http://www.emedicine.com/med/]. The described events concern costs of e-health and e-medical information. However, the decisions about costs of the real health care are much more complicated and concern large parts of the society. Health professionals, especially governmental experts, should be familiar with economical analyses which allow objective choice between different health interventions. Unfortunately, the different economic analyses are often not well understood and they rarely assist in a political discussion or in the development of national health strategies. The cost-effectiveness analysis compares different health interventions by their costs and obtained health effect. It helps to assess which health intervention is worth to be implemented from the economic point of view. The costs are expressed in monetary units, and the health effect in life-years gained. The cost-effectiveness analysis, therefore, followed by sensitivity analysis (which tests the impact of best case and worse case scenarios) becomes a part of the decisionmaking process. The cost-utility analysis (utility is a cardinal value that represents the strength of an individual’s preferences for specific outcomes under condition of uncertainty) is applied when the healthcare area that is likely to provide the greatest benefit has to be identified. The cost-profit or cost-benefit analysis is useful when information is needed to assess which intervention will result in resource savings (that both sides of the equation are in same monetary units). A clear and very useful explanation of these analyses and their terminology can be found in the series named “What is?” sponsored by an educational grant from Aventis Pharma [www. evidence-based-medicine.co.uk], and, in a more

Quality and Reliability Aspects in Evidence Based E-Medicine

sophisticated version, in [http://www.cche.net/ usersguides/main.asp]. The economical analyses, along with survival analysis (Cox model) and many other methods are powerful tools that produce evidence. Although the educational material in the shown sites is only instructive, the references exist that explain the methods, calculations, and limitations, of each approach. Costs in medicine remain an important issue and now are incorporated in any decision analysis and clinical guideline.

QUALITY The following short list of common definitions is useful for better understanding of quality in health care and medicine: •

•

•

•

•

Quality – “Degree to which a set of inherent characteristics fulfills requirements” (ISO 9000, 2000, 3.1.1) Quality assurance – “Part of quality management focused on providing confidence that quality requirements will be fulfilled” (ISO 9000, 2000, 3.2.11). “Quality assurance indicates the quality of process performance” (HS1-A2, 2004). Quality control – “Part of quality management focused on fulfilling quality requirements” (ISO 9000, 2000, 3.2.10). “Quality control indicates the quality of procedural performance” (CLSI, HS1-A2, 2004). Quality improvement – “Part of quality management focused on increasing the ability to fulfill quality requirements (ISO 9000, 2000, 3.2.12). “Continuous quality improvement indicates how to achieve sustained improvement” (CLSI, HS1-A2, 2004). Quality management – “Coordinated activities to direct and control an organization with regard to quality (ISO 9000, 2000, 3.2.8).

•

•

•

Quality management system – “Management system to direct and control an organization with regard to quality” (ISO 9000, 2000, 3.2.3). “Quality management system will support all operational paths of workflow” (CLSI, HS1-A2, 2004). Quality policy – “Overall intentions and direction of an organization related to quality as formally expressed by top management” (ISO 9000, 2000, 3.2.4.) Reliability - the consistency with which the same information is obtained by a test or set of tests in the absence of intervening variables (Tudiver, & al, 2008).

Good quality of healthcare service would not be obtained without serious administrative support. A lot of documents of the Clinical and Laboratory Standards Institute (CLSI, former National Committee for Clinical Laboratory Standards, NCCLS), USA, are developed through a considerable consensus process. After worldwide expert discussion, each document is disseminated for global application [www.nccls.org]. Several CLSI documents describe, in detail, how a health organization that wishes to implement a quality management system can succeed. “These specialized documents are designed for any healthcare service manager who wishes to improve the processes involved in creating customer satisfaction by implementing proven standardized quality system concept” /GP22-A2, 2004, p. 7/. Continuous quality improvement (CLSI, GP22-A2, 2004) is a model focused specifically on the implementation of clinical service quality system management. It represents any healthcare service as working within two forms of infrastructural quality system matrices. The external matrix is created by external guidance from local and governmental administrative-level policies, processes and procedures. The internal matrix is built up from intrinsic quality system managerial-level policies, processes, and procedures. The internal

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matrix contains the quality system and underlines service operations rather than healthcare operation. Another document (CLSI, HS1-A2, 2004) represents organizational hierarchy of quality administration in healthcare establishments. Quality control is the starting level. It is considered as an operational process control technique for measuring the effectiveness of the procedural performance that is expected to fulfill requirements for quality and governmental compliance. It needs quality indicators like precision, accuracy and other statistical variables. Quality assurance is the next level that measures the effectiveness of process performance and provides confidence that the quality requirements are fulfilled. Implementation of a quality management system is the crucial level - the practice facilitating systematic process-oriented improvement. All these levels are included in quality cost management that adds the economic activities – “cost of quality”. The highest level is the total quality management. It identifies the cost of quality for obtaining total quality management and insures sustainable high quality “by focusing on long-term success through customer satisfaction” (CLSI, HS1-A2, 2004, p. vii). In the above motion directed “upstairs” five key activities are implemented in a continuous spiral - quality planning, quality teamwork, quality monitoring, quality improvement and quality review (Plan-Do-Check-Act). The results of the last quality review represent the basis of the next cycle towards the next quality level. All necessary activities have a detailed description in the original document (CLSI, HS1-A2, 2004). The administration of a healthcare establishment, if decided, should surmount step-by-step the hierarchical ladder, from the lowest level of quality to the highest– total quality management – “An organization-wide approach centered on ongoing quality improvement as evidenced by total customer satisfaction and minimal cost of maximized quality” (CLSI, GP22-A2, 2004, p. xii).

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Recently, the MARQuIS project (2009) outlined the EU perspectives in five papers. They commented quality improvement strategies for European cross-border healthcare, national quality improvement policies and strategies, applications of quality improvement strategies in European hospitals, the results of the MARQuIS project, and the future direction of quality and safety in hospital care in EU. However, the lack of staff motivation is able to transform all administrative efforts for high quality in empty formalism and bureaucratic curtain. If medical professionals understand, joint and support the administrative efforts for implementation of quality management system, the team surely will succeed. In fact it is too early to discuss the effectiveness of this systematic approach towards continuous improvement of the healthcare service. More experience should be obtained for a frank assessment of the described administrative approach for obtaining high quality in healthcare.

Quality Indicators Measurement of the quality is essential for its improvement. The tools intended to measure the quality of healthcare in fact measure the evidence for quality and confirm that a desirable level is obtained. •

•

Quality indicators are „observations, statistics, or data defined by the organization or service that typify the performance of a given work process and provide evidence that the organization or service is meeting its quality intentions” (AABB, 2003). The thresholds are “statistical measure of compliance of the specific indicator for acceptable outcome” (CLSI, HS1-A2, 2004).

Quality indicators are objective measures for analysis of quality achievement in health service. Their implementation is an attainable, realistic

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goal for assessing compliance and quantifying improvement. Some quality indicators concern patient preferences and are obtained by patient interviews. These indicators are important for identification of gaps in quality at the population level and can be useful for improvement of health service. These are mainly indicators related to pain, symptom management and access to care. With palliative care and emergency room visits, they are identified as acceptable for assessment of quality. The structure indicators are, for instance, the patient-to-nurse ratio, the existing strategies to prevent medication errors, the measurements of patient satisfaction. The process indicators give a quick view of the quality of care. When indicators such like the length of stay in the hospital, duration of mechanical ventilation, the proportion of days with all beds occupied, the proportion of glucose measurement higher than 8.0 or lower than 2.2 mmol/L, are applied to an intensive care unit, they can characterize the quality of total activity of the unit (Steel, & al, 2004). The outcome indicators are the standardized mortality, the incidence of decubitus, the number of unplanned extubations and others. In each case, however, it is important to assess the validity of data used because the quality ranking of the hospitals depends not only on the indicators used, but also on the nature of the data used. By means of the same indicator, hospitals classified as highquality using routine administrative data have been reclassified as intermediate or low-quality hospitals using the enhanced administrative data (Glance, & al, 2008). The authors emphasize the need to improve the quality of the administrative data if these data are to serve as the information infrastructure for quality reporting. A recent study (Grunfeld, & al, 2008) assesses stakeholder acceptability of quality indicators of end-of-life care that potentially are measurable from population-based administrative health databases. A multidisciplinary panel of cancer care health professionals, patients with metastatic

breast cancer and caregivers for women died of metastatic breast cancer, is used to assess acceptability among the indicators for end-of-life care. The authors conclude that patient preferences, variation in local resources, and benchmarking should be considered when developing quality monitoring systems. Those quality indicators that stakeholders perceive as measuring quality care will be most useful. The acceptance of quality indicators in EU is a subject of another study (Legido-Quigley, & al, 2008). It is noticed that a few EU countries have adopted quality indicators, possibly because some contradictions in EU healthcare policy. Health care is a responsibility of the member states, but the free movement of healthcare customers is regulated by the European law. Some initiatives on quality are coming from national governments but others are from health professionals and providers; some solutions are offered by EU laws but others are entirely nationally based. The following convincing example is shown as evidence for the large variations in the perception of the meaningfulness of quality indicators: in Denmark, the quality of care provided by hospitals for six common diseases (lung cancer, schizophrenia, heart failure, hip fracture, stroke, and surgery for acute gastrointestinal bleeding) is measured for hospital assessment; in UK the performance of general practitioners is assessed with the quality and outcomes framework of about 140 measures developed through evidence and professional consensus. Another very important side of the problem deserves attention: “Quality indicator systems have been criticised for focusing on what is easily measured rather than what is important, and for being used in ways that encourage opportunistic behaviour, either by manipulating data or changing behaviour to achieve targets while compromising care”. (Legido-Quigley & al 2008). The best currently available evidence for quality of any medical care might be the evaluation of patient outcomes. The general criteria for conducting studies on patient outcomes are described

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in a CLSI document (CLSI HS6-A, 2004) and deserve more attention. The document is focused on the planning, conducting and reporting patient outcomes research. It provides a brief review on research methodology of primary patient outcome studies such as observational studies (surveys or cross-sectional studies, case-control studies and cohort studies) and interventional studies (randomized controlled trials and nonrandomized studies). It outlines the role of systematic overviews, meta-analyses, decision analyses, cost-effectiveness analyses and simulations along with a limited reference to some published methodological sources. A good example of the rules implementation on an outcome study is the PATH project (Veillard, & al 2005). It is currently implemented as a pilot project in eight EU countries in order to refine its framework before its further expansion. The project describes the outcomes achieved, specifically the definition of the concept and the identification of key dimensions of hospital performance. It also designs the architecture directed to enhance evidence-based management and quality improvement through performance assessment, selection of the core and tailored set of performance indicators with detailed operational definitions. It identifies the trade-offs between indicators and elaborates on the descriptive sheets for each indicator in order to support the hospitals in interpreting their results, designing a balanced dashboard, and developing strategies for implementation of the PATH framework. The future implementation of this project could be of significant interest for grading of hospitals by quality and effectiveness.

Patient Safety The high quality and reliability in health care are inaccessible without suitable activity to insure patient safety. Recently the General Secretariat of the Council of the EU sent a recommendation on patient safety, prevention and control of

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healthcare associated infections to the Working Party on Public Health [Interinstitutional file 2009/0003 (CNS) 6947/09, 27 February, 2009]. The document offers the important definitions concerning patient safety: • •

•

•

Adverse event – “incident which results in harm to a patient”; Harm – “impairment of structure or function of the body and/or any deleterious effect which arises from that”; Healthcare associated infections – “diseases or pathologies (illness, inflammation) related to the presence of an infectious agent or its products as a result of exposure to healthcare facilities or healthcare procedures”; Patient safety – “freedom for a patient from unnecessary harm or potential harm associated with healthcare”.

The draft document notices an estimation showing that between 8% and 12% of patients in EU Member States admitted to hospitals suffer from adverse events whilst receiving healthcare. That is why the document is focused on the expected action and national policies directed towards improving public health, preventing human illnesses and diseases, and obviating sources of danger to human health: “Community action in the field of public health shall fully respect the responsibilities of the Member States for the organisation and delivery of health services and medical care” the document says. The aim is “to achieve result-oriented behaviour and organizational change, by defining responsibilities at all levels, organizing support facilities and local technical resources and setting up evaluation procedures” [Interinstitutional file 2009/0003 (CNS) 6947/09, 27 February, 2009]. It is also emphasized that the realization of these recommendations needs dissemination of the document content to healthcare organisations, professional bodies, and educational institutions

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along with improvement of the patient information. The document suggests that each institutional level should follow the recommended approaches. This would ensure that the key elements are put into everyday practice and the patient safety is receiving proper attention.

EVIDENCE During the last decades several powerful methods for the distinction of evidence from claims in medicine were effectively disseminated. Evidence can be measured and assessed. For example, the accuracy of the diagnostic tests can be measured by their diagnostic sensitivity (the probability of a positive result amongst target persons) and diagnostic specificity (the probability of a negative result amongst control persons); the post-test probability of disease can be calculated by Bayesian approach from pre-test probability and the new information from the diagnostic test; the ratio of the probability that a given test result is met in a target (ill) person to the probability that the same result is met in a control (healthy) person, is given by the likelihood ratio (LR); the various kinds of effects (risks) for treated and control populations (groups) could be evaluated by the odds ratio; the treatment effect – by the number needed to treat (NNT), that is the inverse of the absolute risk reduction and is translated as “how many patients should be treated to prevent an event”. There are many others. Most of these useful methods can be found in the book-series Clinical Evidence [www.clinicalevidence.com], Wikipedia and others Internet or published resources related to clinical epidemiology. There are other methods that have become more sophisticated. For example, summarizing the results of a number of randomized trials and other independent studies can be done by the statistical technique called meta-analysis. It is used mainly to assess the clinical effectiveness of healthcare interventions as well as to estimate precisely the

treatment effect. Meta-analysis is usually applied in systematic reviews, but as any method has some limitations. The outcome of a meta-analysis will be influenced by the inclusion or exclusion of certain trials, and by the degree of adherence to the rigorous standards for the eligibility criteria explicitness and appropriateness that should characterize a meta-analysis (Fried, & al, 2008). Another example for gathering evidence is the assessment of the future effect of a medical action by decision analysis. It includes building a decision tree, obtaining probabilities and utilities for each outcome, evaluating the outcomes, and performing a sensitivity analysis to compare alternative health strategies (Detsky, & al. 1997, [www.cche.net/usersguides/main.asp], [www. evidence-based-medicine.co.uk] and others). How to obtain evidence for healthcare costs through clinical economic analysis and measurement of the healthcare quality by quality indicators was already discussed. However, any calculations have limitations and need valid primary data.

EVIDENCE BASED MEDICINE Evidence-based medicine (EBM) is the conscientious, explicit and judicious use of the best current evidence in making decisions about the care of individual patients (Sackett, & al 2000). In this context ”best” means that the quality of the evidence can be measured, and “current” means that the best evidence today might not be the best tomorrow. EBM instructs clinicians how to access, evaluate, and interpret the medical publications. The clinician can find the relevant answer of the patient problem by performing critical appraisal of the information resources,. After the decision is taken and best health service applied to the patient, the clinical performance can be evaluated and continually monitored. The goal of this process is to improve patient care by more effective and efficient use of clinical information, including

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diagnostic and prognostic markers, and to ensure effective and safe treatment in compliance with individual patient preferences. EBM also offers an approach to teaching the practice of medicine. Centre for Health Evidence maintains (on behalf of the Evidence-Based Medicine Working Group) the full text of the series “Evidence-Based Medicine: A New Approach to Teaching the Practice of Medicine” [http://www. cche.net/usersguides/main.asp]. The series was originally published in the Journal of the American Medical Association (JAMA) between 1992 and 2000 years and was last updated on August 15, 2007. The educational materials have been enhanced and re-introduced in a new interactive website [http://www.usersguides.org]. Access to the site is available by subscription through JAMA. In the „Users’ Guides to EBM”, the interested medical professional can find a step-by-step explanation how to use electronic information resources, how to select publications that are likely to provide valid results about therapy and prevention, diagnostic tests (including Bayesian approach and likelihood ratios), harm and prognosis, how to use a clinical decision analysis, clinical practice guidelines and recommendations, and many other basic principles of EBM. A study assessing the efficiency of EBM teaching shows the growing popularity of EBM education, practice teaching and evaluation of learning methods, but also indicates the lack of data on the application of the obtained EBM skills in the clinical practice. It is noticed also that users’ perception for the main barriers to EBM practice is the finding of contradictory results in the literature, the insufficient knowledge of English language, the limited access to PCs, and the lack of time and institutional support (Gardois, & al, 2004). The Objective Structured Clinical Examination (OSCE) measures the family medicine resident lifelong learning skills in evidence-based medicine as an important part of the evaluation of physician skills. The authors describe an innovative assessment tool for evaluating the skills of EBM. It was

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found that the competencies are overlapped with well known major skills such as translation of uncertainty for an answerable question, systematic retrieval of best evidence available, critical appraisal of evidence for validity, clinical relevance and applicability, and application of results in practice. These skills are tested in a simulated OSCE. The results show that the development of standardized tools for assessing EBM skills is an essential part of the evaluation of physician skills. They also emphasize the importance of the physician’s understanding of the different levels of evidence (Tudiver, & al 2008) An interesting article describes the development and implementation of an evidence-based practice model named CETEP (Clinical Excellence Through Evidence-Based Practice). The model provides the framework for a process that can be easily adapted for use as a tool to document the critical appraisal process. It is focused on nurse’s activities that lead to evidence-based practice changes and can be adapted for use in any healthcare organization. CETER emphasizes the process evaluating the applicability of the evidence for the clinical practice. It is shown that using research to incorporate essential components into clinical practice needs serious consideration before changing practice at the bedside. The article encourages nurses to conduct research and use evidence that will have meaning for their practice. It is expected that addressing practice problems or evaluating changes in practice with this model will be empowering. Translation of research to practice is more complex than simply writing a policy or procedure based on a research study and expecting the staff to comply. Successful integration of research into practice requires critical appraisal of study methods and results, and most importantly, consideration of the applicability of the evidence to the particular clinical setting (Collins, & al, 2008). Table 1 gives a list of sites that are useful for any reader interested in the principles and application of EBM.

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Table 1. Some websites, with information related to EBM http://www.ahrq.gov/clinic http://www.jr2.ox.ac.uk/bandolier/ http://cebm.jr2.ox.ac.uk/ http://www.clinicalevidence.com http://www.cochrane.org/ http://www.infopoems.com http://www.ICSI.org http://www.guidelines.gov

http://www.acponline.org/journals/acpjc/jcmenu.htm http://www.ceres.uwcm.ac.uk/frameset.cfm?section=trip http://www.ebponline.nehttp://agatha.york.ac.uk/darehp.htm http://www.york.ac.uk/inst/crd/ehcb.htm http://www.evidence-basedmedicine.com http://medicine.ucsf.edu/resources/guidelines http://www.ahrq.gov/clinic/uspstfix.htm http://www.york.ac.uk/inst/crd/

EBM practice starts by converting the need for clinical information into answerable questions. The answers are researched through e-information and library resources. If the Internet is accessible, a lot of library resources become available. A simple enumeration includes MEDLINE, Cochrane Databases of Systematic Reviews and Clinical Trials, DARE, ACP Journal Club, InfoRetriever, InfoPoems, UpToDate, DynaMed and others. The readings found are critically appraised for validity, clinical relevance, and applicability of the evidence. The application of the obtained results in practice and monitoring the effects are focused on the patient. When researching the evidence one should keep in mind the difference between POEM (Patient-Oriented Evidence that Matters) and DOE (Disease-Oriented Evidence). POEM deals with outcomes of importance to patients, such as changes in morbidity, mortality, or quality of life. DOE deals with surrogate end points, such as changes in laboratory values or other measures of response (Slawson & Shaughnessy, 2000). The preferable readings in researching the evidence are the high quality systematic reviews that can be found mainly in The Cochrane Database of Systematic Review, The Cochrane Controlled Trails Register, The Cochrane Review Methodology Database [www.york.ac.uk/inst/crd/ welcome.htm], [www.cebm.jr2.ox.ac.uk], [www. hiru.macmaster/ca/cochrane/default.htm], [www. ich.ucl.ac.uk/srtu] and other sites. The systematic reviews are an important source of evidence because their authors take care for finding all relevant studies on the question, as-

sessing each study, synthesizing the findings in an unbiased way, very often after performing a meta-analysis. Expert critical appraisal about the validity and clinical applicability of systematic reviews can be found in the Database of Abstracts of Reviews of Effects (DARE), at the Website of the Centre for Review and Dissemination, University of York [www.york.ac.uk/inst/crd/welcome. htm], the sites shown in Table 1, as well as in [www.mrw.interscience.wiley.com/cochrane/ cochrane_cldare_articles_fs.html]. The next preferable readings as sources of evidence are randomized controlled trials (RCT) – the most appropriate research design for studying the effectiveness of a specific intervention or treatment. There is considerable information about how to plan, perform, and report a RCT as STARD checklist of items to include in diagnostic accuracy study (Bossuyt, & al, 2003), and in a randomized trial (Altman, & al, 2001). All other study designs, especially already mentioned outcomes studies (CLSI HS6-A, 2004) can also produce high quality evidence. The findings of systematic reviews, RCT and other studies provide the evidence for the development of clinical guidelines based on the principles of evidence-based medicine. The clinical guidelines are destined to bridge the gap between research and practice, to base the clinical decisions on a research evidence, and to make this evidence available globally. The clinical guidelines are systematically developed statements designed to help practitioners and patients to decide on appropriate healthcare regarding specific clinical conditions and/or circumstances. Guidelines de-

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velopment, legal implications, implementations, and evaluation, are described in „What is?” series [www.evidence-based-medicine.co.uk], in [www. healthcentre.org.uk/hc/library/guidelines.htm], [www.nice.org.uk], and some others sites. The guideline development follows the principles of EBM. It starts with a clear definition of key questions about patients involved, alternative strategies, clinical outcomes, and methodological quality of the available evidence. A systematic review of each question should be available, and the evidence in the guideline should be arranged by their levels of power - from randomized controlled clinical trials and basic clinical research to well-conducted observational studies focused on patient-important outcomes and inferences. Nonsystematic clinical studies and expert opinion also are acceptable. A concise, continuously updated, and easy-touse collection of clinical guidelines for primary care, combined with the best available evidence, is

offered by [http://ebmg.wiley.com]. The collection covers a wide range of medical conditions with their diagnostics and treatments. The high-quality evidence is graded from A (that means “strong evidence exists and further research is unlikely to change the conclusion”) to D (that means “weak evidence and the estimate of effect is uncertain”). All reviews cited in the site are coming from The Cochrane Database of Systematic Reviews. It is likely that a complete source of clinical guidelines is the National Guideline Clearinghouse, United States [http//www.guideline.gov/ index.asp]. As recommended by EBM (Sackett, & al, 2000), each guideline contains a description of the methods used to collect and select the evidence and the rating scheme for the strength of this evidence. Such a rating scheme, used in the diabetes mellitus guideline (revised 2007), is given in Table 2 as an example [http//www. guideline.gov].

Table 3. Evidence hierarchy scheme [http//www.guideline.gov] from the guideline of Diabetes mellitus (with small modifications) LEC*

Study Design or Information Type

Comments

1.

RCT** Multicenter trials Large meta-analyses With quality rating

Well-conducted and controlled trials at 1 or more medical centers Data derived from a substantial number of trials with adequate power; substantial number of subjects and outcome data Consistent pattern of findings in the population for which the recommendation is made – generalizable results Compelling nonexperimental, clinically obvious evidence (e.g., use of insulin in diabetic ketoacidosis); “all or none” evidence

2.

RCT Prospective cohort studies Meta-analyses of cohort studies Case-control studies

Limited number of trials, small number of subjects Well-conducted studies Inconsistent findings or results not representative for the target population

3

Methodologically flawed RCT Nonrandomized controlled trials Observational studies Case series or case reports

Trials with 1 or more major or 3 or more minor methodological flaws Uncontrolled or poorly controlled trials Retrospective or observational data Conflicting data with a weight of evidence unable to support a final recommendation

4

Expert consensus Expert opinion based on experience Theory-driven conclusions, Unproven claims, Experience-based information

Inadequate data for inclusion in level-of-evidence categories 1, 2, or 3; data necessitates an expert panel’s synthesis of the literature and a consensus. *Level-of-Evidence Category, **Randomized Controlled Trials.

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According to the principles of EBM, guidelines for clinical practice are also subject of critical appraisal and continuous improvement. For example, the recommendations made in the diabetes mellitus guideline (edition 2000) were found to be in agreement when concerning the general management and clinical care of type 2 diabetes, but some important differences in treatment details were noticed. The influence of professional bodies such as the American Diabetes Association was seen as an important factor in explaining the international consensus. It was also noticed that the globalisation of recommended management of diabetes is not a simple consequence of the globalisation of research evidence and deserves more attention (Burgers, & al, 2002). Sometimes, the principles of EBM might escape the authors of clinical guidelines. The core question of the guideline may not be clearly defined, the outcomes of interest may not be explicit, the systematic reviews may be missing, the quality of the primary studies may contain different kinds of bias or patient values and preferences might be neglected. As a result, the quality of the guidelines may be compromised. On the other side, data from clinical trials are often focused on specific patient populations and conditions. As a result, patient safety and trial efficiency are maximized, but may not reflect the majority of patients in clinical practice. The information obtained from such an artificial environment to clinical practice can be problematic if transferred directly to the clinical practice Nevertheless the usefulness of the clinical guidelines was appreciated from medical professionals soon after their dissemination. The existing considerable variations in resources, needs and traditions, stimulated many countries to establish their own processes for development or adaptation of clinical guidelines. In 2001, the Council of Europe developed a set of recommendations for producing clinical guidelines and for assessing their quality. The international collaboration created the European

research project PL96-3669 (AGREE, Appraisal of Guidelines, Research and Evaluation in Europe). The efforts of researchers from Health Care Evaluation Unit at St George’s Hospital Medical School in London, Guidelines International Network (an international network for guidelines developing organisations), policy makers, and others have contributed substantially for creating an international collaboration for implementation of the project. The project ultimate goal is to improve the quality and effectiveness of clinical practice guidelines by the introduction of an appraisal instrument, development of standard recommendations for guideline developers, programmes, content analysis, reporting, and appraisal of individual recommendations. The development programme concerns guidelines on asthma, diabetes and breast cancer. All of the guidelines are based on the principals of EBM and are presented in [www.agreecollaboration.org/instrument/] and [http://www.agreecollaboration.org]. In connection with the AGREE, several studies show that guidelines on the same topic may differ, possibly due to different medical practice, insufficient evidence, different interpretations of evidence, unsystematic guideline development methods, influence of professional bodies, cultural factors such as differing expectations of apparent risks and benefits, socio-economic factors, cultural attitudes to health or characteristics of health care systems resources, and patterns of disease (Cluzeau, & al, 2003; Burgers, & al, 2003; Berti, & al, 2003). In such countries, a guideline developed on the basis of the best current evidence and prepared for global dissemination, might appear irrelevant (Fried, & al, 2008). Several sessions of AHRQ (an USA Federal Agency for Healthcare Research and Quality) are also directed towards assessment of the guideline implementation. The AHRQ website [www.ahrq. gov] contains high quality information for Clinical practice guidelines, Evidence-based practice, Funding opportunity, Quality assessment, Health IT Home, and others. The critical role of guideline

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implementation is explored. The common conclusions are that access to knowledge must be close to the point of decision and that the importance of guideline implementation into practice depends on using recommendations at the right time for the right patient. The same information may be delivered in different manners and may lead to different results (Slutsky, 2008). Obviously, the opportunity to obtain the relevant information on time at the point of decision-making might influence critically the patient outcomes. On the other side, national evidence-based guidelines are missing in some countries and optimal evidence is often not available for many clinical decisions. Another study considers an important and sometimes contradictory aspect of evidence-based medical practice – rational prescribing. It is shown that many factors other than evidence drive clinical decision-making. Amongst them are patient preferences, social circumstances, disease-drug and drug-drug interactions, clinical experience, competing demands from more urgent clinical conditions, marketing or promotional activity, and system-level drug policies. For example, despite the availability of trials and strong evidence for the positive effect of pravastatin and simvastatine, the new statines with less evident effects are often prescribed. It is likely that the lack of time and training for evidence evaluation allows some well-intentioned clinicians to be influenced by promotional information at the time of pharmaceutical product launch (Mamdani, & al, 2008). The possible interference of industry in clinical trials is well summarized in an expert article. It is shown that the priorities of the pharmaceutical industry, in terms of disease targets and the entities that are chosen to test might not reflect the needs of patients or the community. “The goals of industry can, therefore, become the hidden agenda behind the generation of much of the evidence that is used to construct guidelines. The profusion of clinical trials in lucrative diagnostic categories is testament to this phenomenon” (Fried, & al, 2008).

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Whatever a guideline is, the clinician has to translate the available evidence to the management of an individual patient. However, the patient conditions are complex and often presented with chronic or co-morbid complications for which a suitable guideline may not exist. The real patient can be very different from the population included in clinical trials and the real clinician is, in fact, practicing an “opinion-based medicine” (Hampton, 2002). The evidence-based medicine has made a substantial impact on medical research and practice of medicine through the clinical guidelines. However, the need of regular recalls of EBM concepts and skills becomes necessary for the critical appraisal, validity, and applicability of the guidelines. Only evidence-based recommendations may consider the balance between benefits, risks and burdens, and weigh these considerations using patient and societal, rather than expert, values. With the aim to avoid discrepancies some organizations use different systems to grade evidence and recommendations. As a result, the current proliferation and duplication of guidelines is wasteful (Townend, 2007). A revision of the current practice for development and dissemination of guidelines seems to be adequate for better understanding and easier application. It is proposed that a uniform grading system (GRADE) achieve widespread endorsement. Then, specialty groups can produce a central repository of evidence that is regularly updated with the available systematic reviews. “From this central resource, regional guidelines could be developed, taking into account local resources and expertise and the values and preferences important in that population” (Guyatt, & al, 2006; Fried, & al, 2008),(www. gradeworkinggroup.org/publications/index.htm; http://www.gradeworkinggroup.org/intro.htm. The first step in this direction is already completed. The National Guidelines Clearing House, USA, already developed syntheses of guidelines covering similar topics [http://www.ngc.org/ compare/synthesis.aspx] (accessed 17 October

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2007). BMJ as well requests that authors should preferably use the GRADE system for grading evidence when submitting a clinical guidelines article to the journal [www.bmj.com]. With GRADE the patients and clinicians have the opportunity to weigh up the benefits and downsides of alternative strategies when making healthcare management decisions. Much more about the balance and uncertainty between benefits and risks, decisions about an effective therapy, resource and cost can be learned by UpToDate (an electronic resource widely used in North America), [www.uptodate.com]. “When dealing with resource allocation issues, guideline panels face challenges of limited expertise, paucity of rigorous and unbiased cost-effectiveness analyses, and wide variability of costs across jurisdictions or health-care systems. Ignoring the issue of resource use (costs) is however becoming less and less tenable for guideline panels” (Guyatt, & al, 2006).

CONCLUSION The e-medical resources are useful and probably will continue to improve. With some crucial governmental support the delivery of healthcare services to patients in rural, lonely, or distant regions with shortages of medical staff will be facilitated. The e-medicine undoubtedly can reduce the cost of on-site communication between patients, doctors, and insurance agencies. In many cases, however, the information about the quality of the supporting evidence of the e-health service will remain limited. The e-sources will continue to provide excellent specific information for health professionals who have recognized the need to learn and understand how to discover, assess, and use the best current evidence in decision-making about the individual patient care. The principles of EBM deserve to become a way of thinking for governmental clerks, in particular when decisions for population health strategies are made, and for politicians when promises are disseminated.

The invasion of the market principles into the realm of medicine is a reality and the customer satisfaction tends to become a priority. However, common sense and governmental regulations are needed to avoid the transformation of the quality medical care into hedonistic “satisfaction-based medicine”. The results from outcome studies and assessments based on quality indicators seem to be a more realistic foundation for measuring the quality of healthcare for both the patients and the societies.

ACKNOWLEDGMENT Special thanks to Dr. Geroge Daskalov, St Francis Hospital - Hartford CT, USA for help and support.

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Itagaki, M. W., Berlin, R. B., Bruce, R., & Schatz, B. R. (2002). The Rise and Fall of E-Health: Lessons From the First Generation of Internet Healthcare. Medscape General Medicine 4(2). Retrieved August, 6, 2009 from [http://www. medscape.com/viewarticle/431144] .

Slawson, D. C., & Shaughnessy, A. F. (2000). Becoming an information master: using POEMs to change practice with confidence. Patient-oriented evidence that matters. [Retrieved from]. The Journal of Family Practice, 49, 63–67.

Kolbasuk, M., & McGree, N. (2009). Retrieved June 17, 2009, from http://www.docmemory.com/ page/news/shownews.asp?num=11826

Slutsky, J. (2008). Session Current Care - G-I-N abstracts. Retrieved March, 27, 2009 from [http:// www.g-i-n.net/download/files/G_I_N_newsletter_May_2008.pdf]

Legido-Ouigley, H., McKee, M., & Walshe, K., & al. (2008). How can quality of healthcare be safeguarded across the European Union? [Retrieved from]. British Medical Journal, 336, 920–923. doi:10.1136/bmj.39538.584190.47

Steel, N., Melzer, D., & Shekelle, P. G., & al. (2004). Developing quality indicators for older adults: transfer from the USA to the UK is feasible. Quality & Safety in Health Care, 13(4), 260–264. doi:10.1136/qshc.2004.010280

LeRouge, C., Hevner, A., Collins, R., & al. (2004). Telemedicine Encounter Quality: Comparing Patient and Provider Perspectives of a SocioTechnical System. Proc. 37th Hawaii International Conference on System Sciences. Retrieved March 25, 2009 from [http://www2.computer. org/portal/web/csdl/doi?doc=abs/proceedings/ hicss/2004/2056/06/205660149a.abs.htm]

Townend, J. N. (2007). Guidelines on guidelines. Lancet, 370, 740. doi:10.1016/S01406736(07)61376-2

Mamdani, M., Ching, A., Golden, B., & al. (2008). Challenges to Evidence-Based Prescribing in Clinical Practice. Annals of Pharmacotherapy, 42(5), 704-707. Harvey Whitney Books Company. Posted 07/15/2008. Retrieved from [http://www. medscape.com/viewarticle/576145]. MARQuIS research project. Qual Saf Health Care, 18, i1-i74. (2009) National Guideline Clearinghouse (NGC), Guideline Syntheses (accessed 17 October 2007). Retrieved from [http://www.ngc. org/compare/synthesis.aspx]. Sackett, D. L., Straus, S. E., Richardson, W. S., Rosenberg, W., & Haynes, R. B. (2000). EvidenceBased Madicine. Now to Practice and Teach EBM. Second Edition, Churcill Livingstone, Edinburg, London, New York & al., as well as in [http:// www.library.utoronto.ca/medicine/ebm/].

Tudiver, F., Rose, D., Banks, B., & Pfortmiller, D. (2004). Reliability and Validity Testing of an Evidence-Based Medicine OSCE Station. Society of Teachers in Family Medicine meeting, Toronto, May 14, 2004. Date Submitted: May 22, 2008. Retrieved March 22, 2009 from http://www.stfm. org/fmhub/fm2009/February/Fred89.pdf UEMS. 2007.19 – Bratislava Declaration on eMedicine. (2009, February 25). Retrieved June 17, 2009,from http://admin.uems.net/uploadedfiles/893.pdf. Veillard, J., Champagne, F., & Klazinga, N., & al. (2005). A performance assessment framework for hospitals: the WHO regional office for Europe PATH Project. International Journal for Quality in Health Care, 17(6), 487–496. doi:10.1093/ intqhc/mzi072

This work was previously published in E-Health Systems Quality and Reliability: Models and Standards, edited by Anastasius Moumtzoglou and Anastasia Kastania, pp. 100-117, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global). 189

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Chapter 1.13

E-Medical Education: An Overview

D. John Doyle Case Western Reserve University, USA & Cleveland Clinic Foundation, USA

ABSTRACT The World Wide Web has made available a large variety of medical information and education resources only dreamed of two decades ago. This review discusses a number of Web-based e-Medical education concepts and resources likely to be of interest to the medical education community as well as a number of other groups. The resources described focus especially on those that are free and those that have an interactive component. The importance of interactivity and its role in the “constructivist” approach to educaDOI: 10.4018/978-1-60960-561-2.ch113

tion is emphasized. Problem-based learning in medical education is also discussed. In addition, the importance of “Web 2.0” and related developments is discussed, along with an overview of Web-based medical simulation software that can complement medical education programs. The importance of podcasts and videocasts as an educational resource are also emphasized. Other concepts such as mashups and the semantic Web are briefly described. Intellectual property issues are also discussed, such as Creative Commons license arrangements, as well as the concept of “information philanthropy”. Finally, the importance of peer-review and technology evaluation for online educational materials is discussed.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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INTRODUCTION The Rise of Web-Based Educational Systems “The student begins with the patient, continues with the patient, and ends his studies with the patient, using books and lectures as tools, as means to an end.” -- Sir William Osler, Aequanimitas, 1905 Since the coming of age of the World Wide Web a little over a decade ago, a true revolution in access to information of all kinds has taken place. In particular, recent years have witnessed a quasi-revolution in conventional and distance education as a result. Web-based educational technologies now allow for new approaches to education not previously dreamed of. While this is especially true in the field of distance education generally, it has also had a tremendous impact on medical education in particular (Gallagher, Dobrosielski-Vergona, Wingard, and Williams, 2005; Glinkowski and Ciszek, 2007; Jang, Hwang, Park, Kim and Kim, 2005; McKimm, Jollie and Cantillon, 2003; Sajeva, 2006; Schilling, Wiecha, Polineni and Khalil, 2006; Smith, Roberts and Partridge, 2007; Zary, Johnson, Boberg and Fors, 2006). Clinicians and medical students now have available to them a countless array of online medical journals, CME educational sites, discussion forums, medical search engines, podcasts, wikis, and blogs that they can use to enhance their ongoing learning. A great number of these resources are entirely free.

WEB-SITE DEVELOPMENT SOFTWARE Web pages are a particularly useful means of providing information of almost any kind. They are easily accessed and are updated relatively easily, at least with some development systems. Links to related documents can be provided, while support

for tables, graphics and multimedia objects like audio and video clips can usually be managed with minimal difficulty. As a result, Web pages have proven to be very popular for medical education. Web pages are primarily built using the HTML language or one of its successors (Katzman, 2001; Lynch and Horton, 1998; Ryan, Louis and Yee, 2005; Wiggins, Davidson, Harnsberger, Lauman and Goede, 2001). There are many advantages to using HTML for Web site development, one of which is that one does not need to buy any special software in order to use it - one can write Web pages directly in HTML using almost any text editor. Still, most Web developers prefer to use a specialized Web page editor, and there are a number of inexpensive or free HTML editors available (as a simple Google search will quickly reveal). Professionals usually use packages such as Macromedia’s Dreamweaver or Microsoft‘s Expression Web. While these are excellent, comprehensive, general-purpose Web page editors, they are not especially friendly to beginners, and for a number of small projects I have used a less well-known but very easy-to-use system known as Homestead (www.homestead.com). Regardless of the system chosen, consideration must also be given to the use of appropriate design principles in Web page construction (Cook and Dupras, 2004; Gotthardt et al, 2006; Wong, Greenhalgh, Russell, Boynton, and Toon, 2003). In this respect, Where possible, consideration should also be given to adding some degree of interactivity to the process (Coiera, 2003; Despont-Gros, Mueller, and Lovis, 2005; Reynolds, Mason and Harper, 2008; Ridley, 2007; Stout, Villegas, and Kim 2001). Web-based interactivity, discussed in more detail later in this article, can be implemented in various ways. For instance, a section of a Web page may ask the student a question, and offer four possible answers the student may select from. Depending on the student’s response, the Web page can provide a different commentary. Other forms of Web-based interactivity may involve the use of discussion forums or online surveys and

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polls. Providing e-mail addresses (and “mailto” links) for authors or instructors can also help make material more interactive. Advanced interactivity may also be achieved using JavaScript, a popular and relatively easy to learn programming language for Web pages, as well as via a number of other specialized languages. Another concern is with Web site maintenance. All good web sites need periodic updating – otherwise the content becomes dated, hyperlinks become broken and other problems arise. You should plan for quarterly maintenance updates to your site to keep it topical and respectable. A final point concerns Intellectual Property (IP) issues. It is not uncommon for individuals involved in the development of educational materials to utilize in their work useful material (especially images) found on the Web. However, if done without permission, this practice is potentially problematic, even with appropriate attribution, as the use of the Intellectual Property belonging to others – whether commercial or otherwise generally requires that permission be granted. It should be noted, however, that a number of excellent educational resources such as Wikipedia operate under a “Creative Commons” license arrangement. Creative Commons is “a nonprofit corporation dedicated to making it easier for people to share and build upon the work of others, consistent with the rules of copyright.” Creative Commons works “to increase the amount of creativity (cultural, educational, and scientific content) in “the commons” — the body of work that is available to the public for free and legal sharing, use, repurposing, and remixing.” This arrangement often greatly facilitates the legal reuse of valuable educational materials. The interested reader is directed to http://creativecommons.org for further details. As an example of such an arrangement, an individual seeking to freely use an illustration of a sugammadex molecule encapsulating a rocuronium molecule can get one that the creator has generously released into the public domain at http://

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en.wikipedia.org/wiki/Image:Sugammadex_encaps_rocuronium.jpg).

WHAT IS WEB 2.0 AND WHY IS IT IMPORTANT? The original launch of the World Wide Web circa 1994 soon developed into a series of offerings that took the online world by storm and quickly created no small number of multi-millionaires. Such early offerings soon included corporate and personal Web sites, Web-based mail, Internet commerce, Internet radio, etc. For convenience, some authors describe this “original” version of the Web as Web 1.0. Since that time, however, the Web has advanced and developed so much that some authors now use the term “Web 2.0” to describe the many recent developments that stress openness, sharing, cooperation and collaboration among Web users (Atreja, Messinger-Rapport, Jain, and Mehta, 2006; Boulos, Maramba, and Wheeler 2006; Kamel Boulos, and Wheeler, 2007; McGee, and Begg, 2008; McLean, Richards, and Wardman 2007). Such developments include Wikis, blogs, podcasts, mashups, and RSS feeds (vide infra). While a formal definition of Web 2.0 that everyone agrees on would be hard to find, one way of understanding it would be to point out its emphasis on participation through open applications and services, rather than define it (as others might) in terms of newer languages such as AJAX or XML, or advanced online applications such as Goggle Maps or Wikipedia. The emphasis in Web 2.0 is thus on developing applications and systems so that they get smarter and more comprehensive the more people use them (leveraging collective efforts and intelligence), rather than emphasizing any technical properties or specifications. It is this special ‘collaborationware’ focus of many Web 2.0 applications as well as their simplicity of use and speed of deployment, that has lead their enthusiastic adoption in many medical educational

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arenas. Another emphasis in Web 2.0 is on the automatic granting of permission to use existing content in new and exciting ways (“information philanthropy”) (Doyle, Ruskin and Engel, 1996), discussed later. Yet another focus is that of services over software. Finally, as Web 2.0 continues to develop, it is expected to embrace new modes of communication and interaction such as the use of communication via cell phones, the use of instant messaging, learning via virtual reality techniques, and even learning via online social gaming. In some cases the connections between these technologies and clinical practice may be surprising. For instance, in a study by Rosser, Lynch, Cuddihy, Gentile, Klonsky and Merrell (2007) the authors discovered that surgical trainees who had played video games more than 3 hours per week worked faster and made fewer errors when performing laparoscopic surgical tasks. Let us now look at some of these tools in more detail.

Wikis A Wiki is a collaborative informational Web site whose content is open (freely accessible) and can be edited by anyone who is duly authorized. The name comes from the Hawaiian word wiki, meaning to hurry or swift. The best known Wiki is Wikipedia (www.wikipedia.org), which as of this writing has over 2.6 million articles in English. It is sometimes held out as a one of the best examples of Web 2.0, with its openness in both access and in its participation model. However, Wikipedia’s openness in allowing virtually anyone to participate has lead to criticism (Taylor-Mendes, 2007), even though there is evidence that it may be just as accurate as commercial encyclopedias (Giles, 2005). Still, such concerns have lead to the launching of more rigorously edited free online encyclopedias such as Scholarpedia (http://www.scholarpedia.org) and Citizendium (http://citizendium.org). For instance, the approach of Scholarpedia is to avoid competing with Wikipedia, but rather to comple-

ment it, covering a few narrow fields (such as computational neuroscience) in an exhaustive manner. It should also be pointed out that some of the “alternatives” to Wikipedia that have been developed may be seriously flawed. As an example, Conservapedia (http://www.conservapedia.com) is a Web-based encyclopedia written from a Conservative Christian point of view. As a result, it contains articles that support the “Young Earth” creationist model (the earth being a mere 6000 years old) or that present evolution as a discredited scientific theory in conflict with the fossil record.

Medical Education Wikis One challenge with Wikipedia is that its entries dealing with medical topics tend to vary greatly in quality. While blatant technical inaccuracies are unusual, many of Wikipedia’s medical entries lack the perspective and balance usually found in well-established medical textbooks. This situation exists in part because no special training or clinical experience is required to contribute to any Wikipedia article. While it is true that clinicians unhappy with any Wikipedia medical article can take it upon themselves to edit it to their satisfaction, this seems not to be done frequently. In all likelihood, this is at least partly because academics generally get no credit towards tenure or promotion for contributions of this kind. Because Wikipedia is often weak and incomplete in their treatment of medical subjects, efforts to develop medical Wikis have been launched. Ganfyd (http://ganfyd.org) is an online collaborative medical reference that is edited by medical professionals and other experts. Sermo (http:// sermo.com) is a wiki-like medical resource that is only accessible by physicians practicing in the USA who can prove their medical credentials by providing their “DEA number”. A more specialized medical wiki example is Flu Wiki (http://www. fluwikie.com), which was launched to assist in the planning for possible avian influenza pandemics.

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Wikisurgery (http://www.wikisurgery.com) is a free collaborative surgical encyclopaedia “with over 1,300 articles for surgeons and patients, including news, articles, operation scripts, biographies and images.” Yet another example is the Diabetes Wiki (http://diabetes.wikia.com/wiki/ Diabetes_Wiki). A fairly comprehensive list of medical Wikis is available at http://www.davidrothman.net/ list-of-medical-wikis. For more information on Wikis in a medical context see a review by Johnson, Freeman and Dellavalle (2007).

Blogs Blogs (short for “Web logs”) are Web sites intended for online audiences containing a diary-like account of events and personal insights. While a great many blogs are social, political or technical in nature, a number focus on medical issues. Often written by doctors, these blogs regularly discuss the latest developments in diagnosis, treatment or basic science. Some give the authors a chance to rant at “Big Pharma” and their expensive drugs, to complain about HMOs and their “unreasonable” policies, or to complain about malpractice lawyers and the ever rising cost of medical liability insurance. Others are more focused on providing up-todate clinical information or in telling interesting clinical anecdotes. For an excellent example of a medical blog, see http://www.kevinmd.com. For more general information in a medical context see a review by Sethi (2007). Finally, loosely related to blogging is Twitter (http://twitter.com), a microblogging technology where users can send and receive concise, textbased information. While it has not yet seen deep penetration in the medical field, this may be only a matter of time as this and other social media services, such as YouTube, Facebook, MySpace, and Second Life emerge as popular sources of health information, especially for young adults.

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Critique of Wikis and Blogs One concern with both wikis and blogs is the quality of their content, an issue identified earlier. However, there is hope that, at least for some popular resources, the collaborative nature or the resource will lead to self-correction. For instance, the openness of many wikis has given rise to the concept of “Darwikinism” where “unfit” wiki material content is zealously edited by other users (McLean, Richards, and Wardman, 2007). One interesting question that arises in relation to medical blogging is: “What ethical standards should medical bloggers be held to in their postings?” I would argue that where these individuals are health professionals, ideally at least, they should be ethically bound to the same rules and standards of professionalism as professional journalists: any specific medical information posted should be accurate, should come from a trusted, authoritative source, and a citation to that source should be provided. This is not always the case, as exemplified by some blogs that promote the view that HIV does not cause AIDS (e.g., http:// www.aids-science.blogspot.com) When medical bloggers write about situations or cases encountered in their own clinical practice, specific attention should be given to protecting patient confidentiality. Ideally, medical bloggers may wish to have their blog or medical Web site accredited by the Health on the Net Foundation, which provides endorsement to websites which adhere to the foundation’s guidelines. (More information is available at the foundation’s Web site (http://www.hon.ch), which outlines the eight principles that the Health on the Net Foundation requires for endorsement.)

Podcasts / Videocasts A podcast is a downloadable audio or video presentation for replay on a personal media player such as Apple’s iPod. A rich variety of medical podcasts are available to interested individual

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(e.g., itunes.com, podcast.com), although many require a subscription or payment While podcasts are sometimes used for undergraduate medical education, they are most commonly used in postgraduate medical education. A great many medical education podcasts consist of edited recordings of lectures at major medical conferences. In the case of lecture podcasts offered by Audio Digest and some other suppliers, it is possible to get formal CME credit upon completion of a quiz. This is important to many physicians who must obtain a particular number of CME credits to maintain their medical license. Users wishing free medical podcasts may wish to take advantage of those offered by the New England Journal of Medicine (http://content.nejm.org/misc/podcast.dtl). For more information on podcasts see a review by Abbasi (2006).

Mashups A mashup is a web application that presents information integrated from two or more data sources. An example of a health mashup is HealthMap (http://healthmap.org), which uses information from Google Maps (http://maps.google.com) or other sources to add cartographic context to epidemiological data so as to create a new service not originally provided by either source.

RSS (Really Simple Syndication) An RSS document is a Web resource known as a “feed” or “channel” that scans the Web to provide links to updated content (blog entries, news headlines, podcasts, etc.) in which one has previously indicated an interest. RSS documents also include “metadata” such as publication dates and authorship information in a standardized file format (“XML”) allows the information to be viewed by many different programs. For more information on RSS services in a medical context see a review by Wu and Li (2007).

Semantic Web A member of individuals such as Tim BernersLee, the inventor of Web 1.0, have expressed concerns about a number of aspects of Web 2.0 and have proposed as an alternative the concept of the “Semantic Web”, an ambitious undertaking where information would be automatically exchanged and acted upon on our behalf by the Web itself. While humans are capable of using the Web to carry out tasks such as ordering merchandise on eBay or searching for a good price on a cruise vacation, because web pages are designed to be read by people and not machines it is exceedingly hard for a computer to accomplish the same tasks without human direction. However, the vision offered by the Semantic Web is that the information it carried would be formatted so that it is “understandable” by computers, thereby reducing much of the tedious work involved in information-related web activities. Tim BernersLee originally expressed the vision of the semantic web as follows (Berners-Lee and Fischetti, 1999): “I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A ‘Semantic Web’, which should make this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The ‘intelligent agents’ people have touted for ages will finally materialize.” In the domain of biomedicine, the Semantic Web has now begun to be explored in earnest (Mukherjea, 2005; Good and Wilkinson, 2006; Antezana, Kuiper and Mironov, 2009). Related to the Semantic Web is the notion of “Semantic Publishing”, where information and documents are published with explicit “semantic markups” intended to allow computers to comprehend the structure and meaning of published information. While the Semantic Web and Seman-

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tic Publishing is expected to eventually change the face of web publishing in dramatic ways, there is much progress to be made before it becomes commonplace. For more information the reader is directed to discussions by Berners-Lee and Hendler (Berners-Lee and Hendler, 2001) and Shadbolt and -Lee (Shadbolt and Berners-Lee, 2008).

INFORMATION PHILANTHROPY Recent years have seen the growth of a movement I have called “Information Philanthropy” (Doyle, Ruskin and Engel, 1996), which has as its goal making quality intellectual property available for free, usually as an Internet download. This material might be textbooks, graphic illustrations, photographs, videos, musical compositions, blueprints, schematics, DNA and amino acid sequences, computer programs, mathematical algorithms, poetry, chemical synthesis procedures, detective novels, simulation procedures, and even raw scientific data. In all such cases, the creator/owner of the material makes this material available without charge subject to possible restrictions—such as not allowing the material to be used for commercial or military purposes, or perhaps not permitting the material to be reworked (edited, remixed, etc.) into a derivative product. In a great many cases such materials are governed by a Creative Commons license, as discussed earlier.

Interactivity and the “Constructivist” Approach to Education Constructivism is the view that knowledge is “constructed” by the learner by testing ideas, concepts and approaches based on existing knowledge and one’s actively acquired experiences and those of others. Constructivist theory holds that knowledge is not merely acquired passively and that students learn best when they actively (often collaboratively) participate in problem-solving

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and critical thinking while involved in a wellformulated learning activity. Many experts in adult learning argue that traditional lecture-based didactic teaching all-toooften encourages passive learning instead of the development of the higher order cognitive skills needed for “true” education. Arguing that active involvement is essential for effective learning, they suggest that adults learn best when drawing on previous experiences, using techniques such as group discussion, simulation exercises, and problem solving. That is, going beyond mere looking and listening motivates people to learn on their own, gives students the motivation to try out new ideas, and encourages them to critically examine issues that were once simple accepted passively. One educational element that has been advocated as a means to achieve active learning is “interactivity” (Table 1) since it is well-matched to the constructivist model of learning (ConCeição and Taylor, 2007). The Oxford English Dictionary defines interactivity as reciprocally active, allowing a two-way flow of information between source and user, responding to the user’s input. In recent years interactivity has been viewed as a particularly important component to educational undertakings, although the nature of the “interaction” will naturally vary with factors such as the subject domain (e.g., philosophy versus physics), and format (e.g., conventional versus distance education). Well-designed interactivity in educational systems can, at least in principle, help capture the learner’s interest, has the potential to speed learning, and even allows for continuous assessment of the degree to which the material is mastered. These goals are met by providing frequent and relevant user feedback, by recognizing when students misunderstand a concept, and by providing learning aids such as animations or graphs that vary depending on user input. However, badly designed interactivity can also impede student progress (vide infra). The importance of interactivity in learning is perhaps best illustrated by the fact that a number

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Table 1. Types of Interactivity in teaching and education, with examples focusing on medical Education Person-to-person (student / teacher) interaction with both persons in direct verbal communication (“same place interaction”) formal classroom or auditorium teaching (such as a question and answer period at the end of a lecture) informal hallway or bedside discussions Person-to-person interaction (student / teacher) using remote communication technology (“different place interaction”) telephone and audio conferencing writing (paper, e-mail) video conferencing telemedicine (e.g., clinical discussion centering around a radiographic image) Paper-based interactivity [can also be made into a computer model] interactive paper scenarios (If X is true then go to page Y otherwise go to page Z) Example 1: Patient with chest pain presents to the ER where X= “admit patient to hospital” Example 2: Patient with abdominal pain presents to the ER where X= “operate on patient [laparotomy]” Paper-based interactivity based on “developer pen” technology [can also be made into to a computer model] developer pen technology allows the student to see the result of a study or laboratory test once it has been “ordered”; based on the result of the selected investigation the student selects other clinical options (e.g., asks more questions, attempts to elicit a particular clinical finding, orders another test) Computer based interactivity not using the World Wide Web. This usually involves using programming languages such as C++ or various older authoring platforms. Web-based interactivity. This usually involves using programming languages such as JavaScript or authoring platforms with Web support. Note: further distinctions can to be made between immediate (“synchronous”, “real time”) and delayed (“asynchronous”) interaction.

of journals either directly or peripherally address this topic. For instance, the Journal of Interactive Learning Research (JILR), available online at http://www.aace.org/pubs/jilr/default.htm, publishes manuscripts having to do with “the underlying theory, design, implementation, effectiveness, and impact on education and training of the following interactive learning environments.” Stevan Harnard (1992) has pointed out that electronic publication (and especially electronic publication via the Internet) provides a dimension of interactivity in document publication that is radically new. He notes: No other medium can offer an author the possibility of virtually instantaneous and ubiquitous interaction with his intended audience. Electronic publication is not just a more efficient implementation of paper publishing, it offers the possibility of a phase transition in the form (and hence also the content) of scholarly inquiry. Not only are the

boundaries between “informal” and “formal” literature blurred, but scholarly inquiry has always been a continuum, from the inchoate birth of new ideas and findings to their (in principle) endless evolution as inquiry carries on. Interactivity is also important in distance education. One simple definition of “distance education” is that it is the delivery of instruction that does not require the learner to be present in the same physical location as the instructor. Students in such learning environments are thus said to interact variously with the instructor, the content, the technology, and with other students. In addition to these four forms of interaction, further distinctions must to be made between instantaneous (“real time”, “synchronous”) and delayed (“asynchronous”) interaction, as well as between “same-place” and “different-place” interaction. Generally speaking, delayed and different-place interaction offer the student more flexibility and

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opportunities for thought and reflection, while immediate and same-place interaction, some would say, allow for a greater sense of spontaneity, impulsiveness and even exhilaration. By contrast, with conventional classroombased teaching, the focus is predominantly on student-teacher interaction, with student-content interaction also taking place, especially when outof-classroom reading takes place. In the distance education setting, with the heavy use of self-study materials, the focus is usually on student-content interaction although interaction with technology is also frequently important. Various learners and instructors will have preferences regarding interactivity, and some forms of education will rely on some forms of interactivity more than others. Regardless, the wise use of interactivity will help “engage” students and help ensure an enduring learning experience. One particularly engaging form of interactivity in educational materials is the use of multiple choice questions (MCQs) (Collins, 2006; Moss, 2001). The use of MCQs not only make the material more engaging but also help detect learning gaps. In recent years the most popular format for MCQ’s is the “pick the best single answer” format (e.g., Which one of the following drugs would be the most likely to increase heart rate in a normal adult?). If the question has been answered correctly, the student is given an encouraging note with more details. If answered incorrectly, a different note is shown to the student. Until recent years, educational technology was not especially capable of maintaining a high degree of interactivity with a learner, in contrast, say, to the very high level of possible interaction between a teacher and a student discussing a complex philosophical matter, where a series of verbal exchanges may occur that involves a cognitive effort much more advanced than mere memorization or rote repetition. This matter is expected to change favorably as software technology continues to become more sophisticated

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and as computer hardware advances in speed and power. In particular, advances in Artificial Intelligence (AI), and educational authoring systems will likely have a direct impact on the evolution of computer-based interactivity. Still, computerbased learning is much more complex than working through a textbook, and many students who are uncomfortable with computers see no reason to deviate from a curriculum based on well-written textbooks, particularly well-polished classics that are current and are accompanied by study guides and solutions manuals. Many advocates of computer-mediated distance education draw attention to the helpful aspects offered by computer-based methods and understate the kinds of communicative and technical difficulties learners may experience. When students use complicated equipment in their learning environment, it is important to ensure the technologies used help rather than hinder the learning experience. Students using unfamiliar technologies may experience frustration, anxiety and confusion and students involved in distance education using computer technologies may face numerous stumbling blocks unrelated to the material to be learned. These include computer hardware problems, software problems at the level of the operating system or the various software applications, as well as difficulties with Internet access. Files may mysteriously “disappear”. Attachments sent by e-mail may be “lost” in transit. Random “disconnects” from the Internet may frustrate the user. Unless students are moderately familiar with computers, substantial effort may be expended on technology-related issues rather than learning the intended materials. Thus technologies that are awkward, unintuitive or present the learner with a steep learning curve may hinder interaction and even impede learning. Various means by which technology can impede interactivity include: haphazard, unintuitive or disorganized user interfaces; poor screen layout; poor use of fonts and icons; systems that

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respond too slowly to user inputs; or systems that crash frequently. While a detailed discussion of this matter is beyond the scope of this review, books by design gurus Jakob Nielsen (1994) and Donald Norman (1999) are recommended as a good place to explore the nature of good and bad user-interface design. Studies show that technical problems can occasionally render the educational undertaking to be a substantially negative experience. For instance, Hara and Kling (1999) carried out a study of a web-based distance education course at an American university and commented that distance learners may experience problems and frustrations that are not well documented in the distance education literature. They noted that three interrelated sources accounted for most of the problems: “lack of prompt feedback, ambiguous instructions on the web, and technical problems”. They concluded that the frustrations the students experienced “inhibited their educational opportunity”. In a related paper (Hara and Kling, 2000) similar concerns were voiced. The importance of interactivity in medical education is supported by a landmark metaanalysis of the effectiveness of formal CME by Davis, O’Brien, Freemantle, Wolf, Mazmanian and Taylor-Vaisey (1999) who showed that traditional didactic methods do not generally lead to a change of clinical practice, or to an improvement in patients’ health outcomes, whereas interactive techniques are more likely to. As result, some regulatory bodies, such as the Royal College of Physicians and Surgeons of Canada (RCPSC) specifically mandate that at least 25 per cent of the time of an approved educational event should be allocated for interactive learning, such as in the form of a question and discussion period. This serves to further engage the listener as well as to clarify some of the issues that may remain unanswered at the end of the formal part of the presentation.

PROBLEM-BASED LEARNING Traditional education practices from kindergarten through professional school often produce students who are disenchanted and bored with their education. All too often students are faced with a vast amount of information to memorize, a good deal of which is of little apparent relevance to future career goals. In particular, in the field of medical education, medical students have been traditionally provided with a series of lectures, laboratories and clinical sessions for four years (six years in Europe), and then released into internship and clinical practice with little in the way of actual skills in real-world clinical problem solving, the prevailing view being that these skills will emerge quickly enough in the hustle-bustle of the busy clinical world (Barrows, 1983). Studies in classical medical education have often been critical of this approach. What students learn, despite intense efforts on the part of both students and teachers, often fades quickly (Levine & Forman, 1973) and there is even evidence that natural problem solving skills may be impaired by this process (Barrows & Bennett, 1982). It also seems that many physicians are not able to continue their education after completion of their training (Ramsey & Carline, 1991). Problembased learning (PBL) has been advocated as one means to get around these difficulties (Albanese & Mitchell, 1993). Traditional education involves the delivery of information to students by means of lectures and demonstrations, usually supported by tutorials and laboratory sessions. In this model the instructor provides students with the information that they need. In Problem-based Learning (PBL) the students work in small groups under the guidance of an instructor or tutor to find for themselves the knowledge they need to solve real world problems. The tutors are usually not experts in the subject matter of the tutorial, but rather are facilitators who help the students find the answers for themselves. At the conclusion

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of a PBL module, the students should have encountered the information necessary to solve the problem and, in so doing, should have gained knowledge and skills that in a traditional medical curriculum would have been circulated by lecture. PBL emphasizes group problem-analysis and independent self-directed study over teacher- or examination-driven education. As a consequence, it is believed that PBL may encourage students to become thoughtful problem-solvers and life-long learners (Barrows, 1983). The introduction of a PBL-based medical curriculum began in 1969 at McMaster University in Hamilton, Ontario, Canada (Schmidt et al., 1991). From the school’s outset, McMaster medical school structured the curriculum around actual clinical cases instead of teaching based on the traditional subjects of anatomy, biochemistry, physiology, pharmacology and so on. The introduction of PBL was based on a concern that many medical schools put too much emphasis on memorization of facts and little weight on developing problem solving skills or the self-directed study skills necessary for the life-long practice of medicine. Following its introduction at McMaster University, the PBL teaching model was later adopted by a significant number of other medical schools, as well as by schools teaching other professional disciplines such as business. PBL is now the instructional method of choice in an increasing number of medical schools around the globe (Schmidt et al., 1991). Problem-based learning is a ground-breaking and demanding approach to medical education ground-breaking because it involves a new way of using clinical material to help students learn, and demanding because it requires the instructor to play a facilitating and supporting role rather than a didactic one. For the student, problem-based learning emphasizes the application of knowledge and skills to the solution of problems (clinical cases) rather than the mere recall of facts.

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Since the quantity of direct teaching is substantially reduced in a PBL curriculum, students are expected to assume greater responsibility for their own education, and the instructor’s role becomes variously one of coach, group facilitator, subject matter expert, instructional resource guide, and group consultant, where the instructor is expected to promote and support student involvement, to provide guidance to help keep students on track, to help prevent anyone giving inappropriate negative criticism, and even to assume the role of fellow learner. This arrangement is intended to promote group acquisition of information rather than the mere imparting of information to passive students by faculty (Vernon & Blake, 1993). In a PBL class, students are given a problem to work on. They then proceed through a number of stages as they move towards their solution: exploring the nature of the problem; making a clear statement of the problem; identifying the information needed to understand the problem; identifying resources needed to gather this information; generating possible solutions to the problem; scrutinizing the various solutions for strengths and weaknesses; identifying the best solution among the various possible solutions; and presenting the solution. These stages usually take place over several class discussion sessions along with students working individually on assigned tasks related to the problem outside of class time. Problem-based learning has now entered the realm of eMedical education. For example, Table 2 lists some sample medical PBL problems that may be viewed from the Internet.

WEB-BASED MEDICAL SIMULATION SYSTEMS In many respects, simulators offer the ultimate in interactivity and appear to have an excellent future in medical education. Computer-based simulators used in medical education fall into four general categories: (1) Screen-based text simulators, (2)

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Table 2. Some sample medical Problem-Based Learning (PBL) cases available on the internet Bad Reaction - A Case Study in Immunology http://ublib.buffalo.edu/libraries/projects/cases/immunology/ immunology1.html Pediatric Infection Case http://medicalpblukm.blogspot.com/2009/08/case-files-3-pedsim-hot-and-drooling.html Into Thin Air - A Case Study in Physiology http://ublib.buffalo.edu/libraries/projects/cases/Denali1.html A Series of Cases in the Field of Obstetrics and Gynecology http://www.healthsystem.virginia.edu/internet/obgyn-clerkship/ PBLCases.cfm A Series of Biology Cases http://capewest.ca/pbl.html

Screen-based graphical simulators, (3) Manikinbased simulators, and (4) Virtual reality trainers. We briefly discuss the first two of these here, since both can be Web-based. Screen-based text simulators create verbal scenarios in which the user picks one of several responses and, based on the chosen response, a new text scenario is produced. For instance, in a scenario involving a patient presenting with a severe headache, the user may be offered options such as prescribing an analgesic or getting a CTscan of the head. Based on the user’s choice, a new narrative is then generated and more management choices are offered. Being purely text-based, screen-based text simulators are relatively simple to construct and require little memory, making them popular in early medical simulation efforts. However, since they are lacking in graphical elements, they are rarely used today. Screen-based graphical simulators such as ‘Gasman’ (Philip, 1986) and ‘Body’ (Smith and Davidson, 1999) aim to recreate elements of reality in graphic form on a computer screen, often to elucidate the pharmacokinetic and pharmacodynamic processes associated with drug administration. Usually, only a mouse interface is involved. While these simulations help one understand the

conceptual theory underlying clinical practice, they usually do not confer actual practical skills. Their strength lies in an ability to help one understand abstract concepts while remaining portable and relatively inexpensive. For individuals interested in respiratory medicine, ThoracicAnesthesia.com offers a free “education, information, and reference service” which include a series of didactic pieces and teaching slide sets, a series of journal article synopses, a bronchial anatomy quiz, and a bronchoscopy simulator. The bronchoscopy simulator is unquestionably the best part of the site. Here, using real time video based on an Adobe Flash platform one can navigate through the tracheobronchial tree with controls for moving your virtual bronchoscope forward, backwards, and in diagonal directions. This process is aided by a “macro” view on the left panel (called a “bronchial tree navigational map view”) and a “micro” view on the right panel (“bronchoscope view”). If desired, zooming in and out and adding or removing labels on the displayed material can be controlled simply by clicking on the appropriate controls. Most importantly, clicking on various labels in the bronchoscope view will launch further information in the map view, making the experience intuitive and even enjoyable.

PEER-REVIEW OF ONLINE EDUCATIONAL MATERIALS When an instructor selects teaching resources - especially in the case of medical teaching resources - he or she wants some assurance that the material is timely, accurate, well-written, and developed using sound pedagogical principles. Such assurances, some would argue, can be best obtained via a process of peer-review. While the peer-review process in scientific publishing is as old as scientific publishing itself, the idea of peer-review procedures for educational resources in relatively new. However, it is clearly an idea whose time has come.

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Typically, scientific materials submitted for publication are reviewed by two or more expert referees who are particularly knowledgeable in the subject matter being discussed. Their comments and opinions form the basis upon which an editor will decide whether or not to publish the submitted material, and with what changes, if any. By contrast, formal peer-review of educational materials remains relatively uncommon. Historically, this was because until recently, publishing educational resources like books was usually always done in conjunction with a publishing house with its own internal quality control system. For instance, some book companies employ a pre-publication internal and external reviews to establish whether a manuscript is worthy of publication and to determine how it might be enhanced. With the rise of the Internet, however, the landscape has changed, and publishing no longer requires enormous expense and the cooperation of a publishing house. The prevalence of electronic scholarly publishing has thus increased dramatically in recent years due, largely as a consequence of the reach and immediacy of the Internet and its ability to handle varied forms of electronic media. The idea of establishing a peer-review process for educational resources is not entirely new. For instance, Project Merlot, located at http://merlot. org, seeks to provide high-quality teaching materials in a number of disciplines of higher education. It conducts reviews along three dimensions: 1) quality of the content, 2) usefulness as a teaching tool, and 3) ease of use. For peer-reviewed medical education materials, MedEdPORTAL, an offering from the Association of American Medical Colleges (AAMC) maintains an online presence at http://www.aamc. org/mededportal. According to its Web site MedEdPORTAL “is a Web-based tool that promotes collaboration across disciplines and institutions by facilitating the exchange of peer-reviewed educational materials, knowledge, and solutions” and serves “as a central repository of high quality educational materials such as PowerPoint presen-

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tations, assessment materials, virtual patient cases, and faculty development materials”. Any medical educator may submit materials for possible publication on MedEdPORTAL. The material is then screened by an AAMC staff member to ensure that it meets the minimal requirements for inclusion and does not violate patient privacy. Suitable material is then assigned to three reviewers, at least one who will have expertise in the content area, and at least one who will have expertise in the educational format of the material. Another peer-reviewed medical resource is The Health Education Assets Library (HEAL), which maintains an online presence at http://www.healcentral.org. Established in 2000, HEAL has grown over time to provide over 22,000 resources such as images, video clips, audio clips, PowerPoint slidesets, documents in PDF format, etc. HEAL, MERLOT and MedEdPORTAL all make use of the well-known Creative Commons copyright system with their works. In general, all resources in these collections are licensed for free use, reproduction, and modification by users according to the specific terms of the Creative Commons license associated with that resource. Despite these encouraging developments, formal peer-review of educational resources remains uncommon. However, with academic credit now starting to be granted to individuals who produce freely downloadable peer-reviewed educational material such as that offered by HEAL and MedEdPORTAL, the volume and quality of such resources is bound to increase over time.

EDUCATIONAL TECHNOLOGY EVALUATION As new educational technologies (ETs) become available there is a need to evaluate them in some systematic way. Ideally any new ET would be compared to an existing standard ET using a randomized trial design, such as in randomly assigning students to one of two teaching systems

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and testing for differences in outcome. In reality these kinds of studies are relatively rare because of high resource requirements and for other reasons, and we may be thus forced to use less perfect instruments to gauge the value of a new ET, using measures such as user preference scores, as a surrogate for rigorous outcome data. Thus, for instance, when some educators declare that interactive teaching provides better educational outcomes than the mere non-interactive display of information, they generally do not argue on the basis of randomized clinical trials (as one would generally do, for instance, in evaluating a new drug) but instead support their position on the basis of general principles. Thus the notion that adding interactivity to a teaching event such as a lecture improves learning retention has not been proven to be true on the basis of randomized trials such as those done in domains like oncology, but is held to be true on the basis of a collective wisdom in the medical education community via a process driven primarily by thought leaders in education, where these leaders periodically explore, review and comment on this and other issues on a regular basis.

CONCLUSION In the past, medical education relied predominantly on classic methods of instruction such as formal lectures, laboratory demonstrations, and examining patients in clinics or at the bedside. Even today, these methods of instruction remain important, and in particular, contact with patients as a learning experience remains paramount. However, the use of electronic and Web-based teaching methods has now become an important additional modality in medical education. When properly designed so as to be engaging and interactive, such methods can have a particularly favorable impact on the way medical education is carried out. As these methods continue to be developed and refined

it is expected that they will continue to impact favorably on medical education in the future. As compared to medical education from two decades ago that relied heavily on textbooks and lectures, medical education today utilizes a large number of web-based resources that make learning more engaging, more pleasant, and easier. For example, no longer is a trip to the library required to obtain up-to-date information on an unusual condition that a medical student may encounter in a clinical encounter. Instead, the student can get reliable clinical information virtually instantly from an increasingly rich number of trustworthy online clinical sources. The future of medical education is indeed very promising.

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Donnelly, L. S., Shaw, R. L., & van den Akker, O. B. (2008). eHealth as a challenge to ‘expert’ power: a focus group study of internet use for health information and management. Journal of the Royal Society of Medicine, 101, 501–506. doi:10.1258/jrsm.2008.080156

Atienza, A. A., Hesse, B. W., Baker, T. B., Abrams, D. B., Rimer, B. K., Croyle, R. T., & Volckmann, L. N. (2007). Critical issues in eHealth research. American Journal of Preventive Medicine, 32, S71–S74. doi:10.1016/j.amepre.2007.02.013 Atreja, A., Messinger-Rapport, B., Jain, A., & Mehta, N. (2006). Using Web 2.0 technologies to develop a resource for evidence based medicine. In Proceedings of AMIA Annu. Symp., (pg. 847). Beard, L., Wilson, K., Morra, D., & Keelan, J. (2009). A survey of health-related activities on second life. Journal of Medical Internet Research, 11, e17. doi:10.2196/jmir.1192 Boulos, M. N., Maramba, I., & Wheeler, S. (2006). Wikis, blogs and podcasts: a new generation of Web-based tools for virtual collaborative clinical practice and education. BMC Medical Education, 6, 41. doi:10.1186/1472-6920-6-41 Catwell, L., & Sheikh, A. (2009). Evaluating eHealth interventions: the need for continuous systemic evaluation. PLoS Medicine, 6, e1000126. doi:10.1371/journal.pmed.1000126 Cook, D. A., Beckman, T. J., Thomas, K. G., & Thompson, W. G. (2008). Adapting web-based instruction to residents’ knowledge improves learning efficiency: a randomized controlled trial. Journal of General Internal Medicine, 23, 985–990. doi:10.1007/s11606-008-0541-0

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Hellström, L., Waern, K., Montelius, E., Astrand, B., Rydberg, T., & Petersson, G. (2009). Physicians’ attitudes towards ePrescribing-evaluation of a Swedish full-scale implementation. BMC Medical Informatics and Decision Making, 9, 37. doi:10.1186/1472-6947-9-37 Hughes, B., Joshi, I., & Wareham, J. (2008). Health 2.0 and Medicine 2.0: tensions and controversies in the field. Journal of Medical Internet Research, 10, e23. doi:10.2196/jmir.1056 Lemley, T., & Burnham, J. F. (2009). Web 2.0 tools in medical and nursing school curricula. Journal of the Medical Library Association, 97, 50–52. doi:10.3163/1536-5050.97.1.010 McLean, R., Richards, B. H., & Wardman, J. I. (2007). The effect of Web 2.0 on the future of medical practice and education: Darwikinian evolution or folksonomic revolution? The Medical Journal of Australia, 187, 174–177. Mohammed, S., Orabi, A., Fiaidhi, J., Orabi, M., & Benlamri, R. (2008). Developing a Web 2.0 telemedical education system: the AJAX-Cocoon portal. International Journal of Electronic Healthcare, 4, 24–42. doi:10.1504/IJEH.2008.018919 Nordqvist, C., Hanberger, L., Timpka, T., & Nordfeldt, S. (2009). Health professionals’ attitudes towards using a Web 2.0 portal for child and adolescent diabetes care: qualitative study. Journal of Medical Internet Research, 11, e12. doi:10.2196/jmir.1152 Norman, G. J., Zabinski, M. F., Adams, M. A., Rosenberg, D. E., Yaroch, A. L., & Atienza, A. A. (2007). A review of eHealth interventions for physical activity and dietary behavior change. American Journal of Preventive Medicine, 33, 336–345. doi:10.1016/j.amepre.2007.05.007

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Rethlefsen, M. L., Piorun, M., & Prince, J. D. (2009). Teaching Web 2.0 technologies using Web 2.0 technologies. Journal of the Medical Library Association, 97, 253–259. doi:10.3163/15365050.97.4.008 Somal, K., Lam, W. C., & Tam, E. (2009). Computer and internet use by ophthalmologists and trainees in an academic centre. Canadian Journal of Ophthalmology, 44, 265–268. doi:10.3129/ i09-057 Timpka, T., Eriksson, H., Ludvigsson, J., Ekberg, J., Nordfeldt, S., & Hanberger, L. (2008). Web 2.0 systems supporting childhood chronic disease management: a pattern language representation of a general architecture. BMC Medical Informatics and Decision Making, 8, 54. doi:10.1186/14726947-8-54 van Straten, A., Cuijpers, P., & Smits, N. (2008). Effectiveness of a web-based self-help intervention for symptoms of depression, anxiety, and stress: randomized controlled trial. Journal of Medical Internet Research, 10, e7. doi:10.2196/ jmir.954 van Zutphen, M., Milder, I. E., & Bemelmans, W. J. (2009). Integrating an eHealth program for pregnant women in midwifery care: a feasibility study among midwives and program users. Journal of Medical Internet Research, 11, e7. doi:10.2196/jmir.988

KEY TERMS AND DEFINITIONS Blogs: Web sites intended for online audiences containing a diary-like account of events and personal insights. Information Philanthropy: A movement which has as its goal making quality intellectual property available for free, usually as an Internet download.

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Problem-Based Learning (PBL): An educational modality where students work in small groups under the guidance of an instructor to find for themselves the knowledge they need to solve real world problems. Podcasts: A podcast is a downloadable audio or video presentation for replay on a personal media player such as Apple’s iPod. Web 2.0: A term used to describe the recent technical developments (e.g., Wikis, blogs, podcasts, mashups, and RSS feeds) that stress openness, sharing, cooperation and collaboration among Web users

Wikis: A collaborative informational Web site whose content is open (freely accessible) and can be edited by anyone who is duly authorized. Semantic Publishing: A technology where information and documents are published with explicit “semantic markups” intended to allow computers to comprehend the structure and meaning of published information. Semantic Web: A vision of the future where information is automatically exchanged and acted upon on our behalf by the Web itself by virtue of formatting to make it readily “understandable” by computers.

This work was previously published in Biomedical Engineering and Information Systems: Technologies, Tools and Applications, edited by Anupam Shukla and Ritu Tiwari, pp. 219-238, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 1.14

Nursing Home Shuyan Xie The University of Alabama, USA Yang Xiao The University of Alabama, USA Hsiao-Hwa Chen National Cheng Kung University, Taiwan

ABSTRACT A nursing home is an entity that provides skilled nursing care and rehabilitation services to people with illnesses, injuries or functional disabilities, but most facilities serve the elderly. There are various services that nursing homes provide for different residents’ needs, including daily necessity care, mentally disabled, and drug rehabilitation. The levels of care and the care quality provided by nursing homes have increased significantly over the past decade. The trend nowadays is the continuous quality development towards to residents’ satisfaction; therefore healthcare technology plays a significant role in nursing home operations. This chapter points out the general information about current nursing home conditions and functioning DOI: 10.4018/978-1-60960-561-2.ch114

systems in the United States, which indicates the way that technology and e-health help improve the nursing home development based on the present needs and demanding trends. The authors’ also provide a visiting report about Thomasville Nursing Home with the depth of the consideration to how to catch the trends by implementing the technologies.

INTRODUCTION Nursing home is a significant part of long-term care in the health care system. Nursing homes provide a broad range of long-term care services – personal, social, and medical services designed to assist people who have functional or cognitive limitations in their ability to perform self-care and other activities necessary to live independently. This

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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survey release some information about nursing home, including general introduction of nursing homes in the United States, resident information, quality of life, services that are providing, governmental regulations, and developing trends. Objectives of this chapter are providing the broad information about current nursing homes in the United States to help understand the positive and negative situations that confront to nursing homes. Furthermore, our nursing home visiting report points out the real residents’ experience and opinions on their life qualities, needs and attitudes on technologies. Based on this information we introduce more technological innovations to improve nursing home development.

PART I: NURSING HOMES IN THE UNITED STATES General Information A nursing home, a facility for the care of individuals who do not require hospitalization and who cannot be cared for at home, is a type of care of residents. It is a place of residence of people who require constant nursing care and have significant deficiencies with activity of daily living (“Analysis of the National Nursing Home Survey (NNHS),” 2004). People enter nursing homes for a variety of reasons. Some may enter for a brief time when they leave the hospital because they need subacute care, such as skilled nursing care, medical services, and therapies (“Analysis of the National Nursing Home Survey (NNHS),” 2004). Others, however, need long-term care (LTC). LTC is generally defined as a broad range of personal, social, and medical services that assist people who have functional or cognitive limitations in their ability to perform self-care and other activities necessary to live independently (“Analysis of the National Nursing Home Survey (NNHS),” 2004).

In the United States, nursing homes are required to have a licensed nurse on duty 24 hours a day, and during at least one shift each day, one of those nurses must be a Register Nurse (“Analysis of the National Nursing Home Survey (NNHS),” 2004). A registered nurse (RN) is a health care professional responsible for implementing the practice of nursing in concert with other health care professionals. Registered nurses work as patient advocates for the care and recovery of the sick and maintenance of the health (“Nursing Facts: Today’s Registered Nurse - Numbers and Demographics,” 2006). In April, 2005 there were a total of 16,094 nursing homes in the United States. Some states having nursing homes that are called nursing facilities (NF), which do not have beds certified for Medicare patients, but can only treat patients whose payments sources is Private Payment, Private Insurance or Medicaid (“Medical & You handbook,” 2008). Medicare is a social insurance program administered by the United States government, providing health insurance coverage to people who are aged 65 and over, or who meet other special criteria (Castle, 2008). Medicaid is the United States health program for eligible individuals and families with low incomes and resources. It is a means-tested program that is jointly funded by the states and federal government, and is managed by the states. Among the groups of people served by Medicaid are eligible low-income parents, children, seniors, and people with disabilities. Being poor, or even very poor, does not necessarily qualify an individual for Medicaid (“Overview Medicaid Program: General Information,” 2006).

Services Baseline Services Those services included in the daily rate. The following basic services should be made available to all the residents:

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• • • • • •

•

•

•

•

•

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Lodging-a clean, healthful, sheltered environment, proper outfitted; Dietary services; 24-hour-per-day nursing care; pharmacy services; diagnostic services; the use of all equipment, medical supplies and modalities used in the care of nursing home residents, including but not limited to catheters, hypodermic syringes and needles, irrigation outfits, dressings and pads, etc.(“Durable medical equipment: Scope and conditions,” 2006). general household medicine cabinet supplies, including but not limited nonprescription medications, materials for routine skin care, dental hygiene, care of hair, etc., except when specific items are medically indicated and prescribed for exceptional use for a specific resident (“Durable medical equipment: Scope and conditions,” 2006). assistance and/or supervision, when required, with activities of daily living, including but not limited to toileting, bathing, feeding and assistance with getting from place to place (“Home Health Service,” 2003). use of customarily stocked equipment, including but not limited to crutches, walkers, wheelchairs or other supportive equipments, including training in their use when necessary, unless such items are prescribed by a doctor for regular and sole use by a specific resident (“Durable medical equipment: Scope and conditions,” 2006). activities program, including but not limited to a planned schedule of recreational, motivational, social and other activities together with the necessary materials and supplies to make the resident’s life more meaningful (“Home Health Service,” 2003). social services as needed;

• •

provision of optician and optometrist services (““Home Health Service,” 2003,). physical therapy, occupational therapy, speech pathology services, audiology services and dental services, on either a staff or fee-for-services basis, as prescribed by a doctor, administered by or under the direct supervision of a licensed and currently registered physical therapist, occupational therapist, speech pathologist, qualified audiologist or registered dentist (“Home Health Service,” 2003).

Adult Day Health Care (ADHC) ADHC program provides the health care services and activities provided to a group of persons, who are not residents of a residential health care facility, but are functionally impaired and not home bounded (Castle, 2008). Require supervision, monitoring, preventive, diagnostic, therapeutic, rehabilitative or palliative care or services but not require continuous 24-hour-a-day inpatient care and services to maintain their health status and enable them to remain in the community (“Home Health Service, 2003,) (Castle, 2008).

Behavioral Intervention Services This program must include a discrete unit with a planned combination of services with staffing, equipment and physical facilities designed to serve individuals whose severe behavior cannot be managed in a less restrictive setting (Castle, 2008). The program shall provide goal-directed, comprehensive and interdisciplinary services directed at attaining or maintaining the individual at the highest practicable level of physical, affective, behavioral and cognitive functioning (Castle, 2008).

Clinical Laboratory Service Clinical laboratory means a facility for the microbiological, immunological, chemical, hema-

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tological, biophysical, cytological, pathological, genetic or other examination of materials derived from the human body, for the purpose of obtaining information for the diagnosis, prevention or treatment of disease, or the assessment of a health condition, or for identification purposes (“Home Health Service, 2003,) (Castle, 2008). Such examinations shall include procedures to determine, measure, or otherwise describe the presence or absence of various substances, components or organisms in the human body (“Home Health Service, 2003,) (Castle, 2008).

Coma Services A resident admitted for a coma management shall be a person who has suffered a traumatic brain injury, and is in a coma (Castle, 2008). This resident may be completely unresponsive to any stimuli or may exhibit a generalized response by reacting inconsistently and non-purposefully to stimuli in a nonspecific manner (Castle, 2008).

Government Regulations All nursing homes in the United States that receive Medicare and/or Medicaid funding are subject to federal regulations. People who inspect nursing homes are called surveyors or called as state surveyors (“NURSING HOMESHOMES: Federal Monitoring Surveys Demonstrate Continued Understatement of Serious Care Problems and CMS Oversight Weaknesses,” 2008). The Center for Medicare & Medicaid (CMS) is the component of the Federal Government’s Department of Health and Human Services that oversees the Medicare and Medicaid programs (“Overview Medicaid Program: General Information,” 2006). A large portion of Medicaid and Medicare dollars is used each year to cover nursing home care and services for elderly and disabled. State governments oversee the licensing of nursing homes (“Overview Medicaid Program: General Information,” 2006). In addition, State

have contract with CMS to monitor those nursing homes that want to be eligible to provide care to Medicare and Medicaid beneficiaries (“Overview Medicaid Program: General Information,” 2006). Congress established minimum requirements for nursing homes that want to provide services under Medicare and Medicaid (“NURSING HOMESHOMES: Federal Monitoring Surveys Demonstrate Continued Understatement of Serious Care Problems and CMS Oversight Weaknesses,” 2008). CMS also publishes a list of Special Focus Facilities- nursing homes with “a history of serious quality issues.” The US government Accountability Office (GAO), however, has found that state nursing home inspections understate the numbers of serious nursing home problems that present a danger to residents (“NURSING HOMES” 2008). CMS contracts with each state to conduct onsite inspections that determine whether its nursing homes meet the minimum Medicare and Medicaid quality and performance standards. Typically, the part of state government that takes care of this duty is the health department or department of human services (“NURSING HOMES” 2008) (Inspectors Often Overlook Serious Deficiencies at U.S. Nursing Homes, 2008). A report issued in September of 2008 found that over 90% of nursing homes were cited for federal health or safety violations in 2007, with about 17% of nursing homes having deficiencies causing “actual harm or immediate jeopardy” to patients (Pear, 2008). Nursing homes are subject to federal regulations and also strict state regulations (Inspectors Often Overlook Serious Deficiencies at U.S. Nursing Homes, 2008). The nursing home industry is considered one of the two most heavily regulated industries in the United States (the other being the nuclear power industry) (Wolf, 2003). As for the state and federal regulations that affect health care IT, CMS interpreted HIPAA’s security aspects to cover CIA--confidentiality, integrity, and availability. To date, most of the emphasis has been on confidentiality to reduce

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citizens’ fears that employers, insurers, or governmental agencies could use their personal health data against them (Sloane, 2009). The federal government left the implementation details surrounding HIPAA up to the states to oversee, however, because the states themselves manage the Medicare reimbursements, typically through third-party insurance companies. The result, unfortunately, really does look like a quilted patchwork of confusing and conflicting regulations (Sloane, 2009). Therefore, when information technology steps into the nursing homes, there are some concerns caused by the regulation complications.

Quality of Life Resident-Oriented Care Resident oriented care is designed as the place that nurses are assigned to particular patients and have the ability to develop relationships with individuals (Shields, 2005). Patients are treated more as family members. Using resident-oriented care, nurses are able to become familiar with each patient and cater more to their specific needs, both in emotional and medical aspects (Home Care and Nursing Home Services). According to various findings residents who receive resident-oriented are experience a higher quality of life, in respect to attention and time spent with patients and the number of fault reports (Boumans, 2005). Although resident-oriented nursing does not lengthen life, nursing home residents may dispel many feelings of loneliness and discontent (Boumans, 2005). “Resident assignment” refers to the extent to which residents are allocated to the same nurse. With this particular system one person is responsible for the entire admission period of the resident (Shields, 2005). However, this system can cause difficulties for the nurse or care-giver when one of the residents they are assigned to pass away or move to a different facility, since the nurse may become attached to the resident(s) they are car-

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ing for (Shields, 2005;Home Care and Nursing Home Services). Therefore, three guidelines must be assessed: structure, process and outcome. Structure is the assessment of the instrumentalities of care and their organization (Pear, 2008); Process being the quality of the way in which care is given (Shields, 2005); Outcome is usually specified in terms of health, well-being, patient satisfaction, etc. (Shields, 2005); Using these three criteria, find that they are strengthened when residents experience resident oriented care (Shields, 2005; Home Care and Nursing Home Services) (Boumans, 2005). Communication is also heightened when residents feel comfortable discussing various issues with someone who is experienced with their particular case. In this particular situation nurses are also better able to do longitudinal follow up, and this insures the implementation of more lasting results (Shields, 2005; Boumans, 2005).

Task-Oriented Care Task oriented care is where nurses are assigned specific tasks to perform for numerous residents on a specific ward (Shields, 2005). Residents in this particular situation are exposed to multiple nurses at any given time. Because of the random disbursement of tasks, nurses are declined the ability to develop more in depth relations with any particular resident (Shields, 2005). Various findings suggest that task-oriented care produces less satisfied residents (Shields, 2005). In many cases, residents are disoriented and unsure of whom to disclose information to and as a result decide not to share information at all (Shields, 2005). Patients usually complain of loneliness and feelings of displacement. “Resident assignment” is allocated to numerous nurses as opposed to one person carrying the responsibility of one resident (Shields, 2005). Because the load on one nurse can become so great, various nurses are unable to identify with gradual emotional and physical changes experienced by

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one particular resident (CMS, 2008). Resident information has the ability to get misplaced or undocumented because of the numerous amounts of nurses that deal with one resident (Shields, 2005; CMS, 2008).

Options Current trends are to provide people with significant needs for long term supports and services with a variety of living arrangements (Home Care and Nursing Home Services). Indeed, research in the U.S as a result of the Real Choice Systems Change Grants, shows that many people are able to return to their own homes in the community. Private nursing agencies (Nursing Agency, also known as Nurses Agency or Nurses Registry) is a business that provides nurses and usually health care assistants (such as Certified Nursing Assistants) to people who need the services of healthcare professionals. Nurses are normally engaged by the agency on temporary contracts and make themselves available for hire by hospitals, care homes and other providers of care for help during busy periods or to cover for staff absences; “Assisted Living Facilities (ALF),” 2007) may be able to provide live-in nurses to stay and work with patients in their own homes. When considering living arrangements for those who are unable to live by themselves, potential customers consider it to be important to carefully look at many nursing homes and assisted living; Assisted living residences or assisted living facilities (ALFs) provide supervision or assistance with activities of daily living (ADLs), coordination of services by outside health care providers, and monitoring of resident activities to help to ensure their health, safety, and wellbeing (“Assisted Living Facilities (ALF),” 2007). Assistance may include the administration or supervision of medication, or personal care services provided by a trained staff person (“Assisted Living Facilities (ALF),” 2007), facilities as well as retirement homes, where a retirement home is a

multi-residence housing facility intended for the elderly (“Assisted Living Facilities (ALF),” 2007). The usual pattern is that each person or couple in the home has an apartment-style room or suite of rooms. Additional facilities are provided within the building. Often this includes facilities for meals, gathering, recreation, and some form of health or hospice care (Inspectors Often Overlook Serious Deficiencies at U.S. Nursing Homes, 2008), keeping in mind the person’s abilities to take care of themselves independently. While certainly not a residential option, many families choose to have their elderly loved one spend several hours per day at an adult daycare center, which is a non-residential facility specializing in providing activities for elderly and/or handicapped individuals (Boumans, 2005). Most centers operate 10 - 12 hours per day and provide meals, social/ recreational outings, and general supervision (Inspectors Often Overlook Serious Deficiencies at U.S. Nursing Homes, 2008). Beginning in 2002, Medicare began hosting an online comparison site intended to foster quality improving competition between nursing homes (Boumans, 2005).

Culture Change Nursing homes are leading to the way they are organized and direct to create a more residentcentered environment, making it more home-like and less hospital like (Home Care and Nursing Home Services). In these homes, nursing home units are replaced with a small set of rooms surrounding a common kitchen and living room. The design and decoration are more home style. One of the staff giving care is assigned to be the “household” (Home Care and Nursing Home Services). Residents have way more choices about when and what they want to eat, when they want to wake up, and what they want to do during the day. They also have access to more companionship such as pets, plants (Shields, 2005). Due to the residents’ diversity, the nurses and staff are learning more

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about different cultural difference, and meet more of the residents needs. Many of the facilities utilizing these models refer to such change as the “Culture Shift” or “Culture Change” occurring in the Long Term Care industry (Shields, 2005). Due to the nursing shortage problem, more and more nursing homes are heading the tendency to have more technology involve. Robot nurses are proposed to be part of the nursing home’s asset in the near future (“Robots may be next solution to nursing shortage,” 2003).

Resident Characteristics Nursing home residents are among the frailest Americans. In 2005, nearly half of all residents had dementia, and more than half were confined to a bed or wheelchair. In 2004, nearly 80 percent of residents needed help with 4 or 5 activities of daily living (bed mobility, transferring, dressing, eating, and toileting) (“Analysis of the National Nursing Home Survey (NNHS),” 2004). Most nursing home residents are female, especially at older ages, shown in Table 1 (Carrillo, 2006). Widowhood is a key predictor of nursing

Table 1. Nursing Home Residents by Age and Gender (Carrillo, 2006) Age Group

Men

Women

64 or Younger

175,000

54%

64%

65 to 84

643,000

34%

66%

85 or older

674,000

18%

82%

home use – at time of admission, over half of nursing home residents were widowed, and only 1 in 5 was married or living with a partner (Carrillo, 2006). The number of nursing home residents has remained approximately constant since 1985, but as a proportion of the population likely to need long-term care, it actually has declined (“The 65 and Over Population: 2000-Census 2000 Brief,”; “Nursing Home Data Compendium “, 2007). Over the past 20 years, the age 65 to 84 population increased by more than 20 percentage and the age 85+ population increased by more than 80 percentage, shown in Figure 1. As the percentage of older people living in nursing homes has declined, the number of stays has grown because of increasing use for short-term

Figure 1. AARP public policy institute analysis of 2004 NNHS

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post-acute care (CMS, 2008). There were close to 3.2 million total nursing home stays in Medicare and Medicaid certified facilities during 2005, up from 3 million in 2000 (“Nursing Home Data Compendium “, 2007). Projecting future trends is difficult, since nursing home usage is driven by care preferences as well as life expectancy and disability trends (“Nursing Home Data Compendium “, 2007). Current estimates are that 35% of Americans age 65 in 2005 will receive some nursing home care in their lifetime, 18% will live in a nursing home for at least one year, and 5% for at least five years (“The 65 and Over Population: 2000-Census 2000 Brief,”; Kemper, Komisar, & Alecixh, 2005). Women, with longer life expectancy and higher rates of disability and widowhood, are more likely that men to need nursing home care, and especially likely to need lengthy stays (Kemper et al., 2005).

Concerns on Nursing Home Quality Nursing Home Abuse It seems that a lot of information that we have received are positive and promising, however, negative news would never too small to negligent. Here is a piece of news from New York Times: nursing home inspectors routinely overlook or minimize problems that pose a serious, immediate threat to patients, said by congressional investigators in a new report (Pear, 2008a). In the report, the investigators from the Government Accountability Office claim that they have found widespread ‘’understatement of deficiencies,’’ including malnutrition, severe bedsores, overuse of prescription medications, and abuse of nursing home residents (Pear, 2008a). Nursing homes are typically inspected once a year by state employees working under a contract with the federal government, which sets stringent standards (Pear, 2008b). Federal officials try to validate the work of state inspectors by accom-

panying them or doing follow-up surveys within a few weeks. It released that Alabama, Arizona, Missouri, New Mexico, Oklahoma, South Carolina, South Dakota, Tennessee and Wyoming were the nine states most likely to miss serious deficiencies (Pear, 2008a). More than 1.5 million people live in nursing homes. Nationwide, about one-fifth of the homes were cited for serious deficiencies last year (Pear, 2008a). ‘’Poor quality of care -- worsening pressure sores or untreated weight loss -- in a small but unacceptably high number of nursing homes continues to harm residents or place them in immediate jeopardy, that is, at risk of death or serious injury,’’ the report said (Pear, 2008b). There are several studies point out similar quality problems that occur in nursing homes. As to logical thoughts, nursing homes must meet federal standards as a condition of participating in Medicaid and Medicare, which cover more than two-thirds of their residents, at a cost of more than $75 billion a year (Pear, 2008b). Later section in this survey, there is an illustration on more details about the financial part and dilemmas that bother nursing homes.

Study of Residents Experience in Nursing Homes There has been increasing interest in the quality of care in nursing homes. Nursing homes should protect resident’s integrity and autonomy, and it should be the place that residents can thrive (Anderson, Issel, & McDaniel, 2003). The relationship between management practice (communication openness, decision making, relationship-oriented leadership and formalization) and resident outcome (aggressive behavior, fractures) should be paid attention on. Several studies (Anderson, 2003, 2003) show that lack of competent personnel and nursing home residents with more complex caring needs leads to insufficient care. There are correlations between satisfaction, commitment, stress

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and quality of care as perceived by staff. Reports from family members are often positive but have also led to implementation of emotion-oriented care in some homes. Despite this, while generally positive, the overall assessment of nursing homes by family members has not improved (Harris & Clauser, 2002; Redfern, Hannan, Norman & Martin,2002; Finnema, de Lange, Droes, Ribbe & Tilburg, 2001). The quality of care as assessed by competent nursing home residents has been studied by some authors (Polit & Beck, 2004). They found that the residents were mostly satisfied with the extent to which their wishes were met in regards to meals, shower routines, opportunities to listen to radio and TV, and their feeling of safety. The greatest difference between what residents wanted and what they experienced concerned the opportunities they had for close social relationships (Anderson, 2003). Understanding how residents in nursing homes regard living is important and has implications for nursing care and practice. In the study “Residents are Safe but Lonely”, it describe the experiences of a group of nursing home residents (Anderson, 2003). The two research questions were: How do the residents experience care? How can nurses help the residents to have a good life in the nursing home (Anderson, 2003)? The analyses were conducted by using a qualitative content analysis method [48]. The main finding was that the experience of being ‘safe but lonely’ characterizes residents’ experiences of living in a nursing home (Anderson, 2003). The residents at the nursing home emphasized that moving into the nursing home had made them feel safer than they felt when living in their private home (Anderson, 2003). Many residents told of feeling insecure and anxious when they lived at home. They were afraid that something adverse might happen to them and that they would not be able to get help when they needed it (Harris & Clauser, 2002). The informants stressed that having people in the vicinity at all times was one of the substantial benefits of living in a nursing

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home. However, most of them have a feeling of a lack of trust in the foreign nurses (Anderson, 2003). These nurses had weak language skills which led to problems with communication and understanding both for the residents and the nurses and in turn led to feeling of insecurity for the residents (Anderson, 2003). Another thing was “haste”. The nurses were too busy to attend to the residents properly, so the informants did not feel that they were respected and regarded as unique individuals. This gave them a feeling of insecurity. All informants, however, stressed the importance of safety as the greatest benefit of living in a nursing home (Harris & Clauser, 2002). The informants gave their particular nursing home more credit than they appeared to think that nursing homes were generally given in the mass media (Harris & Clauser, 2002). They felt that they were cared for, but that at the same time the standard of their care would have been even better if there had been more nursing personnel. Feelings of loneliness and sadness are felt strongly by the residents. The days were long, boring, and empty, and informants felt uncared for (Harris & Clauser, 2002). Even though the activities are organized sometimes, it is far from what they have expected. The nurses do not see, nor do they meet the residents’ social needs. Some informants pointed out that a language barrier made communication with some of the nurses difficult (Harris & Clauser, 2002). According to those residents, the lack of personnel led to loneliness among the residents because the nurses had little time to chat with the residents (Harris & Clauser, 2002). Residents felt that this lack of respect was expressed as nonchalance (Harris & Clauser, 2002). It gave the residents a feeling of not being understood. Several informants stressed that they could not discuss their problems with the nurses because the nurses did not understand what they said and thus did not understand their needs. Other informants said that they used humor to solve language problems and that by the combined use of hands and words they could communicate what

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they wanted from the nurses (Harris & Clauser, 2002). As to the feeling a lack of reliability, it is reported that the nurses often did not follow up on agreements such as making training appointments and other agreements related to care (Redfern et al., 2002). The informants stressed that if a nurse had promised the resident something, he or she should attend to this immediately or at an agreed time. This was not always the case, and the residents felt frustrated at having to wait for ‘eternities’ for what they had been promised. This, the informants agreed on, was an aspect of their care that could be improved (Finnema et al., 2001). A central phenomenon in this study is the resident’s feelings of being met with respect. Several of the informants mentioned their feelings of being met with respect as a fundamental aspect of good nursing care (Finnema et al., 2001). To be met with respect means that the person is being met with appreciation for the unique human being he or she is. Residents in a nursing home have an unexpressed right to be taken care of in such a way that he or she feels comfortable and is regarded as a human being (Redfern et al., 2002). A resident is a human being with unique integral qualities and capabilities (Redfern et al., 2002). If residents are met in this way, hopefully they experience that more of their social needs are met so that they will not experience such a high degree of loneliness. The fact is that only competent residents were counted into this report, and 70-80% of the residents in nursing homes suffer from a dementia disease (Finnema et al., 2001). Residents suffering from dementia may have a different experience of living in a nursing home (Polit & Beck, 2004). Even if the informants mentioned positive factors in their experience of living in the nursing homes, they also mentioned deficiencies in the care they experienced (Polit & Beck, 2004). Nurses are therefore faced with the huge challenge of providing holistic care that addresses both the social as well as the physical needs of the residents

(Finnema et al., 2001). Therefore, some nursing homes start reform and try to design different daily activities for their residents.

Daily Activities There are more and more people receiving long term care in an institutional setting and that population is going to continue to grow with the aging of the “baby boomers.”So it is important to find out the what the residents ‘daily life, which is a way to spark more ideas to improve the nursing homes quality. It has been proven that when people move into nursing homes they lose a sense of control and feel a sense of isolation. Activities are one the best ways to fight these feelings and give a sense of empowerment back to the people. The variety of programs available in any nursing home depends on the health and interests of the residents. After doing research, basically, it comes up with a fairly wide range of activities in nursing homes that are designed to meet the needs of the residents. A broad range of programs is directed by the activities coordinator (Daily Activities, 2008). A home certified for Medicare and Medicaid must have someone designated as an activities coordinator (Daily Activities, 2008). The planning and implementation of activities comes from requests by residents, families, staff, and volunteers (Daily Activities, 2008). The activities are usually posted on a calendar of events that is available to each resident. Through the web reveal, here are some of the examples: •

•

Monthly birthday parties. Parties to which all residents are invited. Families and friends may be invited to participate (Fun Times, 2008). Celebrations of various holidays, both secular and religious. Holidays are particularly difficult times for those away from their own homes, families, and friends. Valentine’s Day, Halloween, Christmas,

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•

•

•

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Hannukah, Easter, and Memorial Day are a few examples (Daily Activities, 2008; Fun Times, 2008; Activities, 2008). Musical events can be enjoyed actively or passively depending on the abilities of the residents. Many homes have sing-alongs in which the residents request their favorite songs and sing along with a leader. Again, the involvement of families, and friends is crucial to the success of such a program. Sometimes concerts are given by a church or school group or friend of the nursing home. Hopefully, the public is invited to attend, for this allows the residents to provide a source of pleasure to their community (Daily Activities, 2008; Fun Times, 2008). Games foster both one-to-one relationships and group activity. Bingo is a favorite for many, but bridge, chess, and other games for smaller groups usually are available. Volunteers and families often are the ones to stimulate resident interest in a game and they may be able to help arrange suitable opponents. Contests sometimes are run with work games, and tournaments are arranged for bridge or game players (Daily Activities, 2008; Fun Times, 2008; Activities, 2008). Outdoor activities include gardening, cookouts, or just enjoying time in the sun alone or with a friend. Often the staff does not have the time to take the immobile residents outside (Fun Times, 2008; Activities, 2008). Trips and tours to community events. Some homes have a special resident fund from the sale of arts and crafts made and sold by the residents to finance transportation rentals and ticket purchases. Friends or volunteers may donate to the fund or sometimes the nursing home sets aside money. Transportation can be a problem for those in wheelchairs, but the activities coordina-

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tor usually can find volunteer drivers who are taught to cope with the special needs of disabled people. Some communities have special vans that transport residents in wheelchairs. Trips outside the home offer variety and mental stimulation (Activities, 2008). Nursing home newsletter, especially if published by residents. This is an especially valuable method of expression and uses resident talent that otherwise may lie idle. Poetry, history, birthdays, and resident and staff personality profiles are all topics that can be included (Fun Times, 2008; Activities, 2008). As we know, lots of elders are losing their ability to read as they are getting old, and therefore, it will be great if they can introduce the electronic reading device or arrange a “story time” when one of the caregivers read the news to the residents. Resident discussion groups. Sometimes a resident is an expert on a particular subject and will be the group leader. Other times a volunteer may offer to lead a discussion group. Topics may include current events, literature, and religion. The residents choose the topics and those interested attend. Exercise fun and physical fitness. Community leaders often volunteer to lead yoga or other exercise sessions. Even wheelchair-bound residents find satisfaction in exercising on a regular basis. Books. Volunteers may run a book service, taking a cart of books to the room of immobile residents. There may be a central library or small bookcases on each floor. Talking books for the blind may be part of the service. Families, friends, and volunteers can buy, bring, and hand out books. Many people help with reading to those unable to see well.

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Coffee or cocktail hours. Policies vary from home to home, but social hours provide a time of resident interaction. It is a particularly nice time for volunteers, family, and friends to join the residents (Daily Activities, 2008; Fun Times, 2008; Activities, 2008). Arts and crafts programs. They separate from occupational therapy, and are frequently offered by the activities’ coordinator. Religious services. Every Medicare- and Medicaid-certified nursing home must, by federal regulation, provide the opportunity for residents to attend religious services of their preference. Many nursing homes welcome denominational groups to provide religious services in the home for those who wish to attend. Again, this often provides an opportunity for families and friends to join the resident in worship. The organization of such services is usually handled by the activities’ coordinator (Activities, 2008).

Nursing Home Financial Regulations Billing Model Nursing home billing model is very like hospitals (Webb, 2001). Staff are housed in accessible nursing homes. Residents live in utilitarian, hospitallike rooms with little or no privacy and they sleep on hospital beds and are usually referred to as “patients” by the staff. Hospital pricing models are also used. (Webb, 2001) Residents are charged daily flat rates for semiprivate or private rooms just like a hospital. Extra services and supplies are added to the bill. This pricing model assumes that all residents require the same supervision and care. Of course this is not true (Webb, 2001). A lot could be done to improve the current system. For example, if family or friends were to help in the care of loved ones, these services

could be deducted from the bill (Webb, 2001). A number of residents are also capable of helping with the care of fellow residents or they might help with the facility services such as cleaning, food preparation, social needs, and laundry and so on. These cost savings could be passed on to all residents (Webb, 2001). State and Federal governments pay about 70% of nursing home costs and for about 85% of all residents the government pays part of or all of their costs (More Can be Done, 2002). Because the government pays such a large portion, nursing homes structure their care delivery system around the government payment system (More Can be Done, 2002). Government reimbursement is based on nursing hours and aide hours per patient, plant costs, wages, utilities, insurance, ancillary services, etc. and basically follows a hospital model (Webb, 2001). Because government programs typically are burdened with massive stationery inertia, the current pricing model will be around for a long time (Webb, 2001; More Can be Done, 2002).

Payment Sources Medicare Medicare is the government health insurance plan for all eligible individuals age 65 and older. Because of its universal availability almost everyone over age 65 in this country is covered by Medicare. There are about 40 million Medicare beneficiaries nationwide (ELJAY, 2006). Medicare will pay for 20 days of a skilled nursing care facility at full cost and the difference between $114 per day and the actual cost for another 80 days (ELJAY, 2006). Private Medicare supplement insurance usually pays the 80 day deductible of $114 per day, if a person carries this insurance and the right policy form. However, Medicare often stops paying before reaching the full 100 days. When Medicare stops, so does the supplement coverage (ELJAY, 2006).

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To qualify for Medicare nursing home coverage, the individual must spend at least 3 full days in a hospital and must have a skilled nursing need and have a doctor order it (ELJAY, 2006). The transfer from a hospital must occur within a certain time period (ELJAY, 2006). Medicaid Medicaid is a welfare program jointly funded by the federal government and the states and largely administered by the states (Caring for the Elderly, 1998). In 1998, Medicaid paid for 46.3% of the $88 billion received by all US nursing homes (Caring for the Elderly, 1998). Although the bias is only to cover eligible patients in semiprivate rooms in nursing homes, a recent court decision is forcing the States to consider more Medicaid funding for home care and assisted living (Caring for the Elderly, 1998). To receive a Medicaid waiver for alternative community services, the patient must first be evaluated for 90 days in a nursing home. There is a certain requirement for Medicaid to cover residents’ nursing home care. In order to qualify for nursing home Medicaid, an applicant must meet the “medical necessity” requirement, which generally requires a medical disorder or disease requiring attention by registered or licensed vocational nurses on a regular basis (“National Health Accounts,”). The Director of Nursing in the nursing home or an applicant’s medical doctor should be able to assess whether someone meets the medical necessity requirement (“National Health Accounts,”). Once it is determined that there is a medical necessity for nursing home care, the applicant must meet two financial tests: the income test and the resources test. The general rule is that in order to qualify for Medicaid nursing home care, an unmarried applicant cannot have more than $1,737.00 in monthly income (for the 2005 calendar year) and no more than $2,000.00 in resources or assets. If both spouses apply, the incomes are combined and the income cap is twice the cap for the individual. The resource limita-

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tions for a married couple, both of whom apply, is $3,000.00. However, the limitations are not as stringent when the applicant is married and his or her spouse is not institutionalized (“National Health Accounts,”). Insurance Insurance is an alternative source of funding for long term nursing home care. From virtually nothing 10 years ago, insurance paid 5% of nursing home receipts in 1998 (Sloan & Shayne, 1993). This percentage is increasing every year. The government is also sending a clear message it wants private insurance to play a larger role. This began with the recommendation of the Pepper Commission in 1992 and continued with the HIPAA legislation in 1996 and on to the offering in 2003 of long-term care insurance for federal workers, military, retirees and their families (“National Health Accounts,”; Sloan & Shayne, 1993). There are several bills now pending in Congress allowing full deduction of premiums and the pass-through of premiums in cafeteria plans. These tax breaks are meant as an incentive for the purchase of insurance. Medicare covers about 12% of private nursing home costs while Medicaid covers about 50%. The Veteran’s Administration nursing home operations bring total government support of nursing home costs to about 70% of the total. Such a large reliance on government support has made nursing homes vulnerable to vagaries in state and Federal reimbursement policies towards nursing homes (Sloan & Shayne, 1993).

Current Problems For Nursing Homes Funding Shortfalls The majority of nursing home income comes from government reimbursement (Polit & Beck, 2004). The industry claims that many of its nursing homes are losing money on government payments caus-

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ing yearly net business losses. For those homes that make profit, there’s not margin enough for improving infrastructure or hiring more or better qualified staff to improve quality of care (Polit & Beck, 2004). Quality of care eventually suffers with inadequate income. On the other hand, critics contend the current revelations of poor care with many nursing homes across the country stem not from lack of income but from greedy owners not willing to apply profits to improvement in care. The national chains, in particular, are accused of retaining profits to bolster stock prices in an effort to fund acquisitions (Staff Turnover and Retention, 2007; Peter, 2001).

Shortage of Nurses

Staffing

Elder Abuse and Lawsuits

A recent report from the Government Accounting Office cites widespread understaffing by nursing homes both in levels of nurses and certified nurse’s assistants (Mukamel & Spector, 2000). Staffing in these cases is below government-recommended adequate standards (Mukamel & Spector, 2000). Recently states such as California have mandated higher staffing ratios for hospitals and skilled nursing homes. But in most cases there is not additional money to cover the cost of more staff. So mandated staffing ratios will probably have little effect on the problems facing nursing homes and may actually increase their problems (Mukamel & Spector, 2000).

It shouldn’t come as a surprise with the problems of funding and staffing that reported incidents of patient neglect and abuse are on the rise (Williams, 2003). Of particular concern is the more frequent occurrence of abuse. Abuse is not only just physical assault or threats but it can also be such things as improper use of restraints, failure to feed or give water, failure to bathe, improper care resulting in pressure sores or allowing a patient to lie too long in a soiled diaper or bed linen. Lawsuits are increasing in number. (Williams, 2003; Webb, 2001) There is a great concern at the Federal and state levels to control abuse (Webb, 2001). So far, aside from proposing tougher laws to penalize the industry, there appears to be little effort in finding a way to improve the nursing home system of care delivery (Webb, 2001).

Turnover of Aides There’s no question that tight labor markets over the past decade have made it difficult to recruit and retain workers. But turnover of qualified aides is so high. Nursing homes claim they can’t afford to pay for the higher level of wages and benefits necessary to retain aides who will stay around for a while (Staff Turnover and Retention, 2007).

Next is the problem of nurses. There is currently a nationwide shortage of nurses. Nursing homes are willing to pay the salary to attract nurses but in many areas there aren’t enough nurses to meet demand (Peter, 2001). Nursing homes, as well as hospitals are using innovative work schedules to meet staffing requirements but in many cases, nurses are overloaded with too many patients. In other cases, less qualified workers are substituting for nurses (Peter, 2001). These shortages and high turnover affect the quality of care that a nursing home can provide (Peter, 2001).

PART II: E-HEALTH AND NURSING HOMES E-health is a type of telemedicine that encompasses any “e-thing” in health field; that is, any database or encyclopedia containing information pertinent

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to health, medicine, or pharmacy that is accessible via internet (Breen & Zhang, 2008). It is a typical form commonly referenced and understood as that consists of internet-based services such as WebMD.com, Medlineplus.gov, and Mentalhelp.net. Telemedicine involves the use of telecommunications technologies to facilitate clinical healthcare delivery and exchange at a distance (Thompson, Dorsey, Miller, & Parrott, 2003). This delivery method was initially designed to specifically benefit patients (Breen, & Matusitz, 2006; Wootton, 2001). E-health is a subunit of the “telemedicine umbrella” (Breen & Zhang, 2008). Because of the distance barrier that telemedicine and e-health services are able to transcend, Matusitz and Breen (Breen, & Matusitz, 2006) were convinced, through their published research, that telemedicine would be particularly beneficial in secluded towns and communities deficient in adequate healthcare services, such as bucolic regions, in mountains, on islands, and in locations of arctic climate (Breen & Zhang, 2008). Given the efficacy of telemedicine services noted by these scholars in their work in both metropolitan and more rural or remote regions, it makes logical sense that such e-health services would prove valuable when actively used by nursing staff in a variety of nursing homes, regardless of the structure and organization of the nursing home (e.g., nursing home chains in urban and rural areas, for-profit or non-profit homes, etc.) (Matusitz & Breen, 2007). E-health provides a faster and easier alternative, as well as a more cost-effective means, to accessing healthcare information, as opposed to more traditional and costly physical interactions between doctors and patients. Besides the fact that the use of e-health services is rapidly rising with, according to Matusitz and Breen (Breen, & Matusitz, 2006) 88.5 million American adults on average seeking on-line health information daily, studies have also been published that identify the quantity of and frequency of use among specific medical practitioners in a variety of specialties and settings: psychiatry, pathology, dermatol-

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ogy, cardiology, in intensive care units, and in emergency rooms where surgery is performed (Matusitz & Breen, 2007; Thompson, Dorsey, Miller, & Parrott, 2003). More relevantly, many of these relate to types of care needed and applied in nursing home settings. WebMD.com is preferred by most medical practitioners (Matusitz, 2007). One of the most important financial advantages found within the WebMD.com site (Our Products and Services) for medical practitioners in particular is that “its products and services streamline administrative and clinical processes, promote efficiency and reduce costs by facilitating information exchange, and enhance communication and electronic transactions between healthcare participants” (Our Products and Services). Further, WebMD.com (Our Products and Services) offers, free-of-charge, disease and treatment information, pharmacy and drug information, and an array of forums and interactive e-communities where practitioners and consumers can correspond using specialized e-messages. WebMD.com (Our Products and Services) doesn’t stop there in its free provisions and benefits to medical practitioners and its potential assets to nursing home settings in particular (Breen & Zhang, 2008). E-health should be readily accepted and generally understood by the professional medical population, especially in nursing home settings across metropolitan, suburban, and rural sectors. Mitka (2003) also reported that telemedicine services, particularly e-health applications, improved delivery of psychiatric care to residents of rural nursing homes (Breen & Zhang, 2008).

Health IT Acceptance in Long-Term Care Nursing homes are often depicted as laggards when it comes to embracing technology tools. But an analysis by the American Association of Homes and Services for the Aging shows that they are more than holding their own.

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Nearly all nursing homes at the time had electronic information systems for MDS data collection and billing, and 43% maintained electronic health record systems, according to analysts, who studied data from the 2004 National Nursing Home Survey. By comparison, 25% of doctor offices and 59% of hospitals handled EHRs (O’Connor, 2008).

Staff Survey There are various factors determining the acceptance of health IT applications by caregivers in long-term care facilities, including social influence factors such as subjective norm and image understandable level and demographic variables including age, job level, long-term care work experience and computer skills in regard to their impact on caregivers’ acceptance of health IT applications. A nationwide survey, being applied a modified version of the extended technology acceptance model (TAM) to examine the health IT acceptance in long-term care, reveals a positive results: Although it is suggested that the effect of the Technology Acceptance Model on professionals and general users (King & He, 2006) and on people from different cultures (Schepers & Wetzels, 2007) is different, the survey results clearly suggest that the caregivers’ acceptance of IT innovations in long-term care environment at the pre-implementation stage. In fact, the caregivers show high levels of acceptance of health IT applications. In order to ensure the successful introduction of a new health IT application into a long-term care facility, a positive environment should be established with support from facility managers and RNs for the introduction of the new innovation. As computer skills directly impact on the caregivers’ perceived ease of use and intention to use an application, effort should be made to understand the caregivers’ computer skills, and provide adequate training and support to improve their skills if necessary. “Our findings also suggest that improving the caregivers’ understanding of the

importance of the introduced system for nursing care will improve their acceptance of the new innovation” (Yu, Li, & Gagnon, 2009). In this survey, it appears that 66.4% of the participants would be able to adapt to a health IT application with reasonable training and support 34% of caregivers’ intention to use an introduced IT application before any hands-on experience with the system established (Yu et al., 2009).

Health Information Technology in Nursing Homes Information technology (IT) has significant potential to reduce error and improve the quality and efficiency of health care (Bates et al., 2001; Institute of Medicine [IOM], 2001). Some researchers also believe that computer systems can be used to reduce error and improve the reporting of adverse incidents in health care settings (Wald & Shojania, 2001). Since October 1998, all state licensed nursing homes have been required to electronically transmit data generated by the federally mandated Resident Assessment Instrument (RAI) via state public health agencies to the Centers for Medicare and Medicaid Services (CMS) (Mor et al., 2003). Advanced features in the software were available to most (87% to 98%) of the facilities; however, most features were not being used all the time. There are huge potentials addressing the technology applications with various aspects such as enhancing the administrator’s ability to manage the facility, tracking quality, and monitoring multiple performance indicators. HIT is used to collect, store, retrieve, and transfer clinical, administrative, and financial health information electronically (General Accounting Office, 2004). The IOM publication To Err is Human (Kohn, Corrigan, & Donaldson, 1999) drew attention to the potential for HIT to improve quality of care and prevent medical errors across the health care system. The focus of HIT development and implementation has been

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mainly on acute and ambulatory care settings (American Health Information Management Association [AHIMA], 2005; Hamilton, 2006; Kramer et al., 2004). Commonly found HIT systems provide computerized physician order entry (CPOE), electronic health records (EHR), and point of care (POC) to access data for entry and retrieval (e.g., reviewing records, entering orders at the bedside or in the examination room; Hamilton, 2006). In addition, electronic systems for management operations such as patient scheduling and reimbursement have been available for longer and are used frequently (AHIMA, 2005; Baron, Fabens, Schiffman, & Wolf, 2005; Poon et al., 2006). An EHR is an electronic formatting of medical records documenting the patient’s medical history, written orders, and progress notes. CPOE is a system making information available for physicians at the time an order is entered (Bates et al., 1998; Bates et al., 1999). POC is a technology automating the care provider’s procedure, visit notes, and educational materials at the place of care (Anderson & Wittwer, 2004). Although the implementation of HIT in the long-term care sector is recognized to be lagging behind the acute and ambulatory settings (AHIMA, 2005; Hamilton, 2006), there is a wide range potentials for nursing homes such as functions covering domains such as financial management, administration, ancillary care, support, and resident care (McKnight’s LTC News, 2005).

Technology Streamlines LongTerm Care Operations Technology’s role in nursing homes has been greater importance as these types of facilities serve more older adults. Most of them are expanding their focus on the best ways to deploy technology to improve residents’ quality of care. In this section, we explored some equipments/devices that favor nursing homes, skilled nursing facilities (SNFs) and assisted living facilities.

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Fall Management This care issue includes technologies aimed at decreasing the occurrence of resident falls, as well as technologies that alert caregivers and reduce injury when falls occur (Burland, 2008). Products include grab bars, non slips mats and socks, and differnt types of alarms. What fall management options are currently available? According to Gillespie SM, Friedman SM (2007), the major two categories are as following: •

Technologies aimed at reducing the risk of a fall: ◦⊦ Anti-slip footwears are socks and slippers with anti-slip material incorporated on the bottom. ◦⊦ Anti-slip matting and materials provide a slip resistant surface to stand on in slippery areas such as tubs and bathroom floors. ◦⊦ Grab bars provide stability and support in bathrooms and other areas. ◦⊦ Wheelchair anti-rollback devices prevent a wheelchair from rolling away when residents stand or lower themselves into a chair. ◦⊦ Chair, bed, and toilet alarms signal a caregiver when a resident who is at risk for falling attempts to leave a bed, chair, wheelchair, or toilet unattended. ◦⊦ Rehabilitation equipment and programs geared toward the restoration and maintenance of strength, endurance, range of motion, bone density, balance, and gait. Some examples include unweighting systems that enable residents to perform gait training in a supported reduced weight environment, systems that can test balance, and treadmills.

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Technologies aimed at reducing the risk of injury when falls occur: ◦⊦ Hip protectors are designed to protect the hip from injury in the event of a fall. ◦⊦ Bedside cushions may help reduce the impact of a fall if a resident rolls out of bed. ◦⊦ Technologies that notify caregivers when a resident has fallen: Fall detection devices use technologies that sense a change in body position, body altitude, and the force of impact to determine when a fall has occurred.

Medication Management

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Products geared toward enabling residents to manage and adhere to their medication regime with greater independence (Field, 2008). •

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Medication Applicators enable the user to apply lotions and ointments on hard to reach areas such as the back and feet. They typically consist of a sponge or pad that is attached to a long handle (Lilley RC 2006). Medication Reminder Software is installed on Personal Digital Assistants (PDA’s), personal computers, or mobile phones to provide reminders to take or administer medication at predetermined time. Some of these applications have the ability to manage complex medication regimens and can store medication and medical histories(Wolfstadt, 2008). Pill Organizers keep dry medications and vitamins arranged in compartments to assist with medication compliance and protocol adherence. Compartments are labeled for weekly or daily dosage frequencies and may be marked in Braille for individuals with visual impairments (McGraw, 2004). Multi-Alarm Medication Reminders and Watches are programmed to remind the

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user to take medications at predetermined time schedules. They typically come in the form of a specialized watch, pager or pocket device, or medication bottle cap (Lilley RC 2006). Multi-Alarm Pill Boxes serve two purposes; to store medication and provide reminder alerts to take medications at prescribed times. Most alerts come in the form of an audible tone at specific times of the day or predetermined hourly intervals (Lilley RC 2006). These pill boxes also offer compartments to help organize medications by day of the week and time of day. Personal Automatic Medication Dispensers are programmable, locked devices that will automatically dispense a dose of dry medications at predetermined times. These devices also act as multi-alarm medication reminders that alert the resident when it is time to take their medication with audible alarms, lights, text and voice messages(Lilley RC 2006). Talking Medication Bottles contain recording mechanisms that enable a caregiver or pharmacist to record a message that can be played back anytime by the user. The recorded message verbally identifies bottle contents and provides reminders concerning the medication protocol (Lilley RC 2006). Automated Medication Dispensing Cabinets provide computer controlled storage, dispensation, tracking, and documentation of medication distribution on the resident care unit. Monitoring of these cabinets is accomplished by interfacing them with the facility’s central pharmacy computer. These cabinets can also be interfaced with other external databases such as resident profiles, the facility’s admission/ discharge/transfer system, and billing systems (MacLaughlin, 2005).

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Automated Medication Dispensing Carts offer secure medication dispensation and tracking at the bedside or wherever a resident may be located during their medication time. These carts feature a wireless computer terminal with tracking software to audit access to the system and control electronically locked medication drawers (Lilley RC 2006). Barcode Medication Administration utilizes barcode scanning technology to match the resident with his/her medication order at the point of administration. If any variables of the “5 Rights of Medication Administration” do not match correctly, the administering nurse will be notified via a combination of warning tones and text messages. In many cases, this technology is used to complement automated medication distribution systems (Greenly, 2002).

Assistance Call This care issue, also known as nurse call or emergency call, includes technologies that enable a resident to summon assistance by means of a transmitter that they can carry with them or access from somewhere in their living area (Alexander, 2008). •

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Wired Systems: These systems rely on wires for communication between the main components of the system. Some wired systems allow for the addition of wireless features, such as wireless call stations, wireless phones, pagers, and locator systems. However these systems are not fully wireless and are categorized here as wired systems (Michael, 2000). Wireless Systems: These systems require no wiring for installation. All components of the system communicate wirelessly through radio waves (Douglas Holmes, 2007).

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Telephone Based Systems: These systems use telephone lines to alert caregivers to a resident need. When a resident is in need of assistance, they press a wireless transmitter that they wear (typically a pendant or wristband) or a wall mounted transmitter that sends a signal to dialing device. The dialing device automatically sends a signal to a central CPU that alerts staff to the resident need.

Bathing This care issue includes products that focus on bath safety, enable residents to be more independent with bathing tasks, and that make bathing tasks easier for caregivers (Barrick, 2008). According to Gill and Han (2006), products that enable residents to access showers and tubs more independently and safely: •

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Barrier Free and ADA Compliant Showers offer accessible features that enable residents to enter and exit the shower, sit, and access controls with greater ease and safety. Tub and Shower Chairs can be placed inside of a tub or shower and provide a seating surface while showering. Transfer Benches are placed in a tub and provide a seating surface that extends over the side of the tub, thereby easing transfers, eliminating the need to step over the side of the tub, and providing a place to sit while showering. In-Tub Bath Lifts are powered devices that are placed in a tub, creating an adjustable height surface for transfers. Mobile Commode/Shower Chairs serve several roles. They can be pushed in to barrier free and roll-in showers, thereby providing a means of transporting the resident to the showering area (self propelled or pushed by a caregiver). They provide seat-

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ing during showers, and act as a commode or raised seat for toileting. Grab Bars provide stability and support in bathrooms and other areas. Anti-slip Matting and materials provide a slip resistant surface to stand on in slippery areas such as tubs and bathroom floors.

Products that enable residents to perform bathing tasks more independently: •

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Wash Mitts offer a washing solution for those with decreased fine motor skills and an inability to handle a washcloth. Long Handled Brushes and Sponges enable residents with limited range to wash hard to reach areas such as their back, feet, and head. Rinse-Free Bathing Products enable residents to wash their body and hair without the need for running water or transferring in to a tub or shower (Gill TM, 2006).

According to Moller, Julie etc. (2003), products that assist caregivers with the transfer of residents to showers and tubs: •

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Height Adjustable Bathtubs can be raised and lowered to assist with transfers and place the tub at a comfortable working height for caregivers. Side-Opening Bathtubs provide a side opening that facilitates transfers in and out of the tub. Bath Lifts fully support the resident during bathing as well as transfers in and out of a tub. Depending on the resident’s trunk support and sitting balance, bath lifts are offered that enable the resident to be transferred and bathed in a seated or recumbent (reclined) position. Shower Trolleys and Gurneys enable caregivers to transport and shower fully depen-

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dent residents that need full body support at a comfortable working height. Showering Cabinets allow caregivers to shower residents while standing outside of the cabinet and provide the resident with some privacy. Showering cabinets open in the front to provide access to mobile commode/shower chairs or assist with transfers on to a sliding seat. Mobile Commode/Shower Chairs serve several roles. They can be pushed in to barrier free and roll-in showers, thereby providing a means of transporting the resident to the showering area (self propelled or pushed by a caregiver). They also provide seating during showers and act as a commode or raised seat for toileting (Moller, 2003).

According to Wendt (2007), products that assist caregivers with bathing tasks: •

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Shampooing Basins and Rinse Trays allow caregivers to wash residents’ hair in bed, a chair, or a wheelchair. Rinse-Free Bathing Products enable caregivers to wash and shampoo residents without the need for running water or transferring residents into to a tub or shower. Height Adjustable Bathtubs can be raised and lowered to assist with transfers and place the tub at a comfortable working height for caregivers.

Detailed Examples A Study of Remote Monitoring Test Understanding senior’s attitudes toward technology and their willingness to adopt technological solutions that help them remain independent longer, which presents a significant challenge to the aging industry. (Reilly & Bischoff, 2008; “Intelligent remote visual monitoring system for

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home health care service,” 1996) until recently, limited information regarding the practical impact if remote monitoring systems on the elderly has been available. Fortunately, as the technology development, a recent study has yield new data about this remote monitoring test (“Intelligent remote visual monitoring system for home health care service,” 1996). The April 2008 study consulting firm at four Philadelphia-based NewCourtland Eleder Services communities measured the effectiveness of senior technology and captured the perceptions of residents, family member, and staff employing it (Reilly & Bischoff, 2008). Results of the study indicate that users had a very positive attitude toward the remote monitoring technology. The two greatest advantages of the system, according to the residents’ responses, were the assistance it provided to get help quickly in the event of an emergency, such as a fall or sudden illness, and the added benefit of enabling them to live independently for a longer period of time (Reilly & Bischoff, 2008). They conducted a survey for all the residents, and 100 percent of the survey respondents either “strongly agreed” or “agreed” with the two statements. Among those surveyed, only one resident reported a concern about intrusiveness (Reilly & Bischoff, 2008). The staff study indicated similar sentiments about the system’s ability to provide better care to their residents (“Intelligent remote visual monitoring system for home health care service,” 1996). “We thought at first that adapting to the technology would be a major issue for our residents, but clearly it was not,” says Kim Brooks, NewCourtland’s vice president of housing and community-based services. “The results of the survey demonstrate that even seniors with little or no prior exposure to this technology can readily adapt to it (Reilly & Bischoff, 2008).” A remote monitoring system, typically consisting of small wireless electronic sensors, monitors daily living activities (“Intelligent remote visual monitoring system for home health care service,”). The sensors are placed in stratefic areas of the

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residents, including walls, to detect movements within the rooms; on the kitchen cupboards and refrigerator doors, to monitor whether the resident is eating regularly; and tilt sensors on the medicine boxes to monitor medication usage. The sensor in the bed can detect when a resident gets in or out of bed, toilet sensors monitor toilet usage, and homeor away sensors tell when the resident leaves or return to her residence (Reilly & Bischoff, 2008; “Intelligent remote visual monitoring system for home health care service,” 1996). A call pendant can be used as an emergency call button, which may be worn or carried by the resident. Besides, if the sensor detects abnormal activity, the system calls will help and automatically alters a caregiver via phone. A cancel button clears in-home alerts or emergency calls (Reilly & Bischoff, 2008). There is a central computing component in the nursing home—that receives all information transmitted by the sensors (Reilly & Bischoff, 2008). Based on information that is received, the base station will determine if there is a need to call for help, for example. In such a case, the base station will first sound an “in-home alert.” If the resident is okay, a cancel button can be pressed to discontinue the alert. If it is an emergency and the in-home alert is not cancelled, the system will automatically proceed through a user-defined call list, via the telephone line, until a responder accepts responsibility to check on the user. If no contacts in the personal caregiver network can be reached, an automated call can be placed to facility security services (Reilly & Bischoff, 2008). Forty-three of 54 residents were interviewed, and the response of the seniors who participated indicated that the peace of mind they obtain from knowing that they will receive help by using the new technology (Reilly & Bischoff, 2008). Only one of the residents commented as a doubt attitude to the remote monitoring method at the beginning, since they ran the risk by leaving the nurse/caregiver always “invisible”. After realizing the success on for awhile, she became happy and

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satisfied with the new system (Reilly & Bischoff, 2008). Mitzi Boegly, a 97-year-old resident who was interviewed for the study, said that she welcomed having the sensor technology in her apartment— especially after she used it to summon help after a fall. “It gives you a feeling of security knowing that if something happened to you, you can get help right away. I go to bed at night with peace of mind,” she said. The NewCourtland staff responses indicate that they also feel the sensor technology has improved the residents’ basic security and safety (Reilly & Bischoff, 2008). “The study shows our staffs appreciate the system’s ability to improve the efficacy of care delivery by directing it quickly to where it is most needed,” says Brooks. At the same time, she adds, the staff’s responses suggest a need for additional training, better reporting tools, and extensions into prognostics for chronic conditions (Reilly & Bischoff, 2008). From such a successful testing, we feel that it is promising to adapt this technology system in nursing homes. As to earlier mentioned that the shortage of nurse in the Unite State, while the continuous increasing of demands for caregivers, the installment of remote monitoring system definitely will be helpful for regulate such a lack. One thing, we need concern is about the cultural and situational difference in different areas. Research indicates that in most of northern United States that people are generally more open minded, and easier to accept new methods (“Intelligent remote visual monitoring system for home health care service,” 1996). So that in the later sections will release the indication on the attitudes of nursing home residents here in Alabama, by conducting the personal interviews to different nursing homes.

Carpet Sensor in Bedroom Due to the frequent accident happening on the seniors during the night time, a new technology has been applied to the carpet to make sure their

safety in year 1997, making it as big as a rug. Result: floors that can sound an alert when a nursing-home patient falls or when a nighttime intruder enters (Otis, 2007). It proves that such carpet has been commonly used in health care organizations, especially in nursing homes. Developed by Messet Oy, a five-person company in Kuopio, Finland, with the Technical Research Center of Finland’s VTT Automation Institute in Tampere, the room-size sensors are being tested in nursing homes in Helsinki and Tampere, says Messet Chairman Keijo Korhonen (Otis, 2007). The sensor is a thin polypropylene lamination that goes under carpeting or floor tiles (Otis, 2007). Inside the 0.002-inch-thick structure are tiny pillows of foamed plastic (Otis, 2007). These function as “electrets,” a type of electromagnet used in some microphones. When a weak current is flowing through the top surface, the pillows respond to the slightest changes in pressure by generating an electrical signal (Otis, 2007). Messet says that the structure is so sensitive that it can detect the breathing of a person lying on the floor — through the carpet. This talking carpet is welcomed by the patients as well as the healthcare providers.

Scheduling Software Using specialized software to schedule the nursing staff at St. Joseph Convent retirement and nursing home is easy and affordable. It’s also a time-saver. Marilyn Fuller says she couldn’t do without it (“St. Joseph”, 2007). Fuller, a scheduler at St. Joseph Convent, a home for 170 retired nuns, uses the Visual Staff Scheduler Pro by Atlas Business Solutions to ensure shift coverage, track and reduce overtime, view staff availability and contact information, and define custom shifts. “The software has helped eliminate mistakes and questions in the schedule and provides an accurate report on cost effectiveness by showing me a complete picture of how many hours each staff member is scheduled each week,” Fuller said (“St. Joseph”, 2007).

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The scheduler software eliminates the opportunity for staff to make changes to the work schedule. Only Fuller and the director of nursing can edit the schedule, but each unit can access a copy (“St. Joseph”, 2007). The Visual Staff Scheduler Pro software by Atlas Business Solutions is a quick and flexible tool that makes staff scheduling easy and very affordable (“St. Joseph”, 2007).

Seating System Since 1994, recliner and wheelchair design, developing cushioning and posturing appliances to make the patient comfortable, has been introduced to health care industry (David, 1994). Chairs were designed to fit the largest size potential users and modified on-site with pillows, cushions, foam pads, gel seats and, most recently, active pneumatic technology (David, 1994). This has its obvious limits, especially to whose are small. Such a high-tech seating system encountered a roadblock in nursing homes: Their funding mechanisms haven’t advanced to keep up with developments in equipment (David, 1994). These new seating systems have been developed for use in more intensive rehabilitation environments and haven’t reached long-term care yet, largely because of high cost and reimbursement difficulties (David, 1994). A higher level of seating care becomes available and is widely used nowadays, because there is a change in how therapy services are being provided to nursing homes (David, 1994). As nursing homes contract for physical and occupational therapy, they are being exposed to newer technology. Sub acute care is educating staff and creating advocates for more comprehensive seating solutions (David, 1994). Intelligent surface technology, according to BCA, allows surfaces which come in contact with the body to automatically change shape to facilitate comfort, fit, and safety (David, 1994). It first measures load distribution on the body, then calculates the most comfortable surface shape,

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and changes the shape of the surface to optimize comfort (David, 1994). “There is a dichotomy in seating,” explained by Sprigle, PhD, biomedical engineer at the Center for Assistive Technology, State University of New York at Buffalo, and an expert on therapeutic seating design. “involving two concepts that are always in opposition -- support and mobility.” (David, 1994). By optimizing support and then allowing that support to change as a patient moves in a chair, intelligent surface technology is bringing new comfort and safety to long-duration seating.

Telecommunication The use of telemedicine technology provides an opportunity to bridge the geographic distance between family and nursing home residents (Debra, 2006). Several studies have focused on using communication technologies in community settings (Demiris, 2001). Videophone technology has been used to enhance communication between home-bound patients and healthcare providers. Several studies confirm the feasibility of using videophone technology in a community setting with few technological challenges (Debra, 2006) Generally, home-based research has found that the technology is accepted by the general public and is easily managed in the home setting. “These interventions have been demonstrated to be cost-effective and satisfactory overall to users.” (Whitten, 2001; Parker, Demiris, Day, Courtney, & Porock, 2006) While most studies have focused on home care, a few have been tried in the nursing home setting (Debra, 2006). A video link between a resident and family may also allow staff to use the technology to communicate with the family, enhancing their relationship with distant caregivers. A facility may consider a shared phone for the facility to decrease resident cost and allow family members the opportunity to connect not only with the resident but also with the entire care team (Debra, 2006). Nursing home providers may want to be prepared

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to accommodate family members experienced with various forms of telecommunications and engage them in the life of the resident, regardless of their geographic distance. Facilities should consider participating in projects that evaluate this and other technology designed to enhance and improve resident and family care (Johnson, Wheeler, Dueser, & Sousa, 2000).

Heart Guard Heart disease is the frequent cause of death and early diagnosis is essential to save lives. Monitoring the heart’s rhythm and electrical activity using an electrocardiogram (ECG) provides vital information about abnormalities and gives clues to the nature of a problem (Hoban, 2003). Some cardiac conditions need long-term monitoring inconvenient for patients as it requires them to be away from their everyday environment for indeterminate periods of time (Hoban, 2003). Six years ago, Latvian company Integris Ltd, a specialist in the development of mobile wireless telemedicine ECG recording devices, came up with the concept of an inexpensive, real-time heart activity monitor for personal use. After years research and tests, 3489 HEART GUARD was born (Hoban, 2003). According to Integris, we know that “The HEART GUARD system comprises a lightweight, simple to use, matchbox-size device with five electrodes that are strategically placed on the wearer’s chest. The wireless device transmits data in real time directly to the patient’s pocket computer or desktop PC for instant interpretation by the system’s unique software” (Hoban, 2003). The low-cost device is discreet enough to be worn 24 hours a day, recording, analyzing and reporting not only the rhythm and electrical activity of a patient’s heart but also his physical activity and body positions, as they go about their daily life (Hoban, 2003). “Effectively, it is an early warning system,” explains Juris Lauznis, Director of Integris, the

project’s lead partner. “If HEART GUARD detects a problem, patients are alerted by means of vibration or a buzzer, prompting them to check their PC for further information and advice. At the very least, the device will help to monitor and manage a patient’s condition and it could even save a life.” (Hoban, 2003). Currently HEART GUARD is being developed for home use only, with patients monitoring their own condition and only contacting a doctor or hospital if the system identifies a cause for concern (Hoban, 2003). HEART GUARD also has applications in a number of other areas, including telemedicine, sports medicine, patient rehabilitation following cardiac surgery or a heart attack and as a low-cost ECG monitoring system in hospitals and nursing homes with limited budgets (Hoban, 2003), which will be extremely beneficial for the elders that want to live home with the monitoring system in the nursing home. With the 30-month project completed and clinical trials of the prototype successfully concluded by Kaunas Medical University’s Institute of Cardiology, the Lithuania Academy of Physical Education and the Research Institute of Cardiology at the University of Latvia, the next steps are to satisfy the EU’s strict compliance requirements for medical devices and then source a company to manufacture and distribute the system (Hoban, 2003). If successful, the first commercial HEART GUARD devices could be on the market and saving lives by the end of 2008 or early 2009 (Hoban, 2003).

The Latest Innovations Regarding to HIT According to long-term care (LTC) health information technology (HIT) Summit, touting the trends and innovations in HIT for long-term care, the latest innovations include handheld devices for care documentation by staff; easy-to-use touch screens with graphic icons to assess the documentations and care by direct care staff; hands-free,

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eyes-free voice documentation of assessment and care for certified nursing assistants; and software for electronic prescription processing from electronic physician order systems (O’Connor, 2005). Other technologies include managing medication passes to robotic medication dispensing, capturing quality data electronically for benchmarking and ongoing quality improvement, and operational and management information systems to assist in management functions (Harrison, 2002). According to COMTEX News Network (2009), two of the latest innovations include electronic patient monitoring and e-prescribing devices. Some of the latest technologies include products Vigil Dementia System, which features intelligent software and sensors to detect unexpected behavior, such as extended time out of bed, leaving a room, or incontinence. Others are the monitors for fall prevention, wireless call with location tracking, Wi-Fi coverage for staff communications using voice-over Internet protocol phones, and use of mobile devices necessary for electronic health record (EHR) data entry. In the past, it was a paper-compliant process done at the end of a shift and was not always the most accurate (Andrews, 2009). Facilities moving toward point-of-care systems (like touch pads in the hallways near care delivery) for nursing assistants are seeing more accurate documentation that is impacting their reimbursement rates because it is reflecting what is actually being delivered.

Challenges to Success Wilt admits that training presented one of the biggest challenges to HIT implementation. Employees needed to develop proficiency in computer use, a process that may last for several years. This is one of the more significant barriers to implementation (O’Connor, 2008). A lot of time are needed to spend in training and support to make the communities successful. Nursing homes’ adoption of technology has been slow. We have learned studies from the

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AHCA and the California HealthCare Foundation. Responses to the December 2006 AHCA and National Center for Assisted Living study “A Snap-Shot of the Use of Health Information Technology in Long Term Care” indicated that 46% of long-term care facilities continued to do the majority of their work on paper or were just beginning to use computers. Only 1% reported being paperless, and only 2% considered themselves fully computerized and just beginning to communicate or communicating fully with outside healthcare providers (O’Connor, 2008).

Concern of Standards In terms of other challenges, the issue of standards creates a concern. According to Andrews (2009), the technology integration has been a challenge mostly because there are standards out there, but they give the implementers of them great flexibility in how to go about using them. It enables to improve the monthly nursing summary that shared between the nurses and doctors by providing an electronic exchange between our two clinical systems. This has improved access to information and hopefully influenced decision making properly. “Many nursing homes are adopting a waitand-see approach until software products are certified by the Certification Commission for Healthcare Information Technology (CCHIT) using the Health Level Seven (HL7)-approved LTC EHR-S functional profile”, says Eileen T. Doll, RN, BS, NHA, president of Efficiency Driven Healthcare Consulting, Inc. in Baltimore. HL7 is an American National Standards Institute standard for healthcare-specific data exchange between computer applications (Sloane, 2006). Proper integration of technology is essential. True streamlining occurs when the clinical and administrative sides are fully integrated in all aspects—no duplicate entry, no data inconsistency, and integrated processes. Duplications are reduced or eliminated when common information

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is shared among care settings and information systems (Sloane, 2006). Reduced duplication also improves accuracy, with less likelihood that the same piece of information is different in other systems. One thing that helps with standardization is the LTC HIT Collaborative. CAST, the American Association of Homes and Services for the Aging, the NCAL, and the AHCA are partners in ensuring that the LTC sector is represented and the national HIT standards cater to the unique requirements of LTC applications (Sloane, 2006).

Nursing Home Size How large must nursing homes be to make various technologies cost-effective? What can be done for smaller homes interested in technology but unable to invest as much as larger operations? According to Majd Alwan, PhD, director of the Center for Aging Services Technologies (CAST) in Washington, DC, and a member of the LTC HIT Summit, “It really depends on the size of the facility, where they are in the process, whether they have the basic infrastructure in place or not, whether they are part of a chain or not, the management’s position, etc. Generally speaking, if shown a return on investment potential, including return in terms of quality and competitive advantage, providers should be willing to invest,” Even so, smaller homes should not be discouraged. “The best approach is to have a plan and to take EHR implementation in stages,” he says. “Plan one application or module at a time based on the organization’s tolerance for change, leadership, and financial resources. If resources are limited, start with one or two smaller applications that will make an impact, such as the nursing assistant documentation/kiosk touchpad, and keep building.” For smaller homes interested in technology, Alwan explains that most companies price technology based on usage, so smaller providers pay less than larger providers. “Many smaller homes choose not to host and manage their software and

data,” he says. “With software as a service, even the smallest [nursing home] providers can benefit.”

The Future of Technology After thorough research work, we believe that resident access to the electronic documentation will continue to be an area providers will eventually offer for their residents. We think that technologies that connect the resident and family closer together will continue to be the most requested items. Innovative technology enables residents and their families to access basic information such as lists of problems, medications, allergies, contact information, and lab reports. The ability to make this easy to use for all will be the challenge but we see an opportunity with telehealth devices that are coming onto the market today for other uses that will eventually be extended to the SNF environment (“Telehealth can improve quality of life,” 2009). As mentioned earlier, a variety of exciting technologies are on the drawing board, including advanced total quality systems that integrate several basic components already in existence, such as nurse call, wandering management, fall prevention, resident tracking, resident assessment, electronic medication administration record, and electronic treatment administration record systems. Also in the works are advanced fall prevention systems, advanced beds with embedded sensors, and comprehensive interoperable EHRs that allow sharing health information securely across different settings. Resistance to change presents one of the biggest challenges to technology integration in the nursing home environment. The industry has been manual and paper-based for so long, it requires a cultural shift to a technology base. We believe that communication is the key to overcoming this challenge. We look forward to seeing technology beyond EHR systems. According to CosmoCom Showcases Telehealth at World Health Care Congress (2009), in the near future, we will see

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current high-tech devices integrated with EHRs. Telehealth is an emerging and important technology for the future. Technology is needed to upgrade all equipment used in SNFs as well as changing the homes to meet the cultural change required for a higher quality of life.

PART III: REPORT ON THOMASVILLE NURSING HOME VISITING In this part, we will report our visiting a nursing home in south Alabama.

Background Thomasville Nursing Home is a well known nursing home in south Alabama, which is equipped with standardized facilities. It is a religious nonmedical health care institution with SNF/NF (dually certificated). Five residents are interviewed and four filled out the questionnaire. Three nurses, two social workers, and the administrator are also interviewed by giving out some brief opinions on the technology involved in the nursing home daily bases. Figures 2 (a) and (b) shows the outside photos of the nursing home.

Figure 2. Thomasville nursing home

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Research Purpose Know the actual conditions in Nursing Home in Alabama rural areas. Research are specific in the residents room decoration, overall quality on the residents’ living conditions, residents satisfaction on Nursing Home care, and staff, residents’ attitudes about technology applying.

Outcomes In Thomasville nursing home, what makes residents happy or unhappy? What are their special wishes? In this report, three themes emerged from resident responses: community, care, supportive relationships. In addition to residents’ daily happiness in nursing homes, quality life includes another great factor: technology involvement.

Community As in any community, nursing home residents focus on their living space, neighbors, and what is happening. For some residents, having others around affords conversation and companionship, especially when their roommate is or becomes a friend. The nursing home provides a sense of “fellowship, friendship, and care.” In contrast, other residents may be uncomfortable with the number of people and the amount of activity level within the environment. These residents are unhappy

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because they “always have someone around,” and wish for “a private room” or “a lot of space like I had at home.” So that in Thomasville Nursing Home, they offer different types of rooms for their residents, though it is more demanding on the single rooms, all of the interviewed residents are somewhat satisfied with their accommodations. Figures 3(a) and (b) shows the single- room pictures. To some residents, there is a sensor pad underneath the bed sheet to secure residents’ safety. Though pets are not allowed, some of the patients still feel like to have a kitty or puppy toy around with them. “TV is another important accompanier to stay away from lonely, which helps us kill time and catch up with the everyday ongoing” said couple of the residents. In the single room, some of the residents feel that they have their own privacy and their own time. Residents enjoy going to the dining room and visiting with their tablemates. They are happy when the food is good, there are snacks at night, and they celebrate birthdays together. They are unhappy when the coffee is not freshly brewed, the same foods are served too often, or the food is not cooked to their personal taste. Residents’ special wishes include “to have food and meat like I had on the farm,” “for someone to bring food from outside for my birthday,” and “to have a Residents enjoy going to the dining room and visiting with their tablemates. They are happy

when the food is good, there are snacks at night, and they celebrate birthdays together. They are unhappy when the coffee is not freshly brewed, the same foods are served too often, or the food is not cooked to their personal taste”. Residents’ special wishes include “to have food and meat like I had on the farm,” “for someone to bring food from outside for my birthday,” and “to have a martini once in a while.” The dining room (Figure 4) and activity room (Figure 5) play a significant role in their daily life in the nursing home. The planned activities provided by nursing homes are an important part of the community. Residents like to be busy and enjoy “the chance to play bingo and laugh.” Some are happy because church services are offered in their home. One of the residents says that many residents want to get into town and see new sights, and others simply would like to “get out more and see flowers and trees.” Others want to go shopping or go out to lunch. Most residents are elders, and the nursing home community may be less satisfying for younger residents. A younger resident says, “There’s a bunch of old people here instead of people my own age.” In Thomasville, the nursing home staffs also say that they understand the different demands on the their residents, however, it is hard to satisfy everyone, say, it’s a complicated process to bring the elders to the shopping in terms of their safety concern; because of the

Figure 3. Residents’ bed room with her toy kitty and TV

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Figure 4. Dining room for the residents

small portion of young residents in the nursing home, considering of the budget concern, it is not realistic to arrange activities for couple of them only. Fulfilling residents’ special wishes could increase their sense of community. According to the administrator, Dania, she says that one approach would be for the care planning coordinator or social worker to ask residents about special

Figure 5. Activity scene: Inauguration Day

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wishes when completing the quarterly or annual Myelodysplastic syndromes (MDS), a group of diseases that affect the bone marrow and blood, assessment. Many of the wishes are easy to grant through staff or family members, and that special treat could be added to the respective resident’s care plan as an onetime event. Using the MDS assessment process ensures that residents with limiting physical or mental impairments would

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be included. Other actions to increase residents’ sense of community might include providing quiet places where they can go to be alone or arranging activities targeted for younger residents that offer interactions with age-mates. Access to the Internet, with assistance in learning to use that technology, could facilitate participation in chat rooms and, perhaps, cyber friendships with peers in other facilities. The “Miss. Nursing Home”, shown in Figure 6, who is 90 year-old, told me with a big smile and proud that she has rewarded as the Miss Nursing Home last year, which brings her a lot of confidence and self-esteem. Such rewarding games and programs are consistently held by Thomasville Nursing Home, which bring all the residents get into their fun daily life.

Care Residents come to nursing homes because they need help with basic activities of daily life. Many Figure 6. “Miss Nursing Home” wears her crown

residents are happy because someone else does the housework, prepares the meals, washes the dishes, and does the laundry. They like having someone who pays attention to them and helps them meet their needs. Having things go the resident’s way is important. Residents appreciate special attention to unique interests or needs, such as having staff communicate with sign language, being able to listen to music of their choice, and having coffee with the chaplain. They need flexibility; for instance, too many baths make one resident unhappy, whereas another’s special wish is to have more baths. Residents are happy when they are satisfied with staff and believe they receive good care. They describe good care as having staff listen to what they say, having someone respond quickly to the call light, being handled gently by staff during care, receiving proper medications and treatments, and having the doctor visit. Personal cleanliness is valued--being kept clean, having clean clothes, and having clean teeth. Good care depends on the facility having enough qualified staff to meet resident needs. Long waits--for meals to be served, for help to the bathroom, or for the nurse--make residents unhappy, which has been changed in Thomasville Nursing Home after getting feedbacks from their residents. One of the residents says that sometimes busy staff save time by doing things residents could do for themselves. It may be faster to change a resident’s briefs than to take him to the bathroom, or to push a resident’s wheelchair to the dining room rather than letting her use a walker. High staff turnover also creates problems. One resident says, “The people who help me change all the time; they don’t know what they’re supposed to do.” Some residents are more vulnerable than others because of cognitive impairment, hearing or vision loss, or limited mobility. A touching special wish is that everybody be treated the same. A resident

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observes, “Some people who don’t know where they are get left behind on answering call lights.” According to the nursing home staffs and Diane, It is important that staff seek input from residents and family members about care. Feedback about how their concerns are addressed reinforces a need for their participation. Although maintaining independence is desirable, a resident’s ability to do a task warrants careful evaluation. For example, vision and fine motor skills must be considered in deciding how a resident participates in an insulin injection. Employee recognition programs should emphasize specific staff behaviors that make a difference for residents. They saw a program in another facility where residents and family members nominate staff for a “STAR” award based on outstanding performance, and a star is attached to the employee’s name tag.

Supportive Relationships Establishing new relationships with staff and other residents is part of settling into a nursing home community. Support from family, friends, and staff is critical to new residents’ successful transition and adaptation. Family and friends, “Being with those you love”, make residents happy. Their hearts will be warmed may be just by receiving a small gift, and services from family and friends, such as a cassette player from a nephew, a fish fillet from a friend, and dresses laundered with a daughter’s special touch. Residents who are unhappy about relationships with family most often feel greater separation because of distance or a perceived lack of concern. Special wishes regarding family and friends usually relate to having more contact. However, one man feels that there is too much pressure on his wife and wishes for someone to help her take care of him. Another man wishes that staff would watch his wife, a resident in the same facility, more closely. Staff members make residents happy through interactions based on respect, kindness, and

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concern, and by taking time to help, listen, and share their lives. Residents are happy when staff are attentive, but without fuss; when they invite them to events, but also allow them to be alone; and, in general, when they try to make things better for them. Residents appreciate a pat on the shoulder and staff coming to tell them goodbye. It is important for them to have something in common with staff; for example, boosting the same sports team. Shared laughter is a crucial happiness factor. For some residents, teasing staff and being teased, kidding, telling jokes, playing tricks on each other, and “picking on” staff add spice to the day. In addition to the resident council, residents need an opportunity to voice concerns privately to someone who can do something about them. Residents may feel powerless or threatened by abandonment in conflicts with staff, and may need support from a family member or friend in voicing their concerns. Family members and friends who do support residents and add quality to their daily lives deserve recognition. Examples of uncomplicated acknowledgments include feedback from staff about how much a resident enjoyed a special outing or a snapshot of a resident wearing a new sweater sent by a brother in another town.

Technology The overall quality is satisfying, and the Thomasville nursing home has already been switch to home-like style, which makes the residents more comfortable to live than that of traditional style. From the interviews on the residents, it reveals that most of the patients are willing to accept the new technology equipments. The overall attitudes on their nursing home quality is fairly satisfied, including the hygiene, food, daily activities, and nursing home services. Two of them feel it is too hard to communicate with others, and lonely is the major issue they do not want to stay in the nursing home. All of them think that it is safer to have the advanced facilities to assist their livings,

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and they feel secured to have the sitting pad on the wheel chair, alarm sleeping sheet on the bed, but they reject the clips connect the cloth and the chair, which makes them feel like being framed and controlled by a string. “Tech-Bed” is another favorite assistant at Thomasville Home. It looks and feels the same as other normal beds, so that the residents do not take it as a weird stuff. The nurses absolutely love those tech beds. “It truly is like another pair of eyes.” Sensors in the zip-on, washable mattress cover provide continuous heart and respiratory rate data. Nothing attaches to the patient. A monitor sits next to the bed and picks up the signals. Hoana presets defaults, but nurses can easily program the sensors to alarm at rates specific to their patients. “We have caught someone going into atrial fibrillation, that we had checked vital signs on an hour before,” Diane, the administrator, said. “It’s an early warning system.” Nurses at Thomasville have also picked up respiratory depression in patients receiving patient- controlled narcotics. The nursing home has monitored nurse and residents satisfaction, fall rates and cardiac arrests. Diane notes positive trends in the preliminary data. The nurses felt that the covers provided a valuable service and they found the device easy to use, said Valerie Martinez, RN, BSN. “It’s a great tool for the nurse, to help them monitor the patients when they can’t be in there,” Martinez said. “You can go back and trend things.” The nurse can assess what the alert was for and how many times it activated. “It allows the nurse to be more involved in the early recognition of patients that are starting to fail,” said Heather Long, RN, chief operating officer of Thomasville. “It provides data that brings the nurse to the bedside.” The Tech-Bed also has a bed exit alarm, with three push-button settings for patients at different fall risks. At high risk, the bed will alert if the patient lifts his or her head or shoulders off the bed. For moderate risk patients, it waits until the patient sits up and for low risk it delays issuing

an alarm until the patient actually exits the bed. Loved ones or the nurse can program the device to play a personalized, prerecorded message to get back into bed and wait for the nurse. “It’s very helpful for the elderly,” Diane said. “They hear a human voice, saying ‘I’m coming to get you,’ and they will stop and wait.” Heather said that patients do not feel or see the sensors. Once the patient understands the system, she said, “It makes them feel more secure and safe.” All of the being interviewed are holding a positive attitudes to have made the nursing home computerized, such as EMR, monitoring equipments, and alarm system. Technology makes their work a lot easier, though it took a while to get used to it. It is useful and powerful tool to make the working place more organized. Dianesays though it is getting harder to get funds from the state or federal government, the government is still supporting the ideas of keeping getting technology into long-term care, because it worth investing by thinking of saving money in a long run. She also points out that the facilities usages are not from patients’ expenses, but from the nursing homes. As for the e-prescribing which is another revolution for healthcare industry, Diane said lots of concerns. “Standards for e-prescribing have recently been developed, so this isn’t an area of widespread adoption yet,” says Diane, the administrator of Thomasville Nursing Home, “This is a complex undertaking for long-term care facilities because it requires standards to communicate between three parties: the facility, the pharmacy, and the physician.” To date, cost has been a primary inhibitor. “We as an industry have not compiled and communicated enough quantitative ROI [return on investment] data,” Diane adds. “I believe that technology can improve the quality of care, reduce risk, save time, and grow the business. These data will continue to be made available and will definitively show providers that technology is good for business and good for resident care.”

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Thomasville nursing home has been successfully implementing electronic records management: nurses’ use of clinical notes, clinical assessments, orders, and electronic medication administration records. For nursing aides, the company has implemented electronic activities of daily living documentation. “We are in the process of implementing electronic lab and integration with our Doc EMR [electronic medical record] to allow for even better communication between the teams,” says Diane. “The biggest improvements have been on the management side of the implementation,” she says. “We have been able to provide greater insight into the amount and frequency of the documentation that we have never had before. We are able to monitor more efficiently the documentation process by providing reporting capabilities that are not feasible with paper.” The concern for the Thomasville Home is financial support, “we hope that either the federal government or states will come up with more money to fund general hightech operation and HIT efforts” It is a great visiting to Thomasville Nursing Home, which allowed us to approach the residents’ real life in south United State. Those three themes are the major concern for the residents in this specific nursing home. Additionally, it is a promising future to have technology involve into the nursing homes, because the demanding is still growing, because a lot of facilities in the homes are not greatly developed, however, the residents numbers are still growing. According to the personal visiting and research, it reveals that the elders are willing to accept the new technologies for the sake of their life quality. The staffs as well as the government are taking a positive attitude in considering of long term benefits.

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CONCLUSION This chapter provides broad information on the current and future views of nursing home in the United States, in terms of the condition and management level, residents’ satisfactions and demands, government regulation and influence. The ability of e-health technology enables nursing homes to improve their quality to meet residents’ needs, though there are challenges. The visiting report about Thomasville Nursing Home reaches the depth of the consideration to how to catch the trends by implementing the technologies.

ACKNOWLEDGMENT This work is supported in part by the US National Science Foundation (NSF) under the grant number CNS-0716211, as well as RGC (Research Grants Committee) award at The University of Alabama 2008.

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This work was previously published in Pervasive and Smart Technologies for Healthcare: Ubiquitous Methodologies and Tools, edited by Antonio Coronato, pp. 39-77, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Section II

Development and Design Methodologies This section provides exhaustive coverage of conceptual architecture frameworks to endow with the reader a broad understanding of the promising technological developments within the field of clinical technologies. Research fundamentals imperative to the understanding of developmental processes within clinical technologies are offered. From broad surveys to specific discussions and case studies on electronic tools, the research found within this section spans the discipline while offering detailed, specific discussions. From basic designs to abstract development, these chapters serve to expand the reaches of development and design technologies within the clinical technologies community.

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Chapter 2.1

Improving Clinical Practice through Mobile Medical Informatics Tagelsir Mohamed Gasmelseid King Faisal University, Saudi Arabia

ABSTRACT This chapter introduces the use of mobile medical informatics as a means for improving clinical practice in Sudan. It argues that mobile medical informatics, combined with new techniques for discovering patterns in complex clinical situations, offers a potentially more substantive approach to understanding the nature of information systems in a variety of contexts. Furthermore, the author hopes that understanding the underlying assumptions and theoretical constructs through the use of DOI: 10.4018/978-1-60960-561-2.ch201

the Chaos Theory will not only inform researchers of a better design for studying information systems, but also assist in the understanding of intricate relationships between different factors.

INTRODUCTION Healthcare organisations are undergoing major transformations to improve the quality of health services. While emphasis tends to be made (especially in developing countries) on the acquisition of improved medical technology, part of this transformation is directed towards the management

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Improving Clinical Practice through Mobile Medical Informatics

and use of rapidly growing repositories of digital health data. However, the heterogeneity associated with such types of data (variety of formats, lack of data standardisation, mismatch between data and proprietary software architectures and interfaces, etc) call for addressing the problems of system and information integration. Medical and healthcare applications and services are becoming knowledge intensive. Therefore, advanced information systems’ technology is essential to produce, coordinate, deliver, and share such information. The migration from static “medical” decision support systems technologies (a.k.a medical informatics technologies) towards a new breed of more malleable software tools allow the users of medical information systems to work with data and methods of analysis within the context of a “teleportal hospital”. These medical information systems can remain efficient and effective by continuously adopting and incorporating the emerging mobile technologies. Mobile computing is going a long way in reshaping the context of healthcare provision and its efficiency. The growing physical mobility of patients mandates the use of mobile devices to access and provide health services at any time and any place. Example of such healthcare provisions range from car or sports-accidents through to research and cure of long-lasting diseases such as allergies, asthma, diabetes and cancer. The basic aim of this chapter is to investigate the potential of improving clinical practice in public hospitals in Sudan through the use of mobile medical informatics.

BACKGROUND The health care sector in Sudan is being challenged by many organizational, institutional, technical and technological issues that endangered its ability to provide quality services (UNFPA website, UNCEF website, Ministry of Health website). Because public hospitals are competing with other government units for public funds, they

failed to acquire appropriate medical technology and improve clinical practice through improved diagnosis and staff training and retention. The lack of a sound managing capacity has also reduced their ability to integrate backward (with community and rural hospitals) and coordinate forward (with educational institutions, industry and research community). The recent economic liberalization has also increased both the “financial” and “managerial” overheating of public hospitals who fail to run as self-sufficient units rather than “cost centers’. While the quality of the services provided by private clinics and hospitals (both inside and outside Sudan) tends to be high their paramountly high costs make them out of the reach of many patients. The deterioration of the quality of health services and clinical practice due to the following: 1. The lack of financial resources on the side of public hospitals due to the fact that they compete for “limited” public funds with other institutions. Their failure to acquire funds has also been accompanied with a considerable difficulty in developing appropriate plans for the effective management of healthcare institutions at the primary and secondary health service provision spectrum. Such mis-management issue has resulted into a considerable failure to develop a matrix of priorities according to which tasks “especially at the two main entrants or gates of service provision for critically I and critically III patients”: Accidents & Causality and Intensive Care Units (ICUs). Especially in public clinics and hospitals the deterioration of service quality originates from the fact that there is a lack of medical supplies. Due to the privatization trends” patients are required to pay for basic inputs. Medical consultants, whose presence at this gate is paramountly important, are not prepared to be there because they are spending much time in their private clinics. Although the

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management of ICUs tends to scientific, there is a considerable difficulty regarding the development and adoption of the suitable management model. There are five ICU models: (a) open ICU model, (b) closed ICU model, (c) co-managed ICU model, (d) managed by an intensive model and (e) mixed model. Noticeably all ICUs across the country are managed using the open model and this reflects the fact that those clinics are not patient-centered to address patient service through innovative solutions. The acquisition of medical technology tend also to be affected by the inappropriate policy making where hospital decision makers tend to think and manage in medical orientations in a way that limits their ability to understand the determinants and processes of technology acquisition (including costs of technological infusion, diffusion, maintenance and training) that they fail to address the context of competition and market efficiency forms. Medical training of newly appointed medical staff (houseman-ship training programs) is negatively affected by economic trends that made specialists and consultants unavailable at attainable periods to train graduates as well as medical students. Moreover, clinical training of medical students has been negatively affected by the growing number of medical institutes and the mismatch between the number of patients, clinically-trainable beds, medical specialists and consultants (in different areas of expertise) and the number of students to be trained. 2. The lack of appropriate incident reporting systems, adverse drug events and order issuance and management protocols to address medical errors. It worth mentioning that this component depends upon intensive R&D and is fast moving. The result of such issues is the deterioration of medication quality, error-inclusive clinical prac-

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tice and the growing costs of health and medical services. Because the overall health sectors fails to develop and implement a maintainable financial and technical sustainability, patients continued to travel to other countries for further investigation and management. In addition to medication associated costs, patients continued to face additional travel, accommodation and misconduct costs. Costs of “mediation” and misconduct associated with abuse and payment policies (reserving even bodies till payment is made”, un-necessary medication and operations and others are all examples.

MOBILITY IN HEALTHCARE Mobile and wireless systems are gaining paramount importance and deployment in They have been widely used in medical informatics, e-business, e-learning, supply chain management, virtual enterprises (Jain et al 1999), information retrieval (Cabri, et al 2000), Internet-based auctions (Sandholm & Huai 2000), distributed network management (Du et al 2003), resource management and broadband intelligent networks (Chatzipapadopoulos et al 2000), telecommunication services, and mobile and wireless computing (Keng Siau et al, 2001). Their use in the healthcare system enhances operational and contextual functionality, improving the availability, accessibility and management of decentralized repositories of concurrent data (Gasmelseid, 2007d). Mobility allows different agents to move across different networks and perform tasks on behalf of their users or other agents, by accessing databases and updating files in a way that respects the dynamics of the processing environment and intervention mechanisms (Gasmelseid, 2007c). The complexity exhibited in the healthcare industry is motivating the distribution of both the organisational structure and enabling information systems. The basic objective of such distributed environment is to build a network of complementary and integrated medical and health centres

Improving Clinical Practice through Mobile Medical Informatics

such as hospitals, laboratories, ambulatories, coordination centres, pharmacies, diagnosis centres and support units in accordance with diversity and imperativeness of services. Within this context, although each individual centre operates as an autonomous unit devoted to the delivery of a particular set of services, they are interacting to provide efficient prevention and care. According to Francesco Fedele (1995), the information systems supporting the individual centres must be structured as a federation of autonomous systems, individually optimised according to the specific characteristics of the involved units. In parallel, the individual systems must be consistent with information and procedural standards, in order to permit the mutual interoperability to provide an effective and efficient support to the co-operation individually provided by each single unit. Mobility is defined as the ability to “wirelessly” access all of the services that one would normally have in a fixed wired line environment such as a home or office, from anywhere. It includes terminal mobility (provided by wireless access), personal mobility (based on personal numbers), and service portability supported by the capabilities of intelligent networks. Especially in technology-intensive and information-rich distributed environments, mobile agents interact to gather information, route processing outcomes, update a database and read performance levels, among others. The efficiency of any mobile multi agent system is based on its capacity to achieve objectives and adapt to changing environments (Gasmelseid, 2007c). Mobility in healthcare has been associated with the concepts of ubiquitous healthcare which extends the dimensions of web-based healthcare computation. It allows individuals to access and benefit from healthcare services through mobile computing devices. While such fully or semi automatic access of healthcare information and services assists in reducing operational complexity, it also turns healthcare institutions into effective learning organizations. Knowledge provided

through mobile medical informatics includes clinical, personal, situational (environmental), and historical medical and health histories (relevant past diseases / operations / therapies mirrored upon current symptoms or already available diagnosis) indicators. Although under certain conditions, some of that knowledge may be captured from patients themselves through mobile devices, other knowledge may be accessed through distributed databases located any of the three at levels of the healthcare control process: federal, territorial and patient care centres such as hospitals, family doctors, medical specialists, pharmacy, and diagnostic centres. Because the maintenance of financial sustainability and competitive advantage (market efficiency forms, service differentiation, process efficiency, and improved patient relationship management) is looming very big in the healthcare system, mobile medical informatics offers an ITenabled platform for continuous improvement. The implementation of mobile medical informatics initiatives tend to be based on alternative technological, architectural, and infrastructurebased methodologies. This paper aims at using a mobile agent-enabled architecture to improve clinical practice through improved knowledge intensive cooperation among humans and intelligent software agents involved in producing, delivering, controlling, and consuming health services. The proposed technology proved to be useful also in improving operational efficiency through shared medical processes. The importance and feasibility of using mobile multiagent systems stems from two main reasons: 1. The diversity and multi-dimensionality of the healthcare structure: The healthcare provision system in Sudan is spread over three levels each with different level of complexity. The federal level is concerned with the promotion of national health strategies. At the Territorial (state) level Epidemiological and coordination centres take the responsibility of epidemiological and planning activities.

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At the medical centres level, hospitals operate as the main providers of patient caring. Despite the fact that each hospital is composed of a set of specialized service-based units, they also interact consistently within the context of the overall functionality of the global structure. Mobility helps in improving the quality of healthcare services and medical practice through improved information provision and location-specific organizational and managerial control. It also maintains effective institutional (within each healthcare institution) and structural (across the three levels of health control) coordination. 2. The characteristics of software agents that make them good candidates to implement distributed management. Within the context of a distributed multiagent mobile medical information system, the entire structure can be populated with a wide range of software agents possessing different qualities and attributes. Based on such attributes they can be classified. An intelligent agent is as an autonomous, computational software entity that has access to one or more, heterogeneous and geographically distributed information sources, and which proactively acquires, mediates, and maintains relevant information on behalf of users or other agents (Gasmelseid, 2007a). While autonomy remains a leading agency attribute other attributes like mobility, pro-activity, conviviality, among others are characterizing the use of multiple agents within the context of an integrated organizational structure. The topology of agents is based on their attributes and userspecific functions. Therefore, the classification includes internet agents, information agents, interface agents, shopping agents, search agents etc. However, the autonomous behaviour of these agents is determined by their proactiveness, reactive and deliberative actions, and social interactions. In a multiagent system, agents jointly use

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knowledge and resources to solve problems in a context-dependent way (Gasmelseid, 2007b). Within the context of a distributed healthcare, software agents can provide assistance in different ways. For example, they can facilitate (vertical and horizontal) coordination, cooperation and interaction among different remote health and medical centres at the three levels of the healthcare system. While this will improve the effectiveness of communication it also enhances interaction among medical staff and patient mobility resulting from embedded orchestration of service and physical mobility and service accessibility enabled by the uniformity of service points. The use of mobile agents allows the incorporation of a wide range of object oriented features necessary for information sharing, encapsulation and inheritance. This is because their conceptualization is based on the “articulation” of roles, players and relationships on the one hand and “drawing” road maps for system-wide critical success “technical” factors including security and authentication on the other hand. In addition to their roles as information gatherers, filters and learners, multi agent systems support various phases of decision making and problem solving by serving as “problem analyzers and solvers” and are engaged in “implementation”, “monitoring” and “negotiation” (Gasmelseid, 2006). When multi agent systems are used, the problem-solving tasks of each functional unit becomes populated by a number of heterogeneous intelligent agents with diverse goals and capabilities (Lottaz, et al, 2000; Luo, et al, 2001; McMullen, 2001; Ulieru, et al, 2000; Wu, 2001). The system simplifies problem-solving by dividing the necessary knowledge into subunits, associating an intelligent independent agent to each subunit, and coordinating the agents’ activity. Multi-agent systems offer a new dimension for coordination and negotiation by incorporating autonomous

Improving Clinical Practice through Mobile Medical Informatics

agents into the problem-solving process and improving coordination of different functional unit defined tasks, independent of both the user and the functional units under control (Byung & Sadeh, 2004).

IMPROVING CLINICAL PRACTICE THROUGH MOBILE MEDICAL INFORMATICS Epidemiologic techniques are suited to complications and major transfusion errors that occur with reasonable frequency rather than rare ones. The conceptualization of medical errors is based on the taxonomy provided by Reason (1990; 1997; 2000) who classified errors into active error (occurs at the point of human interface with a complex system) and latent error (represents failures of system

design). Medical errors are also associated with the lack or inappropriateness of existing patient safety practices (mainly Quality improvement practices) that increases the probability of adverse events within the health care system across a range of conditions or procedures. The use of mobile agent-mediated medical informatics to improve clinical practice in Sudan can be conceptualized using the work centred analysis shown in Figure 1 below. Work centred analysis is usually based on the identification of “users”, “major steps”, “basic approach”, and “technology”. The basic aim beyond the use of such kind of analysis as a methodological tool is to ensure the incorporation of an integrated distributed hospital environment. Therefore, emphasis tend to be made maintaining coordination between medical centres and units at the interface between inpatient and outpatient

Figure 1. A work-centered analysis

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settings; promoting performance improvement and decreasing the number of diagnostic errors of omission. As shown in Figure 2, the distributed environment of the system is based on building effective interfaces among the different medical areas of expertise in accordance with their shared functionalities and rule based integration and reasoning of acquired information. The functionality of mobile information systems in clinical fields is contingent upon its operational capacity to provide clinical support as well as the “optimality” of the operating environment surrounding it as shown in Figure 3. The contribution of such agent mediated mobile medical information system in the Sudanese context can be outlined below:

Figure 2. System based interaction

Figure 3. System enabling environment

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Universality of Patient Data and Records The question of managing and using patient’s medical data looms very big in Sudan. Especially for kidney failure and dialysis patients their mobility is growing over the last couple of years due to the lack of reliable medical services in remote trajectories and/or their inability to meet the growing medication costs. Their migration to the capital originates from their interest to benefit from the financial support provided by social funds and the concentration of qualified medical staff. Under many conditions, when they move from one city to the other they miss to bring with them their medical history. Also the same applies for referred cases where the information provided by different hospitals lacks standardization in a

Improving Clinical Practice through Mobile Medical Informatics

way that make it difficult for different medical specialists to use them as valid entities in their operational databases. The use of mobile multiagent systems provides an opportunity for reducing such complexity through the following: 1. Patients’ data can be maintained by the firstto-see medical specialist in his hospital’s data warehouse or knowledge base. Records of different patients are kept updated every time the patient visits his/her medical specialist. To facilitate information access patients’ data can be stored on smart cards and updated frequently. Upon referring patients to another medical specialist or center, patient’s data can be accessed from the smart card and updated accordingly by keeping original data unchanged. Because of the unprecedented technological developments witnessed in the medical field, mobile software agents can room and move across the internet or shared WANs to access the database of the originating medical center or specialist to retrieve necessary data. The advantage of this process are the following: a. Originating medical center or specialist will be informed about the recent developments in his patient’s health record and accordingly he/she can intervene at any point of time because of his access to the resulting real time information. b. Access can be given to a third party for a second opinion about the medical status of the patient as well as the suitability of medical management practices. This process is supported by the possibility of sharing diagnosis data such as MRI and other ultra sound images and the outstanding interface qualities of software agent systems. 2. Due to such integrated counseling medication errors as well as adverse drug events

will be minimized within a context of shared “responsibility”. Because the process enables the update of incident reporting systems in different medical canters it assists in developing reasonable medical and clinical synergies.

Physician Order Entry Validation and Endorsement A review of major studies of adverse medical events and medication errors led to estimations that in the USA between 44,000 to 98,000 people die in hospitals each year as a result of medical errors. The solutions available range from electronic patient records, order processing, clinical documentation and reporting for nursing and physicians, to computerized physician order entry in combination with rules-based decision support and electronic medication dispensing and administration solutions (Baldauf-Sobez, et al, 2003). Incorporating the Computerized Physician Order Entry (CPOE) into the backbone of the entire agent-mediated mobile information system allows both patients and healthcare institutions to gain some benefits (Mekhjian et al, 2002) such as: significant reduction in medication turn-around times, improvement in result reporting times, eliminated all physician and nursing transcription errors, and medication dispensing.

Patient Hospitalization and Recovery-Oriented Support Geriatric Hospitals address the risks of hospitalization and provide inpatient care for elders suffering from multiple co-morbidities by focusing on their special needs and geriatric syndromes. The main strategy to be adopted is to provide care by Geriatric Evaluation and Management (GEM) Units equipped with multidisciplinary teams in pursuit of improved mortality and other clinically relevant

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outcomes. Alternatively, geriatric hospitalization can be provided through comprehensive geriatric consultation service. On the other hand, Acute Care of Elders (ACE) Units incorporate the GEM unit design with additional enhancements and admit patients with acute illnesses. ACE units often provide improved flooring and lighting, reorienting devices, and other environmental improvements such as common rooms for patient use (Palmer, 1994; 1998). Within the context of the ACE unit, the GEM unit concept was enhanced by the use of more nurse-initiated protocols and a greater number of environmental and design modifications to emphasize the early assessment of risk factors for iatrogenic complications and the prevention of functional decline (Landefeld, et al 1995). The integrated agent mediated medical information system provide assistance in this regard by the development of a platform that helps in strengthening and supporting the incorporation the GEM unit into ACE units. Within this context, patients can use the components and symbols of the entire system to know directions, ask for help, get drug and nutrition information and call for assistance in case of falling etc.

can provide useful feedback about the recovery progress of paediatric inpatients.

Paediatric

a. Institutional knowledge-oriented direction: The use of mobile medical informatics not only affects medical and clinical practice but also dictates new axioms for resources sharing, medical accountability (before institutions and patients). Because the majority of healthcare institutions are public establishments their ability to benefit from the use of mobile medical informatics necessitate the conversion of such establishments into learning organizations capable of managing technology intensive acquisitions. There is also a growing need for some degree of organizational flexibility to allow for automating some of the core clinical processes, widening the hand-eye dexterity of physicians to a wider domain of consultation and

Parents used to co-patient heir children in hospitals sometimes for long periods of time. While emphasis tends to be made on clinical management, paediatric inpatients can be supported by a paediatric smart room in which patients and their co-patients (mainly parents) spend sometime entertainment time. The room can also benefit from the entire integrated system and include system-based smart toys, medication-based smart screens, as well as physical and digital tools. In addition to creating a home environment that assists in recovery, such rooms also provide some confidence for parents regarding the health status of their children. Since it operates through the backbone of the entire integrated system, it

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Medical Training The integrated counseling environment constitutes an outstanding training environment for medical parishioners. Because of the growing number of medical and health-related students and graduates both on-study and houseman-ship training is challenged by the inability of public medical and health institutions to develop a conducive learning and training environment. Such an integrated system-based counseling environment provides an environment through which good practices can be shared.

FUTURE DIRECTION The incorporation of agent-mediated mobile medical informatics to improve clinical practice in Sudan (as well as in other developing countries) calls for paradigm shifts in the following directions:

Improving Clinical Practice through Mobile Medical Informatics

enriching all knowledge bases across the federated medical centers and applications. The use of mobile medical informatics also demands careful restructuring of hospitals to orchestrate processes throughout modified and innovative “value chains” rather than “chains of command”. b. Technology-oriented direction: The implementation of innovative agent-mediated mobile medical informatics requires more appreciation of the importance of changing technological orientations of users. While the installation of technological solutions is directly and inexorably linked to “functional” processes, it is also contingent upon the planning paradigms used to ensure system-function match. This direction emphasizes the role to be played by systems analysts in articulating roles, processes and relationships in a useful value-chain-related format that promotes informed utilization and minimize the impact of dysfunctional change agents that move the whole situation into disarray due to the “blind” jump to technological platforms. Based on the above mentioned directions, further research can be directed towards the following critical success factors: 1. Intelligent systems development and assessment with more emphasis on bridging methodological gaps associated with agent oriented software engineering when such systems are reduced to medical applications. While different agent oriented theories and methodologies are being developed and used, little has been done to address and re-engineer them to fit different domains of applications with varying degrees of functional complexity. 2. Orchestrating the qualities of agents with the core value adding medical processes and allowing rooms for both concurrent processing and flexibility

of technological platforms in order to enable the incorporation of technological (and medical) developments and inventions.

CONCLUSION The decreasing efficiency and lack of quality healthcare services in many developing countries motivates patients to consider alternative mediation and hospitalization scenarios. While public healthcare institutions are being challenged by the lack of funds and public service standard operating procedures, private ones are challenged by the growing operational costs associated with the acquisition of advanced medical equipments and specialists. The marathon of private healthcare institutions originates from the need to improve competitive edges within the healthcare system. However, the dysfunctional side effects of such race-for-competitiveness game ranged from high medication and hospitalization costs in Sudan (compared to other countries) to the institutional intension to find escape gates to avoid regulatory controls and quality assurance practices. If managed properly, the use of agent mediated mobile medical informatics provides a wide range of organizational, institutional and process-oriented functionalities that significantly contribute to improving clinical practice.

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Byung, K., & Sadeh, N. (2004). Applying casebased reasoning and multi-agent intelligent system to context-aware comparative shopping. Decision Support Systems, 37(2), 199–213.

Gasmelseid, T. (2007b). From Operational Dash Boards to EBusiness: Multiagent Formulation of Electronic Contracts. International Journal of E-Business Research, 3(3), 77–97.

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Chatzipapadopoulos, F., Perdikeas, M., & Venieris, L. (2000). Mobile agent and CORBA technologies in the broadband intelligent network. IEEE Communications Magazine, 38(6), 116–124. doi:10.1109/35.846081 Datta, A., & Thomas, H. (1993). The cube data model: A conceptual model and algebra for on-line analytical processing in data warehouses. Decision Support Systems, 27(3), 289–301. doi:10.1016/ S0167-9236(99)00052-4 Du, T., Li, E., & Chang, A. (2003). Mobile agents in distributed network management. Communications of the ACM, 46(7), 127–132. doi:10.1145/792704.792710 Fedele, F. (1995). Healthcare and Distributed Systems Technology. Retrieved December 23, from: www.ansa.co.uk/ANSATech/95/ansaworks-95/ hltcare.pdf Gasmelseid, T. (2006). “A Multi Agent Negotiation Framework in Resource Bounded Environments”. Proceedings of the international conference on information and communication technologies: from theory to practice. Damascus Syria, 24-28 April, 2006. Also in Information and Communication Technologies, 2006. ICTTA ‘06. 2(1), 465-470, ISBN: 0-7803-9521-2. Retrieved from http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?tp= &arnumber=1684414&isnumber =35470. Gasmelseid, T. (2007a). A Multi agent Service Oriented Modelling of e-government Initiatives. International Journal of Electronic Government Research, 3(3), 87–105.

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Lottaz, C., Smith, C., Robert-Nicoud, Y., & Faltings, B. V. (2000). Constraint-based support for negotiation in collaborative design. Artificial Intelligence in Engineering, 14(3), 261–280. doi:10.1016/S0954-1810(00)00020-0 Luo, X., Zhang, C., & Leung, H. F. (2001). Information sharing between heterogeneous uncertain reasoning models in a multi-agent environment: a case study. International Journal of Approximate Reasoning, 27(1), 27–59. doi:10.1016/S0888613X(01)00032-9 McMullen, P. R. (2001). An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives. Artificial Intelligence in Engineering, 15(3), 309–317. doi:10.1016/S0954-1810(01)00004-8 Mekhjian, H. S., Kumar, R. R., Kuehn, L., Bentley, T. D., Teater, P., Thomas, A., Payne, B., Ahmad, A. (2002). Immediate Benefits Realized Following Implementation of Physician Order Entry at an Academic Medical Center. Journal of American Medical Information Association, (9), 529-532. Ministry of Health. Sudan. http://www.fmoh. gov.sd Monica, C.hiarini T., Remblay, A., Robert F., Uller, A., Donald, B., Erndt, B., & James, S. (2006). Doing more with more information: Changing healthcare planning with OLAP tools. Decision Support Systems. doi:.doi:10.1016/j. dss.2006.02.008 Münch, E. (2002). Teleportal-Klinik. Internal presentation, TU Ilmenau. Palmer, R. M., Counsell, S., & Landefeld, C. S. (1998). Clinical intervention trials: The ACE unit. Clinics in Geriatric Medicine, (14): 831–849. Palmer, R. M., Landefeld, C. S., Kresevic, D., & Kowal, J. (1994). A medical unit for the acute care of the elderly. Journal of the American Geriatrics Society, (42): 545–552.

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KEY TERMS AND DEFINITIONS Clinical Practice: The activities undertaken by medical staff at the different medical areas of expertise and specialization (such as surgery, pediatrics, etc) within the organizational domains of health-related organizations and prevailing professional medical codes of ethics.

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Healthcare Control Levels: Reflect both the organizational and institutional dimensions of the healthcare system which varies from country to another. Especially in developing countries where public healthcare institutions play a significant role, three control levels tend to be used: federal, territorial and local. The efficiency of such levels is affected by the existing decision making context and technological platforms. ICU Models: The key organizational characterization used for the description, coordination, and re-engineering (if necessary) of medical processes undertaken by medical (and supporting) personnel in the intensive care unit. Medical Informatics: A term widely used to describe the use of information systems (mainly decision support systems) in medical processes and interactions. The basic aim is to improve operational efficiency of medical and health centers, enhance clinical practice and the quality of medical care and promote good practices.

Mobile Agents: Are software programs that use their “mobility” qualities to room across networks in order to access information and carry out tasks for their own processes, on behalf of their owners and/or other agents. Multiagent Systems: A cluster of (homogenous or heterogeneous) software agents possessing diverse agency qualities and attributes orchestrated in an organization structure-alike context to share resources and achieve objectives within a predefined (center-specific) or universal (internet-based) processing environment. Users: All stakeholders and partners who are in direct (main and subordinate) interaction with the provision of health care services (such as physicians, pharmacists, etc) (a.k.a affecters), third party partners (such as suppliers) (a.k.a facilitators) and patients who are directly affected by the quality of medical and clinical processes (a.k.a affected).

This work was previously published in Handbook of Research in Mobile Business, Second Edition: Technical, Methodological and Social Perspectives, edited by Bhuvan Unhelkar, pp. 604-614, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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A Web-Enabled, Mobile Intelligent Information Technology Architecture for On-Demand and Mass Customized Markets M. Ghiassi Santa Clara University, USA C. Spera Zipidy, Inc., USA

ABSTRACT This chapter presents a web-enabled, intelligent agent-based information system model to support on-demand and mass customized markets. The authors present a distributed, real-time, Javabased, mobile information system that interfaces with firms’ existing IT infrastructures, follows a build-to-order production strategy, and integrates order-entry with supply chain, manufacturing, and product delivery systems. The model provides DOI: 10.4018/978-1-60960-561-2.ch202

end-to-end visibility across the entire operation and supply chain, allows for a collaborative and synchronized production system, and supports an event-based manufacturing environment. The system introduces four general purpose intelligent agents to support the entire on-demand and mass customization processes. The adoption of this approach by a semiconductor manufacturing firm resulted in reductions in product lead time (by half), buffer inventory (from five to two weeks), and manual transactions (by 80%). Application of this approach to a leading automotive manu-

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A Web-Enabled, Mobile Intelligent Information Technology Architecture

facturer, using simulated data, resulted in a 51% total inventory reduction while increasing plant utilization by 30%. Adoption of this architecture by a pharmaceutical firm resulted in improving accuracy of trial completion estimates from 74% to 82% for clinical trials resulting in reduced trial cost overruns. These results verify that the successful adoption of this system can reduce inventory and logistics costs, improve delivery performance, increase manufacturing facilities utilization, and provide a higher overall profitability.

INTRODUCTION The globalization of businesses and the infusion of information technology (IT) into every aspect of operations have introduced a strong demand for product variety and transformed business environments from a production-centric model to one that is information and customer-centric (Arjmand & Roach, 1999). Although the Internet has strengthened business with its convenience and 24x7global accessibility, it has also dramatically shifted the traditional business model to a new, competitive market space. People can now purchase anything, anywhere, at any time, and both product customization and customer requirements are increasing exponentially, making sales and inventory prediction a challenge. Meeting the wants and needs of such a heterogeneous customer population, in a global market, inevitably calls for product variety, while every efficiency-seeking supply chain prefers to process as few “flavors” as possible. Mass customization seeks an economical resolution of this fundamental conflict. Taking mass production as a foil implies that a mass customized product should not cost end customers much more than a mass produced near-equivalent, and that the customization process should not create too much of a delay. We believe that this can be realized with consistency and at scale only with a customercentric production system. This is one that enables

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an end-customer to configure (partially design) the product online and provides real-time visibility of the resulting order directly to the manufacturing floor and throughout the supply chain. In such a production system, businesses focus on their core competencies and outsource activities that are not essential to this core. Improvements in information technology infrastructures and worldwide acceptance of the internet have strengthened this transition. As a result, complex products in the market can be the result of collaborative efforts of many companies (Anderson & Lee, 1999). The auto industry is an excellent example of such a collaborative effort. A car can have over 10,000 parts, with multiple stages of production, many suppliers, high degree of product complexity, and high degree of customization. The manufacturing operation of such a business often requires a high production rate, time and space constraints and often long cycle time. High technology is another example. Fabrication-less firms that design new components are common. These firms now concentrate on their core business of designing a new component and outsource the manufacturing to specialized semiconductor and PC board manufacturing contractors. Transportation and logistics systems are additional examples in which the Internet and online commerce have facilitated rapid movements of products and material in a time-sensitive production environment. Pharmaceutical companies are yet another example in which trials for new drugs are often conducted globally and concurrently and can significantly benefit from an on-demand, real-time production control environment. The participants in these markets include suppliers, retailers and transportation services providers. The efficient operation of such markets requires extensive collaboration among its many members. There are several common themes that characterize these markets. The first theme is the timesensitive nature of the demand in such markets. The order stream for these markets can change

A Web-Enabled, Mobile Intelligent Information Technology Architecture

in a short period of time. For example, for the semiconductor manufacturing system described later in this chapter, the order stream can arrive multiple times per day creating a turbulent production environment requiring adjustments to production schedules. Similarly, transportation and delivery systems need to account for last minute changes in orders, cancellation of existing orders, addition of new orders, break downs in the actual transportation facilities and complications due to weather or traffic conditions, all within just a few hours (Dorer & Calisti, 2005). Most mass customized production environments are time-sensitive and therefore exhibit such behavior. Traditional production systems cannot efficiently address these needs. The second theme associated with such markets is the complexity of the supply chain system. The auto industry is an example of such a production environment. The supply chain is often multilayered with complex arrangements. Supporting mass customization of products in these markets can have a major impact on inventory levels for the suppliers that are located upstream from the final production vendor. If timely demand data reflecting the latest change in final product is not available to all suppliers, the inventory bullwhip effect may require upstream suppliers to stock up on the wrong components. The coordination requirements for an efficient operation of such a system are often lacking in traditional production systems. The third theme present in such markets is the adaptive requirements of such operations. Consider clinical trials for new drugs. The supply chain supporting such operations needs to adapt rapidly to reflect intermediate results from several ongoing clinical trials. As results become available, the supply chain must rapidly adapt itself to support and/or shift operations from one trial site to another and to offer complete visibility in real-time and on-demand. These themes, associated with on-demand and mass customized markets, clearly introduce addi-

tional complexity into the production environment. For these markets, optimal production solutions tend to be order/demand driven, are sensitive to each order quantity, and should account for more frequent changes in order streams. Obtaining optimality in such a production environment requires continuous analysis in very short time intervals. Effective analysis in this environment requires visibility of the changing demand data to all participants in the production system in real-time. The firms participating in such a system often follow a short-term optimization approach to production problems that require coordination and collaborations among local decision makers. The decision making for this environment is often: decentralized and distributed; geographically dispersed; more data driven with less human intervention; benefits from a rule based automated negotiation system; attempts to reach local optimal solutions for the participating firms; and performs final coordination and adjustment of these solutions in a short time interval. In contrast, the traditional production models rely on a centralized decision making process that attempts to optimize a global, central production function representing the primary vendor, to address a longer term production system. We note that agent-based systems allow timely actions and provide optimal response when the rule structure – under which agents act – does not change. However when externalities – i.e. elements not modeled in the system – become effective and change the structure of the decision process, then human interaction becomes crucial to re-establish the optimality of the decision process. For on-demand and mass customized production environments exhibiting these attributes, an agent-based information system solution can offer improved performance. We present three case studies that show how the implementation of such a system has improved the productivity and profitability for a semiconductor manufacturing company, an automotive firm, and a pharmaceutical company. Actual and simulated results show that for the semiconductor firm, the implementa-

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tion of our model reduced product lead time by 50%, reduced buffer inventory from five to two weeks, and reduced manual transaction processing by 80%. For the automotive case, our results, using simulated data, show a total inventory reduction of 51%, and an increase in production facilities utilization rates by more than 30%. Results from adoption of this approach has improved accuracy of trial completion estimates from 74% to 82% for clinical trials while reducing trial cost overruns. Other researchers report similar improvements in cost reduction (by 11.7%), reduced traveled kilometers (by 4.2%), and fewer trucks employed (by 25.5%) in a transportation system utilizing an agent-based transportation optimization system (Dorer & Calisi, 2005), and a 15% increase in revenue with improved profit margin from 2% to 4% in a logistics provider after adoption of an agent-based adaptive transportation network (Capgemini, 2004).

and reorder replenishment inventory cycle under this model will be more frequent, involve smaller lot sizes, and require shorter delivery schedules. Such a synchronized production process will necessitate demand, manufacturing and production information transparency and greater cooperation among the participating members, from the manufacturer to the primary and secondary suppliers (Ghiassi, 2001). Truly successful members of such a manufacturing environment must have stronger alliances and be willing to significantly improve their inter-firm communications. In addition, there is a need for an infrastructure that can:

ON-DEMAND AND MASS CUSTOMIZATION BUSINESS ENVIRONMENT

•

The business environment for successful implementation of on-demand and mass customized markets, thus, requires the establishment of business alliances and partnerships with selected suppliers. Mass customized manufacturing systems need to support a “demand–driven” and “make-to-order” production system. Such a production system is often component-based and may involve many business partners, suppliers, and other companies that affect the delivery of products to customers (Gilmore & Pine, 2000). Outsourcing exacerbates the challenge of coordination and planning of any such production system. Supporting a mass customized production system, therefore, requires a supply chain paradigm capable of a great degree of synchronization throughout the entire supply chain, including the entire inventory system. In particular, the order

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• • •

• •

Reduce time-to-market for product development, enhancement, and customization. Provide end-to-end visibility along the entire supply chain. Directly tie order-entry and manufacturing planning systems to speed the availability of demand requirements and to react in real-time. Intelligently and selectively communicate with a manufacturer’s strategic trading partners. Respond expediently to orders, changes in order configuration, and level of demand. Support an event-based supply chain management system in a collaborative environment.

Supporting such a production system requires a collaborative, adaptive, and synchronized supply chain management system that spans multiple firms, is event-driven, distributed, and can operate in real-time and across many computer platforms (Ghiassi & Spera, 2003a). Such a synchronized supply chain operation no longer follows a linear model. This system must be a network-based operation that requires timely availability of information throughout the system in order to allow cooperative and synchronized flow of material, products, and information among all participants. The manufacturing environment for this system

A Web-Enabled, Mobile Intelligent Information Technology Architecture

requires an operational structure and organization that can focus on building and maintaining the capacity to adapt quickly to a changing order stream while minimizing response time. Clearly, the decision making process in this environment needs to shift from a centralized to distributed mode. In this model, operational and strategic decisions may still be driven centrally; however, the responsibility to adapt to a volatile condition needs to be resolved locally. The effects of a response to such events, then, must be communicated to all relevant layers of the organization, including participating suppliers. Obviously, the relationship among the supply chain members of such a production system also must be altered to support this new business paradigm. Transitioning from mass production to mass customization is a complex, enterprise-wide process which affects external supply chain members, which are not typically controlled by the core company, and thus necessitating alliances and collaborations among all supply chain members.

ON-DEMAND AND MASS CUSTOMIZATION IT INFRASTRUCTURE Implementing such a system would require reliance on an interoperable open system IT structure, a high degree of automation in the decision process, and integration of customers, products and production information across many participants with possibly heterogeneous IT infrastructures. The technology infrastructure for an ondemand and mass customized production system requires a collaborative and adaptive supply chain management system that can account for the entire system - the customer, manufacturers, entire supply chain and the supporting market structure (Ghiassi & Spera, 2003b). An effective infrastructure for such a vast domain requires an interoperable open system capable of operating in a multi-firm, heterogeneous IT infrastructure.

In this chapter, we present an IT framework and architecture to support on-demand and masscustomized production system. We report on prototype solutions that have helped firms achieve their mass-customized production strategies. We present an architecture that can manage production resources, perform negotiations, achieve supply chain information transparency, and monitor and manage processes. We use an intelligent agent technology that can tie together resources, parts and process knowledge, in single or multiple locations, to create an agile production system. The system defines “brokers and infomediaries” and uses an intelligent, mobile, agent-based trading system that can be utilized in an interactive mode that can react to job streams with customized orders while bringing together buyers and suppliers in an efficient production environment. We have developed scheduling algorithms to balance the “time” vs. “price” trade-off by examining the relations between manufacturers and their suppliers and subcontractors. A multi-agent technology is used to schedule, control and monitor manufacturing activities and facilitates real-time collaboration among design teams. Agents in this environment can provide information about the product development process, availability of resources, commitments to deliver tasks, and knowledge of expected product completion in a distributed platform. These mobile agents are used to reach across organizational boundaries to extract knowledge from resources in order to reconfigure, coordinate, and collaborate with other agents in solving enterprise problems. The agents can interface with a customer, a manufacturing resource, a database, or other agents anywhere in the network in seconds, enabling the supply chain system to continuously adapt to demand fluctuations and changing events. Agents in such an environment can facilitate a decentralized and bottom-up planning and decision making process. Our IT architecture supports an easily accessible (web-based) order and production management system where customers can:

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•

Configure a product according to their preferences. Monitor progress and obtain status reports. Manage changes and gain overall visibility into production.

• •

In this system the firms can: • •

•

Execute and support a demand driven synchronized supply chain system. Select suppliers for non-strategic commodity components using mobile intelligent agents. Employ intelligent agent technology to support the human stakeholders for strategic sourcing in an expedient manner.

We describe a distributed, Java-based, eventdriven system that uses mobile agent technologies over the Internet to monitor multiple production systems, sense exceptions as they occur and dispatch intelligent agents to analyze broken links and offer alternative solutions. The system is designed to improve the coordination and control of the total supply chain network, through the real-time optimization of material movement. This network encompasses all entities of the supply chain, and builds a demand recognition capability that is as close to the demand source as possible, and then uses this information to drive and synchronize the replenishment of all interim raw materials, components and products. The availability of this system enables all participants to monitor, detect, predict, and intelligently resolve supply chain problems in real-time. Participants can obtain up-to-date, customized visibility into other members of the marketplace and can measure historical supply chain performance activities. This information can lead to a manufacturing operation that supports on-demand or “make-to-order” strategy and avoids inventory bloat. In a mature implementation, the supply chain operation can be transformed into a “demand

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chain” operation, in which inventory is reduced to its minimal level throughout the entire system.

OVERVIEW OF THE MODEL By providing a common communication infrastructure along with intelligent agents and optimization algorithms, we offer a system for transforming static supply chains into dynamic, web-centric trading markets that are closely integrated with the manufacturer’s systems. This system formulates the production problem as a set of distributed, decentralized, event-driven optimization sub-problems that can efficiently support an on-demand and mass customized production environment, which are often characterized by high demand variability, fixed capacity, time to market sensitivity, and/or complex supply chain network. This approach models the supply chain problem as a hierarchical framework and extends it to support a synchronized production system. The framework presented in this chapter partitions the mass customization production environment into hierarchical levels of process, plan, and execution. The framework offers facilities for process monitoring and analysis, short and long term planning capabilities, and efficient, optimized execution of production opportunities. It also provides visibility throughout the system by allowing changes in orders to be communicated to all impacted participants. Each participant in turn reacts to these changes and broadcasts its locally optimized solutions. In the next level of the hierarchy, the local optimum solutions are coordinated and synchronized and discrepancies are broadcasted again to all affected parties. In the final decision making level of the hierarchy, automated and human assisted negotiating agents resolve all production conflicts in support of the final committed production. The responsiveness required for such highly synchronized production systems is significantly higher than those of non-

A Web-Enabled, Mobile Intelligent Information Technology Architecture

synchronized systems. This requirement necessitates real-time collaboration among all supply chain members. The supporting IT infrastructure and applications presented in this model are able to cope with high demand variability, short product life cycles and tight collaboration and interactions with suppliers. In this model, information about the entire supply chain system is visible to all authorized members, especially through any device that is Internet enabled. Our system allows the up-to-date demand data to drive the production system following a “make-to-order” production policy. In solving the production allocation sub-problems, the up-to-date demand data is used to monitor production allocation at various plants. The fluidity of the demand undoubtedly can create many exceptions to the centrally designed production plan. Decentralized decision-making can allow local players to alter execution plans to satisfy their constraints. To manage such scenarios, a system must be able to detect an exception or unplanned event, identify the players impacted by the exception and its resolution, and develop the models necessary to solve the local sub-problem and to resolve the exception. To be fully effective, such a decentralized decision-making environment requires real-time data running across an intelligent peer-to-peer infrastructure and integrated with contextual understanding of the local production sub-problems. In the past years, many researchers have examined using “agent technology” to find solutions to problems arising in supporting masscustomized production. (Sundermeyer, 2001 and Wilke, 2002) present an agent-based, collaborative supply chain system for the automotive industry (DaimlerChrysler). These authors identify rapidly changing consumer demands, planning uncertainties, information flow delays, and sequential, batch oriented policies as elements of traditional production systems that contribute to an inefficient production environment. The authors offer an agent-based, web-centric, collaborative solution

that integrates the entire supply chain and provides a more efficient production system. They report results from a simulation study which show that using a collaborative, distributed, agent-based supply network management system can reduce production and inventory oscillations, safety stock levels, and working capital. Similarly, (Dorer & Calisti, 2005) report on the integration of an agent-based system into a real-world IT architecture in order to solve and optimize transportation problems facing a major logistics provider. They note that “conventional systems for transportation optimization are limited in their ability to cope with the complexity, and especially with the dynamics, of global transportation business where plans have to be adjusted to new, changing conditions within shortest time frames.” In the following, we describe a Java based software system that uses intelligent agents to constitute a theoretical and practical basis for addressing mass customization markets.

Agent Taxonomy We define a mobile intelligent agent to be an autonomous software program, capable of achieving goals defined by a set of rules, with learning and adaptive capabilities that are loosely or tightly coupled with other peers. Software agents are often designed to perform a task or activity on behalf of the user and without constant human intervention (Ma, 1999; Adam et al., 1999). Existing literature lists applications for intelligent agents in a wide array of electronic businesses, including retailing, purchasing, and automated negotiation (Jain et al., 1999; Ma, 1999; Maes et al., 1999; Sandholm, 1999). These software tools are used as mediators that act on behalf of their users. Specifically, these agents perform repetitive and predictable actions, can run continuously without human intervention and are often stationary. The authors in (Singh et al., 2005) present an agent-based model that uses E-marketplaces and

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infomediaries to register suppliers and consumers of an E-supply chain. This system facilitates purchasing of material by collecting demands from the participants, then locating and matching this demand with suppliers. The proposed system develops supplier profiles based on a satisfaction survey of past performances. When extended to multiple E-marketplaces, a global balancing of supply and demand becomes viable. This system, however, does not include the actual manufacturing operations and relies heavily on the existence of E-marketplaces, infomediaries, and the willingness of all participants to register their demands, capacity and their services. Researchers have also applied intelligent agent technology to manufacturing and production systems (Kalakota et al., 1998), and mass customized production environments (Baker et al., 1999). Others have used agent technology to support what they have termed “adaptive virtual enterprises” (Pancerella & Berry, 1999). They have used mobile agents to extend across organizational boundaries. In their model, these agents extract knowledge from resources in order to reconfigure, coordinate, and collaborate with other agents in solving enterprise problems. There are many mobile agent systems that provide a platform for agent applications; most of these systems are Java-based. Some examples include “Aglets” from IBM, “Concordia” from Mitsubishi, and “Voyager” from ObjectSpace (Lang & Oshima, 1998). We have applied agent technology to ondemand and mass customized production problems. We use mobile software agents to support a distributed decision-making process. A mobile agent, unlike a stationary agent, is not bound to one execution environment. A mobile agent can transport itself and its supporting data to other execution environments. It can then begin execution or use data from both sites to perform its tasks. The mobility also allows the agent to interact with other agents or traditional software systems on the new site. Mobile agents addition-

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ally offer the opportunity for execution support across heterogeneous computing environments. They can reconfigure themselves to interface with destination environment’s libraries, database systems, and other computing components to perform their tasks. Mobile agents introduce a new paradigm to distributed computing. In this paradigm, any host computer in the network can benefit from the “know-how”, resources, and processors throughout the network. We introduce an architecture that deploys mobile agents in the value chain to streamline the efficiency and performance of the entire network under various constraints of materials and time. The agents perform the following primary functions: 1. Distribute and collect data at the right time to process and analyze. 2. Coordinate inter-enterprise business processes and propose the best response to avoid disruptions throughout the supply chain. 3. Tactically and strategically adjust the supply network in an optimal and timely manner to changes in market conditions. 4. Assist the decision-makers in their learning processes. The four functionalities (monitoring, analyzing, acting, and coordinating) characterize the first dimension of our agent taxonomy, the second being given by the role that each agent assumes in the value chain. We have identified three types of roles: 1. Consumer Assistant. 2. Demand Management. 3. Supply and Resource Management. The agents performing these roles are mobile and ubiquitous. These agents address the needs of end users, manage product demands, and coordinate supply and resource management continuously. The time-sensitive nature of an

A Web-Enabled, Mobile Intelligent Information Technology Architecture

on-demand production environment requires the decisions to be made expediently. The mobility of the agents allows the communication between them to occur in real-time. For instance, consider the auto industry case discussed in this chapter. The consumer assistant agent is used by the end user to define product features, place an order, check order processing, and monitor production status of the product all the way through actual delivery. Similarly, in a transportation/package delivery system, the consumer assistant agent can be used to even alter the actual delivery location of a package as the recipient relocates before the final routing of the scheduled delivery of the product. The demand and supply management agents can also perform their roles in real-time and assist decision makers in resolving conflicts and coordinating operations. In particular, conflict resolutions caused by supply or capacity shortages can be handled more expediently. The mobility of the agents and their real-time connectivity within the production system and with the actual end user is utilized to alter the production schedule and inventory levels. These agents can be pre-programmed

to handle some decisions automatically to conduct some negotiations on behalf of their clients. For example, when a product feature is in short supply, the demand management agent can notify the consumer assistant agent of possible delays in delivery and to offer alternative resolutions. At the same time, this agent can negotiate with supply management agents to locate new sourcing of components that are hindering timely production of the final product. Additionally, the mobility of the agents allows managers and consumers to participate in decision making in real-time, using the Internet or even hand-held devices. The technology used to implement these agents and their communication protocols are presented in next section. The complete Functionality versus Role determining our agent taxonomy is reported below. Agent goals are reported in the corresponding cell. The entire system uses an event-based architecture (Figure 1). An event in this system is defined as actionable information (an object) that appears as a message in the system. The monitoring agents are registered to listen to certain mes-

Table 1. Agent taxonomy and functionality Functionality vs. Role

Consumer Assistant

Demand Management

Supply & Resource Management

Monitoring

Detect variations of any relevant (for the consumer) product features.

Detect key demand variable changes at product model and location level.

Detect key supply variable changes or resource constraints at product model and location level.

Coordinating

Bring order and semantic structure to the collection of demand and supply events gathered from multiple sources by the mobile assistant.

Bring order and semantic structure to the responses for the mix of demand events that are firing synchronously from multiple sources.

Bring order and semantic structure to the responses for the mix of supply events that are firing synchronously from multiple sources.

Analyzing

Rationalize the follow on actions for comparative analysis based on features and customers’ preferences.

Rationalize the follow on actions from excess inventory projections, sub-optimal minimum inventory levels, networked replenishment requirements and multiple shipping alternatives and constraints.

Rationalize the follow on actions from excess/shortage inventory projections, sub-optimal minimum inventory levels, networked replenishment requirements and multiple assembling shipping alternatives and production constraints.

Acting

Adjust customers’ decisions as their preference changes.

Adjust projected demand through mix of techniques: Optimize base parameters, modify elasticity, adjust for cannibalization and influence demand through dynamic pricing.

Adjust projected supply and production rates through mix of techniques: Optimize base parameters, modify elasticity, adjust price and optimize supply through tight collaboration.

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sages. These messages are interpreted and processed either synchronously (i.e., as soon as they arrive), or asynchronously (pending the agent’s reaching of some pre-defined state). An event bus is introduced as a building block that receives, sends and processes messages as objects. The event bus offers an interface that is visible to any component of the system that wishes to send and/ or receive events (Figure 1). The agents presented in this architecture behave dynamically, are rule-based, mobile and respond to events. The behavior of the agents can be in reaction to both internal and external events. For instance, consider an inventory management scenario; the “analyze” and “act” agents can use parameters defined by other agents (monitoring and/or coordinating agents) to determine inventory replenishment policies that reflect up-to-date information about market conditions or even macroeconomic indicators, in addition to specific product parameters such as available sources, lead time information, the supplier’s recent behavior profile, and the latest speed of inventory deliveries. The supporting agents

(monitoring and coordinating agents) can monitor internal and external events and processes to acquire and coordinate the necessary data that allows the analyzing and acting agents to manage inventory levels dynamically rather than the traditional static models that use predetermined inventory replenishment parameters. Finally, in production environments for which E-marketplaces exist, this architecture allows the agents to seamlessly interface with these markets to execute procurement activities. When E-marketplaces do not exist, the agents can additionally perform market-making activities, such as identifying candidate suppliers, communicating requirements, and soliciting quotations.

AGENT SERVICE ARCHITECTURE FOR MASS CUSTOMIZATION We present an architectural design which is agent-based, supports mass customization, and uses a “cluster model” representation as depicted in Figure 1. This architecture supports the firm’s

Figure 1. An agent-based architecture for mass customization

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IT infrastructure, and interfaces with existing DBMS, ERP, and other manufacturing support database systems. The architecture uses existing IT networking systems to establish connectivity across participating firms in the marketplace. Clusters of agents can support multiple product lines and/or even different features of a product. The main features of the above design are: •

Agents Technology and Architecture: ◦⊦ Agents are developed using a framework based on the ABLE (Agent Building and Learning Environment from IBM) (Bigus et al., 2002) and JADE-LEAP (Java Agent Development Framework and its extension Light Extensible Agent Platform) (Bellifemine et al., 2003, Bergenti & Poggi, 2001) platforms. The multi-agent system introduced in this architecture uses the ACL language (Agent Communication Language) (ACL, 2006) which complies with FIPA (Foundation for Intelligent Physical Agents) specifications. The architecture employed is distributed, and supports multiple hosts and processes, including mobile systems. Each running instance of the JADE runtime environment is called a “container.” A container can include multiple agents (Figure 2). A set of active containers is called a JADE platform which can be distributed across several hosts. The distribution can be from one platform to many hosts and/or many platforms to many hosts. The inter-agent and intra-agent communications occur via ACL messaging. Each agent is represented by one java thread. ◦⊦ Agents are registered in JNDI (Java Naming and Directory Interface) for clustering support.

◦⊦

•

•

•

Agents communicate asynchronously through the event bus amongst themselves, and can interface with external services (web services, application services and external agents) synchronously. ◦⊦ Agents learn and acquire intelligence through optimized algorithms per agent functionality. Control Panel: ◦⊦ The control panel allows users to create a new agent on demand and register it with the event bus and the JNDI. ◦⊦ The control panel allows users to modify agent parameters and thus modify the behavior of existing agents. Communication Interface: ◦⊦ The event bus structure is introduced and developed to facilitate fast internal communications among agents. ◦⊦ The JMS (Java Message Services) services are introduced to facilitate communications among agents and external applications including web services interface, WMA (Windows Media Audio) services, agents from other applications, external users and the web environment at large. Persistence Layer: ◦⊦ The persistence layer assists agents in interfacing with existing databases and data retrieval systems.

These features offer the necessary elements for supporting visibility across the entire supply chain and allowing the establishment of collaborative efforts in support of a demand driven, mass customized production system. To illustrate these concepts, consider an agent-based system implementation for a manufacturing environment. Figure 2 presents the implementation of this technology for a hypothetical manufacturing system. In this system, agents are implemented as Java

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container classes that perform the Monitoring, Coordinating, Analyzing and Acting functionalities. The server side of Figure 2 lists five agent containers. The client side shows how users can use mobile phones, PDAs and similar devices to gain access to the system. In Figure 2, the “I/O Container” offers monitoring agents that communicate with data entry systems and provide interface capabilities with existing DBMS, ERPs, and manufacturing databases. Agents from this container communicate and exchange information with the central coordinating agents. Similarly, the “Inference Container,” an analyzing agent, stores information about the manufacturing operation for all possible product configurations. It continuously analyzes production schedules, capabilities, and equipment capacities. It also analyzes manufacturing requirements based on production status and the order stream information for new orders to form a batch and to hold them until notified by another agent (the optimizing agent) to release its information. The communications among these agents is in a synchronized mode. The “Optimizing Container” is an acting agent that is invoked upon receiving a new batch of orders from the agents of the “Inference Container.” These agents optimize the processing of the orders. The optimization process includes (a) scheduling optimization using tabu search and genetic algorithms, (b) resource planning using linear and integer programming, and (c) inventory optimization using stochastic optimization methods. The “Back-end Splits Container” serves as the interface channel to the client side. The agents in this container receive and record orders from external agents. The orders are filtered and organized according to their manufacturing requirements and are aggregated and presented to the main container for further processing and optimization. The customers and/ or external agents can use this interface to check the status of their orders. Finally, the “Main Container” serves as the coordinating agent. It interfaces with all other agents, provides supervisory

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functions to all agents, performs synchronization among processes, and serves as conflict resolution authority among agents. In this architecture, a persistence layer is provided for interfacing with the external database and data retrieval systems. The entire system is Java based and uses the agent technology framework developed by IBM (the “ABLE” technology) (Bigus et al., 2002) and JADE-LEAP framework from the open source software (Bellifemine et al., 2003; Bergenti & Poggi, 2001) and the ACL messaging language. These frameworks are extended and modified to support the mobile technologies that will allow users access to the system via mobile phones, PDAs, and any web-enabled device or interface (Moreno et al., 2005). The system presented supports the decentralized, distributed decision making model discussed earlier. It is based on a distributed, web-centric, client server architecture in which the server resides at the manufacturing (primary) vendor and the client operates at each supplier site. The clients’ implementations are lightly integrated into the suppliers’ existing IT architectures. The webcentric nature of the system ensures accessibility across the participating members as depicted in Figure 2. The consistency of the system is ensured by the choice of the communications and the agent frameworks used in this implementation. The use of standard protocols such as XML and the collaborative nature of the environment require each participant to either directly comply with these protocols, or provide converters and translators in support of the communication systems. An implementation of such system may include many geographical servers at various locations. Client installations can also be located at geographically and/or organizationally dispersed sites. Real-time production and consumption data is gathered from the entire supply chain system and distributed throughout the system for decision making (Figure 2).

A Web-Enabled, Mobile Intelligent Information Technology Architecture

Figure 2. An agent-based architecture for a manufacturing system

CASE STUDIES In this section we will discuss case studies that report on adoption and implementation of our approach by three companies that produce ondemand or mass customized products. The first case study reports on adoption of this system in a semiconductor manufacturing environment, the second case study presents our experience with implementation of the system in the automotive industry, and the third case study reports use of the system by a pharmaceutical company.

The ST Microelectronics System The first case presents a semiconductor manufacturing firm (STM) that has adopted and implemented our software system. STM is the fourth largest semiconductor company in the world with revenue in excess of eight billion dollars. A large part of this revenue comes from integrated circuits that are heavily customized to STM’s customer specifics.

STM is the primary supplier of assembled chip sets to a number of mobile telephone providers. The mobile telephone companies offer a mass customized product line with multiple features (GPS, Camera, and Phone capabilities). There are a number of valid product configurations for consumers to choose. Orders from the phone companies to STM for the various chip sets often arrive daily or even twice per day. STM’s production system needs to respond to demands on daily or even shift by shift basis. The order/ reorder processing system, defined by the demand at STM centers, requires agile manufacturing facilities capable of supporting a demand-driven supply chain system in real-time. The architecture described here, and its implementation at STM enables the company to operate and manage a manufacturing system far more efficiently than older, batch driven, build-to-forecast alternatives. In July of 2000, STM adopted our software for their mass customized production system. The server side of the software system was installed at STM manufacturing facilities and the clients’ side

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at customers (the telephone manufacturers) and STM suppliers. This implementation has enabled STM to develop collaborative programs with its top customers and has seen notable progress in its delivery schedule, lead time, inventory management, and profit margins. An important requirement of the project was the ability of STM to connect its IT infrastructure to its customers’ existing installed IT systems using mobile intelligent agents. Our software system, sitting on top of STM’s existing planning platform, detects the RosettaNet (RosettaNet, 1998) and XML messages, and provides visibility into different inventory scenarios, such as what parts to build and where to hold inventory to meet the highly customized demand. The system offers a set of agent-based coordination tools for extracting and transmitting data across the entire IT infrastructure. This strategy has offered STM a proactive approach for supporting its mass customization strategy. It has allowed STM to connect its long-range plans with short term execution and to optimize the inventory levels and production schedules in order to quickly build the integrated circuits that meet its customers’ demands. STM supplies IC boards (assembled chip sets) to several mobile phone providers. The boards need to be customized according to each customer specification. The challenges facing STM in supporting their customers are to: • •

•

•

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Develop a long-term planning framework for process and systems improvements. Implement new demand planning, E-commerce communications, capacity planning, and efficient supply chain execution. Roll out E-commerce with supply chain partners, perform end-to-end supply chain planning, and implement an effective available-to-production (ATP) capability. Continue to follow a build-to-forecast (BTF) policy in wafer fabrication and

•

•

probe to maintain a pre-defined level of inventory in the die bank. Implement a build-to-order (BTO) policy in the assembly and test phase of the IC manufacturing. Develop a financial model to trade-off inventory, manufacturing, and supply chain costs with revenue potentials.

STM adopted the agent-based adaptive supply chain software system described earlier to achieve these objectives. The system is presented in Figure 3 and offers a collaborative supply chain network, supports adaptive demand synchronization and adjustment, provides an adaptive inventory optimization and replenishment policy, and optimizes order, material and flow management systems. In Figure 3 the event management system models supply networks, monitors network input, and triggers events. The agents in the system provide demand synchronization, network inventory optimization, multi-echelon inventory replenishment, delivery and routing planning, and coordination of the final decision resolution. Each of these components includes multiple intelligent agents that perform the monitoring, coordinating, analyzing, and acting functions described earlier. The embedded decision support tools of the system offer performance visibility of suppliers through collection of rankings and metrics, and provides a library of optimization algorithms and learning models. In Table 2 we provide a comparison of the demand management activities of STM under both the traditional approach and the new agentbased adaptive approach. Similarly, Table 3 presents the traditional and the new approach for inventory optimization module. The adaptive replenishment collaboration process, its traditional implementation, and its new implementation are presented in Table 4. Finally, the routing process used to manage order and reordering of the IC boards, material

A Web-Enabled, Mobile Intelligent Information Technology Architecture

Figure 3. STM adaptive supply chain system

Table 2. Demand management activities for STM Traditional Approach Forecast sharing is not traditional, but if occurs, follows a passive process like the following: 1. Semiconductor supplier receives forecasts and firm orders from Distributor and OEM on a weekly/daily basis by ship-to location and SKU. 2. Preprocess data to adjust for standard customer request dates, and account for uncertainty. 3. New forecast and firm order data are updated in demand planning system. 4. Supply chain planners use new demand data to reevaluate and adjust production plans.

Agent-Based Demand Management Approach Agent-based adaptive solution is driven by demand changes that impact as far upstream the supply network as possible. 1. Semiconductor supplier receives forecast or actual sales data from Distributors and OEMs on a daily/weekly basis. This will depend on cycle for forecast updates. If actual sales are given, then forecast from Distributor and OEM is also provided as it changes. 2. Semiconductor supplier receives changes to firm orders from OEMs and Distributors on a daily basis 3. Event management system triggers a “New Demand” event whenever a new forecast or firm order is received into the system. 4. MONITOR agents are listening for any demand event and will apply the appropriate business rules to adjust the input streams and trigger the required ANALYZE agents to check for any trending anomalies. 5. ANALYZE agents perform the following actions: €€€€€€€€€€a. ANALYZE “Order Synchronize” is designed to detect inconsistencies with transition from forecasted to firm orders. €€€€€€€€€€b. ANALYZE “Volatility” is designed to check for excessive volatility between successive forecasts. €€€€€€€€€€c. ANALYZE “Forecast Error” is designed to check for significant error between forecast and semiconductor shipments. €€€€€€€€€€d. ANALYZE “POS Sales” is designed to check for significant error between customer forecast and daily/weekly sales from source location in supply network. In this case that is the OEM’s and Distributor’s. 6. ANALYZE agents will coordinate with ACT agents to engage demand synchronization actions €€€€€€€€€€a. ACT “Rationalize Error” agents deduce root cause actions from analysis results. €€€€€€€€€€b. ACT “Optimize Parameters” agents adjust forecasting model parameters to minimize forecast error. €€€€€€€€€€c. ACT “Forecast” agents create and recommend new forecasts.

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Table 3. Inventory management activities for STM Traditional Approach 1. Inventory performance is reviewed on a periodic basis. 2. Viewed from a single enterprise perspective. 3. Review of safety stock levels based on expected demand and variability, general service level targets, lead-times, lead-time variability.

Agent-Based Inventory Management Approach 1. Inventory management is driven from a network perspective. 2. Event management monitors demand trending, demand error trend, lead time trending, supply error trending, inventory level trend and service level performance. If there is a threshold violation, an event is created. 3. MONITOR agents deduce the semantic value of the mix of events and will trigger the appropriate ANALYZE agents. 4. The ANALYZE agents will focus on the following: €€€€€€€€€€a. ANALYZE “Stock Up” agents deduce where and why average inventory is increasing. €€€€€€€€€€b. ANALYZE “Stock Out” agents deduce where and why stock-outs are excessive. €€€€€€€€€€c. ANALYZE “Min Buffer” agents compute the minimum for all key buffers in network. 5. ANALYZE agents will coordinate with ACT agents to engage inventory optimization actions. €€€€€€€€€€a. ACT “Balance Network” agents will be used to adjust inventory positions and levels via ACT “Replenishment” agents to adjust for both inventory shortage and excess scenarios. €€€€€€€€€€b. ACT “Set Level” agents make the appropriate safety stock parameter adjustments at all buffers.

Table 4. Replenishment management activities for STM Traditional Approach 1. Long-term forecasts are created by customers for up to 3 months lead-time. 2. Forecast is frozen and used to negotiate for capacity from Semiconductor company. 3. Semiconductor company incurs significant manual process costs to continually match capacity to expected customer demand. 4. Semiconductor company is required to keep high levels of inventory to protect against high variability of demand. 5. Customers replenish inventory by continually placing orders with Semiconductor company.

Agent-Based Replenishment Approach 1. Updated demand plan is provided for target replenishment customers on a daily basis. 2. Event management system monitors the demand plan changes and inventory buffer changes. If a threshold limit is violated an event is created. 3. MONITOR agents capture such events and engage the appropriate ANALYZE agent. 4. The ANALYZE agents are of the following two types: €€€€€€€€€€a. ANALYZE “Buffer” agents compute the threshold values for all of the demand buffers for a new demand plan. €€€€€€€€€€b. ANALYZE “Shortage” agents evaluate the shortage situation when inventory is low. 5. ACT agents optimize replenishment over a targeted horizon in order to minimize costs and realize targeted service levels established by Semiconductor supplier and all OEM/ Distributor customers. €€€€€€€€€€a. ACT “Replenishment” agents create an optimized (min cost flow) replenishment plan for all items over supply/demand map and within targeted plan horizon. €€€€€€€€€€b. ACT “Shipment” agents coordinate with “Replenishment” agents to create and trigger shipments, with the goal of minimizing shipment costs over target shipping horizon.

flow, WIP, and movement of supplies throughout the supply chain and manufacturing plants are optimized to ensure timely availability of supplies and fulfillment and delivery of customer orders. Table 5 presents the new routing processes employed and compares it with the traditional approach used by STM. Implementation of the agent-based adaptive supply chain management system by STM has resulted in supply chain simplification, logistic process and provider improvements, and better delivery performance. Table 6 summarizes these improvements. 278

The agent-based system presented offers an effective infrastructure that enables and encourages updated demand data to be distributed among members of the production environment on daily or even shift-by-shift bases. In the traditional system, demand data was often only updated weekly. Clearly, accurate information offered by our solution allows decision makers to optimize their operations throughout the system. The distributed nature of this system allows STM to validate orders for consistency by further monitoring and analysis of demand and manufacturing data down the stream, up to the OEMs and dis-

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Table 5. Order routing process management activities for STM Traditional “Commit” Approach Whereas replenishment is used to support the indirect sales process, the “Order Commit” process supports direct sales. 1. Orders are entered and booked with a temporary Scheduled Ship Date and are localized in multiple offices. 2. Within 24 hours the centralized Sales Planning team determines the initial commit date. 3. Sales orders are continuously revisited to bring the Schedule Date closer to the Customer Request Date. 4. Management of sales order rescheduling is usually very manual, with limited ability for managing changes and exceptions.

Agent-Based Routing Optimization Adaptive Order Routing dynamically adjusts the slotting of orders to fulfillment routes that best meet the sales, service level and supply chain cost goals across a network. 1. Event management system is designed to monitor, new orders, inventory, customer request date changes, goods in transit. Significant changes trigger events. 2. MONITOR agents recognize such events and trigger the appropriate ANALYZE agents. 3. ANALYZE agents interpret changes and recommend the best actions: €€€€€€€€€€a. ANALYZE “Order” agents use order prioritization and current supply/ demand state to correct order slotting. €€€€€€€€€€b. ANALYZE “Supply” agents use change prioritization and current supply/demand state to correct order slotting response. 4. ACT agents are engaged by ANALYZE agents to continuously drive order schedule dates to the customer request date: €€€€€€€€€€a. ACT “Simple Slotting” uses heuristics to slot or change an order to respond to a critical supply event. €€€€€€€€€€b. ACT “Allocation Optimization” optimizes order slotting, given the user selectable weightings for sales, service levels and cost or profit goals subject to available material and capacity constraints.

Table 6. Overall improvements for STM manufacturing processes Process

Results

Supply Chain Simplification

1. Non-value-added movement of wafers or packaged part is reduced. 2. Redefined the testing process to increase offshore testing. 3. Reduced end-to-end cycle time by 8 to 10 days.

Logistic Improvements

1. Identified root cause of long transit between Asia and the US. 2. Changed sourcing activities from multiple freight forwards to single 3PL (third party logistic providers). 3. Reduced inter-company door-to-door cycle times from 7 days to 2 days. 4. Reduced total logistic costs by $2M/year.

Delivery Performance

1. Identified the root causes of the poor delivery performance. 2. Used inventory monitoring and optimization for allocating supply in capacity-constrained environments. 3. Improved delivery performance to first commit by more than 35% within five months on pilot product line.

tributors of its customers’ (phone companies) products. Since adoption and implementation of this strategy, STM has cut its product lead time in half, reduced buffer inventory from five to two weeks, and eliminated 80% of its manual transactions (Shah, 2002).

The Automotive Example: The XYZ Pick-up Truck Today, American, European, Japanese and other automakers are fiercely competing among each

other by giving consumers a unique level of choices in configuring their model. This extreme level of mass customization for a market that until few years ago was producing vehicles with very few choices has introduced numerous challenges on how automakers have aligned and adjusted their supply chain. In the following we report on the strategy and the solution adopted by one of the leading car manufacturers in addressing the challenges related to customization of one of the components of the pickup truck: the engine block. This customization introduces a high degree of variability in the cylinder machining operation at 279

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Engine Plant A, the coating process at the Coating Plant B, and the block casting process at the Casting Plant C. The mass customization process can often result in excessive inventory, which is not tolerable financially. The objectives of the automaker were: 1. To increase the responsiveness to high customized demand. 2. To reduce the inventory level in the supply chain. 3. To improve control of managing the overall material flow to the target performance levels. We first summarize the inventory objectives and production features of each plant and report the “as is” supply chain from B to A and C to B in Table 7, and depict them in Figures 4 and 5 respectively. To show the effect of mass customization to the supply chain we consider the customization of the engine. There are two sets of choices for customers: engine size in Liters (4.6, 5.4, and 6.8) and the number of cylinders (8 and 10). The only engine with 10 cylinders is the 6.8L V10. Even this simple set of choices can have a great impact on the production lead time. In particular, each time production shifts from V8 to V10 the line

becomes idle for some period of time in order to set up the new configuration. Similarly, each time there is a change of configuration from 4.6L to 5.4L or 6.8L the set up needs to be reconfigured and the line becomes idle. The inability to proactively forecast and shape customers’ demand results in: • • • • • •

Unscheduled downtime at the block production line Inability of block line to consistently support engine assembly at Plant A Excessive inventory carrying costs Transportation inefficiencies Unplanned overtime at all the plants Scheduling instability

To support this degree of mass customization, we have applied the “agent-based adaptive supply chain model” presented in this chapter to the manufacturing processes at the three plants. Since the auto industry’s supply chain is complex and often includes many tiers, the agent-based solution provided in this model can make changes in the demand data to be simultaneously visible throughout the system. The propagation of this up-to-date information will allow production and inventory level adjustments for all participants, including the entire supply chain members. This

Table 7. Plant inventory objectives and production features Location

Inventory Objectives

Plant A

1. Supply material needs for engine assembly, which is driven by end demand needs. 2. Buffer against short supply from Plant B for the very near-term JIT windows.

1. Unstable machine capability. 2. Low schedule stability.

Plant B

1. Supply scheduled material needs (JIT schedule) at Plant A. 2. Supply unexpected material needs at Plant A, which may be caused by end demand shift, high scrap rate or material consumption, overtime production, etc. 3. Buffer against short supply from Plant C, which may be caused by high changeover rate, unexpected machine downtime at Plant C, etc.

1. Batch production. 2. High demand uncertainty from Plant A. 3. High supply uncertainty from Plant C.

Plant C

1. Supply scheduled material needs (JIT schedule) at Plant A. 2. Buffer against short material supply from internal or external suppliers.

1. Long changeover time. 2. Batch production. 3. Inflexible product switch.

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Production Features

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Figure 4. Supply chain model from Plant B to Plant A

Figure 5. Supply chain model from Plant C to Plant B

model minimizes downtime, reduces inventory throughout the production system, and offers a more efficient transportation system while achieving a more stable scheduling and delivering option. This model is presented in Figure 6. There are seven components of this adaptive model:

1. The Event Management: The Event Management Framework is a foundational capability of the Adaptive Supply Chain solution. Its primary purpose is to monitor events and orchestrate the adaptive response to the significant event changes that occur in the supply chain. An event is any supply chain variable that represents some key dynamic in

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Figure 6. The adaptive supply chain model for engine assembly

the operation of the supply chain process, e.g. demand or inventory level. Throughout the supply chain a change in an event can trigger one of two possible paths. If the change is considered within the normal variability, it will trigger the Changeover Optimization Engine, Pull Replenishment Engine or both. If the change is outside a preset tolerance limit it will trigger an exception and notify the appropriate user. 2 – 3. Optimal Changeover Sequence and Target Inventory: The Optimal Changeover and Target Inventory (OCTI) is an intelligent engine that will calculate both the optimal sequence of product runs and target inventory buffer levels for any series of product changeover constrained operations in the supply chain. The engine uses a mixed integer programming model to minimize the total supply chain costs subject to demand, inventory, and transportation and production constraints. The OCTI engine is used to calculate both the optimal block sequence for each of the supply chain operations and

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adjust the associated min/max buffer levels. The supply chain system uses a push/pull control strategy for coordinating the material flow, with a pull replenishment buffer established at the ready to ship buffer in Coating. The OCTI results are used as input to both Casting and Coating stages to drive the build schedules that are used to push the engine blocks through Coating to the pull buffer. The min and max levels of the interim buffers are adjusted as needed. On the Plant A OP 10 side, the OCTI results are used as input to the OP 10 pull scheduling process to develop production runs that best support the actual engine assembly usage and the calculated optimal block sequence. When the event of the OP 10 input buffers reaches the reorder levels, it will trigger the Pull Replenishment engine to pull in blocks from the Coating replenishment buffer 4. Pull Replenishment: The Pull Replenishment is an intelligent replenishment engine designed

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to trigger and control the “Pull” replenishment from any two entities in the supply chain. It also has a look-ahead capability, driven by forecasted demand that will assess the synchronization viability of the source supply. Plant A supply chain uses the Pull Replenishment engine to coordinate the pull signals between the OP 10 input buffer and the Coating ready to ship buffer. 5. Demand Visibility: The demand forecasting module offers intelligent agents that are invoked by the event management component. These agents use the longer term demand values and parameters as guidelines and receive up-todate demand information from the actual customer base. The agents revise and smooth the order stream using the longer term demand values and parameters. They use the current production status, buffer values, and other production data to determine final manufacturing orders for both the Casting and Coating processes. 6. Resolve Exceptions: Exceptions are defined to be violations of production values from manufacturing thresholds. In this system, thresholds have dynamic values and are determined by the demand levels. Once an exception is reached, i.e., a threshold is violated, the conditions need to be restored to normality and full or near optimality. Reaching a restoration level is attempted while keeping the noise level in the entire manufacturing environment to a minimum and without

causing major interruption to the system. For example, a generated exception might be allowed to proceed, and thus missing fulfilling a portion of the demand portfolio in the short term, in order to honor longer term production goals and constraints. 7. Measure Performance: This module uses intelligent agents to monitor key performances of the system through the event management component. It can measure performance of any event, both internal and external, by collecting statistics dynamically. Examples of internal events include inventory buffer levels, number of changeovers, production line throughput, and total inventory in the pipeline. Similarly, customer satisfaction and supplier performance, external events, can also be monitored and measured by this module. Implementation of this system has used several features of the model, described in this chapter, in the engine manufacturing process. The data requirement of the system necessitated an information technology infrastructure based on the agent technology for decision making, JDBC for data storage, and XML for interfacing and data access. The concept of synchronization was used to ensure sequencing of the steps. The system allowed for interaction with auto dealers, partners, and suppliers. This visibility brought demand information and fluctuation to the manufacturing floor and broadcasted manufacturing status, alerts, and exceptions to the affected targets. Finally, a graphical user interface was produced to allow authorized users to view, input, or edit the entire system information.

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Prior to the implementation of the system, a simulation model was developed to assess the effectiveness of the proposed system and compare its performance to the existing one. The objectives of the simulation were to measure inventory levels, changeover time, and utilization of the manufacturing facilities for given demand values. The demand information represented a dynamic stream of customized orders and reorders recorded daily over a 90 day period. The objective function used represented total production costs while allowing for switching among the different configurations. The constraint set included the various buffer limitations for inventory, changeover capacity and its opportunity cost, maximum throughput capacity, and equipment availability and maintenance constraints. The results of the simulation showed improved performance as measured by the two key indices (inventory levels and utilization rates) for the three plants. Table 8 shows that total inventory level for the system was reduced from 27,000 units to 13,283 units (a 51% reduction). The utilization rate for plant A increased from 60% to 90% and the combined utilization rate for plants B and C was improved from 41% to 61%. These improvements have increased the automaker’s responsiveness and its competitive edge in meeting customers’ demand.

The Pharmaceutical Company Example: Clinical Trials Development of a new drug is a complex process that is difficult to manage, and includes a lengthy Table 8. Simulation results for the engine manufacturing plant Models

Total Inventory

Utilization (Plant A)

Utilization (Plants B & C)

Existing System

27,000

60%

41%

Proposed System

13,283

90%

61%

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cycle lasting often years or even decades. These projects have several variables, many stakeholders with differing interests, and are closely scrutinized because of the urgent need to bring new treatments to market. Study managers and their teams try to control a process that is largely outside of their sphere of influence and scope of control. A typical drug development cycle begins with laboratory research and animal experimentation (pre-clinical phase) and it is followed by five testing phases. Phase zero trials are also known as the “human micro-dosing studies” and are designed to speed up the development of promising drugs. Phase one of the trials usually involves few volunteers (less than 50). Phase two of testing involves larger numbers of volunteer patients with advanced diseases that might benefit from such a new treatment. This is often affiliated with a hospital and a team of research medical doctors. Phase three, the most costly and time consuming phase, involves a much larger number of patients (between 300 -3000) and must follow the FDA (or similar regulatory bodies) guidelines. The results of this phase are submitted to the FDA for final approval. Once the approved drug reaches the market, development enters phase four of the cycle. This phase addresses any anomalies or unforeseen adverse effects that might become present. This research focuses on phase three. As mentioned earlier, this phase is the most elaborate, costly, and time consuming phase. In today’s global market, most international pharmaceutical companies prefer to conduct this phase over geographically dispersed locations often expanding many countries. Clearly, conducting such clinical trials over multiple sites, spreading over many locations can significantly benefit from existence of an advanced IT infrastructure that will enable coordination, monitoring, analyzing, and acting in a timely manner. Adoption of the architecture described in this paper by pharmaceutical firms meets the requirements of this phase of the clinical trials and is described below.

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A typical phase three of a clinical trial consists of the following steps. •

•

•

•

•

•

Since phase three of the clinical trial requires meeting regulatory guidelines, the first step is the approval of the study protocols by the FDA. This approval can range from medical considerations to approval of the sample size for the clinical trials such as the number of patients to be recruited for the study. The next step involves selection of the sites for the trial including location of hospitals, clinics, and doctors’ offices. Once the total number of patients and sites are defined, allocation of patients to sites is determined. In the next step the actual patient recruitments by the local health sites is attempted. The recruitment follows two stages: in stage one, patients are screened for appropriateness of the study. The second stage is the randomization of patients who have successfully passed stage one. In this stage a patient is either assigned to a treatment at a site or is assigned to a placebo group. Once groups are designed, patient monitoring begins. For clinical trials patients must follow a pre-defined visitation and monitoring plan. During this step necessary data are collected and transmitted from the investigators’ sites to a central location in the pharmaceutical company for analysis. Today’s clinical trials follow an advanced approach called “multi-arms.” In this approach a drug may contain many ingredients or components. Trials are designed to test multiple combination of dosage for each version of the drug. This approach results in many configurations of the medicine, allowing the pharmaceutical company to simultaneously evaluate the most effective combinations. Therefore, at each site multiple independent dosages

•

•

(versions) are being evaluated. Patients are assigned to each drug version following a uniform distribution. The prescribed dosage of the drug for each version is administered to members of the group following the pre-defined procedures for the pre-defined period of the treatment. An added complexity of these trials is the globally distributed nature of the trial. Clearly, logistics, management, coordination and visibility of the entire system requires an information architecture that enables all participants to have real-time access to all the necessary information. Once trials have begun, partial results are analyzed to determine whether a particular dosage is a promising one or not. When there is enough evidence that a particular dosage is not effective, that specific configuration (version) is stopped and patients from this group are re-assigned to begin taking other dosages from the beginning cycle of the new dosage. The process continues until the pre-determined trial cycles for all dosages (versions) are complete.

Managing the Supply Chain for distributed Clinical Trials The supply chain management for distributed clinical trials is very complex and includes many steps. Recent advances in medical science have increased introduction of new drugs. However, developments cycles have become more complex, exclusive patent timelines have shrunk, and costs have skyrocketed. Companies look for ways to reduce these costs and to find an answer to the “costs more, takes longer” problem. Monitoring clinical trial cycles, managing the supply chains, and accurately predicting the end date for recruitment are of great interest to pharmaceutical companies. We first discuss a pharmaceutical company’s traditional supply chain system for distributed clinical trials and then present a

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new approach based on the architecture outlined in this research and show how adoption of this new paradigm has reduced cost of the trials and shortened the completion time. The current landscape for distributed clinical trials for the pharmaceutical firm in this study is presented in Figure 7. The process begins with identification and acquisition of raw material, processing, packaging, delivery, administering, investigation, and drug accounting. Existing system has used forecasting techniques, inventory control, Manufacturing Resource Planning (MRP), Intelligent Voice Recognition Systems (IVRS), and Electronic Data Communications (EDC) systems. The traditional approach follows the “maketo-stock” production policy. In this approach once the dosages (versions) are defined, the entire demand for all versions and all sites are produced in batches and are shipped to the sites. Clearly, in this approach when the trials reveal a dosage not to be promising the associated dosage and its supporting material and clinical and support staff are no longer useful and must be discarded or disbanded. The costs associated with the non-

promising dosages are significant and the firm cannot reuse the dosage for other versions. Once “multi-arms” and globalization of the clinical trials are added, complexity of the system increases. Managing this complex system can significantly benefit from visibility, monitoring, coordinating, analyzing, and acting features of the architecture described earlier. One of the most important factors driving the cost and length of phase three of the clinical trial is recruitment time. Recruitment time is defined as the time needed to recruit a pre-determined number of patients for the entire study. In a multi-arms system, the recruitment problem is significantly more complex. The study managers must recruit sufficient number of patients for each dosage of the drug. The traditional approach uses a batch or “make-to-stock” policy, in which the numbers of sites, number of patients per site and per dosage (version) are first defined and then the supply of drug for each site is produced, packaged, and shipped to each site. As depicted in Figure 7, raw materials are converted into compounds or drug ingredients. A dosage or version uses a pre-defined portion of each ingredient per its recipe. In the traditional

Figure 7. The traditional supply chain for clinical trials

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drug production approach, once the number of patients per site and per version is determined, a complete set of dosage for the recipients is produced, packaged, and distributed to each site. Additionally, clinical and support staff as well as instructions, guidelines, and administering procedures are defined and organized for each version proportionally. This approach is neither cost effective nor timely. The process begins with patient arrivals and administration of specific dosage and monitoring of the patients. The traditional approach does not provide a timely support structure for immediate analysis of the results and immediate termination of the less promising dosages. This in turn limits re-use of the ingredients and re-assignment of patients from failed dosages to the ongoing promising ones thus lengthening the entire process or even missing crucial deadlines. Cost overruns for this system are observed in all stages of the process, from wasted dosages (for the less promising versions) to costs associated with recruitment and administration of the trials. We next describe a system that follows the “on-demand” or “make-to-order” policy that with its supporting IT infrastructure can reduce the cost of the trials while simultaneously shortens the trail length.

the company to respond to changes in trial environment in the short term without compromising long term (strategic) goals. This architecture uses agent-based technology to support a distributed decision making environment. The system ensures optimal service to the patients, offers an integrated network, provides visibility into patient recruitment and treatment, and coordinates recruitment and trial program rate forecasts with drug and treatment availability at local trial sites. Figure 8 presents the overall optimal supply chain design based on this paradigm. In this paradigm, a plan consists of a set of activities and processes are event driven. We acknowledge recruitment strategies as a driving force for the entire clinical trial. The recruiting strategy has been a subject of many studies. Literature in this field offer simulation and stochastic modeling as the two most widely used approaches for managing recruitment policies. Patel, et al. (2009) offer simulation modeling and tools for managing drug supply chains. Anisimov & Fedorov (2007) recommend stochastic solutions for recruitment management of a clinical trial. We use a stochastic modeling approach to dynamically manage patient recruitment for the trials. The primary objective is to complete the recruitment of patients within the defined time of the study. Specifically, project objectives are:

An Adaptive, Agile, Agent-Based, Intelligent Supply Chain System

•

We present a new paradigm to manage supply chain for distributed clinical trials and show how our solution architecture can reduce cost of trials and shorten time to market. The paradigm applies the concepts of visibility, monitoring, coordinating, analyzing, and acting presented in this architecture to create an adaptive environment for trial management. The agent technology introduced earlier is combined with web services technology and the RFID technology to track and trace the elements of the trials (dosage, packages, team status, etc.). The system offers agility that allows

•

To enroll the required number of subjects within the specified timeframe, and To manage to the allocated budget for the study.

To achieve these goals, study managers must be able to quickly monitor changes, analyze the new information, decide on corrective course of actions and act expediently. We have introduced intelligent agents that use pre-defined algorithms as a means for providing the needed insights for making intelligent decisions. Essentially, study managers want the algorithms to define:

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Figure 8. Optimized supply chain system for clinical trials

•

•

The distribution of productive sites, showing when they become active and when they are forecast to screen their first patients, and The distribution of screened and randomized/enrolled patients.

These two interdependent time series are distributions of stochastic variables over a time horizon and therefore represent two interlinked “stochastic processes.” The ability to accurately predict these stochastic processes is the key for a study manager to define and maintain a predictable study plan. Many existing analytical tools for such systems have focused on the use of simulation to address this problem (Patel et al., 2009). The system described in this research, however, provides an intelligent user interface with predictive capabilities that eliminates the need for users to deeply understand the framework, yet still apply it when developing plans and managing recruitment. Our solution effectively visualizes these two interdependent time series across all phases of the enrollment process and provides proactive alerts for projected violations of the

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trial’s business goals. We have examined both deterministic and stochastic model frameworks. A deterministic model framework assumes the patient arrival rate that follows a known pattern or estimated deterministic linear or non-linear function, whereas a stochastic model framework allows the definition of probability functions with expected mean values and variance. Our results show that the stochastic framework delivers superior results. Under the stochastic model framework the user is able to define instances of the model by specifying the recruitment problem parameters (expected initial recruitment estimates, by countries and across centers, as well as the rules for recruitment: competitive, balanced, or restricted recruitment). The advantage of this approach is that the model framework remains invariant and multiple instances can be generated and tested. The instanced model can now be solved using an “efficient” algorithm. Efficiency is characterized mainly by two factors: time and accuracy of the result. Because our stochastic model framework allows us to define algorithms that provide closed form solutions, accuracy and time are optimal and outperform existing simulation techniques utilized in traditional models.

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In modelling the recruitment problem we consider the following: •

•

•

•

Uncertainties during the recruitment period. There are uncertainties (unpredictable events) in patient recruitment over the time horizon. Because of this, recruitment rates generally fluctuate over time and centers may delay screening patients due to unexpected events; Expected recruitment mean and confidence boundaries. To correctly model the uncertainties outlined above, we characterize the expected recruitment average for mean and its confidence boundary value. This will allow the algorithm to generate the expected number of randomized patients and its confidence bounds over the defined time horizon; The minimum number of centers required. The model determines the minimum number of centers needed to complete recruitment by a certain date at a given confidence. Estimated future performance of centers. Information about the future initiation of centers and estimated performance is factored in to improve the significance of the prediction.

The above characteristics are the driving assumptions of the model. Literature on patient recruitment (Senn, 1998, Anisimov & Fedorov, 2007, Cox, 1955) offer guidelines for patient arrivals and assignments to the centers. We follow the same two general assumptions: •

•

Given the stochastic fluctuations of recruitment over time, patients arrive at centers following the distribution of a Poisson process. Variations in recruitment rates between centers are estimated as samples from a gamma distributed population.

Additionally, we add some extensions to these assumptions. They are: •

• •

The minimum number of centers required to complete the recruitment by the target date. Knowledge of the information about future performance of centers. Capacity of the centers and their constraints (facility constraints, maximum number of patients).

We use these assumptions to extend the Cox stochastic forecasting model to develop a specific algorithm to determine the distribution of the patient arrival rate for each center. Figure 8 presents the proposed approach based on the outlined architecture to manage the supply chain for such clinical trials, including the multi-arms approached used in this study and uses the extended algorithms for its patient arrival forecasting. Results from adoption of this approach show significant improvements in cost and time to completion. The savings are the consequences of the “visibility, monitoring, coordinating, analyzing, and acting” features of the system. Specifically, the de-centralized framework of the system and the intelligent agents combine to realize the improvements. This architecture uses a de-centralized production policy. In this approach, instead of the finished drug dosage used in the traditional system, ingredients (drug components) and recipes are sent to each site and the actual dosages are produced on an on-demand or “make-to-order” bases at each site and upon patient arrivals. In this strategy once a dosage is deemed non-promising, the production of that dosage immediately stops and the remaining ingredients (drug components) are re-used for more promising dosages using their recipes. Clearly, in this de-centralized production system, expensive drug components are not wasted thus reducing the overall costs of the trial. Similarly, the predictive algorithms used in this study form the intelligence of the system allowing

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the study managers to forecast events accurately, specially the patient recruitments events, and manage the entire process more efficiently. These features have resulted in faster decision making, allowing less promising trials to end sooner and re-assignment of patients and clinical and support staff to more promising cases, thus, completing the trials faster. The production, packaging, shipment, and administration of the dosages are based on on-demand or “make-to-order” policy, are done more expediently, and result in cost reductions and faster completion time of the trials.

Study Results The pharmaceutical company referenced in this study leverages a store of previously completed clinical studies—classified by therapeutic area and organized according to features (such as target number of subjects, number of sites and cycle time)—to test and refine the implementation of the model and its associated algorithms. Studies are sorted by category values from High (+), Medium (O), to Low (-) in order to better understand and analyze possible pattern behaviors, similarities, and differences. To arrive at the results presented in the examples outlined here, a “post-mortem” analysis was conducted to compare our results with the traditional approach. In the traditional approach, before the trials begin, the study manager offers his projection for the Last Subject Randomized (LSR) based on his past experiences. This input is a very rough estimate and is grounded on the manager’s intuition and earlier similar studies. His estimates are used for budgetary purposes, design of the trial framework, and to define system’s parameters. These parameters consists of (i) the number of countries in the study, (ii) the number of centers per country, and (iii) the total recruitment per center. and are used to create the initial recruitment plan. Clearly, these rough estimates need to be revised and corrected as soon as real data from the field becomes available. In

290

the traditional approach, once the process begins and some information from the field is collected, the LSR projections are revised. In this approach, the revision is deterministic and is triggered when 25% of the sites are operational and are actively recruiting. The study manager uses the field data to revise its LSR projections. Therefore, in a post-mortem analysis, the projection error in this approach can be computed by subtracting the LSR projections @ 25% from the actual completion date. We will use this error rate to compare with our corresponding error. We introduced a stochastic approach to compute the LSR projections. To be able to compare our results with the traditional approach, we also choose the 25% site activation metric as our trigger point. The projected LSR values based on this approach, on the other hand, uses the stochastic process described in this section. Therefore, in a post-mortem analysis, the projection error can be computed by subtracting the LSR projections @ 25% from the actual completion date based on the stochastic algorithm used in this approach. The improvement in the process can be computed by comparing these two error values. Table 9 describes the study features of three sample studies, followed by the results demonstrated by our methodology. The results presented below are illustrated through the use of two performance functions: “Absolute Time Error” and “Relative Time Error.” The “Absolute Time Error” measures the differTable 9. Study features of three trials Study Features

Study 1: Oncology

Study 2: Immunology

Study 3: Respiratory

Number of patients

O/-

+

+

Number of centers

O

-/O

+

Estimated recruitment rate

-

+

+

Expected # of active centers

-

O

O

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Table 10. Oncology study targeting 680 patients Study Start

Projected Last Subject Randomized

Planned First Site Initiated

Planned Last Subject Randomized

Deterministic Model – 25% Sites Active

Stochastic Model – 25% Sites Active

Actual – Study Completed

3-Jan2004

3-Jan2006

10-Jun2007

8-Jan2007

1-Feb2007

ence between the model estimates at the 25% trigger point and the actual end day the last patient was enrolled. The “Relative Time Error” measures the per cent error relative to model estimates at the 25% trigger point. The simplicity of these metrics provides for an immediate normalized comparison of our results across different studies as well as an immediately measurable understanding of the tangible economic impact of proper and accurate forecasting. The actual recruitment completion date for the oncology study was 1-Feb-2007, which was 129 days before the originally forecasted plan (based on the traditional approach). Using the stochastic model described in this research, at 25% centers active, the projections would have been within 23 (8-Jan-2007) days of the actual completion time. The improvement rate of the stochastic algorithm over the traditional deterministic approach is 82%. The actual recruitment completion date for the immunology study was 8-Jan-2007, which was Table 11. Immunology study targeting 2000 patients Study Start

Projected Last Subject Randomized

27 days later than the originally forecasted plan (based on the traditional approach). Using the stochastic model described in this research, at 25% centers active, the projections would have been within 6 days. The improvement rate of the stochastic algorithm over the traditional deterministic approach is 78%. The actual recruitment completion date for the respiratory study was 3-Aug-2007, which was 127 days later than the originally forecasted plan (based on the traditional approach). Using the stochastic model described in this research, at 25% centers active, the projections would have been within 33 days. The improvement rate of the stochastic algorithm over the traditional deterministic approach is 74%. The web-enabled, intelligent, agent-based system with specialized patient recruitment algorithms presented offers a more accurate trial progress forecast. Better accuracy improves and impacts the entire process, from dosage production, distribution, delivery, and administering, to drug version monitoring, information analysis, and site management. The cost savings in this system are spread throughout the supply chain and are the results of reduction in expensive dosage production (by immediate stoppage of non-promising versions) to patients’ assignment and drug administration. The stochastic model presented serves as the “intelligent component” of the agent technology. The stochastic model can determine the best and worst case scenarios for trial completion times. Table 12. Respiratory study targeting 2600 patients Study Start

Projected Last Subject Randomized

Planned First Site Initiated

Planned Last Subject Randomized

Deterministic Model – 25% Sites Active

Stochastic Model – 25% Sites Active

Actual – Study Completed

Planned First Site Initiated

Planned Last Subject Randomized

Deterministic Model – 25% Sites Active

Stochastic Model – 25% Sites Active

Actual – Study Completed

1-Jan2006

21-Nov2006

5-Feb2007

2-Jan2007

8-Jan2007

1-May2005

30-Dec2006

28-Mar2007

30-Jun2007

3-Aug2007

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The agents can use these parameters to determine the feasibility of achieving projected goals. When these goals are deemed to be infeasible, a new trigger point is generated. A second agent can then use this trigger point and re-evaluate the LSR values using the latest available information. In the above examples, we used the 25% center activation metric as the trigger point only to allow us to compare our results with the traditional approach. In our model, the value of the trigger point is dynamically computed by the agent technology. Finally, the system presented here has been recently adopted by a pharmaceutical company and early actual results are very promising and are in line with the experimental values. Since such a system brings significant cost reduction, the savings might translate to further research for drugs targeted to specific customer needs, thus allowing the pharmaceutical company to move toward a “personalized medicine” model.

entire supply chain, allows for a collaborative and synchronized production system, and supports an event-based manufacturing environment. The system introduces an agent-based architecture with four general purpose intelligent agents to support the entire mass customization process. Experiences with implementations of this approach at a semiconductor manufacturer, an automotive company, and a pharmaceutical company show the effectiveness of the proposed architecture in enhancing the ‘velocity of execution’ of supply chain management activities, including order management, planning, manufacturing, operations, and distributions. Results verified that successful adoption of this system can reduce inventory and logistics costs, improve delivery performance, increase manufacturing facilities utilizations, and provide a higher overall profitability.

CONCLUSION

ACL. Agent Communications Language. (2006). Retrieved from http://www.fipa.org/repository/ aclspec.html.

The goal of an on-demand and mass customization strategy is to deliver customized products at costs that are near-equivalent to their mass produced versions without significant delays. To achieve this goal, we presented a web-enabled information system model that uses mobile intelligent agent technology and can be interfaced with the firms’ existing IT infrastructures. Successful implementation of any mass customized strategy requires a collaborative environment that follows a build-to-order production strategy. In such an environment, customers can configure their orders online and monitor the orders’ status at any time, suppliers can view demand stream dynamically in real-time, and the manufacturers can react to changing orders efficiently and expediently. We presented a distributed, Java-based, mobile intelligent information system model that is demand driven, supports a build-to-order policy, provides end-to-end visibility along the

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Baker, A.D., Van Dyke Parunak, H., & Kutluhan, E. (1999). Agents and the Internet: Infrastructure for Mass Customization. IEEE Internet Computing, Sept.-Oct., 62-69. Bellifemine, F., Caire, G., Poggi, A., & Rimassa, G. (2003). JADE: A White Paper. Exp 3(3), 6-19. Bergenti, F. & Poggi. A. (2001). LEAP: A FIPA Platform for Handheld and Mobile Devices. In Proc. Eighth International Workshop on Agent Theories, Architectures, and Languages (ATAL2001), Seattle, WA, 303-313. Bigus, J. P., Schlosnagle, D. A., Pilgrim, J. R., Mills, W. N. III, & Diago, Y. (2002). ABLE: A Toolkit for Building Multi-agent Autonomic Systems – Agent Building and Learning Environment. IBM Systems Journal, (September): 1–19.

Ghiassi, M., & Spera, C. (2003b). A Collaborative and Adaptive Supply Chain Management System. Proceedings of the 31st International Conference on Computers and Industrial Engineering., San Francisco, Ca., 473-479. Gilmore, J. H., & Pine, B. J., II. (2000). Markets of One: Creating Customer-Unique Value Through Mass Customization. A Harvard Business Review Book. GPRS: General Packet Radio System. Retrieved from http://www.gsmworld. com/technology/gprs/index.html JADE. http://jade.cselt.it & http://jade.tilab.com Jain, A. K., Aparicio, M. IV, & Singh, M. P. (1999). Agents for Process Coherence in Virtual Enterprises. Communications of the ACM, 42(3), 62–69. doi:10.1145/295685.295702

Capgemini. (2004). A Collection of Agent Technology Pilots and Projects. Retrieved from http:// www.capgemini.com/resources/thought_leadership/putting_agents_towork/

Kalakota, R., Stallaert, J., & Whinston, A. B. (1998). Implementing Real-Time Supply Chain Optimization Systems. Global Supply Chain and Technology Management, POMS, 60-75.

Cox, D. (1955). Some Statistical Methods Connected with Series of Events (with Discussion). Journal of the Royal Statistical Society. Series B. Methodological, 17, 129–164.

Lange, D. B., & Oshima, M. (1998). Programming and Developing Java Mobile Agents with Aglets. Reading, MA: Addison-Wesley.

Dorer, K., & Calisti, M. (2005). An Adaptive Solution to Dynamic Transport Optimization. In Proc. Of the Fourth International Joint Conference on Autonomous Agents & Multi-agent Systems, AAMAS ’05, Utrecht, The Netherlands. Also at:http://www.whitestein.com/pages/downloads/ publications. Ghiassi, M. (2001). An E-Commerce Production Model for Mass Customized Market. Issues in Information Systems, 2, 106–112. Ghiassi, M., & Spera, C. (2003). a). Defining the Internet-based Supply Chain System for Mass Customized Markets. Computers & Industrial Engineering Journal, 45(1), 17–41. doi:10.1016/ S0360-8352(03)00017-2

Ma, M. (1999). Agents in E-Commerce. Communications of the ACM, 42(3), 79–80. doi:10.1145/295685.295708 Maes, P., Guttman, R. H., & Moukas, A. G. (1999). Agents that Buy and Sell. Communications of the ACM, 42(3), 81–91. doi:10.1145/295685.295716 MIDP. Mobile Information Device Profile. Retrieved from http://java.sun.com/products/midp/ index.jsp Moreno, A., Valls, A., & Viejo, A. (2005). Using JADE-LEAP to Implement Agents in Mobile Devices (Research Report 03-008, DEIM, URV). Retrieved fromhttp://www.etse.urv.es/recerca/ banzai/toni/MAS/papers.html Pancerella, A., & Berry, N. (1999). Adding Intelligent Agents to Existing EI Frameworks. IEEE Internet Computing, Sept.-Oct., 60-61. 293

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Singh, R., Salam, A. F., & Iyer, L. (2005). Agents in E-Supply Chains. Communications of the ACM, 48(6), 109–115. doi:10.1145/1064830.1064835

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Sandholm, T. (1999). Automated Negotiation. Communications of the ACM, 42(3), 84–85. doi:10.1145/295685.295866 Senn, S. (1998). Some Controversies in Planning and Analysing Multicentre Trials. Statistics in Medicine, 17(15-16), 1753–1756. doi:10.1002/(SICI)10970258(19980815/30)17:15/16<1753::AIDSIM977>3.0.CO;2-X

Wilke, J. (2002). Using Agent-Based Simulation to Analyze Supply Chain Value and Performance. Supply Chain World Conference and Exhibition, New Orleans, La.

Shah, J. B. (2002). ST, HP VMI Program Hitting Its Stride. Electronics Business News (EBN), 42,http://www.ebnonline.com. This work was previously published in Interdisciplinary Advances in Adaptive and Intelligent Assistant Systems: Concepts, Techniques, Applications, and Use, edited by Gunther Kreuzberger, Aran Lunzer and Roland Kaschek, pp. 83-123, copyright 2011 by Information Science Reference (an imprint of IGI Global).

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A Framework for Information Processing in the Diagnosis of Sleep Apnea Udantha R. Abeyratne University of Queensland, Australia

INTRODUCTION Obstructive sleep apnea (OSA) is one of the most common sleep disorders. It is characterized by repetitive obstruction of the upper airways during sleep. The frequency of such events can range up to hundreds of events per sleep-hour. Full closure of the airways is termed apnea, and a partial closure is known as hypopnea. The number of apnea/hypopnea events per hour is known as the AHI-index, and is used by clinical community as a measure of the severity of OSA. DOI: 10.4018/978-1-60960-561-2.ch203

OSA, when untreated, presents as a major public health concern throughout the world. OSA patients use health facilities at twice the average rate (Delaive, Roos, Manfreda, & Kryger, 1998), causing huge pressures on national healthcare systems. OSA is associated with serious complications such as cardiovascular disease, stroke, (Barber & Quan, 2002; Kryger, 2000,), and sexual impotence. It also causes cognitive deficiencies, low IQ in children, fatigue, and accidents. Australian Sleep Association reported (ASA, 1999) that in the state of New South Wales alone 11,000–43,000 traffic accidents per year were attributable to untreated-OSA.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

A Framework for Information Processing in the Diagnosis of Sleep Apnea

OSA is a highly prevalent disease in the society. An estimated 9% of the women and 24% of the men in the U.S. population of 30 to 60 years was found to be having at least mild OSA (Young, Evans, Finn, & Palta, 1997). In Singapore, about 15% of the total population has been estimated to be at risk (Puvanendran & Goh, 1999). In a recent study in India (Udwadia, Doshi, Lonkar, & Singh, 2004), 19.5% of people coming for routine health checks were found to have at least mild OSA. The full clinical significance of OSA has only recently been understood. Partly as a result of this, the public awareness of the disease is severely lacking. Healthcare systems around the world are largely unprepared to cater to the massive number of OSA patients. This problem is especially severe in the developing world, where OSA diagnostic facilities are rare to find.

The average number of obstructive sleep apnea and hypopnea events per hour of sleep, as computed over the total sleep period, is defined as the Apnea Hypopnea Index (AHI).

BACKGROUND

Drawbacks of PSG and Possible Improvements

Definition of Sleep Apnea and Hypopnea Sleep apnea refers to a cessation of breathing at night, usually temporary in nature. The American Academy of Sleep Medicine Task Force formally defines apnea as: a. Cessation of airflow for a duration ≥10s, or b. Cessation of airflow for a duration < 10s (for at least one respiratory cycle) with an accompanying drop in blood oxygen saturation by at least 3%. Hypopnea is defined as a clear decrease (≥50%) in amplitude from base line of a valid measure of breathing (eg., airflow, air pressure) during sleep for a duration ≥10s, plus either: a. An oxygen desaturation of ≥3%, or b. An EEG-arousal (EEGA) (Flemons & Buysse, 1999).

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The Current Standards in Apnea/Hypopnea Diagnosis The current standard of diagnosis of OSA is Polysomnography (PSG). Routine PSG requires that the patients sleep for a night in a hospital Sleep Laboratory, under video observation. In a typical PSG session, signals/parameters such as ECG, EEG, EMG, EOG, nasal/oral airflow, respiratory effort, body positions, body movements, and the blood oxygen saturation are carefully monitored. Altogether, a PSG test involves over 15 channels of measurements requiring physical contact with the patient.

At present, the hospital-based PSG test is the definitive method of diagnosis of the disease. However, it has the following drawbacks, particularly when employment as a community-screening tool is considered: 1. Poor data integrity is a common problem in routine PSG tests. Even when the test is done in the hospital, it is common to see cases of data loss (or quality deterioration), due to various reasons (eg., improper sensor contact due to electrodes/sensors coming loose, SpO2 sensor falling off, and measurement problems, such as movement artifacts). 2. PSG interpretation is a tedious task, due to the size of the data gathered, complexity of the signals, and measurement problems such as data loss. 3. PSG requires contact instrumentation; channels such as EEG/ECG/EOG/EMG

A Framework for Information Processing in the Diagnosis of Sleep Apnea

require Galvanic contact with the patient. It is especially unsuited for pediatric use. 4. PSG is not suitable for mass screening of the population. A trained medical technician is required to connect the patient to the PSG equipment, and the patient needs to be monitored overnight to avoid incurring data losses. 5. PSG is expensive; this is another factor working against mass screening uses. 6. AHI index (and a variant of it known as the respiratory disturbance index, RDI) is used as the golden clinical measure on the severity of apnea. However, AHI (or RDI) does not always correlate strongly with the symptoms of apnea as experienced by patients. There is an enormous clinical need for a simplified diagnostic instrument capable of convenient and reliable diagnosis/screening of OSA at a home setting (Flemons, 2003). Similarly, hospital-based, full PSG testing requires better measures to characterize the disease. This article explores possible solutions to both of these problems. There has been a flurry of recent activities at developing technology to address the issue of home screening of OSA. Four different classes of OSA monitors are under development (Flemons, 2003, and references therein). These devices varied from two-channel (eg., airflow and oximetry) systems (designated Type-IV), to miniaturized full-PSG (Type-I) units. Their major drawbacks are: •

Existing take-home devices have at least one sensor which requires physical contact. This makes them difficult to use by untrained persons, and cumbersome to use on pediatric populations. TYPE-IV systems, with the smallest number of sensors, suffers from the fact that oxymetry identifies oxygen saturation in blood only as a surrogate for OSA, and the absence of significant desaturation does not mean the absence of the disease (Flemons, 2003).

•

•

Presence of a medical technologist is still required, if acceptable sensitivity/specificity performance is required. High rates of data loss (up to 20% loss at home compared with 5% at sleep lab) (Flemons, 2003) (nine) have been reported when a medical technologist is not in attendance. Unattended systems have not led to high enough sensitivity/specificity levels to be used in a routine home monitoring exercise (Flemons, 2003; Portier, 2000). Type-I and II devices use channel counts from 7–18 and are difficult to use by an untrained person. The quality and the loss of data is a serious problem.

In this article, we present an instrumentation and signal processing framework addressing current problems in OSA diagnosis.

METHODS A Framework for the Noncontact Diagnosis of OSA Snoring almost always accompanies OSA, and is universally recognized as its earliest symptom (Hoffstein, 2000; Kryger, 2000; Puvanendran & Goh, 1999). Logic dictates that it should be providing us with the earliest opportunity to diagnose the disease. At present, however, quantitative analysis of snore sounds is not a practice in OSA detection. The vast potential of using snoring in the noninvasive diagnosis of OSA remains unused. In this article, we argue that the human speech and snore sounds share many similarities (see Figure 1), and biological “wetware” used for the generation processes. The upper airway acts as an acoustic filter during the production of snoring sounds, just as the vocal tract does in the case of speech sounds. Episodes of OSA, by definition, are associated with partial or full collapse of upper airways. As such, changes to the upper airways

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Figure 1. Similarities and differences between speech and snoring: (a),(b),(c) snoring at different time scales; (d),(e),(f) speech at different time scales

brought about by this collapse should be embedded in snoring signals. Figure 2 shows a typical snore sound recorded from a patient undergoing routine PSG testing at the sleep diagnostic laboratory. The data acquisition system used a sampling rate of 44.1k samples/s, and a bit resolution of 16bits/sample. The system had a 3dB bandwidth of 20.8kHz. A typical hospital based routine PSG test runs for up to eight hours, and it is quite common to observe up to 8,000 events of snores within a recorded sound data.

Snore Sounds: A Working Definition One of the major problems towards automation is that there is no objective definition of what a “snore” is (Hoffstein, 2000). Recently, we proposed (Abeyratne, Wakwella, & Hukins, 2005) an objective definition for “snoring” independent of the sound intensity. It is based on the observation that sounds perceived as “snores” by humans are characterized by repetitive packets of energy that are responsible for creating the vibratory sound peculiar to snores (Figure 2, bottom). We call the distance between such packets as the “pitch” of

Figure 2. (Top) A snore episode (SE); (bottom) different segments in SE

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snoring. A snoring episode (SE) will consist of a segment with pitch (“voiced-segment”), and possibly “silence” and segments without pitch (“unvoiced segment”). An inspiration-expiration cycle without any segment associated with a pitch is termed as a pure breathing episode (PB).

Mathematical Framework for Sound Analysis Snoring originates from acoustical energy released by the vibratory motions of upper airway site(s) during sleep. The airway cavity acts as an acoustical filter, and modifies the source sounds to produce snoring sounds we hear. Inspired by the source/vocal-tract system for human speech synthesis, we model a block of recorded sound s(n) as a convolution between: (i) “source signal x(n)” representing the source acoustical energy, and (ii) “Total Airway Response (TAR) h(n),” which captures the acoustical features of the upper airways as given by: s(n)=h(n)∗x(n)+b(n)

(1)

The symbol b(n) denotes background activities, x(n) is the source excitation, “*” denotes convolution, and h(n) is the TAR function. The term b(n) is due to a range of independent reasons and hence considered a Gaussian distribution independent of x(n). The nature of x(n) depends on the nature of the sound segment under consideration. For the case of a segment without any pitch (i.e, either a PB or unvoiced-snoring segment), we model the source excitation, x(n) as a white random process. The x(n) for voiced snoring segments is modeled by a pseudo-periodic pulse-train, drawing from techniques used in speech analysis. The TAR function h(n) is modeled in this article as a mixed-phase system considering that in OSA, multiple-source locations with temporal relations between eachother can be found, and thus, phaseinformation cannot be neglected in general.

It is our hypothesis that the state of the upper airways can be characterized by the pair ζ = {g{x(n)}, f{h(n)}}, where g and f represent the operation of extracting features out of the x(n) and h(n). We developed a snore segmentation algorithm (Abeyratne et al., 2005), which takes in full night sound data, and separates into snoring and pure-breathing episodes. It then further divides snore episodes into sub-categories “voiced,” “unvoiced,” and “silence” sections. The category “voiced” will then be further analyzed to estimate the pitch associated with each segment. Consider an arbitrary j-th Snoring Episode (SE) in the sound recordings. Divide the voiced segment ssv,j of the j-th Snoring Episode into Lj number of data blocks {Bjk},k =1… Lj, each of length N. Thus, at the output of the pitch-detector, each data block in the set {Bjk} is associated with a pitch period µjk. We term the series {µjk}, k=1,2, .., Lj as the Intra-snore Pitch Series for the j-th Snoring Episode. We consider the structure of the Intra-snore Pitch Series, and show that it is characterized by discontinuities, which can be used in the diagnosis of OSA. We propose a new measure called ISPJProbability to capture intra-snore jumps in pitch, via Definition-1 and Definition-2 given below: •

•

Definition-I: Suppose that in the j-th arbitrary snoring episode ssv,j there are at least q (< Nj +1) data blocks in the set {Bjk}, k =1… Nj with pitch periods µjk greater than a pitch threshold γ. Then the entire Snoring Episode j is labeled as having the feature ISPJ at level (q, γ). We call the quantity q as the “jump multiplicity.” Definition II: Define a quantity ISPJprobability Pqγ(rD) at level (q, γ.) for the signal s(n) of a length D as: Pqγ(rD) = 100 nqγ(rD)/rD%, where rD is the total number of snore episodes contained within the data of length D. The quantity nqγ(rD) is the number of episodes within D that were labeled

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as having the feature ISPJ according to Definition-I. The snore sound based test statistic proposed in this article is Pqγ(rD), with the corresponding decision threshold symbolized by Pth. If Pqγ(rD)> Pth, the snore-based test is positive for OSA, and vice versa. For each subject in the database, a set S of ISPJ-Probabilities is defined by: S = {Pqγ(rD)| q = 1,2,3 and γ = 10,11, …25ms}

(3)

where Pqγ(rD) is calculated via (2). We use S to derive ROC curves of Pqγ(rD) at different ISPJ levels (q, γ). To draw Receiver Operating Characteristics (ROC curves, Figure 3), we need to know the “true clinical diagnosis” of the patients. The PSG based diagnosis is considered the absolute truth in this article. The diagnosis based on the test statistic Pqγ(rD)>Pth is compared to the “absolute truth,” and the nature of the decision is noted as one among (i) true positives (TP), (ii) true negatives (TN), (iii) false positives (FP), or (iv) false

negatives (FN). Sensitivity, a measure of success in detecting TPs is defined as: TP/(TP +FN)%. Specificity, a measure of success in rejecting nondiseased subjects, is defined as TN/(TN +FP)%. The ROC curve is a graph of sensitivity vs. 1-specificity. The ROC curve (Figure 3) allows us to conclude that snoring carries valuable information on the disease of OSA. The performance of the ISPJ-probability indicates the possibility of using the feature to diagnose OSA. Large-database trials are needed for further validation of results. The noncontact nature of the snore acquisition process should provide a significant advantage over competing techniques in developing a community-screening device for OSA. Results obtained in this article have been based on snorers referred to the sleep clinic for suspected OSA. For this reason, the screening utility of the proposed method needs further investigation with healthy snorers who do not display symptoms normally associated with OSA. The estimation of the TAR, the quantity h(n), can be carried out by using any established technique of blind signal identification (eg., Abeyratne, 1999). This article will not discuss the methodol-

Figure 3. ROC curves for AHIth =10, 15 and 30 at q=2 and γ = 14−17

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ogy involved. Interested readers are referred to the article by Karunajeewa (2005).

A Framework for Arousal Analysis in Understanding and Characterizing Sleep Disorders Electrical activities measured on the scalp, as emanated from the brain, are called EEG signals. EEG is one of the most important signals in overnight PSG recordings. In assessing sleep disorders such as apnea, EEG plays an indispensable role. In the current clinical practice it is being used to study the sleep structure of a patient during sleep, mainly via determining the sleep stages. It is also used to estimate the total sleep time of a patient in computing the AHI and RDI indices. In the past, (Deegan & McNicholas, 1995; McNicholas, 1998) researchers have developed various hypotheses and experimental methods to investigate the changes in EEG signal during sleep apnea and hypopnea. Spectral analysis of the EEG has revealed that termination of apnea and hypopnea events are often associated with changes in EEG power in the δ- band of frequency (0.5 – 4Hz) (Dingli & Assimakopoulos, 2002; Svanborg, 1996). The issue of asymmetry of brain during sleep has been investigated by groups of researchers (Bolduc, 2002; Goldstein & Stoltzfus, 1972; Sekimoto, 2000). Asymmetry studies have been confined to undisturbed sleep in normal individuals. None of these studies considered the significance of apnea/hypopnea on the hemispheric correlations, or how the correlation behaved in the cases of peoples with symptoms of sleep apnea. Despite the importance of EEG in studying sleep disorders, the main index used to measure the severity of apnea, AHI (or RDI), does not use EEG, except for the total sleep time derived indirectly through EEG. EEG, however, is a high-temporal resolution window to the brain, and contains much more information than what is currently being used in clinical practice. We

hypothesize that EEG may provide one avenue to alleviate the important problem mentioned under the Background Section. Recently, the phenomenon known as the EEG Arousals (EEGA) has received attention as a possible pointer of sleep disorders. EEGA in sleep is defined as an abrupt shift in EEG frequency, lasting for 3s or more. According to the criteria developed by the American Sleep Disorder Association (ASDA), EEGA are markers of sleep disruption and should be treated as detrimental. Excessive Daytime Sleepiness (EDS) is one of the defining symptoms in OSA patients (Martin, & Wraith, 1997; McNicholas, 1998). The reason for EDS in SDB patients is the modification of sleep texture, due to reoccurring episodes of EEGA causing sleep fragmentation. In this article, we show that there exists a relationship between the EEGA and informationflow between the left and right hemispheres of the brain, as revealed via the left-right correlation of EEG. This finding may open up a new vista of research in understanding how information flows in the brain during phenomena associated with sleep (eg., EEGA, sleep spindles, and so on) and help with localizing sources of origin of some brain activities. Furthermore, considering the relationship between EEGA and dominant symptoms of OSA (ie., day time sleepiness), we believe a proper understanding of EEG will help in designing a better measure for OSA severity. Figure 4 shows (Vinayak, 2007) the spectral correlation coefficient computed between EEG signals measured from electrode pairs A1-C4 and A2-C3 (of the International 10/20 electrode system) during an overnight sleep study. In Figure 4, (f) and (g) arousal events and apnea event have been marked to help visualize their relationship with interhemispheric EEG correlation. In Figure 4(a), 4(b), 4(c), and 4(d) correlation coefficients have been computed separately in the δ,θ,α, and β spectral bands of the measured EEG. Our results showed that apnea/EEG arousal events generally lead to an increase in IHA. This

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Figure 4. (a) Delta, (b) Theta, (c) Alpha, (d) Beta, correlation coefficient during the NREM and REM sleep, (e) dleep stages, base line indicates NREM sleep and 1st level indicates REM sleep, (f) arousal events, (g) apnea events

change is most prominent during NREM sleep affected by EEGA, particularly in θ, α, and β frequency bands. Our results strongly suggest that the information flow between the left-right hemispheres of the brain is affected by events of apnea/EEG-arousals (Vinayak, 2007).

expected to expand far beyond its current role of sleep-staging (using decades old criteria), making significant inroads into the subject of characterizing apnea. EEG will also help understand the brain mechanisms associated with the processes of sleep and sleep disturbances.

FUTURE TRENDS

CONCLUSION

With the rapidly increasing public awareness of the apnea syndrome, the demand for in-home noncontact methods for screening sleep apnea are expected to rise in the future. Noncontact measurement techniques based on snore (and breathing) sound analysis will, if successful, provide enabling technology for home-monitoring applications. Once the home-based screening test identifies individuals who should be properly diagnosed, hospital-based PSG testing should be considered. In a hospital-based PSG test, we expect EEG signals to play a pivotal role in the future, in diagnosing sleep apnea. The role of EEG is

In this article, we explored two techniques that addressed a range of difficult problems associated with diagnosing/ screening obstructive sleep apnea. The snore sound-based technique provided a basis for noncontact methods suitable community screening of the disease. Our results indicate that the performance of the feature Intra-Snore-Pitch-Jump Probability (ISPJ-probability) is on par with other competing technologies, in terms of the sensitivity/specificity characteristics. Snore-based diagnosis proposed in this article is superior, in that it does not involve contact instrumentation, thus solving a major problem in population screening of the disease.

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We investigated the interhemispheric asymmetry of the brain during apnea and EEG-arousals (EEGA) events. The EEG asymmetry was determined via the Spectral Correlation coefficient of data, which computed the correlation between EEG measured from the two hemispheres of the brain. Results showed that apnea/EEG arousal events generally lead to a decrease in correlation. This change is most prominent during NREM sleep affected by EEGA, particularly in θ, α, and β frequency bands. Our results strongly suggest that the information flow between the left-right hemispheres of the brain is affected by events of apnea/EEG-arousals. The data used in this article came from: (i) patients medically diagnosed with sleep apnea, and (ii) subjects medically deemed to be without apnea, but nevertheless displaying some symptoms of the disease.

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Swarnkar, V., Abeyartne, U., & Hukins, C. (2007). Inter-hemispheric asynchrony of the brain during events of apnea and EEG arousals. Physiological Measurement, 28, 869–880. doi:10.1088/09673334/28/8/010

Kryger, M. H. (2000). Management of obstructive sleep apnea-hyopnea syndrome: Overview. In The Principles and Practice of Sleep Medicine. W. B. Saunders Co.

Udwadia, Z. F., Doshi, A. V., Lonkar, S. G., & Singh, C. I. (2004). Prevalence of sleep-disordered breathing and sleep apnea in middle-aged urban Indian population. American Journal of Respiratory and Critical Care Medicine, 169, 2.

Martin, S., & Wraith, P. (1997). The effect of nonvisible sleep fragmentation on daytime function. American Journal of Respiratory and Critical Care Medicine, 155(5), 1596–1601.

Young, T., Evans, L., Finn, L., & Palta, M. (1997). Estimation of the clinically diagnosed proportion of sleep apnea syndrome in middle-aged men and women. Sleep, 20(9), 705–707.

McNicholas, W. (1998). Arousal in the sleep apnoea syndrome: A mixed blessing? The European Respiratory Journal, 12(6), 1239–1241. doi:10.1 183/09031936.98.12061239 Portier, F., Portmann, A., Czemichow, L., Vascaut, L., Devin, E., Cuvelier, A., & Muir, J. F. (2000)... American Journal of Respiratory and Critical Care Medicine, 162, 814–818. Puvanendran, K., & Goh, K. L. (1999)... Sleep Research Online, 2(1), 11–14. Sekimoto, M. (2000). Asymmetric interhemispheric delta waves during all-night sleep in humans. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 111, 924–928. Svanborg, V., & Guilleminault, C. (1996). EEG frequency changes during sleep apneas. Sleep, 3(19), 248–254.

KEY TERMS AND DEFINITIONS β: α, θ and δ EEG Frequency Bands: Bandpass components of EEG signals respectively covering the frequency bands of 18-30 Hz, 8-13Hz, 4-7Hz, and 1-3.5Hz. EEG Arousals: An abrupt shift in EEG frequencies during sleep which may include α, θ, and/or frequencies <16Hz, excluding structures known as sleep spindles. International 10/20 Electrode System: A standard placement of electroencephalographic (EEG) electrodes on the scalp to measure signals from the brain. Oxygen Desaturation: A decrease in the blood oxygen levels often associated with sleep apneas. Respiratory Disturbance Index (RDI): The number of all respiratory disturbances per hour, averaged over the entire duration of sleep.

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, pp. 610-617, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Computer-Aided Diagnosis of Cardiac Arrhythmias Markos G. Tsipouras University of Ioannina, Greece Dimitrios I. Fotiadis University of Ioannina, Greece, Biomedical Research Institute-FORTH, Greece, & Michaelideion Cardiology Center, Greece Lambros K. Michalis University of Ioannina, Greece & Michaelideion Cardiology Center, Greece

INTRODUCTION In this chapter, the field of computer-aided diagnosis of cardiac arrhythmias is reviewed, methodologies are presented, and current trends are discussed. Cardiac arrhythmia is one of the leading causes of death in many countries worldwide. According to the World Health Organization, cardiovascular diseases are the cause of death of millions of people around the globe each year. The large variety and multifaceted nature of cardiac arrhythmias, combined with a wide range of treatments and outcomes, and complex relationships with other diseases, have made diagnosis DOI: 10.4018/978-1-60960-561-2.ch204

and optimal treatment of cardiovascular diseases difficult for all but experienced cardiologists. Computer-aided diagnosis of medical deceases is one of the most important research fields in biomedical engineering. Several computer-aided approaches have been presented for automated detection and/or classification of cardiac arrhythmias. In what follows, we present methods reported in the literature in the last two decades that address: (i) the type of the diagnosis, that is, the expected result, (ii) the medical point of view, that is, the medical information and knowledge that is employed in order to reach the diagnosis, and (iii) the computer science point of view, that is, the data analysis techniques that are employed in order to reach the diagnosis.

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Computer-Aided Diagnosis of Cardiac Arrhythmias

BACKGROUND Arrhythmia can be defined as either an irregular single heartbeat (arrhythmic beat), or as an irregular group of heartbeats (arrhythmic episode). Arrhythmias can affect the heart rate causing irregular rhythms, such as slow or fast heartbeat. Arrhythmias can take place in a healthy heart and be of minimal consequence (e.g., respiratory sinus arrhythmia), but they may also indicate a serious problem that may lead to stroke or sudden cardiac death (Sandoe & Sigurd, 1991). Ventricular arrhythmias may be categorized broadly as premature ventricular contractions (PVCs) and ventricular tachyarrhythmias, the latter including ventricular tachycardia (VT) and ventricular fibrillation (VF). Atrial fibrillation (AF) is the most prevalent arrhythmia in the western world, affecting 6% of the individuals over age 65 and 10% of those over age 80.

REVIEW OF THE PROPOSED METHODS There are several aspects that can be addressed in order to review the proposed methods for computer-aided diagnosis of cardiac arrhythmias. The type of the diagnosis is the most important since cardiac arrhythmia is a very complex problem, having several different characteristics that need to be considered before reaching a safe diagnosis. Also, the electrocardiogram (ECG) analysis that is employed for this purpose is another important aspect. Finally, the data analysis and classification algorithms that are used define the accuracy and robustness of each approach.

Type of Diagnosis Concerning the type of the diagnosis, two main approaches have been followed in the literature: (i) arrhythmic episode classification, where the techniques focus on the total episode and not on

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a single beat, and (ii) beat-by-beat classification, in which each beat is classified into one of several different classes related to arrhythmic behavior. Arrhythmic episode classification was performed in most of the methods proposed early in the literature, addressing mainly the discrimination of one or more of ventricular tachycardia (VT), ventricular fibrillation (VF), and atria fibrillation (AF) from normal sinus rhythm (NSR). More recent approaches mainly focus on beat-by-beat classification. In each case, a much larger number of different types of cardiac arrhythmic beats are considered. A combination of these two different approaches has been proposed by Tsipouras (Tsipouras, Fotiadis, & Sideris, 2005), where beat-by-beat classification was initially performed and the generated annotation sequence was used in order to detect and classify several types of arrhythmic episodes.

Medical Data and Knowledge In what concerns medical information, the main examination that leads to cardiac arrhythmia diagnosis is the ECG recording; thus, the majority of the methods proposed in the literature are based on its analysis. In the early studies, the ECG waveform was directly used for the analysis from it. However, more recent approaches are based on ECG feature extraction. In this case, features are mainly on the time and frequency domains in the early studies, while more complex time-frequency (TF) and chaos analysis are employed in the more recent studies, trying to access the nonstationary dynamic nature of the signal. Related to morphological features, QRS detection is the easiest to apply and the most accurate ECG processing method and thus, the most commonly used in the literature: almost all proposed methods include QRS detection in some stage of their analysis. Several other morphological features, inspired from the physiology of the ECG signal, have been employed in the proposed studies. However, the detection and measurement of all morphological

Computer-Aided Diagnosis of Cardiac Arrhythmias

features is seriously affected by the presence of noise, with the R peck being the least distorted. The identification of the importance of the heart rate variability has led to the development of methods based solely on the RR-interval signal and features that are extracted from it, for cardiac arrhythmia assessment. Medical knowledge has been used in most of the proposed methods, mainly defining the features that carry sufficient information to be used for the classification. Rule-based medical knowledge has been employed in limited studies; instead mainly data-driven approaches have been developed. Thus, medical knowledge have also been employed for the generation of the annotation of the datasets, since data-driven approaches require an initial annotated dataset in order to be trained. In Tsipouras (Tsipouras, Voglis, & Fotiadis, 2007), a hybrid technique has been proposed in which both knowledge-based rules and training based on annotated data have been integrated into a single fuzzy model.

Data Analysis and Classification Algorithms The general scheme that dominates the proposed methods for arrhythmia diagnosis is a two-stage approach: feature extraction from the ECG and classification based on these features. For the feature extraction stage, time and frequency analysis techniques have been gradually replaced by TF and chaos analysis. TF distributions and wavelet transform (WT) are commonly employed to measure the signal’s energy distribution over the TF plane. In addition, combination between features originated from two or more different domains, has become a common practice. However, the major shift has emerged in the classification stage: the initial threshold-based techniques and crisp logic rules have been replaced with complex machine-learning techniques and fuzzy models. Most of the proposed approaches are based on several variations of artificial neural networks

(ANNs) for the final classification and, in addition, hybrid approaches employing several ANNs and combining their outcomes have also been widely used. Support vector machines (SVMs) have also been incorporated, while fuzzy logic has been employed, either as part of the classification mechanism or for the definition of the fuzzy classifiers.

PROPOSED APPROACHES FOR COMPUTER-AIDED DIAGNOSIS OF CARDIAC ARRHYTHMIAS Several researchers have addressed the issue of automated computer-based detection and classification of cardiac rhythms. We present methods that are considered in the literature as the most cited in the field. Thakor et al. (Thakor, Zhu, & Pan, 1990) proposed a sequential hypothesis testing (SHT) algorithm for the detection of VT and VF. The detection is based on the evolution of a binary signal, generated using a threshold crossing interval (TCI) technique. The algorithm was tested in a database consisting of 170 ECG recordings (half from each category) having 8-sec length, and the results indicate that the detection was 94.12% for VT and 82.35% for VF after 4 sec, while the results increase to 100% for both arrhythmic types after 7 sec. An improvement of this approach was proposed by Thakor et al. (Thakor, Natarajan, & Tomaselli, 1994), named multiway SHT algorithm. In this case, three types of rhythm are considered, VT, supraventricular tachycardia (SVT), and NSR. The algorithm is evaluated in a dataset including 28, 31, and 43 segments for each type of rhythm and its accuracy is 98% after 1.6-, 5-, and 3.6-sec evolution time of the episode for VT, SVT, and NSR, respectively. Chen et al. (Chen, Clarkson, & Fan, 1996) applied the SHT algorithm, on the malignant arrhythmia subset of the MIT-BIH database (MITADB), resulting in much lower results. Thus, they have proposed a modification for this technique, employing dubbed

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blanking variability as basis for discrimination. Testing both the initial SHT technique and their modified approach to a subset of the database (30 episodes of VF and 70 episodes of VT), the classification accuracy improved from 84% to 95%. A similar approach was proposed by Zhang et al. (Zhang, Zhu, Thakor, & Wang, 1999). Again, a binary signal is generated from the ECG recording using a threshold, and then the normalized complexity measured is calculated. Then, minimum, maximum, mean, and standard deviation features are extracted from it, for a specific window length, and are used for classification using statistical analysis. The classification is performed for three types of cardiac rhythm, VT, VF, and NSR. The dataset was the same as the one used in Thakor (1990), while 34 ECG recordings of NSR were added from the MITADB. A comparative study was performed with respect to the length of the window length, which varied from 1 to 8 sec. Results indicated 100% sensitivity and specificity for all categories when 7-sec window length is reached. The problem of VF detection was also addressed by Alfonso and Tompkins (1995), using TF analysis of ECG recordings. They compared short-time Fourier transform (STFT) and two TF distributions, smoothed pseudo Wigner Ville distribution (SPWVD) and cone-shaped kernel distribution (CSKD), in order to identify the TF analysis that provided more discriminatory features for VF vs. NSR. Recordings of NSR were obtained from the MITADB, while VF recordings were extracted from the Stanley database. Based on these, the authors demonstrated that TF distributions can provide a more detailed inside of the VF than STFT. Khadra et al. (Khadra, Al-Fahoum, & Al-Nashash, 1997) proposed a more spherical approach for the arrhythmic episode classification problem, addressing the classification of VT, VF, atrial tachycardia (AT), and NSR. Their approach was also based on TF analysis, applying WT to ECG recordings in order to produce the TF representation, and measuring the signal’s

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energy distribution in specific time intervals and frequency subbands (related to ECG physiology), thus producing three features. The final classification was provided using these features with a rule-based classifier. Forty-five ECG recordings of 2-sec length were selected from the MITADB and the ECG database of the electronic engineering department of the Yarmouk University (YUDB), with 12 associated with VF, 13 with VT, 12 with AT, and 8 with NSR, to evaluate the proposed technique and the obtained accuracy is 88.89%. A similar approach was proposed by Al-Fahoum and Howitt (1999). In this case, six features were extracted measuring the signal’s energy distribution in specific time intervals related to ECG physiology and frequency subbands. Classification was performed using radial basis functions neural network (RBFNN), based on the leave-one-out evaluation strategy. The employed ECG recordings had 1-sec length, thus focusing on a single beat rather than a group of beats. For the evaluation of the method, 49 beats of VF, 49 beats of VT, 21 beats of AF, and 40 beats of NSR (in total 159 beats) were selected from the MITADB, the YUDB, and the Marquette medical systems ECG database. Comparative analysis was performed between nine different wavelets and the results indicated accuracy ranging from 81.1%-97.5%. TF analysis was also employed by Tsipouras and Fotiadis (2004) for the classification of ECG segments as normal or arrhythmic. In this study, time and TF analysis were used to extract features from the RR interval signal while classification was performed based on mixture-of-classifiers approach, using the combined results of several artificial neural networks (ANNs). In the time domain, six known heart rate variability (HRV) features were extracted, such as standard deviation of the RR intervals and standard deviation of the HRV, and all different combinations among them were used to train the ANNs. In the case of TF analysis, STFT and 18 time-frequency distributions were used to generate the TF representation and then, for each of them, features were extracted

Computer-Aided Diagnosis of Cardiac Arrhythmias

representing the signal’s energy distribution over the TF plane and based on these, an ANN was trained. The evaluation was performed based on 32 RR intervals segments (corresponding to approximately 30-sec ECG recordings), extracted using all recordings from the MITADB. Classification sensitivity and specificity were 87.53% and 89.48%, respectively, for the time domain features, and 89.95% and 92.91%, respectively, for TF domain features. Beat-to-beat classification was addressed by Simon and Eswaran (1997). In their work, analysis of each ECG waveform was performed using discrete cosine transform (DCT) and using the extracted coefficients as an input into a set of ANNs, each of them identifying a specific classification category, while the final classification was defined combining the outputs of all ANNs. The proposed method was used to identify beats belonging to five categories, while using data from the MITADB, the Glasgow database, and scanned waveforms, training and test sets were comprised, including 54 beats for training and 1,040 beats for testing. The evaluation of the method indicated an average sensitivity of 96.04%. Hu et al. (Hu, Palreddy, & Tompkins, 1997) have presented a method for beat classification, based on a mixture of experts approach, which is also a technique based on combining outcomes of ANNs. In this case, the ECG beat waveform was used as input for two ANN-based classifiers, one trained using data from several patients (global expert), and a second using data from a specific patient (local expert), and the combination of their outputs, using a gaiting network, provided the final classification. Although, the authors use a four-category dataset extracted from the MITADB, evaluation is based on the identification of only two categories, namely PVCs and normal (N) beats. Evaluation was based on 43,897 N beats and 5,363 PVC. The reported accuracy was 95.52%. Beat morphology and interval features were also used by Chazal et al. (Chazal, O’Dwyer, & Reilly, 2004) for cardiac beats classification. The extracted features are

used with a linear discriminants classifier, based on maximum likelihood, to classify heart beats into five categories. The MITADB was used for training and evaluating the proposed method: 51,020 beats were used for training and 49,711 for testing, obtaining 86.2% accuracy. Beat-by-beat classification was also addressed by Miniami et al. (Miniami, Nakajima, & Toyoshima, 1999), for supraventricular rhythm (SVR), ventricular rhythm (VR), and ventricular flutter/ fibrillation (VFF). The spectrum of each QRS complex is accessed using FT, extracting five spectral components (features) fed into an ANN, which provides the final classification. The dataset included 700 QRSs, including 200, 100, and 200 for SVR, VR, and VFF, respectively, used for training the ANN and 100, 50, and 100 QRSs used for evaluation. Results indicated 90.33% average sensitivity and 95.33% average specificity. Dokur and Olmez (2001) also exploited beat-by-beat classification based on ANNs. Frequency domain features are extracted from the ECG beat waveform, using two different approaches, one based on FT and the second based on WT. Classification is performed for 10 categories of arrhythmic beats, adopted from the MITADB annotations. A hybrid ANN was trained for each of the two approaches for feature extraction, using 150 beats from each category for training while evaluation was performed using again 150 beats from each category. The obtained results indicated 65.4% and 88% classification accuracy for the FT and WT feature sets, respectively. Osowski and Lihn (2001) developed a method for ECG beat classification based on ANNs. In this case, statistical features were derived from each ECG beat waveform and, based on them, a fuzzy hybrid ANN was used to generate the final classification. The classification categories were 7, again originated from the MITADB annotations, while 4.035 beats were used for training and 3.150 for testing the method, which reported 96.06% classification accuracy. Ge et al., (Ge, Srinivasan, & Krishnan, 2002) proposed a method for classification of cardiac

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beats into six categories: NSR, artial premature contractions (APC), PVC, SVT, VT, and VF. The ECG waveform is analyzed using autoregressive (AR) modeling, and the estimated AR coefficients were used in a generalized linear model to classify the cardiac arrhythmias. Using 856 cardiac beats, obtained from the MITADB, the proposed approach presented 96.85% classification accuracy. In a second approach, Osowski et al. (Osowski, Linh, & Markiewicz, 2004) addressed this problem, using again statistical features, but also a second set of features based on Hermite basis functions (HBF) extraction, while SVMs were employed. The number of categories increased to 13 and the train and test sets were comprised from 6.690 and 6.095 beats, respectively. The average sensitivity was 93.72%, in the case of statistical features, 94.57% in the case of HBF features, and 95.91% when both classifiers were integrated. Ham and Han (1996) proposed the discrimination of N beats and PVCs based on fuzzy adaptive resonance theory mapping (fuzzy ARTMAP). Their approach was based on the analysis of three features, extracted from each cardiac beat: two linear predictive coding coefficients and the mean square value of the QRS complex. Then, a fuzzy ARTMAP, which is an ANN classifier, was employed for the final discrimination. For training purposes, and to test the described method, 12,123 N beats and 2,868 PVCs were used, resulting in 99.88% sensitivity and 97.43% specificity. Fuzzy logic was also employed in the approach proposed by Weibien et al. (Weibien, Afonso, & Tompkins, 1999) for the same problem. In this case, features were extracted from each cardiac beat related to heart rate, morphology, and TF characteristics of the QRS, accessed using a filter-bank for analysis and a fuzzy rule-based classifier was employed for the final classification. For training, 8,089 N beats and 1,080 PVCs were used, and 77,374 and 5,900, respectively, for testing, reporting average sensitivity 74.6% and average positive predictivity of 66.5%. Wang et al. (Wang, Zhu, Thakor, & Xu, 2001) addressed the problem of classification of

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ECG records in three categories: VT, VF, and AF. The analysis was based on features representing the short-time generalized dimensions of multifractal analysis, while classification was provided by a fuzzy Kohonen network. The test set comprised from 60 ECG recordings of each category, 6- or 8-sec long, while the average sensitivity and specificity were 97.2% and 98.63%, respectively. Acharya et al. (Acharya, Bhat, Iyengar, Rao, & Dua, 2003) presented a classification method based solely on the RR interval signal. Four features are extracted from the RR interval signal, the average heart rate, two features representing signals energy on specific frequency subbands and the correlation dimension factor, extracted from 2-D phase-space plot of the signal. Based on these, a fuzzy equivalence relation classifier is generated, for four classification categories: ischemic/dilated cardiomyopathy, complete heart block, sick sinus syndrome, and NSR. Two hundred and seventy six ECG segments were used for training the classifier and 66 for testing. The average sensitivity was 95%. Acharya et al. (Acharya, Kumar, Bhat, Lim, Iyengar, Kannathal, & Krishnan, 2004) also presented a similar approach for eight classification categories, the four mentioned previously, and additionally left bundle branch block, PVC, AF, and VF. In this case, three features from the RR interval signal were employed, the spectral entropy, geometry of the 2-D phase-space plot of the signal, and the largest Lyapunov exponent, while the classifier was the same. Two hundred and seventy nine ECG segments were used for training the classifier and 167 for testing; the average sensitivity was 85.36%. Another approach for arrhythmic beat classification and arrhythmic episode detection and classification which is also based on the RR-interval signal, is proposed by Tsipouras et. al. (Tsipouras, 2005). A three RR-interval sliding window is used in the arrhythmic beat knowledge-based classification algorithm. Classification is performed for four categories of beats: N, PVCs, VFF, and 2o heart block (BII). The beat classification is used as input

Computer-Aided Diagnosis of Cardiac Arrhythmias

of a knowledge-based deterministic automato to achieve arrhythmic episode detection and classification. Six rhythm types are classified: ventricular bigeminy, ventricular trigeminy, ventricular couplet, VT, VFF, and BII. The achieved scores for all beats of the MITADB (approximately 110,000 beats) indicate high performance: 94% average accuracy for arrhythmic beat classification and 94% average accuracy for arrhythmic episode detection and classification. For the same arrhythmic beat classification problem, Tsipouras et al. presented a second approach (Tsipouras, 2007), based on fuzzy logic. A similar knowledge-based initial set of rules was employed, which was then transformed into a fuzzy model, and an optimization technique was used to tune its parameters. Comparative analysis was performed for different fuzzyfication functions and realizations of the fuzzy operators used in the fuzzy model. The same dataset was used for evaluation, indicating 96.5% average accuracy.

FUTURE TRENDS Based on the presented works, there should be no doubt that several challenges remain concerning the computer-aided diagnosis of cardiac arrhythmias. From the medical point of view, a major limitation in all proposed methods is that they are based solely on the analysis of ECG recordings. Other medical information related to the patient, such as medication and medical history, are not included. Genomic data, such as single nucleotide polymorphisms related to cardiac disorders, can also have a significant impact on the diagnostic value of such systems if they are incorporated. From the computer science point of view, several of the proposed techniques are based on “black box” approaches for the classification, such as ANNs, which cannot provide interpretation of the produced diagnosis. This is a very important aspect, since providing a clear insight of their inner process for decisionmaking mechanism is essential for physicians in

order to incorporate such systems in their clinical practice. Moreover, experts rely more on systems that contain, to some extent, established medical knowledge in their decision-making mechanism. Thus, machine learning techniques that can generate transparent classification mechanisms and can also integrate established medical knowledge are the key to creating computer-based arrhythmia diagnosis systems that will be widely used.

CONCLUSION Several computer-based methods for the automated diagnosis of cardiac arrhythmias have been presented. The methods indicate that, under special conditions, they can provide the doctors with diagnosis of arrhythmias. However, most of the methods have been evaluated in existing datasets and not in clinical conditions, where usually the signals are noisy and not easily interpretable. It seems that methods based on knowledge, in general, do not provide with the best results, but the doctor could be helped since the decisions made are easily interpretable. All methods to become a real tool need further refinement in terms of facing real clinical conditions, real-time operation, and extensive evaluation.

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Computer-Aided Diagnosis of Cardiac Arrhythmias

Tsipouras, M. G., & Fotiadis, D. I. (2004). Automatic arrhythmia detection based on time and time-frequency analysis of heart rate variability. Computer Methods and Programs in Biomedicine, 74, 95–108. doi:10.1016/S0169-2607(03)00079-8 Tsipouras, M. G., Fotiadis, D. I., & Sideris, D. (2005). An arrhythmia classification system based on the RR-interval signal. Artificial Intelligence in Medicine, 33, 237–250. doi:10.1016/j. artmed.2004.03.007 Tsipouras, M. G., Voglis, C., & Fotiadis, D. I. (2007). A framework for fuzzy expert system creation – application to cardiovascular diseases. IEEE Transactions on Bio-Medical Engineering, 54, 2089–2105. doi:10.1109/TBME.2007.893500 Wang, Y., Zhu, Y. S., Thakor, N. V., & Xu, Y. H. (2001). A short-time multifractal approach for arrhythmia detection based on fuzzy neural network. IEEE Transactions on Bio-Medical Engineering, 48, 989–995. doi:10.1109/10.942588 Weibien, O., Afonso, V. X., & Tompkins, W. J. (1999). Classification of premature ventricular complexes using filter bank features, induction of decision trees and a fuzzy rule-based system. Medical & Biological Engineering & Computing, 37, 560–565. doi:10.1007/BF02513349 Zhang, X. S., Zhu, Y. S., Thakor, N. V., & Wang, Z. Z. (1999). Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Transactions on Bio-Medical Engineering, 45, 548–555. doi:10.1109/10.759055

KEY TERMS AND DEFINITIONS Artificial Neural Network (ANN): An interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. It has the ability to learn from knowledge, which is expressed through interunit connection strengths, and can make this knowledge available for use. Atrial Fibrillation (AF): Disorganized, highrate atrial electrical activity. Atrial Premature Contractions (APCs): Single or paired extrasystoles that originate in the atrials. Electrocardiogram (ECG): Recording of the electrical activity of the heart. Fuzzy Logic: Derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic. Heart Rate Variability (HRV): The alterations of the heart rate between consecutive heartbeats. Premature Ventricular Contractions (PVCs): Single or paired extrasystoles that originate in the ventricles. QRS Detection: Procedure for detecting the QRS complexes in the ECG signal. RR-Interval Signal: The signal representing the durations between consecutive R waves of the ECG. Ventricular Tachyarrhythmias: Repetitive forms of three or more consecutive ventricular ectopic beats.

This work was previously published in Encyclopedia of Information Science and Technology, Second Edition, edited by Mehdi Khosrow-Pour, pp. 661-666, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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Computer Aided Risk Estimation of Breast Cancer: The “Hippocrates-mst” Project George M. Spyrou Academy of Athens, Greece Panos A. Ligomenides Academy of Athens, Greece

ABSTRACT

INTRODUCTION

In this chapter the authors report about their experiences in designing, implementing, prototyping and evaluating a system for computer aided risk estimation of breast cancer. The strategy and architecture of “Hippocrates-mst” along with its functionalities are going to be presented. Also, the evaluation results in the clinical practice concerning the performance of “Hippocrates-mst” in the “Ippokrateio” University Hospital of Athens will be presented. The feedback from medical experts along with the new features of the system that are under development will be discussed.

Breast cancer is worldwide the most frequent cancer in women. Years of experience have revealed that mammography constitutes the most efficient method in the early diagnosis of this type of cancer (Elmore et al., 2005). Clustered microcalcifications belong to the worthy mammographic findings since they have been considered as important indicators of the presence of breast cancer (Fondrinier et al., 2002, Gulsun et al., 2003, Buchbinder et al., 2002, Bassett, 1992). Several efforts have been made to classify the microcalcifications as benign or malignant according to their characteristics (Lanyi, 1977, 1985, Le Gal et al., 1984, 1976, Timins, 2005). However, problems

DOI: 10.4018/978-1-60960-561-2.ch205

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Computer Aided Risk Estimation of Breast Cancer

still appear in mammographic imaging methods due to subtle differences in contrast between benign and malignant lesions, mammographic noise, the insufficient resolution and local low contrast. These inherent difficulties of mammographic image reading prevent the medical expert from quantifying the findings and from making a correct diagnosis (Wright et al., 2003, Shah et al., 2003, Yankaskas et al., 2001, Elmore et al., 2003). Therefore, a non-negligible percentage of biopsies following mammographic examination are classified as false positive (Esserman et al., 2002, Roque & Andre 2002). Improvements towards the early diagnosis of breast cancer, as far as the computer science is concerned, belong to the field of systems for computer-aided mammography (Doi et al., 1997, Wu et al., 1992). Such systems have mainly dealt with detection and classification of microcalcifications and use image processing and analysis techniques as well as artificial intelligence methods. The most well known methods of computer aided detection or diagnosis in mammography are artificial neural networks (ANN) with different architectures and variations, the segmentation methods, multiscale analysis - wavelets and morphologic analysis to distinguish between malignant and benign cases (Wu et al., 1993, Zhang et al., 1994, Chan et al., 1995, Zhang et al., 1996, Gurcan et al., 2001, Gurcan et al., 2002, Cooley & Micheli-Tzanakou, 1998, Bocchi et al., 2004, Dengler et al., 1993, Gavrielides et al., 2002, Li et al., 1997, Zhang et al., 1998, Lado et al., 2001, Mata Campos et al., 2000, Shen et al., 1994, Chang, et al., 1998, Spyrou et al., 1999). The project that we present here, describes a system that deals with the digital or the digitized mammographic image, offering computer aided risk assessment starting from a selected region with microcalcifications (Spyrou et al., 1999, Spyrou et al., 2002a, 2002b). The proposed system is based on methods of quantifying the critical features of microcalcifications and classifying them as well as their clusters according to their probability of

being cancerous. A risk-index calculation model has been developed and is included in the system. Furthermore, the calculated risk-index can be refined through other information such as the position and the direction of the cluster along with information from the patient record such as the age and the medical history of the patient (Berg et al., 2002, Cancer Facts and Figures, 2004). The design of the interface follows the clinical routine and allows for interaction with the physician at any time, providing information about the procedures and parameters used in the model of diagnosis. Apart from the very encouraging laboratory testing, the prototype system is under a long-term evaluation phase in “Ippokrateio” University Hospital of Athens. Up to now the results from the current evaluation procedures indicate that the system estimates the risk of breast cancer towards the right direction. As far as the malignant cases are concerned, the system has a very high sensitivity. For the benign cases, the system is able to achieve a significant reduction on the unnecessary biopsies. Nevertheless, the sensitivity and specificity levels are not balanced. Actually, there is an overestimation of risk driven from the attempt to minimize the false negative results. Therefore, we further investigate for a better compromise which will emerge after a fine tuning in the calculation of parameters and the setup of thresholds concerning the selected features of the microcalcifications and their clusters.

SYSTEM AND PROGRAM DESCRIPTION The method used in this computer aided risk assessment scheme is outlined in Figure 1. According to this scheme, the doctor may select from the patient’s archive the set of the four mammographic images (Right and Left CranioCaudal-CC and MedioLateral-ML) that corresponds to a specific date of mammographic examination. Every selected image from this set can be displayed

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inside a form with various digital tools. These tools can be applied either to the whole image or to a Region Of Interest (ROI) with the help of a digital lens (a rectangular region where several digital tools can be applied). With the digital lens the user can select a ROI and subsequently apply several techniques such as histogram equalization, zoom, edge detection, differentiation of contrast and brightness. Some of them are very effective visualization tools, especially in breast periphery where the tissue is overexposed and thus very dark. The module for the detection of the microcalcifications implements a very specific function Figure 1. The computer aided risk assessment scheme

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of the digital lens tool described with a flowchart on the corresponding part of Figure 1 and it is based on the application of High Pass filtering, Variance Normalization and Adaptive filtering in order to reveal the microcalcifications from a low contrast image environment (Karssemeijer, 1993, Lorenz, et al., 1993). After the detection of the microcalcifications, image analysis is needed for the microcalcification feature extraction and quantification. The considered microcalcification features are: (i) size, (ii) circularity, (iii) existence of dark center, (iv) level

Computer Aided Risk Estimation of Breast Cancer

of brightness, (v) irregularity regarding the shape, (vi) level of branching and (vii) circumvolution. As far as the shape features are concerned, we apply a four projections method for recognition, based on the principle of viewing each microcalcification from four different points of view (Horizontal, Vertical and two Diagonals) as shown in Figure 2. These calculations take place on every microcalcification of the cluster inside a sub region of the initial ROI selected by the doctor. Subsequently, the risk of each microcalcification is estimated and a classification is taking place, according to an empirical model comprising of rules either from the related literature or from selected medical experts. The final risk assessment may be synthesized from the default base line risk estimation (A), and two possible shifts of risk (B) and (C) as described below: (A) The default base line risk estimation is based on the evaluation of the findings and specifically on the following parameters: (A1) the risk distribution of microcalcifications, (A2) the number of microcalcifications at high risk, (A3) the cluster polymorphism. (B) A first possible shift of risk estimation which is based on the doctor’s expertise regarding Figure 2. The four-projections method for morphology recognition

the cluster location and direction inside breast. (C) A second possible shift of risk estimation which is based on the patient’s record. It can either be provided by the doctor or calculated by a well known algorithm (Gail Model) that has been implemented to the system (Gail et al., 1989, Gail & Costantino, 2001). Finally, the risk estimation can be classified in the four virtual zones of risk: (i) Zone 1, with risk between 0% and 35% (definitely benign case: discouraging biopsy), (ii) Zone 2, with risk between 35% and 55% (benign case with doubts: can not avoid biopsy), (iii) Zone 3, with risk between 55% and 75% (malignant case with doubts: encouraging biopsy), (iv) (Zone 4, with risk between 75% and 100% (definitely malignant case: strong prompt for biopsy).

INTERFACE The above risk assessment scheme has been implemented and integrated into a software system with graphical interface that is adapted to the physician’s needs. The user (physician) may log in with the corresponding username / password and be subsequently driven with forms mainly concerning (i) the patient’s archive management, (ii) general image analysis and visualization tools, (iii) microcalcifications analysis tools, and (iv) risk assessment. A characteristic form is shown in Figure 3, with the digital tools that can be applied to the image. In this form, microcalcifications have been revealed inside a region of interest. There is also a sub region where each of the revealed microcalcifications has been also classified and coloured according to the risk estimation model.

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Figure 3. A characteristic form of the interface concerning the image analysis tools

A second form is shown in Figure 4, where the physician can be informed about the base line risk assessment (A) as it is estimated from the computer model. There is also specific information concerning the topology of the sub region as well as the patient’s record, providing the user with the possibility of two possible shifts of risk estimation (B and C).

HARDWARE The system consists of a modern personal computer with sufficient memory, processing power and storage capability for high-resolution image handling (see Figure 5). The computer must be connected with a high-resolution film scanner or a digital mammography apparatus. The film scan-

Figure 4. A characteristic form of the interface concerning risk estimation

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Figure 5. Image acquisition, processing and archiving is achieved with the system

ner must have optical resolution of at least 300 dpi, wide optical density range (0.0 to at least 3.5 O.D.), at least 12-bit output, quick scan rate and scanning area greater than 18 cm x 24 cm, namely greater than the mammographic film dimensions.

EVALUATION We have done until now three evaluation studies: The first one used an images set that was containing 260 mammograms, of both craniocaudal and mediolateral views, with microcalcifications. Each case was accompanied by a histopathological examination of resected speciment and with the patient’s demographic data and medical history as well. The results from the Hippocrates-mst system are presented in a risk percentage scale from 0 to 100. Percentage lesser than or equal to 35% means there is not enough evidence to support sending the patient for a biopsy test. Percentage from 35% to 55% declares benign state and a biopsy test is suggested, while percentage greater than 55 indicates malignancy and a biopsy test has to be made as soon as possible. At this point it must be stated that the system only makes suggestions to the doctor and it is the doctor that makes the final decision whether a case has to be sent for a biopsy test or not. From these women 73 have malignant histopathological test results (biopsy: mal) and the rest 187 women have benign histopathological test results (biopsy: ben). The average of the results for malignant cases was 81.21, giving the

proposed system a high sensitivity in malignant microcalcifications detection. The average of the results for the benign cases was 49.46. According to those results, the system shows a high sensitivity (98.63%) by classifying correctly all malignant cases except one, which had high density -bad quality mammography- and was detected as a false negative. However, the system suffers from low specificity. Nevertheless its specificity is much better at 29.95% (56 cases correctly identified as benign with a percentage equal or smaller of 35% out of 187 referred cases which were benign according to the biopsy results) compared to the doctors’ decisions for biopsy referrals. The other two studies were performed during the evaluation period of the system in the “Ippokrateio” University Hospital of Athens. The first one included 186 cases with BI-RADS 3,4,5 accompanied with biopsy. From them, 34 were malignant. The system showed high levels of sensitivity identifying correctly the 97.06% of the malignant cases and at the same time succeeded in 28.79% reduction of the unnecessary biopsies. In the other study, 110 cases (6 malignant) were used, exclusively diagnosed as BI-RADS 3. The system showed high levels of sensitivity identifying correctly the 100% of the malignant cases and at the same time succeeded in 31.81% reduction of the unnecessary biopsies.

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DISCUSSION Screening mammography with computer-aided diagnosis systems has been shown to improve radiologists performance in the detection of clinically occult cancers that otherwise would be overestimated. “Hippocrates-mst” presents high sensitivity but it appears to have low specificity (see Figure 6). This indicates that there is an overestimation of the risk, driven from the attempt to minimize the false negative results, which is the most important factor in these systems. However, in any case its specificity is much better than the specificity obtained only with the doctor’s visual examination of the mammogram. What is also very important in the use of CAD systems is their appropriate use. The users or the radiologists should firstly be trained and informed about the systems

software and about what these systems can do and how. The present system aids the radiologists as a second reviewer in evaluating mammograms and these systems are rapidly gaining acceptance in the breast imaging community. However, the radiologists should not become overly reliant on the system for detection of malignant lesions and should continue to search the films diligently. The system estimates the risk of breast cancer towards the right direction. However, although it presents high sensitivity, it appears to have low specificity. This indicates that there is an overestimation of the risk, driven from the attempt to minimize the false negative results. Thus, there is the need for a better compromise which will emerge after a fine tuning in the calculation of parameters and the setup of thresholds concerning the selected features of the microcalcifications

Figure 6. The system “Hippocrates-mst” in clinical evaluation

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and their clusters. For this reason we are planning to perform a more accurate evaluation procedure that is going to take place in several hospitals of Greece. Some of the issues that must be taken into account are the following: (i) all the cases have to contain microcalcifications, related to the existence (or not) of the lesion and (ii) the biopsies have to be taken from the region that contains the microcalcifications. The feedback from such an accurate procedure will lead to a promising methodology refinement.

Chan, H. P., Lo, S.-C., Sahiner, B., Lam, K. L., & Helvie, M. A. (1995). Computer-aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network. Medical Physics, 22, 1555–1567. doi:10.1118/1.597428

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Dengler, J., Behrens, J., & Desaga, J. F. (1993). Segmentation of microcalcifications in mammograms. IEEE Transactions on Medical Imaging, 12, 634–642. doi:10.1109/42.251111 Doi, K., Giger, M. L., Nishikawa, R. M., & Schmidt, R. A. (1997). Computer-Aided Diagnosis of Breast Cancer on Mammograms. Breast Cancer (Tokyo, Japan), 4(4), 228–233. doi:10.1007/ BF02966511 Elmore, J. G., Armstrong, K., Lehman, C. D., & Fletcher, S. W. (2005). Screening for breast cancer. Journal of the American Medical Association, 293(10), 1245–1256. doi:10.1001/ jama.293.10.1245 Elmore, J. G., Nakano, C. Y., Koepsell, T. D., Desnick, L. M., D’Orsi, C. J., & Ransohoff, D. F. (2003). International variation in screening mammography interpretations in communitybased programs. Journal of the National Cancer Institute, 95(18), 1384–1393.

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Spyrou, G., Pavlou, P., Harissis, A., Bellas, I., & Ligomenides, P. (1999). Detection of Microcalcifications for Early Diagnosis of Breast Cancer. In Proceedings of the 7th Hellenic Conference on Informatics, University of Ioannina Press, August 26-28, 1999, Ioannina, Greece, (p. V104). Timins, J. K. (2005). Controversies in mammography. New Jersey Medicine, 102(1-2), 45–49. Wright, T., & McGechan, A. (2003). Breast cancer: new technologies for risk assessment and diagnosis. Molecular Diagnosis, 7(1), 49–55. doi:10.2165/00066982-200307010-00009 Wu, Y., Doi, K., Giger, M. L., & Nishikawa, R. M. (1992). Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks. Medical Physics, 19, 555–560. doi:10.1118/1.596845 Wu, Y., Giger, M. L., Doi, K., Schmidt, R. A., & Metz, C. E. (1993). Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology, 187, 81–87. Yankaskas, B. C., Schell, M. J., Bird, R. E., & Desrochers, D. A. (2001). Reassessment of breast cancers missed during routine screening mammography: a community-based study. AJR. American Journal of Roentgenology, 177(3), 535–541. Zhang, W., Doi, K., Giger, M. L., Wu, Y., Nishikawa, R. M., & Schmidt, R. A. (1994). Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural networks. Medical Physics, 21, 517–524. doi:10.1118/1.597177 Zhang, W., Doi, K., Giger, M. L., Wu, Y., Nishikawa, R. M., & Schmidt, R. A. (1996). An improved shift-invariant artificial neural networks for computerized detection of clustered microcalcifications in digital mammograms. Medical Physics, 23, 595–601. doi:10.1118/1.597891

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KEY TERMS AND DEFINITIONS BI-RADS: Breast Imaging Reporting and Data System.

This work was previously published in Biocomputation and Biomedical Informatics: Case Studies and Applications, edited by Athina A. Lazakidou, pp. 204-214, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Automatic Analysis of Microscopic Images in Hematological Cytology Applications Gloria Díaz National University of Colombia, Colombia Antoine Manzanera ENSTA-ParisTech, France

ABSTRACT Visual examination of blood and bone marrow smears is an important tool for diagnosis, prevention and treatment of clinical patients. The interest of computer aided decision has been identified in many medical applications: automatic methods are being explored to detect, classify and measure objects in hematological cytology. This chapter presents a comprehensive review of the DOI: 10.4018/978-1-60960-561-2.ch206

state of the art and currently available literature and techniques related to automated analysis of blood smears. The most relevant image processing and machine learning techniques used to develop a fully automated blood smear analysis system which can help to reduce time spent for slide examination are presented. Advances in each component of this system are described in acquisition, segmentation and detection of cell components, feature extraction and selection approaches for describing the objects, and schemes for cell classification.

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Automatic Analysis of Microscopic Images in Hematological Cytology Applications

INTRODUCTION Traditionally, visual microscopical examination is used to perform quantitative and qualitative analysis of blood smears, which are very valuable for diagnosis of many diseases. Human visual analysis is tedious, time consuming, repetitive and has limited statistical reliability. Thus, methods that automate visual analysis tasks have been developed for enhancing the performance in hematological laboratories. Since the end of the 80’s, commercially available systems for automatic quantification of blood cells allow to count the numbers and types of different cells within the blood (Beckman Coulter LH series, Sysmex XE-2100, Siemens ADVIA 120 & 2120). These counters use flow cytometry techniques, which measure some physical and/ or chemical characteristics of blood cells going through a detector of light, fluorescence or electrical impedance, allowing to identify the type of cell. Although quantification results are very precise, some morphological abnormalities can be misidentified or not detected by the machine, and then microscopic blood smear analysis is required. The development of automated

methods for classification of blood cells from digitized blood smears started in 70´s decade (Miller, 1972; Bentley & Lewis, 1975) and is now a current problem in pattern recognition. So far, fully automated microscopy systems are under development, which combine advances in image processing and machine learning for reducing the human intervention in the process (Ceelie et al., 2006). An automatic analysis system for blood or bone marrow smears generally consists in the phases illustrated in Figure 1. First, image preprocessing of the digitized smears is applied for suppressing noise and improving luminance and contrast differences in the images. Second, a segmentation process is applied for finding and isolating the interest objects in the image. The third phase aims at characterizing the objects previously extracted to be used in the last phase, i.e. classification stage. Feature selection can be applied to reduce the redundant information. Selected features are used as input to the classification method which makes the decision about the class assignment.

Figure 1. Automatic analysis of blood and bone marrow smears

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BLOOD SMEARS Blood analysis is commonly carried out on peripheral blood smears since anomalies in blood cells are indicators of disturbances in their origin organs (Hoffbrand et al., 2006). However, in some cases bone marrow biopsies or aspirations are indicated to evaluate diseases which cannot be diagnosed and classified in peripheral blood analysis. Bone marrow is a tissue found inside the bones where the blood cells are produced, and released to the peripheral blood once reached their maturity level. Blood smears are thin films of peripheral blood or bone marrow which are examined by a microscopist for visualizing the morphological features of the cells (Jenkins & Hewamana, 2008). A blood smear is made by placing a drop of blood or bone marrow sample on a highest purity, corrosion-resistant glass slide and then dispersed using a spreader slide (Houwen, 2000). It is then fixed and stained for highlighting morphological cell characteristics. Diagnosis of blood diseases is performed by the differential discrimination of normal and abnormal cells, based on identification and quantification of changes in their visual features. Visual features of blood cells depend on smearing, staining and capturing processes. Variations in these processes can mistake the entire technique

and generate different kinds of artifacts, as for instance dye remains, damaged cells or cells with poor morphology (e.g. crenated or superimposed cells). Sample images of typical peripheral blood and bone marrow smears are shown in Figure 2. (top) Although peripheral blood and bone marrow smears are visually similar, specimens of peripheral blood are observed as a homogeneous layer without any apparent structure, whereas bone marrow smears are not homogeneous and contain inner structured portions. On the bottom the same figure shows sample images of different blood cell types, from left to right: a normal erythrocyte, a retyculocyte, a target cell, an erythrocyte infected with malaria, an eosinophil, a lymphocyte, a neutrophil, a myeloblast and a myelocyte. Normal bone marrow contains cells at all stages of development, from the earliest precursor stem-cells to functionally mature cells, including hematopoietic stem cells which are precursors of most blood cells, mesenchymal stem cell which are considered as “gatekeeper “ cells of bone marrow, and endothelial stem cells. Provided all blood cell are derived from a common stem cell, it is impossible to establish rigid morphological distinctions between certain cells in some stages of the maturation process. On the other hand, peripheral blood normally only contains mature leukocytes, erythrocytes and platelets which have

Figure 2. Peripheral blood and bone marrow smear samples

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specific morphological features that allow to distinguish them. The erythrocyte is a non nucleated cell, a biconcave disk of 6-8 microns. In microscopic smear these cells are commonly viewed as a reddish circular cell, lighter towards the center. The leukocytes (9-15 microns), present a spherical shape in suspension, but take an amoeba shape inside a solid substrate. In microscopic smears, leucocytes are viewed as circular or oval cells with a blue cytoplasm and big nucleus with clumped chromatin structure. Finally, the platelets (thrombocytes) are very sparse, small, disk shaped and non-nucleated corpuscles, whose size is between 1-15 microns. In blood smears, they show up agglutinated and present a translucent clear blue part, which contains many purple stained grains. Also, infecting parasites such as the malaria parasite can be seen in a blood smear. Automatic or semi-automatic approaches for analysis of blood and bone marrow smears have been used in many applications, such as differential counting of blood cells (Micheli-Tzanakou et al., 1997; Ongun et al., 2001; Bikhet et al., 2000; Kats,2000), hemoparasite and hematological diseases diagnosis (Di Ruberto et al., 2002; Halim et al., 2006, Tek et al., 2006; Ross et al., 2006; Sio et al., 2007; Díaz et al., 2009; Markiewicz et al., 2005; Sabino et al. 2004), and content based image retrieval systems by supporting medical decisions (Comaniciu et al., 1998; Aksoy et al., 2002). Because features such as morphometry or visual appearance, and their relationships, cannot be modeled accurately for distinguishing different types of cells, learning from sample approaches are commonly used as classification strategies in these applications. This chapter exhibits a comprehensive review of how machine learning and image processing approaches are applied in automatic analysis of blood and bone marrow smears. It presents the general architecture of a general system and describes relevant techniques proposed for performing each stage. Some examples of applications of these techniques are shown.

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IMAGE DIGITIZATION The starting point of any digital image analysis system is the image capturing process. Many image analysis methods have been evaluated on images acquired in controlled conditions. However, a fully automated examination of blood and bone marrow smears requires approaches that can be used in real laboratory conditions. Technologies for digitization of whole slides have been investigated and are commercially available (Aperio, Cellavision, Carl Zeiss). A proper visual analysis requires that images of blood and bone marrow smears should be captured with a 100x objective lens. In this magnification, a whole slide of a 15mm x 15mm tissue area results in an image of 76 GB approximately, whereby automatic selection of regions of interest is an open research topic. In some cases these regions can be single cells, for instance nucleated cells for diagnosis based on differential leukocytes. In other cases, like in hemoparasite diagnosis or differential erythrocyte analysis, a working area should be selected. A technique for optimal area detection when analyzing peripheral blood smears was proposed by Angulo et al. (2003), which firstly extracted erythrocytes from images scanned at low magnification. Then some scattering and overlapping measures are computed for defining the working area.

IMAGE ENHANCEMENT Inherent variability of many factors such as environment illumination conditions, dye duration, film thickness or film defects result in visual artifacts, non uniform background or different image luminance and color distribution in digitized images. So, images should be enhanced for improving particular image characteristics and reducing the processing required in the latter analysis stages.

Automatic Analysis of Microscopic Images in Hematological Cytology Applications

Two major problems are commonly addressed at this stage: noise reduction and feature enhancement. Traditionally, noise has been reduced by application of median filters (Micheli-Tzanakou et al., 1997; Anoraganingrum, 1999; Bikhet et al., 2000, Di Ruberto et al., 2000). While, image enhancement, has been accomplished using a range of tonal, spatial or spectral domain methods (Heijden, 1995). The tonal domain methods are related to the redistribution of pixel grey levels. The most classical transformation is the histogram equalization: vnew=NmaxRI(vold), where vold and vnew are respectively the grey level values before and after equalization, Nmax is the maximal grey level value, and RI is the cumulative distribution of grey level values in image I. In some approaches, the histogram transformation is defined on a target histogram that represents a standardized image. Tek et al. (2006) proposed a normalization procedure using an adapted grey world normalization method based on the diagonal model of illumination change. After a rough segmentation based on a threshold, the background and the foreground values are independently processed so that every pixel value is normalized using the mean background (foreground) values. Spatial domain methods are indeed local contrast enhancement techniques, which are classically performed by combining the image with its derivatives (unsharp mask). Typically: Inew=Ioldα∆Iold, where α>0 is a gain parameter, and ∆I = ∂2I / ∂x 2 + ∂2I / ∂y 2 is the Laplacian of the image. By adjusting the width of the spatial kernel used to compute the derivatives, the local contrast enhancement techniques can be adapted to the scale of the relevant features. Finally, the spectral domain methods refer to techniques operating in transformed domains, such as Fourier or wavelet bases. One useful operation in this category is the homomorphic filter, which allows correcting non-uniform background. Its principle is to model the background variation

by a low frequency multiplicative noise, and to remove it in the log-Fourier domain: I new = e

(F −1 (F (ln(I old )).H f ))



where F and F-1 are the direct and inverse Fourier transforms respectively, and Hf is the ideal highpass filter of minimal frequency f. Figure 3 illustrates two examples of image enhancement procedures. First row corresponds to the original blood smears and bottom row displays their corresponding enhanced images. Left image is the result of applying an unsharp mask on the V component of HSV color space, followed by a color median filter computed by minimization of the L1 distance in the RGB color space. Right image corresponds to the application of the color normalization process proposed by Tek et al. (2006). Note that object properties, such as color or contrast between objects, are visually highlighted.

OBJECT SEGMENTATION Generally, segmentation is the process of partitioning an image into disjoint regions based on a homogeneity criterion (Lucchese & Mitra, 2001). Two main levels of segmentation are commonly used in blood smear analysis: cell level segmentation, which aims at separating the whole cells from the background or plasma, and component level segmentation, which tries to separate the different components into the cell, such as nucleus from cytoplasm or intracell hemoparasites. Latter case is commonly used in applications in which the cell class depends on morphological features of its components. However, when finding the boundaries between components is not feasible, analyzing the components as a whole and trying to describe these morphological features as properties of the cell can be useful (Díaz et al. 2009).

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Figure 3. Image enhancement approaches. Fist row corresponds to the original images. Bottom row displays enhanced images obtained by application of an unsharp masking followed by a color median filter (left), and the color normalization process proposed by Tek et al. (right)

In this section the most popular cell segmentation approaches will be presented. Image segmentation approaches have been divided in two complementary perspectives: the region-based and boundary-based approaches. In the former the goal is to determine whether a pixel belongs to an object or not, whereas in the latter the objective is to locate the frontier curves between the objects and the background.

Region Based Segmentation Thresholding This is the simplest technique used to segment an image. This method allows separating the objects from the background using a pixel feature value that is compared with a threshold value in order to determine the class of the pixel. The feature generally used is the illumination value, although other feature descriptors have been used, such as gradient information (Bacus et al., 1976), entropy histogram (Bikhet et al., 2000) and intensity computed as mean of red, green and blue components 330

from the RGB color space (Hengen et al., 2002). Other authors have found that specific color components in different color spaces stress the differences between blood smear components. Cseke et al.(1992) found that nuclei of white cells are most contrasted on the green component of RGB color space, while differences between cytoplasm and erythrocytes are most perceptible in the blue component. This has been used by other authors for segmenting leukocytes (Katz, 2000) and hemoparasites (Le et al., 2008). The saturation component from the HSV color space also allows distinguishing the nucleated component (Di Ruberto et al. 2002; Ross et al., 2006; Wu & Zeng. 2006). Moreover, thresholds may be either global or local. In the latter case, some information relative to the pixel’s neighborhood is taken into account to perform adaptive threshold (Cseke et al., 1992; Hengen et al., 2002). The critical issue for the performance of the threshold algorithm is a good selection value, which can be determined heuristically from the grey level histogram of the image to be segmented (Micheli-Tzanakou et al., 1997) or computed

Automatic Analysis of Microscopic Images in Hematological Cytology Applications

automatically. Harms et al. (1986) found that it is possible to estimate automatically a threshold for segmenting leukocyte nucleus, using the largest color difference value from a grey level image obtained as the combination of the red, green and blue color components (R + B – 2G). A similar approach was used by Poon et al. (1992), using a subtraction of the red and blue components (R – B +128). Analysis of the histogram shape for detecting the separating value between modal peaks was used by Le for segmenting nucleated components (Le et al., 2008). A straight line is drawn from the first prominent peak to the first empty bin of the histogram; the threshold is then selected as the abscissa of the point of the histogram with maximal perpendicular distance to the line. Clustering algorithms are used as another way of performing automatic thresholding. Originally, these algorithms assume that the image contains two classes of pixels i.e. foreground and background, and calculate the optimum threshold separating those two classes. Otsu (1979) proposes an optimal threshold by maximizing the between-class variance through an exhaustive search. This approach was adapted to multiple thresholds for separating background, cytoplasm and nucleus (Cseke, 1992; Scotti, 2006). In Wu et al. (2006), Otsu’s algorithm was applied on a circular histogram from the HSI color space; this representation prevents to lose the periodicity of the hue attribute (Wu & Zeng, 2006). Figures 4a and 4b shows segmentation results of blood and bone marrow smears shown in Figures 2 and 3. In this case, automatic thresholds on the green value histograms were computed using the Otsu algorithm. Segmentation based on threshold can work well in blood and bone marrow smears because background and foreground objects maintain constant visual feature values which constitute multimodal histograms. In particular, staining procedures highlight cell components containing DNA, such as white cell nuclei and parasites, in such a way that finding appropriate thresholds for

segmenting them becomes easy. However, boundaries between cytoplasm, plasma and earlier parasite stage are harder to find (see Figure 4b).

Pixel Based Classification Given an input image I, the classification of a target pixel I(x,y) is performed according to a feature vector (μ1,…,μm) describing it. Strategies in this category are composed of two steps: feature extraction and pixel class identification. Intensity or chromatic features, along with supervised classification, explained in section 3.5, are commonly used approaches. Tek et al. (2006) classify the pixels in a blood smear as stained or not stained, according to a their R,G,B values using a Bayesian classifier which is trained using a set of pixels classified by an expert. Classification of whole color space was proposed by Díaz et al. (2007) for reducing the image segmentation time. This method is based on a classification process that labels all components of a color space in one of the three classes: background, erythrocytes and parasites. Then, the labeled color space is used as a look-up-table for defining the class of each pixel in the images. The combined choice of a color space representation and a particular classifier was evaluated, showing that a normalized RGB color space together with a K-nearest neighbor (K-nn) classifier obtained the best performance. Images in Figures 4c and 4d show results of pixel classification for images of Figure 2. A K-nn classifier (k set to 15) was trained for separating the normalized RGB color space in three classes. In Figure 4c, parasites, erythrocytes and background were segmented. In Figure 4d, classifier was trained for distinguishing nuclei cells from cytoplasm (erythrocytes and leukocytes) and background.

Region Growing These are classical algorithms for which a set of regions are defined, each one initially constituted by a single pixel, and then an iterative growing

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Figure 4. Segmentation result samples. (a,b) Segmentation obtained by applying the Otsu algorithm on the green value histograms. (c,d) segmentation using a pixel classification approach. (e,f) Segmentation by k-means clustering

procedure is performed according to a homogeneity criterion. Main issues of region growing are: to establish a criterion that decides whether a neighbor pixel is similar to the seed, and to find the suitable seed. Region growing algorithms have been used in segmentation of leukocytes. Pixels inside nuclei, which are easily identified, are used as seeds, and cytoplasm are segmented by growing region; once nucleus is located, cytoplasm is detected by iterative growing of a ring surrounding the nucleus, and a gradient threshold is used as stopping condition (Kovalev et al., 1996). Lezoray et al. (1999) pro-

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posed a leukocyte segmentation approach based on region growing in which region seeds are found by prior knowledge on color information of nucleus pixels. Then a growing procedure is applied based on color homogeneity and gradient information.

Region Clustering Region clustering approaches are similar to region growing, but the region clustering focuses on the region directly without any seed. In contrast with pixel based classification strategies these approaches identify coherent regions instead

Automatic Analysis of Microscopic Images in Hematological Cytology Applications

of classifying single pixels independently. The k-means algorithm is a well-known clustering method. In (Sinha & Ramakrishnan, 2003), pixels represented by a vector of 3 components from the HSV color space were automatically clustered, using k-means algorithm, for identifying leukocyte nucleus, cytoplasm and background. Then results were further refined by an ExpectationMaximization algorithm. In (Thera-Umpon, 2005), fuzzy k-means algorithm was proposed as an over segmentation algorithm applied on grey level image for detecting regions corresponding to nucleus in leukocytes. Comaniciu & Meer (2001) proposed a method, which detects clusters in the L*u*v* color space and delineates the border cells using the gradient ascent mean shift procedure. Segmentations obtained by application of the k-mean algorithm on green value component of previous images are presented in Figures 4e and 4f.

region for determining whether one point is inside the region. Likewise, markers were extracted from bone marrow images by a mean shift procedure, and used as the seed in a watershed segmentation applied on the color gradient image (Pan et al., 2003). Multiple markers within the same object can be obtained when they are extracted by thresholding. Then, a unique marker is associated to one object by performing a morphological closing on the markers so that they touch each other. The parameters used for these operations are related to the maximum object size which can be previously determined from a granulometric analysis (Mahona Rao & Dempster, 2002). In addition, watershed region merging was also used for enhancing the watershed segmentation by Wang et al. (2006). For doing this, a series of rules on surface, depth and volume of adjacent watershed regions were used as merge criteria.

Morphological Segmentation: Watershed Transform

Boundary Based Segmentation

The watershed transform is the fundamental tool of the mathematical morphology for segmentation of images. For a complete review of this topic we refer the reader to (Dougherty, 2003). The watershed concept comes from considering the image as a topographic surface, in which grey levels correspond to terrain altitudes. Segmentation is performed by flooding or rain falling simulation, and the resulting catchment basins (which correspond initially to the regional minima of the image) form the final segmentation. Classical watershed transform is applied to a gradient image, generally presenting many regional minima, which leads to oversegmentation. In order to overcome this problem many strategies have been proposed. An extended watershed approach for color images was proposed by Lezoray et al. (1999, 2002), in which color information is used for extracting the markers and for defining a function that combines the color gradient module with the mean color value of the

Edge Detectors Classical edge detection approaches are based on detection of abrupt neighborhood changes in the pixel values. In many cases, the boundaries between cells and their components are not clearly defined, and then edge detection performs poorly on these images (Wu & Zeng, 2006). Even so, edge operator performance can be improved when it is combined with other techniques. The Teager energy filter and morphological operators were proposed for segmenting leukocytes (Kumar, 2002). Similar scheme was used by Piuri et al. (2004), but the Canny edge operator was used.

Deformable Contour Models or Active Contours A deformable model is a curve that evolves toward boundary contours using local information of the image. Two main models of active contours

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Automatic Analysis of Microscopic Images in Hematological Cytology Applications

have been proposed: explicit models (snakes) and implicit models (level sets). Explicit models aim at minimizing the energy attached to a curve describing the contour of the object. The optimization function is defined as a linear combination of external forces (computed from the image properties) and internal forces (defined by elasticity and stiffness of the curve). Ongun et al. (2001b) proposed a snake for segmenting leukocytes, for which external forces are computed from the intensity and gradient magnitude of the image and a corner force attraction. Likewise, a gradient flow vector was used to define these external forces in (Theerapattanakul et al., 2004; Zamani & Safabakhsh, 2006). This approach was also used by Yang et al. (2005) but the gradient flow vector was computed from the L*u*v* color space (this was also used by Tuzel et al., 2007). Implicit models represent the evolving object contour through a function of higher dimension defined over the image area, which is positive inside the region, negative outside and zero on the boundaries. The deformation of the contour is generally described by a partial differential equation (PDE). Level sets can be viewed as region or boundary based approaches. In boundary based methods, the function determining the evolution of the contour (stopping function) is based on the gradient of the image. Nilsson & Heyden (2001; 2005) used this scheme to segment leukocyte components. For nuclei segmentation, the stopping function is defined by a threshold value on the RGB color space, while the image gradient is used for cytoplasm segmentation. On the other hand, region based level sets define the contour derivative as a function that depends on the stiffness of the curve and an energy term, which is minimum if inside and outside regions are homogeneous. Dejmal et al. (2005) proposed a linear combination of region and boundary criteria for segmenting erythrocytes. Active contours are useful in segmentation of clusters of cells, however they require a relatively

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high computational cost, and resulting contours do not correspond with the exact borders of the cells. Then, they are not appropriate in differentiation process based on the contour shape, like in erythrocyte classification.

Segmentation Improvement After segmentation, an image can be thought of as a binary map of background and foreground objects. In some cases, this initial segmentation is not satisfactory, as holes and artifacts can appear. Improving the segmentation results can be done through a set of operations based on a priori knowledge. Morphological operators are commonly used for this purpose. Basic morphological operators are often used after segmentation process for reducing artifacts, filling holes or removing border objects (Anoraganingrum, 1999; Sabino et al., 2004; Jian et al., 2006, among others). Binary erosion shrinks image regions and eliminates small objects, whereas binary dilatation enlarges image regions, allows connecting separated blobs and fills small holes. Segmented objects that are not interesting for the analysis can be removed using connected operators, such as the opening by reconstruction or the area opening. These operators are filtering techniques that eliminate some regions without modifying the boundaries of the remaining regions. Connected operators implemented as pruning strategies of a tree representation was presented by Díaz et al. (2009) for removing staining artifacts and border touching cells.

SPLITTING OF CLUMPED CELLS An important problem in analysis of blood and bone marrow smears is the clumped or touching cells. Many approaches have been proposed for separating them, some of them included as part of the segmentation and other specifically dedicated to separate superposed cells. For instance, some

Automatic Analysis of Microscopic Images in Hematological Cytology Applications

leukocytes segmentation approaches working on a sub-image that are extracted from the original image, cutting a square around the segmented nucleus (Kovalev et al., 1996; Jiang et al, 2003, Sinha & Ramakrishnan, 2003; Theerapattanakul et al., 2004; Zamani & Safabakhsh, 2006). Under the assumption that each sub-image has only one white cell, other a priori are included for segmenting it. Kovalev et al. (1996) and Katz (2000) introduce circle-shaped restrictions, whereas color information is used for performing clustering around the detected nucleus by Jiang et al. (2006) and by Sinha & Ramakrishnan (2003). On the other hand, restrictions included into the fitness function of deformable models allow to deal with overlapped cells (Liu & Sclaroff, 2001; Theerapattanakul et al., 2004; Zamani & Safabakhsh, 2006). Single and complex morphological operators have proved useful for the segmentation of touching blood cells. Opening operator was applied for separating leukocytes (Kumar, 2002; Dorini et al., 2007; Pan et al., 2003). Hence, a series of morphological operations was applied for splitting composite erythrocytes by Di Ruberto et al. (2002). This procedure was composed of a morphological opening by a flat disk-shaped structuring element, followed by a morphological gradient, an area closing and a thinning operator. Distance transform (i.e. a function associating to every pixel of a binary image its distance to the border) of the segmented clumped shape is also used in some clump splitting approaches. The maxima in the distance image can be used as markers for subsequent segmentation of the original image by the watershed algorithm (Malpica et al. 1997; Angulo & Flandrin, 2003a, Nilsson

& Heyden, 2005) or another region growing approach (Hengen et al., 2002). But the watershed segmentation can also be applied directly on the distance image for separating circular shapes (Lindbland, 2002). These approaches present good results for splitting clumped cells with small overlaps, but they fail when the overlapping area is too important. An improved approach which introduces geometrical restrictions on the distance function was presented by Pan et al. (2006a). The purpose of these restrictions is to improve the segmentation accuracy and to reduce the computational cost of the watershed computation. Figure 5 shows an example of splitting clumped cells using the watershed transform applied on distance transform. On the segmented image (Figure 5b), the Euclidean distance transform is computed (Figure 5c) and the regional maxima of the distance transform (Figure 5d), are used as markers of the watershed transform (Figure 5e). Other approaches for separating clumped cells are based on a concavity analysis, which assume that superimposed objects can be separated by one line joining two specific cut points where the boundary curvature abruptly changes. Concavity analysis is commonly composed of three sequential steps: detection of concavity points, detection of candidate split lines and selection of best split lines. Poon et al. (1992) presented a method for splitting clumped cells that uses the difference of the tangent angles between neighborhood boundary pixels for finding two cut points on the boundary which are then joined by a line for separating the shape. This approach separates pairs of cells but cannot handle clusters of multiple cells.

Figure 5. Clump splitting using distance transform and watershed segmentation

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An approach that allows separating complex clumped cells proposed by Kumar (2002) was used in (Sio et al., 2007). In this approach, cut points are detected by measuring the concavity depth, defined as the distance from the boundary point to the convex hull border. Then candidate split lines are selected from those obtained by joining all possible pairs of concavity pixels, based on the distance and alignment of points. Finally a “measure of split” is computed for selecting the best split lines (Kumar, 2002). The drawback of these methods is that they depend on implicit conditions about cell shape or size. Morphological operations are limited by the overlapping degree between cells since two shapes can be separated if their centers are separated (Serra, 1982). Whereas, concavity analysis demands very accurate segmentation in order to detect the cut points. Template matching strategies attempt to find parts in the image which correspond to predefined shapes named templates, without any other a priori information. These approaches have two important components: the template definition and the matching strategy, which is commonly formulated as an optimization problem. Halim et al. (2006) proposed a template matching strategy for detection of erythrocytes. A healthy erythrocyte represented by a gray scale template was constructed from the original image, based on cross correlations between the predefined binary templates and the image. Then, cross correlation was also used as optimization criterion in order to detect the actual erythrocytes. On the other hand, Díaz et al. (2007a) proposed a matching strategy based on superposition of the chain code representation of the clumped shape contour and an ideal erythrocyte, estimated from the original image by an Expectation-Maximization algorithm. Although these strategies can overcome the drawbacks previously described, they are computationally expensive and are applicable only for separating shapes that do not present large shape and size variations.

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FEATURE EXTRACTION AND SELECTION Differential analysis of blood and bone marrow smears is feasible thanks to the capacity of visual system to distinguish image patterns, which are associated to specific cell classes. Thereby, the challenge of automatic image analysis systems is to carry out computational methods to calculate objective cell measures related to those visual patterns. The aim of feature extraction step is to obtain a set of descriptors, which will be further separated in different classes by a classification procedure. The feature extraction can be formally defined as a function f which maps the original image I onto a feature vector x, i.e. f:I→x=(x1, x2,…, xd), where d is the number of features used for characterizing the image.

Visual Descriptors Computational visual descriptors can be classified as chromatic, textural and shape measures. Chromatic features are related to the color image information, textural features provide statistical information on the local structures in the image, and shape measures describe geometrical information of objects like perimeter, area, circularity, etc. In the rest of this section we discuss some common measurements used for describing the blood and bone marrow cells.

Chromatic Features These features describe the grey-level or color distribution of the images, which are the most discriminative features of blood and bone marrow cells. Let R={p1, p2,…, pn} be a set of pixels that belong to a segmented object (nucleus, cytoplasm, parasite or complete cell). Intensity and chromatic features are computed from histograms of R. Color histograms can be represented by one single multi

Automatic Analysis of Microscopic Images in Hematological Cytology Applications

dimensional histogram or multiple separate 1-D histograms. Considering an image as a stochastic process where every pixel value is modeled as a random variable, the histogram can be regarded as the probability density function of grey level or color distribution. The most used measures from a histogram h are provided by its moments: mean, variance, standard deviation, kurtosis and skewness, which describe the histogram shape and are computed as1:

∑ kh(k ) Variance : v = ∑ (k − µ) h(k ) Mean : µ =

k

2

k

Standard Deviation : σ = v Skewness : µ3 = σ −3 ∑ (k − µ)3 h(k )



k

Kurtosis : µ4 = σ −4 ∑ (k − µ)4 h(k ) − 3 k

Entropy : e = −∑ h(k ) ln(h(k )) k

The standard deviation is a measure of dispersion of the histogram, whereas the skewness and kurtosis respectively measure the dissymmetry and flatness (or peakedness) of the distribution. An important issue in chromatic descriptors is the color space used for representing the color in the images. Traditionally, images are acquired using the RGB color model, in which each color pixel is represented by its three components: R(Red), G(Green) and B(Blue). But the statistical distributions of those three components are highly correlated, and then, many decorrelated models have been proposed for digital image processing (Plataniotis & Venetsanopoulos, 2001) and some of them have been used for blood and bone marrow cells description. Discrimination of platelets, red and white cells in peripheral blood smears has been correctly accomplished using only the average intensities of R, G and B color components and cell size

(Lin et al. 1998). Nevertheless, discrimination of subclass cells as leukocyte or erythrocyte types usually requires other features, such as textural or geometrical measures. Leukocyte classification systems have used chromatic statistical measures as part of a feature vector. Kovalev et al. (1996) used standard deviation of red and green intensities for describing chromatic features of leukocytes. R, G and B components and color ratios green/red and green/ blue were used by Katz (2000), together with the nucleus area and perimeter, for the same purpose. Similar features were used by Song et al. (1998) and by Sinha & Ramakrishnan (2003). Likewise, RGB color histograms computed from original and gradient images, were used along with many other features (Siroic et al., 2007). On the other hand, CieLab and HSV transformations have also been used in leukocyte color characterization (Ongun et al, 2001; Comaniciu & Meer, 2001; Angulo & Serra, 2002). Chromatic features have also been used for distinguishing live stages of hemoparasites, particularly malaria. HSV color model histograms were used for describing segmented parasites which were classified as mature and immature throphozoites (Di Ruberto et al., 2002). For the authors, parasites in these live stages are differentiated by their nucleus and spot of chromatins, which are evident in the hue and saturation components. Statistical features derived from the red, green, blue, hue and saturation components were part of a set of features used by Ross for classifying malaria parasites into different live stages (Ross et al., 2006). Same measures computed from the normalized RGB color model, were used for classifying erythrocytes as healthy or infected at any infection stage by Díaz et al. (2009).

Shape Descriptors Another important feature to the human visual system is the shape. Many shape measurements have been proposed in the literature. A comprehensive

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review of general purpose shape descriptors was published in (Zhang & Lu, 2004). In hematological applications, the geometrical or “simple” shape descriptors are the most popular because they are easy to compute, but indiscriminative for small dissimilarities. Other used descriptors include geometrical moments, structural representations and spectral features.

Geometrical Features Geometrical features describe different aspects of the cell or component structure, such as size or shape. In hematological applications they are widely used because various cells differ greatly by their size or nucleus shapes. Geometrical features are computed on a region of interest R, which has a well defined closed boundary composed of a set of consecutive pixels S={s1, s2,…,sn}. Simplest geometrical features are area, perimeter, centroid, tortuosity (area/perimeter) and radius. The area feature is computed as the number of pixels enclosed by the cell boundaries (Bacus, 1976), A={cord(R)}. The perimeter, n −1

P = | sns1 | +∑ i =1 | sisi +1 | , corresponding to the

total distance between consecutive points of the boundary. The centroid is the average of coordi1 nates of all points of R: x = ∑ x , A (xi ,yi )∈R i y =

1 ∑ y . The radius is measured by A (xi ,yi )∈R i

averaging the length of the radial line segments defined by the centroid and border points n 1 R = ∑ i =1 | si (x , y ) | . Other features for den scribing shape structure include measures of major and minor axis, which are provided by the eigenvalues of the second order moments matrix; the circularity or elongation (Thera-Umpon & Gader, 2002), computed as the ratio of the square of the perimeter to the area (P2/A); the eccentricity, defined as the ratio between the major and

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minor axes (Sinha & Ramakrishnan, 2003); the symmetry, calculated from the distance between lines perpendicular to the major axis and the cell boundary (Stanislaw et al., 2004; Markiewicz & Osowski, 2005); and the spicularity, proposed by Bacus et al. (1976), spicules are defined as the points where chain code derivate change of sign. In addition, other specific features have been proposed, which are more strongly linked with blood cell analysis. For example, the nucleus and cytoplasm areas ratio and the number and structure of nucleus lobes are prominent features used for identifying the class of the leukocytes (Beksak et al., 1997, Sinha & Ramakrishnan, 2003, Hengen et al., 2002, Ballaro et al., 2008). Likewise, number of chromatin dots, and the ratio between parasite area and cell area have been used for identifying the infection stage and class in malarial image analysis (Ross et al, 2006). The main drawback of the geometrical features is that their application demands accurate segmentation of the region of interest, and then they are commonly used together with other features more robust to segmentation errors, such as texture or chromatic descriptors.

Structural Based Representations Structural methods decompose the shape or contour region into parts that are geometrically meaningful for the image. A structural representation widely used is the convex hull (de Berg et al., 2000), corresponding to the smallest convex polygon H containing the region R (R⊂H). Once computed the convex hull, shape can be represented by a string of concavities as chain code representations or by measures based on convex hull such as its area or region compactness computed as cell area/convex hull area ratio (Mircic & Jorgovanovic, 2006; Ballaro et al, 2008). Region skeleton it is another shape representation that allows to encode both boundary and region information for every object (connected component). Many algorithms for generating

Automatic Analysis of Microscopic Images in Hematological Cytology Applications

skeletons have been proposed. Di Ruberto et al. (2002) applied the sequential thinning process for representing segmented malarial parasites as connected thin lines. The end points of the obtained skeletons are used as shape descriptors for distinguishing either immature disk-shaped parasites or mature irregularly-shaped parasites.

Spectral Based Features In order to provide descriptors less sensitive to noise and boundary variations, several authors have proposed to use a representation of pattern shapes in frequency domain. Those spectral features include Fourier descriptors and wavelet descriptors, which are usually derived from spectral transform on shape signatures (functions derived from the shape boundary points). Features obtained from the direct application of the discrete Fourier transformation are not invariant to geometrical transformation, and then different extensions have been proposed. Elliptic Fourier descriptor (EFD) proposed by Kuhl & Giardina (1982) was used for characterizing nuclei shapes of leukocytes (Comaniciu et al., 1999) and megakaryocytes (Ballaro, et al., 2008). EFD is a Fourier expansion of the chain coding contour, which is represented as a composition of ellipses defined as contour harmonics that result from expanding separately the components x(s) and y(s) in the complex function of coordinates u(s)=x(s)+jy(s). The EFD corresponding to any closed curve S with Euclidean length l and composed of k points is described by the nth harmonics given by: ∆x  2πns 2πns  ∑ ∆s i cos l i − cos l i−1  i =1 i k 2πnsi −1  ∆x i  2πnsi l  sin − sin bn = 2 2 ∑  l l 2π n i =1 ∆si  k ∆yi  2πnsi 2πnsi −1  l  cos − cos cn = 2 2 ∑  l l 2π n i =1 ∆si  k 2πnsi −1  ∆yi  2πnsi l  sin dn = 2 2 ∑ − sin  l l 2π n i =1 ∆si  l an = 2 2 2π n

k

i

Where si = ∑ ∆s j , is the length of the first j =1

i vectors, ∆si =

2

(∆x ) i

2

+ (∆yi ) , ∆xi=(x1-xi-1)

and ∆yi=(y1-yi-1). The coefficients of the EFDs are normalized to be invariant with respect to the size, rotation, and starting point, using the ellipse of the first harmonic. Another extension of the Fourier descriptor used as cell shape feature is the UNLF (Universidade Nova de Lisboa, Portugal) descriptor. The UNLF descriptor is computed by applying 2-D Fourier transform on the image curves transformed to the normalized polar coordinates. The main advantage of this descriptor is that it is able to handle open curves, lines and patterns composed of parametric curves as well as cells with interior components. Kyungsu et al. (2001) used it as shape descriptor of erythrocytes in order to include information about its concavity, observed as holes in a binarization process. The main drawback of UNLF descriptor is that it produces feature vectors of high dimensionality, and then feature selection approaches should be applied. The use of wavelet transformation has been proposed as shape descriptor in many applications in order to achieve description of shape features in a joint spatial and frequency space. However, they have not been much used for cell description. In Sheik et al. (1996) only, largest wavelets coefficients were used for classifying cells as platelets, white or red cells.

Textural Features Traditional machine vision and image processing approaches assume uniformity of intensities in local image regions, but some objects present a repeated pattern as principal visual feature, which is called texture. In hematological applications the texture property has proved valuable for distinguishing some abnormal cells and parasite presence and evolution.

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Automatic Analysis of Microscopic Images in Hematological Cytology Applications

Texture analysis is generally applied on a grey scale representation of the image. However, as in segmentation approaches, different color models have been used for textural analysis. Hengen et al. (2002) perform the texture analysis in H component of HSI model (Hengen et al. 2002). Ongun et al. (2001) use an image distance as input for computing a grey level co-occurrence matrix which is computed as the distance between the pixel color and coordinate origin in the CIELab model. Finally, Angulo and Serra (2002) extend the texture analysis to all RGB channels that are then integrated to compose one single texture feature (2002). Methods for extracting textural measures can be classified as: statistical, model based and geometric.

Statistical Approaches These are the most popular texture analysis methods, in which the texture can be defined by its statistical features, related to the pixel gray level conditional probability given its neighborhood. The goal of these methods is to deduce statistical properties of this function from observed data (gray levels value of pixels). These methods are easy to implement, which is their main advantage. The gray-level co-occurrence matrix (GLCM) is a second order statistics, which is defined as follows: GLCM d (i, j ) = #{(x , y ) ∈ {0,W − 1} × {0, H − 1}, I (x , y ) = i ∧ I (x + dx , y + dy ) = j )} / (W * H )

where d=(dx,dy) is a displacement vector, W and H are the width and height of the image I, and #S is the cardinality of set S. Hence, GLCMd(i,j) is the probability of finding two pixels separated by vector d, which have respective grey values i and j. Many feature vectors can be computed from the GLCM (Haralick, 1979, Tuceryan & Jain, 1998). For example, Sinha and Ramakrishnan used the entropy, energy and correlation of the GLCM for

340

characterizing the cytoplasm in leukocytes (Sinha & Ramakrishnan, 2003). Additionally, Sabino et al. (2004a) used the contrast feature for recognition of leukocyte. The main drawback of the GLCM approach is that their calculation is computationally expensive even if the grey level values are quantized. Sum and difference histograms are a modification of GLCM in which two histograms are calculated counting grey level differences and sums for all the pixel couples separated by vector d. In hematological applications this method has been used in the differentiation of blood cells type (Siroic et al., 2007) and the classification of leukemia blast cells (Stanislaw et al., 2004; Markiewicz & Osowski, 2006). Another statistical approach used to characterize blood cells is the autocorrelation coefficients (Sinha & Ramakrishnan, 2003); this second order statistic may be calculated as: W −p

ACC (p, q ) =

∑ i=1 W *H (W − p)(H − q )

p = 1… P, q = 1…Q

∑

H −q j =1 W

I (i, j )I (i + p, j + q ) H

2

∑ i =1 ∑ j =1 I (i, j )

,

where P,Q are neighborhood size parameters.

Model Based Approaches These approaches are based on the assumption that the texture corresponds to instances of mathematical models, in which parameter models should be found. Feature extraction based on the Markov random fields (MRF) is a representative model based approach. It is a generalization of Markov chains in 2-dimensional space, where the time index is replaced by spatial indexes and the Markov restriction is applied into the neighborhood of each pixel. Hence, texture is characterized by the parameters that define the MRF for observed pixel values and a certain topology, defining the local dependencies. According to the Hammersley-Clifford theorem, solving this problem is equivalent to estimating the parameters

Automatic Analysis of Microscopic Images in Hematological Cytology Applications

of a Gibbs distribution if the MRF has translation invariance and isotropic properties, which is assumed in image processing applications. MRFs have been used as feature descriptor of leukemia blast cells (Stanislaw et al., 2004; Markiewicz & Osowski, 2006). Other model based approaches used in blood cells characterization are the autoregressive models, which were used for characterizing bone marrow cells for differential counting analysis (Ongun et al., 2001) and malignant lymphomas and chronic lymphocytic leukemia in peripheral blood smears (Comaniciu et al., 1999; Comaniciu & Meer, 2001). The autoregressive model of order n+2 is a time series of order n, in which the time variable is defined in a 2D spatial domain. The other 2 parameters correspond to the mean and variance of a white noise added to the model. Maximum likelihood estimation is commonly used for inferring the model parameters.

Geometric Approaches Texture features may also be derived from geometric models. In these approaches, texture is defined as a repetitive pattern composed of texture element named textons. Granulometry analysis was proposed as a texture feature for describing nucleus granules, in order to characterize different kinds of leukocytes. Angulo & Serra (2002) applied the granulometry analysis for calculating texture features used in cell retrieval and leukocyte classification systems. A similar work was presented by Theera-Umpon and Dhompongsa in classification of leukocytes (Theera-Umpon, 2007). A texture descriptor based on resulting regions of a watershed segmentation was proposed in (Hengen et al., 2002). A cell nucleus is segmented applying standard watershed and then the ratio between the interior area (i.e. without boundary) of watershed regions and the area of the whole n

nucleus is computed. ( ∑ i =1 Ai / Anucleus with n= number of watershed regions).

Recently, image analysis approaches based on analogy with syntactical document analysis have been proposed. In texture description, the idea of a texton element associated to a visual word was used by Tuzel et al. (2007) for characterizing cell nucleus and cytoplasm. A texton dictionary is generated from a set of training images of each class. For this, a filter bank was designed, composed of two rotationally symmetric filters (Gaussian and Laplacian of Gaussian) and 36 edge and bar filters with different scales and orientations. Then filter responses inside the segmented images were clustered using the k-means algorithm and the cluster centers were the selected textons. Using the resulting texton dictionary, the new cells are represented with their texton histograms.

FEATURE SELECTION Feature selection is a process commonly used in pattern recognition, which allows determining the most relevant, discriminating and uncorrelated features of a classification task, while reducing the dimensionality of vectors associated to objects. Although original feature vectors extracted from blood and bone marrow cells are usually large, feature selection processes are not often applied in published studies. There are many techniques of feature selection, which can be classified into three categories: filter methods, wrapper methods and embedded methods (Molina et al., 2002; Saeys et al., 2007). In the first category, feature selection is based on quantitative measures of how well the features discriminate the classes, independently of a specific classification algorithm. In the wrapper approaches, the contribution performance of every component is taken into account by the classifier algorithm. Embedded methods refer to classification procedures that include the feature selection as a step in the whole classification procedure, for example decision trees. Wrapper models outperform other models because they are

341

Automatic Analysis of Microscopic Images in Hematological Cytology Applications

optimized for the specific classification approach, but they are computationally feasible only for small feature vectors. Another relevant issue in the feature selection processes is the search strategy which can be exhaustive (brute-force or complete search), heuristic (sequential) or non-deterministic (randomized) (Molina et al., 2002). Exhaustive search generates the optimal subset of n features in a search space of possible subsets. Heuristic approaches select features that maximize a criterion function. Sequential forward selection starts selecting the best single feature and successively adds one feature at the time, whilst sequential backward selection starts with the whole set of features and then deletes one feature at the time. In hematological applications, comparison of several feature selection approaches has been performed. Markiewicz et al. (2006) evaluated two filter approaches (correlation analysis, mean and variance measures) and one wrapper of linear support vector machine (SVM) for selecting features from a vector of 164 dimensions, in order to distinguish 10 classes of leukocytes. The best approach was the correlation between the feature and the class as well as the linear SVM ranking. Likewise, a wrapper method with sequential forward search strategy applied on a naive Bayes classifier was used in (Sabino et al., 2004a). 12 relevant features were selected exploring morphometry, texture and color feature sets separately, i.e. 4 features for each one. Genetic algorithms have been proposed as a feature selection process in (Siroic et al., 2007). In this approach, each set of possible features is represented as chromosomes and genetic operators such as mutation and crossover are applied in order to find the best solution(s) according to a fitness function, defined as the classification error on the validation data set when a SVM is used as classifier algorithm. From reported results, features selected with this approach proved better classification performance than the ones selected by wrapper of linear SVM.

342

On the other hand, some approaches called by many authors “feature selection”, do not properly perform a selection of features, but carry out a reduction of dimensionality by projection or combination of them. Principal component analysis (PCA) is the most popular technique for dimensionality reduction. The aim of the PCA is to find a set of orthogonal vectors in feature space corresponding to directions along which the data present the maximal variance. Dimensionality reduction is performed projecting the data from their original space onto the orthogonal subspace. Kyungsu et al. (2001) used PCA for reducing a 76d vector in order to classify erythrocytes and leukocytes, achieving dimension reductions between 12% and 50%. A non-linear extension of PCA, called kernel-PCA, which maps input data into high dimension feature space using the kernel trick was employed by Pan et al. (2006)

SINGLE CELL CLASSIFICATION Once the cell features are extracted and selected, they should be input into a process that classifies the cells according to hematological concepts. An automatic classification system should be able to identify these concepts within the context of real images, i.e. noisy images with visual differences between concepts, which are variable. This problem may be addressed from two different perspectives: analytic and inductive. The analytic standpoint requires a deep understanding of the way the low-level features are combined to structure concepts. The inductive standpoint automatically builds a model of concepts based on a number of training samples, a framework commonly used through several machine learning approaches. Many learning approaches have been used to classify blood cells. The simplest model is the Bayesian classification (Aksoy, 2002; Sabino, 2004; Theera-Umpon, 2004), which is based on applying the Bayes theorem with independence

Automatic Analysis of Microscopic Images in Hematological Cytology Applications

assumptions between features. In the training step the a priori probabilities of each class are computed, and then used for estimating the class of new instances by the maximum a posteriori rule. Despite its simplicity, Bayesian classification can perform accurate classifications if features are discriminative. Conversely, k-NN classifies unlabeled samples based on their similarity with samples in the training set without any knowledge of a priori class probabilities. Given the knowledge of N prototype features and their correct classification into M classes, the k-NN rule assigns an unclassified pattern to the class that is most heavily represented among its k nearest neighbors in the pattern space, under some appropriate metric. k-NN was used by Tek et al. (2006) to classify stained regions as parasites or not. Likewise, Comaniciu et al. (1999) use this technique for distinguishing lymphoproliferative disorders. Artificial neural networks (ANN) are the most used classifiers in hematological applications. ANNs are networks of simple processors that emulate the behavior of human brain. Many network architectures and training algorithms have been proposed and are well described in literature (Bishop, 1995). The most popular learning algorithm is the multilayer back propagation (ML-BP), which was used in many studies (Lin et al, 1998; Kim et al., 2001; Mircic & Jorgovanović, 2006; Ross, 2006; Theera-Umpon & Gader, 2002). Other algorithms used in the literature include ALOPEX (Lin et al., 1998), Radial basis function – (RBF, Djemal et al., 2005) and Linear Vector Quantization (Ongun et al., 2001). Recently Support Vector Machines (SVM) approaches have received increasing interest because they have outperformed other methods in several pattern recognition problems. The SVM is based on the mapping of the input vectors into a high dimensional feature space, induced by a kernel function chosen a priori. In the feature space the learning algorithm produces an optimal separating hyperplane between two classes. In principle,

a SVM is a linear discriminator; however it can perform non-linear discrimination thanks to the fact that it is a kernel method. The multi-class classification problem is solved constructing several binary classifiers and combining them. The most used strategies are one-against-one and one-against-all combinations. In the former, a binary classifier is trained for all combinations of classes and final decision is made by majority rule among the classifiers (Stanislaw et al. 2004; Ramoser et al., 2005; Markiewicz et al, 2005; Guo et al., 2006; Siroic, 2007; Tuzel, 2007). In the latter, for each class a binary classifier is trained by labeling the samples from the class as positive examples and samples from the other classes as negative examples, the final decision is assigned to the class having maximum decision function among all classes (Pan, 2006). An important issue found in hematological applications is the fact that cells can be classified using hierarchical classifier structures, which take advantage of morphological taxonomy of the blood cells for extracting the most discriminative features in each classification level. Kim et al. (2001) proposed a two stage structure of ANN for classifying erythrocytes according to their shape features. Firstly, the most varied shapes were discriminated, and then circular shapes were classified as normal, target, spherocyte or stomatocyte. The same strategy was used by Ross (2006) in the classification of infected erythrocytes; a first stage decides whether erythrocyte is infected or not and a second stage defines the infection stage. Similar strategy was used by Díaz et al. (2009), but based on a SVM classifier.

CASE OF APPLICATION To conclude this chapter with an application case, we briefly present in this section the application of the general framework to the analysis of malarial infected blood smears. More details can be found in Díaz et al. (2009). The main goal of analysis of

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Automatic Analysis of Microscopic Images in Hematological Cytology Applications

malarial infected smears is to estimate the infection level of each smear. For this, we need to count the number of infected and non-infected erythrocytes but also to estimate the life stage of each parasite. As shown in Figure 6, discrimination of these cells is very difficult due to the slight differences between consecutive life stages.

Image Acquisition Images were digitized using a Sony high resolution digital video camera, which was coupled to a Carl Zeiss Axiostar Plus microscope. Use of intermediate lens and a ×100 power objective yielded a total magnification of ×1006. Optical

image corresponded to 102×76μm2 for a 640×480 image size, resulting in a resolution of 0.0252μm2.

Image Preprocessing Original images presented luminance variations mainly due to film inhomogeneities and varying illumination condition of acquisition devices. In order to correct the background inhomogeneities, a local low-pass filter was applied on the luminance channel from the YCbCr color space i.e. a low pass filter applied on sub-windows of approximately the larger size of erythrocytes in the image, and smoothed out using a moving window whose size was adjusted in order to eliminate the tiling

Figure 6. Single erythrocyte samples. From left to right: two healthy erithrocytes, two erythrocytes infected with ring stage parasites, two erythrocytes infected with throphozoite stage parasites and two erythrocytes infected with schizont stage parasites

Figure 7. Processing results of malarial infected blood smear images. (a) Original Image. (b) Pre-processed Image. (c) Pixel-based classification result. (d) Result after filtering by inclusion-tree representation

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Automatic Analysis of Microscopic Images in Hematological Cytology Applications

effect of the filter. Figure 7b shows an example of application of this procedure.

Erythrocyte Segmentation Automatic quantification of malarial infected blood smears has been designed on the base of detecting parasites and erythrocytes independently, and to locate the former into the latter to quantify the infection level. However, in early infection stages, the boundaries are not clearly defined, and segmentation is very difficult. So, the analysis of the erythrocyte-parasite as a whole was proposed as an alternative procedure. This stage tries to extract single erythrocytes from the background as follows: first, a pixel classification allows labeling each image pixel as either background or foreground, based on its color features. Then, segmentation is improved using an Inclusion-Tree structure, which is simplified to satisfy the restrictions imposed by the erythrocyte morphological structure and its spatial organization. This simplification allows removing artifacts generated at the staining or digitization processes. Figure 7c shows an example of an image segmented with pixel classification procedure and its corresponding result after filtering process is shown in Figure 7d. As the presence of clumped cells affects the automation of erythrocyte quantification, splitting was achieved by a template matching strategy that

searched for the best match between a chain code representation of the clumped shape contour and an ideal erythrocyte, estimated from the original image by an Expectation-Maximization algorithm (see Figure 8). This approach attempts to find cells in clumped shapes similar to erythrocytes found in the same image.

Feature Extraction Visual features of erythrocytes were described by the moments (mean, variance, standard deviation, kurtosis and skewness) of a set of histograms that represent the probability density function of the color, saturation, intensities, texture and edges inside the whole cell. So, an image was represented by 25 features that characterize the five histograms.

Cell Classification Erythrocyte classification was carried out by learning from samples approach composed of a hierarchical (two levels) ensemble of classifiers. At first level, a classifier was trained for deciding whether or not an erythrocyte was infected. Once an erythrocyte classified as infected, a second level determined the stage of infection using a set of classifiers, composed of four learning models: one model for each class and one more for detecting possible artifacts. The erythrocytes

Figure 8. Splitting of clumped cells. Top: Original overlapped cells. Down: splitting results. In white image edges, in green cells found by the proposed approach (Díaz et al. 2009)

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Automatic Analysis of Microscopic Images in Hematological Cytology Applications

that were misclassified into none, two or more classes were ruled out and left to the user for a later visual decision. Training algorithm for any learning model was selected by evaluation of their performance using a one-against-all strategy, resulting in an array of: one polynomial SVM, two radial basis function SVM and a neural network training models. The performance of classification task was evaluated using a Fβ measure, which is related to effectiveness. Parameter β allows assigning relative weights to the true positive and precision rates for dealing with class imbalance problem. A set of 12,557 erythrocytes composed of 11,844 healthy ones, 521 in ring stage infection, 109 in trophozoite stage infection and 83 in schizont stage infection, was used, which were automatically extracted from malarial infected blood smears. The whole set was randomly divided into training (40%), validation (30%) and test (30%) sets. Training and validation sets were used for parameter tuning of the classifiers while the test set was used for the final evaluation of the method performance. Using the strategy of classification presented above, this approach achieves good performance in classification of the different stages of infection on single erythrocytes extracted from malarial blood smears. Table 1 presents result obtained for the two levels of classification. Each Fβ value corresponds to the average of 10 experiments from a 10-fold cross validation process.

Table 1. Fβ measures for training, validation and test sets of the automatic infection stage Infection Detection

Ring stage

Throphozoite

Schizont

Training Set

0.937

0.973

0.771

0.780

Validation Set

0.954

0.923

0.519

0.739

Test Set

0.961

0.947

0.677

0.882

346

FUTURE RESEARCH DIRECTIONS An open issue in automatic hematological diagnosis is the development of algorithms that perform accurate segmentation of single cells and their components, and splitting of the cell clusters. Likewise, an emerging trend is the development of feature extraction techniques that allow a suitable object representation, which is less dependent on the quality of the segmentation. Generalization of learning models is another open research question. Approaches for classifying many types of cells have been developed, however most of them are very specialized. The framework for analyzing these images is very similar and the main difference lies in the features used for describing the objects, and the classification models varying by the training data. Modern machine learning approaches include on-line learning classifiers, which can dynamically extract object features and perform classification without retraining the learning model. These technique combined with robust descriptors can be used for constructing a reliable hematological analysis system.

SUMMARY AND CONCLUSION Automatic differential diagnosis of blood and bone marrow smears is a typical problem of pattern recognition, which is usually realized in four stages: preprocessing, segmentation, feature extraction/ selection, and classification. This chapter presents a comprehensive review of the methods proposed in the literature for performing each stage. The accuracy of computer-assisted hematology image analysis depends on the slide preparation, staining procedures and digitization settings. Consequently, algorithm efficiency may be enhanced by developing optimized acquisition protocols. Unfortunately there exist few system studies on this issue for this kind of images.

Automatic Analysis of Microscopic Images in Hematological Cytology Applications

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ENDNOTE 1



In some cases only mean and standard deviation are utilized together with features from other categories such as texture or geometrical measures (Beksak, 1997; Bikhet, 2000; Hengen, 2002.)

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Computational Methods in Biomedical Imaging Michele Piana Universita’ di Verona, Italy

INTRODUCTION Biomedical imaging represents a practical and conceptual revolution in the applied sciences of the last thirty years. Two basic ingredients permitted such a breakthrough: the technological development of hardware for the collection of detailed information on the organ under investigation in a less and less invasive fashion; the formulation and application of sophisticated mathematical tools for signal processing within a methodological setting of truly interdisciplinary flavor. A typical acquisition procedure in biomedical imaging requires the probing of the biological tissue by means of some emitted, reflected

or transmitted radiation. Then a mathematical model describing the image formation process is introduced and computational methods for the numerical solution of the model equations are formulated. Finally, methods based on or inspired by Artificial Intelligence (AI) frameworks like machine learning are applied to the reconstructed images in order to extract clinically helpful information. Important issues in this research activity are the intrinsic numerical instability of the reconstruction problem, the convergence properties and the computational complexity of the image processing algorithms. Such issues will be discussed in the following with the help of several examples of notable significance in the biomedical practice.

DOI: 10.4018/978-1-60960-561-2.ch207

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Computational Methods in Biomedical Imaging

BACKGROUND The first breakthrough in the theory and practice of recent biomedical imaging is represented by X-ray Computerized Tomography (CT) (Hounsfield, 1973). On October 11 1979 Allan Cormack and Godfrey Hounsfield gained the Nobel Prize in medicine for the development of computer assisted tomography. In the press release motivating the award, the Nobel Assembly of the Karolinska Institut wrote that in this revolutionary diagnostic tool “the signals[...]are stored and mathematically analyzed in a computer. The computer is programmed to reconstruct an image of the examined cross-section by solving a large number of equations including a corresponding number of unknowns”. Starting from this crucial milestone, biomedical imaging has represented a lively melting pot of clinical practice, experimental physics, computer science and applied mathematics, providing mankind of numerous non-invasive and effective instruments for early detection of diseases, and scientist of a prolific and exciting area for research activity. The main imaging modalities in biomedicine can be grouped into two families according to the kind of information content they provide. •

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Structural imaging: the image provides information on the anatomical features of the tissue without investigating the organic metabolism. Structural modalities are typically characterized by a notable spatial resolution but are ineffective in reconstructing the dynamical evolution of the imaging parameters. Further to X-ray CT, other examples of such approach are Fluorescence Microscopy (Rost & Oldfield, 2000), Ultrasound Tomography (Greenleaf, Gisvold & Bahn, 1982), structural Magnetic Resonance Imaging (MRI) (Haacke, Brown, Venkatesan & Thompson, 1999) and some kinds of

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prototypal non-linear tomographies like Microwave Tomography (Boulyshev, Souvorov, Semenov, Posukh & Sizov, 2004), Diffraction Tomography (Guo & Devaney, 2005), Electrical Impedance Tomography (Cheney, Isaacson & Newell, 1999) and Optical Tomography (Arridge, 1999). Functional imaging: during the acquisition many different sets of signals are recorded according to a precisely established temporal paradigm. The resulting images can provide information on metabolic deficiencies and functional diseases but are typically characterized by a spatial resolution which is lower (sometimes much lower) than the one of anatomical imaging. Emission tomographies like Single Photon Emission Computerized Tomography (SPECT) (Duncan, 1997) or Positron Emission Tomography (PET) (Valk, Bailey, Townsend & Maisey, 2004) and Magnetic Resonance Imaging in its functional setup (fMRI) (Huettel, Song & McCarthy, 2004) are examples of these dynamical techniques together with Electroand Magnetoencephalography (EEG and MEG) (Zschocke & Speckmann, 1993; Hamalainen, Hari, Ilmoniemi, Knuutila & Lounasmaa, 1993), which reproduce the neural activity at a millisecond time scale and in a completely non-invasive fashion.

In all these imaging modalities the correct mathematical modeling of the imaging problem, the formulation of computational algorithms for the solution of the model equations and the application of image processing algorithms for data interpretation are the crucial steps which allow the exploitness of the visual information from the measured raw data.

Computational Methods in Biomedical Imaging

MAIN FOCUS From a mathematical viewpoint the inverse problem of synthesizing the biological information in a visual form from the collected radiation is characterized by a peculiar pathology. The concept of ill-posedness has been introduced by Jules Hadamard (Hadamard, 1923) to indicate mathematical problems whose solution does not exist for all data, or is not unique or does not depend uniquely on the data. In biomedical imaging this last feature has particularly deleterious consequences: indeed, the presence of measurement noise in the raw data may produce notable numerical instabilities in the reconstruction when naive approaches are applied. Most (if not all) biomedical imaging problems are ill-posed inverse problems (Bertero & Boccacci, 1998) whose solution is a difficult mathematical task and often requires a notable computational effort. The first step toward the solution is represented by an accurate modeling of the mathematical relation between the biological organ to be imaged and the data provided by the imaging device. Under the most general assumptions the model equation is a non-linear integral equation, although, for several devices, the non-linear imaging equation can be reliably approximated by a linear model where the integral kernel encodes the impulse response of the instrument. Such linearization can be either performed through a precise technological realization, like in MRI, where acquisition is designed in such a way that the data are just the Fourier Transform of the object to be imaged; or obtained mathematically, by applying a sort of perturbation theory to the non-linear equation, like in diffraction tomography whose model comes from the linearization of the scattering equation. The second step toward image reconstruction is given by the formulation of computational methods for the reduction of the model equation. In the case of linear ill-posed inverse problems, a wellestablished regularization theory exists which

attenuates the numerical instability related to illposedness maintaining the biological reliability of the reconstructed image. Regularization theory is at the basis of most linear imaging modalities and regularization methods can be formulated in both a probabilistic and a deterministic setting. Unfortunately an analogously well- established theory does not exist in the case of non-linear imaging problems which therefore are often addressed by means of ‘ad hoc’ techniques. Once an image has been reconstructed from the data, a third step has to be considered, i.e. the processing of the reconstructed images for the extraction and interpretation of their information content. Three different problems are typically addressed at this stage: •

•

•

Edge detection (Trucco & Verri, 1998). Computer vision techniques are applied in order to enhance the regions of the image where the luminous intensity changes sharply. Image integration (Maintz & Viergever, 1998). In the clinical workflow several images of a patient are taken with different modalities and geometries. These images can be fused in an integrated model by recovering changes in their geometry. Image segmentation (Acton & Ray, 2007). Partial volume effects make the interfaces between the different tissues extremely fuzzy, thus complicating the clinical interpretation of the restored images. An automatic procedure for the partitioning of the image in homogeneous pixel sets and for the classification of the segmented regions is at the basis of any Computed Aided Diagnosis and therapy (CAD) software.

AI algorithms and, above all, machine learning play a crucial role in addressing these image processing issues. In particular, as a subfield of machine learning, pattern recognition provides a sophisticated description of the data which, in

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medical imaging, allows to locate tumors and other pathologies, measure tissue dimensions, favor computer-aided surgery and study anatomical structures. For example, supervised approaches like backpropagation (Freeman & Skapura, 1991) or boosting (Shapire, 2003) accomplish classification tasks of the different tissues from the knowledge of previously interpreted images; while unsupervised methods like Self-Organizing Maps (SOM) (Kohonen, 2001), fuzzy clustering (De Oliveira & Pedrycz, 2007) and ExpectationMaximization (EM) (McLachlan & Krishnan, 1996) infer probabilistic information or identify clustering structures in sets of unlabeled images. From a mathematical viewpoint, several of these methods correspond more to heuristic recipes than to rigorously formulated and motivated procedures. However, since the last decade the theory of statistical learning (Vapnik, 1998) has appeared as the best candidate for a rigorous description of machine learning within a functional analysis framework.

FUTURE TRENDS Among the main goals of recent biomedical imaging we point out the realization of •

•

•

microimaging techniques which allow the investigation of biological tissues of micrometric size for both diagnostic and research purposes; hybrid systems combining information from different modalities, possibly anatomical and functional; highly non-invasive diagnostic tools, where even a modest discomfort is avoided.

These goals can be accomplished only by means of an effective interplaying of hardware development and application of innovative image processing algorithms. For example, microtomography for biological samples requires the introduc-

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tion of both new X-ray tubes for data acquisition and computational methods for the reduction of beam hardening effects; electrophysiological and structural information on the brain can be collected by performing an EEG recording inside an MRI scanning but also using the structural information from MRI as a prior information in the analysis of the EEG signal accomplished in a Bayesian setting; finally, non- invasivity in colonoscopy can be obtained by utilizing the most recent acquisition design in X-ray tomography together with sophisticated softwares which allow virtual navigation within the bowel, electronic cleansing and automatic classification of cancerous and healthy tissues. From a purely computational viewpoint, two important goals in machine learning applied to medical imaging are the development of algorithms for semi-supervised learning and for the automatic integration of genetic data with information coming from the acquired imagery.

CONCLUSION Some aspects of recent biomedical imaging have been described from a computational science perspective. The biomedical image reconstruction problem has been discussed as an ill-posed inverse problem where the intrinsic numerical instability producing image artifacts can be reduced by applying sophisticated regularization methods. The role of image processing based on machine learning techniques has been described together with the main goals of recent biomedical imaging applications.

REFERENCES Acton, S. T., & Ray, N. (2007). Biomedical Image Analysis: Segmentation. Princeton: Morgan and Claypool.

Computational Methods in Biomedical Imaging

Arridge, S. R. (1999). Optical Tomography in Medical Imaging. [Evolutionary Programming, Genetic Algorithms. Oxford University Press.]. Inverse Problems, 15, R41–R93. doi:10.1088/02665611/15/2/022 Bertero, M., & Boccacci, P. (1998). Introduction to Inverse Problems in Imaging. Bristol: IOP. Boulyshev, A. E., Souvorov, A. E., Semenov, S. Y., Posukh, V. G., & Sizov, Y. E. (2004). Threedimensional Vector Microwave Tomography: Theory and Computational Experiments. Inverse Problems, 20, 1239–1259. doi:10.1088/02665611/20/4/013 Cheney, M., Isaacson, D., & Newell, J. C. (1999). Electrical Impedance Tomography. SIAM Review, 41, 85–101. doi:10.1137/S0036144598333613 De Oliveira, J. V., & Pedrycz, W. (2007). Advances in Fuzzy Clustering and its Applications. San Francisco: Wiley. Duncan, R. (1997). SPECT Imaging of the Brain. Amsterdam: Kluwer. Freeman, J. A., & Skapura, D. M. (1991). Neural Network Algorithms: Applications and Programming Techniques. Redwood City: AddisonWesley. Greenleaf, J., Gisvold, J. J., & Bahn, R. (1982). Computed Transmission Ultrasound Tomography. Medical Progress Through Technology, 9, 165–170.

Hadamard, J. (1923). Lectures on Cauchy’s Problem in Partial Differential Equations. Yale: Yale University Press. Hamalainen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., & Lounasmaa, O. V. (1993). Magnetoencephalography: theory, instrumentation and applications to non-invasive studies of the working human brain. Reviews of Modern Physics, 65, 413–497. doi:10.1103/RevModPhys.65.413 Hounsfield, G. N. (1973). Computerised Transverse Axial Scanning (Tomography). I: Description of System. The British Journal of Radiology, 46, 1016–1022. doi:10.1259/0007-1285-46-5521016 Huettel, S. A., Song, A. W., & McCarthy, G. (2004). Functional Magnetic Resonance Imaging. Sunderland: Sinauer Associates. Kohonen, T. (2001). Self-Organizing Maps. Berlin: Springer. Maintz, J., & Viergever, M. (1998). A Survey of Medical Imaging Registration. Medical Imaging Analysis, 2, 1–36. doi:10.1016/S13618415(01)80026-8 McLachlan, G., & Krishnan, T. (1996). The EM Algorithm and Extensions. San Francisco: John Wiley. Rost, F., & Oldfield, R. (2000). Fluorescence Microscopy: Photography with a Microscope. Cambridge: Cambridge University Press.

Guo, P., & Devaney, A. J. (2005). Comparison of Reconstruction Algorithms for Optical Diffraction Tomography. Journal of the Optical Society of America. A, Optics, Image Science, and Vision, 22, 2338–2347. doi:10.1364/JOSAA.22.002338

Shapire, R. E. (2003). The Boosting Approach to Machine Learning: an Overview. Nonlinear Estimation and Classification, Denison, D. D., Hansen, M. H., Holmes, C., Mallik, B. & Yu, B. editors. Berlin: Springer.

Haacke, E. M., Brown, R. W., Venkatesan, R., & Thompson, M. R. (1999). Magnetic Resonance Imaging: Physical Principles and Sequence Design. San Francisco: John Wiley.

Trucco, E., & Verri, A. (1998). Introductory Techniques for 3D Computer Vision. Englewood Cliffs: Prentice Hall.

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Valk, P. E., Bailey, D. L., Townsend, D. W., & Maisey, M. N. (2004). Positron Emission Tomography. Basic Science and Clinical Practice. Berlin: Springer. Vapnik, V. (1998). Statistical Learning Theory. San Francisco: John Wiley. Zschocke, S., & Speckmann, E. J. (1993). Basic Mechanisms of the EEG. Boston: Birkhaeuser.

KEY TERMS AND DEFINITIONS Computer Aided Diagnosis (CAD): The use of computers for the interpretation of medical images. Automatic segmentation is one of the crucial task of any CAD product. Edge Detection: Image processing technique for enhancing the points of an image at which the luminous intensity changes sharply. Electroencephalography (EEG): Noninvasive diagnostic tool which records the cerebral electrical activity by means of surface electrodes placed on the skull. Ill-Posedness: Mathematical pathology of differential or integral problems, whereby the solution of the problem does not exist for all data, or is not unique or does not depend continuously on the data. In computation, the numerical effects of ill-posedness are reduced by means of regularization methods.

Image Integration: In medical imaging, combination of different images of the same patient acquired with different modalities and/or according to different geometries. Magnetic Resonance Imaging (MRI): Imaging modality based on the principles of nuclear magnetic resonance (NMR), a spectroscopic technique used to obtain microscopic chemical and physical information about molecules. MRI can be applied in both functional and anatomical settings. Magnetoencephalography (MEG): Noninvasive diagnostic tool which records the cerebral magnetic activity by means of superconducting sensors placed on a helmet surrounding the brain. Segmentation: Image processing technique for distinguishing the different homogeneous regions in an image. Statistical Learning: Mathematical framework which utilizes functional analysis and optimazion tools for studying the problem of inference. Tomography: Imaging technique providing two-dimensional views of an object. The method is used in many disciplines and may utilize input radiation of different nature and wavelength. There exist X-ray, optical, microwave, diffraction and electrical impedance tomographies.

This work was previously published in Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, Julian Dorado and Alejandro Pazos, pp. 372-376, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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Visual Medical Information Analysis Maria Papadogiorgaki Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece Vasileios Mezaris Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece Yiannis Chatzizisis Aristotle University of Thessaloniki, Greece George D. Giannoglou Aristotle University of Thessaloniki, Greece Ioannis Kompatsiaris Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece

INTRODUCTION Images have constituted an essential data source in medicine in the last decades. Medical images derived from diagnostic technologies (e.g., Xray, ultrasound, computed tomography, magnetic resonance, nuclear imaging) are used to improve the existing diagnostic systems for clinical purposes, but also to facilitate medical research. Hence, medical image processing techniques are constantly investigated and evolved. DOI: 10.4018/978-1-60960-561-2.ch208

Medical image segmentation is the primary stage to the visualization and clinical analysis of human tissues. It refers to the segmentation of known anatomic structures from medical images. Structures of interest include organs or parts thereof, such as cardiac ventricles or kidneys, abnormalities such as tumors and cysts, as well as other structures such as bones, vessels, brain structures and so forth. The overall objective of such methods is referred to as computer-aided diagnosis; in other words, they are used for assisting doctors in evaluating medical imagery or in recognizing abnormal findings in a medical image.

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Visual Medical Information Analysis

In contrast to generic segmentation methods, techniques used for medical image segmentation are often application-specific; as such, they can make use of prior knowledge for the particular objects of interest and other expected or possible structures in the image. This has led to the development of a wide range of segmentation methods addressing specific problems in medical applications. In the sequel of this article, the analysis of medical visual information generated by three different medical imaging processes will be discussed in detail: Magnetic Resonance Imaging (MRI), Mammography, and Intravascular Ultrasound (IVUS). Clearly, in addition to the aforementioned imaging processes and the techniques for their analysis that are discussed in the sequel, numerous other algorithms for applications of segmentation to specialized medical imagery interpretation exist.

BACKGROUND Magnetic Resonance Imaging Magnetic Resonance Imaging (MRI) is an important diagnostic imaging technique attending to the early detection of the abnormal conditions in tissues and organs because it is able to reliably identify anatomical areas of interest. In particular for brain imaging, several techniques which perform segmentation of the brain structures from MRIs are applied to the study of many disorders, such as multiple sclerosis, schizophrenia, epilepsy, Parkinson’s disease, Alzheimer’s disease, and so forth. MRI is particularly suitable for brain studies because it is virtually noninvasive, and it achieves a high spatial resolution and high contrast of soft tissues. To achieve the 3D reconstruction of the brain morphology, several of the existing approaches perform segmentation on sequential MR images. The overall process usually includes noise filtering of the images and edge detection for

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the identification of the brain contour. Following, perceptual grouping of the edge points is applied in order to recover the noncontinuous edges. In many cases, the next step is the recognition of the various connective components among the set of edge points, rejection of the components that consist of the smallest number of points, and use of the finally acquired points for reconstructing the 3D silhouette of the brain, as will be discussed in more detail in the sequel.

Mammography Mammography is considered to be the most effective diagnostic technique for detecting abnormal tissue conditions on women’s breast. Being used both for prevention and for diagnostic purposes, it is a very commonly used technique that produces mammographic images by administering a low-dose of x-ray radiation to the tissue under examination. The analysis of the resulting images aims at the detection of any abnormal structures and the quantification of their characteristics, such as size and shape, often after detecting the pectoral muscle and excluding it from the further processing. Methods for the analysis of mammographic images are presented in the sequel.

Intravascular Ultrasound IVUS is a catheter-based technique that renders two-dimensional images of coronary arteries and therefore provides valuable information concerning luminal and wall area, plaque morphology and wall composition. An example IVUS image, with tags explaining the most important parts of the vessel structure depicted on it, is shown in Figure 1. However, due to their tomographic nature, isolated IVUS images provide limited information regarding the burden of atherosclerosis. This limitation can be overcome through 3D reconstruction techniques in order to stack the sequential 2D images in space, using single-plane

Visual Medical Information Analysis

Figure 1. Example IVUS image with tags explaining the most important parts of the vessel structure depicted on it

VISUAL MEDICAL INFORMATION ANALYSIS TECHNIQUES Magnetic Resonance Imaging Analysis

or biplane angiography for recovering the vessel curvature (Giannoglou et al., 2006; Sherknies, Meunier, Mongrain, & Tardif, 2005; Wahle, Prause, DeJong, & Sonka, 1999). The analysis of IVUS images constitutes an essential step toward the accurate morphometric analysis of coronary plaque. To this end, the processing of IVUS images is necessary so that the regions of interest can be detected. The coronary artery wall mainly consists of three layers: intima, media and adventitia, while three regions are supposed to be visualized as distinguished fields in an IVUS image, namely the lumen, the vessel wall (made of the intima and the media layers) and the adventitia plus surroundings. The above regions are separated by two closed contours: the inner border, which corresponds to the lumen-wall interface, and the outer border representing the boundary between media and adventitia. A reliable and quick detection of these two borders in sequential IVUS images constitutes the basic step towards plaque morphometric analysis and 3D reconstruction of the coronary arteries.

Several techniques have been proposed for the analysis of MR images. In Grau, Mewes, Alcaniz, Kikinis, and Warfield (2004), a modification of a generic segmentation technique, the watershed transform, is proposed for knee cartilage and gray matter/white matter segmentation in MR images. This introduces prior information in the watershed method via the use of a previous probability calculation for the classes present in the image and via the combination of the watershed transform with atlas registration for the automatic generation of markers. As opposed to Grau et al. (2004), other methods are more application specific; in Woolrich, Behrens, Beckmann and Smith (2005), for example, segmentation tools are developed for the study of the function of the brain, that is, for the classification of brain areas as activating, deactivating, or not activating, using functional magnetic resonance imaging (FMRI) data. This method performs segmentation based on intensity histogram information, augmented with adaptive spatial regularization using Markov random fields. The latter contributes to improved segmentation as compared to nonspatial mixture models, while not requiring the heuristic fine-tuning that is necessary for nonadaptive spatial regularization previously proposed. Because MR images contain a significant amount of noise caused by operator performance, equipment, or even the environment, the segmentation on them can lead to several inaccuracies. In order to overcome the effects of noise, Shen, Sandham, Granat and Sterr (2005) propose a segmentation technique based on an extension to the traditional fuzzy c-means (FCM) clustering algorithm. The segmentation performance is improved using neighborhood attraction, which

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depends on the relative location and features of neighboring pixels. The degree of attraction is optimized by applying a neural network model. Greenspan, Ruf and Goldberger (2006) have also developed an algorithm for the automated brain tissue segmentation on noisy, low-contrast (MR) images. Under their approach, the brain image is represented by a model that is composed of a large number of Gaussians. For the algorithm’s initialization an atlas or parameter learning are not required. Finally, segmentation of the brain image is achieved by affiliating each voxel to the component of the model that maximizes an a posteriori probability. In Valdes-Cristerna, Medina-Banuelos and Yanez-Suarez (2004) a hybrid model for the segmentation of brain MRI has been investigated. The model includes a radial basis network and an active contour model. The radial basis network algorithm generates an initial contour, which is following used by the active contour model to achieve the final segmentation of the brain.

Mammography Image Analysis Several applications have been proposed, which process the mammographic images in order to assist the clinicians in their diagnostic procedure. In Székely, Toth and Pataki (2006) the mammographic images are analyzed using segmentation in order to identify regions of interest. The applied segmentation technique includes texture features, decision trees, and a Markov random field model. The extracted features which refer to the object’s shape and texture parameters are linearly combined to lead to the final decision. Because the pectoral muscle should be excluded from processing on a mammogram intended for the breast tissue, its identification is important. Kwok, Chandrasekhar, Attikiouzel and Rickard (2004) have developed an adaptive segmentation technique for the extraction of the pectoral muscle on digitized mammograms. The method uses knowledge about the position and shape of the pectoral muscle. Other approaches, such as

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Cascio, Fauci, Magro, Raso, Bellotti, De Carlo et al. (2006) use supervised neural networks for detecting pathological masses in mammograms. A segmentation process provides features of geometrical information, or shape parameters which constitute the input to the neural network that computes the probability of the lesion existence.

IVUS Image and Image Sequence Analysis Traditionally, the segmentation of IVUS images is performed manually, which is a time consuming procedure with results affected by the high inter- and intra-user’s variability. To overcome these limitations, several approaches for semiautomated segmentation have been proposed in the literature. In Herrington, Johnson, Santago and Snyder (1992) after the manual indication of the general location of the boundary of interest by the user, an edge detection filter is applied to find potential edge points within the pointed neighborhood. The extracted image data are used for the estimation of the closed smooth final contour. Sonka, Zhang, Siebes, Bissing, DeJong, Collins et al. (1995) implemented a knowledge-based graph searching method incorporating a priori knowledge on coronary artery anatomy and a selected region of interest prior to the automatic border detection. Quite a few variations of active contour model have been investigated, including the approach of Parissi et al. (2006), where a user interaction is required, by drawing an initial contour as close as possible to its final position. Thus, the active contour is initialized and tends to approximate the final desired border. The active contour or deformable models principles have been used to allow the extraction of the luminal and medial-adventitial borders in three dimensions after setting an initial contour in Kovalski, Beyar, Shofti and Azhari (2000). However, the contour detection fails for low contrast interface regions such as the luminal border where the blood-wall interface in most images

Visual Medical Information Analysis

corresponds to weak pixel intensity variation. In order to improve the included active surface segmentation algorithm for plaque characterization, Klingensmith, Nair, Kuban and Vince (2004) use the frequency information after acquiring the radiofrequency (RF) IVUS data through an electrocardiogram scheme. Radio frequency data are also used in Perrey et al. (2004), after in vivo acquisition for the segmentation of the luminal boundary in IVUS images. According to this approach, tissue describing parameters are directly estimated from RF data. Subsequently, a neuro-fuzzy inference system trained to several parameters is used to distinguish blood from tissue regions. For clinical practice the most attractive approaches are the fully automatic ones. A limited number of them has been developed so far, such as the segmentation based on edge contrast (Zhu, Liang, & Friedman, 2002); the latter is shown to be an efficient feature for IVUS image analysis, in combination with the gray level distribution. Specific automated approaches which utilize the deformable models principles in combination with other various techniques and features reported in the related literature have been investigated. Brusseau, de Korte, Mastik, Schaar and van der Steen (2004) exploited an automatic method for detecting the endoluminal border based on an active contour that evolves until it optimally separates regions with different statistical properties without using a preselected region of interest or initialization of the contour close to its final position. Another automated approach based on deformable models has been reported by Plissiti, Fotiadis, Michalis and Bozios (2004), who employed a Hopfield neural network for the modification and minimization of an energy function as well as a priori vessel geometry knowledge. An automated approach for segmentation of IVUS images based on a variation of an active contour model is presented in Giannoglou et al. (2007). This technique is in vivo evaluated in images originated from human coronary arteries. The initialization of the contours in each IVUS frame

is automatically performed using an algorithm, which is based on the intensity features of the image. The initially extracted boundaries constitute the input to the active contour model, which then deforms the contours appropriately, identifying their correct location on the IVUS frame. A fuzzy clustering algorithm for adaptive segmentation in IVUS images is investigated by Filho, Yoshizawa, Tanaka, Saijo and Iwamoto (2005). Cardinal et al. (2006) present a 3D IVUS segmentation applying Rayleigh probability density functions (PDFs) for modeling the pixel gray value distribution of the vessel wall structures. Other approaches are based on the calculation of the image’s energy. In Luo, Wang and Wang (2003) the lumen area of the coronary artery is estimated using the internal energy, which describes the smoothness of the arterial wall and the external energy which represents the grayscale variation of images that constitute an IVUS video. The minimal energy which defines the contour is obtained using circular dynamic programming. Other methods include statistical analysis, such as Gil, Hernandez, Rodriguez, Mauri and Radeva’s (2006), where the presented approach uses statistical classification techniques for the IVUS border detection. In Papadogiorgaki, Mezaris, Chatzizisis, Kompatsiaris and Giannoglou (2006), a fully automated method for the segmentation of IVUS images and specifically for the detection of luminal and medial-adventitial boundaries is presented. This technique is based on the use of the results of texture analysis, performed by means of a multilevel Discrete Wavelet Frames decomposition. Following image preprocessing, to remove catheter-induced artifacts, a two-step process is employed for the detection of the boundaries of interest. Objective of the first step, termed contour initialization, is the detection of pixels that are likely to belong to the lumen and media-adventitia boundaries, taking into consideration the previously extracted texture features. As a result of this step, initial contours are generated; however, these are not smooth and are characterized by

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Figure 2. Sample IVUS image (left) and the corresponding analysis result of a texture-based approach to the detection of luminal and medial-adventitial boundaries (right)

discontinuities, as opposed to the true lumen and media-adventitia boundaries. Thus, at the second step, a filtering or approximation procedure is applied to the initial contour functions, so as to result in the generation of smooth contours that are in good agreement with the true lumen and media-adventitia boundaries. This approach does not require manual initialization of the contours and demonstrates the importance of texture features in IVUS image analysis. A sample IVUS image and the corresponding analysis result of this approach are illustrated in Figure 2.

FUTURE TRENDS With the number of medical imaging techniques used in everyday practice constantly rising and the quality of the results of such imaging techniques constantly increasing, to the benefit of the patients, it is clear that the need for accurate and automated to the widest possible extent analysis of medical images is a key element in supporting the diagnosis and treatment process for a wide range of medical conditions. To this end, future research will continue to concentrate on the development of analysis methods that are automated, robust and reliable. In addition to that, particular emphasis is expected to be put on the coupling of

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the results of automated analysis with techniques for the formal representation of them. Examples of early systems for the formal representation of knowledge extracted from medical images, though not in an automated manner, include those discussed in Dasmahapatra et al. (2006), Hu, Dasmahapatra, Lewis and Shadbolt (2003). These are concerned with the annotation of medical images used for the diagnosis and management of breast cancer, such as those generated by mammography and MRI, by expressing all the extracted features and regions of interest using domain knowledge and assigning them to specific concepts of a knowledge structure. In an analogous approach, in Gedzelman, Simonet, Bernhard, Diallo and Palmer (2005) a knowledge structure of cardiovascular diseases is constructed in order to be used for the representation of the findings of the relevant imaging techniques, so as to support concept-based information retrieval. Combining automated analysis results with techniques such as those briefly discussed above for the formal representation of them will empower new possibilities in the areas of retrieval in extensive medical databases and reasoning over the results of analysis, consequently providing the physicians not only with the analysis results themselves but also with hints on their meaning, minimizing the risk of misinterpretation.

Visual Medical Information Analysis

CONCLUSION In this article, visual medical information analysis was discussed, starting with an introduction on the current use of medical imaging and the needs for its analysis. The current article then focused on three important medical imaging techniques, namely Magnetic Resonance Imaging (MRI), Mammography, and Intravascular Ultrasound (IVUS), for which a detailed presentation of the goals of analysis and the methods presented in the literature for reaching these goals was given. The future trends identified in the relevant section provide insights on how the algorithms outlined in this article can be further evolved, so as to more efficiently address the problem of medical image analysis and consequently pave the way for the development of innovative doctor decision support applications that will make the most out of the available image data.

REFERENCES Brusseau, E., de Korte, C. L., Mastik, F., Schaar, J., & van der Steen, A. F. W. (2004). Fully automatic luminal contour segmentation in intracoronary ultrasound imaging—a statistical approach. IEEE Transactions on Medical Imaging, 23(5), 554–566. doi:10.1109/TMI.2004.825602 Cardinal, M.-H. R., Meunier, J., Soulez, G., Maurice, R. L., Therasse, E., & Cloutier, G. (2006). Intravascular ultrasound image segmentation: A three-dimensional fast-marching method based on gray level distributions. IEEE Transactions on Medical Imaging, 25(5), 590–601. doi:10.1109/ TMI.2006.872142 Cascio, D., Fauci, F., Magro, R., Raso, G., Bellotti, R., & De Carlo, F. (2006). Mammogram segmentation by contour searching and mass lesions classification with neural network. IEEE Transactions on Nuclear Science, 53(5), 2827–2833. doi:10.1109/TNS.2006.878003

Dasmahapatra, S., Dupplaw, D., Hu, B., Lewis, H., Lewis, P., & Shadbolt, N. (2006). Facilitating multi-disciplinary knowledge-based support for breast cancer screening. International Journal of Healthcare Technology and Management, 7(5), 403–420. Dos, S., Filho, E., Yoshizawa, M., Tanaka, A., Saijo, Y., & Iwamoto, T. (2005, September). Detection of luminal contour using fuzzy clustering and mathematical morphology in intravascular ultrasound images. In Proceedings of the 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE-EMBS), China. Gedzelman, S., Simonet, M., Bernhard, D., Diallo, G., & Palmer, P. (2005, September). Building an ontology of cardiovascular diseases for concept-based information retrieval. Computers in Cardiology, Lyon, France. Giannoglou, G. D., Chatzizisis, Y. S., Koutkias, V., Kompatsiaris, I., Papadogiorgaki, M., & Mezaris, V. (2007). (accepted for publication). A novel active contour model for fully automated segmentation of intravascular ultrasound images: In-vivo validation in human coronary arteries. Computers in Biology and Medicine. Giannoglou, G. D., Chatzizisis, Y. S., Sianos, G., Tsikaderis, D., Matakos, A., & Koutkias, V. (2006). In-vivo validation of spatially correct threedimensional reconstruction of human coronary arteries by integrating intravascular ultrasound and biplane angiography. Coronary Artery Disease, 17(6), 533–543. doi:10.1097/00019501200609000-00007 Gil, D., Hernandez, A., Rodriguez, O., Mauri, J., & Radeva, P. (2006). Statistical strategy for anisotropic adventitia Modelling in IVUS. IEEE Transactions on Medical Imaging, 25(6), 768–778. doi:10.1109/TMI.2006.874962

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Grau, V., Mewes, A. U. J., Alcaniz, M., Kikinis, R., & Warfield, S. K. (2004). Improved watershed transform for medical image segmentation using prior information. IEEE Transactions on Medical Imaging, 23(4), 447–458. doi:10.1109/ TMI.2004.824224 Greenspan, H., Ruf, A., & Goldberger, J. (2006). Constrained gaussian mixture model framework for automatic segmentation of MR brain images. IEEE Transactions on Medical Imaging, 25(9), 1233–1245. doi:10.1109/TMI.2006.880668 Herrington, D. M., Johnson, T., Santago, P., & Snyder, W. E. (1992, October). Semi-automated boundary detection for intravascular ultrasound. In Proceedings of Computers in cardiology (pp. 103-106). Durham, NC, USA. Hu, B., Dasmahapatra, S., Lewis, P., & Shadbolt, N. (2003, November). Ontology-based medical image annotation with description logics. In Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence, Sacramento, CA, USA. Klingensmith, J. D., Nair, A., Kuban, B. D., & Vince, D. G. (2004, August). Segmentation of three-dimensional intravascular ultrasound images using spectral analysis and a dual active surface model. In Proceedings of the IEEE Ultrasonics Symposium, Montreal, Canada. Kovalski, G., Beyar, R., Shofti, R., & Azhari, H. (2000). Three-dimensional automatic quantitative analysis of intravascular ultrasound images. Ultrasound in Medicine & Biology, 26(4), 527–537. doi:10.1016/S0301-5629(99)00167-2 Kwok, S. M., Chandrasekhar, R., Attikiouzel, Y., & Rickard, M. T. (2004). Automatic pectoral muscle segmentation on mediolateral oblique view mammograms. IEEE Transactions on Medical Imaging, 23(9), 1129–1140. doi:10.1109/TMI.2004.830529

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Luo, Z., Wang, Y., & Wang, W. (2003). Estimating coronary artery lumen area with optimizationbased contour detection. IEEE Transactions on Medical Imaging, 22(4), 564–566. doi:10.1109/ TMI.2003.811048 Papadogiorgaki, M., Mezaris, V., Chatzizisis, Y. S., Kompatsiaris, I., & Giannoglou, G. D. (2006, September). A fully automated texture-based approach for the segmentation of sequential IVUS images. In Proceedings of the 13th International Conference on Systems, Signals & Image Processing (IWSSIP), Budapest, Hungary. Parissi, E., Kompatsiaris, Y., Chatzizisis, Y. S., Koutkias, V., Maglaveras, N., Strintzis, M. G., et al. (2006, December). An automated model for rapid and reliable segmentation of intravascular ultrasound images. In Proceedings of the 7th International Symposium on Biological and Medical Data Analysis (ISBMDA), Thessaloniki, Greece. Perrey, C., Scheipers, U., Bojara, W., Lindstaedt, M., Holt, S., & Ermert, H. (2004, August). Computerized segmentation of blood and luminal borders in intravascular ultrasound. In Proceedings of the IEEE Ultrasonics Symposium, Montreal, Canada. Plissiti, M. E., Fotiadis, D. I., Michalis, L. K., & Bozios, G. E. (2004). An automated method for lumen and media–adventitia border detection in a sequence of IVUS frames. IEEE Transactions on Information Technology in Biomedicine, 8(2), 131–141. doi:10.1109/TITB.2004.828889 Shen, S., Sandham, W., Granat, M., & Sterr, A. (2005). MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural network optimization. IEEE Transactions on Information Technology in Biomedicine, 9(3), 459–467. doi:10.1109/TITB.2005.847500 Sherknies, D., Meunier, J., Mongrain, R., & Tardif, J.-C. (2005). Three-dimensional trajectory assessment of an IVUS transducer from singleplane cineangiograms: A phantom study. IEEE Transactions on Bio-Medical Engineering, 52(3), 543–549. doi:10.1109/TBME.2004.843295

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Sonka, M., Zhang, X., Siebes, M., Bissing, M. S., DeJong, S. C., & Collins, S. M. (1995). Segmentation of intravascular ultrasound images: A knowledge-based approach. IEEE Transactions on Medical Imaging, 14(4), 719–732. doi:10.1109/42.476113

Zhu, H., Liang, Y., & Friedman, M. H. (2002). IVUS image segmentation based on contrast. In Proceedings of SPIE, Durham, NC, USA, (Vol. 4684, pp. 1727-1733).

Székely, N., Toth, N., & Pataki, B. (2006). A hybrid system for detecting masses in mammographic images. IEEE Transactions on Instrumentation and Measurement, 55(3), 944–952. doi:10.1109/ TIM.2006.870104

KEY TERMS AND DEFINITIONS

Valdes-Cristerna, R., Medina-Banuelos, V., & Yanez-Suarez, O. (2004). Coupling of radial-basis network and active contour model for multispectral brain MRI segmentation. IEEE Transactions on Bio-Medical Engineering, 51(3), 459–470. doi:10.1109/TBME.2003.820377 Wahle, A., Prause, G. P. M., DeJong, S. C., & Sonka, M. (1999). Geometrically correct 3-D reconstruction of intravascular ultrasound images by fusion with biplane angiography—methods and validation. IEEE Transactions on Medical Imaging, 18(8), 686–699. doi:10.1109/42.796282 Woolrich, M. W., Behrens, T. E. J., Beckmann, C. F., & Smith, S. M. (2005). Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data. IEEE Transactions on Medical Imaging, 24(1), 1–11. doi:10.1109/TMI.2004.836545

Active Contour Model: Energy-minimizing parametric curve that is the basis of several medical image analysis techniques. Computer-Aided Diagnosis: The process of using computer-generated analysis results for assisting doctors in evaluating medical data. Coronary Angiography: X-ray diagnostic process for obtaining an image of the coronary arteries. Intravascular Ultrasound (IVUS): Diagnostic catheter-based technique that renders twodimensional images of coronary arteries. Magnetic Resonance Imaging (MRI): Imaging technique that uses a magnetic field to provide two-dimensional images of internal body structures. Mammography: Diagnostic X-ray technique which produces breast images and is used to detect breast tissue abnormalities. Medical Image Segmentation: The localization of known anatomic structures in medical images.

This work was previously published in Encyclopedia of Information Science and Technology, Second Edition, edited by Mehdi Khosrow-Pour, pp. 4034-4040, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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Angiographic Images Segmentation Techniques Francisco J. Nóvoa University of A Coruña, Spain Alberto Curra University of A Coruña, Spain M. Gloria López University of A Coruña, Spain Virginia Mato University of A Coruña, Spain

INTRODUCTION Heart-related pathologies are among the most frequent health problems in western society. Symptoms that point towards cardiovascular diseases are usually diagnosed with angiographies, which allow the medical expert to observe the bloodflow in the coronary arteries and detect severe narrowing (stenosis). According to the severity, extension, and location of these narrowings, the expert pronounces a diagnosis, defines a treatment, and establishes a prognosis. DOI: 10.4018/978-1-60960-561-2.ch209

The current modus operandi is for clinical experts to observe the image sequences and take decisions on the basis of their empirical knowledge. Various techniques and segmentation strategies now aim at objectivizing this process by extracting quantitative and qualitative information from the angiographies.

BACKGROUND Segmentation is the process that divides an image in its constituting parts or objects. In the present context, it consists in separating the pixels that

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Angiographic Images Segmentation Techniques

compose the coronary tree from the remaining “background” pixels. None of the currently applied segmentation methods is able to completely and perfectly extract the vasculature of the heart, because the images present complex morphologies and their background is inhomogeneous due to the presence of other anatomic elements and artifacts such as catheters. The literature presents a wide array of coronary tree extraction methods: some apply pattern recognition techniques based on pure intensity, such as thresholding followed by an analysis of connected components, whereas others apply explicit vessel models to extract the vessel contours. Depending on the quality and noise of the image, some segmentation methods may require image preprocessing prior to the segmentation algorithm; others may need postprocessing operations to eliminate the effects of a possible oversegmentation. The techniques and algorithms for vascular segmentation could be categorized as follows (Kirbas, Quek, 2004): 1. Techniques for “pattern-matching” or pattern recognition 2. Techniques based on models 3. Techniques based on tracking 4. Techniques based on artificial intelligence 5. Main Focus This section describes the main features of the most commonly accepted coronary tree segmentation techniques. These techniques automatically detect objects and their characteristics, which is an easy and immediate task for humans, but an extremely complex process for artificial computational systems.

Techniques Based on Pattern Recognition The pattern recognition approaches can be classified into four major categories:

Multiscale Methods The multiscale method extracts the vessel method by means of images of varying resolutions. The main advantage of this technique resides in its high speed. Larger structures such as main arteries are extracted by segmenting low resolution images, whereas smaller structures are obtained through high resolution images.

Methods Based on Skeletons The purpose of these methods is to obtain a skeleton of the coronary tree: a structure of smaller dimensions than the original that preserves the topological properties and the general shape of the detected object. Skeletons based on curves are generally used to reconstruct vascular structures (Nyström, Sanniti di Baja & Svensson, 2001). Skeletonizing algorithms are also called “thinning algorithms”. The first step of the process is to detect the central axis of the vessels or “centerline”. This axis is an imaginary line that follows each vessel in its central axis, i.e. two normal segments that cross the axis in opposite sense should present the same distance from the vessel’s edges. The total of these lines constitutes the skeleton of the coronary tree. The methods that are used to detect the central axes can be classified into three categories:

Methods Based on Crests One of the first methods to segment angiographic images on the basis of crests was proposed by Guo and Richardson (Guo & Ritchardson, 1998). This method treats angiographies as topographic maps in which the detected crests constitute the central axes of the vessels. The image is preprocessed by means of a median filter and smoothened with non-linear diffusion. The region of interest is then selected through thresholding, a process that eliminates

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the crests that do not correspond with the central axes. Finally, the candidate central axes are joined with curve relaxation techniques.

Methods Based on Regions Growth Taking a known point as seed point, these techniques segment images through the incremental inclusion of pixels in a region on the basis of an a priori established criterion. There are two especially important criteria: similitude in the value, and spatial proximity (Jain, Kasturi & Schunck, 1995). It is established that pixels that are sufficiently near others with similar grey levels belong to the same object. The main disadvantage of this method is that it requires the intervention of the user to determine the seed points. (Figure 1) O’Brien and Ezquerra (O’Brien & Ezquerra, 1994) propose the automatic extraction of the coronary vessels in angiograms on the basis of temporary, spatial, and structural restrictions. The algorithm starts with a low-pass filter and the user’s definition of a seed point. The system then starts to extract the central axes by means of the “globe test” mechanism, after which the detected regions are entangled through the graph theory. The applied test also allows us to discard the regions that are detected incorrectly and do not belong to the vascular tree.

Figure 1. Regions growth applied to an angiography

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Methods Based on Differential Geometry The methods that are based on differential geometry treat images as hypersurfaces and extract their features using curvature and surface crests. The points of hypersurface’s crest correspond to the central axis of the structure of a vessel. This method can be applied to bidimensional as well as tridimensional images; angiograms are bidimensional images and are therefore modelled as tridimensional hypersurfaces. Examples of reconstructions can be found in Prinet et al (Prinet, Mona & Rocchisani, 1995), who treat the images as parametric surfaces and extract their features by means of surfaces and crests.

Correspondence Filters Methods The correspondence filter approach convolutes the image with multiple correspondence filters so as to extract the regions of interest. The filters are designed to detect different sizes and orientations. Poli and Valli (Poli, R & Valli, 1997) apply this technique with an algorithm that details a series of multiorientation linear filters that are obtained as linear combinations of Gaussian “kernels”. These filters are sensitive to different vessel widths and orientations.

Angiographic Images Segmentation Techniques

Mao et al (Mao, Ruan, Bruno, Toumoulin, Collorec & Haigron, 1992) also use this type of filters in an algorithm based on visual perception models that affirm that the relevant parts of the objects in images with noise appear normally grouped.

Morphological Mathematical Methods Mathematical morphology defines a series of operators that apply structural elements to the images so that their morphological features can be preserved and irrelevant elements eliminated (Figure 2). The main morphological operations are the following: • • • • • •

Dilatation: Expands objects, fills up empty spaces, and connects disjunct regions. Erosion: Contracts objects, separates regions. Closure: Dilatation + Erosion. Opening: Erosion + Dilatation. “Top hat” transformation: Extracts the structures with a linear shape “Watershed” transformation: “Inundates” the image that is taken as a topographic map, and extracts the parts that are not “flooded”.

Eiho and Qian (Eiho & Qian, 1997) use a purely morphological approach to define an algorithm that consists of the following steps:

1. Application of the “top hat” operator to emphasize the vessels 2. Erosion to eliminate the areas that do not correspond to vessels 3. Extraction of the tree from a point provided by the user and on the basis of grey levels. 4. Slimming down of the tree 5. Extraction of edges through “watershed” transformation

MODEL-BASED TECHNIQUES These approaches use explicit vessel models to extract the vascular tree. They can be divided into four categories: deformable models, parametric models, template correspondence models, and generalized cylinders.

Deformable Models Strategies based on deformable models can be classified in terms of the work by McInerney and Terzopoulos (McInerney & Terzopoulos, 1997). Algorithms that use deformable models (Merle, Finet, Lienard, & Magnin, 1997) are based on the progressive refining of an initial skeleton built with curves from a series of reference points: • •

Root points: Starting points for the coronary tree. Bifurcation points: Points where a main branch divides into a secundary branch.

Figure 2. Morphological operators applied to an angiography

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End points: Points where a tree branch ends.

•

These points have to be marked manually.

Deformable Parametric Models: Active Contours These models use a set of parametric curves that adjust to the object’s edges and are modified by both external forces, that foment deformation, and internal forces that resist change. The active contour models or “snakes” in particular are a special case of a more general technique that pretends to adjust deformable models by minimizing energy. (Figure 3) Klein et al. (Klein, Lee & Amini, 1997) propose an algorithm that uses “snakes” for 4D reconstruction: they trace the position of each point of the central axis of a skeleton in a sequence of angiograms.

Figure 3. “Snakes” applied to a blood vessel. http://vislab.cs.vt.edu/review/extraction.html

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Deformable Geometric Models These models are based on topographic models that are adapted for shape recognition. Malladi et al. (Malladi, Sethian & Vemuri, 1995) for instance adapt the “Level Set Method” (LSM) by representing an edge as a level zero set of a hypersurface of a superior order; the model evolves to reduce a metric defined by the restrictions of edges and curvature, but less rigidly than in the case of the “snakes”. This edge, which constitutes the zero level of the hypersurface, evolves by adjusting to the edges of the vessels, which is what we want to detect.

Propagation Methods Quek and Kirbas (Quek & Kirbas, 2001) developed a system of wave propagation combined with a backtracking mechanism to extract the vessels from angiographic images. This method basically labels each pixel according to its likeliness to belong to a vessel and then propagates a wave through the pixels that are labeled as belonging to the vessel; it is this wave that definitively extracts the vessels according to the local features it encounters. Approaches based on the correspondence of deformable templates: This approach tries to recognize structural models (templates) in an image by using a template as context, i.e. as a priori model. This template is generally represented as a set of nodes connected by a segment. The initial structure is deformed until it adjusts optimally to the structures that were observed in the image. Petrocelli et al. (Petrocelli, Manbeck, & Elion, 1993) describe a method based on deformable templates that also incorporates additional previous knowledge into the deformation process.

Angiographic Images Segmentation Techniques

Parametric Models These models are based on the a priori knowledge of the artery’s shape and are used to build models whose parameters depend on the profiles of the entire vessel; as such, they consider the global information of the artery instead of merely the local information. The value of these parameters is established after a learning process. The literature shows the use of models with circular sections (Shmueli, Brody, & Macovski, 1983) and spiral sections (Pappas, & Lim, 1984), because various studies by Brown, B. G., (Bolson, Frimer, & Dodge, 1977) (Brown, Bolson, Frimer & Dodge, 1982) show that sections of healthy arteries tend to be circular and sections with stenosis are usually elliptical. However, both circular and elliptical shapes fail to approach irregular shapes caused by pathologies or bifurcations. This model has been applied to the reconstruction of vascular structures with two angiograms (Pellot, Herment, Sigelle, Horain, Maitre & Peronneau, 1994), which is why both healthy and stenotic sections are modeled by means of ellipses. This model is subsequently deformed until it corresponds to the shape associated to the birth of a new branch or pathology.

Generalized Cylinder Models A generalized cylinder (GC) is a solid whose central axis is a 3D curve. Each point of that axis has a limited and closed section that is perpendicular to it. A CG is therefore defined in space by a spatial curve or axis and a function that defines the section in that axis. The section is usually an ellipse. Tecnically, GCs should be included in the parametric methods section, but the work that has been done in this field is so extense that it deserves its own category. The construction of the coronary tree model requires one single view to build the 2D tree and estimate the sections. However, there is no infor-

mation on the depth or the area of the sections, so a second projection will be required.

ARTERIAL TRACKING Contrary to the approaches based on pattern recognition, where local operators are applied to the entire image, techniques based on arterial follow-up are based on the application of local operators in an area that presumibly belongs to a vessel and that cover its length. From a given point of departure the operators detect the central axis and, by analyzing the pixels that are orthogonal to the tracking direction, the vessel’s edges. There are various methods to determine the central axis and the edges: some methods carry out a sequential tracking and incorporate connectivity information after a simple edge detection operation, other methods use this information to sequentially track the contours. There are also approaches based on the intensity of the crests, on fuzzy sets, or on the representation of graphs, where the purpose lies in finding the optimal road in the graph that represents the image. (Figure 4) Lu and Eiho (Lu, Eiho, 1993) have described a follow-up algorithm for the vascular edges in angiographies that considers the inclusion of branches and consists of three steps: 1. Edge detection 2. Branch search 3. Tracking of sequential contours The user must provide the point of departure, the direction, and the search range. The edge points are evaluated with a differential smoothening operator in a line that is perpendicular to the direction of the vessel. This operator also serves to detect the branches.

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Figure 4. Tracking applied to an angiography

TECHNIQUES BASED ON ARTIFICIAL INTELLIGENCE Approaches based on Artificial Intelligence use high-level knowledge to guide the segmentation and delineation of vascular structures and sometimes use different types of knowledge from various sources. One possibility (Smets, Verbeeck, Suetens, & Oosterlinck, 1988) is to use rules that codify knowledge on the morphology of blood vessels; these rules are then used to formulate a hierarchy with which to create the model. This type of system does not offer any good results in arterial bifurcations or in arteries with occlusions. Another approach (Stansfield, 1986) consists in formulating a rules-based Expert System to identify the arteries. During the first phase, the image is processed without making use of domain knowledge to extract segments of the vessels. It is only in the second phase that domain knowledge on cardiac anatomy and physiology is applied. The latter approach is more robust than the former; but it presents the inconvencience of not combining all the segments into one vascular structure.

FUTURE TRENDS It cannot be said that one technique has a more promising future than another, but the current tendency is to move away from the abovementioned

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classical segmentation algorithms towards 3D and even 4D reconstructions of the coronary tree. Other lines of research focus on obtaining angiograph images by means of new acquisition technologies such as Magnetic Resonance, Computarized High Speed Tomography, or two-armed angiograph devices that achieve two simultaneous projections in combination with the use of ultrasound intravascular devices. This type of acquisition simplifies the creation of tridimensional structures, either directly from the acquisition or after a simple processing of the bidimensional images.

REFERENCES Brown, B. G., Bolson, E., Frimer, M., & Dodge, H. T. (1977). Quantitative coronary arteriography. Circulation, 55, 329–337. Brown, B. G., Bolson, E., Frimer, M., & Dodge, H. T. (1982). Arteriographic assessment of coronary atherosclerosis. Arteriosclerosis (Dallas, Tex.), 2, 2–15. Eiho, S., & Qian, Y. (1997). Detection of coronary artery tree using morphological operator. Computers in Cardiology, 1997, 525–528. Gonzalez, R. C., & Woods, R. E. (1996). Digital Image Proccessing. Addison-Wesley Publishing Company, Inc. Reading, Massachusets, USA.

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Greenes, R. A., & Brinkley, K. F. (2001). Imaging Systems. De Medical informatics: computer applications in health care and biomedicine. Pp. 485 – 538. Second Edition. 2001. Ed. SpringerVerlag. New York. USA. Guo, D., & Richardson, P. (1998). Automatic vessel extraction from angiogram images. Computers in Cardiology, 1998, 441–444. Jain, R. C., Kasturi, R., & Schunck, B. G. (1995). Machine Vision.McGraw-Hill. Kirbas, C., & Quek, F. (2004). A review of vessel extraction techniques and algorithms. ACM Computing Surveys, 36(2), 81–121. doi:10.1145/1031120.1031121 Klein, A. K., Lee, F., & Amini, A. A. (1997). Quantitative coronary angiography with deformable spline models. IEEE Transactions on Medical Imaging, 16(5), 468–482. doi:10.1109/42.640737 Lu, S., & Eiho, S. (1993). Automatic detection of the coronary arterial contours with sub-branches from an x-ray angiogram.In Computers in Cardiology 1993. Proceedings., 575-578. Malladi, R., Sethian, J. A., & Vemuri, B. C. (1995). Shape modeling with front propagation: a level set approach. Pattern Analysis and Machine Intelligence. IEEE Transactions on, 17, 158–175. Mao, F., Ruan, S., Bruno, A., Toumoulin, C., Collorec, R., & Haigron, P. (1992). Extraction of structural features in digital subtraction angiography. Biomedical Engineering Days, 1992.,Proceedings of the 1992 International, 166-169. McInerney, T., & Terzopoulos, D. (1997). Medical image segmentation using topologically adaptable surfaces. In CVRMedMRCAS ‘97: Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery, 23-32, London, UK, Springer-Verlag.

Nyström, I., Sanniti di Baja, G., & Svensson, S. (2001). Representing volumetric vascular structures using curve skeletons. In Edoardo Ardizzone and Vito Di Gesµu, editors, Proceedings of 11th International Conference on Image Analysis and Processing (ICIAP 2001), 495-500, Palermo, Italy, IEEE Computer Society. O’Brien, J. F., & Ezquerra, N. F. (1994). Automated segmentation of coronary vessels in angiographic image sequences utilizing temporal, spatial and structural constraints. (Technical report), Georgia Institute of Technology. Pappas, T. N., & Lim, J. S. (1984). Estimation of coronary artery boundaries in angiograms. Appl. Digital Image Processing VII, 504, 312–321. Pellot, C., Herment, A., Sigelle, M., Horain, P., Maitre, H., & Peronneau, P. (1994). A 3d reconstruction of vascular structures from two x-ray angiograms using an adapted simulated annealing algorithm. Medical Imaging. IEEE Transactions on, 13, 48–60. Petrocelli, R. R., Manbeck, K. M., & Elion, J. L. (1993). Three dimensional structure recognition in digital angiograms using gauss-markov methods. In Computers in Cardiology 1993. Proceedings., 101-104. Poli, R., & Valli, G. (1997). An algorithm for realtime vessel enhancement and detection. Computer Methods and Programs in Biomedicine, 52, 1–22. doi:10.1016/S0169-2607(96)01773-7 Prinet, V., Mona, O., & Rocchisani, J. M. (1995). Multi-dimensional vessels extraction using crest lines. In Engineering in Medicine and Biology Society, 1995. IEEE 17th Annual Conference, 1:393-394. Quek, F. H. K., & Kirbas, C. (2001). Simulated wave propagation and traceback in vascular extraction. In Medical Imaging and Augmented Reality, 2001. Proceedings. International Worksho, 229-234.

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Shmueli, K., Brody, W. R., & Macovski, A. (1983). Estimation of blood vessel boundaries in x-ray images. Optical Engineering (Redondo Beach, Calif.), 22, 110–116. Smets, C., Verbeeck, G., Suetens, P., & Oosterlinck, A. (1988). A knowledge-based system for the delineation of blood vessels on subtraction angiograms. Pattern Recognition Letters, 8(2), 113–121. doi:10.1016/0167-8655(88)90052-9 Stansfield, S. A. (1986). Angy: A rule-based expert system for automatic segmentation of coronary vessels from digital subtracted angiograms. PAMI, 8(3), 188–199.

KEY TERMS AND DEFINITIONS Angiography: Image of blood vessels obtained by any possible procedure. Artery: Each of the vessels that take the blood from the heart to the other bodyparts. Computerized Tomography: Exploration of X-rays that produces detailed images of axial cuts of the body. A CT obtains many images by rotating around the body. A computer combines all these images into a final image that represents the bodycut like a slice.

Expert System: Computer or computer program that can give responses that are similar to those of an expert. Segmentation: In computer vision, segmentation refers to the process of partitioning a digital image into multiple regions. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (structures) in images, in this case, the coronary tree in digital angiography frames. Stenosis: A stenosis is an abnormal narrowing in a blood vessel or other tubular organ or structure. A coronary artery that’s constricted or narrowed is called stenosed. Buildup of fat, cholesterol and other substances over time may clog the artery. Many heart attacks are caused by a complete blockage of a vessel in the heart, called a coronary artery. Thresholding: A technique for the processing of digital images that consists in applying a certain property or operation to those pixels whose intensity value exceeds a defined threshold.

This work was previously published in Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, Julian Dorado and Alejandro Pazos, pp. 110-117, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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Chapter 2.10

Segmentation Methods in Ultrasound Images Farhang Sahba Medical Imaging Analyst, Canada

ABSTRACT Ultrasound imaging now has widespread clinical use. It involves exposing a part of the body to high-frequency sound waves in order to generate images of the inside of the body. Because it is a real-time procedure, the ultrasound images show the movement of the body’s internal structure as well. It is usually a painless medical test and its procedures seem to be safe. Despite recent improvement in the quality of information from an ultrasound device, these images are still a challenging case for segmentation. Thus, there is much interest in understanding how to apply an image segmentation task to ultrasound data and any improvements in this regard are desirable. DOI: 10.4018/978-1-60960-561-2.ch210

Many methods have been introduced in existing literature to facilitate more accurate automatic or semi-automatic segmentation of ultrasound images. This chapter is a basic review of the works on ultrasound image segmentation classified by application areas, including segmentation of prostate transrectal ultrasound (TRUS), breast ultrasound, and intravascular ultrasound (IVUS) images.

INTRODUCTION Among different image modalities, ultrasound imaging is one of the most widely used technologies for the diagnosis and treatment of diseases such as breast and prostate cancer. Ultrasound

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Segmentation Methods in Ultrasound Images

equipment is less expensive to purchase and maintain than many other imaging systems such as X-ray, computed tomography (CT), or magnetic resonance imaging (MRI). These images are the result of reflection, refraction, and deflection of ultrasound beams from different types of tissue with different acoustic impedances. The detection of the object boundaries in such images is crucial for diagnostic and classification purposes. However, attenuation, speckle, shadows, and signal dropout can result in missing or diffused boundaries. Also the contrast between areas of interest is often low. These obstacles make segmentation of these images a challenge. Further complications arise when the quality of the image is influenced by the type and particular settings of the machine. Despite these factors, ultrasound imaging still remains an important tool for clinical applications and any effort to improve segmentation of these images is highly desirable. Thus, there is currently an interest in understanding how to apply image segmentation to ultrasound data. Figure 1 demonstrates the basic principle of an ultrasound imaging transducer. Using an ultrasound transducer, a pulse of energy is transmitted into the body along the path shown by line 1. After this beam encounters any surface, including tissue or structures within an organ, a part of the transmitted energy is backscattered along the original trajectory and received by the transducer which now acts as a receiver. These returning waves are converted to electrical signals, amplified, and finally shown. After that, the direction of the transmitted beam changes to attain the data from the next line close to the first one. The ultrasound transducer repeats the same procedure to cover 64-256 lines and makes the entire image (Webb, 2003). This chapter contains an overview of the ideas representing the ultrasound segmentation problem in particular clinical applications.

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Figure 1. The basic principle of an ultrasound imaging transducer (© 2003 IEEE, Reprinted, with permission from IEEE Press Series in Biomedical Engineering 2003. “Introduction to Biomedical Imaging”, by A. Webb).

BACKGROUND Many methods have been introduced to facilitate more accurate segmentation of ultrasound images. The performance of these methods is generally improved through the use of expertise or prior knowledge. All segmentation methods usually require at least some user interaction to adjust critical parameters. The type of user interaction varies, depending on the amount of time and effort required from the user. This chapter is a review of ultrasound image segmentation methods and focuses on clinical applications that have been investigated in different clinical domains. It centers on reviewing the ideas behind the incorporated knowledge of ultrasound physics such as speckle structure, as well as prior information about the intensity or shape model. We review some principal works in this area according to their applications where the majority of efforts have been focused. These include segmentation of prostate transrectal ultrasound (TRUS), breast ultrasound, and intravascular ultrasound (IVUS) images.

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ULTRASOUND IMAGE SEGMENTATION ACCORDING TO CLINICAL APPLICATIONS Based on clinical application, ultrasound image segmentation can be categorized in various groups. In this section, we mention some important methods in each group.

Prostate Segmentation Prostate cancer is one of the most frequently diagnosed malignancies in the adult and aging male population (Mettlin,1995). Ultrasound imaging is a widely used technology for the detection and intervention of this cancer and may help to reduce death rate if used in early stages. As the prostate boundaries play an important role in the diagnosis and treatment of prostate cancer, it is crucial for many clinical applications to accurately detect them. These applications include the accurate placement of needles during the biopsy, accurate prostate volume measurement from multiple frames, constructing anatomical models used in treatment planning, and estimation of tumor border. These images are the result of reflection, refraction, and deflection of ultrasound beams from different types of tissues with different acoustic impedances (Insana et al., 1993). Some factors, such as poor contrast, speckle, and weak edges, however, make the ultrasound images inherently difficult to segment. Furthermore, the quality of the image may be influenced by the type and particular settings of the machine. Currently, the prostate boundaries are generally extracted from TRUS images (Insana et al., 1993). This kind of imaging has been a fundamental tool for prostate cancer diagnosis and treatment. Prostate boundaries must generally be outlined in 2D TRUS image slices along the length of the prostate. But as previously mentioned, the signal-to-noise ratio in these images is very low. Therefore, traditional edge detectors fail to extract the correct boundaries.

Consequently, many methods have been developed to facilitate automatic or semi-automatic segmentation of the prostate boundaries from the ultrasound images. Figure 2 shows segmentation of some sample ultrasound images of the prostate using the method presented by Nanayakkara et al. (2006); it contains the ground truth boundaries drawn by an expert as well as contours generated by the computerized method. Knoll et al. (1999) proposed a technique for elastic deformation of closed planar curves restricted to particular object shapes. Their method is based on a one-dimensional dyadic wavelet transform as a multi-scale contour parameterization technique to constrain the shape of the prostate model. Important edges at multiple resolutions are extracted as the first step. Then a template matching procedure is used to obtain an initial shape of the contour. The shape of the contour is constrained to predefined models during deformation. While they reported that the method provides an accurate and fully automatic segmentation of 2D objects, the dependence of the statistically derived prior model has limited its capability for segmentation of aberrant shapes. Richard et al. (1996) presented a texturebased algorithm for prostate segmentation. This method segments a set of parallel 2D images of the prostate into prostate and non-prostate regions to form a 3D image. This algorithm is a pixel classifier which classifies each pixel of an ultrasound image using four associated texture energy measures. One of the drawbacks of this approach is that the number of clusters cannot be predicted for an image; therefore, the resulting image may be represented by a set of disconnected regions and no post-processing technique is proposed for preventing or overcoming the problem of such discontinuities. Ladak et al. (2000) proposed a cubic spline interpolation technique for semi-automatic segmentation of the prostate. The algorithm uses an initial contour based on four points given by the user. The user selects four points around the pros-

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Figure 2. Four samples for prostate boundary detection. The solid line shows the contour generated by the computerized method and the dotted line shows the corresponding manual outline (Reprinted (partially), from Physics in Medicine and Biology, 51, N.D Nanayakkara, J. Samarabandu, and A. Fenster, “Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations”, pp. 1831–1848, 2006, with permission from IOP publishing Ltd).

tate and then uses the discrete dynamic contour. A model is used to refine the boundary. Although this semi-automatic algorithm can segment a wide range of prostate images, at least four initial points must be defined accurately by the user (radiologist). In addition, it is less satisfactory when the prostate has an irregular shape and cannot be perfectly approximated by the initial points. For such cases, further human intervention is required to achieve satisfactory results. Wang et al. (2003) presented two methods for semi-automatic three-dimensional (3D) prostate boundary segmentation using 2D ultrasound images. The segmentation process is initiated by manually placing four points on the boundary of a selected slice. Then an initial prostate boundary is determined. It is refined using the discrete dynamic contour until it fits the actual prostate boundary. The remaining slices are then segmented by iteratively propagating the results to other slices and implementing the refinement.

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Hu et al. (2003) proposed an algorithm for semi-automatic segmentation of the prostate from 3D ultrasound images. In this method, the authors use model-based initialization and mesh refinement using deformable models. Six points are required to initialize the outline of the prostate using shape information. The initial outline is then automatically deformed to better fit the prostate boundary. Chiu et al. (2004) introduced a semi-automatic segmentation algorithm based on the dyadic wavelet transform and the discrete dynamic contours. In this method, a spline interpolation is first used to determine the initial contour based on four userdefined initial points. Then the discrete dynamic contour refines the initial contour based on the approximate coefficients as well as the wavelet coefficients generated using the dyadic wavelet transform. A selection rule is also used to choose the best contour.

Segmentation Methods in Ultrasound Images

Abolmaesumi et al. (2004) used an interactive multi-model probabilistic data association filter to extract prostate contours from transrectal ultrasound images. As the first step, a Sticks filter is used to enhance the image. The problem is then addressed by considering several equally spaced radii from a seed point towards the boundary of the prostate. In this method, the border of the prostate is represented as the trajectory of a moving object. This motion is modeled using a set of dynamical models where their measurement points are presented as candidate edge points along each radius. As the method does not use any numerical technique, their results show that the convergence is also fast. Pathak et al. (2000) proposed an edge-guided boundary delineation algorithm for prostate segmentation provided as a visual guide to the observer. This step is followed by manual editing. For automatic edge detection, the algorithm first uses a Sticks filter to enhance contrast and reduce speckle. Then, an anisotropic diffusion filter is applied to smooth the result of the previous stage. Finally, some basic prior knowledge such as shape and echo pattern is used to extract the most probable edges. After these stages, by using a manual linking procedure on the detected edges, the final boundary is indicated. Shen et al. (2003) presented a statistical shape model for segmentation of the prostate in ultrasound images. A Gabor filter bank is employed in both multiple scales and multiple orientations to represent the image characteristics around the prostate boundaries. As these features give both edge directions and edge strengths, they can provide the information for deformation of the prostate model. This strategy can generate both coarse and fine image features that further allow the model to focus on particular features at different deformation steps. To make the proposed method more robust against local minima, several hierarchical deformation strategies are proposed as well. In another work, the authors have also introduced an adaptive focus deformable model,

which uses the concept of an attribute vector (Shen et al., 2001). Figure 3 shows segmentation of an ultrasound image of the prostate using the method presented in (Shen et al., 2003). This figure demonstrates the results of the algorithm in the different steps (iterations). As can be seen, the method is robust with respect to a bad initialization. Betrounia et al. (2005) proposed a method for the automatic segmentation of trans-abdominal ultrasound images. Adaptive morphological and median filtering are employed together to detect the noisy areas and smooth them. Using this method, the contours of the prostate can be enhanced without changing the critical information in the image. An optimization algorithm is then employed to search for the contour initialized from a prostate model. The algorithm has been shown to have accurate results in terms of average distance and average coverage index in comparison to those obtained by manual segmentation.

Breast Cancer Breast cancer is one of the leading causes of death in women. Along with digital mammography, ultrasound has been one of the most commonly used methods for early detection and diagnosis of breast cancer in the last decade. Ultrasound can help to distinguish whether a mass is benign or malignant (cancerous). Automatic segmentation of breast tumors using ultrasound imaging can assist physicians in making faster and more accurate diagnoses (Noble et al., 2006). The problem with segmenting ultrasonic breast images is mostly the variance of the lesion’s shape and the fact that often the borders of the lesion are not well distinguished. Figure 4 demonstrates three different manually segmented ultrasonic breast lesions with great differences in their shapes and sizes as represented in (Madabhushi et al., 2003). Huang et al. (2004) proposed an approach that combines a neural network classifier with a morphological watershed segmentation method to

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Figure 3. Demonstration of the algorithm represented in (Shen et al., 2003). The dashed contour indicates the manually segmented prostate. The solid contours show the resulting the automatic algorithm in the different steps (iterations). The final segmentation results are shown in the right-bottom corner; a case of the bad initialization. (© 2003 IEEE, Reprinted with permission from IEEE Transaction on Medical Imaging, 2003, 22(4), pp. 539-551, “Segmentation of Prostate Boundaries from Ultrasound Images Using Statistical Shape Model”, by D. Shen, Y. Zhan and C. Davatzikos).

Figure 4. Three manually segmented tumors with different shapes and sizes (© 2003 IEEE, Reprinted with permission from ╯IEEE Transaction on Medical Imaging, 2003, 22(2), pp. 155–169, “Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions”, by A. Madabhushi and D. N. Metaxas).

extract precise contours of a breast tumor from ultrasound images. Textural analysis is employed to generate the inputs of the neural network to classify US images. The features of the texture contain auto covariance coefficients to classify US images using a self-organizing map. After

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these features have been classified, an adaptive preprocessing procedure is selected by neural network output and then watershed transformation automatically determines the contours of the tumor. This method contains a preprocessing step which helps the watershed algorithm through a

Segmentation Methods in Ultrasound Images

good selection of markers. The self-organizing map is used in order to select the appropriate preprocessing filter locally from a set of nine predefined filters. Figure 5 demonstrates two malignant and benign cases of breast ultrasound images. In this figure, the first, second, and third rows show the original magnified monochrome breast image, the contours manually sketched, and the contours determined by the proposed system in (Huang et al., 2004), respectively. Chen et al. (2002) presented a method based on a neural network. The aim of this method is to make the classification based on a set of input features. These features are variance contrast, autocorrelation contrast, and the distribution

distortion in the wavelet coefficients. These are inputs of a multilayer perceptron neural network with one hidden layer which is trained by error backpropagation. Image texture is an important component in their method. Xiao et al. (2002) discussed a method for simultaneous estimation of video-intensity inhomogeities and segmentation of US image tissue regions. The number of regions (classes) needs to be specified. It employs a combination of the maximum a posteriori (MAP) and Markov random field (MRF) methods to estimate the US image distortion field. The approach shows consistent segmentations under different time gain compensation (TGC) settings on the tested data.

Figure 5. Contour segmentation. The first row: Original magnified monochrome breast ultrasound images (left malignant and right benign case); the second row, manual sketch contour; and the third row, automatic sketch contour. (Reprinted from Ultrasound in Medicine & Biology, 30(5), Y. L Huang and D. R. Chen, “Watershed segmentation for breast tumor in 2-D sonography”, pp. 625–632, 2004, with permission from Elsevier).

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Horsch et al. (2001) introduced a segmentation technique that is based on maximizing a utility function over partition margins which are defined through gray-value thresholding of a preprocessed image that has enhanced mass structures. It requires the manually defined lesion center. The problem of shadowing is not discussed. Shape, echogeneity, margin, and posterior acoustic behavior are computed as four features to test the effectiveness when directed at distinguished malignant and benign masses. The authors have further evaluated their method in (Horsch et al., 2004) to assess the advantages of the different mentioned features using linear discriminant analysis. It is shown that the two best features are depth-to-width ratio for shape and normalized radial gradient for margin. Sahiner et al. (2004) developed 2D and 3D intensity-gradient active contour models for automated segmentation of the mass volumes. For initialization of the active contour, texture and morphological features were automatically extracted from the segmented masses and their margins. Algorithm parameters were determined empirically. To find the segmentation result, depth to width ratio, a posterior shadowing feature measure, and texture features were computed around the boundary of each 2D slice and then linear discriminant analysis was used to classify volumes. Madabhushi et al. (2003) proposed a method that uses the combination of intensity and texture with empirical domain-specific knowledge, along with directional gradient and a deformable shape-based model. A second-order Butterworth filter is used to remove speckle noise, and then the contrast of the tumor regions is enhanced. The image pixels are probabilistically classified based on intensity and texture information and then a region growing technique is used to obtain an initial segmentation of the lesion. The boundary points are found to supply an initial estimate to a deformable model. It attempts to limit the effects of shadowing and false positives by incorporat-

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ing empirical domain-specific knowledge. The method requires a small database for training. There are also some other methods that take into account the domain knowledge and consider the specification of ultrasound images such as speckle or artifacts.

Intravascular Ultrasound Intravascular Ultrasound (IVUS) is a non-invasive technique which provides real-time high-resolution images with valuable anatomical information about the coronary arterial wall and plaque. IVUS consequently provides new insights into the diagnosis and therapy of coronary disease. IVUS imaging can be used as a complementary imaging tool to contrast X-ray angiography (Radeva et al., 2003), (Noble et al., 2006). Due to the ultrasound speckle, catheter artifacts, or calcification shadows, processing of IVUS image sets is a challenge. To perform an accurate quantitative analysis, a good segmentation of the lumen, the plaque, and the wall borders is required. Methods used for IVUS segmentation usually apply contour modeling (Kovalski et al., 2000; Sonka et al., 1995; Cardinal et al., 2003; Olszewski et al., 2004; Noble et al., 2006). Figure 6 shows a schematic form of a cross-sectional anatomy of a diseased coronary artery (Sonka et al., 1995). Cardinal et al. (2003) presented a three-dimensional IVUS segmentation model based on the fast-marching method and gray level probability density functions of the vessel wall structures. A mixture of Rayleigh PDFs model the gray level distribution of the whole IVUS pullback. Using this method, the lumen, intima plus plaque structure, and media layers of the vessel wall were computed simultaneously. The results were obtained with average point to point distances between segmented vessel wall borders and ground truth. The authors have also shown the potential of gray level PDF and fastmarching methods in 3D IVUS image processing.

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Figure 6. A schematic cross-sectional anatomy of a diseased coronary vessel. (©1995 IEEE, Reprinted with permission from IEEE Transaction on Medical Imaging, 1995, 14(4), pp. 719–732, “Segmentation of intravascular ultrasound images: A knowledge-based approach”, by M. Sonka, X. M. Zhang, M. Siebes, M. S. Bissing, S. C. DeJong, S. M. Collins and C. R. McKay).

Sonka et al. (1995) introduced a semi-automatic knowledge-based method for segmentation of intravascular ultrasound images which identifies the internal and external elastic laminae and the plaque-lumen interface. This approach attempts to incorporate a priori knowledge about crosssectional arterial anatomy, such as objectshape, edge direction, double echo pattern, and wall thickness. The method uses a cost function based on edge strength to find the border. However, the method does not take into account speckle statistics. To assess the performance of the method, they compared five quantitative measures of arterial anatomy derived from borders extracted by the algorithm, with measures derived from borders manually indicated by an expert. Shekhar et al. (1999) developed a threedimensional segmentation technique, called active surface segmentation, for semi-automatic segmentation of the lumen and adventitial borders in serial IVUS images in examinations of coronary arteries. The authors also presented a faster method, based on a fast active contours technique

(a neighborhood-search method) (Klingensmith et al., 2000). Both of their works used only intensity gradient information for snakes. The technique was assessed by computing correlation coefficients and by comparing the results to the expert tracings. Takagi et al. (2000) presented an automated contour analysis assisted by a blood noise reduction algorithm used as preprocessing step. Subtraction of two consecutive IVUS images acquired at the same position in time can increase the signal-to-noise ratio of the lumen area, i.e., obtain good contrast between the lumen and other parts in the image. As the blood echo speckles have higher temporal and spatial variations than the arterial wall, an adaptive filtering of speckle was applied based on pre-segmentation of blood and tissue areas. Their preliminary results showed that automated contour detection facilitated with a blood noise reduction algorithm appeared to be a reliable technique for area measurements in 40-MHz IVUS imaging. Kovalski et al. (2000) developed an algorithm to identify the lumen border and the media-adventitia border (the external elastic membrane). To represent the contours in this method, a 3D balloon model is recruited by using polar coordinates. In this representation, the control points can only move along the radial direction. The reported inter-observer area variability is comparable to the result in (Klingensmith et al., 2003) except that the method in (Kovalski et al., 2000) is automatic. Figure 7 illustrates three sets of images with the lumen and media-adventitia tracings with shadowing artifacts from (Kovalski et al., 2000). The left, middle, and right columns demonstrate the original image, the manual contours, and the result of automatic procedures, respectively. The features are extracted in three dimensions. The results for border contours obtained by this method were compared to the manually extracted results. The method suffers from some tuning of the algorithm’s parameters when using different scanners.

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Figure 7. The comparison between the manual and the automatic tracings of the lumen and mediaadventitia for three different slices. Left: Original image; middle: Manual tracing and right: automatic detection. (Reprinted from Ultrasound in Medicine & Biology, 26(4), G. Kovalski, R. Beyar, R. Shofti, and H. Azhari, “Three-dimensional automatic quantitative analysis of intravascular ultrasound images”, pp. 527–537, 2000, with permission from Elsevier).

Haas et al. (2000) used an algorithm based on the optimization of a maximum a posteriori estimator. It implements an image generation model based on the Rayleigh distribution of the image pixels and a priori information about the contours. Using the property of the closed contours, they are modeled by first-order Markov random fields, expressing a strong correlation between a contour point and its two next neighbors. During the first stage, the algorithm detected the reliable points and then chose either the global or the first maximum along the radial direction depending on the contour prior energy. Dynamic programming is used to accelerate the estimation process. Additional information from the blood flow is used to initialize the segmentation in 3D image sets. Pardo et al. (2003) proposed a statistical deformable model for 3D segmentation of anatomical organs applied to IVUS images. A bank of Gaussian derivative filters is used to generate a feature space at different orientations and scales, to locally describe each part of the object

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boundary. The feature space was reduced by linear discriminant analysis and a statistic discriminant snake is used in a supervised learning scheme to guide the model deformation. It is performed by minimizing the dissimilarity between the learned and found image features. The proposed approach is of particular interest for tracking temporal image sequences. Anatomical organs including IVUS are segmented and the results compared to expert tracings for validation. As one of the approaches incorporating highlevel knowledge in IVUS image segmentation, Olszewski et al. (2004) proposed a learning technique based on the human visual system. This method mimics the procedure performed by human experts for automatic detection of the luminal and the medial-adventitial borders. The approach requires no manual initialization or interaction. To verify the accuracy of the method, it is applied on a reasonable data set. A key drawback for Intravascular Ultrasound imaging is its inability to consider the vessel cur-

Segmentation Methods in Ultrasound Images

vature and the orientation of the imaging catheter (Noble et al., 2006). Therefore, the information extracted from this data is distorted, since the vessel curvature remains unconsidered. An answer for correct 3D reconstruction of the IVUS can be derived from the fusion between intravascular ultrasound images and biplane angiography (Noble et al., 2006).

FUTURE TRENDS Ultrasonic imaging stands as one of the most important medical imaging applications of physics and engineering. There have been recent advances in transducer technology and image formation procedure which significantly improve the quality of information obtained from ultrasound devices. There is a need for more effort in the area of segmentation validation to better evaluate the strengths and limitations of the existing methods on larger and more varied databases of images. This would be especially useful for the adoption of methods which are more appropriate in clinical applications. Future efforts for ultrasound segmentation methods should also more effectively consider imaging physics. Basically, a 3D ultrasound image can be constructed from 2D slices which are put together. For such an application, the size and shape of the object of interest is to be extracted in each slice. This means that a fast and reliable segmentation method for individual slices is needed. Considering the correlation between the sequential frames, this can be subjected to further research. 4D ultrasound system provides a next generation for medical imaging applications which allow faster diagnosis and improve treatment success rates. A 4D ultrasound device takes multiple images in rapid succession and creates a 3D motion video, which is very valuable for diagnosis purposes. Any effort towards applying current segmentation methods in these successive frames would be desirable.

CONCLUSION Ultrasound imaging is a non-invasive, easily portable, and relatively inexpensive image modality that has been used for clinical applications for a long time. Ultrasound imaging can be used to visualize the anatomy of the body organs. It also has excellent temporal resolution. In this technique, by exposing a part of the body to high-frequency sound waves, we can generate images of the inside of the body. On the other hand, segmentation is an important image processing task that partitions the image into meaningful regions. These regions are homogeneous with respect to specific characteristics or features such as gray level, texture, etc. Segmentation is an important tool in medical imaging and it is useful for many applications such as feature extraction and classification. Although ultrasound imaging is one of the most widely used technologies for diagnosis and treatment, it is still a challenging case for segmentation tasks due to attenuation, speckle, shadows, and signal dropout. Improvements in this area of research are thus highly desirable. Many methods have been introduced in existing literature to facilitate more accurate automatic or semi-automatic segmentation of ultrasound images. This chapter reviews the ultrasound image segmentation methods by focusing on clinical applications that contain important ideas demonstrating significant clinical usefulness. The focus of this chapter is on reviewing the works which have incorporated prostate transrectal ultrasound (TRUS), breast ultrasound, and intravascular ultrasound (IVUS) images. The future trend for ultrasound image segmentation can focus on improvement of the current methods such that they can be adopted for real clinical applications. Development of new and effective methods for construction of 3D images from 2D slices is also another interesting area of research.

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As the next generation of ultrasound imaging devices, the 4D ultrasound system should be positioned as a top research area for image segmentation.

REFERENCES Abolmaesumi, P., & Sirouspour, M.R. (2004). Segmentation of prostate contours from ultrasound images. ICASSP04, 3, 517-520. Betrounia, N., Vermandela, M., Pasquierc, D., Maoucheb, S., & Rousseaua, J. (2005). Segmentation of abdominal ultrasound images of the prostate using a priori information and an adapted noise filter. Computerized Medical Imaging and Graphics, 29, 43–51. doi:10.1016/j.compmedimag.2004.07.007 Cardinal, M. H. R., Meunier, J., Soulez, G., Thrasse, E., & Cloutier, G. (2003). Intravascular ultrasound image segmentation: A fastmarching method. Medical Image Computing and Computer Assisted Intervention, Lecture Note Computer Science. Berlin: Springer-Verlag, (pp. 432–439). Chen, D. R., Chang, R. F., Kuo, W. J., Chen, M. C., & Huang, Y. L. (2002). Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks. Ultrasound in Medicine & Biology, 28(10), 1301–1310. doi:10.1016/S0301-5629(02)00620-8 Chiu, B., Freeman, G. H., Salama, M. M. A., & Fenster, A. (2004). Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour. Physics in Medicine and Biology, 49, 4943–4960. doi:10.1088/00319155/49/21/007 Haas, C., Ermert, H., Holt, S., Grewe, P., Machraoui, A., & Barmeyer, J. (2000). Segmentation of 3-D intravascular ultrasonic images based on a random field model. Ultrasound in Medicine & Biology, 26(2), 297–306. doi:10.1016/S03015629(99)00139-8 388

Horsch, K., Giger, M. L., Venta, L. A., & Vyborny, C. J. (2001). Automatic segmentation of breast lesions on ultrasound. Medical Physics, 28(8), 1652–1659. doi:10.1118/1.1386426 Horsch, K., Giger, M. L., Vyborny, C. J., & Venta, L. A. (2004). Performance of computer-aided diagnosis in the interpretation of lesions on breast sonography. Academic Radiology, 11(3), 272–280. doi:10.1016/S1076-6332(03)00719-0 Hu, N., Downey, D. B., Fenster, A., & Ladak, H. M. (2003). Prostate boundary segmentation from 3D ultrasound images. Medical Physics, 30(7), 1648–1659. doi:10.1118/1.1586267 Huang, Y. L., & Chen, D. R. (2004). Watershed segmentation for breast tumor in 2-D sonography. Ultrasound in Medicine & Biology, 30(5), 625– 632. doi:10.1016/j.ultrasmedbio.2003.12.001 Insana, M. F., & Brown, D. G. (1993). Acoustic scattering theory applied to soft biological tissues. In Ultrasonic Scattering in biological tissues, Boca Raton, CRC Press. Klingensmith, J. D., Shekhar, R., & Vince, D. G. (2000). Evaluation of three dimensional segmentation algorithms for the identification of luminal and medial-adventitial borders in intravascular ultrasound images. IEEE Transactions on Medical Imaging, 19(10), 996–1011. doi:10.1109/42.887615 Klingensmith, J. D., Tuzcu, E. M., Nissen, S. E., & Vince, D. G. (2003). Validation of an automated system for luminal and medial-adventitial border detection in three-dimensional intravascular ultrasound. The International Journal of Cardiovascular Imaging, 19(1), 93–104. Knoll, C., Alcaniz, M., Grau, V., Monserrat, C., & Juan, M. C. (1999). Outlining of the prostate using snakes with shape restrictions based on the wavelet transform. Pattern Recognition, 32, 1767–1781. doi:10.1016/S0031-3203(98)00177-0

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Kovalski, G., Beyar, R., Shofti, R., & Azhari, H. (2000). Three-dimensional automatic quantitative analysis of intravascular ultrasound images. Ultrasound in Medicine & Biology, 26(4), 527–537. doi:10.1016/S0301-5629(99)00167-2

Pathak, S. D., Chalana, V., Haynor, D. R., & Kim, Y. (2000). Edge-guided boundary delineation in prostate ultrasound images. IEEE Transactions on Medical Imaging, 19, 1211–1219. doi:10.1109/42.897813

Ladak, H. M., Mao, F., Wang, Y., Downey, D. B., Steinman, D. A., & Fenster, A. (2000). Prostate boundary segmentation from 2D ultrasound images. Medical Physics, 27, 1777–1788. doi:10.1118/1.1286722

Radeva, P., Suri, J. S., & Laxminarayan, S. (2003). Angiography and plaque imaging: Advanced segmentation techniques. Boca Raton, FL: CRC Press.

Madabhushi, A., & Metaxas, D. N. (2003). Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Transactions on Medical Imaging, 22(2), 155–169. doi:10.1109/ TMI.2002.808364 Mettlin, C. (1995). American society national cancer detection project. Cancer, 75, 1790–1794. doi:10.1002/10970142(19950401)75:7+<1790::AIDCNCR2820751607>3.0.CO;2-Z Nanayakkara, N. D., Samarabandu, J., & Fenster, A. (2006). Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations. Physics in Medicine and Biology, 51, 1831–1848. doi:10.1088/00319155/51/7/014 Noble, J. A., & Boukerroui, D. (2006). Ultrasound Image Segmentation: A Survey. IEEE Transactions on Medical Imaging, 25, 987–1010. doi:10.1109/TMI.2006.877092 Olszewski, M. E., Wahle, A., Mitchell, S. C., & Sonka, M. (2004). Segmentation of intravascular ultrasound images: A machine learning approach mimicking human vision. International Congress Series, 1268, 1045–1049. doi:10.1016/j. ics.2004.03.252

Richard, W. D., & Keen, C. G. (1996). Automated texture-based segmentation of ultrasound images of the prostate. Computerized Medical Imaging and Graphics, 20(3), 131–140. doi:10.1016/08956111(96)00048-1 Sahiner, B., Chan, H. P., Roubidoux, M. A., Helvie, M. A., Hadjiiski, L. M., & Ramachandran, A. (2004). Computerized characterization of breast masses on threedimensional ultrasound volumes. Medical Physics, 31(4), 744–754. doi:10.1118/1.1649531 Shekhar, R., Cothren, R. M., Vince, D. G., Chandra, S., Thomas, J. D., & Cornhill, J. F. (1999). Three-dimensional segmentation of luminal and adventitial borders in serial intravascular ultrasound images. Computerized Medical Imaging and Graphics, 23, 299–309. doi:10.1016/S08956111(99)00029-4 Shen, D., Herskovits, E. H., & Davatzikos, C. (2001). An adaptive-focus statistical shape model for segmentation and shape modeling of 3D brain structures. IEEE Transactions on Medical Imaging, 20, 257–270. doi:10.1109/42.921475 Shen, D., Zhan, Y., & Davatzikos, C. (2003). Segmentation of Prostate Boundaries From Ultrasound Images Using Statistical Shape Model. IEEE Transactions on Medical Imaging, 22(4), 539–551. doi:10.1109/TMI.2003.809057

Pardo, X. M., Radeva, P., & Cabello, D. (2003). Discriminant snakes for 3-D reconstruction of anatomical organs. Medical Image Analysis, 7(3), 293–310. doi:10.1016/S1361-8415(03)00014-8 389

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Sonka, M., Zhang, X. M., Siebes, M., Bissing, M. S., DeJong, S. C., Collins, S. M., & McKay, C. R. (1995). Segmentation of intravascular ultrasound images: A knowledge-based approach. IEEE Transactions on Medical Imaging, 14(4), 719–732. doi:10.1109/42.476113 Takagi, A., Hibi, K., Zhang, X., Teo, T. J., Bonneau, H. N., Yock, P. G., & Fitzgerald, P. J. (2000). Automated contour detection for highfrequency intravascular ultrasound imaging: A technique with blood noise reduction for edge enhancement. Ultrasound in Medicine & Biology, 26, 1033– 1041. doi:10.1016/S0301-5629(00)00251-9 Wang, Y., Cardinal, H. N., Downey, D. B., & Fenster, A. (2003). Semiautomatic three-dimensional segmentation of the prostate using two dimensional ultrasound images. Medical Physics, 30(5), 887–897. doi:10.1118/1.1568975 Webb, A. (2003). Introduction to Biomedical Imaging. IEEE Press Series in Biomedical Engineering.

Xiao, G. F., Brady, M., Noble, J. A., & Zhang, Y. Y. (2002). Segmentation of ultrasound B-mode images with intensity inhomogeneity correction. IEEE Transactions on Medical Imaging, 21(1), 48–57. doi:10.1109/42.981233

KEY TERMS AND DEFINITIONS Breast Ultrasound: Generation of ultrasound images from the breast for diagnostic and treatment purposes. Image Segmentation: This is an important image processing task that partitions the image into meaningful regions. Intravascular Ultrasound: This is an ultrasound medical imaging methodology that uses a catheter with a miniaturized ultrasound probe to visualize the inside walls of blood vessels. Prostate Ultrasound: Generation of ultrasound images from the prostate for diagnostic and treatment purposes. Ultrasound Imaging: This is an ultrasoundbased diagnostic imaging technique used for visualizing internal organs and their structures and possible lesions.

This work was previously published in Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications, edited by Themis P. Exarchos, Athanasios Papadopoulos and Dimitrios I. Fotiadis, pp. 473-487, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Exploring Type-and-IdentityBased Proxy Re-Encryption Scheme to Securely Manage Personal Health Records Luan Ibraimi University of Twente, The Netherlands Qiang Tang University of Twente, The Netherlands Pieter Hartel University of Twente, The Netherlands Willem Jonker University of Twente, The Netherlands

ABSTRACT Commercial Web-based Personal-Health Record (PHR) systems can help patients to share their personal health records (PHRs) anytime from anywhere. PHRs are very sensitive data and an inappropriate disclosure may cause serious problems to an individual. Therefore commercial Web-based PHR systems have to ensure that the patient health data is secured using state-of-theart mechanisms. In current commercial PHR DOI: 10.4018/978-1-60960-561-2.ch211

systems, even though patients have the power to define the access control policy on who can access their data, patients have to trust entirely the access-control manager of the commercial PHR system to properly enforce these policies. Therefore patients hesitate to upload their health data to these systems as the data is processed unencrypted on untrusted platforms. Recent proposals on enforcing access control policies exploit the use of encryption techniques to enforce access control policies. In such systems, information is stored in an encrypted form by the third party and there

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme

is no need for an access control manager. This implies that data remains confidential even if the database maintained by the third party is compromised. In this paper we propose a new encryption technique called a type-and-identity-based proxy re-encryption scheme which is suitable to be used in the healthcare setting. The proposed scheme allows users (patients) to securely store their PHRs on commercial Web-based PHRs, and securely share their PHRs with other users (doctors).

INTRODUCTION Recently, healthcare providers have started to use electronic health record systems which have significant benefits such as reducing healthcare costs, increasing the patient safety, improving the quality of care and empowering patients to more actively manage their health. There are a number of initiatives for adoption of electronic health records (EHRs) from different governments around the world, such as the directive on privacy and electronic communications in the U.S. known as the Health Insurance Portability and Accountability Act (HIPAA) (The US Department of Health and Human Services, 2003), which specify rules and standards to achieve security and privacy of health data. While EHR systems capture health data entered by health care professionals and access to health data is tightly controlled by existing legislations, personal health record (PHR) systems capture health data entered by individuals and stay outside the scope of this legislation. Before going into details on how to address the confidentiality issues, let us introduce the definition of PHR system (The personal health working group final report, 2004): “An electronic application through which individuals can access, manage and share their health information, and that of others for whom they are authorized, in a private, secure, and confidential environment.”

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PHR systems are unique in their design since they try to solve the problem that comes from scattering of medical information among many healthcare providers which leads to unnecessary paper work and medical mistakes (The personal health working group final report, 2004). The PHR contains all kinds of health-related information about an individual (say, Alice) (Tang, Ash, Bates, Overhage & Sands, 2006). Firstly, the PHR may contain medical data that Alice has from various medical service providers, for example about surgery, illness, family history, vaccinations, laboratory test results, allergies, drug reactions, etc. Secondly, the PHR may also contain information collected by Alice herself, for example weight change, food statistics, and any other information connected with her health. Controlling access to PHRs is one of the central themes in deploying a secure PHR system. Inappropriate disclosure of the PHRs may cause an individual serious problems. For example, if Alice has some disease and a prospective employer obtains this, then she might be discriminated in finding a job. Commercial efforts to build Web-based PHR systems, such as Microsoft HealthVault (Microsoft, 2007) and Google Health (Google, 2007), allow patients to store and share their PHRs with different healthcare providers. In these systems the patient has full control over her PHRs and plays the role of the security administrator - a patient decides who has the right to access which data. However, the access control model of these applications does not give a patient the flexibility to specify a fine-grained access-control policy. For example, today’s Google Health access control is all-or-nothing - so if a patient authorizes her doctor to see only one PHR, the doctor will be able to see all other PHRs. Another problem is that the data has to be stored on a central server locked by the access control mechanism provided by Microsoft HealthVault or Google Health, and the patient loses control once the data is sent to the server. PHRs may contain sensitive information such as details of a patients disease, drug usage,

Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme

sexual preferences, therefore, many patients are worried whether their PHRs will be treated as confidential by companies running data centers. Inappropriate disclosure of a PHR can change patients life, and there may be no way to repair such harm financially or technically. Therefore, it is crucial to protect PHRs when they are uploaded and stored in commercial Web-based systems.

PROBLEM STATEMENT The problem addressed in this paper is the confidentiality of patient PHRs stored in commercial Web-based PHR systems. A solution to the problem is a system which would have the following security requirements: •

• • •

Protect patient PHRs from third parties (from the commercial Web-based PHR systems) Provide end-to-end security Allow only authorized users to have access to the patient PHRs Allow the patient to change the access policy dynamically

Contributions To solve the identified problem we propose a new public key encryption scheme called typeand-identity-based proxy re-encryption which helps patients to store their PHRs on commercial Web-based PHR systems, and to share their

PHRs securely with doctors, family and friends. In public key encryption, each user has a key pair (private/public key) and everyone can encrypt a message using a public key, also referred to as the encryption key, but only users who have the associated private key, also referred to as the decryption key, can decrypt the encrypted message. Proxy re-encryption is a cryptographic method developed to delegate the decryption right from one party, the delegator, to another, the delegatee. In a proxy re-encryption scheme, the delegator assigns a key to a proxy to re-encrypt all messages encrypted with the public key of the delegator such that the re-encrypted ciphertexts can be decrypted with the private key of the delegatee. The Proxy is a semi-trusted entity i.e. it is trusted to perform only the ciphertext re-encryption, without knowing the private keys of the delegator and the delegatee, and without having access to the plain text. In the context of public key encryption, proxy reencryption is used to forward encrypted messages without revealing the plaintext. For example, in Figure 1, Alice (delegator) is president of a company who wants to allow her secretary, Bob (delegatee), to read encrypted emails from Charlie when Alice is on vacation (Alice cannot give to Bob her private key). Using proxy re-encryption Alice can compute a re-encryption key which would allow the Proxy to transform a ciphertext for Alice generated by Charlie into a ciphertext for Bob, thus, Bob can decrypt the re-encrypted data using his private key. In practice the role of the Proxy can be played by a commercial enterprise which has enough computation power to perform

Figure 1. Proxy re-encryption

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re-encryption services for a large number of users. Our proposed scheme is suitable for the healthcare setting and has the following benefits: •

•

•

Allow the patient to store her PHRs in a protected form on semitrusted commercial Web-based PHR system. The commercial Web-based PHR system cannot access the data since the data is stored in an encrypted form. Allow the patient to define a fine-grained access control policy. The patient only has to compute the re-encryption key and forward it the Proxy which will re-encrypt the data without decrypting them such that the intended user (e.g., doctor) can decrypt the re-encrypted data using his private key. In addition to that, the scheme allows the patient to change dynamically the access policy without necessarily decrypting the data. Allows the patient to categorize her messages into different types, and delegate the decryption right of each type to the doctor through a Proxy. Data categorization is needed since different data may have different levels of privacy requirements.

WEB-BASED PHRS In this section we discuss current Web-based PHRs systems such as Microsoft HealthVault and Google Health and their access control mechanisms. Moreover, we discuss existing techniques to enforce access policies using cryptography and discuss their limitations. In Microsoft HealthVault each patient has an account and an identifier. Each account has a special role called a custodian who has the right to view and modify all records, and grant or revoke access to users (e.g., doctors) and applications. Both users and applications have to be registered with HealthVault in order to access other accounts. An application is a third party service which can

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read data stored in HealthVault records, store new data, or transfer data from one account to another account. An application can access patient accounts in two ways: a) online access - access the account when the patient is logged on to her account, and b) offline access – access the account at any time. If the patient uses an application for the first time, and the application requires access to a health record, the application sends an access request to the patient. After the patient approves the request, the application can access the health information. HealthVault uses discretionary access control (DAC) (Graham and Denning, 1971) where access to a patient PHR is based on user identity (e.g. email). For example, if the patient wants to allow the doctor to see her record, the patient has to send a sharing invitation to the doctors’ email and within 3 days the doctor has to accept or reject the invitation. The doctor may have one of the following permissions: a) view patient information, b) view and modify patient information or, c) act as a custodian. Microsoft HealthVault defines 74 types of information and allows granular access control for non-custodians. For example, the patient can allow the doctor to have access only to records of type Allergy, and block access to records of type Family History. However, the patient does not have the flexibility to grant access to the doctor to an individual record. When the patient grants access to the doctor to her health information, the patient can also specify an expiration date. After the expiration date, the doctor would not be able to access the patient information. However, the patient has the option to remove sharing access at any time (even before the expiration date). Similar to Microsoft HealthVault, Google Health allows a patient to import her PHRs, add test results and add information about allergies, among others. In Google Health each patient has an account and an identifier and a user (e.g., doctor) has to be registered to Google Health to access others health information. If the patient wants to allow the doctor to see her PHRs, the patient has

Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme

to send an invitation to doctors’ email, and within 30 days the doctor has to accept or reject the invitation. A patient can share her PHRs with other users or Google partners including Walgreens, CVS Long Drugs pharmacies, Cleveland Clinic and etc. However, data sharing in Google Health is all-or-nothing. The patients does not have the flexibility to chose a fine-grained access policy to share her data. For example, once the patient allows the doctor to see her blood result test, the doctor can access all health information of the patient. In the access-control mechanisms of Microsoft HealthVault and Google Health the receiving end of the information must provide a set of credentials to the access control manager (ACM) who is responsible for enforcing the access control policies. The ACM checks whether user credentials satisfy the access control policy. If so, the user can read the resource, otherwise not. However, the main limitation of this is that the patient still needs to trust the Microsoft HealthVault and Google Health to enforce access control policies when disclosing data, and specially the access control decisions and privacy enforcement has to be enforced when the data is moving from one enterprise to another. Another limitation is that despite the privacy policy the leakage of confidential information can happen due to compromise of the database maintained by the enterprise. Therefore, to solve the aforementioned problem, recent proposals on enforcing access control policies exploit the use of encryption techniques. In such systems, information is stored in an encrypted form by an enterprise and there is no need for an access control manager to check user credentials. Thus, each user can get the encrypted data, but only users who have the right credentials (the right key) can decrypt the encrypted data. This implies that data confidentiality is preserved even if the database maintained by the enterprise is compromised.

Current Solutions (And Their Drawbacks) Which Enforce Access Control Policies Using Encryption To prevent commercial Web-based PHR systems to access the content of patients PHRs, the patient can encrypt her data using traditional public key encryption algorithms and store the encrypted data (ciphertext) in a database, and then decrypt the ciphertext on demand. In this case, the patient only needs to assume that Microsoft HealthVault and Google Health will properly store her encrypted data, and even if Microsoft HealthVault and Google Health get corrupted, patients PHR will not be disclosed since the data is stored in an encrypted form. The problem with this solution is that the patient needs to be involved in every request (e.g., from her doctor, hospital) and perform the decryption. This is because only the patient knows the decryption key. One possible solution is to use more advanced public key encryption schemes such as CiphertextPolicy Attribute-Based Encryption (CP-ABE) scheme (Bethencourt, Sahai, & Waters, 2007), (Cheung & Newport, 2007), (Ibraimi, Tang, Hartel, & Jonker, 2009). The CP-ABE scheme is a type of attribute-based encryption scheme in which the data owner encrypts the data according to an access control policy τ defined over a set of attributes, and where the receiving end can decrypt the encrypted data only if his private key associated with a set of attributes satisfies the access control policy τ. For example, suppose Alice encrypts her data according to an access policy τ=(a1 AND a2) OR a3. Bob can decrypt the encrypted data only if his private key is associated with a set of attributes that satisfy the access policy. To satisfy the access control policy τ, Bob must have a private key associated with at least one from the following attribute sets: (a1, a2), (a3) or (a1, a2, a3). In CP-ABE the mapping user-attribute is many-to-many, which means that one user may possess many attributes and one attribute may be possessed by many users. The main drawbacks of

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using CP-ABE to securely manage PHRs are: a) the patient has to know the list of users associated with an attribute, and b) an attribute is possesed by many users. Therefore, using CP-ABE, the patient cannot encrypt a PHR such that only one healthcare provider, with whom the patient has contract, can access it at a time. This is because one attribute can be possesed by many healthcare providers. This is one of the main reasons why both Microsoft HealthVault and Google Health use DAC where access to patients records is based on doctors identity, instead of using Attribute-Based Access Control (ABAC) where access to patients records is based on doctors attributes. Another solution is to use existing identitybased proxy re-encryption schemes (Green and Ateniese, 2007), in which the patient assigns a re-encryption key to the Proxy which re-encrypts the encrypted PHR under patients’ public key into encrypted PHR under doctors’ public key. However this approach has the following drawbacks: •

•

The Proxy is able to re-encrypt all ciphertexts of the patient such that the doctor can decrypt all ciphertexts using his private key. Thus, the patient does not have the flexibility to define fine-grained access control. If the Proxy and the delegatee get corrupted, then all PHRs may be disclosed to an illegitimate entity based on the fact that the re-encryption key can re-encrypt all ciphertexts.

THE CONCEPT OF TYPEAND-IDENTITY-BASED PROXY RE-ENCRYPTION In order to solve the aforementioned problems a new encryption scheme which would cryptographically enforce access policies is needed. We propose a type-and-identity-based proxy re-encryption scheme which consists of six algo-

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rithms: Setup, Extract, Encrypt, Pextract, Preenc and Decrypt. The basic building block of the type-andidentity-based proxy re-encryption scheme is the Identity-Based Encryption (IBE) scheme (Figure 2). The concept of IBE was proposed by Shamir (Shamir, 1985), however IBE has become practical only after Boneh and Franklin (Boneh and Franklin, 2001) propose the first IBE scheme based on bilinear pairings on elliptic curve (In appendix A we review in more detail the concept of bilinear pairing). The IBE scheme consist of four algorithms: Setup, Extract, Encrypt and Decrypt. Unlike a traditional public key encryption scheme, an IBE does not require a digital certificate to certify the encryption key (public key) because the public key of any user can be an arbitrary string such as an email address, IP address, etc. Key escrow is an inherent property in IBE systems, i.e., the trusted authority (TA), also referred to as Key Generation Center (KGC), can generate each users’ private key, because the TA owns the master key mk used to generate users’ private keys. IBE is a very suitable technique to be used in healthcare to exchange emails more securely. For example, in Figure 2, when Alice (the patient) wants to send an encrypted email to Bob (the doctor), Alice can encrypt an email using the encryption key derived from the doctors identity and send the email via an insecure channel. The doctor can authenticate himself to the TA to get the decryption key (private key). After the private key is generated the doctor can decrypt the encrypted email. Unlike in traditional public-key encryption schemes where the private key and the public key has to be created simultaneously, in IBE the private key can be generated long time after the corresponding public key is generated. A type-and-identity-based proxy re-encryption scheme extends the IBE scheme by adding the Proxy entity to the existing two entities: the TA and users. Another type of extension has been made to the number of algorithms. In addition to the four algorithms of IBE scheme, the type- and-

Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme

Figure 2. Identity-based encryption

identity-based proxy re-encryption scheme uses Pextract algorithm to generate a re-encryption key, and Preenc algorithm to re-encrypt the ciphertext. These algorithms are needed to enable the patient (delegator) to specify a fine-grained access control policy for her PHRs. In the type-and-identity-based proxy reencryption scheme (Figure 3), Alice (delegator) using one key-pair can categorize messages (data) into different types and delegate the decryption right of each type to Bob (delegatee) through a Proxy. Grouping the data into different categories is needed since different PHRs may have different levels of privacy requirements. For example, Alice may not be seriously concerned about disclosing her food statistics to other persons, but she might wish to keep her illness history as a top secret and only disclose it to the appropriate person. In addition to categorizing her PHRs according to the sensitivity level, Alice may categorize her PHRs according to the type of information or according to the device which generated the PHR. There are a number of measurement devices in the market which can be used by the patient and can be connected via home hubs to remote back-end servers. Such examples are disease management services (such as Philips Motiva and PTS) or emergency response services (Philips Lifeline) in which the healthcare provider can remotely access the measurement data and help the patients. As illustrated in Figure 3, Alice uses only one public key to

encryp all messages, and delegates her decryption right (computes a re-encryption key) only for one type (type 1), and Bob can use his private key to access only messages which belong to type 1. If the Proxy and Bob get corrupted, then only health records belonging to type 1 may be disclosed to an illegitimate entity, while the other types of information remains secure. A full construction of the type-and-identity based re-encryption scheme is given in Appendix B. The six algorithms are defined as follows: •

•

•

•

Setup(k): run by the TA, the algorithm takes as input a security parameter k and outputs the master public key pk and the master private key mk. pk is used in the encryption phase by each user, and mk is used by the TA to generate users private keys. Extract(mk,id): run by the TA when a user request a private key. The algorithm takes as input the master key mk and an user identity id, the algorithm outputs the user private key skid. Encrypt(m,t,pkid): run by the encryptor, the algorithm takes as input a message to be encrypted m, a type t, and an the public key pkid associated with the identity id, and outputs the ciphertext c. Pextract(idi , id j , t, skid ) run by the delei

gator, this algorithm takes the delegator’s identifier idi, the delegatee’s identifier idj,

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Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme

Figure 3. A type-and-identity-based proxy re-encryption

the type t, and the delegator’s private key skid as input and outputs the re-encryption

A formal security model and a formal security proof is given in appendix B.1. and B.2.

key rkid →id .

Applying the Scheme in Practice

i

i

•

j

Preenc(ci , rkid →id ) run by the Proxy, this i

j

algorithm, takes as input the ciphertext ci and the re-encryption key rkid →id , and i

•

j

outputs a new ciphertext cj Decrypt(c j , skid ) run by the decryptor, j

the algorithm takes as input the ciphertext cj and the private key skid , and output a message m.

j

Figure 4. Secure management of PHR

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Figure 4 illustrates a general architecture of a PHR system that uses our type-and-identity-based proxy re-encryption scheme. The architecture consists of Trusted Authorities (TAs), a patient, an application hosting device, a Web PHR, a Proxy and a doctor. The TAs are used to generate key pairs for the patient, respectively the doctor. We assume that the patient and the doctor are from different security domains. The application hosting device can be implemented on a home PC of the data source (a patient) or as a trusted service. Its

Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme

role is to encrypt PHRs and forward them to the Web PHR. The Web PHR stores encrypted PHRs, and Proxy is used to re-encrypt encrypted data and forward them to the doctor. There are five basic processes in the management of PHRs: •

•

•

Setup: In this phase, TAs run the Setup and Extract algorithm and distribute the public parameters needed to run the algorithms of the type-and-identity-based proxy re-encryption scheme, and distributes the private keys, which are needed to decrypt encrypted messages, to the patient and doctor (1). We assume that there is a secure channel between the TA and the user, respectively the doctor. Note that the doctor can get his key pair during the decryption phase, while the Proxy can perform re-encryption of encrypted data under doctors public key, even if the doctor does not have a private key. This is possible since the doctors public key can be computed by everyone who knows doctors’ identity. The TA does not have to be online as long as each user gets his/her key pair. Data creation: The patient uses a number of healthcare devices and creates measurement data and forwards them to the application hosting device (2). In addition to that, the patient can forward to the application hosting device all kinds of information that the patient has from various medical service providers. Data protection: The patient categorizes her PHR according to her privacy concerns. For instance, she can set her illness history as type t1, her food statistics as type t2, and the necessary PHR data in case of emergency as type t3. Then the patient generates the encryption key (public key) derived from her identity (identity can be any type of public string) and run the Encrypt algorithm using the generated public key. After the encryption is performed, the pa-

•

•

tients uploads the encrypted data to Web PHR (3). As in the previous step, all this (data categorization, data encryption and data uploading) can be done by a hosting device on behalf of the patient. Data Sharing (allow the doctor to see patient data): In this phase, the patient runs the Pextract algorithm and generates the re-encryption key which will be used by the Proxy to re-encrypt encrypted data under patients public key to encrypted data under doctors public key, such that the doctor can decrypt the encrypted data using his private key. The generated key is forwarded to the Proxy (4). As in the above steps, all this can be done by a hosting device on behalf of the patient who specifies the access control policy. Data consumption (doctors’ request-response): When a doctor wants to use patient data, he contacts the Web PHR and specifies the ciphertext that he wants to decrypt (5). We assume that each ciphertext is associated with appropriate metadata - descriptive information about the patient. The encrypted data is forwarded to the Proxy (6). The Proxy checks if the doctor is allowed to see patient data (checks if it has a re-encryption key rkPatient→Doctor), and, if so, the Proxy runs the Preenc algorithm to re-encrypt the encrypted data. The re-encrypted data is sent to the doctor (7). After receiving the re-encrypted data the doctor runs the Decrypt algorithm using his private key.

Trust Assumptions User trust is very important aspect when deploying a Web-based PHR system. In practice, users have greater trust on systems where they can control access to their information, and lower trust on systems where they have to trust someone else to control access to their information (e.g., the user

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Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme

has to trust the access control manager of the Web PHR). Therefore in this paper we provide a solution which compared to existing solutions reduces the trust that patients need to have on commercial Web-based PHR systems. As mentioned above, in our proposal the role of the Web PHR is twofold: a) to provide storage for PHRs, and b) to maintain the Proxy. Next to that, the patient has to put the following trust on Web PHR: •

•

400

The Web-based PHR system is trusted to store PHRs in publicly accessible storage only in an encrypted form, therefore the patient does not have to trust the Web-based PHR system to provide data confidentiality service. The data confidentiality service is provided by the patient at the moment when the data is encrypted. Encryption prevents sniffing software to access the data when the data travels from the user to the storage, and the storage cannot decrypt the data without having the private key. The Proxy is trusted to maintain a list of re-encryption keys, and to enforce the access policy by properly re-encrypting the encrypted data when the identity of the doctor (requester) is part of the re-encryption key. Note that, the list of re-encryption keys should be secret (if the list of reencryption keys is public then the patient cannot prevent an authorized doctor to see her data after the access decision is made), therefore the patient has to trust the Proxy to store all re-encryption keys securely. The difference between our approach and the access control mechanisms in existing Web PHR is that in our approach the Proxy who plays the role of the ACM cannot access the content of PHRs, therefore, the patient does not have to fully trust the Proxy, while in existing commercial Web-based PHR systems the patient has to fully trust the ACM because the ACM can access the content of PHRs.

•

The user should trust the Proxy to securely delete re-encryption keys when the user wants to prevent an authorized doctor to access users data further. For example, a patient might change her healthcare provider, and after some time she wants to prevent the doctor from an old healthcare provider to access her data.

Policy Updating To allow someone to access her data, the patient has to compute a new re-encryption key. For example, if Alice wants to allow Bob and Charlie to see her PHRs belonging to category tAlergy, Alice has to create two re-encryption keys: rkAlice→Bob and rkAlice→Charlie. Transmission of the reencryption keys to the Proxy should be secured using cryptographical protocols such as Transport Layer Security (TLS) which allows two entities to securely communicate over the internet. In practice the patient might want to update her access policy. Using our approach the patient might do this task without entirely decrypting the ciphertext. To update an access policy means to create new, and delete old, re-encryption keys. For example, if Alice wants to update her access policy from τ=Bob OR Charlie (this access policy implies that Bob and Charlie can access the data) to a different access policy τ ' = Bob OR Dave (this access policy implies that Bob and Dave can access the data), she has to follow the following two procedures: •

•

Notify the Proxy to delete (revoke) the re-encryption key rkAlice→Charlie. This would prevent the Proxy to re-encrypt encrypted data under Alice’s public key to encrypted data under Charlie’s public key. Create a new re-encryption key rkAlice→Dave and send it to the Proxy. This would allow the Proxy to re-encrypt encrypted data

Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme

under Alice’s public key to encrypted data under Dave’s public key.

RELATED WORK ON PROXY RE-ENCRYPTION Since (Mambo and Okamoto, 1997) first proposed the concept, a number of proxy re-encryption schemes have been proposed. (Blaze, Bleumer, and Strauss, 1998) introduce the concept of atomic Proxy cryptography which is the current concept of proxy re-encryption. In a proxy re-encryption scheme, the Proxy can transform ciphertexts encrypted with the delegator’s public key into ciphertexts that can be decrypted with the delegatee’s private key. (Blaze, Bleumer, & Strauss, 1998) propose a proxy re-encryption scheme based on the (ElGamal, 1985) encryption scheme. One property of this scheme is that, with the same re-encryption key, the Proxy can transform the ciphertexts not only form the delegator to the delegatee but also from the delegatee to the delegator. This is called the “bi-directional” property in the literature. Bi-directionality might be a problem in some applications, but it might also be a desirable property in some other applications. (Jakobsson, 1999) addresses this “problem” using a quorum controlled asymmetric proxy re-encryption where the Proxy is implemented with multiple servers and each of them performs partial re-encryption. (Ivan & Dodis, 2003) propose a generic construction method for proxy re-encryption schemes and also provide a number of example schemes. Their constructions are based on the concept of secret splitting, which means that the delegator splits his private key into two parts and sends them to the Proxy and the delegatee separately. During the re-encryption process the Proxy performs partial decryption of the encrypted message using the first part of the delegator’s private key, and the delegatee can recover the message by performing partial decryption using the second part of the delegator’s private key. One disadvantage of this method is that it is not collusion-safe, i.e. the

Proxy and the delegatee together can recover the delegator’s private key. Another disadvantage of this scheme is that the delegatee’s public/private key pair can only be used for dealing with the delegator’s messages. If this key pair is used by the delegatee for other encryption services, then the delegator can always decrypt the ciphertexts. (Ateniese, Fu, Green, & Hohenberger, 2006) propose several proxy re-encryption schemes based on the (ElGamal, 1985) scheme. In their schemes, the delegator does not have to interact and share his private key with the delegatee. The delegator stores two private keys, a master private key and a “weak” private key. The ciphertext can be fully decrypted using either of the two distinct keys. Their scheme is collusion safe, since only the “weak” private key is exposed if the delegatee and the Proxy collude but the master key remains safe. In addition, (Ateniese, Fu, Green, & Hohenberger, 2006) also discuss a number of properties for proxy re-encryption schemes. Recently, two IBE proxy re-encryption schemes were proposed by (Matsuo, 2007) and (Green & Ateniese, 2007), respectively. The Matsuo scheme assumes that the delegator and the delegatee belong to the same KGC and use the (Boneh & Boyen, 2004) encryption scheme. The (Green & Ateniese, 2007) scheme assumes that the delegator and the delegatee can belong to different KGCs but the delegatee possess the public parameter of the delegator’s KGC. (Sahai and Waters, 2005) introduce the concept of Attribute-Based Encryption (ABE) which is a generalized form of IBE. In ABE the ciphertext and user private keys are associated with a set of attributes. A user can decrypt the ciphertext if the user private key has the list of attributes specified in the ciphertext. In Ciphertext-Policy Attribute-Based Encryption (CP-ABE) the user private key is associated with a set of attributes and a ciphertext is associated with an access control over some attribute. The decryptor can decrypt the ciphertext if the list of attributes associated with the private key satisfies the access policy.

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Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme

In Key-Policy Attribute-Based Encryption (KPABE) (Goyal, Pandey, Sahai, & Waters, 2006) the idea is reversed and the private key is associated with an access control over some attributes and the ciphertext is associated with a list of attributes. The decryptor can decrypt the ciphertext if the list of attributes associated with the ciphertext satisfy the access policy associated with the private key. (Liang, Cao, Lin, & Shao, 2009) proposed an attribute-based proxy re-encryption scheme. The scheme is based on the (Cheung & Newport, 2007) CP-ABE scheme and inherits the same limitations that (Cheung & Newport, 2007) has: it supports only access policies with AND boolean operator, and the size of the ciphertext increases linearly with the number of attributes in the system. Proxy re-encryption has many promising applications including access control in file storage (Ateniese, Fu, Green, & Hohenberger, 2006), email forwarding (Wang, Cao, T. Okamoto, Miao & E. Okamoto, 2006), and law enforcement (Mambo & Okamoto, 1997). With the increasing privacy concerns over personal data, proxy re-encryption, in particular IBE proxy re-encryption schemes, will find more and more applications. As we show in this paper, proxy re-encryption is a powerful tool for patients to enforce their PHR disclosure policies.

CONCLUSION This paper presents a new approach for secure management of PHRs which are stored and shared from a semitrusted web server (the server is trusted to perform only the ciphertext re-encryption, without having access to the plain text). We gave an overview of access control mechanisms employed in current commercial Web-based PHR systems and show that traditional access control mechanisms as well as traditional encryption techniques which enforce access control policies are not suitable to be used in scenarios where the data is outsourced to a third party data center. In

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this paper we propose a type-and-identity-based proxy re-encryption scheme which allow patients to reduce the trust on commercial Web-base PHR systems and enable patients to provide different re-encryption capabilities to the Proxy while using the same key pair. This property has been shown to be useful in our PHR disclosure system, where an individual can easily implement fine-grained access control policies to her PHRs.

REFERENCES Ateniese, G., Fu, K., Green, M., & Hohenberger, S. (2006). Improved proxy re-encryption schemes with applications to secure distributed storage. [TISSEC]. ACM Transactions on Information and System Security, 9(1), 1–30. doi:10.1145/1127345.1127346 Bethencourt, J., Sahai, A., & Waters, B. (2007). Ciphertext-policy attribute-based encryption. In D. Shands (Ed.), Proceedings of the 28th IEEE Symposium on Security and Privacy (pp. 321-334). Oakland, CA: Citeseer. Blaze, M., Bleumer, G., & Strauss, M. (1998). Divertible protocols and atomic proxy cryptography. In K. Nyberg (Ed.), Proceeding of Eurocrypt 1998 (Vol. 1403 of LNCS) (pp. 127-144). New York: Springer Verlag. Boneh, D., & Boyen, X. (2004). Efficient selectiveid secure identity-based encryption without random oracles. In C. Cachin & J. Camenisch (Eds.), Proceedings of Eurocrypt 2004 (Vol. 3027 of LNCS) (pp. 223-238). New York: Springer Verlag. Boneh, D., & Franklin, M. K. (2001). Identitybased encryption from the weil pairing. In J. Kilian (Ed.), Proceedings of Crypto 2001 (Vol. 2139 of LNCS) (pp. 213-229). New York: Springer Verlag.

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Chen, L. (2007). An interpretation of identitybased cryptography. In A. Aldini & R. Gorrieri (Eds.), Foundations of Security Analysis and Design, IV FOSAD 2006/2007 Tutorial Lectures (Vol. 4677 of LNCS) (pp. 183-208). New York: Springer Verlag. Cheung, L., & Newport, C. (2007). Provably secure ciphertext policy ABE. In P. Ning (Ed.), Proceedings of the 14th ACM Conference on Computer and Communications Security (pp. 456–465). New York: ACM. ElGamal, T. (1985). A public key cryptosystem and a signature scheme based on discrete logarithms. In G. R. Blakley & D. Chaum (Eds.), Proceedings of Crypto 1984(Vol. 196 of LNCS) (pp. 10-18). New York: Springer Verlag. Google. (2008). Google Health. Retrieved September 15, 2009 from http://www.google.com/ health Goyal, V., Pandey, O., Sahai, A., & Waters, B. (2006). Attribute-based encryption for finegrained access control of encrypted data. In A. Juels (Ed.), Proceedings of the 14th ACM Conference on Computer and Communications Security (pp. 89-98). New York: ACM. Graham, G. S., & Denning, P. J. (1971). Protection: principles and practice. In Proceedings of the November 16-18, 1971, Fall Joint Computer Conference (pp. 417–429). Green, M., & Ateniese, G. (2007). Identity-based proxy re-encryption. In J. Katz & M. Yung (Eds.), Proceedings of Applied Cryptography and Network Security (Vol. 4521 of LNCS) (pp. 288-306). New York: Springer Verlag. Ibraimi, L., Tang, Q., Hartel, P., & Jonker, W. (2009). Efficient and provable secure ciphertextpolicy attribute-based encryption schemes. In F. Bao, H. Li, & G. Wang (Eds.), Proceedings of Information Security Practice and Experience (Vol. 5451 of LNCS) (pp. 1-12). New York: Springer Verlag.

Ivan, A., & Dodis, Y. (2003). Proxy cryptography revisited. In C. Neuman (Ed.), Proceedings of the Network and Distributed System Security Symposium. Citeseer. Jakobsson, M. (1999). On quorum controlled asymmetric proxy re-encryption. In H. Imai & Y. Zheng (Eds.), Proceedings of Public Key Cryptography (Vol. 1560 of LNCS) (pp. 112–121). New York: Springer Verlag. Liang, X., Cao, Z., Lin, H., & Shao, J. (2009). Attribute based proxy re-encryption with delegating capabilities. In W. Li, W. Susilo, & U. Tupakula (Eds.), Proceedings of the 4th International Symposium on Information, Computer, and Communications Security (pp. 276-286). New York: ACM. Mambo, M., & Okamoto, E. (1997). Proxy Cryptosystems: Delegation of the power to decrypt ciphertexts. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Science, 80(1), 54–63. Matsuo, T. (2007). Proxy re-encryption systems for identity-based encryption. In T. Takagi, T. Okamoto, E. Okamoto, & T. Okamoto (Eds.), Proceedings of Pairing-Based Cryptography Pairing 2007 (Vol. 4575 of LNCS) (pp. 247-267). New York: Springer Verlag. Microsoft. (2007). HealthVault Connection Center. Retrieved September 15, 2009 from http:// www.healthvault.com/ Sahai, A., & Waters, B. (2005). Fuzzy identitybased encryption. In R. Cramer (Ed.), Proceedings of Eurocrypt 2005 (Vol. 3494) (pp. 457-473). New York: Springer Verlag. Shamir, A. (1985). Identity-based cryptosystems and signature schemes. In G. R. Blakely & D. Chaum (Eds.) Proceedings of Crypto1984 (Vol. 196 of LNCS) (pp. 47-53). New York: Springer Verlag.

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Shoup, V. (2006). Sequences of games: a tool for taming complexity in security proofs. Retrieved October 15, 2009 from http://shoup.net/papers/ games.pdf Tang, P. C., Ash, J. S., Bates, D. W., Overhage, J. M., & Sands, D. Z. (2006). Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption. Journal of the American Medical Informatics Association, 13(2), 121–126. doi:10.1197/jamia.M2025 The personal health working group final report. (2004). Connecting for health. Retrieved October 2, 2009 from http://www.connectingforhealth.org/ resources/wg_eis_final_report_0704.pdf

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The US Department of Health and Human Services. (2003). Summary of the HIPAA privacy rule. Retrieved October 2, 2009 from http://www.hhs. gov/ocr/privacy/hipaa/understanding/summary/ privacysummary.pdf Wang, L., Cao, Z., Okamoto, T., Miao, Y., & Okamoto, E. (2006). Authorization-limited transformation-free proxy cryptosystems and their security analyses. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Science, (1): 106–114. doi:10.1093/ ietfec/e89-a.1.106

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APPENDIX A. Review of Pairing We briefly review the basis of pairing and the related assumptions. More detailed information can be found in the seminal paper (Boneh and Franklin, 2001). A pairing (or, bilinear map) satisfies the following properties: 1. G and G1 are two multiplicative groups of prime order p; 2. g is a generator of G; 3. eˆ : G × G → G1 is an efficiently-computable bilinear map with the following properties: ◦⊦ Bilinear: for all u.v∈G and a, b ∈ Z*p , we have eˆ(u a , vb ) = eˆ(u, v )ab . ◦⊦ Non-degenerate: eˆ(g, g ) ≠ 1 . As defined in (Boneh and Franklin, 2001), G is said to be a bilinear group if the group action in G can be computed efficiently and if there exists a group G1 and an efficiently-computable bilinear map eˆ as defined above. The Bilinear Diffie-Hellman (BDH) problem in G is as follows: given g, ga, gb, gc ∈ G as input, output eˆ(g, g )abc ∈ G1 . An algorithm A has advantage ε in solving BDH in G if: Pr[ A(g, g a , g b , g c ) = eˆ(g, g )abc ] ≥ ε. Similarly, we say that an algorithm A has advantage ε in solving the decision BDH problem in G if: |Pr[A(g, ga, gb, gc, gabc)=0] – Pr[A(g, ga, gb, gc, T)=0]|≥ε. Here the probability is over the random choice of a, b, c ∈ Z*p , the random choice of T∈G1, and the random bits of A (the adversary is a nondeterministic algorithm). Definition 1.We say that the (decision) (t,ε) -BDH assumption holds in G if no t-time algorithm has advantage at least ε in solving the (decision) BDH problem in G. As in the general group, the Computational Diffie-Hellman (CDH) problem in G is as follows: given g, ga, gb ∈ G as input, output gab ∈ G. An algorithm A has advantage ε in solving CDH in G if:

Pr[A (g, ga, gb) = gab]≥ε.

Definition 2.We say that the (t,ε) -CDH assumption holds in G if no t-time algorithm has advantage at least ε in solving the CDH problem in G.

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Given a security parameter k, a problem (say, BDH) is believed to be intractable if any adversary has only negligible advantage in reasonable time. We usually define a scheme to be secure if any adversary has only a negligible advantage in the underlying security model. The time parameter is usually be ignored. Definition 3.The function P(k):Z→R is said to be negligible if, for every polynomial f(k), there exists 1 an integer Nf such that P (k ) ≤ for all k≥Nf. f (k )

B. Our Construction In this section we give the construction of the type-and-identity-based proxy re-encryption scheme. In our scheme, the delegator and the delegatee are allowed to be from different domains, which nonetheless share some public parameters. •

Suppose that the delegator is registered at KGC1 in Boneh-Franklin IBE scheme (Setup1, Extract1, Encrypt1, Decrypt1) and the delegatee is registered at KGC2 in Boneh-Franklin IBE scheme (Setup2, Extract2, Encrypt2, Decrypt2). The algorithms are defined as follows. ◦⊦ Setup1 and Extract1 are the same as in the Boneh-Franklin scheme, except that Setup1 outputs an additional hash function H2 : {0,1}* → Z*p . The public parameter is params1 = (G, G1, p, g, H1, H2 , eˆ, pk1 ) , and the master key is mk1=α1. Encrypt1(m,t,id): Given a message m, a type t, and an identifier id, the algorithm outputs the ciphertext c= (c1, c2, c3) where r ∈R Z*p ,

◦⊦

r ⋅H (sk ||t ) 2 id

c1 = g r , c2 = m ⋅ eˆ(pkid , pk )

Decrypt1(c,skid): Given a ciphertext c= (c1, c2, c3), the algorithm outputs the message

◦⊦ m=

, c3 = t .

c2 H (sk ||c ) 2 id 3

eˆ(skid , c1 )



Without loss of generality, suppose the delegator holds the identity idi and the corresponding private key skid . Apart from the delegator, another party cannot run the Encrypt1 algorithm under the delegai

tor’s identity idi since he does not know skid . i

Suppose that the delegatee (with identity idj) possesses private key skid registered at KGC2 in the j

Boneh-Franklin IBE scheme, where the public parameter is params2 = (G, G1, p, g, H1, eˆ, pk2 ) , the α

master key is mk2=α2, and skid = H1(id j ) 2 . For the ease of comparison, we denote the IBE scheme as j

(Setup2, Extract2, Encrypt2, Decrypt2) although these algorithms are identical to those described in Section B.

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The Delegation Process If the delegator wants to delegate his decryption right for messages with type t to the delegatee, the algorithms of the proxy re-encryption scheme are as follows. Pextract(idi , id j , t, skid ) : Run by the delegator, this algorithm outputs the re-encryption key

•

i

rkid →id , where X∈RG1 and i

j

−H (sk ||t ) 2 id i

rkid →id = (t, skid i

•

j

i

⋅ H1(X ), Encrypt2 (X , id j )).

Preenc(ci , rkid →id ) : Run by the Proxy, this algorithm, takes a ciphertext ci=(ci1, ci2, ci3) and the i

j

re-encryption key rkid →id as input where t=ci3, and outputs a new ciphertext cj=(cj1, cj2, cj3), i

where cj1=ci1 and

−H (sk ||c ) 2 id i 3 i

c j 2 = ci 2 ⋅ eˆ(ci 1, skid α

= m ⋅ eˆ(g 1 , pk

i rH (sk ||t ) 2 id i id i

j

⋅ H1 (X )) −H (sk ||t ) 2 id i

) ⋅ eˆ(g r , skid

i

= m ⋅ eˆ(g r , H1(X )),

⋅ H1(X ))

and cj3=Encrypt2(X,idj). •

Decrypt(c j , skid ) .Given a re-encrypted ciphertext cj, the delegatee can obtain the plaintext m by j

computing: m′ =

cj 2 eˆ(c j 1, H1 (Decrypt2 (c j 3 , skid ))) r

=

m ⋅ eˆ(g , H1 (X ))

eˆ(g r , H1(X )) = m.

j



B.1. Security Model We assume that the Proxy is semi-trusted in the following sense: it will honestly convert the delegator’s ciphertexts using the re-encryption key; however, it might act actively to obtain some information about the plaintexts for the delegator and the delegatee. As mentioned in (Chen, 2007), the key escrow problem (TA owns a master key which can be used to decrypt each encrypted data) can be avoided by applying some standard techniques (such as secret sharing) to the underlying scheme, hence, we skip

407

Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme

any further discussion in this paper. The delegatee may be curious in the sense that it may try to obtain some information about the plaintexts corresponding to the delegator’s ciphertexts which have not been re-encrypted by the Proxy. As a standard practice, we describe an attack game for modeling the semantic security against an adaptive chosen plaintext attack for the delegator (IND-ID-DR-CPA security) for our scheme. The INDID-DR-CPA game is carried out between a challenger and an adversary, where the challenger simulates the protocol execution and answers the queries from the adversary. Note that the allowed queries for the adversary reflect the adversary’s capability in practice. Specifically, the game is as follows: 1. Game setup: The challenger takes a security parameter k as input, runs the Setup1 algorithm to generate the public system parameter params1 and the master key mk1, and runs the Setup2 algorithm to generate the public system parameter params2 and the master key mk2. 2. Phase 1: The adversary takes params1 and params2 as input and is allowed to issue the following types of queries: a. Extract1 query with any identifier id: The challenger returns the private key sk corresponding to id. b. Extract2 query with any identifier id ′ : The challenger returns the private key sk ′ corresponding to id ′ . c. Pextract query with (id, id ′, t ) : The challenger returns the re-encryption key rkid →id ′ for the type t. d. Preenc† query with (m, t, id, id ′) : The challenger first computes c=Encrypt1(m,t,id) and then returns a new ciphertext c ′ which is obtained by applying the delegation key rkid →id ′ to c, where rkid →id ′ is issued for type t.

Once the adversary decides that Phase 1 is over, it outputs two equal length plaintexts m0, m1, a type t , and an identifier id*. At the end of Phase 1, there are three constraints here: *

a. id* has not been the input to any Extract1 query. b. For any id ′ , if (id * , id ′, t * ) has been the input to a Pextract query then id ′ has not been the input to any Extract2 query. c. If there is a Preenc† query with (m, t, id, id ′) , then (id, id ′, t ) has not been queried to Pextract. 3. Challenge: The challenger picks a random bit b∈{0,1} and returns c* = Encrypt1 (mb, t*, id*) as the challenge to the adversary. 4. Phase 2: The adversary is allowed to continue issuing the same types of queries as in Phase 1. At the end of Phase 2, there are the same constraints as at the end of Phase 1. 5. Guess (game ending): The adversary outputs a guess b ′ ∈ {0,1}. 1 | . Compared with 2 the CPA security formalizations in (Ateniese, Fu, Green and Hohenberger, 2006), in our case, we also take into account the categorization of messages for the delegator. The Preenc† query reflects the fact that a curious delegatee has access to the the delegator’s plaintexts. At the end of the game, the adversary’s advantage is defined to be | Pr[b ′ = b ] −

408

Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme

B.2. Security Proof Theorem 1.For the type-and-identity-based proxy re-encryption scheme described in Section B, any adversary’s advantage is negligible. Proof sketch. We suppose that the total number of queries issued to H1 and H2 is bounded by integer q1 and q2, respectively. Suppose an adversary A has the non-negligible advantage ε in the IND-ID-DR-CPA game. The security proof is done through a sequence of games. Game0: In this game, B faithfully answers the oracle queries from A. Specifically, B simulates the random oracle H1 as follows: B maintains a list of vectors, each of them containing a request message, an element of G (the hash-code for this message), and an element of Z*p . After receiving a request message, B first checks its list to see whether the request message is already in the list. If the check succeeds, B returns the stored element of G; otherwise, B returns gy, where y a randomly chosen element of Z*p , and stores the new vector in the list. A' simulates the random oracle H2 as follows: B maintains a list of vectors, each of them containing a request message and an element of Z*p (the hash-code for this message). After receiving a request message, B first checks its list to see whether the request message is already in the list. If the check succeeds, B returns the stored element of Z*p ; otherwise, B returns u which is a randomly chosen element of Z*p , and stores the new vector in the list. 1 |= ε . 2 Game1: In this game, B answers the oracle queries from A as follows.

Let δ0 = Pr[b ′ = b ] , as we assumed at the beginning, | δ0 −

• •

•

Game setup: B faithfully simulates the setup phase. Phase 1: B randomly selects j∈{1,2,…,q1+1}. If j=q1+1, B faithfully answers the oracle queries  and B answers the oracle queries from A from A. If 1≤j≤q1, we assume the j-th input to H1 is id as follows: Answer the queries to Extract1, Extract2, Pextract, and Preenc† faithfully, except that  is the input to a Extract query. B aborts as a failure when id 1 Challenge: After receiving (m0, m1, t*, id*) from the adversary, if one of the following events occurs, B aborts as a failure. a. id* has been issued to H1 as the i-th query and i≠j, b. id* has not been issued to H1 and 1≤j≤q1.

 is the input to j-th H Note that, if the adversary does not abort then either 1≤j≤q1 and id * = id 1 * query or j=q1+1 and id has not been the input to any H1 query. B faithfully returns the challenge. • •

Phase 2: B answers the oracle queries faithfully. Guess (game ending): The adversary outputs a guess b ′ ∈ {0,1} .

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Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme

The probability that B successfully ends is execution is

1 , i.e. the probability that B does not abort in its q1 + 1

1 . Let δ1 = Pr[b ′ = b ] when B successfully ends, in which case |δ1=δ0|. Let θ1 be the q1 + 1

probability that B successfully ends and b ′ = b . We have θ1 =

δ1

. q1 + 1 Game2: In this game, B simulates the protocol execution and answers the oracle queries from A in the following way. α

Game setup: B faithfully simulates the setup phase. Recall that pk1 = g 1 . Phase 1: B randomly selects j∈{1,2,…,q1+1}. If j=q1+1, B faithfully answers the oracle queries from A. If 1≤j≤q1, B answers j-th query to H1 with gβ where β ∈R Z*p , and answers the oracle  . queries from A as follows. Suppose the input of the j-th query to H1 is id  is the input a. Answer Extract and Extract faithfully, except that B aborts as a failure when id

• •

1

2

to a Extract1 query.  , B returns the re-encryption key rk , where Pextract query with (id, id ′, t ) : If id = id id →id ′

gtid ′ ∈R G, Xtid ′ ∈R G1, rkid →id ′ = (t, gtid ′ , Encrypt2 (Xtid ′ , id ′)). Otherwise, B answers the query faithfully. If id ′ has been queried to Extract2, when Xtid ′ is queried

−1 to H1 then B returns gtid ′ ⋅ htid where htid ′ ∈R G . ′ †  , B returns Preenc query with (m, t, id, id ′) : If id = id

r ∈R Z*p , Xtid ′ ∈R G1, c ′ = (g r , eˆ(g r , H1 (Xtid ′ )), Encrypt2 (Xtid ′ , id ′)). Otherwise, B answers the query faithfully. •

Challenge: After receiving (m0, m1, t*, id*) from the adversary, if one of the following events occurs, B aborts as a failure. a. id* has been issued to H1 as the i-th query and i≠j, b. id* has not been issued to H1 and 1≤j≤q1.

 is the input to j-th H Note that, if the adversary does not abort then either 1≤j≤q1 and id * = id 1 query or j=q1+1 and id* has not been the input to any H1 query. In the latter case, B sets H1(id*)=gβ where β ∈R Z*p , and returns c * = (c1* , c2* , c3* ) as the challenge to the adversary, where: b ∈R {0,1}, r ∈R Z*p , T ∈R G1, c1* = g r , c2* = mb ⋅ T , c3* = t * .

410

Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme

• •

Phase 2: B answers the oracle queries from A as in Phase 1. Guess (game ending): The adversary outputs a guess b ′ ∈ {0,1} . Let θ2 be the probability that B successfully ends and b ′ = b . We have θ2 =

1 since T∈RG1. 2(q1 + 1) α ⋅β

Let E1 be the event that, for some id ′ and t, the adversary issues a H2 query with the input g 1 || t or Xtid ′ is issued to H1 while id ′ has not been issued to Extract2. Compared with Game1, Game2 differs when E1 occurs. From the difference lemma (Shoup, 2006), we have |δ2-δ1|≤ε2 which is negligible in the random oracle model based on the BDH assumption. Note that (Setup2, Extract2, Encrypt2, Decrypt2) is one-way based on the BDH assumption and BDH implies CDH. 1 1 1 − θ1 |≤ ε2 . In addition, from | δ0 − |= ε , we have | From |θ2-θ1|≤ε2 and θ2 = 2 2(q1 + 1) 2(q1 + 1) ε1 ε ≤ + ε2 . Because εi (1≤i≤2) are negligible and ε is q1 + 1 q1 + 1 q1 + 1 assumed to be non-negligible, we get a contradiction. As a result, the proposed scheme is IND-ID-DRCPA secure based on the CDH assumption in the random oracle model, given that (Setup2, Extract2, Encrypt2, Decrypt2) is one-way. , |δ1-δ0|≤ε1 and θ1 =

δ1

, we have

This work was previously published in International Journal of Computational Models and Algorithms in Medicine (IJCMAM), Volume 1, Issue 2, edited by Aryya Gangopadhyay, pp. 1-21, copyright 2010 by IGI Publishing (an imprint of IGI Global).

411

412

Chapter 2.12

Integration of Clinical and Genomic Data for Decision Support in Cancer Yorgos Goletsis University of Ioannina, Greece Themis P. Exarchos University of Ioannina, Greece Nikolaos Giannakeas University of Ioannina, Greece Markos G. Tsipouras University of Ioannina, Greece Dimitrios I. Fotiadis University of Ioannina, Greece, Michaelideion Cardiology Center, Greece & Biomedical Research Institute, Greece

INTRODUCTION Computer aided medical diagnosis is one of the most important research fields in biomedical engineering. Most of the efforts made focus on diagnosis based on clinical features. The latest breakthroughs of the technology in the biomolecular sciences are a direct cause of the explosive growth of biological data available to the scientific community. New technologies allow for high volume affordable production and colDOI: 10.4018/978-1-60960-561-2.ch212

lection of information on biological sequences, gene expression levels and proteins structure on almost every aspect of the molecular architecture of living organisms. For this reason, bioinformatics is asked to provide tools for biological information processing, representing today’s key in understanding the molecular basis of physiological and pathological genotypes. The exploitation of bioinformatics for medical diagnosis appears as an emerging field for the integration of clinical and genomic features, maximizing the information regarding the patient’s health status and the quality of the computer aided diagnosis.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Integration of Clinical and Genomic Data for Decision Support in Cancer

Cancer is one of the prominent domains where this integration is expected to bring significant achievements. As genetic features play a significant role in the metabolism and the function of the cells, the integration of genetic information (proteomics-genomics) to cancer-related decision support is now perceived by many not as a future trend but rather as a demanding need. The usual patient management in cancer treatment involves several, usually iterative, steps consisting of diagnosis, staging, treatment selection, and prognosis. As the patient is usually asked to perform new examinations, diagnosis and staging status can change over time. On the other hand, treatment selection and prognosis depend on the available findings, response to previous treatment plan and, of course, clinical guidelines. The integration of these evolving and changing data into clinical decision is a hard task which makes fully personalised treatment plan almost impossible. The use of clinical decision support systems (CDSSs) can assist in the processing of the available information and provide accurate staging, personalised treatment selection, and prognosis. The development of electronic patient records and of technologies that produce and collect biological information have led to a plethora of data characterizing a specific patient. Although this might seem beneficial, it can lead to confusion and weakness concerning the data management. The integration of the patient data (quantitative) that are hard to be processed by a human decision maker (the clinician) further imposes the use of CDSSs in personalized medical care (Louie, Mork, Martin-Sanchez, Halevy, & Tarczy-Hornoch, 2007). The future vision—but current need—will not include generic treatment plans according to some naive reasoning, but totally personalised treatment based on the clinicogenomic profile of the patient. In this article, we address decision support for cancer by exploiting clinical data and identifying mutations on tumour suppressor genes. The goal is to perform data integration between medicine

and molecular biology by developing a framework where clinical and genomic features are appropriately combined in order to handle cancer diseases. The constitution of such a decision support system is based on (a) cancer clinical data and (b) biological information that is derived from genomic sources. Through this integration, real time conclusions can be drawn for early diagnosis, staging and more effective cancer treatment.

BACKGROUND Clinical Decision Support Systems are active knowledge systems which use two or more items of patient data to generate case-specific advice (Fotiadis, Goletsis, Likas, & Papadopoulos, 2006). CDSSs are used to enhance diagnostic efforts and include computer based programs that, based on information entered by the clinician, provide extensive differential diagnosis, staging (if possible), treatment, follow-up, and so forth. CDSSs consist of an inference engine that is used to associate the input variables with the target outcome. This inference engine can be developed based either on explicit medical knowledge, expressed in a set of rules (knowledge based systems) or on data driven techniques, such as machine learning (Mitchel, 2006) and data mining (intelligent systems) (Tan, Steinbach, & Kumar, 2005). CDSSs require the input of patient-specific clinical variables (medical data) and as a result provide patient specific recommendation. Medical data are observations regarding a patient, including demographic details (i.e., age, sex), medical history (i.e., diabetes, obesity), laboratory examinations (e.g., creatinine, triglyceride), biomedical signals (ECG, EMG), medical images (i.e., MRI, CT), and so forth. Demographic details, medical history, and laboratory data are the most easily obtained and recorded and, therefore, most commonly included in electronic patient records. On the other hand, biomedical signals and medical images require more effort in order to be acquired

413

Integration of Clinical and Genomic Data for Decision Support in Cancer

in a digital format and must be processed for useful feature extraction. Apart from these, several types of genomic data can be generated from laboratory examinations, that is, gene DNA or protein sequences, gene expression data, microarray images, and so forth. Genomic data can also be used for medical diagnosis, disease prevention, and population genetics studies. Although medical data are sufficient for the diagnosis of several diseases, recent studies have demonstrated the high information value of genomic data, especially in specific types of diseases, such as cancer diseases. The great amount and the complexity of the available genetic data complicates their analysis from conventional data analysis methods and requires higher order analysis methods such as data mining techniques. Lately, data mining has received much attention in bioinformatics and molecular biology (Cook, Lawrence, Su, Maglothin, & Jonyer, 2001). Data mining methods are usually applied in the analysis of data coming from DNA microarrays or mass spectrometry. Over the last few years, several scientific reports have shown the potential of data mining to infer clinically relevant models from molecular data and thus provide clinical decision support. The majority of papers published in the area of data mining for genomic medicine deals with the analysis of gene expression data coming from DNA microarrays (Jiang & Gruenwald, 2005; Shah & Kusiak, 2007; Walker et al., 2004) consisting of thousands of genes for each patient, with the aim to diagnose types of diseases and to obtain a prognosis which may lead to therapeutic decisions. Most of the research works are related to oncology (Louie et al., 2007), where there is a strong need for defining individualized therapeutic strategies. Another area where data mining has been applied is the analysis of genes or proteins, represented as sequences (Exarchos, Papaloukas, Lampros, & Fotiadis, 2006); sequential pattern mining techniques are suitable for analyzing these types of data (Zaki, 2000).

414

Several CDSSs for cancer have been proposed in the literature. Most of the approaches are based solely on clinical data and a few methods exist that provide cancer decision support using microarray gene expression data. The cancer CDSSs concern several different types of cancer and employ various techniques for their development. The majority of systems are still in a research level and only a few are being used in clinical practice. A CDSS which is already in clinical use is PAPNET (Boon & Kok, 2001) which deals with cervical cancer. PAPNET uses ANNs to extract abnormal cell appearances from vaginal smear slides and describe them in histological terms. Other CDSSs for cervical cancer concentrate on the evaluation of the benefits of the PAPNET system (Doornewaard, 1999; Nieminen, Hakama, Viikki, Tarkkanen, & Anttila, 2003). Colon cancer has also been studied using clinical data and fuzzy classification trees (Chiang, Shieh, Hsu, & Wong, 2005) or pattern analysis of gene expression levels (Alon et al., 1999). A CDSS that combines imaging data with pathology data for colon cancer has also been proposed (Slaymaker, 2006). CDSSs proposed for prostate cancer, employ prostate specific antigen (PSA) serum marker, digital rectal examination, Gleason sum, age, and race (Remzi et al., 2003). Another approach for decision support in prostate cancer is based on gene expression profiles (Singh et al., 2002). Regarding bladder cancer, a CDSS has been developed based on proteomic data (Parekattil, Fisher, & Kogan, 2003). Concerning breast cancer, the potential of microarray data has been analysed (Van’t Veer et al., 2002). Also, a recent CDSS has been developed that integrates data mining with clinical guidelines towards breast cancer decision support (Skevofilakas, Nikita, Templakesis, Birbas, Kaklamanos, & Bonatsos, 2005). It should be noted that all CDSSs mentioned above are just research attempts and only PAPNET is in clinical use.

Integration of Clinical and Genomic Data for Decision Support in Cancer

CLINICAL DECISION SUPPORT USING CLINICOGENOMIC PROFILES Methodology Conventional approaches for CDSS focus on a single outcome regarding their domain of application. A different approach is to generate profiles associating the input data (e.g., findings) with several different types of outcomes. These profiles include clinical and genomic data along with specific diagnosis, treatment and followup recommendations. The idea of profile-based CDSS is based on the fact that patients sharing similar findings are most likely to share the same diagnosis and should have the same treatment and follow-up; the higher this similarity is, the more probable this hypothesis holds. The profiles are created from an initial dataset including several patient cases using a clustering method. Health records of diagnosed and (successfully or unsuccessfully) treated patients, with clear follow-up description, are used to create the profiles. These profiles constitute the core of the CDSSs; each new case that is inserted is related with one (or more) of these profiles. More specifically, an individual health record containing only findings (and

maybe the diagnosis) is matched to the centroids. The matching centroids are examined in order to indicate potential diagnosis (the term diagnosis here refers mainly to the identification of cancer subtype). If the diagnosis is confirmed, genetic screening may be proposed to the subject and then, the clusters are further examined, in order to make a decision regarding the preferred treatment and follow-up. The above decision support idea is shown schematically in Figure 1.

General Description of the System Known approaches for the creation of CDSSs are based on the analysis of clinical data using machine learning techniques. This scheme can be expanded to include genomic information, as well. In order to extract a set of profiles, the integration of clinical and genomic data is first required. Then data analysis is realized in order to discover useful knowledge in the form of profiles. Several techniques and algorithms can be used for data analysis such as neural approaches, statistical analysis, data mining, clustering and others. Data analysis is a two stage procedure: (i) creation of an inference engine (training stage) and (ii) use of this engine for decision support. The type of

Figure 1. Decision support based on profiles extraction. Unknown features of patient are derived by known features of similar cases.

415

Integration of Clinical and Genomic Data for Decision Support in Cancer

analysis to be used greatly depends on the available information and the desired outcome. Clustering algorithms can be employed in order to extract patient clinico-genomic profiles. An initial set of records, including clinical and genomic data along with all diagnosis/treatment/follow-up information, must be available for the creation of the inference engine. The records are used for clustering and the centroids of the generated clusters constitute the profiles. These profiles are then used for decision support; new patients with similar clinical and genomic data are assigned to the same cluster, that is, they share the same profile. Thus, a probable diagnosis, treatment, and follow-up is selected. Both, the creation of the inference engine and the decision support procedure are presented in Figure 2.

Types of Data Clinical data that are contained in electronic patient records (i.e., demographic details, medical history, and laboratory data) are usually presented in a simple and structured format, thus simplifying the analysis. On the other hand, genomic data are not structured and, therefore, appropriate prepro-

cessing is needed in order to transform them into a more structured format. Three different kinds of biological data may be available to clinicians: (i) genomic data, often represented by a collection of single nucleotide polymorphisms (SNPs), DNA sequence variations that occur when a single nucleotide in the genome sequence is altered and since each individual has many SNPs, their sequence forms a unique DNA pattern for that person; (ii) gene expression data, which can be measured with DNA microarrays to obtain a snapshot of the activity of all genes in one tissue at a given time or with techniques that rely on a polymerase chain reaction (PCR) and real-time PCR when the expression of only a few genes needs to be measured with better precision; (iii) protein expression data, which can include a complete set of protein profiles obtained with mass spectra technologies, or a few protein markers which can be measured with ad hoc essays.

Data Processing Depending on the type of the available biological data, different preprocessing steps should be performed in order to derive structured biological

Figure 2. Representation of a general scheme for a CDSS, integrating clinical and genomic information

416

Integration of Clinical and Genomic Data for Decision Support in Cancer

information, while expert knowledge could favor the preprocessing steps. The processing stage is necessary in order to transform the genomic data into a more easy-to-analyse form, allowing their integration along with the clinical data into the data analysis stage. Also, the genomic data processing might take advantage of expert knowledge, that is, known genomic abnormalities. Finally, the integrated data (clinical and genomic) are analysed in order to discover useful knowledge that can be used for decision support purposes. This knowledge can be in the form of associations between clinical and genomic data, differential diagnosis, treatment, and so forth. The initial dataset (clinical or genomic) is defined by the experts and includes all features that according to their opinion are highly related with the domain at hand (clinical disease). After acquiring the integrated data, a feature selection technique is applied in order to reduce the number of features and remove irrelevant or redundant ones. Finally, the reduced set of features is used by a clustering algorithm. K-means (MacQueen, 1967), fuzzy k-means (Bezdek, 1981), and expectation maximization (Dempster, Laird, & Rubin, 1977) are known approaches for clustering and can be involved for profile extraction. The profiles are the output of the clustering procedure (centroids). A deficiency of several clustering algorithms is that the number of centroids (profiles) must be predefined; this is not always feasible. Thus, in order to fully automate the profile extraction process, a meta-analysis technique is employed, which automatically calculates the optimal number of profiles.

Application to Colon Cancer Colon cancer includes cancerous growths in the colon, rectum, and appendix. It is the third most common form of cancer and the second leading cause of death among cancers in the developed countries. There are many different factors involved in colon carcinogenesis. The associa-

tion of these factors represents the base of the diagnostic process performed by medics which can obtain a general clinical profile integrating patient information using his scientific knowledge. Available clinical parameters are stored together with genomic information for each patient to create (as much as possible) a complete electronic health record. Several clinical data that are contained in the electronic health records are related to colon cancer (Read & Kodner, 1999): age, diet, obesity, diabetes, physical inactivity, smoking, heavy alcohol consumption, previous colon cancer or other cancers, adenomatous polyps which are the small growths on the inner wall of the colon and rectum; in most cases, the colon polyp is benign (harmless). Also, other diseases or syndromes such as inflammatory bowel disease, ZollingerEllison syndrome, and Gardner’s syndrome are related to colon cancer. In the context of genomic data related with colon cancer, malignant changes of the large bowel epithelium are caused by mutations of specific genes among which we can differentiate (Houlston & Tomlinson, 1997): •

•

•

Protooncogenes. The most popular mutated protooncogenes in colon cancer are: K-RAS, HER-2, EGFR and c-MYC. Suppressor genes-anticogenes. In colorectal cancer the most important are DCC, TP53 and APC. Mutator genes. So far, six repair genes of incorrectly paired up bases were cloned from humans. Four are related to Hereditary Nonpolyposis Colon Cancer (HNPCC). These are: the hMSH2- homolog of yeast gene MutS, the hMLH1 - homolog of bacterial MutL, the hPMS1 and hPMS2 - from yeast equivalent.

An efficient way to process the above gene sequences is to detect Single Nucleotide Polymorphisms (SNPs) (Sielinski, 2005). SNPs data

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Integration of Clinical and Genomic Data for Decision Support in Cancer

are qualitative data providing information about the genomic at a specific locus of a gene. An SNP is a point mutation present in at least 1% of a population. A point mutation is a substitution of one base pair or a deletion, which means the respective base pair is missing or an addition of one base pair. Though several different sequence variants may occur at each considered locus, usually one specific variant of the most common sequence is found, an exchange from adenine (A) to guanine (G), for instance. Thus, information is basically given in the form of categories denoting the combinations of base pairs for the two chromosomes, for example, A/A, A/G, G/G, if the most frequent variant is adenine and the

single nucleotide polymorphism is an exchange from adenine to guanine. According to previous medical knowledge, there are several SNPs with known relation to colon cancer. Some indicative SNPs already related to colon cancer, according to several sources in the literature, identified in TP53 gene, are presented in Table 1. The expert knowledge contains information about the position of the SNPs in the gene sequence (i.e., exon, codon position and amino acid position), the transition of the nucleotides and the translation of the mRNA to protein. Based on the list of known SNPs related to colon cancer, appropriate genomic information is derived, revealing the existence, or not, of these SNPs in the patient’s genes.

Table 1. Indicative SNPs transitions and positions in the TP53 gene, related with colon cancer mRNA pos.

Codon pos.

Amino acid pos.

Function

Transition

Protein residue transition

exon_10

1347

1

366

nonsynonymous

G/T

Ala [A]/Ser [S]

exon_10

1266

1

339

nonsynonymous

A/G

Lys [K]/Glu[E]

exon_9

1242

3

331

synonymous

A/G

Gln [Q]/Gln[Q]

exon_8

1095

1

282

nonsynonymous

T/C

Trp [W]/Arg[R]

exon_8

1083

1

278

nonsynonymous

G/C

Ala [A]/Pro[P]

exon_8

1069

2

273

nonsynonymous

A/G

His [H]/Arg[R]

Region

exon_7

1021

2

257

nonsynonymous

A/T

Gln [Q]/Leu[L]

exon_7

998

3

249

nonsynonymous

T/G

Ser [S]/Arg[R]

exon_7

994

2

248

nonsynonymous

A/G

Gln [Q]/Arg[R]

exon_7

984

1

245

nonsynonymous

A/G

Ser [S]/Gly[G]

exon_7

982

2

244

nonsynonymous

A/G

Asp [D]/Gly[G]

exon_7

973

2

241

nonsynonymous

T/C

Phe [F]/Gly[G]

exon_5

775

2

175

nonsynonymous

A/G

His [H]/Arg[R]

exon_5

702

1

151

nonsynonymous

A/T/C

Thr [T]/Ser[S]/Pro [P]

exon_5

663

1

138

nonsynonymous

C/G

Pro [P]/Ala [A]

exon_5

649

2

133

nonsynonymous

C/T

Thr [T]/Met [M]

exon_4

580

2

110

nonsynonymous

T/G

Leu [L]/Arg [R]

exon_4

466

2

72

nonsynonymous

G/C

Arg [R]/Pro [P]

exon_4

390

1

47

nonsynonymous

T/C

Ser [S]/Pro [P]

exon_4

359

3

36

synonymous

A/G

Pro [P]/Pro [P]

exon_4

353

3

34

synonymous

A/C

Pro [P]/Pro [P]

exon_2

314

3

21

synonymous

T/C

Asp [D]/Asp [D]

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Integration of Clinical and Genomic Data for Decision Support in Cancer

Some of the described genes are acquired from the subjects and based on the SNP information regarding every acquired gene, such as SNPs in Table 1 for TP53 gene, new features are derived. These new features contain information regarding the existence or not of these SNPs in the patient’s gene sequence. The derived features along with the aforementioned clinical data that are related to colon cancer are the input to the methodology and, after following the above described inference engine creation methodology, clinicogenomic profiles are generated. These profiles are able to provide advanced cancer decision support to new patients.

FUTURE TRENDS There should be no doubt that several challenges remain regarding clinical and genomic data integration to facilitate clinical decision support. The opportunities of combining these two types of data are obvious, as they allow obtaining new insights concerning diagnosis, prognosis, and treatment. According to this, medical informatics are combined with bioinformatics towards biomedical informatics. Biomedical Informatics is the emerging discipline that aims to put these two worlds together so that the discovery and creation of novel diagnostic and therapeutic methods is fostered. A limitation of this combination is that although data exist, usually their enormous volume and their heterogeneity constitute their analysis and association a very difficult task. Another challenge is the lack of terminological and ontological compatibility, which could be solved by means of a uniformed representation. Besides new data models, ontologies are/have to be developed in order to link genomic and clinical data. Furthermore, standards are required to ensure interoperability between disparate data sources.

CONCLUSION Advances in genome technology are playing a growing role in medicine and health care. With the development of new technologies and opportunities for large-scale analysis of the genome, genomic data have a clear impact on medicine. Cancer prognostics and therapeutics are among the first major test cases for genomic medicine, given that all types of cancer are related with genomic instability. The integration of clinical and genomic data makes the prospect for developing personalized health care ever more realistic.

REFERENCES Alon, U., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S., & Mack, D. (1999). Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Cell Biology, 96, 6745–6750. Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. Plenum Press, USA. Boon, M. E., & Kok, L. P. (2001). Using artificial neural networks to screen cervical smears: How new technology enhances health care. Clinical applications of artificial neural networks. Cambridge: Cambridge University Press (pp. 81-89). Chiang, I. J., Shieh, M. J., Hsu, J. Y., & Wong, J. M. (2005). Building a medical decision support system for colon polyp screening by using fuzzy classification trees. Applied Intelligence, 22(1), 61–75. doi:10.1023/B:APIN.0000047384.85823. f6 Cook, D. J., Lawrence, B. H., Su, S., Maglothin, R., & Jonyer, I. (2001). Structural mining of molecular biology data: A graph-based tool for discovering and analyzing biological patterns in structural databases. IEEE Engineering in Medicine and Biology, 4, 67–74. doi:10.1109/51.940050

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Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B. Methodological, 39(1), 1–38. Doornewaard, H., van der Schouw, Y. T., van der Graaf, Y., Bos, A. B., Habbema, J. D., & van den Tweel, J. G. (1999). The diagnostic value of computerassisted primary cervical smear screening: A longitudinal cohort study. Modern Pathology, 12(11), 995–1000. Exarchos, T. P., Papaloukas, C., Lampros, C., & Fotiadis, D. I. (2006). Protein classification using sequential pattern mining. In Proceedings of the 28th Annual International Conference of the Engineering in Medicine and Biology Society, USA (pp. 5814-5817). Fotiadis, D. I., Goletsis, Y., Likas, A., & Papadopoulos, A. (2006). Clinical decision support systems. Encyclopedia of Biomedical Engineering, Wiley. Houlston, R. S., & Tomlinson, I. P. M. (1997). Genetic prognostic markers in colorectal cancer. Journal Clinical Pathology: Molecular Pathology, 50, 281–288. doi:10.1136/mp.50.6.281 Jiang, X. R., & Gruenwald, L. (2005). Microarray gene expression data association rules mining based on BSC-tree and FIS-tree. Data & Knowledge Engineering, 53(1), 3–29. doi:10.1016/ S0169-023X(04)00110-7 Louie, B., Mork, P., Martin-Sanchez, F., Halevy, A., & Tarczy-Hornoch, P. (2007). Data integration and genomic medicine. Journal of Biomedical Informatics, 40, 5–16. doi:10.1016/j.jbi.2006.02.007 MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 281-297).

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Mitchell, T. (2006). Machine learning. Springer, McGraw-Hill Education (ISE Editions). Nieminen, P., Hakama, M., Viikki, M., Tarkkanen, J., & Anttila, A. (2003). Prospective and randomised public-health trial on neural networkassisted screening for cervical cancer in Finland: Results of the first year. International Journal of Cancer, 103(3), 422–426. doi:10.1002/ijc.10839 Parekattil, S. J., Fisher, H. A., & Kogan, B. A. (2003). Neural network using combined urine nuclear matrix protein-22, monocyte chemoattractant protein-1 and urinary intercellular adhesion molecule-1 to detect bladder cancer. The Journal of Urology, 169(3), 917–920. doi:10.1097/01. ju.0000051322.60266.06 Read, T. E., & Kodner, I. J. (1999). Colorectal cancer: risk factors and recommendations for early detection. American Family Physician, 59(11), 3083–3092. Remzi, M., Anagnostou, T., Ravery, V., Zlotta, A., Stephan, C., & Marberger, M. (2003). An artificial neural network to predict the outcome of repeat prostate biopsies. Urology, 62(3), 456–460. doi:10.1016/S0090-4295(03)00409-6 Shah, S., & Kusiak, A. (2007). Cancer gene search with data-mining and genetic algorithms. Computers in Biology and Medicine, 37(2), 251–261. doi:10.1016/j.compbiomed.2006.01.007 Sielinski, S. (2005). Similarity measures for clustering SNP and epidemiological data. Technical report, University of Dortmund. Singh, D., Febbo, P. G., Ross, K., Jackson, D. G., Manola, J., & Ladd, C. (2002). (2002). Gene expression correlates of clinical prostate cancer behavior. Cancer Cell, 1(2), 203–209. doi:10.1016/ S1535-6108(02)00030-2

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Skevofilakas, M. T., Nikita, K. S., & Templakesis, P. H. Birbas., K.N., Kaklamanos, I.G., & Bonatsos, G.N. (2005). A decision support system for breast cancer treatment based on data mining technologies and clinical practice guidelines. In Proceedings of the 27th Annual Conference of IEEE Engineering in Medicine and Biology (pp. 2429-2432), China. Slaymaker, M., Brady, M., Reddington, F., Simpson, A., Gavaghan, D., & Quirke, P. (2006). A prototype infrastructure for the secure aggregation of imaging and pathology data for colorectal cancer care. In Proceeding of IEEE Computer Based Medical Systems (pp. 63-68). Tan, P. N., Steinbach, M., & Kumar, V. (2005). Introduction to data mining. Addison Wesley. USA. Van‘T Veer, L.J., Dai, H., Van De Vijner, M.J., He, Y.D., Hart, A.A.M., et al. (2002). Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415, 530–536. doi:10.1038/415530a Walker, P. R., Smith, B., Liu, Q. Y., Famili, A. F., Valdes, J. J., & Liu, Z. (2004). Data mining of gene expression changes in Alzheimer brain. Artificial Intelligence in Medicine, 31(2), 137–154. doi:10.1016/j.artmed.2004.01.008 Zaki, M. J. (2000). Sequence mining in categorical domains: Incorporating constraints. In Proceedings of the 9th international conference on information and knowledge management, USA (pp. 422-429).

KEY TERMS AND DEFINITIONS Cancer Staging: Knowledge of the extent of the cancer at the time of diagnosis. It is based on three components: the size or depth of penetration

of the tumour (T), the involvement of lymph nodes (N), and the presence or absence of metastases (M). Clinical Decision Support Systems (CDSS): Entities that intend to support clinical personnel in medical decision-making tasks. In more technical terms, CDSSs are active knowledge systems that use two or more items of patient data to generate case-specific advice. Cluster Analysis: The task of decomposing or partitioning a dataset into groups so that the points in one group are similar to each other and are as different as possible from the points in other groups. Data Integration: The problem of combining data residing at different sources and providing the user with a unified view of these data. This important problem emerges in several scientific domains, for example, combining results from different bioinformatics repositories. Data Mining: The analysis of observational datasets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner. Mutation: A change in the genetic material (usually DNA or RNA) of a living being. Mutations can happen for a lot of different reasons. They can happen because of errors during cell division, because of radiation, chemicals, and so forth. Single Nucleotide Polymorphism (SNP): A DNA sequence variation occurring when a single nucleotide—A, T, C, or G—in the genome differs between members of a species or between paired chromosomes in an individual. Tumour Suppressor Gene: A gene that reduces the probability that a cell in a multicellular organism will turn into a tumor cell. A mutation or deletion of such a gene will increase the probability of the formation of a tumor.

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, pp. 768-776, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 2.13

Module Finding Approaches for Protein Interaction Networks Tero Aittokallio University of Turku, Finland

ABSTRACT This chapter provides an overview of the computational approaches developed for exploring the modular organization of protein interaction networks. A special emphasis is placed on the module finding tools implemented in three freely available software packages, VisANT, Cytoscape and MATISSE, as well as on their biomedical applications. The selected methods are presented in the broader context of module discovery options, ranging from approaches that rely merely on topological properties of the underlying network to those that take into account also other compleDOI: 10.4018/978-1-60960-561-2.ch213

mentary data sources, such as the mRNA levels of the proteins. The author will also highlight some current limitations in the measured network data that should be understood when developing and applying module finding methodology, and discuss some key future trends and promising research directions with potential implications for clinical research.

INTRODUCTION Recent advances in experimental technologies and computational methods have made it possible to measure and predict protein-protein interactions on a global scale (see Chapters I-VI and

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Module Finding Approaches for Protein Interaction Networks

Shoemaker & Panchenko, 2007a, b). While the large-scale interaction datasets can provide an unprecedented glimpse into the cellular mechanisms underlying the behavior of various biological systems, the increasing sizes and densities of the protein interaction networks available today pose also many challenging computational problems. In particular, the inherent complexity of most biological processes and the large number of possible interactions involved can make it difficult to interpret and mine the interaction networks only by eye, even with the help of sophisticated visualization and layout tools available. Software packages that implement computational tools for more advanced network data mining can facilitate the explorative network analysis by identifying the key players and their interactions that contribute to the cellular processes of interest. This may allow e.g. to pinpoint errors in experimentally or computationally derived interaction links, identify proteins directly involved in the particular process, and to formulate hypotheses for follow-up experiments. It should be realized, however, that even if the rapidly developing complex network theory has successfully been applied to analysis of various social and technological networks, such as the Internet and computer chips, its impact on studying molecular interaction networks is still an emerging area of research and therefore all these methods should be considered as experimental. At the moment, the computational tools are best used together with network visualization and analysis software that enable interactive and fully-controlled mining of the complex protein interaction networks. One of the most fundamental properties found in many biological networks is their modular organization (Hartwell et al., 1999). Consequently, the decomposition of large networks into a hierarchy of possible overlapping sub-networks (so-called modules) has become a principal analytical approach to deal with the complexity of large cellular networks (Barabási & Oltvai, 2004). In protein interaction networks, a functional module refers

to a group of physically connected proteins that work together to carry out a specific cellular function in a particular spatio-temporal context. A large number of computational tools of increasing complexity have recently been developed for investigating the modular organization of interaction networks. These tools cannot only identify whether a given network is modular or not, but also detect the modules and their interrelationships in the underlying network. By relating the found sub-networks with complementary functional genomics or proteomics data, such as gene expression profiles from genome-wide microarray experiments or protein abundance measurements from mass-spectrometry-based assays, it is also possible to identify a hierarchy of connected groups of components that show coherent expression patterns. Such functionally organized modules cannot only emphasize the biological meaning of the modules discovered, but also allow us to gradually focus on the active subsystems of particular interest (active modules), which can lead to concrete hypotheses about the regulatory mechanisms and pathways most important for the given process (Ideker et al., 2002). Moreover, these modules can subsequently be used in predictive modeling studies, with the aim of suggesting new biological hypotheses, such as unexplored new interactions or the function of individual components, or even distinguishing different biomedical phenotypes (discriminative modules). While several computational approaches to discovering functional modules in protein interactions networks are available to molecular biologists, and new ones are constantly being developed by the methodologists, there is no general consensus on how to choose between the different strategies and definitions. The choice of the network mining approach is an important aspect which depends both on the network and questions under the analysis. Therefore, basic understanding of the graph-theoretic concepts and algorithms behind the software packages

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is required so that the specific questions can be addressed and biologically meaningful interpretations can be made. This chapter describes several computational strategies developed for investigating the modularity of protein interaction networks, together with their relative merits and potential pitfalls. Since this topic has witnessed great progress lately, only representative examples of the different approaches can be presented. Among the multitude of algorithms and methods available in the literature and Internet, I describe here those features and options available in three popular software tools, VisANT, Cytoscape and MATISSE, which support the discovery of functionally coherent network modules. These publicly available software were selected because they come with a convenient graphical user interface making the methods easily accessible also to the end-users without programming skills. A special emphasis is placed on four specific tools |E|=18implemented in these software, which represent typical computational strategies in the wide spectrum of module finding options, ranging from approaches that are based on topological network properties, such as nodes with a particular connectivity level (e.g. VisANT) or sub-networks that are highly connected (MCODE), to more sophisticated approaches that take into account also other related data sources, such as significant differences or co-expression patterns in the mRNA levels of the proteins (jActiveModules and JACS, respectively).

BACKGROUND AND MOTIVATION Many advanced network analysis software complement the visual network exploration with options that facilitate the characterization of topological and functional properties of the underlying network (comparison of the features available in the popular software tools can be found from Chapter XVI and Suderman & Hallett, 2007; Zhang et al., 2007; Cline et al., 2007). Typical

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topological properties include, e.g., the degree distribution for determining hub nodes, clustering coefficient for detecting dense neighborhoods, and shortest paths for finding n-neighborhoods of selected nodes; see Figure 1 and Box 1 for the basic graph-theoretic concepts and their biological counterparts. Such measures, either alone or when compared to those of the whole network or its randomized counterpart, can be used to identify topologically coherent sub-networks, which may encode functional modules – i.e. groups of protein nodes and their interactions that can be attributed to a particular biological function (Hartwell et al., 1999). Several computational definitions of the topological properties of specific sub-networks, together with their biological interpretations, have been proposed during the past few years (Barabási & Oltvai, 2004), many of which – e.g. clusters, complexes and motifs – are described in detail in Chapters VIII and IX. In this chapter, the focus is on those network properties that have widely been recognized in many protein interaction networks: (1) modules are highly connected sub-networks (i.e. intra-module connectivity is higher than intermodule connectivity), and (2) protein interaction networks have hierarchical modular organization (modules are not isolated but they can interact and overlap at multiple levels). The first criterion reflects the fact that certain proteins are involved in common elementary biological functions, while the second implies that the same proteins can be active in multiple cellular processes at different points of time or under different conditions. In addition to such topological properties, requirement of functionally coherent modules often involves also some type of complementary information on the protein nodes or their interactions that can facilitate revealing the multi-level functional organization of the protein interaction networks. A useful source of complementary information on the activity of the protein nodes originates from the high-throughput transcriptomics and proteomics technologies. In particular, genomewide mRNA or protein expression measurements

Module Finding Approaches for Protein Interaction Networks

Figure 1. Basic graph-theoretic concepts. An example network graph G describing 18 binary interactions (edges) among 12 proteins (nodes), i.e. the cardinalities are |V|=12 and The neighborhood graph Na=(Va,Ea) of the node a is the set of nodes in G and their interactions that are directly connected to a; the left-hand circle in the figure identifies Na. By definition, the node a itself and its own connections are excluded from the sets Va and Ea, respectively. The degree d of a node is the number of its first neighbors, e.g. d(a)=|va|=5, making it a hub protein in the network. The path length between two nodes is the number of edges on a shortest path between them; for instance, node b belongs to the 4-neighborhood of a. This particular toy network is said to be connected, because there exists a path connecting each pair of the nodes. A sub-network is called a k-core if the minimal degree among its nodes is k. The density D of a graph G is the fraction of edges it has out of all possible node pairs; formally, D(G)=2|E|/|V|(|V|-1). The clustering coefficient c of a node defines the proportion of its first neighbors that are connected to each other; for instance, since there is only one edge connecting the neighbors of a, c(a)=D(Na)=2∙1/20. In other words, clustering coefficient quantifies how close the connectivity of the neighborhood of a node is to a sub-network in which every node is connected to every other node (so-called clique). For instance, the neighborhood of b in the right-hand circle forms a fully connected sub-network, and therefore c(b)=1.

using microarrays or mass-spectrometry, respectively, are increasingly being used to illuminate the molecular mechanisms involved in the control of cellular systems in various organisms. However, interpretation of the resulting lists of genes or proteins remains a labor-intensive and error-

prone task, because listing the individual elements alone can provide only a limited insight into the multitude of biological processes the individual elements participate in. To facilitate distinguishing elements directly involved in a particular process from bystander elements, whose expres-

Box 1. Network nomenclature and biological counterparts A graphG=(V,E) is a mathematical object that represents a protein interaction network. Conventionally, protein-protein interactions are modeled by undirected graphs G, in which V is a set of vertices (protein nodes), and E is a set of edges connecting pairs of nodes (binary interactions). The activity states of the proteins can be represented as node attributes (e.g. continuous weighting by the mRNA level of the gene encoding the particular protein under a specific condition), and the information how and when these biomolecules interact with each other as edge attributes (e.g. discrete labeling by the conditions under which the particular interaction is active). A module is a sub-network of G with specific properties. Typical topological properties include neighborhoods of highly connected genes (hubs), highly connected subnetworks (clusters), and highly-occuring sub-networks with particular connectivity patterns (motifs). Additionally, it can be required that the sub-networks show coherent expression changes (active modules), or allow distinguishing between distinct phenotypes (disriminative modules). In cell biology, such modules may correspond to functionally coherent groups of biomolecules, which act together in cellular mechanisms or processes, e.g., sub-units of protein complexes or biological pathways.

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sion have been altered by secondary effects or technical artifacts, the expression data can be mapped on a protein interaction networks to provide better understanding of the overall organization of the system in its entirety, rather than investigating individual genes, proteins or pathways separately. Moreover, instead of using the conventional pathway enrichment analysis among pre-defined gene/protein sets, which are limited to well-studied systems and known pathways only, identification of network modules that are active in the particular experiment is better targeted at finding novel interactions, cross-talks between the established pathways, and eventually the biological meaning of each individual component. Therefore, analyzing experimental data in the context of protein interaction networks can, in general, help to determine system-level cellular mechanisms explaining the experimental observations. Functional modules extracted from the human interaction networks, in particular, that are becoming available at an increasing rate, can provide systematic strategies to unravel the molecular basis of complex human diseases, and therefore enable identification of novel molecular markers for pharmaceutical and/or diagnostics developments for many disorders (Ideker & Sharan, 2008).

MODULE DISCOVERY ON THE BASIS OF NETWORK TOPOLOGY ALONE At the most general abstraction level, the protein interaction networks can be investigated and compared on the basis of their topology (i.e. the connection structure between the nodes). The representation of protein interaction networks as undirected graphs makes it possible to systematically investigate the structure-function relationships of these networks using well-understood graphtheoretical concepts and algorithms (Przulj et al., 2004). Global network measures, such as distribu-

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tions of the node degrees, clustering coefficients, or path lengths, that can capture the overall network organization, have been proposed as topological correlates of the network evolution, function, stability or even dynamic responses (Yook et al., 2004; Albert, 2005). Alternatively, characterizing local interconnectivity patterns in terms of relative importance of individual nodes (centrality measures) or connecting paths (pathway redundancy), can provide more detailed functional information on the node neighborhoods and alternative pathways, and hence facilitate understanding of fundamental biological concepts, such as proteins’ essentiality and robustness of a particular system under internal or external perturbations (Estrada, 2006; Aittokallio & Schwikowski, 2006). Since the topological parameters can be rather sensitive to the chosen experimental setup and data analysis protocol (Hakes et al., 2008), it is essential to utilize these network measures in software packages, which allow multi-resolution network display and mining, to confirm that the properties discovered are not due to experimental or computational artifacts only.

Characterizing Network Connectivity Patterns Using VisANT VisANT software package is a visual analysis tool for biological networks and pathways, which provides a number of useful tools for both exploring and displaying the topological properties of a given protein interaction network (Hu et al., 2004; Hu et al., 2008). For instance, the user can easily display a scatter-plot or log-linear fit of the degree distribution of the connections in a separate window, which is directly linked to the main network view so that nodes with a particular level of connectivity can be readily identified. Such plot allows for distinguishing highlyconnected nodes (hubs) from weakly-connected nodes (leaves), whereas the degree exponent of the log-linear fit can be used to characterize the scale of the network (Barabási & Oltvai, 2004).

Module Finding Approaches for Protein Interaction Networks

Without entering to the debate whether or not protein interaction networks exhibit a scale-free topology, the degree distribution provides a quick summary of the overall connectivity structure and enables differentiation between distinct classes of network topologies (e.g. power-law type of distribution from a peaked one as seen in random network topologies). For identifying the modular organization of the network, VisANT provides similar plots for the distribution of the clustering coefficient over all of the nodes in the network, which can characterize the overall tendency of the nodes to form clusters or groups. Moreover, VisANT enables plotting of the average clustering coefficient of the nodes as a function of their degree k: C (k ) =

1 nk

∑ D(N

d (v )=k

v

),

where nk is the number of nodes with degree k. The function C(k) can be used to characterize the network’s hierarchical structure, and it allows the user to identify local neighborhood of nodes at a particular connectivity and density level. VisANT software also enables evaluation of shortest path lengths between nodes, which can help to further expand the selected modules, and the occurrence of sub-networks with a pre-defined connection structure (network motifs). One way to evaluate the modular organization of the network with VisANT is to exclude part of nodes according to their degree value. Interactive experimenting with increasing degree cut-off thresholds, coupled with dynamic navigation of the reduced networks, can reveal densely connected areas when sufficiently many low-degree regions have been removed. The downside of this rather crude multi-level filtering process is that it may also destroy the intermodule connections. Once the user has selected the node groups, based on the topology statistics or motif configurations, or imported existing groupings from external databases or from own

files, VisANT provides many options for displaying and manipulating these higher-level graph structures as meta-graphs, with the possibility to expand and collapse them iteratively to show reduced network views at multiple scales. Nested meta-nodes allow also one node to have multiple instances in the network and these instances are automatically tracked by the software.

Identifying Highly-Connected Sub-Networks Using MCODE Cytoscape software platform has a wide spectrum of tools and plug-ins for extracting the basic topological properties, such degree distributions, clustering coefficients, shortest paths, centrality statistics, and over-represented motifs, in addition to the advanced filtering and displaying options based on such attributes available in the core of the software package (Shannon et al., 2003; Cline et al., 2007). Cytoscape also includes specific plug-ins devoted for discovering modules – or putative complexes and pathways – in the imported networks. The molecular complex detection (MCODE) algorithm by Bader and Hogue (2003) was developed to enable an automated discovery of densely-connected sub-networks in large protein interaction networks. The requirement of high density originates from the observation that highly interconnected regions of protein interaction networks often correspond to known protein complexes and parts of biological pathways (Barabási & Oltvai, 2004). Like VisANT, MCODE considers local groups of nodes with a given connectivity and density level, but it can also expand these core modules automatically by recursively including nearby nodes that either can preserve or increase the sub-graph density. More formally, the operation of the module detection algorithm is organized through three stages. First, the node weighting is done on the basis of the density of the node’s neighborhood. Instead of using the standard clustering coefficient, the algorithm uses so-called core clustering coefficient of a node a,

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which is the density of the largest k-core Sa in its neighborhood (including also a itself): C k (a ) = D(Sa ),

where Sa = arg max {| S |: S ⊆ N a ∪ a, ∀v ∈ S : d (v ) ≥ k }

Depending on the chosen value of k, the definition of Ck(a) can emphasize the hub nodes, while giving smaller weights to the less-connected nodes. The final weight attached to the node a is the product of its core clustering coefficient Ck(a) and the size |Sa| of the highest k-core in its neighborhood. In the second stage, greedy optimization algorithm is used, which starts from the seed nodes with highest weights, and then recursively traverses their neighborhoods by including those nodes into the module whose weights deviate from the seed node’s weight by less than a user-defined cutoff value. This parameter controls the size and density of the resulting sub-networks. The third stage involves post-processing steps that can filter out or add nodes based on their connectivity. The output of the MCODE algorithm is a list of possible overlapping modules ranked according to their density and size values. The algorithm includes several useradjustable parameters, with different impacts on the results. The fast running time makes it possible to experiment with different parameter combinations, and their fine-tuning can be carried out according to existing biological knowledge by starting the optimization algorithm from known seed nodes. A further advantage over many traditional network clustering algorithms is that MCODE assigns to the modules only those nodes above a given local threshold density, whereas the rest of the nodes remain un-clustered (Figure 2).

MODULE DISCOVERY ON THE BASIS OF TOPOLOGY AND EXPRESSION One potential pitfall of the module finding approaches that rely solely on the topology of the networks is that false negative and positive inter-

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actions can have severe effects on their results, regardless of whether one identifies protein hubs, clusters or network motifs. Since the current interaction networks are still limited both in accuracy and coverage, especially in more complex organisms like human, the topological structure alone may not always be sufficient for fully understanding of the complex cellular mechanisms and functions. To make the identification of functional modules more robust against experimental and modeling artifacts, a number of approaches have proposed the usage of protein interaction networks in conjunction with complementary information such as protein localization, expression or function (Tornow and Mewes, 2003; Lu et al., 2006; Lubovac et al., 2006; Cho et al., 2007). The presumption behind all such integrative module discovery approaches is that exploiting heterogeneous types of data sources in concert may be impacted less by the noise and biases associated with each individual data source if they were used separately. However, such integrative analysis also complicates the validation of the methods due to the overlap between complementary data sources and functional annotations, and thereby may increase the risk for circular reasoning. Here, I focus on those approaches only that combine global interaction networks with mRNA expression data, although any continuous type of state data on the nodes, such as protein abundance, could in principle be used instead. Most computational approaches that use both expression and network datasets are motivated by those early experimental studies which demonstrated that genes with similar expression profiles are more likely to encode interacting proteins (Ge et al. 2001). In particular, it has been shown that many stable protein complexes show significant co-expression in their mRNA levels (Jansen et al., 2002). Inspired by these observations, several computational studies have used genome-wide mRNA expression data to characterize and predict protein interactions (Tirosh & Barkai, 2005; Sprinzak et al., 2006; Shoemaker & Panchenko

Module Finding Approaches for Protein Interaction Networks

Figure 2. An example output from the MCODE. The selected sub-network corresponds to the 3rd highest ranked module in a sample interaction network of Cytoscape (galFiltered). The red intensity in the node color reflects its score, while the white nodes remain unscored and therefore unclustered. The shape of the nodes indicates their roles in the clustering process (seed node – square, core cluster member – circle and non-core cluster member – diamond). The default parameter values were used both in scoring and finding the core clusters. The Include Loops option controls whether or not also self-edges are used when calculating the neighborhood density. The Degree Cutoff parameter controls the minimum number of connections for a node to be scored. When the Haircut option is turned on, all the singly-connected nodes will be removed from the core cluster. The Fluff option enables expanding the cluster cores by one neighbor outwards. The Node Score Cutoff is the most influential parameter for the cluster size. New cluster members are added only if their node score deviates from the seed node’s score by less than the set cutoff percentage. The Size Threshold slider on the right allows the user interactively shrink or expand the core clusters by adjusting the threshold below or above its original value (^).The Node Attribute Enumerator provides a summary of the protein attributes and their frequency in the module (here the GO Biological Process annotation is selected). The K-Core parameter enables filtering out modules that do not contain an inter-connected sub-network of at least k degrees. The Max. Depth parameter controls how far from the seed node the search for new members can extend (virtually unlimited by default). For a more detailed and up-to-date parameter information, consult the MCODE website (http:www.baderlab.org/Software/MCODE) and the original article (Bader and Hogue, 2003).

2007b), or used these complementary data resources together to reveal dynamic organization of modularity in protein interaction networks (Han et al., 2004). Most network visualization software tools also allow mapping gene expression data on the global interaction networks to facilitate their interpretation and integrated analysis. For instance, VisANT 3.0 enables the user to select node groups based on expression ranges or profile

correlations, making it possible e.g. to discover new proteins with similar function to a given list of query proteins already known to have closely related function (Hu et al., 2007). Multiple experiments can be navigated using a sliding bar and the navigation process can be animated using color coding. However, VisANT currently lacks more automated module finding options based

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on both the networks connectivity and expression correlation structure.

Identifying Sub-Networks with Similar Expression Profiles Using MATISSE MATISSE software by Ulitsky & Shamir (2007) provides automated module detection tools that makes use of both the topology of interaction patterns as well as similarity sets of nodes inferred from expression data. In addition to several visualization and clustering options for network and expression datasets separately, it implements an efficient algorithm for identifying jointly active connected sub-networks (JACS) – i.e. connected sub-networks in the interaction data that show high internal similarity in their expression profiles (Ulitsky & Shamir, 2007). The algorithm finds such putative modules using statistical hypothesis testing framework, which evaluates the significance of the pairwise expression similarities Sij between all those node pairs (i, j) that belong to a connected sub-network U (candidate modules). Pearson correlation between the expression patterns of nodes i and j is used as a default Sij, but in principle any similarity measure could be used instead. More formally, let γijm = β Pi Pj be the probability that the nodes i and j are in the same group (so-called module hypothesis), where β is a parameter reflecting the general probability of any pair to group together and Pi is a prior probability describing how likely is it that the node i is transcriptionally regulated. Similarly, let γijn = pm Pi Pj be the probability that the nodes i and j are in the same group under random selection of pairs (the null hypothesis), where pm is the fraction of node pairs expected at random. The putative module U is then evaluated using the log-likelihood ratio score: L(U ) =

∏

i , j ∈U

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γijm P (Sij | M ij ) + (1 − γijm )P (Sij | N ij ) γijn P (Sij | M ij ) + (1 − γinj )P (S ij | N ij )

This formula stems from the assumption that the pairwise similarities Sij are conditionally independent, and that they originate from a mixture of two Gaussian distributions, in which the two normal components P(Sij|Mij) and P(Sij|Nij) correspond to the node pairs (i,j) that have high or low expression similarity, respectively. These distributions, together with the prior probabilities under the two rivaling hypotheses, are automatically learned with the expectation-maximization algorithm. Using this scoring function, MATISSE identifies modules through a heuristic multi-stage optimization procedure. First, relatively small, high-scoring, connected node sets are detected as seeds. Second, a greedy iterative algorithm is used to optimize all the seeds simultaneously by considering the following four operations: (i) adding an unassigned node to a module, (ii) removing a node from a module, (iii) exchanging a node assignment between two modules, and (iv) merging two modules by taking their union. An operation is accepted if it improves the overall score calculated over all the modules and also preserves the connectivity of the modules. In the last stage, significance filtering of the resulting modules is performed by sampling random node sets of the same size and comparing their scores to those of the detected ones. While it assumes no prior knowledge on the number of modules (size constraints can be introduced), the optimization procedure forces the modules to be non-overlapping, which may hinder the assignment of proteins whose expression levels are a result of multiple cellular functions reflected in the experiment.

Identifying Sub-Networks with Coherent Expression Changes Using jActiveModules The usage of pairwise similarities, like Pearson correlation, in the identification of functional modules makes it possible to use various types of expression dataset, e.g., time-series or steady-state experiments. This comes with the potential limita-

Module Finding Approaches for Protein Interaction Networks

tion that such overall measures of co-expression may ignore the fact that a protein can be active in a particular process over a subset of experimentally measured conditions or time-points only – a phenomenon termed just-in-time assembly (De Lichtenberg et al., 2005). Experimental studies have indeed demonstrated that some components of protein complexes may show coherent expression changes under a few conditions only and those components often correspond to known sub-complexes with specific functions (Simonis et al. 2006). This motivates those computational approaches that search for connected sub-networks whose nodes show differential expression over a particular subset of conditions only. One such method by Ideker et al. (2002) is implemented in the Cytoscape software as a plug-in called jActiveModules. The algorithm requires as an input the condition-specific pa,c-values representing the significance of differential expression of a node a under each condition c. The method first converts the p-values to z-scores, using the inverse normal cumulative distribution function, that is, za,c=P-1(1-pa,c), and then derives an aggregate score Sc(A) for a sub-network A under the condition c by averaging the z-scores over the nodes in A. Using the simplifying assumption that these conditionspecific scores are independent, the significance of the jth highest scoring condition Sj(A) can be computed using the binomial order statistic: m   h m −h m p j (A) = ∑   1 − P (S j (A)) P (S j (A)) .      h  h=j 

The pj(A) -value gives the probability that at least j of the m conditions have scores above Sj(A), which reflects the activity of the sub-network A under conditions ranked 1 through j. After adjusting the scores for the rank and correcting for the background score distribution by sampling randomly node sets of similar size, independently of the underlying interactions, the scoring function can be use to rank both the sub-networks and

the subsets of conditions. For identifying highscoring connected sub-networks, jActiveModules enables using a stochastic optimization heuristic based on simulated annealing (Kirkpatrick et al., 1983). Briefly, in each iteration, a random node of the network is either removed or added to the module and the score for the resulting new subgraph is calculated. If the new score is larger than the previous one, then the move is accepted; otherwise the move is accepted with a probability proportional to the current temperature, which decreases geometrically with the iteration number. Finally, the temperature is set to zero, meaning that the optimization becomes greedy, until the algorithm reaches the closest local maximum. The algorithm can be adjusted by allowing the user to define the number of modules searched simultaneously and by reducing the effect of hub nodes on the search results (Ideker et al. 2002). To output smaller modules for visualization, one can repeat the search within each original sub-network using the local search option (see Figure 3). Several modifications to the original scoring function and search algorithm have been developed since then (Sohler et al., 2004; Rajagopalan & Agarwal, 2005; Cabusora et al., 2005; Nacu 2007; Guo et al., 2007; Dittrich et al. 2008).Figure 3. Network modules involved in HIV-1 reactivation. An example of integrated analysis using both gene expression profiling of human T-cell lines infected with human immunodeficiency virus (HIV), as well as curated human-human and human-HIV protein interactions from HPRD and HIV-1 interaction database, respectively. The active sub-networks during different stages of the process were identified using a local search, with parameters depth=2 and max depth=2, in the jActiveModules plugin to Cytoscape software (Ideker et al. 2002). The significance of differential expression at different activation stages was assessed using p-values from the t-test. The color of the nodes corresponds to the mean expression change and the shape indicates the species (HIV – diamond and human - circle). The sub-network shown in

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the inset was active during the early stages of the reactivation. This connected sub-network included five hub-proteins (Rb1, Polr2a, Jun, HSPca and Pten), each of which had significant numbers of differentially expressed neighbors (p<0.01). As expected, the module contains HIV-1 proteins, such as p53 (TP53), which co-operates with Tat in the activation of HIV-1. Moreover, casein kinase 2 (Csnk2a1) is involved in the activation of the HIV-1 replication and it interacts with PTEN

and HSPca, both of which are known to interact with Tat. In addition to the known players, the active sub-network can suggest novel functions relevant for the HIV pathogenesis (Bandyopadhyay et al., 2006). The figure was reproduced with permissions from the authors of the original work and the editors of the Proceedings of the Pacific Symposium on Biocomputing 2006.

Figure 3. Network modules involved in HIV-1 reactivation. An example of integrated analysis using both gene expression profiling of human T-cell lines infected with human immunodeficiency virus (HIV), as well as curated human-human and human-HIV protein interactions from HPRD and HIV-1 interaction database, respectively. The active sub-networks during different stages of the process were identified using a local search, with parameters depth=2 and max depth=2, in the jActiveModules plugin to Cytoscape software (Ideker et al. 2002). The significance of differential expression at different activation stages was assessed using p-values from the t-test. The color of the nodes corresponds to the mean expression change and the shape indicates the species (HIV – diamond and human - circle). The sub-network shown in the inset was active during the early stages of the reactivation. This connected sub-network included five hub-proteins (Rb1, Polr2a, Jun, HSPca and Pten), each of which had significant numbers of differentially expressed neighbors (p<0.01). As expected, the module contains HIV-1 proteins, such as p53 (TP53), which co-operates with Tat in the activation of HIV-1. Moreover, casein kinase 2 (Csnk2a1) is involved in the activation of the HIV-1 replication and it interacts with PTEN and HSPca, both of which are known to interact with Tat. In addition to the known players, the active sub-network can suggest novel functions relevant for the HIV pathogenesis (Bandyopadhyay et al., 2006). The figure was reproduced with permissions from the authors of the original work and the editors of the Proceedings of the Pacific Symposium on Biocomputing 2006.

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EVALUATION OF THE SIGNIFICANCE OF THE MODULES DISCOVERED

Evaluating the Methods in Terms of Robustness Against Technical Factors

An increasing number of methodological papers have recently introduced new and improved computational methods for finding functional modules from protein interaction networks and other information sources; see e.g. Chapter IX and the recent review by Sharan et al (2007). Each of these papers concludes that the modules found by the particular method significantly overlap with known protein complexes (extracted from databases such as MIPS) and show significant homogeneity in their functional annotation (typically assessed using enriched GO categories). Given the known association of protein interaction patterns and expression profile similarity with the established protein complexes and functional classifications available, these results are not very surprising and cannot indisputably demonstrate the relative benefits gained by using the particular method, unless a set of reference methods has been applied to the same datasets. As a reference method, many methodology papers use random node sets of similar sizes to asses the statistical significance of the modules detected, and evaluate overlap with known functional classes to show functional homogeneity observed in these sets. However, it could be argued that such comparisons against random sub-networks, regardless whether or not the random model preserves the connectivity structure of the modules, should be considered more like a sanity check for the proposed method, and as such can provide only a little indication of the biological significance of the putative modules discovered. A potential caveat of using functional annotations either in module finding or in their validation is the limited accuracy and availability of annotations for some organisms, especially for human, and for some biological processes that are not yet well described in GO or other knowledge databases.

In the very systematic comparison by Brohée & van Helden (2006), the authors evaluated a number of topology-based module finding algorithms also in terms of their sensitivity to their user-adjustable parameters values and against different levels of false positive and negative links known to occur in the measured protein interaction networks. The authors found that the most robust method was the one based on the Markov clustering algorithm (Pereira-Leal 2004). Recently, Sharan et al. (2007) reviewed a number of computational approaches to protein interaction network-based function prediction and classified them into direct methods, which infer the functional role of a protein through its connections in the network, and module-assisted methods, which use the detected sub-networks for the annotation task. Using a small-scale comparison in a single protein interaction network from BioGRID, they observed that the straightforward neighbor-counting method of Schwikowski et al. (2000) had a higher specificity in predicting GO Biological Process annotations when compared to the MCODE-assisted method (Sharan et al., 2007). The authors attributed this difference in the function prediction performance to the fact that the current module discovery methods are biased towards those processes that impose highly-connected sub-networks. However, more comprehensive evaluation studies are critically needed that consider also other technical factors, e.g. how the module findings are affected by the different graph models used for representing the measured interaction patterns (Scholtens et al. 2005; Rungsarityotin et al., 2007), and what is the contribution of the particular experimental design, known sampling biases, or the varying experimental techniques for measuring the protein interactions (Pu et al., 2007; Gentleman & Huber, 2007).

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Assessing the Biological Significance of the Modules Discovered Beyond evaluating the modules in statistical or technical terms only, the true relevance of the computational approaches can be assessed with concrete biomedical applications by showing how the putative modules can provide e.g. added biological insights into the molecular processes being studied, or even novel medical information e.g. in terms of molecular markers for diseases. While the technical comparisons are important prerequisites for performing such real applications, their practical contribution may remain very limited from the biologist point of view. At the moment, there seems to be no systematic comparative studies among the different module discovery approaches that would demonstrate their relative benefits in more biological terms. The situation is very similar with the ongoing debate on the biological meaning of other topological properties, such as the scale-free property (Stumpf et al., 2005; Han et al., 2005; Khanin & Wit, 2006; Friedel & Zimmer, 2006a), or the concepts of ‘date hubs’ and ‘party hubs’ (Ekman et al., 2006; Batada et al., 2006; Bertin et al., 2007). The biological significance of these rather abstract network properties are still partly unclear and open to many opinions and speculations. This means that the biological conclusions drawn from the global topological measures should be viewed with some skepticism (Hakes et al., 2008). The same caution is also needed when interpreting the modular organization of the protein interaction networks. An additional dimension that may be needed when assessing the eventual biological relevance of the network hubs and modules comes from an evolutionary perspective (see Chapter X and Poyatos & Hurst, 2004; Batada et al., 2006; Wang & Zhang, 2007).

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Elucidating the Biological Processes Implicated in Human Diseases While the success of the module-based mining and interpretation of biological processes has originally been demonstrated in model organisms, such as yeast, functional modules have increasingly been identified also in more complex organisms, especially in the human interactome. Showing concrete implications, such as disease mechanisms or molecular marker, for clinically important phenotypes can be regarded as one important form of evaluation of the significance of the putative network modules. Similarly as the topology-based modules can be very sensitive to the artifacts in the interaction data, it has increasingly been recognized that disease biomarkers discovered solely from microarray or mass-spectrometry experiments may show very poor reproducibility due to several sources of experimental, technical and biological variation that can severely confound the actual phenotypic variability. Integrated computational analysis of expression datasets together with network modules has been shown to improve elucidating the underlying biological processes associated with complex disorders, like cancer phenotypes, as well as to provide more reproducible set of biomarkers having better coverage with known disease risk genes and improved sample classification accuracy as compared to the standard analysis of differential expression alone (Efroni et al., 2007; Chuang et al., 2007). Other promising applications of molecular networks to human disease include identifying new disease gene nodes and their network properties, as well as identifying diseaserelated protein complexes or sub-networks (Hu et al., 2007; Lage et al., 2007; Ideker & Sharan, 2008). Such integrated analysis can e.g. identify relevant components, which are not differentially expressed or may even not have been profiled under a given condition (e.g. JUN protein in Figure 3). Several additional approaches and illustrative examples how protein interaction networks can

Module Finding Approaches for Protein Interaction Networks

help prioritizing disease genes and understanding disease pathways are described in Chapter XIV.

LIMITATIONS, EXTENSIONS AND FUTURE RESEARCH DIRECTIONS Perhaps the biggest challenges the current computational approaches are facing still originate from the limited coverage and accuracy of the interaction data available. Protein-protein interaction maps for yeast and human were estimated to contain only roughly 50% and 10% of all of the possible interactions in 2006 (Hart et al., 2006). There are various experimental techniques for identifying protein interactions, the major differences arising from their scale and ability to detect binary or complex interactions (Shoemaker & Panchenko, 2007a). However, all the techniques currently used in large-scale interactions screens are poor at detecting very transient interactions and those that depend on posttranslational modifications. Another technical complication comes from the fact that these techniques detect interactions that are biophysically possible but may not actually occur in the living cells. Moreover, the measured interactions are very much dependent upon the cell types studied and on the developmental stages or external conditions under which the measurements were performed. In particular, the completion of the human interactome will pose many experimental problems, not least because of its large estimated scale and relative sparseness (Stumpf et al. 2008). The context-dependency together with the large number of false positive and false negative interactions, that are bound to occur in the current human protein interaction datasets, also explains their small overlap (Ramírez et al., 2007; Futschik et al., 2007). These experimental limitations should be understood when developing and applying data mining methods. Computational strategies can also help to increase the coverage and accuracy of the interaction networks by predicting and prioritizing those components and

their interactions, either observed or missing, that warrant further experimental testing (Qi et al., 2006; Qiu & Noble, 2008; Skrabanek et al., 2008). It can be expected that the development of data mining approaches to protein interaction networks will benefit greatly also from the ongoing community efforts to standardize the representation and storage of the interaction datasets (Orchard et al., 2007; Kerrien et al., 2007; Klipp et al., 2007), as well as from the integrated tools for accessing and querying the public interaction databases (Cerami et al., 2006; Strömbäck et al., 2006; Dhanapalan & Chen. 2007; Newman et al., 2008; Splendiani, 2008). Albeit being error-prone and incomplete, the protein interaction networks mapped so far have already proved useful in understanding biological processes and have also triggered many biomedical applications with relevance to human health. The large-scale experimental datasets for human cell biology, in particular, that are constantly improving both in quality and coverage, are enabling computational methods to systematically address exciting biological questions, such as identifying the key players and their roles responsible for the multi-factorial behavior in complex diseases. An approach with a particular biomedical potential involves using protein interaction networks as priors when analyzing genome-wide gene or protein expression datasets; network modules can be used for distinguishing those genes and proteins directly involved in a particular disease process from the background variation originating from other biological factors or experimental noise. Besides such false positives detections, the use of connectivity patterns can help to decrease the number of false negatives as well, caused e.g. by those members of the module that are expressed at low or constant levels. The need of such integrated analysis is emphasized when the sample sizes are small and the phenotypes of interest involve also components with subtle expression changes. Accordingly, such network-based analysis of complex phenotypes is likely to be extremely

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useful in diseases like diabetes (Liu et al., 2007). Possible future extensions of the network-based identification of potential biomarkers include accounting for inter-individual variability in the disease responses: by aggregating the modulelevel expression changes between different subjects, it may be possible to provide multiple sets of molecular markers that correspond to different subsets of patients. Such modular approach to biomarker discovery should be robust to high measurement noise and intrinsic biological variation, thus enabling in the future tests to distinguish between different disease subtypes, progression stages, individual risk factors, or even individual response to different treatments. To increase the coverage and accuracy of the protein interaction networks, the module searches can also be extended across multiple species (Dutkowski & Tiuryn, 2007; Kalaev et al., 2008). Such conserved sub-networks can suggest e.g. undescribed protein functions or interactions (Sharan et al., 2005), and when integrated with transcriptional interaction data, the cross-species discoveries can also provide insights into the evolutionary dynamics of protein complex regulation (Tan et al., 2007). Beyond different species, similar approaches could be extended to include also information on the varying external conditions, interaction types or time points, thus making the predictions more reliable by uncovering modules that are supported by interactions patterns of different types. An interesting future investigation would also be to assess how much adding complementary data sources can really contribute to the success of the module finding algorithms; in particular, the extent to which gene expression data can help to improve the pure topology-based methods and what is the optimal way of incorporating the high-throughput expression measurements into the module detection algorithms. An inherent limitation of the current methods that utilize both the network connectivity and expression levels, including JACS and jActiveModules algorithms, is that they cannot assign into the same module

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genes that show different behavior in their co-expression or differential expression patterns under the specific spatio-temporal context (e.g. just-intime assembly). These limitations demonstrate that there is still room for many improvements in the module finding methodology, perhaps one of the most striking challenges being how to take the full advantage of the temporal information in the many time-series expression datasets available today, rather than treating the time points as independent conditions. By addressing these and related questions, we may in the future be able to more accurately distinguish between different types of network modules, with distinct biological roles, such as those corresponding to stable protein complexes and dynamic functional modules (Spirin & Mirny, 2003; Komurov & White, 2007). Most computational approaches to module finding can be formulated as an optimization problem, which takes into account the networks topological properties and possible other data sources in their scoring function. Depending on the search strategy used, the solution can output either distinct or overlapping sub-networks. While greedy search is typically used because it is computationally inexpensive and provides reasonably good results in practice (Breitling et al., 2004; Nacu et al., 2008), more comprehensive search strategies have also been used (King et al., 2005; Rajagopalan & Agarwal, 2005). In the initialization phase, one can either use completely random seeds, high scoring nodes or proteins that are a part of the complex or pathway under investigation (Altaf-Ul-Amin et al., 2006; Maraziotis et al., 2007). The network information can be incorporated into the analysis of genomic datasets also using probabilistic frameworks, such as Markov random field models (Segal et al., 2003; Wei & Li, 2007) or spatially-correlated mixture models (Ulitsky & Shamir, 2007; Wei & Pan, 2008). Several network properties can be considered when decomposing the network into modules (Aittokallio & Schwikowski, 2006). An interesting approach frequently used when

Module Finding Approaches for Protein Interaction Networks

discovering community structures in complex social networks is the so-called edge-betweenness centrality (Girvan & Newman, 2002). It has been shown that protein clusters defined by removing the edges with the highest betweenness centralities tend to share similar functions (Dunn et al. 2005), and nodes with high betweenness centrality (e.g. the node connecting the two modules in Figure 1) are more likely to be essential proteins as compared to the those with high degree (Yu et al., 2007). This local node property can also be applied when searching modules in weighted graphs (Yoon et al., 2006), making it possible to weight the interactions using e.g. gene expression microarray measurements (Chen & Yuan, 2006). Regardless of the topological property used as an objective function in the optimization, however, it is important to take into account the limited sampling and the large proportion of false positive and negative interactions when estimating the particular network property (Stumpf et al., 2005; Friedel & Zimmer, 2006b; Scholtens et al., 2008).

after contrasting the interaction patterns with other large-scale functional genomics or proteomics measurements. In particular, when mining human interaction networks, that are still limited both in their accuracy and coverage, integrated analysis of interaction networks together with complementary datasets is probably needed to identify robust, functional modules, which may eventually prove valuable also in many medical applications. The limitations of the current protein interaction datasets should be taken into account when developing new data mining methods and when applying these methods to real biological problems. The future success of the module-based analysis is likely to be driven also by parallel improvements both in the experimental techniques for mapping global interaction networks as well as in the modeling frameworks for representing the existing and emerging types of interaction information.

CONCLUDING REMARKS

The author thanks Jukka Hiissa for his technical assistance with the figures and Igor Ulitsky for the helpful discussions on the JACS procedure. The work was supported by the Academy of Finland (grant 120 569).

Graph-based models of protein interaction networks can combine heterogeneous system-level data sources and their integrative mining can provide valuable information about local and global characteristics of their modular and functional organization. The global structural properties try to characterize the networks as a whole, whereas local network analyses aim at discovering such individual interaction patterns that may carry significant information about their roles in specific cellular mechanisms. Module analyses seek to partition the complex networks into functionally organized hierarchy of interconnected groups that are involved in common cellular functions. While most current network modularization approaches are based solely on the topological properties, such as network hubs, clusters or motifs, it is likely that biologically relevant findings can only be made

ACKNOWLEDGMENT

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This work was previously published in Biological Data Mining in Protein Interaction Networks, edited by Xiao-Li Li and SeeKiong Ng, pp. 335-353, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 2.14

Networks of Action for Anti Retroviral Treatment Information Systems Elaine Byrne University of Pretoria, South Africa Roy D. Johnson University of Pretoria, South Africa

INTRODUCTION The South African government has an impressive constitution and legislative framework that recognizes the right of its citizens to quality health care (Government of South Africa, 1996). In South Africa, approximately 80% of the population relies on state-provided health care. Health workers in the public health sector provide services at the formal health facilities and to the various outreach programs in the community (i.e., immunization drives). The effective management and delivery DOI: 10.4018/978-1-60960-561-2.ch214

of these diverse services requires regular reporting of routine and exceptional information by health care workers. These workers spend a significant amount of time collecting, recording, storing, and transmitting data in various forms. With the commencement of the Anti-Retroviral Treatment (ART) program in selected clinics throughout South Africa in 2003 (Department of Health, 2003), treating and supporting clients attending ART clinics places great pressure on the health staff, not only because of insufficient human resources and time, but also with the associated severe emotional strain. Pressure is escalating as the number of clients requesting ART is increasing

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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daily (Stewart, Padarath & Bamford, 2004). An effective Information System (IS) is needed to manage this increase in clients as well as support a variety of reporting requirements. A national survey in South Africa of health personnel, ambulatory and hospitalized patients, and health facilities substantiates that a weak patient IS (a) was an impediment to ensuring ongoing and correct treatment, (b) increased staff workloads, and (c) led to unnecessary duplication of effort and time. Additionally, Shisana, et al. (2002) argue that ensuring that a single electronic IS is in place to assist in treatment of patients is an essential yet often neglected aspect of the health system. In 2005, the clinical director of the Batho Pele clinic1 in the Gauteng province in South Africa requested the assistance of the Department of Informatics at the University of Pretoria in addressing their IS issues. This request fitted the department’s research interests in health information systems (HIS), as well the broader research focus and commitment to provide outreach services to the community. Knowing the problems of commencing projects without having planned for sustainability and scalability, the HIS research group elected to use the “networks of action” concept to partner and collaborate with the various role players, institutions, and other ART entities. This process of developing interconnecting networks of human and nonhuman entities in South Africa and beyond its borders raised a number of opportunities, challenges, and tensions in initiating this project. To provide a background to this process, the next section introduces the concept of “networks of action” and a brief description of the ART clinic. The following section develops the main focus of this chapter, which is the process of developing these networks. The last section suggests the necessity of developing networks of action as a future trend for sustainable IS.

BACKGROUND Networks of Action In addressing why so many action research efforts fail in the long term, Braa, Monteiro, and Sahay (2004) argue that the two major challenges in the development of a successful HIS are the interrelated factors of sustainability and scalability. Sustainability refers to making the IS work over time through the institutionalization of routines and the development of local learning processes. Scaling concerns the spreading of a working solution to other sites (Braa et al., 2004). However, scalability is not merely a technical problem but encompasses a sociotechnical network, comprised of people, technology, and processes within an institutional context and relates to a process of specifically what is being scaled and how it is being scaled (Sahay & Walsham, 2006). As argued by Braa, et al. (2004), scalability requires local interventions to be part of or connected to broader networks in order for sustainability to occur. They argue that local action research interventions need to be conceptualized and approached as one element in a larger network of action in order to ensure sustainability. Sustainability cannot occur just through action at a local level, and scaling needs to occur through the creation of multiple interconnecting networks. A flexible and adaptive process, which accommodates planned and unplanned events, or as Giddens (1984) would say, anticipated and unanticipated consequences, needs to be adopted in order for scaling to occur successfully. Building on the Scandinavian-based action research’s recognition of the need to perform action as part of a network rather than as a sole local venture (Chisholm & Elden, 1993; Elden & Chisholm, 1993), Braa, et al. (2004) argue that the need to develop an institutionalized and sustainable system is not a luxury but a necessity and needs to be part of larger interventions. Networking enables the sharing of experience,

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knowledge, and technology and thereby scaling the learning process. A key concept from this learning process is an alternative approach to action research; namely, the development of networks of action. These are characterized as: 1. “Abandoning singular, one-site action research projects in favor of a network of sites 2. Generating local, self-sufficient learning processes together with working mechanisms for the distribution of appropriately formatted experiences across sites in the form of vertical and horizontal flows 3. Nurturing a robust, heterogeneous collection of actors likely to pursue distinct yet sufficiently similar agendas 4. Aligning interventions with the surrounding configurations of existing institutions, competing projects, and effort, as well as everyday practices” (Braa et al., 2004, p. 359) The development of networks of action is pivotal to addressing the challenges of sustainability and scalability and is especially important to the project described in this chapter, the Anti-Retroviral Treatment Information System (ARTIS) Project.

Background of ART Program The Batho Pele ART clinic was recently established (2006) and is housed in a district hospital in the province of Gauteng. The clinic is part of the government’s plan to improve the delivery of HIV/AIDS-related services (Department of Health, 2003). Principally, there are three phases in processing a patient at this ART clinic: •

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Phase 1. Making an initial appointment. This is based on the patient having a CD4 count <200 cells/mm3 or a WHO Stage IV disease (Department of Health, 2004). The

•

•

patient has to be referred to the clinic from another clinic or medical practitioner. Phase 2. Preprescription of ART. Depending on the health and emotional status of the patient, acceptance for ART can occur, starting with the third visit or later. Phase 3. ART prescription and followup. Once accepted onto ART, there are regular return visits scheduled for pill counting, patient assessment, and prescription renewal.

Each visit requires the patient to check in with the ART administrator and register with the hospital administrator. Once registered, the patient is seen on each visit by nurses for tests (i.e., blood, weight, urine, blood pressure) and by the doctor, who reviews and analyzes the test results and prescribes the treatment regimen. The pharmacist dispenses the antiretroviral drugs. Consultations with the counselor, social worker, and dietician occur as the need arises (De Freitas, 2005). Although there are official workflow processes for an ART clinic, these workflow processes are not adhered to stringently for a variety of reasons. Staff members are allocated to particular positions with clearly defined roles and duties, but due principally to shortages of staff, little segregation of duties occurs. When there is a shortage of staff or a staff member is experiencing a heavy workload, it is common for staff members to assist in completing the work tasks of their colleagues. For example, the administrator will assist the data entry operator when few patients are waiting at the reception. This implies that staff members are performing duties for which they have not received an adequate orientation, professional training, or supervised practice. This causes problems on many levels, such as security for accessing data and the quality of data entered incorrectly, incompletely, or duplicated (De Freitas, 2005). Based on this background of the meaning of networks of action and the situation at the ART

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clinic, the next section describes our experience of developing these networks.

ESTABLISHING NETWORKS OF ACTION From the preliminary review of the paper-based IS currently in place at the District Hospital ART clinic (De Freitas & Byrne, 2006), the IS does not meet the requirements of the staff and hence does not support the effective and efficient delivery of services to ART clients. The IS at the ART clinic is a paper-based system and a partial computer-based system using an Excel spreadsheet. The paper-based system consists of a patient file, which contains all the necessary health-related documents on the patient’s health condition and is stored in a filing room. The longer a patient stays on treatment, the bulkier and more cumbersome the documents become. The process of reviewing data in the paper file then becomes unwieldy, frustrating, and time consuming. The computer-based part of the system is an Excel spreadsheet originally designed at another ART clinic. These Excel spreadsheets contain basic demographic data, CD counts, and appointments. In these initial stages of design and development, a conscious effort was made to establish networks of action. This process has been time consuming, frustrating, and confusing. However, the focus on sustainability overcomes these challenges. The various actors involved to date in the process of developing our network of actions are as follows: •

•

Alignment of interests and focus. In 2005, the Informatics Department established an HIS research group. The team meets regularly, and HIS has become one of three research focus areas for the department. Memorandum of understanding. A memorandum of understanding, which clearly outlines the role and responsibilities

•

•

•

of the clinic and the university, has been established between the ART clinic and the Department of Informatics. The ART clinic agrees to give access at prearranged times; participate in IS requirements analysis, design, and implementation; and provide staff training on the system. The Department of Informatics agrees to perform an IS requirements analysis and develop a system; support the IS; assist in training health staff and students; and conduct research in IS design, development, and training. User requirements. A preliminary review of the IS requirements was obtained in 2005. In early 2006, an initial group meeting was held with all the clinic staff on the user requirements. However, at the following meeting, none of the clinic staff showed up. Later, they were evasive about the lack of support from the clinic staff but showed incredible interest in the documentation and insisted on keeping it. Building upon networks. The department has relationships with HIS research networks, such as Informatics Development for Health in Africa (INDEHELA) and Building Europe Africa collaborative Network for applying IS (BEANISH). INDEHELA is a Finnish initiative for collaborative research between Finland and Africa (Nigeria, Mozambique, and South Africa). Part of their research focuses on sustainability and contextual HIS design. BEANISH is a collaborative HIS initiative whose overall goal is to build networks of researchers and development between and within countries in Africa and Europe by studying practical applications of IS technology in the health sector. Linking existing ART modules or systems. One of the first steps of our project was a review of the existing ART modules or open source patient-based HIS. The Care2x hospital management system, the

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OpenMRS data model, the District Health Information System (DHIS) ver1.4 and 2.0, the Ethiopian ART module, and the Excel spreadsheet from another hospital have been explored to varying degrees. An ART module prototype has been developed and the possibility of exporting aggregated data to the DHIS, which is currently the official district HIS in South Africa (HISP, 2006), and the potential of integrating as a module into Care2x is being investigated. The most intensive review was a workshop in October 2006 on the refactoring of an existing ART module running in clinics of Ethiopia. The ART workshop was facilitated by the Open Source Centre from the Council of Scientific and Industrial Research in South Africa, and the refactoring process continued at the center the following week. Representatives from HIS networks in Ethiopia, Norway, India, and Vietnam attended the workshop. The development of the ART prototype took place during December 2006 and January 2007. Deployment of the system is still underway at the time of this writing. This review of the process of developing the networks of action has implications for future IS development projects and are now discussed.

FUTURE TRENDS From the review of the process of the development of the networks of action, it is apparent that this is a time-consuming process. However, developing such networks should be viewed as a necessity for the long-term sustainability of the systems. There is a number of implications of adopting such an approach, which surfaced within our process. •

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Time constraints. There is a constant battle to deliver health services to implement an ART solution quickly, yet the need is not to reinvent the wheel or develop paral-

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•

•

•

lel systems. An agile and staged approach to systems development is appropriate, but the scope still needs to be delimited. Various approaches. Transferring tools and techniques from one sector to another may not be appropriate. Gaining user requirements from health practitioners may be more fruitfully done through participatory prototyping as illustrated in the development of DHIS rather than gaining all user requirements upfront. Partnerships. Within the health sector, there is a need to build relationships with the clinic staff, middle and top management, and across research groups, universities, clinics, hospitals, government departments, and NGOs. Cultural differences and different work practices across cultures also pose a challenge. Understanding existing artifacts. Given the number of patient-based HIS and ART modules, the review of existing artefacts could be paralyzing. This, however, raises opportunities for pooling resources (expertise and financial), which would otherwise not be possible. Institutional building. The institution in which the research is to be conducted needs to be clearly defined, establish institutional backing, and have a long-term commitment.

Insufficient attention to the process of establishing networks can severely impact the sustainability and scalability of the IS. However, the process of establishing these networks is not generally described in action research projects or in IS development initiatives. From our experience, this process has been time-consuming but needs to form a substantial part of any IS that wishes to be sustainable and scalable.

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CONCLUSION Within the IS discipline, there has been a movement away from the previously dominant technical orientation of systems developers, stemming primarily from a computer science tradition to include and actively recognize the needs, aspirations, and expertise of users and to understand IS as social processes (Cornford & Smithson, 1996; Roode, 2003). However, there is little focus on how to develop the networks of action within this sociotechnical process. Missing this vital process in terms of planning and allocating resources can have a negative impact on IS projects. This stage of IS design and development should receive more attention in project management and IS development methodology.

REFERENCES Braa, J., Monteiro, E., & Sahay, S. (2004). Networks of actions: Sustainable health information systems across developing countries. Management Information Systems Quarterly, 28(3), 337–362. Chisholm, R. F., & Elden, M. (1993). Features of emerging action research. Human Relations, 46(2), 275–298. doi:10.1177/001872679304600207 Cornford, T., & Smithson, S. (1996). Project research in information systems: A student’s guide. London: Macmillan. De Freitas, M. R. (2005). Quality and use of data for decision support: A case study of the antiretroviral clinic. Pretoria, South Africa: Department of Informatics, University of Pretoria. De Freitas, M. R., & Byrne, E. (2006). Activity theory as an analytical tool: A case study of IS development for an anti-retroviral treatment clinic in South Africa. Proceedings of the South African Institute of Computer Scientists and Information Technologists (SAICSIT) conference, South Africa, 90–99.

Department of Health. South Africa. (2003). The operational plan for comprehensive HIV/AIDS treatment. Retrieved September 2006, from http:// info.gov.za/issues/hiv/careplan.htm Department of Health. South Africa. (2004). National antiretroviral treatment guidelines. Retrieved June 2006, from www.doh.gov.za/docs/ factsheets/guidelines/artguidelines04/intro.pdf Elden, M., & Chisholm, R. F. (1993). Emerging varieties of action research: Introduction to the special issue. Human Relations, 45(2), 121–142. doi:10.1177/001872679304600201 Giddens, A. (1984). The constitution of society: Outline of the theory of structuration. Cambridge: Polity. Government of South Africa. (1996). Constitution of the Republic of South Africa. Act 108 of 1996 as adopted on 8 May 1996 and amended on 11 October 1996 by the Constitutional Assembly. Retrieved October 2006, from http://www.polity.org.za/html/govdocs/constitution/saconst. html?rebookmark=1 HISP. (2006). History of HISP-SA. Retrieved October 2006, from http://www.hisp.org/ Roode, D. (2003). Information systems research: A matter of choice? South African Computer Journal, 30, 1–2. Sahay, S., & Walsham, G. (2006). Scaling of health information systems in India: Challenges and approaches. Journal of Information Technology for Development, 12(3), 185–200. doi:10.1002/ itdj.20041 Shisana, O., et al. (2002). The impact of HIV/AIDS on the health sector. National survey of health personnel, ambulatory and hospitalised patients and health facilities. A collaborative effort of the Human Sciences Research Council (HSRC), the Medical University of Southern Africa (MEDUNSA) and the Medical Research Council (MRC).

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Stewart, R., Padarath, A., & Bamford, L. (2004). Providing antiretroviral treatment in Southern Africa. A Literature Review. South Africa: Health Systems Trust.

KEY TERMS AND DEFINITIONS Action Research: A collaborative cyclical inquiry process that involves diagnosing a situation, taking action, and reflecting on the outcome before recommencing the process. ART: The treatment of people infected with HIV/AIDS by incorporating antiretroviral drugs, counseling, and guidance on how to live a healthier life.

Developing Countries: Countries generally found in the southern hemisphere and characterized by low levels of human development. Health Information Systems: Sociotechnical combinations of human and nonhuman artefacts for coordination and communication work, or social activities in the health sector. Networks of Action: A term used in action research that involves the cultivation and alignment of groups of people, institutions, sites, and artefacts. Scaling: The spreading of working sociotechnical solutions to other sites, including people, technology, and processes. Sustainability: Involves a long-term working solution.

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, pp. 977-981, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Socio-Technical Structures, 4Ps and Hodges’ Model Peter Jones NHS Community Mental Health Nursing Older Adults, UK

ABSTRACT This chapter explores the potential of a conceptual framework –Hodges’ model – both as a socio-technical structure and means to explore such structures of relevance to nursing informatics theory and practice. The model can be applied universally by virtue of its structure and the content which it can encompass. In apprehending this chapter, readers will be able to draw, describe and explain the scope of Hodges’ model within contemporary healthcare contexts and the wider global issues presented by the 21st Century that influence and shape nursing informatics. Critically, the reader

will also gain insight into how socio-technical structures can facilitate cross fertilization of clinical and informatics theory and practice; drawing attention to information as a concept that provides a bridge between socio-technical, clinical, and informatics disciplines. This chapter will review the socio-technical literature and venture definitions of socio-technical structures related to Hodges’ model and advocate the need for sociopoliticaltechnical structures. The chapter also proposes the 4Ps as a tool to facilitate reflection upon and the construction of socio-technical structures. The adoption and significance of the hyphenated form as per “socio-technical” will also be explained.

DOI: 10.4018/978-1-60960-561-2.ch215

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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INTRODUCTION Data is a plural noun (Pearsall, et al., 1998). Technology has plural - compound uses. The word technology is somewhat unique among the family of ‘-ologies’. The word is applied as a noun, adjective and used in everyday conversation and media to an extent that no other -ology can match. The word refers of course, not only to the study of the technical, but a phenomenon: a ubiquitous, pervasive presence in our lives. The extent to which we take technology for granted, is evident in our missing this other meaning. How often do we refer to: This biology is playing up (which may well be the case!)? Geology never lasts very long! Sociology just adds to the noise. Maths and English [all languages] are similar in not only referring to the study of a subject area, but being applied in day-to-day life – essential forms of literacy. Depending on definitions technology is of course an adjunct to literacy and expression, from the caves of Lascaux to virtual reality communities. It is only ‘now’ that technology is considered as the latest – the third ology - to become ubiquitous. Technology presents challenges by virtue of its ability to liberate or constrain (Cooley, 1987; Nevárez, 2008). While this can confuse and disorientate us, technology also offers opportunities for discovery and integration. Viewed through the compound eye of Hodges’ model (see below), socio-technology can liberate by creating fractures of the model’s axes allowing leakage, seepage of meaning. We can look upon the seepage as soap that assists conceptual hygiene, as we make sense of technology across several knowledge domains. This affords us the opportunity to break the constraints of time, distance, culture (with translation) and intra-interdisciplinary theory and practice. If however, technology is poorly managed and implemented it can again in terms of Hodges’ model constrain the movement of information and meaning to just one or two knowledge domains? When allied with (clinical) language and profes-

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sional practice, technology facilitates categorisation which can depersonalise and alienate human actors. Alternately, positive effects are witnessed in the social networking phenomenon with its tags and labels. From a socio-technical perspective technologies ability to fracture is not catastrophic, but is a circumstance that carries an ecological impact. It helps us to conjoin what are usually disparate disciplines of theory, practice and policy and also highlights the need for a philosophy of technology (Scharff and Dusek, 2002) and elaborated (integrated) definitions of informatics. Elsewhere (Jones, 2008), the author discusses how Michel Serres (1995), the French philosopher, employs the ancient god Hermes as a trope to explain technology and communication. Hermes is well suited to this task being the philosopher of plural spaces. Hodges’ model constitutes a plural - pantological space (Jones, 2008). Perhaps this plurality explains the extended significance of technology in our (clinical) language and practice. Historically, our culture is built upon layers of technology: fire, the wheel, agricultural tools, weapons through to the rapid lifecycle rate experienced today in the technopolis (Nevárez, 2008). This chapter begins with a brief introduction to Hodges’ model, followed by definitions of sociotechnical structure. Then several key sources in the socio-technical literature are introduced leading to the formulation of socio-technical structures within Hodges’ model. These are explicated by introducing the 4Ps (e.g., process) followed by closing discussion. If a paper is afforded one gross assumption, then at this point let me suggest that nurses and the majority of other health and social care practitioners are either suspicious of ICT due to previous experiences at work, or they are pragmatic in their expectations. Pragmatic in that they recognise the inevitability that in the 21st century informatics will figure in their working lives, just as it does in their personal lives. Therefore, this paper also addresses how (nursing) informatics can be informed by a model of nursing and how

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health can contribute to informatics. This connection utilises Hodges’ model and the concept of information.

Hodges’ Model: A Cognitive Periplus for Life-Long Learning1 Developed in the UK during the early 1980s, Hodges’Health Career Model (hereafter referred to as h2cm) is a conceptual framework that is personcentred and situation based. In structure h2cm combines two axes to create four care (knowledge) domains (Figures 1 and 2). Academics and practitioners in many fields create models that help support theory and practice (Wilber, 2000). Models act as a memory jogger and guide. In health care generic models can encourage holistic practice directing the user to consider the patient as a whole person and not merely as a diagnosis derived from physical investigations? Exposure of h2cm is limited to a small (yet growing) cadre of practitioners; several published articles (Hinchcliffe2, 1989; Adams, 1987; Jones 2004). In addition to a website (Jones, 1998) a blog and podcast were published in 2006. The most recent paper is that already mentioned which relates h2cm and the work of Michel Serres to social informatics (Jones, 2008).

Figure 1.

The best way to explain h2cm is to review the questions that Brian Hodges originally posed. To begin, who are the recipients of care? Well, first and foremost individuals of all ages, races and creed, but also groups of people, families, communities and populations. Then Hodges asked: what types of activities - tasks, duties, and treatments - do nurses carry out? They must always act professionally, but frequently according to strict rules and policies, their actions often dictated by specific treatments including drugs, investigations, and minor surgery. Nurses do many things by routine according to precise procedures, rather like the stereotypical matron with machinelike efficiency? If these actions are classed as mechanistic, they contrast with times when healthcare workers give of themselves to reassure, comfort, develop rapport and engage therapeutically. This is opposite to mechanistic tasks and is described as humanistic; what the public usually think of as the caring nurse. In use this framework prompts the user to consider four major subject headings or care domains of knowledge. Namely, what knowledge is needed to care for individuals - groups and undertake humanistic - mechanistic activities? Through these questions Hodges’ derived the model depicted above. Initial study of h2cm on the website has related Hodges’ model to the multicontextual nature of Figure 2.

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health, informatics, consilience (Wilson, 1998), interdisciplinarity, and visualization. H2cm says nothing about the study of knowledge, but a great deal about the nature of knowledge is implied in Figures 1 and 2. This prompted two web pages devoted to the structural and theoretical assumptions of h2cm (Jones, 2000a, b.). Although the axes of h2cm are dichotomous, they also represent continua. This duality is important as an individual’s mental health status for example is situated on a continuum spanning excellent to extremely unwell. There are various states in-between affected by an individual’s beliefs, response to stress, coping strategies, epigenetic and other influences. H2cm was created to meet four educational and serviceside objectives: 1. To produce a curriculum development tool. 2. Help ensure holistic assessment and evaluation. 3. To support reflective practice. 4. To reduce the theory-practice gap. Since h2cm’s formulation these objectives have grown in relevance. The 1980s may seem remote, but these problems are far from archaic as expansion of points 1-4 reveals. Student life is preparation for life-long learning. Curricula are under constant pressure. Despite decades of policy declarations, truly holistic care (combining physical, mental and pastoral care) remains elusive. The concept and practice of reflection swings like a metronome, one second seemingly de rigour, the next moment the subject of web based polls. H2cm can be used in interviews, outlining discussion and actions to pursue, an agenda - agreed and shared at the end of a session. The model is equally at home on paper, blackboard, flipchart and interactive whiteboard. Finally, technology is often seen as a way to make knowledge available to all practitioners; the means to bridge theorypractice gap through activities such as e-learning, governance and knowledge management.

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The model’s expressive power arises from its structure; a conceptual space created by diagrammatic representation of four pivotal concepts – individual, group, humanistic and mechanistic. This construct leads to a conceptual framework with generic and specific, broad and detailed capacities. The conceptual dynamics of the model can be represented as a horse and litter. The horse and its direction of travel constitutes the situation – context. Hodges’ model is the litter, a framework which can carry our ideas, concepts, problems, issues (our ‘sick and unwell’); plus hypotheses and solutions. The challenge follows though from the fact that health and social care is multi-contextual - multiaxial. One horse and litter is simply not enough. The horse and litter need to travel in several directions at once. What Hodges’ model encourages and provides in one context is vicarious travel. The axes within h2cm create a conceptual space as explored by Gärdenfors (2000). A third axis projecting through the page can represent history; be that an educational, health or other ‘career’. It is ironic, that an act of partition can simultaneously represent reductionism and holism. Reductionism has a pivotal role to play, which h2cm acknowledges in the sciences domain. What h2cm can do is prompt the expert (single domain) practitioner that there are three other pages to reflect and write upon. In total with the addition of faith h2cm could also be said to represent the spiritual domain. The model’s title was extended with ‘Care Domains’ in order to differentiate it from job and professional career website and resources.

Definitions: What do We Mean by ‘Socio-Technical’, ‘SocioTechnical Structure’ and ‘Socio-Political-Technical’? Nursing informatics can be considered as a sociotechnical structure. This structure and health informatics more generally boasts not only a history, but an autobiography (Hayes and Barnett, 2008). The

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multi-axial nature of health has been recognised for millennia from the fundamental dichotomy of life-death to more recent pre-occupations of supply-demand, public-private and many others. There are more specific axes instantiated in terminology systems such as SNOMED CT; for example, diseases, drugs, function, procedure and event (Melton, et al.; 2006). Relating this to h2cm, a dichotomy such as subjective - objective can be defined as spanning and linking all four care domains. This may seem so general that it is useless as the basis for a meaningful definition; but a definition of socio-technical structure should demarcate the space it inhabits whether physical or conceptual (psychophysical). Being situated in application h2cm informs novices and experts alike of the primacy of context in health and social care (Berg and Goorman, 1999; Bricon-Souf and Newman, 2007; Westbrook, et al., 2007). Defining these terms is difficult and what follows will not find agreement with everyone. For all that globalisation says about the world today, our appreciation of the world remains less than wholly. The world is constantly partitioned: NorthSouth and East-West. C.P. Snow’s seminal book Two Cultures (1959) noted the fracture within the pursuit of knowledge; with the existence of two camps the sciences and the humanities that were divorced and non-communicative. This dichotomy is symptomatic of a profound tendency for human beings to polarise, as evinced in the psychological literature through Tomkins’Polarity theory (Stone and Schaffner, 1997). As all students quickly realise, a key part of learning entails unpacking dichotomies, continua, axes and contexts. It could be argued that our capacity to dichotomize in situations assures the creation of socio-technical structures even if this ticket to enlightenment is only ‘half-used’ (in the socio-technical sense). After the finale many ticket stubs litter the ground. In informatics the need to tackle the skills divide provoked the quest for hybrid managers; senior personnel gifted with management acumen and information skills to deliver (socio-technical)

change in the new organisation (Earl and Skyrme, 1990). The objective and subjective are clearly germane to health as Sullivan (2003) argues: If medicine is now to aim for patient-centred outcomes, it needs a new object of study. Outcomes research is as yet undecided if this will be the patient’s health or the patient’s life. Each step in this direction brings medicine closer to pursuing “what really matters to patients” and also brings greater scientific, ethical and social complexity. Subjective health is more meaningful to patients than objective health measures … p.1602 If I ask you the reader to place, first the social on Hodges’ model followed by the technical, you quickly realise looking at the structure of Hodges’ model that it provides an immediate high level template and prescription for assessment, planning, intervention and evaluation. The social obviously belongs to the humanistic left-hand side of the model, while the technical is to the right – the mechanistic. Hodges’ model can provide a conceptual and propositional (Jones, 2008) bridge from the socio-technical to health and social care as shown in Figure 3. Our definitions – working or otherwise - must be cognisant of history. The socio-technical movement began in the late 1940s, giving rise to the Tavistock Institute (Mumford, 1983; Coakes,╯Willis and Lloyd-Jones, 2000). After the Second World War, science and the earliest modern computers had proved themselves, but in the search for increased production and commercial advantage it became apparent that technical solutions alone do not guarantee success. If attempts to apply technology are not managed properly with Figure 3. “Socio–technical” in h2cm east-west

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the impact considered holistically, then technical solutions can become part of the problem. At the Tavistock Institute a series of field studies provided the first major contributions to the theory of socio-technical design with research in the British coal industry (Mumford, 1983).

Definitions: ‘Socio-Technical’ From the outset it is important to differentiate between socio-technical as applied within the informatics contexts of information systems design, software development plus project management; and definitions that operate in organisational management. So, for the purposes of this paper socio-technical is defined as the combination of two types of knowledge (objective and subjective) comprised of people, artefacts, a retinue of concepts and dynamic (chaotic?) fields of human practice notably management, finance and organisation. Whether consciously acknowledged or not, this combination may be coherent or incoherent, depending upon the structures (see below) used to integrate and manage conceptually proximate and disparate parts.

‘Socio-Technical Structure’ Building on the definition of socio-technical, the structural aspect is a hybrid, an in-situ analoguedigital creation made up of physical (devices/ infrastructure), concepts, human and political ingredients based on various theoretical conceptual schemas and methodologies. Socio-technical structures are multiple and composite in that they span human-machine-organisational boundaries: subject to mathematics and nebulosity. They are transient, their existence and timescales ranging across the human-machine interface, database, available network configuration, hardware and software life-cycle management, organisational and governmental longevity. Socio-technical structures are curricula for change, especially in the form of Masters in Informatics and MBA

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courses. The clinical-education partnership necessarily entails socio-technical structures from the research councils - Economic and Social Research Council (ESRC), Engineering and Physical Sciences Research Council (EPSRC), the JANET-NHS partnership through to group and one-to-one e-supervision. Education and training (lifelong learning and business change) act as a transport system for ideas that test the business, social, finance and infotech ideologies of the day. Socio-technical structures must also be defined economically as key drivers of creativity and innovation. The result is an amalgam comprised of humanistic and mechanistic systems, software (infrastructure), policy and social groupings.

‘Socio-Political-Technical’ In formulating h2cm, Hodges’ inclusion of the political domain is pivotal. In any intrainterpersonal - social scenario, politics holds sway. Not necessarily in that formal legal, party political sense, but an informal socially conveyed politics encompassing for example; power relations, gender, choice, consent and autonomy. Globally - creativity, innovation and leadership (design) are no longer relatives twice removed from enterprise whether social, technical or sociotechnical. Hodges’ model can represent through its structure the need for rules in the organisation of the many. For at least two decades the debate about technology shaping society versus society shaping technology has been ongoing (Bijker, et al., 1987): Informal and formal politics in action; as Aarts and Gorman (2007) put it “To Err is System”. The NHS boasts a prime example in the National Programme for Information Technology managed by Connecting for Health. This ongoing programme is remarkable for its funding, ambition, the attention of the media and academia (see: http://www.nhs-it.info retrieved June 8 2008) its socio-technical impact thus far and the need for a socio-technical approach in the years to follow (Peltu, et al., 2008).

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Background: Existing SocioTechnical Structures and Methods This section scratches the pages of the sociotechnical literature by introducing two seminal contributions and briefly discusses other sources. The two authors discussed are Mumford (1983) in the socio-technical sense and Giddens (1984) who is more generic socially and organisational oriented. Enid Mumford created ETHICS, a systems design methodology: Effective Technical and Human Implementation of Computer-based Systems. The need for ETHICS is to help manage change with three objectives. First, ETHICS stresses that the future users of computer systems, whether directly or indirectly involved should play a major part in designing these systems. User involvement is closely related to subsequent job satisfaction and efficiency gains and hence the realization of benefits. The users of systems are credited as experts; if this knowledge and experience is recognized and utilized then job satisfaction gains are likely as the users are active agents in the change process and not passive. There is an interesting correspondence here with the continuing emphasis on patients and carers being acknowledged as experts in their care assessment, management and evaluation. The second objective focuses on the human – behavioural response to change. It is important that specific job satisfaction objectives are factored into the design from the outset and not left to chance, lost amid technical specifications and objectives. In this way potential negative change impacting the quality of work life can be anticipated and avoided or at least ameliorated. Technology has frequently been associated with deskilling and of course the loss of traditional jobs. The prospect of technical, management led change can cause consternation in the user community. If alienated, employees may be absent, seek alternate employment, and overall be less productive. ETHICS is not restricted to the computer system; the third objective highlights the need

for a new computer system to be ‘surrounded by a compatible, well functioning organizational system’ (Mumford, 1983). Design must be viewed globally as a whole. The technical design is just one part of a very complex design process that must also incorporate the details of human-machine interaction; what would be called gap analysis, the differences in existing processes and proposed new processes and procedures. In addition, as per objectives one and two, individual jobs and workgroup activities must be reviewed; how are existing roles and relationships altered and newly defined? What new management arrangements are needed, since middle management is rarely untouched (Pinsonneault and Kraemer, 1997)? Giddens’ structuration theory seeks to integrate structure and agency. Giddens (1984) defines structuration as “the structuring of social relations across time and space, in virtue of the duality of structure” (p.376). The duality arises from Giddens’ emphasis on social action as being comprised of human agents and social structures: The basic domain of study of the social sciences, according to the theory of structuration, is neither the experience of the individual actor, nor the existence of any form of social totality, but social practices ordered across space and time. Human social activities, like some self-reproducing items in nature, are recursive. That is to say, they are not brought into being by social actors but continually recreated by them via the very means whereby they express themselves as actors. In and through their activities agents reproduce the conditions that make these activities possible. (Giddens, p.2). This definition allows Giddens to traverse the human individual experience through to the macro level structures of institutions and organisations. A focus purely centred on the technical ignores people as knowledgeable agents; people must not be lost amid objectivism. The links between the thought of Mumford and Giddens are clear. The items that follow are mentioned briefly, they are

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more specific with a technical bias focusing on IT services, customer services and project management. The complexity of software and hence software development has led to the production and ongoing refinement of tools and frameworks to assure safe, fit for purpose, reliable and efficient code on the one hand and the effective management of projects on the other, such that risk reduction ensures success as in the deployment of a new (or updated) information system. Influential for several decades Soft Systems Methodology (SMS) (Checkland, 1981) is introduced by Lane and Oliva (1998): SMS was developed during the 1970s by Peter Checkland and his colleagues at the University of Lancaster’s Department of Systems. The methodology emerged from an action-research process that to date includes 400+ cases and still continues the evolution of the methodology. (pp.216-217) SMS incorporates an organizational history, real world problem situation, a rich picture which includes input from the would-be improvers of the situation. This leads to descriptions of the situation described as streams of culture and tasks and issues. Two forms of analysis are applied cultural and logic-based. This analysis enables identification of differences between the models produced and the real world with proposed changes that are systematically desirable and culturally feasible. Within SMS actions to improve the situation should then follow as a final output. (Lane and Oliva, 1998, p.281). The essential perspectives of SMS are captured in the mnemonic “CATWOE”: • • • • • •

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Customers Actors Transformation process Worldview Owner Environmental constraints (Checkland and Scholes, 1999).

Atkinson’s (2001) Soft Information Systems and Technologies Methodology (SISTeM) was developed from SSM and provides a contingency approach to integrated decision making and development. Launched in 1996 PRINCE2 - PRojects IN Controlled Environments is a process-based method for effective project management. PRINCE2 is an Open Source international de facto standard used extensively by the UK Government in the public and a skill also sought in the private sector. The history of PRINCE can be traced back to 1975. According to Wikipedia Structured Systems Analysis and Design Method (SSADM) is a systems approach to the analysis and design of information systems. In SSADM information systems design is derived through a waterfall method and is seen as a rigorous document-led approach. PRINCE2 and SSADM are being challenged by more socially-iterative methods of what is called Rapid Application Development (RAD) and Agile Software Development (Thomas, et al.; 2007). IT Infrastructure Library (ITIL) refers to a series of five volumes that describe a lifecycle framework for IT Service Management: the volumes include Service Strategy, Service Design, Service Transition, Service Operation and Continual Service Improvement. ITIL can be adapted to various business settings (e.g. telecommunications, helpdesk, information system customer services) the framework defines how Service Management is applied within an organisation and is associated with the international standard - ISO 20000. There are many other tools including the SocioTechnical Framework. Web links to this and other methods follow the references.

Socio-Technical Structures of h2cm and Nursing Informatics: The 4Ps The previous discussion and citations highlight that existing socio-technical structures are employed to help realise effective information

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systems. The idealised objectives are systems that can facilitate fluid organisational change through the efficient utilisation (dissemination, reuse and refinement) of skills and knowledge. Essentially, this same motivation applies to h2cm, but in a caring context. The fusion of h2cm and the socio-technical depends upon either: 1. Users bring their existing socio-technical structures (as above) and deploy them through the reflective lens of h2cm, confirming a high-level role for Hodges’ model or; 2. Researchers study Hodges’ model and the extent to which it can support socio-technical perspectives within customary information systems environments. Novel formulations between informatics disciplines or other areas tainted (as are most) by informatics (office applications) may be revealed by this more powerful socio-technical light. Figure 4 below extends Figure 3 through the 4Ps: PROCESS; PURPOSE; POLICY and PRACTICE each is associated with a knowledge domain. The adoption of the 4Ps arises from the combined clinical and informatics experience of the author and the significance accorded these concepts in the literature. For example, while admittedly a crude device a word search within Ure, et al. (2007) for policy, practice, process and purpose produces two, fifteen, fifteen and six occurrences respectively. The 4Ps then are the constants of the socio-technical program – a unique program that involves people.

PURPOSE To be at cross-purposes is a sorry state; it variously denotes disarray, uncertainty, disorganisation and confusion. The NHS finds itself at cross-purposes; the commonly cited anecdote that ‘NHS’ really stands for National Ill-Health Service bears repeating here. Given our propensity to polarise, the profusion of dichotomies in health care and the regular cycles of re-organisation, it is strange that these two world’s of disease and health promotion still wear the same uniform. This state of affairs has not gone unnoticed amid health-social care structural and policy review. PURPOSE lies at the heart of socio-technical structures as a fusion of people (as individuals and groups), skills, knowledge, function and form. Key social structures are obviously embedded in the humanistic domains. For the individual, motivation is a primary psychological factor. Having a sense of PURPOSE is essential in terms of response to a given situation and any subsequent problems solving. People are sense making machines (Kurtz and Snowden, 2003). They need to be fed with a sense of purpose. Not programmed. Nursing should not be purely task-oriented; nursing is about people not (just) PROCESS. Purpose matters from the level of technical with local project planning and management to National engagement in informatics programmes. Whilst organisations are primarily group centred (production, payroll, sickness) planning and change must be framed in individual terms.

PRACTICE

Figure 4. Hodges’ model and the 4P’s

If clinically, informatics is seen as little more than a management abacus, then the user’s sense of purpose will be reduced to that of a bean counter. The much vaunted aim of personalised care then becomes nothing more than numbers on paper related to serious untoward incidents, the number of complaints and similar data. Tasks

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have been viewed pejoratively in the past within nursing, but tasks are an essential consequence of time and currency of description. Tasks can readily be quantified and they are a vital part of the totality of care and informatics. For staff to be motivated and have a sense of purpose, qualitative considerations must be brought into play. Care is always situated; priorities, education and training, the environment and technology and much more all affect PRACTICE. In making these distinctions this is not to divorce PURPOSE and PRACTICE from the SCIENTIFIC and POLITICAL domains. The 4Ps are interdependent. They can support or hinder the learning organisation. Effective socio-technical structures must create and respond to change; producing improvement through data-to-knowledge transformation that delivers meaning at all levels. Meaning is nothing if not disseminated. There is an acute issue in nursing, that of ensuring that user engagement: • • •

Actually takes place and is actively pursued by project leads and developers; Is recognised and valued by nursing managers; Provision is made by senior managers to allow clinical users time-out to engage with backfill of posts if needed;

PROCESS Whether sequential or parallel, PROCESSES are first and foremost grounded in the SCIENCES domain. A beginner’s guide to IT might include a description of what computers do best. Computers excel in their number crunching abilities. It is their capacity to represent, simulate and transform processes that makes them so powerful. Although PROCESS may have something to say about delivery of narrative, it seems reasonable to declare that what narrative is to the SOCIOLOGICAL domain, so PROCESS is to the SCIENTIFIC? In socio-technical structures however, health care

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is about PEOPLE and PROCESS. The debacle in November 2007 with the merger of HM Revenue and Customs and loss of the personal data of millions of UK citizens is a stark reminder of the dependencies between the 4Ps and PEOPLE. The advent of the nursing process, with its 4-6 iterative stages - assessment; diagnosis; planning; implementation and evaluation (Wilkinson, 2006) - saw some nurses concerned that patients were literally being ‘processed’. PROCESS in informatics now has a sociotechnical home in clinical practice through role based access and control [RBAC] (El-Hassen and Fiadeiro; 2007), as deployed in the UK’s National Programme for Information Technology. Now roles take on an extended socio-technical meaning within the organisation. Roles are also of import in gap analysis, a key activity completed in the preparation, roll-out and review of new information systems. What are the existing processes? Are there any ‘show-stoppers’, new processes that do not support vital tasks, for example, specific reporting requirements or referral pathway events? It is salutary to reflect on the distance between practice and software development approaches such as computation, coordination, configuration [CCC] (El-Hassen and Fiadeiro; 2007); models, views and controllers [MVC] (Thomas, et al., 2007) and developer-analyst engagement with end-users? Given their properties and purposes as formal tools computer programming languages reside in the sciences domain. This means there is wide gap between the language and syntax of the programmer and the domain expert (nurse). Computing now includes a diverse range of so-called Domain Specific Languages (DSL). Although definitions remain a contentious point there are signs that DSLs could bring these disparate worlds closer together with increased productivity for all. The challenge remains however in that existing DSLs seem confined to computing, finance and technical applications (Olsen, 2007). Wither the business rules for nursing?

Socio-Technical Structures, 4Ps and Hodges’ Model

Figure 5. “Socio–technical” in h2cm: north-south

POLICY Figure 3 showed socio-technical as a vertical bisection of h2cm. Is there a horizontal form as per Figure 5? In the same way that the individual precedes the group as the focus within Hodges’ model, so too for the technical which can also be said to occupy the INTRA-INTERPERSONAL and SCIENCES domains. Distributed social networks are a product of devices and other technologies that are designed and used by individual users. It follows then that the socio component spans the SOCIOLOGY and POLITICAL domains. This is why the construct of socio-political-technical previously mentioned is needed. PROCESS is found in the SCIENCES, but is enacted (or not!) in the humanistic domains. A further justification of the need to add politics to our socio-technical definitions could rest solely on standards: and information standards in particular. Technical standards provide the intelligent glue that holds the structures together and releases them appropriately. Standards are of course subject to evaluation and supervision, being embedded and enshrined within professional practice. Information standards are critical in assuring the quality of data and safety of the public. The realisation of health care quality information systems is an ongoing quest, as reported by Niland, et al. (2006). So, to conclude the 4Ps we tend to think of the parts-whole debate as intimately connected with reductionist SCIENCES – PROCESSES. In attempting to grasp (sense make!) the 4Ps, perhaps there is a need to consider mereology,

the study of whole-part relations across all the h2cm knowledge domains? Perhaps, a key to fully apprehending socio-technical structures is to treat them as organic and study their morphology (Bortoft, 1996)? We need new ways of seeing. Before there is a vision we need to envision – in new ways.

A World of Two Halves or Four Quarters and Enforced Relations Figures 3, 4 and 5 maintain the two distinct sociotechnical camps. A more accurate (and rather obvious) depiction is that of a subset combining both social and technical factors as in Figure 6. To contemplate the socio-technical is to contemplate a world of knowledge, experience and thought that is so obviously divided. I have adopted the convention of the hyphen, inspired by the work of Serres (1995), plus the nexus of Hodges’ model and because small things can denote large. Let us ask the question then: how hard does that hyphen have to work? Is the union forced and the two worlds produce seismic waves; or is the union one of ready accommodation with a regulated need to re-focus eased by artifice on both sides? Perhaps, the hyphen is not as passive as may first appear? It makes it possible insofar as Hodges model is concerned to use our imagination and travel between the social and the technical. At times it does appear we are dealing purely with a sociological agenda, at other times technical. The hyphen becomes a switch either analogue or digital seeing the technical from an analogue perspective and vice-versa; facilitating Figure 6. Socio-technical as Venn diagram

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Socio-Technical Structures, 4Ps and Hodges’ Model

inclusion as recognised above in the socio-political-technical. The hyphen can act like the lunar terminator (the dividing margin between night and day), waxing and waning according to context. Experienced lunar observers recognise that the terminator is where shadows are to be found, providing contrast more detail. The full moon [TECHNICAL] brings illumination for focused action, but the glare of Harold Wilson’s ‘white heat’ (Cooley, 1987) can be blinding. The new moon [SOCIAL] promotes a need for collective safety, reflection and planning. The half moon (both ‘quarters’) relates to the socio-technical in balance; a time for connection, creativity and fusion. It may seem strange that an object of myth can feature within informatics. In truth myth must have a place in the socio-technical. Just as circuit boards are the substrate for technology; so myth is the substrate for society and history. One of the problems in melding together and conceptually sustaining socio-technical structures is a lack of unifying concepts. Information is the concept of choice (Stamper, 1985; Jones, 1996). If information is not the logical choice, then it is surely the holistic candidate? Informatics might provide the basis for a foundation that is not only flexible, multi or non-contextual, but can also Figure 7. Stress-vulnerability model

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operate at the required levels of description and across them; for example, machine code – public mental health strategy; information strategy - information prescription; ontology – patient advice. Nursing informatics practitioners readily embrace the vocabulary and techniques of the informatics world. They can also utilise and champion tools developed in health care that can provide the bracing to reinforce the integrity, resilience, longevity and functionality of our socio-technical structures. A case in point is the stress vulnerability model of Zubin and Spring (1977), which helps to explain an individual’s response to stress with reference to level of stress exposure and the person’s degree of vulnerability. This model can also be used to characterise information use and information overload (as per Figure 7) plus the transformational benefits of education and training.

Closing Discussion The market, policy makers and enthusiasts have sold informatics on the back of promised benefits. In these most commercial of times, we must ask what of value Hodges’ model adds to the sociotechnical debate? As the nursing literature reveals concept analysis has long been a tool of nurse theorists. Hodges’ model can stimulate and aug-

Socio-Technical Structures, 4Ps and Hodges’ Model

ment conceptual analysis, encouraging exploration and identification of concepts and thereby highlight ill-defined or much needed conceptual cross-members to marry the social (political) and technical realms. It is no accident that scaffolding is a common metaphor and technique in education, engineering, cognitive science and software development. Safety first principles dictate that due care and diligence is taken as we leverage existing structures and when necessary build new ones. For all informatics practitioners and participants the issue is not just the existence and creation of socio-technical structures, but access and utilisation and how this is measured. Nursing as a profession must constantly aspire to make a difference and effect positive change. In the decades ahead (nursing) informatics can help leverage positive change in tackling health inequalities, and addressing the ongoing revision of the new health agenda of health promotion and education. To do so though nursing informatics must also recognise its limits; it must seek out or help create new socio-technical structures through partnerships with other informatics disciplines, such as - community, social (care), urban, and citizen. Although 2 x 2 matrices are ubiquitous as a structuring device for concepts, ideas and much more, they are also much maligned: a ready reckoner for gross assumptions. Upon first encounter Hodges’ model may be considered merely as a brainstorming tool. On the contrary, h2cm can act as a framework for weaving; a template for a socio-technical tapestry. The framework provides a lattice upon which vital conceptual connections can be displayed explicitly on its public face, or privately when the handicraft is viewed from the back – the (propositional) infrastructure.

ACKNOWLEDGMENT I would like to express my sincere thanks to the editors for their interests and the very helpful

comments from the referees on early drafts of this paper.

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Coakes, E., Willis, D., & Lloyd-Jones, R. E. (Eds.). (2000). The New Sociotech: Graffiti on the Long Wall: London: Springer-Verlag. Cooley, M. (1987). Architect or Bee? The Human Price of Technology. The Hogarth Press, p.117. Earl, M. J., & Skyrme, D. J. (1990, May). Hybrid managers: what should you do? The Computer Bulletin, 19–21. ElHASSEN, O., & Fiadeiro, J. L. (2007). Rolebased architectural modelling of socio-technical systems. Electronic Notes in Theoretical Computer Science, 181, 5–17. doi:10.1016/j. entcs.2007.01.050 Gärdenfors, P. (2000). Conceptual Spaces: The Geometry of Thought. MIT Press. Giddens, A. (1984). The Constitution of Society: Outline of the Theory of Structuration. Berkeley, University of California Press. Hayes, G., & Barnett, D. (Eds.). (2008). UK Health Computing: Recollections and Reflections, BCS. Hinchcliffe, S. M. (Ed.). (1989). Nursing Practice and Health Care, [1st Edition only], London, Edward Arnold.

Jones, P. (2004). Viewpoint: can informatics and holistic multidisciplinary care be harmonised? British Journal of Healthcare Computing & Information Management, 21(6), 17–18. Jones, P. (2008). Exploring serres’ atlas, hodges’ knowledge domains and the fusion of informatics and cultural horizons, in kidd, t., chen, I. (eds.) Social Information Technology Connecting Society and Cultural Issues. IGI Global. Kurtz, C. F., & Snowden, D. J. (2003). The new dynamics of strategy: Sense-making in a complex and complicated world. IBM Systems Journal, 42, 3. Lane, D. C., & Oliva, R. (1998). The greater whole: Towards a synthesis of system dynamics and soft systems methodology. European Journal of Operational Research, 107, 214–235. doi:10.1016/ S0377-2217(97)00205-1 Melton, G. B., Parsons, S., Morrison, F. P., Rothschild, A. S., Markatou, M., & Hripcsak, G. (2006). Inter-patient distance metrics using snomed ct defining relationships. Journal of Biomedical Informatics, 39(6), 697–705. doi:10.1016/j. jbi.2006.01.004

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Olsen, R. (2007). Design Patterns in Ruby, Addison-Wesley, Chapter 16. Pearsall, J., et al. (Eds.). (1998). The new oxford dictionary of english, oup. p. 468. Peltu, M., Eason, K., & Clegg, C. (2008). How a sociotechnical approach can help npfit deliver better nhs patient care, report produced following bcs workshop 7 February 2008. Retrieved 8 June 2008, from http://www.bcs.org/server. php?show=nav.9932 Pinsonneault, A., & Kraemer, K. L. (1997). Information technology and middle management downsizing: a tale of two cities, CRITO Working Paper URB-115, 4/16/97. Retrieved June 8, 2008 from http://repositories.cdlib.org/cgi/viewcontent.cgi?article=1090&context=crito Scharff, R., & Dusek, V. (2002). Philosophy of Technology: The Technological Condition - An Anthology. Blackwell Philosophy Anthologies. Serres, M. (1995). Conversations on Science, Culture, and Time. With Latour, B., University of Michigan Press. Snow, C. P. (1959). The Two Cultures. Cambridge University Press. Reissued 1993. Stamper, R. K. (1985). towards a theory of information: information: mystical fluid or a subject for scientific enquiry. [BCS, Oxford Journals.]. The Computer Journal, 28, 195–199. doi:10.1093/ comjnl/28.3.195 Stone, W. F., & Schaffner, P. E. (1997). The Tomkins polarity scale: recent developments. Bulletin of the Tomkins Institute, 4, 17–22. Sullivan, M. (2003). The new subjective medicine: taking the patient’s point of view on health care and health. Social Science & Medicine, 56(7), 1595–1604. doi:10.1016/S0277-9536(02)001594

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SOCIO-TECHNICAL WEBSITES http://wisdom.usc.edu/stf/ (Sociotechnical Framework) http://www.itil.org.uk/ (ITIL) http://www.prince2.com/what-is-prince2.asp (PRINCE2) http://www.sociotechnical.org/ (British Computer Society Sociotechnical Specialist Group)

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ENDNOTES 1



This brief introduction to Hodges’ model has been published previously online and in print (Jones, 2008).

2



Hinchcliffe includes a chapter by Brian E Hodges (1st Edition only).

This work was previously published in Nursing and Clinical Informatics: Socio-Technical Approaches, edited by Bettina Staudinger, Victoria Höß and Herwig Ostermann, pp. 160-174, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 2.16

Techniques for Decomposition of EMG Signals Arun Kumar Wadhwani MITS, India Sulochana Wadhwani MITS, India

ABSTRACT

INTRODUCTION

The information extracted from the EMG recordings is of great clinical importance and is used for the diagnosis and treatment of neuromuscular disorders and to study muscle fatigue and neuromuscular control mechanism. Thus there is a necessity of efficient and effective techniques, which can clearly separate individual MUAPs from the complex EMG without loss of diagnostic information. This chapter deals with the techniques of decomposition based on statistical pattern recognition, cross-correlation, Kohonen self-organizing map and wavelet transform.

The electrical signals produced by the muscles and nerves are analyzed to assess the state of neuromuscular function in subjects with suspected neuromuscular disorders. The repetitive activation of several individual motor units (MUs) results in a superposed pulse train and constitutes the electromyogram (EMG) signal. The analysis of the EMG is based on its basic constituent i.e. motor-unit action potentials (MUAPs). The motor unit is the smallest functional unit of a muscle, which can be activated voluntarily. It consists of a group of muscle fibers, which are innervated from the same motor nerve. The shape of MUAP reflects the pathological and functional states of

DOI: 10.4018/978-1-60960-561-2.ch216

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Techniques for Decomposition of EMG Signals

the motor unit. With increasing muscle force, the EMG signal shows an increase in the number of activated MUAPs recruited at increasing firing rate, making it difficult for the neurophysiologist to distinguish individual MUAP waveforms. In most of the clinical EMG examinations, it is the shape of the action potential that is analyzed for diagnostics The shape and amplitude of MUAP waveform generally differ from motor unit to motor unit due to unique geometric arrangement of the muscle fibers in each motor unit. However, the MUAP waveforms from different motor units may also be nearly similar in amplitude and shape when the muscle fibers of two or more motor units have a similar spatial arrangement in detectable vicinity of electrode. The variations in the MUAP waveform are greater when the MUAP waveform actually arises from two or more of the motor units fibers at approximately same distance from the electrode and thus of similar amplitude. The variable latencies in the onset of individual muscle fiber action potentials may produce large uncorrelated changes in compound MUAP waveform from one firing to the next. When few (less than three) motor unit action potential train (MUAPTs) are present in the EMG signal, much of the noise actually results from variations in an MUAP waveform from one firing to the next. When a greater number of MUAPTs is present, the additional noise is also produced by many low amplitude MUAP waveforms. Number of different factors attribute for noise in MUAPs, namely, increase in degree of severity, instrument noise, neuromuscular junction jitter, detection electrode movement, and interference from the MUAPs of nearby motor units [2]. The shape of the MUAP waveforms within a MUAPT remains constant, if (i) all the initial relationships between the electrode and the active muscle fibers remain constant (i.e. there is no variation in geometrical arrangement between the recording electrode and active muscle fibers), (ii) the relative time difference between the initiation of

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each constituent fiber action potential, and (iii) the properties of the recording electrode remain constant, and (iv) there are no significant biochemical changes in the muscle tissues. The presence of noise in the EMG signal is a major problem to obtain clean MUAPs after decomposition. Due to overlapping of MUAPs in the EMG signal, physicians have great difficulty in classifying superimposed waveforms and have no valid reason for their conclusions. With the help of decomposition technique, it is possible to provide more accurate information w.r.t. the EMG signal and MUAPs to know the state of a subject’s peripheral neuromuscular system.

DECOMPOSITION OF EMG The parameters of the MUAPs have proven to be of major importance for the clinical diagnosis of myopathies and neuropathies [1]. For perfect diagnosis, accurate decomposition of EMG signal is needed. The success of EMG signal decomposition is decided by the accurate classification of the MUAPs. There is a long list of limiting factors, which hamper the process of successful decomposition. Some of them arise from the characteristics of the MUAPs. The MUAPs have different durations (finite) and overlap in time and frequency domains. They occur at different time instants are time varying in nature and contaminated by noise and background activity produced by non detectable MUAPs. The repetition frequencies are so high that individual MUAPs rarely appear as isolated potentials [1]. The major challenge in decomposition is to break down the waveforms and analyze them accurately as these are the outcome of a complicated activity and require complete understanding of the electrical fields generated within and around the muscle fibers. Manual decomposition of EMG signal has been successfully done for a long time for clinical diagnosis. It has been shown that the relevant MUAP parameters such as shape, amplitude, duration, and number of phases and turns can

Techniques for Decomposition of EMG Signals

be identified manually. However, manual analysis is tedious, time-consuming and inaccurate, and therefore, the investigators have worked for long time to automate the process of decomposition. Significant features and other details of various techniques for automatic decomposition of an EMG signals. are given in (Figure 1). It has been found that most automatic detection schemes cannot accommodate slow changes in shape or amplitude in MUAPs.

SEGMENTATION The segmentation (scan the EMG signal) is done to determine the number and shape of different MUAPs present, to know the approximate location

of each peak and to know the amount of change in the MUAP waveforms throughout the EMG signal. In segmentation phase, the EMG signal is cut into the segments of possible MUAP waveform and the low activity signal is eliminated. The segmentation technique algorithm calculates threshold depending on maximum value of the complete signal. Peaks over the calculated threshold are considered as the candidate MUAPs. This selected threshold is dependent upon level of background noise in the data. Determination of this threshold is very critical. Generally speaking, lower value of threshold may generate extra number of peaks and tend to increase the error, which mistakenly classifies the spikes belonging to the same motor unit into different groups. On the other hand, if threshold is higher, it may generate less number

Figure 1. Comparison of various decomposition techniques

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Techniques for Decomposition of EMG Signals

of groups and increase the error, which fails to distinguish the spikes of different motor units.

CLASSIFICATION TECHNIQUES As per literature survey, it has been found that to obtain clean MUAPs, the statistical pattern recognition, cross correlation coefficients, Kohonen self-organizing feature map, and Wavelet transform methods can be used. Therefore these methods have been described here in detail.

Statistical Pattern Recognition Technique The statistical pattern recognition approach is based on the Euclidean distance [2]. Euclidean distance is determined to identify and group similar waveforms(segment) using a constant threshold. The MUAP waveform, which is having minimum distance (having greatest similarity) from the reference waveform and is above the threshold, is considered belonging to the same class. The steps of the decomposition technique are as follows: STEP 1. Start with the first segment, x, as the reference waveform; being the first member of the class. STEP 2. a. Calculate the Euclidean distance, lx of the reference waveform as follows: lx =

N

∑x i=1

2 i

where N is number of samples,

xi is the ith sample of waveform x. b. Calculate the Euclidean distance, dxy, between the x and all the other segmented waveforms dxy =

N

2

∑ (x i − yi ) where, yi is the ith i=1

sample of waveform y.

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STEP 3. Find those waveforms which have dxy ≤ 0.125 * lx (set threshold) If number of identified waveforms > 2, then form a new MUAP class and calculate its average waveform and store; else class members are superimposed STEP 4. Take the new reference from the remaining segments and go to step 2(b) and determine the Euclidean distance. If it satisfies the threshold criteria, then MUAP class is determined accordingly. Threshold values are chosen after extensive testing. It was found that there are no widely applicable threshold criteria for assigning a MUAP a class. This threshold is critical because a very low value may spilt a MUAP class with high waveform variability in two or more subclasses, whereas, a greater value may merge the resembling classes of MUAPs.

Cross-Correlation Coefficient Technique This cross-correlation coefficient technique is widely used for detecting the individual wave shapes from a complex signal. This technique is also suitable for the MUAP detection. The principle of this technique is based on template matching. The cross -correlation coefficient indicates the degree of similarity between templates. In this method, from the recorded EMG signals, the individual segments having high correlation coefficients in a template matching are recognized as the MUAP. All possible combinations of predetermined templates are determined by searching for the best match. In case of clustering, feature analysis criteria show that the clusters can be better separated and boundaries can be better detected by using cross-correlation coefficients. And finally, in case of the supervised classification of active

Techniques for Decomposition of EMG Signals

segments, cross-correlation coefficients analysis achieves a higher recognition rate compared to other techniques. Following steps are used to calculate the cross -correlation coefficient between the segments obtained after segmentation of the EMG signal: • •

•

•

Start with the first waveform, x, as reference, being the first member of the class. Calculate the cross -correlation coefficient, C(r), of this reference waveform with all the other segmented waveforms. Find those waveform for which, C (r) ≥ 0.82, (a set threshold), if the number of the waveform satisfying the above relation is > 2, then form a new MUAP class and calculate its average and store; else class members are superimposed. Take the new reference from the remaining segments and go to ‘STEP 2’ and determine the cross-correlation coefficient C(r) in a similar manner; if it satisfies the threshold criteria, then MUAP class is determined accordingly.

For determination of other classes, any one of remaining segments is taken as the reference and its cross -correlation coefficients with the segments is calculated. This procedure is repeated till all segments are classified.

Kohonen Self-Organizing Feature Map Technique The unsupervised, Kohonen self-organizing neural network (NN) is considered a potential technique for the decomposition of EMG signal. The classification of the MUAPs into groups of similar shapes is a typical case of an unsupervised learning pattern recognition problem. The identified MUAPs are classified by an unsupervised NN. The term unsupervised identifies a structure able to recognize the presence of different classes in the input set, without any priori knowledge. When

an input pattern is presented, each unit in the first layer takes a value of the corresponding entry in the input pattern. The output layer then sums up inputs and completes it to find a single winning unit. Each interconnection in the Kohonen feature map has an associated weight value. The initial state of the network has randomized values for the weights. The weight values are updated during training of the network. In this technique, there is no prior knowledge of correctly labeled (classified) inputs, but the system is expected to discover statistically salient features of the input population. With this algorithm, the training process involves the presentation of pattern vectors from the training set one at a time. A winning neuron (node) is selected in a systematic manner after all input vectors are presented. A weight adjustment process takes place by using the neighborhood concept that shrinks over time. After several input vectors are presented, weights form clusters or vector centers that sample the input space such that the point density function of the vector centers tend to approximate the probability density function of the input vectors. The weights are also organized such that topologically close output nodes are sensitive to inputs that are physically similar. Thus, the output nodes are ordered in a natural way. The Kohonen network provides advantages over classical pattern-recognition techniques because it utilizes the parallel architecture of a neural-network and provides a graphical organization of pattern relationships. It is found from literature that the maximum number of MUAPs that can be identified for needle EMG at low to moderate force level is at most 6 [2,3]. Therefore 8 output nodes are sufficient in the map. Following are the steps for training the Kohonen-NN: STEP 1. Initialize the weights. STEP 2. Create a competitive layer of eight nodes. STEP 3. Train the neural network.

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Techniques for Decomposition of EMG Signals

STEP 4. The training stops when number of epochs (replications) is reached to its maximum set value. STEP 5. After training, the competitive layer is used as classifier with each neuron in the competitive layer as a class. STEP 6. All the segmented MUAPs are given to this trained competitive layer and the output is obtained in the form of sparse matrix, then it contains the winner neuron for each candidate MUAP.

Wavelet Based Technique Wavelet Transform (WT) is a powerful alternative to Fourier Transform, Discrete Fourier Transform and Fast Fourier Transform for the analysis of EMG signal. Wavelet transforms (WT) have become powerful alternative to Fourier transforms in medical applications, where the signals are nonstationary in nature like EMG. Wavelet algorithms process data at different scales and resolutions. If we look at a signal with a large ‘window’, we should notice gross features. Similarly, if we look at a signal with a small window, we should notice detail features. In addition to helping in recognition and detection of key diagnostic features, they provide a powerful tool for the disease diagnostics. In order to select a suitable WT,that matches the physics of the problem, due weightage be given to some important properties of the wavelets such as smoothness, spatial localization, frequency localization, and ability to represent local polynomial function, orthogonality, and symmetry. A good wavelet is the one which results in a small number of non negligible WT coefficients or a best approximation of a given signal up to a given scale. Thus, the scaling function associated with a good wavelet usually has a shape similar to the shape of the given signal. WT is a very promising technique for time-frequency analysis. By decomposing signals into elementary building blocks that are well localized both in time and frequency, WT can characterize the local

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regularity of the signal. This feature can be used to distinguish EMG signal from serious noise, artifacts and baseline drift. In the present technique, the second order Daubechies wavelet has been used as its waveform is biphasic similar to most of the spikes of the MUAPs. This is also orthogonal and compactly supported. The 1st, 2nd and 3rd decompositions, namely w1, w2 and w3, of each waveform using db-2 wavelet have not give the considerable difference therefore these decompositions are not taken into account. The decomposition steps are as follows: STEP 1. Calculate 4th, 5th and 6th decompositions, namely w4, w5 and w6, of each waveform using db-2 wavelet and save all the decompositions. STEP 2. Take first waveform, x, as reference waveform being first member of the class. STEP 3. Determine distances of all the decompositions separately. Calculate three Euclidean distances (d4, d5 and d6) between the waveform, x, and all the other segmented waveforms yi as d4 =

2

Length (w 4 )

∑ (x i=1

4i

− y 4i )

where, i = ith sample in 4th decomposed signal. d5 =

2

Length (w5 )

∑ (x j=1

5j

− y5 j )

where, j = jth sample in 5th decomposed signal. d6 =

2

Length (w6 )

∑ (x k=1

6k

− y 6k )

where, k = kth sample in 6th decomposed signal.

Techniques for Decomposition of EMG Signals

STEP 4. Find the similarity measure, D = max (d4, d5 and d6) STEP 5. Find all those waveforms having D ≤ 0.9 (a set threshold). The maximum values of all three calculated distances are compared to the threshold for classification of MUAPs. If the maximum value of the distance is less than or equal to the threshold, then detected MUAP belongs to the class of reference MUAP; if not, then it belongs to some other class.

a. Take average MUAP of class 1. Reduce its length in such away that its start and end points have magnitude less than 10μV. Take this waveform as a reference. b. Take any superimposed waveform x. c. Slide the reference waveform (reduced MUAP waveform) on superimposed waveform and find the best matching value, where Cross correlation coefficient has its maximum value. This value indicates the best matching point.

STEP 6. If number of waveforms (satisfying above condition) > 2, form a new class and take the average of all and store. STEP 7. Take the new reference from the remaining segments and go to step (ii) and determine the Euclidean distance in a similar manner; if it satisfies the threshold criteria, then MUAP class is determined accordingly.

For each matching point, calculate the normalized Euclidean distance (Nd), with the reference waveform.

It is observed that db-2 wavelets has proved to be the best suitable for MUAP separation. There is one advantage of this technique as the classification procedure is based on spectrum matching. The spectrum matching technique is considered to be more effective than the waveform matching technique in the time domain, especially when the interference is induced by baseline drift or by high-frequency noise. In both the cases, interference affects two small portions of the frequency spectrum.

PERFORMANCE COMPARISION AND DISCUSSIONS

DECOMPOSITION OF SUPERIMPOSED WAVEFORMS It was found after classification phase that superimposed waveform were also present in the segmented waveform. Therefore it is necessary to separate out these MUAPs from the superimposed waveform. The following steps are implemented for the decomposition of superimposed MUAP waveforms:

Nd =

2

M

∑ (x i=1

i

M

− yi ) / ∑ yi Nd< Th = 0.2 i =1

The general expected accuracy of the decomposition is dependent on the quality of data. The quality of the data is determined by its signal to noise ratio, stability, and actual separation of the motor unit class templates. If any one of these aspects of the data is degraded, accuracy of the process suffers. When there is increased recruitment or noise, one must acquire a large number of MUAP discharges for all above methods except wavelet technique and to identify a modest number of similar templates. Statistical pattern recognition and crosscorrelation techniques require recordings of the EMG signal that are more free of noise. Kohonen self-organizing feature technique is a fast method for extracting MUAPs that requires recordings of the EMG signal relatively free of noise. Overall Wavelet technique has the best performance. This study demonstrates the versatility of Kohonen technique. When the recordings are relatively free of background activity, the decomposition of EMG

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signal by this method are similar to those obtained by the statistical pattern recognition and crosscorrelation techniques. When background activity is increased and statistical pattern recognition and cross-correlation techniques fail, decomposition of EMG signal are similar to Wavelet technique. Cross-correlation technique examines all possible combinations of templates and their waveforms shifts in each possible time location (exhaustive search scheme performed in the time domain) to determine the best template match. Obviously, any exhaustive search technique is usually time consuming and is normally applicable only when the number of available templates is small. The final task in MUAP classification is the selection of reference template. Identification of MUAP templates mainly relies on the number of MUAPs present in one set of data, which indicates the number of repetitions of a MUAP. Therefore, a group containing more MUAPs has a higher chance to be selected as a reference template. Training effort for the wavelet technique and Kohonen self-organizing feature map were significantly less as compared to the cross-correlation and statistical pattern recognition techniques. Decomposition performances of correlation approach and statistical technique were found to be similar. Statistical technique has the disadvantages of using a constant threshold for classification that make it less flexible, especially when the signal is noisy and with high variability. It is observed that number of classes which can be detected by the statistical technique is unlimited depending only on the number of actual MUAP classes existing in the signal. The Kohonen-NN performed better than the statistical pattern recognition technique and yielded a higher success rate. Wavelet spectrum of a spike describes not only frequency components of the spike but also their time locations. Thus, waveform matching in the wavelet preserves the best properties of matched filter techniques in both the time and frequency domains. The excitability and variability of MUAPs are easily evaluated with the help of Kohonen map and wavelet based

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techniques. The success-rate of classification for NOR, MYO and MND cases is found to be highest using wavelet technique. When decomposition and clustering were performed on the acquired myoelectric signal, it was found that the number of action potentials were missing due to unresolved superpositions which occur at times when many neurons fire almost simultaneously. When the myoelectric signal is recorded, it is initially decomposed into individual action potentials. The decomposed signals are then clustered, using physical characteristics, into classes representing single motor neuron sources and the lumping of more than one neuron’s firings into a single class. The clustering step, however, is never fully successful due to unresolved signal superpositions and, less frequently, missed detection by the electrode. Number of action potential firings are therefore missed resulting in an incomplete data set. This often manifests itself in the data as an inter pulse interval (IPI) that is too large. It was also observed that in statistical pattern recognition and cross-correlation techniques, due to waveform variability, the MUAP classes coming from the same motor unit, although looked similar, were not grouped together. Merging of these classes can be achieved by using higher constant threshold. It is seen visually from the MUAPs of NOR, MYO and MND cases that there is a wide variation in some of the clinically important parameters. The duration measure is the key parameter being used in the clinical practice. Myopathy patients usually have MUAPs with short durations, low amplitudes and a small number of phases; whereas the MND patients have MUAPs with long durations, high amplitudes and a large number of phases. Sometimes waveform are more complex and have as many as six-phase reversal even in normal muscles. In complex EMG signals, where more active motor units were present, the physician was not able to confidently classify MUAPs based on a visual analysis of the data. The physician could only suggest how many motor units were active in a signal. The decomposition techniques

Techniques for Decomposition of EMG Signals

were able to confidently classify motor units in a complex EMG signal. The decision of reference MUAP is very important, because sometimes, it happens that when we are taking any one MUAP as a reference and find the members of the class and second time when we are taking the reference MUAP from this obtained class then the number of members change for the same class. This is due to location of the peak in the given MUAPs. In all above techniques, which are fully automatic, the data processing permits analysis without any loss or distortion of information.

CONCLUSION The following major conclusions are drawn on the basis of present work: 1. The performance of decomposition by Cross-Correlation approach is comparable to statistical pattern recognition technique.

The main problem of both the techniques is the large amount of computational time to process all the possible combinations of time shifted templates by taking different template as the reference. 2. The Kohonen-NN require smaller architecture and thus less computational effort and is found more appropriate for the classification of MUAPs because of their ability to adapt and to create complex classification boundaries. When Cross-Correlation approach is combined with the Kohonen-NN, then it results in an integrated approach for decomposition, which is fast, efficient, accurate and requires less computational time. 3. Wavelet technique is easily implementable and has better performance in respect of speed and accuracy. In the wavelet technique, it is observed that in all above five decompositions 4. 3rd , 4th and 5th decompositions show significant difference for MUAPs classification.

Figure 2. Performance comparison of techniques

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5. If number of templates is small, then statistical pattern recognition technique and Cross-Correlation approach are good; and if number of templates is high, then KohonenNN and wavelet decomposition technique are very good. 6. Wavelet-based analysis provides the MUAP morphology in the time-frequency plane. It allows fast extraction of localized frequency components and there is no need of correction for baseline drift or high-frequency noise. Therefore, wavelet technique has proved to be the best for decomposition and classification of MUAPs. The outcome of this work will be very useful for diagnosing the neuromuscular disorders in planning aspects of curative and rehabilitation therapy and for evaluating response to treatment.

REFERENCES Chauvet, E., Fokapu, O., Hogrel, J.-Y., Gamet, D., & Duchene, J. (2003, November). Automatic Identification of Motor Unit Action Potential Trains from Electromyographic signals Using Fuzzy Techniques. Journal of Medical and Biological Engineering and Computing. SpringerLink, 41(6), 646–653. Christodoulos, I., & Pattichis, C. S. (1999, February). Unsupervided pattern recognition for the classification of EMG signals. IEEE Trans. on BME, 46(2), 169–178. doi:10.1109/10.740879 De Luca, C. J., Alexander, A., Wotiz, R., Gilmore, L. D., & Nawab, S. H. (2006). Decomposition of Surface EMG Signal. Journal of Neurophysiology, 96, 1646–1657. doi:10.1152/jn.00009.2006 Fever, R. S., & Luca, C. J. (1982, March). A procedure for decomposing the myoelectric signal into its constituent action potentials-part 1: Technique, Theory and Implementation. IEEE Trans. on BME, 29(3), 149–157. doi:10.1109/TBME.1982.324881 476

Fever, R. S., Xenakis, A. P., & Luca, C. J. (1982, March). A procedure for decomposing the myoelectric signal into its constituent action potentialspart 2: Execution and Test for Accuracy. IEEE Trans. on BME, 29(3), 158–164. doi:10.1109/ TBME.1982.324882 Gerber, A., Studer, R. M., Figueiredo, R. J. P., & Moschytz, G. S. (1984, December). A new framework and computer program for quantitative EMG signal analysis. IEEE Trans. on BME, 31(12), 857–863. doi:10.1109/TBME.1984.325248 Gill, K. C. M., Cummins, K. L., & Dorfman, L. J. (1985, July). Automatic decomposition of the clinical Electromyogram. IEEE Trans. on BME, 32(7), 470–477. doi:10.1109/TBME.1985.325562 Holobar, A., & Zazula, D. (2004). Correlationbased decomposition of surface electromyograms at low contraction forces. Journal of Medical and Biological Engineering and Computing. SpringerLink, 42(4), 487–495. Khandpur, R. S. (1987). Hand book of biomedical instrumentation. New York: McGraw-Hill Publishing Company Limited. Kleine, B. J., vanDijk, B., Lapatki, M., & Zwarts, D., Stegeman. (2007). Using two-dimensional spatial information in decomposition of surface EMG signals. Journal of Electromyography and Kinesiology, Elsevier, 17(5), 535–548. doi:10.1016/j. jelekin.2006.05.003 Loudon, G. H., Jone, N. B., & Sehmi, A. S. (1992, November). New signal processing techniques for the decomposition of EMG signals. Medical & Biological Engineering & Computing, 30(6), 591–599. doi:10.1007/BF02446790 Mallet, Y., Coomans, D., Kautsky, J., & Olivier, D. V. (1997, October). Classification using adaptive wavelets for feature extraction. IEEE Trans. on PAMI, 19(10), 1058–1066.

Techniques for Decomposition of EMG Signals

Nandedkar, S. D., & Sanders, D. B. (1989, November). Median averaging of electromyographic motor unit action potentials: comparison with other techniques. Medical & Biological Engineering & Computing, 27(6), 566–572. doi:10.1007/ BF02441637

Stashuk, D., & Bruin, H. D. (1988, January). Automatic decomposition of selective needle detected myoelectric signals. IEEE Trans. on BME, 35(1), 1–10. doi:10.1109/10.1330

This work was previously published in Biomedical Engineering and Information Systems: Technologies, Tools and Applications, edited by Anupam Shukla and Ritu Tiwari, pp. 187-197, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 2.17

Prototype Based Classification in Bioinformatics Frank-M. Schleif University of Leipzig, Germany Thomas Villmann University of Leipzig, Germany Barbara Hammer Technical University of Clausthal, Germany

INTRODUCTION Bioinformatics has become an important tool to support clinical and biological research and the analysis of functional data, is a common task in bioinformatics (Schleif, 2006). Gene analysis in form of micro array analysis (Schena, 1995) and protein analysis (Twyman, 2004) are the most important fields leading to multiple sub omicsdisciplines like pharmacogenomics, glycoproteomics or metabolomics. Measurements of such studies are high dimensional functional data with few samples for specific problems (Pusch, 2005). This leads to new challenges in the data analysis. DOI: 10.4018/978-1-60960-561-2.ch217

Spectra of mass spectrometric measurements are such functional data requiring an appropriate analysis (Schleif, 2006). Here we focus on the determination of classification models for such data. In general, the spectra are transformed into a vector space followed by training a classifier (Haykin, 1999). Hereby the functional nature of the data is typically lost. We present a method which takes this specific data aspects into account. A wavelet encoding (Mallat, 1999) is applied onto the spectral data leading to a compact functional representation. Subsequently the Supervised Neural Gas classifier (Hammer, 2005) is applied, capable to handle functional metrics as introduced by Lee & Verleysen (Lee, 2005). This allows the classifier to utilize the functional

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Prototype Based Classification in Bioinformatics

nature of the data in the modelling process. The presented method is applied to clinical proteome data showing good results and can be used as a bioinformatics method for biomarker discovery.

BACKGROUND Applications of mass spectrometry (ms) in clinical proteomics have gained tremendous visibility in the scientific and clinical community (Villanueva, 2004) (Ketterlinus, 2005). One major objective is the search for potential classification models for cancer studies, with strong requirements for validated signal patterns (Ransohoff, 2005). Primal optimistic results as given in (Petricoin, 2002) are now considered more carefully, because the complexity of the task of biomarker discovery and an appropriate data processing has been observed to be more challenging than expected (Ransohoff, 2005). Consequently the main recent work in this field is focusing on optimization and standardisation. This includes the biochemical part (e.g. Baumann, 2005), the measurement (Orchard, 2003) and the subsequently data analysis (Morris, 2005)(Schleif 2006).

PROTOTYPE BASED ANALYSIS IN CLINICAL PROTEOMICS Here we focus on classification models. A powerful tool to achieve such models with high generalization abilities is available with the prototype based Supervised Neural Gas algorithm (SNG) (Villmann, 2002). Like all nearest prototype classifier algorithms, SNG heavily relies on the data metric d, usually the standard Euclidean metric. For high-dimensional data as they occur in proteomic patterns, this choice is not adequate due to two reasons: first, the functional nature of the data should be kept as far as possible. Second the noise present in the data set accumulates and likely disrupts the classification when taking a

standard Euclidean approach. A functional representation of the data with respect to the used metric and a weighting or pruning of especially (priory not known) irrelevant function parts of the inputs, would be desirable. We focus on a functional distance measure as recently proposed in (Lee, 2005) referred as functional metric. Additionally a feature selection is applied based on a statistical pre-analysis of the data. Hereby a discriminative data representation is necessary. The extraction of such discriminant features is crucial for spectral data and typically done by a parametric peak picking procedure (Schleif, 2006). This peak picking is often spot of criticism, because peaks may be insufficiently detected and the functional nature of the data is partially lost. To avoid these difficulties we focus on a wavelet encoding. The obtained wavelet coefficients are sufficient to reconstruct the signal, still containing all relevant information of the spectra, but are typically more complex and hence a robust data analysis approach is needed. The paper is structured as follows: first the bioinformatics methods are presented. Subsequently the clinical data are described and the introduced methods are applied in the analysis of the proteome spectra. The introduced method aims on a replacement of the classical three step procedure of denoising, peak picking and feature extraction by means of a compact wavelet encoding which gives a more natural representation of the signal.

BIOINFORMATIC METHODS The classification of mass spectra involves in general the two steps peak picking to locate and quantify positions of peaks and feature extraction from the obtained peak list. In the first step a number of procedures as baseline correction, denoising, noise estimation and normalization are applied in advance. Upon these prepared spectra the peaks have to be identified by scanning all local maxima. The procedure of baseline cor-

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rection and recalibration (alignment) of multiple spectra is standard, and has been done here using ClinProTools (Ketterlinus, 2006). As an alternative we propose a feature extraction procedure preserving all (potentially small) peaks containing relevant information by use of the discrete wavelet transformation (DWT). The DWT has been done using the Matlab Wavelet-Toolbox (see http://www.mathworks.com). Due to the local analysis property of wavelet analysis the features can still be related back to original mass position in the spectral data which is essential for further biomarker analysis. For feature selection the Kolmogorov-Smirnoff test (KS-test) (Sachs, 2003) has been applied. The test was used to identify features which show a significant (p < 0.01) discrimination between the two groups (cancer, control). In (Waagen, 2003) also a generalization to a multiclass experiment is given. The now reduced data set has been further processed by SNG to obtain a classification model with a small ranked set of features. The whole procedure has been cross-validated in a 10-fold cross validation.

WAVELET TRANSFORMATION IN MASS SPECTROMETRY Wavelets have been developed as powerful tools (Rieder, 1998) used for noise removal and data compression. The discrete version of the continuous wavelet transform leads to the concept of a multi-resolution analysis (MRA). This allows a fast and stable wavelet analysis and synthesis. The

analysis becomes more precise if the wavelet shape is adapted to the signal to be analyzed. For this reason one can apply the so called bi-orthogonal wavelet transform (Cohen, 1992), which uses two pairs of scaling and wavelet functions. One is for the decomposition/analysis and the other one for reconstruction/synthesis, giving a higher degree of freedom for the shape of the scaling and wavelet function. In our analysis such a smooth synthesis pair was chosen. It can be expected that a signal in the time domain can be represented by a small number of a relatively large set of coefficients from the wavelet domain. The spectra are reconstructed in dependence of a certain approximation level L of the MRA. The denoised spectrum looks similar to the reconstruction as depicted in Figure 1. One obtains approximation- and detail-coefficients (Cohen, 1992). The approximation coefficients describe a generalized peak list, encoding primal spectral information. For linear MALDITOF spectra a device resolution of 500−800Da can be expected. This implies limits to the minimal peak width in the spectrum and hence, the reconstruction level of the Wavelet-Analysis should be able to model corresponding peaks. A level L = 4 is appropriate for our problem (see Figure 1). Applying this procedure including the KS-test on the spectra with an initial number of 22306 measurement points per spectrum one obtains 602 wavelet coefficients used as representative features per spectrum, still allowing a reliable functional representation of the data. The coefficients were used to reconstruct the spectra and the final functional representation of the signal.

Figure 1. Wavelet reconstruction of the spectra with L = 4, 5, x-mass positions, y-arbitrary unit. Original signal - solid line. One observes for L = 5 (right plot) the peak approximate is to rough.

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PROTOTYPE CLASSIFIERS Supervised Neural Gas (SNG) is considered as a representative for prototype based classification approaches as introduced by Kohonen (Kohonen, 1995). Different prototype classifiers have been proposed so far (Kohonen, 1995) (Sato, 1996) (Hammer, 2005) (Villmann, 2002) as improvements of the original approach. The SNG has been introduced in (Villmann, 2002) and combines ideas from the Neural Gas algorithm (NG) introduced in (Martinetz, 1993) with the Generalized learning vector quantizer (GLVQ) as given in (Sato, 1996). Subsequently we give some basic notations and remarks to the integration of alternative metrics into Supervised Neural Gas (SNG). Details on SNG including convergence proofs can be found in (Villmann, 2002). Let us first clarify some notations: Let cvin L be the label of input v, L a set of labels (classes). Let V in RDV be a finite set of inputs v. LVQ uses a fixed number of prototypes (weight vectors, codebook vectors) for each class. Let W={wr} be the set of all codebook vectors and cr be the class label of wr. Furthermore, let Wc={wr|cr = c} be the subset of prototypes assigned to class c in L. The task of vector quantization is realized by the map Ψ as a winner-take-all rule, i.e. a stimulus vector vin V is mapped onto that prototype s the pointer ws of which is closest to the presented stimulus vector v, measured by a distance dλ (v,w). dλ (v,w) is an arbitrary differentiable similarity measure which may depend on a parameter vector λ. For the moment we take λ as fixed. The neuron s (v) is called winner or best matching unit. If the class information of the weight vector is used, the above scheme generates decision boundaries for classes (details in (Villmann, 2002)). A training algorithm should adapt the prototypes such that for each class c in L, the corresponding codebook vectors Wc represent the class as accurately as possible. Detailed equations and cost function for SNG are given in (Villmann, 2002). Here it is sufficient to

keep in mind that in the cost function of SNG the distance measure can be replaced by an arbitrary (differentiable) similarity measure, which finally leads to new update formulas for the gradient descent based prototype updates. Incorporation of a functional metric to SNG As pointed out before, the similarity measure dλ (v,w) is only required to be differentiable with respect to λ and w. The triangle inequality has not to be fulfilled necessarily (Hammer, 2005). This leads to a great freedom in the choice of suitable measures and allows the usage of non-standard metrics in a natural way. For spectral data, a functional metric would be more appropriate as given in (Lee, 2005). The obtained derivations can be plugged into the SNG equations leading to SNG with a functional metric, whereby the data are functions represented by vectors and, hence, the vector dimensions are spatially correlated. Common vector processing does not take this spatial order of the coordinates into account. As a consequence, the functional aspect of spectral data is lost. For proteome spectra the order of signal features (peaks) is due to the nature of the underlying biological samples and the measurement procedure. The masses of measured chemical compounds are given ascending and peaks encoding chemical structures with a higher mass follow chemical structures with lower masses. In addition, multiple peaks with different masses may encode parts of the same chemical structure and, hence, are correlated. Lee proposed an appropriate norm with a constant sampling period τ: 1

D p L (v ) = ∑ (Ak −1 (v) + Ak +1 (v))p    fc p

k =1

with τ    2 | vk | Ak (v) =   τ vk2     2 |vk |+|vk −1 |

 τ | v | 2 k Bk (v)=   τ vk2  if 0 > vk vk − 1    2 |vk |+|vk +1 | if 0 ≤ vk vk − 1

if 0 ≤ vk vk + 1 if 0 > vk vk + 1

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are respectively of the triangles on the left and right sides of xi. Just as for Lp, the value of p is assumed to be a positive integer. At the left and right ends of the sequence, x0 and xD are assumed to be equal to zero. The derivatives for the functional metric taking p = 2 are given in (Lee, 2005). Now we consider the scaled functional norm where each dimension (0, 1], vi is scaled by a parameter λi > 0 and all λi sum up to 1: 1

D p L (λv) = ∑ (Ak −1(λv) + Ak +1(λv))p    fc p

k =1

with τ  τ λ | v | if 0 ≤ v v + 1   2 k k k k  2 λk | vk | if 0 ≤ vk vk − 1 Ak (λ v) =  τ Bk (λ v)=  τ λk2vk2 λk2vk2   e lse else    2 λk |vk |+λk +1 |vk +1 |  2 λk |vk |+λk −1 |vk −1

The prototype update changes to: ∂δ22 (x,y, λ) ∂xk

=

τ2 (2 −U k −1 −U k +1 )(Vk −1 + Vk +1 )∆k 2

cancer, 50 control samples). Sample preparation and profile spectra analysis were carried out using the CLINPROT system (Bruker Daltonik, Bremen, Germany [BDAL]). The preprocessed set of spectra and the corresponding wavelet coefficients are then analyzed using the SNG extended by a functional metric. We reconstructed the spectra based upon the discriminative wavelet coefficients determined by the Kolmogorov-Smirnoff test as explained above and used corresponding intensities as features. We used all features for the parameterized functional norm i.e. all λi = 1. The original signal with approx. 22000 sampling points had been processed with only 600 remaining points still encoding the significant parts of the signal relevant for discrimination between the classes. The SNG classifier with functional metric obtains a crossvalidation accuracy of 84% using functional metric and 82% by use of standard Euclidean metric. The results from the wavelet processed spectra are slightly better than using standard peak lists, with 81% crossvalidation accuracy.

FUTURE TRENDS with 0 if 0 ≤ ∆ ∆   k k +1  0 if 0 ≤ ∆k ∆k −1 2 2 U k −1 =  , U =   λk +1∆k +1 λk −1∆k −1 k +1   else  λk |∆k |+λk +1 |∆k +1 |   λk |∆k |+λk −1 |∆k −1 |   1λ if 0 ≤ ∆ ∆  1λk if 0 ≤ ∆k ∆k −1 k k +1  k  Vk −1 =  ,Vk +1 =   λk |∆k | λk |∆k |   else else   λk |∆k |+λk +1 |∆k +1 |   λk |∆k |+λk −1|∆k −1 |

(

)

(

)

And ∆k=xk-yk using this parameterization one can emphasize/neglect different parts of the function for classification.

ANALYSIS OF PROTEOMIC DATA The proposed data processing scheme is applied to clinical ms spectra taken from a cancer study (45

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The proposed method generates a compact but still complex functional representation of the spectral data. While the bior3.7 wavelet gives promising results they are still not optimal, due to signal oscillations, leading to negative intensities in the reconstruction. Further, the functional nature of the data motivates the usage of a functional data representation and similarity calculation but there are also spectra regions encoded which do not contain meaningful biological information but measurement artefacts. In principle it should be possible to remove this overlaying artificial function from the real signal. Further it could be interesting to incorporate additional knowledge about the peak width, which is increasing over the mass axis.

Prototype Based Classification in Bioinformatics

CONCLUSION The presented interpretation of proteome data demonstrate that the functional analysis and model generation using SNG with functional metric in combination with a wavelet based data pre-processing provides an easy and efficient detection of classification models. The usage of wavelet encoded spectra features is especially helpful in detection of small differences which maybe easily ignored by standard approaches as well as to generate a significant reduced number of points needed in further processing steps. The signal must not be shrinked to peak lists but could be preserved in its functional representation. SNG was able to process high-dimensional functional data and shows good regularization. By use of the Kolmogorov-Smirnoff test we found a ranking of the features related to mass positions in the original spectrum which allows for identification of most relevant feature dimensions and to prune irrelevant regions of the spectrum. Alternatively one could optimize the scaling parameters of the functional norm directly during classification learning by so called relevance learning as shown in (Hammer, 2005) for scaled Euclidean metric. Conclusively, wavelet spectra encoding combined with SNG and a functional metric is an interesting alternative to standard approaches. It combines efficient model generation with automated data pre-treatment and intuitive analysis.

REFERENCES Baumann, S., Ceglarek, U., Fiedler, G. M., & Lembcke, J. (2005). Standardized approach to proteomic profiling of human serum based magnetic bead separation and matrix-assisted laser esorption/ionization time-of flight mass spectrometry. Clinical Chemistry, 51, 973–980. doi:10.1373/ clinchem.2004.047308

Cohen, A., Daubechies, I., & Feauveau, J.-C. (1992). Biorthogonal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics, 45(5), 485–560. doi:10.1002/ cpa.3160450502 Hammer, B., Strickert, M., & Villmann, T. (2005). Supervised neural gas with general similarity measure. Neural Processing Letters, 21(1), 21–44. doi:10.1007/s11063-004-3255-2 Haykin, S. (1999). Neural Networks (2nd ed.). Englewood Cliffs, NJ: Prentice Hall. Ketterlinus, R., Hsieh, S.-Y., Teng, S.-H., Lee, H., & Pusch, W. (2005). Fishing for biomarkers: analyzing mass spectrometry data with the new clinprotools software. BioTechniques, 38(6), 37–40. doi:10.2144/05386SU07 Kohonen, T. (1995). Self-Organizing Maps, volume 30 of Springer Series in Information Sciences. Springer, Berlin, Heidelberg, (2nd Ext. Ed. 1997). Lee, J., & Verleysen, M. (2005) Generalizations of the lp norm for time series and its application to self-organizing maps. In Marie Cottrell, editor, 5th Workshop on Self-Organizing Maps, volume 1, pages 733–740. Mallat, S. (1998) A wavelet tour of signal processing. San Diego, CA: Academic Press. Martinetz, T., Berkovich, S., & Schulten, K. (1993). ’Neural-gas’ network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks, 4(4), 558–569. doi:10.1109/72.238311 Morris, J., Coombes, K., Koomen, J., Baggerly, K., & Kobayashi, R. (2005). Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum. Bioinformatics (Oxford, England), 21(9), 1764–1775. doi:10.1093/bioinformatics/bti254

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Orchard, S., Hermjakob, H., & Apweiler, R. (2003). The Proteomcs Standards Initiative. Proteomics, 3, 1274–1376. Petricoin, E. F., Ardekani, A., Hitt, B., & Levine, P. (2002). Use of proteomic patterns in serum to identify ovarian cancer. Lancet, 359, 572–577. doi:10.1016/S0140-6736(02)07746-2 Pusch, W., Flocco, M., Leung, S. M., Thiele, H., & Kostrzewa, M. (2003). Mass spectrometrybased clinical proteomics. Pharmacogenomic, 4, 463–476. doi:10.1517/phgs.4.4.463.22753 Ransohoff, D. F. (2005). Lessons from controversy: ovarian cancer screening and serum proteomics. Journal of the National Cancer Institute, 97, 315–319. Rieder, A. Louis, A.K. & Maaß, P. (1998) Wavelets: Theory and Applications. Wiley. Sachs, L. (2003) Angewandte Statistik. Springer. Sato, A., & Yamada, K. (1996) Generalized learning vector quantization. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems 8. Proceedings of the 1995 Conference, pages 423–9. MIT Press, Cambridge, MA, USA. Schena, M., Shalon, D., Davis, R. W., & Brown, P. O. (1995). Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270(5235), 467–470. doi:10.1126/science.270.5235.467 Schleif, F.-M. (2006) Prototype based Machine Learning for Clinical Proteomics. Technical University Clausthal, PhD-Thesis. Twyman, R. M. Principles of proteomics BIOS Scientific Publishers, NY,2004. Villanueva, J., Philip, J., Entenberg, D., & Chaparro, C. A. (2004). Serum peptide profiling by magnetic particle-assisted, automated sample processing and maldi-tof mass spectrometry. Analytical Chemistry, 76, 1560–1570. doi:10.1021/ac0352171

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KEY TERMS AND DEFINITIONS Bioinformatics: Generic term of a research field as well as a set of methods used in computational biology or medicine to analyse multiple kinds of biological or clinical data. It combines the disciplines of computer science, artificial intelligence, applied mathematics, statistics, biology, chemistry and engineering in the field of biology and medicine. Typical research subjects are problem adequate data pre-processing of measured biological sample information (e.g. data cleaning, alignments, feature extraction), supervised and unsupervised data analysis (e.g. classification models, visualization, clustering, biomarker discovery) and multiple kinds of modelling (e.g. protein structure prediction, analysis of expression of gene, proteins, gene/ protein regulation networks/interactions) for one or multidimensional data including time series. Thereby the most common problem is the high dimensionality of the data and the small number of samples which in general make standard approach (e.g. classical statistic) inapplicable. Biomarker: Mainly in clinical research one goal of experiments is to determine patterns which are predictive for the presents or prognosis of a disease state, frequently called biomarker. Biomarkers can be single or complex (pattern)

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indicator variables taken from multiple measurements of a sample. The ideal biomarker has a high sensitivity, specificity and is reproducible (under standardized conditions) with respect to control experiments in other labs. Further it can be expected that the marker is vanishing or changing during a treatment of the disease. Clinical Proteomics: Proteomics is the field of research related to the analysis of the proteome of an organism. Thereby, clinical proteomics is focused on research mainly related to disease prediction and prognosis in the clinical domain by means of proteome analysis. Standard methods for proteome analysis are available by Mass spectrometry. Mass Spectrometry: An analytical technique used to measure the mass-to-charge ratio of ions. In clinical proteomics mass spectrometry can be applied to extract fingerprints of samples (like blood, urine, bacterial extracts) whereby semiquantitative intensity differences between sample cohorts may indicate biomarker candidates Prototype Classifiers: Are a specific kind of neural networks and related to the kNN classifier. The classification model consists of so called

prototypes which are representatives for a larger set of data points. The classification is done by a nearest neighbour classification using the prototypes. Nowadays prototype classifiers can be found in multiple fields (robotics, character recognition, signal processing or medical diagnosis) trained to find (non)linear relationships in data. Relevance Learning: A method, typically used in supervised classification, to determine problem specific metric parameter. With respect to the used metric and learning schema univariate, correlative and multivariate relations between data dimensions can be analyzed. Relevance learning typically leads to significantly improved, problem adapted metric parameters and classification models. Wavelet Analysis: Method used in signal processing to analyse a signal by means of frequency and local information. Thereby the signal is encoded in a representation of wavelets, which are specific kinds of mathematical functions. The Wavelet encoding allows the representation of the signal at different resolutions, the coefficients contain frequency information but can also be localized in the signal.

This work was previously published in Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, Julian Dorado and Alejandro Pazos, pp. 1337-1342, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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An Integrated System for E-Medicine: E-Health, Telemedicine and Medical Expert Systems Ivan Chorbev Ss. Cyril and Methodius University, Republic of Macedonia Boban Joksimoski European University, Republic of Macedonia

ABSTRACT This chapter presents an overview of an integrated system for eMedicine that the authors propose and implement in the Republic of Macedonia. The system contains advanced medical information systems, various telemedicine services supported by modern telecommunication technologies, and decision support modules. The authors describe their telemedicine services that use wireless broadband technologies (WiMAX, 3G, Wi-Fi).

A significant part of the chapter presents a web based medical expert system that performs self training using a heuristic rule induction algorithm. The data inserted by medical personnel while using the e-medicine system is subsequently used for additional learning. The system is trained using a hybrid heuristic algorithm for induction of classification rules that we developed. The SA Tabu Miner algorithm (Simulated Annealing and Tabu Search based Data Miner) is inspired by both research on heuristic optimization algorithms and rule induction data mining concepts and principles.

DOI: 10.4018/978-1-60960-561-2.ch218

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

An Integrated System for E-Medicine

INTRODUCTION E-medicine can be viewed as a symbiosis between medicine, informatics and telecommunication technologies. Basically, e-medicine incorporates the use of computer technologies, multimedia systems and global networking in the provision of medical services. It is an area of great scientific and research interest, followed by fast implementation of novel commercial functionalities. A common simple definition describes e-medicine as the use of multimedia technologies like text, pictures, speech and/or video for performing medical activities. The goal of our research is to define a prototype of an integrated system for e-medicine that enables application of information and communication technologies over a wide spectrum of functionalities in the health sector including medical personnel, diagnostics, therapy, managers, medical insurance and patients. Additionally we aim at incorporating artificial intelligence in various modules of the system making it a useful partner to all entities using the system. We present algorithms for building medical decision support and expert systems as part of the e-medicine system. The chapter is organized as follows. The first section gives a short overview of e-medicine, telemedicine and medical expert systems. The second part explains in detail our model of a system for e-medicine, its main modules, the used technologies and the implemented functionalities. The third section gives as overview of the medical expert subsystem we implemented, along with the SA Tabu Miner rule induction algorithm for classification that we developed for that purpose.

BACKGROUND E-medicine (sometimes referred to as e-medicine or eHealth) is a rather new term for describing the medical care that is supported by modern electronic processes and modern telecommunications. It is sometimes used to describe the use of computers in

health institutions, for providing medical services via Internet or simply a new name for telemedicine. In fact, it is used to describe a wide spectrum of services that are part of the medical practice supported by the aid of information technology. The provided services include: •

•

•

•

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The use of Electronic Medical Records (EHR) for easy storing, retrieving and sharing data between medical personnel (doctors, pharmacists, therapists etc). Telemedicine, as a way to provide medical services remotely and as a way for providing teleconsultations and assistance to other doctors Public Health Informatics, where the population and/or patients could get informed about relevant medical information. Management of medical information and medical knowledge, using the data in data mining research Mobile e-medicine, a field that includes the use of mobile devices for various purposes including real-time monitoring of patients, diagnosis, gathering and providing data for the doctors and mobile telemedicine

Medical Information Systems Information systems have been developing rapidly through the past decades, and we have now means of managing and organizing large quantities of data, methods of validating the data, and ways of processing the data for retrieving valuable information as well as learning from the data. However, for practical implementations, there are a lot of requirements that should be satisfied in order to make a healthcare information system usable. A lot of the tasks are concerned with gathering and manipulation of data provided by patients, doctors and insurance companies. The medical information is critical, and should be accessible, up to date and coherent at all times. Also, the data must be secure, confidential and protected from

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unauthorized access. Security of medical data is regulated by various requirements, like HIPAA and European Commission’s Directive, and all systems should strictly implement them. The evolution of Healthcare Information Systems has gone through different phases (Vogel & Perreault, 2006). The first phase was marked by development of specific applications that would help healthcare workers in a specific area (like patient registration or laboratory results). Access to data was mostly at department level, and there was the problem of transferring data between departments in a hospital. To overcome the inoperability between different systems, an approach was taken to interconnect them. The second phase was the development of appropriate interface engines to make interconnection possible. The goal is to make data accessible wherever it is required. Progress is still made in interconnecting different countries or regions and it has been regarded as a challenging task.

Telemedicine Initially telemedicine was defined by Bird (1971) as “the practice of medicine without the usual physician-patient confrontation …via an interactive audio-video communications system” (p.67). Telemedicine basically provides options for neutralizing the geographical distance between the users. It is useful to spare the ill patients from the discomfort and expenses of traveling. Saving the medical personnel from travelling to remote areas leaves more time for the medical problems. The transfer of data from site to site ensures better judgment and informed decisions. Application areas of telemedicine expand to almost all the fields of medicine (Zielinski & Duplaga, 2006). Applications of telemedicine include: Consultation, Diagnostic Consultation, Monitoring, Education, Disaster management (Olariu et al., 2004), Virtual Microscopy (Fontelo et al., 2005), Homecare, Diagnosis, Treatment and Therapy (Psychology). Along the development of telemedicine, new

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terms like eHealth have emerged. According to (Maheu, 2001), “E-health refers to all forms of electronic healthcare delivered over the Internet, ranging from informational, educational, and commercial “products” to direct services offered by professionals, nonprofessionals, businesses or consumer themselves” (p. 3). The history of telemedicine shows evident correlation with the developments in communication technology and IT software development. Researches categorize the telemedicine history into three eras based on the technological development (Bashshur et al., 2000; Tulu & Chatterjee, 2005). All the definitions during the first era of telemedicine focused on medical care as the only function of telemedicine. The first era can be named as telecommunications era of the 1970s (Bashshur et al., 2000). Applications in this era were dependent on broadcast and television technologies. Telemedicine was not integrated with any other clinical data. It was based on transmission of a TV and audio signal both ways, and lacked the ability to connect other devices and transmit data automatically. Hence, the use was limited to basic teleconsultations and low quality video communication with patients. The second era of telemedicine evolved with the digitalization in telecommunications (Bashshur et al., 2000). The transmission of data was supported by various communication mediums ranging from telephone lines to Integrated Service Digital Network (ISDN) lines. However, the high costs attached to the communication mediums that can provide higher bandwidth became an important bottleneck for telemedicine. This era overcame a lot of the problems in the first era, like patient feedbacks, data transfer, but was not applicable for general use, mostly because the expensive services needed (ISDN, satellite communication). The third era has been named “Internet era”, where the main burden for interconnecting is transferred to the Internet network, as a robust system that is accessible, already deployed, relatively

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cheap and constantly growing. Also, the technologies to access the Internet are constantly evolving for better performance and higher speeds. Regardless of the technology used to access the network, previous services still function without modifications, even faster and more capable (Bashshur et al., 2000). The development of mobile wireless Internet access, by using 3G wireless networks and high-speed WiMAX provides new ways of implementing telemedicine services. Other telemedicine projects are based on alternative ways of establishing communication, like satellite-based telemedicine (Healthware project - http://healthware.alcasat.net/), and although this provides effective large scale connectivity, its deployment and maintenance is difficult and thus its services are expensive. The wireless networks are ultimately the best cost-effective solution for fast deployment and large area covering, and WiMAX as the leading technology for fast, broadband Internet access.

Expert Systems Expert Systems belong to the broader class of Decision Support Systems, and are viewed as consultants to human decision makers. The use of expert systems in medical problems has always been a topic of interest, and an integrated system for e-medicine must include a decision support system in order to gain the advantages that artificial intelligence can offer. Some of the reasons why decision support systems are necessary include the following challenges that the medical personnel is facing: • • •

•

Classification – deciding who is treated first, and who is sent back Treatment - the decisions for treatment must be informed and expert consulted The physicians’ knowledge in a particular area might be outdated, and the time for reading new publications is always limited Physicians go into diagnostic habits

•

• • •

The physician might be inexperienced with diagnosing or treating particular rare diseases The treatment might be too expensive and the doctor is hesitating to prescribe Time constraints to make a decision The doctor might be unaware of the existence of some new drugs or their side effects

Medical expert systems have been an area of research and implementation since the 1970s. A major part of the research in Artificial Intelligence was focused on expert systems. Various programming languages like LISP and Prolog were introduced for the purpose of declarative programming and knowledge representation, later to be used for development of expert systems. Famous medical expert systems include Mycin®, designed to identify bacteria causing severe infections and to recommend antibiotics, CADUCEUS®, embracing all internal medicine, also Spacecraft Health Inference Engine (SHINE)®, STD Wizard® etc. There are two fundamental approaches for knowledge base construction for the expert system: knowledge acquisition from a human expert, and empirical induction of knowledge from collections of training samples. In our research we choose the later. We used an algorithm for rule induction that we developed to create a medical expert system.

MODEL OF AN INTEGRATED SYSTEM FOR E-MEDICINE Services in E-Medicine are growing rapidly with the development of the underlying telecommunication and computer technologies. The developed applications quickly get replaced by newer services that use the bandwidth and accessibility of the newest communication technologies. Still, by analyzing the incoming technologies and anticipated commercial needs, the medical services,

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architectures and design of the e-medicine systems of the future can be predicted to some extent. The system must be based on reliable communication technologies. Cable and optical networks will always be faster, more secure and reliable than wireless technologies, hence ensuring their infrastructural function as backbones. Wireless networks have the drawbacks of providing less resource in terms of data traffic and bandwidth, but add the very significant mobility to the users. Speeds in wireless networks are constantly growing, and with standards like WiMAX of 4G mobile telephony, the implementation of cheaper and faster e-medical services is ensured. Integrating different multiplatform services is the crucial phase of the development of an integrated system for e-medicine. IT Systems in general are in constant development, and as such should have modular design. Older modules should be replaced with newer without interfering with the overall functioning of the system. The Healthcare Information System should be designed to provide complete and reliable storage of electronic records, implementing the concerns of privacy, confidentiality and security. Furthermore, the use of unique electronic identifications in form of chip cards, proximity cards or RFID Figure 1. Diagram of a modular e-health system

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can help improve patient care by identifying and getting medical records from patients that can’t communicate (unconscious patients). The diagram of an integrated system is shown on Figure 1. Users of the system can use various devices to access parts of the system. With the implementation of encrypted communication with XML web services, the modules do not depend on the telecommunication technology or the devise used for access (Notebook, PDA, mobile phone or other terminal). Because of modularity, newer services can be deployed when devices that support that service will be available. Of course, the new devices should support older services that are still in use. The integrated and coherent patient data, through wireless network, are accessible to personnel in hospitals, ambulance vehicles, in a medical campus and even in homes. Data that can be transmitted can be in form of text, patient records, video streaming, audio recordings, real-time data from sensors etc. The services can also be used for management purposes, like allocation of human resources, checking supplies of medical equipment, making appointments, control of patients etc.

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Implementing an E-Medicine System in a Developing IT Society In the Republic of Macedonia there is no integrated health information and communication system. There are only individual multiplatform systems at some of the hospitals, usually based on Microsoft SQL Server databases supporting various Windows forms or recently web applications. The analysis of the current solutions show significant differences among hospitals, but the overall conclusion is serious lack of Information and Communication Technology (ICT) and nonexistence of integrated hospital information systems, with small number of exceptions. Also, it is evident that information systems that operate in some facilities are challenging to integrate and extremely difficult to enable information exchange. While researchers in developed countries can have different goals, our objectives locally had to be scalable, ranging from establishment of basic telemedicine services up to advanced up to date functionalities. The main concepts that our system was based on included creation of necessary basic Medical Information Systems (MISs) where hospitals had none, interfaces for various MISs, using modern telecommunication technologies for creating an integrated MIS and provision of advanced medical services at remote locations and other telemedicine applications. (Chorbev & Mihajlov, 2008) There are many prerequisites for the integrated e-medicine system to be implemented. Organizational prerequisites include cooperation and communication between actors in the complex organizational structures. Human resources need to be trained to a level of understanding and familiarity with ICT. Legal prerequisites include regulating patient related information. The Republic of Macedonia already integrates Diagnosis Related Groups (DRG) into patient’s medical data, but the records have so far been distributed among various hospitals. In developed

integrated medical information systems there are numerous standardized records of patient data. With some initial organized data we already started research with diseases that are endemic for the region. By using data mining techniques, we developed modules that serve as diagnosis consultants. We used already available data in form of blood tests from 70000 patients in the Ohrid Orthopedic hospital. This data serves as training data input in our research where our rule induction algorithm SA TABU Miner (Chorbev et al., 2009) is used for generating classification rules. Other aspects of artificial intelligence, like combinatorial optimization, can also be integrated in the system. Using optimization tools to obtain optimal drug amounts in treatment can reduce the burden on the state health insurance fund. The limited number of existing surgery teams and equipment, kidney dialysis sets, MRI devices can be scheduled for use with combinatorial optimization, achieving maximal utilization according to specific patient needs and urgency. However, scattered results in research must be integrated in an e-medicine system for the benefits to be readily available. The modularity will enable every new diagnostic tool to be quickly integrated and delivered to physicians and patients. All developed modules of the integrated MIS need to be readily available to both patients and physicians throughout the country. By designing the system as a web and PDA application, available through statewide wireless network and secured with adequate authentication and encryption methods, it is expected that it will increase severely the quality of medical service.

Wireless Infrastructure In order to implement our telemedicine system we used the backbone network of a fast growing privately owned data communication provider. The backbone network consists of some fiber optic connections in the city limits of Skopje and among some major cities and mostly 802.16 (WiMAX)

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base stations throughout the country. The optic fiber connections are used for provision of fast bandwidth services where possible. The WiMAX antennas are used for connecting hospitals where the optic fiber has not reached yet and 802.11 hotspots are used for wireless devices (PDAs, notebook PCs etc.) Due to the sufficient bandwidth of WiMAX, it is used to cover most of the needs of our telemedicine system. WiMAX is a telecommunication technology aimed at providing broadband wireless data connectivity over long distances. It is based on the IEEE 802.16 standard. The high bandwidth and increased reach of WiMAX make it suitable for providing a wireless alternative to cable and DSL for last mile broadband access. We tested the performance of both fixed outdoor and fixed indoor WiMAX antennas and the results are very promising, since both provided robust connections. Latest systems built using 802.16e2005 and the OFDMA PHY™ as the air interface technology are called “Mobile WiMAX” and are expected to provide broadband connections while the client is moving. Within the city limits of the capital Skopje there is a functional fiber optic Metro Ethernet network. The fiber optical connection enables Figure 2. Optical network in Macedonia

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fast and robust connectivity for provision of advanced telemedicine services like high quality video streaming of surgical procedures, medical visualization etc. Even when the fiber optical lines are used for communication, the WiMAX wireless lines could be used for backup in case of disrupted cable communication. While cables can be physically cut, the WiMAX connections are stable even in severe weather conditions. A wireless backbone network is established throughout the country, and hospitals in different cities are (or will be) connected to the network. Antennas are placed on hills overseeing cities, and coverage with the radio signal is good and robust. The backbone network is depicted in Figure22, while one antenna in Skopje.

Implemented Services and Functionalities In the initial stages we included two hospitals in the pilot project: The Institute for respiratory diseases in children-Kozle and the University clinical center in Skopje. Due to the lack of a modern Medical Information Systems (MIS) in the hospitals, we developed a prototype of a modular MIS that can later be distributed to all the hospi-

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tals. The main database is centralized, since the data for approximately 2.000.000 inhabitants in the Republic of Macedonia can easily be handled by the contemporary database engines. Because of the idea for centralization of the database and since some hospitals cannot afford to maintain an IT department, the MIS is hosted on the Internet Service Provider’s (ISP) servers. Knowing that connectivity speeds are high enough when using WiMAX, there is no need to host the MIS locally at the hospital. The MIS is developed as a web application that can be accessed by a common Internet browser. Querying data in the web based MIS is possible using multiple criteria. Data can be searched from other patients with similar symptoms in order to learn from other previous experiences. Entire patient history is accessible online, with strong regard to privacy issues. While patient identity details are available to the physician in charge of the particular case, for other medical personnel with lower access privileges, only medical information is available, without disclosing the identity of the particular patient. The developed system includes software components specialized for use by PDA devices. Both patients and staff can wirelessly access different software modules. Physicians can access patient’s data, results from laboratory analyses, forums and chats, web sites with medical scientific papers. Patients can access their results from different analyses, make appointments, and check the availability of certain physicians. We paid great attention to the usability of the user interface in the PDA applications. Due to the resolution and dimension limitations, significant effort was made to maximize the utilization of the given space on the small screens and to enable easy navigation through the user interface. We adopted a policy of gradual increase of details presented on demand, since scrolling and navigating large texts is unpleasant on a PDA device. The system includes a Short Message Service (SMS) gateway that is used for SMS notifications for both physicians and patients. Current func-

tionalities include confirmation of appointments for patients, notification for completed laboratory analyses, SMS emergency calls for physicians on stand-by etc. The system can even notify the patient for the upcoming time for therapy or treatments. WiMAX is also used for Voice over IP (VoIP) services. PSTN telephone bills are drastically reduced as a result of the use of VoIP for communication among hospitals. A vital part of a telemedicine system is the sharing of knowledge, experience and expertise. The implemented MIS includes a forum and a virtual chat room where physicians can consult each other. The forum enables posting various laboratory results and even video and audio sequences from various diagnostic procedures for the consultations to be supported by appropriate information. Since the system is centralized, consultations are possible among physicians from all hospitals included in the system. Posted materials in the virtual chat room cannot be connected to the patient identity. Our system incorporates modules that enable laboratory results and other analyses to be submitted for review to the specialists. Physicians working in smaller towns can access the system using their accounts and can submit questions along with supporting materials electronically. Special web application software modules are developed for submitting images (MRI, X-Ray, CAT scan) from remote hospitals in the country to the specialist working in the capital. Also results from blood analysis are filled in online forms. Specialists review the results and can post their reply to the sender. This system enables reduction of transport costs, response times are drastically smaller and patients do not have to suffer through long trips to the specialist. We introduced a system of grading each submitted material giving it different priority according to the contents and level of urgency demanded by the sender. Extremely urgent submissions can even cause the SMS gateway to notify the specialist for the incoming request. We tested streaming video through the WiMAX

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connections and we set up a system where experts from one hospital can oversee complex surgical operations performed by the surgeons elsewhere. Also, students at the University hospital in Skopje will be able to learn from the live feed from surgeries performed elsewhere. We performed several video streaming experiments from a paramedic’s vehicle to the hospital. In the first experiment we used a vehicle equipped with a WiMAX antenna and an MPEG coding device. A continual video stream was established, and used for transmitting live feed from the patient in the ambulance to the hospitals. The video link enables specialists to give advice to first aid workers on the scene of an accident, based on real time video feed from the patient’s condition. Paramedics could be supervised by experienced medical personnel while performing necessary life support interventions. In the second experiment we used a 3G mobile telephony device for the same use, but the bandwidth was insufficient for a higher quality video. Due to current limitations of WiMAX, the ambulance must not move while being connected online. However new equipment based on Mobile WiMAX (802.16e-2005) is expected to overcome this issue. The equipment used in the experiment was SCOM® MPEG-2 Digital Video Encoder/ Decoder. The used WiMAX antennas support 2-10 Mbit/s. The particular experiment used 2 Mbit/s, but an acceptable video quality is achieved even with a 512 Kbit/s connection. A third experiment was conducted using a personal computer instead of a specialized MPEG coding device; however a noticeable delay was evident in the video stream. The later architecture is applicable for a smaller spectrum of services. The small indoor antennas were also used for video telephony experiment. We tested a scenario where an older woman suffering from strong pain in the back and almost immobilized, had to communicate with her doctor for consultation. Since transportation of the patient was difficult and painful, we brought the WiMAX antennas and IP video phones at both locations (the patient’s and

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doctor’s) and established a video link that they used for consultation. We also used the video phones for establishing sign language communication for patients with impaired hearing. We used Leadtek® IP broadband videophones (BVP8882). They use H.323 protocol for high performance and good quality video communication. The quality of the video stream using only 256 Kbit/s was sufficient for the common sign language to be used and understandable by the communicating parties. The video signal that we used in most of the testing originated from a digital video camera. Another even more import feature is streaming of digitalized video signals received from analogous endoscopy equipment. We worked on digitalization of an analogous signal from a fluoroscopic camera using a Plextor® MPEG encoder. The digital output from the encoder was easily streamed. The received live video could be used to consult subspecialists not present at the location where the exam is performed. Using VoIP and chat on PDA devices, the specialist could provide feedback and guidance to the person performing the exam in the field or in the remote hospital. The implementation of the system consists of three main parts: the database, the online web and PDA applications and a standalone application that performs batch data processing and performs scheduled jobs and maintenance functionalities. Most of the applications are developed in Microsoft® .NET technology, using SQL Server 2005® as a database engine, and some are coded in PHP and hosted on Apache servers using MySQL databases.

OUR MEDICAL EXPERT SYSTEM A crucial module in the integrated system was a decision support subsystem that would provide useful advice to users based on information gathered from the medical information system in use. For that purpose we developed several expert subsystems, the main of which is a diagnostic module.

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The goal of the web medical expert subsystem presented here is to serve as a consultant to physicians when setting a diagnosis. The physician logs in the system and chooses the appropriate input form that suite the test data that he/she is going to enter. We have previously collected training data from various patient histories from hospitals and training datasets from the UCI machine learning repository™ (http://archive.ics.uci.edu/ml/datasets.html). By using the training data we trained classifiers using the SA Tabu Miner algorithm. After the doctor inputs the patient’s data and submits the form, the classifier assigns a class (disease) to the patient. The classifier also presents the percent of its predictive certainty, hence the certainty of its diagnosis. The data entered by the physician is stored. Later, when the diagnosis is confirmed and the entered data is rechecked, the confirmed records are added to the training data for a new training cycle of the classifiers, each time with more data. The training cycle is repeated in different intervals for different classifiers, since not all diagnostic forms are used with the same frequency. The tendency is to run the training when new training cases exceed 5% of the previous number of training records. The training process is based on a well-known 10-fold cross-validation procedure (Weiss & Kulikowski, 1991). Each data set is divided into 10 mutually exclusive and exhaustive partitions and the algorithm is run once for each partition. Each time a different partition is used as the test set and the other 9 partitions are grouped together and used as the training set. The predictive accuracies (on the test set) of the 10 runs are then averaged. Eventually the training is performed with the entire dataset so that the generated rules are based on the entire knowledge available. These final rules have significantly more impact in the deciding process when the system is in use. The rules generated in each iteration are stored. When deciding, the different groups of rules vote for the final classification of the new case with different

impact, dependant on the predictive accuracy. The system adopts the weighted majority vote approach to combine the decision of the rule groups. The average predictive accuracy is necessary to estimate the reliability of the system when used as a diagnosis consultant.

Extracting Knowledge in Expert Systems, Rule Induction Some efforts were made to implement an expert system entirely using the ID3 algorithm (Mingers 1986, Quinlan 1986), while other expert systems use combination of data mining and human knowledge verification (Holmes & Cunningham, 1993). Empirically derived knowledge is commonly represented by classification rules, gained using specific algorithms. A number of induction methods were devised for extracting knowledge from the data, and most common known are decision trees (Breiman et al., 1984), rough set theory (Tsumoto et al., 1995), artificial neural networks, etc. This text describes an algorithm for rule induction called SA Tabu Miner (Simulated Annealing and Tabu Search based Data Miner). The goal of this rule induction algorithm is a type of data mining that aims to extract knowledge in form of classification rules from data. Simulated Annealing (SA) (Kirkpatrick et al., 1982) and Short-term Tabu Search (TS) algorithm (Zhang & Sun, 2002; Sait & Youssef, 1999; Glover, 1989) are used to develop the algorithm since the rule discovery problem is NP-hard. To the best of our knowledge, the use of SA and TS algorithms for discovering classification rules in data mining is still unexplored research area. Despite the accuracy of the discovered knowledge, it is equally important for the derived rules to be comprehensible for the user (Fayyad et al., 1996; Freitas & Lavington, 1998). Comprehensibility is very important especially when the discovered knowledge will be used for supporting a decision made by a human user. Comprehensible knowledge can be interpreted and validated by a

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human. Validated knowledge will be trusted and actively used for decision making. When deriving rules for classification, comprehensibility is achieved with discovering rules that contain less terms in the “if” part. Also, fewer rules are easier to comprehend.

An Overview of Rule Induction Algorithms Several methods have been proposed for the rule induction process such as ID3 (Quinlan, 1986), C4.5 (Quinlan, 1993), CN2 (Clark & Boswell, 1991), CART(Breiman et al., 1984), AQ15 (Holland, 1986) and Ant Miner (Parepinelli et al., 2002). All mentioned algorithms can be grouped into two broad categories: sequential covering algorithms and simultaneous covering algorithms. Simultaneous covering algorithms like ID3 and C4.5 generate the entire rule set at once, while the sequential covering algorithms like AQ15 and CN2 learn the rule set in an incremental fashion. The algorithm ID3 (Iterative Dichotomiser 3) is used to generate a decision tree. The ID3 algorithm takes all unused attributes and evaluates their entropy. It chooses the attribute for which the entropy is minimal and makes a node containing that attribute. Always using the attribute with minimal entropy rule can lead to trapping in local optima. C4.5 is an extended version of ID3. It implements a “divide-and-conquer” strategy to create a decision tree through recursive partitioning of a training dataset. The final tree is transformed into an equivalent set of rules, one rule for each path from the root to a leaf of the tree. Creating decision trees means using quite a lot of memory which grows exponentially when the number of attributes and classes increases. CN2 works by finding the most influential rule that accounts for part of the training data, adding the rule to the induced rule set, removing the data it covers, and then iterating this process until no training instance remains. The most in-

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fluential rule is discovered by a beam search, a search algorithm that uses a heuristic function to evaluate the promise of each node it examines. Always using the most influential rule can also lead to trapping in local optima.

Heuristic Search Algorithms in Data Mining and Rule Induction Ant Colony Optimization (ACO) has recently been successfully used for rule induction by Parepinelli et al. (2002). He developed an ACO based algorithm that managed to derive classification rules with higher predictive accuracy than CN2. Also, the ACO derived rules were quite simpler and more comprehensible for humans. Some general use of Simulated Annealing and Tabu Search in data mining applications can be found in literature. Zhang has used TS with short-term memory to solve the optimal feature selection problem (Zhang et al. 2002). Tahir et al. (2005) also propose a feature selection technique using Tabu Search with an intermediate-term memory. TS is used (Bai, 2005) for developing a Tabu Search enhanced Markov Blanket (TS/MB) procedure to learn a graphical Markov Blanket classifier from data sets with many discrete variables and relatively few cases that often arise in health care. Johnson and Liu (2006) have used the traveling salesman approach for predicting protein functions. Unlike SA and TS, Genetic algorithms (GA) can be found related to classification more often. Bojarczuk et al. (2000) use genetic programming for knowledge discovery in chest pain diagnosis. Weise et al. (2007) developed a GA based classification system to participate in the 2007 DataMining-Cup Contest, proving that combinatorial optimization heuristic algorithms are emerging as an important tool in data mining. Other cases of algorithms for deriving classification rules using Genetic Algorithms are referenced in Gopalan et al., (2006); Otero et al., (2003); Yang et al., (2008); Podgorelec (2005).

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SA Tabu Miner - Algorithm for Rule Induction The SA Tabu Miner algorithm incrementally constructs and modifies a solution - a classification rule of the form: IF < term1 AND term2 AND ...> THEN

Each term is a triple . Since the operator element in the triple is always “=”, continuous (real-valued) attributes are discretized in a preprocessing step using the C4.5 algorithm (Quinlan, 1993). A high level description of the SA Tabu Miner algorithm is shown in Algorithm 1. The algorithm creates rules incrementally, performing a sequential process to discover a list of classification rules

Algorithm 1. SA Tabu Miner, the rule induction algorithm. TrainingSet = {all training cases}; DiscoveredRuleList = [ ]; /*initialized with an empty list*/ While (TrainingSet > Max_uncovered_cases) Calculate entropy and hence probability Start with an initial feasible solution S ∈ Ω. Initialize temperature While (temperature > MinTemp) Generate neighborhood solutions V* ∈ N(S). Update tabu timeouts of recently used terms Sort by (quality/tabu order) desc sol-s S* ∈ V* S* = the first solution ∈ V* While(move is not accepted or V* is exhausted) If metrop(Quality(S) - Quality(S*)) then Accept move and update best solution. Update tabu timeout of the used term break while End if S* = next solution ∈ V* End while Decrease temperature End While Prune rule S Add discovered rule S in DiscoveredRuleList TrainingSet = TrainingSet - {cases covered by S}; End while where • Ω is the set of feasible solutions, • S is the current solution, • S∗ is the best admissible solution, • Quality(S) is the objective function, • N(S) is the neighborhood of solution S, • V∗ is the sample of neighborhood solutions. 497

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covering as many as possible training cases with as big quality as possible. At the beginning, the list of discovered rules is empty and the training set consists of all the training cases. Each iteration of the outer WHILE loop of SA Tabu Miner, corresponding to a number of executions of the inner WHILE loop, discovers one classification rule. After the rule is completed, it is pruned from the excessive terms, to exclude terms that were wrongfully added in the construction process. The created rule is added to the list of discovered rules, and the training cases covered by this rule are removed from the training set. This process is iteratively performed while the number of uncovered training cases is greater than a user-specified number called Max_uncovered_cases, usually 5% of all cases. The selection of the term to be added to the current partial rule depends on both a problemdependent heuristic function (entropy based probability), a tabu timeout for the recently used attribute values and the metropolitan probability function based on the Boltzman distribution of probability. The algorithm keeps adding one term at a time to its partial rule until one of the following two stopping criteria is met:

able values in the rule terms, but a combination of less probable ones. The entropy based probability guides and intensifies the search into promising areas (attribute values that have more significance in the classification), therefore intensifying the search. The tabu timeouts that recently used attribute values are given, discourage their repeated use, therefore diversifying the search. The metropolitan probability function controls the search, allowing greater diversification and broader search at the beginning, while the control parameter temperature is big, and later in the process, when the control parameter temperature is low, it intensifies the search only in promising regions. An important step in the rule construction process is the neighborhood function that generates the set V* of neighborhood solutions of the current rule S. The neighborhood contains as many rule proposals as there are values of attributes in the dataset. Let termij be a rule condition of the form Ai = Vij, where Ai is the i-th attribute and Vij is the j-th value of the domain of Ai. The selection of the attribute value placed in the term is dependent on the probability given as follows:

•

Pij =

•

Any term to be added to the rule would make the rule cover a number of cases smaller than a user-specified threshold, called Min_cases_per_rule. The control parameter “temperature” has reached its lowest value.

Every time an attribute value is used in a term added to the rule, its tabu timeout is reset to the number of values of the particular attribute. In the same time, all other tabu timeouts of the other values for the particular attribute are decreased. This is done to enforce the use of various values rather than the most probable one, since often the difference in probability between the most probable one and the others is insignificant. Therefore the final solution might not include the most prob-

498

φij λ ×TabuTimeoutij



(1)

where φij =

log2 k − H ij bi

∑ (log j =1

2



(2)

k − H ij )

where: • • • •

bi is the number of values for the attribute i. k is the number of classes. Hij is the entropy H(W|Ai=Vij). TabuTimeoutij is a parameter for the attribute value Vij, reset to the number of values

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•

bi of the attribute Ai, when Vij is used in a term. λ is a multiplier of the tabu timeout used to increase/decrease the influence of the tabu timeout to the probability of using the particular attribute value.

For each termij that can be “added” to the current rule, SA Tabu Miner computes the value Hij of a heuristic function that is an estimate of the term quality, with respect to its ability to improve the predictive accuracy of the rule. This heuristic function is based on the Information Theory (Cover & Thomas, 1991). More precisely, the value of Hij for termij involves a measure of the entropy (or amount of information) associated with that term. The entropy of each termij (of the form Ai=Vij,) is given in the formula: H ij ≡ H (W Ai = Vij ) = −∑ (P (w Ai = Vij ) ⋅ log2 P (w Ai = Vij )) k

w =1

(3)

where: •

•

W is the class attribute (i.e., the attribute whose domain consists of the classes to be predicted). P(w|Ai=Vij) is the empirical probability of observing class w conditional on having observed Ai=Vij.

The higher the value of the entropy H(W|Ai=Vij), the classes are more uniformly distributed and so, the smaller the probability that termij would be part of the new solution. If the entropy and the tabu timeout are smaller, the more likely the attribute’s value is going to be used. H(W|Ai=Vij) of termij is always the same, for a constant dataset. Therefore, to save computational time, the H(W|Ai=Vij) of all termij is computed as a preprocessing step to every while loop. If the value Vij of attribute Ai does not occur in the training set, then H(W|Ai=Vij) is set to its maximum value of log2k. This corresponds to

assigning the lowest possible predictive power to termij. Second, if all cases belong to the same class then H(W|Ai=Vij) is set to 0. This corresponds to assigning the highest possible predictive power to termij. This heuristic function used by SA Tabu Miner, the entropy measure, is the same kind of heuristic function used by decision-tree algorithms such as C4.5 (Quinlan, 1993). The main difference between decision trees and SA Tabu Miner, with respect to the heuristic function, is that in decision trees the entropy is computed for an attribute as a whole, since an entire attribute is chosen to expand the tree, whereas in SA Tabu Miner the entropy is computed for an attribute-value pair only, since an attribute-value pair is chosen to expand the rule. The tabu timeout given to recently used attribute values servers as diversifier of the search, forcing the use of unused attribute values. Each time an attribute value Vij is used, its tabu timeout TabuTimeoutij is reset to the number of values bi of the attribute Ai. In the same time, the tabu timeouts of all remaining values of the attribute Ai are decreased by 1. The parameter λ serves to increase or decrease the influence of the tabu timeouts to the probability of using the particular attribute value. The usual value of λ is one, but in certain datasets, its value may vary to achieve better results. Once a solution proposal is constructed, it is evaluated using the quality measure of the rule. The quality of a rule, denoted by Q, is computed by the formula: Q = sensitivity • specificity (Lopes et al., 1998), defined by: Q=

TP TN ⋅ TP + FN FP + TN

(4)

where: TP - true positives, FP - false positives, FN false negatives, TN - true negatives. Q´s value is within the range 0 < Q < 1 and, the larger the value of Q, the higher the quality of the rule.

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The quality of the rule is the “energy” parameter of the SA metropolitan function that decides if the new solution proposal will be accepted as the next solution. As soon as our algorithm completes the construction of a rule, the rule pruning procedure is invoked. The term whose removal most improves the quality of the rule is effectively removed from it, completing each iteration. This process is repeated until there is no term whose removal will improve the quality of the rule. The computational complexity of SA Tabu MinerThe most outer while loop of the algorithm will execute r times, r being the number of discovered rules. This number is highly variable depending on the particular dataset. Before a new rule is constructed, the algorithm calculates the probability of using each attribute value as a preprocessing step. This step has the complexity of O(n×a), where n is the number of instances in the training dataset, and a is the number of attributes. The inner while loop will execute t times, where t is the number of temperature decrements. Its contents have the complexity of: choosing a probable attribute value of each attribute - O(a×v2) and calculating the coverage and quality of each solution proposal - O(a×n×a), bubble sorting the solution proposals and choosing the most probable - O(a2). The rule pruning initially consists of evaluating k new candidate rules, derived by removing each one of the k terms from the rule. The next pruning iteration will evaluate k-1 new candidates etc., until pruning makes no improvement. k2 is the worst case scenario. Every evaluation has the complexity of O(n×k), hence the pruning process has the complexity of O(n×k3). Therefore the entire algorithm complexity is O(r(n×a+t×a×v2+t×n×a2+t×a2+n×k3)). Since the average number of values per attribute v is insignificant compared to n, it can be excluded. Some of the other components can collapse, so the final complexity is O (r×n×[t×a2+k3]).

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Experimental Results and Discussion We compared the performance and results of our SA Tabu miner with the results derived by CN2, C4.5 and Ant Miner (Parepinelli et al., 2002). We used the CN2 version integrated in the WEKA™ package (http://www.cs.waikato.ac.nz/ml/weka), and for the Ant Miner we developed our source code based on the description in the published materials. We coded versions in C#.NET and in Pyton. The performances were evaluated using public-domain datasets from the UCI (University of California at Irvine) machine learning repository™ (http://archive.ics.uci.edu/ml/datasets.html). The comparison was performed across two criteria, the predictive accuracy of the discovered rule lists and their simplicity (hence comprehensibility). Predictive accuracy was measured by a well-known ten-fold cross-validation procedure (Weiss & Kulikowski, 1991). The comparison of predictive accuracies after the 10-fold cross-validation procedure is given in table 1. As shown in the Table 1, SA Tabu Miner achieved better predictive accuracy than CN2 in the Dermatology dataset, ANN, BUPA, Echocardiogram, Haberman and better accuracy than Ant Miner in tic-tac-toe, AllBP, BUPA New Tyroid, Echocardiogram and Haberman sets. In the other cases, the predictive accuracy is almost equal or slightly smaller than the other two. Despite the insignificantly smaller accuracy on some of the datasets, the advantage of SA Tabu Miner is in the robustness of the search, and its independence from the dataset size and number of attributes. While other algorithms that build a decision tree, might need exponential time or extremely large portions of memory to work, SA Tabu Miner can perform its search quickly, utilizing modest memory resources. Figure 3 shows a graphical representation of the results in Table 1. An important feature of classification algorithm is the simplicity of the discovered rule list, mea-

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Table 1. The predictive accuracy of the SA Tabu Miner algorithm compared with the predictive accuracy of CN2 and Ant miner. Predictive accuracies (%) Data Sets Ljubljana breast cancer

SA Tabu Miner 65,1

CN2 67,69

Ant Miner 75,28

C45 73,22

Wisconsin breast cancer

90,3

94,88

96,04

93,28

tic-tac-toe

84,7

97,38

73,04

61,81

Dermatology

91,3

90,38

94,29

91,43

Hepatitis

89,2

90,00

90,00

83,33

AllBP

96,91

97,2

94,99

97,33

ANN

92,71

92,07

92,72

93,64

BUPA

63,70

58,53

57,23

59,12

New Tyroid

92,44

93,79

87,96

91,43

Echocardiogram

54,40

53,33

54,36

53,00

Haberman

74,86

66,33

73,32

72

Mamography

79,4

80,84

83,23

81,58

Transfusion

75,56

77,3

74,33

74,87

Nursery

43,7

74,08

86,38

82,72

Pima

68,4

67,5

67,96

66,58

PostOp

68,9

73,75

58,89

73,61

Heart disease

52,55

53

56,26

57,24

Parkinson

88,65

88

80,08

90

sured by the number of discovered rules and the average number of terms (conditions) per rule. Simplicity is very important for any use of the rules by humans, for easier verification and implementation in praxis. The results comparing the simplicity of the rule lists discovered by SA TabuMiner, CN2 and Ant Miner are reported in Table 2. SA Tabu Miner achieved significantly smaller number of simpler rules in all datasets compared with CN2, while deriving simpler rules than Ant Miner in the case of Wisconsin breast cancer, dermatology, Hepatitis, AllBP, ANN, New Tyroid, Haberman etc. In the Ljubljana breast cancer and tic-tac-toe, the simplicity is very similar with the one achieved by Ant Miner. Figure 4 shows a graphical representation of the results in Table 2. The parameters of the meta-heuristic algorithms used (SA, TS) have significant impact on

the result quality. The given results are achieved by using a SA starting temperature of n×a/4, n being the number of training samples and a being the number of attributes. The temperature decrement and the final temperature were set to 1. The dependency of the SA parameters from the dataset size in not linear and aside from some nonlinear functions that we derived, different values can be derived empirically for each dataset.

CONCLUSION This chapter describes the basic framework of an integrated system for e-medicine. The prototype of the system is described along with its most essential modules. Using the contemporary communication and software technologies we tried to define ways to significantly improve medicine and

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Figure 3. Graphical representation of the predictive accuracy of SA Tabu Miner algorithm compared with the CN2, Ant miner and C4.5

health care. Most of the essential modules were developed, implemented and practically tested. Also, the performances of the used telecommunication technologies were put to the test. The proposed framework especially applies to a growing information society like the one in the Republic of Macedonia. In such places, e-medicine should follow step along with other growing IT areas in order to close the gap with developed countries. The framework proposed here and the steps already taken to implement it promise a fast trip toward a modern system that could enhance

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the quality of medical services, reduce costs and increase patients satisfaction and health. The quality of mobility offered by modern wireless communication technologies like WiMAX enforce their use for various applications. They enable the provision of telemedicine services to places previously unreachable by landlines. Web services and XML enable integration of various Medical Information Systems into an Integrated System for E-Medicine. High bandwidth and reliability of WiMAX helps the integration with bringing remote hospitals ever closer.

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Table 2. Simplicity of rules discovered by the compared algorithms. The number of rules and terms per rule # of Rules; Conditions per Rule Data Set

SA Tabu miner

CN2

Ant Miner

C45

Ljubljana breast cancer

8,55;1,70

55,40;2,21

7,10;1,28

9,7;2,56

Wisconsin breast cancer

6,10;2,15

34,67;1,94

12,6;1,07

37,9;1,85

tic-tac-toe

8,62;1,30

39,70;2,90

8,50;1,18

95;5,76

Dermatology

6,92;4,08

18,50;2,47

7,30;3,16

31;5,1

Hepatitis

3,21;2,54

7,20; 1,58

3,40;2,41

8;1,88

AllBP Thyroid disease

1:1

66,00;2,29

12,00;2,9

13;1,92

ANN Thyroid disease

1;1

86;3,45

11;2,99

27;3,19

BUPA liver disorders

9,9;0,95

98;3,03

8,2;0,98

24,4;2,91

New Tyroid gland data

5,8;0,87

24,7;1,85

7,2;1,07

13,5;1,40

Echocardiogram

8,3;1,25

40,7;2,33

6;1,55

11;1,91

Haberman’s survival

6,4;0,85

76,7;2,40

6,6;0,85

3,8;1,37

PostOp

7;1,23

29,4;2,68

5;1,80

19,89;0,35

Nursery

9,2;1,39

298,5;5,09

5,4;1,00

328;7,43

Pima

9,8;0,90

166,2;2,94

8,6;2,76

47,4;2,91

Mamography

8,5;0,89

117,3;2,87

9;0,88

17,9;1,86

Transfusion

8,8;0,90

43,2;2,43

6,3;1,21

12,9;1,79

Heart disease

8,3;1,06

132;3,28

11,1;1,84

94,20;4,11

Parkinson

7,80;0,9

23,1;2,05

5,9;1,14

33,8;3,92

We also present a web based medical expert system that performs self training using a heuristic rule induction algorithm. The system has a self training component since data inserted by medical personnel while using the integrated system for e-medicine is subsequently used for additional learning. For the purpose of training, the system uses a hybrid heuristic algorithm for induction of classification rules that we previously developed. The SA Tabu Miner is inspired by both research on heuristic optimization algorithms and rule induction data mining concepts and principles. We have compared the performance of SA Tabu Miner with CN2, C45 and Ant miner algorithms, on public domain data sets. The results showed that, concerning predictive accuracy, SATabu Miner obtained similar and often better results than the other approaches. Since comprehensibility is important whenever discovered knowledge will be used for supporting a decision made by a human user, SA Tabu Miner

often discovered simpler rule lists. Therefore, SA Tabu Miner seems particularly advantageous. Furthermore, while CN2 and C4.5 have its limitations when large datasets with big number of attributes are in question, SA Tabu Miner can still be applicable and will obtain good results due to the heuristic local search. Important directions for future research include extending SA Tabu-Miner to cope with continuous attributes, rather than requiring that this kind of attribute be discretized in a preprocessing step.

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Figure 4.Graphical representation of the rule simplicity of SA Tabu Miner algorithm compared with CN2 and Ant miner

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ADDITIONAL READING Chorbev, I., & Mihajlov, D. (2007). Integrated system for eMedicine in a developing information society. In International Multiconference. Ljubljana, Slovenija: Information Society

An Integrated System for E-Medicine

Cooke, C. D., Santana, C. A., Morris, T. I., DeBraal, L., Ordonez, C., & Omiecinski, E. (2000). Validating expert system rule confidences using data mining ofmyocardial perfusion SPECT databases. [Cambridge, MA, USA]. Computers in Cardiology, 2000, 785–788.

Okuyama, F., Hirano, T., Nakabayasi, Y., Minoura, H., Tsuruoka, S., & Okayama, Y. (2006). Telemedicine Imaging Collaboration System with Virtual Common Information Space. Sixth IEEE International Conference on Computer and Information Technology

Cover, T. M., & Thomas, J. A. (1991). Elements of Information Theory. New York: John Wiley & Sons. doi:10.1002/0471200611

Olariu, S., Maly, K., Foudriat, E. C., & Yamany, S. M. (2004). Wireless support for telemedicine in disaster management. Tenth International Conference on Parallel and Distributed Systems (pp. 649- 656)

Dhillon, H., & Forducey, P. G. (2006). Implementation and Evaluation of Information Technology in Telemedicine. 39th Hawaii International Conference on System Sciences. Holopainen, A., Galbiati, F., & Voutilainen, K. (2007). Use of smart phone technologies to offer easy-to-use and cost-effective telemedicine services. First International Conference on the Digital Society (p. 4). Hu, P. J. (2003). Evaluating Telemedicine Systems Success: A Revised Model. 36th Hawaii International Conference on System Sciences Kohavi, R., & Sahami, M. (1996). Error-based and entropy-based discretization of continuous features. In 2nd International Conference Knowledge Discovery and Data Mining (pp. 114-119). Menlo Park, CA: AAAI Press. Lach, J. M., & Vázquez, R. M. (2004). Simulation Model Of The Telemedicine Program, Winter Simulation Conference (Vol. 2, pp. 2012-2017). LeRouge, C., & Hevner, A. R. (2005). It’s More than Just Use: An Investigation of Telemedicine Use Quality. 38th Hawaii International Conference on System Sciences (pp. 150b - 150b). Maia, R. S., von Wangenheim, A., & vNobre, L. F. (2006). A Statewide Telemedicine Network for Public Health in Brazil. 19th IEEE Symposium on Computer-Based Medical Systems (pp. 495-500).

Paul, D. L. (2005). Collaborative Activities in Virtual Settings: Case Studies of Telemedicine. 38th Hawaii International Conference on System Sciences. Sadat, A., Sorwar, G., & Chowdhury, M. U. (2006). Session Initiation Protocol (SIP) based Event Notification System Architecture for Telemedicine Applications. 5th IEEE/ACIS International Conference on Computer and Information Science and 1st IEEE/ACIS International Workshop on Component-Based Software Engineering, Software Architecture and Reuse (ICIS-COMSAR’06) (pp. 214-218). Yamauchi, K., Chen, W., & Wei, D. (2004). 3G Mobile Phone Applications in Telemedicine - A Survey. The Fifth International Conference on Computer and Information Technology (CIT’05). J. Mauricio Lach, Ricardo M. Vázquez. Simulation Model Of The Telemedicine Program, 2004 Winter Simulation Conference (pp. 956-960). Zielinski, K., Duplaga, M., & Ingram, D. (2006). Information Technology Solutions for Health Care. Health Informatics Series. Berlin: SpringerVerlag.

This work was previously published in Ubiquitous Health and Medical Informatics: The Ubiquity 2.0 Trend and Beyond, edited by Sabah Mohammed and Jinan Fiaidhi, pp. 104-126, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global). 507

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The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy Simulation Analysis Mahendran Maliapen University of Sydney, Australia, National University of Singapore, Singapore and UCLAN, UK Alan Gillies UCLAN, UK

ABSTRACT This paper uses simulation modelling techniques and presents summarized model outputs using the balanced scorecard approach. The simulation models have been formulated with the use of empirical health, clinical and financial data extracted from clinical data warehouses of a healthcare group. By emphasising the impact of strategic financial and clinical performance measures on healthcare institutions, it is argued that hospitals, in particular, DOI: 10.4018/978-1-60960-561-2.ch219

need to re-focus cost-cutting efforts in areas that do not impact clinicians, patient satisfaction or quality of care. The authors have added a real time component to business activity monitoring with the executive dashboards shown as graphs in this paper. This study demonstrates that it is possible to understand health policy interactions and improve hospital performance metrics through evaluation using balanced scorecards and normalized output data. Evidence from this research shows that the hospital executives involved were enthusiastic about the visual interactive interface that pro-

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The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy

vides the transparency needed to isolate policy experimentation from complex model structures that map strategic behaviour.

cators such as patient satisfaction, clinical utilization and outcomes, financial performance as shown in Table 1:

INTRODUCTION

• • •

The provision of health care is a complicated activity requiring a multitude of skills, experiences and technologies. No one person or discipline can be responsible for poor or excellent performance. Similarly, hospitals are complex organizations that cannot be measured on a single dimension of performance. A balanced scorecard includes financial measures that capture the organisation’s ability to survive and grow. However, it complements financial measures with operational measures on customer satisfaction, internal processes, and the organization’s innovation and improvement activities (Caldwell, 1995). If well chosen, these operational measures capture the organisation’s operating performance, which is the ultimate driver of both current and future financial performance. The power of the balanced scorecard derives from its ability to present a succinct yet multifaceted picture of an organization to top management and a board of directors. A “balanced scorecard” for measuring the multiple dimensions of hospital performance is shown as four quadrants in Table 1. The objectives of this research paper was to develop, test and evaluate a sustainable hospital performance model for Senior Executives that would use both qualitative and quantitative indi-

• • •

•

•

Cash flow in the private hospital; Net cash balance; Patient satisfaction with hospital and clinician services; Clinician satisfaction with hospital management; Hospital bed occupancy; Deviations between the national average length of stay (NLOS) and the hospital’s LOS by Diagnostic Related Group (DRG) for patient admissions; Gap in available bed days comparing NLOS and hospital’s LOS for the patient admissions; and Average marginal costs per patient.

These cardinal dimensions are visually represented in radar chart format so that the simulated outcomes across the dimensions under different combination of hospital policies and scenarios can be visually compared against a reference baseline for these metrics. The Balanced Scorecard approach to represent the results was then integrated the simulation outputs so that each time a policy variation or scenario was tested, users could see the differences across all dimensions simultaneously the impact of the policy variations.

Table 1. The four quadrants’ of hospital performance Patient Satisfaction Examines patient perceptions of their hospital experience including the overall quality of care, outcome of care and unit-based care

Clinical Utilization and Outcomes Describes the clinical performance of PCH and refers to access to hospital services, clinical efficiency and quality of care

Financial Performance and Condition Describes how PCH manages the financial and human resources. Refers to the hospital’s financial health, efficiency, management practices and HR allocations

System Integration and Change Describes PCH’s ability to adapt, including how clinical information technologies, work processes and hospital-community relationships function within the hospital system.

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RESEARCH METHODS AND METHODOLOGY The model development was conducted in consultation with key hospital executives, who identified the following key influencing factors as the enablers of strategic hospital business success. We illustrate how these factors have influenced the development and prototyping of the systems dynamics model, and in particular the development of the dynamic hypothesis for the Clinician & PCH Patient Market domain is described here. The feedback loops for other domains such as Clinical Governance, Financial Management and Hospital Management were developed applying the same methodology. Five hospital focus group members were actively involved in the model development process. The focus group, which included two clinicians, helped to identify the key trails in the flows of patients and monetary transactions taking a “process-orientated” view through the organization (Vissers & Beech, 2005). In order to understand hospital throughput and contributing drivers to workload it is necessary to understand the linkages between patient referrals, clinician interactions, hospital performance and costs. A visual walkthrough of the business processes was also conducted. The dynamic relationships and linkages between the various variables were identified for each sector. The model structure for each was developed based on these information linkages and the dynamic hypotheses and delays in response to actions have been included where appropriate. The relationships in model structure were developed based upon the experience of hospital professionals, researched literature references and evidence of documented hospital practices. The key factors as identified by the hospital focus group that would impact the Clinician and Patient Market domain in the typical Australian healthcare market were identified as:

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• • • • • • • • •

Clinician / Hospital Alliances Clinician Consultation Charges Capitation & Private Insurance/Hospital Alliance Waiting Times and Delays Case mix Capitation and Hospital Fees Preference for “No-Gap” in Fees Co-location and Service Partnerships Outsourcing Targeted Market Growth

Clinician / Hospital Alliances Clinicians are pursuing various initiatives designed to increase their revenues and market share that put them in competition with hospital systems. A variety of clinician initiatives such as ‘focused-factory’ specialty hospitals, ambulatory care centres, disease management initiatives have mushroomed according to Dickinson et al. (1999). These are designed to compete directly for a share of the most lucrative hospital and ancillary revenue sources. As a collaborative strategy, hospitals are increasing using incentive schemes or gain sharing models to secure the loyalty of clinicians. These schemes are designed to provide incremental compensation to the participating clinician for achieving agreed upon reductions of inpatient costs or improvements in quality of care by following improved processes and standardized protocols. The economic linkage between the hospital and the clinician can be used to focus on hospital cost reduction and quality improvement (Chilingerian, 1992). The hospital focus group unanimously agreed the hospital should carefully evaluate its market strengths and vulnerabilities in light of the relative indispensability of targeted clinicians. Dickinson (1999) regards the balance of power or indispensability as the ability for the hospital to retain patients in the face of a clinician threat. Clinician and hospital alliances through various

The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy

mechanisms such as gain sharing, and collaboration can help to improve patient supply. The main source of the referrals to a clinician is the general practitioner (GP). Hence, the relationship between Clinicians and GP’s alliance is a key factor in determining the volume of patients referred for treatment. Successful treatment regimes and reputation of clinicians are created through the network of GP’s, which strengthens the relationships. The reinforcing loop R1 in Figure 1 exemplifies this monotonic tendency. The wider the GP network the greater the patient referrals and cash flow. The focus group participants felt that any strategic moves by the Board to increase the polarity of these relationships by providing clinicians in house residency with medical clinics at the hospital supports vertical downward integration to patient supply sources and would significantly increase patient admissions. Clinicians with high historical patient admissions should be targeted. Clinicians tend to form practice provider groups and consortiums to allow doctors and locums much more clout and equity in the delivery system. Practice consolidation also provides

larger practice volume and dominance in the marketplace. Clinicians have a one-to-many relationship with hospitals and therefore are at liberty to refer patients to hospitals of their choice. The objective of gaining patient dominance enhances the possibility of providing patient services in cardiology, general surgery, oncology, obstetrics and paediatrics. Partnerships with employers and workmen compensation groups to manage their medical components in multiple geographies and postcodes pose a serious threat to clinicians without such alliances or partnerships. The group felt that the Hospital should take cognizance of consortiums in their strategic analysis, as they are a threat to the supply of patients. The rate of disease incidence or epidemiological rate determines the number of cases occurred in the past. It is a mandatory requirement for GP’s and clinicians to report certain notifiable diseases that are likely to cause an epidemic. Public health statistics in primary care provides such analysis in an effort to control the growth of communicable diseases such as HIV/AIDS. Mortality rates, which are taken into account in computing inci-

Figure 1. Dynamic hypothesis for clinician & PCH patient market

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dence rates, also provide useful indicators of the occurrence of heart failure, coronary thrombosis, diabetes, kidney disease and various forms of cancer in the community. Clinician caseload, long consultation hours and case complications can have both positive and negative effects on the quality of care and productivity of clinicians. Based on actual patient/ clinician admission ratios for PCH in 1998/1999 and 1999/2000, the focus group estimated that the clinician market share is estimated to be at least twice the number of PCH admissions. The backlog, thus created, in turn has the effect of reducing patient satisfaction and increasing waiting times for acute care. The prospect of delays due imaging, diagnostic and/or pathological investigations further reduces patient satisfaction.

Clinician Consultation Charges In a well-functioning competitive market, doctors’ charges would tend to reflect their relative costs (i.e., resource inputs). However, it is generally accepted that in Australia the market for medical services is imperfect. Factors such as the supply of doctors within the various disciplines, the impact of medical insurance, and the capacity of patients to pay and the ability of doctors to influence the demand for their services act to distort the market and therefore influence the pricing of services. In the context of a market approach if the going price for a particular surgical procedure is, say, AU$1,200 and a cost based relative value study indicates that the Medicare fee should be reduced from say AU$900 to AU$600 then the “gap” to the patient would widen. On the other hand, those specialists in relative oversupply could find themselves with no alternative but to accept prices dictated by third parties such as health funds, hospitals or both. In the private sector, market forces regulate the pricing mechanisms for patient consultations and clinicians have some discretion to set their consultation and procedure fees in accordance to

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the AMA’s guidelines. Increase in consultation and anaesthetist fees above the Commonwealth Medical Benefits (CMB) scheduled rates will increase the patient or payer gap for the procedure and hence patient dissatisfaction. The higher the payment gap the more likely it is that patients will seek alternatives such as less costly clinicians for treatment or join a public hospital waiting list depending of the severity of the illness and degree of pain. In 1998, private healthcare insurance organizations, being well aware of the negative effects of clinician consultation and procedure fees, and in a bid to help their consumers (patients) alleviate the financial pressure, initiated the “no-gap” clinician’s scheme. Clinicians can therefore partner with insurance organizations for practice in designated hospitals to create a price cap for medical and surgical procedures. In return for clinician participation in the no-gap scheme, the hospital fees for theatre and facility usage by the clinician are reduced. In general, the implications of the price cap are to encourage more patient referrals to clinicians, which have the cascading effects of higher hospital admissions. This results in longer patient waiting times for surgery and patient dissatisfaction with the clinician (Martin et al., 1995).

Capitation & Private Insurance/ Hospital Alliance The growth of hospital capitation has been slow because most hospitals have not reached their “capitation threshold”. In the Australian context, capitation arises from the use of standardized cost and service weights to provide case mix adjusted revenue streams to hospital. In addition, most markets have not provided private healthcare insurance organizations with adequate incentives to share risk. Conditions that will drive private healthcare insurance organizations to adopt hospital capitation include true integration of care delivery systems and information systems,

The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy

sustained medical losses, and strong market position of hospitals. Hospital capitation has not become a prevalent payment mechanism, despite expectations that it would become the predominant method for controlling healthcare expenditures. Private healthcare insurance organizations will continue to be reluctant to share risk unless sufficient market pressure dictates a move to capitation contracts. Therefore, the motivation for hospital management to seek alliances with private healthcare insurance funds is a critical success factor. Arrangements with private insurance organizations to cap hospital fees have a significant impact on patient referrals for admission. Private insurance provides the second tier refund to the patient for the use of hospital beds. The accommodation charge refund is fixed for per day of overnight hospital stay. Day procedures performed at a hospital do not accrue an accommodation fee refund. The amount of refund is a function of the insurance premium paid by the patient (insurance table) and the level of healthcare cover held by the patient. Spiralling hospitals costs, on the one hand and the imposition of government control on insurance refunds on the other, create an unhappy marriage between the partners. Partnerships with private healthcare insurers and other managed care organizations are needed to keep the refunds at current levels as shown in the Balancing Loop, B3 in Figure 1. The net effect of

higher hospital accommodation charges is patients turning away to the public system.

Waiting Times and Delays The proportion of elective surgery patients waiting longer than the accepted standard is a nationally recognized indicator of access to acute care hospitals (Health Department of WA, 1998). Hospitals also collect waiting time data for internal management purposes. From a patient’s perspective, the relevant question is, ‘If I need surgery, what is the likelihood that I will have to wait longer than is considered desirable?’ Clinical judgments about need for surgery, and allocations by surgeons into the three categories of urgency, would need to be consistent across the hospital. As a private hospital, waiting times and delays are relatively short compared to the public system but this is an important factor for patient and clinician satisfaction (Australian Institute of Health and Welfare, 1995). In Figure 2, there are 3 balancing loops labelled B1, B2 and B3 and 2 reinforcing loops labelled R1 and R2. Loop B1, a balancing loop with delay, an archetype, where clinician referrals to the PCH hospital increases the proportion of the clinicians’ workload for PCH. The higher volume of referrals results in longer patients waiting times for PCH admission, which in turn increases patient dissatisfaction and that, slows down the rate at which patients are referred to that clinician. The opposing

Figure 2. The balanced scorecard: a potent tool (source: adapted from Chow et al., 1998)

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effect in the loop is from link 10, which implies shorter waiting times reduce patient dissatisfaction and this characteristic behaviour has been observed in patient surveys conducted at PCH. The turn away loop is a balancing loop, shown as B2 in Figure 2, which dominates the rates of PCH admissions. Delays in pathological assessment exacerbate patients with critical and serious illnesses, in particular, who are placed on the waiting list. Clinicians or GP’s therefore, prefer to admit these episodes to other hospitals. As a consequence, it reinforces PCH hospital’s loss in market share. The same feedback loop occurs if the delay was due to a lack of operating theatre time slots or availability of beds at the hospital. This loop clearly demonstrates the leverage clinicians have in admitting patients and confirms the focus group’s belief that PCH has “no control” over the patient supply network. Also, in Loop B2, the higher patient volume results in the offer of medical clinics from PCH management to house the prospective high volume clinician. Such a facility inducement provides outpatients visiting their clinicians, with a “onestop service centre”. Such clinicians have full access to the diagnostic and pathological facilities at hospital. The proximity to hospital facilitates shortens waiting times for diagnostic procedures to be completed. The convenience offered to patients especially to geriatrics and patients with limited mobility increases patient satisfaction. The prompt service leads to higher referrals to clinicians with premises at PCH medical clinics from the goodwill and ‘word of mouth’ of satisfied patients.

Case Mix Capitation and Hospital Fees Hospitals that accept both case mix capitation and fee-for-service payments have contradictory performance incentives that make it difficult to determine optimal strategies. Case mix capitation promotes decreases in the frequency and intensity

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of healthcare services, whereas fee-for-service rewards utilization increases with additional revenue. Consequently, hospitals’ profits suffer if they receive a portion of their revenues from capitation while maintaining fee-for-service as the primary payment mechanism. As the hospital’s percentage of gross revenue from capitation increases, the negative financial impact of misaligned incentives also increases. Private healthcare insurance organizations often stipulate that hospitals provide complete, “seamlessly” coordinated healthcare services across the continuum of care, cover an extensive geographic area, and be affiliated with a broad panel of primary care clinicians and specialists. To meet these requirements, many hospitals have expanded their service offerings in an attempt to create systems capable of delivering comprehensive, coordinated care. However, many “systems” actually are loosely aligned ambulatory, inpatient, home care services, and other business units that, although financially linked, do not offer truly coordinated care. A hospital, with a significant market presence (30 percent market share or more) with minimal excess capacity in the market, is in a strong negotiating position with private healthcare insurance organizations (Timothy et al., 2000). The combination of the hospital’s marketability with employers and the market’s capacity constraints will position the hospital as a “must have” in a private healthcare insurance organization’s network.

Preference for “No-Gap” in Fees In Australia, the CMB Schedule is a government benchmark reference for clinician consultation charges. Patients are concerned about “gaps” between fees and rebates. Most doctors would consider that equity in relation to the fees they consider as appropriate is synonymous with equity for patients in relation to their rebates. The Government expects reasonable efficiency from expenditure on health and reasonable assurance

The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy

that the fees charged by doctors will not leave patients facing substantial out-of-pocket costs. In Loop B3, the lesson for PCH management is that patients show a preference for hospitals with lower fees and that are fully covered (reimbursable) by private medical insurance. Hospitals that form alliances with private insurers to fully refund the cost of the accommodation charge thereby minimize any patient “out of pocket” cost component are more likely to meet the patients’ affordability and therefore increase patient satisfaction. The reinforcing loop, R1 in Figure 1, represents the typical clinician preference for the “no-gap” scheme launched by private medical funds such as Medibank Private, Health Benefits Fund (HBF) or Medical Benefits Fund (MBF). The scheme has gained popularity and the patient can proactively inquire at the private insurer’s website for such “no-gap” clinicians by the postal district and can request their GP’s to refer them to such clinicians. The market diversion and shift of patients is a factor, which increases the desire for clinicians to strengthen their alliances with the private healthcare insurance organizations. Reinforcing loop, R2 in Figure 1, represents the escalating effects resulting from market potential of PCH to accept or turn away patients either by virtue of capacity or operating theatre resources or availability of ‘state of art’ medical technology. Clinicians treat patients at PCH based on the choices that can be offered to patients by the hospital facility and therefore can opt to admit paying patients to other hospitals with better facilities. In addition the following other factors that we discovered through focus group investigations which were also supported in the literature and were developed as part of the integrated simulation model.

Co-Location and Service Partnerships In a hospital research case study in New York, Timothy et al. (2000) support the view that most hospital facilities have joined or formed a network to enhance market power and achieve efficiencies. Hospital executives were particularly concerned with the one-sided nature of their relationships with private healthcare organizations. They believed that these mergers and affiliations enable them to bring more leverage and more bargaining power when negotiating with these organizations. They obtained better prices for their services as a group. Co-location also enables hospitals to combine functional tasks such as purchasing, lab services, laundry and housekeeping. In most cases, these administrative efficiencies achieve cost savings almost immediately. Some hospital executives expressed the view that while consolidation was attractive, they feared losing patients who might choose to go to another facility due to a location change of the traditional facility.

Outsourcing Hospitals maintain several key ancillary facilities in a bid to maintain a “one-stop” service to clinicians and patients. However, under-utilization of such resources is a key decision factor in the consideration for the facility to be privatized or the whole service outsourced and managed by healthcare contractors. In many cases, the demand for services is too small to generate a market yet the pathological need is real. Similarly, contracts are awarded through competitive bidding for major purchases of medical supplies and pharmaceuticals. Specifications for the purchase of these products are presented and price solicitations are sought form vendors. The process strives to obtain the lowest price for products and reduce the possibility of kickbacks in the bidding process. Tight expenditure controls restrict the freedom to operate can reduce

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significant opportunity costs to productivity and often focus strictly on the nominal value of the bid. In cases where professional services are being procured, procurement may be difficult in a competitive context due to the need to disqualify irresponsible or unreliable contractors (Lynch, 1995; Block, 1993).

Targeted Market Growth In order to build a new direction for the hospital, the focus group members have to realize and learn more about the private patient profile and its competitive value. Customers not only included patients but staff members, clinicians and private healthcare insurance organizations such as insurance companies and employers. In the communities surrounding the geography of the hospital, the fastest growing population segment was the age group over 55 years in age. Analysis of the case mix data showed that 59% of 1995 revenue for hospital under investigation came from inpatients above the age of 61 years from the outer suburbs. The losses came from the inner suburbs in the age groups between 19 and 47 years of age. Demographics of the patient population demonstrate the untapped market for the hospital in the primary and secondary catchments. According the recent census and with a population that is aging, those in the 50 – 60 age group are indeed projected to be the fastest growing cohort over the next 5 years and hospital’s target market.

MODEL EVALUATION AND VALIDATION The focus group participants from PCH were interviewed to investigate their perceptions about learning and applying the system dynamics (SD) models for decision-making. Issues investigated during interviews included: the understanding of the model design and its purpose; the value

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of the visual user interface to the decision maker and its contribution to understanding the model structure; the decision maker’s learning curve with the visual interface; the impact of the visual interface on decision making; interpretation of the balanced scorecards, the benefits and the perceived advantages of simulation models with balanced scorecards (radar charts). A questionnaire, consisting of 40 questions, was developed to probe hospital managers’ views of SD-based visual interface models. The questionnaire was applied to the 5 focus group members and the 30 participants independently. All respondents were provided with the questionnaires two weeks prior to the interviews. The focus group was regarded as the reference group of modelbuilders’ as they were involved in all phases of model development, evaluation and analysis. The responses were analyzed in a descriptive fashion across four dimensions namely model characteristics, manipulation, evaluation and performance. The differences between the focus group members’ responses and the hospital managers’ responses were determined. The similarity in the responses helps build confidence that other managers within the hospital population held concurrent views as the focus group towards the interpretation of simulation results with balanced scorecards. The reference mode was fundamental to model structure evaluation - for the purposes of discovering patterns and model validation, analysis of simulated policies and scenarios. The model was tuned until it produced acceptable error rates in replicating historical financial performance, bed occupancies and seven other key performance indicators. The model was validated against two years of historical case mix data using actual patient arrival patterns. Clinicians and hospital administrators validated the model responses in the case study and compared the simulated results against the actual financial performance - called the Reference Mode.

The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy

However, it needs to be pointed out that occurrence of events in the past alone cannot be interpreted to mean that events in the future will follow accordingly. While both historical financial behaviour and a reference mode can be expressed in either quantitative or descriptive terms, a reference mode is essentially a qualitative and intuitive concept since it represents a pattern rather than a precise description of a series of events (Saeed, 1998). A reference mode also subsumes past history, extended experience and a future inferred from projecting the inter-related past trends based on the experience of the experts. Also, a reference mode will not contain random noise normally found in historical trends, as noise represents problem behaviour rather than the system behaviour. The caveat is with the exclusive use of historical case mix data to construct the reference mode.

BALANCED SCORECARD APPLICATION Kaplan and Norton (1992) found that neither financial performance measures nor operational performance measures alone provided senior managers in leading manufacturing firms the information they needed to operate complex organizations in globally competitive markets. Converting to a clinical setting, managers increasingly relied on what is best described as a comprehensive business picture of organizational effectiveness, including metrics for financial performance, market share, clinical outcomes, patient safety, customer satisfaction and community benefit. In their study of health care organizations and their use of integrated performance measurement systems, Chow et al. (1998) found that the design and implementation of a balanced scorecard is intimately intertwined with planning and control processes. Figure 2 illustrates the interrelationship between the design and use of a balanced scorecard and an organization’s planning and control

processes. In particular, this can be divided into four stages: • • • •

Translating the vision and gaining consensus; Communicating the objectives, setting the goals, and linking strategies; Setting targets, allocating resources, and establishing milestones; and Feedback and learning.

As Lockamy and Cox (1994) observe, a performance measurement system includes performance criteria, standards, and measures. Further, the performance criteria, standards, and measures of local business processes must be linked to other processes and also to the strategic objectives of the organization as a whole in order to support management’s decision-making needs.

HOSPITAL POLICY ANALYSIS AND DECISION MAKING In New Zealand, an elaborate study at Central Health concluded that the active co-operation of clinicians and other health professionals is required in the operation of case mix based accounting and control systems designed to monitor medical activity (Doolin, 1999). Managerial rationality underlies the notion that comparative information can be constructed as a management tool from which changes in the way clinicians manage their practice will and naturally follow. The appropriate levels of resource use that occur are often debated between managerial and clinical cultures (Parker, 1997). For example, managers at Central Health viewed the use of DRGs in their case mix from a funding and costing perspective, while clinicians tended to view the DRG framework as a medical classification system. The belief that peer review is influential in modifying clinician behaviour is well established

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(Symes, 1992). However, in most cases of viewing case mix analysis, whether by financial manager, clinicians or healthcare professionals, only a static snapshot of the data has been considered in a specific time frame. The impact of simulating events and occurrences in future time periods with the aid of simulation technology using case mix data has not been published to date in the literature. This is the most significant paradigm shift, for clinicians and hospital managers, in analyzing case mix data and the major objective of this research study. Hospital executives involved in this research decided that strategic business decisions in a hospital that would significantly alter the trajectory of the hospital into “best clinical practices” and healthy surpluses could include following: •

•

•

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Cost Reduction Policy- The aim here is to identify and consistently reduce the occurrences of high length of stay outliers and high cost DRGs and is of importance to the strategic financial management of hospitals. Although hospitals cannot refuse admission, they can redirect patients to appropriate institutions specializing in these high cost DRGs. It also requires stringent fiscal control policies, efficient manpower resource use monitoring systems and an increase in standardization practices across clinical, hotel services, pharmaceuticals, medical consumables and products. Market Growth and Intervention Policy - Negotiated schemes with clinicians and private insurers that would introduce an umbrella of private health insurance cover that reduces or eliminates the patient or payer gap. The strategy would be to increase market share, as most patients would have the ‘best of both worlds’, clinicians of their choice and no cash component or gap to pay for the treatment. Outsourcing or Co-location Policy – There are many options to consider with management of facilities and services in a private

hospital which could include sharing facilities (co-location) with another provider for pathology, radiology, surgical and other medical related services (Greco, 1996). For non-medical type services such as laundry and food catering, building and equipment maintenance, the outsourcing option is considered contracting and removes the burdens of unionized staff with workplace agreements. The opportunity for streamlining purchases from a single supplier who manages all the distribution and delivery to various hospitals can effectively cut costs and overheads through supply chain management (Greco, 1996). •

•

Clinical Pathways Policy – Clinical Pathways describes the usual way of providing multidisciplinary care for a particular type of patient and allows annotation of deviations from the norm for the purpose of improvement (Hindle, 1998). Clinical pathways can provide reasons for variance and empower management to redress the system. Analysis of individual clinician variance could possibly result in suggestions for changes in care, economic punitive action (such as withdrawal of accreditation), or demands that a clinical pathway be adhered to as a condition of future accreditation or agreement between clinician and hospital. With a simulation model, it possible to understand the “most cost effective clinical practice” within the hospital for a particular DRG profiled against the national benchmarks for that DRG. Clinicians’ Succession Policy – Clinicians and specialists who contract or commit their services to hospitals also negotiate utilization targets for the use of expensive hospital facilities such as operating theatres, a practice prevalent in the US. Based on these committed targets, the hospital management can forecast the revenue

The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy

bandwidth that a specialist would fit into in future production time periods. A clear understanding for revenue streams would allow for strategic resource allocation and facilities planning. Likewise, a specialist who is planning to retire or leave can be replaced with an equally reputed professional who is likely to bring the same level of commitment and revenue as the incumbent to the hospital. •

General Practitioner Network Policy – The General Practitioners (GP’s) form the ‘backbone’ of the patient supply chain in Australia. However, few GP’s have admitting rights to hospitals but the GP usually makes a recommendation or selection as to the clinician or specialist who could treat the patient based on their suspected aliment.

The GP network can be managed to ensure high hospital occupancy levels. Vertical integration into the GP network is a market supply policy issue that would affect long term planning decisions for hospitals.

POLICY EXPERIMENTS The simulation model is used to assess the effectiveness of each of the policies in isolation to determine the value of implementing such a policy in the organization. The effectiveness of the policy is gauged against the performance metrics discussed earlier in this paper. A strategic switch in the simulation model controls each policy and activation of the switch in the iThink Visual Interactive User Interface launches the implementation of the policy in the model.

Market Growth and GP Networks Policy High performance in the private sector patient market can be achieved by increased GP network alliance and consortium partnerships and the concurrent availability of PCH medical clinics to tap this market. The policy activates the PCH market potential for patient admissions by shifting the clinician’s market to the PCH hospital. An increase in the strength of the GP networks exhibits itself as increased patient growth. Before the policy switch is activated, the “Baseline Case” situation in Figure 3 is considered. Plots of actual patient admissions (Plot 1) and simulated patient admissions (Plot 2) have the same magnitude and pattern. However, in Figure 4, after the market growth policy implementation, Plot 2, the simulated admissions, peaks at week 60 with about 500 admissions per week compared to the base case average of 127 admissions. The market growth then declines, as the hospital capacity is not able to cope with the excessive growth, shown by the occupancy plot in Figure 6. So increases in the GP networking put an upper limit on patient admissions. Cash flow increases as patient arrivals increase and erodes with loss of admissions in week 60 as shown in Figure 5. Marginal costs as seen in Figure 7 are fairly consistent and on average have decreased from the base case of AU$1284 per patient to AU$1021 (consistent with economies of scale) indicating that there is some reduction in cost growth per patient as patient volume increases with the implementation of the market policy.

OUTSOURCING POLICY The fundamental assumption for this policy is to effectively control the hiring of healthcare contractors at an organizational level. PCH experts’ group hypothesized that there be many redundant

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Figure 3. Market policy –base case

Figure 4. Market policy – increase in GP networking

contractors in the system as hiring decisions are currently made at a departmental level. The decision to hire or layoff contractors in the model is a global decision based on the total consumables cost and the costs of operating theatre and ancillary services such as diagnostic imaging and pathology provided to support patient care in the hospital. Price competitiveness and the desire for outsourcing services are based on the derived average cost per episode of these services. Figure 8 shows the level of contracting with high (Plots 2 & 4) and low (Plots 1 & 3) patient growth re-

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spectively. The marked decrease in contractor use over a period of two years with the outsourcing policy in effect can be observed in Plots 2 & 4. The sharp increase in price competitiveness is observable in Plots 1 & 2 in Figure 9 with the increase in admissions volume, an indication that the cost of providing the service with full time salaried staff rather than with contractors becomes a viable and economically sensible option. The utilization gap (unused capacity) for the use of operating theatres and ancillary hospital facilities in Figure 10 shows a decrease with increased

The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy

Figure 5. Market policy – cash flow and mean cash balancest

Figure 6. Market policy – occupancy plot

Figure 7. Market policy – marginal costs plot

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Figure 8. Outsourcing policy –level of contracting

patient admissions (Plots 1 & 2) as expected. This implies that Outsourcing Policy has not impact on the utilization of available hospital bed capacity. A sharp reduction in simulated overhead costs in Figure 11 (Plot 4) coincides with the reduction in contract workforce in Figure 8 (Plot 4). It can be seen that the outsourcing policy effectively curtails the engagement of contractors with high throughput of patients, as the price effectiveness factor regulates contractor use based Figure 9. Outsourcing policy – price competitiveness

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on the average cost of service provision. And the converse is true when the price effectiveness decreases.

CLINICAL PATHWAYS POLICY The Clinical Pathways policy is the result of PCH management’s desire to reduce medical errors and deviations in clinical practice and also to reduce

The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy

Figure 10. Outsourcing policy – utilization gap

Figure 11. Outsourcing policy – overheads

clinicians with low case volumes. A simulation switch labelled ‘Clinical Governance Policy’ ensures the same patient volume to allow realistic experimentation of the effectiveness of Clinical Pathway (CP) policies. Each DRG or disease group has its own CP documenting the details of the recommended procedures, expected patient outcomes, pharmaceuticals, clinical investigations, prognosis monitoring and instrumentation for the surgical procedure. The formalization of CP in any hospital environment is a tedious and challenging implementation and

requires many hours of education, protocol explanation, monitoring and control against targets. There are benefits for such a long-term view as this clinical methodology introduces standardization in medical practice. In the simulation model, the desire to minimize clinicians with low practice volume is PCH management’s central objective. In applying the CP policy, it was decided that patient admissions be held constant throughout the experimentation with the CP variables such as the rate of CP implementation, the desire to minimize medical

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Figure 12. Clinical pathways policy – net hospital costs variation

procedure deviations and the level of CP varied across the DRGs. The simulation results demonstrate (Figure 12) that hospital costs decrease with increased CP implementation. It brings with it a potential increase in capacity to treat more patients (deFigure 13. Clinical pathways policy – mean cp level

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scribed by variations in occupancy) resulting from the overall reductions in length of stay (due to adherence to recommended CP procedures). The different levels of CP across the DRGs are shown in Figure 13. It is clear that the model represents PCH management’s desire to reduce

The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy

Figure 14. Clinical pathways policy – bed occupancy

Figure 15. Clinical pathways policy – variations in occupancy

clinical and treatment variances related to the divergent number of clinicians practicing at the hospital. Clinical treatment variations from a large number of clinicians dissipate the efforts of the clinical pathways policy to streamline clinical practices. The number of practicing clinicians for DRG G45B (Gastroscopy) was reduced to test this policy. A smaller and more manageable group of clinicians with a focus on implementing the CP system will bring better benefits to the organization.

It is also worth noting that the overall bed occupancy of the hospital peaked much earlier (week 79 in Plot 1, Figure 14) for the same volume of patients than the actual performance over two years. PCH management indicated that, from their experience, CP implementation actually has a regulatory effect on the use of shared resources within the hospital. The variations in bed occupancies enable the treatment of more patients, a side effect of overall shorter lengths of stay and more efficient clinical care, the desired effect from CP imple-

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Figure 16. Clinical pathways policy – quality of care

Figure 17. Clinical pathways policy – hospital cash flow

mentation is shown in Figure 15 for each of the simulations. A notable and desirable consequence of the CP implementation is the albeit increase in quality of care, even though it is delayed until week 82 seen in Figure 16 on Plot 4. As observed the full implementation of CP is the result of the organizational and cultural change in the clinician

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and nurses attitudes with greater due diligence in patient management and care. Finally, the CP implementation results in an increase in cash flow less the cost of CP implementation. A comparison of Plots 1 & 2 and Plots 3 & 4 separately in Figure 17 shows that the reductions in low volume clinicians for DRG G45B

The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy

Figure 18. Radar chart of reference modes

did not have a significant impact on cash flow as their patient volume is low. In Figure 18, the balanced scorecards of the base case models are compared against the Actual Financial Performance of the hospital. The nine cardinal performance indicators are Occupancy, NLOS Gap (Gap in Available Bed days), NLOS Deviation (Deviation from National Average LOS), Marginal Costs, Cash flow per patient, Cash Balance per patient, Clinician Satisfaction, Patient Satisfaction and Quality of Care. A numerical summary of the output values are provided in Table 2 along with a comparison against

the actual performance metrics of the hospital over two years. The values of these indices in Table 2 were in different dimensions and these have been normalised with no dimensions and indexed against the Actual Financial Performance base value of 1 (-1 for NLOS Deviation) for the purposes of comparison in Figure 18. So, for example, the average cash balance (in Table 2) under the Actual Financial Performance, Average Cash Balance, (ACB) i.e. (ACB AFP) was $37, 56773 and in the simulated model (ACB ST) the value was $36, 44426. Thus the normalised value would be (ACB ST)/(ACB AFP) which would be 0.97 as plotted on the radar chart in Figure 18. High values of these indicators demonstrate their enhanced strategic value and contribution for all except the Marginal Costs and the NLOS Gap. For these two indicators, the smaller the value the better as it means that the actual gaps in available bed days and marginal costs are significantly lower than the national average. Lower marginal costs per patient and lower gaps in available bed days are highly desirable. In particular, these two indicators show significant differences in Figure 18 from the Actual Financial Performance in the radar chart. It indicates that the simulation models and their variants have better base case performance characteristics

Table 2. Numerical values of simulation outputs against actual performance measures PCH Performance Indicators Bed Occupancy (max 1) NLOS Available Beds Gap Deviation from NLOS Average Marginal Costs per Head (AU$)

Actual Financial Performance

Simulation model (Systems Thinking)

0.28

0.29

-97.08

-143.89

1.08

3.29

1738.20

2557.61

Sum of Cash flow (AU$)

894378

981605

Average of Cash Balances (AU$)

3756773

3644426

Clinician Satisfaction (max 1)

0.60

0.60

Patient Satisfaction (max 1)

0.65

0.56

Quality of Care (max 1)

0.70

0.60

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The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy

Table 3. Model policy variable settings for different scenarios (ST) Scenario Type/ Policy Market Growth Policy

Variable

Middle of the Road

‘Doldrums’

0.15

10% Increase to 0.165

0.15

10% Decrease to 0.135

Consortium Partnerships

0.2

10% Increase to 0.22

0.2

10% Decrease to 0.18

Market Growth Fraction

0.1

0.1

0.1

10% Decrease to 0.09

Policy Not Implemented

Implemented

Implemented

Implemented

Desire to Reduce Medical Deviations

0.11

Increase to 1 (max)

Increase to 1 (max)

Decrease to 0 (min)

Desire to reduce low volume clinicians

0.17

Increase for DRG G45B only to mean value of 7.76

Increase for DRG G45B only to mean value of 7.76

Decrease to 0 for DRG G45B only to mean value of 0.07

than the Actual Financial performance for the two fiscal years.

SCENARIO ANALYSIS Scenario analysis is applied by combining the implementation of a combination of market growth, outsourcing and clinical pathway policies. The simultaneous implementation of the 3 policies based on a combination of model input parameters as defined in Table 3 produced the scenarios namely the Base Case; Nightingale; Middle of the Road; Doldrums. The strength and influence of each policy (by fine-tuning simulation model parameters) was developed by the PCH hospital executive team such that the four classic scenarios namely Base Case, Nightingale (best case), Middle of the Road and Doldrums (worst case) were used to study the strategic combination of the above policies. The experimental evidence from these simulated scenarios suggests that a combination of market growth, clinical governance and managed contracting policies are necessary for business success. In the Nightingale scenario, the hospital experiences high patient growth with strong ele-

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‘Nightingale’

Strength of GP Alliance

Outsourcing Policy Clinical Pathways Policy

Base Case

ments of partnerships with clinicians because of the implementation of clinical governance. The scenario shows that high cash flow can be expected in spite of both increased internal net hospital cost variations and reductions in simulated overheads from significant contractor workforce reduction and increased facilities utilization. But the efforts to sustain cash flow declines as capacity and workforce reductions are limiting factors. It is important to recognize that quality of care drops Figure 19. Radar chart of scenarios in Table 3

The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy

for some time due to excessive patient admissions in this scenario. The Middle of the Road scenario predicts that cash flow will fall below the base case if only clinical pathways and outsourcing polices is implemented without any serious efforts to improve patient supply. The Doldrums scenario is the worst case scenario and reports that cash flow will fall even below the Middle of the Road scenario level if PCH management opts to reduce the contractor workforce by implementing an outsourcing policy without commensurate actions to implement clinical pathways and increase market growth. The simulation (ST) model scenarios (1–4) developed are summarized in Figure 19, showing the effectiveness of the Nightingale scenario. This suggests that, by selectively implementing clinical pathways and reducing low volume clinicians, cash flow per patient rises. More significantly the normalized value of NLOS deviation increases. As a result of normalization (used for the purposes of comparison), a higher value is indicative of shorter lengths of stay. This shows that with the Nightingale strategies in place, there is greater alignment to the critical success factors that drive successful hospital performance. Clearly, the effect of increasing the pressure to manage staff and clinicians’ recruitment policies with greater management scrutiny has a significant impact on revenue with its goal seeking behaviour. The hospital has the responsibility of weighting these indicators. Preference for the Nightingale strategy is a management choice but the simulation model indicates performance above current levels.

IMPLICATIONS AND CONCLUSION The use of the balanced scorecard approach with radar charts enabled hospital executives to visually compare the simulation model outputs across all the nine performance metrics and subsequently

benchmark the strategies and scenarios that offered the best performance for the hospital. While the simulation models permitted detailed analysis of individual policy variables and behaviour over a two year horizon, it was useful to fine tune policy parameters to understand the influence of the variables and other parametric changes. The use of a balanced scorecard provided the snapshot of strategy performance, averaged over the 2-year simulation period, the hospital executives needed to understand the interaction of the various policies. The simulation model results presented in balanced scorecard format is an opportunity for “experimenting” with new ideas about hospital management policies without endangering their existing operations. Hospital managers could learn by trial and error the policies that would have a better success rate in this rapidly changing environment. It is a “no risk” tool for strategic thinking that most hospital managers, clinicians and administrators have little experience with. Qudrat-Ullah et al. (2007) report a research study for Changi General Hospital, Singapore where a balanced scorecard was developed and deployed. Researchers identified the need to have decision support systems that are sufficiently robust to incorporate a better understanding of the external environment (demand for services and the market competition) on the internal processes of the hospital. They also developed a broader understanding of organizational performance by identifying areas that had not received attention previously. Most notably these were procedures and methods for enhancing feedback and learning across several dimensions; for example, deferring patients with the consequent linkages to organizational performance indicators. The current research takes this further by linking the portrayal of a balanced scorecard with the outputs from a dynamic model of hospital operations. A similar use in a business management context has recently been made available (Bianchi, 2008) who quotes the originators of the Balanced

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The Integration of Systems Dynamics and Balanced Scorecards in Strategic Healthcare Policy

Scorecard: “The Balanced Scorecard can be captured in a system dynamics model that provides a comprehensive, quantified model of a business’s value creation process” (Kaplan & Norton, 1996, p. 67) and “…dynamic systems simulation would be the ultimate expression of an organisation’s strategy and the perfect foundation for a Balanced Scorecard” (Norton, 2000, pp. 14-15). Balanced scorecards can be employed in a framework for learning how to think systemically and in particular to understand system feedback. It was clear that hospital managers typically took action based on direct impacts that were expected. But any action is likely to ricochet and sometimes even boomerang affecting other parts of the hospital with far-reaching implications. The methodology improves the understanding that actions can have indirect as well as direct consequences and there is a need to think systemically when taking action. There is also a need for collaboration to successfully achieve financial and clinical outcomes since the pressures of change tend to promote a focus on hospital managers’ selfinterest. The methodology offers the opportunity to understand surprisingly large differences in the “mental models” among executives, clinicians, nurses and administrators (Cavana et al., 1999). It generates a shared vision amongst participants, providing policy convergence and consensus.

REFERENCES Andersen, D. F., Richardson, G. P., & Vennix, J. A. M. (1997). Group model building: Adding more science to the craft. System Dynamics Review, 13(2), 187–201. doi:10.1002/ (SICI)1099-1727(199722)13:2<187::AIDSDR124>3.0.CO;2-O Australian Institute of Health and Welfare (AIHW). (1995). Australian Hospital Statistics 1995; Canberra. Retrieved from http://www. aihw.gov.au

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Bianchi, C. (2008). Enhancing Strategy Design and Planning in Public Utilities through ‘Dynamic’ Balanced Scorecards: insights from a project in a city water company. System Dynamics Review, 24(2), 175–213. doi:10.1002/sdr.395 Block, P. (1993). Stewardship: Choosing Service over Self Interest. San Francisco, CA: BerretKoehler Publishers. Caldwell, C. (1995). Mentoring Strategic Change in Healthcare. Seattle: Quality Press. Cavana, R. Y., Davies, P. K., Robson, R. M., & Wilson, K. J. (1999). Drivers of quality in health services: different worldviews of clinicians and policy managers revealed. System Dynamics Review, 15(3), 331–340. doi:10.1002/ (SICI)1099-1727(199923)15:3<331::AIDSDR167>3.0.CO;2-G Chilingerian, J. (1992). New directions for hospital strategic management: The market for efficient care. Health Care Management Review, 17(4), 73–80. Chow, C. W., Garulin, D., Tekina, O., Haddad, K., & Williamson, J. (1998). The Balanced Scorecard: A potent tool for energizing and focusing healthcare organization management. Journal of Healthcare Management, 43, 263–280. Cisneros, R. J. (1998). Are providers constructing towers of Babel? Managed Healthcare, 21-23. Deloitte & Touche. (2000). U.S. Hospitals and the Future of Health Care Survey. Modern Healthcare, 30(26). Dickinson, R. A., Thomas, S. M., & Naughton, B. B. (1999). Rethinking specialist integration strategies. Journal of Healthcare Financial Management Association, 53(1), 42–47. Doolin, B. (1999). Case mix Management in a New Zealand Hospital: Rationalization and Resistance. Financial Accountability & Management, 15(3-4).

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Greco, J. (1996). Physicians heal themselves. The Journal of Business Strategy, 17(6). doi:10.1108/ eb039817 Health Department of Western Australia. (1998). Annual Report 1997-1998. Certification of Performance Indicators. Hernandez, S. R. (2000). Horizontal and Vertical Healthcare Integration: Lessons Learned from the United States. Healthcare Papers, 1(1). Hindle, D. (1998). Classifying the care needs and services received by Home and Community Care (HACC) clients. Aged and Community Care Service Development and Evaluation Reports. Kaplan, R., & Norton, D. (1992, January). The balanced scorecard-measures that drive performance. Harvard Business Review, 71–79. Kaplan, R., & Norton, D. (1996). Linking the Balanced Scorecard to Strategy. California Management Review, 39(1), 53–79. Lockamy, A., & Cox, J. F. (1994). Reengineering performance measurement: How to align systems to improve processes, products, and profits. Burr Ridge, IL: Irwin. Lynch, T. D. (1995). Public Budgeting in America. Upper Saddle River, NJ: Prentice Hall. Martin, S., & Smith, P. (1995). Modeling waiting times for elective surgery (Tech. Rep.). York, UK: University of York, Center for Health Economics. Norton, D. (2000). Is Management finally ready for the ‘Systems’Approach? Balanced Scorecard Report.

Parker, M. (1997). Dividing Organizations and Multiplying Identities. In Hetherington, K., & Munro, R. (Eds.), Ideas of Difference (pp. 112–136). Oxford, UK: Blackwell. Qudrat-Ullah, H., Chow, C., & Goh, M. (2007). Towards a dynamic balanced scorecard approach: the case of Changi General Hospital in Singapore. Int. J. Enterprise Network Management, 1(3), 230–237. doi:10.1504/IJENM.2007.012756 Richmond, B., & Peterson, S. (1997). An Introduction to Systems Thinking: IThink Analysis Software. Hanover, NH: High Performance Systems Inc. Saeed, K. (1998). Defining a problem or constructing a reference mode. In Proceedings of the 16th System Dynamics Conference, Quebec, Canada. Symes, D. (1992). Resource Management in the National Health Service. In Pollitt, C., & Harrison, S. (Eds.), Handbook of Public Services Management (pp. 194–204). Oxford, UK: Blackwell. Timothy, S. P., Haslanger, K., Delia, D., Fass, S., Salit, S., & Cantor, J. C. (2000). Hospital Markets, Policy Change and Access to Care for Low-Income Populations in New York. New York: Rutgers. Vissers, J., & Beech, R. (2005). Health Operations Management. London: Routledge.

ENDNOTE 1



PCH is an acronym for the hospital participating in the study

Nunamaker, J. F., Chen, M., & Purdin, T. D. M. (1990). System development in information systems research. Journal of Management Information Systems, 7(3), 89–106. This work was previously published in International Journal of Healthcare Delivery Reform Initiatives (IJHDRI), Volume 2, Issue 2, edited by Matthew W. Guah, pp. 10-34, copyright 2010 by IGI Publishing (an imprint of IGI Global).

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Chapter 2.20

Use of Clinical Simulations to Evaluate the Impact of Health Information Systems and Ubiquitous Computing Devices Upon Health Professional Work Elizabeth M. Borycki University of Victoria, Canada Andre W. Kushniruk University of Victoria, Canada

ABSTRACT Health information systems, and in particular ubiquitous computing devices (UCD), promise to revolutionize healthcare. However, before this can be widely achieved UCD need to be adapted to fit the information, workflow and cognitive needs of users of such devices. Indeed systems and devices that are not developed appropriately may inadvertently introduce error in healthcare (“technology-induced error”). This chapter deDOI: 10.4018/978-1-60960-561-2.ch220

scribes an approach to applying clinical simulations to evaluate the impact of health information systems and ubiquitous computing devices on health professional work. The approach allows for an assessment of “cognitive-socio-technical fit” and the ability to modify and improve systems and devices before they are released into widespread use. The application of realistic clinical simulations is detailed, including the stages of development of such simulations (from the creation of representative clinical environments to subject selection and data collection approaches). In order

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Use of Clinical Simulations to Evaluate the Impact of Health Information Systems

to ensure the success and widespread adoption of UCD, it is argued that greater emphasis will need to be placed on ensuring such systems and devices have a high degree of fit with user’s cognitive and work processes.

INTRODUCTION Health information systems (HIS) and in particular ubiquitous computing devices (UCD) have promised to revolutionize healthcare. Early research in this area demonstrated the value of integrating varying HIS and UCD into clinical settings in terms of improving the quality of patient care and reducing medical error rates (e.g. Bates et al., 1999; Chaudry et al., 2006). More recent research has called into question some of these findings. Some researchers have identified that poor cognitive, social and technical fit may influence some implementations of HIS and UCD and be the cause of health professional adoption and appropriation failures (Borycki et al., 2009a). In healthcare the hospital and clinic costs associated with implementing and re-implementing a HIS and UCD can be significant. There is a need to develop new evaluation methodologies that allow for testing of differing constellations or groupings of HIS and UCD prior to their implementation in clinical settings to reduce the likelihood of costs being associated with health professional adoption and appropriation failure. The authors of this book chapter will introduce a novel methodology that can be used to evaluate the cognitive-socio-technical fit of HISs and UCD prior to their implementation in real world clinical settings. This may prevent poor adoption and appropriation of these technologies. The authors will begin this chapter by first introducing the reader to the literature involving HIS and UCD successes and failures. This will be followed by a brief introduction to the theory of cognitive–sociotechnical fit as applied to HIS and UCD in clinical settings. Following this, the authors of the book

chapter will describe the new and emerging area of HIS evaluation involving the use of clinical simulations. This section of the book chapter will not only describe the background and rationale for using clinical simulations to evaluate HIS and UCD, but will also describe the steps in the evaluation method and provide examples from the authors’ work. The authors will use examples from their previous research to illustrate the use of clinical simulations as an evaluation methodology for HIS and UCD. The application of clinical simulations to the evaluation of HIS and UCD was pioneered by the authors and builds upon their previous work in the areas of clinical simulation and usability engineering (e.g. Borycki et al., 2009b; Kushniruk et al., 1992). Clinical simulations as applied to HIS and UCD evaluation in health informatics have their origins in the medical education and usability engineering literatures. Clinical simulations, when applied to the evaluation of a technology(ies), borrow from medical education, where clinical simulations are used to train physicians and evaluate their competency as health care practitioners (Issenberg et al., 1999). The author’s work also extends the usability literature to clinical simulation by using many of the methods and analysis approaches employed by usability engineers to collect, record and analyze data involving HIS and UCD. Unlike the clinical simulation and usability literatures, clinical simulation when applied to the evaluation of HIS and UCD involves a more holistic evaluation of technology’s impact upon the cognitive-socio-technical fit of health professionals’ work. Lastly, this work also adds to the scientific knowledge in health informatics by identifying a new methodological approach that can be used by health informaticians to evaluate cognitive-socio-technical fit prior to HIS and UCD release and implementation. This approach has not been described elsewhere in the mainstream health informatics evaluation literature (e.g. Anderson & Aydin, 2005; Friedman & Wyatt, 2006; Shortliffe & Cimino, 2006) and is emergent in nature.

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Use of Clinical Simulations to Evaluate the Impact of Health Information Systems

UNDERSTANDING THE NEED FOR APPLYING CLINICAL SIMULATIONS TO THE EVALUATON OF HEALTH INFORMATION SYSTEMS AND UBIQUITOUS COMPUTING DEVICES The published research literature demonstrating the ability of HIS and UCD to improve patient and health care system processes and outcomes has been mixed. For example, Chaudry et al. (2006) in his systematic review of the health informatics literature found that HIS in conjunction with varying devices: (a) improved physician use of guidelines, (b) monitoring and surveillance of patient disease, and (c) decreased medication error rates (but only in healthcare organizations where home grown systems were being used). There were no proven improvements in the quality of patient care associated with the use of commercial HIS. As well, Chaudry and colleagues found that research on the cost-effectiveness of HIS/UCDs was limited and the findings that had been published in the literature were inconclusive. Eslami and colleagues in their 2007 and 2008 systematic reviews of the literature had similar findings. Eslami et al. (2007; 2008) found that implementing a computerized physician order system (i.e. the ordering of medications via computer) increased physician adherence to guidelines in outpatient settings, but that there was a lack of evidence to support the system’s ability to improve patient safety and reduce the costs of providing patient care in outpatient care settings. Lastly, Ammenwerth et al. in her 2008 systematic review found that HIS/UCD may reduce the risk of medical errors when used by physicians, but at the same time acknowledged there is a need for more research in this area as the quality of the studies published to date are variable. Other researchers found that some types of HIS and UCD may facilitate medical errors (e.g. Koppel et al. 2005). Koppel et al. (2005) found HIS system features and methods of implementing could facilitate medical errors in real-world

534

settings if not designed and implemented properly. Kushniruk et al. (2005) suggested the interaction between HIS and UCD (i.e. handheld devices) could potentially lead to technology-induced errors (e.g. where interface design features may induce users to unknowingly prescribe medications incorrectly and UCD features may influence decision making). Lastly, Schulman et. al. (2005) found that while some types of errors were reduced by HIS, new types of errors were also introduced or emerged with the introduction of the technology. This has lead some researchers (e.g. Borycki & Kushniruk, 2008; Koppel & Kreda, 2009) and healthcare organizations (e.g. The Joint Commission for Health Care Quality, 2008) to suggest there is a need to exercise caution and apply rigorous evaluation when implementing HIS and UCD in healthcare organizations. In response to these suggestions researchers have begun to call for the evaluation of differing constellations of HIS and UCD cognitive-socio-technical fit to prevent medical errors involving patients and health professionals from occurring (Borycki & Kushniruk, 2005; Borycki et al., 2009a). As Berg (1999), Kushniruk et al. (1992) and Koppel et al. (2005) suggest improving this fit may reduce the likelihood of unintended consequences. When a healthcare organization (e.g. hospital or clinic) identifies that a HIS and UCD affects the quality of patient care, the organization’s leadership typically tries to address these HIS and UCD issues (Koppel et al., 2005). However, there are high costs associated with modifying a HIS and/or replacing a UCD after a hospital or clinic has implemented it. Yet, in healthcare, HISs and/or UCDs often require modification and/or reimplementation in order to address the above mentioned issues and improve the cognitive-sociotechnical fit of the system with health professional work (i.e. physician, nurse and other allied health professional work) (Ash et al., 2004; Borycki et al., 2009b). In some cases, where systems have not achieved full cognitive-socio-technical fit with health professional work, physicians and

Use of Clinical Simulations to Evaluate the Impact of Health Information Systems

nurses have chosen to boycott the HIS and/ or UCD (LaPoint & Rivard, 2005; LaPoint & Rivard, 2006). In other cases health professionals have chosen to not use many of the HIS and UCD features and functions that would have lead to significant reductions in medical errors or improvements in patient outcomes (Granlien et al., 2008; Kushniruk et. al., 2006). As a result, some organizations have “turned off” HISs and/ or removed some types of UCD from the clinical setting, instead attempting to redesign HIS or select new UCD and then re-implement them at a later date and time (thereby improving fit and health professional adoption and appropriation) (Ash et. al., 2004). Some health informaticians have suggested there is a 65% failure rate associated with implementing HIS and their associated devices (Rabinowitz et al., 1999). Others have suggested this failure rate may be higher when one considers the number of HIS and/or UCD that are not used to their fullest extent (i.e. in terms of their features and functions) (Granlien et al., 2008). Therefore, there is a need for new approaches that can be used to evaluate the cognitive-socio-technical fit of HIS and UCD. As well, poor software and hardware designs (including UCD), and poor implementations can lead to significant healthcare organizational costs (e.g. hospital or clinic) (LaPoint & Rivard, 2005; LaPoint & Rivard, 2006). Costs arise from HIS/UCD device removal, redesign and/ or reimplementation (including costs associated with retraining health professional staff) when such failures occur. In a time where there is a scarcity of healthcare resources (i.e. financial and human) to provide patient care such expenditures can have a significant impact upon regional health authority and federal healthcare budgets. This has led some policy makers and researchers to also conclude that there is a need for new evaluation methods that can be used to inform systems/UCD design and implementation prior to deployment in order to reduce the need for costly redesign and

reimplementation (Ash et al., 2004; Borycki et al., 2009a; Borycki & Kushniruk, 2006).

THE THEORY OF COGNITIVESOCIO-TECHNICAL FIT, HEALTH INFORMATION SYSTEMS AND UBIQUITOUS COMPUTING DEVICES Assessing cognitive-socio-technical fit involves examining the degree of integration between cognitive, social and technical aspects of work. The effects of poor cognitive-socio-technical fit are often significant. The process of evaluating cognitive-socio-technical fit involves taking an integrated, holistic approach towards understanding how differing constellations of HIS and UCD interact in healthcare settings and identifying those HIS and UCD constellations that best match work processes. Cognitive-socio-technical fit addresses the cognitive aspects of fit involving health professionals’ cognition, the social aspects of fit including social interactions that take place between health professional, patient and technology and the use of the technology to provide patient care. Research suggests poor cognitive-socio-technical fit often leads to unintended consequences that were not expected by either the technology’s designer or the organization that procured and implemented the HIS and/or UCD (Borycki et al., 2009a). Poor cognitive-socio-technical fit may result in health professional process and outcomes changes. Process changes include those changes that alter how work is done when a new technology(ies) is implemented. As outcome changes arise from process changes (McLaughlin & Kaluzny, 2006), outcome changes include those changes that affect a patient’s health and wellness and are as a direct result of interactions between the health professional, the HIS/UCD that supports work and the social systems of the organization (e.g. hospital or clinic) (Borycki et al., 2009a). Research has found cognitive-socio-technical fit affects processes and has led to health profes-

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sionals bypassing some HIS and UCD functions (Kuwata et. al., 2006; Patterson et al., 2002) or experiencing increased cognitive load (Borycki et. al. 2009b; Shachuk et al., 2009), and/or experiencing cumbersome workflows (Kuwata et al., 2006; Patterson et al., 2002). For example, research has shown that that introducing a computer workstation into a physician’s office affects the nature of physician-patient communication and physician workflow (Ludwick & Doucette, 2009; Shachuk et al., 2009). Ludwick and Doucette (2009) found that the positioning of a computer workstation in typical physician office led some physicians to modify their workflow and in some cases affected their ability to conduct sensitive communications about health issues with their patients. In such cases, the physicians chose to not document in the electronic patient record on the workstation in the examination room, instead choosing to document on a paper patient record. In another study Shachuk et al., (2009, p.345) found that 92% of physicians found that having an electronic medical record in the exam room “disturbed” their communication with patients in some way. In order to overcome some of these issues physicians developed new skills (i.e. used blind typing) or used templates to improve the workflow that emerged from interactions between the workstation, electronic medical record and the patient. Shachuk et al. (2009) also noted the positioning of a computer workstation in a clinic setting (i.e. social context) influenced workflow and increased cognitive work. These findings are not unique to the physician office setting. Poor workflows emerging from interactions between UCD-HIS and organizational context (i.e. environment) are also present in the hospital setting. For example, wireless carts that provide ubiquitous access for health professionals to electronic patient records may provide timely patient information, but when used to provide certain types of patient care (i.e. medication administration), they may lead health professionals to develop unplanned workarounds. Publications by Granlien et al. (2008), Kushniruk et al. (2006),

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Kuwata et al., (2005) and Patterson et al., (2004) reveal wireless carts in conjunction with medication administration systems (i.e. systems that support nurse administration and documentation of medications administered to patients), bar code scanners, patients, and the hospital environment may lead clinicians to undertake significant workarounds and bypassing of some system functions in order to provide timely patient care. Each of these studies has identified that there is a need to better understand the cognitive-socio-technical fit between the worker and system/UCD in order to optimize fit and improve adoption and appropriation of these technologies (Borycki et al., 2009a; Granlien et al., 2008).

Cognitive-Socio-Technical Fit and Healthcare Processes and Outcomes The theory of cognitive-socio-technical fit involving HIS and UCD influences healthcare processes and subsequent outcomes. According to the healthcare quality improvement literature, process improvements may lead to significant improvements in outcomes (McLaughlin & Kaluzny, 2006). Along these lines, process changes may also lead to workarounds or bypassing of system functions (if not effectively changed), thereby leading to poor or unintended outcomes such as no reductions in health professional error rates or improvements in patient health status) (Koppel et al., 2005; Kushniruk et al., 2005). Healthcare process changes arising from poor cognitivesocio-technical fit include the development of new unintended interactions (not expected by the technology’s designer nor the adopting organization) between a HIS, the UCD and the health professional that in turn influences patient outcomes. For example, in some cases HIS have failed to reduce medication error rates projected by procuring organizations (Koppel et al., 2005). In other cases researchers have reported that the attributes of UCD (e.g. small screen size) have served as a barrier to clinicians use of the UCD

Use of Clinical Simulations to Evaluate the Impact of Health Information Systems

(e.g. a handheld device, cellular phone) in the clinical practice setting (Chen al., 2004). Lastly, still other researchers have found that interactions between HIS and UCD can introduce a new class of “unintended” consequences (Ash et al., 2004) such as technology-induced errors (Kushniruk et al., 2005). These unintended consequences have their origins in the interactions between the HIS and the UCD within a given healthcare organizational context. In these cases HIS and UCD have introduced new types of errors to the organization. Such instances of poor cognitive-socio-technical fit need to be identified and addressed to prevent harm from occurring to patients prior to HIS and UCD implementation in real-world settings (Borycki & Kushniruk, 2008). In summary poor-cognitive-socio-technical fit involving HIS, UCD, the healthcare organization and the patient can have significant impact upon healthcare processes and patient outcomes. The substantive costs associated with addressing “unintended” process and outcome changes arising from poor cognitive-socio-technical fit following the introduction of HIS and UCDs in healthcare requires that researchers develop new methods of identifying potential process and outcome changes before a HIS/UCD is implemented to prevent unintended consequences from occurring. According to Boehm’s seminal research (1976), the relative costs of repairing software errors increase exponentially the further the software product moves through the design, coding, testing and implementation phases of the software development lifecycle (SDLC). Although this work focuses on software products, the quality improvement literature in healthcare identifies introducing new processes using HIS and UCD can also lead to significant costs - costs associated with modifying the HIS to meet UCD specifications or the need to identify a new UCD that has high cognitivesocio-technical fit with health professional work (McLaughlin & Kaluzny, 2006). Lastly, the adverse events literature in healthcare identifies the human cost of death and disability arising from software

and UCD failure during medical treatment (Levenson, 1995). In summary it is important to detect unintended consequences associated with using HIS and UCD early in the SDLC, rather than during the operations and maintenance phase (i.e. when HIS and UCD are implemented in a hospital or clinic) and they may lead to medical errors. In the next section of the book chapter the authors will discuss the use of clinical simulations, a methodology for evaluating the impact of HIS and UCD upon health professional work.

CLINICAL SIMULATIONS: A METHODOLOGY FOR EVALUATING THE IMPACT OF HEALTH INFORMATION SYSTEMS AND UBIQUITOUS COMPUTING DEVICES UPON HEALTH PROFESSIONAL WORK One emerging methodology that may be an effective approach to evaluating the impact of a HIS and UCD upon health professional work processes and patient outcomes is clinical simulation as applied to the evaluation of HIS and UCD in healthcare. Clinical simulations involve the use of real-world examples of complex clinical work activities involving health professionals (e.g. doctors or nurses) being observed as they carry out a work task involving a HIS and UCDs in a hospital or clinic. In the next section of this book chapter, the authors will describe the development and history of clinical simulations in healthcare, their subsequent application and use in health informatics in the evaluation of HIS and UCD, and their use in detecting “unintended” consequences associated with poor cognitive-socio-technical fit.

History of the Use of Clinical Simulations is Healthcare Clinical simulations have been used in the healthcare industry for many years. Several researchers

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have successfully developed and utilized clinical simulations as a way of preparing medical and nursing students for real-world practice. Initially, instituted as a method for providing clinicians with opportunities to practice clinical skills and procedures in preparation for work involving live patients, clinical simulation has become an accepted methodology for training physicians and nurses prior to their real-world encounters with patients (Issenberg et al. 2005; Kneebone et al., 2005). Research examining the effectiveness of clinical simulation as a training methodology has shown that student participation in clinical simulations can improve the quality of their clinical decision-making and practice. Clinical simulation can also provide opportunities for students to develop expertise in a given clinical area as well as learn about how to manage a patient’s states of health and illness in a safe environment where there are no live patients and instructors can provide constructive feedback (Issenberg et al., 1999; Issenberg et al., 2005; Kneebone et al., 2005). Clinical simulations may take many forms. For example, a clinical simulation may involve live actors who are “playing” the part of an ill patient – acting out the signs and symptoms of a disease such as pain or melancholy. In such clinical simulations health professional students such as medical students have the opportunity to practice skills such as patient interviewing while an instructor observes and later provides feedback. Instructors review the medical students’ performance and provide feedback aimed at improving the health professionals’ diagnostic and procedural skills. Simulations may also take the form of interactions with a mannequin (that represents a patient) in a laboratory setting. The health professional has the opportunity to perform procedures on the mannequin that would typically be done on a patient in the clinical setting. Procedures are practiced until the health professional achieves competence in performing the activity, skill or procedure (Issenberg et al., 2005; Kneebone et al., 2005).

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Clinical Simulations and Health Professional Education In the laboratory setting instructors can observe students applying theory to practice using such clinical simulations. For example, an instructor can observe a student performing an activity or a procedure that was discussed in the classroom. Once the student has completed the activity or procedure, the instructor can provide the student with constructive feedback and thereby help the student to achieve a higher level of knowledge and improve the student’s ability to perform the activity or procedure. More recently, the tools used in training health professionals (e.g. physicians and nurses) have improved significantly. Instead of using unresponsive mannequins’, health professional training programs are using computerized mannequins and actors to help build clinician skills. Mannequins have been developed that can simulate real patient signs and symptoms of disease over time. Furthermore, these computerized mannequins can be used to train health professionals to respond to life critical events (such as heart attacks) and to perform complex procedures within the context of simulated, life threatening events. This gives the health professional student an opportunity to experience events that are more representative of the real-world and an opportunity to learn about how to respond to these events over time as the patient’s condition deteriorates or improves in response to student interventions. Once competencies are achieved, health professionals can move to assessing actors playing the role of a patient. Following this students can learn to manage real patients and perform procedures on real patients in real-world clinical settings under the supervision of a health professional (having practiced these activities on a mannequin and with actors) (Cioffi, 1997; Issenberg et al., 2005).

Use of Clinical Simulations to Evaluate the Impact of Health Information Systems

APPLICATION OF CLINICAL SIMULATIONS TO THE EVALUATION OF HEALTH INFORMATION SYSTEMS AND UBIQUITOUS COMPUTING DEVICES Researchers have begun to apply key learning’s gained from clinical simulation research involving medical and nursing students to the testing and evaluation of HIS and UCDs prior to their implementation in real-world clinical settings. Initial research in this area has employed clinical simulations as a methodology for evaluating the impact of systems and UCDs upon physician cognition and work. Early research in this area took place in the 1990’s. Cognitive researchers attempted to determine what effect a HIS deployed on a desktop computer would have upon physician-patient interviews and a physician’s cognition. The researchers simulated a typical physician office and asked physician subjects to conduct an interview with an actor, while using the HIS. The researchers found the introduction of a HIS had significant cognitive implications for physicians, as the HIS altered the nature of the patient-physician interview, caused some physicians to become “screen driven” (Kushniruk et al., 1992) and in some cases altered diagnostic reasoning processes (Patel et al., 2000). More recent research has extended this work to evaluate the impact of HIS deployed on differing UCDs (i.e. hand held UCDs). In one line of research, researchers asked physician subjects to perform a number of prescribing tasks in a simulated clinical environment using a prescription writing system on a personal digital assistant (PDA). The researchers studied the effects of the HIS/UCD upon physician prescribing error rates. In the clinical simulation the researchers were able to observe interactions between a HIS and a hand held computing UCD upon physician prescribing and subsequent medical error rates. The researchers observed that HIS and UCD usability problems led to technology-induced errors.

They were able to identify a number of hand held UCD and HIS interface features that could lead to errors. In addition to this, the researchers noted that the size of the UCD and its responsiveness influenced subject error rates, demonstrating an interaction effect between the HIS and UCD. This research was successful in predicting differing types of technology-induced errors prior to HIS/UCD deployment and implementation (Kushniruk et al., 2005). In a subsequent study the researchers extended their work to additional health professionals (i.e. nurses and physicians) to evaluate the use of a mobile wireless medication cart with a laptop and a barcode scanner. Subjects were asked to administer medications (i.e. oral medications, injectible medications and intravenous medications) to a mannequin who was wearing a bar-coded bracelet for identification. In this study the researchers again conducted a clinical simulation where health professionals were asked to administer the medications and document their administration on a HIS using a laptop on a mobile cart. The researchers noted the interactions between the health professional, HIS, UCDs (i.e. laptop and bar code scanner) and the mannequin (who took the place of the patient). The researchers observed that the medication administration task involving the UCD and system in the clinical environment led to the development of cumbersome workflows involving the UCD, system and patient. In addition to this, the researchers found the interactions between the subject, UCD, system and patient could lead to increased cognitive load. In some cases subjects engaged in cumbersome and confusing workarounds in order to be able to successfully complete the task of medication administration (including documentation) (Borycki et al., 2009a; Kushniruk et. al. 2006). The researchers were able to provide the healthcare organization that was implementing the HIS/UCDs with feedback about how the system/UCDs could be modified, how to simplify some of the emergent workflows arising from using systems/UCDs and how to improve

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health professional training so that health professionals spent less time learning about how to use the integrated system (Kushniruk et al., 2006). In summary the use of clinical simulations in the evaluation of interactions between health professionals, HIS, UCDs and patients has demonstrated the value of using clinical simulations to improve health professional work processes while at the same time identifying key ways outcomes can be improved when using HISs/UCDs (i.e. reducing the likelihood of technology-induced errors or unintended consequences). In the next section of this chapter the authors will provide an overview of the clinical simulation methodology.

The Clinical Simulation Methodology In order for clinical simulations to be effectively used in evaluating the process and outcome impacts of implementing a HIS and its associated UCDs, researchers need to ensure that clinical simulations have a high level of ecological validity. Ecological validity refers to the ability to create clinical simulations that are representative of the real-world settings so that the results of the clinical simulations are generalizable (Vogt, 1999). Therefore, ecological validity ensures the quality and value of the clinical simulation results when they are used to: (1) redesign a system/UCD, (2) procure a system, (3) understand the effects of system customization in conjunction with UCD selection and (4) as an aid to providing technology trainers with information about how to train individuals to use such systems/devices to increase the speed of learning how to use the HIS/UCD in a given clinical setting. In the next section of the chapter we will discuss a sequence of steps that can be used to create clinical simulations that can test aspects of cognitive-socio-technical fit in more detail. This involves creating representative clinical environments (that would typically be found in healthcare organizations where the HIS/UCD would be designed for or deployed). This involves: (1) using representative equipment

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from that organization, (2) selecting a range of representative tasks health professionals would typically engage in, (3) developing a range of representative scenarios, and (4) inviting a range of representative health professional subjects to participate in the clinical simulations as part of the evaluation.

Creating Representative Clinical Environments The complexity of representative clinical environments required for clinical simulations in health informatics where a HIS/UCD is being evaluated varies from low to high fidelity. A low fidelity clinical environment somewhat approximates the real world. In a high fidelity clinical simulation every attempt is made to imitate the real world. In creating high or low fidelity environments there is a need to attend to ecological validity (as outline earlier). As part of creating an ecologically valid environment, the essential features of a real-world environment would need to be re-created so that the subject that is participating in the clinical simulation responds as they would in the realworld when confronted with a similar situation. Therefore, creating a representative organizational or clinical environment is critical to ensuring the ecological validity of the clinical simulation and the validity and value of the results. High ecological validity will lead to representative results in terms of observed impacts of systems and UCDs upon health professional processes and outcomes where as low ecological validity will not.

Tangible and Intangible Aspects of Creating Representative Clinical Environments Clinical environments (e.g. hospital rooms, nursing stations) have both tangible and intangible components. Tangible aspects of organizations include those visible structures that can be easily replicated such as the physical layout of a hospital

Use of Clinical Simulations to Evaluate the Impact of Health Information Systems

room, desks, chairs, hospital beds, intravenous equipment, oxygen equipment etc. Intangible aspects of an organization include those that are not readily visible such as underlying organizational policies, procedures, social norms and culture. Intangible aspects of the clinical environment are difficult to identify and replicate, yet they have a significant impact upon how work is done within an organizational context (Shortell & Kaluzny, 2006). Tangible aspects of a clinical environment should be evaluated and judged by experts to be representative of typical clinical settings to ensure the setting’s ecological validity (Haimson & Elfenbein, 1985; Shortell & Kaluzny, 2006). Experts with clinical, management and health information technology backgrounds should be used to evaluate the ecological validity of a clinical environment in conjunction with health informaticians (Shamian, 1988). Clinical and management experts can be used to judge the representativeness of the simulated clinical environment in terms of layout, furniture and equipment. Information technology and health informatics experts can be used to judge the representativeness of the systems/UCDs that will be used or are currently being used in a “typical” clinical environment. These experts will also be expected to select the “ideal” or planned UCDs for use in the clinical simulation and integrating the system/UCDs for the simulation. For example, if the intent of a clinical simulation is to re-create a representative hospital environment that is “typical”(where the system/UCD would be implemented) of a hospital room with healthcare equipment, this can be done in a laboratory setting. The results of this clinical simulation would be generalizable to most typical hospital environments, but may not be typical of the organization where the HIS/UCDs would actually be implemented (Borycki et al., 2009b; Haimson & Elfenbein, 1985). Alternatively, if the intent of the clinical simulation is to create a representative hospital environment that is ecologically valid and to ensure the results of the clinical simulation are representative

of the local healthcare organization (e.g. hospital) every effort should be taken to conduct the clinical simulation in the local organization (Haimson & Elfenbein, 1985; Kushniruk & Borycki, 2005; Shortell & Kaluzny, 2006). For example, if a hospital is implementing a specific HIS/UCD that has been customized for the hospital, an empty hospital room can be used with the HIS/UCD to be tested (Kushniruk & Borycki, 2005). As a result, both the tangible and intangible aspects of the organizational environment are replicated in using an empty hospital room within the local organization. In conducting the clinical simulation in the organizational environment where the HIS/UCD will be deployed there will be full replication of the tangible aspects of the organization in the clinical simulation (e.g. room layout, hospital equipment etc.). As well, the intangible aspects of the organizational environment will also be present in the clinical simulation (e.g. organizational culture, social norms in behaviour etc.). The health professionals who participate in the simulation within the context of their own local organization may engage in those organizational and practice norms that underlie all health professional practice and are unique to that organizational context. It must be noted that if a health professional participates in the clinical simulation and they are not familiar with the intangible aspects of the organization they may not practice according to local organizational practices and thereby influence the quality of the clinical simulation results for the local organization. Alternatively, if the health professional participates in a clinical simulation outside their organization (i.e. in a laboratory setting) they may not use the same organizational cultural and behavioural norms. This will also influence the quality of the clinical simulation results (Haimson & Elfenbein, 1985; Shortell & Kaluzny, 2006). Therefore, ecological validity is necessary in order to ensure the process and outcomes changes that are observed during the clinical simulation are representative of those involving the HIS/UCD. Furthermore, there is a

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need to attend to the tangible and intangible aspects of an organization. Tangible and intangible aspects of organizations may influence health professional activities, practices and behaviours during a clinical simulation and therefore, the quality of the simulation outcomes are affected.

Use of Representative Equipment The complexity of the equipment used in clinical simulations varies depending on whether the clinical simulation is low or high fidelity. As a low fidelity clinical simulation roughly approximates the real-world, the equipment is often not complex and may be limited to only the HIS/UCDs that are to be evaluated. Alternatively, as we increase the fidelity of the clinical simulation the equipment that is added to the clinical simulation is more representative of the clinical environment. For example, a clinical simulation in an empty hospital room would also include the use of equipment that would typically be used by health professionals in managing a patient’s care such as a hospital bed, bedside table, IV poles, oxygen equipment etc. As well, the HIS/UCD that is planned for use would also be used in the clinical simulation environment. This might include the use of a computer workstation placed beside the patient’s bed with a copy of the patient’s electronic patient record available for viewing during the clinical simulation (Kushniruk et al., 2006; Kuwata et al., 2005).

Use of Representative Patients and Other Health Professionals The use of representative patients and other health professionals will also range from low to high fidelity. In a low fidelity clinical simulation a mannequin may be used to represent the patient. As the fidelity of the clinical simulation increases a computerized mannequin (that simulates the signs and symptoms of disease) may be used to represent the patient. The fidelity may further increase with instructors controlling the signs

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and symptoms of disease that the mannequin is experiencing and providing the mannequin with a “voice” via a speaker or instructor from the control room. Lastly, a high fidelity situation may be constructed where the health professional interacts with an actor playing the part of a patient, other health professionals as well as the systems/UCDs that will be evaluated. The authors of the book chapter have found that one can create clinical simulations that can be used to evaluate the HIS/UCD. Low and high fidelity simulations have been created by the authors. Low and high fidelity simulations involving a health professional, HIS and UCD can be easily created for as little as $2000 (to evaluate cognitive-socio-technical fit). These simulations involve using real-world equipment and health professionals that can be found in any healthcare facility globally (see Kushniruk & Borycki, 2006). Simulations involving computerized mannequins can also be constructed in facilities that employ such technology to train medical and nursing students as they are now an important part of the training these health professionals globally. Here, such simulations can be conducted in a medical school, nursing school or government organization that uses such technology to train health professionals or a teaching hospital where such equipment resides (Issenberg et al. 2005; Kneebone et al., 2005).

Use of Scenarios Scenarios are an important component of clinical simulations. Careful attention should be paid to the attributes or qualitative dimensions of each scenario used in the clinical simulation. Scenarios are used to select or create the environment where the clinical simulations will take place (e.g. a hospital room, an operating room, a hallway). Scenarios are also used to set out the context for the clinical simulation. Scenarios should include varying levels of complexity and urgency. For example, if the researcher wishes to examine the

Use of Clinical Simulations to Evaluate the Impact of Health Information Systems

ability of a system to address subject information needs in time limited, demanding environments, he or she may observe the subject attempting to acquire information from the system given varying time constraints and urgency (e.g. 5 minutes use of the system to address an information need about a patient’s condition versus 20 minutes of possible system use to make a decision about a patient’s condition after a clinic visit). Scenarios should take into account routine and atypical cases. For example, in conducting evaluations involving medication order entry systems, the evaluator may include routine cases (e.g. the prescribing of medication in a physician’s office to a patient with a chronic illness) (as illustrated in Kushniruk et al, 2005) to atypical cases (e.g. a physician giving medications immediately (i.e. stat) during a critical, life threatening event such as a patient experiencing a myocardial infarction (see Kushniruk et al., 2006 for more details). Scenarios should be representative of a range of situations that would be encountered by subjects. They should also outline possible sequences of activities that will take place. This will serve as a comparison point between existing processes and outcomes and those that are expected as a result of introducing the technology and those that emerge during a scenario as unintended consequences once the HIS/UCD are introduced (Borycki et al., 2009b; Orlinkowski, 1992). For example, when studying the adequacy of a HIS in addressing information needs, the researcher must first select scenarios that stimulate subjects’ need for information such as the need to assess a patient’s condition (e.g. Borycki & Lemieux-Charles, 2009). Then the researchers might compare what is routinely done by health professionals in a paper environment to that done to complete the same work in a HIS/UCD environment. Scenarios can involve low fidelity, simple written medical case descriptions that are given to subjects to read and respond to. Higher fidelity clinical simulation scenarios can involve more complex scenarios involving computer controlled

mannequins that display preset programmed signs and symptoms of disease over time for health professionals to respond to. Even higher fidelity simulations may involve actors with specifically developed scripts that engage the health professional and involve the HIS/UCD to be evaluated. For example, a simple clinical simulation may involve presenting physicians with a short written case description of a patient and asking them to enter the information about the patient into a patient record system. A high fidelity simulation would reproduce more closely the real world situation being studied. This might involve actors playing the roles of patients and staff in a clinic exam room in a study of how doctors’ use of an electronic patient record system on a hand held UCD. Such a study may involve multiple recordings of UCDs to precisely document all subject interactions (e.g. audio and video recording of all verbalizations, computer activities and the hospital room or clinic environment as a whole to document actions). For example, in studying the impact of hybrid electronic-paper environments upon novice nurse information seeking, Borycki et al., (2009b) recreated a typical nursing unit environment that would be found in a hospital (i.e. desk, telephone, chairs, workstation, software and paper and electronic sources of nursing and medical information about a typical patient). In order to capture the effects of the hybrid environment upon novice, nurse information seeking the researcher recorded each nurses’ verbalizations while reviewing their patient’s information by using an audio recorder. The nurses’ actions and interactions with their environment were recorded by using a video camcorder. Lastly, the nurses’ interactions with the software and hardware (i.e. the workstation) were recorded using Hypercam®, a screen recording program.

Selecting Representative Tasks This stage involves the selection of representative tasks users are expected to undertake when

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using the system and UCD under study. A range of tasks should be selected (Kushniruk & Patel, 2004). Ideally, tasks should be in keeping with the environment where the task will be performed and the scenario that has been constructed (Borycki et al., 2005). For example, a nurse may be asked to administer routine medications to a patient in a hospital room. Tasks that are part of this scenario include: logging on to the electronic medication administration system (eMAR), searching for a patient that will be receiving the medication in the electronic medication administration system (eMAR), electronically identifying the patient, identifying the medications that need to be administered at that time, scanning the patients’ identifying bar-coded bracelet with a bar code scanner, scanning the medication package, pouring the medication, verifying the patient’s identity verbally, giving the medication to the patient and then documenting the administration of the medication in the eMAR that is provided on a laptop mounted on a portable medication administration cart (Altman, 2004; Patterson et al., 2002). This process ensures the medication that is being given by the nurse is given to the correct patient, is the correct medication, is in the correct dose, and is provided by the correct route (e.g. by mouth). This process also ensures the administration of the medication is documented in the eMAR (Altman, 2004).

Inviting Representative Participants This step involves the identification and selection of representative subjects for the simulation. Subjects should be representative of end users of the system under study in terms of level of disciplinary, domain and technology expertise. Research suggests health professional background can have a significant impact upon processes that involve patient care, systems and UCDs and patient and health professional outcomes (e.g. medical error rates). For example, health professionals of differing disciplinary backgrounds may

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respond differently to the same scenario (Johnson & Turley, 2006). Research has also demonstrated that a domain of expertise will influence health professional behaviours and outcomes (Lammond et al., 1996). For example, a medical nurse will attend to differing types of information as compared to a surgical nurse when presented with the same task - searching an electronic patient record. Organizational experience has also been shown to have an effect upon worker behaviours (Jablin & Putnam, 2001). Lastly, more recent research by Patel and colleagues (2000) reveals computer expertise can have a significant impact upon health professional use of systems. In addition to this, research suggests experience with differing UCDs may have an impact upon health professional behaviours and outcomes. Evaluators must take into account disciplinary, domain, organizational and technology expertise when selecting representative participants. Ideally a range of participants should be selected to understand differing users’ responses to the system/UCD. If the intent is to study user ability to learn about the new HIS/UCD constellation or group of software and devices, then novice users are best to be invited to participate in the clinical simulations (Borycki et al., 2009b).

Data Collection The authors of this book chapter recommend that audio and video data be collected. Individuals may be asked to “think aloud” as they work with the HIS/UCD individually. Groups of health professionals working with the system/UCD should have their conversations recorded. Audio data of individual’s thinking aloud or the conversations of groups of users working with the system/UCD is essential. Audio data provides insights as to what information in the system representative participants are attending to as well as data about potential usability issues arising from system and UCD attributes while the individual or group is working with the HIS and UCD constellation

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(Kushniruk & Patel, 2004). Other forms of data may include video recordings of subjects interacting with systems, UCDs, other health professionals and simulated patients. Video data allows the researcher to observe emergent workflows arising from interactions between system, UCD and technology (Borycki & Kushniruk, 2005). Computer screen recordings of participants interacting with the system allow the researchers to identify sources of poor or cumbersome workflows. Screen recordings also illuminate human computer interaction issues and issues arising from poor system usability (Kushniruk et al., 2006). Video data and computer screen recordings can be triangulated with the audio data. Increasingly, the role of computer screen recordings and video data has been found to inform and contextualize information (Borycki et al., 2009). These two types of data have provided researchers with additional insights and understanding of the underlying cognitive processes of subjects and the effects of systems and UCDs upon them. For example, in a recent study by the author that examined the relationship between medical error and system usability, video and computer screen data was used to inform audio transcripts of subjects’ “thinking aloud” while interacting with a system on a hand held device. During analysis, audio data indicated subjects were under the impression they had entered the correct prescription data when using an electronic prescribing program on a hand held device. Corresponding video data and computer screen recordings revealed subjects had not entered the correct information and they experienced usability issues. As a result, subjects unknowingly entered the wrong prescription (Borycki & Kushniruk, 2006; Kushniruk et al., 2005). As described above, clinical simulations as a methodological approach can be used to study the effect of systems and UCDs upon health professional work processes and outcomes. Clinical simulation unlike other evaluation approaches in health informatics (e.g. randomized clinical control trials) allow one to observe clinicians (e.g.

physicians or nurses) interacting with HIS/UCDs under simulated conditions. Clinical simulations allow one to study the effects of the system, UCD, health professional and patient interactions upon clinical processes and outcomes. Clinical simulations can be conducted early in the software development lifecycle to inform design, development, customization, procurement, implementation, operation and maintenance of software and hardware. It must be noted that clinical simulations conducted in a laboratory setting offer significant insights into interactions between UCD, systems and health professionals within an organizational environment (e.g. a hospital). In conducting clinical simulations (as outlined above) all aspects of the interaction are recorded. Researchers can make observations about cognitive-socio-technical fit and changes can be made to the UCD/system to improve fit (Borycki & Kushniruk, 2006). Recordings allow multiple researchers to review the collected data. Inter-rater reliability can be calculated as a result. Researcher reviews of video and audio recordings can prevent researcher and subject recall bias and researcher recording bias. Subject’s may experience recall bias – being unable to remember all the interactions between the system, UCD and organizational environment, if other forms of data collection are used such as semi-structured interviews or focus groups. In such cases subjects may not be able to recall all the events that occurred in their interactions with the UCD and system within in a given organizational environment (Jackson & Verberg, 2006). Researcher bias involves a researcher not being able to physically record all of the information when taking notes or being selective about the information that is recorded during observations of subjects interacting between the UCD and technology. Some researchers have attempted to conduct these types of studies in naturalistic settings involving real-world interactions between patients, health professionals, systems, UCDs and organizational contexts. Many of these studies (e.g. Ash

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et al., 2004) have involved semi-structured oneon-one interviews and focus groups with key informants (i.e. health professionals who have been affected by these UCD/HIS implementations). As well, researchers have conducted observational analyses of subjects working in these environments. Researchers observed subjects while they were performing tasks and took notes.

Data Analysis Data analysis, where clinical simulations are concerned, is both qualitative and quantitative in nature. Data can be qualitatively coded using empirically accepted coding schemes to identify subject patterns of interactions with a system, workflow issues, usability problems and recurring system issues. Audio, video and computer screen data can be qualitatively coded. Data can be coded using inductive, deductive and mixed method approaches. Deductive approaches involving the coding of qualitative data involve the use of existing empirically accepted coding schemes, model(s) or theory (ies) (Ericsson & Simon, 1993). Alternatively, when inductive approaches are used, data drives coding scheme development (e.g. grounded theory approaches) (Kuziemsky et al., 2007). Mixed method approaches towards the analysis of qualitative data combine both inductive and deductive methods. Here inductive and deductive approaches are combined. Empirically validated coding schemes from the research are used while at the same time new codes can emerge from the data as prior empirical work may not be sufficient to fully inform the coding of the data. In combining the approaches, transcripts of audio, video and computer screen recordings are initially coded using existing empirically validated coding schemes, models or theories. Then, inductively extensions are made to the empirically validated coding schemes, models or theories (Borycki et al., 2009b; Ericsson & Simon, 1993; Kushniruk & Patel, 2004; Patel et al., 2000).

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Qualitative data are not limited to coded audio, video or computer screen data. Video data and computer screen recordings can be reviewed. Workflow and data flow diagrams can be constructed to observe for differences in before and after HIS/UCD use. Such diagrams allow the evaluator to identify cumbersome workflows as well as interactions between HIS/UCDs that increase the complexity of work and cognitive load (Borycki et. al., 2009a). Qualitative data (i.e. coded verbal transcripts, actions on the computer screens and observed behaviours) can be quantified (Borycki & Lemieux-Charles, 2008). Coded verbal data may be reduced to individual items “intended to mean on thing” (Sandelowski, 1999, p. 253). Frequencies can be tabulated for each code in the transcribed data. Then, inferential statistics can be done (Borycki & Lemieux-Charles, 2008; Ericsson & Simon, 1993). In summary clinical simulations in health informatics involve imitating real-world situations in a laboratory setting or a organizational environment (e.g. a clinic, hospital room or operating room). Clinical situations are used to assess the impact of new software, UCDs and interactions between the two upon health professional work. In health informatics clinical simulations allow health informaticians to observe health professionals with differing backgrounds (e.g. nurses or physicians) while they perform tasks typical of their profession as they would perform them in real-world settings while engaging with other health professionals and patients. In addition to aiding in the assessment of these UCDs, clinical simulations allow one to assess differing constellations of HIS and UCDs in laboratory or real-world environments to be tested and evaluated prior to their real-world or organization wide implementation - in the process reducing the risk to the healthcare organization by minimizing the likelihood of an institution wide redesign and reimplementation arising from poor cognitive-socio-technical fit. In undertaking clinical simulations in health informatics, HIS and UCD designers as well as the organization’s where

Use of Clinical Simulations to Evaluate the Impact of Health Information Systems

these systems and UCDs are implemented are able to assess the impact of the system and UCD upon health professional processes and outcomes without patient involvement and without placing the health professional in a situation where the new system and UCD may impact upon their ability to work with other health professionals or with a patient. In this case, the health professional encounters difficulties while working with the HIS and UCD when using the system/device in the context of a “safe” laboratory environment and clinical situation (i.e. not involving patients).

FUTURE RESEARCH DIRECTION Future research involving the use of clinical simulations will involve applying the methodology to new and emerging types of UCD and HIS. For example, many healthcare organizations are attempting to determine the UCDs that would best meet the needs of clinicians in the hospital and clinical setting. In addition to this as UCDs have become more prevalent in healthcare, HIS that have historically been made available to clinicians via desktop workstations will need to be evaluated in terms of their ability to support the cognitive and social aspects of health professional work using varying types of UCD/HIS in combination. This will help to identify the best constellations of HISs and UCDs that lead to improvements in health care processes and the quality of patient care while at the same time reducing the likelihood of a medical error occurring. The authors are currently working on developing advanced simulations to assess potential issues in the integration of electronic health records with a range of physical devices (e.g. monitoring devices, bar coded patient bracelets etc). Lastly, there will be a need to identify the range of routine to urgent and typical to atypical situations that can be used to test HIS and UCD.

CONCLUSION Clinical simulations in health informatics have allowed software and hardware vendors as well as healthcare organizations to better customize HIS to the local organizational environments prior to implementation. Clinical simulation also allows organizations to assess the impact of HIS and UCD constellations upon health professionals cognitivesocio-technical fit. Such knowledge is significant as it influences the design and development of software and UCDs at the vendor level. Such knowledge also influences the customization of software by organizations and their procurement of software and UCDs for use in the clinical setting. Lastly, such information offers organizations the opportunity to develop training strategies for health professionals that take into account the cognitive-socio-technical learning needs of health professionals as they learn to use the new HIS and the UCDs that will be used in conjunction with the HIS in a real-world setting. The outcomes of using clinical simulations to evaluate HIS and UCDs are significant. Clinical simulations have been effectively used to evaluate the impact of a technology (i.e. HIS and UCDs) upon health professional processes such as patient interviews (involving actors who are playing the role of patient) (Kushniruk et al., 1996), information seeking (Borycki et al., 2009b), decision-making (Patel et al., 2000), knowledge organization, reasoning (Patel et al., 2001), workflow (Borycki et. al., 2009a), and patient safety (Kushniruk et al., 2005). They have also been used to evaluate the impact of interactions between UCDs, HIS and health professionals where patient care processes and outcomes are concerned (Kushniruk et. al., 2005; Patel et. al., 2000). Clinical simulations when applied to the evaluation of cognitive-socio-technical fit involving HIS and UCDs can provide significant benefit to technology designers and organizations who are procuring these systems and UCDs. They allow technology designers to evaluate the impact

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of systems/UCDs upon health professional work prior to systems implementation (thereby preventing possible system/UCD implementation failures or failures to fully use HIS/UCD functionality). From an organizational perspective, clinical simulations allow organizations to evaluate the interactions between HIS, UCD, organizational environment and health professional. The use of clinical simulations ensures that organizations achieve a strong cognitive-socio-technical fit for health professionals. This lessens the likelihood of implementation failure and poor health professional adoption and appropriation of technology(ies). Such use of clinical simulation is needed to evaluate HIS/UCD cognitive-socio-technical fit with health professional work. Such evaluation is important to prevent the need for the modification of HIS/UCDs after implementation when the cost of implementation is significantly higher. The use of clinical simulations in the evaluation of HIS/UCDs is a significant advancement over an evaluation of HIS/UCD after implementation using semi-structured interviews or observation of clinicians in the healthcare work environment (as both these methodologies are prone to recall and researcher recording bias) and may not allow for full documentation of the interactions. This is a new and emerging methodology that extends work in the area of usability engineering by borrowing methods of recording data and applying those recording methods to the evaluation of cognitive-socio-technical fit involving HIS and UCDs. This work also borrows methods from the training of health professionals and extends them for use in evaluating the effects of HIS/UCDs upon cognitive-socio-technical fit involving organizational processes and outcomes.

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ADDITIONAL READING Ammenwerth, E., Schnell-Inderst, P., Machan, C., & Siebert, U. (2008). The effect of electronic prescribing on medication errors and adverse drug events: A systematic review. Journal of the American Medical Informatics Association, 15(5), 585–600. doi:10.1197/jamia.M2667 Ash, J. S., Berg, M., & Coiera, E. (2004). Some unintended consequences of information technology in health care: The nature of patient care information system related errors. Journal of the American Medical Informatics Association, 11(2), 121–124. Borycki, E. M., & Kushniruk, A. W. (2008). Where do technology induced errors come from? Towards a model for conceptualizing and diagnosing errors caused by technology. In Kushniruk, A. W., & Borycki, E. M. (Eds.), Human and Social Aspects of Health Information Systems (pp. 148–166). Hershey, PA: IGI Global. Borycki, E. M., Kushniruk, A. W., Kuwata, S., & Watanabe, H. (2009a). Simulations to assess medication administration systems. In Staudinger, B., Hob, V., & Ostermann, H. (Eds.), Nursing and Clinical Informatics (pp. 144–159). Hershey, PA: IGI Global.

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Borycki, E. M., Lemieux-Charles, L., Nagle, L., & Eysenbach, G. (2009b). Evaluating the impact of hybrid electronic-paper environments upon novice nurse information seeking. Methods of Information in Medicine, 2, 1–7. Chaudhry, B., Wang, J., Wu, Sh., Maglione, M., Mojica, W., & Roth, E. (2006). Systematic review: Impact of health information technology on quality, efficiency and costs of medical care. Annals of Internal Medicine, 144, E-12–E22. Eslami, S., Abu-Hanna, A., & deKeizer, N. F. (2007). Evaluation of outpatient computerized physician medication order entry systems: A systematic review. Journal of the American Medical Informatics Association, 14(4), 400–406. doi:10.1197/jamia.M2238 Eslami, S., deKeizer, N. F., & Abu-Hanna, A. (2008). The impact of computerized physician medication order entry in hospitalized patients: A systematic review. International Journal of Medical Informatics, 77, 365–376. doi:10.1016/j. ijmedinf.2007.10.001 Koppel, R., Metlay, J. P., Cohen, A., Abaluck, B., Localio, R., Kimmel, S. E., & Strom, B. L. (2005). Role of computerized physician order entry systems in facilitating medication errors. Journal of the American Medical Association, 293(10), 1197–1203. doi:10.1001/jama.293.10.1197 Kushniruk, A. W., & Borycki, E. M. (2006). Low-cost rapid usability engineering: Designing and customizing usable healthcare information systems. Healthcare Quarterly (Toronto, Ont.), 9(4), 98–100, 102. Kushniruk, A. W., Triola, M. M., Borycki, E. M., Stein, B., & Kannry, J. L. (2005). Technology induced error and usability: The relationship between usability problems and prescription errors when using a handheld application. International Journal of Medical Informatics, 74, 519–526. doi:10.1016/j.ijmedinf.2005.01.003

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KEY TERMS AND DEFINITIONS Clinical Simulation: A re-creation or imitation of the key characteristics of a real-world healthcare environment or setting (e.g. hospital room, emergency room, physician office) for the purpose of evaluating a group of HIS/UCD. Cognitive-Socio-Technical Fit: Cognitivesocio-technical fit refers to the cognitive aspects of fit involving health professionals’ cognition, the social aspects of fit including social interactions that take place between the health professional(s), patient and technology and the use of the technology to provide patient care. Computerized Physician Order Entry (CPOE): Computerized physician order entry is a health information system that allows for the electronic entry of physician orders for patients under a physician’s care. These orders are sent over a computer network to other health professionals so that they may be fulfilled. Ecological Validity: Ecological validity refers to the ability to create clinical simulations that are representative of real-world healthcare settings (e.g. hospitals and clinics) so that the

results of a clinical simulation are generalizable to the real-world. Electronic Medication Administration Record (eMAR): An electronic medication administration record is an electronic health information system that uses bar code reader technology, an electronic medication record system and a wireless medication cart to aid in the process of medication administration. Intended Consequences: Those real-world processes and outcomes that HIS and UCD are expected to change by the technology’s designers or the organization that implemented the HIS/UCD after the HIS/UCD has been implemented. Technology-Induced Error: A new type of medical error that arises from the introduction of a technology or from the interactions that take place between a technology, a health professional and a real-world healthcare environment. Unintended Consequences: Those realworld processes and outcomes of HIS and UCD that were not predicted and that did not change as was originally expected by the technology’s designers or the organization that implemented the HIS/UCD.

This work was previously published in Ubiquitous Health and Medical Informatics: The Ubiquity 2.0 Trend and Beyond, edited by Sabah Mohammed and Jinan Fiaidhi, pp. 552-573, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Using New Model-Based Techniques for the User Interface Design of Medical Devices and Systems A. Janß RWTH Aachen University, Aachen, Germany W. Lauer RWTH Aachen University, Aachen, Germany F. Chuembou Pekam RWTH Aachen University, Aachen, Germany K. Radermacher RWTH Aachen University, Aachen, Germany

ABSTRACT Studies concerning critical incidents with technical equipment in the medical/clinical context have found out, that in most of the cases non-ergonomic and non-reliable user interfaces provoke use deficiencies and therefore hazards for the patient and the attending physician. Based on these studies, the authors assume that adequate and powerful tools for the systematic design of error-tolerant and ergonomic Human-Machine-Interfaces for DOI: 10.4018/978-1-60960-561-2.ch221

medical devices are missing. In this context, the Chair of Medical Engineering (mediTEC) has developed the new software-based tool mAIXuse in order to overcome these difficulties and to support designers as well as risk assessors. Based on two classical formal-analytical approaches, mAIXuse provides a high-performance modelling structure with integrated temporal relations in order to visualise and analyse the detailed use process, even with complex user interfaces. The approach can be used from the very early developmental stages up to the final validation process. Results

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Using New Model-Based Techniques for the User Interface Design of Medical Devices and Systems

of a comparative study with the new mAIXuse tool and a conventional process-FMEA (Failure Mode and Effect Analysis) show, that the new approach clearly outperforms the FMEA technique.

INTRODUCTION The major objective of the rapidly evolving technological progress and automation in the field of medical devices and systems is an enhancement of the efficiency and effectiveness of diagnostic, therapeutic procedures. This is partially associated with fundamental changes of the HumanMachine-Interaction characteristics. To ensure safe and reliable user interfaces not only ergonomic but also error-tolerant interface design has to be taken into consideration by the design engineer. In this context, a new software-based tool mAIXuse for formal-analytical usability evaluation and use-oriented risk analysis of medical devices and systems has been developed at the Chair of Medical Engineering and will be presented in this chapter. The in depth analysis of human error in the clinical context of use are of major importance particularly for the introduction of new technical equipment into the medical work system. State of the art methods and tools in usability engineering and risk analysis still have some problems and bottlenecks related to a systematic a-priori as well as a-posteriori review and analysis of human induced risks in risk sensitive work processes. As the complexity and speed of development of medical devices is increasing (more than 50% of medical products typically are less than 2 years on the market) and as the incidents of human error in medicine increases, more sophisticated tools for human error risk analysis seems to be mandatory. The main focus of this chapter is on the development and evaluation of the mAIXuse approach for model-based usability evaluation and use-oriented risk analysis.

BACKGROUND The overall risk for manufacturers of risk-sensitive products (e.g. in aeronautics, nuclear engineering, medical technology and pharmaceuticals) has increased in recent years. Expensive products can only be lucrative if they are distributed to a large number of customers worldwide. This increases not only the cumulative potential damage, but also the potential consequences. Especially in the medical field of diagnostic and therapeutic systems undetected remaining failures or residual risks, which occur in the development process, often cannot be tolerated. Bringing defective and erroneous products to the market means a high potential of severe consequences for the manufacturers. Apart from ethical considerations, related costs can endanger the livelihood of enterprises. It ranges from warranty and goodwill costs to product recalls as well as liability for consequential damages. The medical branch is, similar to almost all manufacturing industries, under a high cost and time pressure, which is characterised by globalisation and dynamic markets, innovation and shorter product life cycles. The increase of the complexity of products and production processes is another factor determining the situation of the risk (Doubt, 2000). The necessity to respond to the risks, is also reflected by the fact that the introduction of systematic risk management processes including the early application of an usability engineering process in accordance with established national and international standards (EN ISO 14971, IEC 62366, MDD (Medical Device Directive), FDA (Food and Drug Administration)) are obligatory for medical device manufacturers. The risk management process can be seen as a “systematic application of management principles, procedures and practices to identify, analyse, treat and monitor understanding of risks, allowing organisations to minimise losses and maximise opportunities” (Hoffmann, 1985). However, these goals are often missed in practice (Hansis, 2002).

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Using New Model-Based Techniques for the User Interface Design of Medical Devices and Systems

There are about 40.000 malpractice complaints and the evidence of more than 12.000 malpractice events per year in Germany (Kindler, 2003). More than 2.000 cases of this so-called “medical malpractice” can be traced to medical and surgical causes, leading in the end to the death of the patient (Hansis, 2001). The expert’s opinion on the development of health services since the year 2007 is that in Germany approximately 17.000 deaths each year are due to “preventable adverse events”. In addition to false decisions and medication errors, use errors are a major cause of deaths in the medical context (Merten, 2007). Harvard Medical Practice Studies confirmed the important role of human factors in safety aspects of Human-Machine-Interaction in clinical environment. Following their statement, 39.7% of avoidable mistakes in operating rooms arise from “carelessness” (Leape, 1994). Avoidable mistakes, especially in combination with the use of technical equipment in the medical context, are, with a disproportionately high rate, due to human-induced errors (Leape, 1994). Results from the Harvard Medical Practice Study were confirmed by a number of other epidemiological studies. In the retrospective study by Gawande et al. regarding 15000 surgically treated patients in 3% of the cases user-based injuries have been found, of which 54% have been preventable errors (Gawande, Thomas Brennan and Zinner, 1999). According to incident reports in orthopaedic surgery avoidable mistakes with technical equipment are in 72% due to human error (Rau et al., 1996). Thus, human-oriented interface design plays an important role in the introduction of new technology and specific devices into medical applications. To ensure safe and reliable user interfaces not only ergonomic but also error-tolerant interface design has to be taken into consideration for the acceptance as well as for the routine application of medical devices (Radermacher, 2004). With the increasing complexity of tasks and systems, reliability can only be guaranteed by an optimized Human-Machine Interaction (HMI)

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(Giesa and Timpe, 2000). From the cognitive psychological point of view, inadequate coordination of the design of the work equipment and environment with the task and situation, the erroneous interpretation of information in user interfaces and situational factors as well as external environmental factors and extrinsic stress are the main causes of “human error” in medicine (Bogner, 1994). Statistics on malpractice incidents and the handling of medical devices seem to prove this fact (Kohn, Corrigan and Donaldson 2000). Related studies state that an inadequate design of working task and situation with respect to working equipment and environment can be regarded as a major trigger for user errors (Hyman 1994). The advances in automation abets the number and complexity of technical components in medicine in recent years (e.g. in the clinical context or in home care). In general, this provides more effective and efficient treatment options, but devices with a broad range of functions and complex user interfaces, additionally, potentially bring along use problems. This trend is reflected e.g. in the risk-sensitive work system operating room (OR) characterized by a high amount of Human-Machine- and HumanHuman-Interaction. Increasing functionality and complexity of new technical medical equipment in the OR – such as multi modal image data acquisition and processing, navigation telemanipulators and robot systems - can implicate deficiencies in the use process, bringing along high potential for hazardous human-induced failure implicating higher risk for the patient and the attending physician. Nowadays, in addition to conventional medical tasks, physicians have to cover a multitude of technical monitoring and control duties. Moreover, the boundary conditions are characterized by various potentially adverse performance shaping factors (climate, light and sterile working conditions, clothing, risk of infection and irradiation, time pressure…), which amongst others can lead to a higher physical and mental workload as well as to a deprivation of situation awareness

Using New Model-Based Techniques for the User Interface Design of Medical Devices and Systems

and therefore to potential human failure (Cook et al., 1996, Radermacher, 1999). When performing under extreme time pressure, the combination of high physical effort and multimodal information processing (e.g. interpretation of several X-ray images while perceiving acoustic alarms and tactile feedback concerning tissue manipulation) brings along an increased risk potential for the patient and the surgical team. The progressive change in the interaction characteristics between surgical staff and computer/ machine (e.g. increase of control and monitoring functions, the change in focus concerning operational area vs. imaging and navigation systems etc.) and subsequent use errors induce a significantly increasing risk potential (Radermacher et al., 2004). Last but not least, task analysis in surgery show, that task sequences to be supported by Computer Aided Surgery (CAS) techniques, represent only a relatively small fraction of time in daily clinical routine work of a surgeon. This implies on one hand that, time efficiency of novel technical procedures introduced into the common workflow, is of major importance. On the other hand, practical use of these techniques may be adversely affected by routine deficiencies as well as due to the fact, that time consuming technical training can only be realized to a very limited amount. This especially holds for technical equipment explicitly dedicated to support rare and complicated cases. Nevertheless, even in situations with high workload in the OR (e.g. in emergency situations) a reliable performance of the operation must be ensured. To guarantee patient and user safety, when applying medical technical components, manufacturers of medical devices have to fulfill specific legislative and regulatory requirements. Essential requirements according to the risk management process are in terms of ergonomic analysis and optimization of Human-Machine Interaction (HMI), thus in assuring the usability and reliability of new medical products. To assure high reliability of medical risk-sensitive systems, e.g. in the work-

ing field OR, it is necessary to conduct a usability evaluation in relation to the use context and the user. Since 2006, as part of the risk management for medical devices, the harmonization of the DIN EN 60601-1-6 standard requires a documented usability assessment of medical electrical equipment. Since 2007, the international standard IEC 62366 describes a process for the establishment and implementation of usability engineering for all medical devices. A systematic usability evaluation and risk analysis enables the development team to identify risks and hazards in products and (manufacturing and usage) processes early developmental phases and to treat them efficiently in order to avoid serious adverse events in the field and to minimise in the end harm for patients, users or third parties. It is meaningful to conduct the risk analysis (which includes the identification and the evaluation of risks) using appropriate methods and tools, which, particularly in the area of diverse and complex Human-Machine-Interaction, are inevitable for the performance of a systematic and scientifically-grounded investigation.

Tools and Methods for Risk Analysis and Usability Assessment The effective implementation of risk management requires not only a systematic approach concerning management principles but also an appropriate methods and tools for the different stages of implementation. Based on the Failure Mode and Effects Analysis (FMEA), a product or a process can be systematically investigated in the early development stages in order to initiate prevention measures (Pfeifer, 2001). There are two forms of the FMEA, the product-FMEA and the process FMEA, which analyses possible errors in the process of product manufacturing (VDA, 2006). In addition, there are special developments such as the Human FMEA analysis, which focuses on potential human errors (Algedri, 2001). The disadvantages of the method-

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Using New Model-Based Techniques for the User Interface Design of Medical Devices and Systems

ology, mentioned by many practitioners, are high effort and the complexity of the analysis, which increases significantly with a rise in the range of functionality of the analysed system (Pfeifer, 1999). A partial reduction of the effort can be achieved by using appropriate software support. With regard to the triple “error, consequence, cause”, which defines a sequential analysis, the FMEA doesn’t allow to model complex causal failure chains within the investigation. Besides the FMEA, the fault tree analysis (FTA) is one of the most commonly used methods in risk and quality management. The core of the method is the development of the fault tree, based on a previously performed system analysis. In this inductive approach, initially on the basis of potential error, all possible consequences and failure combinations are analysed. Finally the detected errors or malfunctions are rated with regard to the probability of occurrence (Pfeifer, 2001). As with the FMEA, complex relationships between failures, which seem to be time and space independent in their appearance at first sight, cannot be identified. The calculation of component-based error probabilities can be done in product-based experiments, but the difficulty to evaluate humaninduced errors in a realistic dimension to capture and quantify them in a sufficient way, however, remains unanswered. In particular, studies concerning Human-Machine-Interaction of medical experts in the specific clinical context are often only conducted during the clinical testing at the end of the product development - and therefore too late for effective risk management. Due to the complexity of cause-effect relationships, especially in Human-Machine-Interaction, these methods are always associated with compromises. Risk models - like all models - are inevitably simplistic, often isolated considerations of the system and therefore not able to detect inherent influence on the particular error or hazard. As a result of above-described problems, in particular ergonomic aspects have to be already considered in the prospective identification and

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analysis of risks. Hereby, especially the estimation of error probabilities and influencing factors as well as the availability of suitable tools and methods for effective and efficient risk assessment and the derivation of appropriate countermeasures mean serious problems. The aim of a clinical trial of medical products in the framework of the accreditation process is the gathering of empirical data, which is difficult to obtain in a controlled laboratory environment. Previous attempts to identify human-induced risk potentials in the use of medical devices can be devided in the personal and the systemic approach (Giesa, 2000). In the personal-based approach the error tendency is seen as the individual status of a special person. Troubleshooting therefore focuses on identifying the user’s action leading to the failure. Empirically, however, it has been proven that under the same conditions with different actors similar error patterns occur, therefore the hypothesis of personal accident proneness is not sustainable (Hacker, 1998). In contrast to the personal approach the system-oriented approach defines errors as consequences from a number of systemic triggers, such as an inadequate design of the workplace and work organization (Bubb, 1992). The system-oriented approach by Reason (Reason, 1990) distinguishes active failures of the actors and latent error-promoting system states. The goal in system design must therefore be the avoidance of conditions which provoke human errors. It is shown, however, that the users’ actions are designed to minimise the workload and to test out the limits of acceptable behavior (Rasmussen, Pejtersen and Goodstein, 1994). With the harmonisation of the IEC 62366 (Medical devices - Application of usability engineering to medical devices) standard, which has been coming into effect in the year 2007, the experimental and criteria-oriented usability-evaluation of medical systems are receiving increasing attention. The design engineer has to examine and determine the usability of an envisioned system already in the very early developmental phases,

Using New Model-Based Techniques for the User Interface Design of Medical Devices and Systems

often without being able to interact with a first mock-up. Experimental usability testing and heuristic analysis (conducted by usability experts) offer many advantages for manufacturers, but are still connected to method-related disadvantages. The experimental usability testing provides, in contrast to formal analytic procedures, a potentially high validation level as, in addition to tasks and systemspecific characteristics, also (partly implicit) complex environment and user-specific factors (such as stress, fatigue, awareness, temperature, climate) can be included within the laboratory and field tests by the analysis of e.g. video analysis, physiological data, observations and interviews. However, the existence of an interactive mockup (also necessary for heuristic analysis), and the presence of a representative user group are necessary for the conduction of these tests, which usually cause high time-related and infrastructural costs. Results from usability tests are often acquired too late for a proper integration in the ongoing development process without inducing high costs. Moreover, in general realistic contextspecific stress or emergency situations are in the experimental laboratory setup poorly mapped. In various stages of the developmental process a multiplicity of appropriate methods for the support of usability evaluation can be applied. Model-based approaches can be utilized in the definition and specification phase (Kraiss, 1995). Approaches for modelling user, interaction and system behavior are e.g. cognitive modelling and task analysis. Due to the lack of efficient and easyto-use modelling tools, cognitive architectures are currently mainly used for fundamental research in the area of cognitive psychology. Pertaining to the (cognitive) task analysis, there are several methodologies (e.g. hierarchical task analysis) which can be applied depending on the specific questions and cases. The application of task analysis for supporting user interface design of envisioned and redesigned interactive systems can be a fundamental contribution to enhance human-

centered development (Diaper, 2004). Regarding model-based usability examination approaches, there is often the lack of an application-oriented software tool, although the theoretical framework behind shows well-developed syntax models and structures. The majority of commercially available software tools concerning (cognitive) task analysis enables interactive data storing and provides different task modelling options. Unfortunately none of these tools offers a subsequent failure analysis, in order to analyse potential risks in the use process and to derive accurate information concerning proper design of the user interface. Furthermore, with existing software-based tools/methodologies the detailed modelling of all possible interaction tasks with a complex human-computer-interface (e.g. with reference to the task taxonomy and time dependencies) is not accomplishable. To integrate a model of the user, the system and the interaction tasks and their dependencies, a model-based usability-evaluation software tool has to comply with a lot of methodological and application-specific requirements. In reference to the future of the medical and clinical field (e.g. integration and interoperability in the OR), it is of great importance being able to model the comprehensive functional spectrum of modern (graphical) user interfaces. Furthermore, Human-Human-Interaction has to be taken into account with regards to multi-disciplinary and multi-personnel (OR) work systems. The main focus lies within the detailed modelling of cognitive information processing (e.g. OR personnel and the surgeons during the intraoperative workflow). In conjunction with this, assured information flow (e.g. verbal instructions) and coordination are indispensable as well as observing and controlling various signals from discriminative sources (e.g. bio-physical and image data in the OR) (Woods, 1995). Based on these inspections, evaluation criteria have to be predefined, in order to discover and examine contradictions in the allocation of hu-

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Using New Model-Based Techniques for the User Interface Design of Medical Devices and Systems

man cognitive resources. Finally, a model-based usability-evaluation tool should be able to model external performance shaping factors (e.g. environmental conditions occurring in the OR). To apply the standards IEC 60601-1-6 for medical electrical equipment and the IEC 62366 for all medical products in the framework of usability evaluation, methods on the basis of specific models are important tools for prospective usability evaluation. These approaches should support the analysis of the stress/strain on the patient and practitioner as well as the resulting risks. By the use of formal-analytic procedures the focus on potential ergonomic deficiencies can be set at the early developmental stage. The iterative process of usability engineering and the effort related to usability experiments can potentially be reduced. In future this may represent a significant cost and time factor for manufacturers when developing new safety-critical (medical) products -especially when qualified subjects are needed for interaction-centered user testing- and thus facilitate the accreditation process.

A NEW MODEL-BASED USABILITY ASSESSMENT TOOL Based on an initial concept, developed in the framework of the BMWi funded project INNORISK (run-time: 07.2006-12.2008), a software-based usability and risk analysis tool has been created at the Chair of Medical Engineering at the RWTH Aachen University. This method supports designers and development engineers of complex medical equipment/systems in conducting a formal-analytical usability evaluation and risk analysis, which can be applied from the specification and definition phase up to the validation phase. The methodology and the corresponding software tool mAIXuse, which are permanently developed further, are based on a twofold modelbased approach. Additionally, a systematic error analysis concerning human-induced failures has

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been integrated. The novel tool has been created on the basis of several standards for the development of medical devices (e.g. ISO 14971:2006 - Application of risk management to medical devices and IEC 62366:2007 - Application of usability engineering to medical devices) The aim of the formal-analytical approach is to provide an overview of the Human-MachineInteraction process, being helpful for the prospective evaluation of a new system/interface as well as for the redesign process of an existing system. Task models describe the necessary activities of the user, the system and the interaction steps to achieve a specific goal. Complex tasks can be hierarchically divided into sub-tasks. Based on an initial risk analysis (e.g. according to DIN EN ISO 14971 for medical devices) use-oriented process, sequences and their potential impact on the overall process are initially evaluated. The proposed formal-analytic mAIXuse procedure is based on a twofold strategy (Figure 1).

High-Level Task Analysis The first part of our procedure is a methodological extension of the ConcurTaskTree approach (Paternò, Mancini and Meniconi 1997), which provides a graphical syntax for the specification of task models, taking into account defined task categories and temporal relations. Within this initial task analysis, the usability investigator gains a systematic overview of the high-level operations of the user, the system and the interactions which are required to achieve a specific task objective within the task fulfilment. As part of the graphical notation, it is possible to describe different task categories and types as well as various attributes and objects. Within the mAIXuse modelling, there are five task categories. Human-systeminteraction tasks are subdivided into the classical information processing sequence “Perception”, “Cognition” and “Action” in combination with an analysis of human-human-interactions (“HumanHuman-Interaction“). The before-mentioned task

Using New Model-Based Techniques for the User Interface Design of Medical Devices and Systems

Figure 1. mAIXuse method for model-based usability assessment and prospective risk analysis

categories describe operations in which human errors can potentially occur. System tasks (“System”) however, are carried out independently by the system/technical product. The category “Abstraction” could include all the previously listed types of tasks and provides a higher-ranking task type (mathematically a parenthesis). Unlike traditional task analysis (Diaper, 2004), within the mAIXuse modelling not only top-down dependencies are presented, but with the help of temporal relations (e.g. sequence, concurrency, disabling, sharing, choice, etc.) coequal tasks are linked with temporal interconnections. The descriptions refer to the form “Task A [operator] Task B”. The operators are used to specify the relationships and dependencies between tasks. Examples for temporal relations and their corresponding symbols are shown in Table 1. The relationship between tasks and their subtasks are modelled in a net-like structure. The number of subtasks per task is arbitrary. Each sub-task is assigned to a higher-level task, but

may also have subtasks. Using the above-mentioned modelling syntax, process states arise, which define the task execution order of subtasks by various temporal operators. If the interaction process information has been entirely modelled, it is necessary to determine whether a “parent” task or a “child” subtask can be affected by potential errors and which consequences could result. It must be examined within each sub-task, how a deviation from the planned course of action may cause an error-prone Table 1. Excerpt of temporal relations and corresponding coding symbols Concurrency with information exchange

T1 |[ ]| T2

Sequence with information exchange

T1 []>> T2

Choice

[T]

Return Iteration

T1↖ T2

Finite Iteration

Ti

T*

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Using New Model-Based Techniques for the User Interface Design of Medical Devices and Systems

situation. To identify human-induced errors the task categories “Perception”, “Cognition” and “Human-Human-Interaction” have to be analysed. The task category “System”, if necessary, may be analysed in a product-related risk analysis (e.g. FMEA, FTA), in order to identify and assess potential system-inherent technical errors or failures in the manufacturing process. For the usability evaluation, however, we will assume that the system/device works according to its technical requirements. To detect potential errors early in the user interface design process a table of potential human errors (classification according to its external appearance) is given. A classification of defects due to the external appearance is defined as an error that occurs when directly interacting with the system (in the category “Action”). The occurring errors are classified into omission, execution and additional failures, whereas execution failures are further subdivided into selection, time, qualitative, sequential and mix-up failures. To identify potential human-induced errors in Human-Machine-Interaction and Human-Human Interaction, first the motor actions in the task category “Action” are filtered and analysed regarding errors between different tasks (inter-task error) or within the respective tasks (intra-task error). Furthermore, potential causes of the abovementioned failures are defined for the cognitive information processing and the perception process (information acquisition). A possible cause of error e.g. lies within the cognitive information processing. For this purpose, a failure checklist has been derived on the basis of the cognitive modelling system called the Generic Error Modelling System. The Generic Error Modeling System (GEMS) is a framework for understanding error cause types and designing error prevention strategies. This model is based on the cognitive psychological approach by Reason (Reason, 1987). Here, the cognitive information process of human beings is divided into three levels of regulation (skill-, rule- and knowledge-based). The skill-based performance

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mode is characterized by routine actions in familiar settings. The rule-based performance mode is characterized by pre-defined actions performed because of the recognition of a similar situation. The rule-based performance mode requires an individual to use analytical skills and judgment to complete a task in an unfamiliar situation. Each cognitive processing level is analysed for potential failures in the specific task step of the use process. Potential error causes in a cognitive regulation level can then semi-automatically be integrated into an FMEA form sheet and finally evaluated. Additionally, on the basis of relevant standards (e.g. 9241-12) a checklist for the analysis of potential error causes in the acquisition and perception of information has been developed. Due to the provision of this checklist potential sources of errors and guidelines for the interface design can be derived. The procedure of analysing potential errors and failure causes in the information processing of the human being are shown in Figure 2, additionally exemplary modelling with the software tool is presented here. An exemplary excerpt of the comprehensive modelling and analysis of the use process with a surgical planning and navigation system, developed at the Chair of Medical Engineering, is shown in Figure 3. Figure 4 shows an excerpt of the model of the use process with two dialogues of the planning and navigation system for hip surface replacement. Here the different task categories and the temporal relations between various tasks are depicted. Next to the different process steps potential humaninduced failures are integrated and listed.

Low-Level Task Analysis In the second part of the mAIXuse analysing process, a cognitive task model based on the initial high-level task analysis is created. Here, parallel tasks concerning cognitive, perceptual and motor low-level operators are decomposed.

Using New Model-Based Techniques for the User Interface Design of Medical Devices and Systems

Figure 2. Systematic failure analysis on the basis of derived checklists concerning the information processing and visualization of modelling structure

Figure 3. Excerpt of the mAIXuse modelling and analysis using the example of the use process with two GUI dialogues of a planning and navigation system for hip surface replacement

Breaking down the previously detected high-level decompositions into low-level operators eases the acquisition of cognitive processing steps involved. However, the success of this step is depending on the accuracy of the previous high-level task modelling.

Following this approach, a methodological extension of CPM-GOMS (Cognitive Perceptual Motor – Goals Operators Methods Selection Rules) (John and Gray, 1995), a cognitive task analysis, based on the Model Human Processor (MHP) (Card, Moran and Newell, 1983), can be

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Using New Model-Based Techniques for the User Interface Design of Medical Devices and Systems

Figure 4. Excerpt of the modelling of the use process with two dialogues of the planning and navigation system for hip surface replacement

conducted subsequently to the high-level task analysis. The MHP architecture is developed on the basis of a computer-oriented architecture and integrates various categories of repository (visual and auditory cache, working and long-term storage). Furthermore, the average processor cycle times are specified by several rules (e.g. Hick’s law, power law of practice, Fitt’s law etc.). Here, concurrent-working processors are able to model low-level operators (perceptual, cognitive and motor-driven) in a parallel mode, in order to generate an exact and detailed overview of the information processing tasks of the human operators. Especially the parallelism between the processors/operators can be helpful in order to model and analyse multimodal Human-MachineInteractions, typically occurring in the context of clinical work systems. Dependencies between the users’ perceptual, cognitive and motor-driven activities are mapped out in a schedule chart (PERT - Program Evaluation Review Technique), where the critical path represents the minimum required execution time. John and Gray have provided templates for cognitive, motor-driven and perceptual activities alongside their dependencies under various conditions (John and Gray, 1995). In project Ernestine

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CPM-GOMS has been validated to real-world task performance by assessing Human-MachineInterfaces of telephone companies’ workstations (Gray, 1992). Additionally, within this cognitive task analysis approach different cognitive task steps are matched with appropriate cognitive processing levels. In the course of these analyses potential contradictions and conflicts in concurrent cognitive resource utilisation can be detected and several risks and hazards in Human-Machine-Interaction can be identified and assessed. Supplementary, the second part of the methodology developed includes, in contrast to original CPM-GOMS, the distinction of skill-, rule- and knowledge-based behavior according to Rasmussen (Rasmussen, 1983) in the PERTChart. Moreover, external performance shaping factors are currently being integrated, in order to provide supplementary qualitative evaluation criteria specifically taking into account the clinical context of use. Although, time performance predictions can only be made for primitive skill-based operators without special contextual PSF’s, there is a great benefit concerning the detailed overview. In addition, a process bar is integrated into the PERT Chart in order to transcribe human and system

Using New Model-Based Techniques for the User Interface Design of Medical Devices and Systems

activities (obtained in the high level analysis) to facilitate decomposition of high-level tasks and to create additional help for the user. The inclusion of these new features into the low level task modelling provides more detailed qualitative evaluation criteria and an extensive overview of the complete interaction. In combination with existing quantitative statements on learning and operating time, as well as on failure rates and efficiency, the inclusions mean a powerful extension of CPM-GOMS. In addition, a failure analysis on the basis of above-mentioned failure taxonomies will also be possible after the cognitive task analysis.

Experiences with the New Approach The mAIXuse method and the corresponding software tool have been evaluated in several workshops with industrial partners. Systems investigated in these workshops have been e.g. a shockwave therapy system, planning and navigation systems for orthopaedic- and neurosurgery, a robotic system for orthopaedic surgery, a pacemaker and the corresponding pacemaker system analyser in the context of cardio-electrotherapy systems as well as a semiautomatic safety trepanation system for neurosurgery. The workshops (participants have been employees from different departments: risk management, quality management, design, verification, validation, marketing, etc.) have initially been paper-based. Further details have been reported in (Schmidt et al., 2009). Meanwhile, a first software version has been implemented and the partners used this software tool during the workshops. As a result of the very positive experience with the mAIXuse methodology and the corresponding software tool several partners continued to use the mAIXuse tool for the evaluation and design of human-machineinterfaces. Furthermore, the mAIXuse method and tool have been investigated in comparison to classic risk analysis methods concerning efficiency, ef-

fectiveness and usability aspects in the framework of the risk analysis with an existing planning and navigation system for hip resurfacing in orthopaedic surgery. This tool is currently being developed at the Chair of Medical Engineering. The results of the mAIXuse application in a two days workshop with different test groups have been compared to the results with a conventional risk analysis method, the Process-FMEA (Failure Mode and Effect Analysis). The hypothesis was that with the new method more and risk-sensitive failures should be found within a risk investigation in the same time in comparison to the conventional method. Two test groups, each including three test subjects (age between 24-30), had to apply the Process-FMEA and the mAIXuse tool on the same system/interface in varying order, so as to exclude dependencies and correlations between working time and results. Application time for each analysis has been set up to 2½ hours. Two consecutive dialogues of the computer-assisted planning of the implant-based resurfacing of an affected femoral head had been investigated regarding potential use errors. In the assessed computer-assisted orthopaedic intervention, a high precision planning and execution is essential in order to achieve a sustained therapeutic outcome. The corresponding planning and navigation system is currently being developed at the Chair of Medical Engineering. The dialogues “Positioning of the safety zone for the femoral neck” and “Positioning of the implant”, which are conducted interactively using a graphical user interface, have been modelled and analysed concerning (especially risk-sensitive) potential human failures. Finally the subjects had to answer a questionnaire in order to evaluate both methods relatively and absolutely concerning user-satisfaction, understandability and learnability. Objective and subjective test data are shown in Table 2. Results show the advantages of the mAIXuse method in contrast to the conventional Process-

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Using New Model-Based Techniques for the User Interface Design of Medical Devices and Systems

Table 2. Evaluation results of the comparison between the mAIXuse and the Process-FMEA application Group A

Group B

Criteria

FMEA

mAIXuse

mAIXuse

FMEA

Detected risks

14

27

29

16

Critical risks

5

9

12

7

Application time

2½ hours

2½ hours

2½ hours

2½ hours

Evaluation (1 = very good, 2 = good, 3 = satisfactory, 4 = adequate, 5 = poor) User satisfaction

3-4

2

1-2

4

Understandability

2-3

1-2

2

3

Intuitiveness

3-4

2

1-2

4

Learnability

3

2

1-2

3

FMEA. The new modelling and evaluation tool shows better results regarding all subjective and objective criteria. Usability aspects as user satisfaction, understandability, intuitiveness and learnability have been rated much better for the mAIXuse approach then for the FMEA. Concerning detected (critical) risks mAIXuse clearly outperforms the FMEA. Surprisingly, when the subjects had worked in the first session with the mAIXuse tool, afterwards they were not even able to find an equal number of risks respectively critical risks with the process FMEA, although the subjects then in summary worked twice as much time with the planning and navigation system. Analysis of the questionnaires and the discussion with the subjects revealed, that the predefined lowest level of the modelling technique and the use of temporal relations in the mAIXuse approach are responsible for its detailed modelling analysis and therefore for its higher effectiveness and efficiency. The results of the questionnaire and the objective test data show that mAIXuse supports the user and eases the application process. Objective results are corroborated by the results of the questionnaire. Even the cost benefit evaluation has been rated as “good”, because almost two times more potential system-inherent failures have been

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detected with the mAIXuse method than with the Process-FMEA. Additionally, the application of the software-assisted tool doesn’t show a reduction of the communication or the collaboration of the test subjects. This estimated negative effect has not been observed during the evaluation. On the contrary, the software tool supports the efficient and effective analysis and the comprehensive overview of the modelling. The described new method for prospective usability-evaluation and the corresponding software tool have proven their applicability and the practicability in various investigations. After a short introduction concerning the application of the methodology all test subjects have been able to successfully use the mAIXuse method and the corresponding software tool on their own. A comparison of the modelling results of the interaction process concerning the same user interface with different test groups shows nearly identical outcome. This leads to the hypothesis that, because of the predefined lowest level of modelling and the integration of temporal relations, a standardised modelling structure has been developed. The way of coding and presenting is intended to support designers and engineers with the internal communication and to ease the understanding of the specific Human-Machine-In-

Using New Model-Based Techniques for the User Interface Design of Medical Devices and Systems

teraction. Furthermore the mAIXuse tool supports medical device developers and manufacturers in conducting a model-based usability-evaluation and a usage-oriented risk analysis on their own. The standardised documentation required for the risk management and the usability-engineering process, is automatically generated in a FMEA form-sheet. When using the mAIXuse technique for modelling a prototypical interface an interesting and productive side-effect has been observed. In order to model the current state of the use process regarding the prototypal system with the mAIXuse tool, the test subjects have shown explorative test behaviour. The combination and integration of explorative user tests in the framework of the mAIXuse modelling process show a promising approach for an enhanced usability evaluation method, extending the conventional Cognitive Walkthrough approach.

FUTURE RESEARCH Effective and efficient usability engineering and risk management will have a growing impact on the success of medical device manufacturers in the future. The increasing complexity and functionality of medical devices together with the need for interoperability and integration of modern work systems (e.g. in modern operating theaters) bring along additional system-inherent risks and potential use problems. Thus, cognitive engineering is becoming more and more a part of medical equipment design, enabling the improvement of patient safety measures. A systematic consideration of cognitive information processing in Human-Machine-Interaction regarding medical work systems, particularly within the scope of multifunctional applications, is a special requirement for usability assessment in the future. Particularly, ergonomic quality and reliability will become important factors for reducing safety-critical risks in the medical field.

A prospective systematic and methodological acquisition of all task steps/types and subsequently of potential human errors in a formal-analytical modelling and simulation can be expedient and useful for medical device manufacturers, as it allows a more comprehensive and earlier overview on potential risks and bottlenecks than user-interactive tests with the final medical device. An early usability evaluation can be a significant advantage with regard to saving time and resources. Therefore, easy-to-handle methods and all-purpose tools are needed in order to fulfill the requirements of normative and legislative regulations in usability engineering and risk management. In general, user-centered design and cognitive engineering will become more and more important especially in the medical field. Particularly in this area, there will be an increased emphasis on research, trying to understand and predict the specific contextual requirements and needs of medical users of (risk-sensitive) systems. Early cognitive modelling and simulation of an envisioned system or of a prototypical interface will become more important in order to minimize additional time and financial costs related to summative usability tests. Emerging nations are developing quickly also on the medical technology market and there will be a rapid evolution in business. In this context usability and safety will become selling points of increasing importance.

CONCLUSION The usability engineering process as a basis for developing usable and error-tolerant user interfaces should start already during the definition and specification phases and continue through every phase of the product life cycle. User-centered usability tests (e.g. in special usability labs) are a mandatory element of the approval process for medical products. There is no doubt that a lot

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of potential hazards and derived solutions have been identified through the use of such labs and that these tests are an important part of usability improvement. However, the costs of running such tests sometimes significantly increase the overall cost of the system/device development. One possible solution to reduce these costs could be the early and consistent application of software-based modelling and simulation tools. With a-priori data, the usability assessment and reporting can be improved and expedited. Due to the lack of efficient and easy-to-use modelling tools, cognitive architectures have been mainly used for fundamental research in the area of cognitive psychology. Future research in human-centred design will claim the development of easy-to-apply cognitive architectures, in order to model and simulate the interaction process in detail. The motivation for cognitive engineering and for developing new prospective usability-evaluation tools is to provide adequate models of human performance for designing and assessing user interfaces already in the very early developmental stage. In this framework the developed software tool mAIXuse provides useful information (e.g. overview of tasks and relations, potential use errors and design rules) in the specification, definition and validation phase. Taking into account the mental workload e.g. in clinical interventions, the mAIXuse approach is intended to be a useful approach for modelling cognitive information processing in Human-Machine-Interaction, especially in complex risk-sensitive medical work systems. Nevertheless, interface and technical designers shall be supported methodologically with the help of a reliable as well as easy-to-use analysis tool, in order to systematically identify potential hazards related to Human-Machine-Interaction. Particularly, manufacturers of medical devices and especially small and medium-sized enterprises (SME), which represent the majority of enterprises in the branch of medical device industry could benefit from such a standardised coding and modelling approach concerning the design

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process of medical device interfaces. Thus, SME shall be supported within the application of the usability-engineering and risk management process as crucial elements of the device approval.

REFERENCES Algedri, J., & Friehling, E. (2001). Human-FMEA - Menschliche Handlungsfehler erkennen und vermeiden. Carl Hanser Verlag München Wien. Bogner, M. S. (1994). Human Error in Medicine. Mahwah, NJ: Lawrence Erlbaum Associates. Bubb, H. (1992). Menschliche Zuverlässigkeit. Landsberg/Lech: ecomed. Card, S. K., Moran, T. P., & Newell, A. (1983). The psychology of Human-Computer-Interaction. Hillsdale, NJ: Lawrence Erlbaum Associates. Cook, R. I., & Woods, D. D. (1996). Adapting to the new technology in the operating room. Human Factors, 38, 593–613. doi:10.1518/001872096778827224 Diaper, D., & Stanton, N. (2004). The Handbook of Task Analysis for Human-Computer Interaction. London: Lawrence Erlbaum Associates. Gawande, A. A., Thomas, E. J., Zinner, M. J., & Brennan, T. A. (1999). The incidence and nature of surgical adverse events in Colorado and Utah in 1992. Surgery., 126(1), 66–75. doi:10.1067/ msy.1999.98664 Giesa, H. G., & Timpe, K. P. (2000). Technisches Versagen und menschliche Zuverlässigkeit: Bewertung der Zuverlässigkeit in Mensch-MaschineSystemen. In Timpe, K.-P., Jürgensohn, T., & Kolrep, H. (Eds.), Mensch-Maschine-Systemtechnik. New York: Symposion Publishing. Gray, W. D., John, B. E., & Atwood, M. E. (1992). The precis of Project Ernestine or an overview of a validation of GOMS. Proceedings of CHI,pp.307-312. New York: ACM.

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Hacker, W. (1998). Allgemeine Arbeitspsychologie. Bern: Huber. Hansis, M. L. (2002). Medizinische Behandlungsfehler in Deutschland. Schleswig-Holsteinisches Ärzteblatt, 06(02), 51–59. Hansis, M. L., Hart, D., Hansis, D. E., & BeckerSchwarze, K. (2001). Gesundheitsberichterstattung des Bundes. Heft 04/01: Medizinische Behandlungsfehler. Robert-Koch-Institut. Hoffmann, K. (1985). Risk Management – Neue Wege der betrieblichen Risikopolitik. Karlsruhe: Verlag Versicherungswirtschaft. Hyman, W. A. (1994). Errors in the Use of Medical Equipment. In Bogner, M. S. (Ed.), Human Error in Medicine. Hillsdale, NJ: Lawrence Erlbaum. John, B. E., & Gray, W. D. (1995). CPM-GOMS - An Analysis Method for Tasks with Parallel Activities. In: Conference companion on Human factors in computing systems, pp. 393-394. New York: ACM Press. Kindler, M. (2003). Risikomanagement von medizintechnischen Geräten bei Entwicklung, Zulassung und Anwendung. Weiterentwicklung der Arbeitsprozesse in der Krankenhaustechnik. Strategien, Administration, Kooperation. Kongress TK. Kohn, L. T., Corrigan, J., & Donaldson, M. S. (2000). To Err Is Human: Building a Safer Health System. Washington, DC: National Academy Press. Kraiss, K.-F. (1995). Modellierung von MenschMaschine Systemen. In Willumeit, H.-P. und H. Kolrep (Eds.). ZMMS-Spektrum, Band 1, Verlässlichkeit von Mensch-Maschine-Systemen. S. 15–35. Pro Universitate Verlag. Leape, L. L. (1994). The Preventability of Medical Injury. In Bogner, M. S. (Ed.), Human Error in Medicine. Hillsdale, NJ: Erlbaum Publ.

Merten, M. (2007). Risikomanagement: Den Ursachen auf der Spur. Deutsches Ärzteblatt. Deutscher Ärzte-Verlag GmbH. Paternò, F., Mancini, C., & Meniconi, S. (1997). ConcurTaskTrees: A Diagrammatic Notation for Specifying Task Models. Proc. of IFIP Int. Conf. on Human-Computer Interaction Interact ‘97, pp. 362–369. London: Chapman & Hall Pfeifer, T. (2001). Qualitätsmanagement: Strategien, Methoden, Techniken. München, Germany: Carl Hanser Verlag. Radermacher, K. (1999). Computerunterstützte Operationsplanung und -ausführung mittels individueller Bearbeitungsschablonen in der Orthopädie. In Rau, G. (Ed.), Berichte aus der Biomedizinischen Technik. Aachen: Shaker. Radermacher, K., Zimolong, A., Stockheim, M., & Rau, G. (2004). Analysing reliability of surgical planning and navigation systems. In Lemke, H.U., & Vannier, M.W. (Eds.), International Congress Series 1268, 824-829. Rasmussen, J. (1994). Skills, Rules, Knowledge: Signals, Signs, Symbols and other Distinctions in Human Performance Models. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3, 257–267. Rasmussen, J., Pejtersen, A. M., & Goodstein, L. P. (1994). Cognitive Systems engineering. New York: Wiley-Interscience Publications. Rau, G., Radermacher, K., Thull, B., & v. Pichler, C. (1996). Aspects of Ergonomic System Design Applied to Medical Worksystems. In Taylor, R. H. (Ed.), Computer-integrated surgery: technology and clinical applications (pp. 230–221). Cambridge, MA: MIT Press. Reason, J. (1990). Human Error. Cambridge, UK: Cambridge University Press.

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Reason, J. T. (1987). Generic error-modelling system (GEMS). A cognitive framework for locating human error forms. In Rasmussen, J., Duncan, K., & Leplat, J. (Eds.), New technology and human error. London: Wiley.

Hollnagel, E. (1991). The phenotype of erroneous actions: Implications for HCI design. In Weir, G. W. R., & Alty, J. L. (Eds.), Human-computer interaction and complex systems. New York: Academic Press.

Schmitt, R. H., Rauchenberger, J., Radermacher, K., Lauer, W., & Janß, A. (2009). Neue Wege für das Risikomanagement bei der Entwicklung risikosensitiver Produkte am Beispiel der Medizintechnik. In FQS (Eds.) FQS-DGQ Band 88-04, Beuth Verlag.

Hollnagel, E. (1993). Human reliability analysis: Context and control. New York: Academic Press.

VDA. (2006). Sicherung der Qualität vor Serieneinsatz - Produkt- und Prozess-FMEA. Henrich Druck und Medien. Woods, D. D., Cook, R. I., & Billings, C. E. (1995). The impact of technology on physician cognition and performance. Journal of Clinical Monitoring, (11): 5–8. doi:10.1007/BF01627412 Zimolong, A., Radermacher, K., Zimolong, B., & Rau, G. (2001). Clinical Usability Engineering for Computer Assisted Surgery. In Stephanides, C. (Eds.), Universal Access in HCI – Towards an Information Society for All, pp. 878-882. Mahwah, NJ:Lawrence Erlbaum Ass. Publ., Zweifel, P., & Eisen, R. (2000). Versicherungsökonomie. Berlin:Springer.

ADDITIONAL READING

Hollnagel, E. (1998). Cognitive reliability and error analysis method: CREAM. New York: Elsevier. Holzinger, A. (2005). Usability Engineering for Software Developers. Communications of the ACM, 48(1), 71–74. doi:10.1145/1039539.1039541 Kirwan, B. (1994). A Guide to Practical Human Reliability Assessment. New York: Taylor & Francis. Kirwan, B., & Ainsworth, L. (Eds.). (1992). A guide to task analysis. New York: Taylor & Francis. Nielsen, J. (1993). Usability Engineering. Boston: Academic Press. Norman, D. (1988). The psychology of everyday things. New York: Basic Books. Paternò, F. (2000). Model-based design and evaluation of interactive applications. New York: Springer. Rasmussen, J. (1986). Information processing and human-machine interaction: An approach to cognitive engineering. New York: Wiley.

Davies, J. B., Ross, A., Wallace, B., & Wright, L. (2003). Safety Management: a Qualitative Systems Approach. New York: Taylor and Francis. doi:10.4324/9780203403228

Reason, J. (1990). Human error. Cambridge, UK: Cambridge University Press.

Gaba, D. M., Fish, K. J., & Howard, S. K. (1998). Zwischenfälle in der Anästhesie – Prävention und. Management. München, Germany: Elsevier.

Vicente, K. J. (1999). Cognitive Work Analysis: Toward safe, productive, and healthy computer based work. Mahwah, NJ: Erlbaum.

Hacker, W. (1995). Arbeitstätigkeitsanalyse Analyse und Bewertung psychischer Arbeitsanforderungen. Heidelberg: Roland Asanger Verlag.

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Williams, J. C. (1985) HEART – A proposed method for achieving high reliability in process operation by means of human factors engineering technology. Proceedings of a Symposium on the Achievement of Reliability in Operating Plant, Safety and Reliability Society. NEC, Birmingham. Woods, D. D. (1990). Modeling and predicting human error. In Elkind, J., Card, S., Hochberg, J., & Huey, B. (Eds.), Human performance models for computer-aided engineering (pp. 248–274). New York: Academic Press. Woods, D. D., Johannesen, L., Cook, R., & Sarter, N. (1994). Behind human error: Cognitive systems, computers, and hindsight. CSERIAC SOAR Report 94-01. Crew Systems Ergonomics Information Analysis Center, Wright-Patterson Air Force Base, Ohio.

KEY TERMS AND DEFINITIONS Cognitive Engineering: Cognitive engineering is a multidisciplinary field concerned with the analysis, design, and evaluation of HumanMachine-Interfaces. It combines knowledge and

experience from cognitive science, human factors, human-computer interaction design and systems engineering. Cognitive Modelling: Cognitive modelling is an area of computer science which deals with simulating human problem solving and mental task processes in a computerized model. Such a model can be used to simulate or predict human behavior or performance on tasks. Cognitive models can be used in human factors engineering and user interface design. Human-Machine-Interaction: An interdisciplinary field which focuses on the interactions between users and systems, including the user interface and the inherent processes. Use Error: The use error occurs due to latent error-promoting system states. Use-Oriented Risk Analysis: A risk analysis, which supports designers and assessors to investigate use deficiencies with Human-Machine-Interfaces, in contrast to system and (manufacturing) process risk analysis. User-Centered Design: User-centered design involves simplifying the structure of tasks, making things visible, getting the mapping right, exploiting the powers of constraint, and designing for error.

This work was previously published in Human-Centered Design of E-Health Technologies: Concepts, Methods and Applications, edited by Martina Ziefle and Carsten Röcker, pp. 234-251, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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The European Perspective of E-Health and a Framework for its Economic Evaluation Paola Di Giacomo University of Udine, Italy

ABSTRACT E-health is a priority of the European i2010 initiative, which aims to provide safe and interoperable information systems for patients and health professionals throughout Europe. Moreover, the use of electronic storing and transmission of data to patients is increasing while through the deployment of e-health applications, health care is improved in terms of waiting time for patients. The concentration results from the cumulative DOI: 10.4018/978-1-60960-561-2.ch222

incidence of chronic-degenerative pathologies, the greater utilization of biomedical technologies, and the increased health services demand. Finally, the interest towards electromechanical systems means the realization of tools of small dimensions, which have tremendous advantages thanks to their invasivity and greater diagnostictherapeutic effectiveness. Therefore, an economic analysis has to take into consideration the use of biomedical technology, the analysis of alternatives, the selection of the economic evaluation technique, and the identification and quantification of the costs and benefits.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

The European Perspective of E-Health and a Framework for its Economic Evaluation

INTRODUCTION Innovation is essential to improve accessibility, effectiveness and efficiency of healthcare delivery. E-health promises these improvements, provided we comply with fundamental requirements with respect to quality and safety. E-health must be implemented thoughtfully to provide the maximum advantage of the innovation. However, there exists no structured framework of the basic requirements of quality and safety issues. This often hampers development, implementation and usage. Therefore, a framework of quality and safety requirements must evolve to support and encourage innovation. The analysis of an innovation, which contributes to quality, safety and efficiency, leads to an evaluation process, which should be followed to assess an e-heath application. The issue of minimum requirements is a political one while the requirements themselves are the subject of research. Furthermore, one problem in addressing this issue is the mere fact that e-health is under development and that it can assume various forms and sizes. It is, therefore, hardly likely to formulate all requirements in advance. For each service offered to the market, it is anticipated that different aspects will have to be assessed while a rigorous program of requirements will make innovation difficult. It is worthwhile mentioning that e-health is a priority of the European i2010 initiative, which aims to provide safe and interoperable information systems for patients and health professionals throughout Europe. Based on a pan-European survey on electronic services in healthcare, 87% of general practitioners use a computer and 48% have a broadband connection. Moreover, the use of electronic storing and transmission of data to patients is increasing while through the deployment of e-health applications, health care is improved in terms of waiting time for patients. There is, however, considerable space for improvement since ICT assists many aspects of the doctor-patient

relationship. This includes remote monitoring services (used in Sweden, the Netherlands and Iceland), electronic prescriptions (used by only 6% of EU general practitioners, and only three Member States, which include Denmark, Netherlands and Sweden), and medical care, practiced by only 1% of general practitioners, with the highest percentage in the Netherlands (5%). The survey also shows that e-health services are used where the broadband Internet use is widespread. In Denmark, for example, where the penetration of broadband Internet is the highest in Europe, 60% of doctors are currently exchanging emails with patients. On the contrary, the EU average is stuck at 4%. Overall, despite considerable progress, there are still notable differences between different countries. The main barriers to adoption of new technologies in health care include the lack of training and technical support. To encourage the routine use of ICT in health care and accelerate the adoption of appropriate strategies at the country level, the Commission adopted an action plan for e-health, under the title ‘Lead Market Europe’ (Commission of the European Communities, 2007). The analysis aimed at the cost-benefit of the patient treatment, an essential part of the quality evaluation, is widely documented in healthcare literature. The focus results from the cumulative incidence of chronic-degenerative pathologies, the greater utilization of biomedical technologies, and the increased health services demand. The method is based on the formulation of testable criteria and standard values with respect to the particular parts of the service. Therefore, it can determine the criteria, which are not met, leading to a provisional acceptance of the e-health application. Methods of comparative analysis, like the cost-benefit approach have to be adapted to the various aspects of the healthcare process, in order to determine the advantage of a traditional model of care with respect to an integrated one, which is based on new computer science technologies. Among the new methods of book keeping that

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operate in this direction, the most widely used is the Activity Based Costing (ABC). ABC is a method of imputation of the costs incurred by the activities of the cost centers. Therefore, every reengineering decision can be isolated, and estimated in relation to the cost that it incurs. In this chapter, we will provide an overview of the European strategy in e-health, the research programs and initiatives of the European Union, as well as a framework for the economic evaluation of e-health applications (Drummond et al., 2005; Scrivens, 1997; Hughes & Humphrey, 1990).

BACKGROUND Commissioner for Health and Consumer Protection Markos Kyprianou said: ‘e-health can improve health care. Even more important, it is possible to reduce medical errors and save lives. We need a partnership between the ministers of health, technology providers, patient associations and NGOs to realize the full potential of e-health in Europe’. ‘The EU-US e-health is important because there are large economic areas with the same characteristics, such as the aging population. We need to coordinate the development of standards and interoperability in this area,’ said the General Director of the Commission for Information Society, Fabio Colasanti. He also clarified that the technology itself is not enough. It needs to be accompanied by an appropriate legal environment and education of health professionals, said Frans de Bruin of the EU Directorate General for Information Society: ‘The e-health should no longer be a subject of specific conferences, but simply the normal way of doing health care,’ said Petra Wilson, Director for the public health sector and Cisco Internet Business Solutions Group. ‘We need to give incentives to doctors to encourage them to adopt and use technology.’ Peter Langkafel of SAP agrees that the incentive of physicians is

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important, stressing that it is not enough: ‘You have to demonstrate value and a business case for e-health - a better quality of care and patient safety, as well as improved use of resources in health care (human resources, processes). The business case must be presented with the costbenefit analysis’. ‘Why should not patients have the same type of service in health care such as in the banking sector? (...) Patients should contact their health care providers and start putting pressure on them claiming greater use of technology’, said Baldur Johnsen, Hewlett Packard Director of Development of health services in the e-health market. . ‘The one devoted to health is the segment with the highest growth rate in our society,’ according to Charles Scatchard, Vice-president of health sciences at Oracle, ‘... and we also know that all new health technologies need a period of 17 years to finally be usable’. ‘The e-health helps to overcome the obstacles related to the distance of the organization and the delivery of health services and is, therefore, an important tool for both rural and urban areas. The development of these technologies contributes to the development of a global economic region, attracting firms specializing in this area and the creation of new employment opportunities, ‘said Agneta Granström, Advisor of Norrbotten. ‘We must begin with the strengthening of cooperation between hospitals and between other sectors of health care across Europe. We do not need a single system, the same for everyone, but interoperable systems. Both universities and companies should fully appreciate the enormous potential of e-health and provide the necessary tools along with the regions that should work for the development of e-health’, he added. The Group of Pharmacists of the European Union (PGEU) calls the national and European authorities to help pharmacists to understand the advantages and opportunities of the greater use of data from the Internet and the development of e-health applications. PGEU promotes the development of European standards and the certification of P2P

The European Perspective of E-Health and a Framework for its Economic Evaluation

applications, including the necessary standards for the electronic transmission of prescriptions. The standards should help all stakeholders to expand existing services and draw medical attention to the security of the Internet applications. Finally, in 2007, the European Data Protection Statement approved a working document that discusses the legal requirements and application parameters for the structure and management of a national system of electronic health records. The document is open for public consultation to solicit contributions and comments.

THE EUROPEAN STRATEGY IN E-HEALTH ‘E-health’ implies e-health applications of Information and Communication Technologies (ICT) for health, which encompass hospital doctors, patients and social security data processing. The goal is to improve the quality, access and efficiency of health services for all. In the EU, e-health plays a key role in the strategy for action on innovation, which was launched in 1999. A series of action plans followed in 2002 and in 2005(eEurope 2005: An information society for all). The Action Plan i2010 - A European Information Society for growth and employment, was followed by the Communication ‘Preparing Europe’s digital future i2010: Mid-Term Review’ in April 2008. Regional networks of health, electronic health records, primary care and deployment of electronic health cards have contributed to the rise of ehealth, which has the potential to become the third largest industry, after the pharmaceutical industry and the medical imaging. In particular, for the period 2006-2009, Member States and the Commission promoted interoperability and encouraged greater standardization. They then set the target to establish the basis of European E-health services by the end of 2009.

The key stages included: December 2005: E-health stakeholder’s group meets for the first time. The group is an organization that brings together the leading European actors involved in the standardization (such as CEN - European Committee for Standardization, CEN / ISSS - Information Society Standardization System, CENELEC - Comité Européen de Normalization Electrotechnique, ETSI - European Telecommunications Standards Institute), industry associations, and user groups. June 2006: the Assembly of European Regions (AER) is organizing an international conference and presents a session on the e-health (Thematic Dossier of the Assembly of European Regions). June 2006: The Commission for Health Information Technology adopted a new strategy (ICT for Health and 2010, ‘Transforming the European healthcare landscape: Towards a strategy for ICT for Health’) to promote the transformation of the European health systems. The strategy argues that in order to meet the challenges of aging, Europe needs a new model of health care, which should be based on prevention and people-centeredness. The approach involves the E-health Action Plan and includes research under the Seventh Framework Program for Science and Research. The strategy is also in line with the communication of the new framework of i2010 (i2010 Annual Information Society Report 2007), which promotes the application of ICT to improve social inclusion, public services and quality of life. October 2006: The results of the study on the impact e-health are published (economic benefits of the e-health implementation in several European sites). April 2007: Conference and exhibition on e-health April 2007: A report argued that Member States have made considerable progress in implementing the strategy for e-health, but had failed to establish educational and socioeconomic guidelines for matters falling under their responsibility.

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May 2007: Workshop on e-health, which was organized by the European Commission and the U.S. Department of Health & Human Services, in collaboration with the European-American Business Council. The main issues included the health practices which were supported by information technology, interoperability, certification and improvement of patient safety July 2007: Presentation of a draft recommendation on interoperability December 2007: The Task Force on e-health publishes a report on Accelerating the Development of the E-health Market in Europe. December 2007: Publication of a kick-off study by the EU Commission on financing e-health. The Commission adopts a Communication on the action guide for the European market (‘A lead market initiative for Europe’), with e-health as one of the top six markets. April 2008: A study provides an overview on e-health emerging markets regulation and a report on the use of ICT among general practitioners in Europe. May 2008: The EU Portorož Declaration on the e-health is adopted. It reaffirms the need to share experiences and collaborations carried out in Europe, and the terms of interoperability for cross-border care. June 2008: Two European pilot projects are launched to check the European cooperation in the use of data relating to emergencies and prescriptions. In 2008, the Commission issued a Recommendation on cross-border e-health systems. By the end of the year, the Commission had also planned to publish a Communication on telemedicine and innovative ICT solutions for the management of chronic diseases. It is worthwhile mentioning that the Action Plan for 2004-2010 focused on three priority areas: •

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Dealing with common challenges and creating an appropriate framework to support e-health, such as the interoperability

•

•

of health information systems, the electronic health records, and patient and staff mobility Accelerating the implementation of the of e-health use in the areas of health education and disease prevention, and promoting the use of electronic health cards linking state efforts in monitoring, benchmarking and dissemination of best practices

European E-Health Research Projects The EU is supporting research in e-health for nearly two decades. The technologies developed in hundreds of successful projects have helped to improve health care provision in many different fields. Research on e-health is a priority in the Seventh Framework Program, which is valid until 2013. The Seventh Framework Program for Research (presented in the Decision of December 18, 2006) will continue to this direction until 2013, with a total budget more than 50 billion euros. It aims to create an ‘intelligent environment’ in which the state of health of each individual can be monitored and managed continuously. The key points of the program on e-health are the following: • • •

•

Personal Health Systems (PHS) Patient Safety (PS) Software tools, which help health professionals to find the available information each time they have to draw conclusions. Virtual Physiological Human (VPH) Framework

Multidisciplinary networks of researchers in the field of bioinformatics, genomics and neuroinformatics, which create a new generation of e-health systems.

The European Perspective of E-Health and a Framework for its Economic Evaluation

Research Projects Bepro Enabling Best Practices for Oncology (IST2000-25252 BP) The project focused on a multinational network of oncology and assessed the effectiveness of oncology services. The project involved three phases: establishment, medical evaluation, and assessment. The results were disseminated to the most influential European medical communities and, in some cases, to standardization bodies.

Diaffoot Remote Monitoring of Diabetic Foot (IST2001-TR 33281) Diabetes mellitus is a growing problem in European countries with a high impact both on the quality of life of millions of citizens, and the financial situation of national health systems. The technical objective of the project was to test the use of a remote system, capable of measuring data on clients and sending data to the hospital for further analysis and monitoring of patients with diabetes mellitus. Another objective was to compare current procedures and techniques in order to redefine the protocol for the treatment of diabetes mellitus and propose an operational procedure.

age and communication. The digital microscopy could then allow the integration of PACS (Picture Archiving and Communication System), HIS (Hospital Information System) and information available in hospitals.

Ihelp Electronic remote operating room (IST-200133269 BP) The project Ihelp analyzed the state of art of ehealth platforms and investigated the methodology for establishing best practices for online support, 24 hours, anywhere in the world, with the latest systems in neurosurgery. The project aimed to ensure the proper use of the new technologies by the surgeons and the drastic reduction of complications. The service is integrated with treatment systems already in use for the operations of neurosurgery.

Prideh Privacy in data management for e-health (IST2001-TR 32647) Technologies for securing privacy, using encryption methods, are complex. Prideh takes advantage of the experience gained from its partners (Custodix as a supplier and as Wren’s) for the implementation of services in the pharmaceutical and health industries.

E-Scope

Reshen

Digital microscopy for diagnostics and data integration in the hospital (IST-2001-33294 BP) The last decade, the technology of a pathology diagnosis takes advantage of multimedia systems, which include standards for images (DICOM Visible Light), electronic forms of reporting, storage systems and support procedures for accreditation and certification. The project, therefore, aimed to prepare a digital microscopy to effectively integrate the standard for medical imaging, stor-

Security in Regional Networks of Health (IST2000-25354 BP) The project aimed at the demonstration of best practices in health care information exchange. The goal was to develop a business case for information security in health care and highlight the best practices, which ensure communication and information exchange between all participants (providers of health care services, end users).

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Screen-Trial Regional Secure Healthcare Networks (IST2001-TR 33439) Millions of women perform a mammography screening in Europe. The new technologies of digital mammography and Soft-Copy Reading (SCR) will have an impact on radiology and the quality of service. SCR allows the use of computer-aided detection, as well as the use of digital images and digital communications. SCR is the key to the integration of screening programs in the e-health while it will accelerate the adoption of a SCR system, which was developed in the Screen project.

Spirit Priming The Virtuous Spiral for Healthcare: Implementing an Open Source Approach to Accelerate the Uptake of Best Practice and Improvement, Regional Healthcare Network Solutions (IST-2000-26162 BP) Spirit is a pioneering initiative, which accelerates the adoption of regional health network solutions. The project aimed to establish best practices in Open Source Business software for health care. This will enable the implementation, use and dissemination of regional networks for health. The project created a partnership between the worldwide community of software developers and healthcare professionals to share, enhance, and create innovative solutions for health care.

Stemnet Information Technology for the transplantation of stem cells (IST-2000-26117 BP) The project aimed to demonstrate best practices in a network database of stem cells donors. The project facilitates the sharing of best European practices, including standards, the best laboratory practices, and quality control procedures.

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This translates into greater efficiency and quality, reducing waiting time and cost.

Woman European network of services for the health of women (IST-2001-TR 32672) Woman II, a project funded by the EC in 19982000, has created an innovative approach for the protection of women’s health, with the combination of a Web portal and the Electronic Patient Record (EPR). The multilingual portal provides access to women and professionals with respect to women’s health information. The project’s objective was to increase the number of Web sites, in order to create a European network working for the collection and compilation of data, and promote the use of the Web portal among citizens and experts. The work ahead is adaptation, personalization and improvement of the results of Woman.

The Economic Evaluation of E-Health Health care expenditures are increasing in the industrialized countries (Scrivens, 1997; Hughes & Humphrey, 1990; Ranci Ortigiosa, 2000). Therefore, European countries spend, in the health sector, 8-10% and the U.S.A 15% of their Gross Domestic Product. The interest towards electromechanical systems (MEMs - Micro-ElectroMechanical to you Systems) means the realization of tools of small dimensions, which have remarkable advantages thanks to their invasivity and greater diagnostic-therapeutic effectiveness. Moreover, telemedicine requires a methodology, which analyzes its potential effects, taking into account the different actors of the health industry. Therefore, an economic evaluation has to take into consideration the following aspects/variables (Gerhardus, 2003; Luke et al., 2004; Zuckerman & Coile, 2003): •

Use of biomedical technology: The perspective of evaluation is limited to a single

The European Perspective of E-Health and a Framework for its Economic Evaluation

•

•

•

•

service or is extended to the entire health system. Therefore, it is necessary to assess the effects of change, estimating the total economic impact. That way, we estimate the cost of the changes, produced from the new technology, on the entire health system; Identification of the point of view: The financial resources for the patient admission are usually provided by a public system. However, although the main reference is the entire society it is necessary to identify the gains and losses of the single groups. Analysis of the alternatives: The scarce resources make choice necessary. Therefore, we have to compare the new and existing technology. Selection of the economic evaluation technique: Levels of different sophistication exist in the economic analysis, all deriving from the approach cost-benefits (cost analysis, cost minimization, cost-effectiveness, cost-utility, cost-benefits) that differ essentially because of the different ways they estimate benefits. However, the technique of evaluation is tied to the question we must answer. Identification and quantification of the costs and benefits: The studies reported in the literature do not show the same amplitude and variety of costs and benefits. For example, the capital costs are often omitted while the complexity in quantifying benefits is increasing. Normally, for the economic evaluation we use market prices or proxies, which are referred as prices, and we have to take into account the different aspects of outcome evaluation comparing the existing and the new model (Walston & Bogue, 1999; Coile, 2000; Bolon, 1997).

CONCLUSION Healthcare supply and the public health concept are being modified according to new standards. Nevertheless, although the budget constraints, e-health represents an interesting opportunity for the exchange, integration and sharing of information among health care providers and patients (Grimson et al., 2001; Maceratini et al., 1995; Leisch et al., 1997).

REFERENCES Bolon, D. S. (1997). Organizational citizenship behavior among hospital employees: a multidimensional analysis involving job satisfaction and organizational commitment. Hospital & Health Services Administration, 42(2), 221–241. Coile, R. (2000). E-health: Reinventing healthcare in the information age. Journal of Healthcare Management, 45(3), 206–210. Commission of the European Communities. (2007). A lead market initiative for Europe. Brussels: Commission of the European Communities. Drummond, M., Sculpher, M., Torrance, G., O’Brien, B., & Stoddart, G. (2005). Methods for the Economic Evaluation of Health Care Programmes. Oxford, UK: Oxford University Press. Gerhardus, D. (2003). Robot-Assisted Surgery: The Future Is Here. Journal of Healthcare Management, 48(4), 242–251. Grimson, J., Stephens, G., Jung, B., Grimson, W., Berry, D., & Pardon, S. (2001). Sharing HealthCare Records over the Internet. IEEE Internet Computing, 5(3), 49–58. doi:10.1109/4236.935177 Hughes, J., & Humphrey, C. (1990). Medical Audit in General Practice: Practice Guide to the literature. London: King’s Fund Centre.

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Leisch, E., Sartzetakis, S., Tsiknakis, M., & Orphanoudakis, S. C. (1997). A framework for the integration of distributed autonomous healthcare information systems. Informatics for Health & Social Care, 22(4), 325–335. doi:10.3109/14639239709010904 Luke, R., Walston, S., & Plummer, P. (2004). Healthcare strategy: in pursuit of competitive advantage. Chicago, IL: Health Administration Press. Maceratini, R., Rafanelli, M., & Ricci, F. L. (1995). Virtual Hospitalization: reality or utopia? Medinfo, 2, 1482–1486.

Ranci Ortigiosa, E. (2000). La valutazione di qualità nei servizi sanitari. Milano, Italy: Franco Angeli Edizioni. Scrivens, E. (1997). Accreditamento dei servizi sanitari: Esperienze internazionali a confronto. Torino, Italy: Centro Scientifico Editore. Walston, S. L., & Bogue, R. J. (1999). The effects of reengineering: Fad or competitive factor? Journal of Healthcare Management, 44(6), 456–474. Zuckerman, A., & Coile, R. (2003). Competing on Excellence: Healthcare Strategies for a Consumer-Driven Market. Chicago, IL: Health Administration Press.

This work was previously published in E-Health Systems Quality and Reliability: Models and Standards, edited by Anastasius Moumtzoglou and Anastasia Kastania, pp. 28-36, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 2.23

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders Benjamin I. Rapoport Massachusetts Institute of Technology, USA & Harvard Medical School, USA Rahul Sarpeshkar Massachusetts Institute of Technology, USA

ABSTRACT Algorithmically and energetically efficient computational architectures that operate in real time are essential for clinically useful neural prosthetic devices. Such architectures decode raw neural data to obtain direct motor control signals for external devices. They can also perform data compression and vastly reduce the bandwidth and consequently power expended in wireless transmission of raw data from implantable brain–machine interfaces. DOI: 10.4018/978-1-60960-561-2.ch223

We describe a biomimetic algorithm and micropower analog circuit architecture for decoding neural cell ensemble signals. The decoding algorithm implements a continuous-time artificial neural network, using a bank of adaptive linear filters with kernels that emulate synaptic dynamics. The filters transform neural signal inputs into control-parameter outputs, and can be tuned automatically in an on-line learning process. We demonstrate that the algorithm is suitable for decoding both local field potentials and mean spike rates. We also provide experimental validation of our system, decoding discrete reaching

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

decisions from neuronal activity in the macaque parietal cortex, and decoding continuous head direction trajectories from cell ensemble activity in the rat thalamus. We further describe a method of mapping the algorithm to a highly parallel circuit architecture capable of continuous learning and real-time operation. Circuit simulations of a subthreshold analog CMOS instantiation of the architecture reveal that its performance is comparable to the predicted performance of our decoding algorithm for a system decoding three control parameters from 100 neural input channels at microwatt levels of power consumption. While the algorithm and decoding architecture are suitable for analog or digital implementation, we indicate how a micropower analog system trades some algorithmic programmability for reductions in power and area consumption that could facilitate implantation of a neural decoder within the brain. We also indicate how our system can compress neural data more than 100,000-fold, greatly reducing the power needed for wireless telemetry of neural data.

INTRODUCTION Brain–machine interfaces have proven capable of decoding neuronal population activity in real time to derive instantaneous control signals for prosthetics and other devices. All of the decoding systems demonstrated to date have operated by analyzing digitized neural data (Chapin, Moxon, Markowitz, & Nicolelis, 1999; Hochberg et al., 2006; Jackson, Mavoori, & Fetz, 2006; Musallam, Corneil, Greger, Scherberger, & Andersen, 2004; Santhanam, Ryu, Yu, Afshar, & Shenoy, 2006; Taylor, Tillery, & Schwartz, 2002; Wessberg et al., 2000). Clinically viable neural prosthetics are an eagerly anticipated advance in the field of rehabilitation medicine, and development of brain–machine interfaces that wirelessly transmit neural data to external devices will represent an important step toward clinical viability. The general model for such devices has

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two components: a brain–implanted unit directly connected to a multielectrode array collecting raw neural data; and a unit outside the body for data processing, decoding, and control. Data transmission between the two units is wireless. A 100-channel, 12-bit-precise digitization of raw neural waveforms sampled at 30 kHz generates 36 Mbs-1 of data; the power costs in digitization, wireless communication, and population signal decoding all scale with this high data rate. Consequences of this scaling, as seen for example in cochlear-implant systems, include unwanted heat dissipation in the brain, decreased longevity of batteries, and increased size of the implanted unit. Recent designs for system components have addressed these issues in several ways, including micropower neural amplification (Holleman & Otis, 2007; Wattanapanitch, Fee, & Sarpeshkar, 2007); adaptive power biasing to reduce recording power in multielectrode arrays (Sarpeshkar et al., 2008); low-power data telemetry (Ghovanloo & Atluri, 2007; Mandal & Sarpeshkar, 2007, 2008; Mohseni, Najafi, Eliades, & Wang, 2005); ultralow-power analog-to-digital conversion (Yang & Sarpeshkar, 2006); low-power neural stimulation (Theogarajan et al., 2004); energy-efficient wireless recharging (Baker & Sarpeshkar, 2007); and low-power circuits and system designs for brain–machine interfaces (Sarpeshkar et al., 2008; Sarpeshkar et al., 2007). Power-conserving schemes for compressing neural data before transmission have also been proposed (Olsson III & Wise, 2005). However, almost no work has been done in the area of power-efficient neural decoding (Rapoport et al., 2009). Direct and power-efficient analysis and decoding of analog neural data within the implanted unit of a brain–machine interface could facilitate extremely high data compression ratios. For example, the 36 Mbs-1 required to transmit raw neural data from 100 channels could be compressed more than 100,000-fold to 300 bs-1 of 3-channel motoroutput information updated with 10-bit precision at 10 Hz. Such dramatic compression brings

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

concomitant reductions in the power required for communication and digitization of neural data. Ultra-low-power analog preprocessing prior to digitization of neural signals could thus be beneficial in some applications. Related considerations arise in the design of cochlear implants, and prior work in that field has demonstrated that powerefficient, analog preprocessing before digitization can be used to achieve high data compression ratios, leading to order-of-magnitude reductions in power consumption relative to fully digital data-processing schemes (Sarpeshkar, Baker et al., 2005; Sarpeshkar, Salthouse et al., 2005). Such power savings have been achieved while preserving programmability, as well as robustness to multiple sources of noise and transistor mismatch. Importantly, a processor based on this low-power design paradigm functioned successfully in a deaf patient, enabling her to understand speech on her very first attempt (Sarpeshkar, 2006). In this chapter we describe an approach to neural decoding using low-power analog preprocessing methods that can handle large quantities of high-bandwidth analog data, processing neural input signals in a slow-and-parallel fashion to generate low-bandwidth control outputs. Parallel architectures for data compression constitute an area in which analog systems perform especially well relative to digital systems, as has been demonstrated analytically (Sarpeshkar, 1998), and as exemplified by biological systems such as the retina and the cochlea (Mead, 1989; Sarpeshkar, 2006). Multiple approaches to neural signal decoding have been implemented successfully by a number of research groups, as mentioned in the section entitled “Implementation of the Decoding Algorithm in Analog Circuitry.” All of these have employed highly programmable, discrete-time, digital algorithms, implemented in software or microprocessors located outside the brain. We are unaware of any work on continuous-time analog decoders or analog circuit architectures for neural decoding (Rapoport et al., 2009). The neural signal decoder

we present here is designed to complement and integrate with existing approaches. Optimized for implementation in micropower analog circuitry, it sacrifices some algorithmic programmability to reduce the power consumption and physical size of the neural decoder, facilitating use as a component of a unit implanted within the brain. We demonstrate a method of designing highly power-efficient adaptive circuit architectures for neural decoding in two stages. We first generalize a discrete-time algorithm for adaptive-filter decoding to a continuous-time analog decoder. We then provide an approach to translating this decoder into a circuit architecture. In this chapter we show that our algorithm and analog architecture offer good performance when learning to interpret real neural cell ensemble codes in the rat thalamus, from which we decode continuous head direction trajectories, and the macaque monkey parietal cortex, from which we decode discrete reach decisions. We further show that the system is sufficiently flexible to accept local field potentials or mean spike rates as inputs. Circuit simulations of a subthreshold analog CMOS implementation of our circuit architecture suggest that a spike processor and decoder with 100 input channels and 3 output control parameters could be built with a total power budget of approximately 42 μW; this would reduce the power expended in wireless telemetry to approximately 300 nW and the total power consumption of the implanted unit would be under 43 μW. Our decoding scheme sacrifices the flexibility of a general-purpose digital system for the efficiency of a special-purpose analog system. This tradeoff may be undesirable in some neural prosthetic devices. Therefore, our proposed decoder is meant to be used not as a substitute for digital signal processors but rather as an adjunct to digital hardware, in ways that synergistically fuse the efficiency of embedded analog preprocessing options with the flexibility of a general-purpose external digital processor. The schematic for such a combined system is shown in Figure 1. First,

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A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

an external digital processor transiently analyzes raw, high-bandwidth neural data to determine spike thresholds and DAC-programmable analog parameters for the implanted analog decoder. Such parameters could control which input channels are turned on or off, set filter time constants for converting spikes to mean firing rates, and establish amplitude thresholds that ensure optimal spike sorting. Such analysis requires a great deal of flexible programmability and so is best done digitally, by an external processor, taking into account all available raw neural data. The analysis generates configuration settings that can be downloaded over a low-bandwidth link to reconfigure the implanted unit, including a low-power analog decoder, digitally. The analog system then learns, decodes, and outputs low-bandwidth information in a low-power fashion for most of the time that the brain–machine interface is operational. Digital analysis and recalibration of the analog system may be done infrequently (perhaps once a day, as in early reports of a clinically useful brain–machine interface (Hochberg et al., 2006)) so that the average power consumption is always low even though transient power consumption of the external digital system may be relatively high during recalibration. The architecture of Figure 1 permits exploration of other decoding algo-

rithms in the external unit, allowing high-power operation when necessary to achieve sufficient programmability. For clinical neural prosthetic devices, the necessity of highly sophisticated decoding algorithms remains open to question, since both animal (Carmena et al., 2003; Musallam et al., 2004; Taylor et al., 2002; Velliste, Perel, Spalding, Whitford, & Schwartz, 2008) and human (Hochberg et al., 2006) users of even first-generation neural prosthetic systems have proven capable of rapidly adapting to the particular rules governing the control of their brain–machine interfaces. In the present work we focus on an architecture to implement a simple, continuous-time analog linear (convolutional) decoding algorithm. The approach we present here can be generalized to implement analog-circuit architectures of more general Bayesian algorithms; examples of related systems include analog probabilistic decoding circuit architectures used in speech recognition and error correcting codes (Lazzaro, Wawrzynek, & Lippmann, 1997; Loeliger, Tarköy, Lustenberger, & Helfenstein, 1999). Related architectures can be adapted through our mathematical approach to design circuits for Bayesian neural decoding. This chapter is organized as follows: The section entitled “Methods of Designing and Testing

Figure 1. Schematic diagram of a brain–machine interface system incorporating an implantable analog neural decoder

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A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

a Micropower Neural Decoding System” begins by presenting a derivation of our adaptive-kernel decoder; a corresponding description of an example instantiation in subthreshold micropower analog VLSI is presented in the subsection “Implementation of the Decoding Algorithm in Analog Circuitry.” “Methods of Testing the Neural Signal Decoding System” describes methods for testing the algorithm and corresponding circuit architecture in several decoding tasks: continuous trajectory decoding from simulated local field potential (LFP) input signals, continuous headdirection trajectory decoding from spike-train inputs recorded from the thalamus of a freely moving rat, and discrete arm-reach intentions decoded from spike-train inputs recorded from the parietal cortex of a macaque monkey. The results of these tests are subsequently presented in the section entitled “Evaluating the Performance of the Neural Decoding System.” The following section, “Evaluating Design Tradeoffs in the Construction of an Implantable Neural Decoder,” discusses the simulated performance characteristics of our decoding system and alternative digital implementations from the perspectives of reducing power consumption for implantable brain–machine interfaces. The chapter concludes with a summary of our work and anticipated directions for future research in the field of neural decoding in the context of brain–machine interfaces.

METHODS OF DESIGNING AND TESTING A MICROPOWER NEURAL DECODING SYSTEM An Algorithm for Adaptive Convolutional Decoding of Neural Cell Ensemble Signals The function of neuronal population decoding is to map neural signals onto higher-level cognitive processes to which they correspond. This section presents the mathematical foundations of an adap-

tive convolutional decoder whose kernels can be modified according to a gradient-descent–based learning algorithm that optimizes decoding performance in an on-line, real-time fashion. In convolutional decoding of neural cell ensemble signals, the decoding operation takes the form   M (t ) = W (t )  N (t ) n

M i (t ) = ∑Wij (t )  N j (t )



(1.1)

j =1

 where i∈{1,…,m}; N (t ) is an n-dimensional vector containing the neural signal (n input channels of neuronal firing rates, analog signal values, or local field potentials, for example) at time t;  M (t ) is a corresponding m-dimensional vector containing the decoder output signal (which in the examples presented here corresponds to motor control parameters, but could correspond as well to limb or joint kinematic parameters or to characteristics or states of nonmotor cognitive processes); W is a matrix of convolution kernels Wij(t) (formally analogous to a matrix of dynamic synaptic weights), each of which depends on a set of p modifiable parameters, Wijk , k ∈ {1,…,p}; and ∘ indicates convolution. Accurate decoding requires first choosing an appropriate functional form for the kernels and then optimizing the kernel parameters to achieve maximal decoding accuracy. Since the optimization process is generalizable to any choice of kernels that are differentiable functions of the tuning parameters, we discuss the general process first. We then explain our biophysical motivations for selecting particular functional forms for the decoding kernels; appropriately chosen kernels enable the neural decoder to emulate the real-time encoding and decoding processes performed by biological neurons. Our algorithm for optimizing the decoding kernels uses a gradient-descent approach to

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A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

minimize decoding error in a least-squares sense during a learning phase of decoder operation. ˆ During this phase the correct output M (t ) , and   ˆ hence the decoder error e (t ) = M (t ) − M (t ) , is available to the decoder for feedback-based learning. We design the optimization algorithm to evolve W(t) in a manner that reduces the squared decoder error on a timescale set by the parameter τ, where the squared error is defined as t

) ∫ e (u )

(

E W (t ), τ =

t −τ m

t

2

du 2

= ∑ ∫ ei (u ) du

(1.2)

i =1 t −τ m

≡ ∑ Ei , i =1

and the independence of each of the m terms in Equation (1.2) is due to the independence of the m sets of n × p parameters Wijk , j∈{1,…,n}, k∈{1,…,p} associated with generating each component Mi(t) of the output. Our strategy for optimizing the matrix of decoder kernels is to modify each of the kernel parameters Wijk continuously and in parallel, on a timescale set by τ, in proportion to the negative gradient of E(W(t),τ) with respect to each parameter:

 ∂E −∇kij E W (t ), τ ≡ − ∂Wijk

(

)

  t   n   ∂Wij (u )     N j (u ) = − ∫ du  2 M i (u ) − ∑Wij (u )  N j (u ) × − k    ∂ W    = 1 j  t −τ ij     t  ∂W (u )   ij = 2 ∫ ei (u )   N j (u ) du.  ∂W k  t −τ   ij

(1.3)

The learning algorithm refines W in a contin uous-time fashion, using −∇E (t ) as an error feedback signal to modify W(t), and incrementing each of the parameters Wijk (t ) in continuous time  by a term proportional to −∇kij E W (t ) (the

(

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)

proportionality constant, ε, must be large enough to ensure quick learning but small enough to ensure learning stability). If W(t) is viewed as an array of linear filters operating on the neural input  signal, the quantity −∇kij E W (t ), τ used to

(

)

increment each filter parameter can be described as the product, averaged over a time interval of length τ, of the error in the filter output and a secondarily filtered version of the filter input. The error term is identical for the parameters of all filters contributing to a given component of the output, Mi(t). The secondarily filtered version of the input is generated by a secondary convolution ∂Wij (u ) , which depends on the funckernel, − ∂Wijk tional form of each primary filter kernel and in general differs for each filter parameter. Figure 2 shows a block diagram for an analog circuit architecture that implements our decoding and optimization algorithm. Many functional forms for the convolution kernels are both theoretically possible and practical to implement using low-power analog circuitry. Our approach has been to emulate biological neural systems by choosing a biophysically inspired kernel whose impulse response approximates the postsynaptic currents biological neurons integrate when encoding and decoding neural signals in vivo (Arenz, Silver, Schaefer, & Margrie, 2008). Combining our decoding architecture with the choice of a first-order low-pass decoder kernel enables our low-power neural decoder to implement a biomimetic, continuous-time artificial neural network. Numerical experiments have also indicated that decoding using such biomimetic kernels can yield results comparable to those obtained using optimal linear decoders (Eliasmith & Anderson, 2003). But in contrast with our on-line optimization scheme, optimal linear decoders are computed off-line after all training data have been collected. We have found that this simple choice of kernel

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

Figure 2. Block diagram of an analog circuit architecture for linear convolutional decoding and learning

offers effective performance in practice, and so we confine the present analysis to that kernel. We also offer a heuristic justification for using first-order low-pass kernels. Kernels for decoding continuous trajectories should be designed to anticipate low-frequency variation in the input and output signals. Two-parameter first-order low-pass filter kernels account for trajectory continuity by exponentially weighting the history of neural inputs: Wij =

Aij τij

e

t − τij

Wijk =2 = τij , the decay time over which past inputs  N (t '), t ' < t , influence the present output es  timate M (t ) = W  N (t ) . The filters used to tune the low-pass filter kernel parameters can be implemented using simple and compact analog circuitry. The gain parameters are tuned using low-pass filter kernels of the form ∂Wij (t ) ∂Wijk =1

,

t

1 − τij = e , τij

(1.5)

(1.4)

where the two tunable kernel parameters are Wijk =1 = Aij , the low-pass filter gain, and

while the time-constant parameters are tuned using band-pass filter kernels:

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A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

∂Wij (t ) ∂Wijk =2

=

Aij τij2

e

−

t τij

t     − 1 .   τij

(1.6)

When decoding discontinuous trajectories, such as sequences of discrete decisions, we use the limiting case of this kernel as τij → 0: Wij (t ) = Wijk =1δ (t ) = Aij δ (t ) .

(1.7)

Such a decoding system, in which each kernel is a zeroth-order filter characterized by a single tunable constant, performs instantaneous linear decoding, which has successfully been used by others to decode neuronal population signals in the context of neural prosthetics (Hochberg et al., 2006; Wessberg & Nicolelis, 2004). With kernels of this form, W(t) is analogous to matrices of synaptic weights encountered in artificial neural networks, and our optimization algorithm resembles a ‘delta-rule’ learning procedure (Haykin, 1999).

Implementation of the Decoding Algorithm in Analog Circuitry In this section we describe the implementation of our adaptive decoder in an analog circuit architecture. This approach represents a divergence from conventional approaches to neural signal decoding. The ease of implementing sophisticated algorithms in digital systems has led to a proliferation of effective approaches to decoding and learning. Some of the most popular such algorithms in the context of neural decoding involve construction of optimal linear filters (Serruya, Hatsopoulos, Fellows, Paninski, & Donoghue, 2003; Warland, Reinagel, & Meister, 1997); training of artificial neural networks (Sanchez, Erdogmus, Principe, Wessberg, & Nicolelis, 2005); use of Kalman filters (Wu, Black et al., 2004; Wu, Gao, Bienenstock, Donoghue, & Black, 2006; Wu, Shaikhouni, Donoghue, & Black, 2004) and adaptive Kalman filters (Wu & Hatsopoulos, 2008); estimation

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based on Bayesian inference techniques (Brockwell, Rojas, & Kass, 2004; Srinivasan, Eden, Mitter, & Brown, 2007) and point-process models (Eden, Frank, Barbieri, Solo, & Brown, 2004); and decoding on the basis of frequency-domain data (such as the spectral content of local field potentials) (Musallam et al., 2004; Pesaran, Pezaris, Sahani, Mitra, & Andersen, 2002; Shenoy et al., 2003) or wavelet decompositions of neural signals (Musallam et al., 2004). However, to our knowledge the present work represents the first description of an analog circuit architecture for neural signal decoding. We separate the present discussion into three parts. The first part treats the preprocessing of the two principal classes of neural input signals, local field potentials (LFPs) and action potentials (‘spikes’). We then address the decoding architecture itself. Finally, we describe our estimates of power consumption by each module of the decoder architecture. Figure 3 is a diagram of the circuit modules required to implement a single adaptive kernel of the decoder. These modules fall into six functional classes: (1) Adaptive filters corresponding to the kernels Wij(t); (2) Parameter-

{ ( ) } = {A , τ } ;

learning filters to tune the Wij

k

ij

ij

(3) Biasing circuits for the parameter-learning filters; (4) Multipliers; (5) Adders and subtracters; and (6) Memory units for storing learned parameter values. The second part of this section presents the design for each functional subunit of our decoding architecture in turn. Note that the descriptions given here correspond to an unoptimized, proof-of-concept circuit implementation. Current-mode techniques, circuit optimizations not described here, and noise-robust analog biasing techniques such as those described in (Sarpeshkar, Salthouse et al., 2005) will be necessary to ensure robust, programmable, and efficient operation in a practical implementation.

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

Figure 3. Block diagram indicating the functional component circuits required to implement a single adaptive kernel of the convolutional decoder

Input Signals for the Neural Decoder Local-Field-Potential–Based Decoding Local field potentials encode information about well defined cognitive states and can also be used as control signals for neural prostheses. Furthermore, it has been found that particular classes of information are encoded in distinct frequency bands of the LFP power spectrum (Pesaran et al., 2002). Information encoded in this manner can be extracted from the LFP by passing the raw LFP signal through a band-pass amplifier tuned to the spectral band of interest, rectifying the output of the band-pass filter, and passing the result through a peak-detection circuit to generate an envelope waveform. Prior work solved this design problem in the context of ultra-low-power bionic-ear (cochlear implant) processors (Sarpeshkar, Baker et al., 2005; Sarpeshkar, Salthouse et al., 2005) for which micropower band-pass amplifier (Salthouse & Sarpeshkar, 2003) and envelope detector circuits (Zhak, Baker, & Sarpeshkar, 2003) were built that can be employed to process neural signals at lower frequencies. Spike–Based Decoding Neuronal action potential voltage spikes typically have widths on the order of 1 ms, corresponding to frequencies in the kilohertz range. Spike-

based inputs are transformed to lower-frequency signals (of order 1–10 Hz) through time-domain averaging and the resulting spike rates are used as input signals for the decoder. Such averaging can be implemented by low-pass interpolation filters. The simplest such filter is a first-order low-pass filter with frequency-domain transfer 1 , and cutoff frequency function H 1 (s ) = 1 + τ1s fc = (2πτ1)-1 Hz. Smoother interpolation can be obtained by cascading first-order filters. The analog implementation of such filters can be achieved using a Gm–C design: Gm refers to the transconductance of an operational transconductance amplifier (OTA) component, while C denotes 1 Gm , a filter capacitance. In such a filter fc = 2π C so a low cutoff frequency requires C to be large or Gm to be small. Circuit layout area restrictions will constrain the maximum value of C to approximately 4 pF, so a low fc requires Gm to be small. Wide-linear-range transconductors with subthreshold bias currents (Sarpeshkar, Lyon, & Mead, 1997) allow Gm to be small enough to achieve such low corner frequencies in a reliable fashion. We convert raw neural input waveforms into smooth time-averaged spike rates (Figure 5) using the circuitry shown in Figure 4, which uses dual thresholding to detect action potentials on

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A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

Figure 4. Analog preprocessing circuitry for converting raw neural signals into smooth analog inputs for an analog decoder

Figure 5. Analog preprocessing of raw neural signals to generate smooth analog inputs to an implantable analog decoder. The raw neural signal (top trace) is thresholded to generate a spike train (middle trace), which is converted to a smooth time-averaged spike rate (bottom trace) using a second-order low-pass filter

spectively. As a result, power consumption of the preprocessing stages will be dominated by the requirements of the comparator; a comparator of the kind described in (Yang & Sarpeshkar, 2006), operating at 30 kHz, requires approximately 240 nW.

Functional Circuit Subunits of the Decoder Architecture Primary Adaptive Filters Each of the m × n tunable kernels used to implement our decoding architecture can be understood as an adaptive filter whose frequency-domain transfer function is obtained from the Laplace transform of the time-domain kernel in Equation (1.4): Wij (s ) =

each input channel and then smoothens the resulting spike trains to generate mean firing rate input signals. Circuit simulations indicate that the charge pump and smoothing filter modules consume approximately 1.1 nW and 40 pW of power from a 1 V supply in 0.18 μm CMOS technology, re-

590

Aij 1 + τij



(1.8)

where the gain Aij can be positive or negative. This transfer function can be obtained from a filter having the topology shown in Figure 6, which contains four standard, nine-transistor wide-range operational transconductance amplifiers (OTAs) of the form described in (Mead, 1989). Every OTA is operated subthreshold such that its transconductance is linear in its bias current. The gain of the filter is determined by the three (A ) (A ) (R ) OTAs, Gm +ij , Gm −ij , and Gm ij , which have trans-

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

Figure 6. Adaptive filter with tunable parameters for learning the optimal convolution kernels for neural signal decoding

(A ) κI + ij

(A ) κI − ij

(R ) κI ij , reVT VT VT spectively, where κ denotes the gate-coupling coefficient of the MOS transistor, and in this analysis κ = 0.7 is assumed for all transistors; and VT = kT/q, where k denotes the Boltzmann constant, q denotes the electron charge, and T denotes the Kelvin temperature. By applying Kirchoff’s Current Law (KCL) at node Vx, we obtain the voltage gain from Vin to Vx as

conductances

(Aij )

,

, and

1 . Cτ ij 1+s τ Gmij

(1.11)

As a result, we can express the overall transfer function from Vin to Vout as (A ) (A ) 2I + ij − I totij (s ) = (R ) Vin I ij

Vout

1 1+s

Cτ

.

(1.12)

ij

τ

Gmij

=

(1.9)

(Aij )

(Aij )

If we set the sum of I + and I − equal to a (A ) constant current I totij , the expression in Equation (1.9) can be reduced to (A ) (A ) 2I + ij − I totij = . (R ) Vin I ij

Vx

Vx

(s ) =

(Aij )

Gm + − Gm − (R ) Vin Gm ij (A ) (A ) I + ij − I − ij . = (R ) I ij Vx

Vout

(1.10)

The transfer function from Vx to Vout can be expressed as

Comparing Equation (1.12) to Equation (1.8), we obtain expressions for the gain and time constant parameters: (A ) (A ) 2I + ij − I totij Aij = (R ) I ij Cτ ij . τij = (τij ) Gm

(1.13)

(A ) (A ) Since I + ij can be adjusted from 0 to I totij , (A ) (R ) (A ) (R ) Aij can vary from −I totij / I ij to I totij / I ij .

591

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

The time constant τij can be adjusted by tuning (τ ) the bias current in Gm ij . Secondary Parameter Tuning Filters Real-time gradient-descent–based optimization of the convolution kernels can be achieved through

{ ( )}

tuning the filter parameters Wij = {Aij , τij }  (k ) using signals proportional to −∇ij E . Construction of such signals requires convolution kernels ∂Wij (t ) . These convolution proportional to (k ) ∂Wij k

kernels can be implemented by ‘parameterlearning filters’ of the kind shown in Figure 7. Figure 7(a) shows a first-order low-pass Gm–C 1 A filter with transfer function Wij ij (s ) = 1 + τij s to be used as a ‘gain-learning filter.’ Figure 7(b) shows a second-order band-pass Gm–C filter with τij s τ transfer function Wij ij (s ) = to be used 2 1 + τ s ( ij ) as a ‘time-constant–learning filter.’ The time Cτ ij for the two parameterconstant τij = (τij ) Gm

learning filters is identical to that of the adaptive filter, as described in the section entitled “Primary adaptive filters.” Correspondingly, the bias (τ ) currents and therefore the transconductances Gm ij in all three types of filter are identical, so the time constants of all filters in the learning architecture are updated simultaneously. Note that the actual transfer function of the time-constant–learning filter need only be proportional to Wij ij (s ) , so τ

the factor of τij/Aij between the transfer function of Equation (1.8) and the filter shown in Figure 7(b) is acceptable. Implementation of the negation required to adapt the time constants is addressed in the section entitled “Multipliers.” Multipliers The multipliers that perform the operations ∂Wij (t ) ei (t ) × required by the gradient-descent (k ) ∂Wij algorithm, denoted by the symbol × in Figure 2 and Figure 3, can be implemented using widerange four-quadrant Gilbert multipliers of the kind described in (Mead, 1989), which accept four voltage inputs Vi, i ∈ {1, 2, 3, 4} and a bias current Ib, and generate an output current; low-power performance can be obtained by operating the

Figure 7. Parameter-learning filters for tuning adaptive filter parameters based on error-signal feedback. 1 A to be used as a ‘gain(a) A first-order low-pass Gm–C filter with transfer function Wij ij (s ) = 1 + τij s learning filter.’ (b) A second-order band-pass Gm–C filter with transfer function Wij ij (s ) = τ

τij s 2

(1 + τ s ) ij

to be used as a ‘time-constant–learning filter’

592

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

multiplier circuit with all transistors in the subthreshold regime. The input-output characteristic of the Gilbert multiplier is given by I out = I b tanh

κ (V1 −V2 ) 2VT

2

tanh

κ (V3 −V4 ) 2VT



 κ   (V1 −V2 ) (V3 −V4 ), ≈ I b   2VT 

(1.14)

where the approximation of Equation (1.14) is valid in the intended operating region, where V1 ≈ V2 and V3 ≈ V4. Noninverting multiplication, as required for adapting the Aij, can be implemented ∂Wij (t ) into V3, and setting by feeding ei into V1, (A ) ∂Wij ij V2 and V4 to a constant reference voltage. On the other hand, inverting multiplication, as required for adapting the τij, can be implemented by inter∂Wij (t ) changing the roles of V3 and V4, feeding (τ ) ∂Wij ij into V4, and setting V3 to a constant reference voltage. The output of each multiplier is a current, Iout, so the integrations required in Equation (1.3) for

{ ( ) } = {A , τ } are

updating the parameters Wij

k

ij

ij

conveniently implemented by linear capacitors,  (k )  Wij  C

as indicated in Figure 3. The voltages V the capacitors C

W (k )   ij 

(k )

on

and used in adapting the

parameter values Wij are therefore given by W (k )   ij 

VC

=

Ib W (k )   ij 

C

κ2 4VT2

 ∂W u   ij ( )   e u N u  ( ) ( )  du j ∫u=0 i  ∂W (k )   ij t

(1.15)

which has the form required by Equation (1.3). The filter parameters therefore vary continuously in

time, and the time variation of the control voltages can be obtained by differentiating Equation (1.15). Biasing Circuits As discussed in the section entitled “Multipliers,” the filter parameters Aij and τij defining the transfer function of adaptive filter Wij are stored on the (A ) (τ ) capacitors C ij and C ij , respectively. Furthermore, as indicated in the section entitled “Primary adaptive filters,” the values of the filter parameters can be tuned by adjusting the bias (A ) (τ ) currents that determine Gm ij and Gm ij . Since IA ( ij ) the gain Aij depends on ∝ I A , while the ( ij ) IR ( ij ) Cτ 1 ij ∝ , realtime constant τij depends on (τ ) Iτ Gm ij ( ij ) time adaptive parameter tuning requires a scheme (A ) for modifying I A in proportion to VC ij and ( ij ) (τ ) I τ in inverse proportion to VC ij . ( ij ) Tuning I A in proportion to variations in the ( ij ) (A ) capacitor voltage VC ij can be accomplished by (A ) converting VC ij into a current proportional to (A ) VC ij and then using a current mirror to generate a copy of that current that is in turn used to set (A ) the transconductance Gm ij of the adaptive filter. (A ) The conversion of VC ij into a current propor(A ) tional to VC ij can be performed by a wide-linearrange transconductance amplifier (WLR) of the form described in (Sarpeshkar et al., 1997). Figure 8(a) shows a schematic of the gain-biasing (A ) circuit used to generate I A ∝ VC ij . This biasing ( ij ) (A ) (A ) (A ) circuit is intended to make I + ij + I − ij = I totij . Assuming that all of the NMOS transistors are well matched, the current in M3 must be equal to A

that in M2, which is I +ij . Using KCL at the drain

593

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

Figure 8. Circuits for setting the bias currents and transconductances that determine the adaptive filter parameters: (a) Bias-current–setting circuit for filter gains, (b) Bias-current–setting circuit for filter time constants

(A ) (A ) of M3, the current in M4 is thus I totij − I + ij , which (A ) (A ) (A ) is equal to the current in M5, so I − ij = I totij − I + ij as required. Tuning I τ in inverse proportion to variations ( ij ) (τ ) in the capacitor voltage VC ij can be accomplished using the circuit shown in Figure 8(b), which (τ ) operates as follows. First, VC ij is converted into

a proportional current I pτ as in the gain-biasing ( ij ) circuit. A translinear circuit, formed by the four well matched MOS transistors M1–M4, is then I2 used to invert I pτ , producing I τ = ps , where ( ij ) ( ij ) Iτ ( ij )

594

Is is a current reference that scales the inversion. A mirror copy of I τ is then used as the bias ( ij ) (τ ) current that sets transconductance Gm ij . Adders and Subtracters Each adder, denoted by the symbol Σ in Figure 2 and Figure 3, sums the n outputs of each set {Wij}, j ∈ {1, …, n} of adaptive filters contributing to ˆ (t ) . The adders can be W (t )  N (t ) = M i

(

)

i

implemented using a follower-aggregation circuit of the kind described in (Mead, 1989). The corresponding error signal, ei(t), is generated by ˆ (t ) . This performing the subtraction M i (t ) − M i

operation can be implemented by another adder,

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

with a unity-gain inverting amplifier negating the ˆ (t ) . adder input from M i Parameter Memory A completely analog implementation of our decoder could include analog memory units for storing parameter values when learning ends. Such units could consist of analog memory elements operating in a switched sample-and-hold scheme to permit memoryless adaptation during the learning phase and parameter storage as soon as learning terminates. Analog memory circuits with 8-bit hold times of 3.9 hours and 12-bit hold times of 14.5 minutes have been developed (O’Halloran & Sarpeshkar, 2004, 2006) and could be used in this context. Output from the memory and biasing circuits could be multiplexed onto the adaptive filter nodes whose voltages correspond to the adaptive filter parameters, using a CMOS transmission gate. This scheme is indicated in Figure 3. However, even using digital memory elements does not increase total power consumption significantly, since the termination of a learning phase is a rare event and therefore writing to memory, with its associated power cost, occurs only infrequently. Power Consumption of the Decoder Table 1 shows an estimate of the power consumed by each circuit block needed to implement our neural decoder, based on a SPICE simulation of the decoder, the performance of which is shown in Figure 11. The supply voltage of the entire system is 1 V. The bias current of each module is chosen so that the circuit has enough bandwidth to process input signals band-limited to 1 kHz (note that this choice of input signal bandwidth is more than sufficient because the bandwidth of meanfiring-rate changes is typically much smaller than the reciprocal of a refractory period. As indicated in Table 1, the total power consumption of one decoding module is approximately 54 nW. In order to ensure robust, stable decoding, the learned (optimized) filter parameters for the neu-

ral decoder will be programmed using digital-toanalog converters (DACs) as indicated in the “Introduction” section. These DACs do not appreciably increase system power consumption. One DAC is required per filter parameter to set the bias current for each filter constant, so a system decoding 3 motor parameters from 100 neural input channels would require 600 current DACs. The static current in a current DAC is approximately equal to that of a transistor whose gate is connected to the associated capacitors in Figure 3, which is approximately 100 pA. The total power consumed by all 600 DACs, when operated from a 1.8-V supply, is therefore only approximately 110 nW.

Methods of Testing the Neural Signal Decoding System In this section we describe three approaches to testing the ability of our system to decode neural signals. The first experiment is based on a simulation of local field potential input signals, which are used to decode continuous-time motor trajectories. The second experiment involves continuous-time decoding of head direction from spike train data recorded from a small set of neurons in the thalamus of an awake, behaving rat. In the third experiment we decode discrete arm-reach Table 1. Power consumption in decoder circuit modules Portion of Channel

Power Consumption (nW)

Tunable Decoding Filter

16

Gain-Learning Filter

2

Time-Constant–Learning Filter

2

Multipliers (2)

2

Gain Biasing Circuit

10

Time Constant Biasing Circuit

10

Analog Memories (2)

5

Total (One Decoder Module)

54 nW

595

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

intentions from spike train data recorded using a multielectrode array in the posterior parietal cortex of a macaque monkey.

normalized mean squared error, η, of the estimated trajectory, defined as

Testing the Ability to Decode Trajectory Information from Multichannel Local Field Potential Neural Input Signals A recent set of experiments has shown that parietal cortex neurons tend to exhibit electrical activity predictive of arm or eye movement in a single preferred direction. Increases in γ -band (25–90 Hz) spectral activity of such tuned parietal neurons anticipate movements in the preferred directions of those neurons, so a potentially useful signal for decoding intended movement from neural activity is therefore an envelope curve describing the modulated amplitude of the power transmitted in the gamma band (Andersen, Musallam, & Pesaran, 2004; Musallam et al., 2004; Pesaran et al., 2002). As a preliminary demonstration of the ability of our convolutional decoder to interpret LFP-type neural input signals, we therefore generated simulated γ -band power envelopes in order to model the local field potentials recorded by a set of n neural recording electrodes. We modeled γ-band power envelopes using a set of sinusoids with randomized amplitudes and phases and a constant offset term, and stored the corresponding  waveforms in the vector N (t ) , which was used

1 η≡ T

as the input to the decoder. We then randomly generated an m×n matrix, W*, and used it to construct a vector of m motor control parameters,  M (t ) . The decoder was permitted to observe  M (t ) during a learning period of variable length, over which it sought to optimize its m×ndimensional convolution kernel W(t). The parameter ε was set to 0.1 during these simulations, and both the Ni(t) and the Mi(t) were transformed using a hyperbolic-tangent normalization to constrain them to the interval [-1, 1]. We evaluated decoder performance using a scale-invariant and dimensionless conventional figure of merit, the

596

∫

t2 =2T

t1 =T

2

 M (t ) − M ˆ (t )  i i  , (1.16) dt ∑    Li  i =1   m

where Li denotes the maximum extent of excursions permitted to Mi(t) and the time T denotes the length of the training interval; η(1) is used to denote the average value of η for a single output dimension. It is possible to consider other figures of merit, including ones based on correlation  rather than absolute error between M (t ) and ˆ M (t ) , but other authors have agreed that η-like figures of merit tend to reflect decoding system performance most reasonably (Wu et al., 2006). The results of these simulations are provided in the section entitled “Model Motor Trajectory Decoding from Simulated Multichannel Local Field Potential Inputs.” Testing the Decoding of Continuous Trajectories Using Neuronal Spike Recordings from the Thalamus of an Awake Behaving Rat Head direction cells of the rat thalamus are neurons known to exhibit receptive fields tuned to specific orientations of the head relative to the environment (Taube, 1995). We explored the ability of our system to decode the temporal firing patterns of such cells in real time. The spike trains used as input signals to the decoder in this set of experiments were derived from tetrode recordings made in the thalamus of a laboratory rat that rat ran back and forth between two points on a circular maze for food reward over a 30-minute period. The position and head direction of the animal were continuously tracked using a pair of head-mounted light-emitting diode (LED) arrays imaged at a sampling frequency of 30 Hz using a 300 × 300-pixel charge-coupled device (CCD)

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

array. Data from the imager were time-stamped in order to ensure synchronization with the neural recordings, and were used to generate a target ˆ output signal M (t ) for the decoder. In order to avoid learning the discontinuity associated with a mod-2π–based definition of the head direction ˆ angle θ(t), M (t ) was constructed as a two-dimensional vector having components M1(t) ≡ cosθ(t) and M2(t) ≡ sinθ(t). Spike-sorting analysis of the tetrode-derived waveforms isolated n = 6 single units, and the activity of each of these units was converted to a normalized spike rate Ni(t), i  ∈ {1, …, n = 6} so that N (t ) could be used as an n-channel analog input to the decoder. As indicated in the section entitled “Input Signals for the Neural Decoder,” the conversion of spike trains to analog input signals was achieved by treating each spike train as a train of pulses having uniform amplitude and pulse width, then passing the pulse trains through a third-order interpolation filter with transfer function H 3 (s ) =

1 3

(1 + τ s )



(1.17)

1

with τ1 = 500 ms. The filter output was then rescaled and recentered about a zero-offset in order to generate the normalized spike rates Ni(t). The performance of the convolutional decoding algo  rithm in learning to map N (t ) to M (t ) was studied using a software implementation of the decoder, as well as a SPICE analog-circuit simulation of the convolutional decoder, the details of which are described in the section entitled “Functional Circuit Subunits of the Decoder Architecture.” We discuss the results of these simulations in the section entitled “Continuous Real-Time Decoding of Head Direction from Neuronal Spike Activity in the Rat Thalamus.”

Testing the Decoding of Discrete Decisions Using Neuronal Spike Recordings from the Posterior Parietal Cortex of a Macaque Monkey Engaged in an Arm-Reaching Task Neurons in the posterior parietal cortex have been shown to encode intention to execute limb movements in both humans (Connolly, Andersen, & Goodale, 2003) and nonhuman primates (Snyder, Batista, & Andersen, 1997). The ability to decode signals from this region has provided proof of principle for neural prosthetic devices based on cognitive (as opposed to explicitly motor) control signals (Musallam et al., 2004). Using neural spike trains recorded during these proof-of-principle experiments, we investigated the performance of our system in the real-time decoding of discrete arm-reaching decisions. The spike trains used as input signals to the decoder in this set of experiments were derived from recordings by a multielectrode array chronically implanted in the medial intraparietal area (within the ‘parietal reach region’) of a macaque monkey. The animal had previously been trained to perform a standard stimulus-response task involving center-out arm-reaching movements between visual targets. This task, which has been described in detail elsewhere (Musallam et al., 2004), was designed to isolate the neural correlates of motor intention from those of actual movement. The monkey initiated each iteration of the task by touching a central cue target and looking at a nearby visual fixation point at t = – 800 ms (its gaze was monitored using an eye-tracking device, and cues were presented on a touch-sensitive screen). After a delay of 500 ms a peripheral cue target was flashed from t = – 300 to t = 0 ms at one of four locations displaced up, right, down, or left from the starting point. The animal was rewarded if it touched the indicated target at the end of a memory period of 1500±300 ms. Neural activity as monitored by the implanted electrode array was recorded continuously during each trial. A spike-sorting algorithm isolated 54 units from the recorded signals, and spike trains from

597

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

each unit were smoothed to spike rate waveforms using a filter of the form provided in Equation (1.17), with τ1 = 50 ms. Filter output was then rescaled and recentered about a zero-offset in order to generate the normalized spike rate input signals Ni(t) for each of the isolated units indexed by i ∈ {1, …, n – 1 = 54}; the (n = 55)-dimen sional neural input signal N (t ) contained an

additional constant-offset component Nn(t) = 1. The task of the decoder was to predict imminent arm movement on the basis of intention-related neuronal activity. Therefore, in order to ensure the  absence from N (t ) of residual neuronal activity

corresponding to actual arm movement (such artifact signals are sometimes present at the beginning and end of a memory interval), only the segment of  N (t ) from t ∈ [200, 1100] ms was fed into the  decoder for each reach. The motor output M (t )

was defined as a two-dimensional position vector corresponding to the target to which the monkey reached at the end of a corresponding memory period. Reach target positions were encoded as follows: Analog outputs generated by the decoder were thresholded according to Mi→sgnMi,

(1.18)

so that positive and negative outputs were interpreted as +1 and –1, respectively. We studied the performance of a software implementation of our convolutional decoding system in learning to use neural signals from the parietal reach region to predict the direction of subsequent reaching movements. We discuss the Table 2. Encoding vectors for reach targets Direction

M1

M2

Up

+1

+1

Right

+1

–1

Down

–1

–1

Left

–1

+1

598

results of these simulations in the section entitled “Decoding Discrete Arm Reaches from Neuronal Spike Activity in the Macaque Monkey Posterior Parietal Cortex.”

EVALUATING THE PERFORMANCE OF THE NEURAL DECODING SYSTEM Model Motor Trajectory Decoding from Simulated Multichannel Local Field Potential Inputs Parts (a) and (b) of Figure 9 respectively illustrate the training and post-training phases of decoder operation as the system learns to trace a threedimensional trajectory in real time during a simulation of the kind described in the section entitled “Testing the Ability to Decode Trajectory Information from Multichannel Local Field Potential Neural Input Signals,” with (n, m) = (10,  3). The figure illustrates qualitatively that M (t ) ˆ converges toward M (t ) reasonably quickly on the timescale set by full-scale variations in the trajectory. Figure 10 presents the results of a set of computations of η(1) for the performance of the neural decoding system in simulations of the kind described in the section entitled “Testing the Ability to Decode Trajectory Information from Multichannel Local Field Potential Neural Input Signals.” The system was trained for intervals of varying length up to one minute, T ∈ [0, 60] s, and the value of η(1) was computed for each of 50 trials at each value of T. In a fraction f of trials at each value of T, the decoding performance as reflected by η(1)(T) was significantly worse than in the remaining fraction, 1–f, of cases. In such cases markedly improved decoding, comparable to that achieved in the (1–f)-majority of cases, could be achieved by randomly reinitializing the

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

Figure 9. Trajectory decoding from simulated local field potential input signals. (a) The correct trajecˆ tory, M (t ) , is plotted in light gray over the initial segment t ∈ [0, 10] s of a 40-second training interval,   while the decoded trajectory, M (t ) ≡ W  N (t ) , is plotted as a darker line whose shade lightens as  ˆ time progresses; M (t ) converges toward M (t ) . (b) For t ∈ [40, 80] s following training, the correct trajectory is plotted in light gray while the decoded trajectory evolves with time from dark to lighter shades

(k )

parameters Wij and decoding again. The data presented in Figure 10 were obtained by setting f = 0.1. Error in trajectory estimation by the decoder decreases rapidly as the training interval increases. At T = 30 s, for example, 〈η(1)〉≈0.008±0.008, as compared with a baseline value of 〈η(1)〉≈0.118±0.106 computed for an untrained system (T = 0) over 1000 trials.

Continuous Real-Time Decoding of Head Direction from Neuronal Spike Activity in the Rat Thalamus Head direction was decoded from the activity of the n = 6 isolated thalamic neurons according to the method described in the section entitled “Testing the Decoding of Continuous Trajectories Using Neuronal Spike Recordings from the Thalamus of an Awake Behaving Rat.” The adap-

{ ( ) } = {A , τ } were

tive filter parameters Wij

p

ij

ij

optimized through gradient descent over training intervals of length T during which the decoder error, ˆ (t ) ei (t ) = M i (t ) − M i  M (t ) = cos θ (t ), sin θ (t ) ,

(

)

(1.19)

(where θ denotes the head direction angle) was made available to the adaptive filter in the feedback configuration described in the section entitled “An Algorithm for Adaptive Convolutional Decoding of Neural Cell Ensemble Signals” for t ∈ [0, T]. Following these training intervals feedback was discontinued and the performance of the decoder was assessed by comparing the decoder  ˆ output M (t ) with M (t ) for t>T. Figure 11(a) compares the output of the decoder to the measured head direction over a 240s interval. The filter parameters were trained over the interval t ∈ [0, T = 120] s. The figure shows 599

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

Figure 10. Mean squared trajectory prediction error as a function of training time for the convolutional decoding algorithm

 ˆ M (t ) (thick, light line) tracking M (t ) (thin, dark line) with increasing accuracy as training progresses, illustrating that while initial predictions are poor, they improve with feedback over the course of the training interval. Feedback is discontinued at t = 120 s. Qualitatively, the plots on the interval t ∈ [120, 240] s illustrate that the output of the neural decoder reproduces the shape of the correct waveform, predicting head direction on the basis of neuronal spike rates. Figure 11(b) displays output over a brief interval from the neurons in the ensemble whose activity was decoded, showing individual action potentials as raster plots below the corresponding smoothened firing rate curve for each cell, as well as a plot of head direction over the same time interval; individual neurons evidently have distinct receptive fields in head-direction space, and their spatial selectivity makes decoding both possible and, in this case, intuitive. The performance of the decoder in predicting head direction was assessed quantitatively using the normalized mean-squared error measure η(1) (in this context Li = 2, t1 = T, and t2 = T + 60 s). In

600

order to quantify the accuracy of head direction decoding as a function of training time T, η(1) was computed for a set of training and decoding trials with increasingly long training periods, averaging over randomized initial settings of the filter parameters and different choices of training interval. The results of this computation are displayed in Figure 12, which shows improving accuracy of head direction decoding with increased training.

Decoding Discrete Arm Reaches from Neuronal Spike Activity in the Macaque Monkey Posterior Parietal Cortex The input signals to the neural decoder when decoding discrete reaches were piecewise-constant, as described in the section entitled “Testing the Decoding of Discrete Decisions Using Neuronal Spike Recordings from the Posterior Parietal Cortex of a Macaque Monkey Engaged in an Arm-Reaching Task.” This form of input reflects a qualitative difference between the decoding problem in this experiment, which requires the decoder to make a series of decisions from among

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

Figure 11. (a) Continuous decoding of head direction from neuronal spiking activity. (b) Spiking activity in head direction cells and corresponding head direction plotted as functions of time. The paired plots illustrate neuronal receptive fields and the distribution of their peaks over the range of possible head direction angles

a finite set of options, and the decoding problems framed in the sections entitled “Testing the Ability to Decode Trajectory Information from Mul-

tichannel Local Field Potential Neural Input Signals” and “Testing the Decoding of Continuous Trajectories Using Neuronal Spike Recordings

Figure 12. Performance improvements in head direction decoding with increasing training times

601

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

from the Thalamus of an Awake Behaving Rat,” and simulated in the sections entitled “Model Motor Trajectory Decoding from Simulated Multichannel Local Field Potential Inputs” and “Continuous Real-Time Decoding of Head Direction from Neuronal Spike Activity in the Rat Thalamus,” which require the decoder to estimate smooth trajectories as functions of time. While the gradient-descent least-squares approach is applicable to both kinds of problem, the convolution kernel chosen to implement the neural decoder, Wij =

Aij τij

e

−

t τij

, is designed to exploit the

predictive value of past input signals. The degree  to which past inputs N (t ' < t ) have predictive value is reflected by the value of the time constant τij, and as τij → 0 the time interval over which  N (t ' < t ) contributes significantly to the present time output M (t ) correspondingly vanishes. In this experiment the signal to be decoded corresponds to a time series of discrete decisions made every Δt = w; consecutive reach cues were guaranteed to be independent through experimental  design. Consequently, N (t ') is completely uncor  related from N (t ) and M (t ) for t − t ' ≥ w . (In concrete terms, since successive reaches are independent, neural activity preceding one reach contains no predictive information concerning the direction of the next reach.) As a result, meaningful decoding requires τij = 0, which we enforce by taking τij to be a small, unmodifiable constant. Therefore, our decoding scheme as applied to the reach-intention neural data reduces to an instantaneous linear decoder, analogous to a singlelayer artificial neural network implemented in continuous time and trained with continuous-time feedback. The neural data used in the experiments reported here were first obtained and analyzed in connection with a previously reported set of experiments (Musallam et al., 2004). The decod-

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ing method used in those experiments involved an analysis of variance to preselect a subset of neurons exhibiting the greatest directional tuning, followed by Bayesian inference on the mean firing rates and higher-order Haar wavelet coefficients of the signals obtained from the selected neurons. We consequently had the opportunity to compare the performance of the adaptive-filter decoder to that of the Bayesian decoder. The principal performance measure reported using the Bayesian decoder was a 64.4% success rate in predicting the correct one of four allowed reach directions (Musallam et al., 2004). Under corresponding training conditions the neural decoding system described in the present work generated accurate predictions in 65% ± 9% of trials (the uncertainty figure preceded by the ± symbol indicates the magnitude of one standard deviation). Improved decoding performances were demonstrated in (Musallam et al., 2004) by considering higher-order coefficients in the Haar wavelet decomposition of the neural input signals, but in the present study we considered only the zeroth-order coefficients, corresponding to mean firing rates. The decoding scheme originally used to decode the data analyzed in this section was based in part on the known tendency of direction-sensitive neurons to ‘tune’ to preferred directions in the sense that only movement in certain preferred directions induces such neurons to modulate their firing rates away from a baseline (Cohen & Andersen, 2002). In preparing the Bayesian decoder, an off-line analysis of variance on the training set of spike trains (corresponding to arm reaches in each direction for each isolated neuron) was required to rank the isolated neurons by degree of directional sensitivity. The computational intensity of this decoding scheme was sufficiently high that inputs from only a subset of isolated neurons were used in decoding after the learning period ended, and this ranking provided a means of prioritizing neurons for use as decoder inputs. By contrast, the adaptive-filter decoder described here easily handles all 54 neuronal inputs in computer

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

simulations of real-time decoding; in a certain sense the adaptive-filter decoder automatically learns which neural inputs are the most highly directionally tuned (Rapoport, 2007). Moreover, the analog-circuit–based implementation of the decoder described here processes all neuronal inputs in parallel, so the computational intensity of the decoding task does not constrain the number of neuronal inputs the system can handle. Consequently, the adaptive-filter approach to decoding scales favorably with the number of neuronal inputs to the system. This is an important virtue of adaptive-filter decoding, as decoding accuracy typically improves and more complex decoding tasks can be performed without sacrificing accuracy as more neuronal inputs are used (Chapin, 2004). Furthermore, improvements in multielectrode neural recording methodologies and technologies continue to facilitate recording from increasing numbers of neurons (Harrison et al., 2007; M. A. L. Nicolelis et al., 2003; Suner, Fellows, Vargas-Irwin, Nakata, & Donoghue, 2005; Wise, Anderson, Hetke, Kipke, & Najafi, 2004). The ability of the adaptive-filter decoder to handle large numbers of neurons provides an opportunity to explore a decoding regime less amenable to the Bayesian analysis of (Musallam et al., 2004). Figure 13 plots decoder performance as a function of the number of neuronal inputs (the horizontal line across the plot indicates the 0.25 threshold corresponding to unbiased guessing). While the corresponding performance curves for the Bayesian decoding algorithm were originally analyzed for up to sixteen neurons (Musallam et al., 2004), the computational efficiency of the adaptive filter enables performance to be evaluated for considerably larger numbers of neuronal inputs; the computation illustrated in Figure 13 is limited only by the total number of neuronal inputs available. The computation was performed under the standard training condition of 30 trials, and the error bars indicate the magnitude of a standard deviation after averaging over sets of randomized initial conditions and training inputs.

As expected, decoder performance increases from just above the 25% chance threshold to the maximum of approximately 65% reported earlier in this section. The lower curve corresponds to random selections of the input neurons, while the upper curve corresponds to preselection of neurons in order of decreasing variance in the mean firing rates over the four reach directions (higher variance indicates greater directional selectivity). The latter curve suggests that decoding input signals from the subset of neurons transmitting the greatest amount of directional information results in performance nearly equivalent to that obtained from using the full set of available signals. The 65% success rate of the decoding system, while comparable to that of other decoding algorithms tested on the same data (Musallam et al., 2004), indicates that there is considerable room for improvement. An outstanding question in the field of neural prosthetics concerns the degree to which intelligent users can compensate for imperfect decoding through biofeedback. Marked improvements in performance along these lines have been observed over time in both monkeys and humans (Carmena et al., 2003; Hochberg et al., 2006; Musallam et al., 2004; Taylor et al., 2002), but such contributions from biological learning are evidently insufficient. The work of (Musallam et al., 2004) indicates that performance can be improved by considering the temporal structure of the input neural signals at higher than zeroth order, and this might be achieved through changing the form of the filter kernels Wij.

EVALUATING DESIGN TRADEOFFS IN THE CONSTRUCTION OF AN IMPLANTABLE NEURAL DECODER Power Efficiency Simulations using the circuit designs presented in the section entitled “Implementation of the Decoding Algorithm in Analog Circuitry” indicate

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A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

Figure 13. Decoding performance as a function of neuron number for randomly selected neurons (dark) and neurons selected on the basis of directional selectivity (light)

that a single decoding module (corresponding to an adaptive kernel Wij and associated optimization circuitry, as diagrammed in Figure 3) should consume approximately 54 nW from a 1-V supply in 0.18 μm CMOS technology (and require less than 3000 μm2 in area). This low power consumption is achieved through the use of subthreshold bias currents for transistors in the analog filters and other components. Analog preprocessing of raw neural input waveforms can be accomplished by dual thresholding to detect action potentials on each input channel and then smoothing the resulting spike trains to generate mean firing rate input signals. Simulations of the circuits presented in the section entitled “Spike–based decoding” indicate that each analog preprocessing module should consume approximately 241 nW from a 1-V supply in 0.18 μm CMOS technology. A full-scale system with n = 100 neuronal inputs  comprising N (t ) and m = 3 control parameters  comprising M (t ) would require m × n = 300 decoding modules and consume less than 17 μW in the decoder and less than 25 μW in the preprocessing stages.

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Savings in Telemetry Power An advantage of our system is that its suitability for power- and area-efficient analog implementation can enable decoding in the implanted unit of a brain–machine interface, saving power by obviating the need for analog-to-digital conversion of neural signals before decoding, and by compressing data before wireless transmission. In this section we compare the power costs of internal analog decoding to those of external digital decoding. System power will depend on the bandwidth required to transmit digitized neural data to an external unit, so we consider three alternatives for digitization and telemetry, reflecting three approaches to trading algorithmic flexibility in decoding for total system power: (1) Transmission of digitized raw neural waveforms, preserving the greatest amount of neural information and requiring the greatest bandwidth; (2) Transmission of threshold-crossing events from neural waveforms, preserving only spike timing information and requiring intermediate bandwidth; (3) Transmission of control-parameter outputs from an implantable analog decoder, requiring minimal bandwidth

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

and internal power consumption but sacrificing algorithmic flexibility. In cases (1) and (2) initial 8-bit digitization of the input data at 30 kHz would require approximately 1 μW per channel (Yang & Sarpeshkar, 2006), contributing 100 μW in total. In these cases we consider implementing the external decoding system using a highly power-efficient digital signal processor (DSP) (“TMS320C55x Technical Overview (Literature Number SPRU393),” 2000) and analog-to-digital converters (A/Ds) (Yang & Sarpeshkar, 2006). (1) The first alternative requires transmitting all of the digitized data, corresponding to a bandwidth of 24 Mbs-1 for all n = 100 channels; at a rate of 1 mW per Mbs-1 (assuming typical link geometries and efficient topologies) (Mandal & Sarpeshkar, 2007, 2008) wireless data telemetry would consume 24 mW. (2) The second alternative requires dual thresholding for spike detection, followed by transmission of one bit per channel indicating the presence of a detected spike. Two comparison operations per period at 30 kHz across n = 100 input channels constitutes 6 × 106 operations per second. In practice (due to chip and board level parasitics) the most power-efficient DSPs operate at efficiencies of several hundred microwatts per MIPS (Millions of Instructions Per Second) (Verret, 2003); assuming an efficiency of 250 μW/MIPS, the power associated with these thresholding operations would be 1.5 mW. The bandwidth required to transmit the thresholded waveforms, assuming a maximal spike rate of 1 Hz per channel, would be 0.1 Mbs-1 for all n = 100 channels, corresponding to a transmission power of 0.1 mW. So the total power consumed by a system adopting this alternative for data digitization, compression and transmission would be approximately 1.7 mW. In estimating the power consumed in cases (1) and (2) we have not included the overhead of a DSP in excess of the power consumed in computations. In practice such overhead costs can be significant, contributing several milliwatts or more, depending on the processor used and its operating

settings (Verret, 2003). However, it is important to note that all DSPs incur static power costs due to CMOS leakage currents, independent of device activity and operating frequency. Leakage power depends primarily on operating voltage and temperature, and the processing core of one highly power-efficient DSP dissipates approximately 180 μW at 1.6 V and room temperature (25°C); this leakage power increases to approximately 277 μW at body temperature (37°C) (Verret, 2003). Therefore, our power estimates for DSP-based processing represent lower bounds on what would be consumed in a practical implementation. When our decoding algorithm is executed by a power-efficient DSP, the power expended in performing computations is negligible relative to the leakage power of the processor core. In a digital implementation of our decoder the adaptive filter kernels are transformed into discrete-time filters. A discrete-time implementation of the −

t

filter kernel Ae τ has a two-parameter z-transform, so each update cycle requires two multiplications. An update frequency of 10 Hz requires 20 multiplications per second, and scaling by n × m = 300 yields the computational rate required to execute the algorithm, 0.006 × 106 multiplications per second. At an efficiency of 250 μW/ MIPS, the power cost of the algorithm itself would therefore be only approximately 1.5 μW. (3) As discussed in the section entitled “Power Efficiency,” our simulations indicate that a lowpower-analog implementation of our first-order adaptive kernel decoder with (n, m) = (100, 3) could be built with a power budget of approximately 17 μW (54 nW per decoding module) and that an additional 25 μW is required for the preprocessing stages. Transmission of m = 3 decoded parameters for real-time control of an external device with 10-bit precision and an update frequency of 10 Hz requires a transmission rate of 300 bs-1 and an associated power of 300 nW. (Here we have assumed an impedance modulation telemetry scheme similar to the one reported in (Mandal &

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A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

Sarpeshkar, 2007) and (Mandal & Sarpeshkar, 2008), which operates at an efficiency of <1 nJ per bit even for transmission rates greater than 1 Mbs-1.) The total power consumption associated with internal decoding would therefore be approximately 43 μW. So an implantable analog decoding system is capable of operating with 40- to 500-fold less power than would be required by digital preprocessing and telemetry in external decoding systems. Moreover, the 43 μW required by an implantable decoding system is 4–6 times less than even the leakage power of state-of-the-art power-efficient DSPs; this implies that when the implantable analog decoder is operating, it should consume at least 4 times less power than an external digital decoder that is doing nothing. The efficiency of our analog architecture arises from its exploitation of subthreshold currents (which are wasted as ‘leakage’ power in traditional digital implementations) to perform computations. A further point of comparison concerns the sizes of the devices required to perform the decoding. In our simulations, a single decoder module implemented in 0.18 μm technology would require less than 3000 μm2, so a system decoding 3 control parameters from 100 input channels would consume less than 1 mm2 of chip area. By contrast, industry-standard low-power DSPs are typically 100 times larger, consuming approximately 1 cm2 of board area (“TMS320C54x BGA Mechanical Data (Document MPBG021C),” 2002). Space limitations and skull curvature restrict the planar surface area of devices implanted within the cranial cavity to approximately 2×2 cm2, so individual components that consume 1 cm2 of area are likely impractical as elements of implantable units.

FUTURE RESEARCH DIRECTIONS Neural Decoding in the Context of Implantable Brain– Machine Interfaces In this section we describe five important emerging areas in the field of neural decoding for brain– machine interfaces: (1) Biocompatibility and Stability: Ideal neuroprosthetics must be capable of implantation in the brain for indefinite periods without damaging or eliciting unintended reactions from surrounding neural tissue, macro- and microanatomically optimized for the appropriate neural targets, and capable of stably recording neural signals of the selected types for indefinite periods of time; (2) Biological versus Artificial Learning: Achieving optimal long-term performance from brain–machine interfaces requires understanding the interplay between artificial and biological learning; (3) Closed-Loop Neuroprosthetics: Neuroprosthetics must be able to adapt in real time based on feedback from sensory input, neural populations, and other environmental variables; (4) Integrated Electronic Systems: Generalpurpose devices suitable for clinical intervention or neuroscientific research must have all functional subcomponents integrated within a single modularized package; (5) Scalable Multi-Region Brain–Machine Interfaces: Brain–machine interfaces should be capable of parallel or networked operation, in which multiple independent modules can operate either independently from neighboring or disparate neural populations without sacrificing performance quality, or while sharing information in a networked fashion in real time.

Biocompatibility and Stability The difficulty of maintaining stable single-unit neural recordings for extended periods has long been acknowledged by electrophysiologists, and while some notable examples have been reported in the literature, such reports tend to be

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the exception rather than the rule. The field of brain–machine interfaces has responded to this problem in two principal ways, at once seeking to optimize electrode design and electrode-tissue interactions for stable long-term recording, while simultaneously exploring a range of neural signals, including local field potentials and unsorted multiunit activity as alternatives to single-unit action potentials. Biocompatibility as a concern for implantable brain–machine interface systems extends to multiple aspects of the implanted system, including the design and materials chosen for the packaging, but we restrict our comments here to neural electrodes. Decoding neural signals presupposes the availability of such signals, and the quality of neural decoding depends not only on the particular algorithms used for the decoding, but also on the quality of the neural signals used as inputs to the neural decoder. An ideally biocompatible neural recording electrode must satisfy two sets of constraints, which respectively concern the static and dynamic properties of the tissue-electrode interface. Ideal static properties of the electrode include the following: (1) Suitability for insertion in a manner that does minimal damage to surrounding neural and vascular tissue; (2) Mechanical stiffness equal to that of the surrounding tissue, in order to avoid tissue-electrode shearing and electrode displacement during head acceleration; (3) Minimal electrode volume to minimize neural tissue displacement after insertion; (4) Shank and recording site geometry optimized for stereotactic placement relative to desired macro- and microanatomic targets; and (5) Tip or recording site geometry optimized for the neural signal of choice. Ideal dynamic properties of a neural recording electrode include the following: (1) Optimized electrode impedance given the local tissue properties and neural signals of interest; and (2) Ability to regulate the response of neural tissue to the presence of the electrode over time. Almost all of these static and dynamic properties represent active areas of current research and

potentially fruitful future work. In particular, microfabrication techniques (Kipke et al., 2008; Wise et al., 2004) and nontraditional materials (such as conducting polymers) (Abidian & Martin, 2008; Ludwig, Uram, Yang, Martin, & Kipke, 2006; Richardson-Burns, Hendricks, & Martin, 2007) present large parameter spaces within which to optimize electrode designs. Additionally, it is well known that the microanatomic environment of a recording electrode changes over time, generally in ways that degrade electrode performance, as neighboring neurons tend to die or recede from the electrode, for example, and astrocytes and microglia ensheath the electrode and change the impedance at the tissue-electrode interface. Consequently, understanding and controlling the dynamic response of neural and neurovascular tissue to neural recording electrodes is also an extremely important area for future research (Bjornsson et al., 2006; Shain et al., 2003). Some differences of opinion exist as to the most appropriate signals to use as inputs for neural prosthetic devices. The difficulty of first reliably obtaining and subsequently maintaining stable single-unit recordings for indefinite periods has motivated the use of other forms of input signal, which in a number of studies have yielded equivalent decoding performance while exhibiting greater stability over time. Action potentials, multiunit activity, and local field potentials associated with movement, imagined movement, and motor planning have all been shown capable of affording neural control of external devices in conjunction with a variety of neural decoding techniques (Andersen, Burdick, Musallam, Pesaran, & Cham, 2004; Mehring et al., 2003; Pesaran et al., 2002; Shenoy et al., 2003; Stark & Abeles, 2007). A related question concerns the ways in which the brain represents motor control information: Precisely what kinds of state space variables do neural populations encode, and how stable are the representations (Rokni, Richardson, Bizzi, & Seung, 2007)? In particular, in the context of neuro-

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A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

motor prosthetics, under what circumstances does neural activity reflect internal signals to muscles, external parameters such as those relating to limb kinematics or dynamics, or higher-level control signals such as those related to movement planning (Kakei, Hoffman, & Strick, 1999)? Questions such as these are particularly relevant to systems such as those in which functional electrical stimulation (FES) of muscles in a paralyzed limb is placed under real-time neural control to restore lost function (Moritz, Perlmutter, & Fetz, 2008).

Biological vs. Artificial Learning A major question in the area of neural decoding to achieve neural control of prosthetic devices concerns the extent to which the brain can learn to decode processed neural data on its own. Given a particular prosthetic interface controlled by neural signals from a subject that receives feedback on decoder performance, what is the minimal decoder required to operate the device reliably? Under what circumstances, and with what degree of reliability, can biological learning compensate for deficiencies in an artificial neural decoder? As indicated earlier in this chapter, experiments in both animals (Musallam et al., 2004; Taylor et al., 2002) and humans (Hochberg et al., 2006) have provided early evidence that users of neural prosthetic systems are capable of adapting to the particular rules governing the control of their brain–machine interfaces, demonstrating improved performance over time even after termination of the learning phases for their software-based decoders. Nevertheless, some of the same experiments have also demonstrated the apparent insufficiency of such contributions from biological learning, and so the degree to which a neural prosthetic system should rely on artificial or biological learning remains unclear. More recent work (Ganguly & Carmena, 2009) has helped clarify some of these issues by underscoring the importance of stable long-term neural recordings for reliable neural decoding and improvements

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through biological learning. In particular, it appears that the brain is capable not only of learning complex decoders, but also of correcting errors in corrupt decoders, provided the decoders are based on stable neural recordings. Much of the existing work on neural decoding has been limited by the inability to maintain stable recordings from multiple units over long time periods, and by the consequent need to retrain decoders at each training session. As biological learning can occur over longer timescales than those set by the instability of typical single- or multiunit neural recordings, optimal conditions for biological learning in the context of neural prosthesis control have rarely been achieved. Recent evidence suggests that a reliably high level of performance can be obtained as the brain forms motor memories in response to learning a decoder based on stable, long-term recordings. The implications of these findings for the design of neural decoders, neural electrodes, and neural prosthetic systems in general remain to be explored and will be an important area for future research. Relatedly, decoder optimality is likely to be application dependent, as specific contexts will determine the constraints and performance criteria to be optimized by a particular neural decoder. Nevertheless, it may be important to ask under what circumstances is it possible to define and construct optimal neural decoders, and to understand how to make tradeoffs between biological and artificial learning that optimize system performance.

Closed-Loop Neuroprosthetics Translating advances in neural decoding into clinical practice will require prosthetic devices, including robotic prosthetic limbs, that confer natural functionality (Aaron et al., 2006; Adee, 2009; Kuiken et al., 2009; Kuniholm, 2009). The form and quality of sensory and direct neural feedback signals are important in enabling devices to achieve such lifelike performances. Therefore, in

A Biomimetic Adaptive Algorithm and Micropower Circuit Architecture for Implantable Neural Decoders

the context of motor prosthetics, realistic sensory feedback from proprioceptive, haptic, and other modalities encoded by physiologic signals will improve motor prosthetic performance (Miguel A. L. Nicolelis, Chapin, & Wessberg, 2007). Implementation of such a system will require simultaneous neural recording and neural stimulation of somatosensory and motor brain regions. An important class of future neural prosthetic systems will involve closed-loop neural decoding and neural stimulation, in which decoded neural firing patterns can be used to control neural stimulation in a feedback loop. This paradigm is applicable to deep brain stimulation (DBS) in which the stimuli delivered depend on sensory data and the state of local neural networks; current DBS systems use fixed, nonadaptive stimulation paradigms. Neural decoding for seizure detection, coupled with stimulation to preempt the seizure in a feedback fashion, represents another potential clinical application for closed-loop neural decoding and stimulation. The scope of clinical problems amenable to solutions of this kind is likely to expand over time.

Integrated Electronic Systems We make reference in the Introduction to recent progress developing modular electronic components of implantable brain–machine interfaces, particularly in the areas of micropower neural amplification, low-power data telemetry, ultra-lowpower analog-to-digital conversion, low-power and power-conserving schemes for neural stimulation, adaptive power biasing of multi-electrode arrays, and energy-efficient wireless recharging circuits (Sarpeshkar et al., 2008). Significant progress has also been made toward integrating and packaging these individual modules into functioning systems, in several cases together with neural recording electrodes (Harrison, 2008; Song et al., 2007; Wise et al., 2004). Work in this area is an important avenue for continuing research toward

brain–machine interface systems that can be used in both clinical and experimental neuroscience. Power efficiency and power management are extremely important concerns in small, biologically implantable systems. The development of high-performance biologically implantable power sources that are safe, compact, and easily rechargeable or schemes for scavenging energy from the biological environment could lift a variety of design constraints on the implantable electronics, as implantable batteries are typically the largest and most massive components of biologically implantable electronic devices.

Scalable Multi-Region Brain– Machine Interfaces Scalability represents an important trend in contemporary neuroscience (Narasimhan, 2004). Neural computations underlying motor and other functions are distributed across a variety of brain regions, so it is likely that optimal control of neural prosthetic devices will be achieved using neural decoders that coordinate input signals from multiple brain regions (Andersen, Burdick et al., 2004; Chapin, 2004; M. A. L. Nicolelis et al., 2003). Coordinating signal processing over multiple input streams derived from distinct regions requires that brain–machine interface systems be designed in a modular fashion that permits individual modules not only to report data independently, but also to share information in a networked fashion in real time. Such scalable, networked systems represent an extremely interesting direction for future research, particularly in light of the observation that even rudimentary artificial networking of neural regions can induce long-term motor plasticity (Jackson et al., 2006). Systems of this kind might be used not only to establish functional connections among multiple regions within an individual brain, but also to connect individual brains to one another.

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CONCLUSION We have described a continuous-time, adaptive, biomimetic algorithm and a corresponding micropower analog circuit architecture for decoding neural signals (Figure 1 and Figure 2). The system is suitable for decoding both local field potentials and mean spike rates; in experimental trials it has successfully decoded both discrete decisions from the parietal cortex of a behaving primate (Figure 13) and continuous trajectories from the thalamus of a behaving rodent (Figure 11). The algorithm and architecture presented here offer a practical approach to computationally efficient neural signal decoding, independent of the hardware used for their implementation. While the system is suitable for analog or digital implementation, we discuss how a micropower analog implementation trades some algorithmic programmability for reductions in power consumption that could facilitate implantation of a neural decoder within the brain. In particular, circuit simulations of our analog architecture (comprised of standard building-block circuits described in the section entitled “Functional Circuit Subunits of the Decoder Architecture”) indicate that a 100-channel, 3-motor-output neural decoder can be built with a total power budget of approximately 43 μW. Our work could also enable a 100,000-fold reduction in the bandwidth needed for wireless transmission of neural data, thereby reducing to nanowatt levels the power potentially required for wireless data telemetry from a brain implant. Our work suggests that highly power-efficient and area-efficient analog neural decoders that operate in real time can be useful components of brain–implantable neural prostheses, with potential applications in neural rehabilitation and experimental neuroscience. Through front-end preprocessing to perform neural decoding and data compression, algorithms and architectures such as those presented here can complement digital signal processing and wireless data transmission

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systems (Figure 1), offering significant increases in power and area efficiency at little cost.

ACKNOWLEDGMENT This work was supported in part by a grant from the National Institutes of Health, NS-056140, and in part by an MIT–CIMIT Medical Engineering Fellowship. Neural data were provided by Sam Musallam of the Andersen Laboratory at the California Institute of Technology, and by Hector Penagos of the Wilson Laboratory at the Massachusetts Institute of Technology.

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KEY TERMS AND DEFINITIONS Adaptive Algorithm: In the present work we use the term “adaptive algorithm” to denote an optimization process in which a filter adjust its own transfer function. The associated filter is referred to as an “adaptive filter,” and the adaptation process is referred to as “tuning.” In other scientific literature, the term “adaptive algorithm” is sometimes used to refer to an algorithm that

modifies its behavior in accordance with the computational resources available. Biomimetic: Designed according to principles used by biological systems. Brain–Machine Interface (BMI): A system enabling direct transfer of information between a brain and a machine; when the machine in question is a computer, such a system is sometimes called a “brain–computer interface” (BCI). The transfer of information is typically “direct” in one of two senses: Either information obtained from the brain remains encoded in physiologic signals (such as intracortical or electroencephalographic [EEG] voltage traces) that are recorded from the brain and transmitted to an external system for decoding, or information is sent to the brain by electrochemical stimulation of neural tissue. The present chapter focuses on brain-machine interfaces of the former kind, which decode information from electrophysiologically recorded neural signals. Motor Control: “Motor” in the neurophysiologic sense refers to muscle activity and associated body movements coordinated by the nervous system; such neural coordination is referred to as “motor control.” Neural Cell Ensemble (Neuronal Ensemble): A group of neurons whose activity is considered simultaneously. The cells in the ensemble may or may not share functional or anatomic connections. Neural Decoding: A process by which physiologic neural signals are translated into objectively defined state variables (which may correspond to parameters such as position, speed, or discrete values from a set of possibilities). Neural Prosthetic (Neural Prosthesis): The term “prosthesis” refers to an artificial replacement for a body part, usually one whose function has been lost or impaired. The term “neural prosthesis” may refer literally to a system designed to replace or augment part of a brain or nervous system. The term may also refer to a prosthetic device, such as an artificial limb, that is controlled by a brain–machine interface.

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Parietal Cortex: An anatomic region of the cerebral cortex whose functions relate primarily to spatial orientation and proprioception (the perception of body positioning), based on integration of information from multiple sensory information pathways. Experiments described in the present chapter involve neural data from the macaque monkey posterior parietal cortex, which computes transformations between eyebased and head-based coordinate systems, and which has been correspondingly implicated in the coordination of limb and eye movements for reaching and grasping. Receptive Field: The “receptive field” of a neuron consists of all locations from which a stimulus is capable of altering the firing pattern of the neuron in question. When the cell of interest belongs to a sensory pathway, its receptive field may be spatial, corresponding to regions of the visual field for neurons of the visual system or to regions on the body surface for touch-sensitive

neurons; the receptive field may also exist in a more abstract parameter space, corresponding, for example, to ranges in pitch for neurons of the auditory system. Functionally connected neurons may also be considered to comprise receptive fields for one another: One neuron whose firing constitutes a stimulus for a second neuron can be considered part of the receptive field of the second cell. Thalamus: A structure in the brain whose functions include transmitting sensory information to the cerebral cortex, and relaying motor information from the cerebral cortex to the peripheral nervous system. The present chapter describes a set of experiments involving neural decoding from “head direction cells” of the rat thalamus, which are neurons within this region of the rat brain that are known to exhibit receptive fields tuned to specific orientations of the head relative to the environment.

This work was previously published in System and Circuit Design for Biologically-Inspired Intelligent Learning, edited by Turgay Temel, pp. 216-254, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Section III

Tools and Technologies

This section presents an extensive treatment of various tools and technologies existing in the field of clinical technologies practitioners and academics alike must rely on to develop new techniques. These chapters enlighten readers about fundamental research on the many methods used to facilitate and enhance the integration of this worldwide phenomenon by exploring software and hardware developments and their applications—an increasingly pertinent research arena. It is through these rigorously researched chapters that the reader is provided with countless examples of the up-and-coming tools and technologies emerging from the field of clinical technologies.

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Chapter 3.1

Avatars and Diagnosis: Delivering Medical Curricula in Virtual Space Claudia L. McDonald Texas A&M University-Corpus Christi, USA

ABSTRACT Medical education faces a host of obstacles in coming decades requiring that it rethink the way it delivers medical curricula, especially with regard to critical thinking and differential diagnostics. Traditional didactic curricula must be coupled with emerging technologies that provide experiential learning without risk to patients while not eroding the clinical effectiveness of advanced medical learners. Virtual-world technologies have advanced to a level where they must be considered as a method for delivering medical curricula effectively and safely; moreover, research must establish that such systems are reliable and valid means for delivering medical curricula; otherwise, they are of no use to the medical community, reDOI: 10.4018/978-1-60960-561-2.ch301

gardless of their technical sophistication. Pulse!! The Virtual Clinical Learning Lab is a project designed to explore these issues by developing a reliable and valid learning platform for delivering medical curricula in virtual space, space usually reserved for entertainment videogames.

INTRODUCTION Medical education must reform to stay abreast of rapid change. Baby-boom retirements from academic faculties and other demographic factors are creating looming shortages of medical personnel, especially physicians and nurses (Rasch, 2006). Shorter hospital stays and medical residents’ workweeks are reducing clinical training opportunities and expertise development (e.g., Verrier, 2004). Continuously changing warfare and terrorist

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Avatars and Diagnosis

technology and methods drive a need for rapid deployment of training for continuously evolving medical treatment (Zimet, 2003). Battlefield fatalities from potentially survivable injuries might have been prevented with consistent application through more effective training of U.S. armed forces’ Tactical Combat Casualty Care (TCCC) guidelines (Holcombe, et al., 2006). Simulation is an established, effective means for providing medical training and education. Issenberg et al. (2005) conclude “that high-fidelity medical simulations facilitate medical learning under the right conditions” (p. 24). This first comprehensive review of 34 years of research on the efficacy of simulation in medical training found that “high-fidelity medical simulations are educationally effective and simulation-based education complements education in patient care settings” (Issenberg et al., 2005, p. 10). Issenberg notes that the medical community’s increasing interest in simulation and virtual reality (VR) is driven in part by “the pressures of managed care” that lead to fewer clinical opportunities for medical learners, including practicing physicians constrained by shrinking financial resources to spend less time keeping abreast of developing medical topics. Issenberg cites a 1997 study by Mangione & Nieman, which found a decline in bedside acumen and urged “the use of simulation systems for training” (Issenberg et al., 2005, p. 12). Reznick and MacRae (2006) observe that well-established motor learning theory (Fitts & Posner, 1967), as it applies to surgical training, ratifies the use of simulators in the acquisition of skills through three stages – cognitive, integrative, and autonomous. Ericsson (1996) applies a thicker description directly to surgical training with the concept of “deliberate practice” defined as “repeated practice along with coaching and immediate feedback on performance” (Reznick & MacRae, 2006, p. 2665). Watters et al (2006) propose that substantial learning occurs through what has become standard entertainment-game architecture, including

instantaneous feedback, a rising scaffold of challenges, visible goal indicators, personalization and customization, fluidity and contextual grounding. Watters and Duffy (2004) propose “a framework of motivational constructs” (p. 16) found in games that are applicable in developing interactive healthrelated software: self-regulation, or autonomy; relatedness, which includes role-playing, narrative and personalization; and competency, or self-efficacy built upon completion of meaningful tasks. Virtual-world technologies are emerging as the next big step for medical simulation. “We are in a complete revolution in surgical education,” according to Richard Satava, a professor of surgery at the University of Washington. “If history serves us well, such a revolution occurs only once every hundred years, as evidenced by the fact that the last revolution began in 1908 with the Flexner Report. Whatever is established during these next 10 years is likely to endure for the next century” (Satava, 2008, p. 2). Entertainment has been the main use of virtual reality, but virtual training in various formats also has been explored. In the academy, there has been incidental development of human-simulation trainers anchored to computer-assisted case presentations for surgeons, U.S. Army combat medics engage electronic role-playing games aimed at clinical soft-skills education. The military originally developed the state-of-the-art America’s Army online war game for recruitment, using the ‘first-person shooter’ game genre. A modified version of America’s Army became a standard aspect of some training at the U.S. Military Academy after research showed that those who used virtual-reality training achieved better scores in a in ground-maneuver warfare course than those who had used traditional print materials. Farrell et al. (2003) said in conclusion: “The result is excited and motivated cadets who take ownership of their military development, in terms of what they study, when they study it, and how both cadets and instructors receive feedback on performance” (p. 1).

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This chapter will address specific issues based on the development of Pulse!! The Virtual Clinical Learning Lab, including funding and organization; the interface among various fields of expertise brought into play by Pulse!! development, and the process of development that evolved from the project. This chapter also will recommend a model for developing medical education in virtual space with regard to funding, management, case-based development, curricular standards and embedded evaluation.

BACKGROUND Military and civilian health-care delivery systems are at risk from medical errors, their related costs, and rapidly diminishing educational and training resources. The Institute of Medicine (IOM) reports that errors are responsible for 44,000 to 98,000 deaths annually; and the Agency for Healthcare Research and Quality estimates that related costs are between $17 billion and $29 billion annually. Patients’ length of stay has shortened dramatically, giving students significantly fewer and briefer opportunities for clinical experience, including exploring further diagnostic options and examining treatment results (Kohn, Corrigan, & Donaldson, 1999). A growing shortage of nurses and looming shortage of physicians contribute to the safety issue, which is compounded by case-mix intensity at health-care facilities – more and sicker people requiring more attention from a dwindling base of caregivers. A number of demographic factors, including baby-boom retirements – 55 percent of nurses and a third of physicians intend to retire between 2011 and 2020 (Hader et al., 2006; AAMC, 2006) – and increased numbers of women physicians since the 1970s, which due to child-rearing reduce overall full-time equivalents (FTEs), are driving an estimated shortage of 200,000 physicians, beginning in 2010. Even conservative estimates, such as that reported

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in 2005 by the Council on Graduate Medical Education (COGME), indicate that the domestic shortage of physicians will be no less than 85,000 (COGME, 2005). The U.S. Census Bureau predicts that the number of elderly will increase 104 percent from 2000 to 2030; meanwhile, firstyear medical-school enrollment per 100,000 has steadily declined since 1980 (Campbell, 1996; AAMC, 2006). The U.S. Department of Health and Human Services warns that, despite increased numbers of nursing programs, projected demand for nurses substantially exceeds projected supply. Capacity in nursing education programs is at a maximum, faculty are retiring in unprecedented numbers, care patterns are changing, and according to the National League for Nursing (2009), 99,000 qualified nursing students, at all levels, were denied admission in 2006. The development of three-dimensional virtual simulation comes at a moment in the history of American health care when the treatment paradigm is shifting to include greater emphasis on care as the population ages. The current system is built on the concept of cure, but that goes against the grain of U.S. demographics and society, which are dominated by aging baby boomers living longer than any previous generation due to pharmaceutical intervention, surgical advances, and the successful treatment of chronic disease. U.S. medicine is evolving a new concept of care that has implications for medical education and underscores the need for virtual learning space. Medical practice in the future will become more complex as the concept of care for older patients matures and technology continues to advance. Virtual simulation of such complexity makes possible the kind of education and training that will rise to these challenges. Modeling and simulation are generally recognized in the medical community as performance enhancers. A U.S. Food and Drug Administration panel has recommended virtual-reality training for carotid artery stenting: “Given the advances in technology and the accruing evidence of their

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effectiveness, now is the time to take stock of the changes we can and must make to improve the assessment and training of surgeons in the future” (Reznick & MacRae, 2006, p. 2668). An Accreditation Council on Graduate Medical Education (ACGME) “toolbox” of clinical assessment methods recommends simulation and models as the most desirable means for evaluating residents’ competence in medical procedures and the next best method for the development and execution of management plans, investigatory and analytic thinking, knowledge. and application of basic science and ethical practices. Simulators and models are deemed potentially applicable to physicians’ self-analysis of performance and where improvements might be made (AGCME Outcomes Project, 2000). Medical-education is shifting, perforce, from traditional process evaluation to performance outcome. The Association of American Medical Colleges (AAMC) notes that “measurement of performance outcome is gradually replacing process evaluation alone” due to “increasing regulatory and payor influences [that] constrain the conduct and governance of clinical teaching activities” (2005, p. 6). The ACGME performance now requires outcomes evaluation in six domains of competency: medical knowledge, patient care, practice-based learning and improvement, systems-based practice, professionalism, and interpersonal and communication skills (2006). Responding to the IOM report, a partnership between the National Academy of Engineering and the Institute of Medicine formed to conduct a study of health care mistakes and to identify future remedies. The academy’s 2005 report, Building a Better Delivery System: A New Engineering/ Health Care Partnership, is a culmination of that team’s research. The report concludes that the “U.S. health care industry neglected engineering strategies and technologies that have revolutionized quality, productivity, and performance in many other industries,” and calls for an array of powerful new tools in medicine (p. 1). Virtual

environment simulations are being explored for use in various formats and are one solution for a necessary paradigm shift in clinical health-care education. Virtual-reality learning takes into account the remarkable development of computer technologies as tools for teaching the “Net Generation,” born since 1982, of which 89.5 percent are computer literate, 63 percent are Internet users, and 14.3 percent have been using the Internet since age four (U.S. Department of Commerce, 2002). Research by the Kaiser Family Foundation found that American children four to six years of age “are living richly media-centric lives”—90 percent using “screen media” two hours a day; 43 percent using a computer and 24 percent playing video games several times a week (Rideout, 2006, p. 33).

DESIGNING MEDICAL EDUCATION IN VIRTUAL SPACE The Pulse!! platform design is consistent with the Whitelock-Brna-Holland (1996) model for conceptual learning in virtual reality, which posits three essential properties of such domains for “implicit” or experiential learning: representational fidelity, “the extent to which the environment that is simulated is familiar to the user;” immediacy of control through a continuum of media from imitative haptics to command-line coding; and presence, viewed “as subjectively reported phenomenon. .. and as a set of repeatable objective measures” (p. 6). The authors conclude that “a high presence value and a high degree of immediacy of control (i.e., autonomy and interaction) leads to a high degree of implicit learning. .. the ability to perform tasks consistent with an improved understanding” as opposed to mere “conceptual understanding,” which is the projected outcome of low-value immediate control (p. 7). Thus conceived, the Pulse!! project models a strategy for providing cross-disciplinary expertise and resources to educational, governmental and

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business entities engaged in meeting looming health-care crises with three-dimensional virtual learning platforms that provide unlimited, repeatable, immersive clinical experience without risk to patients anywhere there is a computer. Pulse!! has been in development since 2005 by an international team of academicians, subject matter experts in medicine and human-factors research and top-flight simulation industry professionals. The project has been supported so far by more than $14 million in federal grants through the Office of Naval Research. Based on industry standards, development from scratch of the base layer of a platform such as Pulse!! requires $25 million to $30 million. Government investment is the most likely source of funding for virtual-world research and development, which, at least for the time being, competes for private investment with the lucrative entertainment industry. Pulse!! products will be grounded in research findings and equipped with tools and generators that enable clients to author cases and create scenarios within a variety of virtual environments. The Pulse!! platform is being rigorously researched, developed and tested extensively for reliability and validity, yielding a product for delivering medical and health-care curricula with confidence.

ESSAY: ON AVATARS AND DIAGNOSIS In the beginning, Pulse!! was about establishing meets and bounds. In general, the project was to (1) develop a (2) training platform using (3) game-based technologies for (4) health-care providers. Decision-making formed around those four general topics. Pulse!! was envisioned as a high-fidelity virtual-world platform from the beginning, because, in the author’s view, only intensively realistic virtual reality provides the immersive experience required for problem-based medical education in real time. Pulse!! thus distinguishes itself from

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“computer-assisted” learning, which delivers didactic material electronically, and interactive platforms that render cases in abstract imagery or so-called “stick figures.” Pulse!! is a wholly new approach in curriculum-delivery and virtual-world development: Every case is generated from standard medical curricula, rendered in virtual space and evaluated for learning effectiveness and the capability to measure/assess outcomes against objectives; moreover, it’s portable and thus costeffective. The Pulse!! virtual learning lab can be run wherever there is a properly configured and sufficiently powered computer or network. Pulse!! was a research project from the beginning because the virtual-reality platform had to be shown to be a reliable and valid delivery system for medical education – a first in the field of VR simulation. Evaluation was the “third leg of the milking stool” that gave the project its stability amid the demands of academic medicine. It wasn’t enough to depict cases in an immersive, interactive virtual environment. Medical educators want to know whether simulators actually teach critical thinking in the appropriation of medical curricula. It was critical that innovation in the delivery system not alter the substantive content of the accredited curricula. Pulse!! is a teaching strategy not a curriculum generator. The curriculum decision, moreover, was a major decision in developing the platform: Everyone got around the table – medical subject-matter experts, evaluators and game producers – to determine what cases actually would be presented in virtual space from the infinite spectrum of all possible standard medical cases.

Collaboration There is no one way to structure management and execution of a virtual-world project such as Pulse!!, but the following discussion touches upon some major issues that may confound those seeking to develop and produce virtual-reality (VR) educational platforms that can be used

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with confidence to deliver complex curricula to high-level, motivated learners. These issues fall under the general topic of collaboration and include funding, technology and evaluation. This discussion will conclude with what lies ahead for the Pulse!! research and development project. Collaboration is a polite term for the inevitable culture clash that makes for spirited conversation around the conference-room table. Academics, subject-matter experts (SMEs) and business executives don’t always operate from the same point of view; in fact, they seldom see the world – or their collaborative project – in quite the same way. The product of their collaboration is a synthesis of often antithetical standpoints. Pulse!! experience indicates that there must be a “decider” at the heart of the process. Achieving consensus is improbable in a project that brings three such diverse fields into collaboration. When the project is research and development, the principal investigator (PI) – and, in this case, the chief fundraiser – is the most likely decision-maker if for no other reason than to ensure the integrity of the research. In corporate language, the PI is the chief executive officer (CEO), the captain of the ship, whose decisions make or break the project. Still, the project’s ultimate success depends on collaborative performance. The team is interdependent such that when one piece falters, all falter. Each piece of the project is continually modified as other pieces provide input, feedback, and expertise. The PI may be the captain of the ship, but midcourse corrections are numerous and inevitable, especially when the course is uncharted and the seas unknown. As a public/private venture – funded by government but driven in part by private-sector entrepreneurs – Pulse!! actually leans toward being a small business. The Pulse!! project lays a foundation through learning research and development of the core engines and features of the learning platform. As a business venture, however, the Pulse!! collaboration also must be aware of market developments, technological shifts, and

medical trends. The three-way conversation that informs Pulse!! development, production, and research never ends, and neither does the role of the PI/CEO. There always must be a captain/ decider at the helm. Business executives and their employees are constrained by costs and are wont to put parameters on the PI/CEO’s vision. The PI/CEO, meanwhile, pushes back with expectations that the product will be technologically innovative, even trailblazing, as well as pedagogically sound. SMEs bring deep knowledge to the depiction of medical cases in virtual reality, and their desire for realism – without which the integrity of the Pulse!! vision would be compromised – often reaches far beyond what the business executive thinks is either necessary or affordable. The evaluation team, meanwhile, sets a high standard for the platform’s interface, tutorials, and instructional materials to ensure that learners are not inhibited by the platform itself in their appropriation of the all-important curricula. The military, meanwhile, wants bang for its buck and so does the U.S. Congress. The clock is always ticking, and everyone feels the pressure. The Pulse!! project’s private-sector partner describes stumbling blocks for VR educational products based on years of experience in the interface of academe and the “serious games” industry (McDonald, 2009). Some things academics think are difficult are actually easy; such as creating demonstrations with off-the-shelf VR engines. On the other hand, some things academics think are easy are actually difficult; such as publicly showing a product in development, developing user interfaces and depicting the world “exactly as it looks in real life” (McDonald, 2009, p. 29). And the hardest things of all? “Getting a subject matter expert to tell you what you should put into the game” and “getting all the SMEs to agree” (McDonald, 2009, p. 31). The collaborative relationship is symbiotic, but there must be a single authority responsible for the outcome of the whole emulsion of virtual-world technologies, medical curricula, and learning

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evaluation. For Pulse!! the single authority is the principal investigator/producer, whose position is defined at least in part by her pivotal role in fund-raising and in recruiting and coordinating subject-matter experts. It is a collaborative partnership with genuine respect on both sides – but someone must be in charge, at whose desk the buck stops.

Terminology Matters Sorting out nomenclature issues among collaborators is a significant undertaking. It matters in how the project’s story is told to the outside world wherein resides customers, critics, and potential investors. One nomenclature issue, for example, has been whether the Pulse!! project is about “training” or “education.” The medical field regards training as something one can do with simians, while “education” is what must occur for medicine to be practiced critically as well as skillfully. “Training,” however, is something taken seriously for human beings by those in the academy whose field is education. It’s not just for simians; ergo, there is a bit of creative tension in how to speak internally and externally about Pulse!! It’s a full-spectrum “learning platform,” from training to continuing education for healthcare professionals, terminology that also finesses the education-training debate. And then, there’s the term “game,” which is the bread and butter of the project’s privatesector partner but which is anathema in selling Pulse!! to congressional committees and military review boards. Games are not serious business and education, except to gamers and companies that develop games for entertainment or serious simulation. Games are “played,” while simulations are “learned.” Pulse!! collaborators have crafted the phrase “learning platform,” yet there has been a steep learning curve, especially in private conversation, in not referring to Pulse!! as a game. How to overcome the nomenclature issues? Clarify, clarify, clarify, and be consistent as pos-

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sible, especially in public conversation but also in private consultation among collaborators.

Case Development There’s a scene in the film “A River Runs Through It,” in which a young man is being taught to write by his exacting father, an educated Presbyterian pastor. It’s a study in refinement, as each effort is returned to the young writer with the instructions to use “half-again” as many words in the next draft. Because few VR learning projects have unlimited resources, they must accomplish as much as possible with no superfluous features, which requires a similar kind of “half-again” refinement. It is traditional in medical education to teach by body system – endocrine, respiratory, muscular – or by clinical area – pediatrics, geriatrics, emergency medicine and the like. Pulse!!, however, had to take a different approach – “outside in” – because the level of visual fidelity required in a whole-body model, especially internal fluid dynamics, is currently beyond reach of virtualworld technologies, given available funding and the state of development of programming and animation. Pulse!!, moreover, had something to prove – to the military and to Congress but also among its collaborators. The project was conceived as research, after all, and even though everyone at the table anticipated the outcome, the data had to speak for themselves. The Pulse!! learning platform had to show it was more than computer-assisted learning, that it was a trailblazing application of virtual-world technologies used by millions in the entertainment game industry. The original case design for Pulse!! was rooted in the need to show the platform’s diversity, the width, breadth and depth of it as a delivery system for medical curricula and that it could deliver at every level of medical training and education, from entry-level first-responder skills to residents and practicing physicians doing continuing education.

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First-generation case selection was tempered by the availability of financial and technical resources and putting together the talent team, as well as the need to deliver results for government and military stakeholders. Pulse!! collaborators decided not to develop virtual patient physiology by body-systems or medical discipline; instead, collaborators went with a whole-body physiology approach, from outside in, and cases that initially would not require technically difficult invasive procedures. The first four cases developed were for various types of shock common to battlefield trauma. Consequently, Pulse!! case-development delivers problem-based curricula stemming from trauma and transmitted through the experience of the project’s medical subject-matter experts. Every aspect of the virtual-world medical facility and operation depicted on-screen is shaped by experience and established medical practice. All interactive components of the platform reflect real-world possibilities and probabilities; moreover, the patient’s physiology is programmed to be responsive to treatment in real time. If the problem-based case is not properly understood, diagnosed, and treated, the patient suffers; therefore, learners are required not only to know what procedures must be performed but must think critically and differentiate amid several possible diagnoses. Meanwhile, an on-screen clock ticks off the minutes, cardio-vascular monitors operate realistically, and sometimes the virtual patient’s condition deteriorates rapidly. Pulse!! problem-based cases in virtual reality offer repeated practice for gaining expertise in the simulated conduct that prevents real-world errors. In theory, iterative training in VR leads to a level of expertise in which there’s less hesitation and less chance for error on procedures that have been done many times in virtual space. Pulse!! is predicated in part on the assumption that opportunities for practicing a wide range of clinical skills will continue to decline in coming years, such that the number and range of opportunities offered won’t

offer sufficient opportunity to achieve optimum learning outcomes. VR technology is poised to fill that void with immersive, experiential, iterative learning experience. VR also offers the possibility of training practicing health-care professionals in new techniques that require repeated practice to move from novice to expert. Moreover, it’s not just a matter of physical dexterity but also mental dexterity, which makes the immersive VR environment all the more applicable not only to medical education but also continuing education.

Engines Whether to build your own engines or modify commercially available products off-the-shelf was a major decision for the Pulse!! project. The collaborators decided it was better to start from scratch, because it made little difference whether project resources were invested in purchasing and modifying someone else’s product or developing something new; moreover, existing engines could not produce soft-tissue and fluid effects without major, time-consuming modifications. Time and money considerations for both strategies came down to a wash, which doesn’t mean there wasn’t a grand debate within the development team. Those in favor of purchasing commercial engines argued that case-development would begin sooner. Another group also postulated that Pulse!! could be developed as an open-source modification of an existing core engine with which others could create their own cases. Quality-control, however, would have required an enormous expenditure of time and resources. There would have been no guarantee that others would create Pulse!! cases in compliance with accreditation standards; moreover, it would be difficult to establish the platform’s reliability and validity had it been an open-source project built by modifying existing virtual-world development engines. The fact that no engine could emulate human physiology was the capstone argument in favor of building from scratch.

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Funding Funding is a major issue in developing virtualworld educational platforms. It requires upward of $30 million to develop a complex commercial entertainment videogame, and that level of funding is simply not available from academic granting sources. Private investment is an unlikely source for educational learning platforms, because entertainment is much more profitable and the educational genre is not yet a proven product. Public investment has driven Pulse!! development through the Office of Naval Research, but it has come incrementally – almost $15 million since 2005 – so development has been relatively slow compared with the entertainment industry. Eventually, Pulse!! is expected to pay for itself through licensing and client arrangements for case development, but not before the learning research is complete – and not before the platform includes robust case-authoring tools and assets. Congressional funding drove some significant strategic decisions for the Pulse!! project; for example, the project’s base of operations; money needed to flow to the congressional district in Texas where Pulse!! was conceived. That meant creation of a new production studio far removed from the private-sector partner’s corporate headquarters in Maryland. Logistical issues have sometimes been inconvenient but generally not insurmountable; and in any case, the project’s “geography” has not inhibited Pulse!! from achieving its strategic goals. Funding for ventures such as Pulse!! does not match the level of general interest in virtual-reality as a simulation strategy for enhancing medical education. There are few domestic programs similar to that recently announced by the South Korean government, which will provide the equivalent of $63.5 million in seed money for the so-called serious-games market. The government hopes that market will grow to almost $400 million by 2012 (Korea IT Times, 2009). Nowhere near enough private capital has been attracted to educational serious games such as Pulse!!

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Hefner’s market study (2007) concludes that while VR technology will be in higher demand as a medical-training strategy over the next 10 to 15 years, development costs will continue to be prohibitive for the private sector. (The study suggests that establishing technological standards for VR health-education systems would lead to more rapid commercial development of learning platforms such as Pulse!! [p. 107].) It is likely, then, for the foreseeable future, that government funding similar to that of the South Korean plan will be key to seeding the “serious-games” market with sufficient resources to establish, among other things, that VR medical education is reliable and valid and that technologies and conventions common to entertainment videogames are sufficiently playable to be of value to emergent generations of health-care learners, from first responders to medical residents. Interest in these developments, meanwhile, remains high, as evidenced by the Robert Wood Johnson Foundation’s sponsorship of the Games for Health program. The American College of Surgeons (ACS) has demonstrated its general interest in VR technologies as well as its particular interest in Pulse!!, which was demonstrated by invitation in 2007 at the ACS Clinical Congress in New Orleans, La. U.S. House and Senate caucuses have formed around modeling and simulation technologies with annual demonstrations on Capitol Hill. Both major U.S. political parties have supported congressional resolutions calling for public-private partnerships in developing these technologies, including virtual-world simulation, toward their having a more significant role in military and civilian training and education. The U.S. military has a longstanding interest in simulation technologies, which are used extensively not only for training but also for strategic planning. Pulse!! is a logical extension of the military’s continued exploration of computer-based simulation, which since 2002 has supported the remarkably popular multi-player online game America’s Army, an adaptation of

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which has proved effective in training cadets at the U.S. Military Academy (Farrell, 2003). To date, no definitive cost-benefit analyses have been completed on the use of virtual-reality systems in health-care training and education. Rizzo & Kim (2005) observe: “Without such cost-benefit proofs, healthcare administrators, and mainstream practitioners who are concerned with the economic ‘bottom-line’ may have little motivation to spend money on high tech solutions if there is no expected financial gain” (pp. 15-16).

Future Directions Pulse!! research indicates that the platform is a viable delivery system for medical curricula and that users not only enjoy using it but believe it is an effective educational strategy they would recommend to others. Three generations of problembased cases have been developed, including firstresponder training in tactical combat casualty care (TCCC). The project has reached a plateau from which to launch its next major phases, including the following: •

•

A case-authoring system is in preliminary stages of development. These tools will enable medical trainers and educators to develop problem-based cases from the accumulated assets of the Pulse!! learningplatform database. Case-authoring development also includes creation of new assets for the database, including virtual personnel, equipment, environments and conditions that are not associated with early generations Pulse!! cases. Pulse!! will be integrated with standard medical reference curricula that learners will be able to access from the platform. This component will be one with an integrated “smart tutoring” system capable of assisting learners before, during or after engaging a problem-based case.

•

•

Long-range Pulse!! development calls for creating a server-based, multiuser system within which teams of learners will assume roles and work cases. It will not be unlike “America’s Army” in this regard. This development also will include inter-user voice communication, and will not require that users be in the same location to train together. Pulse!! collaborators are exploring whether voice-recognition technology is sufficiently advanced that it can be integrated with the platform such that users can exchange oral conversation with virtual medical personnel.

CONCLUSION The Pulse!! project is predicated on the belief that virtual-world technologies, which have in recent years achieved a remarkably high level of realism, present medical education with an opportunity to appropriate these media not only to convey curricula but to make them come alive, as it were, presented as problem-based cases that induce critical thinking and differential diagnostics. Continued technological development, moreover, will only enhance the educational community’s ability to present curricula in dynamic, realistic ways that improve performance and reduce medical errors due to insufficient clinical training. These technologies never will replace other means of providing medical curricula, but will so dramatically enhance medical-education experience that the whole spectrum of medical and paramedical practitioners, from first-responders to practicing physicians will become more proficient in practice. In effect, over time, Pulse!! collaborators believe that this cutting-edge project is but one of many that will change the face of medical education. Pulse!! collaborators are absolutely persuaded that VR medical-education learning platforms must rise to the high standard of reliability and validity through rigorous field testing and evaluation

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that is embedded in the development process from the inception of a learning platform. The medicaleducation community will hold VR technologies to this high standard, so it must be an assumption for any who would contribute to this significant paradigm shift in training and educating medical and paramedical practitioners. Adoption of the technology alone will require a quantum leap for the medical-education community. It simply must be able to assume that VR technologies have demonstrated value as an educational strategy. The Pulse!! project provides a model based on experience since 2005 of the collaborative process required to achieve the high standards and goals of medical education in virtual reality. It is a collaboration fraught with natural tensions and competing interests, none of which are insurmountable but all of which must come into play as each takes ownership of its piece of a complex development process aimed at producing VR learning platforms that will enhance and improve U.S. military and civilian health care for decades to come.

Accreditation Council for Graduate Medical Education (ACGME) Outcomes Project. (2006). Educating physicians for the 21st century. Chicago, IL: Author.

NOTE

Farrell, C. M., Klimack, W. K., & Jaquet, C. R. (2003). Employing interactive multimedia instruction in military science education at the U.S. Military Academy. In Proceedings of the Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC); Orlando, FL, Dec. 1-4, 2003.

Texas A&M University-Corpus Christi technical writer Ronald E. George contributed to this chapter.

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This work was previously published in Serious Game Design and Development: Technologies for Training and Learning, edited by Jan Cannon-Bowers and Clint Bowers, pp. 233-245, copyright 2010 by Information Science Reference (an imprint of IGI Global).

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Agile Patient Care with Distributed M-Health Applications Rafael Capilla Universidad Rey Juan Carlos, Spain Alfonso del Río Universidad Rey Juan Carlos, Spain Miguel Ángel Valero Universidad Politécnica de Madrid, Spain José Antonio Sánchez Universidad Politécnica de Madrid, Spain

ABSTRACT This chapter deals with the conceptualization, design and implementation of an m-health solution to support ubiquitous, integrated and continuous health care in hospitals. As the life expectancy of population grows in modern societies, effective healthcare becomes more and more important as a key social priority. Medical technology and high quality, accessible and efficient healthcare is currently demanded by citizens. Existing technologies from the computer field are widely used to improve DOI: 10.4018/978-1-60960-561-2.ch302

patient care but new challenges demand the use of new communication, hardware and software technologies as a way to provide the necessary quality, security and response time at the point of care need. In this scenario, mobile and distributed developments can clearly help to increase the quality of healthcare systems as well as reduce the time needed to react to emerging care demands. In this chapter we will discuss important issues related to m-health systems and we deeply describe a mobile application for hospital healthcare. This application offers a modern solution which makes more agile doctor and nurse rounds on behalf of an instant online access to patient records through

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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wireless networks. We also provide a highly usable application that makes simple patient monitoring with handheld devices.

INTRODUCTION The origins of health telematics and telemedicine did mainly focus on the benefits of communicating and making available medical information from a patient to a remote medical expert instead of having to displace the injured person to a health centre (Bashur, R., 1997). Thus, telemedicine aimed to support people at the point of care wherever, for different reasons, neither the health professional nor the patient could easily travel to meet each other face to face. In this context, multiple new scenario were imagined taking into advantage Information and Communication technologies (ICT) to provide care to people in isolated regions, emergency situations or environments where a difficulty exists to displace a patient who cannot receive on site medical attention. At a parallel pace, medical informatics started to devote significant efforts to deploy health information systems that ensure medical data availability “at the point of care”. Both Hospital Information Systems (HIS) and Department Information Systems (DIS) aimed to provide health professionals with adequate tools that may integrate all the medical data required by health staff (medical doctors, nursery, administrative, health managers, etc.), to treat a patient (Winter A., 2003). Consequently, the concept of Electronic Health Record (EHR) raised and, with diverse levels of success at the market level, important standardization work have been active (CEN TC251, ISO215, HL7) making efforts to structure in a secure and efficient way the enormous amount of data that can be associated to a person´s health history (Dolin R., 2006). However, the traditional view of Medical Informatics focused more on those situations where the health professionals are, for instance, present at their hospital or care centre office providing a consultation service rather than

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those scenarios that oblige them to be displacing in order to assist in-bed patients in the hospital or elderly and disabled people at home. A solution for the challenge of mobile care support came from the concept of m-health proposed in the ‘90s to exploit the potentiality of mobile communications to assist care professionals or patients “in movement” (Istepanian R.S.H, 2004). The original scenarios of health telematics were changing and technologies were no longer expected to only provide medical information at a fixed computer or medical device where the specialist is supposed to be located, but to “bring” valuable information to the professional wherever he or she is located, in movement, whichever mobile or wireless network is available. Mobile networks were initially used to transmit data from mobile patients; furthermore, the m-health concept started to think about mobile professionals or wirelessly connected citizens who are displaced from a fixed location. A typical example from the first ideas was the utilization of emerging GSM systems to transmit biomedical signals, like an ECG or blood pressure, in emergency situations where an injured patient is moved from a mobile ambulance unit to the hospital (Pavlopoulos S., 1999). Most advanced research on m-health has mainly treated with the unobtrusive and ubiquitous integration of e-care or telemedicine services with remote health information systems through GPRS/ UMTS, WLAN or WPAN technologies as well as provision of context aware health care ad-hoc support including Quality of Service features (Oliver N., 2006) (Wac K., 2007). Innovative m-health solutions for health professionals rely on the possibilities to exploit the potential of next generation advanced mobile or wireless technologies (3G and above, WiFi/ WiMax) together with the availability and affordability of usable and portable electronic devices such a PDA, smartphone, tablet PC or handheld that may provide health care professionals with a full EHR to support medical attention at the patient´s point of care. It is unquestionably that

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successful e-health and m-health systems must be adapted to user’s needs and capabilities. Whether the end user is a nurse or a patient, for example, the system will only be valuable if it provides in a usable and accessible way the health care information needed (Safran C., 2005). System utilization must provide useful, quick and effective information to users who, especially in medical scenarios, cannot afford delays or typical problems with computer tools that makes difficult or frustrating to receive the expected data. Most common communication networks must be considered by an m-health solution and the system should be supported by either available mobile communication technologies or private wireless networks ensuring additional security and performance requirements. In the past 10 years, the enormous advances in PDA capabilities, linked to cost reductions have brought that many health care professionals have familiarized with this kind of portable and easy devices. Initially, non-connected PDAs were used to facilitate professionals a daily agenda or local useful tools such as a vademecum or a small database to store patients´ records. These initial solutions faced a big inconvenient: they could not be connected and synchronization with medical information stored in a health care server was not possible. The increasing integration of WiFi and GPRS / UMTS capabilities into PDA or Smartphone devices has increased the number of potential mobile applications that can be applied to the health domain having always on mind safety and RF restrictions which might be compulsory in certain locations of health centers (DoH, 2007).

M-HEALTH RESEARCH OVERVIEW IN EUROPE At present, the applicability of m-health has spread out in the economically developed countries were emerging mobile networks are having a broader penetration. Both in USA, Canada, Australia

and European countries like Sweden, Norway, Spain, Great Britain or Greece, a range of mobile systems research and development (R&D) have been documented. For the purpose of this chapter, the focus is given on the European activity as the pace of European Commission Framework Programmes as representative of the evolution of m-health trends. A significant boost to m-health R&D was given in European Commission V Framework Programme (1998-2002), where the IST Programme Key Action 1 (System and Services for the Citizen) defined a priority research line called Applications relating to Health. In particular, the cluster of projects called intelligent systems for the health professionals aimed at assisting health professionals to cope with major challenges, including enhancement of health services provision and continuous learning and training, through innovative, user-friendly, fast and reliable IST technologies and systems. The main research work of the projects, covered topics as new generations of computerized clinical systems, advanced interactive environments for remote and timely access of available best medical practice and patient’s medical files from anywhere, collaborative healthcare provision, evidence-based medicine and systems supporting continuous education. The added value of the cluster was centered on interoperability, standardization, clinical validation, awareness and dissemination. Among the large number of projects regrouped in the cluster, two main thematic sub-clusters were identified and reached critical mass and visibility: Intelligent systems for minimally invasive diagnosis and treatment planning and intelligent systems for mobility of health professionals. The projects in this last sub-cluster aimed at enabling health professionals to access remotely the best available medical advice and consult patient medical files whether from the surgery room, the hospital, the patients’ home or site of accident. The work was mainly based on the integration of advanced interfaces (voice recognition, natural language understand-

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ing, etc) and mobile multimedia workstations with embedded means of communication referred to as advance mobile and wireless systems for health. In the Area of Mobile and Wireless Systems for Health, work in the domain of Medical Digital Assistants at EC V FP concerned with the implementation and validation of speech recognition technologies for specific clinical environments such as the WARD IN HAND project (Mobile workflow support and information distribution in hospitals via voice operated, wireless networked handheld devices) in addition to the use of natural language understanding systems in health care like MOBIDEV (mobile devices for healthcare applications). Other experiences focused on evidence based medicine and decision support tools (SMARTIE, smart medical applications repository of tools for informed expert decision), as well as mobile and wireless technologies to support intelligent collaborative environments (CHILDCARE, intelligent collaborative environment for out-of-hospital children healthcare and TELELOGOS, and next generation of methods and tools for team work based care in language and speech therapy). Special consideration was given to the market of Medical Digital Assistants and business models for making a success of these applications; the MEMO project (Accompanying Measure for medical mobile devices) was coordinating these activities. In the area of Portable and Wearable Health Monitoring, EPI-MEDICS (enhanced personal, intelligent and mobile system for early detection and interpretation of cardiological Syndromes); AMON (advanced care and alert portable telemedical monitor); LIFEBELT (intelligent wearable device for health monitoring during pregnancy); WEALTHY (wearable healthcare system); and MOBIHEALTH (mobile health care) were the most mentionable projects. The EC VI Framework Programme (20032007) dealt with m-health solutions by defining areas of interest like the development of telemonitoring personal intelligent systems with

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embedded biosensors; interactive teleassistance systems for patients and citizens; decision supported systems; and intelligent environments for professionals. The outcome of the ALLADIN project (natural language based decision support in neurorehabilitation) was a personal digital assistant for neurorehabilitation activities with speech recognition, DICOEMS (diagnosis collaborative environment for medical relevant situations) developed for portable system to support remote management of medical emergencies. In addition, MYHEART (fighting cardio-vascular diseases by preventive lifestyle & early diagnosis) developed and tested personal intelligent systems to prevent and monitoring heart diseases. As in previous EC FP, the development of Personal Health Systems continue in the VII Framework Programme (FP) by focusing on wearable, implantable or portable health systems, as well as Point-of-Care diagnostic devices, enabling independent living for elderly people. In the VII FP it is proposed to develop Personal Health Systems focusing on non-invasive, multiparameter health monitoring; multianalytical screening and analysis; and disease management within the citizens’ ordinary living environments.

M-HEALTH CHALLENGES, BENEFITS AND DRAWBACKS In addition to future issues such us context aware services or “always best connected” solutions coming from 4G perspectives, the main challenges that currently rise for m-health refers to the secure and interoperable integration with health information. Current commercial tools do still offer proprietary and close solutions whose interoperability is hardly available. Therefore, ongoing developments like the work presented in this chapter opens the way for interconnectivity reducing dependency from ad hoc solutions and offering expected usability and organizational benefits.

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The most important factors that have propelled the implantation of m-health in clinical care routine deal with quality of care, accessibility and efficiency issues. In particular, the time reduction to identify and react to a medical emergency is seeing as an important added value thanks to the enhanced access to data and medical information. Furthermore, improvements in care, perceived by patients, and care services are being identified linked to added value possibilities for specialized care and increases in medical productivity (Yu P., 2006). It is envisaged that the time reductions can be also obtained from digital information collection at the point of care as well as included processing mechanisms. Also, one of the major drawbacks for m-health comes from the lack of relationship between devices and networks as well as standards for mobile devices. Each user may have a different operative environment (Windows Mobile, J2ME, and Symbian), different devices with distinct Graphical User Interface and different mobile network facilities (from GSM or GPRS to EDGE, UMTS, HDSPA or HSUPA), or wireless network technologies (WiFi, WiMax, Bluetooth, ZigBee, RFID). Certain highly cost hardware infrastructure, specifically for Body Area Network sensors or advanced PDA is still a problem, and m-health have to be extended to the users’ community. Besides, health care industry and organizations are very practical and will not invest on m-health services unless sort time benefits may be demonstrated in clinical routine. From a HW point of view, devices autonomy (battery, weight) is still an issue to be solved as, for example, the daily and quick activity of health care staff should be supported independently of connectivity restrictions. From the software point of view, usability, accessibility and the complexity in the protocols stack are still problems to be solved in order to easily extend, develop and use services in the m-health world, including care professionals, patients, disabled people, relatives and informal careers.

AN M-HEALTH SYSTEM FOR REMOTE PATIENT CARE On behalf of the wonders of wireless networks, there is an increasing popularity for developing and using mobile applications. Ubiquitous and pervasive computing (Hassmann U., Merk L., Nicklous, M.S., Stober, T., Korhonen P., Khan P. and Shelness N., 2003) exploit the advantages of handheld devices and provide efficient and modern solutions for users who demand mobility. The need of nurses and doctors to make more efficient patient care procedures has brought the development of a PDA application able to increase the response for those patients who demand a quick attention. This section addressed several issues related to the construction and usage of mobile applications for the health care domain and describes an m-health application developed at the University Rey Juan Carlos. Hospitals and geriatric centers employ an increasing number of nurses that take care of patients using the classical “nursing rounds” protocol. Nurses’ rounds are conducted regularly every 1 or 2 hours to decrease patients’ use of call lights. As a result, an increasing level of safety and patient satisfaction is achieved. The usage of mobile devices like a PDA may improve hospital health care systems by making more agile nurse rounds and having a better control of the regular cares patients need. The agility that mobile devices can offer to doctors and nurses during the regular daily schedule, is achieved by exploiting on-line access to patients’ records using handheld devices. Therefore, the importance for having available such key information for monitoring the evolution of patients (e.g.: elderly, critical cases) along the day, becomes a strong motivation to use specific handheld applications. In this research we have developed GEMA, which is a PDA application aimed to support ubiquitous patient care. This application modernizes a very few similar products traditionally used by Spanish hospitals and enhances and extends some

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of the capabilities offered by other systems. The aforementioned application was mainly thought for clinical institutions, hospitals and geriatrics centers, in which most of the medical staff need to perform a precise and continuous control of elders and for those people with special cares. Therefore, the scheduling of nurse rounds during the patient visits can be optimized using mobile technologies because patient records are already available using the PDA application. First, will we explain the main features of the functionality supported by GEMA. Second, we will outline the high level description of the system by detailing its software architecture and the main functional parts of the application. A connection to Web services is described for remotely accessing the corporate hospital database system. In addition, security issues such us authentication using an LDAP server will be discussed. Third, we will describe in detail the functionality offered by GEMA to support doctor and nurse rounds for monitoring patients with high availability to patient records to make more agile hospital rounds.

As mentioned before, GEMA supports different stakeholders’ roles for which each one may have different permission levels which have to be authenticated against and LDAP server. Patients’ records have to be previously stored by the own hospital information system, and such databases could be exported to be used by GEMA. In other case, GEMA should be fully integrated with the HIS platform. In addition, GEMA was specifically designed to assist nurses during their rounds but also doctors can be key users of the system. The application can be tailored for each hospital or medical center that want to define their own medical protocols, constants registry, or patient evaluation levels. The main features of the system are summarized as follows: •

Functionality and Main Features of GEMA Because hospitals are special organizations that frequently need an updated and on-line access to patients’ records, doctors and nurses are the principal stakeholders and potential users of mobile applications that provide access to patients´ information, and make more agile the rounds for monitoring the status of their patients, in particular those who need special cares. The main goal of GEMA is to provide mobility and agility for nurses and also doctors who want to have access to patients´ records using handheld devices. With GEMA, nurses and doctors will have an instant and on-line access to patient records during their rounds. Also, such information will be automatically updated to hospital server databases on behalf of the wireless connection.

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•

Access to GEMA: Only authorized hospital members may access to different information levels depending on the role assigned to each user type. Initially, three types of users (i.e.: nurse, doctor, auxiliary staff) are defined. Each type of user has different privileges for accessing relevant information from patient records. In addition, each specific user may browse which patients have been assigned daily by the schedule system. For each patient, nurses and doctors can browse the patient’s record as well as know which current cares have to be performed, the specific medication needed, or the status of a particular diagnostic. Patient monitoring and evaluation: GEMA facilitates users to monitor each patient’s status and check current medication. The aim of monitoring is to define the correct actions ensure that the patient’s status evolves positively. Monitoring activities in GEMA includes the following tasks: ◦⊦ Patient evaluation: Nurses can fill evaluation sheets for each patient to reflect the medical symptoms dur-

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•

ing the evaluation. These evaluation sheets have different evaluation criteria that can be defined by each particular hospital and they should be grouped into subgroups in order to facilitate the input data. For instance, a group of symptoms might be breathing difficulty, which can be associated to certain difficulty signs as well as implies an effort from the patient like: rapid breathing, apnea, and lung diseases among others. ◦⊦ Insert comments on patient’s evolution: GEMA will allow the insertion during the rounds of new comments about the evolution of patients. ◦⊦ Introduction of vital constants: GEMA allows users the input of vital constants during monitoring. The allowed types and ranges of vital constants should be defined by each hospital, but most of them are common for all medical centers. ◦⊦ Query the evolution of patient’s constants: The application also permits users to query the sequence of the constants of each patient. Such information can be displayed graphically to clearly show the evolution of the patient. ◦⊦ Incidents: All the issues happened during monitoring of enough relevance that cannot be registered using the options offered by GEMA can be registered as incidents in simple text form. Maintenance: The mobile application GEMA allow patient healthcare tasks basically consisting in the administration of medicines, as well as personalized programmed care plans defined for specific care agendas. The basic tasks defined for this functionality are the following.

Care agenda: Each patient should have a personalized healthcare agenda elaborated by the doctor and monitored by the nurse or any other specialized medical staff. This medical staff will administer each event and this will be registered in the care agenda of each patient. Initially, all the cares should be pre-programmed by the hospital before the rounds. ◦⊦ Medical agenda: Each patient should have an agenda for the medical prescriptions containing all the medicines and the amount of medicines each patient needs. After this, the agenda must be updated using the application. ◦⊦ Incidents: Similarly as for patient monitoring, every incident happened during patient maintenance tasks should be registered in the agenda. Incidents that might be produced by the administration of a specific care or medicament must be necessary registered in the system, in such a form that specifies the origin and the causes observed. Common infrastructure: The proposed applications include some features common to the entire application, and we highlight the following ones. ◦⊦ Communication between clients: The application permits an instant text message communication between PDA users by means of the facilities provided by the network infrastructure. A user name or code identifies who sent a message to another user. ◦⊦ Legal issues: All the communications and actions realized with GEMA should be registered according to specific country regulations. In the case of Spain, the LOPD (“Ley Orgánica de Protección de Datos”) law, that ◦⊦

•

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◦⊦

◦⊦

develops the European recommendations given by the European Network and Information Security Agency (ENISA), should be taken into account to protect confidential information of patients. ° Access to patient records: Only authorized user with appropriated permissions can access patient records. Home healthcare: In special situations when needed, GEMA can be used for home healthcare in a transparent form by means of appropriate wireless radio protocols (e.g.: GPRS/ UMTS).

All these features represent the core of the functionality of GEMA, were specified with 27 functional requirements, 4 non-functional requirements and 3 hardware requirements. Because the goal of this chapter is to show how a mobile healthcare applications works, and its benefits over the traditional healthcare, we have preferred to omit a detailed list of software requirements. Instead, we will focus on the description of the security issues and how HIS databases will be accessed. After this, we will

explain the main parts of the software architecture we developed to support GEMA’s features.

The GEMA’s Architecture GEMA is designed for use inside buildings with wireless technologies like WiFi, but GPRS/UMTS can be also employed for connecting to remote databases when needed. Traditionally, GPRS/ UMTS radio networks are not longer used inside hospitals to avoid the potential impact of radio transmissions. Thus, the most effective connection to the remote databases for accessing patient record is offered using wireless networks. The physical architecture deployed for GEMA application is shown in Figure 1. As shown in Figure 1, the mobile GEMA application installed on the PDAs may connect to the hospital or specific GEMA databases via GPRS/ UMTS or by means of the private hospital wireless network. The way in which GEMA mobile users access remotely the patient records is transparently to the wireless protocol selected. GPRS for those patients who need distance healthcare. Otherwise, inside the hospital the remote connection will be provided by the wireless infrastructure. In both

Figure 1. Physical architecture of GEMA using PDA devices for remote access to patient records

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cases, the connection will be established to an IP address for any authorized IP addresses valid range.

Securing Remote Connections Information from patient records constitutes valuable and confidential information that must be legally protected and secured against unauthorized users. Therefore, authentication for hospital information system (HIS) becomes and important issue that should be carefully treated. In GEMA, the first thing we did for PDA users is to authenticate them to a secured users database, which consists on an LDAP Lightweight Directory Access Protocol) server that contains the list of authorized users (e.g.: doctors, nurses, etc.) of the hospital. Because security is a key piece for any HIS, password encryption is required for safety remote communications. Additional security can be offered by the wireless access points by means of wireless encryption protocols like WEP or WPA-PSK or MAC validation. In order to avoid the possibility of data interception transmitted between the clients and the server, the connections should use the Secure Sockets Layer (SSL) protocol to guarantee the integrity of the data during the remote connections to the server. The SSL protocol runs above TCP/IP and below higher-level protocols such as HTTP, and it uses TCP/IP on behalf of the higher-level protocols. SSL authenticates SSLenabled clients to an SSL-enabled server through encrypted connections. For GPRS/UMTS remote connections, intranet services offered by mobile phone operators will allow secured accesses on behalf of Virtual Private Networks (VPN) and using secure protocols like IPsec (IP security). IPsec is implemented by a set of cryptographic protocols for securing packet flows and for internet key exchange. IPsec can be used to create VPN for end-to-end communications between clients and the server. Integrity validation (ensuring traffic has not been modified along its path) and

authenticating the peers (ensuring that traffic is from a trusted party) are facilities offered by IPsec. Using this scheme, home care patient records can be securely accessed from networks external to the hospital.

Web Services for Accessing HIS Databases Many PDA applications that need to access information stored in databases employ specific connectors or drivers that have to be installed and configured for each PDA client. In many cases and depending on the target language or technology employed, compatibility problems between the language (e.g.: Java 1.1.8) and the PDA operating system may appear for a specific remote database connector (e.g.: Connector/J) or driver. Besides, modifying the PDA registry to indicate where the connector is located in the PDA directory may be a problem for configuring the application. Web services (Alonso G., Casati, F., Kuno H. and Machiraju V., 2003) are reusable components that can be invoked using Internet protocols and they offer a transparent solution to the user described in the service specification. In order to overcome the potential problems using specific database connectors, we used Web services (WS) to authenticate users in the GEMA application and to provide the remote connection to GEMA databases. With this approach we can isolate the business logic of the GEMA system from the connections made from the PDA clients, and also because Web services can be used independently of any particular database engine of current use in the hospital information system. As a result we decouple the data layer from the business application layer, and provide a modern and scalable solution that can be integrated with different HIS, in a transparent way to the user. Another advantage of using Web services is the reuse of code for similar applications. Web services may facilitate maintenance tasks when new system

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requirements demand, for instance, a platform change. The compatibility of PDA users with other existing applications at the hospital requires that GEMA mobile application may allow different ways to authenticate users. The Web service provides adequate authentication support through specific connections to the LDAP server. In other environments, other authentication methods can be considered and be integrated appropriately with GEMA. Again, security for HIS databases is considered a key issue to deploy a Web server, where specific methods have to be included in order to protect critical data against embedded SQL sentences (SQL-InJection). In this way, the Web server will check the SQL sentences before its execution and will test that the arguments provided by the user do not contain such embedded SQL sentences. In other case, the service will block the user query.

General Use Case In order to understand the main functionality of GEMA from a high level point of view, in this subsection we show one of the most interesting use cases (UCs) which describes the main activities carried out by the nurse subsystem. As shown Figure 2. Use case for the nursing care subsystem

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in Figure 2, nurses can interact with patients assigned to a specific hospital unit. The nurse will monitor the status of each patient and will carry out the pre-programmed care plans including the administration of medicines. In addition, the role of the doctor stakeholder is to elaborate healthcare plans and is the unique authorized user to alter the diagnostic and the prescriptions of medicines. Therefore, specific profiles with individual grants are defined in GEMA in such a way that permissions are loaded when the users logons in the system. Depending of each particular hospital, doctors may be involved in patient monitoring activities. In addition, auxiliary staff belongs to nurses with restricted tasks aimed to apply the cares defined in the care agenda. More specifically, the “diagnostics and prescription” use case (UC) deals with the evaluation made by the doctor who, in consequence, prescribes the most appropriate medicaments to the patient. In tight relationship with this task, the medical agenda reflects each prescription. The “care plans” UC covers the realization of a specific care plan made by the doctor. This use case generates a set of tasks which are gathered in the care agenda. Each authorized hospital staff may have a care agenda for each individual patient.

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The whole set of care agendas form the care plan. Moreover, patient records can be accessed to browse some personal data of each patient and a brief summary of his/her disease. The maintenance of the care and medical agendas belong to the “patient maintenance” UC. The other important part of this subsystem focuses on patient monitoring activities, which result of extremely importance for controlling the status of the patient. GEMA makes patient monitoring more agile by a quick access to patient records as well as an instant browsing and updating operations of the care agenda. The characteristic of mobility offered by PDA devices clearly improves the schedule of nurses. The “patient monitoring” UC comprises to check the patient’s vital constants. This, an evaluation of the patient is based on the introduction of the specific data according to its evolution which will be stored in the system database for a later use, as well as a registry with any incidents that may happen during the monitoring activity.

Software Architecture Software architectures are used to depict a high level view which reflects the design of the main functional parts of a software system (Bass L, Clements P. and Kazman R., 2003), and often represented from different viewpoints (Clements P., Bachmann F., Bass L., Garlan D., Ivers J., Little R., Nord R. and Stafford J., 2002). In this chapter we describe the “structural view” of GEMA by means of a description of the most important packages and subsystems and including some classes. We have used UML 2.0 (Unified Modelling Language) to represent the high level architecture of GEMA and the main relationships between the packages and classes, such as Figure 3 shows. The description of the architecture of Figure 3 is as follows. This two-layered architecture describes how GEMA is structured. A third layer under the middleware layer belonging to the hospital databases is not shown in the figure for practical reasons. The upper layer describes the overall functionally of the client application in-

Figure 3. Software architecture (structural view) of the GEMA application

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stalled on the PDA. Part of this functionality has been detailed in the nurse use case described in the previous section. The architecture of Figure 3 shows the decomposition into subsystems (packages), each package containing appropriate classes that implement the functionality of GEMA and accordingly to the requirements. The “vital constants” package defines appropriate classes for checking, introducing, and visualizing a patient’s vitals constants. These constants are used by the package “patient monitoring” to control the status of the patient and register incidents when needed. The “hospital unit management” package displays the bed map with the occupancy of the hospital and shows the age and the gender of each patient. Doctors and nurses logon on GEMA using the methods implemented of the class authentication and they can browse patient records on behalf of the functionality implemented on the class browse patient records. The “planification” and “patient maintenance” packages implement most of the functionality of the nurse care UC previously described. These fine packages use the services described in the “communications” packages, which implement the message service between PDA users and a specific communication service which call the Web service by means of the “pocketSOAP” protocol for accessing remotely the hospital databases. The middleware layer mostly defines a class for the Web service for accessing such databases as well as the LDAP server for user authentication.

GEMA’s User Interface Because agility in nursing scheduling is a main concern to achieve, the organization of the menus has been designed according to the way nurses and doctors work. In addition, some of the menu options have big and representative icons in order to facilitate a quick identification by the user and a proper selection with the stylus of the PDA. The GEMA user interface has been designed according to the following characteristics:

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• • • •

High usability level based on: ◦⊦ Structure of the menus according to the normal hospital procedures so that GEMA users can feel comfortable with GEMA operation mode. ◦⊦ No more than 3 menu levels. ◦⊦ Landscape mode to avoid the horizontal scroll as well as to ease the presentation of the information in the screen, such as the bed map. Screen resolution of at least 320x240. Reduced number of colours. Smooth colours. A unique font for the menus and information shown in the screen.

After the user logons into the system, the bed map with the occupancy for that user is displayed. Figure 4 shows how the bed map is organized in the PDA. As we can see in Figure 4, the gender and the age of the patient are clearly shown in the bed map. In addition, an icon in the left side of the screen allows the user to display all the beds or only those one that are occupied with patients. Using this simple and usable screen, the user can make a quick selection of the patient who is going to be monitored. Also, the application provides an easy access to all the functions related to the care agenda and patient records. Once the patient Figure 4. Bed map with a partial view of the occupancy

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Figure 5. Contants input and graphics menu options

to be monitored has been selected, a new menu (see Figure 5) containing two representative icons is shown. This menu provides access for patient care options and for administering medicines. Both icons can be easily recognized by nurses and doctors. Also, the usability of the GEMA application provides in this case big icons, so they can be easily and quickly selected by the user even without the classical PDA stylus. The reduced size of PDA screens needs a considerable design effort of the user interface to make it as much usable as possible to users, so they can easily recognize the menu options. In other cases, poorly designed interfaces may delay the selection of these options, and for hospital patient monitoring, time is often considered a critical factor.

according to GEMA schema. Because many PDA installation and configuration problems with specific database connectors, we have developed a Web service that resolves the database connection by making it transparently to the user. In our case, a MySQL server is accessed from the Web service using a JDBC. The GEMA data model comprises 25 MySQL tables that store the information needed to implement the functionality described in the software architecture. In addition, the application is easily modifiable to change to a different database system like Oracle or Informix, which are frequently used in health care scenarios, without changing the functionality already implemented. In addition to the database model, the use of a Web service for database operations require the use of common data formats for all the operations with the data. For instance, when we query the database, the problem turns out how to return a common structure that may include a different number of rows and cols as a result of a given query. To overcome this problem we decided to use the standard XML (eXtensible Markup Language) as an intermediate exchange data format, and the structure returned will dynamically compose the fields and the records obtained from the execution of SELECT sentence. Figure 6 shows an example of the common exchange data format using XML.

Database and Exchange Data Formats

IMPLEMENTATION AND INTEGRATION

Because this first prototype of GEMA was implemented independently of any particular HIS, the database has not been fully integrated with any existing hospital server. In fact, GEMA was designed to be not only extensible to incorporate new features, but also opened to be integrated with existing platforms. The GEMA database system can be easily adapted or integrated with other database servers or existing data can be imported

GEMA was implemented using PocketBuilder as the target language. In this section we will describe some relevant parts of the implementation and we will illustrate the problems encountered and the solutions provided. We prefer to explain the solutions already implemented describing the methods and the logical steps performed rather than showing a large number of source code sentences.

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Figure 6. XML common format for exchanging information using a Web service for accessing hospital databases

Web Service for Accessing HIS Databases In order to avoid the potential problems that some PDA suffers when they directly use a specific database connector, we decide to implement a single Web services for managing the remote connections to databases server. Thus, the Web service (so-called GEMA_WS_DB), and implemented in Java, assumes the responsibility to interact with specific database connectors like JDBC for accessing MySQL tables. The GEMA_WS_DB service is composed by a single class that access GEMA databases and provides user authentication through the LDAP server. Figure 7 shows the methods implemented in the class that describes the GEMA_WS_DB Web service, as Figure 7 shows.

The GEMA_WS_DB Web service defines five public methods; four of them, that is: query(), insert(), delete(), and update()) are used for accessing the hospital database, while the fifth one (authenticateLDAP()) implements the authentication process that validates users against the LDAP server. The remainder three methods are defined as private methods and the functionality of each of them is the following. •

The getValueDB() method returns the value of a field converted to a string field independently of the original type of the field selected. The field RsRows uses the “RecordSet” pre-defined Java type for managing the database records from a query already executed, as well as name and type of a field. The function returns the value of the field as a string.

Figure 7. UML class describing the operations implemented in the GEMA_WS_DB Web service

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•

•

The getConnection() method returns a connection to the database before operations are launched. The parseSQL() method checks the integrity of the any SQL sentence before execution.

As an example, a typical scenario where a doctor wants to insert some specific data using GEMA, the application will create an instance of the GEMA_WS_DB service and build the call that invokes the insert() method in the service. Then, an internal process checks the integrity of the SQL sentence and stores it as an internal log file. This log file contains all the queries that have been executed due to security and legal reasons. After an SQL sentence is executed, the result is returned to the user if succeed. Otherwise, in case of failure and error is returned. Finally, the screen is refreshed with the information requested. Another example of Web service usage happens when users need to authenticated to the LDAP server. The service checks the user and password in the LDAP directory using the following process. • •

• •

Clean the data buffers Call the Web service. On behalf of method authenticateLDAP() described in the Web service, we check if the parameter that controls the LDAP validation is active or not. If this parameter is active, an object of the “validationLDAP” class produces a call to a function called validation() which checks the validation of users in the LDAP server. Because the implementation of the service was done in Java, we used the JNDI (Java Naming and Directory Interface) component, which uses a standardized API for authenticating in different distributed directory systems like Microsoft Active Directory, Novell eDirectory, etc. Build the SQL sentence. Import the data from the buffer.

•

•

Check if the user has been validated and if so, we select the profile for that specific user. The user is logged into the system, but a limited number of trials are permitted.

XML Data Exchange As mentioned before, GEMA uses the XML format to display the data retrieved from the database. GEMA processes the data as XML files by exploiting the import facilities offered by PocketBuilder. The steps to do these tasks can be summarized in the following steps. • • • •

Define the windows where the data will be presented. Invoke the query() method of the Web service. Import the data in XML format to the aforementioned window. The user access to the data to perform the desired operations.

One of the problems we encountered during the development of the first version of GEMA was the exchange of binary files. This is the case of, for instance, jpeg images containing the picture of the patient. If we are able to store these photos in the database we will need to implement a new function in the Web service able to return a BLOB (Binary Large Object) object. Nevertheless, we found an incompatibility problem with the BLOB field using the pocketSOAP protocol. This is due because the current implementation of the pocketSOAP component for pocketBuilder stores the data retrieved in string variables. During the communication between the service and the database, the pocketSOAP API realizes a conversion from BLOB to string but altering the content. Thus, the BLOB retrieved by the service cannot be properly interpreted and displayed. This problem doesn’t appear if we use SOAP for transmitting

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the images from a PC or laptop. One alternative solution is to store the images locally in the PDA but this doesn’t seem a realistic solution for the future. The second and most visible alternative is to change the transmission data protocol for accessing the images and replace pocketSOAP by FTP or HTTP. In other case, we should wait for an improved version of pocketSOAP for pocketBuilder. In this subsection we have discussed some key implementation issues of GEMA that we believe they could be useful for implementing similar applications, both in the same domain or in a different one. In next section we will provide an overview of the use of GEMA with appropriate screenshots guiding the habitual working schedule of nurse rounds.

AGILE NURSING ROUNDS WITH GEMA In this section we will describe the operation practice using GEMA for m-health patient care. We will illustrate the logical steps of the activities usually performed by doctors and nurses to show how GEMA can help care assistance by making more agile the processes that most of them are often carried out manually. The instant on-line access to patient records during nurse rounds provides a powerful tool to get more agile care

to patients, in particular to elderly or those who need special cares. The first thing the user must do before using GEMA, is to log into the system with a user and a password, which are validated against the LDAP server using the Web service. Once the login information has been successfully, the application shows the user the profiles which are in current use for that user. GEMA users can select a specific role or clinic profile (i.e.: nurse, doctor, other staff) and a unit (e.g.: cardiology) for that profile. Figure 8 shows both screenshots used for authentication in the system and for selecting the profile and specific unit attached for that profile. Once the user has selected his/her specific role and unit, the bed map of Figure 4 is displayed in the PDA showing the occupancy of the subset of patients assigned to that particular doctor or nurse. The top part of the screen of Figure 4 shows in the left side the type of user and the unit or service to which belongs. Also, three additional icons are defined to: update the data in the screen (the green recycle icon), show if user has incoming messages from other PDA user (an envelope turns its color to red), and the exit icon. The biggest area of Figure 4 is used exclusively to display the bed map. Each nurse and doctor can select one of the patients shown in the bed map to start the patient’s medical history. After the selection of a particular patient, GEMA shows the user its entire function-

Figure 8. Both screenshots constitute de main entrance to the GEMA system for user authentication and user profile selection

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Figure 9. GEMA main menu options for mobile health care

ality for mobile health care with on-line access to patient’s records stored in the hospital information system or in a different database server. Figure 9 shows the main menu options with the processes supported by GEMA. Using the functionality shown in Figure 9, a typical nurse round will start, for instance, checking the evolution of a patient that has just suffered a surgery operation. The nurse or doctor will start filling a symptomatology sheet using the symptomatology menu option. GEMA allows the user to select one symptom from a predefined list already store in the database servers and for that symptom the application displays more specific symptoms that can me marked according to the patient’s status. Figure 10 shows an example of symptoms that have been checked during the round. The name of the patient and the nurse appear above the symptomatology features. After the user has update the symptomatology of the patient, the constants option can be selected and a screen will appear to fill the appropriate constants for a patient who is being monitored. When all the constants have been recorded in the database, the user can check if every thing is right by a graphic dynamically built with the data already measured. A sample graphic displaying the constant for the diastolic tension is shown in Figure 11.

Figure 10. Symptomatology for a specific patient

Figure 11. Histogram bar showing the diastolic tension constant evolution

GEMA provides different visualization options for the graphics in the shape of histogram bars, pies, 3D view, solid line, etc, by only pressing the change view button and selecting the desired view (see Figure 12). In addition, the graphics as well as some other data from other menu options can be remotely printed using, for instance, a bluetooth printer which should be previously configured for the GEMA application. Another key option for patient health care is the medical agenda, which stores the medicine doses that have to be administered to each patient. GEMA users can insert or new medicines or modify existing ones in the agenda for a specific date depending of the evolution of the patient. Therefore, the medical agenda is used by nurses to know each individualized patient care plan. Also, comments about

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Figure 12. Two different views of the same vital constant

Figure 13. Perspective of the medical agenda with the insertion of a new care

the evolution of the patients can be added as a guide for the doctor to know if the medical agenda has to be changed or suppressed. Figure 13 shows a screenshot of the medical agenda. According to Figure 5, the mission of the care agenda is to define the set of specific care actions for each patient. Hospitals in which nurses, on average, take care of eight patients each have risk-adjusted mortality rates following common inpatient surgical procedures that are 31% higher than hospitals, in which nurses care for four patients each. Staffing hospitals uniformly at four versus eight patients per nurse would be expected to prevent 5 deaths per 1000 patients (Aiken L.H., Clarke S.P., Sloane D.M., Sochalski J. and Silber J.H., 2002). Therefore, there is a need for nurse staffing to receive more attention with regard to its potential to improve patient

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safety. The GEMA application eases this on behalf of the agility using PDA devices with on-line access to patient records and health care agendas for an effective surveillance of patients. When the nurse opens the care agenda, it shows the list of pending cares for the current day. After the patient has received one of the cares in the list, the nurse confirms in the PDA the care has been made. If an unexpected care is required, the nurse can go through the incidences menu option and introduce the incidence caused by a particular care. After, the nurse can go back to the care agenda and recover the care list to continue with the tasks until all the programmed cares have been applied and quickly move to the next patient. In order improve patient safety against unexpected situations, GEMA allows the user to send an instant message to another user like, as for instance, a doctor. The

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Figure 14. An incoming message is displayed for the destination user

formation can be available in seconds without interrupting the nurse round. Figure 15 shows an example of basic patient record.

DISCUSSION

user will use the message icon to type a short message and selecting first the user where the message goes to. As the user receives the message, the icon message turns to red color indicating an incoming message. Figure 14 shows the message window. This simple but effective communication systems between the handheld devices reduces the time needed to search for a particular doctor and contribute to patient’s perception of doctor availability, as it enable best practices aimed to improve the communication of the hospital staff. To conclude, the goal of the patient option is to display a brief information of the patients records in order to clearly identify the basic history of the patient as well as his/her picture. This facility avoids that many times the doctor or nurse needs access to patient records and need to go to an authorized computer for this, which make take several minutes. With GEMA, basic patient inFigure 15. Abbreviated patient record information

In this chapter we have addressed the importance of mobile application for hospital healthcare and we have stated this with the description of GEMA, a modern mobile application for m-health. The special requirements of hospital and similar medical centers requires the design and use of appropriate usable and accessible systems in order to cover the functionality demanded by patients, in particular elderly and disabled people, as well as those others with specific care needs. At present, until being able to carry out a big scale hospital evaluation is too soon to extract results of the use of GEMA by doctors and nurses, and see how GEMA can make more agile nurse and doctor rounds. Is our aim to release GEMA as a commercial application, but at present we only have done three demos to hospital managers and computing chief directors. The feedback from these experiences was very positive but we will need more time to evaluate the benefits of using GEMA as an integrated application with any HIS platform. From the more technical point of view, GEMA offers a usable and extensible solution for m-health which employs Web service technologies permit to connect with different databases and authentication servers what fosters real integration with existing HIS. Thus, the integration with other platforms should not be a problem and also to extend the functionality of GEMA allowing Web clients to connect to GEMA databases. Moreover, the use of open standards for data exchange like XML facilitate the description, storage, and exchange of patient records and other useful information between hospital databases. In addition, the modularity of the software architecture described in the

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chapter facilitates the extensibility of GEMA and fosters code reuse. Finally, we have tested all the functionality described in GEMA both using a private wireless network and GPRS connections. We perceived that GPRS goes a little bit slower compared to Wi-Fi networks, but we believe UMTS or the more recent HSPDA protocol will provide more efficient ways of utilization. The advantage of using GPRS/UMTS protocols is that outside from the hospital, tele-medicine and tele-care can be improved by using m-health systems like GEMA.

CONCLUSION This chapter has described a modern real-time mhealth application and service with the capability of quick reaction and on-line data access in order to offer a proactive help to improve healthcare systems. As mean age is rapidly increasing in most societies, modern healthcare applications should be developed to provide a better, more accessible and quicker support to these citizens. The increasing interest in this kind of applications has brought the attention of researchers and developers and more applications like these are demanded. The satisfaction building GEMA can be mainly increased with its longer use in a real hospital environment to measure how patient care may become more and more effective. One issue that needs to be analyzed in more detail is the proper integration with other HIS platforms and environments including the validation of use of appropriate standard data formats, like HL7 or CENTC251 among others, like XML to share medical information. More to the point, the use of Web services and other modern Internet technologies will facilitate the integration challenges that might appear in the future.

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REFERENCES Aiken, L. H., Clarke, S. P., Sloane, D. M., Sochalski, J., & Silber, J. H. (2002). Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA. Retrieved from http:// www.webmm. ahrq.gov/ perspective. aspx? Perspective ID=7 Alonso, G., Casati, F., Kuno, H., & Machiraju, V. (2003). Web services, concepts, architectures and applications. Springer. ANSI/IEEE Std. 1471-2000 (2000). Recommended practice for architectural description of software intensive systems. Bashur, R. (1997). Telemedicine and the health care system in telemedicine, theory and practice. Thomas, Charles C. (Ed.). Springfield, pp. 5-35. Bass, L., Clements, P., & Kazman, R. (2003). software architecture in practice, 2nd edition. SEI series in software engineering.Addison-Wesley. Clements, P., Bachmann, F., Bass, L., Garlan, D., Ivers, J., Little, R., et al. (2002). Documenting software architectures. SEI series in software engineering. Addison-Wesley. Department of Health - Estates and Facilities Division. (2007). Using mobile phones in NHS hospitals, (pp.5-10). Retrieved from http://www.dh.gov. uk/ en/ Publications and statistics/ Publications/ Publications Policy And Guidance/ DH_074396 Dolin, R. H., Alschuler, L., & Boyer, S. (2006). HL7 clinical document architecture, release 2. Journal of the American Medical Informatics Association, 13, 30–39.medical Informaticsdoi:10.1197/jamia.M1888 Hassmann, U., Merk, L., Nicklous, M. S., Stober, T., Korhonen, P., Khan, P., & Shelness, N. (2003). Pervasive computing: The mobile world. Springer

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Healy, J. C. (2004). Applications relating to health. fifth research and development framework programme 1998-2002. Office for Official Publications of the European Communities. Luxembourg, ISBN: 92-894-7432-7. Retrieved from╯http:// ec.europa.eu/ information_ society/ activities/ health/ docs/ publications/ fp5- ehealth- applications- relating- to- health.pdf ICT for Health. (2006). Resource book of eHealth projects. Office for official publications of the European Communities. Luxembourg. Retrieved from http://ec.europa.eu/ information_ society/ activities/ health/ highlights/ 2006/ index_ en.htm Istepanian, R. S. H., Jovanov, E., & Zhang, Y. T. (2004). Guest editorial introduction to the special section on m-Health: Beyond seamless mobility and global wireless health-care connectivity. IEEE Transactions on Information Technology in Biomedicine, 8(4), 405–414. doi:10.1109/ TITB.2004.840019 Oliver, N., & Flores-Mangas, F. (2006). Healthgear: A real-time wearable system for monitoring and analyzing physiological signals. In Proceedings of Int. Conf. on Body Sensor Networks (BSN’06). MIT, Boston. USA. Pavlopoulos, S., Kyriacou, E., Berler, A., Dembeyiotis, S., & Koutsouris, D. (1999). A novel emergency telemedicine system based on wireless communication technology: AMBULANCE. IEEE Transactions on Information Technology in Biomedicine, 2(4), 261–267. doi:10.1109/4233.737581 Safran, C. Pompilio-Weitzner, Emery, G. K. D. and Hampers L., (2005). Collaborative approaches to e-Health: Valuable for users and non-users, connecting medical informatics and bio-informatics. R. Engelbrecht, et al. (Eds). (pp. 879-884).

Wac, K., Van Halteren, A., Bults, R., & Broens, T. (2007). Context-aware QoS provisioning in an m-health service platform. International Journal of Internet Protocol Technology, 2(2), 102–108. doi:10.1504/IJIPT.2007.012373 Winter, A., Brigl, B., & Wendt, T. (2003). Modeling hospital information systems (Part 1), the revised three-layer graph-based meta-model 3LGM2. Methods of Information in Medicine, 42(5), 544–551. Yu, P., Wu, M. X., Yu, H., & Xiao, G. C. (2006). The Challenges for the Adoption of M-Health. IEEE International Conference on Service Operations and Logistics and Informatics (SOLI 2006). Shanghai, China, pp. 181-186.

KEY TERMS AND DEFINITIONS E-Health: Electronic Health. The term ehealth encompasses all of the information and communication technologies (ICT) necessary to make the health system work (International Telecommunication Union – ITU, 2003) Hospital Information System (HIS): Central medical information system in hospitals where health care related data (e.g.: personnel, patients and their medical history etc.) is stored. Lightweight Directory Access Protocol (LDAP): Is a software protocol for enabling anyone to locate organizations, individuals, and other resources such as files and devices in a network, whether on the public Internet or on a corporate intranet (SearchMobileComputing. com definitions). M-Health (Mobile Health): It can be understood as mobile computing, medical sensor, and communications technologies for e-health (Istepanian R.S.H., Jovanov E. and Zhang Y.T., 2004). Medical Informatics: The rapidly developing scientific field that deals with biomedical information, data, and knowledge - their storage,

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retrieval, and optimal use for problem solving and decision making. Mobile Computing: Is a generic term describing your ability to use technology ‘untethered’, that is not physically connected, or in remote or mobile (non static) environments. The term is evolved in modern usage such that it requires that the mobile computing activity be connected wirelessly to and through the Internet or to and through a private network (Wikipedia). Pervasive / Ubiquitous Computing: Mobile Computing: Pervasive Computing is the trend towards increasingly ubiquitous (another name for the movement is ubiquitous computing), connected computing devices in the environment, a trend being brought about by a convergence of advanced electronic - and particularly, wireless - technologies and the Internet (SearchNetworking.com).

Software: Architecture: Architecture is defined as the fundamental organization of a system, embodied in its components, their relationships to each other and the environment, and the principles governing its design and evolution (ANSI/IEEE Std. 1471-2000, 2000). Telemedicine: The use of medical information exchanged from one site to another via electronic communications for the health and education of the patient or healthcare provider and for the purpose of improving patient care. Telemedicine includes consultative, diagnostic, and treatment services (Websters’s new world medical dictionary). Web Service: A reusable component that can be registered, discovered, and invoked using standard internet protocols.

This work was previously published in Handbook of Research on Distributed Medical Informatics and E-Health, edited by Athina A. Lazakidou and Konstantinos M. Siassiakos, pp. 282-304, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 3.3

Affect-Sensitive Virtual Standardized Patient Interface System Thomas D. Parsons University of Southern California, USA

ABSTRACT Virtual Standardized Patients (VSPs) are advanced conversational virtual human agents that have been applied to training of clinicians. These interactive agents portray standardized patient scenarios involving VSPs with clinical or physical conditions. VSPs are capable of verbal and nonverbal interaction (receptive and expressive communication) with a clinician in an effort to enhance differential diagnosis of psychiatric disorders and teach interpersonal skills. This chapter describes the design and development of both software to create social interaction modules on a VSP platform and individualized affective models DOI: 10.4018/978-1-60960-561-2.ch303

for affect recognition. This author describes clinically relevant scenarios for affect elicitation and protocols for reliable affect recognition. Further, there is an elucidation of a VSP interface system that has the capacity to monitor the trainee’s affective response using physiological signals. Research findings will be summarized from studies on (1) the usability and applicability of VSPs with training clinicians on various mental health disorders (e.g., adolescent male with conduct disorder; adolescent female who has recently been physically traumatized); and (2) preliminary use of the affect-sensitive system to systematically assess and manipulate aspects of VSPs to more fully develop cognitive and affective models of virtual humans with pathological characteristics.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Affect-Sensitive Virtual Standardized Patient Interface System

INTRODUCTION Traditional approaches to training clinicians in the interpersonal communication skills needed for assessment, diagnosis, and interview performance rely upon a combination of classroom learning and role-playing with human standardized patients. The importance of interpersonal communication is reflected in recent requirements for communication evaluation in medical schools. The Accreditation Council for Graduate Medical Education (ACGME; 2007) has emphasized the importance of interpersonal and communication skills in training clinicians. Residents are expected to: (1) create and sustain a therapeutic and ethically sound relationship with the patient; (2) use effective listening skills, eliciting and providing information using effective nonverbal, explanatory, questioning, and writing skills; and (3) work in an efficient manner with others. However, evaluation studies have revealed methodological deficiencies in many cases (Chant et al., 2002) and limited positive training effects (Hulsman et al., 1999). In an effort to increase interpersonal communication assessment, standardized patients (paid human actors) have been recruited and trained to exhibit the characteristics of an actual patient, thereby affording novice clinicians a realistic opportunity to practice and to be evaluated in a mock clinical environment. Although a valuable training approach, there are limitations with the use of human standardized patients that can be mitigated through simulation technology. For example, human standardized patients are expensive and cost several thousand dollars per student. Further, given the fact that there are only a handful of sites (for over 130 medical schools in the U.S.) providing standardized patient assessments of the clinician in training’s communication ability as part of the U.S. Medical Licensing Examination (USMLE), the current model provides limited availability. Another concern is the issue of standardization. Despite the expense of standardized patient programs, the standardized patients themselves are

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typically unskilled actors. As a result of common turnover, administrators face considerable challenges for offering psychometrically reliable and valid interactions with the training clinicians. A related issue is the limited scope that the actors are able to portray. As a result, there tends to be an inadequate array of developmentally, socially, and culturally appropriate scenarios. For example, when a clinician has a pediatric focus and needs access to children, it is difficult for the clinician to pretend that the actor is a child. Finally, many clinical cases (e.g., traumatic brain injury) have associated physical symptoms and behaviors (e.g., dilated pupils, spasms, and uncoordinated movements) that simply cannot be accurately portrayed by human actors.

Design and Simulation of Pathologies One proposed answer to some of the difficulties inherent in training persons with standardized patients, hence human actors, is to use virtual humans as patients. Virtual humans (VH) are developing into powerful interfaces that can enable greatly increased intuitive human like interactions. These virtual human systems consist of characters that have realistic appearances, can think and act like humans, and can express themselves both verbally and non-verbally. Additionally, these virtual humans can listen and understand natural language and see or track limited user interactions with speech or vision systems. Advances in simulated virtual humans afford the possibility of virtual standardized patients that reduce cost, ensure standardization and faithfully model physical symptoms. Virtual standardized patients (VSPs) are artificially intelligent (AI) virtual human agents that control computer generated bodies and can interact with users through speech and gesture in virtual environments (Gratch, et al., 2002). Advanced virtual humans are able to engage in rich conversations (Traum et al., 2008), recognize nonverbal cues (Morency et al., 2008), analyze

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social and emotional factors (Gratch and Marsella, 2004) and synthesize human communication and nonverbal expressions (Thiebaux et al., 2008). Building virtual humans requires fundamental advances in AI, speech recognition, natural language understanding and generation, dialog management, cognitive modeling and reasoning, virtual human architectures and computer graphics and animations. All of these technologies need to be integrated together into a single system that can work in unison, be expandable, flexible and plug-and-play with different components. The University of Southern California’s Institute for Creative Technologies (USC/ICT) is a world-recognized leader in virtual-human research. The VH work at USC/ICT offers an interactive experience. Further, our virtual humans are capable of listening to users, reasoning about the situation at hand based on sensor input and the situational context and choosing appropriate dialog and actions and delivering those actions in generated verbal output that is synchronized to realistic human like non-verbal animations. The virtual human systems we develop include complex cognitive agents, question response agents, and culturally specific characters. These

virtual characters have beliefs, desires and intentions, as well as daily routines that can be carried out within virtual worlds. These agents are based around a set of distributed components that communicate with each other and perform specific tasks (e.g. speech recognition). Although these agents do require time and effort for adequate development, we have tried to address this with a distributed underlying virtual human architecture. Given the distributed nature of the architecture, we are able to replace components without large integration efforts, which reduces the time needed to build or expand a given application. Additionally a set of tools is continually being developed to ease the task of creating these characters. The major components in the system, as seen in Figure 1 are: •

•

Speech Recognition: digitizes the user’s speech and produces a string of words as output. Natural Language Understanding and Dialogue Management: parses the word string produced by speech recognition and extracts meaning and concepts to form an internal semantic representation.

Figure 1. Virtual patient data flow diagram

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•

•

•

•

•

Intelligent Agent: reasons about plans and generates actions based on its internal state and the input from the verbal text or other multi-modal devices. Complex agents can be created using a cognitive architecture that reason about plans and actions and integrate a dialog manager and emotion model. Simple agents can be created with a question response system that picks a response based on statistical analysis of the input text. The output of the agent is a response output string and actions. Non-Verbal Behavior Generation: takes the response output string and applies a set of rules to select gestures, postures and gazes for the virtual characters. Procedural Animation System: synchronizes speech output with gestures and other non-verbal behavior to control the characters in the virtual environment. Speech Generation: performs speech synthesis from the text generated by the agent. Or alternatively a set of pre-recorded voice strings can be used as the speech. Game Engine Graphic Display: is the current underlying graphics engine used to display the character and virtual environment.

Building on the VH technology, VSPs fulfill the role of standardized patients by simulating a particular clinical presentation with a high degree of consistency and realism (Stevens et al., 2005). There is a growing field of research that applies VSPs to training and assessment of bioethics, basic patient communication, interactive conversations, history taking, and clinical assessments (Bickmore &Giorgino, 2006; Bickmore et al., 2007; Lok et al., 2006; Parsons et al., 2008). Results suggest that VSPs can provide valid and reliable representations of live patients (Kenny et al., 2007; Triola et al., 2006; Andrew et al., 2007). Additionally VSPs enable a precise presentation and control of dynamic perceptual stimuli; along

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with conversational dialog and interactions, they have the potential to provide ecologically valid assessments that combine the veridical control and rigor of laboratory measures approaching a verisimilitude that reflects real life situations (Parsons et al., 2008; Andrew et al., 2007). Herein the reader will find a description of attempts in this author’s lab to advance the state of the art in VSPs. Prototypes have been developed for mental health assessment (Kenny et al., 2007; Kenny et al., 2008a; Kenny et al., 2008b; Kenny et al., 2009; Parsons et al., 2009a) and extended the behavioral fidelity necessary to support such diagnoses (e.g., physical appearance, gestures and facial expressions, reactions to pain, sweating, etc.).

VIRTUAL STANDARDIZED PATIENT The general architecture supports a wide range of VHs from simple question/answering to more complex ones that contain cognitive and emotional models with goal oriented behavior. The architecture is a modular distributed system with many components that communicate by message passing. Interaction with the system works as follows: the trainee talks into a microphone which records the audio signal that is sent to a speech recognition engine. The speech engine converts the signal into text. The text is then sent to a statistical natural language system that matches the input text to a question/answer pair which selects an answer. The answer is sent to a non-verbal module which applies rules to create the appropriate gestures and behaviors. A procedural animation system then synchronizes the gestures, speech and lip synching and plays a pre-recorded or generated voice of the input text for the character for final output to the screen. The user then listens to the response and asks more questions to the character.

Affect-Sensitive Virtual Standardized Patient Interface System

Design and Simulation of Pathologies One of the challenges of building complex interactive VSPs that can act as simulated patients has been in enabling the characters to act and carry on a dialog like a real patient that has the specific mental condition in the domain of interest. Additional issues involve the breadth and depth of expertise required in the psychological domain to generate the relevant material for the character and dialog.

Virtual Standardized Patient: Conduct Disorder Teaching interviewing skills with VHs and VSPs is still a young discipline. There are no standard methods and metrics. The larger problem of teaching general interviewing skills is even less distinct as there are many techniques and it is not well understood how to properly implement those with a VSP. To alleviate this problem this author’s lab at USC/ICT is concentrating on teaching skills required to diagnose a particular disorder. For example, we have developed a VSP with conduct disorder. Through an iterative

process a proficient training set was developed. The first project involved the construction of a natural language-capable VSP named “Justin.” The clinical attributes of Justin were developed to emulate a conduct disorder profile as found in the Diagnostic and Statistical Manual of Mental Disorders (DSM IV-TR). Justin (see Figure 2) portrays a 16-year old male with conduct disorder who is being forced to participate in therapy by his family. Justin’s history is significant for a chronic pattern of antisocial behavior in which the basic rights of others and age-appropriate societal norms are violated. He has stolen, been truant, broken into someone’s car, been cruel to animals, and initiated physical fights (see Figure 2). Our goal was to obtain objective data from an initial intake interview. This was accomplished by evaluating the questions asked by the trainee to the VSP and the corresponding answers. The trainee’s interview questions were guided by the need to determine if the patient is internalizing or externalizing behaviors and for eliciting information regarding the four general symptom categories prevalent in conduct disorder: •

Aggressive behavior: fighting, bullying, being cruel to others or animals

Figure 2.

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• • •

Destructive behavior: arson, vandalism Deceitful behavior: repeated lying, shoplifting, breaking into homes or cars Violation of rules: running away, engaging in non appropriate behavior for age

The VSP system is designed to provide answers to questions that target each of these categories and will respond to a variety of questions pertinent to these areas. Some responses by the VSP may be on target, off target, involve “brush away” responses, and in some cases, they may be irrelevant replies. The probability of a specific response being emitted is rated to the question asked. For example if the trainee asks: “How are things going at home” or “Are you having any problems at home” or “How are things going?”. The system will respond with “My parents think I messed up.” Further questions will lead to finding out that the patient has been running away. This will lead to marking one of the above categories true for the diagnosis in the trainees’ interview. In order for the trainee to pass it will require responses in all of the categories. The total set of questions and responses are extracted from role playing exercises, initial subject testing, interviews with doctors and common sense for specific responses. In total a question set would consist of over 100-200 lines of text. The matching of questions to responses is a manual process with automated learning techniques to generate probability values. Research has been completed to assess the system by (1) experimenter observation of the participants as they communicated with the VSP; and (2) questionnaires. To adequately evaluate the system, a number of areas were determined that were believed needed in the questionnaires: •

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Consistency: The behavior of the VSP should match the behavior one would expect from a patient in such a condition (e.g. verbalization, gesture, posture, and appearance)

•

• •

Adequacy: The discourse between the VSP and the participants should provide adequate verbal and nonverbal communication Proficiency: The clarity, pace, utility of VSPs discourse with the participant Quality: The quality of the speech recognition of utterances spoken.

Basic usability findings revealed that the VSP had high-quality overall system performance. Participants reported that the system (1) simulated real-life experience; and (2) the verbal and nonverbal behavior was satisfactory. However, results also revealed that some participants found aspects of the experience “frustrating”. For example, some participants complained that they were receiving anticipated responses and the system tended to repeat some responses too frequently. This was due to the speech recognition’s inability to evaluate certain of the stimulus words. Further, there were too many “brush off” responses from the VSP when participant questions were outside the VSP’s dialog set. Although in the early stages of system development, initial outcomes have been favorable. We have collected (and continue to collect) quantitative and qualitative results. The VSPs fit well into usability testing. The clinicians in training were videotaped as they performed the interview with the VSP and this recording was stored for later analyses of their verbal communication, nonverbal behavior (e.g., gaze), and overall reaction to their interaction with the VSP. Ultimately, this reflects a general construct of believability. The construct of believability is typically understood as the extent to which data is regarded as true and credible. Among other factors, it may reflect an individual’s assessment of the credibility of the data source, comparison to a commonly accepted standard, and previous experience (MagnenatThalmann et al., 2005). For the VSP work, this means that the clinicians compared their experience with the VSP to their general experience with human standardized patients and actual patients

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they see as a part of their clinical training. Findings suggested that the clinicians in training had positive response to the VSP and behaved as they normally would during a clinical encounter. However, two issues remain: (1) what was the phenomenological experience of these training clinicians as they interacted with the VSPs; and (2) how adequate are the appraisal models used in virtual human research at generating cognitive and emotional models (see discussion below: “2.2 Psychophysiology to enhance cognitive and affective models of virtual humans”). While the latter is lends itself well to a psychophysiological assessment approach (see discussion below: “2.2 Psychophysiology to enhance cognitive and affective models of virtual humans”), a review of the literature reveals that the former lends itself to theoretical speculation of the phenomenological experience of the clinician in training.

Phenomenonology of Clinical Encounters with Virtual Standardized Patients The task of a clinician is exceedingly multifaceted, involving concurrent awareness of the patient’s verbal and nonverbal communication, self-regulation of the clinician’s own observation and management of countertransference reactions (Sternberg, 2000). The training of the clinician grows out of, and is governed by, therapeutic experience. To focus on a clinical encounter with a virtual standardized patient, then, is to focus on a complex context characterized by the clinician in training as he or she interacts with the simulation. For the clinician in training, the VSP will need to aid their commitment to the integrity of the context within which each novice clinician encounters the VSPs and that is organized around certain determinable constitutive features. The phenomenological approach taken in this author’s lab reflects the four phenomenological areas postulated by Zaner (2006) for phenomenologicaly addressing the therapeutic relation: (1) Immer-

sion: attainment of clinical knowledge by a trainee requires that the novice clinician put out of action all the convictions he or she has been accepting up to now; (2) Reflective-Attentive shift: herein reflection involves a shift of focal attention from active involvement in clinical cases for their own sake, to considering them as examples of the practice; (3) Appeal to Evidence: regardless of the clinician in training’s desire for “Immersion” and “Reflective-Attentive shift”, there still needs to be a judgment on the basis of soundest available evidence; and (4) Clinical Ethics: to consult as an ‘‘ethicist’’ on a simulated case means that the training clinician be focused on the simulated situation (e.g. context, persons simulated, issues, etc.) itself, for its own sake. It seems apparent that virtual patients will play an important role in the future of psychotherapy education for psychiatry residents and psychology trainees. The use of virtual patients could be implemented in several ways. For example, virtual patients could be developed to recognize the essential features and common pitfalls of an initial psychotherapy interview so that they could give more specific, relevant, and reliable verbal feedback to the residents involved. In addition, the use of virtual patients illustrating common problems such as acting out, transference, intrusive questions, or seductive behavior would allow residents to have an experience with these anxiety provoking situations in a simulated setting before they occur in their practice. Finally, performance in virtual patient scenarios could be used as an additional source of data for the assessment of resident competency in the psychotherapy domain. An issue that is bound to come up when discussing the training of clinicians using a simulated patient is the resident’s perception of being assessed against a non-human agent. For this author’s work, this issue is understood as a phenomenological issue inherent in any therapeutic relation. Following Zaner’s (2006) phenomenological approach (mentioned above), there are four areas of interest for the novice clinician’s interaction with the virtual

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patient. First, there is the issue of “Immersion” and the extent to which the clinician in training immersible. This is an important issue, because this author’s own research has found that the extent to which a participant is capable of “absorption” and “hypnotism” are very important for social science research using virtual reality. The propensity of participants to get involved passively in some activity and their ability to concentrate and block out distraction are important factors to consider when conducting a study. Likewise, evidence suggests that immersiveness and hypnotizability play a role in the outcome of studies using virtual environments. Research into these moderating individual traits is of value because such research may augment participant selection (Macedonio et al., 2007). Second, there is the related issue of the “Reflective-Attentive shift”, in which the clinician’s acceptance of a virtual patient approach may be limited by the extent to which the trainee is able to shift attention from active involvement in clinical cases with virtual standardized patients, to considering them as examples of the practice. Of course, there is still the issue of the “Appeal to Evidence” and this is where the simulation really needs to present a very plausible and believable scenario so that the clinician in training is able to Figure 3.

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offer a judgment on the basis of soundest available evidence. Finally, it will be up to the trainer to help the novice clinician understand that the interaction with the scenarios involves an ethical focus that “suspends belief” in the fidelity of the graphics so that the focus can be on a potential real life scenario.

Virtual Standardized Patient: Trauma Exposure For the next VSP, this author’s lab constructed an adolescent female character called Justina that had been the victim of an assault and showing signs of PTSD (see Figure 3). The technology used for the system is based on the virtual human technology developed at USC and is the same as what was used with the previous VSP ‘Justin’. PTSD Domain The experience of victimization is a relatively common occurrence for both adolescents and adults. However, victimization is more widespread among adolescents, and its relationship to various problem outcomes tends to be stronger among adolescent victims than adult victims. Whilst much of the early research on the psychological

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sequelae of victimization focused on general distress or fear rather than specific symptoms of PTSD, anxiety, or depression, studies have consistently found significant positive correlations between PTSD and sexual assault, and victimization in general and violent victimization in particular (Norris et al., 1997). Although there are a number of perspectives on what constitutes trauma exposure in children and adolescents, there is a general consensus amongst clinicians and researchers that this is a substantial social problem (Resick & Nishith, 1997). The effects of trauma exposure manifest themselves in a wide range of symptoms: anxiety, post-trauma stress, fear, and various behavior problems. New clinicians need to come up to speed on how to interact, diagnose and treat this trauma. According to the most recent revision to the American Psychiatric Association’s DSM Disorders (2000), PTSD is divided into six major categories; refer to the DSM-IV category 309.81 for a full description and subcategories; A. Past experience of a traumatic event and the response to the event. B. Re-experiencing of the event with dreams, flashbacks and exposure to cues. C. Persistent avoidance of trauma-related stimuli: thoughts, feelings, activities or places, and general numbing such as low affect and no sense of a future. D. Persistent symptoms of anxiety or increased arousal such as hyper vigilance or jumpy, irritability, sleep difficulties or can’t concentrate. E. Duration of the disturbance, how long have they been experiencing this. F. Effects on their life such as clinically significant distress or impairment in social or educational functioning or changes in mental states. Diagnostic criteria for PTSD includes a history of exposure to a traumatic event in category

A and meeting two criteria and symptoms from each B (i.e., re-experiencing), C (i.e., avoidance), and D (i.e., hypervigilance). The duration of E (i.e., duration of disturbance) is usually greater than one month and the effects on F (i.e., social functioning) can vary based on severity of the trauma. Effective interviewing skills are a core competency for the clinicians, residents and developing psychotherapists who will be working with children and adolescents exposed to trauma. A clinician needs to ask questions in each of these categories to properly assess the patient’s condition. We aimed primarily to evaluate whether a VSP generate responses that elicit user questions relevant for PTSD categorization. Findings suggest that the interactions between novice clinicians and the VSP resulted in a compatible dialectic in terms of rapport, discussion of the traumatic event, and the experience of intrusive recollections. Further, there appears to be a satisfactory amount of discussion related to the issue of avoidance. These results comport well with what one may expect from the VSP (Justina) system. In some of our earlier work, we found that the individual characteristics of study participants may impact the immersiveness and subsequent findings of a given study. Of primary importance is the extent to which a participant is capable of “absorption” and “hypnotism.” Hence, individual differences may moderate presence and confound findings. The propensity of participants to get involved passively in some activity and their ability to concentrate and block out distraction are important factors to consider when conducting a study. Likewise, evidence suggests that hypnotizability plays a role in the outcome of studies using VR. Research into these moderating individual traits is of value because such research may augment participant selection. We applied these findings to our work with VSPs to investigate the relationship between a number of psychological variables and the resulting VSP responses. The primary goal in this study

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was evaluative: can a virtual standardized patient generate responses that elicit user questions relevant for PTSD categorization? Findings suggest that the interactions between novice clinicians and the VSP resulted in a compatible dialectic in terms of rapport, discussion of the traumatic event, and the experience of intrusive recollections. Further, there appears to be a fair amount of discussion related to the issue of avoidance. These results comport well with what one may expect from the VSP (Justina) system. Much of the focus was upon developing a lexicon that, at minimum, emphasized a VSP that had recently experienced a traumatic event and was attempting to avoid experienced that my lead to intrusive recollections. However, the interaction is not very strong when one turns to the issue of hyper-arousal and impact on social life. While the issue of impact on social life may simply reflect that we wanted to limit each question/response relation to only one category (hence, it may have been assigned to avoidance instead of social functioning), the lack of questions and responses related to hyperarousal and duration of the illness reflects a potential limitation in the system lexicon. These areas are not necessarily negatives for the system as a whole. Instead, they should be viewed as potential deficits in the systems lexicon. A secondary goal was to investigate the impact of psychological variables upon the VSP Question/ Response composites and the general believability of the system. After controlling for the effects of these psychological variables, increased effects were found for discussion of the traumatic event, avoidance, individualized models for affect recognition hyper-arousal, and impact on social life. Further, the impact of psychological characteristics revealed strong effects upon presence and believability. These findings are consistent with other findings suggesting that hypnotizability, as defined by the applied measures, appears moderate user reaction. Future studies should make use of physiological data correlated with measures of

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immersion to augment and quantify the effects of virtual human scenarios. There are several modalities such as facial expression, vocal intonation, gestures, and postures that are regularly used to evaluate and model the affective states of individuals interacting with a virtual human. In this author’s lab, there is an attempt to take a take a multimodal approach that builds on the work of virtual human researchers, but extends the cognitive and affective modeling through the incorporation of psychophysiological data. This is done for the following reasons: (1) Psychophysiology is an important component left out of many cognitive and affective models used in virtual human research; and (2) physiological signals are continuously available and are arguably not directly impacted by technological limitations inherent in virtual human assessment of facial expression, vocal intonation, gestures, and postures.

Psychophysiology to Enhance Cognitive and Affective Models of Virtual Humans Current cognitive and affective models found in virtual human research tend to use appraisal models that specify how events, agents and objects are used to elicit an emotional response depending on a set of parameters (e.g., goals, standards and attitudes) representing the subject. In principle, it is possible to model appraisal processes using classical symbolic AI techniques (Picard, 1997; Chwelos & Oatley, 1994). However, cognitive and affective models of virtual humans do not generally account for neurophysiological data (Fellous, Armony, & LeDoux, 2003). Further, as MagnenatThalmann and Thalmann (2005) have pointed out in their review of virtual human research, virtual human models of emotional responses tend to be generated from a cognitive point of view and do not adequately take into account the psychophysiological response. Although appraisal does play a role in many current theories of emotion, most

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contemporary psychologists studying emotion emphasize the importance of psychophysiological arousal and that emotions are to be understood as cognitive appraisals and are accompanied by autonomic nervous system activity. Although many appraisal models contend that cognitive processes (e.g., sensory perception) present verification for the preeminence of appraisal in emotion, other theorists indicate that appraisal processes occur following perception and represent a separate cognitive process (Izard, 1993). Of course, while most would agree that perception is a necessary part of any sensory experience, it is not known whether perceptual processes are the foundation of cognitive models of emotion or if these emotions are concerned with higher order cognitive appraisals that assign meaning and valence (Eckhardt, Norlander, & Deffenbacher, 2004). A major limitation to many appraisal models found in virtual human research is that they follow outdated appraisal models that assert specific patterns of physiological changes that may be observed in affect occurrence after the subjective experience of affect. Research in psychophysiology has not supported these cognition first models (Cox & Harrison, 2008). In fact, a common frustration to attempts at developing an adequate scientific approach to emotion has been focus upon constructing theories of the subjective appraisals. Again studies of the neural basis of emotion and emotional learning have instead focused on how the brain detects and evaluates emotional stimuli and how, on the basis of such evaluations, emotional responses are produced (Magnenat-Thalmann & Thalmann, 2005). This author believes that a preferred approach to developing cognitive and emotional virtual human models would include psychophysiological inputs from the humans to the virtual humans during interactions. It is believed that these additional inputs can be developed into affect-sensitive VSP interfaces that go beyond conventional virtual human models designed by pure (i.e., devoid of

psychophysiological metrics) cognitive appraisal principles. The resulting affect-sensitive VSP interfaces would be similar to brain-based-devices (BBDs) that are being designed based on biological principles and are programmed to alter their behavior to the environment through self-learning (Edelman, 2006). An example of such research is found in work to develop intelligent robots. A series of devices with sensors and computer-simulated brains have been built in Gerald Edelman’s (2006) Neurosciences Institute in La Jolla. The brains are modeled on human anatomy, complete with versions of visual cortex, inferotemporal cortex, and hippocampus. They are not pre-programmed, but evolve neuronal connections in response to experience. These devices can learn to recognize patterns and navigate novel environments. Although the development of such computational models for virtual humans would be difficult, researchers (Magnenat-Thalmann & Thalmann, 2005) have pointed out that computational approaches to emotional processing are both possible and practical. In the following, there is a description of preliminary attempts at incorporating psychophysiological metrics into an affect-sensitive VSP interface system. Our goal is to first develop the interface and then build upon the interface to model affect and enhance the VSPs’ cognitive and affective models.

Psychophysiology to Enhance Cognitive and Affective Assessment of User The tendency of virtual human researchers to rely upon modalities such as facial expression, vocal intonation, gestures, and postures results in limitations due to “communicative impairments” (both nonverbal and verbal) inherent in the technology. This is very much the case regarding expression of affective states. Although these vulnerabilities place limits on traditional conversational and observational methodologies found in much virtual human research, psychophysiological signals are

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(1) continuously available; and (2) are arguably not directly impacted by these difficulties. As a result, psychophysiological metrics may proffer an approach for gathering robust data despite potential virtual human technology limitations. Furthermore, there is evidence that psychophysiological activity of persons immersed in virtual environments is associated with (1) trait differences (immersability; Macedonio, 2007) and (2) state differences (intensity of the environment; Parsons et al., 2009b, Parsons et al., 2009c; Meehan et al., 2005). These findings from virtual reality research reflect the finding that transition from one affective state to another is accompanied by dynamic shifts in indicators of autonomic nervous system activity (Bradley, 2005).

Psychophysiologically-Driven Interfaces for Adaptive Virtual Standardized Patients According to Fairclough (2009), the next generation of intelligent technology will be characterized by increased autonomy and adaptive capability. Such intelligent systems need to have ample capacity for real-time responsivity (Aarts, 2004). For example, to decrease the intensity of a simulation if a user is overloaded by stimuli within a learning environment, or to make the simulation more challenging if the user is bored. The psychophysiological computing approach proffers the VSP a means of monitoring, quantifying, and representing user context and adapt in real-time. A primary focus of the work described herein is the development of VSPs that are capable of real-time adaptive responding to affective processing of novice clinicians during clinical training paradigms and to enhance cognitive and affective models of virtual human technology. In what follows, there is a description of the current VSP task design and affective modeling techniques that are used during studies of interactions between novice clinicians and VSPs. The biocybernetic adaptation (Pope et al., 1995) of the affect-sensitive VSP

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system during interaction with a novice clinician can be summarized as follows: First, psychophysiological signals from the novice clinician are recorded while she is interacting with the VSP system. Next, the psychophysiological signals are filtered and quantified to operationalize relevant psychological constructs (e.g., frustration, user engagement, alertness) for incorporation into a unique psychophysiological profile for that user. 3) The psychophysiological profile is input into a database that has been developed from expert opinion, psychophysiology literature, and multimodal data (facial expression, vocal intonation, gestures, postures, dialectical interactions, and psychophysiology) gleaned from previous subjects interacting with VSPs (Kenny et al., 2007; Kenny et al., 2008a; Kenny et al., 2008b; Kenny et al., 2009; Parsons et al., 2009a). The VSP system next finds the best matching stimuli from its databases with respect to the intensity level of an affective state. An assessment of user state may be achieved via the development of discriminant algorithms (Liu, Rani, & Sarkar, 2005) or neural networks (Gevins et al., 1998; Laine et al., 2002). Finally, the system adapts the VSP’s representation of the interaction and behavior accordingly.

Psychophysiology and Affect Modeling Individual (Parsons et al., 2009b) and cohort (Parsons et al., 2009d) differences have been shown to impact results gleaned from psychophysiological assessments using virtual environments, which reflects the need for psychophysiological assessment of persons interacting with VSPs. In addition to extending the validation of VSPs, this author’s lab uses psychophysiological metrics to develop a psychophysiological interface for VSPs that can adapt VSP scenarios to the user’s psychophysiological processing of information. More specifically, psychophysiological measures such as heart rate, skin conductance responses, facial electromyographic response recordings, respira-

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tion, electroencephalographic recordings, and eyetracking can be continuously recorded while subjects interact with the VSP. These recordings can be processed in real time to gain information about the user’s current state of emotional and cognitive processing. This information can then be relayed to the virtual environment in order to change for example, the behavior of the VSP. If the user is distressed by the current state of the interaction, a psychophysiological pattern of increased heart rate, increased skin conductance levels, a more rapid rate of respiration, increased corrugator muscle activity, decreased alpha wave activity and diversion of gaze may develop. Allowing for access to this information about the user’s current emotional state offers the VSP an increased capability to understand the dynamics of the current interaction and develop a context appropriate response or behavior change. In order to have reliable reference points, this author’s lab has been running subjects through a protocol in which they interact with our VSPs. A primary goal has been to develop a reference knowledge database that is based on relevant data from literature, expert consensus, and actual psychophysiological data gleaned from persons interacting with VSPs.

Virtual Standardized Patient: Assessing Bias Data for the reference knowledge database is initially being modeled off of data from previous experiments. In addition to the experiments mentioned above, this author’s lab has also run subjects through protocols in which we measured the activation and control of affective bias using 1) startle eye blink responses; and 2) self-reports as human participants interacted with VSPs representing both the human’s race and another race (Parsons et al., 2009a). We also assessed the differences in psychophysiological responses of humans interacting with VSPs representing same and different sex groups. By measuring eyeblink

responses to startle probes occurring at short and long latencies following the onset of same compared with other ethnicity VSPs, we were able to examine affective processes associated with both the activation and potential control of bias. A number of studies have used startle eye blink to successfully detect positive and negative affective responses (Blascovich, 2000; Lang, 1995). Startle eye blink has been found to adequately detect implicit affective race bias (Amodio, Harmon-Jones, & Devine, 2003). Part of the reason that the startle response is a good choice for this research is that a relatively simple and brief, but intense stimulus (i.e., usually a 100 db, 50 ms burst of static) can result in a primitive, defensive reflex. In previous studies, researchers have found that the startle response is increased or potentiated by stimuli that evoke a negative affective response and decreased or inhibited by stimuli that evoke a pleasant affective response or appetitive stimuli (Lang, 1995). The startle response has also been shown to be effective in predicting affective reactions to people of a particular race (Amodio et al., 2004). Individual differences in levels of bias were predicted using E. A. Plant and P. G. Devine’s (1998) Internal and External Motivation to Respond without Prejudice scales (IMS/EMS). Since participants with varying levels of IMS and EMS may differ in their responses to general affective stimuli unrelated to ethnicity, we presented general affective pictures from the International Affective Picture System (IAPS; Lang, Bradley, & Cuthbert, 2005). Finally, we obtained participants’ responses to the Attitude Toward Blacks/Whites scale to compare the predictive ability of both the VSP interaction and the IMS–EMS with a traditional attitude measure of prejudice. Eye blinks were collected and scored as electromygraphic (EMG) activity of the orbicularis oculi muscle of the left eye according to standard procedures (Blumenthal et al., 2005; Biopac, Santa Barbara, CA). One small (4 mm) silver-silver chloride (Ag-AgCl) electrode was placed on the

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left eyelid directly below the pupil while a second 4 mm electrode was placed approximately 1 cm lateral to the first. The impedance between the two electrodes was measured and deemed acceptable if below 10 KΩ. A large (8 mm) Ag-AgCl electrode was placed behind the left ear to serve as a common ground. Startle blinks were identified in each portion of the recordings as follows: an interval (from 20 to 150 ms) adjacent to each startle probe was searched for the largest spike whose absolute value exceeds a high threshold (of 150 µV). A difference score between the median blink amplitude to African American VSPs and Caucasian VSPs was determined. The difference score was then analyzed in terms of IMS and EMS scores. As all participants reported similar levels of internal motivation to respond without racism, they were separated into two groups of higher and lower external motivation scores. A one-way ANOVA was performed to determine if high vs. low external motivation related to physiological responses to different racial groups of virtual patients. The difference score tended to be lower in those with high external motivation to behave in a non-racist manner. Those who were lower in external motivation had a larger difference score between startle amplitudes while looking at African American vs. Caucasian VSPs. The larger difference score reflects larger startle amplitudes to African American VSPs, suggesting an implicit negative bias towards that group. The difference between the low EMS and high EMS groups was not significant but suggests need for future analyses into the ways in which motivations can influence behavior at even automatic levels. While the findings of the study are interesting, our current interest in this dataset is the modeling of user representation. We are currently running additional subjects to enhance the modeling through the inclusion of other modalities such as facial expression, vocal intonation, gestures, and postures, which may be amalgamated with

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psychophysiology to increase the complexity of the user representation.

Psychophysiological Computing as a Means of Providing User Context Psychophysiological computing represents an innovative mode of human computer interaction (HCI) where system interaction is achieved by monitoring, analyzing and responding to covert psychophysiological activity from the user in realtime (Parsons et al., 2009a; Parsons et al., 2009c; Plant & Devine, 1998; Allanson & Fairclough, 2004). These systems operate by transforming psychophysiological data into a control signal (or an input to a control signal) without a requirement for any overt response from the user. Psychophysiological computing captures spontaneous and subconscious facets of user state, opening up bandwidth within the HCI by enabling an additional channel of communication from the user to the VSP. As such, information exchange between human and VSP is rendered symmetrical as the psychophysiological interface constructs, consults and responds to a dynamic representation of the user.

Psychophysiological Assessment We are collecting psychophysiological (e.g. heart rate; skin conductance; respiration; etc) data from users at USC via Biopac sensors. These data are being filtered and quantified to operationalize relevant psychological constructs (e.g., frustration, user engagement, alertness). These data will be correlated with other metrics (e.g. verbal and nonverbal receptive language) to enhance user interactions with the VSP. Specifically, we aim to gain psychophysiological data that we will analyze in order to quantify or label the state of the user so that we may develop an affect-sensitive VSP interface system. Following Fairclough (2009), we envision user state assessment as something being made with reference to absolute (e.g., heart rate

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exceeds 80 percent of normal baseline) or relative criteria (e.g., heart rate has risen 20 percent since the previous data collection epoch); alternatively, the assessment provided by the system may be categorical in nature (e.g., pattern of heart rate activity and changes in skin conductance level indicate that the person is in a negative rather than a positive emotional state). Based upon the user’s psychophysiological responses to interacting with the VSP, enhancements may be made to the VSP’s interactivity.

Psychophysiological Validity As part of our ongoing data collection and data analytic process, we are endeavoring to validate the psychophysiological protocol: 1) Content validity is being established through careful selection of psychophysiological variables based on a review of existing literature (i.e., that a precedent exists for specific variables to tap those psychological constructs targeted by the system designer); and 2) Concurrent validity of the psychophysiological interface (i.e., degree to which a particular psychophysiological measure(s) can be demonstrated to predict the target psychological dimension). Further, we are establishing the reliability of the psychophysiological inference across a range of representative test conditions (e.g., high vs. low levels of operator stress) and individual differences among users.

VIRTUAL STANDARDIZED PATIENTS FOR TECHNOLOGY INTEGRATION IN HIGHER EDUCATION Although traditional approaches to training clinicians in the interpersonal communication skills currently include a combination of classroom learning and role-playing with human standardized patients, there are limitations that can be mitigated through the integration of simulation technology. For example, while human standard-

ized patients are expensive and cost several thousand dollars per student, the integration of virtual patient technology into the curriculum would be relatively inexpensive because it would require a one time programming cost. Further, given the fact that there are only a handful of sites (for over 130 medical schools in the U.S.) providing standardized patient assessments of the clinician in training’s communication ability as part of the U.S. Medical Licensing Examination (USMLE), the current model provides limited availability. The integration of virtual patient technology would allow for the exact same virtual patient technology to be integrated and implemented at multiple locations. Another concern is the issue of standardization. Despite the expense of standardized patient programs, the standardized patients themselves are typically unskilled actors. As a result of common turnover, administrators face considerable challenges for offering psychometrically reliable and valid interactions with the training clinicians. The integration of virtual patients would allow for a standardized administration, standardized scoring, and standardized interpretation of the novice clinicians learning across multiple presentations and interactions. A related issue is the limited scope that the actors are able to portray. As a result, there tends to be an inadequate array of developmentally, socially, and culturally appropriate scenarios. For example, when a clinician has a pediatric focus and needs access to children, it is difficult for the clinician to pretend that the actor is a child. Finally, many clinical cases (e.g., traumatic brain injury) have associated physical symptoms and behaviors (e.g., dilated pupils, spasms, and uncoordinated movements). While these signs and symptoms simply cannot be accurately portrayed by human actors, they are a relatively straightforward programming issue for use with a virtual standardized patient. As discussed above there are a number of ways in which the integration of virtual standardized patient technology can enhance clinical training in communication. The contribution of a virtual

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standardized patient to increased doctor patient communication is important and may enhance multifarious aspects of health outcomes. For example, increased doctor patient communication has been found to offer elevated compliance (Cegala, 2000), improved health outcomes (Stewert et al., 1995), increased patient satisfaction (Jackson, 2001), and a decrease in malpractice risk (Levinson and Roter, 1997). Given a virtual standardized patient’s ability to simulate a particular clinical presentation with a high degree of consistency and realism, a virtual standardized patient may increase communication skills. Further, virtual standardized patients can be developed for special populations (e.g., children; octogenarians; neurological disorders) that cannot be adequately represented by a human actor (human standardized patient). Hence, a virtual standardized patient may help clinicians, educators, and health service administrators better understand clinician-patient communication that is unique.

CONCLUSION In this chapter, there was a discussion of the ways in which advanced technologies (i.e., virtual standardized patients can move beyond traditional approaches to training clinicians in assessment, diagnosis, interviewing and interpersonal communication. The traditional approaches rely upon a combination of classroom learning and roleplaying with human standardized patients. Much of this work is done with actors that have been recruited and trained to exhibit the characteristics of an actual patient, thereby affording novice clinicians a realistic opportunity to practice and to be evaluated in a mock clinical environment. Although a valuable training approach, there are limitations with the use of human standardized patients that can be mitigated through simulation technology. For example, human standardized patients are expensive and cost several thousand dollars per student. Further, given the fact that

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there are only a few sites providing standardized patient assessments as part of the U.S. Medical Licensing Examination, the current model provides limited availability. In addition to issues of availability of trained actors, there is the issue of standardization. Despite the expense of standardized patient programs, the standardized patients themselves are typically unskilled actors. As a result of common turnover, administrators face considerable challenges for offering psychometrically reliable and valid interactions with the training clinicians. A related issue is the limited scope that the actors are able to portray. As a result, there tends to be an inadequate array of developmentally, socially, and culturally appropriate scenarios. For example, when a clinician has a pediatric focus and needs access to children, it is difficult for the clinician to pretend that the actor is a child. Finally, many clinical cases (e.g., traumatic brain injury) have associated physical symptoms and behaviors (e.g., dilated pupils, spasms, and uncoordinated movements) that simply cannot be accurately portrayed by human actors. In this chapter a series of experiments were described to elucidate the usefulness and effectiveness of an affect-sensitive VSP Interface System. While self- report data are widely used in virtual human research, they are susceptible to modification by a participant’s awareness of the social desirability of particular responses, reducing the sensitivity of the measures, implicit behavioral and psychophysiological responses are automatic and thus considered less susceptible to self-conscious influences (Schwarz, 1999). A further issue discussed in this paper was that the current cognitive and affective models found in virtual human research tend to use appraisal models generated from a cognitive point of view and do not adequately take into account the psychophysiological response. It was contended that a preferred approach to developing cognitive and emotional virtual human models would include psychophysiological inputs

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from the humans to the virtual humans during interactions. It is believed that these additional inputs can be developed into affect-sensitive VSP interfaces that go beyond conventional virtual human models designed by pure (i.e., devoid of psychophysiological metrics) cognitive appraisal principles. The resulting affect-sensitive VSP interfaces would be similar to brain-based-devices (BBDs) that are being designed based on biological principles and are programmed to alter their behavior to the environment through self-learning. In summary, effective interview skills are a core competency for training clinicians. Although schools commonly make use of standardized patients to teach interview skills, the diversity of the scenarios standardized patients can characterize is limited by availability of human actors. Further, there is the economic concern related to the time and money needed to train standardized patients. Perhaps most damaging is the “standardization” of standardized patients—will they in fact consistently proffer psychometrically reliable and valid interactions with the training clinicians. Virtual Human Agent technology (e.g., virtual standardized patients) has evolved to a point where researchers may begin developing mental health applications that make use of virtual reality patients for training.

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KEY TERMS AND DEFINITIONS Standardized Patient: Actor that has been trained to act as a real patient in order to simulate a set of symptoms or problems. Virtual Human: Embodied agent with a graphical front-end that is capable of engaging in conversation with humans employing the same verbal and nonverbal means that humans do (e.g., gesture, facial expression). Virtual Standardized Patients: Advanced conversational virtual human agents programmed to portray standardized patient scenarios (e.g., mental or physical conditions) for the training of clinicians. Immersion: State of consciousness where a person immersed in a virtual environment has diminished awareness of physical self due to his or her being surrounded in an engrossing virtual environment.

Psychophysiology: Domain of psychology that is emphasizes the physiological bases of psychological processes Psychophysiological Computing: Computer system that can recognize, interpret, and process real-time psychophysiological activity and use it as input data Affect Modeling: Detection of emotional information using sensors that capture data about the user’s physical state or behavior. Recognizing emotional information requires the extraction of meaningful patterns from the gathered data (e.g., parsing the data through various processes such as speech recognition, natural language processing, or facial expression detection). Validity: extent to which a concept, conclusion or measurement is well-founded and corresponds accurately to the real world.

This work was previously published in Technology Integration in Higher Education: Social and Organizational Aspects, edited by Daniel W. Surry, Robert M. Gray Jr and James R. Stefurak, pp. 201-221, copyright 2011 by Information Science Reference (an imprint of IGI Global).

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Chapter 3.4

Telemedicine and Biotelemetry for E-Health Systems: Theory and Applications Elif Derya Übeyli TOBB Ekonomi ve Teknoloji Üniversitesi, Turkey

ABSTRACT This chapter develops an integrated view of telemedicine and biotelemetry applications. The objective of the chapter is coherent with the objective of the book, which includes techniques in the biomedical knowledge management. Telemedicine is the use of modern telecommunications and information technologies for the provision of clinical care to individuals at a distance and the transmission of information to provide that care. The medical systems infrastructure underpinning this form of medicine, consisting of the equipment DOI: 10.4018/978-1-60960-561-2.ch304

and processes used to acquire and present clinical information and to store and retrieve data are explained in detail. An investigation of telemedicine applications in various fields is presented and the likely enormous impact of telemedicine systems on the future of medicine is discussed. For example, bioelectric and physiological variables could be measured by biotelemetry systems. Developing a biotelemetry system and the principal operation of such a system are presented, and its components and the telemetry types are explained. The author suggests that the content of the chapter will assist the medical sector and the general reader in gaining a better understanding of the techniques in the telemedicine and biotelemetry applications.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Telemedicine and Biotelemetry for E-Health Systems

INTRODUCTION Literally, telemedicine means medicine at a distance. Telemedicine has been defined as the electronically-transmitted rapid exchange of medical information between sites of clinical practice for the purposes of relief and/or education. Telemedicine is also defined as the use of electronic information and communication technologies to provide and support health care when distance separates the participants. A broader definition is the use of telecommunication technologies to provide medical information and services (Chen et al, 1999; Foster et al, 1998; Güler & Übeyli, 2002a; Moore, 1999). Telemedicine includes diagnosis, treatment, monitoring and education of patients using systems that allow ready access to expert advice and patient information no importance where the patient or relevant information is located. The fundamental concepts of telemedicine technology including: Basic principles of telecommunications and internetworking of computer systems, use of communications software, forms of telecommunications. The use of telemedicine systems in hospitals, clinics, longterm care facilities and home care is becoming well established and evolving in effectiveness and efficiency (Garshnek et al, 1997; Stanberry, 2001). Telemedicine can therefore be divided into three areas: Use for decision making; remote sensing; and colloborative arrangements for the real time management of patients at a distance. Each of the three areas are limited to aspects of medical diagnosis, patient care and education (Merrell & Doarn, 2007; Sood et al, 2007). Biomedical telemetry is a special field of biomedical instrumentation that often permits transmission of biological information from an inaccessible location to a remote monitoring site. When direct observation is impossible, biotelemetry can be used to obtain a wide spectrum of environmental, physiological and behavioural data (Güler & Übeyli, 2002b; Hines, 1996; Jones & Normann, 1997; Ziaie et al, 1997). The purpose

of biotelemetry includes the capability for monitoring humans and animals with minimum restraint and to provide reproduction of the transmitted data. If measurements and monitoring techniques are applied to restrained humans and animals, stress of immobilization causes alterations of measured variables. According to this concept, the advantage of biotelemetry is the measurement of physiological variables in conscious, unrestrained humans and animals. The method of biotelemetry is offering wireless, restraint-free, simultaneous, long-term data gathering (Axelsson et al, 2007; Lee et al, 2007; Mussivand et al, 1997). Measurements which have been done in biotelemetry can be determined in two categories: 1. Bioelectrical variables, such as electrocardiogram (ECG), electromyogram (EMG) and electroencephalogram (EEG); 2. Physiological variables that require transducers, such as blood pressure, gastrointestinal pressure, blood flow and temperatures. By using suitable transducers, telemetry can be employed for the measurement of a wide variety of physiological variables (Rocchitta et al, 2007; Welkowitz et al, 1976).

TELEMEDICINE TECHNOLOGIES The use of telecommunications and information technology is central in providing health services- regardless of location. The investigation, monitoring and management of patients and the education of patients and staff using systems which allow ready access to expert advice and patient information, no matter where the patient or relevant information is located (Chen et al, 1999; Garshnek et al, 1997). In addition to the aspects covered by these definitions, telemedicine involves a combination of topics from the fields of telecommunication, medicine and informatics. The application of telecommunication technology to health care requires integration of technology,

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tools and training with medical care practices and problems, although it is not necessary to be an expert in all these components to effectively use a telemedicine system. Telemedicine achieve its potential to improve delivery of health care in rural or remote areas only through cooperation among health professionals, computer system developers, telecommunication providers and educators. Telemedicine includes transfer of basic patient information over computer networks (medical informatics), diagnosis, treatment, monitoring and education of patients using systems that allow access to expert advice and patient information. During these processes location of patient or relevant information is not important. The fundamental concepts of telemedicine technology, including: • •

•

Basic principles of telecommunications and internetworking of computer systems; Use of communications software, including electronic mail and browsers for the World Wide Web; Forms of telecommunications, including videoconferencing, remote data monitoring and file transfer, applicable to medical care in remote or rural environments.

Communication networks are becoming increasingly large in size and heterogeneous in nature. Recent advantages in communication technologies have contributed to an explosion of new products and services directed at the medical environment. An abstract mapping between medical applications and network evolution is given in Figure 1 (Pombortsis, 1998). The general goal of using communication technologies in medical environments is to improve the overall quality of health care at an affordable cost. This requires close interaction between health care practitioners and information technologists to ensure that the proposed technologies satisfy current user’s needs and anticipate future ones. The practice of telemedicine may be broken into two categories: Store-and-forward and interactive. Store-and-forward technology is a low cost method of transmitting images by computer. This technology is most frequently used for transmitting radiological pictures and is used by most of the hospitals and clinics. Interactive telemedicine implies face-to-face interaction with a patient, health professional or both. This requires videoconferencing technology at both sites, which is usually between a large medical center or hospi-

Figure 1. An abstract mapping of medical applications to network evolution (source: After Pombortsis, 1998)

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tal and another facility (usually rural) requiring a specialist’s opinion (Brown, 1998). Telemedicine can also be synchronous or asynchronous and increasing numbers of programs provide both services. Synchronous services occur in real time and primarily include audio, interactive full motion video and still images. Synchronous services are often used for live patient consultations and for large group continuing education and meetings when interactive communication is required. Systems used for synchronous communication may include specialized telemedicine about interactive video units, interactive video room systems, computer-based desktop videoconferencing units, videophones. These may be supplemented with peripherals including electronic stethoscopes, other electronic scopes, view boxes, combination of telemonitoring devices, graphics stands and more. Sometimes hand-held mobile and wireless systems are used for records, prescriptions and orders. Asynchronous services are viewed at different times than the time of transmission and generally consist of still images, e-mail and video clips. Asynchronous telehealth is more often used when the patient does not need to be present for interactive communication and for independent continuing education. Store-and-forward technologies used in asynchronous services are mostly PC based (Moore, 1999).

TELEMEDICINE INFRASTRUCTURE The telecommunications infrastructure provides the technology to move information electronically between geographically dispersed locations. Participating sites are linked through electronic networks. The telecommunication medium utilized by telemedicine programs is determined in large part by the available local infrastructure. These can include satellite, microwave link or terrestrial lines (either twisted copper phone lines or fiber optic cable) (Murakami et al., 1994). The medical systems infrastructure consists of the equipment

and processes used to acquire and present clinical information and to store and retrieve data. Acquisition and presentation technologies include teleconferencing, data digitizing and display (remote X-ray, laboratory tests), text processors (scanners, fax), or image processors (video cameras, monitors). Data storage and retrieval include storage devices (disks, tape, CD-ROM) along with technology to compress, transmit and store data. The applications are based on a variety of networks, ranging from the ordinary telephone network to specialized networks. Telemedicine therefore involves a spectrum of technologies including computer technology, digital imaging, videoconferencing, remote monitoring, file sharing, networking and telecommunications (Conner et al., 2000). Technology is necessary to support long-distance communications. Electronic devices like telephones, TV cameras or computers allow people to create information that is then transmitted through media such as cables, satellite systems or computer networks. Most of these systems interpose switches or routers in the medium that relay information from one intermediate point to another, deciding along the way how to get an electronic message to its intended destination in the best manner (Chen et al, 1999; Ruskin et al, 1998; Sargsyan et al, 1999; Sheng et al, 1999; Takeda et al, 1999; Tanrıverdi & Iacono, 1999). The Internet allows any computer with an internet connection to communicate with any other connected computer, no matter where each of them are. Internet supports telecommunications in number of ways. Composing, sending and receiving electronic mail between users has been possible. A user can also make copies of files from other computers onto their own by using programs for file transfer such as FTP. The foundation of the World Wide Web is organized group of computers (sites) accessible from internet containing files that can be viewed by using browser software (Ruskin et al, 1998; Sargsyan et al, 1999). With the rapid development of internet

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and internet based applications, telemedicine can potentially provide important health care coverage for remote and rural area where specialized medical expertise is lacking. Using internet based telemedicine application, the records and vital signs of patients in remote or rural areas can be made available to medical experts in major medical centers for real-time evaluation or diagnosis (Bellazzi et al, 2001). In order to do this effectively, asynchronous transfer mode (ATM) is the preferred technology in medical communications. Many medical applications require the higher bandwidth and guaranteed qualities of service supported by ATM. Interest first came from the carriers and the manufactures of wide area network (WAN) equipment and then interest is growing in the application of ATM technology to local area networks (LANs) and campus area networking environments, as well as to desktop area networks. In a typical scenario there are several different networks carrying voice, data and video. An ATM based network can be used to consolidate these into one network, with the initial candidate networks being data and video, and voice networks being the last added (Gomez et al, 2001; Moore, 1999; Pombortsis, 1998). The Integrated Services Digital Network (ISDN) provides various telecommunication services through one common interface to the subscriber line. Enhanced features for data communication is a particularly important service of ISDN. The services include telephony, telex, teletext, facsmile, videotext, videophony, videoconferencing and data communication (including mail, conferencing, databases, etc.). In the health sevices, use of ISDN for the exchange of administrative information has great potential. Making an appointment for an X-ray examination may be an example. The primary health care clinic’s system for medical records can establish a connection to the local hospital’s booking system while the patient is still at the practitioner’s office. Then the patient, the practitioner and the clerical officer at the hospital cooperate on deciding a suitable

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time and make the appointment. Then necessary information on the patient can be transmitted. After a completed examination, the X-rays and the radiologist’s diagnosis can be transmitted back to the primary health clinic system for medical records (Tanrıverdi & Iacono, 1999). The integration of fixed and mobile networks also makes new applications possible for telemedicine. Mobile information and communication systems in clinical routine have the potential to greatly improve communication, facilitate information access, eliminate double documentation and increase quality of patient care. With a view to providing paramedical care within moving vehicles, a telemedicine technique using mobile satellite communication is proposed. In a moving vehicle, a color image, an audio signal and physiological signals, such as ECG and blood pressure are obtained from a patient. They are multiplexed and transmitted to a satellite. In a fixed station, the signals received from the satellite are demultiplexed and presented to a medical doctor or automatic monitoring system. The instructions from the doctor are transmitted back to the mobile station via satellite. The usage of mobile satellite communication in telemedicine makes some particular applications more possible: emergency medicine in moving vehicles, emergency medicine during disasters, physiological monitoring of pilots and/or astronauts, etc. In addition, there will be many new applications of telemedicine using mobile satellite communications developed as this technology becomes more widely used (Murakami et al, 1994). Videophony and videoconferencing over ISDN and internet are useful tools for communication within the health services. Videoconference standards emerged at the end of the 1980s to work over synchronous communication networks, such as ISDN. Videoconferencing is an effective way for people to meet and work together, without being at the same place. It may also be a useful tool when transmitting medical information in the form of video sequences. Videoconferencing

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connects a group of hospitals on a ‘one-to-many’ basis, whereby a single hospital acts as a chairman of the conference. The chairing hospital will decide if any of its participating hospitals will become its conferencing partner at any time by channeling the partner’s video and audio signals to appear alongside it on the main screen. Any other hospital in the group can join in at any time during the conference (Callas et al, 2000; Klutke et al, 1999). Finally, teleconsultation is a consultation between two or more physicians about the diagnostic work-up and therapeutic strategy in the treatment of an individual case by means of modern telematics. Teleconsultations can be used in two modes: Live consultations via videoconferencing systems and store-and-forward consultations via the Internet. Through communication among doctors or between doctors and other related personnel, the knowledge of experts can be transmitted to non-specialists to support medical care (Moore, 1999; Sood et al, 2007).

APPLICATIONS OF TELEMEDICINE The wide scope of applications for telemedicine include patient care, education, research and public health to diagnose, deliver care, transfer health data, provide consultation, educate health professionals. Telemedicine has become an integral part of medical care in space flight (Hines, 1996). In addition to these applications, telemedicine is utilized in a growing number of medical specialities by health providers (Brown, 1998; Chen et al, 1999; Ruskin, et al, 1998). Home health service is one of the fastest growing areas of health care in many countries. Among the reasons for this rapid growth are several factors, including an aging population, patient preference for care provided in their own homes and earlier discharge from acute care settings (Chae et al, 2001; Ruggiero et al, 1999; Sood et al, 2007). Linking patients and physicians through internet may increase

the involvement of patients in supervising and documenting their own health care, processes that may activate patients and contribute to improved health (Mandl et al, 1998). Telemedicine may even be useful in developing more cost effective methods of providing health care in private clinics or organizations which present difficult computer integration and communication challenges due to their size and geographic distribution (Broens et al, 2007; Whitten et al, 2007). The doctors and/or support people who are working in the medical field often have geographical disadvantages. Their support systems constantly attempt to providesthem with the most up-to-date information. Telemedicine can therefore also be used for educating health professionals in rural or remote communities and to give them access to specialized information that might be difficult to obtain otherwise. Internet teleconferencing software can be used to hold virtual meetings, during which participants around the world can share ideas. For example, anesthesiologists have rapidly adopted teleconferencing as part of scientific meetings and educational activities (Broens et al, 2007; Sood et al, 2007). Distant learning may be used for educating patients, as well as health care personnel. In addition to this usage, patient records from multiple remote sites could be searched for symptoms similar to those observed in a patient. This could greatly enhance the chances of correct diagnosis of particular illness and possible suggest courses of treatment that had been successful in other patients. Telemedicine can also be used in public health programs to assist local organizations in health related campaigns such as accident, pre-natal care, etc. (Moore, 1999). Telemedicine can also enhance efforts for disaster response. Disasters are catastrophic events that can overwhelm a community’s emergency response capacity, threatening health and safety of the public and environment. In the pre-disaster situation, telemedicine could be employed in the education and training of health care personnel and

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the general community in disaster management practices. Telemedicine could support disaster plan implementation, modification, assist with management of critical resources, and provide consultation from within and outside the disaster area. In post-disaster rehabilitation, telemedicine can provide a variety of traditional medical consultations and continue to provide support to both resource management and continuing assessment activities. It can also provide urgently needed specialist health care in instances of natural disaster (Garshnek & Burkle, 1999). This is because in many areas ambulances and emergency rescue teams are equipped with telemetry equipment to allow physiological data to be transmitted to a nearby hospital for interpretation. Through the use of telemetry equipment, physiological data can be interpreted and treatment begun even before a patient arrives at the hospital. However, to be effective, the system must be capable of providing reliable reception and reproduction of the transmitted signals under any conditions, including that of natural and man-made disasters. Emergency medical care has become an important part of the overall health delivery system (Anantharaman & Han, 2001; Benger, 2000; Binks & Benger, 2007). Telemedicine is also not a new concept to space flight. Since its very beginning space medicine has used communications and information processing technologies. Remote monitoring of crew, spacecraft and environmental health has always been an integral part of the US National Aeronautics and Space Administration’s (NASA’s) operations, as it has been of other space travelling nations. Faced with the prospect of astronauts needing medical attention in space, NASA began remote monitoring of astronauts in the early 1960s (Garshnek et al, 1997). During long-duration missions biomedical data transmitted to earth from space included astronaut heart rates, body temperature, ECG, oxygen and carbon dioxide concentration (Hines, 1996). In many respects operational boundary conditions in space medicine such as remoteness,

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telediagnostics and biotelemetry are characteristics of telemedicine applications for remote communities on the earth (Williams et al, 2000). Telemedicine is thus used by health providers in a growing number of medical specialities including: Cardiology, pathology, radiology, endoscopy, pediatry, orthopedics, dermatology, psychiatry, pharmacy, surgery, obstetrics, diabetic patients management, ophthalmology, otolaryngology, space medicine, etc. (Güler & Übeyli, 2002a; Moore, 1999; Sood et al., 2007).

THE COMPONENTS OF A BIOTELEMETRY SYSTEM Size, cost, circuit complexity, power requirements (and operational lifetime), type of transducer, nature of data to be transmitted and performance are important in the design of a telemeter. First of all, a simple system is described to illustrate the basic principles involved in telemetry. The stages of a typical biotelemetry system are shown in Figure 2 (Güler & Übeyli, 2002b), and can be divided into functional blocks as transmitter and receiver (Lee et al, 2007; Welkowitz et al, 1976). Physiological signals are obtained from the subject by means of appropriate transducers. Then, signal is passed through a stage of amplification and processing circuits that include generation of a subcarrier and modulation stage for transmission. The receiver consists of a tuner to select transmitting frequency, a demodulator to separate the signal from the carrier wave and a method of displaying or recording the signal. The transmitter generates the carrier and modulates it. The receiver is capable of receiving the transmitted signal and demodulating it to recover the information. Information to be transmitted is impressed upon the carrier by a process known as modulation. The distance the transmitted signal can be received is called as the range of the system. The range of the system depends on the power and frequency of the transmitter,

Telemedicine and Biotelemetry for E-Health Systems

Figure 2. Block diagram of a biotelemetry system

the relative locations of transmitting and receiving antennas and the sensitivity of the receiver. There are several important factors that telemetry users should be familiar with when using antennas. Some of these are: keep clear of the antenna when taking a bearing; do not stand within 1/2 wavelength of the antenna elements; protect the antenna elements to prevent them from being bent out of shape; and keep all metal objects from interfering with the antenna. In this way, confusion will be reduced and success in tracking will be increased (Lee et al, 2007). Transmitting devices built for patient use must be light weight and compact enough to ensure adequate user comfort. In some cases it is desirable to implant the telemetry transmitter or receiver subcutaneously. The transmitter is swallowed or surgically implanted in the subject. These systems provide monitoring and collecting data from conscious freely moving subjects. Conscious subjects provide data free from the effects of anesthesia. This is important as it has been clearly shown in the literature that anesthetic agents can change blood pressure, heart rate, peripheral vascular resistance, thermo-regulation, gastrointestinal function and other body functions. The highest quality data is therefore be best collected by implanted units. The function of the instrumented

implant and the external system components are described by Graichen et al (1996). However, there are some important requirements for the usage of implantable telemetry. Implantable units must have relatively small size and light weight, and their internal power source has to be capable of use for a long time in many cases. Even so, miniaturization and the long-term use of implant electronic systems for medical applications have resulted in a growing necessity for an external powering system. Another requirement is encapsulation of the unit. There remain unmet needs in packaging, as well as in the development of potentially useful materials and techniques for packaging implantable electronic devices or systems for use in chronic situations. Problems of packaging solid-state transducers, where the volume and weight of the packaging material exceeds that of the operational unit are noted (by Park et al (1994). The use of implantable units also restricts the distance of transmission of the signal. Body fluids and skin greatly attenuate the signal and because of this an implanted unit must be small. Therefore, most units have little power and the range of their signals is quite restricted. This disadvantage has been overcome by picking up the signal with a nearby antenna and retransmit-

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ting it. If applications involve monitoring over relatively short distances then retransmission is not necessary.

TYPES OF TELEMETRY There are two types of biotelemetry units: Single channel and multichannel unit. In general, more than one channel of physiological information is studied. The simplest encoding can be used for telemetering a single channel of slow data. Pulse interval modulation or pulse width modulation can

be used for a single slow variable. Telemetering a single channel of fast data requires quite different encoding because the carrier must be continuously varied. Either its amplitude or its frequency may be modulated (Welkowitz et al, 1976). A multiple subject biotelemetry system is composed of an implantable system, which consists of a command receiver, a subject selection receiver, a conditioner and a transmitter and external systems, such as a power command signal transmitter and a subject selection signal transmitter, as shown in Figure 3 (Park et al., 1994). As indicated in Figure 3, such

Figure 3. Block diagram of a multiple subjects telemetry system (Source: Park et al., 1994)

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a system is capable of accepting signals from a variety of sensors. Biomedical data has been telemetered through every medium between two sites including air, space, water and biologic tissue by using a variety of modulated energy forms like electromagnetic waves, light and ultrasound. In general, biotelemetry systems involve the use of radio transmission. A radio frequency carrier is a high efficiency sinusoidal signal propagated in the form of electromagnetic waves when applied to an appropriate transmitting antenna. Radio telemetry is an excellent tool for gathering data on the biology of animals and their interactions with the environment they inhabit (Salvatori et al, 1999). Infrared (IR) telemetry also has a very wide field of application. IR radiation enables transmission of different physiological parameters from moving subjects like patients in intensive care units, wards, newborn babies in incubators and animals in biological and hospital laboratories. In a typical IR biotelemetry system, the patient carries a batterypowered transmitter and one or more small arrays of infrared light-emitting diodes (IRLEDs) that send encoded data to remotely located photo detector-based receivers (Santic, 1991). Acoustic energy can also be used to send a signal over a distance. Ultrasonic ranges from 30-100kHz are above most animal auditory ranges and transmitted with low energy loss through seawater or gel media (Voegeli et al, 2001).

APPLICATIONS OF BIOTELEMETRY As we have seen, during space travel biomedical data transmitted to earth from space included astronaut heart rates, body temperature, ECG, oxygen and carbon dioxide concentration. Telemetry was employed to establish an understanding of and monitor the health and well-being of astronauts while they were in space. In the early days of human space flight, NASA for example used biotelemetry to provide biomedical data

from orbiting astronauts to medical personnel. So NASA has been involved in the development and application of biotelemetry since the Agency’s beginnings (Güler & Übeyli, 2002b). Because of the great distance from the earth, systems and procedures were developed to support medical operations inflight. All astronauts wore a biosensor harness, which provided for transmitting critical physiological data back to the earth from space craft and lunar surface. This real time telemetry was also available to monitor astronauts in the event of illness inflight. Advanced biosensors and biotelemetry systems are used in sensing a wide variety of phenomena in the body and transmitting this information to receivers near the body. These sensors can provide remote, continuous biomedical monitoring of patient data (Rocchitta et al, 2007).A pressure/temperature pill-transmitter is the first of a family of implantable and/or ingestible pill-transmitters that will measure a variety of physiological parameters. The pill-shape and small size of the transmitter, its ultra-low power consumption, long lifetime and the powerful capabilities of data analysis software make this system unique. Applications of these pill transmitters are very common and go beyond fetal surgery. A complete biotelemetry system can be designed for the telemetry of various physiological signals such as ECG, EEG, etc. The main advantage of such biotelemetry systems is that they provide an easy and practical way for the long term monitoring of various physiological signals from a patient’s body while maintaining a low implementation cost (Axelsson et al, 2007; Jaana et al, 2007) The use of telemetry to monitor a swimmer’s rectal core temperature has been developed and measurements taken from swimmers have been analysed (Finlay et al, 1996). Progress in the development of an implantable telemetry system for assessing blood oxygen saturation and hematocrit is described. The key element of the system is an optical sensor which employs optoelectronics and on-chip signal processing electronics to measure

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light backscattered by blood. Long-term monitoring of central haemodynamics with implanted monitoring systems might be valuable in managing heart failure patients (Ohlsson et al., 1996). Alongside these uses emergency medical care has also become an important part of the overall health delivery system. In many areas ambulances and emergency rescue teams are equipped with telemetry equipment to allow electrocardiograms and other physiological data to be transmitted to a nearby hospital for interpretation (Anantharaman & Han, 2001). A compact, low power, implantable system for in vivo monitoring of oxygen and glucose concentrations has been developed. The telemetry instrumentation system consists of two amperometric sensors: one oxygen and one glucose biosensor and two potentiostats for biasing the sensors, an instrumentation amplifier to subtract and amplify sensor output signals and a signal transmitter subunit to convert and transmit glucose dependent signals from the sensors to a remote data acquisition system (Salehi et al., 1996; Beach et al., 1999). A system has also been designed to simultaneously acquire ECG and respiration and send them to a receiver over a telephone line. Respiration is acquired by measuring the transthoracic impedance between two electrodes (Reisman, 1984). Remote biotelemetry systems can also provide power, remote monitoring and control (Mussivand et al, 1997). A single channel implantable microstimulator for functional neuromuscular stimulation can be inserted into paralyzed muscle groups by expulsion from a hypodermic needle, thus reducing the risk and discomfort associated with surgical placement. The device receives power and data from outside by radio frequency (RF) telemetry (Ziaie et al., 1997). The walking (gait) analysis telemetry system (walking analyzer) consists of miniature sensor/transmitters that are affixed directly over the leg muscle group being studied. The muscle activity sensed by the electrodes is transmitted to a computer by biotelemetry

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process. This system is used to determine the degree and location of abnormal muscle activity and in prescribing treatment. A system for measuring force in both legs and crutches or cane during walking uses a special sensor based on infrared radiation changes to achieve force measurement in the crutches or cane. To extend a patients free mobility, an infrared telemetry system is applied (Lackovic et al, 2000) The most reliable operation of IR biotelemetry has been found in hospitals and in biological laboratories (Hagihira et al., 2000). An IR diffuse telemetry system for ECG and temperature transmission will typically consist of a basic patient unit with IR receiver and transmitter because biological data and signals have to be transmitted and commands to the basic patient unit have to be received (Santic, 1991). A wireless biotelemetry system for the transfer of digital data through intact skin tissue has been developed to provide a safe and noninvasive communication between implanted medical devices and the outside of the body. With the development of more powerful telemetric data transmission technologies, such a method could be extended in the near future to a truly ambulatory urodynamic recording with real-time on-line facilities, either at home or in the clinic, and usable for both adults and children (Mohseni et al, 2005; Neihart & Harrison, 2005). A through-water ultrasonic data telemetry system burst-mode frequency shift keying (FSK) has been developed. The system can be adapted for the transmission of data from various sensors, but it has been designed principally for monitoring the respiratory rate, heart rate, temperature and depth of a free-swimming diver over a range of up to 300 meters (Woodward & Bateman, 1994) Different biotelemetric applications have been adeveloped for wide variety of animal species since the 1950s. Information about wild-life biotelemetry activity with some historical perspective are described by Long & Weeks (1980). For many species, determination of habitat selection is based on habitat-use data obtained through

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radio telemetry (Rettie & Mcloughlin, 1999). Some species of animals are currently used in biomedical research through radio telemetry systems consisting of implantable transmitters and receivers. Implantable devices are used to monitor various physiological parameters in mice, hamsters, rats, rabbits, ferrets, dogs, cows, sheep, bears and other species. Implants that measure ECG waves have flexible leads extending from the housing similar to those used in heart pacemakers. When measuring telemetered ECG, sensing leads are placed under skin at locations similar to surface electrodes. For EEG measurements, the transmitter leads can be connected to screw electrodes or deep electrodes to monitor various sites within the brain. For EMG measurements, the transmitter leads can be connected to fine braided stainless steel wire to be threaded through or buried in the muscle. On the other hand, implants that measure temperature generally have a sensor imbedded in the electronics module. When measuring core temperature, the device is often placed in the peritoneal cavity. This is because core body temperature is often a critical measure in studies of behavioural and physiological control of metabolism and body temperature regulation (Beach et al, 1999; Rocchitta et al, 2007; Axelsson et al, 2007). A wireless telemetry system to assess heart rate, core temperature and gross locomotor activity in freely moving rats while performing a behavioural task allows measurement of these variables. The telemetry system consists of a small implantable transmitter and a receiver connected to a computer, with data acquisition controlled by a computer board and software package (Mussivand et al, 1997; Mohseni et al, 2005).

form of voice, data or video imagery. Most of the existing literature on telemedicine has taken as its primary focus the utility and efficacy of the technology itself, as it is applied to particular clinical problems and settings. This is primarily a clinical literature that is about establishing the safe practice of medicine using a diverse set of communications technologies. Since the rapid growth in telecommunications and computer technology over the last decade, telemedicine has become an important part of medical development, with the potential to greatly improve the quality of future health care. Biomedical telemetry is a special area of biomedical instrumentation that permits transmission of physiologic information from an inaccessible location to a remote monitoring site. The goals of biotelemetry include the capability to monitor humans and animals with minimum restraint and to provide faithful reproduction of the transmitted data. Size, cost, circuit complexity, power requirements (and operational lifetime), packaging, transducers, nature of data to be transmitted and performance are important parameters in the design of a backpack or implanted biotelemetry unit. It seems likely that future development will be in the further miniaturization and integration of biotelemeters and transducers, improved power sources and improved packaging. Techniques and applications of biotelemetric methods continue to expand and are limited only by the imagination of the investigators in using new technologies as they evolve. Since the rapid growth in technology different applications of biotelemetry can be used for data gathering from a remote location, and these have been described in this chapter.

CONCLUSION

FUTURE RESEARCH DIRECTIONS

Telemedicine represents a combination of expertise and technology that delivers medical services and information over distance. Telecommunications technology delivers this information in the

There are several barriers inherent in overcoming distance in delivering health care – these are technological, political and professional. Some of these barriers are described in the following:

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1. Infrastructure planning and development. It is unusual for health care applications to be considered in planning and developing new telecommunications and information technology. Policy makers at the regional and national level must consider needs and solutions for multiple agencies and activities. Lack of uniform policies and standards creates problems for health care organizations. Absence of policies also creates confusion about patient privacy rights and how to enforce them; 2. Economic impact. Competition for telecommunications services, particularly in rural areas, has resulted in arbitrary boundaries between service areas and high costs for transmission services of the power needed to support telemedicine. There is no clear policy at the national level anywhere that allows the costs of telemedicine systems to be reliably covered, but public and private payers are reluctant to set reimbursement policy at a lower level until more viable applications for telemedicine are developed; 3. Security and confidentiality. While the Internet is a marvelous medium for transmitting information between remote computers, it is notoriously susceptible to security problems. Since confidentiality of patient information is a foundation of medical care in most countries, the problems of keeping information transferred between computers away from unauthorized access must be solved. Special consideration and care must be taken to ensure the safety of patient and medical data; 4. Speed of communication. The Internet carries millions of pieces of information every minute and demand gets larger all the time. Some critics have predicted the eventual collapse of the Internet from the traffic it is forced to carry. Even when operating at full speed, it is difficult for internet media to transmit large amounts of information (like

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that needed for videoconferencing) at sufficient rates to avoid delays and degradation of the images or sound. New networking media must be developed that is capable of carrying more information at faster speeds. While these challenges are difficult, they can be overcome with cooperation among health care professionals, technology specialists and governments. Such cooperation is the key to telemedicine’s achieving its potential to improve health care.

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Moore, M. (1999). The evolution of telemedicine. Future Generation Computer Systems, 15(2), 245–254. doi:10.1016/S0167-739X(98)00067-3 Murakami, H., Shimizu, K., Yamamoto, K., Mikami, T., Hoshimiya, N., & Kondo, K. (1994). Telemedicine using mobile satellite communication. IEEE Transactions on Bio-Medical Engineering, 41(5), 488–497. doi:10.1109/10.293224 Mussivand, T., Hum, A., Holmes, K. S., & Keon, W. J. (1997). Wireless monitoring and control for implantable rotary blood pumps. Artificial Organs, 21(7), 661–664. doi:10.1111/j.1525-1594.1997. tb03718.x Neihart, N. M., & Harrison, R. R. (2005). Micropower circuits for bidirectional wireless telemetry in neural recording applications. IEEE Transactions on Bio-Medical Engineering, 52(11), 1950–1959. doi:10.1109/TBME.2005.856247 Ohlsson, A., Bennett, T., Ottenhoff, F., Bitkover, C., Kjellstrom, B., & Nordlander, R. (1996). Long-term recording of cardiac output via an implantable haemodynamic monitoring device. European Heart Journal, 17(12), 1902–1910. Park, J., Choi, S., Seo, H., & Nakamura, T. (1994). Fabrication of CMOS IC for telemetering biological signals from multiple subjects. Sensors and Actuators. A, Physical, 43, 289–295. doi:10.1016/0924-4247(93)00682-T

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Pombortsis, A. S. (1998). Communication technologies in health care environments. International Journal of Medical Informatics, 52(1-3), 61–70. doi:10.1016/S1386-5056(98)00125-7 Reisman, S. S. (1984). Instrument for remote ECG and respiratory monitoring. IEEE Transactions on Biomedical Engineering. BME, 31(8), 577. Rettie, W. J., & Mcloughlin, P. D. (1999). Overcoming radiotelemetry bias in habitat-selection studies. Canadian Journal of Zoology, 77(8), 1175–1184. doi:10.1139/cjz-77-8-1175 Rocchitta, G., Migheli, R., Dedola, S., Calia, G., Desole, M. S., & Miele, E. (2007). Development of a distributed, fully automated, bidirectional telemetry system for amperometric microsensor and biosensor applications. Sensors and Actuators. B, Chemical, 126(2), 700–709. doi:10.1016/j. snb.2007.04.019 Ruggiero, C., Sacile, R., & Giacomini, M. (1999). Home telecare. Journal of Telemedicine and Telecare, 5, 11–17. doi:10.1258/1357633991932333 Ruskin, K. J., Palmer, T. E. A., Hagenouw, R. R. P. M., Lack, A., & Dunnill, R. (1998). Internet teleconferencing as a clinical tool for anesthesiologists. Journal of Clinical Monitoring and Computing, 14, 183–189. doi:10.1023/A:1007499017113 Salehi, C., Atanasov, P., Yang, S. P., & Wilkins, E. (1996). A telemetry instrumentation system for long-term implantable glucose and oxygen sensors. Analytical Letters, 29(13), 2289–2308. Salvatori, V., Skidmore, A. K., Corsi, F., & Van der Meer, F. (1999). Estimating temporal independence of radio-telemetry data on animal activity. Journal of Theoretical Biology, 198(4), 567–574. doi:10.1006/jtbi.1999.0936 Santic, A. (1991). Theory and application of diffuse infrared biotelemetry. Critical Reviews in Biomedical Engineering, 18(4), 289–309.

Sargsyan, A. E., Doarn, C. R., & Simmons, S. C. (1999). Internet and world-wide-web technologies for medical data management and remote access to clinical expertise. Aviation, Space, and Environmental Medicine, 70(2), 185–190. Sheng, O. R. L., Hu, P. J. H., Wei, C. P., & Ma, P. C. (1999). Organizational management of telemedicine technology-conquering time and space boundaries in health-care services. IEEE Transactions on Engineering Management, 46(3), 265–278. doi:10.1109/17.775279 Sood, S., Mbarika, V., Jugoo, S., Dookhy, R., Doarn, C. R., Prakash, N., & Merrell, R. C. (2007). What is telemedicine? A collection of 104 peerreviewed perspectives and theoretical underpinnings. Telemedicine Journal and e-Health, 13(5), 573–590. doi:10.1089/tmj.2006.0073 Stanberry, B. (2001). Legal ethical and risk issues in telemedicine. Computer Methods and Programs in Biomedicine, 64, 225–233. doi:10.1016/S01692607(00)00142-5 Takeda, H., Minato, K., & Takahasi, T. (1999). High-quality image oriented telemedicine with multimedia technology. International Journal of Medical Informatics, 55(1), 23–31. doi:10.1016/ S1386-5056(99)00017-9 Tanrıverdi, H., & Iacono, C. S. (1999). Diffusion of telemedicine: a knowledge barrier perspective. Telemedicine Journal, 5(3), 223–244. doi:10.1089/107830299311989 Voegeli, F. A., Smale, M. J., Webber, D. M., Andrade, Y., & O’Dor, R. K. (2001). Ultrasonic telemetry tracking and automated monitoring technology for sharks. Environmental Biology of Fishes, 60, 267–281. doi:10.1023/A:1007682304720 Welkowitz, W., Deutsch, S., & Akay, M. (1976). Biomedical Instruments Theory and Design. San Diego: Academic Press.

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Whitten, P., Buis, L., & Love, B. (2007). Physician-patient e-visit programs – Implementation and appropriateness. Disease Management & Health Outcomes, 15(4), 207–214. doi:10.2165/00115677-200715040-00002 Williams, D. R., Bashshur, R. L., Pool, S. L., Doarn, C. R., Merrell, R. C., & Logan, J. S. (2000). A strategic vision for telemedicine and medical informatics in space flight. Telemedicine Journal and e-Health, 6(4), 441–448. doi:10.1089/15305620050503924 Woodward, B., & Bateman, S. C. (1994). Diver Monitoring by Ultrasonic Digital Data Telemetry. Medical Engineering & Physics, 16(4), 278–286. doi:10.1016/1350-4533(94)90051-5 Ziaie, B., Nardin, M. D., Coghlan, A. R., & Najafi, K. (1997). A single-channel implantable microstimulator for functional neuromuscular stimulation. IEEE Transactions on Bio-Medical Engineering, 44(10), 909–920. doi:10.1109/10.634643

KEY TERMS AND DEFINITIONS Applications of Biotelemetry: Transmitted biomedical data included heart rate, body temperature, ECG, oxygen and carbon dioxide concentration. Telemetry was employed to establish an understanding and monitor health and well-being of the subjects. Applications of Telemedicine: The wide scope of applications for telemedicine includes patient care, education, research and public health to diagnose, deliver care, transfer health data, provide consultation, educate health professionals.

Biotelemetry: Biomedical telemetry is a special field of biomedical instrumentation that often permits transmission of biological information from an inaccessible location to a remote monitoring site. When direct observation is impossible, biotelemetry can be used to obtain a wide spectrum of environmental, physiological and behavioural data. Telemedicine Infrastructure: The telecommunications infrastructure provides the technology to move information electronically between geographically dispersed locations. The telecommunication medium utilized by telemedicine programs is determined in large part by the available local infrastructure. These can include satellite, microwave link or terrestrial lines (either twisted copper phone lines or fiber optic cable). Telemedicine Technologies: The use of telecommunications and information technology is central in providing health services- regardless of locations. Telemedicine involves a combination of topics from the fields of telecommunication, medicine and informatics. The application of telecommunication technology to health care requires integration of technology, tools and training with medical care practices and problems. Telemedicine: Literally, telemedicine means medicine at a distance. Telemedicine is defined as the use of electronic information and communication technologies to provide and support health care when distance separates the participants. Telemetry Types: Biomedical data has been telemetered through every medium between two sites including air, space, water and biologic tissue by using a variety of modulated energy forms like electromagnetic waves, light and ultrasound. Telemetry types are defined according to the transmission medium.

This work was previously published in Biomedical Knowledge Management: Infrastructures and Processes for E-Health Systems, edited by Wayne Pease, Malcolm Cooper and Raj Gururajan, pp. 1-17, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Tele-Audiology in the United States: Past, Present, and Future John Ribera Utah State University, USA

ABSTRACT The incorporation of telehealth into the daily clinical practice of audiologists in the United States is in its early stages of development. Some initial research has been conducted in order to validate the use of telehealth technologies in providing hearing and balance evaluation and management services (Krumm, Huffman, Dick, & Klich, 2008; Krumm, Ribera, & Klich, 2007;Krumm, Ribera, & Schmiedge, 2005; Lancaster, Krumm, & Ribera, 2008). More research is needed. This chapter suggests possible applications using existing DOI: 10.4018/978-1-60960-561-2.ch305

technology and explores the possibility of virtual audiology clinics nation-wide and internationally. Whatever the mind of man can conceive and believe, it can achieve—Napoleon Hill

INTRODUCTION The above quote seems at the root of much of the technological revolution we are experiencing today. This is the stuff of which dreams are made as well as the inspiration for avant-guard thinking such as science-fiction. Who would have imagined 50 years ago that we would someday

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talk into little plastic boxes without wires and communicate with someone hundreds of miles away? It is thrilling to stand on the threshold of progress with the hopes of adapting modern and future technology to improve the quality of life for countless patients in need of medical evaluation and treatment. As an audiologist I have given much thought to how technology can be inculcated into healthcare delivery systems for patients with hearing and or balance disorders. Most audiology healthcare providers are located in metropolitan and urban areas where population is denser and where there is access to a continuum of healthcare providers and services (ASHA, 2006). However, it is interesting to note that the prevalence of hearing impairment is greater in rural areas (Holt, Hotto, & Cole, 1994). Projections are that audiologists will be in demand for the foreseeable future (Bureau of Labor Statistics, 2008) with an estimated increase of 10% over the next six years. As patients tend to have a longer life expectancy than in the past, there will be an increasing need for audiological services. As universal newborn hearing screening compliance increases there will be additional requirements for pediatric audiologists with specialized training in evaluating and managing children with hearing disorders. Telehealth/telemedicine seems to possess inherent capabilities of delivering hearing healthcare to remote areas or underserved populations. This chapter will address the history of telemedicine in the United States, recent research in the application of telehealth in audiology, challenges to be overcome, and what the future may hold.

BACKGROUND Benefits It seems almost intuitive that there are advantages to providing healthcare at a distance by a) filling a void in healthcare delivery to remote geographic

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regions, b) expanding access to essential medical services, c) reducing practitioner and/or patient travel time, d) reducing of cost in providing healthcare, d) expanding the dissemination of medical information to patients, e) and enhanced interaction (counseling and consultations) among healthcare providers and between clinicians and patients, to mention only a few. Another possible benefit from telehealth technology is in support of the international movement to “go green” meaning to be more environmentally friendly and responsible about the carbon footprint we are leaving and to be more conscious of how we manage natural resources. Going green through telehealth seems to be a natural outcome. Use of existing and future technology will allow providers to reduce the amount of paper used, as well as the need for travel resulting in less consumption of fossil fuels. This might be a welcome by-product of adopting telehealth in general and tele-audiology specifically. Telehealth delivery in audiology is truly in its infancy. There has been reticence on the part of audiologists to adopt this expansion of their scope of practice. There are several possible reasons for a delay in incorporating telehealth as part of the delivery model used by audiologists a) unfamiliarity with the technology, b) lack of confidence in outcomes, c) initial upfront cost for equipment and connectivity, d) training, e) licensure issues, f) reimbursement issues, g) paucity of validation studies, h) lack of standardization, i) safety and security issues, and j) patient acceptance/satisfaction. The challenge for pioneers in this area is to overcome the inertia and develop this delivery system more widely. There is need for more data to validate telehealth technology in audiology. The author invites colleagues from around the world to begin investigating how telehealth can be integrated into the audiologist’s practice. The Alaska Federal Healthcare Access Network (AFHCAN) has been providing audiological services via store and forward digital scans and high resolution images with ear, nose, throat (ENT)

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specialty physicians. This has resulted in a) reducing patient travel from rural villages, b) reducing wait time, c) reducing ENT patient backlog, and d) a savings of over $100,000 in travel costs per annum (Hofstetter, Kokesh, & Ferguson, 2008). The American Speech Language and Hearing Association (ASHA) has developed several documents relating to the use of telehealth in speech-language pathology and audiology (ASHA, 2005a, 2005b, & 2005c). The American Academy of Audiology (AAA) has organized a Telehealth Task Force to consider the application of telehealth in the practice of audiology. A group of experienced audiologists with a vision of how this delivery system could be more fully used has been recently asked by AAA to establish guidelines for the use of telehealth in the practice of audiology. Several of the task force members have been involved in telehealth research related to audiology. Recently a resolution was passed by AAA to the effect that a) diagnostic and rehabilitative audiology telehealth or telemedicine services should always be provided by, or supervised by, a qualified audiologist, b) telehealth or telemedicine services should be primarily provided to individuals who have limited access to audiologists in their communities (e.g., homebound), and c) that audiology telehealth or telemedicine services should be validated before implementation to assure confidentiality and accuracy as well as to evaluate feasibility, particularly with difficult to test populations (newborns, infants, individuals with developmental disabilities) for which little or no validation of telehealth or telemedicine services currently exists”(AAA, 2008).

Research Most of the hearing and balance related research in telehealth within the United States has been conducted at East Carolina University (North Carolina), Kent State University (Ohio), Minot State University (North Dakota), Tripler Army Medical Center (Hawaii), University of Hawaii,

(Hawaii), and Utah State University (Utah). A review of the literature on telehealth and audiology can be broken down into categories such as a) overviews of telehealth applications in audiology (Givens, 2003; Ribera & Krumm, 2002), b) auditory brainstem response and otoacoustic emissions (Krumm, Huffman, Dick, & Klich, 2008; Krumm, Ribera, & Klich, 2007; Krumm, Ribera & Schmeidge, 2005; Towers, Pisa & Froelich, 2005), c) speech in noise testing (Ribera, 2005), d) counseling (Andersson, Stromgren, Strom, & Lyttkens, 2002; Kaldo-Sandstrom, Larsen, Andersson, 2004; Laplante-Leveque, Pichora-Fuller & Gagne, 2006), e) professional issues (Denton & Gladstone, 2005), f) audiometrics (Choi, & Oh, 2007; Elangovan, 2005; Lancaster, Krumm, & Ribera, 2008), g) vestibular testing (Yates, & Campbell, 2005), h) training, (Dansie, & Muñoz, 2008; Givens, Blanarovich, Murphy, Simmons, Blach, & Elangovan, 2003; Yates & Campbell, 2005), and i) video otoscopy (Hofstetter, Kokesh, & Ferguson, 2008). Pilot data at Utah State University suggest that programming of hearing aids and mapping of cochlear implants can also be accomplished using a telemedicine approach.

Delivery Models The question should be asked “Which delivery model(s) might work best for audiology?” This can be answered by determining what the objectives are for providing audiological services via telehealth. The traditional model of synchronous telehealth is one where a facilitator provides the faceto-face services while the audiologist supervises at a distance. A facilitator may be a nurse or other trained support staff, located at a remote site, and who is trained to assist the audiologist in such tasks as intake history, otoscopy (visual inspection of ear canal and tympanic membrane), patient instruction for each test, placement of earphones, and audiometric testing (air and bone conduction as well as speech tests). A clinic protocol is

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implemented under the supervision of an audiologist via web camera allowing him/her to visually observe how the tests are being administered. The advantage is that the audiologist can be reassured that the testing protocol is followed correctly and the data are reliable. The disadvantage is that the audiologist is spending time observing and less able to engage in other simultaneous activities at the local (hub) site. The key to the successful use of a facilitator lies in the level and quality of training. If a “hub and spoke” model is used, training may be conducted simultaneously at several remote sites (the spokes) and broadcast from a central location (the hub) via videoconferencing. The audiologist must have confidence in the skill and knowledge of each remote site facilitator. There might also need to be some face-to-face training to ensure facilitator competency. In the final analysis, it is the healthcare provider (in this case the audiologist) who is ultimately responsible for the evaluation, care and treatment of the patient seen via a teleaudiology delivery system. What is involved in being a facilitator? How much training is necessary? What is the best way to train a facilitator? These and other questions would need to be addressed during the initial stages of system development. There is evidence that training of a facilitator can be conducted via teleconferencing and results in skill and knowledge acquisition similar to that provided face-to-face (Dansie, & Muñoz, 2008). Remote computing is an approach that allows the audiologist at the hub to assume control of a computer-based audiometer or other test equipment at the remote site where the patient is located. The role of the facilitator is simplified in this approach. He or she is responsible for all the hands-on tasks that cannot be performed by the audiologist, such as otoscopy, insertion of probe for immittance testing, and placement of earphones, bone oscillator, or electrodes. PCbased audiometers are becoming more and more available. With the right set up of computer ap-

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plication sharing and teleconferencing software an audiologist can take over the commands of a remote audiometer and conduct a comprehensive audiometric test battery and communicate directly with the patient (Krumm, Ribera, & Klich, 2007). This then becomes a virtual audiometer. Store and forward is a relatively easy asynchronous approach, provided time is not an issue or there is no need for immediate referral. How would this be set up? Data could be collected and sent via regular mail, or transferred by electronic media such as email or by Internet using a web camera and displaying the data for the audiologist to see and interpret. For instance, a tracing of a tympanogram or the photo from a video otoscope could be viewed and interpreted by an audiologist. This would, of course, require good resolution for the audiologist to be able to read the tracing and data. Another approach that might be considered is to have the facilitator do the testing and record the results for the audiologist to review at a later time. This could include a video stream of the test to ensure proper technique was used. What we do not know at this time is how well tele-audiology will be tolerated by patients with hearing or balance disorders. There may be cultural issues with which to deal. We need studies that evaluate a patient’s perception of being treated at a distance and confiding in someone who is not in the same room. Does the patient feel he is receiving the same quality of medical attention via telehealth as he would face-to-face? We just do not know at this point.

Telehealth Technology There is a continuum of technologies that can be incorporated into a tele-audiology practice. The technology set-up can be very simple such as plain old telephone systems (POTS), or more advanced such as facsimile (fax), email, video conferencing, and video streaming. When using tele-videoconferencing as a mode of hearing healthcare service delivery, it is preferable to

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have a camera with pan, tilt, and zoom capability. There also exists technology that allows the viewer to track a voice or face. This is a handy feature when there are multiple participants in a broadcast/teleconference. One application of such technology might be the use of tele-audiology in group counseling at one or more remote sites. Audiologists could use this service delivery approach after patients have been fitted with hearing aids. Several post fitting broadcasts would allow the audiologist to ascertain that new hearing aid users receive instruction in communication strategies, and hearing aid maintenance. In addition, the hearing healthcare provider could reiterate information that was presented during the initial hearing aid fitting, but which may have been forgotten or misunderstood. In this type of setting, patients would be free to ask questions of the audiologist or to interact with other hearing aid users either on site or located at other participating sites. Where feasible, more than one camera might be preferable. For example, if a traditional (synchronous) model of delivery is being used, one camera could focus on the patient and the second could track the actions of the facilitator. Another consideration is the resolution of the video. The higher the resolution is the more expensive the camera. The question that needs to be asked is “What is the purpose of the video?” If it is to verify the correct placement of an insert phone into a new born infant’s ear, then high resolution would be the system of choice. If the purpose is merely to see if the facilitator has correctly placed supra aural headphones on the ears of an adult, then a camera with low to mid resolution would suffice. More and more audiometers are being developed and manufactured that are personal computer (PC)-based. This makes it relatively straightforward to use application sharing software between sites. Much of the research in tele-audiology that has been conducted has relied on computer-based applications. For example, a PC-based audiometer is located at a remote test site and is connected to the Internet. The computer at the remote site has

not only the software to run the test equipment, but also video conferencing software with camera and audio capability. The audiologist at the hub (central location) located at a distance from the test site is also connected to the Internet and has compatible video conferencing software with application sharing capability. Once connected, the two sites enable real time interaction. Once control of the remote computer has been relinquished to the audiologist, he/she may begin to control the computer and audiometric test equipment at the remote site. Telephone consults are probably the most basic and frequently used mode of telehealth. Video phones can provide some information that might be of value to the audiologist. While the video quality is not high definition at this time, the quality and resolution continues to improve with new developments in technology. The use of facsimile (fax) is another approach to hearing healthcare delivery and although the resolution is not as good as the original document being scanned, it does allow for the transmission of valuable information to the audiologist, such as an audiogram from a previous test at another clinic. Connectivity can present challenges for all end users. There are some limitations with regard to bandwidth. Much depends on how the information is being sent whether by dial-up, DSL, or wide band. Usually the complaint from the end user is there is not enough bandwidth, particularly when using audio and video features simultaneously. There are also network issues such as what type of network to use e.g., local area networks (LAN), wide area networks (WAN), virtual private networks (VPN), or grid clusters. There are security issues such as how to get through fire walls. It is always best to ensure that data are encrypted in order to preclude unauthorized access to sensitive (personal medical) data. Grid technology addresses security issues and should make the transmission of sensitive data safe from outside intrusion.

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Challenges to Overcome

FUTURE RESEARCH DIRECTIONS

There are several issues that have proven challenging to the incorporation of tele-audiology into every clinical practice. Progress has been slow in the area of reimbursement for services delivered via telehealth. Providers need to understand that insurance companies (payors) are not going to reimburse for services unless they are convinced that the services/procedures via telehealth are as valid as those provided face-to-face. This is where validation studies by researchers at universities and other laboratories can make a difference. Insurance companies want data to justify paying a claim for an audiological or other hearing/ balance-related procedure. A second challenging issue is that of licensure. Currently there is no legislation to allow the provision of audiological services across state borders in the United States. There is, however, a model that seems to be working for nursing. Twentythree states have entered into a Nurse Licensure Compact (Nurse Licensure Compact, 2008) that allows practitioners to provide information to patients living in participating states without needing to be licensed in that state. This is similar in concept to the use of a driver’s license issued in the state of California that allows a driver to operate a vehicle in any of the other 49 states, provided he or she abides by the local state laws. A third issue deals with the cost of tele-audiology. No research to date has been published on the cost benefit for tele-audiology. This is not to suggest that there is no benefit. There is anecdotal information that suggests that without telehealth we face a) an increase in patients who cannot be treated because of distance from the healthcare provider, or b) loss of valuable time of the audiologist spent in travel and related costs when visiting outlying (remote) areas, resulting in periodic or no service.

Audiologists may want to begin to do some creative thinking in what the future might hold in terms of providing hearing healthcare in the future. One might take a conservative and simple approach. This approach would include more research, as well as actual implementation of a tele-audiology starting within one or two states. The objective would be to see what works best by starting small and building upon success. A simple approach in telehealth usually means less initial expense; however, it behooves all hearing healthcare providers to make their services available to as many people as possible. While there is evidence that audiometric testing can be accomplished via telehealth, there is now a need for pioneers in the field to begin putting this evidence to work by incorporating this technology into clinical practice. An audiological test battery might include any or all of the following: air conduction, bone conduction, speech audiometry, electrophysiological tests (auditory brain stem response), and otoacoustic emissions. Further research is needed in immittance testing (middle ear analysis), psychoacoustic tests for tinnitus evaluation, cortical responses, hearing aid adjustments, electroacoustic analysis, as well as cochlear implant mapping. The day may come when a small box is connected to a patient’s PC that can be controlled by an audiologist located at a considerable distance to facilitate making adjustments to a cochlear implant or hearing aid in real-time. The possibilities are limitless.

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Global Virtual Clinic In an ideal model there could be a world-wide virtual clinic. Imagine the day when a Frenchspeaking patient goes on line and needs medical intervention for tinnitus (ringing in his ears). Several audiologists, otologists, nurses (who might be from different countries) have previously joined a virtual medical group (team) and agree to be on call

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for a specified number of hours each week. This could be an application of grid technology on a medical cluster. A call comes into a central clearing house that forwards the question or complaint to the provider (in this case a nurse) who is next in the queue. The nurse responds either by phone or by email to the inquiring patient. This immediate feedback helps to determine the nature and extent of the complaint. An online medical intake history can be filled out by the patient. The nurse makes a referral to the on-call audiologist based on the patient’s input or questions. The audiologist can easily gain access to the patient’s online responses and determine if he should have audiometric testing or be referred directly to a physician in his immediate area/region for a diagnostic evaluation. If a referral were needed, then the on-call provider could place a referral request into a queue. The next available otologist would respond to the referral and through further interaction with the patient determine the necessity and immediacy of a face-to-face consult. Given the information provided above, a patient could then be referred to a local clinic (one in his close proximity). If that clinic has limited services a facilitator or nurse could assist in the evaluation, while the physician or audiologist conducts the evaluation at a distance. The clinic would be equipped with a system that is PC-based and connected to the Internet. The audiologist could gain access to the software running the PC and control all the test parameters. At the conclusion of the testing he/she could explain the results to the patient face-to-face using a camera and monitor. There are several advantages for such a virtual clinic. First, there is a large pool of hearing-related healthcare providers. Second, is the ready and easy access to professionals anytime day or night. Third, is the convenience for the clinician to provide at least initial counseling and triage (screening). Fourth, there is no travel cost or time involved in the initial encounter. This could ensure timely diagnosis and treatment.

There are also disadvantages with this novel idea such as reimbursement issues. How would the hearing team members used in the scenario above be recompensed for their time and expertise? This might not be an issue among European countries where the Euro is the common currency. It could become quite a challenge, however, to bill for services if other currencies were involved. Also, to be evaluated by an international team may result in communication challenges (language differences). What would be the language for the encounter or for reports? Maybe the pool could be restricted to those who were fluent in one or more specific languages. For instance all French-speaking countries could be on the same grid/network or medical team. Third, different protocols or philosophies exist on how to evaluate and treat (manage) a patient. This is not a new challenge. There are areas of healthcare in the United States where there is no consensus on how to treat a certain condition. For example, the issue often arises as to whether or not children with recurring otitis media should have pressure equalization tubes (grommets) inserted. These differences would need to be addressed. Fourth, is the issue of the initial cost, depending on the sophistication of the system to be used. These and other issues are not insurmountable, but they need to be addressed if tele-audiology is going to be considered as one of many possible approaches to world-wide healthcare. Tele-audiology is like a tool belt. It is only one tool among many and has a specific range of capabilities or applications. There is no one tool or test or therapy that fits the needs of every patient. It is up to the skilled professional to determine what the best use is and when is the best time to employ tele-audiology in providing hearing healthcare services.

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CONCLUSION Audiology is an allied healthcare profession in the United States and encompasses evaluation and treatment of hearing and balance related disorders. While delivery of these services may be different from state to state, the need continues to exist for healthcare providers to service not only those individuals in metropolitan areas, but also those in more remote areas. Although there has been a paucity of research in the area of telehealth and audiology compared to other disciplines, evidence suggests that the field of hearing and balance disorders, no matter who provides the services, is poised to begin embracing telehealth/ telemedicine. It will take a few brave professionals to take the initial step and begin developing and implementing delivery models that are compatible with acceptable clinic protocols. We must learn to crawl before we walk and to walk before we run. This is natural progression for the human race; so it is with telehealth and audiology. There is an ongoing technical revolution with no end in sight. That is both the good news and the bad news. The good news is new technology expands our current capabilities; the negative side of this is the technology changes rapidly, making incorporation of new technology challenging for the healthcare provider. Teleaudiology is really a multidisciplinary issue, where program and systems developers, technicians, healthcare providers, professional organizations, healthcare administrators, insurance payors, and legal counselors, need to jointly examine relevant issues. Each discipline can offer a unique perspective that will enrich the outcome and should ensure consensus. Grid technology in audiology will undoubtedly start out small and evolve. Maybe it will begin in just one state; then adjoining states might enter into a compact, permitting telehealth patient encounters across state lines. The eventual goal would be to have a virtual clinic with a full spectrum of services that could be delivered via telehealth nation-wide

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and a protected database with restricted access to qualified grid users. Once the systems, protocols, licensure, reimbursement and connectivity issues are resolved at that level, then a progressive, systematic and responsible approach to international interaction would be a natural next step. This, of course, would be the ideal progression; however, a more likely scenario would follow a parallel evolution model. In this model, there might be simultaneous inter and intra-state development of tele-audiology applications. As further research data become available we may be able to more clearly see how tele-audiology fits into the healthcare delivery model. It may not be feasible or even desirable to use tele-audiology in all situations. Evidence-based research may well suggest that tele-audiology is best suited for initial screening and that diagnostic evaluations are better conducted face-to-face. Another application of telehealth would be grand rounds or continuing education where case studies are available for comment and discussion internationally. This is rather an intriguing notion that would allow interaction among healthcare providers, facilitating dialogue among international healthcare colleagues, and promoting diverse thinking and problem solving.

REFERENCES American Academy of Audiology. (2008). AAA Resolution 2008-06. The use of Telehealth/Telemedicine to Provide Audiology Services. Retrieved November 15, 2008 from http://www.audiology. org/advocacy/publicpolicyresolutions/Documents/TelehealthResolution 200806.pdf American Speech-Language-Hearing Association. (2005a). Audiologists Providing Clinical Services via Telepractice: Technical Report [Technical Report]. Retrieved November 27, 2008, from www.asha.org/Policy

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American Speech-Language-Hearing Association. (2005b). Audiologists Providing Clinical Services via Telepractice: Position Statement [Position Statement]. Retrieved November 28, from www.asha.org/policy. American Speech-Language-Hearing Association. (2005c). Knowledge and Skills Needed by Audiologists Providing Clinical Services via Telepractice [Knowledge and Skills]. Retrieved November 27, 2008, from www.asha.org/policy American Speech-Language-Hearing Association. (2006). 2006 Audiology Survey report: Survey methodology, respondent, demographics, and glossary. Rockville, MD: Author. Andersson, G., Stromgren, T., Strom, L., & Lyttkens, L. (2002). Randomized Controlled Trial Of Internet-Based Cognitive Behavior Therapy for Distress Associated with Tinnitus. Psychosomatic Medicine, 64, 810–816. doi:10.1097/01. PSY.0000031577.42041.F8 Bureau of Labor Statistics. (2008). Occupational Outlook Handbook, 2008-2009 edition. Retrieved November 28, 2008, from: http://www.bls.gov/ oco/oco20016.htm Choi, Park, Oh, Park (2007). PC-based TeleAudiometry. Telemedicine and e-Health 13(5), 501-508. Dansie, J., & Muñoz, K. (2008). Providing Training for Otoacoustic Emissions (OAE). Salt Lake City, Utah: Hearing Screening Using Telehealth. Intermountain Area Speech and Hearing Convention. [poster session] Denton, D., & Gladstone, V. (2005). Ethical and Legal Issues Related to Telepractice. Seminars in Hearing, 26(1), 43–52. doi:10.1055/s-2005-863794

Givens, G., Blanarovich, A., Murphy, T., Simmons, S., Blach, D., & Elangovan, S. (2003). Internet-based tele-audiometry system for the assessment of hearing: a pilot study. Telemedicine Journal Electronic Health, 9, 375–378. doi:10.1089/153056203772744707 Hofstetter, P., Kokesh, J., & Ferguson, S. (2008). A telehealth model—delivering hearing healthcare to remote sites. Telemedicine and e-Health, March 1, 2008, 14(s1), 24-81. Holt, J., Hotto, S., & Cole, K. (1994). Demographic aspects of hearing impairment: questions and answers, (3rd ed.). Retrieved November 25, 2008, from http://gri.gallaudet.edu/demographics/ factsheet.html Kaldo-Sandstrom, V., Larsen, H., & Andersson, G. (2004). Internet-based cognitive-behavioral selfhelp treatment of tinnitus: clinical effectiveness and predictors of outcome. American Journal of Audiology, 13(2), 185–192. doi:10.1044/10590889(2004/023) Krumm, M., & Huffman, T., Dick, & Klich, R. (2008). Providing infant hearing screening using OAEs and AABR using telehealth technology. Journal of Telemedicine and Telecare, 14(2), 102–104. doi:10.1258/jtt.2007.070612 Krumm, M., Ribera, J., & Klich, R. (2007). Providing basic hearing tests using remote computing technology. Journal of Telemedicine and Telecare, 13, 406–410. doi:10.1258/135763307783064395 Krumm, M., Ribera, J., & Schmiedge, J. (2005). Using a Telehealth Medium for Objective Hearing Testing: Implications for Supporting Rural Universal Newborn Hearing Screening Programs. Seminars in Hearing, 26(1), 3–12. doi:10.1055/s-2005-863789

Elangovan, S. (2005). Telehearing and the Internet. Seminars in Hearing, 26(1), 19–25. doi:10.1055/s-2005-863791

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Lancaster, P., Krumm, K., & Ribera, J. (2008). Remote hearing screenings via telehealth in a rural elementary school. American Journal of Audiology, 17(2), 114–122. doi:10.1044/10590889(2008/07-0008) Laplante-Leveque, A., Pichora-Fuller, M., & Gagne, J. (2006). Providing an internet-based audiological counseling programme to new hearing aid users: A qualitative study. International Journal of Audiology, 45, 697–706. doi:10.1080/14992020600944408 Nurse Licensure Compact Administrators. (2008). Nurse Licensure Compact. Retrieved December1, 2008 from https://www.ncsbn.org/nlc.htm Retrieved November 30, 2008, from doi:10.1089/ tmj.2008.9979.supp Ribera, J. (2005). Interjudge Reliability and Validation of Telehealth Applications of the Hearing in Noise Test. Seminars in Hearing, 26(1), 13–18. doi:10.1055/s-2005-863790 Ribera, J., & Krumm, M. (2002) Telepractice in Audiology. Audiology Online (www.audiologyonline.com), Posted April 1, 2002. Towers, A., Pisa, J., & Froelich, T. (2005). The Reliability of Click-Evoked and FrequencySpecific Auditory Brainstem Response Testing Using Telehealth Technology. Seminars in Hearing, 26(1), 26–34. doi:10.1055/s-2005-863792

Yates, J., & Campbell, K. (2005). Audiovestibular education and services via telemedicine technologies. Seminars in Hearing, 26, 35–42. doi:10.1055/s-2005-863793

KEY TERMS AND DEFINITIONS Asynchronous Telehealth: Delivery of healthcare or medically related information via electronic media after data have been collected. Audiology: An allied healthcare profession dealing with the evaluation and treatment of hearing and balance disorders. Pc-Based Audiometer: Software and hardware system that connects to and is controlled through a personal computer. Remote Computing: Controlling software applications at a remote site from a local computer. Tele-Audiology: Provision of audiological services via telehealth technology. Telehealth: Provision of healthcare services via electronic media. Synchronous Telehealth: Real-time collection or dissemination of medically-related data. Virtual Clinic: Provision of clinic services via cyberspace.

This work was previously published in Grid Technologies for E-Health: Applications for Telemedicine Services and Delivery, edited by Ekaterina Kldiashvili, pp. 205-214, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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SIe-Health, e-Health Information System Juan Carlos González Moreno University of Vigo, Spain Loxo Lueiro Astray University of Vigo, Spain Rubén Romero González University of Vigo, Spain Cesar Parguiñas Portas University of Vigo, Spain Castor Sánchez Chao University of Vigo, Spain

ABSTRACT In recent years, the incessant development of new communication technologies has provided a better way for accessing information and also a lot of useful opportunities. The implementation of these new technologies gives us an ideal environment for transmitting and receiving real-time information from anywhere. One of the sectors that have a great potential to use and exploit this kind of DOI: 10.4018/978-1-60960-561-2.ch306

technologies is the healthcare sector. Nowadays, the application of all these new technologies to support the clinical procedures has taken part in the definition of a new concept known as e-Health. This concept involves a lot of different services related with the medicine/health terms and the information technologies. However, to provide emergency transportation with better care capabilities to the patient is something that still has a lot to improve. Within this context SIe-Health comes into being a software platform oriented for developing Telemedicine solutions. The solution

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model proposed here allows remote assistance for a mobile health emergency (for example, an ambulance), integrating in this service electromedical devices and videoconference services.

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INTRODUCTION The peak of communication between mobile devices and the potential that they have for their integration in a distributed software platform, makes possible the development of new solutions, more dynamic than before, and also more useful in this information society. The implementation of these new systems in every sector of our society, especially in public utilities, could be viewed as a revolution in the way the users can use the different services. This statement is easily verifiable, just consider the increase in the number of simple actions (recharge a phone card, control of television programming, news, real-time access, or shopping, ...) that are fully integrated into modern distributed software systems which have been widely adopted by the vast majority of users, giving them the impression that are able to do anything anywhere. One of the sectors with greater potential to use and exploit these technologies is the health sector. Healthcare involves a large number of different services whose integration into the modern devices offered by new communication technologies is highly dependent on context. The care for dependents through services such as tele care and remote assistance, centralized control of the patient in hospitals, or the current telemedicine systems used by the Army, which are being implemented for civilian use, are just the beginning. Specifically, the implementation of these new technologies to support clinical practice has given birth to a new concept known as e-Health. This term includes a wide range of different services related to medicine / health terms and information technology:

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Electronic Medical Records (EMR): allow easy communication of patient data between different healthcare professionals (GPs, specialists, care team, pharmacy). Unfortunately as stated in Marion (2006) “fewer than 10 percent of the state’s physicians and 25 percent of its hospitals have functioning EMRs” (p. 78). Personal and Electronic Health Records (PHR and EHR): Considering the information appearing in Connecting for Health (2006), the Markle Foundation defines the PHR as “an electronic application through which individuals can access, manage and share, their health information in a secure and confidential environment. It allows people to access and coordinate their lifelong health information and make appropriate parts of it available to those who need it”. Thus, it differs from the EHR, which is “an electronic version of the patient medical record kept by physicians and hospitals”. The data in the EHR are controlled by and intended for use by medical providers. Telemedicine: Using the definition appearing in the Wikipedia. “Telemedicine is a rapidly developing application of clinical medicine where medical information is transferred through the phone or the Internet and sometimes other networks for the purpose of consulting, and sometimes remote medical procedures or examinations”. Telemedicine generally refers to the use of communications and information technologies for the delivery of clinical care. It could include all types of physical and psychological measurements that do not require a patient going to a specialist. When this service works, patients need to travel less to see a specialist or conversely the specialist has a larger attention range. Evidence Based Medicine: Using again the Wikipedia: “Evidence-based medicine

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(EBM) aims to apply the best available evidence gained from the scientific method to medical decision making. It seeks to assess the quality of evidence of the risks and benefits of treatments (including lack of treatment)”. Moreover, EBM involves a system that provides information of a suitable treatment under certain patient conditions. A healthcare professional can look up whether his/her diagnosis is in line with scientific research. The advantage here is that the data can be kept up to date. Consumer Health Informatics (or citizen-oriented information provision): both healthy individuals and patients want to be informed on medical topics. Health knowledge management (or specialist-oriented information provision): e.g. an overview of latest medical journals, best practice guidelines or epidemiological tracking. Virtual healthcare teams: they consist of healthcare professionals who collaborate and share information on patients through digital equipment (for transmural care). Health or m-Health: includes the use of mobile devices in collecting aggregate and patient level health data, providing healthcare information to practitioners, researchers, and patients, real-time monitoring of patient vitals, and direct care provision (via mobile telemedicine). Medical research uses eHealth Grids that provide powerful computing and data management capabilities to handle large amounts of heterogeneous data. Healthcare Information Systems: also often refer to software solutions for appointment scheduling, patient data management, work schedule management and other administrative tasks surrounding health. Whether these tasks are part of eHealth depends on the chosen definition, they do, however, interface with most e-

Health implementations due to the complex relationship between administration and healthcare at Health Care Providers. The work presented in this paper arose from the observation of members of various sectors of health. The main objective is to provide emergency transports with an increased healthcare capacity over the patient to increase the quality of service and the possible action over that patient. This requirement generally is specified in the lack of information and training of staff involved in service. Keep in mind that most of them have no medical training (are no doctor, neither nurse). This problem appears when a simple patient transfer, becomes a vital urgency. Consider for example the following possible real situation: While transferring a patient who suffers an unspecified ailment and whose state, at first, is not a vital urgency. According to the protocol operation of most of the emergency health centers these services are attended by an ambulance without medical staff, that is with no Doctors. In the event of a sudden worsening of the patient state, it is very important that the patient receives immediate attention by a Doctor. In this case we find a clear shortcoming of the attention that the patient is receiving from the ambulance staffs that are covering this service. A system like ours provides the ability to transmit the most relevant data on the patient in real time, along with video and audio of care. Thus any medical emergency service of any hospital that was connected to the system, can make an initial diagnosis and advise the ambulance staff on how to proceed. Besides the obvious improvement in the quality of service provided during the emergency, there is another added benefit: The system even allows the emergency department physician who will serve the patient knows the exact status of the patient before, during and at the time of admission, moreover they could be the optional medical consultant that tracks and advises the patient during transport.

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There are currently platforms that are trying to solve certain deficiencies which we have mentioned before. Some, like BioShirt (Michalek, 2006), focuses primarily on capturing clinical data on a continuous basis, moving into the background the real-time monitoring of an emergency. There has also been another feature referring to specify the type of patients. This feature is also shared by the system HMES (Loos, 006) of Akogrimo Consortium. Both systems are focusing their attention on a group of patients defined as high-risk with a certain pathology previously diagnosed (heart chronic diseases). Another system, eSana (Savini, 2006), serves for most types of patient; however, it does not contemplate the possibility of using data transmitted in real time to establish an early diagnosis. All these systems are deeper presented in a section. There are a number of international studies like Korpela et al. (2005) that support both the benefits and the possibility of establishment of such architectures. Others like BC eHealth System Concept Architecture (2005) support a macro system for comprehensive integration of any element that generates information relevant to the health of a patient. Software architectures for digital healthcare are another proposal for the overall management. These architectures are focused on the use of an international protocol for the exchange of communication known as HL7. SIe-Health was born to give full solution to the problem in development this project. The creation of a platform which allows solving current shortcomings in care systems of medical emergencies has been marked as its main objective, helping the flow of useful information in real time, among all the subjects involved in the care of urgency. At this point, the system proposed allows doing the following: (i) To obtain from the Coordination Centre, realtime information on all vehicles intended for emergency care.

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(ii) To connect multiple electromedicine devices to the patient to collect clinical data. (iii) To dispatch of the clinical data in real time from the ambulance toward the Coordination Centre, Hospital and / or CAP. (iv) To monitor and track multiple emergencies from the Centre for Coordination at the same time. (v) To support communication through audio, video and data channels, between the ambulance and Coordination Centre, and simultaneously, with any other destination referred to in the architecture.

TECHNOLOGIES, INFORMATION SOURCES AND STANDARDS USED The use of standards and information technology provides a starting point for the system analysis and his future development. For this reason, this section presents a study of what it is available at the moment and some possible contributions to the development of the system.

Development Organizations, Standards and HealthCare initiatives IHE13 (Integrating the Healthcare Enterprise): IHE Europe: IHE Spain This is a joint initiative of industry and health professionals to improve the communication ways on the health information systems. It promotes the coordinated use of established standards, as HL7, XML and DICOM to solve the specific needs of the patient care and improve their quality. IHE ensures that the specifications must produce systems that communicate with one another more easily and are easier to implement. Also, these systems allow health professionals a more efficient use of the information.

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HL7 (Health Level Seven): HL7 Spain All HL7 standards are used for the electronic exchange of medical information. The Level 7 refers to the existence of a level seven on the OSI layer that provides support services to network applications. HL7 was developed by ANSI and in their Version 3 several of the approved standard definitions include the use of the UML modeling language and the XML meta-language. HL7 is the standard that is currently being used in Europe, getting several agreements with CEN, European Standardization Committee. These agreements led to the compatibility of their standards, what enables any system developed under the standard HL7 the ability to communicate with a system developed under the standards of CEN.

XML The meta-language XML is used extensively on the standardization and data management between computer systems. The use of XML in the clinical research industry and health care is widespread. An e-Health application should be based on an architecture specified using XML, what provides connectivity necessary for high levels of efficiency in the processing information and in the management of a high workload.

Technologies and Information Sources A Middleware is a software connectivity that offers a set of services to help in the functioning of distributed applications on heterogeneous platforms. This software is used as an abstraction layer to distributed software, between the application layer and lower layers (operating system and network). The Middleware provides an abstraction level for the complexity and heterogeneity that are present on the underlying communications networks, as well as on the operating systems and in the programming languages. Standards as

CORBA or RMI provide different Middleware solutions for distributed objects on a same basis. CORBA (Common Object Request Broker Architecture) is an architecture standard which provides a platform for the development of distributed systems. One of their main goals is to help on the remote method invocation under the object-oriented paradigm. It also enables the implementation of services regardless of the programming language in which they are written. This mechanism is a quite relevant abstraction on the e-Health domain for any software engineer, specially taking in mind the range of possible digital health devices that must be connected in a software Health-care system. RMI (Remote Method Invocation Java) is a mechanism provided by the language Java that allows to remotely invoke a method on a distributed object. It is important to remark that the communication interface for distributed applications, are significantly simplified because it is integrated on the RMI Java Runtime Environment. The use of agent technology in the sector of information technology (IT) has been increased in the last years as a consequence of the improvements it brings. This technology combines several other types that have as common goal the fact that they act autonomously to replace the behavior of some entity (a person for example). The application of this technology provides an abstraction of autonomous entities for exchanging information. Finally it is remarkable the role it can play the Java Mutimedia Framework (JMF) on the data streaming management. Especially when combined with any API for processing XML data; JAXB and JAXP allow the management of XML files from a defined pattern or analysis of a fast.

E-HEALTH SYSTEMS Systems that integrate technologies that serve as mobile platforms for the development of solutions in the social and health environments, represents

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a significant portion of the software development and innovation for specific health services. These kinds of systems have to take account important issues, many of whom are oriented to a overall improvement of services provided to citizens, allowing more services and actions that technological progress may become outdated. In this way, SIe-Health aims to provide an application environment designed initially to improve emergency health care services, under established protocols of most health agencies which actually coordinates this type of service. From a general point of view, over an area of control and coordination of any emergency, the different public administrations usually manage all resources, both human and material, through small territorial delegations, which in turn are coordinated by central coordinating body. In this pyramidal structure is common a delegation of tasks and resource allocation while maintaining some control over them. It represents an apparent inherent decentralization. In the particular case of medical emergency services is very important that the management of available resources and control of tasks are performed, the performance with maximum guarantees. In this way, it must be considered that the emerging mobile technology revolution is an important part in the process of improving these services. The solution presented in this paper starts from a preliminary analysis of the performance of emergency medical transport services. Typically these services are coordinated at regional level by a central emergency telephone exchange available, through which citizens with a health emergency requiring help. This is the trigger that initiates the action of the focal point of public health emergencies, which immediately mobilize the resources available to meet the emergency, within their respective territorial area. Resources, meaning ambulances are limited. Within these limitations, there is also an intrinsic deficiency of the current system, most of the health emergency service ambulances have

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medical staff, but don’t have medical personnel aboard them. There are a few vehicles that are medicalized ready to cover the priority emergencies, established through the coordination center. Note that in most situations, given the high level of training of personnel working in these services and the proper management and organization of resources, it makes possible that the current system work covering current health emergencies as satisfactory way. However, in this work, we propose a further step in order to improve these services through application of current technological possibilities. The current system, as mentioned above, works well. But sometimes while transmitting the state of a patient, changes became unpredictable and then, many complications could appear. In other words, the problem arises when a simple routine patient transfer becomes a vital urgency. An example of that situation could be the following: Moving a patient who suffers from an unspecified ailment and whose condition, in principle, is not serious. According to the protocol operation of most of the focal points of health emergencies the service is attended by ambulances, that are manned by technical personnel without doctor and nurse. In the event of a sudden worsening of the patient, what implies a worsening direct that endanger their life, is really important that patients receive immediately medical attention. Therefore, in this case detected a deficiency of the attention it is receiving the transferred patient (remembering, ambulance without doctor and nurse). Being aware that it is economically unfeasible to maintain a system in which all vehicles have doctors and nurses on health services, 24 hours/day, it is considered that the best solution requires establishing mechanisms to maintain channels of continuous information between emergency units in health services (ambulances), and groups of trained medical personnel (doctors) to coordinate and assist in the development of the

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performance of these urgent services. And here comes in the growing development of mobile technologies applied to social-health field. In essence, the E-Health aims to provide capabilities to communicate in real time the most relevant data about a patient, in addition to video and audio, to allow make an initial diagnosis by doctors and advice ambulance staff about the procedure. Besides the obvious improvement in the quality of the service during the transfer, there is another added benefit: The medical emergency service to attend to the patient, can know exactly the status of the patient before and at the time of arrival to the hospital. Moreover the same doctor who advise about the procedures to the ambulance staff during the transfer, can attend the patient when it arrives. In addition to the welfare and care of the patient coming, this system provides a mechanism for centralized control of all emergency services performed in a given geographical region through their respective focal point. This focal point, the emergency management center, is the one who establishes the video-conference sessions with mobile units for each emergency, then delegating these communications with the medical centers or hospitals, destiny of that emergencies. The practical approach of this system is very broad, and this particular case about health services was taken as a starting point for our development. However, the potential of this system is not confined exclusively to improving emergency health services, but can become established in other areas, such as improved care services like attention to elderly people, and migrate to other areas of social sector health.

RELATED WORKS Related to the work presented, as pointed in the introduction, it could be found similar projects that make up an initial state of the art. In this section some of these project are analyzed to remark a set of important features that do not have and that the

solution proposed in the paper provide in order to improve the general services of healthcare.

The eSana Framework: Mobile Services in eHealth using SOA Developped by Marco Savini, Andreea Ionas, Andreas Meier, Ciprian Pop, Henrik Stormer on the University of Fribourg, the eSana framework (Savini, 2006) was developed as an integrated system to connect patients with several medical experts. This framework offers application developers a solid base in order to create their applications. The scenario is used to get medical data from mobile patients (e.g. patients with chronic diseases as diabetes) and transmit them directly to a server; this data is afterwards offered in some way to a set of subscribed recipients that can build their own services with it. The data information may be used for different means, but the architecture has not been thought to be used on line in an emergency situation.

E-Health with Mobile Grids: The Akogrimo Heart Monitoring and Emergency Scenario. This system proposed by Christian Loos from the Universität Hohenheim is based on the use of Mobile Grids. These grids form networks of mobile and stationary monitoring and diagnosis facilities around the patient, electronic health records, medical decision support, diagnosis and analysis services, as well as even mobile medical experts and physicians. Thus, Mobile Grids provide an infrastructure for an efficient development, provision and maintenance of complex e-health applications.

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Realization of an e-Health System to Perceive Emergency Situations

Figure 1. Ambulance electro-medical devices

This work realized by S. C. Shin, C. Y. Ryu, J. H. Kang, S. H. Nam, Y. S. Song, T. G. Lim, J. W. Lee, D. G. Park, S. H. Kim, and Y. T. Kim, presents an e-health system to perceive emergency situations of a patient. The system need to use an awearable shirt (BioShirt) and a personal monitoring system (PBM). This system obtains the body signals of a user. A monitoring system collects and transmits the vital signs to a personal digital assistant (PDA) throw BlueTooth communication module. To detect emergency from the received data, a simple detection algorithm is performed in the PDA. And the PDA forwards the data to an e-health central monitoring room (ECMR), if necessary. In the ECMR, several operators supervise the registered users based on incoming body signals from each users device. If an automatic decision-making algorithm generates an emergency alarm, the operators try to contact the corresponding patient and recognize his status. These three projects are designed to work in controlled environments with limited space and with a very specific group of patients. Its capabilities are fully defined in the way of covering specific application cases and for certain diseases. However, our system combines aspects such as decentralization and transfer of data, but for cases and situations much more general. In our project, the main function of the system is to provide the necessary mechanisms to establishing and transmitting video signal, voice and data from electromedical devices on board a vehicle, usually an ambulance, located anywhere in emergencies, and attending to any type of patient.

this way having in mind practical aspects as the geographic location of each of the applications that shape this system, and other criteria such as workload distributions, available resources and technology constraints. Moreover, the scalability of the systems that shape this type of architecture has been also taken into account. These kind of systems often tend to have a growth both horizontally and vertically. The server duplication leads to the increase in the robustness or to an improved performance. The duplication of clients allows an increase in availability of systems. For these reasons, both clients and thick servers have been developed, as they often tend to be complementary. The next sections present each part of the developed architecture:

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The presented platform architecture is divided into three main parts: On Board, Server Area and Monitoring Station. It has been structured

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On Board The main function supplied by the architecture of this platform’s part is to obtain information from devices connected to the system and transmit it. Additionally it also gives feedback of what is happening to the central or to the center where these request are treated. Summarizing, their basic objectives are the following ones: Getting the data devices connected to the system. Storing the data information obtained.

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Presenting data through an appropriate user interface. Establishing communication with the server area. Creating a log history for the system in operation

Server Area The main function of this part is to provide a communication link between any mobile unit and the monitoring station. The information of the emergency is then managed by the provincial’s architecture part of SIe-Health. Taking into account that could exist many monitoring stations, the adoption of this solution by the SIe-Health platform allows to the health professionals in a hospital or emergency department getting, with an admissible delay, the data information that the system obtains on board using their own devices. Moreover this information is also sending to the Coordination Centre for tracking and storage. More in detail, the basic

goals for this part of the architecture are the following: • • • • • •

Capturing of data from mobile units. Managing communications between mobile units and the monitoring station. Presenting information about the state of the units associated with a service Storing data and logs. Authentication management. Managing local operations when the monitoring station has hands over control the server area.

Monitoring station This part of the architecture centralizes the most important control operations of the platform, providing full control over all connections that are served in this system. By the use of this part of the architecture, the coordination Center could attend the incoming emergency call, or could derive it to a Hospital Emergency Department.

Figure 2. Provincial Architecture Diagram

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Figure 3. Coordination Centre architecture diagram

Figure 4. Videoconference window

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Addressing all requests from the platform. Transmitting data information between mobile units and the integrated Action Clients at various points of architecture. Managing and delegating control transmissions on a given server area. Doing a mass storage of data.

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SIE-HEALTH SYSTEM DEPLOYMENT In this section it is explained a possible implementation of the architecture. To improve understanding, it presents the operation of the architecture by using prototypes of the graphical user interfaces that could be used in each part of the final system previously introduced.

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Main View: Control Interface Figure 4 shows a possible implementation of the application, which would be installed inside the ambulance. Making a preliminary analysis, based on consultation from experts, it has been stated that the application should support at least the following utilities:

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Gadget Support for each of the devices connected to the system. This will allow easily add new devices to the application, enhancing its growth potential extensions. The use of plugins designed as Gatgets graph allows the incorporation of new functions to be incorporated later. Support video conferencing duplex. One of the major milestones of the implementation was to support video-conferencing to emergency assistance. Figure used to illustrate the application of “on board” can be seen as part of the interface is dedicated to fill this gap, in a more than acceptable, because it could support design for multiconference, allowing different experts can maintain consistent attention to the emergency immediately. Duplex audio support. Provide audio conferencing system is also marked as another milestone for this project. Although the use of voice communication system is common during a transfer. The SIe-Health system aims to go one step further and allow that are taking place several conversations at once, from one device and accompanied by its corresponding image, in real time. Support chat console. Although it may seem like an added without much utility, the chat system allows a conversation si-

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multaneously with different experts, with a minimum computational cost. This has been implemented with the intent to support a possible cover unexpected situation, and not always have a bandwidth wide enough to transmit or receive over a distance. That is why for maintaining high availability of the system is necessary to rely on simple communication systems. Once implemented the functionality discussed above, also has set up a support system in case of failure and recovery. The supported system is based on the following points: •

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System log: The system log will allow the record of all events during an emergency assistance. In a normal situation the system will store data concerning the patient, technicians, doctors, devices and drive history. The implementation of this service will cover any type of event that could happen during service. Support system transmission: During a process of transmitting or receiving data should be checked several factors that can affect this. Thus, we considered different data transmission networks, GPRS, UMTS and 802.11g, and have implemented various protocols of action, depending on

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number of frames lost. These include the reduction of services offered by the interface for transmission capacity available, or changing from one network to another with less noise. STORAGE data: All data transmitted and received during a service are stored in part for their subsequent study and improve our services. This type of data is that will be useful for support services, while observing their actions during an emergency, may provide a better service by setting new performance protocols. Data from patient monitoring: Real-time data obtained from a patient is shown and transmitted from the vehicle that covers the emergency. Videoconference Window. The architecture design of this part of the system has been stated on González et al. 2009.

Main View of Interface Monitoring: Monitoring Control Interface This interface will show all the information available about the ambulances under centralized control. The information is presented as a list

Figure 5. Data interface

Figure 6. Video-conference interface

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divided into sectors corresponding to different geographical areas that are managed by the application. Associated with each service offered by the system, SIe-Health store data tables that could be managed underway at any given time. To cover this service the following concepts must be taken into account: • • • • •

State of flow: indicating the current state of communication with the mobile unit. Status information: indicating the service state, as transfer protocol. Date: indicating the date of commencement of service. Type: indicating the type of unit is performing the service. Destination: indicating the destination of the shipment. In most cases they refers to the hospital destination.

The protocol for coordination of a service usually comes preset in advance by the existing health system in each autonomous community. For this test case, it has simply been monitored from the central system that the service is carried out properly at all times. As in the system “on board”, it has also remained the registration service and storing data logs concerning any service that is being conducted.

Figure 7. Main view of interface monitoring

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•

•

Monitoring and advice during surgery: Integrating and implementing a system of video conferencing and data transfer electro-medical devices present in the operating theatre. Module access to patient medical history: The integration into the platform system of digitized medical records is one of the most important works to be done. Integrator for MDP SIe-Health on the platform: MDP (Diagnostic Module Preliminary), which is in its early stage of development. Its aim is to provide an initial diagnosis and guidelines for action in an emergency medical via mobile device isolation.

CONCLUSION FUTURE RESEARCH DIRECTIONS There are several changes that could be applied to the SIe-Health architecture. Several of the most relevant works to be done are: •

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Monitoring patient’s state through mobile devices: In this line of work, it would try to keep monitoring the patient’s general condition in a controlled environment (hospital complex, geriatric nursing home, etc...) through the transmission of information from different electro-medical devices.

The need for obtaining information from the real world for analysis is increasingly required more quickly. If this is applied to the medicine domain, the speed increases exponentially. This affirmation is evidenced regarding the numbers of devices, which emerge daily and that allow access to such data or information. Regardless of that, it must be taken into account that in many cases getting information and data is difficult due to several obstacles (distance, format...). Moreover, it is also possible that the data could not be analyzed by competent personnel or by appropriate devices. The development of Sie-Health has succeeded in

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solving these problems and unifying the solution in a single platform. In addition, Sie- Health is an open platform capable of annexing new features that may be sued by the staff who really are the end users of the system. It is also capable of adapting to the needs that are arising from technological advances, improved communications, new devices, etc. One of the biggest problems we found at the beginning of the development of Sie-Health was the need for access to information in a timely manner and with a high degree of integrity. It was added that the barrier of the town, which has led to rule out any means of transferring data from its origin to the location where they will be analyzed, which did not have a high quality during transmission and a wide coverage space ensure a minimum acceptable coverage. That is why we have selected technologies such as GPRS, UMTS, election is not only provides a reliable solution, but it also implies a lower cost and greater ease of development than other discarded options (Satellite, etc). Sie-Health is a platform that facilitates obtaining information from patients in a vehicle intended for emergency health and onward shipment to a plant where they will be analyzed by qualified personnel to provide support during the patient care, staffs that are covering such urgency.

REFERENCES Ball, M. J., et al. (2006). Banking on Health: Personal Records and Information Exchange. Journal of Healthcare Information Management — Vol.20, No.2 BC eHealth Steering Committee (2005). “BC eHealth Conceptual System Architecture”. British Columbia Ministry of Health Services. National Library of Canada Cataloguing. From: http:// www.healthservices.gov.bc.ca/library/publications/year/2005/BC_eHealthcas.pdf

Connecting for Health. (2004, July). Connecting Americans to Their Healthcare. Final Report of the Working Group on Policies for Electronic Information Sharing Between Doctors and Patients. Markle Foundation and Robert Wood Johnson. Korpela, M., Mykkänen, J., Porrasmaa, J., & Sipilä, M. (2005). Software architectures for digital healthcare. HIS R&D Unit, University of Kuopio, Finland. From: http://www.uku.fi/ tike/his/exporthis/CHIMA2005-architecturescorrected160605.doc Loos, C. (2006). E-Health with Mobile Grids: The Akogrimo Heart Monitoring and Emergency Scenario. EU Akogrimo project Whitepaper. Retrieved from: http://www.mobilegrids.org/ Michalek, W. (2006). BIO-SHIRT. Retrieved from http://www.ele.uri.edu/courses/ele382/F06/ WhitneyM_1.pdf Moreno, G., Carlos, J., & Rodríguez, G. Mª, A., González, R.,Astray, R. (2009). V-MAS: a Video conference. Multiagent System. Volume 55, Advances in Soft Computing, (pp. 284-292). New York: Springer-Verlag. Null, R.,& Wei, J. (2009). Value increasing business model for e-hospital. International Journal of Electronic Healthcare 2009 - Vol. 5(1), 48-63 Riedl, B., Grascher, V., & Neubauer, T. (2002). A Secure e-Health Architecture based on the Appliance of Pseudonymization. Secure Business. Austria, Vienna. From: http://www.academypublisher. com/jsw/vol03/no02/jsw03022332.pdf Savini, M., Ionas, A., Meier, A., Pop, C., & Stormer, H. (2006). The eSana Framework: Mobile Services in eHealth using SOA. In Proceedings of EURO mGOV 2006.

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ADDITIONAL READING Alberto Hernández Abadía de Barbará. “Sistema de Telemedicina de las Fuerzas Armadas Españoles”. Unidad de Telemedicina del Hospital Central de la Defensa. IGESAN. Ministerio de Defensa. From: http://www.csi.map.es/csi/tecnimap/tecnimap_2006/05T_PDF ANSI, American National Standards Institute. From http://www.ansi.org CEN. Comité Europeo Normalización. Disponible: www.cen.org Christian Loos. (2006). “E-Health with Mobile Grids: The Akogrimo Heart Monitoring and Emergency Scenario”. Universität Hohenheim, Information Systems II. From: http://www. akogrimo.org/download/White_Papers_and_ Publications/Akogrimo_eHealth_white_papershort_20060207.pdf CORBA. Common Object Request Broker Architecture. Available at: http://www.corba.org DICOM. Digital Imaging and Comunications in Medicine. From: http://medical.nema.org Garets D., Davis M. (2005, August 26). Electronic Medical Records vs. Electronic Health Records: Yes, There Is a Difference. A HIMSS Analytics White Paper. Chicago, IL: HIMSS Analytics. Gunther Eysenbach. (2001). “What is e-Health?”. Journal of Medical Internet Research (J Med Internet Res 2001;3(2):e20). Available at: http:// www.jmir.org/2001/2/e20/ HL7, Health Level Seven Spain. From: http:// www.hl7spain.org Hubert Zimmermann. (1980). OSI Reference Model-The IS0 Model of Architecture for Open Systems Interconnection. From: http://www. comsoc.org/livepubs/50_journals

IHE. Integrating the Healthcare Enterprise. From: http://www.ihe-e.org Java Remote Method Invocation, R. M. I. Available at: http://java.sun.com Marco Savini, Andreea Ionas, Andreas Meier, Ciprian Pop, Henrik Stormer. “The eSana Framework: Mobile Services in eHealth using SOA”. University of Fribourg. From: http:// www.mgovernment.org/resurces/euromgvo2006/ PDF/21_Savini.pdf Norman López-Manzanares. (2004). Sevicios Móviles en la Sanidad: Un paso más en la Mejora de la Salud, Telefónica Móviles España. From: http:// www.csi.map.es/csi/tecnimap/tecnimap_2004/ comunicaciones/tema_05/5_007.pdf Olga Ferrer-Roca. (2001). Telemedicina. Editorial Panamericana. Pedro Álvarez Díaz. (2007). “Teleasistencia Médica, ¿hacia dónde vamos?”. RevistaeSalud. com Vol 3, Nº 10. From: http://www.revistaesalud.com/index.php/revistaesalud/article/viewArticle/155/411 Shin, S. C., Ryu, C. Y., Kang, J. H., Nam, S. H., Song, Y. S., Lim, T. G., et al. (2004). “Realization of an e-Health System to Perceive Emergency Situations”. Microsystems Research Department, Electronics and Telecommunications Research Institute, Deajeon, Korea. From: http://ieeexplore. ieee.org/iel5/9639/30463/01403930.pdf Waegemann, C. Peter (2002). “Status Report 2002: Electronic Health Records”. Medical Record Institute. From: http://www.medrecinst. com/uploadedFiles/MRILibrary/StatusReport.pdf XML. Extensible Mark-up Language. World Wide Web Consortium. From: http://www.w3c.org

This work was previously published in Handbook of Research on Mobility and Computing: Evolving Technologies and Ubiquitous Impacts, edited by Maria Manuela Cruz-Cunha and Fernando Moreira, pp. 445-458, copyright 2011 by Information Science Reference (an imprint of IGI Global). 716

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Sensing of Vital Signs and Transmission Using Wireless Networks Yousef Jasemian Engineering College of Aarhus, Denmark

ABSTRACT People living with chronic medical conditions, or with conditions requiring short term monitoring, need regular and individualized care to maintain their normal lifestyles. Mobile healthcare is a solution for providing patients’ mobility while their health is being monitored. Existing studies show that mobile healthcare can bring significant economic savings, improve the quality of care, and consequently the patient’s quality of life. However, despite all progresses in advanced information and telecommunication technologies, DOI: 10.4018/978-1-60960-561-2.ch307

there are still very few functioning commercial wireless mobile monitoring devices present on the market, which most work off-line, are not proper for m-health services and there are still many issues to be dealt with. This chapter deals with a comprehensive investigation of feasibility of wireless and cellular telecommunication technologies and services in a real-time m-health system. The chapter bases its investigation, results, discussion and argumentation on an already developed remote patient monitoring system by the author. The implemented m-health system has been evaluated and validated by a number of well defined tests and experiments. The designed and implemented system fulfils the requirements.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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The suggested system is reliable, functions with a clinically acceptable performance, and transfers medical data with a reasonable quality, even though the system was tested under totally uncontrolled circumstances during the patients’ daily activities. Both the patients and the involved healthcare personnel expressed their confidence in using it. It is concluded that the system is applicable in clinical setup, and might be generalized in clinical practice. Finally, the chapter suggests improvement approaches for more reliable, more secure, more user-friendly and higher performance of an m-health system in future.

INTRODUCTION Telemedicine has been defined as “medicine practiced at distance”. This encompasses diagnosis and treatment as well as medical education. The European Commission Information Society Directorate-General (2001), defines telemedicine as “the use of remote medical expertise at the point of need, which includes two major areas: Home care, as care at the point of need through connected sensors, hubs, middleware and reference centers, and co-operative working, as a network of medical expertise linked together”. The definition of telemedicine differs depending on the background of the user and the applications aim. From a clinician’s point of view, telemedicine can be defined as “practicing healthcare delivery such as consultation, transferring medical data, monitoring, diagnosis, treatment and patient education, using interactive audio/video facilities and a telecommunication network”. This definition leads to the creation of “E-health” and “Tele-health” terms. Tele-health is the use of information and communication technology to deliver health services, expertise and medical information over a distance. Whereas, e-Health is broader than either Telemedicine or Tele-health and can be described as an emerging field composing medical informatics,

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public health and business, which enables health services and medical information to be delivered or enhanced through the internet or other related communication technologies. So, a telemedicine system encompasses information technology, biomedical engineering and telecommunication technologies, serving healthcare providers and patients at a distance. Thus, the used terminology to describe healthcare services at a distance will probably change as fast as the used technology. Telemedicine is showing its value in a rapidly increasing number of clinical situations (Agha, Schapira & Maker, 2002; Bai et al. 1999; Freedman, 1999; Kyriacou et al., 2003; Magrabi, Lovell & Celler, 1999; Scalvini et al., 1999). Moreover, telemedicine applications may include but are not limited to, rural healthcare, public healthcare service, humanitarian efforts, school-based health services, disaster medicine, prison healthcare and nursing home care (Berek & Canna, 1994; Perednia & Allen, 1995). There have been many studies around the world which have shown the feasibility and usefulness of telemedicine in remote areas (Jasemian & Arendt-Nielsen, 2005c; Lin, Chiu, Hsiao, Lee & Tsai, 2006; Jasemian, 2006; Jasemian, 2008; Bai et al. 1999; Freedman, 1999; Kyriacou et al., 2003; Magrabi et al., 1999; Gott, 1995; Coyle, Boydell & Brown, 1995; Dansky, Palmer, Shea & Bowles, 2001). Fixed communication networks have been used in different telemedicine setups for some years (Gott, 1995; Coyle et al., 1995; Rezazadeh & Evans 1990; Patel & Babbs 1992), whereas wireless and cellular technologies within telemedicine have been in focus in the latest few years (Freedman, 1999; Kyriacou et al., 2003; Orlov, Drozdov, Doarn & Merrell 2001; Satava, Angood, Harnett, Macedonia & Merrell, 2000; Shimizu, 1999; Uldal, Manankova & Kozlov, 1999; Woodward, Istepanian & Richards,2001). Mobile healthcare is a solution for providing patients’ mobility while their health is being monitored. It has real benefits which are often significant; this can become evident through clini-

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cal trials (Jasemian et al., 2005c; Lin et al., 2006). Based on the existing studies it is clear that mobile healthcare can bring significant economic savings, improve the quality of care and consequently the patient’s quality of life. The performance of communication links has greatly improved, and communications networks have been extended throughout most of the world. Mobile communications, in particular, and particularly in combination with the internet, have brought new possibilities to various fields. Therefore, advanced telecommunication technologies may make the realization of new and innovative monitoring and healthcare delivery a reality. Despite all progress in advanced telecommunication technologies, there are still very few functioning commercial wireless mobile monitoring devices present on the market, which most work off-line, and there are still many issues to be dealt with. The present chapter intends to explore the state-of-the-art of the wireless and mobile technologies applied in an M-Health service and the most important issues in design and implementation of mobile and pervasive healthcare services for sensing vital signs; suggesting improvement approaches for more reliable, more secure, more user-friendly and higher performance of an M-Health system.

BACKGROUND AND MOTIVATION People living with chronic medical conditions, or with conditions requiring short term monitoring, need regular and individualized care to maintain their normal lifestyles. The life of these patients is often centered on check-ups and hospital visits. According to the Administration on Aging (2002), the elderly population is increasing worldwide and it is expected that the number of people over 65 will increase even more in the coming decades. Older adults, 65 years plus, comprise about 12.4% of the U.S. population. By 2030, this will have increased to 20%, representing twice the number as in 2000. Eighty five years and older seniors

represent the fastest growing group (He, Sengupta, Velkoff & DeBarros, 2005). An aging population will impact upon traditional healthcare delivery methods. Older people have different requirements from society and government as opposed to young people, and frequently differing values as well. These issues alone will have an enormous impact on the demands for care services for the elderly. Many elderly people suffer from physical and mental disabilities that affect their everyday life. Many older adults require long-term care due to diseases associated with aging. According to the Administration on Aging (2002), in U.S.A, About 7 million adults over 65 have mobility or self-care limitations. This often makes it necessary to constantly monitor the vital functions of these patients in hospitals or nursery homes which are both costly and uncomfortable. By 2030, the average number of children per family will be about 2 compared to 3 in 1990 (Fields & Casper, 2001). Smaller family sizes along with geographically dispersed family members make it difficult to provide long-term care without some type of external support system. These demographic trends highlight the need for innovative support systems for family members and their caregivers. Nowadays, in many Western countries, there is a tendency for health authorities to optimize resources by introducing pervasive and mobile healthcare services. In many cases it has become a preference to treat and/or monitor as many patients as possible in their homes. The benefits of mobile healthcare are to decrease hospital-based resource utilization, improve patient compliance with treatment plans, improve the level of patient satisfaction with healthcare services, and improve patients’ perceived quality of life. In many cases, continuous monitoring has allowed patients to return to productive work. This reduces the burden on the society and most specifically healthcare system. For people with diseases such as cardiac arrhythmia, Chronic Obstructive Pulmonary Disease

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(COPD), asthma, and for others in need of frequent medical monitoring, it is easier to enjoy everyday activities when they have mobile healthcare available. An important area of the mobile healthcare service is the mobile monitoring of the patient’s vital signs outside the clinical environment. Mobile healthcare can monitor common vital signs such as blood pressure, electro cardiogram (ECG), pulse rate, blood oxygenation (SpO2), breathing rate, body temperature, body activity and weight, and other measures. Questionnaires and several blood tests can also be integrated to remote monitoring systems such as the monitoring of pain or blood glucose. While health providers are already geared up to dealing with an increasing number of elderly patients, and could probably cope with a rise in instances of obesity related diseases, they cannot do both without automating clinical processes and using technology to improve public health. For the healthcare sector, wireless and mobile technologies have appeared at an opportune time. For decades the healthcare sector has lagged behind the manufacturing and financial sectors in the adoption of automated processes. Now it can use mobile and wireless technology to realize the sort of efficiency gains achieved by banks and large businesses. Pervasive and mobile healthcare services can apparently create insecurity when the patients are remote from access to medical systems. However, the capturing of real-time data has been proven to significantly improve the observed effects of treatment (Philips Medical Systems, 2005; BASUMA Project, 2008; MobiHealth Project, 2008; Lo & Yang, 2005; Jasemian et al., 2005c; Lin et al., 2006). Frequent monitoring reassures patients that their condition is being regularly followed, providing them with security and peaceof-mind. The patients can feel less limited in their daily lives (Jasemian, 2006; Jasemian, 2008). Moreover, with wireless technology, physicians can use their hand-held devices such as Personal Digital Assistant (PDA), to access the clinical data

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repository and can view both the historical and real-time patient data such as laboratory result quickly and conveniently. Inevitably, healthcare goes mobile. Recently developed mobile healthcare (i.e., m-health) services allow healthcare professionals to monitor mobile patient’s vital signs and provide feedback to this patient anywhere at any time. Due to the nature of current mobile service platforms, mhealth services are delivered with a best-effort, i.e., there are no guarantees on the delivered Quality of Service (QoS). To estimate the global progress of telemedicine and mobile healthcare a search in the PubMed database and some simple statistics shows that from 1974 to 2007 approximately 7,772 published articles on telemedicine and mobile healthcare were registered, of which relatively few were review articles and the rest representing great activity in the field reporting the design, implementation, evaluation and application of telemedicine and mobile healthcare in all areas. These are of course only the registered papers in the PubMed database and there would be more if other database were searched too. It is only a rough estimate, but one can have a notion of the course which progress in this field has taken in the last 34 years. Figures 1 and 2 illustrate the numbers of published telemedicine papers from 1974 to 1991, and from 1991 to 2007 respectively. The years with higher publication rates are those in which the feasibility, applicability and implementation of new technologies in telemedicine and mobile healthcare were under investigation, evaluation and validation, however on the research and prototyping level. It seems that there was a significant decrease in years 2006 and 2007. Probably these are the years in which the technologies became more mature and there was more focus on the commercialization of different solution/products. Very few numbers of mobile healthcare systems are commercialized, clinically applied but not all evaluated scientifically. Mobile healthcare

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Figure 1. The distribution of the published papers on telemedicine, in PubMed database, from 1974 to 1991.

Figure 2. The distribution of the published papers, in PubMed database, on telemedicine from 1991 to 2007.

provision in the home environment or outdoors presents many challenges. Patients are becoming more informed about the management of chronic conditions and the use of technology to support the process is rising. Thus, it is unavoidable to address issues such as system interoperability, cost, security, reliability, performance, patient’s compliance and training in order to ensure effective use of mobile devices within the home healthcare arena.

SYSTEM REQUIREMENTS FOR AN M-HEALTH SERVICE Taking the recommendations and advice from the literature in addition to the practical and

technological possibilities into consideration, a common mobile healthcare system should satisfy the following requirements: •

The system must be secure and safe (Specific Absorption Rate (SAR) < 2.0 W/Kg in Europe, according to the international commissions’ recommendations (International Commission on NonIonizing Radiation Protection [ICNIRP], 1996; Standard for Safety levels with respect to human exposure to radio frequency electromagnetic fields [ANSI/ IEEE C95.1-1992, NJ 08854-1331], 19911999; International Radiation Protection Association, Health Physics [IRPA], 1988; World Health Organization, Environmental

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• •

• •

•

•

• • •

•

Health Criteria 137, Electromagnetic Fields [WHO], 1993) and SAR < 1.6 W/ Kg in USA (www.conformity.com, 2002) The M-Health device must be user friendly [Design requirement] The operation of the M-Health device must be fast and simple for the patients as well as for the healthcare personnel [Design requirement] The system should works interactively [Design requirement] The system should give the patients full mobility and freedom, in that way it would be possible to monitor them wherever they are, as long as the remote area has network coverage [Design requirement] The system should improve the present monitoring method significantly [Design requirement] The start-up must be fast and easy (< 25 s the time required for link establishment) (Kammann, 2001) The system uptime must be > 99.95% The transfer rate should be at least ≥ 3.4 kbps [Design requirement] The data transmission must be without information loss or artifact gain (Bit Error Rate<10-4 at application level (Melero, Wigard, Halonen & Romero, 2002) and Signal to Noise Ratio > 22 dB [Design requirement]) The system should have high performance (Delay < 7.5 s application-level Round Trip Time, Dropped Call Rate < 1.8%, Call Success Rate > 95%, Packet Error Rate < 10-4, Packet Lost Rate < 10-4) (Melero et al., 2002; Romero, Martinez, Nikkarinen & Moisio, 2002).

Figure 3. A simplified sketch of a telemedicine system

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TELEMEDICINE AND MOBILE HEALTHCARE SYSTEM AND TECHNOLOGY A telemedicine system, in general, is composed of four main parts; 1) Patient unit /client side, 2) Communication network, 3) Receiver unit / server side, and 4) Presentation unit / user interface. Figure 3 is a simplified sketch of a general telemedicine system. A patient unit collects information / vital data from a patient, sends it as an analogue signal or performs A/D conversion and error correction, controls the data flow, and performs data transmission, using a transmission media. The type of the information that might be sent could be: audio, data, motion video, still images or fax. The network is responsible for data security and data transmission from patient side to hospital / server side. The architecture of the communication network could contain one or a combination of telecommunication services such as: Plain Old Telephone Service (POTS), Integrated Services Digital Network (ISDN), Asynchronous Transfer Mode (ATM), Global System for Mobile communication (GSM), Universal Mobile Telecommunications System (UMTS), Microwaves radio system and Satellite link. A server unit receives the data from the communication network, processes and converts it into a well defined format and then sends it to a presentation unit. The received data could be analogue or digital, and could be in a bit stream or data packets. A presentation unit receives the data from the server and transforms it into either a text or a graphical format, and presents the data to the user via a user interface (e.g. at the hospital).

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No matter which technology or network a telemedicine system has, patient safety, data integrity and, privacy, system’s performance and reliability, data quality, applicability and interoperability are common aspects which should be considered, during a telemedicine system design and setup. A number of trials in telemedicine field such as modeling and system simulation, design and development, have been carried out by different research groups. These found a number of valuable issues to deal with, from which the following were selected. Istepanian et al. (Istepanian, Woodward, Gorilas & Balos, 1998) modeled and simulated a complete cellular digital telemedicine system, using cellular telephone channels. The system was studied using both Interim Standard 54 (IS54) and GSM cellular telephone standard. The feasibility of transmitting and receiving Photoplethesmography (PPG) data using simulated GSM and IS-54 cellular channels was investigated. The results showed a successful PPG transmission over both simulated standards. They found that the performance of the mobile telemedicine system was dependent on the degree of multi-path, noise and interference, which were presented in the specified communication channel. To test the performance of the GSM standard, the input PPG data were tested for different power delay profiles (rural environment, hilly environment, urban environment or flat fading), mobile speeds, and noise and interference conditions. In another study (Freedman, 1999), investigated two important issues in a telemedicine system, namely, timeliness and ease of ECG transmission, using a cellular phone (Nokia 9000) and GSM standard. To study the mentioned issues experimentally, an ECG transmission system to a mobile phone was developed. The system was an off-line (saves and forward) using the mobile phones inbuilt fax software and modem. The results showed that the system could solve the above mentioned issues. Namely, it required

very little effort for acquiring the ECG and the transmission to the fax mail-box was rapid, in this way the healthcare process was not delayed. Shimizu (Shimizu, 1999), proposed a concept providing a mobile telemedicine system for emergency care in moving vehicles. An experimental system for transmission of color images, an audio signal, three-channel ECG and blood pressure was developed. The system used a satellite link, and both a fixed and a cellular communication network for real-time medical data transmission. Some problems with the used technique were identified (the problems are not mentioned in his paper), but the theoretical analysis verified the feasibility of the proposed technique. Later on in 2001 Woodward et al. (Woodward, Istepanian & Richards, 2001), presented the design of a prototype integrated mobile telemedicine system that was compatible with the existing mobile telecommunication networks. The system utilized an Infrared Data Association standard (IrDA), GSM and ISDN. The system was in its first phase of development and the research was carried out producing a working system that allowed the transmission of only one parameter of data (ECG). The system was simulated but not fully developed. In 2003, Kyriacou et al. (Kyriacou et al., 2003) designed and tested a Multi-purpose HealthCare Telemedicine System using satellite, POTS system and GSM mobile communication link support, which combined real-time, and store and forward facilities. It seems that the final system was installed and used in Greece and Cyprus later on. They reported that the results from the system application were very promising using the GSM system. Zhao et al. (Zhao, Fei, Doarn, Harnett & Merrell, 2004) designed and developed a Telemedicine System for Wireless Home Healthcare Based on Bluetooth and the Internet. The system uses Bluetooth as wireless technology connecting the client side to the server side and conversely via

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internet using dial-up modem, Digital Subscriber Line, or cable modem. Medical information and data were transmitted over short-range interface (Universal Serial Bus (USB) and RS232), wireless connection and the internet. The system was tested and the results showed promising performance for the wireless Bluetooth connection. It was revealed that the latency could be reduced as bandwidth increases. They found that the major source of data error arose from the serial port communication, when working in high noise environments. Jasemian and Arendt-Nielsen (2005a), have designed and implemented a telemedicine system using Bluetooth and GSM/GPRS, for real time remote patient monitoring. They evaluated the system applying a number of well defined tests and experiments. The results showed that the system fulfilled most of the requirements. They concluded that the implemented system was reliable, was functioning with a clinically acceptable performance, and was transferring medical data with a reasonable quality, even though the system was tested under totally uncontrolled circumstances during the patients’ daily activities (Jasemian & Arendt-Nielsen, 2005a & 2005b). They concluded that the system was applicable in clinical practice, since the patients as well as the involved nurses expressed their confidence and acceptance in using it. Therefore, they suggested that the real-time remote monitoring system might be generalized in clinical practice e.g. cardiology, and with small adjustment could be applied for monitoring other patient categories e.g. epileptic and diabetic patients. In spite of the differences in system setups, materials and methods, the encountered issues such as feasibility, applicability, bandwidth, throughput, performance, reliability and data quality of the telemedicine system, were common investigation issues in all the above mentioned studies. They are also the concern of this present chapter.

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The State of the Art in Telemedicine Inevitably, healthcare goes mobile. Recently developed mobile healthcare (i.e., m-health) services allow healthcare professionals to monitor mobile patient’s vital signs and provide feedback to them anywhere at any time. The recent studies in the telemedicine field showed that wireless and mobile telemedicine systems are needed and are in focus at the present time. In this relation cellular networks and communication technologies such as GSM cellular telephone, infrared, Bluetooth, Satellite link, TCP/IP over the GSM/GPRS network or internet connection, Wireless Local Access Network (WLAN) have been utilized. Thus, using wireless and mobile technologies utilizing the existing public cellular network for telemedicine system design and setup is the stateof-the-art at the present time. Due to the nature of current supporting mobile service platforms, M-health services are delivered with a best-effort, i.e., there are no guarantees on the delivered Quality of Service (QoS) yet.

MOBILE HEALTHCARE SYSTEM STRUCTURE Mobile Healthcare (M-Health) can be regarded as a medical assistance service that integrates the technologies of medical sensors, mobile computing, information technologies and wireless communications into one complete remote system. Advances in technology have led to development of various sensing, computing and communication devices that can be woven into the physical environment of our daily lives. Such systems enable on-body and mobile health-care monitoring, can integrate information from different sources, and can initiate actions or trigger alarms when needed. An M-health system could be composed of four main parts; (1) Wireless patient units including wireless Network Access Point, (2) Mobile communication network, (3) Server unit, and (4)

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Presentation unit. Figure 4 illustrates a simplified sketch of an M-Health system.In the successive subsections, those four mentioned parts will be described using the scenario in Figure 4.

Wireless Patient Units and Network Access Point Wireless patient units are a set of wireless devices that monitor patient’s vital signs and transmit measured data wirelessly to a mobile gateway or access point. In the scenario illustrated in Figure 4 four types of patient unit are applied. 1. A Bluetooth Pedometer or Step Counter; it is a Bluetooth enabled wireless device, that counts each step a patient takes by detecting the motion of his/her hips. Pedometers are inexpensive body-worn motion sensors that can easily be used by researchers and practitioners to assess and motivate physical activity behaviors (Tudor-Locke & Bassett, 2004). The device determines the patient’s physical activity indices and transmits those data via a Bluetooth connection to a mobile

access point, which the patient has on him/ her. 2. A Bluetooth enabled monitoring device for home telemonitoring of patients with chronic diseases. This mobile device is able to measure: Blood Oxygen saturation (SpO 2), Pulse rate, Breath rate and Body acceleration. The measured vital signs are transmitted to the mobile access point by Bluetooth connection. 3. A digital Bluetooth enabled device which monitors, measures and transmits the patients Blood Pressure over a Bluetooth connection to the mobile access point. 4. A Bluetooth enabled Precision Health Scale. The device is able to measure patient’s weight with a reasonable precision, and it is capable of wireless communication by Bluetooth to the mobile access point. A Bluetooth and GSM/GPRS enabled Personal digital assistant (PDA) device. The device acts as master in an ad-hoc network, which communicates with all patients’ units and establishes GSM/GPRS connection to a public mobile network when it is

Figure 4. The structure of an M-Health system; (1) Bluetooth Pedometer or Step Counter. (2) Bluetooth Monitoring Device for home telemonitoring of patients with chronic diseases. The device is able to measure: Blood Oxygen saturation, Pulse rate, Breath rate and Body acceleration. (3) Bluetooth Blood Pressure Monitor. (4) Bluetooth Precision Health Scale. (5) Bluetooth and GSM/GPRS enabled Network Access Point e.g. a smart mobile phone or a personal digital assistant (PDA) device applying mobile communication network, remote server at hospital and a monitoring device (presentation unit).

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necessary. The availability of compact IP stacks has made it possible for Bluetooth to adopt the protocol suite which drives the Internet, and it is the Personal Area Network (PAN) profile which lays out the ground rules for doing that. The PAN profile provides the rule for carrying IP traffic across Bluetooth connection. A PAN user can connect to a Network Access point (NAP) in order to access a remote public network. Thus, the NAP provides a bridge to a public network. This NAP can be mobile (A) or fixed (B), as it is illustrated in Figure 5. These two ways of connection require three different roles: (1) Network Access Point (NAP) – A device acting as a bridge to connect a Pico net to an IP network. It forwards data packets to and from the network composed of PAN user. (2) Group ad-hoc network (GN) - A NAP device which connects to one or more PAN users, forwarding data packets between more than one connected PAN users. (3) PAN user – A client which uses the Group ad-hoc Network or NAP service (Bray & Sturman, 2002). In the scenario (Figure 4) a NAP is applied, which is a Bluetooth enabled access point. This access point automatically detects and connects Bluetooth devices to the Bluetooth network, confirming and connecting users with security and access rights configurations specified for each user. The current Bluetooth technology provides a data

transfer rate of 1Mbps, with a personal area range of up to 10m in client-to-client open air (5m in a building). In terms of client-to-access point, the current range is 100m in the open air and 30m in buildings. The present scenario utilizes client-toaccess point within the range of 30-100 m.

An Overview of Bluetooth Architecture Bluetooth is a wireless technology with a universal radio interface within the globally available 2.4 GHz frequency band, which makes the wireless data and telephony communication, in both fixed and mobile environment, possible. Bluetooth employs the globally available, license free Industrial Scientific Medical (ISM) frequency bands. Bluetooth technology is based on a low cost short distance wireless connection, which eliminates the necessity of cable connections. It provides movement freedom, via a wireless connection. It has a number of different security algorithms, such as authentication and encryption. By Bluetooth, it is easy to establish an ad hock connection for a local network. It is versatile system, since it can interact with different devices regardless of manufacturer. It is reliable within an open frequency band, ISM. Bluetooth does not exactly match the well known Open System Interconnect (OSI) standard reference model, which is an ideal

Figure 5. (A) Bluetooth enabled Precision Health Scale and a Bluetooth Blood Pressure monitor using mobile access point to communicate with a public communication network and/or Internet. (B) The same Personal Area Network (PAN) establishing Bluetooth connection to a fixed access point (AP) to communicate with a public communication network and/or Internet.

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reference model for a communication protocol stack. Figure 6 illustrates an OSI reference model contra Bluetooth protocol stack for comparison. In OSI model, the Physical Layer is responsible for the electrical interface to the communications media, including modulation and channel coding. This covers the Radio and a part of Baseband tasks in Bluetooth. The Data Link Layer in OSI is responsible for transmission, framing, and error control over a particular link. This overlaps the control part of the Baseband including error checking and correction and the Link Controller in Bluetooth. The Network Layer in OSI is responsible for data transfer across the network. This covers the higher end of the Link Controller (which is setting up and maintaining multiple links), and also covers most of the Link Manager (LM) task in Bluetooth. In OSI, The Transport Layer is responsible for reliability and multiplexing of data transfer across the network to the level provided by the application. It covers the higher end of LM and the Host Controller Interface (HCI) in the Bluetooth. HCI indeed provides the actual data transport mechanisms. The Session Layer in OSI is responsible for the management and data flow control services. This is provided by Logical Link Control and Figure 6. OSI reference model contra Bluetooth protocol stack

Adaptation Protocol (L2CAP) and the lower end of the RFCOMM (protocol for RS-232 serial cable emulation)/SDP (Service Discovery Protocol) in the Bluetooth protocol. Finally, the Application Layer in OSI is responsible for managing communications between host applications, which covers the Applications in the Bluetooth. In spite of the mentioned differences, Bluetooth covers all necessary communication protocol layers for a reliable wireless connection.

Bluetooth Device, Discovery and Ad Hock Connection Bluetooth is able to establish an ad-hock wireless connection to a variety of Bluetooth enabled mobile and fixed devices in surroundings. The connection establishment can be arranged to happen selectively and automatically. In order to establish a connection, both ends of the link have to be willing to connect. Thus, a connection can not be forced to accept if it is not selected or not in the correct mode. Figure 7 illustrates the steps when a Bluetooth enabled telemetry device (patient’s unit) finds the correct NAP, in order to establish a connection to the monitoring centre at hospital for medical data transmission. The PDA here acts as a Network Access Point (NAP) using the Dial-Up Networking (DUN) profile. It periodically scans the surroundings to see if any device wants to use it. When the patient’s unit wants to establish a connection to the remote monitoring centre it needs a DUN connection to connect to NAP. To do that, it employs the DUN profile of the Bluetooth protocol. The first stage in such connection is to find out if any Bluetooth enabled device in surroundings supports the DUN profile. The Bluetooth enabled patient’s unit performs an inquiry, by sending a set of inquiry packets to look for NAP devices in the neighborhood. The NAP replies with a Frequency Hop Synchronization (FHS) packet which contains all the information that the patient’s unit needs for a

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Figure 7. The necessary steps to take when a patient’s unit using the Bluetooth protocol finds the correct NAP in order to establish a connection to the remote monitoring centre for medical data transmission

connection establishment to the NAP. The information packet contains also the device class. Every existing Bluetooth enabled device in the neighborhood that scans for inquiries, will respond with a similar FHS packet, thus the Bluetooth enabled patient’s unit accumulates a list of devices. However, the patient’s unit automatically chooses the NAP device with pre-known address among the found devices. The next stage is to find out if the NAP supports the DUN profile. To do that, the patient’s unit uses the Service Discovery Protocol (SDP). First the patient’s unit pages the NAP, using the gathered information during the inquiry. The NAP responds if it is scanning for pages, then an Asynchronous Connection Less (ACL) baseband connection is set up to transfer data (control and configuration data) between these two entities. After an ACL connection establishment, a Logical Link Control and Adaptation Protocol (L2CAP) is set up and used. The patient’s unit uses the L2CAP channel to set up a connection to the SDP server in the NAP.

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The SDP client in the patient’s unit asks the SDP server in the NAP to send all the information it has about the DUN profile. The SDP server in the NAP searches its database and returns the attributes relating to the DUN. This SDP information provides the patient’s unit all needed information to connect to the DUN service on the NAP. The patient’s unit configures the link using the Link Management Protocol (LMP) to meet its requirement, which in this case is DUN connection. Once the ACL and L2CAP connection are set up, an RFCOMM (an RS-232 emulator layer) connection it also set up, and then the DUN profile uses the RFCOMM. Each protocol has its own channel number. In this case, the NAP’s channel number for DUN was previously sent to the patient’s unit in the DSP packet. So, the patient’s unit knew in advance which number it should be used for setting up a RFCOMM connection. Thus, the DUN is set up using the RFCOMM connection and the patient’s unit starts to use the DUN service of the NAP.

Sensing of Vital Signs and Transmission Using Wireless Networks

Finally, the patient’s unit uses the NAP to perform connection across the public mobile network.

Bluetooth Security Since anyone, possibly, could eavesdrop wireless transmission, security has become an important key issue for wireless communication systems. Bluetooth uses SAFER+ cipher, which generates 128-bit cipher keys from a 128-bit pain text input, for link encryption and authentication in order to insure that the device at the two ends of the communication are who they claim to be. The SAFER+ has been designed by Cylink Corporation as a candidate for the U.S. Advanced Encryption Standard (AES) (Bray et al., 2002). The basic objective of security arrangements in Bluetooth is to provide means for a secure link layer, which encompasses entity authentication (facilitate access control and “Hardware” identification), and link privacy (eavesdropping is not easy). The applied key types are Link Keys (128 bit) and Encryption keys (8-128 bit). For Pairing it establishes secret keys and authentication. For encryption algorithm uses a stream cipher algorithm. The encryption engine is initiated with a random number using a random number machine. When the initiation is succeed the encryption engine uses four inputs for the encryption process, namely, (1) A number to be encrypted or decrypted

(this is the data being passed between devices), (2) The Master’s Bluetooth device address (the device address of the initiator device), (3) the Master’s Bluetooth slots clock, (4) A secret key which is shared by both devices. Additional to the above mentioned security arrangement the high speed pseudo-random frequency hopping algorithm in Bluetooth makes it very difficult to eavesdrop a wireless connection (Bray et al., 2002).

MOBILE COMMUNICATION NETWORK, SERVICES AND WIRELESS TECHNOLOGIES This section explores the most applied wireless and cellular technologies in most part of the world, in order to get an overview on the key enabling technologies for a mobile healthcare system. Figure 8 summarizes frequency allocations for mobile phones, cordless phones, and Wireless Local Access Network (WLAN) technologies in Europe, USA and Japan (Schiller, 2000).

Global System for Mobile Communication (GSM) GSM is a most successful digital mobile telecommunication system. It is the most popular digital system in Europe, approximately 100 million individuals use GSM, more than 130 countries use

Figure 8. frequency allocations for some of most used mobile phone, cordless phone, Wireless Local Access Network (WLAN) in Europe, USA and Japan

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this system, and it has 40% of the market share. GSM allows integration of different telephony and communication services, and it adapts to the existing network. A GSM mobile device has approximately 0.125 – 0.25 W maximum output power. It has full duplex, synchronous, 1.2, 2.4, 4.8 and 9.6 kb/s data rate, and full duplex asynchronous, from 300 to 9.6 kb/s data rate (Schiller, 2000; Melero, wigard, Halnen & Romero, 2002). It has 9.6 kb/s and 14.4 kb/s Bandwidth. GSM interplays with PSTN, ISDN, and Public Switched Packet Data Network (PSPDN).

High Speed Circuit Switched Data (HSCSD) The High Speed Circuit Switched Data (HSCSD) is designed to enhance the data transmission capabilities in GSM system. HSCSD is circuit switched as basic GSM is, and it is based on connection-oriented traffic channel of 9.6 kbps each channel. By combining several channels, HSCSD increases the bandwidth and enhances the data transmission capabilities. A Mobile Station, in theory, could use all eight time slots within a Time Division Multiple Access (TDMA) frame to achieve an air interface user rate of 115.2 kbps (Schiller, 2000; Melero et al., 2002). HSCSD seems to be attractive at first glance, but HSCSD exhibits some major disadvantages. It applies connection-oriented mechanism of GSM, Figure 9. Channel arrangement in GSM and GPRS

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which means the connection setup is performed before information transfer take place. In this way, transferring a large amount of bursty data (e.g. computer data traffic) may require all channels reserved. This channel allocation will be reflected in the service cost directly. Furthermore, for n channel allocation, HSCSD requires n time signaling for connection setup, connection release and during handover, as in HSCSD each channel is treated separately. Thus, the probability of blocking or service degradation increases during handover and connection setup or release, as Base Station Controller (BSC) has to check the resources for n channels. HSCSD might be an attractive solution for higher bandwidth and constant traffic (e.g. file download) in spite of being a costly solution.

General Packet Radio Service (GPRS) GPRS is a data communication service for a GSM system. As it is illustrated in Figure 9, for one GPRS radio channel the GSM can allocate 1-8 timeslots within one Time Division Multiplexing frame (TDM). The time slots are allocated on demand and are not fixed. All time slots can be shared by the active users. This means that each time slot is multiplexed for up to 8 active users. Allocation of the time slots is based on current

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load and operator preferences (Schiller, 2000; Romero, Martinez, Nikkarinen & Moisio, 2002). The traffic channel is dependent on the coding scheme. Allocating all time slots using the coding for 14.4 kbps traffic channel, results in 115.2 kbps channel. GPRS is independent of channel characteristic and channel type. It does not set any limitation regarding the maximum data rate (just the GSM transport system limits the transmission rate). GPRS keeps an open connection, staying online to receive and send data at all times when it is needed. In GPRS, data is sent in packets, and up to three time slots can be combined to provide the necessary bandwidth, up to 39.6 kbps for receiving data, depending on coding scheme (Schiller, 2000; Romero et al., 2002). GPRS is an IP-based connection, which means that a high transmission capacity is only used when needed. This makes it possible to stay connected via GPRS, whereas keeping a constant circuit switched connection would be more expensive. Using GPRS, this provides data and internet/ intranet access, for a PC, PDA or any handheld device connected via Bluetooth wireless technology, infrared or cable.

Enhanced Data Rates for GSM Evolution (EDGE) EDGE is an enhanced version of GPRS, as it combines digital Time Division Multiple Access (TDMA) and GSM. It is anticipated that by using EDGE you can reach 85% of the world, using dual-mode handsets. EDGE applies enhanced modulation schemes and another technique, using the same 200 kHz wide carrier and the same frequency as GSM. In an arrangement of 48-69.2 kbps per time slot it offers up to 384 kbps data rate (Schiller, 2000; Hakaste, Nikula & Hamiti, 2002).

Universal Mobile Telecommunications System (UMTS) The European proposal for IMT-2000 prepared by ESTI is called Universal Mobile Telecommunications System (Dasilva 1997 and Ojanperä 1998). IMT-2000 is called future public land mobile telecommunication system. The major aims of UMTS are personal mobility, removing any distinctions between mobile and fixed networking and supporting the Universal Personal Telecommunication concept of ITU. UMTS offers both narrowband (2 Mbps) and broadband (> 100 Mbps in 60 GHz band) type of services (Schiller, 2000). By UMTS implementation any user will be able to approach any fixed or mobile UMTS terminal nationally or internationally. UMTS intends to provide several bearer services, real-time and non real-time services, circuit and packet switched transmission, and many different data rates (Schiller, 2000). High Speed Downlink/Uplink Packet Access (HSDPA and HSUPA) are advanced data services that are now being deployed on the UMTS networks worldwide. Together, HSDPA and HSUPA offer reduced latency and much higher data rates on the downlink and uplink. They are expected to help users in a mass market for mobile IP multimedia services. UMTS was not fully implemented at the time of the study; therefore it was not possible to investigate its communication general aspects in practice.

Digital Enhanced Cordless Telecommunication (DECT) Digital enhanced cordless telecommunication (DECT) standard was developed in 1992 within the European Telecommunication Standard Institute (ETSI). DECT technology is successor technology for CT2 and CT3 digital wireless telephone systems in Europe. Comparing DECT with Bluetooth, DECT has lack of spontaneous and ad hock network management mechanism. Furthermore, Bluetooth is more protected in

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Figure 10. Properties of digital enhanced cordless telecommunication compared to the properties of Bluetooth

respect to the data security management. Figure 10 compares Bluetooth contra DECT properties (Schiller, 2000). DECT is dependent on a fixed infrastructure while Bluetooth is not. This makes Bluetooth more feasible for the implementation of an M-health system comparing with DECT.

Infra Red Data Association (IrDA) Infra Red Data Association (IrDA) with 900 nm wavelengths provides data communication system based on infrared light. The technology is simple and cheap and is integrated in almost all mobile devices today. Electrical devices do not interfere with infrared transmission (Schiller, 2000). The main disadvantage is that infrared is quite easy shielded (transmission can not penetrate walls or obstacles). Infrared contra Bluetooth properties are compared in Figure 11. IrDA creates only data communication by applying infrared light, while Bluetooth creates its connection by Radio Frequency (RF). IrDA is limited to an optical directional wireless connection, while Bluetooth is Omni-directional. IrDA is not able to penetrate furniture and wall, while Bluetooth does that. Bluetooth and IrDA are both

short distance wireless technologies, but IrDA has much less range than Bluetooth. Bluetooth has more complete network architecture, compared to IrDA which has no capability of internet working, media access, or other enhanced communication mechanisms. IrDA is typically limited to only two participants (point-to-point connection). Data security in Bluetooth is more protected than in IrDA.

Wireless LAN Wireless Local Access Networks (LAN) offer much higher access data rates than do cellular mobile networks, but provide limited coverage – typically up to 50 meters from the radio transmitter, while GSM/GPRS and WCDMA offer widespread – typically nationwide – coverage. Ericsson provides IEEE 802.11b based 2.4GHz WLAN enterprise systems today, and will continue to update this service with new capabilities. WLAN IEEE 802.11 technology offers infrared transmission in addition to radio transmission, whereas High Performance Local Area Network (HIPERLAN) and Bluetooth rely only on radio transmission (Schiller, 2000).

Figure 11. Bluetooth compared with Infra red data association (IrDA)

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Figure 12. Bluetooth properties compared with the properties of WLAN IEEE 802.11

In this respect, WLAN compared to Bluetooth has a limitation concerning patient’s mobility, as it needs an infrastructure and it depends on a fixed access point in the patient’s house and partly line-of-sight (LOS) between sender and receiver. Bluetooth does not need a fixed infrastructure and is not restricted to LOS. Therefore Bluetooth provides more mobility when integrated in a device using a mobile cellular network. Figure 12, summarizes the difference between Bluetooth and WLAN 802.11.

Terrestrial Trunked Radio (TETRA) TETRA is one of a number of digital wireless communication technologies standardized by European Telecommunications Standards Institute (ETSI). ETSI Technical Committee (TC) Terrestrial Trunked Radio (ETSI TC TETRA) shall produce standards for a frequency spectrum for Professional Mobile Radio (PMR) and Public Access Mobile Radio (PAMR) Operators to support voice and data services using techniques such as Trunking, Time Division Multiple Access (TDMA) methods in narrow band RF channels and/or a variety of efficient modulation schemes in multiple allocations of narrow band RF channels for increased data throughput. TETRA applies another arrangement for wireless data transmission. This system uses many different carriers but assigns only one specific carrier to a certain user for a short period of time according to a demand. This radio system offer interfaces to a fixed telephone network, i.e., voice

and data, but is not publicly accessible. It is reliable and cheap, but it does not have nationwide coverage. TETRA offers bearer services up to 28.8 kbps and 9.6 kbps. It offers two standards, namely, Voice + Data (V+D) and Packet Data Optimized (Schiller, 2000).

Zigbee Specification for High Level Communication Protocols ZigBee is a low-cost, low-power, wireless standard which applies digital radio based on the IEEE 802.15.4 standard. ZigBee uses self-organizing mesh networks that can be used for industrial control, embedded sensing, medical data collection, smoke and intruder warning, building automation, home automation, etc. The low cost attribute allows the technology to be widely deployed in wireless control and monitoring applications. The low power-usage allows longer life with smaller batteries, and the deployment of mesh networking characteristic provides high reliability and larger range (ZigBee Standards Organization, 2008). ZigBee operates in the industrial, scientific and medical (ISM) radio bands; 868 MHz in Europe, 915 MHz in countries such as USA and Australia, and 2.4 GHz in most other countries. The technology is intended to be simpler and cheaper than other Wireless Personal Area Networks (WPANs) such as Bluetooth (ZigBee Standards Organization, 2008). The raw, over-the-air data rate is 250 Kbit/s per channel in the 2.4 GHz band, 40 Kbit/s per channel in the 915 MHz band, and 20 Kbit/s in the 868 MHz band. Transmission range is between 10

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Figure 13. Bluetooth properties compared with the properties of ZigBee IEEE 802.15.4

and 300 meters, however it is heavily dependent on the particular environment (ZigBee Standards Organization, 2008). The maximum output power of the radios is generally 0 dBm (1 mW). Figure 13 summarizes the differences between ZigBee and Bluetooth technologies.

Wi-Fi Wi-Fi is the global brand name across all markets for any 802.11-based wireless LAN products. Wi-Fi stands for Wireless Fidelity and is meant to be used generically when referring of any type of 802.11 network, whether 802.11b, 802.11a, dual-band, etc. The term is formally proclaimed by the Wi-Fi Alliance. Any products tested and approved as “Wi-Fi Certified” (a registered trademark) by the Wi-Fi Alliance are certified as interoperable with each other, even if they are from different manufacturers. Formerly, the term “Wi-Fi” was used only in place of the 2.4GHz 802.11b standard, in the same way that “Ethernet” is used in place of IEEE 802.3. The Wi-Fi Alliance expanded the generic use of the term in an attempt to stop confusion

about wireless LAN interoperability. Typically, any Wi-Fi product using the same radio frequency (for example, 2.4GHz for 802.11b or 11g, 5GHz for 802.11a) will work with any other, even if not “Wi-Fi Certified. As Wi-Fi is widely used WLAN, some of its important features are compared with ZigBee, and Bluetooth in Figure 14.

Server Unit A server unit receives medical data from the communication network, processes and converts it into a well defined format and then sends it to a presentation unit. The server unit could be e.g. a Pentium 4 computer, 2GHz or faster running on Windows 2000 or XP operating system, with minimum 1 GB RAM, 80 GB hard disk with a built in database. It could be connected either to a mobile modem or to the internet, and could be situated at a hospital, care/ health centre or alarm centre. The server software carries out the following functions: •

It allows the registration of client terminals to make them able to communicate to the

Figure 14. the properties of Bluetooth IEE 802.15.1, ZigBee IEEE 802.15.4 and WI-Fi IEEE 802.11b are compared

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•

•

•

remote server. The process uses the stored information in the database of the server to identify each user; telephone number, device address and IP address assigned by e.g. the GPRS network. It detects the communication request from a caller trying to communicate with the server. If the caller is not registered in the server, the caller will be warned that a conversation is not possible. When the server accepts the incoming call of a user and the communication channel is established, the server drives the data traffic between the client and the server. It receives the medical information as data packets, interprets and converts them to a synchronized data stream, before sending it to the monitoring/presentation unit.

Presentation Unit A presentation unit receives medical data from the server and transforms it into either a text or a graphical format, then presents the data to the user via a user interface (e.g. at the hospital). The presentation unit could be a computer, applying a user application program to convert the received medical data to a user-friendly graphical image or text format. The application program controls and detects true alarms and ignores false alarms. The presentation unit can monitor many patients simultaneously. Each patient has a unique

identification number in addition to his/her name. Through the application program, the caregiver can communicate with the patient at any time either via text massage or by phone (e.g. IP-telephony). The patient also can communicate with the health personals either via Short Message Service (SMS) or by phone.

Patient’s Safety (Electromagnetic Waves and Specific Absorption Rate) Electromagnetic waves have a spectrum range from 30 Hz with a wavelength of approximately the half of the earth’s diameter to more than 1022 Hz high-frequency cosmic rays with a wave length smaller than the nucleus of an atom (Goleniewski, 2002; Schiller, 2000). Figure 15 summarizes the designation of the frequency bands and the corresponding wave length in this spectrum. In mobile telephony, radio waves within 450 – 2200 MHz frequency range are used (Schiller, 2000), which are a part of microwave frequencies. In fact, all modern communications are based on manipulation and controlling signals within this electromagnetic spectrum. The power density or intensity of radio signals emitted from a mobile telephone or a base station, wanes quickly following the Inverse square law (Parsons, 2000). Radio waves’ tendency to lose power intensity as distance from the antenna increases is considered to be useful from the safety point of view. Furthermore, in order to serve more

Figure 15. the designation of the frequency bands in electromagnetic spectrum and the corresponding wave lengths

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subscribers, network providers develop networks of higher density. This higher density networks make it possible to keep the transmission power low, this property has been investigated and considered as an advantage in relation to an M-health system (Jasemian & Arendt-Nielsen, 2005a). The absorption of electromagnetic energy causes temperature rise in a tissue (Bernardi, Cavagnaro, Pisa & Piuzzi, 2000). Specific Absorption Rate (SAR) is defined as the mass averaged rate of energy absorption in tissue, and can be considered as a measure of the local heating rate. Testing the SAR of a mobile phone is an effective method of quantifying the amount of energy absorbed by biological tissue, particularly in the human body. To test the SAR value, standardized methods are utilized, with the phone transmitting at its highest certified power level (2 W) in all used frequency bands (Poljak, 2004; Ramqvist, 1997). Many different devices are tested using the same method and not only mobile handsets. Wireless LAN and Bluetooth are just a few of the available technologies, which have had SAR measurements taken. Patient safety is investigated in a previous research work (Jasemian et al., 2005a), where the applied mobile phone and Bluetooth module had 0.89 W/Kg maximum SAR value. This is less than the recommended SAR value (< 2.0 W/Kg in Europe, and < 1.6 W/ Kg in USA) (ICNIRP, 1996; ANSI/IEEE C95.11992, NJ 08854-1331], 1991- 1999; IRPA, 1988; WHO, 1993).

medical data should be impersonalized and access controlled. Data security in such M-health systems has also been investigated by the author (Jasemian et al., 2005a) using telecommunication technologies and services such as Bluetooth, GSM and GPRS. The applied technologies offer Access Control, Authentication, Data Encryption, and User Anonymity for data security and access arrangements.

MOBILE HEALTHCARE SYSTEMS AND STANDARDS Mobile healthcare and telemedicine systems like other fields must, of course, obey standard rules and regulations. Indeed, telemedicine benefits from a large number of standards such as medical informatics, digital images, messaging, telecommunications, equipment specifications and networking (Sosa-Iudicissa, Luis & Ferrer-Roca, 1998). Most used telemetry devices obey the following standards: European Telecommunications Standards Institute (ETSI 300 220), International Electro technical Commission (IEC 529), International standard for Electromagnetic compatibility (EMC) and testing of Medical Electrical Equipment (EN60601-1 and EN60601-1-2). The most used mobile phone and Bluetooth modules obey the ETSI as well as the Institute of Electrical and Electronics Engineers (IEEE) standards.

DATA SECURITY

GENERAL DISCUSSION, IMPLICATION AND CONCLUSION

Data security is an essential aspect in telemedicine, particularly in mobile healthcare systems, as the patient is moving from one location to another, and is not bound to a specific place. A mobile healthcare system applies wireless and cellular technologies. Wireless communication contrary to a cable connection is more likely to be exposed to eavesdropping. Therefore, the transmitted

The present chapter dealt with a comprehensive investigation of feasibility of wireless and cellular telecommunication technologies and services in a real-time M-health system. The chapter based its investigation, results, discussion and argumentation on a remote patient monitoring scenario (figure 4). GSM allows 8 timeslots allocation for one GPRS radio channel within one Time Division

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Multiplexing frame (TDM), which results in an effective resource utilization for up to 8 active user and consequently higher bandwidth (115.2 kbps), however allocation of the time slots is based on current load and operator preferences (Schiller, 2000; Romero et al., 2002). GPRS is an IP-based connection, keeps an open connection, staying online to receive and send data at all times when it is needed, which make the service less expensive in respect to a standard GSM circuit switched connection. Using GPRS provides data and internet/intranet access, for a PC, PDA or any handheld device connected via Bluetooth wireless technology, infrared or cable. These properties make GPRS more attractive for implementation of an M-health system. The investigation shows that EDGE is an enhanced version of GPRS, which offers up to 384 kbps data rate as it combines digital Time Division Multiple Access (TDMA) and GSM. This can be a promising enhancement of, and an alternative to, the GPRS service. Although HSCSD appeared to be an attractive and promising alternative to GPRS, it exhibited some disadvantages, such as higher service costs comparing to a traditional GSM data service. Moreover, HSCSD required more signaling during each handover, connection setup and release, thus the probability of blocking or service degradation increased during each handover, and as a consequence the QoS in general was reduced. The HSCSD is an ideal alternative to the GPRS if the patient is monitored only at home, however this is an essential limitation for realization of an M-health system. DECT acquires a higher bandwidth and communication range; however Bluetooth has more protected data security arrangements compared to DECT. Bluetooth supports spontaneous and ad hock networking while DECT does not. This makes Bluetooth more feasible for the implementation of an M-health system comparing with DECT.

TETRA offers bearer services op to 28.8 kbps and 9.6 kbps. It offers two standards, namely, Voice + Data (V+D) and Packet Data Optimized. Its radio system offer interfaces to fixed telephone network, i.e., voice and data, it is reliable and cheap, it does not have nationwide coverage and it is not publicly accessible. Therefore, it doesn’t fulfill the requirement of an M-health system. ZigBee is a low-cost, low-power, wireless standard, uses self-organizing mesh networks that can be used for embedded sensing and medical data collection. The low cost attribute allows the technology to be widely deployed in M-health and monitoring applications, the low power-usage allows longer life with smaller batteries, and the deployment of mesh networking characteristic provides high reliability and larger range. The technology is simpler and cheaper than other Wireless Personal Area Networks (WPANs) such as Bluetooth, however the raw, over-the-air data rate (250 Kbit/s per channel) is much less than Bluetooth data rate. The technology is more attractive compared with Bluetooth as long as medical application requires data transmission with low data rate. Despite the high bandwidth of Wi-Fi which is the most widely used WLAN, it has essential limitations compared with ZigBee and Bluetooth. It requires more memory space, its battery life is very much shorter and it is dependent on a fixed infrastructure. However, its bandwidth is much higher than ZigBee and Bluetooth. Therefore, Wi-Fi is more suitable for home healthcare but not for an M-health solution which are intended to be used outdoors. The Universal Mobile Telecommunications System (UMTS) as a third generation of GSM mobile network was also considered as an alternative network. UMTS was not fully implemented at the time of the study; therefore it was not possible to investigate its communication general aspects in practice.

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It has been shown that development of an M-health system, utilizing Bluetooth, GSM and GPRS is entirely possible (Jasemian et al., 2005a & 2005b & 2005c). To implements a real-time M-health system a wireless communication platform, utilizing Bluetooth protocol was designed, implemented and tested (Jasemian et al., 2005a). It has been shown that there is interplay between high throughput and high QoS (Jasemian et al., 2005a). To increase the probability of errorless packet transmission, and increase the QoS, the TCP/IP packet size was decreased, and by this the transmission traffic was reduced, as the transmission of a long packet generates more traffic. On the other hand, the error correction mechanism in GPRS and RFCOMM reduces the bandwidth, as it was dealt with a distributed data line. Thus, TCP/IP was triggered to retransmit a packet, which was stuck in the buffer waiting for retransmission; this increased the traffic unnecessarily. This issue appeared to be one of the limitations in the implementation process. The limited memory capacity in the patient unit or the Network Access Point (NAP) was another limitation; however it is believed that a patient unit with a higher memory capacity would easily overcome this problem. Furthermore, with dedication of telemedicine transmission channels by network providers, an additional system improvement can be achieved. It has also been demonstrated (Jasemian et al., 2005a) that a Bluetooth enabled M-health service combined with GSM/GPRS solution was applicable, but while promising, further work needs to be carried out before reaching ideal reliability and performance. The M-health system was tested and evaluated on healthy volunteers and heart patients while they were in their daily environments. Generally, the test results showed that the system, in a more realistic environment, had a reasonable reliability, performance and quality (Jasemian et al., 2005b; Jasemian et al., 2005c), compared to the system behavior in the laboratory (a more controlled circumstance/environment).

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Comparing these two conditions, there was no significant difference in throughput, but there were clear differences in PER, PLR and Up-time. These variations can probably be due to the fact that in the more realistic (uncontrolled) conditions, volunteers and patients attended to different indoor and outdoor activities. These included moving in vehicles at varying speeds, being in different landscapes and building environments and possibly being close to different sources of interference. All factors which can significantly influence the reliability and the performance of the system. This drawback can probably be avoided by asking the patients to avoid specific circumstances, which may influence the systems behaviors during the monitoring period. The quality of the transmitted data was generally good, and only 9.6% of the transferred ECG, in the more realistic environments, had an unacceptable quality. It is believed that the data quality could be improved by using the most suitable disposable electrodes, and by instructing the patients in skin preparation, since most of the impaired quality originated from a bad skin-electrode connection. The M-health system has been evaluated and validated by a number of well defined tests and experiments. Comparing the characteristics of the designed system with the system requirements, it can be concluded that the designed and implemented system fulfils the requirements. The suggested system is reliable, functions with a clinically acceptable performance, and transfers medical data with a reasonable quality, even though the system was tested under totally uncontrolled circumstances during the patients’ daily activities. Thus, it can be concluded that the system is applicable in clinical practice. Both the patients and the involved healthcare personnel expressed their confidence in using it. Therefore, it can also be concluded that the M-health real-time remote monitoring system might be generalized in clinical practice e.g. cardiology.

Sensing of Vital Signs and Transmission Using Wireless Networks

FUTURE RESEARCH ON AN M-HEALTH SYSTEM When implementing a mobile or wireless healthcare solution, a complicated set of choices must be faced. Combinations of devices, network, software, infrastructure, application design and security can make or break a mobile project. Thus, the system requirements should be based upon a set of real scenarios. Knowledge about the existing technologies, services and their advantages and disadvantages, in addition to knowing what is practical, ease the choice and aid its success. The scenarios may be built upon two different conditions: 1. Providing mobility by M-health system only for monitoring vital medical signs on patients at home (Home Healthcare Monitoring) 2. Providing mobility by M-health system for monitoring vital medical signs on patients everywhere at any time (Pervasive Healthcare) In the first case it would be advantageous to apply a fixed Access Point (AP), Figure 5b, TCP/ IP backbone and internet connection for long rage communication, in order to use the power line to supply the fixed AP power, as there are always battery life limitations in an M-health solution. Based on the nature of the monitored signal, the required measuring frequency and the required bandwidth one of the globally applied wireless technologies can be chosen; namely, Bluetooth or ZigBee for short range communication. The choice should be made as a trade-off between what is feasible and what is necessary. In the second case (Figure 5a), it is suggested to take advantage of The UMTS properties for long distance communication. Again based on the nature of the monitored signal, the required measuring frequency and the required bandwidth one of the globally used wireless technologies can be chosen; namely, Bluetooth or ZigBee for short range communication. In this case power

consumption and battery life are the two main issues to deal with, as both the measuring unit and the mobile AP are battery dependent. Recent advances in embedded computing systems have led to the emergence of wireless sensor networks, consisting of small, batterypowered units with limited computation and radio communication capabilities. Sensor networks permit data gathering and computation to be deeply embedded in the physical environment. This technology has the potential to allow vital signs to be automatically collected and fully integrated into the patient care record and used for real-time triage, correlation with hospital records, and long-term observation. During the past few years, wireless sensor technology has shown great potential as an enabler of the vision of ubiquitous computing. One promising application of wireless sensor networks (WSNs) is healthcare. Recent advances in sensor technology have enabled the development of small, lightweight medical sensors such as pulse oximeters and electrocardiogram leads that can be worn by the patient while wirelessly transmitting data. This frees the patient from the confinement of traditional wired sensors, increasing his or her comfort and quality of life. However, wireless sensor networks generally have limited bandwidth and high data loss rate. However, based on the nature of the monitored signal, the required measuring frequency, the required bandwidth, power consumption and sufficient memory space are still the main issues. By reducing the amount of signal processing in the patient unit or the WSN and letting most of the required signal processing be done on the remote server, longer battery life can be achieved. In any case the most fundamental challenge is the security and privacy of sensitive patient data. Encryption must be done to ensure the confidentiality of the data. At the same time, a sensor receiving a query from a base station (and likewise a base station receiving data from a sensor) needs to have some way of verifying the identity of the

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other party, and of ensuring that the data has not been altered from its source. Hence, mechanisms must exist for data authenticity and integrity. What makes security uniquely challenging for WSNs is that the computational, memory and bandwidth costs must be carefully balanced against the limited resources of the individual nodes. As it has been explored and described in the present section, the future trend is toward the patient’s involvement in her/his own treatment and health monitoring in own natural everyday’s environment. Thus, the patient needs to be equipped with mobile health care devices that provide him/her full mobility in a secure manner. To fulfill this need, the device must be user friendly, lightweight and battery driven. This requires a low power conception device using longer lifetime batteries. The sensors need to be wireless, small, and lightweight and preferably integrated in the patient’s cloths or should be worn easily. Moreover, the mobile health device needs to be equipped with high security arrangements. Thus, the trend of the future research will be development of embedded software design with minimum memory requirement for M-health services. The devices should have a build in intelligent data flow control mechanism with higher data security that is integrated in a cost-effective wireless sensor network. Moreover, there is an insisting demand for development of lightweight batteries with longer life-time. All these will enhance the M-health services significantly in the future, ensuring the patients, their relatives and the healthcare providers and elevate their compliance to M-health services.

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Agha, Z., Schapira, R. M., & Maker, A. H. (2002). Cost effectiveness of telemedicine for the delivery of outpatient pulmonary care to a rural population. Telemedicine Journal and e-Health, 8, 281–291. doi:10.1089/15305620260353171 Bai, J., Zhang, Y., Shen, D., Wen, L., Ding, C., & Cui, Z. (1999). A portable ECG and Blood Pressure Telemonitoring System. IEEE Engineering in Medicine and Biology, 18(4), 63–70. doi:10.1109/51.775490 BASUMA Project. Berek, B., & Canna, M. (1994). Telemedicine on the move: healthcare heads down the information superhighway. AHA Hospital Technology Series, 13, 1–55. Bernardi, P., Cavagnaro, M., Pisa, S., & Piuzzi, E. (2000). Specific Absorption Rate and Temperature Increases in the Head of a Cellular-Phone User. IEEE Trans. MTT, 48(7). Bray, J., & Sturman, C. F. (2002). BLUETOOTH 1.1: Connect Without Cables. In J. Bray C. F. Sturman, B. Goodwin (Eds.), (pp. 12, 33, 52-53, 112, 224-230, 320-328). New Jersey. Coyle, G., Boydell, L., & Brown, L. (1995). Home telecare for the elderly. Journal of Telemedicine and Telecare, 1, 183–185. Dansky, K. H., Palmer, L., Shea, D., & Bowles, K. H. (2001). Cost analysis of telehomecare. Telemedicine Journal and e-Health, 7, 225–232. doi:10.1089/153056201316970920 European Commission Information Society Directorate-General. (2001). Telemedicine Glossary, 3rd Edition, Glissary of standard, concepts, technologies and users. Unit B1: Applications relating to Health, working Document. Fields, J., & Casper, L. M. (2001). America’s families and living arrangement population characteristics. P20-537. U.S. Bureau of the Census, U.S. Department of Commerce, Washington, D.C.

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Freedman, S. B. (1999). Direct Transmission of Electrocardiograms to a Mobile Phone for Management of a Patient with Acute Myocardial Infarction. Journal of Telemedicine and Telecare, 5, 67–69. doi:10.1258/1357633991932315 Goleniewski, L. (2002). Telecommunications Essentials. P. Education (Ed.), (pp. 28-30 & 444449). Bosyon: Addition-Wesley Gott, M. (1995). Telematics for health: The role of Telemedicine in Homes and Comminities. Oxford, UK: Radcliffe Med. Press. Hakaste, M., Nikula, E., & Hamiti, S. (2002). GSM/EDGE Standard Evolution. In T. Halonen, J. Romero, & J. Melero, (Eds.), GSM, GPRS and EDGE performance, Evolution Towards 3G/UMTS (pp. 3-63). West Sussex: John Wiley & Sons LTD. He, W., Sengupta, M., Velkoff, V. A., & DeBarros, K. A. (2005). 65+ in the United States: 2005. Special report issued by the U.S. Department of Health and Human Services and the U.S. Department of Commerce. Washington, DC. ICNIRP. (1996). Health issues related to the use of hand-held radiotelephones and base transmitters. International Commission on Non-Ionizing Radiation Protection, Health Physics (pp. 70-4, 587–593). IEEE (1991- 1999). Standard for Safety levels with respect to human exposure to radio frequency electromagnetic fields (3 kHz to 300 GHz). ANSI/ IEEE C95.1-1992, NJ 08854-1331. New York. IRPA. (1988). Guidelines on limits of exposure to radio frequency electromagnetic fields in the frequency range from 100 kHz to 300 GHz. International Radiation Protection Association. Health Physics, 54, 115–123.

Istepanian, R. H., Woodward, B., Gorilas, E., & Balos, P. A. (1998). Design of Mobile Telemedicine Systems using GSM and IS-54 Cellular Telephone Standards. Journal of Telemedicine and Telecare, 4(Supplement 1), 80–82. doi:10.1258/1357633981931579 Jasemian, Y. (December 2006). Security and privacy in a wireless remote medical system for home healthcare purpose. 1st International Conference on Pervasive Computing Technologies for Healthcare [CD-ROM] s. 3, & Proceedings in IEEE Xplore, peer reviewed conference article, 29 November-1, Innsbruck, Austria. Jasemian, Y. (2008). Elderly Comfort and Compliance to Modern Telemedicine System at home. 2nd International Conference on Pervasive Computing Technologies for Healthcare, Proceedings, peer reviewed conference article, ISBN 978-9639799-15-8. Jasemian, Y., & Arendt-Nielsen, L. (2005a). Design and implementation of a telemedicine system using BLUETOOTH and GSM/GPRS, for real time remote patient monitoring. The International Journal of Health Care Engineering, 13, 199–219. Jasemian, Y., & Arendt-Nielsen, L. (2005b). Evaluation of a real-time, remote monitoring telemedicine system, using the Bluetooth protocol and a mobile phone network. Journal of Telemedicine and Telecare, 11(5), 256–260. doi:10.1258/1357633054471911 Jasemian, Y., & Arendt-Nielsen, L. (2005c). Validation of a real-time wireless telemedicine system, using Bluetooth protocol and a mobile phone, for remote monitoring patient in medical practice. European Journal of Medical Research, 10(6), 254–262. Kammann, J. (2001). Proposal for a Mobile Service Control Protocol. Institute for Communications and Navigation German Aerospace Centre (DLR), PDCS Anaheim (USA),

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Kyriacou, E., Pavlopoulos, S., Berler, A., Neophytou, M., Bourka, A., Georgoulas, A., et al. (2003). Multi-purpose HealthCare Telemedicine System with mobile communication link support. BioMedical Engineering OnLine. 1-12. Lin, C. C., Chiu, M. J., Hsiao, C. C., Lee, R. G., & Tsai, Y. S. (2006). Wireless health care service system for elderly with dementia. IEEE Transactions on Information Technology in Biomedicine, 10(4), 696–704. doi:10.1109/TITB.2006.874196 Lo, B. P. L., & Yang, G. Z. (2005). Key Technical Challenges and Current Implementations of Body Sensor Networks. In Proc. of 2nd Intl. Workshop on Wearable and Implantable Body Sensor Networks (BSN 2005) (p. 1). London, UK. Magrabi, F., Lovell, N. H., & Celler, B. G. (1999). A Web-based approach for electrocardiogram monitoring in the home. International Journal of Medical Informatics, 54, 145–153. doi:10.1016/ S1386-5056(98)00177-4 Melero, J., Wigard, J., Halonen, T., & Romero, J. (2002). Basics of GSM Radio Communication and Spectral Efficiency. In T. Halonen, J. Romero, & J. Melero (Eds.), GSM, GPRS and EDGE performance, Evolution Towards 3G/UMTS (pp. 145- 190). West Sussex: John Wiley & Sons LTD. MobiHealth Project (last update, 2008), Orlov, O. I., Drozdov, D. V., Doarn, C. R., & Merrell, R. C. (2001). Wireless ECG monitoring by telephone. Telemedicine Journal and e-Health, 7, 33–38. doi:10.1089/153056201300093877 Parsons, J. D. (2000). Fundamental of VHF and UHF Propagation. In J. D. Parsons (Ed.), the Mobile radio propagation channel (pp. 15-18). West Sussex: John Wiley & Sons LTD. Patel, U., & Babbs, C. (1992). A computer based, automated telephonic system to monitor patient progress in home setting. Journal of Medical Systems, 16, 101–112. doi:10.1007/BF00996591

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Perednia, D. A., & Allen, A. (1995). Telemedicine technology and clinical applications. Journal of the American Medical Association, 273, 483–488. doi:10.1001/jama.273.6.483 Philips Medical Systems. (February 2005). Striving for cable less monitoring. In C. Ragil (Ed.), Philips Medical Perspective Magazine, 8, 24-25. Poljak, D. (2004). Human Exposure to Electromagnetic Fields. In D. Poljak (Ed.), (pp. 11-40 & 49-52). Southampton: WIT press. Ramqvist, L. (1997). Health and Safety in Mobile Telephony. EN/LZT 123 4060, Ericsson Radio Systems AB, 164 80 Stockholm, 1-14. Rezazadeh, M., & Evans, N. (1990). Multichannel physiological monitor plus simultaneous fullduplex speech channel using a dial-up telephone line. IEEE Transactions on Bio-Medical Engineering, 37, 428–432. doi:10.1109/10.52352 Romero, J., Martinez, J., Nikkarinen, S., & Moisio, M. (2002). GPRS and EGPRS Performance. In T. Halonen, J. Romero, & J. Melero (Eds.), GSM, GPRS and EDGE performance, Evolution Towards 3G/UMTS (pp. 230-248). West Sussex: John Wiley & Sons LTD. Romero, J., Martinez, J., Nikkarinen, S., & Moisio, M. (2002). GPRS and EGPRS Performance. In T. Halonen, J. Romero, & J. Melero (Eds.), GSM, GPRS and EDGE performance, Evolution Towards 3G/UMTS (p. 291). West Sussex: John Wiley & Sons LTD. Satava, R., Angood, P. B., Harnett, B., Macedonia, C., & Merrell, R. (2000). The physiologic cipher at altitude: telemedicine and real-time monitoring of climbers on Mount Everest. Telemedicine Journal and e-Health, 6, 303–313. doi:10.1089/153056200750040165

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Scalvini, S., Zanelli, E., Domenighini, D., Massarelli, G., Zampini, P., & Giordano, A. (1999). Telecardiology community: A new approach to take care of cardiac patients. Cardiologia (Rome, Italy), 921–924. Schiller, J. H. (2000). Mobile Communications. In J. H. Schiller (Ed.) (pp. 24, 27, 83-92, 86, 105113, 119-125). London, UK: Addition-Wesley. Shimizu, K. (1999). Telemedicine by Mobile Communication. IEEE Engineering in Medicine and Biology, (pp. 32-44). Sosa-Iudicissa, M., Luis, J., & Ferrer-Roca, O. (1998). Annex I: Standardisation Bodies. In O. Ferrer-Roca & M. Sosa-Iudicissa (Eds.), Handbook of telemedicine (pp. 197-209). Amesterdam: IOS press, Amesterdam. Tudor-Locke, C., & Bassett, D. R. Jr. (2004). How many steps/day are enough? Preliminary pedometer indices for public health. Sports Medicine (Auckland, N.Z.), 34(1), 1–8. doi:10.2165/00007256-200434010-00001

Uldal, S. B., Manankova, S., & Kozlov, V. (1999). Choosing a PC-based ECG System for a Mobile Telemedicine unit. The Health News magazine (pp. 30-31). Woodward, B., Istepanian, R. S. H., & Richards, C. I. (2001). Design of a Telemedicine System Using a Mobile Telephone, IEEE, (pp. 13-15). World Health Organization WHO. (1993). Environmental Health Criteria 137, Electromagnetic Fields (300 Hz to 300 GHz), Geneva. World Wide Web. (July 2002). Zhao, X., Fei, D., Doarn, C. R., Harnett, B., & Merrell, R. (2004). A Telemedicine System for Wireless Home Healthcare Based on Bluetooth and the Internet. Telemedicine Journal and e-Health, 10(supplement 2), 110–116. doi:10.1089/1530562042632038 ZigBee Standards Organization (January 17, 2008). Copyright © 2007 All rights reserved, ZigBee Specification, ZigBee Document 053474r17.

This work was previously published in Mobile Health Solutions for Biomedical Applications, edited by Phillip Olla and Joseph Tan, pp. 180-207, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 3.8

Neural Networks in Medicine: Improving Difficult Automated Detection of Cancer in the Bile Ducts Rajasvaran Logeswaran Multimedia University, Malaysia

ABSTRACT Automatic detection of tumors in the bile ducts of the liver is very difficult as often, in the defacto non-invasive diagnostic images using magnetic resonance cholangiopancreatography (MRCP), tumors are not clearly visible. Specialists use their experience in anatomy to diagnose a tumor by absence of expected structures in the images. Naturally, undertaking such diagnosis is very difficult for an automated system. This chapter DOI: 10.4018/978-1-60960-561-2.ch308

proposes an algorithm that is based on a combination of the manual diagnosis principles along with nature-inspired image processing techniques and artificial neural networks (ANN) to assist in the preliminary diagnosis of tumors affecting the bile ducts in the liver. The results obtained show over 88% success rate of the system developed using an ANN with the multi-layer perceptron (MLP) architecture, in performing the difficult automated preliminary detection of the tumors, even in the robust clinical test images with other biliary diseases present.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Neural Networks in Medicine

INTRODUCTION There are a large number of algorithms and applications that have been and are actively being developed to assist in medical diagnosis. As medical problems are biological in nature, it is expected that nature-inspired systems would be appropriate solutions to such problems. Among the most popular of such nature-inspired tools is the artificial neural network (ANN), which has lent itself to applications in a variety of fields ranging from telecommunications to agricultural analysis. There is a large amount of literature on the use of ANN in medical applications. Some examples of medical systems developed employing neural networks include those for screening of heart attacks (Furlong et al., 1991) and coronary artery disease (Fujita et al., 1992), facial pain syndromes (Limonadi et al., 2006), diabetes mellitus (Venkatesan & Anitha, 2006), psychiatric diagnosis (NeuroXL, 2003), seizure diagnosis (Johnson et al., 1995), brain injuries (Raja et al., 1995), and many more. There is an increasing number of cancer cases in most countries, with an increasing variety of cancers. Over the years, ANN has been actively employed in cancer diagnosis as well. ANN systems have been developed for cancer of the breast (Degenhard et al., 2002), skin (Ercal et al., 1994), prostate (Brooks, 1994), ovaries (Tan et al., 2005), bladder (Moallemi, 1991), liver (Meyer et al., 2003), brain (Cobzas et al., 2007), colon (Ahmed, 2005), lung (Marchevsky et al., 2004), eyes (Maeda et al., 1995), cervix (Mango & Valente, 1998) and even thyroid (Ippolito et al., 2004). ANN has also been used for cancer prognosis and patient management (Naguib & Sherbet, 2001). Although there has been extensive development of ANN systems for medical application, there are still many more diagnostic systems for diseases and organs that would be able to gain from this nature-inspired technology. This chapter proposes a multi-stage nature-inspired detection

scheme that mimics the radiologist’s diagnosis strategy, where most of the algorithms employed are themselves nature-inspired. The scheme is augmented with the nature-inspired neural networks to improve the system performance in tackling automatic preliminary detection of a difficult and much less researched set of tumors affecting the bile ducts, using the defacto diagnostic imaging technology for the liver and pancreato-biliary system.

BACKGROUND Bile is used in the digestion and absorption of fat-soluble minerals and vitamins in the small intestines. In addition, it also has the function of removing soluble waste products from the body, including cholesterol. Diseases affecting the biliary tract cause distension (swelling) in the bile ducts, blockages, swelling of the liver and build up of toxic waste in the body, which can be fatal. Tumor of the bile ducts, medically known as cholangiocarcinoma, is the second most common primary malignant tumor of the liver after hepatocellular carcinoma and comprises approximately 10% to 15% of all primary hepatobiliary malignancies (Yoon & Gores, 2003). The incidence of this disease has been on the rise in recent decades (Patel, 2002). It is highly lethal as most tumors are locally advanced at presentation (Chari et al., 2008). These tumors produce symptoms by blocking the bile duct, often seen in clinical diagnosis as clay colored stools, jaundice (yellowing of the skin and eyes), itching, abdominal pain that may extend to the back, loss of appetite, unexplained weight loss, fever, chills (UCSF, 2008) and dark urine (Chari et al., 2008). The clinical diagnosis of the biliary tumor depends on appropriate clinical, imaging, and laboratory information (Yoon & Gores, 2003). Generally, after taking the medical history and performing a physical examination, the doctor would order one or more tests to get a better view

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of the affected area before making the diagnosis. The common follow-up test for biliary tumors include non-invasive medical imaging tests such as computed tomography (CT) scans, magnetic resonance imaging (MRI) scans or ultrasound; (minimally) invasive imaging tests such as endoscopic retrograde cholangiopancreatography (ERCP), endoscopic ultrasound (EUS) or percutaneous transhepatic cholangiography (PTC); or even a bile duct biopsy and fine needle aspiration (UCSF, 2008). Treatments for this disease include surgery, liver transplantation, chemotherapy, radiation therapy, photodynamic therapy and biliary drainage (Mayo, 2008). Medical imaging has become the vital source of information in the diagnosis of many diseases, work-up for surgery, anatomical understanding of the body’s internal systems and functions etc. It is also the preferred technological method in aiding diagnosis as most of the medical imaging techniques are either non-invasive or minimallyinvasive, thus causing minimal discomfort to the patient and does not involve long (or any) hospitalization for recovery. Magnetic resonance cholangiopancreatography (MRCP) is a special sequence of magnetic resonance imaging (MRI) that is used to produce images of the pancreatobiliary region of the abdomen. This area covers the biliary system within and just outside the liver, as shown in Figure 1 (NIDDK, 2008). MRCP is now the preferred imaging test for biliary diseases as it is non-invasive, non-ionizing, has no side-effects nor requires any hospitalization. However, as MRI uses electromagnets, this technique is not applicable to any patients with implants affected by magnets. So far, there has been very little work done on developing automated computer-aided diagnosis (CAD) systems for tumor detection in MRCP images due to the high complexity in accurately identifying such diseases in a relatively noisy image. Although there are articles on clinical work, case studies and on the MRCP technology, the only automated biliary tumor detection scheme using MRCP

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Figure 1. Location of the biliary tract in the abdomen

images readily available in the literature to date is the one developed by Logeswaran & Eswaran (2006). The performance of the ANN model proposed in this chapter will be compared against the published non-ANN model, in the Results section. To illustrate the complexity faced in automatic detection of tumors affecting the bile ducts, let us look at an example. Figure 2 shows a sample MRCP image containing a tumor at the indicated area. It is observed that there is no apparent identifiable structure in the area of the tumor, nor is the tumor boundary distinguishable from the background. Secondly, it should be observed that the image is noisy and affected by a lot of background tissue. Very often MRCP images are severely influenced by the presence of other organs (e.g. stomach and intestines) and tissue (e.g. liver). In addition, MRCP images are also prone to artifacts and bright spots. The orientation of the image during acquisition may also not be optimal in detecting a tumor, as there may be parts of the biliary structure itself overlapping the tumor. The intensity ranges of MRCP images are very susceptible to parameter settings, which are routinely tweaked by the radiographer in an attempt to best visualize the regions of interest.

Neural Networks in Medicine

Figure 2. MRCP image containing tumor of the bile duct (indicated by the arrow)

The signal strength or signal to noise ratio (SNR) decreases with the thickness of the image slices, but thicker slices are subject to greater partial volume effect (where larger amounts of the 3D volume is squashed into a 2D image causing loss of depth, and thus, structural information).

ANN Tumor Detection System With the complexities described in the previous section, conventional image processing techniques for structure detection and modeling would be hard-pressed to detect the presence of the tumor. As such, it was decided that it would be best to attempt to mimic as much of the processes involved in the natural methodology of diagnosis of such diseases by the specialists, and adapt them for implementation in computer systems. Through collaboration with a leading medical institution in Malaysia, which is also the liver diseases referral center of the country and a modern paperless hospital, radiologists were interviewed and observed on the diagnosis patterns used for identifying tumors in these MRCP images. Their diagnoses stemmed from their educational background and experience, the human visual system and biological neural network system in the brain, allowing

for powerful noise compensation and making associations within structures in an image. The important aspects observed that were vital in the diagnosis were basic knowledge of identifying characteristics, the ability to seek out important structures through the image noise and artifacts in the MRCP images, being able to compensate for inter-patient variations and inhomogeneity in intra-patients images (e.g. different orientations), in addition to the issues raised in the previous section. Based on the observed radiologist diagnosis methodology, a nature-inspired scheme comprising various nature-inspired components is proposed in an attempt to adapt computer systems, albeit in a rudimentary fashion, to mimic parts of their diagnosis method. The computer-aided system to be developed should be automatic as far as possible for ease of use and to allow automatic detection of the tumors. It is not meant to replace the medical experts in diagnosis but instead to assist them in screening large numbers of images and tagging those with potential tumors. Such a system can then be extended to highlight, navigate and manipulate pre-processed information, structures and parts of images, to aid the medical practitioners further in their diagnosis. Enabling an interactive interface also allows such a system to operate in a semi-automatic fashion, thus allowing

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the medical practitioner to correct or modify the parameters and results at various stages of the implementations to overcome weaknesses of the automated system, for instance when faced with the inability to handle a very difficult pathology. Essentially, the algorithm involves several parts, as shown in Figure 3. Firstly, some image preparation or pre-processing is required to highlight the region(s) of interest and reduce the influence of noise in the image. Then, segmentation is required for preliminary abstraction of the objects from the background. Structure identification would be required for refining the segmentation process and labeling the biliary structures of interest. This will then be followed by the tumor detection phase, and finally enhanced with decision-making via the ANN implementation. Although at first glance the above steps may appear to be standard computer-aided methodology, each step taken consists of nature-inspired components. Some of the steps above are taken for granted by many as our natural human visual system undertakes them automatically. For instance, noise reduction, brightness and contrast compensation and image enhancement are all handled by our visual cortex such that when a radiologist studies MRCP images with such Figure 3. Flowchart of algorithm for tumor detection

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“problems”, he/she will not face much difficulty or conscious effort in overcoming them and analyzing the important information within the images. Similarly, although the method of segmentation differs, when observing a picture we do naturally break it up into smaller regions to which we can assign some meaning, in order to understand the different parts of the picture and get a better sense of the entire picture. The segmentation algorithm employed in this work is natureinspired by rainfall on mountains, as will be described later. Structure identification is usually a conscious task as we identify the structures of interest (in this case the biliary ducts and tract) from other structures in the image (blood vessels, fat, tissue, artifacts, noise, organs etc.). The algorithm used in the structure identification consists of modified region-growing, which is inspired by fluid spreading to areas with desired characteristics (e.g. down a stream). From these structures, the next step, naturally, is to shortlist the potential tumor areas for further analysis. The final decision is made using the ANN. Although now more associated with their mathematical algorithms (Sarle, 1994), ANNs were originally nature-inspired after a crude model of the brain. It is used in this scheme to incorporate some intelligence,

Neural Networks in Medicine

tolerance to data inconsistencies, and as a tool to incorporate “learning” and storage of adaptive knowledge through training. In the interest of faster processing and reduced complexity, one major aspect of the natural diagnosis is not undertaken in the proposed scheme, which is the use of multiple MRCP slice images to conduct the diagnosis. A typical MRCP examination consists of a number of series of images, composed of locator slices, axial T2 sequence series, axial in-phase (fat saturation) series, MRCP thin slice series and MRCP thick slab images (Prince, 2000), and additional series as deemed necessary by the radiologist. Often, the total number of images acquired by the radiographer during a single examination may be in excess of a hundred images. In the event of doubt and for better understanding of the anatomy, a radiologist would scan through a number of the images, if not all of them. Implementing such ability in a CAD system is very tedious due to the large amount of variations in orientations and even sequences that may be used in a single examination, in addition to the need to approximate volumetric information lost due to the partial volume phenomenon during acquisition. Furthermore, it would be very processing intensive, taking up a lot of resources and time. Although not undertaken in this implementation, some recent work towards more rigorous 3D reconstruction of the biliary tract has been done in Robinson (2005) and with sufficient resources such as GPU (graphic processing unit) programming, may be undertaken in the near future to enhance the system proposed here. Such considerations will be described in the Discussion and Future Trends sections later in this chapter.

Image Preparation Image preparation essentially consists of two parts. Firstly, the region of interest (ROI) in the images should be determined, and then the image needs to be normalized it so that it is more consistent with the other images. ROI identification

may be undertaken in several ways. A popular manual method is by a rectangular selection box. Another method, in the case that the ROI is spread throughout most of the image, is to remove the parts that are not of interest, such as other organs. The image shown in Figure 2 is the result of a freehand selection area (not box), with obvious organs and artifacts removed from the boundary areas. For the ROI stage, it will be assumed that only appropriate MRCP images focusing on the biliary tract would be presented to the system. The images should have good orientation where the structures of interest are not unduly obstructed. Ideally, only perfectly oriented, clear, high quality images would be presented to the system, but that would be an unrealistic expectation even if most of the images were manually selected. So the system and consecutive stages in the algorithm will be developed to handle various imperfections in the images presented. The most popular method of normalization, which often reveals acceptable noise reduction, is via thresholding. However, due to the nature of MRCP, fixed values cannot be used as there is often a lot of inter-image variation, especially in terms of brightness and contrast. Instead, the thresholds need to be dynamically determined in each of the non-normalized MRCP images due to their varying intensity ranges and parameter settings. When a radiologist analyzes MRCP images with different intensity distributions (i.e. change in brightness and/or contrast), the relative information within the intensities is usually used, as opposed to the absolute intensity value. The human visual system compensates for this, and also fills in missing details to reconstruct the whole picture. A similar facility has to be implemented in the proposed system. First, histogram analysis is used to collect information relating to the image. Robinson (2005) studied typical MRCP images and found that the patterns in the histogram provided good estimates of the intensity range of the biliary structures in the image. The frequency histogram of the intensities

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in an MRCP image is shown in Figure 4(a). From an analysis of a large number of images, it was found that the first global peak (P1) in an MRCP image corresponded to the intensity of most of the air present in the abdominal area, whilst the second peak (P2) corresponded to the intensity of the soft tissues (which includes the lower intensity biliary ducts). As such, the intensity distribution, if not the exact values, could be used to normalize the brightness and contrast between images. The minimum point between the two peaks (i.e. the trough, X) is used as the threshold intensity (the value of which varies for each image) to remove most of the air. The rest were found to be different intensities of the various parts corresponding to the biliary tract (as well as some noise, artifacts, background and other structures). The very high intensities (after Z) tend to be a few very bright spots, which make the rest of the image appear dull in comparison. Adapting these thresholds, by truncating to the right of Z, the biliary structures could be enhanced. To improve the enhancement, the middle area between Y (a point estimated 2/3 ways down the slope, considered as the beginning intensity of the more significant parts of the bile ducts) and Z are stretched so that the histogram between X and Z stretches to cover the original x-axis, enhancing the intensities of the main parts of the biliary ducts in the image. In practical implementations, the x-axis (intensity) would be scaled down from the original 12 bits per pixel (bpp) representation (i.e. 212

intensity range) in the images to the 8 bpp resolution of the conventional computer monitor (i.e. 28 intensities, ranging from 0-255). The processed histogram is shown in Figure 4(b). This represents the normalized intensities correctly on the conventional monitor. If the scaling is not done, there is a tendency for the monitors and programs to represent the 12 bpp intensities as cycles of the 0-255 intensity, i.e. intensity 256 in the 12 bit format would be displayed as 0 on the conventional monitor, thus wrongly representing the image. The outcome of this stage on a sample MRCP image is shown in a later section (in Figure 5).

Segmentation Good segmentation allows an image to be broken up into meaningful homogenous sections. Although infamous for its over-segmentation problem where images tend to be broken up into too many segments, the watershed algorithm (Vincent & Soille, 1991) does produce relatively accurate segmentation of edges. This morphological (shape based algorithm) is also nature-inspired as it was derived from the idea of watersheds formed in mountain ranges during rainfall. The topography (elevation) of the mountain represents the image intensity. The flow of the water down the gradients is the inspiration behind how the intensity flows in the image is analyzed. The water catchment basins (which are very often lakes) is the central

Figure 4. Dynamic histogram thresholding of an MRCP image

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Figure 5. Effects of image preparation, segmentation and biliary structure detection

idea of the segments. Taking the intensity gradient image, the watershed algorithm produces segment boundaries at the steepest points of the gradient. This allows the image to be separated into semihomogeneous segments. Once the image has been segmented, as is expected with watershed, small spurious segments (cf. puddles) may occur in various parts of the image, most notably in the background. Also, as the intensity of the biliary structures do fluctuate throughout its length and width, which often makes it difficult to differentiate parts of the structure from the background, the spurious and very small segments need to be consolidated into the larger structures (either background or bile ducts). In order to overcome inter-pixel variations in a segment, the segments (instead of pixels) are considered as the minimal unit, and each segment’s average intensity is calculated, and all pixels within the segment are assigned this average intensity value. This is then followed by region merging, where small segments that are similar in terms of average intensity are merged to form larger, more meaningful segments.

The segments that merge most in this phase are those of the background. Ideally, the segmentation identifies all the background with a single label 0 and removes it. The intensity averaging and merging, although very useful in simplifying processing and overcoming intra-segment inconsistencies, should be taken with utmost caution as it could lead to elimination of parts of the biliary structure. In practice, using a test set of 256x256 MRCP images, it was found that the segments that generally fit the criteria for merging were those that were less than 30 pixels in size, and are merged to other segments with intensities within 10% of their own.

Biliary Structure Identification In the case of most of the tumors, the bile ducts that are of interest (in nearby locations) are those that are distended (enlarged) from the bile buildup due to the blockage caused by the tumor. As such, the first clue to the possible existence of a tumor (and for that matter, many of the diseases affecting the biliary ducts) is the presence of uncommonly large biliary structures in the MRCP image. The

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segmentation should, ideally, have identified each meaningful segment as a separate biliary structure. However, due to the nature of the MRCP images, partial volume problems and intensity inconsistencies, some parts of the background may appear to be bile ducts and vice versa. Several strategies including scale-space analysis (another nature-inspired algorithm based on the human visual system) were attempted. Scalespace examines the same image at different scales (implemented as different levels of blurring of the image) in order to discern the significance of the different parts of the image. This is likened to looking at a picture of a fruit tree and first just seeing the overall shape of the tree, before focusing on the fruits, type of leaves etc. This method is effective in hierarchical analysis of an image, but only moderate results were achieved as the blurring process (even anisotropic blurring) caused too much loss of information. A related scheme using multiresolution analysis through directional wavelets such as contourlets also failed to produce sufficiently accurate results. A more promising yet simple approach of merging the segments meaningfully, and discarding non-biliary segments, was segment growing (i.e. an adaptation of the region growing algorithm at the segment level). The region-growing strategy too is nature-inspired as it mimics the natural expansion to areas or materials of similar characteristics. Such a growing strategy requires identification of appropriate starting source or seed segments. As the proposed system is to be able to undertake automatic detection, automatic seed selection is required. To identify the characteristics for determining the appropriate seed selection, test MRCP images were segmented and manually labeled into background and bile ducts, to collect the statistics and identify the characteristics for merging. Out of the many features analyzed in Logeswaran (2005), it was found that only average intensity, location and size influenced the correct selection of seed segments. A seed segment, it was found, is usually

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a segment within the top 20% highest intensity, close to the middle of the image (i.e. the focus of the region of interest) and not too small (minimum 10 pixels in size). Using the characteristics above, all seeds meeting the criteria are selected automatically and grown. Many seeds are used due to the disjoint nature in tumor affected biliary ducts, where in a typical 2D MRCP image, the tumor obscures parts of the biliary tree structure, causing them to appear disjoint. Also, in the case of patients with other biliary diseases and prior biliary surgeries, as well as to compensate for possible low intensity areas of the biliary structures in the MRCP image, several seeds allow for all potential biliary structure areas to be identified. To grow the segments, once again appropriate characteristics had to be identified. These characteristics were discerned from the previously manually labeled images. Grown segments had to meet the criteria of having similar high average intensities (i.e. intensity greater than 200 with a intensity difference of less than 20%), small segment size (less than 100 pixels as larger segments were usually background), located nearby (maximum distance between the centers of gravity of the two corresponding segments was 10 pixels apart) and sharing a small border (maximum 30 pixels). Large segments with large borders often tend to be background or noise. For clarity, the effects of steps above until the structure detection of part of the biliary tract in an MRCP image are shown in Figure 5 (Logeswaran, 2006). Take note that the MRCP image in Figure 5(a) is that of a 50mm thick slab (i.e. 50mm volume squashed into 1 pixel thickness in a 2D image). Figure 5(c) shows the segments with each pixels assigned its average segment intensity. Through the various steps, the algorithm managed to successfully eliminate most of the background and even provide better 3D information approximation. The lower intensities usually represent parts further away; although lower intensities also represent structures deeper in the body and those influenced by some obstructions (e.g. fat).

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It should be observed that although most of the background was removed, some still remains between the biliary tree branches on the right side of Figure 5(d). Increasing the thresholds or making the segment growing more stringent will not be able to completely remove such influences, as minor branches of the biliary tree are already adversely affected by the processing (see missing minor ranches on the left side of the same image).

Tumor Detection A tumor affects the bile ducts by impeding the flow of bile in the ducts, causing the mentioned swelling or distention in the biliary structures around the tumor. The tumor itself is not seen clearly in a T2 MRCP image, as it is solid and appears to be black (fluid is represented by higher intensities in such images). In radiological diagnosis, the presence of a tumor is suspected if the distended biliary structure appears disjoint (i.e. a black object overlapping part of the biliary structure). In the typical radiological diagnosis, the suspicion is confirmed by examining the appropriate series of MRCP images, other series (different orientations and even different sequence) and possibly ordering more tests and/or images, if necessary. As the proposed system is a preliminary detection system to aid in the diagnosis of the tumor, without implementing sophisticated and rigorous processing or requiring intensive processing power, the system is required at this stage to only shortlist images with potential tumors for further analysis, whilst discarding those that do not fit the profile. The algorithm looks for two or more disjoint biliary structures in moderately close proximity. The distance information to be incorporated is very subjective as the orientation of the images in the 3D plane heavily influences the way disjoint sections are viewed in a 2D plane. For example, two disjoint sections as seen in the coronal (front) view may appear joined in the sagital (side) view, with decreasing distance between the disjoint sections in the rotational angles between the coronal and

sagital views. A sample of this problem is given in the Discussion section later. As such, only the worst case scenario for distance, i.e. maximum distance, is used. From the test images, the sizes of the tumors were found to be less than 50 pixels wide, and consequently the maximum distance is set likewise. Each structure has to be sufficiently large to be considered as distended. As such, structures less than 50 pixels in size were not considered as a significant biliary structure. In the case of Figure 5(d), the small patch above the biliary structure could be eliminated this way. Ideally, the duct thickness (or a simple approximation of area divided by length) would be used as a measure of the distension, but it was found that the distension of the biliary structures in the case of biliary tumors were not significant enough (see Figure 2) to be incorporated as a criteria, as opposed to other biliary diseases such as cyst. So, the area (i.e. total number of pixels in a biliary segment) is used as the criterion. The actual structure analysis implementation in the developed system labels each biliary segment (i.e. after the segment merging and growing above) with different numbers. The size (in pixels) of each biliary segment is then calculated. If two or more sufficiently large biliary segments exist in the MRCP image, the image is considered as possibly containing a tumor.

ANN Implementation Issues Unfortunately, assumption of the size of disjoint segments can vary depending on the orientation of the acquired image. Furthermore, it is also greatly influenced by the size of the focus area in the image. To add to the complication, not all disjoint structures necessarily indicate cancer. An ANN is employed to improve the automatic learning and decision-making performance of the system. Inspired by the crude model of the brain, the ANN is powerful in detecting patterns in data and predicting meaningful output. For the purposes of this work, a training set of images are ascer-

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tained, both with tumors and without. However, medical diagnosis is rarely that straightforward, as patients often suffer from more than one disease, and even preliminary detection should be able to exclude other common diseases present in the type of images being analyzed. To make the implementation realistic and applicable in the clinical environment, MRCP images with other biliary diseases present (such as stones, cyst and non-tumor swelling in the bile duct) are also included in the training and test sets. An ANN essentially approximates functions, and the characteristics pivotal to its performance rely very much on its architecture or network topology. Lately, there have also been many architectures of ANN introduced for specific problems. In addition to the very popular multi-layer perceptron (MLP) network, linear networks, recurrent networks, probabilistic networks, self-organizing maps (SOM), clustering networks, Elman, Kohonen and Hopfield networks, regression networks, radial basis functions, temporal networks and fuzzy systems, are just a few of the many existing ANN architectures that have been developed and incorporated into numerous applications to handle different classes of problems. Choosing the best network topology for the ANN allows it to produce the best results, but this is rarely obvious and certainly not easy. Literature such as (Sima & Orponen, 2003) and (Ripley, 1995) may be referred for a more detailed discussion on choosing an appropriate ANN architecture for specific problems, while there are numerous books and even Wikipedia (2008) providing information on the architectural configuration, learning algorithms and properties of the ANN. In the case of multi-layered architectures, the number of hidden layers corresponds to the complexity of problems that the network is able to process, while the number of neurons generally denotes the number of patterns that may be handled. Many of these networks are able to ascertain patterns in the input automatically, through supervised or unsupervised learning algorithms,

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making them a powerful tool in data analysis and prediction. Decision-making information is usually stored as weights (coefficients) and biases that influence the neurons and the connections between them. There are many different learning algorithms as well as activation / transfer / squashing functions that may be used in the neurons. The learning or training algorithms determine how the appropriate values of the weights and biases are assigned to each neuron and connection, based on the data it is given during training (or at run-time, in the case of unsupervised or adaptive networks). The speed and quality of training is determined by a learning rate, whilst a learning goal (i.e. and error rate) is set to indicate that the network should be trained until the error achieved is below the set training goal. An important aspect with regards to the training of an ANN is that over-training an ANN makes it rigid and hinders its ability to handle unseen data. The error tolerance property of the ANN is very much dependent on its generalization ability, which is the strength of the ANN. As such, caution must be exercised during training such that over-zealous training to achieve high precision output must not render the ANN rigid. At the same time, the choice of training data is significant, and a good distribution of data covering typical and atypical scenarios improves the ANN’s ability in decision-making for a larger group of unseen data. In many cases, a maximum number of iterations (epochs) is also set to forcibly terminate training, thus preventing it from going on indefinitely, even when the desired training goal is not achieved. Bender (2007) states that when testing a large number of models, especially when using flexible modeling techniques like ANN, one that fits the data may be found by pure chance (Topliss & Edwards, 1979). A discussion on this issue, with regards to ANN can be found in Livingstone & Salt (2005). It is recommended that feature selection be used and refined until the “best” model is found. Good classification or regression result may be obtained by shortlisting possible features,

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generating many models, conducting validation, and repeating the process until the “best” model based on the user’s preference is found. The rational is that very often, the actual feature may not be known at the onset, and will only be realized through such a refining process. The initial features in this work were selected manually through the observations described in the previous section. The model significance, often measured through metrics such as the “F measure” has been shown to vary with feature selection (Livingstone & Salt, 2005) and thus, such correlation coefficients and RMSE by themselves may not reflect the performance of the model. The necessary factors to be taken into account to reflect the model’s performance include the number of descriptors (parameters), the size of the dataset, the training/test/validation set split, the diversity of structures (in this work reflect the cancerous bile ducts), and the quality of the experimental data (Bender, 2007). Model validation requires splitting the data into 3 sets: training (to derive the parameters), validation (assess model quality) and testing (assess model performance). Such splitting is undertaken in this work. Alternatives to this method, especially for small datasets, include using cross-validation (split data to multiple equal parts – use one for testing and the rest for training) or leave-multiple-out splits. K-fold validation or receiver operating characteristic (ROC) curve could be used as well, to evaluate the statistical significance of a classifier. Ideally a clean dataset, measured by a single, well-defined experimental procedure, should be used in any experiment. This is not possible in this work as when dealing with typical medical data, it involves a variety of radiographers, patients, diseases and their many inconsistencies. However, the training, validation and test sets will each encompass a representative sample of the structure diversity of the data.

ANN Setup and Training The issues discussed in the previous section are those that should be taken into account when developing an optimal ANN system. However, very often, some compromises are made depending on the objectives of the work, available resources and other compounding factors. The objective of this chapter is not to produce the best bile duct tumor detection system, as that requires much more indepth research and implementation, but rather to show that the nature-inspired ANNs could be a possible solution in the ultimate development of a CAD system for that purpose using MRCP images. As such, this section concentrates on employing a popular but relatively simple ANN to the task and showing that even such an implementation with low resource requirements could provide good results, as will be discussed further in the following sections. Although facing many valid critiques in recent times, such as those in Roy (2000), Thornton (1994) and Smagt & Hirzinger (1998), the feedforward MLP ANN is still arguably the most popular ANN. It has, over the decades, provided good performance in a large number of applications with a broad base of data types. Easy availability, convenient and intuitive setup, automatic setting of weights and biases, and ability to be instructed on expected outcomes through supervised training, are among the reasons the MLP is set up as the final component of the proposed system to make the binary selection between tumor and non-tumor cases. The reader, however, is advised that alternative architectures and ANN setups may be experimented with for improved results. Some possible alternatives that may be implemented in the near future are discussed later in the chapter. The ANN used in this implementation is set up as shown in Figure 6. Figure 6(a) shows the basic architecture of a single perceptron model taking in j number of input streams or parameters. Each input is multiplied by its weight coefficient (w) acting on that stream, before being sent to the

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perceptron node (i.e. the black circular node). The node itself will be subjected to a constant bias (b), producing the result f(x) as given by the formula in the figure. The output is dependent on the activation function that is used in the node. The output of a traditional hardlimiter (step activation function) perceptron is binary, so O(x) would be a 0 or 1 depending on a set threshold of the result f(x). Note that the output is usually denoted by f(x) itself as that is the activation function, but we are splitting it here and denoting a separate term O(x) to describe the calculation and the output individually. An MLP is made up of multiple layers of one or more perceptrons in each layer, as shown in Figure 6(b) for a fully connected MLP architecture. The nodes of the input layer in Figure 6(b) are shown in a different color (grey instead of black) as these are usually not perceptrons but merely

an interface to accept the input values in their separate streams. The input layer of the MLP used for this work consists of a neuron for each of the parameters identified in the previous section, namely, the average segment intensities and sizes of the two largest biliary segments, thus four neurons. Additional sets of input neurons could be used if more than two biliary segments are to be analyzed, although this was found to be unnecessary in the test cases. Depending on the stringent control of images being presented to the system, further input neurons to incorporate parameters including distance between the midpoint of the segments, thickness of each segment (can be approximated by dividing the area of the segment by its length), as well as shape characteristics, could be added. This was not undertaken in the proposed system as the available images were retrieved from the radiological archives spanning

Figure 6. Fully-connected feedforward MLP ANN with one hidden layer

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a number of years. The acquisitions were by various radiographers under the supervision of various radiologists, and thus, were not taken under a stringent enough control environment for a more precise study. A priority in the setup is low computing power requirements and fast processing time. Since the features used as input to the system were few and of low complexity, a single hidden layer sufficed. 10 neurons were used to delineate the network in this layer, the number found experimentally to be suitable in producing the best results. Lesser number of hidden neurons caused the overall detection rate to deteriorate, whilst increasing the number of neurons (only up to 20, as the complexity was to be kept low) did not cause any appreciation of the results. As for the output, only a single neuron was required as the output would be binary, i.e. 0 indicating negative tumor detection or 1 indicating possible tumor detection. All neurons used were standard. For generalization and function approximation, the activation functions used in the neurons were sigmoidal and linear, in the hidden and output layers, respectively. No activation function is used in the input layer, but the input is normalized to a range of 0.0-1.0, where the maximum size would be the size of the image and the maximum intensity 255. As the ANN used is a supervised network, a set of training data and confirmed diagnosis results are required. 20% (120 images) of the acquired 593 MRCP images (see next section) were used as the training set. The images were selected pseudorandomly to include a distribution comprising proportionately of images consider as normal (healthy), diagnosed as bile duct tumor, and those with other diseases. The confirmed diagnoses for these images were used to determine the binary expected output which was fed to the MLP during training, labeled as 1 (containing tumor) or 0 (no tumor), for the corresponding image. The MLP uses the backpropagation learning algorithm to adjust the coefficients (weights) on each neuron interconnection (lines in the figure), such that the

error between the actual results produced by the network and the expected result is minimized. There are several variants but the basic gradient descent backpropagation was used in this work. The system is trained until the training converges to the training goal (mean squared error of 0.05) or maximum limit of training cycles (30,000 epochs) to prevent the training from going on indefinitely if it does not converge. In handling robust data, as the case was in this work, some cases of nonconvergence is to be expected. The maximum training limit was set to 30,000 as there was no observable improvement in the convergence beyond that. Based on past experience, a learning rate of 0.15 was used in this work as it was found that minor changes in the learning rate did not affect the outcome for these images, whilst large learning rates produced poor results.

RESULTS Before the system is tested in a robust clinical environment, it should be first tested for proof of concept. For this test, the algorithm is evaluated on its ability to differentiate images containing tumor from images of healthy patients. It must be remembered that the tumor may be present in different parts of the bile ducts, and that in biological systems, even the images of healthy patients can differ considerably. The choice of parameters, intensity thresholds, values of the segment sizes for merging and growing, and biliary segment size for tumor detection were varied and tested. The performance of the proposed system is very dependent on the choices made, which is in turn dependent on the quality of MRCP images obtained. The configuration and results presented in this chapter are for the setup that performed the best for the available 2D MRCP images. All the images used in this work, inclusive of the training and test data, were obtained from the archives of the collaborating medical institution. As the images were acquired there, and the

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patients were also treated at the same place, the actual medical records and diagnosis information (in some cases further confirmed through ERCP or biopsy) were available. The presence and absence of tumors and other biliary diseases was further confirmed by the expert medical consultant, who is the senior radiologist heading the digital imaging department where the clinical and radiological diagnoses were made. The proposed ANN system is trained with 20% of the clinical 2D MRCP images acquired from the collaborating hospital, containing a distribution of diseases. The results obtained for 55 images in this first test using the trained ANN above are given in Table 1. As is observed from the table, the accuracy level is very good when validating the detection against normal healthy images. In cases where the classification was inaccurate, the description of the reason for the error is given in the table. In one of the cases where the tumor was not identified correctly, it was due to lack of distention which made the structures appear to be that of a healthy patient, whilst in the other case, the orientation made the biliary structures appear joint, indicating other biliary diseases. The normal image that was detected wrongly was not detected as a tumor, but merely as distended (dilated), as the biliary structure appeared unusually large, possibly due to some zooming during image acquisition. Such cases are unavoidable through a simple detection system using a single image. Table 1. Results of the tumor detection validation testing Medically Diagnosed Detected Accuracy % Error Description of Error

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Normal

Tumor

27

28

26

26

96.3%

92.8%

1

2

Large biliary structures

1 normal, 1 non-tumor dilation

Next, for realistic medical diagnosis, all the available images (including those of healthy patients as well as those of patients with various biliary diseases) were used in the second test. Many patients suffer from more than one biliary affliction, and as such, many of the test images were influenced by more than one disease. This kind of testing allows for robust evaluation of the proposed system in the clinical environment for which it is targeted. This set of 593 images contained 248 healthy (normal), 61 tumor and the remainder images with other biliary diseases such as stones, cyst, residual postsurgery dilation, Klatskin’s disease, Caroli’s disease, strictures, and obstruction from other liver diseases. In some of the images with more than one disease, some of the visual characteristics were very similar between the different afflictions (e.g. dilation / swelling / distention is common in most biliary diseases). The results obtained are given in Table 2. As this is a robust testing, the evaluation criterion known as “Overall accuracy” is calculated as the percentage of the sum of the true positives and true negatives over the total test set. This overall accuracy takes into account how well the system performed in detecting the correct disease, as well as in rejecting the wrong diagnosis. For comparison, results from the earlier published work on automated tumor detection using MRCP images is given in the last row. As is observed from the table, the ANN implementation successfully improved the detection results for both the cases with tumor as well as for the healthy normal images. In terms of specificity (i.e. the non-tumor images correctly identified as not containing tumor), the ANN implementation performed well too, achieving 94.70% (an improvement from 89.85% without ANN). The algorithm prototype was developed in IDL as it allowed for fast development with readily available tools and libraries, without incorporating any significant optimization. In terms of timing performance, the entire process from pre-

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Table 2. Results of robust testing using multidisease clinical MRCP images Normal Test images

Tumor

Others

Total

248

61

284

593

Overall accuracy (ANN)

83.64%

88.03%

-

-

Overall accuracy (Non-ANN)*

76.90%

86.17%

-

-

* Based on results from (Logeswaran & Eswaran, 2006)

processing until ANN output for each image on a standard Pentium IV desktop personal computer took less than a minute. IDL is a matrix-based software, and programs running in its run-time environment would not be as fast as those compiled into machine language. Thus, the timing performance could be dramatically enhanced by changing the platform and using optimized code in a language that allowed compilation to machine code, e.g. C or C++. The timing performance enhancement falls out of the scope of this chapter and is left for the reader to experiment with. The classifier’s performance could also be analyzed using machine learning research tools such as MLC++, WEKA etc.

DISCUSSION The results obtained show good overall accuracy for the ANN system, proving that the natureinspired strategy employed has merit. Although errors were present, the accuracy achieved is amicable taking into account that even visual diagnosis using single 2D MRCP images is very difficult and impossible in certain cases. This is compounded by the fact the test data contained a large number of images with various diseases, in which accurate detection algorithms had not been developed. Very often, even experienced radiologists would look through a series of images before confidently making a diagnosis.

In most of the failed cases, the influence of other diseases and inconsistency in image acquisition by various radiography technologists affected the detection. Examples of some of the failed cases are shown in Figure 7. The image in Figure 7(a) was acquired in the very same MRCP examination of the patient in Figure 2, taken at a different orientation that makes it impossible for the developed system and even a human observer to conclusively diagnose the tumor. In the case of Figure 7(b), the signal strength for the acquisition was poor (a norm with MRCP thin slices) and the liver tissue intensity masked the biliary structures. Again, such images would defeat the human as well. Although the proposed algorithm does incorporate several features at various stages to minimize the influence of a certain amount of weaknesses in the acquired images, problems due to cases as shown in Figure 7 are too severe. Overcoming such problems requires strict adherence of image acquisition standards and uniformity, which is beyond the scope of the work conducted. Some information relating to the MRCP protocol, examination and relevant information can be found at (Prince, 2000). What is important in the presented results is that even when faced with a difficult test set of mostly unseen data, a simplified implementation of a non-optimal ANN was able to perform well through its nature-inspired learning and processing capabilities, as the improvement in results obtained against the system in the literature relied entirely on the ANN’s learning abilities from the parameters fed into it. The non-ANN implementation used experimental hard-coded decisionmaking. Further benchmarking may be possible once such automated cholangiocarcinoma detection using MRCP implementations using other algorithms (possibly those employing advanced classifiers such as Support Vector Machine, Adaboost etc.) are available. Of course, to be fair for the purposes of benchmarking, an optimally selected and configured ANN topology with appropriate

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Figure 7. Example MRCP images that caused tumor detection inaccuracies

customizations should be the used in the tests. Performance comparisons would also need to be undertaken under a controlled environment, implemented on the same platform, and using the same set of images. Of interest to this discussion is also the appropriate and correct use of the ANN in medical applications. There have been issues raised on the misuse of ANN in oncology / cancer in the past, as highlighted by Schwarzer et al. (2000). In the article, the authors studied literature published between 1991-1995 where ANN were used in oncology classifications (43 articles) and identified several common shortcomings in those articles. These include: •

•

•

•

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mistakes in estimation of misclassification probabilities (in terms of biased estimation due to neglecting the error rate, and inefficient estimation due to very small test sets). fitting of implausible functions (often leading to “overfitting” the ANN with too many hidden nodes) incorrectly describing the complexity of a network (by considering the complexity to only cover only one part of the network, leading to underestimating the danger of overfitting), no information on the complexity of the network (when the architecture of the net-

•

• •

work used in terms of number of hidden layers and hidden nodes are not identified), use of inadequate statistical competitors (feedforward ANN are highly flexible classification tools and as such, it should only be compared against statistical tools of similar flexibility), insufficient comparison with statistical methods, and naïve application of ANN to survival data (building and using ANN with shortcomings to the data and situations being modeled, such as not guaranteeing monotonicity of estimated survival curves, omission of censored data, or not providing data with the proper relationships).

The above problems are still very common and should be taken into account for better understanding and use of ANNs. Care must always be taken, especially in developing medical applications, such that they are rigorously tested for accuracy and robustness before being adapted to the clinical environment. Pertinently, it must always be remembered that no matter how good a tool is, it is just a tool to facilitate the medical practitioner in obtaining the necessary information so that he/ she can made the diagnosis. A tool, such as the proposed system, is never to be used to replace the medical practitioner’s responsibility in making the final diagnosis. The objective of such a tool as

Neural Networks in Medicine

the one proposed here is to help flag suspicious images so that the medical practitioner may proceed with further analysis as necessary.

FUTURE TRENDS From its humble beginnings of a single layered, hardlimiter / step activation function and binary output perceptron, the ANN has evolved impressively over the years. In the Introduction section of this chapter, a very small number of examples of the vast work that has been done in employing ANN in medical applications were mentioned. From this, it is obvious that ANN is already a very established nature-inspired informatics tool in medical applications, in addition to its contributions in many other fields. More interesting is the development of hybrid networks that allow the strengths of various architectures and algorithms to be combined into a single network, giving it the ability to handle a greater variety of data and highly complex pattern recognition, apt for the increasingly complex modern systems. Such optimized ANNs boast of superior performance to traditional architectures. In addition to the ANN, there has been a marked increase in development of other nature-inspired algorithms and tools as well. Some of the popular ones include simulated annealing (mimics the crystallization process), genetic algorithms (mimics population growth), particle swarm optimization (mimics social behavior of birds flocking), ant colony algorithm and many more. These stateof-the-art algorithms often provide solutions to difficult problems. In this author’s opinion, many such algorithms can be adapted to collaboratively work with ANNs, to develop even more powerful tools. As such, there is definitely a trend for nature-inspired informatics in general, and neural networks specifically, to be used in larger proportions in the foreseeable future. A majority of implementations of ANN systems treat the ANN as a “black box” system where the

ANNs, through the training routine, determine its internal coefficient values. The setup proposed in this chapter is an example of that. Lately, there have been efforts for possible rule extraction from the trained network. Once the rules are known, they may be further optimized and/or be applied for other similar problems. Once perfected, such rule extraction becomes a powerful companion to the ANN systems. There has been a lot of work done in the handling of 3D data. Although most of the frontiers in this technology appear in the computer gaming as well as the film and animation worlds, the medical field has also begun employing more of such technology. Conventional MRI equipment visualizes 3D volume as a static series of 2D images, such as those used in this work. Although this is still the norm at most medical institutions worldwide, technological advancement has seen the introduction of many real-time and 3D systems such as 3D ultrasound, 3D CT, fMRI and many others. Furthermore, computer processing power, technology and memory are increasing dramatically. New paradigms in programming, such as those for the graphics processing units (GPU), are becoming popular. All these enable more 3D volume reconstruction and manipulation. The future in medical diagnosis is in 3D and as such, the next step is to upgrade systems such as the proposed one to become a complete 3D graphical user interface (GUI) ANN CAD system.

CONCLUSION This chapter started off by providing a brief introduction to the increasing efforts in incorporating automated and computerized technology that employs nature-inspired artificial neural networks (ANN) to undertake difficult diagnosis of various diseases in different organs. It then proceeded to the disease of interest, i.e. cancer, which is fast becoming a serious threat and cause of death in many nations. It is expected that there will a

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significant increase in cancer deaths in the years to come. The central focus of the chapter was to introduce a nature-inspired algorithm mimicking the natural diagnosis strategies of medical specialists, aided by the nature-inspired methods and technology, for the preliminary detection of tumors affecting the bile ducts. The algorithm developed consisted of image enhancement, watershed segmentation, region growing, diagnosis-based evaluation, and ANN pattern recognition for detection. Almost all steps employed were inspired in one way of another by natural systems. The results obtained prove that such a nature-inspired approach can provide improved detection of difficult to observe tumors in 2D MRCP images in a clinically robust multi-disease test set, even when using a non-optimized simplistic setup. From the trends observed, it is expected that many more successful systems would be developed in the future, gaining from a broader outlook at various natural systems. With the formidable track record, ANNs are sure to lead the way to many more accomplishments. It is hoped that this chapter contributes ideas and examples that propagate such advancement to nature-inspired systems, especially in developing and improving much needed medical applications that would benefit from computer-aided technology.

ACKNOWLEDGMENT This work is supported by the Ministry of Science, Technology and Innovation, Malaysia, the Academy of Sciences Malaysia, and the Brain Gain Malaysia program. The author would like to express his appreciation to Dr. Zaharah Musa and Selayang Hospital, Malaysia for the medical consultation and clinical data (MRCP images and medical diagnosis) used in this research work.

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This work was previously published in Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science, and Engineering, edited by Raymond Chiong, pp. 144-165, copyright 2010 by Information Science Reference (an imprint of IGI Global).

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Chapter 3.9

Image Registration for Biomedical Information Integration Xiu Ying Wang BMIT Research Group, The University of Sydney, Australia Dagan Feng BMIT Research Group, The University of Sydney, Australia & Hong Kong Polytechnic University, Hong Kong

ABSTRACT The rapid advance and innovation in medical imaging techniques offer significant improvement in healthcare services, as well as provide new challenges in medical knowledge discovery from multi-imaging modalities and management. In this chapter, biomedical image registration and fusion, which is an effective mechanism to assist

medical knowledge discovery by integrating and simultaneously representing relevant information from diverse imaging resources, is introduced. This chapter covers fundamental knowledge and major methodologies of biomedical image registration, and major applications of image registration in biomedicine. Further, discussions on research perspectives are presented to inspire novel registration ideas for general clinical practice to improve the quality and efficiency of healthcare.

DOI: 10.4018/978-1-60960-561-2.ch309

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Image Registration for Biomedical Information Integration

INTRODUCTION With the rapid advance in digital imaging techniques and reduction of cost in data acquisition, widely available biomedical datasets acquired from diverse medical imaging modalities and collected over different imaging sessions are becoming essential information resources for highquality healthcare services. Anatomical imaging modalities such as Magnetic Resonance (MR) imaging, Computed Tomography (CT) and X-ray mainly provide detailed morphological structures. Functional imaging modalities such as Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) primarily reveal information about the underlying biochemical and physiological changes. More recently, the combination of functional and anatomical imaging technologies into a single device, PET/CT and SPECT/CT scanners, has widened the array of biomedical imaging approaches and offered new challenges in efficient and intelligent use of imaging data. Since each of these imaging technologies can have their own inherent value for patient management, and ideally all such imaging data would be accessible for the one individual when they are required, huge volumes of biomedical imaging datasets are generated daily in the clinical practice (Wang et al, 2007). However, these ever-increasing huge amounts of datasets unavoidably cause information repositories to overload and pose substantial challenges in effective and efficient medical knowledge management, imaging data retrieval, and patient management. Biomedical image registration is an effective mechanism for maximizing the complementary and relevant information embedded in various image datasets. By establishing spatial correspondence among the multiple datasets, biomedical image registration enables seamless integration and full utilization of heterogeneous image information, thereby providing a more complete insight into medical data (Wang, Feng, 2005) to

facilitate knowledge discovery and management of patients with a variety of diseases. Biomedical image registration has important applications in medical database management, for instance, patient record management, medical image retrieval and compression. Image registration is essential in constructing statistical atlases and templates to capture and encode morphological or functional patterns across a large specific population (Wang and Feng, 2005). The automatic registration between patient datasets and these available templates can be used in the automatic segmentation and interpolation of structures and tissues, and the detection of pathologies. Registration and fusion of information from multiple, diverse imaging resources is critical for accurate clinical decision making, treatment planning and assessment, detecting and monitoring dynamic changes in structures and functions, and is important to minimally invasive treatment (Wang, Feng, 2005). Due to its research significance and crucial role in clinical applications, biomedical image registration has been extensively studied during last three decades (Brown, 1992; Maintz et al., 1998; Fitzpatrick et al. 2000). The existing registration methodologies can be catalogued into different categories according to criteria such as image dimensionality, registration feature space, image modality, and subjects involved (Brown, 1992). Different Region-of-Interests (ROIs) and various application requirements and scenarios are key reasons for continuously introducing new registration algorithms. In addition to a large number of software-based registration algorithms, more advanced imaging devices such as combined PET/CT and SPECT/CT scanners provide hardware-based solutions for the registration and fusion by performing the functional and anatomical imaging in the one imaging session with the one device. However, it remains challenging to generate clinically applicable registration with improved performance and accelerated computation

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for biomedical datasets with larger imaging ranges, higher resolutions, and more dimensionalities.

Concepts and Fundamentals of Biomedical Image Registration Definition Image registration is to compare or combine multiple imaging datasets captured from different devices, imaging sessions, or viewpoints for the purpose of change detection or information integration. The major task of registration is to search for an appropriate transformation to spatially relate and simultaneously represent the images in a common coordinate system for further analysis and visualization. Image registration can be mathematically expressed as (Brown, 1992): I R (X R ) = g(I S (T (X S )))

(1)

where IR and IS are the reference (fixed) image and study (moving) image respectively; T:(XS)→(XR) is the transformation which sets up spatial correspondence between the images so that the study image XS can be mapped to and represented in the coordinate system of reference image XR; g:(IS)→(IR) is one-dimensional intensity calibration transformation. Figure 1. Framework of Image Registration

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Framework As illustrated in Figure 1, in registration framework, the study dataset is firstly compared to the reference dataset according to a pre-defined similarity measure. If the convergence has not been achieved yet, the optimization algorithm estimates a new set of transformation parameters to calculate a better spatial match between the images. The study image is interpolated and transformed with the updated transformation parameters, and then compared with the reference image again. This procedure is iterated until the optimum transformation parameters are found, which are then used to register and fuse the study image to the reference image.

MAJOR COMPONENTS OF REGISTRATION Input Datasets The characteristics of input datasets, including modality, quality and dimensionality, determine the choice of similarity measure, transformation, interpolation and optimization strategy, and eventually affect the performance, accuracy, and application of the registration.

Image Registration for Biomedical Information Integration

The input images for registration may originate from identical or different imaging modality, and accordingly, registration can be classified as monomodal registration (such as CT-CT registration, PET-PET registration, MRI-MRI registration) or multimodal registration (such as CT-MRI registration, CT-PET registration, and MRI-PET registration). Monomodal registration is required to detect changes over time due to disease progression or treatment. Multimodal registration is used to correlate and integrate the complementary information to provide a more complete insight into the available data. Comparatively, multimodal registration proves to be more challenging than monomodal, due to the heterogeneity of data sources, differing qualities (including spatial resolution and gray-level resolution) of the images, and insufficient correspondence. Registration algorithms for the input datasets with different dimensionalities are used for other applications and requirements. For instance, two-dimensional image registration is applied to construct image mosaics to provide a whole view of a sequence of partially overlapped images, and is used to generate atlases or templates for a specific group of subjects. Although threedimensional image registration is required for most clinical applications, it is challenging to produce an automatic technique with high computational efficiency for routine clinical usages. Multidimensional registration is demanded to align a series of three-dimensional images acquired from different sessions for applications such as tumor growth monitoring, cancer staging and treatment assessment (Fitzpatrick et al, 2000).

Registration Transformations The major task of registration is to find a transformation to align and correlate the input datasets with differences and deformations, introduced during imaging procedures. These discrepancies occur in the form of variations in the quality, content, and information within the images, therefore posing a

significant obstacle and challenge in the area of image registration. In addition, motions, either voluntary or involuntary, require special attention and effort during the registration procedure (Wang et al., 2007; Fitzpatrick et al., 2000). Rigid-body Transformations include rigid and affine transformations, and are mainly used to correct simple differences or to provide initial estimates for more complex registration procedures. Rigid transformation is used to cope with differences due to positional changes (such as translations and rotations) in the imaging procedure and is often adopted for the registration of brain images, due to the rigid nature of the skull structure. In addition, affine transformation can be used to deal with scaling deformations. However, these transformations are usually limited outside of the brain. Deformable Transformations are used to align the images with more complex deformations and changes. For instance, motions and changes of organs such as lung, heart, liver, bowel, need to be corrected by more comprehensive non-linear transformations. The significant variance between subjects and changes due to disease progression and treatment intervention require nonlinear transformations with more degrees of freedom. In deformable registration, a deformation field which is composed of deformation vectors, needs to be computed. One displacement vector is decided for each individual image element (pixel for 2D or voxel for 3D). Compared to rigid-body transformations, the complexity of deformable transformations will slow down the registration speed, and efficient deformable registration remains a challenging research area.

Interpolation Algorithms Interpolation is an essential component in registration, and is required whenever the image needs to be transformed or there are resolution differences between the datasets to be registered. After the transformation, if the points are mapped

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to non-grid positions, interpolation is performed to approximate the values for these transformed points. For the multimodal image registration, the sample space of the lower-resolution image is often interpolated (up-sampled) to the sample space of the higher-resolution image. In the interpolation procedure, the more neighbouring points are used for the calculation, the better accuracy can be achieved, and the slower the computation. To balance interpolation accuracy and computational complexity, bilinear interpolation technique which calculates the interpolated value based on four points, and trilinear interpolation, are often used in registration (Lehmann et al., 1999).

Optimization Algorithms Registration can be defined as an iterative optimization procedure (Equation 2) for searching the optimal transformation to minimize a cost function for two given datasets: Toptimal = arg min f (T (X S ), X R ) (T )

(2)

where T is the registration transformation; f is the cost function to be minimized. Gradient-based optimization methods are often used in registration, in which the gradient vector at each point is calculated to determine the search direction so that the value of the cost function can be decreased locally. For instance, Quasi-Newton methods such as Broyden-FletcherGoldfarb-Shanno (BFGS), has been investigated and applied in medical image registration (Unser, Aldroubi, 1993; Mattes et al., 2003). Powell optimization (Powell, 1964) is another frequently adopted searching strategy in registration. Powell method performs a succession of one-dimensional searches to find the best solution for each transformation parameter, and then the single-variable optimizations are used to determine the new search direction. This

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procedure is iterative until no better solution or further improvement over the current solution can be found. Because of no derivatives required for choosing the searching directions, computational cost is reduced in this algorithm. Downhill Simplex optimization (Press et al., 1992) does not require derivatives either. However, compared with Powell algorithm, Downhill Simplex is not so efficient due to more evaluations involved. For a given n-dimensional problem domain, the Simplex method searches the optimum solution downhill through a complex n-dimensional topology through operations of reflection, expansion, contraction, and multiple contractions. Because it is more robust in finding the optimal solution, Simplex optimization has been widely used in medical registration. Multi-resolution optimization schemes have been utilized to avoid being trapped into a local optimum and to reduce computational times of the registration (Thévenaz, Unser, 2000; Borgefors, 1988; Bajcsy, Kovacic, 1989; Unser, Aldroubi, 1993). In multi-resolution registration, the datasets to be registered are firstly composed into multiple resolution levels, and then the registration procedure is carried out from low resolution scales to high resolution scales. The initial registration on the global information in the low resolution provides a good estimation for registration in higher resolution scales and contributes to improved registration performance and more efficient computation (Wang et al, 2007). Spline based multi-resolution registration has been systematically investigated by Thévenaz (Thévenaz, Unser, 2000), and Unser (Unser, Aldroubi, 1993). In these Spline-based registration, the images are filtered by B-spline or cubic spline first and then down-sampled to construct multi-resolution pyramids. Multi-resolution Bspline method provides a faster and more accurate registration result for multimodal images when using mutual information as a similarity measure.

Image Registration for Biomedical Information Integration

BIOMEDICAL IMAGE REGISTRATION METHODOLOGIES AND TECHNIQUES Registration methods seek to optimize values of a cost function or similarity measure which define how well two image sets are registered. The similarity measures can be based on the distances between certain homogeneous features and differences of gray values in the two image sets to be registered (Wang et al, 2007). Accordingly, biomedical image registration can be classified as feature-based or intensity-based methods (Brown, 1992).

Feature-Based Registration In feature-based registration, the transformation required to spatially match the features such as landmark points (Maintz, et al, 1996), lines (Subsol, et al, 1998) or surfaces (Borgefors, 1988), can be determined efficiently. However, in this category of registration, a preprocessing step is usually necessary to extract the features manually or semi-automatically, which makes the registration, operator- intensive and dependent (Wang et al, 2007).

Landmark-Based Registration Landmark-based registration includes identifying homologous points which should represent the same features in different images as the first step, and then the transformation can be estimated based on these corresponding landmarks to register the images. The landmark points can be artificial markers attached to the subject which can be detected easily or anatomical feature points. Extrinsic landmarks (fiducial markers), such as skin markers, can be noninvasive. However, skin markers cannot provide reliable landmarks for registration due to elasticity of human skin. The invasive landmarks such as stereotactic frames are able to provide robust basis for registration,

and can be used in Image Guided Surgery (IGS) where registration efficiency and accuracy are the most important factors. Since they are easily and automatically detectable in multiple images to be registered, extrinsic landmarks can be used in both monomodal and multimodal image registration. Intrinsic landmarks can be anatomically or geometrically (such as corner points, intersection points or local extrema) salient points in the images. Since landmarks are required to be unique and evenly distributed over the image, and to carry substantial information, automatic landmark selection is challenging task. Intensive user interaction is often required to manually identify the feature points for registration (Wang et al, 2007). Iterative closest point (ICP) algorithm (Besl, MaKey 1992) is one of most successful landmark-based registration methods. Because no prior knowledge on correspondence between the features is required, ICP eases the registration procedure greatly.

Line-Based Registration and Surface-Based Registration Line-based registration utilizes line features such as edges and boundaries extracted from images to determine the transformation. “Snakes” or active contours (Kass et al, 1988) provide effective contour extraction techniques and have been widely applied in image segmentation and shape modelling, boundary detection and extraction, motion tracking and analysis, and deformable registration (Wang and Feng 2005). Active contours are energy-minimizing splines, which can detect the closest contour of an object. The shape deformation of an active contour is driven by both internal forces, image forces and external forces. To handle the difficulties of concavity and sensitivity during initialization, classic snakes, balloon model (Cohen and Cohen 1993) and gradient vector flow (GVF) (Xu and Prince 1998) were proposed.

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Surface-based registration uses the distinct surfaces as a registration basis to search the transformation. The “Head-and-Hat” algorithm (Chen, et al, 1987) is a well-known surface fitting technique for registration. In this method, two equivalent surfaces are identified in the images to be registered. The surface extracted from the higher-resolution images, is represented as a stack of discs, and is referred to as “head”, and the surface extracted from the lower-resolution image volume, is referred to as “hat”, which is represented as a list of unconnected 3D points. The registration is determined by iteratively transforming the hat surface with respect to the head surface, until the closest fit of the hat onto the head is found. Because the segmentation task is comparatively easy, and the computational cost is relatively low, this method remains popular. More details of surface-based registration algorithms can be found in the review by Audette et al, 2000.

Intensity-Based Registration Intensity-based registration can directly utilize the image intensity information without segmentation or intensive user interaction required, and thereby can achieve fully automatic registration. In intensity-based registration, a similarity measure is defined on the basis of raw image content and is used as a criterion for optimal registration. Several well-established intensity-based similarity measures have been used in the biomedical image registration domain. Similarity measures based on intensity differences including sum of squared differences (SSD) (Equ. 3) and sum of absolute differences (SAD) (Equ.4) (Brown, 1992), are the simplest similarity criteria which exhibit minimum value for perfect registration. As these methods are too sensitive to the intensity changes and significant intensity differences may lead to false registration, SAD and SSD are limited in application and as such, are mainly used to register monomodal images (Wang et al, 2007).

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2

N

SSD = ∑ (I R (i) − T (I S (i)))

(3)

i

SAD =

1 N

N

∑I i

R

(i ) − T (I S (i ))

(4)

where IR(i) is the intensity value at position i of reference image R and IS(i) is the corresponding intensity value in study image S; T is geometric transformation. Correlation techniques were proposed for multimodal image registration (Van den Elsen, et al, 1995) on the basis of assumption of linear dependence between the image intensities. However, as this assumption is easily violated by the complexity of images from multiple imaging devices, correlation measures are not always able to find optimal solution for multimodal image registration. Mutual information (MI) was simultaneously and independently introduced by two research groups of Collignon et al (1995) and Viola and Wells (1995) to measure the statistical dependence of two images. Because of no assumption on the feature of this dependence and no limitation on the image content, MI is widely accepted as multimodal registration criterion (Pluim et al., 2003). For two intensity sets R={r} and S={s}, mutual information is defined as: I (R, S ) = ∑ pRS (r , s ) log r ,s

pRS (r , s ) pR (r ) ⋅ pS (s )



(5)

where pRS(r,s) is the joint distribution of the intensity pair (r,s); pR(r) and pS(s) are the marginal distributions of r and s.Mutual information can be calculated by entropy: I (R, S ) = H (R) + H (S ) − H (R, S ) = H (R) − H (R | S ) = H (S ) − H (S | R)

(6)

Image Registration for Biomedical Information Integration

H(S|R) is the conditional entropy which is the amount of uncertainty left in S when R is known, and is defined as:

Figure 2. Registration for Thoracic CT volumes from different subjects

H (S | R) = ∑ ∑ pRS (r , s ) log pS |R (s | r ) r ∈R s ∈S

(7)

If R and S are completely independent, pRS (r , s ) = pR (r ) ⋅ pS (s ) and I(R,S)=0 reaches its minimum; if R and S are identical, I(R,S)=H(R)=H(S) arrives at its maximum. Registration can be achieved by searching the transformation parameters which maximize the mutual information. In implementation, the joint entropy and marginal entropies can be estimated by normalizing the joint and marginal histograms of the overlapped sections of the images. Although maximization of MI is a powerful registration measure, it cannot always generate accurate result. For instance, the changing overlap between the images may lead to false registration with maximum MI (Studholme, et al., 1999).

Case Study: Inter-Subject Registration of Thorax CT Image Volumes Based on Image Intensity The increase in diagnostic information is critical for early detection and treatment of disease and provides better patient management. In the context of a patient with non-small cell lung cancer (NSCLC), registered data may mean the difference between surgery aimed at cure and a palliative approach by the ability to better stage the patient. Further, registration of studies from healthy lung and the lung with tumor or lesion is critical to better tumor detection. In the example (Figure 2), the registration between healthy lung and the lung with tumor is performed based on image intensity, and normalized MI is used as similarity measure. Figure 2 shows that affine registration is not able to align the common structures from different subjects

correctly and therefore deformable registration by using spline (Mattes, et al 2003) is carried out to further improve the registration accuracy.

Hardware Registration Although continued progress in image registration algorithms (software-based registration) has been achieved, the software-based registration might be labor intensive, computationally expensive, and with limited accuracy, and thus is impractical to be applied routinely (Townsend, et al, 2003). Hardware registration, in which the functional imaging device, such as PET is combined with an anatomical imaging device such as CT in the one

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instrument, largely overcomes the current limitations of software-based techniques. The functional and anatomical imaging are performed in the one imaging session on the same imaging table, which minimizes the differences in patient positioning and locations of internal organs between the scans. The mechanical design and calibration procedures ensure that the CT and PET data are inherently accurately registered if the patient does not move. However, patient motion can be encountered between the CT and PET. This not only results in incorrect anatomical localization, but also artifacts from the attenuation correction based on the misaligned CT data (Wang et al, 2007). Misalignment between the PET and CT data can also be due to involuntary motion, such as respiratory or cardiac motion. Therefore, although the combined PET/CT scanners are becoming more and more popular, there is a clear requirement for software-registration to remove the motions and displacements from the images captured by the combined imaging scanners.

APPLICATIONS OF BIOMEDICAL IMAGE REGISTRATION Biomedical image registration is able to integrate relevant and heterogeneous information contained in multiple and multimodal image sets, and is important for clinical database management. For instance, registration is essential to mining the large medical imaging databases for constructing statistical atlas of specific disease to reveal the functional and morphological characteristics and changes of the disease, and to facilitate a more suitable patient care. The dynamic atlas in turn is used as a pattern template for automated segmentation and classification of the disease. Image registration is also critical for medical image retrieval of a specific type of disease in a large clinical image database, and in such a scenario, the functional or anatomical atlas provides prior knowledge and is used as a template for early-

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stage disease detection and identification (Toga, Thompson, 2001). Registration has a broad range of clinical applications to improve the quality and safety of healthcare. Early detection of tumors or disease offers the valuable opportunity for early intervention to delay or halt the progression of the disease, and eventually to reduce its morbidity and mortality. Biomedical image registration plays an important role in detection of a variety of diseases at an early stage, by combining and fully utilizing complementary information from multimodal images. For instance, dementias are the major causes of disability in the elderly population, while Alzheimer’s disease (AD) is the most common cause of dementia (Nestor, et al, 2004).. Registration of longitudinal anatomical MR studies (Scahill, et al, 2003) allows the identification of probable AD (Nestor, et al, 2004) at an early stage to assist an early, effective treatment. Breast cancer is one of major cause of cancer-related death. Registration of pre- and post-contrast of a MR sequence can effectively distinguish different types of malignant and normal tissues (Rueckert, et al, 1998) to offer a better opportunity to cure the patient with the disease (Wang et al, 2007). Biomedical image registration plays an indispensable role in the management of different diseases. For instance, heart disease is the main cause of death in developed counties (American Heart Association 2006) and cardiac image registration provides a non-invasive method to assist in the diagnosis of heart diseases. For instance, registration of MR and X-ray images is a crucial step in the image guided cardiovascular intervention, as well as in therapy and treatment planning (Rhode, et al, 2005). Multimodal image registration such as CT-MR, CT-PET allows a more accurate definition of the tumor volume during the treatment planning phase (Scarfone et al 2004). These datasets can also be used later to assess responses to therapy and in the evaluation of a suspected tumor recurrence (Wang et al, 2007).

Image Registration for Biomedical Information Integration

FUTURE TRENDS Image registration is an enabling technique for fully utilizing heterogeneous image information. However, the medical arena remains a challenging area due to differences in image acquisition, anatomical and functional changes caused by disease progression and treatment, variances and differences across subjects, and complex deformations and motions of internal organs. It is particularly challenging to seamlessly integrate diverse and complementary image information in an efficient, acceptable and applicable manner for clinical routine. Future research in biomedical image registration would need to continuously focus on improving accuracy, efficiency, and usability of registration. Deformable techniques are in high demand for registering images of internal organs such as liver, lung, and cardiac. However, due to complexity of the registration transformation, this category of registration will continuously hold research attention. Insufficient registration efficiency is a major barrier to clinical applications, and is especially prevalent in the case of whole-body images from advanced imaging devices such as the combined PET/CT scanners. For instance, whole-body volume data may consist of more than 400 slices for each modality from the combined PET/CT machine. It is a computationally expensive task for registering these large data volumes. With rapid advance in medical imaging techniques, greater innovation will be achieved, for instance, it is expected that a combined MRI/PET will be made available in near future, which will help to improve the quality of healthcare significantly, but also pose a new set of challenges for efficiently registering the datasets with higher resolution, higher dimensionality, and wider range of scanning areas. Multi-scale registration has the potential to find a more accurate solution with greater efficiency. Graphics Processing Units (GPUs) may provide a high performance hardware platform

for real-time and accurate registration and fusion for clinical use. With its superior memory bandwidth, massive parallelism, improvement in the programmability, and stream architecture, GPUs are becoming the most powerful computation hardware and are attracting more and more research attention. The improvement in floating point format provides sufficient computational accuracy for applications in medical areas. However, effective use of GPUs in image registration is not a simple issue (Strzodka, et al 2004). Knowledge about its underlying hardware, design, limitations, evolution, as well as its special programming model is required to map the proposed medical image registration to the GPU pipeline and fully utilize its attractive features. Medical image registration, particularly for high-dimensional data, which fully utilizes the outstanding features of graphics hardware to facilitate fast and cost-saving real-time clinical applications, is new and yet to be fully explored.

CONCLUSION Registration of medical images from multiple imaging devices and at multiple imaging times is able to integrate and to facilitate a full utilization of the useful image information, and is essential to clinical diagnosis, treatment planning, monitoring and assessment. Image registration is also important in making the medical images more ready and more useful to improve the quality of healthcare service, and is applicable in a wide array of areas including medical database management, medical image retrieval, telemedicine and e-health. Biomedical image registration has been extensively investigated, and a large number of software-based algorithms have been proposed alongside the developed hardware-based solutions (for instance, the combined PET/CT scanners). Among the comprehensive softwarebased registration, the feature-based techniques are more computationally efficient, but require

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a preprocessing step to extract the features to be used in registration, which make this category of registration user-intensive and user-dependent. The intensity-based scheme provides an automatic solution to registration. However, this type of registration is computationally costly. Particularly, image registration is a data-driven and caseorientated research area. It is challenging to select the most suitable and usable technique for specific requirement and datasets from various imaging scanners. For instance, although maximization of MI has been recognized as one of the most powerful registration methods, it cannot always generate accurate solution. A more general registration is more desirable. The combined imaging devices such as PET/CT provide an expensive hardwarebased solution. However, even this expensive registration method is not able to always provide the accurate registration, and software-based solution is required to fix the mis-registration caused by patient motions between the imaging sessions. The rapid advance in imaging techniques raises more challenges in registration area to generate more accurate and efficient algorithms in a clinically acceptable time frame.

ACKNOWLEDGMENT This work is supported by the ARC and UGC grants.

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Lehmann, T. M., Gönner, C., & Spitzer, K. (1999). Survey: Interpolation methods in medical image processing. IEEE Transactions on Medical Imaging, 18(11), 1049–1075. doi:10.1109/42.816070 Maintz, J. B. A., van den Elsen, P. A., & Viergever, M. A. (1996). Evaluation of ridge seeking operators for multimodality medical image registration. IEEE Trans. PAMI, 18(4), 353–365. Maintz, J. B. A., & Viergever, M. A. (1998). A Survey of Medical Image Registration. Medical Image Analysis, 2(1), 1–36. doi:10.1016/S13618415(01)80026-8 Mattes, D., Haynor, D. R., Vesselle, H., Lewellen, T. K., & Eubank, W. (2003). PET-CT image registration in the chest using free-form deformations. IEEE Transactions on Medical Imaging, 23(1), 120–128. doi:10.1109/TMI.2003.809072 Nestor, P. J., Scheltens, P., & Hodges, J. R. (2004, July). Advances in the early detection of Alzheimer’s disease. Nature Reviews. Neuroscience, 5(Supplement), S34–S41. doi:10.1038/nrn1433 Pluim, J. P., Maintz, J. B. A., & Viergever, M. A. (2003, August). Mutual-information-based registration of medical images: a survey. IEEE Transactions on Medical Imaging, 22(8), 986–1004. doi:10.1109/TMI.2003.815867 Powell, M. J. D. (1964). An efficient method for finding the minimum of a function of several variables without calculating derivatives. The Computer Journal, 7, 155–163. doi:10.1093/ comjnl/7.2.155 Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1992). Numerical Recipes in C. Cambridge Univ. Press, Cambridge, U.K. Rhode, K. S., Sermesant, M., Brogan, D., Hegde, S., Hipwell, J., Lambiase, P., & Rosenthsal, E., bucknall, C., Qureshi, S. A., Gill, J.S., Razavi, R., & Hill, D. L.G. (2005, November). A system for real-time XMR guided cardiovascular intervention. IEEE Transactions on Medical Imaging, 24(11), 1428–1440. doi:10.1109/TMI.2005.856731

Rueckert, D., Hayes, C., Studholme, C., Summers, P., Leach, M., & Hawkes, D. J. (1998). Non-rigidregistration of breast MR images using mutual information. MICCAI’98 lecture notes in computer science, Cambridge, (pp.1144-1152). Scahill, R. I., Frost, C., Jenkins, R., Whitwell, J. L., Rossor, M. N., & Fox, N. C. (2003, July). A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. Archives of Neurology, 60(7), 989–994. doi:10.1001/archneur.60.7.989 Scarfone, C., Lavely, W. C., Cmelak, A. J., Delbeke, D., Martin, W. H., Billheimer, D., & Hallahan, D. E. (2004, Apr). Prospective feasibility trial of radiotherapy target definition for head and neck cancer using 3-dimensional PET and CT imaging. Journal of Nuclear Medicine, 45(4), 543–552. Strzodka, R., Droske, M., & Rumpf, M. (2004). Image registration by a regularized gradient flow a streaming implementation in DX9 graphics hardware. Computing, 73(4), 373–389. doi:10.1007/ s00607-004-0087-x Studholme, C., Hill, D. L. G., & Hawkes, D. J. (1999). An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition, 32, 71–86. doi:10.1016/S0031-3203(98)00091-0 Subsol, G., Thirion, J. P., & Ayache, N. (1998). A scheme for automatically building three dimensional morphometric anatomical atlases: application to a skull atlas. Medical Image Analysis, 2(1), 37–60. doi:10.1016/S1361-8415(01)80027-X Thévenaz, P., & Unser, M. (2000). Optimization of mutual information for multiresolution registration. IEEE Transactions on Image Processing, 9(12), 2083–2099. doi:10.1109/83.887976 Toga, A. W., & Thompson, P. M. (2001, september). The role of image registration in brain mapping. Image and Vision Computing Journal, 19, 3–24. doi:10.1016/S0262-8856(00)00055-X

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Townsend, D. W., Beyer, T., & Blodgett, T. M. (2003). PET/CT Scanners: A Hardware Approach to Image Fusion. Seminars in Nuclear Medicine, XXXIII(3), 193–204. doi:10.1053/ snuc.2003.127314 Unser, M., & Aldroubi, A. (1993, November). A multiresolution image registration procedure using spline pyramids. Proc. of SPIE 2034, 160170, Wavelet Applications in Signal and Image Processing, ed. Laine, A. F. Van den Elsen, P. A., Maintz, J. B. A., Pol, E.-J. D., & Viergever, M. A. (1995, June). Automatic registration of CT and MR brain images using correlation of geometrical features. IEEE Transactions on Medical Imaging, 14(2), 384–396. doi:10.1109/42.387719 Viola, P. A., & Wells, W. M. (1995, June) Alignment by maximization of mutual information. In Proc. 5th International Conference of Computer Vision, Cambridge, MA, 16-23. Wang, X., Eberl, S., Fulham, M., Som, S., & Feng, D. (2007). Data Registration and Fusion, Chapter 8 in D. Feng (Ed.) Biomedical Information Technology, (pp.187-210), Elsevier Publishing

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KEY TERMS AND DEFINITIONS Image Registration: The process to search for an appropriate transformation to spatially align the images in a common coordinate system. Intra-Subject Registration: Registration for the images from same subject/person. Inter-Subject Registration: Registration for the images from different subjects/persons. Monomodal Images: Refers to images acquired from same imaging techniques. Multimodal Images: Refers to images acquired from different imaging.

Wang, X., & Feng, D. (2005). Active Contour Based Efficient Registration for Biomedical Brain Images. Journal of Cerebral Blood Flow and Metabolism, 25(Suppl), S623. doi:10.1038/ sj.jcbfm.9591524.0623 This work was previously published in Data Mining and Medical Knowledge Management: Cases and Applications, edited by Petr Berka, Jan Rauch and Djamel Abdelkader Zighed, pp. 122-136, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 3.10

Cognition Meets Assistive Technology: Insights from Load Theory of Selective Attention Neha Khetrapal University of Bielefeld, Germany

ABSTRACT This chapter deals with the issue of treating disorders with the help of virtual reality (VR) technology. To this end, it highlights the concept of transdiagnostic processes (like cognitive biases and perceptual processes) that need to be targeted for intervention and are at the risk of becoming atypical across disorders. There have been previous theoretical attempts to explain such common processes, but such theoretical exercises have DOI: 10.4018/978-1-60960-561-2.ch310

not been conducted with a rehabilitative focus. Therefore, this chapter urges greater cooperation between researchers and therapists and stresses the intimate links between cognitive and emotional functioning that should be targeted for intervention. This chapter concludes by providing future directions for helping VR to become a popular tool and highlights issues in three different areas: (a) clinical, (b) social and (c) technological. Coordinated research efforts orchestrated in these directions will be beneficial for an understanding of cognitive architecture and rehabilitation alike.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Cognition Meets Assistive Technology

INTRODUCTION

BACKGROUND

This chapter will begin with background on the concept of cognitive rehabilitation and will serve to illustrate a recently successful popular example of it. It will then describe cognitive models that explain cognitive and emotional functioning and how these could give rise to disorders. A major focus of the chapter is to highlight the manner in which various disorders could be treated in a similar manner and how technology could aid this process—bringing in the concept of transdiagnostic approach—the basic tenet of which is to emphasize that the processes that serve to maintain disorders cut across these disorders and hence could be dealt with a single appropriately built technology. Though there has not been much research in this direction because therapists prefer to specialize in particular treatment approaches and disorders, this kind of work has picked up momentum (due to the recent scientific focus on an interdisciplinary framework). This chapter will make an initial attempt in this direction by describing how cognitive theories could be applied in understanding the transdiagnostic processes like attentional biases and perceptual processing. This chapter will also attempt to describe the merger of cognitive architecture, specially the transdiagnostic processes and recent rehabilitative tools. Since there remains much work to be done in this direction, this chapter will highlight the areas that require much needed research attention, and at the same time, will provide future directions for embarking upon this process. This chapter will provide an important resource for understanding the transdiagnostic process in terms of assistive technology to psychologists, cognitive scientists, teachers, parents, students of psychology, neuroscientists and rehabilitation professionals.

Cognitive Rehabilitation

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The aim of rehabilitation is to maintain an optimal level of functioning in domains like physical, social and psychological (McLellan, 1991). Therefore, a rehabilitation program is designed for a particular individual and is conducted over a period of time based on the nature of impairment. The basic goal is not to enhance performance on a set of standardized cognitive tasks, but instead to improve functioning in the day-to-day context (Wilson, 1997). Models of cognitive rehabilitation stress the need to address cognitive and emotional difficulties in an integrated manner and not as isolated domains (Prigatano, 1999). Therefore, cognitive training could be of immense help in this endeavor. Cognitive tasks could thus be designed to deal with cognitive functions like memory, attention, language, and so on, and the level of difficulty could also be varied to suit individual specification (Clare & Woods, 2004).

Techniques for Cognitive Rehabilitation An exciting new development in the field of cognitive rehabilitation is the use of virtual reality (VR). Virtual environments (VE) could be built by keeping in mind the needs of the individual. Examples include presenting a specific number of stimuli to an autistic child that can be gradually increased as the treatment progresses (Max & Burke, 1997) or virtual wheelchair training for people afflicted by physical disabilities (Stephenson, 1995). Schultheis and Rizzo (2001) define VR for behavioral sciences as, “an advanced form of human-computer interface that allows the user to interact with and become immersed in a computergenerated environment in a naturalistic fashion.” Virtual reality could also be viewed as an excellent example of assistive technology (AT) because it can be used to build upon the existing strengths

Cognition Meets Assistive Technology

of an individual who in turn helps in offsetting the disability or, in other words, provides an alternate way of completing a task which also helps in compensating for the disability (Lewis, 1998). Virtual reality technology has yielded promising results in terms of cognitive functioning (Rose, Attree, & Johnson, 1996), social benefits (Hirose, Taniguchi, Nakagaki, & Nihei, 1994), and has proved to be less expensive than the real-world simulators. The previous discussions show that VR as AT could be fruitfully employed to treat disabilities. But it is also important to take into account the functioning of human cognitive systems while designing the VR/VE or any other AT and rehabilitation program. So far in the scientific literature there have been discussions about both cognitive psychology and AT, but each one has remained isolated from the other. Due to the significant contributions from both fields, it becomes essential that both are discussed in relation to each other so that each of these fields could be utilized to maximize the benefits that the other can confer. To begin with, one needs to adopt a good working definition of deficient cognitive components that require rehabilitative attention and which also cut across various disabilities. An important concept in this regard is the concept of “transdiagnostic approach.” According to this approach, behavioral and cognitive processes that serve to maintain disorders are transdiagnostic in nature (Mansell, Harvey, Watkins, & Shafran, 2008). The transdiagnostic approach has many advantages to itself and these include a better understanding about comorbidity—generalization of knowledge derived from cognitive model(s) to explain a particular disorder. Therefore, when the processes are seen as cutting across the disorders, it becomes easier to generalize one explanation to other processes that are similar in nature. The next advantage is the development of treatment approaches. If the processes are assumed to be common, then it becomes easier and even cost-effective to treat various disorders. Stud-

ies in cognitive psychology indeed support the transdiagnostic approach. For instance, attention to external or internal concern related stimuli have been found to be common across various psychological disorders like social phobia, panic disorder, depression, eating disorder, psychotic disorder, posttraumatic stress disorder and so on. Other processes that are also transdiagnostic are memory, thought, reasoning and the like (Mansell et al., 2008). But how exactly are transdiagnostic processes implicated in disorders? How could such processes serve as targets for rehabilitation? The following discussion on cognitive models will make this clearer.

COGNITIVE MODELS FOR EMOTIONAL PROCESSING Any program of cognitive rehabilitation is built upon a comprehensive understanding of the cognitive and behavioral processes/architecture. Cognitive models that explain cognitive functioning and behavior are good candidates on which rehabilitation endeavors could be built. Current scientific theorizing proposes a more intimate link between cognition and emotion than has been proposed before. Therefore, it may be useful as rehabilitation programs are planned to keep both cognitive and emotional processing in mind because of the intimate interaction. Describing all such theories is outside the scope of this paper; however, the following discussion will touch upon some of these theories.

A Cognitive Model for Selective Processing in Anxiety Mathews and Machintosh (1998) proposed a cognitive model to explain selective processing in anxiety. Anxiety is usually the experience of unpleasant feelings of tension and worry in reaction to unacceptable wishes or impulses. A popular finding in anxiety research is attentional

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bias towards anxiety relevant concerns only under competitive conditions (where the competition is between neutral and anxiety relevant stimuli). The Mathews and Macintosh (1998) model provides a parsimonious explanation for this finding. The model essentially explains that stimuli attributes are processed in a parallel manner and compete for attention due to the involvement of two different routes. A threat evaluation system (TES) is in place that provides help in easing the competition between the two routes by interacting with the level of anxiety and consequently strengthens activations of anxiety relevant attributes. Within their model, a slower route is used when the threat value is appraised by the consciously controlled higher level, such as in a novel anxious situation. Repeated encounters with similar situations will store the relevant cues in the TES and, therefore, a later encounter will produce anxiety through the shorter route bypassing the slower one. As a result, attention will be captured automatically in a competing situation (where the competition is between neutral and anxiety relevant stimuli). Cues encountered in a novel situation that resemble attributes already stored in TES will also tend to elicit anxiety automatically and receive attentional priority. In the model, some danger related attributes are innate while the others are learned. At the same time, other neurobiological evidence suggests that the threat value of a stimulus is evaluated and determined in two distinct ways (LeDoux, 1995). One is a shorter and quicker route that directly runs from the thalamus to the amygdale and the other is a slower route, which is mediated by higher cortical level resources. The proposal of two routes within the model proposed by Mathews and Machintosh (1998), implies that a threatening cue that matches the current concern of anxiety disordered people will sufficiently attract attention under competing situations due to the involvement of the shorter route and will counteract the functioning of the higher level; but following treatment, these patients would no longer show the attentional effects mediated by

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the shorter route. This is because in the later case, the higher level counteracts the processing of the faster route. Their model also implies that when people encounter threatening cues (no neutral cue), the most threatening one will capture attention and the least threatening one will be inhibited. The model is also applicable in an evolutionary framework because it is certainly more adaptive to process the most potent source of danger and its consequence as a result of mutual inhibition within the TES. The model proposed by Mathews and Machintosh (1998) deals exclusively with selective processing in anxiety and even though it has implications for treatment, it does not give a detailed account of the how treatment will counteract the effects of anxiety. Their model entails a link between cognitive and emotional processing, but a different model proposed by Power and Dalgleish (1997) serves to document a closer relationship between cognitive and emotional processing. The model proposed by Power and Dalgleish (1997) deals with explaining the processing of five basic emotions, such as sadness, happiness, anger, fear, disgust, as well as complex emotions. Since this model is more comprehensive in nature it will be detailed here rather than other models designed to explain processing in specific disorders.

The SPAARS Approach SPAARS (Schematic, Propositional, Analogical and Associative Representational Systems) is the integrated cognitive model of emotion proposed by Power and Dalgleish (1997). It is a multilevel model. The initial processing of stimuli occurs through specific sensory systems that are collectively termed the analogical processing system. This system can play a crucial role in emotional disorders, for instance, where certain sights, smells, noise, etc. become inherent parts of a traumatic event. The output from this system then feeds into three semantic representation systems. These systems operate in parallel. At

Cognition Meets Assistive Technology

the lowest level is the associative system which takes the form of a number of modularized connectionist networks. The intermediate level has the propositional system that has language like representation though it is not language specific. There is no direct route from intermediate level to emotions, but they feed either through appraisals at the schematic level or directly through the associative system. The highest level is called the Schematic Model level. It has the merit of storing information in a flexible manner along with the traditional schema approach. At this level, the generation of emotion occurs through the appraisal process. Appraisal refers to the evaluation of meaning of affective stimuli and is considered causal in the generation of an emotional response. Different types of appraisals exist for eliciting the five basic emotions of sadness, happiness, anger, fear and disgust. An appraisal for sadness would focus on the loss (actual or possible) of some valued goal to an individual and pathological instances, for sadness appraisal could be termed as depression. An individual will feel happy when he or she successfully moves towards the completion of some valued goal. When an appraisal of physical or social threat to self or some valued goal is done by the person, then he or she will experience fear and, when such an appraisal is done for a harmless object, it could result in instances of phobia or anxiety. The appraisal of blocking or frustration of a role or goal through an agent leads to feelings of anger. A person will feel disgust when he or she appraises elimination from a person, object or idea, repulsive to the self or some valued goal (Power & Dalgleish, 1997). These appraisals provide the starting point for complex emotions or a sequence of emotions. In this scheme, complex emotions as well as the disorders of emotions are derived from the basic emotions. A second important feature of emotional disorders is that these may be derived from a coupling of two or more basic emotions or appraisal cycles that further embroider on the

existing appraisals through which basic emotions are generated or through the integration of appraisals which include the goals of others. Examples include the coupling of happiness and sadness, which can generate nostalgia. Indignation can result from the appraisal of anger combined with the further appraisal that the object of anger is an individual who is inferior in the social hierarchy. Empathy results from sadness when combined with the loss of another person’s goal. The model acknowledges the need for two routes for the generation of emotions and this need is based in part on the fact that basic emotions have an innate pre-wired component; additionally, certain emotions may come to be elicited automatically. These two routes are not completely separable. Thus genetics provides a starting psychological point, though the subsequent developmental pathways may be different for each individual. An additional way in which emotions might come to be generated through the direct route is from repeated pairings of certain event-emotion sequences that eventually leads to the automatization of the whole sequence. This repetition bypasses the need for any appraisal. An example of the involvement of the direct route in an emotional disorder, which is an instance of phobia or anxiety where the automatic processing of the objects is anxiety provoking even though it is non-threatening—but due to previous encounter with the object in an individual’s past always in an anxiety provoking situation—it comes to be associated with anxiety. The two routes, for example, can also sometimes generate conflicting emotions as in when the individual may appraise a situation in a happy way, while the direct route is generating a different emotion. The therapeutic technique for working with emotional disorders varies depending on which route the emotion is involved in the disorder. For instance, the person can be provided with a new schematic model for the appraisal of events. Once this model has been accepted the recovery is faster.

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This type of therapy will work in situations where the schematic level is involved in the disorder. This is an example of fast change processes occurring in therapy. But the patient may continue to experience the maladaptive emotions through the activation of the direct route that is slow to change and is an example of slow processes in recovery. In such cases, exposure-based techniques (as used in the case of phobias) can be helpful. There may also be cases in which a combination of the two techniques will be most effective. Therapies that try to focus on the propositional level of representation only may not be successful if the higher schematic models are flawed. The description of the SPAARS approach shows various similarities with the model proposed by Mathews and Machintosh (1998). Both the models posit two different routes for emotion generation. The models are parsimonious since they advance the same explanation for both normal and disordered cognition, though the SPAARS approach has a broader scope and also gives more detailed specification for treatment choices.

ASSISTIVE TECHNOLOGY AND HUMAN COGNITION Current rehabilitative tools and assistive technologies could be significantly improved by considering the architecture of human cognition during the design. The principles derived from cognitive models described, if incorporated into AT tools will help the tools to serve effectively with the target population. Before embarking on the process of merging the nature of human cognition with assistive tools, I will discuss another popular theory of how selective attention operates—load theory of selective attention, which could be utilized to explain the nature of emotional and cognitive functioning in both order and disorder.

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Load Theory of Selective Attention Goal-directed behavior requires focusing attention on goal relevant stimuli. The load theory of selective attention proposes two mechanisms for selective control of attention (Lavie, Hirst, Fockert, & Viding, 2004). The first is a perceptual selection mechanism, which is passive in nature and ensures the exclusion of distractors from perception under high perceptual load (Lavie, 1995). The distractors are not perceived under high perceptual load as the target absorbs all the available processing capacity. But under conditions of low perceptual load, spare processing capacity left over from processing task relevant stimuli “spills-over” to irrelevant stimuli that are processed accordingly (Lavie & Tsal, 1994). Loading perception would require either adding more items to the task at hand or a more perceptually demanding task on the same number of items. The second mechanism of attentional control is more active in nature and is evoked for the purpose of rejecting distractors that have been perceived under low perceptual load. This type of control depends on higher cognitive functions, like working memory. Therefore, loading higher cognitive functions that maintain processing priorities result in increased distractor processing. The effect occurs because the reduced availability of control mechanisms in turn reduces the ability to control attention according to the processing priorities. Supporting the theory, Lavie and Cox (1997) have shown that an irrelevant distractor failed to capture attention under high perceptual load conditions as compared to low perceptual load. The load was manipulated by either increasing the number of stimuli among which the target had to be detected or by increasing the perceptual similarity between the target and distractors making the task increasingly perceptually demanding in nature. This result was cited as a support for passive distractor rejection in contrast to active inhibitory mechanisms that are employed for

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the purpose of rejecting distractors under low perceptual load conditions.

Load Theory and Disorders The two mechanisms of selective attention could be distributed in disorders and hence the load theory of selective attention could serve as an aid while describing the attentional deficits encountered in both cognitive and emotional disorders. A complete description of all the disorders and how these attentional deficits are present in each are out of the scope of this chapter, but for illustrative purpose, a few disorders will be chosen here to further illustrate the transdiagnostic processes. Consistent with the theories previously introduced, Bishop, Jenkins, and Lawrence (2007) showed that anxiety modulated the amygdalar response to fearful distractors that interfered with the task performance only under low perceptual load conditions. But this effect was observed for state anxiety (current anxious state) rather than trait anxiety (a permanent personality feature). Trait anxiety, on the other hand, correlated with reduced activity in brain regions responsible for controlled processing under low perceptual load. This result implies that trait anxiety is associated with poor attentional controls. Therefore, state and trait anxiety potentially produce interactive effects and disturb task performance because of the disturbed passive mechanisms and faulty attentional control which in turn does not prevent irrelevant emotional distractors from capturing attention under conditions of load. Deficient attentional control was also observed for aged participants by Lavie (2000; 2001). Maylor and Lavie (1998) investigated the role of perceptual load in aging. They showed that distractor processing was decreased for older participants at lower perceptual loads as compared to the younger ones. Similarly high level affective evaluation (appraisals that are necessary for emotion generation as described in the SPAARS approach) requires attention and working memory, and as a result,

is disrupted under high cognitive load. Kalisch, Wiech, Critchley, and Dolan (2006) varied cognitive load, while at the same time, anxiety was induced with the help of anticipation of an impending pain. They observed no change in subjective and physiological indices of anxiety expectations under conditions of load. They did obtain reductions in the activity of brain areas responsible for controlled processing under conditions of high load indicating that high level appraisal was suppressed. Their results did not only show dissociation between brain areas responsible for higher and lower level appraisals, but also how these interact with the manipulations of load.

Merging Technology and Cognition Having described the intimate role between cognitive and emotional processing in both order and disorder and their interaction with perceptual and cognitive load, what should be the next step if we need to plan a rehabilitative program considering the aforementioned principles of human cognition? Therapy with VR, as previously described, has shown promising results. For instance, VR has been employed effectively for the treatment of phobias, that are usually described as intense and irrational fears of objects or events, like acrophobia (Emmelkamp, Krijn, Hulsbosch, de Vries, Schuemie, & van der Mast, 2002), fear of flying (Rothbaum, Hodges, Smith, Lee, & Price, 2000), spider phobia (Garcia-Palacios et al., 2002) and social phobia (Roy, Légeron, Klinger, Chemin, Lauer, & Nugues, 2003). Clinicians also consider phobias as part of anxiety disorders. VR as a potential tool for dealing with phobias has several advantages. Because the essential component in the treatment of phobias is exposure to the threat related object (like spiders in case of spider phobia) either in the form of imagery or in vivo (the latter involves graded exposure), VR as a treatment device could be employed effectively. When working with VR/VE, the therapist can control feared situation and graded exposure with a significant

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degree of safety. VR thus turns out to be more effective than the imagination techniques/sessions, where the patients are simply left to themselves to imagine the feared object. Under the imagination procedure the therapist not only lacks control on the imagination of the patient, but it also becomes hard for the therapist to determine whether the patient is actually following the imagination procedure leading to poor treatment generalization outside the treatment clinics. On the other hand, real exposure to the feared object could lead the patient to be traumatized, making him/her more fearful of it. Consequently VR could be employed fruitfully to overcome the difficulties of both the imagination techniques and real exposure. The other most important advantage that VR confers on the treatment process is the opportunity for interoceptive exposure (Vincelli, Choi, Molinari, Wiederhold, & Rive, 2000). This becomes important given the fact that bodily sensations are interpreted as signs of fear in the presence of feared object. Virtual reality also turns out to be effective when higher level distorted cognitions need to be challenged (Riva, Bacchetta, Baru, Rinaldi, & Molinari, 1999).

FUTURE RESEARCH DIRECTIONS Future research efforts on VR as a successful application for rehabilitation should concentrate on three major issues and associated problems: (a) clinical, (b) social and (c) technological issues.

Clinical Issues The previous discussion clearly shows that VR as a rehabilitative tool has shown promising results and, therefore, has implications for further improvement. Virtual reality could be better suited to rehabilitate a range of disorders by meshing it with the functioning of human cognition. Much remains to be done in order to pinpoint the specific transdiagnostic processes that cuts across disorders

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and are also found to be deficient. A promising direction in this regard is the application of load theory of selective attention. Though the studies conducted by Bishop et al. (2007) and Kalisch et al. (2006) show that atypical cognitive bias interacts with behavior and neural responses under differing conditions of load, these kinds of results still await to be incorporated into a rehabilitative VR endeavor. As we have previously stated, VR has been used successfully for treating various phobias like acrophobia (Emmelkamp et al., 2002), fear of flying (Rothbaum et al., 2000), spider phobia (Garcia-Palacios et al., 2002) and social phobia (Roy et al, 2003). What the literature lacks currently is an intimate link between cognitive architecture and the basis for VR successes. Cognitive psychologists, rehabilitative therapists, and VR professionals will stand to gain much if more studies are planned in this direction. For instance, VR is a good choice in exposure techniques for phobias, but since the SPAARS framework and the model proposed by Mathews and Machintosh (1998) show that there could be two routes to emotions—and exposure technique is useful when the faster route that runs from thalamus to amygdala is involved—it will be fruitful to plan future VR studies as was done by Bishop et al. (2007). If such studies show improved attentional control under different conditions of load and prevent anxiety from modulating amygdalar response to anxiety relevant distractors (that disrupt task performance under low perceptual load) with VR treatment, then this will strengthen the link between cognitive models and rehabilitation. The prior theorizing also shows that for successful treatment, practitioners need to provide the patient with a new schematic model for the appraisal of events other than exposure techniques. Once this model has been accepted, recovery is faster. This type of therapy will work in situations where the schematic level is involved in the disorder. This is an example of fast change processes occurring in therapy. For the future, VR could be

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used in conjunction with brain imaging techniques to study the brain responses along with behavioral responses before and after treatment. Researchers need to meticulously plan such studies by manipulating cognitive load in participants to study the effect of treatment on cognitive appraisals as was done by Kalisch et al. (2006). Once such future endeavors show successful results for anxiety treatment, practitioners will be more confident about the transdiagnostic processes that become atypical and give rise to cognitive biases. How do researchers and practitioners know which route to emotion (faster or slower) is involved in atypical functioning before embarking on such endeavors? This again calls for stronger links with neuropsychology and thorough assessments before chalking out a treatment plan. Finally, if both the routes are involved, then a mixture of techniques can be used. If the decision is to concentrate on both the routes, then it is essential to increase the load on perceptual and cognitive processes parametrically in an orthogonal manner; this is a very important concept because if both kinds of load were to be increased simultaneously, then it would become difficult to discern the effect of each individually. Moreover, since VR allows for interoceptive exposure, which becomes important given the fact that bodily sensations are interpreted as signs of fear in the presence of feared object, it would make sense to study the effect of treatment on the schematic level while the bodily responses are also monitored. If the treatment also shows improvement in bodily responses, then one can be even more confident of the VR intervention.

Social Issues Before VR could become a part of mainstream use, researchers and practitioners need to overcome several social obstacles. In many traditional schools of therapies, a personal relationship between the therapist and the client is given a high degree of importance. For some, VR could be

viewed as disruptive to this relationship. This issue is even more important for a culture that does not emphasize individualism, for instance in some Eastern societies. In this scenario, it becomes important to consider even technologically less developed societies. Apart from this hindrance, any new therapy initially faces resistance from the broader clinical society. This was even true for behavioral therapy when it was introduced, and hence in the field of mental health, there are other issues that determine the acceptance of a new rehabilitative method rather than just documented efficacy. Until the relevant social problems connected to VR are solved, the best course of action might be to adopt VR in conjunction with other traditional modes of rehabilitation.

Technological Issues Research on social and clinical issues is not enough to promote VR; it is also essential to concentrate on the technological aspects of it. Currently, VR devices and protocols lack standardization, while many others are developed for a specific context making generalization poor (Riva, 2005). Though VR systems cost less than real world simulators, VR is expensive considering that many are built for specific purposes only. In addition, VR is very time-consuming to build.

CONCLUSION Given that VR research proceeds along three different directions, the future of VR as a rehabilitative tool is promising. Current research efforts and scientific discussions do focus on VR and human cognition, but these have so far remained isolated from each other. But the advent of cognitive science and multidisciplinary frameworks calls for a better cooperation between the two. First, fruitful research direction in this regard will be to focus on transdiagnostic processes that cut across various disorders and need to be targeted with rehabili-

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tative efforts. This will bring down the cost of building rehabilitative tools for specific contexts and will also save precious time. Next, once the transdiagnostic processes have been examined, practitioners would apply these as models of human cognition that explain typical and atypical cognition. There have been few theories, and some popular ones have been described, but still a lot work needs to be done to develop them further and make them the basis of cognitive rehabilitation with VR. Once such efforts are in place, we will truly be able to understand comorbidity, generalize knowledge, and bring down the cost of treating various disorders. The day is not far off when mass rehabilitation over the Internet would be possible with such exciting tools!

REFERENCES Bishop, S. J., Jenkins, R., & Lawrence, A. D. (2007). Neural processing of fearful faces: Effects of anxiety are gated by perceptual capacity limitations. Cerebral Cortex, 17(7), 1595–1603. doi:10.1093/cercor/bhl070 Clare, L., & Woods, R. T. (2004). Cognitive training and cognitive rehabilitation for people with early-stage Alzheimer’s disease: A review. Neuropsychological Rehabilitation, 14(4), 385–401. doi:10.1080/09602010443000074 Emmelkamp, P. M., Krijn, M., Hulsbosch, A. M., de Vries, S., Schuemie, M. J., & van der Mast, C. A. (2002). Virtual reality treatment versus exposure in vivo: A comparative evaluation in acrophobia. Behaviour Research and Therapy, 40(5), 509–516. doi:10.1016/S0005-7967(01)00023-7 Garcia-Palacios, A., Hoffman, H., Carlin, A., Furness, T. A., & Botella, C. (2002). Virtual reality in the treatment of spider phobia: A controlled study. Behaviour Research and Therapy, 40(9), 983–993. doi:10.1016/S0005-7967(01)00068-7

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Hirose, M., Taniguchi, M., Nakagaki, Y., & Nihei, K. (1994). Virtual playground and communication environments for children. IEICE Transactions on Information & Systems. E (Norwalk, Conn.), 77D(12), 1330–1334. Kalisch, R., Wiech, K., Critchley, H. D., & Dolan, R. J. (2006). Levels of appraisal: A medial prefrontal role in high-level appraisal of emotional material. NeuroImage, 30(4), 1458–1466. doi:10.1016/j.neuroimage.2005.11.011 Lavie, N., & Tsal. (1994). Perceptual load as a major determinant of the locus of selection in visual attention. Perception & Psychophysics, 56(2), 183–197. Lavie, N. (1995). Perceptual load as a necessary condition for selective attention. Journal of Experimental Psychology. Human Perception and Performance, 21(3), 451–468. doi:10.1037/00961523.21.3.451 Lavie, N. (2000). Selective attention and cognitive control: Dissociating attentional functions through different types of load. In Monsell, S., & Driver, J. (Eds.), Control of cognitive processes: Attention & performance XVIII (pp. 175–194). Cambridge, MA: MIT Press. Lavie, N. (2001). The role of capacity limits in selective attention: Behavioural evidence and implications for neural activity. In Braun, J., & Koch, C. (Eds.), Visual attention and cortical circuits (pp. 49–68). Cambridge, MA: MIT Press. Lavie, N., & Cox, S. (1997). On the efficiency of visual selective attention: Efficient visual search leads to inefficient distractor rejection. Psychological Science, 8(5), 395–398. doi:10.1111/j.1467-9280.1997.tb00432.x Lavie, N., Hirst, A., Fockert, J. W. D., & Viding, E. (2004). Load theory of selective attention and cognitive control. Journal of Experimental Psychology. General, 133(3), 339–354. doi:10.1037/0096-3445.133.3.339

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LeDoux, J. E. (1995). Emotion: clues from the brain. Annual Review of Psychology, 46, 209–235. doi:10.1146/annurev.ps.46.020195.001233

Riva, G. (2005). Virtual reality in psychotherapy [Review]. Cyberpsychology & Behavior, 8(3), 220–230. doi:10.1089/cpb.2005.8.220

Lewis, R. B. (1998).Assistive technology and learning disabilities: Today’s realities and tomorrow’s promises. Journal of Learning Disabilities, 31(1), 16–26, 54. doi:10.1177/002221949803100103

Riva, G., Bacchetta, M., Baru, M., Rinaldi, S., & Molinari, E. (1999). Virtual reality based experiential cognitive treatment of anorexia nervosa. Journal of Behavior Therapy and Experimental Psychiatry, 30(3), 221–230. doi:10.1016/S00057916(99)00018-X

Mansell, W., Harvey, A., Watkins, E. R., & Shafran, R. (2008). Cognitive behavioral processes across psychological disorders: A review of the utility and validity of the transdiagnostic approach. International Journal of Cognitive Therapy, 1(3), 181–191. doi:10.1521/ijct.2008.1.3.181 Mathews, A., & Machintosh, B. (1998). Cognitive model of selective processing in anxiety. Cognitive Therapy and Research, 22(6), 539–560. doi:10.1023/A:1018738019346 Max, M. L., & Burke, J. C. (1997). Virtual reality for autism communication and education, with lessons for medical training simulators. In Morgan, K. S., Hoffman, H. M., Stredney, D., & Weghorst, S. J. (Eds.), Studies in health technologies and informatics, 39. Burke, VA: IOS Press.

Rose, F. D., Attree, E. A., & Johnson, D. A. (1996). Virtual reality: An assistive technology in neurological rehabilitation. Current Opinion in Neurology, 9(6), 461–467. Rothbaum, B. O., Hodges, L., Smith, S., Lee, J. H., & Price, L. (2000). A controlled study of virtual reality exposure therapy for the fear of flying. Journal of Consulting and Clinical Psychology, 68(6), 1020–1026. doi:10.1037/0022-006X.68.6.1020 Roy, S., Légeron, P., Klinger, E., Chemin, I., Lauer, F., & Nugues, P. (2003). Definition of a VR−based protocol for the treatment of social phobia. Cyberpsychology & Behavior, 6(4), 411–420. doi:10.1089/109493103322278808

Maylor, E. A., & Lavie, N. (1998). The influence of perceptual load on age differences in selective attention. Psychology and Aging, 13(4), 563–573. doi:10.1037/0882-7974.13.4.563

Schultheis, M. T., & Rizzo, A. A. (2001). The application of virtual reality technology in rehabilitation. Rehabilitation Psychology, 46(3), 296–311. doi:10.1037/0090-5550.46.3.296

McLellan, D. L. (1991). Functional recovery and the principles of disability medicine. In Swash, M., & Oxbury, J. (Eds.), Clinical Neurology (Vol. 1, pp. 768–790). London: Churchill Livingstone.

Stephenson, J. (1995). Sick kids find help in a cyberspace world. Journal of the American Medical Association, 274(24), 1899–1901. doi:10.1001/ jama.274.24.1899

Power, M., & Dalegleish, T. (1997). Cognition and emotion: From order to disorder. London: The Psychology Press.

Vincelli, F., Choi, Y. H., Molinari, E., Wiederhold, B. K., & Rive, G. (2000). Experiential cognitive therapy for the treatment of panic disorder with agoraphobia: Definition of a clinical protocol. Cyberpsychology & Behavior, 3(3), 375–385. doi:10.1089/10949310050078823

Prigatano, G. P. (1999). Principles of neuropsychological rehabilitation. New York: Oxford University Press.

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Wilson, B. A. (1997). Cognitive rehabilitation: How it is and how it might be. Journal of the International Neuropsychological Society, 3(5), 487–496.

ADDITIONAL READING Baumgartner, T., Speck, D., Wettstein, D., Masnari, O., Beeli, G., & Jäncke, L. (2008). Feeling present in arousing virtual reality worlds: Prefrontal brain regions differentially orchestrate presence experience in adults and children. Frontiers in Human Neuroscience, 2(8). doi:.doi:10.3389/ neuro.09.008.2008 Buxbaum, L. J., Palermo, M. A., Mastrogiovanni, D., Read, M. S., Rosenberg-Pitonyak, E., Rizzo, A. A., & Coslett, H. B. (2008). Assessment of spatial attention and neglect with a virtual wheelchair navigation task. Journal of Clinical and Experimental Neuropsychology, 30(6), 650–660. doi:10.1080/13803390701625821 Capodieci, S., Pinelli, P., Zara, D., Gamberini, L., & Riva, G. (2001). Music-enhanced immersive virtual reality in the rehabilitation of memory related cognitive processes and functional Abilities: A case report. Presence (Cambridge, Mass.), 10(4), 450–462. doi:10.1162/1054746011470217 Glantz, K., Durlach, N. I., Barnett, R. C., & Aviles, W. A. (1996). Virtual reality (VR) for psychotherapy: From the physical to the social environment. Psychotherapy (Chicago, Ill.), 33(3), 464–473. doi:10.1037/0033-3204.33.3.464 Harvey, A. G., Watkins, E. R., Mansell, W., & Shafran, R. (2004). Cognitive behavioral processes across psychological disorders: A transdiagnostic approach to research and treatment. Oxford, UK: Oxford University Press. Khetrapal, N. (2007a). Antisocial behavior: Potential treatment with biofeedback. Journal of Cognitive Rehabilitation, 25(1), 4–9.

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Khetrapal, N. (2007b). SPAARS Approach: Integrated cognitive model of emotion of Attention Deficit/Hyperactivity Disorder. Europe’s Journal of Psychology. Khetrapal, N. (in press). SPAARS Approach: Implications for Psychopathy. Poiesis & Praxis: International Journal of Technology Assessment and Ethics of Science. Lavie, N., & Fockert, J. W. D. (2005). The role of working memory in attentional capture. Psychonomic Bulletin & Review, 12(4), 669–674. LeDoux, J. E. (1996). The emotional brain. New York: Simon & Schuster. McGee, J. S., van der Zaag, C., Buckwalter, J. G., Thiebaux, M., Van Rooyen, A., & Neumann, U. (2000). Issues for the Assessment of Visuospatial Skills in Older Adults Using Virtual Environment Technology. Cyberpsychology & Behavior, 3(3), 469–482. doi:10.1089/10949310050078931 Parsons, T. D., & Rizzo, A. A. (2008). Initial validation of a virtual environment for assessment of memory functioning: Virtual reality cognitive performance assessment test. Cyberpsychology & Behavior, 11(1), 17–25. doi:10.1089/ cpb.2007.9934 Renaud, P., Bouchard, S., & Proulx, R. (2002). Behavioral avoidance dynamics in the presence of a virtual spider. Information Technology in Biomedicine. IEEE Transactions, 6(3), 235–243. Riva, G. (1998). From toys to brain: Virtual reality applications in neuroscience. Virtual Reality (Waltham Cross), 3(4), 259–266. doi:10.1007/ BF01408706 Riva, G., Botella, C., Légeron, P., & Optale, G. (Eds.). (2004). Cybertherapy: Internet and virtual reality as assessment and rehabilitation tools for clinical psychology and neuroscience. Amsterdam: IOS Press.

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Riva, G., Molinari, E., & Vincelli, F. (2002). Interaction and presence in the clinical relationship: Virtual Reality (VR) as communicative medium between patient and therapist. IEEE Transactions on Information Technology in Biomedicine, 6(3), 1–8. doi:10.1109/TITB.2002.802370

Strickland, D., Marcus, L., Mesibov, G. B., & Hogan, K. (1996). Brief report: Two case studies using virtual reality as a learning tool for autistic children. Journal of Autism and Developmental Disorders, 26(6), 651–660. doi:10.1007/ BF02172354

Riva, G., Wiederhold, B. K., & Molinari, E. (Eds.). (1998). Virtual Environments in Clinical Psychology and Neuroscience. Amsterdam: IOS Press.

Williams, J. M., Watts, F. N., MacLeod, C., & Mathews, A. (1997). Cognitive psychology and emotional disorders (2nd ed.). Chichester, UK: John Wiley & Sons.

Srinivasan, N., Baijal, S., & Khetrapal, N. (in press). Effects of emotions on selective attention and control. In Srinivasan, N., Kar, B. R., & Pandey, J. (Eds.), Advances in cognitive science (Vol. 2). New Delhi: SAGE.

This work was previously published in Handbook of Research on Human Cognition and Assistive Technology: Design, Accessibility and Transdisciplinary Perspectives, edited by Soonhwa Seok, Edward L. Meyen and Boaventura DaCosta, pp. 96-108, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Wearable Kinesthetic System for Joint Knee Flexion-Extension Monitoring in Gait Analysis Mario Tesconi University of Pisa, Italy Enzo Pasquale Scilingo University of Pisa, Italy Pierluigi Barba University of Pisa, Italy Danilo De Rossi University of Pisa, Italy

INTRODUCTION Posture and motion of body segments are the result of a mutual interaction of several physiological systems such as nervous, muscle-skeletal, and sensorial. Patients who suffer from neuromuscular diseases have great difficulties in moving and walking, therefore motion or gait analysis are widely considered matter of investigation by the clinicians for diagnostic purposes. By means of DOI: 10.4018/978-1-60960-561-2.ch311

specific performance tests, it could be possible to identify the severity of a neuromuscular pathology and outline possible rehabilitation planes. The main challenge is to quantify a motion anomaly, rather than to identify it during the test. At first, visual inspection of a video showing motion or walking activity is the simplest mode of examining movement ability in the clinical environment. It allows us to collect qualitative and bidimensional data, but it does not provide neither quantitative information about motion performance modalities (for instance about dynamics and muscle activity),

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Wearable Kinesthetic System for Joint Knee Flexion-Extension Monitoring in Gait Analysis

nor about its changes. Moreover, the interpretation of recorded motion pattern is demanded to medical personnel who make a diagnosis on the basis of subjective experience and expertise. A considerable improvement in this analysis is given by a technical contribution to quantitatively analyse body posture and gesture. Advanced technologies allow us to investigate on anatomic segments from biomechanics and kinematics point of view, providing a wide set of quantitative variables to be used in multi-factorial motion analysis. A personal computer enables a real-time 3D reconstruction of motion and digitalizes data for storage and off-line elaboration. For this reason, the clinicians have a detailed description of the patient status and they can choose a specific rehabilitation path and verify the subject progress. In this context, the Gait Analysis has grown up and currently provided a rich library about walking ability. In a typical Motion Analysis Lab (MAL), multiple devices work together during walking performance, focusing on different motion aspects, such as kinematics, dynamics, and muscular activity. The core of the equipment used in the MAL is a stereophotogrammetric system that measures 3D coordinates of reflecting markers placed on specific anatomic “repere” on the body (Capozzo, Della Croce, Leardini, & Chiari, 2005). This instrumentation permits to compute angles, velocities, and accelerations. The MAL is also equipped with force platforms, which reveal force profiles exchanged with ground on landing. The electromyographic unit is deputed to measure muscle contraction activity by using surface electrodes. Pressure distributions can be obtained by means of baropodometric platforms, made up of a matrix of appropriately shaped sensors. A unique software interface helps the operator to simply manage data from the complex equipment.

BACKGROUND Although its remarkable advantages, the Quantitative Gait Analysis require large spaces, appropriate environments, and cumbersome and expensive equipment that limit the use to restricted applications. Moreover, the stereophotogrammetric system requires a pretest calibration and complex procedure which consists in the placement of reflecting markers on the subject body. Electromyography may be obtrusive if needle electrodes are used to investigate deeper muscles or single motor unit activity. Because of this, although it is a widely accepted methodology, Gait Analysis is still quite difficult to be used in clinical contexts. For these motivations, in the last few years technological research accepted the challenge of transferring any support and high level information close to the patient, better on the patient himself, by creating a user friendly patient-device interface. This means that health services can be brought out of their classic places with a remarkable improvement in health service and in health care based on prevention. Patient can be monitorized for longer time, ensuring the same high service quality at lower costs. A profitable combination of expertise and know how from electronic engineering, materials science and textile industry led to develop effective Wearable Health Care Systems (or simply Wearable Systems) (Lymberis & De Rossi, 2004). These latter ones had electronics integrated on traditional clothes, like on a breadboard. In order to allow subjects to freely move without constraints, improvements in materials manufacture, microelectronics, and nanotechnologies allowed us to develop a new generation of wearable sensing systems characterized by multifunctional fabrics (referred to as e-textiles or smart textiles), where electronic components are made up of polymeric materials, weaved or impressed on the fabric itself, without losing their original mechanical and electrical properties.

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The main advantages of this advanced technology consist of: • •

• •

Strict contact of fabric with skin, the natural human body interface Flexibility and good adherence to the body, which helps avoiding motion artefacts affecting the measurement Washability to be reused Lightness and unobtrusivity, so the patient can naturally move in his usual environment

By means of wearable systems, a large set of physiological variables (ECG, blood pressure, cardiac frequency, etc.) can constantly be monitored and quantitative parameters can be computed, from motion sensing, by dedicated software. Important applications related to health care deal with Ergonomics, Telemedicine, Rehabilitation, and any health service (elderly or impaired people).

MATERIALS AND METHODS As described in the introductive section, Gait Analysis is usually performed for measuring kinematic variables of anatomic segments with tracking devices, which have their main disadvantages in being complex, expensive, and not easy to be used in clinical environments. Thanks to the development of wearable systems, a new class of smart Figure 1. Sensorized shoe for marking the gait cycle

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textiles (De Rossi, Santa, Mazzoldi, 1999) allows us to design and produce special garments using electrically conductive elastomer composites (CEs) (Post, Orth, Russo, & Gershenfeld, 2000). CEs show piezoresistive properties when a stress is applied, and in several applications they can be integrated into fabrics or other flexible substrates acting as strain sensors (Scilingo, Lorussi, Mazzoldi, & De Rossi, 2003). Such a sensors are just applied in sportive medicine for rehabilitation in soccer post-strokes, for athletic gesture analysis, for angles measurement, and for lower limb force analysis. We used a wearable prototype based on CEs sensors for the first time in a clinical environment. It consists of a sensorized fabric bend wrapped around the knee, very light, well adherent and elastic, hence very suitable for the chosen application. A sensing garment has been realized deposing the CEs over a sublayer of cotton Lycra® and building a sensorized shoe devoted to the gait cycle synchronism. The sensorized shoe consists of a pressure sensor realized of the same material of the knee-band (KB), and is placed on the sole near the heel, as shown in Figure 1. The CEs mixture is smeared on the fabric previously covered by an adhesive mask cut by a laser milling machine. The mask is designed according to the shape and the dimension desired for sensors and wires. After smearing the solution, the mask is removed. Then the treated fabric is placed in an oven at a temperature of about 130 centigrade degrees. During this phase, the cross-

Wearable Kinesthetic System for Joint Knee Flexion-Extension Monitoring in Gait Analysis

linking of the solution speeds up, and in about 10 minutes the sensing fabric is ready to be employed. This methodology is used both for sensing areas and connection paths, thus avoiding employing metallic wires to interconnect sensors or link them to the electronic acquisition unit (Lorussi, Rocchia, Scilingo, Tognetti, & De Rossi, 2004). In this way, no rigid constraints or linkages are present and movements are unbounded (Tognetti et al., 2005). Section (a) of Figure 2 shows the mask for the realization of the KB; the bold black track represents four sensors connected in series and covers the knee joint. The thin tracks represent the connection between the sensors and the electronic acquisition system. Being the thin tracks are made of the same piezorestive mixture, they undergo a not negligible (and unknown) change in their resistance when the knee bends. To face this inconvenience, the analog front-end of the electronic unit is designed to compensate the resistance variation of the thin tracks during the deformations of the fabric. The electric scheme is shown in section (b) of Figure 2. While a generator supplies the series of sensors Si with a constant current I, the acquisition system is provided by a high input impedance stage realized by instrumentation amplifiers and represented in section (b) of Figure 2 by the set of voltmeters. The voltages measured by the instrumentation

amplifiers are equal to the voltages which fall on the Si that is related to resistances of the sensors. In this way, the thin tracks perfectly substitute the traditional metallic wires and a sensor consisting of a segment of the bold track between two thin tracks can be smeared in any position to detect the movements of a joint. The KB acquires information on the flexion-extension of the knee from four sensors spread on a leotard and a step-signal from the sensorized shoe (Morris & Paradiso, 2002). Moreover, the prototype consisted of an “on body unit” dedicated to the acquisition of signals from the KB and the Bluetooth transmission to a remote PC as shown in Figure 3.

EXPERIMENTAL PROTOCOL The prototype was used in a clinical application consisting of the gait monitoring on subjects affected by venous ulcers localized in the lower limbs. A test for discriminating between pathologic and normal walking was performed by the acquisition of flexion-extension signals from the knee joint during movement. Next, experimental data were validated in order to assess goodness of the methodology. Nine subjects, five females and four males, volunteered to participate in the study. The “normal” sample was made of four voluntaries, three males and one female, belong-

Figure 2. (a) The mask used for the sensorised KB. (b) The equivalent electric scheme of the KB.

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Figure 3. The overall prototype consisting of a KB, sensorized shoe and an “on body unit.” Front (left side) and back (right side) views

ing to university context with a mean age of 32 years. Each subject was required to fill in a suitable anamnesis questionnaire in order to verify they did not undergo lower-limb injuries, disease, or trauma. The “pathologic” sample consisted of five patients, four females and one male (mean age about 73 years), in-patient in a clinic specialized in vascular diseases, with special competences in cutaneous ulcers treatment (the medical partner in this work). The presence of the pathology under study was certified by consulting clinical folders. After the measurement system was correctly applied, taking care of arranging the sensors upon the knee articulation, each subject was required to walk freely on a level ground. By means of a wireless communication system, based on Bluetooth protocol, data were transferred in real time from an on-body unit to a personal computer, where a software interface let the operator monitorize signals from the sensorized garment, including diagnostics and current settings. In a five-gait cycle observation interval the flexion-extension signals were acquired and the correspondent files stored for off-line elaboration.

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DATA ANALYSIS In the typical signal from a sensor of KB (continuous curve in Figure 4), resistance increases during flexion and decreases while the articulation extends, according to the piezoresistive effect. Voltage shows the same behaviour because the supply current was fixed for this kind of measurement. The synchronized matching with stepsignal acquired from the sensorized shoe (dashed line) allowed us to detect the “gait cycle,” or the elemental reference interval of the analysis. A single step-signal is an “on-off” signal where the low-level indicates that the foot is in contact with the ground, while the high-level indicates lifting. In order to extract significant variables for discrimination, two different approaches were studied. The first one, based on the evaluation of flexion-extension capability, was referred to as the width excursion parameter E, defined in Equation (1). (| V −V |)  B E = VM −  A +VA    2

(1)

VM represents the maximum voltage width in the maximum flexion instant; VA is the volt-

Wearable Kinesthetic System for Joint Knee Flexion-Extension Monitoring in Gait Analysis

Figure 4. Flexion-extension of the central sensor of KB (continuous curve) and signal coming from the shoe sensor (dashed curve) which marks the gait cycle

Figure 5. Graphical representation of the width excursion

Em =

EC + EC 1

Em = L

2

2

C

EL + EL 1

2

2



(2)



(3)

ECi (i = 1, 2) indicates the width excursion regarding to central sensor i, while ELi represents the width excursion related to lateral sensor i. IR parameters were computed by “nondistortion” method such as shown in Equation (4):

IR = age value when flexion starts; VB indicates the voltage value in the maximum extension instant such as shown in Figure 5. The second approach proposes to analyze gait discontinuity during the observed interval. To do this we referred to the standard deviation over a sample made up of average width excursion (E) values, on the five-gait cycles. This parameter called irregularity (IR) was calculated both for central (IRc) and lateral (IRL) sensors. In order to integrate information coming from each couple of sensors, a standard deviation was calculated with respect to their average value:

2  _   ∑ x − x    (n − 1)

(4)

where x represents the sample arithmetical mean, while n is the dimension of the sample.

RESULTS According to graphic representation of both IRc and IRL parameters over the entire recruited population (see Figure 6), several important issues have to be addressed: •

IR values showed fundamentally to be higher for pathologic subjects than for normal ones, confirming how they reveal an

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Wearable Kinesthetic System for Joint Knee Flexion-Extension Monitoring in Gait Analysis

Figure 6. Graphical representation of IRc parameter

• •

•

irregular knee flexion-extension during normal walking. A good discrimination between the two populations was obtained. IR values is higher in patients 5 and 1, which have a larger ulcer size, as it is known in clinical folder. Figure 6 reports on the discrimination on the basis of the irregularity of the mean value of central sensors IRc and Figure 7 shows the graphical discrimination of subjects according to the irregularity of the mean value of lateral sensors IRL. The graph IRc reports a normal subject (normal 4) classified as belonging to the pathologic group, while in IRL graph he is rightly recognized as normal. This means that, for our application, lateral sensors of KB resulted more specific than the central ones.

TEST EVALUATION The test validity has to be intended as the capability by the test to discriminate between normal and pathological subjects within the recruited population. Receiver Operative Characteristic (R.O.C.) curves analysis provided its scientific support in

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this evaluation, mainly to decide the best cut-off value of parameters for discrimination. A R.O.C. curve is a graphic representation of sensitivity vs. false positive rate (FPR) for different cut-off levels of the measurement variable. The best decisional value ensures maximum of sensitivity to the minimum FPR. By this method, optimal cut-off value of irregularity was detected both for central sensors and for lateral ones (see Figure 8) and the test characteristic parameters were computed, in terms of sensibility, specificity and positive predictive value (PPV), as shown in the Table 1. Positive predictive value is directly proportional to disease prevalence in the study population. For a good test a high PPV is more significant if prevalence is high too. In our study, prevalence was high enough (55%), so PPV values of 80% and 100% gave to the test a good validity score, together with high sensibility and specificity. Lateral sensors showed to be equally sensitive but better specific and predictive than central ones. However these results should be verified over a wider population.

CONCLUSION In this article, we reported on the possibility of using a wearable kinesthetic system for moniFigure 7. Graphical representation of IRL parameter

Wearable Kinesthetic System for Joint Knee Flexion-Extension Monitoring in Gait Analysis

Figure 8. Central sensor and Lateral R.O.C. curves

Table 1. Sensitivity, specificity, ppv and vpn value of the sensors central and lateral Central sensors

Lateral Sensors

Sensitivity

80%

Sensitivity

80%

Specificity

75%

Specificity

100%

PPV

80%

PPV

100%

VPN

75%

VPN

80%

toring through gait analysis the improvement of patients suffering from venous ulcers after undergoing surgical operation. As a validation tool, a preliminary pilot test has been realized, which reports a good capability of discriminating health from injured subjects. Furthermore, results demonstrated the possibility of identifying the severity of the disease in the pathologic group by analyzing of walking activity. Commonly in the clinical practice, these patients are monitored by means of obtrusive instrumentation aimed at evaluating how the circumference of a thigh increases over time. We used an alternative approach to investigate the illness progress. As patients suffering from this illness exhibit an irregular gait, we customized a sensing garment in order to analyze the gait discontinuity.

Therefore this prototype could be a means that allows clinicians to monitor patients without causing any discomfort during the recovery process after a surgical operation. Next, developments aim at extending this application to the study of hip motion. Finally, it has been pointed out that the use of these sensorized garments can be considered a valid alternative and comfortable instrumentation applicable in several rehabilitation areas, such as in sport disciplines and in multimedia fields.

REFERENCES Capozzo, A., Della Croce, U., Leardini, A., & Chiari, L. (2005). Human movement analysis using stereophotogrammetry part 1: Theoretical background. Gait & Posture, 21(21), 186–196. De Rossi, D., Lorussi, F., Scilingo, E. P., Carpi, F., Tognetti, A., & Tesconi, M. (2004). Artificial kinesthetic systems for telerehabilitation. In A. Lymberis & D. de Rossi, (Eds.), Wearable ehealth systems for personalised health management - state of the art and future challenges (pp. 106-114). Amsterdam: IOS Press.

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De Rossi, D., Santa, A. D., & Mazzoldi, A. (1999). Dressware: Wearable hardware. Materials Science and Engineering C, 7, 31. doi:10.1016/S09284931(98)00069-1 Lorussi, F., Rocchia, W., Scilingo, E. P., Tognetti, A., & De Rossi, D. (2004, December). Wearable redundant fabric-based sensors arrays for reconstruction of body segment posture. IEEE Sensors Journal, 4(6), 807–818. doi:10.1109/ JSEN.2004.837498 Morris, S. J., & Paradiso, J. A. (2002). Shoeintegrated sensor system for wireless gait analysis and real-time feedback. In Proceedings of the 2nd Joint Meeting of IEEE EMBS and BMES, Houston, TX (pp. 2468-2469). Post, E. R., Orth, M., Russo, P. R., & Gershenfeld, N. (2000). Design and fabrication of textile-based computing. IBM Systems Journal, 39(3-4). Scilingo, E. P., Lorussi, F., Mazzoldi, A., & De Rossi, D. (2003). Sensing fabrics for wearable kinaesthetic-like systems. IEEE Sensors Journal, 3(4), 460–467. doi:10.1109/JSEN.2003.815771

KEY TERMS AND DEFINITIONS Anatomical Repere: A prominent structure or feature of the human body that can be located and described by visual inspection or palpation at the body’s surface; used to define movements and postures. Also known as anatomical landmark. Conductive Elastomer Composites: A rubberlike silicone material in which suspended metal particles conduct electricity. Piezoresistive Effect: The changing electrical resistance of a material due to applied mechanical stress. Quantitative Gait Analysis: Useful in objective documentation of walking ability as well as identifying the underlying causes for walking abnormalities in patients with cerebral palsy, stroke, head injury, and other neuromuscular problems Wearable Systems: Devices that are worn on the body. They have been applied to areas such as behavioral modeling, health monitoring systems, information technologies, and media development.

Tognetti, A., Lorussi, F., Bartalesi, R., Tesconi, M., Quaglini, S., & Zupone, G. (2005, March). Wearable kinesthetic system for capturing and classifying upper limb gesture in post-stroke rehabilitation. Journal of Neuroengineering and Rehabilitation, 2(8).

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, pp. 1390-1397, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 3.12

Wearable Systems for Monitoring Mobility Related Activities:

From Technology to Application for Healthcare Services Wiebren Zijlstra University Medical Center Groningen, The Netherlands Clemens Becker Robert Bosch Gesellschaft für medizinische Forschung, Germany Klaus Pfeiffer Robert Bosch Gesellschaft für medizinische Forschung, Germany

ABSTRACT Monitoring the performance of daily life mobility related activities, such as rising from a chair, standing and walking may be used to support healthcare services. This chapter identifies available wearable motion-sensing technology; its (potential) clinical application for mobility assessment and monitoring; and it addresses the need to assess user perspectives on wearable monitoring systems. Given the basic requirements for apDOI: 10.4018/978-1-60960-561-2.ch312

plication under real-life conditions, this chapter emphasizes methods based on single sensor locations. A number of relevant clinical applications in specific older populations are discussed; i.e. (risk-) assessment, evaluation of changes in functioning, and monitoring as an essential part of exercise-based interventions. Since the application of mobility monitoring as part of existing healthcare services for older populations is rather limited, this chapter ends with issues that need to be addressed to effectively implement techniques for mobility monitoring in healthcare.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Wearable Systems for Monitoring Mobility Related Activities

INTRODUCTION One of the major challenges in health care is the ability to timely initiate interventions that prevent loss of functional abilities and maintain or improve quality of life. The individual capacity for safe locomotion is a major indicator for independent functioning in older people. However, within the growing population of older people, safe and independent mobility can be at risk due to age-related diseases such as osteo-arthritis, Parkinson’s or Alzheimer’s disease, and stroke. In addition, older people may become inactive and develop frailty without overt pathology. The latter increases the incidence and impact of falls which are a major threat for health related quality of life in older people (Skelton and Todd 2004). In the next decades, Europe will face a sharp increase, in both relative as well as absolute terms, in the number of older adults. This development is a result of an increasing number of older adults and an average ageing of the population. In 2008, less than 15% of the Dutch population was aged 65 or older; by 2040 this percentage will have increased and reached its peak at approximately 26% (CBS, 2009). Estimates of ageing in other European countries, such as Germany and Italy, are even higher (Eurostat, 2008). The demographic trend towards an ageing society poses social as well as economic challenges. While the demands on health care services are steadily increasing, the (relative) number of persons to give care and to finance health care decreases. Thus, there is a need to adapt health care services. New technologies may aid in providing solutions. Effective interventions are needed to maintain functioning and prevent the loss of independent mobility in older people. Wearable technology for monitoring the performance of daily life mobility related activities, such as lying, rising from a chair, standing and walking may be used to support interventions, which aim to maintain or restore independent mobility. However, at present the routine-use of movement monitoring for the

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clinical management of care in older populations is limited. Therefore, this chapter aims to identify the potential relevance of wearable systems for monitoring mobility for exercise-based interventions and healthcare services by addressing: available wearable motion sensing technology and its application in methods for mobility assessment and monitoring; clinical applications of mobility monitoring; user perspectives on mobility monitoring; and present shortcomings that prevent an effective implementation of wearable solutions for mobility monitoring in health care services.

AVAILABLE WEARABLE TECHNOLOGY FOR MONITORING HUMAN MOVEMENTS A general principle underlying studies of human movements is to consider the human body as a set of rigid bodies (e.g. foot, shank, or thigh), interconnected by joints (e.g. ankle, or knee). Human movement analyses thus require measuring the kinematics of one or more body segments, e.g. by a camera-based system for position measurements or by different motion sensors. The resulting kinematic data are input into further analyses. Depending on research aims, measurements may be simple (e.g. head or trunk position to study walking distance and speed), or highly complex (e.g. full-body measurements to study inter-segmental dynamics). Recent developments in the miniaturization of movement sensors and measurement technology have opened the way for wearable motion sensing technology (Bonato 2005) and the ambulatory assessment of mobility related activities (e.g. Aminian & Najafi 2004, Zijlstra & Aminian 2007). The recent advances even allow full-body ambulatory measurements by motion sensors. However, since monitoring techniques need to be applied over long durations and under real-life conditions, a number of feasibility criteria should be taken into account. These criteria encompass technical criteria (e.g.

Wearable Systems for Monitoring Mobility Related Activities

the degree to which energy consumption limits the use of multiple sensors) as well as criteria which relate to user acceptance (e.g. the methods need to be non-obtrusive and easy-to-use). The latter aspects will be addressed in a subsequent section, in this section an a-priori choice will be made which facilitates technical and practical feasibility; namely a focus on single-sensor approaches to monitoring mobility. The next sub-sections give a short evaluation of available sensors, signal analysis aspects, and methods for mobility assessment and monitoring. The latter subsection will primarily address current possibilities and limitations of methods using single sensor locations, such as on trunk or leg segments.

Wearable Movement Sensors Goniometers can be used to directly measure changes in joint angle. Regardless of sensor type, the basic principle consists of attaching the two axes of the goniometer to the proximal and distal segments of a joint and measuring the angle between these two axes. Drawbacks of using goniometers are sensor attachments which may hinder habitual movement, limited accuracy, and vulnerability of the sensors. Accelerometers measure accelerations of body segments. Piezo-electric type accelerometers measure accelerations only. They are mainly used for quantifying activity related accelerations. Other accelerometers, i.e. (piezo)resistive or capacitive type accelerometers, measure accelerations (a) and the effect of gravitation (g). In the absence of movement, they can be used to calculate the inclination of the sensor with respect to the vertical. Thus, by attaching accelerometers on one or more body segments (e.g. trunk, thigh, and shank), the resistive type can be used to detect body postures at rest (e.g. standing, sitting, and lying). During activities, movements of body segments induce inertial acceleration, and a variable gravitational component depending on the change of segment inclination with respect to the vertical axis. Both

acceleration components are superimposed and their separation is necessary for a proper analysis of the movement. Miniature gyroscopes are sensitive to angular velocity. Although their use for analyzing human movements is still rather new, gyroscopes are promising since they allow the direct measurement of segment rotations around joints. Unlike resistive type of accelerometers, there is no influence of gravitation on the signal measured by gyroscopes. The gyroscope can be attached to any part of a body segment: as long as its axis is parallel to the measured axis, the angular rotation is still the same along this segment. Rotation angles can be estimated from angular velocity by simple numeric integration. However, due to integration drift, it is problematic to estimate absolute angles from angular velocity data. Earth magnetic field sensors or magnetometers measure changes in the orientation of a body segment relative to the magnetic North. Earth magnetic field sensors can be used to determine segment orientation around the vertical axis. Hence, they provide information that cannot be determined from accelerometers or by the integration of gyroscope signals. However, a disadvantage of magnetometers is their sensitivity to nearby ferromagnetic materials (e.g. iron) and magnetic fields other than that of the earth magnetic field (e.g. electro-magnetic fields produced by a TV screen). Pressure sensors or foot switches attached to the shoe or foot sole can be used to detect contact of the foot with the ground. These sensors allow the identification of different movement phases during walking (i.e. swing, stance and bipedal stance phases), or they can provide pressure distribution of the foot during stance phases. These techniques allow the measurement of longer periods of walking with many subsequent stride cycles. A barometric pressure sensor technically is not a movement sensor, but its sensitivity to air pressure can be used to estimate movement characteristics. Changes in altitude are reflected

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Wearable Systems for Monitoring Mobility Related Activities

in a different air pressure, thus changes in the height of a body segment can be estimated from the measured pressure signal. Use of a Global Position System (GPS) offers the possibility to locate (changes in) the position of body segments. However, the system can only be used outside and the accuracy of determining position is limited to ca. 0.3 m. The latter accuracy can be improved by use of so called differential GPS (dGPS). At present, the most relevant movement sensors for use in wearable systems for mobility monitoring are (resistive type) accelerometers, gyroscopes and foot switches. Sometimes combinations of sensors are used (i.e. hybrid sensing) to overcome limitations of specific sensor types and therewith obtain optimal kinematic data of body segments. A well-known example of such a hybrid sensor consists of a combination of three-dimensional accelerometers, gyroscopes and earth-magnetic field sensors.

Analysis of Data from Motion Sensors Unlike conventional camera-based methods for movement analyses, most motion sensors do not provide information about position of body segments, therefore the use of these sensors requires intelligent signal processing and appropriate methods to obtain relevant movement parameters. Advanced signal processing is necessary to extract relevant information from movement sensors. To begin with, appropriate signal processing methods are required in order to estimate body kinematics with an acceptable accuracy. Often the data analysis requires that kinematic data are transformed from a local to an inertial (i.e. gravity- or earth-oriented) reference frame. Accelerometers and gyroscopes measure within a local frame of reference, and the orientation of this local reference frame depends on the orientation of the body segment to which the sensor is attached. To transform the sensor data from a local segment

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oriented reference frame to an inertial reference frame requires knowledge of sensor orientation in space. This orientation can be estimated using data from different sensor types and adequate signal processing algorithms. For example, it has recently been demonstrated that the use of hybrid sensors, consisting of accelerometers, gyroscopes and magnetometers, and signal processing algorithms using a Kalman filter allows for an accurate sensor-based analysis of movements in an inertial frame of reference (e.g. see Luinge & Veltink 2005, Roetenberg et al. 2005). Another recent development is the possibility to combine hybrid sensors for analyzing movements of body segments and GPS for overall changes in position (e.g. Terrier & Schutz 2005). Since the kinematics of body segments are task dependent, the analysis of time varying properties of segment kinematics requires methods that vary in dependence of the specific movement task (e.g. sit-to-stand, standing, or walking). In addition, the choice for signal processing of sensor data depends on the specific goal of the measurements. Not all analyses of human activities need a complete description of the movements of all body segments. The same activity, for example gait, may be analyzed differently depending on whether overall measures (e.g. duration of walking and estimated walking distance) or specific measures (e.g. gait variability, joint rotations) are required. As will be seen in the next section, data acquisition and signal analyses can be simplified if a-priori knowledge of a specific movement is available.

Methods for Mobility Assessment and Monitoring Sensor-based assessments of mobility related activities can target both quantitative and qualitative aspects of movement performance. To quantify movement performance requires methods to detect specific postures, activities, or events from data measured by motion sensors. The qualitative

Wearable Systems for Monitoring Mobility Related Activities

analysis of mobility related activities requires algorithms, which extract relevant biomechanical parameters of movement performance from sensor data. As will be seen in the next sub-sections, a wide variety of different sensor configurations (i.e. the specific type of sensors and the location of sensors on body segments) has been used to extract quantitative and qualitative parameters of movement performance. The detection of frequency and duration of specific body postures and activities from sensor signals, in the literature often indicated as “Activity Monitoring”, is at the heart of any method which aims to monitor mobility over long durations. The underlying principle for the detection of different postures is that during static conditions (e.g. lying, sitting or standing) the orientation of body segments can be determined from the static component in the measured acceleration signal. Thus, it is possible to deduce whether a person is standing, sitting or lying from the orientation of leg and trunk segments. Using (video-based) observations as a reference, several papers have demonstrated the validity of discerning different postures and activities based on accelerometers on trunk and leg segments (e.g. Bussmann et al. 1995, Veltink et al. 1996, Aminian et al. 1999). A sensitivity and specificity higher than 90% has been reported for the detection of different postures and activities based on multiple sensors. However, it should be noted that available validation studies typically are based on data-sets which have been obtained under conditions which are different from activity patterns in daily life. Whereas the first activity monitoring studies were all based on the use of sensors on multiple body segments (mostly trunk and leg), recent studies have shown possibilities to obtain similar information about frequency and duration of activities based on single sensor approaches. For example, based on the detection of transitions between postures by a hybrid sensor which combines a two-dimensional (2D) accelerometer and a gyroscope on the ventral thorax (Najafi et al. 2003) or based on a 3D ac-

celerometer at the dorsal side of the lower trunk (Dijkstra et al. 2008, Dijkstra et al. 2010) postures and activities can be detected. Fall detection can be considered a special case within activity monitoring. Sensor-based fall detectors are highly relevant as the basis for reliable fall reports and automatic fall alarm systems. The advantage of sensor-based fall alarm systems over existing alarm systems is that in the event of a serious fall, the alarm can be triggered automatically. Thus, services can be initiated immediately even if a person is unable to trigger an alarm manually, or otherwise call for help. The available literature on sensor-based fall detection methods presents different approaches, which most often are based on accelerometers at body locations such as head, trunk, or wrist (e.g. Lindemann et al. 2005, Noury et al. 2006, Karantonis et al. 2006, Zhang et al. 2006, Bourke et al. 2007, Kangas et al. 2008). However, at present, there is little published information about the real-life validity of sensor-based fall detectors. The validity of fall detection methods is determined by their sensitivity to falls (i.e., “Does the method detect all falls?”) and specificity (i.e., “Does the method correctly classify those situation where no falls occurred?”). The available studies often have followed a validation approach in which the sensitivity and specificity of fall detection algorithms are determined based on data measured in healthy subjects who simulate different type of falls. Although this approach is an essential first step, the real validation should come from real-life falls obtained in the target populations for applying a fall detection method. In the last decades a steadily increasing number of papers report the use of sensor-based methods for a qualitative analysis of the performance of mobility related activities such as standing, walking, sit-to-stand movement (e.g. see Zijlstra & Aminian 2007). A considerable part of these papers are based on measurements at a single sensor location. The latter simplifies the procedures for data-acquisition, and is possible when a-priori

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knowledge exists about the characteristics of the movements to be measured. For example, many studies are based on measuring the kinematics of the lower trunk at a position close to the body’s centre of mass, which during static standing conditions is located within the pelvis at the level of the second sacral vertebrae. The subsequent data-analyses and interpretations are then based on the assumption that the measured kinematics are similar to the movement patterns of the body’s centre of mass. A conventional approach to determine postural stability during standing is to analyze how well a subject is able to maintain his body position without changing the base of support. Data measured by a force plate under both feet allows for an analysis of the changes in centre of pressure position under the feet. Often these analyses are based on measures of the amplitude and frequency content of changes in the centre of pressure. During quiet standing conditions, the accelerations of the body’s centre of mass are proportional to the changes in centre of pressure (Winter 1995). Thus, accelerations measured at the height of the body’s center of mass should yield similar information about postural stability as the data measured by a force plate. The latter assumption seems to be confirmed by accelerometry based studies of trunk sway during different standing conditions (e.g. Mayagoitia et al. 2002, Moe-Nilssen & Helbostad 2002). In addition to accelerometry-based approaches, the use of gyroscopes to measure trunk rotations during standing has shown to be sensitive for difference in postural stability (e.g. Allum et al. 2005). Spatio-temporal gait parameters such as walking speed, step length and frequency, duration of swing and stance phases can be determined from sensors on leg or trunk segments. Hausdorff et al. (1995) used foot switches to measure (the variability of) temporal gait parameters. Aminian et al. (2002) used gyroscopes on shank and thigh segments of both legs in combination with a geometrical model of the leg to estimate spatial

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parameters in addition to temporal gait parameters. Based on knowledge of the mechanical determinants of trunk movements during walking (i.e. inverted pendulum characteristics (see Zijlstra & Hof 1997, Zijlstra & Hof 2003)), Zijlstra (2004) estimated spatio-temporal gait parameters using 3D accelerometry at a lower trunk position close to the body’s centre of mass. Sabatini et al. (2005) used knowledge of specific constraints for human walking (i.e. repeated stance phases where the foot has no forward velocity) to overcome typical limitations of sensors and determine spatio-temporal gait parameters from a 2D hybrid sensor on the foot. When more than one sensor location is used, the basic spatio-temporal gait parameters can be complemented with additional parameters which depend on the exact sensor configurations (e.g. joint kinematics (Dejnabadi et al. 2005, 2006), trunk angles (Zijlstra et al. 2007), or joint dynamics (Zijlstra & Bisseling 2004). Performance of the Sit-to-Stand (STS) transfer requires the ability to maintain balance while producing enough muscle force to raise the body’s centre of mass from a seated to a standing position (e.g. see Schenkman et al. 1999, Ploutz-Snyder et al. 2002, Lindemann et al. 2003). Recent studies demonstrated that hybrid sensors can be used to obtain temporal measures of STS (e.g. Najafi et al. 2002), and estimations of the power to lift the body’s center of mass during the STS movement (Zijlstra et al. 2010). Usually, the latter analyses of muscle power are restricted to a laboratory-based approach using cycle- or rowing-ergometers, or opto-electronic camera systems and force plates. However, based on laboratory validations, the recent study demonstrated that motion sensors can be used to obtain measures of muscle strength and power during the Sit-to-Stand (STS) movement in young and older (70+) subjects. To summarize this section: the present literature indicates the availability of wearable technology and suitable methods for assessment of relevant quantitative and qualitative aspects of mobility related activities. As a general rule,

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the use of hybrid sensors and complex sensor configurations on the body offer better analytical solutions. However, even complex configurations of multiple hybrid sensors are limited with regard to the analysis of (changes in) position of body segments in relation to the support surface or objects in the environment. Furthermore, practical considerations may necessitate the choice for as few sensors as possible. It should be noted that the wealth of sensor-based approaches to mobility assessment and monitoring has not yet resulted in a standardization of procedures for data-acquisition, data-analysis and the validation of assessment methods.

EMERGING HEALTHCARE SERVICES BASED ON MOBILITY MONITORING The World Health Organization’s International Classification of Functioning (i.e. the ICF-model (see references)) presents a framework for studying the effects of disease and health conditions on human functioning. The model indicates that insight in the underlying mechanisms of functional decline requires insight in the complex relationships between body functions (e.g. joint flexibility, muscle force), activities (e.g. gait & balance capacity), and actual movement behavior in daily life. The ICF model underlines the need of assessing both capacity (“What a person can do under standardized conditions”) and performance (“What a person really does in his or her daily life”). During daily life a person may use specific assistive devices and services (facilitators), or vice versa may encounter specific problems (barriers) in his or her personal situation. Thus, capacity and performance measures yield different information and the ICF model very clearly indicates the need to assess capacity and to monitor the performance of mobility related activities in real-life. The ICF model is widely used as a framework for clinical research, but the model does not

specify how capacity and performance should be measured. At present, the extent to which the ICF model is systematically used as part of the daily work routines of those disciplines which address impaired motor functioning and mobility seems rather limited. One reason for this situation is that tools to systematically address capacity and performance aspects of mobility have not been available for application in clinical practice. The tools which are based on wearable technology, as described in the preceding section, are based on recent developments, and there still is the need to resolve many issues before these new tools may become a standard part of clinical routine. First of all, an undisputed evidence-base is needed for the clinical relevance of monitoring based health-care services, and secondly the monitoring approaches need to be acceptable to the potential users. Thus, it is of paramount importance to not only have technical solutions available, it is also necessary to evaluate the clinical validity of tools for mobility assessment and monitoring, and to assess and incorporate user perspectives in the development of monitoring-based health-care services. The next sub-sections will address some of the issues relating to clinical relevance by focusing on aspects of current clinical practice and the potential contribution of mobility monitoring techniques in new health care services. Examples will be given in relation to different conditions, which may lead to mobility impairments. This section will be followed by a next section, which specifically addresses the user perspective on wearable technology.

Current Clinical Practice When aiming to provide optimal care to a patient, health-care professionals are typically confronted with a number of recurrent issues; e.g. the need to diagnose certain conditions, the need to evaluate outcome of an intervention, and the need to predict the risk of future adverse events. All these issues require decisions, which are based on adequate

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assessment tools. The quality of available tools for clinical assessment varies strongly between different clinical disciplines, and at present the clinical fields, which address (potential) mobility problems in different populations still need to further develop assessment tools of acceptable quality. At present, the clinical assessment of mobility related activities mainly encompasses the use of field tests, observational methods, and self-report instruments. Rarely are objective quantitative methods used as part of the routine clinical assessments. The available clinical instruments that are used can be disease specific or generic, but generally, there are serious limitations to their use. For example, at present, there is little information to give evidence-based recommendations for specific existing field tests for balance and mobility in frail older persons (see the Prevention of Falls Network (www.ProFaNE.eu.org) and see Gates et al. 2008). Although many conventional clinical tests for balance and mobility (such as the Timed Up & Go (TUG), or Berg Balance Scale (BBS)) are easy-to-use, and hence allow for integration in clinical practice, they do have serious disadvantages. Most of the present field tests lack a conceptual fundament for balance assessment (e.g. Huxham et al. 2001); the scores which result from these tests are usually based on observation and simple time measures (e.g. time one one-leg, time required for standing up from a chair walking 3m, turning walking back and sitting down); and at present there is only limited evidence for the clinical validity of these measures for diagnosis, prognosis, or outcome evaluation. Similarly, there are serious limitations to the use of self-report methods (such as diaries, questionnaires, or interviews) for obtaining information about the quantity of mobility related activities in daily life. Recalling physical activity is a complex cognitive task and the available self-report instruments vary in their cognitive demands. Older adults in particular may have memory and recall skill limitations, and generally people tend

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to overestimate their physical activity levels. In addition, the methods do not give a detailed profile of type, duration, frequency, and intensity of the most common daily physical activities. Although out-of-home activities, such as leisure time and sportive activities can be assessed by questionnaire, it is hard to assess non-exercise activities with thermogenesis (NEATs). In older people, more than 70% of physical activities occur at home and this should be included in an appropriate analysis. Particularly in sedentary frail older people, self-reports are insensitive to (small) changes in patterns of physical activity. Currently, the available figures about the incidence of falls primarily depend on oral reports of the subjects themselves or their proxies. Thus, also the evaluation of fall prevention methods, in terms of a reduction in number of falls, depends on subjective reports. These reports can be biased, for example due to difficulties in defining a fall, not-remembering a fall, or due to the cognitive status of the subject who fell (e.g. see Zecevic et al. 2006, Hauer et al. 2006). In addition, the circumstances of a fall and the fall characteristics are seldom reported in a structured manner (Zecevic 2007). The lack of objective fall data seriously hampers the development and evaluation of effective fall prevention programs.

Potential Contribution of Mobility Monitoring to New Health Care Services A large number of studies have demonstrated the potential clinical relevance of sensor-based mobility assessment and monitoring in older people (e.g. see Zijlstra & Aminian 2007). The shortlist of available sensor-based methods in the previous section clearly indicates that a variety of clinically relevant applications seem possible. However, at present there are no standardized sensor-based clinical tests for the assessment of balance and mobility. Furthermore, a recent systematic review (de Bruin et al. 2008) indicates that the majority

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of available studies, which have used wearable systems for monitoring mobility related activities in older populations have merely addressed the technical validity of monitoring methods. The number of studies that actually applied monitoring methods over long durations in support of clinical goals was (very) limited. Thus, the review unfortunately demonstrates that there is still the need to develop evidence-based applications of the available monitoring technology. At present, a number of clinically relevant applications of monitoring techniques can be identified. First of all, a generic aspect of exercise-based interventions in different target groups is setting (individual) target levels for physical activity. To control whether target levels are reached (or not) requires appropriate monitoring information. These can be obtained by different monitoring techniques. Second, the quality of care to specific patient groups with mobility problems may be improved based on the systematic use of monitoring techniques. Third, the use of monitoring techniques can be used to identify persons with an increased risk of falling. Fourth, when a high fall risk has been established, reliable fall detection methods may be used to automatically detect a fall and initiate alarm services when necessary. These four examples of clinically relevant application of monitoring techniques will be elaborated in the next subsections.

Mobility Monitoring to Support Interventions Aiming to Stimulate Physical Activity An increasing body of evidence demonstrates the importance of physical activity for general health in older adults (e.g. see position stand by the American College of Sports Medicine (ACSM), 2009). Regular physical activity has proven to be effective in the primary and secondary prevention of several chronic conditions and is linked to a reduction in all-cause mortality (e.g. U.S. Department of Health and Human Services

1996, Warburton et al. 2006). In addition, regular physical activity can enhance musculoskeletal fitness (Warburton et al. 2001, 2001), and cognitive functioning (Kramer et al. 2006, Hillman et al. 2008) which both are major prerequisites for functional autonomy. Physical inactivity, on the other hand, is associated with premature death and specific chronic diseases such as cardiovascular disease, colon cancer, and non-insulin dependent diabetes (Warburton et al. 2006). It is widely recognized that, in western societies, many older adults do not achieve sufficient levels of physical activity according to guidelines by the ACSM and the American Heart Association (cf. Nelson et al. 2007, Haskell et al. 2007). However, most of the present knowledge about daily physical activity patterns has been obtained through selfreport based methods, and there still is a lack of objective data about physical activity in different older populations. Recent data collected on 7 consecutive days by a body-fixed sensor in community-dwelling older adults demonstrate that on average the older persons spent ca. 5 hours in an upright position (i.e. standing and walking), the average cumulative walking time was approximately 1.5 hours. However, a strong inter-individual, and intraindividual day-to-day variability was observed (Nicolai et al. 2010). Monitoring variables such as cumulative walking and standing time can be used as a robust indicator of overall physical activity. This allows for self-management or remote counseling of older adults and specific patients groups as part of exercise-based interventions. Particularly when more specific information about qualitative aspects of movement performance can be monitored it will be possible to initiate remotely supervised exercise-programs (e.g. see Hermens & Vollenbroek-Hutten 2008). An individual personal coaching based on objective monitoring data might be used to optimize levels of performance. In specific patient groups, monitoring might also prove to be useful for the

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early detection of clinical deterioration and the management of chronic diseases.

Mobility Monitoring to Support Interventions in Specific Patient Groups In several ways, the monitoring of quantitative and qualitative aspects of mobility related activities in patients with impaired mobility is likely to result in a better disease management and counseling. Not only can patients be monitored to see whether target levels for physical activity are reached (cf. preceding sub-section), monitoring may also contribute to a better diagnosis, prognosis, and evaluation of treatment effects. Thus, the treatment and guidance can be tuned to the individual needs of a patient. The following examples demonstrate potential clinical applications in specific diseases with a high prevalence, severe mobility problems, and considerable associated medical costs: •

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Arthrosis: A very common age-associated problem is hip or knee arthrosis. In these conditions, the early course of the disease usually consists of pharmaceutical and/ or exercise-based treatment. In the more advanced stages, hip or knee replacement are the most common treatment options to regain pain relief, functional improvement and normal participation levels. Recent studies demonstrated systematic improvements in gait parameters (van den AkkerScheek et al. 2007), and a poor relationship between self-reported and performancebased measures of physical functioning in patients after total hip arthroplasty (van den Akker-Scheek et al. 2008). Thus, the use of mobility monitoring seems ideally suited to document physical activity patterns and the treatment response of the patients in an objective and valid manner. The monitoring can demonstrate the improvement in walking distances and duration after the application of pain treatment as well as the

•

treatment response to an operative procedure. Although Morlock et al. (2001) used an ambulatory system to investigate the duration and frequency of every day activities in total hip patients, mobility monitoring has, to our knowledge, not yet been used to systematically document treatment effects in patients with arthrosis. Chronic Obstructive Pulmonary Disease (COPD): COPD refers to chronic lung conditions in which the airways become narrowed; i.e. chronic bronchitis and emphysema. This results in a limited air flow to and from the lungs causing a shortness of breath and, thus, a limited capacity for physical activities. The disease usually is progressively getting worse over time. Medication can improve lung function and reduce the risk of pulmonary inflammations and thus enhance exercise tolerance. In addition it has been demonstrated that pulmonary rehabilitation can enhance exercise tolerance, improve symptoms and reduce exacerbations. Increasing exercise tolerance and the quantity of daily life physical activities is of crucial importance for COPD patients, because it has been shown that regular physical activity reduces hospital admissions and mortality (Garcia-Aymerich et al. 2006). A recent monitoring-based study confirmed that physical activity indeed is reduced in COPD patients (Pitta et al 2005). A subsequent study then showed that whereas pulmonary rehabilitation can improve exercise capacity, muscle force, and functional status after 3 months of rehabilitation, the improvements in daily life physical activities were first demonstrated after 6 months (Pitta et al. 2008). The latter finding demonstrates the need to develop effective strategies to enhance daily life physical activities in COPD patients. It can be expected that mobility monitoring could be used

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•

to support such strategies and thus enhance functioning and life-expectancy in COPD. Parkinson’s Disease (PD): This neurodegenerative disease is common, its prevalence in the population above 65 is approximately 1-2 percent. Due to the increasing percentage of old people as part of the total population, its prevalence is increasing. Disease progression is mainly characterized by increasing postural instability, balance problems, and mobility impairment (cf. Hoehn & Yahr 2001), which also lead to an increased risk for falls and fall related injuries. In addition, PD patients suffer from symptoms such as a slowing of movement (bradykinesia) and tremors. Particularly in the later stages of the disease, additional difficulties may arise like emotional problems and cognitive deficits. The early stages of PD can be treated effectively with medication, but, after some years many patients develop disease related problems that can no longer fully be treated by medication. In these later stages, additional therapeutic strategies, such as deep brain stimulation may be initiated (Fahn, 2010).

During the early course of the disease, mobility monitoring can be used to document the effect of the medication on movement patterns, and to reach an optimal individual dosage of medication. This is highly relevant since the numbers of patient visits with the specialist usually are few and brief. The observed movement performance during these visits is not necessarily representative for movement performance in daily life. Thus, remote monitoring might lead to modifications in pharmacological treatment, which else would not have been possible. Due to the specific patho-physiology of PD, patients may profit from external information, which facilitates their movements. Provided that adequate algorithms and actuators are available, mobility

monitoring in PD may be used as a foundation for initiating feedback-based interventions, or auditory and/or visual cues that facilitate movement performance. Anecdotal evidence and some studies indicate that PD patients do profit from such interventions. Recently, these possible treatment strategies have been addressed in research projects financed by the European Commission (e.g. see www.rescueproject.org, www.sensaction-aal.eu). In later stages of the disease, PD patients may develop motor problems such as hyperkinesias, freezing or off-periods that are intermittent and cannot be documented during the visit. Here, the movement monitoring allows the development of patient profiles to document changes due to the course of the disease or the changes in treatment. In addition, monitoring may be used to detect gait arrhythmia, which indicates an increased risk of falling that should be discussed with the patient and care-givers.

Identification of Persons at an Increased Risk for Falls Regarding fall risk, a decreased gait speed (e.g. Lamb 2009), an increased variability in temporal gait parameters such as stride duration (e.g. Hausdorff 1997, 2001), variability in lower trunk accelerations (Moe-Nilssen & Helbostad 2005) and frontal plane trunk rotations during walking (de Hoon et al. 2003), and the duration and variability of body transfers such as during sit-to-stand (Najafi et al. 2002), have all shown to be sensitive in distinguishing older fallers from non-fallers, or frail from fit elderly. Using wearable sensors, it is possible to monitor such parameters and other parameters, such as instabilities during missteps or tripping, over long duration. However, the predictive validity of these and similar measures still needs to be demonstrated in larger cohorts. Ideally, a monitoring approach would allow to evaluate whether a person is at an increased risk for falling before a fall occurs. Thus, a timely initiation of preventive measures could avoid future

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falls and injuries. Such preventive measures could encompass exercise-based interventions to optimize balance (i.e. ‘capacity’) and daily physical activity (i.e. ‘performance’), counseling strategies, home-adaptations, or compensating interventions (for example the use of assistive devices such as a cane or a walker, or wearing hip protectors).

Monitoring of Mobility and Fall Incidents in High-Risk Groups The epidemiology of falls is threatening. Approximately 30% of persons older than 65 years fall each year, and after the age of 75 fall rates are even higher (e.g. O’Loughlin et al. 1993). In Germany more than 5 millions falls occur each year, with a substantial part of these falls leading to hospital admissions. It has been estimated that the fall-related costs in European countries, Australia and the USA are between 0.85% and 1.5% of the total health care expenditures (Heinrich et al. 2009). Given the huge impact of falls, strategies that effectively reduce the number of falls or the consequences of falls are highly relevant. Thus, the development of reliable monitoring approaches which monitor parameters, which indicate fall-risk (cf. the previous sub-section), physical activity patterns and the occurrence of falls is one of the most urgent needs in health care services to older people. Given its relevance, it is no surprise that in the last two decades an increasing effort is made to develop reliable fall detection methods. At present, a number of different approaches, either based on ambient technology (i.e. smart homes) or body worn sensors (e.g. N´I Scanaill et al. 2006), have been reported (see also in earlier section). However, it is astonishing, how little real-life falls data are available and it must be stressed that currently there are no sensor-based fall detection systems available that have been tested in large scale trials. Therefore, the development of adequate sensors and fall-detection algorithms, and their real-life validation in different older populations continues to be an enormous challenge.

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The development of automatic fall-alarms needs to encompass an analysis of the fall and its impact as well as the post-fall phase. Sensorbased signals during the falling and impact phase are needed to identify a fall. Depending on sensor type and location, information on body acceleration, jerk, impact height, fall direction, protective mechanisms and body position while hitting the ground can be derived from the data. These can be used to develop algorithms to detect a fall. Figure 1 presents an example of accelerations measured at the lower trunk before, during and after a real-life fall. In this specific example the circumstances of the fall and the nature of the fall itself were well-documented. It should be noted that, given the variety of falls and their pre-conditions, the categorization and recording of different fall types is highly relevant for the development of sensitive fall detection methods in different populations. The movement pattern in the time period following the impact phase reflects the consequences of the fall. Consequently, their interpretation is crucial for the decision whether or not to initiate an automated alarm response. The post-fall situation might be life threatening (e.g. syncope or epilepsy), very serious (fracture), or without serious consequences. From a user perspective it is crucial that a major event is recognized immediately (e.g. calling an emergency) and that a minor event does not result in an overreaction of the social environment.

THE USER PERSPECTIVE In respect to monitoring solutions over longer durations, questions about the users’ acceptance become more and more relevant next to aspects of effectiveness and (social) significance. The successful application of technologies in this field is strongly influenced by factors that relate to the fit between the demands of the technology and the specific capabilities of the user, the obtrusiveness of the monitoring method, and not least by intrinsic

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Figure 1. An example of acceleration signals in vertical (x-axis), medio-lateral (y-axis) and sagittal (z-axis) direction during a real-life fall with impact at t = 30 sec

and extrinsic motivational factors. Furthermore non-acceptance and non-usage can be regarded partly as a consequence of the failure of designs and operational procedures to respond to the wishes and feelings (of this very heterogeneous group) of older people (Fisk 1998). This seems to be a very important issue because even very simple and already widely used systems like wearable alarm-buttons seem to be poorly used for their intended purpose: the case of emergency. In a recent study it was shown that alarmbuttons were not used in most cases where a fall was followed by long period of lying on the floor (Fleming & Brayne 2008). It was shown that 94% of the person who were living in institutional settings, 78% in the community, and 59% in sheltered accommodations did not use their call alarm to summon help after they felt alone and were unable to get up without help. 97% of the person lying on the floor for over one hour did not use their alarm to summon help. “Barriers to using alarms arose at several crucial stages: not seeing any advantage in having such a system, not developing the habit of wearing the pendant even if the system was installed, and, in the event of a fall, not activating the alarm – either as a conscious decision or as a failed attempt” (Fleming & Brayne 2008). One reason probably could be found in the fact that

35% of the participants were severely cognitive impaired. Another aspect of these results may show the danger in the overemphasis of clinical objectives within service frameworks for tele-care in people’s own homes. Technologies in the home may be viewed as more intrusive (and, therefore, less acceptable) than in other, more institutional contexts (Fisk 1997). For a successful extension of (commercial) monitoring technology and its integration into tele-health systems a better understanding of acceptance and non-acceptance for specific target groups is indispensable. The following briefly outlines three theoretical constructs, which address important key aspects relevant to this question.

The Human Factors Approach The human factors approach refers to “the role of humans in complex systems, the design of equipment and facilities for human use, and the development of environments for comfort and safety” (Salvendy 1997). The aim of this approach is to match the demands of a system to the capabilities of the user; as illustrated in Figure 2, described by Rogers & Fisk 2003 and originally adopted by the Center for Research on Aging and Technology Enhancement (Czaja et al. 2001). “The system

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imposes certain demands on the user as a function of the characteristics of the hardware, software, and instructional support that is provided for it. The operator of the system has certain sensory/ perceptual, cognitive, and psychomotor capabilities. The degree of fit between the demands of the system and the capabilities of the user will determine performance of the system as well as attitudes, acceptance, usage of the system, and self-efficacy beliefs about one’s own capabilities to use that system” (Rogers & Fisk 2003). To develop good fitting technologies, various approaches like exact analysis of the necessary tasks when using the technology, observation of users, manipulation of the different designs, and the development of proper training and instructional support are recommended by the authors.

The Obtrusiveness Concept The obtrusiveness concept in tele-health was defined by (Hensel et al. 2006a) “as a summary evaluation by the user based on characteristics or effects associated with the technology that are

perceived as undesirable and physically and/or psychologically prominent”. This concept (see Figure 3; Hensel et al 2006a) was constructed inductively by 22 categories found in the literature. It adds some further and more specific aspects compared to the more general “Human factors approach”. In a secondary analysis within two studies based on focus groups and interviews with residential care residents 16 of the postulated 22 subcategories could be confirmed (*= confirmed in 1 study, **= in both studies; Courtney, Demiris, & Hensel 2007). Restrictively, it has to be mentioned that only two of the 29 participants of the two studies were using information-based technologies at the time of the interview.

The Expectations-Confirmation Theory From a consumer behavior perspective the expectations-confirmation theory (ECT) adds a further aspect to user satisfaction with including initial expectations of a specific product or service prior to purchase or delivery. This theoretical construct

Figure 2. Matching system demands to user capabilities (see text for further explanation)

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Figure 3. The obtrusiveness concept (see text for further explanation)

posits that the expectations lead, after a period of initial experience, to post-purchase satisfaction. The primary reason that we experience disappointment or delight is because our expectations have not been met (negative disconfirmation) or they have been superseded (positive disconfirmation) and we have noticed and responded to the discrepancy (Figure 4; Oliver 1980; Spreng et al. 1996).

Integration of User Perspectives in the Development of Monitoring Approaches When summarizing the previous three concepts, user satisfaction with wearable activity monitoring systems should be seen in the context of a good fit between the demands of the device and

Figure 4. The expectations-confirmation theory (see text for further explanation)

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the capabilities of the user, and furthermore the meeting or exceeding of the user’s expectations in regard of a favorite balance between the perceived benefit and obtrusive aspects. Considering aspects of user satisfaction is becoming more recognized as an important criterion for the development and application of long term and unsupervised monitoring approaches. However, there still seems to be a lack of pertinent data. In a recent systematic review of studies using wearable systems for mobility monitoring in older people (de Bruin et al. 2008), it was noted that only few studies had actually applied monitoring methods over long durations, and that the number of studies that addressed feasibility and user acceptance aspects was even smaller. The identified feasibility and adherence aspects mainly related to the reliability of various devices in unsupervised measurement settings and the acceptance of devices by the populations under study. The user acceptance seemed to vary with sensor location and attachment method. Our own experience in recent or ongoing studies indicates a reasonable user acceptance of singlesensor monitoring techniques. The adherence was above 90% for short periods (24-48 hours) of monitoring with a sensor on the trunk. However, with repeated and extended measurements the acceptability decreased to 50-60%, and in patients with severe cognitive impairment, or during hot periods, adherence can be considerably lower. These experiences indicate that there is a need for sensor miniaturization and/or the integration of small sensors in clothing. Ultimately, monitoring systems need to be evaluated over longer periods and by taking into account different aspects of user perspectives. To our knowledge, there currently is no comprehensive assessment tool available that evaluates user satisfaction with monitoring technologies. Therefore, we have started to develop a questionnaire with six dimensions and 30 items in total. The five-point level Likert scaled items are phrased with regard to sensor based applications and comprise the following six dimensions: (perceived) benefit, usability, self-concept, privacy &

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loss of control, quality of life, wearing comfort. The questionnaire is currently under validation in a number of ongoing studies. Further information is available under www.aktiv-in-jedem-alter.de.

CONCLUSION Demographic changes confront most industrialized countries with the challenge of an ageing population together with a decreasing work force. Innovative solutions are needed to maintain the quality of our health care systems. Monitoring based health-care services can potentially play a role in decreasing the workload of health care professionals. For example by increasing the effectiveness of existing work routines, or by supporting effective preventive strategies that avoid the extensive use of regular health care services. Given the huge challenges in the next decades, the latter preventive medicine approach deserves increased attention. The present contribution has shown that basic technical solutions for wearable systems for monitoring mobility are available, and that relevant clinical applications can be identified for different older populations. However, from the preceding sections, it will be clear that there still is a gap to bridge before mobility monitoring approaches can become an accepted part of regular healthcare services. Without claiming to be complete, we think at least the following issues need to be resolved to facilitate the successful application of wearable systems for mobility monitoring as part of healthcare services. First of all, the present evidence-base for the clinical relevance of existing clinical assessment tools for mobility related activities is insufficient, and, at this time, this shortcoming is also true for the new sensor-based assessment tools. Although several studies suggest a high potential for relevant applications in older people, the new sensor-based approaches for mobility assessment and monitoring still need to be applied in appropriately designed clinical studies to unequivocally demonstrate their

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clinical validity for specific purposes. Evidencebased clinical applications of new monitoring techniques should rely on epidemiological studies with non-selected cohorts as well as in well-defined patient groups. The additional benefit of monitoring should be documented in controlled trials. Secondly, there is little doubt that ongoing and future research in the next years will only further increase the abundance of different sensor-based assessment approaches. This will lead to further improvements and innovations, but it will also increase the need for standardization of instruments and procedures for data-acquisition and data-analysis. In addition, it can be expected that there will be a need to define standards for the clinical validation procedures, in order to be able to compare different approaches. At present, such considerations have hardly received attention. Third, the application of monitoring based health-care services requires the development of monitoring approaches which are acceptable to its potential users. Thus, it is essential to assess and incorporate user perspectives in the development process. It should be noted that these user perspectives must not only comprise the patient perspective, but also the perspectives of health care professionals who are involved in using the system. Both types of users will need to be convinced of the potential benefits of the monitoring techniques. It is likely that the willingness to accept will not only depend on an existing evidence-base for the clinical relevance, but also on the practicability of the systems in the home environment and in the clinical environment.

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ADDITIONAL READING American College of Sports Medicine, ChodzkoZajko, W.J., Proctor, D.N., Fiatarone Singh, M.A., Minson, C.T., Nigg, C.R., Salem, G.J. & Skinner, J.S. (2009). American College of Sports Medicine position stand. Exercise and physical activity for older adults. Medicine and Science in Sports and Exercise, 41(7), 1510–1530.

Hermens, H. J., & Vollenbroek-Hutten, M. M. (2008). Towards remote monitoring and remotely supervised training. Journal of Electromyography and Kinesiology, 18(6), 908–919. doi:10.1016/j. jelekin.2008.10.004 International Classification of Functioning. Disability and Health (ICF).(n.d.). World Health Organisation, Geneva (www.who.int/classification/icf). Luinge, H. J. (2002). Inertial sensing of human movement. PhD thesis. The Netherlands: Twente University Press. Mathie, M. J., Coster, A. C., Lovell, N. H., & Celler, B. G. (2004). Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological Measurement, 25(2), R1–R20. doi:10.1088/0967-3334/25/2/R01

Aminian, K., & Najafi, B. (2004). Capturing human motion using body-fixed sensors: outdoor measurement and clinical applications. Computer Animation and Virtual Worlds, 15(2), 79–94. doi:10.1002/cav.2

N’I Scanaill, C., Carew, S., Barralon, P., Noury, N., Lyons, D. & Lyons, G.M. (2006). A Review of Approaches to Mobility Telemonitoring of the Elderly in Their Living Environment. Annals of Biomedical Engineering, 34, 547–563. doi:10.1007/s10439-005-9068-2

Bonato, P. (2005). Advances in wearable technology and applications in physical medicine and rehabilitation. Journal of Neuroengineering and Rehabilitation, 2, 1–4. doi:10.1186/17430003-2-2

Noury, N., Fleury, A., Rumeau, P., Bourke, A. K., Laighin, G. O., Rialle, V., & Lundy, J. E. (2007) Fall detection principles and methods. Conference Proceedings - IEEE Engineering in Medicine and Biology, 1663-1666.

de Bruin, E. D., Hartmann, A., Uebelhart, D., Murer, K., & Zijlstra, W. (2008). Wearable systems for monitoring mobility related activities in older people. Clinical Rehabilitation, 22, 878–895. doi:10.1177/0269215508090675

Roetenberg, D. (2006). Inertial and magnetic sensing of human motion. PhD thesis. The Netherlands: University of Twente.

Hausdorff, J. M. (2005). Gait variability: methods, modeling and meaning. Journal of Neuroengineering and Rehabilitation, 2, 19. doi:10.1186/17430003-2-19

Skelton, D. A., & Todd, C. (2004). What are the main risk factors for falls amongst older people and what are the most effective interventions to prevent these falls? How should interventions to prevent falls be implemented? Health Evidence Network. Denmark: World Health Organisation.

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Terrier, P., & Schutz, Y. (2005). How useful is satellite positioning system (GPS) to track gait parameters? A review. Journal of Neuroengineering and Rehabilitation, 2, 2–28. doi:10.1186/17430003-2-28 Winter, D. A. (2009). Biomechanics and Motor Control of Human Movement (4th ed.). New York: John Wiley & Sons. doi:10.1002/9780470549148 Zijlstra, W., & Aminian, K. (2007). Mobility assessment in older people, new possibilities and challenges. European Journal of Ageing, 4, 3–12. doi:10.1007/s10433-007-0041-9

KEY TERMS AND DEFINITIONS Movement: Movement is a change of position, or a rotation, of an object or (parts of) the body. Physical Activity: Physical activity is any body movement produced by skeletal muscles that results in energy expenditure.

Mobility (as used in this contribution): Mobility relates to voluntary movements that involve a change of overall body position, for example rising from a chair, or walking. A person with a mobility impairment may have difficulty with walking, standing, lifting, climbing stairs, carrying, or balancing. Motion Sensor: An instrument that senses and measures movement. Assessment (as used in this contribution): The systematic collection of information about an individual’s motor and/or cognitive functioning. Monitoring (as used in this contribution): Repeated assessments of aspects of an individual’s motor and/or cognitive functioning. Fall: An unexpected event in which a person comes to rest on the ground, floor, or a lower level. fall detection: An automated recognition of the occurance of a fall, for example based on video data or based on motion sensors.

This work was previously published in E-Health, Assistive Technologies and Applications for Assisted Living: Challenges and Solutions, edited by Carsten Röcker and Martina Ziefle, pp. 244-267, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 3.13

RFID in Hospitals and Factors Restricting Adoption Bryan Houliston Auckland University of Technology, New Zealand

ABSTRACT Hospitals are traditionally slow to adopt new information systems (IS). However, health care funders and regulators are demanding greater use of IS as part of the solution to chronic problems with patient safety and access to medical records. One technology offering benefits in these areas is Radio Frequency Identification (RFID). Pilot systems have demonstrated the feasibility of a wide range of hospital applications, but few have been fully implemented. This chapter investigates the factors that have restricted the adoption of RFID technology in hospitals. It draws on related DOI: 10.4018/978-1-60960-561-2.ch313

work on the adoption of IS generally, published case studies of RFID pilots, and interviews with clinicians, IS staff and RFID vendors operating in New Zealand (NZ) hospitals. The chapter concludes with an analysis of the key differences between RFID and other IS, and which RFID applications have the greatest chance of successful implementation in hospitals.

INTRODUCTION In 1989 management guru Peter Drucker described hospitals as prototypical knowledge-based organisations (Drucker, 1989). Considering the variety and volume of information that hospitals,

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RFID in Hospitals and Factors Restricting Adoption

and other health care organisations, deal with, it is easy to see why they might be expected to be early adopters of IS. Kim and Michelman (1990) identify the components of a typical integrated Hospital Information System (HIS): General accounting and budgeting; Staff payroll; Patient demographic information and medical records; Nursing care plans; Treatment orders; Test results; Surgery and resource schedules; and Databases of clinical information relating to Radiology, Pharmacology, Pathology, and other specialist departments. Yet research suggests that in comparison to other industries, the health care sector invests relatively little in IS. Twelve years on from Drucker’s statement, a British survey of annual IS spending per employee found that the health care sector spent approximately one-third that of the manufacturing sector, one-fifth that of the distribution sector, and one-ninth that of the financial sector (Wallace, 2004). This low level of investment has led various stakeholders to demand greater use of IS in the health care sector. Two key areas in which significant potential benefits have been identified are improving patient safety, and sharing electronic medical records amongst all the health care organisations that may treat a patient. Patient safety is, of course, paramount in health care. ‘First, Do No Harm’ is the fundamental principle of the medical profession. Yet each year medical mistakes take a heavy toll in both human life and health care resources. For example, errors in administering drugs, known as Adverse Drug Events (ADEs), are believed to result in tens of thousands of deaths, many more serious injuries, and to cost the health care sector tens of billions of dollars (Classen, Pestotnik, Evans, Lloyd, & Burke, 1997; Davis et al., 2003; Johnson & Bootman, 1995; Wilson et al., 1995). The US Institute of Medicine (IOM) strongly advocate the use of IS to reduce the incidence of ADEs (Institute of Medicine, 2001). Regulatory agencies, such as the Food and Drug Administration (FDA) and Joint Commission on Accreditation on Healthcare

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Organisations (JCAHO), have mandated the use of barcode technology in US hospitals to improve identification of medications and patients (Merry & Webster, 2004). The NZ Ministry of Health has recently announced plans to spend NZ$115 million to implement systems such as Computerised Physician Order Entry (CPOE) and Barcoded Medication Administration (BCMA) (Johnston, 2007). The leading cause of ADEs is the prescription of unsuitable drugs (Bates, Cullen, & Laird, 1995; Leape, Bates, & Cullen, 1995). Unsuitable prescriptions result primarily from clinicians lacking ready access to patients’ medical records, and thus being unaware of drug allergies, existing conditions, and current prescriptions. Storing medical records in electronic form, in a centralised database, enables timely access to such information for all clinicians who may treat a patient. In the UK, the government is planning to spend around £6 billion on an Electronic Patient Records (EPR) system for the National Health Service (NHS) as part of the ‘Connecting for Health’ initiative (Wallace, 2004). In NZ, the WAVE (Working to Add Value through E-information) Advisory Board to the Director-General of Health recommended a similar system (WAVE Advisory Board, 2001), which has been included in the country’s Health Information Strategy (Health Information Strategy Steering Committee, 2005). In Australia a non-profit company created by federal and state governments, the National e-Health Transition Authority (NEHTA), invests in IS that supports sharing of EPRs. The same approach has been taken in Canada, with the Health Infoway corporation. In the US, major insurers, such as Medicare, require hospitals to provide details of treatment in electronic form (Jonietz, 2004). One technology that has been gaining attention in the health care sector for its potential to address these issues is RFID. It offers very similar functionality to barcode technology, but with a number of advantages (Schuerenberg, 2007). Most notably, RFID allows multiple labels to be scanned

RFID in Hospitals and Factors Restricting Adoption

simultaneously without requiring a line-of-sight between the scanner and the label. A range of RFID applications, from real-time stocktaking, to tracking patients, staff and equipment to storing patient data, have been successfully trialled by hospitals around the world (Wasserman, 2007). Based on published case studies, relatively few hospitals have fully implemented such systems. At the time of writing, NZ hospitals are just beginning to RFID pilots. This chapter seeks to investigate the factors that have restricted the adoption of RFID technology in hospitals. The next section, RFID and Hospital IS, describes the various RFID applications piloted in hospitals. It then reviews research on the adoption of IS generally by hospitals, identifying a number of key organisational and technological factors. To gain greater insight of these factors in the NZ context, a series of interviews were conducted with clinicians, IS staff and RFID vendors operating within the NZ hospital environment. The section Interviews and Findings briefly describes the interview process and presents the results. The Discussion section considers some implications of this research for RFID pilots in NZ hospitals, and some possible areas for further research.

RFID AND HOSPITAL IS This section describes the various RFID applications piloted in hospitals. It then reviews research on the adoption of IS generally by hospitals, and concludes with the aims of the current research.

RFID Applications for Hospitals Hospitals all around the world have successfully trialled RFID applications. These applications can be broadly organised into four types. Identification applications involve a single reader interrogating a single tag. Data storage applications are those where a single reader reads and writes to a single tag. Location-based applications involve a single

reader reading multiple tags. Tracking applications involve multiple readers reading single or multiple tags. Examples of each application are given below.

Identification Applications Identification of people, objects and locations is an obvious application of RFID technology. Security systems that use RFID proximity cards to control access to restricted areas are perhaps the most common RFID application in use today. Even in developing countries, such as Bangladesh, hospitals are using RFID-enabled staff ID cards (Bacheldor, 2008). Other hospitals have extended the use of staff ID cards to control access to computers and applications (Bacheldor, 2007d). Patient identification appears to be the next most common application, after access control. A survey of US health care organisations found that 29% expected to be using RFID wristbands for patient identification by the end of 2007 (Malkary, 2005). One example is the US Navy, whose medics in Iraq have trialled RFID dog-tags and wristbands in the field, attaching them to injured servicemen, refugees and prisoners of war. The tags can be read even in harsh desert and battlefield conditions (Greengard, 2004). Drug identification is an application attracting growing interest, as it is the basis for both medication administration and e-pedigrees. Intended to combat the counterfeiting, dilution and diversion of drugs, e-pedigrees are mandated in the US under the Prescription Drug Marketing Act. The FDA, which will administer the law, advocates RFID as their preferred technology for keeping e-pedigrees. Drug manufacturers such as Pfizer, GlaxoSmithKline, and Johnson & Johnson have trialled RFID ‘e-pedigrees’ for their products (Herper, 2004) At the Georgia Veteran’s Medical Centre RFID has been used for location identification (Ross & Blasch, 2002). Patients with visual impairments can navigate around the hospital by reading these

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location tags with using a cane containing an RFID reader.

Location-Based Applications Once patients, staff, and drugs and other medical consumables are identified by RFID tags, it becomes possible to automate a number of manual processes that happen at specific locations. One example is prevention of retained surgical items. Sometimes known as the NoThing Left Behind (NTLB) policy, this involves checking that surgical implements and supplies used during an operation have been removed from the patient before they leave theatre. More than 30 hospitals around the US already employ a system that allows surgeons to use an RFID wand to detect tagged surgical sponges left inside patients (Japsen, 2008). Medication administration is another example. At the University of Amsterdam Medical Centre, patients awaiting blood transfusion are issued RFID wristbands. The blood products are also tagged. By reading the two RFID tags prior to starting the transfusion, the risk of a patient receiving incorrect blood is reduced (Wessel, 2007b). Similar systems apply the same principle to patients receiving general, anaesthetic, and chemotherapy drugs. ‘Smart’ cabinets, are already in use in a number of hospitals (Collins, 2004). Drugs and other consumables are tagged, allowing RFID readers inside the cabinets to read them. Inventory levels can then be monitored in real time, and warnings given about expired items or items low stock levels. The cabinets may also read the RFID badges of staff, allowing only authorised people to access restricted drugs. Smart cabinets are relatively expensive, and tagging large numbers of items can be costly and time-consuming. As a result, some companies have started to provide and monitor cabinets as a managed service, with monthly fees rather than a large up-front cost (Bacheldor, 2007a, 2007f).

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RFID makes it safer to take inventory of sterilised items, hazardous chemicals, and other objects with which people should have minimal contact. Sokol and Shah (2004) describe how a US surgical supplies company have embedded RFID tags in over 1 million re-usable surgical gowns and drapes. These are cleaned, assembled into surgical packs, and sterilised. As a final step in the process an RFID reader scans the contents of the pack, verifying that it is complete without compromising the sterilisation. National University Hospital and Alexandra Hospital in Hong Kong used a location-based application for quarantine management during the SARS epidemic of 2002/03 (Roberti, 2003). Two areas were set aside for patients with SARS-like symptoms. All patients, staff and visitors entering these areas were issued with RFID-enabled cards and their movements recorded. Had any patients within these areas been diagnosed with SARS, it would have been possible to identify all the people they had been in contact with during the incubation period.

Tracking Applications RFID technology is being used to track thousands of wheelchairs, beds, IV pumps and other pieces of expensive medical equipment at several hospitals, including Bielefeld City Clinic in Germany (Wessel, 2007a), Lagos de Moreno General Hospital in Mexico (Bacheldor, 2007c), and the Walter Reed Army Medical Centre in the US (Broder, 2004). Each item is tagged and can be located in real time via a network of RFID readers throughout the hospital. Some such systems, often referred to as Real Time Location Systems (RTLSs), also keep track of whether the equipment is available, in use, awaiting cleaning, or undergoing maintenance. If patients and staff are tagged for identification, then patient tracking and staff tracking are also possible. Systems are already available commercially for tracking babies. Osborne Park Hospital in Australia places RFID bands on the

RFID in Hospitals and Factors Restricting Adoption

legs of newborn babies (Deare, 2004), following a kidnapping from its maternity ward. Readers around the ward allow the location of each baby to be tracked. Readers at the exits set off an alarm if they detect a baby being removed from the ward without the presence of an authorised staff member. Alerts are also raised if the ankleband is cut, or if it remains stationary for some time. This may indicate that the band has been removed, or that there is something wrong with the baby. By collecting tracking data over time, hospitals can also use workflow analysis to gain insights into, and identify possible improvements to, their procedures. Huntsville Hospital (Bacheldor, 2007e) is using a system to analyse patients and staff movements up to and following surgery. Ospdedale Traviglio-Caravaggio in Italy has been using RFID to analyse the progress of patients from first arrival in the Emergency Department (ED) (Swedberg, 2008). The cost of tracking systems can be significant, depending on the area to be covered, the number of items to be tracked, and the accuracy required. This cost can be reduced by employing existing wireless networks. For example, Lagos de Moreno General Hospital’s system operates over its WiFi (802.11) network, using the access points as RFID readers and simplified network cards as tags. Walter Reed Army Medical Centre’s RTLS works over a Zigbee (802.15) network. RTLS systems are also available as a managed service, with monthly fees rather than a large up-front cost (Bacheldor, 2007b).

Data Storage Applications The applications described above typically read only the RFID tag’s identifier, and retrieve or update data across a network. But more advanced tags also permit data to be stored on the tag itself. Georgetown University Hospital has trialled a system where the patient’s RFID wristband stores their blood type, allergies and medications (Schuerenberg, 2007). This allows the data to be

viewed quickly even when network access is not available.

IS Adoption in Hospitals The case studies above contain individual hospitals’ experiences with RFID technology. But there is little research on the overall uptake of RFID in the health care sector. In comparison, there is a more significant body of research on adoption of IS generally. The research suggests that hospitals, and the health care sector as a whole, spend relatively little on IS. A recent survey of US hospitals shows that almost 50% spend between 1% and 2.5% of their budget on IS, with 20% spending less and 30% spending more (Morrissey, 2004). According to the IOM, the health care sector ranks 38th out of 53 industries in IS spending per employee, at about 1/25th the level of securities brokers (Institute of Medicine, 2001). A more recent survey in the UK showed a similar pattern, with IS spending per employee in the health care sector approximately one-ninth of that in the financial industry (Wallace, 2004). There are a number of reasons why measuring IS investment per employee may give low figures for the health care sector. One reason is the nature of work in a hospital. Hospitals generally operate 24/7. Medical staff spend a large part of their day on rounds or in theatre, rather than at a desk (Reddy & Dourish, 2002). So while a personal computer (PC) in an office may be used by one person for eight hours a day, a hospital PC is likely to be used by three shifts a day and multiple people during each shift. A second reason is that in comparison to a financial institution, hospitals employ a lot of support staff. Cleaners, caterers, orderlies and the like typically have less need for IS than, for instance, securities brokers. IS has also frequently proven to be a bad investment for hospitals in the past. Rosenal et al (Rosenal, Paterson, Wakefield, Zuege, & LloydSmith, 1995) state that “the set of all successful

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HIS implementations is only slightly larger than the null set”. Littlejohns et al (Littlejohns, Wyatt, & Garvican, 2003) suggest that around 75% of hospital IS projects fail. Heeks et al (Heeks, Mundy, & Salazar, 1999) offer a brighter picture, giving a failure rate of only 50%. The work of Heeks et al is one of the few pieces of research to analyse a number of HIS projects and attempt to generalise some theory as to why they fail. They conclude that the probability of failure is proportional to the size of ‘ConceptReality’ gaps. The larger the gap between the HIS designers’ concept of a system and the end users’ reality, the more likely the system is to fail. While this conclusion is hardly novel, and is not specific to hospitals, the authors do identify seven dimensions on which gaps may occur: Information, Technology, Processes, Objectives, Skills, Management and Resources. A more common approach taken by researchers of HIS success and failure has been to contextualise a general model. For example, England et al (England, Stewart, & Walker, 2000) apply some of Rogers’ seminal work on the diffusion of innovation within organisations (Rogers, 1995) to the diffusion of IS within hospitals. Rogers identified eight organisational factors, shown in Table 1, and five technological factors, shown in Table 2. Evaluating the organisational factors from an analysis of Australian hospitals, England et al conclude that IS innovations would be expected to diffuse slowly. Based on the technological factors, they further conclude that administrative IS, such as accounts and payroll, would diffuse more quickly than more strategic or clinical IS. Although England et al have attempted to base their arguments on previously published research, they note that there is a dearth of suitable material. Thus a number of their arguments seem to be based on their personal experience, which is not documented in any detail. In addition, their framework is relatively simple. Pettigrew et al (Pettigrew, Ferlie, & McKee, 1992) and Greco and Eisenberg (1993), for instance, have developed

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Table 1. Organisational factors affecting the diffusion of IS (Adapted from England et al., 2000) Factors that lead to faster diffusion

Present in Australian hospital environment?

Executive sponsorship

Unclear

Decentralised IS control

No

Complex work and educated staff

Yes

Lack of formal procedures

No

Highly interconnected departments

No

Readily available resources

No

Large size

Yes

Openness to outside ideas

No

Table 2. Technological factors affecting the diffusion of IS (adapted from Rogers, 1995) New technology will diffuse faster to the extent that it… Provides relative advantage over existing technology Is compatible with existing technologies Has low complexity Has been observed to be successful for other users Can be trialled with minimal disruption to normal activities

models of innovation in hospitals that also include their general environment. An earlier model developed by Stocking (1985) also includes the characteristics of individuals. Rogers’ own work highlighted the importance of this factor. He classified individuals into five types, based on how early they adopt innovation: Innovators, Early adopters, Early majority, Late majority, and Laggards. The work of England et al does have the advantage of being the most recent of the models considered. In addition, it relates to the Australian hospitals, which are commonly used as comparisons in NZ health research (Jackson & Rea, 2007).

Organisational Factors The findings of Rogers and England et al on organisational factors are summarised in the fol-

RFID in Hospitals and Factors Restricting Adoption

lowing sections. Related information on the NZ health care sector is also presented, based on the general environmental factors identified by Pettigrew et al and Greco and Eisenberg.

Figure 1. Stakeholders in the NZ health care sector (Ministry of Health, 2008b) (NGO = Non-Government Organisation; PHO = Primary Health Organisation)

Executive Sponsorship Rogers finds that if an organisation’s leaders have a positive attitude towards change, then innovations will diffuse more quickly. England et al find that there is not enough published research on the attitude of hospital executives towards IS to evaluate this factor. In the NZ health care sector there are a number of executive layers that may impact on hospital operations. As Figure 1 illustrates the Minister of Health, the Health Ministry, and the relevant District Health Board (DHB) have direct input. The Accident Compensation Corporation (ACC), an agency of the Department of Labour, also has some indirect input. These executive layers can have different objectives. Figure 2 shows the Ministry’s current objectives for the health care sector. Figure 3 shows the current objectives of the Auckland DHB. As the Minister and the executive members of each DHB change on a regular basis, the level of sponsorship and objectives can also vary over time. Easton (2002) provides an excellent review of how changes in government and in the role of DHBs have impacted substantially on the health sector over the last twenty years.

Decentralised IS Control Rogers finds that the more control of resources is centralised, the less innovative an organisation becomes. England et al report that hospitals tend to have centralised IS control for major projects, while day-to-day IS control is less centralised. In NZ, there are levels of IS management in the Ministry, the DHBs and individual hospitals. The WAVE Advisory Board advocates greater centralisation of IS in the health care sector, in order to

reduce duplication of effort and create economies of scale (WAVE Advisory Board, 2001).

Complex Work and Educated Staff Rogers finds that more complex work presents more opportunities for innovation, but only if staff are sufficiently educated to recognise and exploit those opportunities. England et al suggest that the medical work in a hospital is complex, and that the staff who carry it out are highly educated. Medical work carried out in NZ hospitals is certainly complex. An illustration of the variety of services performed by the NZ health care sec-

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Figure 2. Ministry of Health’s Health Strategy objectives (Ministry of Health, 2003b)

Figure 3. Key objectives from Auckland DHB Strategic Plan 2002-2007 (adapted from Auckland District Health Board, 2002)

nurses and general practitioners (GPs) in NZ (Engelbrecht, Hunter, & Whiddet, 2007).

Lack of Formal Procedures Rogers finds that an organisation with formal procedures is less likely to innovate. England et al suggest that hospitals do officially have many formal procedures, but in everyday work are somewhat more flexible. An example of this is provided by Heeks et al, who describe a scenario Table 3. Health care events in an average day in New Zealand (adapted from 1Ministry of Health, 2008a; 2Ministry of Health, 2006) 160 people are born1 78 people die1 83,000 prescriptions are filled1 66,000 laboratory tests are analysed2 6,000 outpatients visit hospitals for treatment1 275 people have elective surgery1 29 people are diagnosed with diabetes2 1,450 people visit an Accident and Emergency department2

tor is given in Table 3 (WAVE Advisory Board, 2001). It is less clear whether the administrative work is any more complex than that carried out in other organisations. Medical staff in NZ must be well educated. However, as Chassin points out, medical training conditions doctors to be self-reliant, and to reject support systems (Chassin, 1998). This is supported by a recent survey of administrative staff,

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1,350 people are admitted to hospital1 94,000 people take an anti-depressant medication2 235,000 people take a cholesterol lowering drug2 25 people have a heart attack1 127 children are immunised2 2,124 children and teenagers visit the dentist2 47 asthmatics are admitted to hospital2 ACC receives 4,100 new claims2 55,000 people visit their GP1

RFID in Hospitals and Factors Restricting Adoption

where nurses followed a procedure that was morally and clinically sound, but officially prohibited.

Highly Interconnected Departments Rogers finds that highly interconnected departments lead to a faster rate of diffusion, because innovations can flow more easily between them. England et al point out that hospital departments are not generally highly interconnected. They particularly note the different perspectives of medical staff and managers, and the professional sub-cultures or ‘tribes’ that are formed by medical staff with the same specialities.

Readily Available Resources Rogers finds that innovation is more likely to happen if time and resources are available to do it. England et al suggest that public hospitals are unlikely to have such resources due to “constant funding pressure”. The NZ government funds around 77% of total health care funding. The remaining 23% is paid by individuals or private insurers for treatment in private hospital. Public and private spending on health care have both risen by an average of 5% per year over the past 10 years. According to Organisation for Economic Co-operation and Development (OECD) figures, NZ’s health care spending per capita and as a proportion of GDP are both just below the median for developed countries. Health care spending per capita is approximately 81% of that in the UK health care sector, 66% of that in Australia, and 34% of that in the US (Ministry of Health, 2007). Public hospitals in NZ are extremely limited in their ability to borrow from private sector financial institutions (Ministry of Health, 2003a). The government runs the Crown Funding Agency (CFA) to provide loans and credit to public sector bodies, including DHBs.

Large Size Rogers finds that innovation happens more quickly in larger organisations, as they have more people to identify opportunities and more resources available to exploit those opportunities. England et al have classified hospitals as large, but don’t state what metric they have based this on. Hospitals are certainly large organisations in the NZ context, where 96% of businesses employ fewer than 20 people (Ministry of Economic Development, 2007). But in comparison to hospitals in other countries, NZ’s are relatively small by most measures. Considering budget, the figures given above illustrate that NZ’s is substantially less than the UK, Australia and the US. Considering infrastructure, as approximated by the number of public hospital beds per capita, NZ has half the level of Australia and 60% that of the UK (Jackson & Rea, 2007). Considering the number of staff, NZ pales in comparison to the UK’s NHS, which is the third largest employer in the world (Wallace, 2004).

Openness to New Ideas Rogers finds that organisations that are open to new ideas are able to learn about innovations and evaluate them more easily. England et al state that medical staff tend to be open to new ideas only from within their professional sub-culture. In terms of Rogers’ classification of individuals’adoption of IS, Mathie (1997) finds that doctors generally view Innovators as “misfits”, but respect Early adopters as “opinion leaders”. Mathie suggests that the adoption of clinical IS is slowed by the principle of Evidence-Based Medicine (EBM). EBM requires that a new medical practice should not be used until there is sufficient evidence that it is safe and effective. Table 4 shows a typical scale for levels of evidence (Canadian Task Force on Preventive Health Care, 1997). In reviewing literature for this chapter, no IS evaluations were

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Table 4. Levels of evidence in EBM (adapted from Canadian Task Force on Preventive Health Care, 1997) I

Evidence from at least one well-designed randomized controlled trial

II – 1

Evidence from well-designed controlled trials without randomization

II – 2

Evidence from well-designed cohort or case–control analytic studies, preferably from more than one centre or research group

II – 3

Evidence from comparisons between times and places with or without the intervention; dramatic results from uncontrolled studies (e.g., results of treatment with penicillin in 1940s)

III

Opinions of respected authorities, based on clinical experience; descriptive studies or reports of expert committees

found that might be considered ‘well-designed randomized controlled trials’. Even when an IS is adopted by some clinicians, there is no assurance of widespread diffusion. For example, Morrissey (2003) reports that Computerised Physician Order Entry (CPOE) has recently been approved as an evidence-based standard by an, albeit privately managed, medical standards organisation in the US. Yet only 24% of hospitals are planning to implement it. Of those that aren’t, 54% state that it is because doctors are resistant to using it.

Technological Factors The findings of Rogers and England et al on technological factors are summarised in the following sections.

Relative Advantage Rogers finds that innovations that offer many advantages over the status quo tend to diffuse more quickly than those that offer few or no advantages. England et al seem to suggest that IS managers in hospitals believe that IS produces relative advantage, but don’t know how to prove it. The literature clearly indicates that hospitals have problems evaluating IS. As Littlejohns et al (2003) point out, “the overall benefits and costs of hospital IS have rarely been assessed”. Ammenwerth et al (2004) discuss some of the common barriers to performing and publicizing evaluations. These reflect a number of the organi-

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sational factors above, including a lack of formal procedures, a lack of resources, and insular departments. The authors summarise previous attempts at HIS evaluation frameworks, and conclude by drafting the Declaration of Innsbruck. Although it contains 12 valid recommendations, they are quite generic and relate to the evaluation process rather than to the evaluation details. For example, one recommendation is that an evaluation should be sufficiently funded.

Compatibility Rogers finds that innovations that are highly compatible with the status quo tend to diffuse more quickly than ones that aren’t. England et al suggest that, in the hospital context, administrative applications have the most compatibility because most people know how to use them. Larger, strategic applications have less compatibility because they generally require accompanying organisational changes. Clinical applications can also have frequent compatibility problems because of the difficulty of translating paper-based medical forms to electronic format.

Complexity Rogers finds that innovations that are less complex tend to diffuse more quickly than those that are more complex. England et al don’t appear to explicitly address complexity in their analysis.

RFID in Hospitals and Factors Restricting Adoption

Observability Rogers finds that innovations that can be observed working successfully elsewhere will diffuse more quickly than those that can’t. England et al suggest that successful HIS projects are so rare that observability is problematic. At the time of writing, the only RFID application that is observable in NZ hospitals is access control. There are a small number of vendors developing or importing RFID systems for hospitals, that are observable in other countries (Hedquist, 2007). Outside of the health care sector, a number of RFID systems have been trialled or fully implemented in NZ: Livestock identification and tracking (Stringleman, 2003); Tracking carcasses on a meat processing line (Anonymous, 2004b); Identification of dogs and other pets (Department of Internal Affairs, 2005; New Zealand Veterinary Association, 2002); Timing runners competing in road races (Timing New Zealand, 2004); Identifying and tracking books in libraries (Anonymous, 2004a); and Identifying farm and forestry equipment to support a fuel delivery service (Anonymous, 2007). There have also been trials conducted by a small number of major retailers and manufacturers, and by customs officials for tracking cargo containers and identifying passports (Bell, 2004).

Trialability A trial is defined by Rogers as testing an innovation in part before committing to it fully. He finds that innovations that can be easily trialled diffuse more quickly than those that can’t. England et al suggest that small, stand-alone operational systems are more easily trialled than strategic or clinical systems.

Research Aims At the time of writing, NZ hospitals are just beginning trials of RFID. Two pilot projects are in the

early planning stages. The first will employ RFID for patient identification and patient tracking in an ED. The second will use RFID to replace or complement barcodes in an existing BCMA system used during anaesthesia (Houliston, 2005). The researcher has been an observer of the former project, and an active participant in the latter. This research has been conducted partly to inform the further development of these two projects, and others that may follow. The analysis of England et al lead them to conclude that the organisational characteristics of Australian hospitals partly explain why they have been slow to adopt IS generally, and that they would be expected to adopt small, standalone, operational IS more quickly than strategic or clinical IS. The first aim of the current research was to investigate whether these conclusions are equally valid for NZ hospitals. The second aim of the research was to consider RFID applications for hospitals in light of these conclusions. As NZ hospitals begin to pilot RFID applications, what implications do the hospitals’ organisational characteristics, the applications’ technology factors, and the strengths and weaknesses of RFID, as discussed in previous chapters, have for the likelihood of success?

INTERVIEWS AND FINDINGS This section briefly describes the interview process and presents the findings.

Interviews A number of research methods were considered for this research project. Surveys were ruled out primarily because other recent surveys on IT in NZ hospitals had shown a very low response rate (Lau, 2003). Observation was considered to be especially appropriate for gauging ‘fuzzy’ factors such as ‘Executive sponsorship’, ‘Complex work’ and ‘Interconnected departments’. However, this

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was judged to be a high-risk approach, given the researcher’s inexperience with observational methods and the hospital environment. Interviews offered the best balance. They could be performed in less time than observation, but go into more detail than surveys. Given the lack of experience with RFID, interviews also provided an opportunity to introduce the technology to interviewees. A total of nine people were interviewed. All were known to the researcher or his supervisor, or were referred by another interviewee. It had been the researcher’s intention to interview more people, but availability was an issue. This may in itself be an indication of the time pressures resulting from organisational factors such as ‘Complex work’ and ‘Readily available resources’. A brief background on each interviewee is shown in Table 5.

The interviews with M, G and C were done primarily in the context of designing a prototype for the anaesthetic RFID pilot. Therefore the technology factors were discussed more than the organisational factors. Organisational factors were mostly discussed in relation to their experiences in getting the existing BCMA system implemented. The interviews with S, E and R were more structured. All followed a similar, two-part pattern. In the first part, interviewees were asked to describe at least one IS they had been involved with. For each IS, they were then asked whether it had been a success or failure, and what factors they perceived as being most significant in that outcome. The researcher explicitly mentioned Rogers’ organisational factors only once, when E appeared to struggle with identifying factors. In the second part, the researcher briefly described

Table 5. Backgrounds of interviewees Interviewee

Background

M

M is a practicing doctor. He has recently been successful in getting a BCMA system implemented for anaesthesia in his DHB. He is the main sponsor for the anaesthetic RFID pilot. Interviews with M took place in person and by e-mail.

G

G is also a practicing doctor, and a colleague of M. Interviews with G took place in person.

C

C is doctoral researcher, primarily interested in systems that support patient safety. He has been researching alongside M. Interviews with C took place in person and by e-mail.

S

S is an experienced nurse in an Intensive Care Unit (ICU). She is an early adopter of IS, and is currently researching in the area of medical data mining. Interviews with S took place in person and by e-mail.

E

E is a nursing manager, with previous experience as a practicing nurse. Her current role includes some responsibility for the design, implementation and support of nursing IS. Interviews with E took place in person.

R

R is currently a management consultant in the private sector, but was previously the Chief Information Officer (CIO) of a DHB, and a project manager within the Ministry of Health. Interviews with R took place by e-mail.

D

D is a vendor who markets an RFID application for tracking staff and equipment to hospitals (so far with no sales in NZ) and companies in the transport industry. Interviews with D took place in person.

P

P is a vendor who does bespoke IS development for various organisations including hospitals. He is leading the vendor consortium involved in the ED RFID pilot. Interviews with P took place by telephone.

B

B is a technician for electronic medical equipment. Interviews with B took place by telephone.

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RFID technology and the four types of application identified in the previous section. If the interviewee had not already done so, they were asked to indicate which application they thought would be most useful to them. They were then asked to point out obvious problems that they could foresee with any of the applications. The interviews with D and P were also structured. They were asked questions about making initial contact with a hospital, the management level of hospital staff they dealt with, how well-informed the staff were about RFID and its applications, requirements for reference sites and trials, and the most common reasons for IS implementations being delayed or abandoned. The interview with B covered only RFID technology, particularly compatibility with existing hospital equipment and applications.

she eventually stopped using it because it had no executive sponsorship. As she puts it:

Findings

There’s a pattern when you implement a new system. For the first two weeks users moan and complain because it’s different. Then they’re quiet for about a month. At six weeks there’s another peak of grumbling. Then they realise that management are committed to the system, and they just accept it…

This section presents the findings from the interviews. The findings are grouped under the eight organisational and five technology factors identified in the previous section.

Organisational Factors The organisational factors considered were: Executive sponsorship; Decentralised IS control; Complex work and Educated staff; Lack of formal procedures; Highly interconnected departments; Readily available resources; Large size; and Openness to new ideas.

Executive Sponsorship England et al suggested that hospital executives could not be considered to have a consistently positive or consistently negative attitude towards IS. The interviews supported this. S described a barcode-based costing system that she had implemented several years previously. Although the system was a success in her eyes,

Management just didn’t want to know that we spent $250,000 on this burns patient. A lack of executive sponsorship does not necessarily mean the demise of an IS. M successfully implemented an anaesthetic BCMA system apparently without executive sponsorship. However, it should be noted that M is a senior staff member in his department, and getting his system implemented was: … a long, and not altogether happy, tale. An example of positive executive sponsorship was provided by E:

R noted that even when an IS had executive sponsorship, it did not necessarily make the IS a success: We had systems on our books that should have been scrapped but weren’t, because they were a manager’s pet project. Officially they still exist, but no-one, except maybe the manager, uses them… P stated that he had never had a DHB or hospital manager present at one of his product presentations. D also stated that he rarely has contact with managers, although the ED RFID pilot is an exception. The DHB’s Chief Information Officer (CIO) been involved with the project since its inception.

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E expressed a belief that executive sponsorship can be diluted by the large number of executives around hospitals. D gave an example from the ED RFID pilot. While the project has sponsorship from the DHB’s CIO, IS management in the Ministry are ambivalent. They have expressed concern that it may conflict with Ministry plans for barcode-based systems.

Complex Work and Educated Staff

Decentralised IS Control

I believe that all the medical staff I encountered, regardless of their paper qualifications, had the equivalent of a Masters’ education.

England et al suggested that hospitals tend to have centralised IS control of major projects, but not necessarily of day-to-day operations. The interviews seem to support this. As far as R was aware, the ministry and each DHB had a central IS group. However, he agreed that these IS groups exercise control mostly at the level of IS strategy and significant new projects. A large measure of this control is to satisfy the requirements of government. He noted that this is criticised in the WAVE report (WAVE Advisory Board, 2001): “There is a strong focus on spending [IS] money on governance and compliance, rather than on systems aligned to health goals”. R suggested that in day-to-day dealings with end-users, each hospital’s IS group focused on support, not on control. This is supported by S, E and G, who were not aware of any policies enforced by the IS group at their hospital. E went so far as to suggest that the IS group wouldn’t have sufficient influence with management and clinicians to enforce any major policies. She recalled an incident: IS sent out a memo saying they were no longer going to support systems developed by the departments, without IS knowledge. They were very quickly asked, or instructed rather, by management to drop that particular policy. P generally has IS staff attending his product presentations, but only from the individual hospital, never from the DHB or Ministry.

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England et al suggest that the medical work in a hospital is complex, and that the staff who carry it out are highly educated. The interviews support both these points. All interviewees agreed with both points. R noted that:

Rogers found that educated staff are more likely to recognise and exploit opportunities for innovation. However, this is not always the case in hospitals. E pointed out that even highly educated and experienced staff are not necessarily natural innovators: We get lots of complaints when new systems go in. But hardly any suggestions on how to make things better. Normally they just ask to go back to the way the previous system did things. R suggested that senior doctors who work only part-time for the hospital might be more inclined to apply innovations in their private practice than in the hospital.

Lack of Formal Procedures England et al suggested that hospitals had formal procedures, although they were not always followed. The interviews support this point. R noted that, in his experience as a CIO: Clinicians will typically not be comfortable with being limited to a single method of operation for any given system. This can be at variance with the desire of the IS professional to develop integrated and efficient systems.

RFID in Hospitals and Factors Restricting Adoption

This is illustrated well by M and C’s anaesthetic BCMA system. It is a highly procedural system, carefully designed to reduce the likelihood of drug administration errors during surgery. Yet during simulated operation trials, half of the participants neglected the simple step of scanning the barcode before administering the drug (Merry, Webster, Weller, Henderson, & Robinson, 2002). Another example was given by both S and E. Patients in their hospital were given barcoded wristbands, but neither knew which staff or procedures actually made use of the barcodes.

Highly Interconnected Departments England et al suggest that hospital departments are not generally highly interconnected. The interviews support this. R expressed his outsiders view: The [healthcare] sector is highly tribal: There are at least 12 different practitioner groups. Within the doctors’ ambit alone there are around 15 specialist colleges. There is traditionally a high level of distrust between practitioner groups. Even within one group of practitioners, there can be a high level of mistrust [sic]. All of this was exacerbated in NZ by the endless reforms and re-structures. S expressed this ‘mistrust’ indirectly: Whenever we (the ICU) get a new patient, we always go through the admission form again. The people in admissions never do it properly. It’s not surprising – they’re very busy.

Readily Available Resources England et al suggest that public hospitals are unlikely to have readily available resources. The interviews support this point. R believed that NZ hospitals do have readily available resources. He pointed to repeated in-

creases in government funding and recent statistics on DHB deficits. The 21 DHBs had a combined deficit of NZ$58 million as at June 2004, although that was a significant decrease from the NZ$170 million deficit the previous year (Pink, 2004). But those resources were not readily available to IS: Another factor that is, I think, unique to healthcare is that there is never enough money. Every dollar that is not directly spent on the delivery of care is questioned. E agreed that investment in IS is very closely scrutinised, due to its unfortunate track record. Quoting from one of her Ministry of Health directives: “Following concern regarding the quality of health [IT] investment, the Minister of Health has directed that a stepped approval be required for information systems and communication technology” (Ministry of Health, 2003a). Any IS investment over $500,000 must be approved by the Director-General of Health. Any investment over $3 million must be approved by the Minister. P believed that the time and effort required for hospital operational staff to get IS funding was the major cause for projects being delayed, cancelled or reduced in scope. He gave an example: One hospital wanted [his product] to track their wheelchairs. I gave them a quote for a full system to cover the entire complex. They went with someone else who just put readers at the exits. They wanted something quickly and couldn’t wait for approval. S still had the barcode scanners from her abandoned costing system in a box under her desk. This raises the interesting question of what other unused IS resources from failed projects are in a similar position.

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Large Size England et al have classified hospitals as large. The issue of size was not raised in any of the interviews. Rogers finds that innovation happens more quickly in larger organisations, as they have more staff to identify opportunities and more resources to exploit those opportunities. Given that ‘Readily available resources’ and ‘Openness to new ideas’ already appear as separate factors, it may be that size is redundant in the case of hospitals.

Openness to New Ideas England et al suggested that medical staff were generally not open to new ideas. The interviews support this point. R suggested that being closed to new ideas was the result of medical training: Healthcare workers take a very long term view. It takes ten years for a doctor to become fully qualified, so there is an inherent conservatism. M and C provide an illustration of the challenges posed by the requirements of evidencebased medicine. They carried out two sets of simulated operation trials of their barcode-based ID (Merry et al., 2002; Webster, Merry, Gander, & Mann, 2004). The trials were level II-1 on the evidence-based medicine scale shown in Table 4. The results were positive, yet there are still only a limited number of doctors using the system. P and D both agreed that initial interest in RFID nearly always came from operational staff, and never from the medical staff. As P put it: My first contact always comes from the guy who looks after, say, the wheelchairs. He finds that a lot are going missing. He searches the ‘net and finds us.

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P believes that operational staff are generally open to new ideas. Part of his standard product presentation is discussing how the staff could employ RFID beyond their initial problem.

Technological Factors The technological factors considered were: Relative advantage over existing technology; Compatibility with existing technology; Low complexity; Trialability; and Observability.

Relative Advantage M, G, C, S, E and R were asked which of the RFID applications described in the previous section would be the most useful to them. The responses illustrate that relative advantage is in the eye of the beholder. M and G selected drugs identification. In particular, M was interested in using RFID to replace barcodes in their anaesthetic BCMA system, leading to the initiation of the anaesthetic RFID pilot. This would remove the need for anaesthetists to scan barcodes, which has proven to be a major usability issue for the BCMA system. C was most interested in RFID ‘smart’ cabinets. He expressed particular interest in having alerts when drugs were about to reach their expiry date, and when quantities reached minimum levels. S nominated data storage. If every person had an RFID chip containing their medical history embedded under their skin, this would mitigate the problem of having inaccurate or incomplete details on patient admission forms. She went so far as to suggest a ‘Top 10’ list of data to store. E believed that nurses would be interested in anything that made their medication rounds easier. She thought that a medication administration would be most useful. R suggested RTLS. He noted a problem that many hospitals have with theft of laptop computers, personal digital assistants (PDAs) and similar items. The ability to raise an alert when a piece of

RFID in Hospitals and Factors Restricting Adoption

equipment leaves a particular area, and to track it, may help to prevent stolen items from leaving the building. The issue of evaluating IS was raised with E and R, as the only two interviewees with any official responsibility for evaluation. R had a formal evaluation process, albeit inherited from a previous role in the private sector. E had no formal evaluation process, and tended to rely heavily on user feedback. She gave the example of an IS being selected by the doctors in one particular department: They’ve been looking for nearly four years, and they’re finally about to make a choice. Basically, we’re so desperate to get something in place, we’ll go along with whatever they decide. D and P were asked how often the systems were replacing existing systems, and what benefits their clients expected. The ED RFID pilot, in which D was involved, was not replacing any existing system. P stated that his RTLS is often intended as a replacement for some form of manual ‘equipment booking’ system that hospital staff are too busy to use. The advantage is having a system automatically detect where a piece of equipment is, rather than staff having to write that on a board or enter it into a PC.

Compatibility The interviewees raised a number of compatibility issues that they believed might impact on the adoption of particular RFID applications. They are discussed here grouped under the dimensions identified by Heeks et al. Information: Two major issues regarding compatibility of information were raised by S. First, the medical data stored in embedded RFIDs must be compatible with the needs of end users. As noted above, the major reason for the failure of S’s costing IS was that management did not want the information it produced. She also cited

the National Health Index system (NHI) (NZ Health Information Service, 2004). Although an estimated 95% of the population are recorded in the system, the information kept is too basic to be of use. Second, the data must be consistent with that held in paper-based records. Technology: B confirmed that there were potential compatibility issues with RFID technology and existing electronic medical devices, such as pacemakers. However, he suggested that a more likely problem would be other devices interfering with communication between RFID readers and tags. Surgical and medical diathermy machines are two examples of devices that emit much stronger electromagnetic fields than RFID. They are widely recognised as major sources of interference (Bassen, 2002). D highlighted the importance of technology compatibility in his RFID pilot: We had a few hospitals express an interest in running the trial. We chose [the pilot DHB] because they have the wireless network infrastructure that makes it easiest for us. The other interviewees didn’t mention any issues related to technology compatibility. Although RFID is novel in the hospital environment, all interviewees were familiar with related technologies such as barcodes and proximity cards. Processes: All interviewees made the point that adopting RFID for a particular process should not prevent the process from being carried out as it is now. For example, M, G and C required that, if RFID labels were to replace barcode labels in their drug identification application, then the barcode must be printed on them. Then, if the RFID application failed, the existing barcode application could be reverted to. E and S both suggested that staff would be reluctant to wear RFID tags. Both accepted that there were valid reasons for wearing them, such as authenticating access to smart cabinets. However, they expressed concern that the data gathered

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might be used for other purposes, such as covert tracking. As E put it: There are managers who would love to know exactly how much time doctors and nurses spend on their feet. E, S and R all believed that management would have to make the adoption of any RFID applications that involve tagging staff as transparent as possible. Skills: D and P both reported that the hospital staff they interacted with knew little or nothing about RFID technology when they first gave product presentations. But all interviewees seemed to agree that medical staff already had the skills to deal with the RFID applications discussed. When it was found that few of the staff in S and E’s departments used barcode scanners, the researcher pointed out that patient and drug identification applications would probably require the use of handheld devices, such as PDAs or tablet PCs. Even though the staff had little or no experience with these either, S and E believed they would have no difficulty learning to use them. Some RFID applications may also require new skills to be learned by patients. S noted that if medical data were to be stored on embedded RFID chips, then the general public may have to be educated in protecting that data. Resources: M, G and C are the only interviewees with whom the costs of RFID were discussed in any detail. The tag and reader prices mentioned did not appear to be a major concern. The researcher suggested that with the interest shown in RFID by pharmaceutical manufacturers, and the possible influence of DHBs and PHARMAC over pharmaceutical suppliers, the cost involved in tagging drugs may largely be met at those levels of the supply chain. M considered this unlikely at the current time.

Complexity Two interviewees also raised complexity issues that they believed might impact on the adoption of particular RFID applications. E suggested that getting all parties involved in the health care sector to agree on what medical data should be stored on an embedded RFID chip would be: …next to impossible. That’s why data in the NHI is so basic. It’s mostly stuff that’s available publicly from other sources. B believed that designing networks of RFID readers to support an asset tracking application would be complex. While some IS groups have experience with RF engineering through the implementation of wireless networks, such networks consist largely of homogeneous components. In contrast, RFID tags are more heterogeneous. Patient identification applications, for example, are likely to use short-range, passive tags while RTLSs are likely to use longer-range, active tags. He also expected that keeping up to date with the RFID standards would be a challenge.

Trialability The importance of trialability varied between the interviewees. It was vital for M, G and C. They regard the principles of evidence-based medicine as very important, particularly for the clinical systems that they use in theatre. This is illustrated by the simulated operation trials they performed on their own IS. D and P both stated that they always recommend trials of their systems before full implementation. E provided a contrasting view. When asked specifically about trialability, her reply indicates that it isn’t essential: We don’t normally have time for trials. We just put systems in. If they don’t work we stop using them.

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Observability

Diffusion of IS in NZ Hospitals

Observability appeared to be important for most of the interviewees. It was one of R’s evaluation criteria, and he noted that it regularly appeared in proposals for major new IS projects. For instance, a recent proposal for a clinical HIS by one DHB is based largely on the success of the same system at a neighbouring DHB (Wright, 2003). Both D and P stated that their clients always asked for reference sites. D recalled a meeting he attended where a few DHB CIOs were hosting a visiting Canadian CIO:

The interview findings seem to suggest that there are three organisational factors where England et al’s evaluation of Australian hospitals may differ for NZ hospitals. These are: Decentralised IS control, Lack of formal procedures, and Large size. Assigning No to ‘Decentralised IS control’ conceals the fact that day-to-day IS control is largely decentralised. Likewise, assigning No to ‘Lack of formal procedures’ conceals the fact that such procedures exist primarily at high levels. It is therefore suggested that these factors be assigned a value of Unclear. It is suggested that the ‘Large size’ factor also be assigned a value of Unclear. The basis on which England et al classified Australian hospitals as large is not clear. However, on measures such as number of employees, spending per capita, and public hospital beds per capita, NZ hospitals are significantly smaller. Finally, the factor ‘Large size’ may be redundant, given the presence of the ‘Readily available resources’ factor. Table 6 shows a comparison of the values suggested by England et al and the values suggested by this research, with differences highlighted. If each of these organisational factors had equal weighting in determining the rate of diffusion, then removing two Nos and one Yes should result in a higher rate. Thus it might be expected to observe NZ hospitals diffusing IS, including RFID, more quickly than Australian hospitals. Yet that does not appear to be the case. This anomaly may be explained in a number of ways. One possibility is that the organisational factors do not have equal weightings in determining the rate of diffusion, perhaps specifically in hospitals. The factors ‘Highly interconnected departments’, ‘Readily available resources’, and ‘Openness to new ideas’ appear to outweigh the factors ‘Decentralised IS control’, ‘Complex work and educated staff’, ‘Lack of formal procedures’, and ‘Large size’. Based simply on the number of times that these factors were mentioned in the interviews,

He asked what their innovation strategy was. [A NZ CIO] replied ‘We look at what other hospitals have been doing for at least three years. A further example of the importance of observability was provided at a demonstration of the prototype anaesthetic RFID system. In previous demonstrations the application had been standalone, simply displaying the RFID tag numbers of the items it was reading. In the last demonstration it was integrated with the existing BCMA system, providing the same functionality as using barcodes. As G commented: It’s one thing to see the tag numbers come and go on the screen. It’s something quite different to see the drug names come up in [the existing IS].

DISCUSSION This section proposes some refinements to the conclusions of England et al in light of the interview findings, and considers the implications for RFID applications. Some possible research areas for further grounded theory iterations are also highlighted.

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Table 6. Organisational factors affecting the diffusion of IS in NZ hospitals (adapted from England et al., 2000) Factors that lead to faster diffusion

Present in Australian hospital environment ?

Executive sponsorship

Unclear

Decentralised IS control

No

Complex work and educated staff

Yes

Lack of formal procedures

No

Highly interconnected departments

No

Readily available resources

No

Large size

Yes

Openness to outside ideas

No

Present in NZ hospital environment ? Unclear Unclear Yes Unclear No No Unclear No

this seems plausible. However, that is based on the researcher’s interpretations of interviewee responses. A more explicit, quantitative research method, such as a questionnaire with all the factors listed, should be used to confirm this. Another possibility is that there are additional organisational factors, perhaps specific to hospitals, that are not included in Rogers’ work and have not been considered by England et al. In analysing the interviews for this research, statements were associated with the established factors. Analysing the interviews without these a priori classifications may highlight new factors. A third possibility is that RFID technology is sufficiently different from the generic innovation considered by Rogers and the general IS considered by England et al, that some of the organisational factors have a different effect on its diffusion. Specifically, the factor ‘Large size’ may have an exaggerated effect on the diffusion of RFID, or the factors ‘Decentralised IS control’ and ‘Lack of formal procedures’ may have a minimal or even negative effect.

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Decentralised IS control may in fact decrease the diffusion rate of RFID. RFID is an infrastructure technology, similar to other networking technologies such as mobile phones or wireless networks. The pattern seen with other infrastructure technologies is that diffusion is slow until a critical mass of end users is reached, at which point diffusion increases sharply (Rogers, 1995). Reaching the critical mass of end users is likely to be easier for an organisation with centralised IS control than for an organisation with decentralised IS control. Centralised IS control should ensure that all end users have compatible technology. With decentralised IS control, end users may independently choose incompatible technologies. This is likely to result in a longer wait for one technology to reach a critical mass of end users. Even when this does occur, there may be resistance from end users of other technologies to adopt a new one. This resistance may be exacerbated in the health care sector by the presence of non-interconnected departments. A lack of formal procedures may also decrease the diffusion rate of RFID. A formal, documented procedure lends itself to automation more easily than an informal, unwritten procedure. This is illustrated by the fact that physical security, a domain with many formal procedures, is currently the most widespread application for RFID technology. Walker et al suggest that the greatest value of RFID will come from using it for “the real work of organising data to help trigger transactions and business rules” (Walker, Spivey Overby, Mendelsohn, & Wilson, 2003). It is less obvious how large size might directly affect the diffusion of RFID. Large size implies more staff who may identify opportunities to apply RFID technology. But it has already been established that clinicians tend not to be open to outside ideas. Large size suggests more resources to exploit opportunities. But in the health care sector, resources are not readily available for investment in IS. Large size in terms of physical area or services provided may increase diffusion,

RFID in Hospitals and Factors Restricting Adoption

as it offers improved trialability. RFID applications are frequently trialled in distinct areas of the hospital, such as the ED, or for specific services, such as blood transfusion. But diffusing an RFID application over a large physical area would be impeded by the lack of readily available resources to create a reader network. Diffusing across a range of services would be complicated by noninterconnected departments and a lack of openness to outside ideas.

Diffusion of RFID Applications The interviews seem to suggest that all of Rogers’ technological factors will impact on the diffusion of particular RFID applications, but not to the same extent. For instance, all interviewees raised issues about compatibility, but complexity issues were noted by only two. The interviews also seem to support England et al’s conclusion that small, administrative or operational IS should diffuse more quickly than large, strategic or clinical IS. The three largest, most strategic applications discussed were data storage - RFID chips holding a person’s medical data embedded under their skin - and hospital-wide staff tracking, and RTLS. More concerns were raised about the first two of these applications than any others. Four classes of RFID application were identified in the section on RFID and Hospital IS. A discussion of the factors impacting on each class is presented in the following sections.

Identification Applications Patient identification supports the major health care concern of easily sharing medical information. Drug identification directly supports the major concern of patient safety. In addition, drug identification was nominated by two interviewees as the applications that would be of the most benefit to them. Overall this suggests a high level of relative advantage. Thus it is no surprise to see that patient and drug identification are the major

components of the first two RFID pilots in NZ hospitals. As the first experience with RFID technology, there are likely to be issues around technology standards, integration with existing IS, training and other aspects of compatibility and complexity. But there are many patient identification systems already deployed overseas, so it should be more observable than other types of application. Trialability is possible by limiting the application to a single area of the hospital, as is being done in the NZ anaesthetic drug and ED patient RFID trials. The cost of handheld RFID readers suitable for identification applications is relatively small. The cost of RFID wristbands for patient identification is minimal, since they can be re-used. However, the cost of tagging individual drug doses is likely to be the major impediment to the adoption of drug identification applications. The Ministry of Health gives cost as the primary reason for recommending the use of barcodes in its proposed BCMA system. Cost may become less of an issue if more pharmaceutical manufacturers and distributors choose, or are required by law, to adopt RFID for drug e-pedigrees.

Location-Based Applications A number of location-based applications directly support the major health care concern of patient safety. Medication administration, prevention of retained surgical items, and quarantine management are clear examples. Smart cabinets offer indirect support by ensuring that sufficient quantities of drugs and supplies are in stock, and that they are not expired. Medication administration and smart cabinets were each nominated by one interviewee as the application that would be of the most benefit to them. Overall this suggests a reasonably high level of relative advantage. These systems are typically automating standard operational practices, so should not be complex. But the interviewees did raise some compatibility issues, notably that staff may feel

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they are being monitored. Trialability of smart cabinets, for example, may be difficult if staff are able to avoid using them by obtaining drugs or supplies from other storage areas. The number of such systems implemented around the world is relatively low, which may make observability an issue. As noted above, the cost of tagging individual drug doses and surgical items will be significant. Smart cabinets themselves are also expensive. There are currently no companies in NZ providing them as a managed service, as there are in other countries.

Tracking Applications Patient tracking applications directly support the major health care concern of patient safety. Staff tracking and RTLSs offer indirect support by ensuring that staff and equipment can be found quickly if required for an emergency. RTLS was nominated by one interviewee as the application that would be of the most benefit to them. Both vendors interviewed also deal mainly in patient tracking and RTLS, although not with any sales in NZ. Overall this suggests a medium level of relative advantage. Tracking applications are the most complex of the four types discussed here. Some idea of the complexity is given by Sokol and Shah’s detailed cost-benefit analysis for an RTLS (Sokol & Shah, 2004). Designing an effective RFID reader network requires expertise in RF engineering. Making use of existing wireless networks is one way to reduce complexity, but that creates potential compatibility issues with existing IS and requires more expensive tags. Tracking applications also have the most potential compatibility issues identified by interviewees. Most significant are the privacy of staff, and potential electromagnetic interference between RFID readers and common medical electronic devices. Despite these issues there have been a relatively high number of RTLSs piloted overseas, so observability should not be difficult.

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Trialability can be made easier by limiting the tracking to a specific area, such as a single ward. The cost profile of tracking applications differs from the other four types discussed. They require more RFID readers, but significantly fewer tags than, for example, drug identification. The cost can be controlled by varying the degree to which existing wireless networks are used, the area to be covered, the number of people or items to be tracked, and the accuracy required. There are currently no companies in NZ providing RTLSs as a managed service, as there are in other countries.

Data Storage Applications Data storage applications offer one solution to the major concern of easily sharing medical records. Storing patient medical data in an RFID tag was nominated by one interviewee as the application that would be of the most use to them. Overall this suggests a reasonably low level of relative advantage. Compatibility of information is the most significant issue identified by interviewees. There must be agreement among potential users on what information is stored. Assuming that the information is encoded for security, and to compress it into the small storage space available on an RFID tag, there must also be agreement on encoding standards. Writable RFID tags are currently more expensive than the read-only versions, but the cost difference is likely to decrease over time.

Future Research This research would benefit from further interviews with a more representative range of people. The current group of interviewees is dominated by clinicians, who could be considered to be ‘Innovators’ or ‘Early Adopters’ under Rogers’ classification, and whose experience is in public hospitals in the same DHB. Further interviews should include staff from other DHBs, and health

RFID in Hospitals and Factors Restricting Adoption

care organisations other than public hospitals, more managers, more ‘Late Adopters’, and possibly some patients. There is a danger in interviews that people give ‘correct’ responses rather than truthful responses. While the researcher didn’t sense such behaviour during interviews conducted in person, it is more difficult to discern in an e-mail or telephone interview. As already noted, observation may have been a more suitable methodology for judging the true state of factors such as ‘Executive sponsorship’, ‘Complex work and educated staff’ and ‘Highly interconnected departments’. Privacy has emerged as a clear concern for RFID applications that involve staff tracking and smart cabinets. Further investigation seems warranted. What exactly are the privacy concerns of staff? What measures, if any, could be taken to reduce these concerns? Security of RFID tags has been identified as a key issue for data storage applications. A number of approaches have been suggested in the research (Juels, Rivest, & Szydlo, 2003) but these mostly relate to the retail scenario. Are they suitable for securing tags containing medical information? Chao, Hsu and Miaou (2002) propose a scheme intended specifically for keeping medical data on an RFID chip confidential. There remain a number of aspects still to be investigated. For instance, how will the source of data written to a tag be authenticated? If it is done by a medical data certification authority, how might that function? It is clear that most RFID trials and implementations are taking place in US hospitals. This may simply reflect the fact that the recent development of RFID technology has taken place largely in the US. It might also indicate that some aspect of US hospitals increases the diffusion of RFID. The major feature distinguishing US hospitals from those of other countries is the level of competition. Might operating in a competitive market affect the diffusion of RFID? Is the ‘Need to compete’ reflected in Rogers’ organisational factors?

Executive sponsorship is widely regarded as a critical success factor for most types of IS. Further research on the attitude of hospital executives to IS, and RFID in particular, would be valuable.

CONCLUSION This chapter has investigated the factors that have restricted the adoption of IS by hospitals, and specifically of RFID technology in NZ hospitals. The health care sector has historically invested relatively little in IS, in comparison to the manufacturing sector, the financial sector and most others. But this is to be expected. Hospitals and other health care organisations exhibit characteristics associated by Rogers with slow diffusion of innovation: No clear executive sponsorship; Centralised IS control; Formal procedures; Shortage of readily available resources; Little or no connection between departments; and Lack of openness to new ideas. When hospitals have implemented IS, there has been a high rate of failure. In recent years governments, regulators, insurers, and consumers have been calling for the greater use IS to, among other things, make it easier to improve patient safety and more easily share patient medical records. The NZ government has played its part, supporting a National Health Index and Health Information Strategy, and planning to invest in CPOE and BCMA systems. Innovative hospitals overseas, notably in the US, have been applying RFID technology to these problems. A range of applications, from simple patient identification to medication administration to real-time tracking of patients, staff and equipment have been successfully piloted. Despite this, and successful applications of RFID in other industries in NZ, local hospitals are just beginning RFID pilots. Interviews conducted with practicing clinicians, a nurse manager, a CIO, an RF technician, and RFID vendors suggest that NZ hospitals may operate day-to-day with less centralised IS con-

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trol and fewer formal procedures than hospitals in Australia, and perhaps other countries. While this might be expected to hasten the adoption of IS generally, it may explain why an infrastructural technology such as RFID has been slower to diffuse. The two RFID pilots that are about to begin – anaesthetic drug identification and patient tracking in ED - will be closely observed by other NZ hospitals. The pilots have technological profiles that give them a good chance of success. They don’t raise issues of privacy, security, or any of the other major concerns highlighted in the interviews. However both will be expensive to implement beyond the trial, and will have to demonstrate real benefits to the inherently cautious health care sector.

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Greco, P. J., & Eisenberg, J. M. (1993). Changing physicians practices. The New England Journal of Medicine, 329, 1271–1274. doi:10.1056/ NEJM199310213291714 Greengard, S. (2004). A Healthy Dose of RFID. Retrieved September 8, 2004, from www.rfidjournal.com Health Information Strategy Steering Committee. (2005). Health Information Strategy for New Zealand 2005. Wellington: Ministry of Health. Hedquist, U. (2007). Orion Health teams with Oracle and Intel in Spain. Retrieved February 12, 2008, from www.computerworld.co.nz Heeks, R., Mundy, D., & Salazar, A. (1999). Why Health Care Information Systems Succeed or Fail. In A. Armoni (Ed.), Health Care Information Systems: Challenges of the Next Millenium: Idea Group Publishing. Herper, M. (2004). Tiny Chips Could Combat Counterfeit Pills. Retrieved August 1, 2004, from www.forbes.com Houliston, B. (2005). Integrating RFID Technology into a Drug Administration System. Paper presented at the Health Informatics NZ Conference, Auckland, New Zealand. Institute of Medicine. (2001). Crossing the Quality Chasm: A New Health System for the 21st Century. Washington DC: National Academies Press.

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Jackson, G., & Rea, H. (2007). Future hospital trends in New Zealand. The New Zealand Medical Journal, 120(1264).

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Japsen, B. (2008, 2 January 2008). Technology cuts risk of surgical sponges. Retrieved February 12, 2008, from www.chicagotribune.com

Merry, A., & Webster, C. (2004). Bar codes and the reduction of drug administration error in anesthesia. Seminars in anesthesia. Perioperative Medicine and Pain, 23, 260–270. doi:10.1053/j. sane.2004.05.004

Johnson, J. A., & Bootman, J. L. (1995). Drugrelated morbidity and mortality: a cost of illness model. Archives of Internal Medicine, 155, 1949–1956. doi:10.1001/archinte.155.18.1949 Johnston, M. (2007). Wired for saving lives. Weekend Herald, August 25, p. B4. Jonietz, E. (2004). Making Medicine Modern. Retrieved August 1, 2004, from www.technologyreview.com Juels, A., Rivest, R. L., & Szydlo, M. (2003). The Blocker Tag: Selective Blocking of RFID Tags for Consumer Privacy. Paper presented at the Tenth International Conference on Computer and Communication Security, Washington, DC. Kim, K. K., & Michelman, J. E. (1990). An examination of factors for the strategic use of information systems in the healthcare industry. MIS Quarterly, 14(2), 201–215. doi:10.2307/248778 Lau, L. (2003). Pen Based Computers in Health Care. Unitec New Zealand, Auckland. Leape, T., Bates, D. W., & Cullen, D. J. (1995). Systems analysis of adverse drug events. Journal of the American Medical Association, 274, 35–43. doi:10.1001/jama.274.1.35 Littlejohns, P., Wyatt, J. C., & Garvican, L. (2003). Evaluating computerised health information systems: hard lessons still to be learnt. British Medical Journal, 326, 860–863. doi:10.1136/ bmj.326.7394.860 Malkary, G. (2005). Healthcare without Bounds: Trends in RFID. Menlo Park, CA: Spyglass Consulting Group.

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Merry, A., Webster, C., Weller, J., Henderson, S., & Robinson, B. (2002). Evaluation in an anaesthetic simulator of a prototype of a new drug administration system designed to reduce error. Anaesthesia, 57, 256–263. doi:10.1046/j.00032409.2001.02397.x Ministry of Economic Development. (2007). SMEs in New Zealand: Structure and Dynamics. Wellington: Ministry of Economic Development. Ministry of Health. (2003a). Guidelines for Capital Investment. Wellington: Ministry of Health. Ministry of Health. (2003b). New Zealand Health and Disability Sector Overview. Wellington: Ministry of Health. Ministry of Health. (2006). A Day in the Life statistics. In DayInTheLife.xls (Ed.). Wellington, New Zealand. Ministry of Health. (2007). Health Expenditure Trends in New Zealand 1994-2004. Wellington: Ministry of Health. Ministry of Health. (2008a). Every Day in New Zealand: DVD. Wellington: Ministry of Health. Ministry of Health. (2008b). Statement of Intent 2008-11. Retrieved September 26, 2008, from www.moh.govt.nz Morrissey, J. (2003). An info-tech disconnect. Modern Healthcare, 33, 6. Morrissey, J. (2004). Capital crunch eats away at IT. Modern Healthcare, 34, 32.

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New Zealand Veterinary Association. (2002). Annual Report. Retrieved November 1, 2004, from www.vets.org.nz NZ Health Information Service. (2004). National Health Index FAQ. Retrieved August 31, 2004, from www.nzhis.govt.nz Pettigrew, A., Ferlie, E., & McKee, L. (1992). Shaping Strategic Change. London: Sage. Pink, B. (2004). District Health Board Deficit Decreases. Retrieved November 1, 2004, from www.stats.govt.nz Reddy, M., & Dourish, P. (2002). A Finger on the Pulse: Temporal Rhythms and Information Seeking in Medical Work. Paper presented at the ACM Conference on Computer-Supported Co-operative Work, New Orleans, Louisiana. Roberti, M. (2003). Singapore Fights SARS with RFID. Retrieved August 1, 2004, from www. rfidjournal.com Rogers, E. M. (1995). Diffusion of Innovations. New York: The Free Press. Rosenal, T., Paterson, R., Wakefield, S., Zuege, D., & Lloyd-Smith, G. (1995). Physician involvement in hospital information system selection: a success story. Paper presented at the Eighth World Conference on Medical Informatics, Vancouver, Canada. Ross, D. A., & Blasch, B. B. (2002). Development of a wearable computer orientation system. ACM Personal and Ubiquitous Computing, 6(1), 49–63. doi:10.1007/s007790200005 Schuerenberg, B. K. (2007). Bar Codes vs RFID: A Battle Just Beginning. Retrieved October 2, 2007, from www.healthdatamanagement.com Sokol, B., & Shah, S. (2004). RFID in Healthcare. Retrieved October 30, 2004, from www. rfidjournal.com

Stocking, B. (1985). Initiative and Inertia. London: Nuffield Provincial Hospital Trust. Stringleman, H. (2003). Electronic identification comes a step closer. Retrieved November 1, 2004, from www.country-wide.co.nz Swedberg, C. (2008). Italian Hospital Uses RFID to Document Patient Location, Treatment. Retrieved February 12, 2008, from www.rfidjournal.com Timing New Zealand. (2004). Winning Time Timing System. Retrieved November 1, 2004, from www.poprun.co.nz Walker, J., Spivey Overby, C., Mendelsohn, T., & Wilson, C. P. (2003). What You Need to Know About RFID in 2004. Retrieved February 9, 2004, from www.forrester.com Wallace, P. (2004). The health of nations. The Economist, 372, 1–18. Wasserman, E. (2007). A Healthy ROI. Retrieved October 12, 2007, from www.rfidjournal.com WAVE Advisory Board. (2001). From Strategy to Reality: The WAVE Project. Webster, C., Merry, A., Gander, P. H., & Mann, N. K. (2004). A prospective, randomised clinical evaluation of a new safety-orientated injectable drug administration system in comparison with conventional methods. Anaesthesia, 59, 80–87. doi:10.1111/j.1365-2044.2004.03457.x Wessel, R. (2007a). German Hospital Expands Bed-Tagging Project. Retrieved February 12, 2008, from www.rfidjournal.com Wessel, R. (2007b). RFID Synergy at a Netherlands Hospital. Retrieved October 31, 2007, from www.rfidjournal.com Wilson, R. M., Runciman, W. B., Gibberd, R. W., Harrison, B. T., Newby, L., & Hamilton, J. (1995). The quality in Australian health care study. The Medical Journal of Australia, 163, 458–471.

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Wright, D. (2003). Business Case for Clinical Information System - Phase 1. Auckland: Waitemata District Health Board.

KEY TERMS AND DEFINITIONS Clinical HIS: IS that supports day-to-day clinical activity in radiology, pathology, pharmacy, and other specialist areas. Diffusion of Innovation: The process by which adoption of an innovation spreads through an organisation. Diffusion may be informally, through social peer networks, and/or formally, through organisational hierarchies. Electronic Patient Record (EPR): Also known as Electronic Health Record (EHR) or Electronic Medical Record (EMR). An electronic longitudinal collection of personal health information relating to an individual entered or accepted by health care providers, and organised primarily to support ongoing, efficient and effective health care.

Evidence Based Medicine (EBM): The use of current best evidence in making decisions about the care of patients. Evidence comes from both the individual physician’s clinical expertise, to accurately diagnose a patient, and external research, to identify the safest and most effective treatment for a given diagnosis. Operational Hospital Information System (HIS): IS that supports day-to-day non-clinical activity, such as accounting, payroll, inventory, and patient (customer) management. Patient Safety: The minimisation of unintended injury to a patient as a result of health care practices (as opposed to injury resulting from the patient’s underlying disease). Injury includes death, permanent and temporary disability, prolonged hospital stay, and/or financial loss to the patient. Strategic HIS: IS that supports analysis of day-to-day activity in order to identify potential improvements, such as traffic patterns in ED, equipment utilisation, and workflow.

This work was previously published in Auto-Identification and Ubiquitous Computing Applications, edited by Judith Symonds, John Ayoade and David Parry, pp. 91-118, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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Chapter 3.14

Transcranial Magnetic Stimulation (TMS) as a Tool for Neurorehabilitation in Parkinson’s Disease Pablo Arias University of A Coruña, Spain Nelson Espinosa University of A Coruña, Spain Javier Cudeiro University of A Coruña, Spain

ABSTRACT Transcranial Magnetic Stimulation (TMS) is a stimulation technique introduced to clinical practise by Anthony Baker in 1985. TMS has become very valuable either for neurophysiological examination as for research. When use in a repetitive way it has shown to have a therapeutic role for treatment of neurological and psychiatric disorders. This chapter summarizes the basic prinDOI: 10.4018/978-1-60960-561-2.ch314

ciples of the technique focusing on its interaction with the neural tissue along with the analysis of different stimulation protocols, potential risks, and its effectiveness on Parkinson’s disease.

INTRODUCTION TMS is based on the generation of magnetic fields to induce currents in the cortical tissue (or any excitable tissue) interfering, therefore, with the electrical activity of neurons. TMS was introduced

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Transcranial Magnetic Stimulation (TMS) as a Tool for Neurorehabilitation in Parkinson’s Disease

by Anthony Barker in 1985 as a painless and noninvasive technique that, based on the principles of electromagnetic induction, is able to generate currents in the brain and modulate the function of the cortex. The prototype presented by Barker was capable of generating current pulses of 110μsec at a frequency of 0.3Hz and an intensity of 4kA. This device (developed by Magstim and under an exclusive license to the University of Sheffield) was the first magnetic stimulator built for commercial purposes. Today there are new versions of magnetic stimulators manufactured by different companies specially designed for clinical and research applications and able to work with higher current and frequency ranges. The following is a review of the technique that includes the general aspects of the device, the physics behind TMS and its main applications in both, clinical and experimental protocols, focused on Parkinson’s disease.

Basic Setup of TMS: A TMS apparatus is composed by (see Figure 1): A: A capacitive systems of charge-discharge current designed to store an electric charge Figure 1. Transcranial magnetic stimulator Magstim model Rapid. A: two capacitive systems to accumulate electric charge above 2.8 kV. B: control system to adjust stimulation parameters and safeguards. C: circular coil and two figure-eight coils with different diameters, each of them induces a magnetic field with different characteristics

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at high potential (in the kV range) and to supply a high current (in the kA range). B: A control system which allows to determine the current level, according to the user needs, and generates a magnetic stimulus either manually or by an external trigger circuit. In addition, it governs all electric and thermal protections for a safe operation. C: A stimulating coil whose design is crucial because it determines the profile of the induced magnetic field and because it is the only component in direct contact with the subject to study.

Operating Principles Whenever the capacitor bank is discharged by the action of the control system, a pulse of current I flows through the stimulating coil and, according to Ampère’s Law (eq. [1]), induces a magnetic  field H surrounding the coil. Figure 2A shows a simplified circuit equivalent to a figure of eight (or butterfly) coil (Figure 1C, center & right). Note that the induced magnetic field is greater (2H) where the more current flows.  Because I varies with time (pulse) so does H and, by virtue of the Faraday’s law (eq. [2] in Figure 3), an electric field is induced in the air Figure 2 A. a circulating current I induces a magnetic field H. The magnetic field is greater (2H) at points with greater current flow and is zero where the net current is zero. B: The right-hand rule is a mnemonic rule to determine the spatial relationship between current and magnetic field. The Ampère’s law (eq. [1]) expresses the relation between the current and the magnetic field

Transcranial Magnetic Stimulation (TMS) as a Tool for Neurorehabilitation in Parkinson’s Disease

Figure 3. Electric field A: 3D and B: 2D induced by a figure of eight coil (Fig 1C, right) at 100% of the capacity of the Magstim Rapid. The maximum value is located at the center of the coil (Jalinous, 1996). Equation [2] shows the relation between the electric field E and the magnetic field H. ∇× is the curl operator and 𝜇 is the permeability of the medium

(see Figure 3) and all tissues located on the coil vicinity. Then, if the coil is placed on the scalp, the magnetic field crosses the scalp, skull, cerebrospinal fluid and brain. As a result, the induced electric field gives rise to eddy currents whose magnitude depends on the conductivity of each medium (Wagner et al., 2004). TMS stimulus intensity can be inferred from the equation [2] in such a way that the bigger the slope of the current pulse, the higher the electric field intensity. Therefore, both the capacitive charge-discharge system design and the electrical parameters of the stimulation coil determinate the stimulus strength. The characteristics of the spatial distribution of the induced electric field (i.e. the precision and penetration depth of the stimulus) depend on the region where the eddy currents are induced (Starzynski et al., 2002, Miranda, Hallett and Basser, 2003, Roth et al., 1991, Nadeem et al., 2003) as well as on the geometric characteristics of the stimulation coil.

Coil Configurations and Companies Different coil shapes (see Figure 1C) are available from different companies and the choice depends on the objective of the study. “Figure of eight” or “round” coils are those more often used in clini-

cal practise, though some other shapes are also being developed. Circular coil: Circular coils (Figure 1C, left) are developed in different sizes and produce a wide electric field, allowing bihemispheric stimulation. Large 90mm mean diameter coil is most effective in stimulating the motor cortex and the spinal nerve roots. Using this kind of coil the stimulation is maximum under the winding of the coil, but tends to zero at the center. It is important to point out the role of current direction in the case of applying stimulation with a circular coil; given both sides of the coils have stimulating properties the appropriate direction must be chosen in order to preferentially activate each hemisphere. When the coil powered from a monophasic stimulator is placed centrally over the vertex of the skull and the current induced in the tissue flows clockwise, the stimulated hemisphere is chiefly the left one, and viceversa; given the motor cortex is more sensitive when current flows from posterior to anterior (Hovey and Janilous, 2008). Figure of eight coil: eight-shaped coils (see Figure 1C, center & right) are built from two windings place side by side, so that the induced tissue current reaches its maximum under the centre with a lower electric field in the remaining area, allowing a very good definition of the stimulated area. At this point, coil orientation critically influences the way stimulation interferes with the stimulated area, if a monophasic pulse is applied with this coil with latero-medial orientation corticospinal activity emerges from activation of either activation of the cortico-spinal tract directly, as well as from trans-synaptic activation of the piramidal cells, whereas stimulation with a posterior-anterior orientation elicits only the latter (Patton and Amassian, 1954; Kaneko et al., 1996; Nakamura et al., 1996; Di Lazzaro et al. 2004) Some other coil shapes: Apart from these classic coil shapes some other special configurations have been devised, allowing a more focused stimulation on the lower limb areas; some dedicated to

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Transcranial Magnetic Stimulation (TMS) as a Tool for Neurorehabilitation in Parkinson’s Disease

sham stimulation or a recently developed coil to deeper stimulation. Double Cone Coil: it has two large cup shaped windings side by side that allows a very good coupling to the skull, so that the induced current at the central fissure is very high, and the areas controlling lower limbs and torso are better stimulated (Hovey and Janilous, 2008). Sham Coil: it looks like a standard coil but just a very small portion of the energy is delivered to the head of the coil, since the coil is routed via a special box. Inside the box most of the energy is discharged but the characteristic ‘click’ is heard when the stimulator is fired (Hovey and Janilous, 2008). Sham coils have been a matter of debate, given a perfect sham coil should match a number of features to perfectly mimic real stimulation (i.e. identical coil positions on the scalp, acoustic artefacts, and identical sensations evoked on the scalp). Traditionally sham stimulation has been performed by tilting the coil 90º, however this technique is known not to fulfil the conditions mentioned beforehand. In addition some degree of real stimulation might also be present. To overcome the problems some works have proposed to use two superimposed coils (Okabe et al., 2003), and new sham coils are being developed such as the one composed by joining two coils, one real coil, and one sham coil, place upside-down so that the real coil provides some key elements to achieve realistic sham stimulation, like noise artefacts, and the sham coil some others, like coil’s position on the skull. Despite these steps forward the development of sham stimulators keeps on being a target given the devices developed so far do no match sideeffect scalp stimulation provided by real coils. Some researches (Okabe et al. 2003) have dealt with this issue by applying cutaneous electrical stimulation of the scalp. For instance, Rossi et al., (2007) have recently presented an adaptation of the traditional figure of eight coil which is built in compact wood. This is attached to the real

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coil by Velcro strips, so that the first one acts as a physical spacer between the real coil and the scalp; also the latter houses a bipolar electrical stimulator within the surface which is in contact with the scalp to reproduce cutaneous sensations felt by the subject when is really stimulated. H-coil: recently a way to obtain deeper TMS stimulation of the brain has been proposed, it is based on a new configuration of the coil. This coil allows to generate a summation of the evoked electric fields in a given brain region by means of placing several coil elements around this area; all this elements induce a electric field in the same direction (Roth, et al., 2002; Zangen et al. 2005) Coil companies: From a theoretical point of view one coil-shape will generate a given magnetic field, and will induce a characteristic electric field. However based on everyday practice things are not so easy and a new source of variability appears given several companies have developed differently the same kind of coil. For instance, figure of eight coils have been developed with both windings in the same plane, side by side, or in different plane and overlapping. The difference in configuration has been proved to have different neurophysiological effects when generating a pulse to interact with the cortex. Figure 4 illustrates how the output intensity from the stimulator at which motor threshold is determine varies depending on the coil manufacturer, regardless pulse waveform or the current direction (Kammer et al., 2001). This is a critical point to take into account when interpreting data, given motor thresholds are usually the key point in order to determine stimulation intensity in experimental or clinical procedures.

STIMULATION PARAMETERS Pulse Waveform Pulse waveform: this feature is also a basic element to take into account during TMS. According

Transcranial Magnetic Stimulation (TMS) as a Tool for Neurorehabilitation in Parkinson’s Disease

Figure 4. Variation in motor threshold in 8 subjects expressed as percentage of the stimulator as a function of the manufacturer, the pulse waveform and the current direction (anterior-posterior or postero-anterior) (modified from Kammer et al., 2001)

to the technical specifications of the equipments, single pulse may be monophasic, biphasic and polyphasic. Each type leads to different neurophysiological interactions with the tissue, and leads to different applications (Di Lazzaro et al., 2001). Monophasic pulse is more accurate, and produces lower noise and heat, but bilateral responses are not easy achievable (Hovey and Janilous, 2008). Neurophysilogical studies demonstrated that monophasic and biphasic pulses require different output intensities in order to determinate the motor threshold (MT), either active (AMT), or at rest (RMT) (Sommer et al., 2006).

Stimulation Frequency Depending on the frequency of the pulses there are three possible stimulation protocols: •

Simple stimulation: TMS is applied with a frequency below 1Hz (Barker, 2002). This is suitable for studies of peripheral nerves, being able to depolarize neurons in the motor cortex and to induce a motor evoked potential (MEP) that can be measured in a given muscle of the contralateral hemibody (usually in the hand). This type of stimulation is applied, for example, to evaluate the

•

•

motor conduction time, which is altered in cases of multiple sclerosis or amyotrophic lateral sclerosis (de Noordhout et al., 1998). Paired pulse stimulation: It consist of two TMS pulses applied in the same or different brain areas. Pulses are usually of different intensities and presented within an interval of milliseconds (Kujirai et al., 1993). When applied to motor cortex and varying the interval or intensity of the first pulse, the response to the second pulse can be inhibited or facilitated (Ziemann et al., 1998, Di Lazzaro et al., 1999, Ilic et al., 2002). These two phenomena are known respectively as intracortical inhibition and facilitation since, supposedly, it would be mediated by intracortical connections in pyramidal neurons, being inhibitory or excitatory depending on the interval between stimuli (Kujirai et al., 1993, Ziemann, Rothwell and Ridding, 1996). For other applications, further studies have proposed a range of protocols, varying the number of pulses, intensity and the interval between stimuli (Ziemann et al., 1998, Bestmann et al., 2004, Tokimura et al., 1996). Repetitive stimulation: Repetitive TMS (rTMS) consists of pulse trains delivered with a maximum frequency of 50 Hz during tens, hundreds or thousands of milliseconds. It is useful to modify the excitability of brain areas at the site of stimulation or in other cortical areas due to cortico-cortical connections (Jalinous, 1991), lasting even several minutes after the end of the rTMS protocol (Robertson, Theoret and PascualLeone, 2003), “off-line effect” or “after effect” (Siebner and Rothwell, 2003)). Depending on the frequency of stimulation, rTMS have been classified as of low and high frequency, being the separation between types around 5 Hz. This division is not random and is due mainly to results

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Transcranial Magnetic Stimulation (TMS) as a Tool for Neurorehabilitation in Parkinson’s Disease

Other Parameters

obtained in experiments devoted to analyze this frequency range. They concluded that frequencies above 5 Hz tended to increase corticospinal excitability (Berardelli et al., 1998, Maeda et al., 2000a, Pascual-Leone et al., 1994, Pascual-Leone et al., 1998). In contrast, frequencies below 5 Hz tended to depress the excitability (Pascual-Leone et al., 1998, Chen et al., 1997, Muellbacher et al., 2000). One special type of repetitive stimulation is called “theta burst” and consists of trains of pulses of short duration applied with a repetition rate of 5 Hz (it has also been tested in humans with 10 and 20 Hz, see De Ridder et al., 2007). Each train consists normally of 3 to 5 pulses with an intra-train frequency of 100 Hz; although human safety restrictions (Wassermann, 1998, Rossi et al. 2009, Table 1) limits the stimulation trains to 3 pulses with an intra-train frequency of 50 Hz (Huang and Rothwell, 2004). Its effect can be controlled, being facilitator in the “intermittent mode” (using groups of trains of 2 sec duration every 10 sec) or inhibitor in the “continuous mode” (Huang et al., 2005, Di Lazzaro et al., 2005). Its main advantage is that to achieve a specific period of delayed effect it requires a comparatively smaller period of stimulation than conventional rTMS (Huang and Rothwell, 2004).

Other parameters to be determined are the current that flows through the stimulation coil (i.e. the magnitude of the magnetic field, see eq. [1]) and the period of stimulation. Several studies have shown that frequency, intensity and duration are closely dependent variables. For example, a facilitatory effect similar to that obtained using 10 TMS pulses at 20 Hz and 150% of motor threshold (Pascual-Leone et al., 1994) is obtained by applying a train of 240 pulses at the same frequency but with an intensity of 90% (Maeda et al., 2000a, Maeda et al., 2000b). On the other hand, interdependence between frequency and duration of the stimulus is revealed in a second study devoted to analyze visual evoked potentials after rTMS applied on occipital cortex of the cat at low and high frequencies during periods ranging from 1 to 20 minutes (Aydin-Abidin et al., 2006). Trains of low frequency (1 to 3 Hz) require a long period of application (20 min) to significantly decrease the amplitude of the evoked potential; in contrast, high frequency trains (10 Hz) require less time (1 to 5 min) to increase the amplitude of evoked potentials and appear to be more effective than longer trains (20 min).

Table 1. Maximal rTMS train duration (secs) as a function of stimulation frequency to be bound to safety guidelines. Values have been calculated from data obtained from motor cortex stimulation; therefore it is worth remarking cortical excitability varies from area to area (Wassermann, 1998) Frequency (Hz)

Stimulation Intensity (% RMT)

1

>1800

>1800

360

>50

>50

>50

>50

27

11

11

8

7

6

5

>10

>10

>10

>10

7.6

5.2

3.6

2.6

2.4

1.6

1.4

1.6

1.2

10

>5

>5

4.2

2.9

1.3

0.8

0.9

0.8

0.5

0.6

0.4

0.3

0.3

20

2.05

1.6

1.0

0.55

0.35

0.25

0.25

0.15

0.2

0.25

0.2

0.1

0.1

25

1.28

0.84

0.4

0.24

0.2

0.24

0.2

0.12

0.08

0.12

0.12

0.08

0.08

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Transcranial Magnetic Stimulation (TMS) as a Tool for Neurorehabilitation in Parkinson’s Disease

CLINICAL APPLICATIONS OF TMS The use of TMS as a tool for clinical diagnosis is widely recognized. It has become a regular technique in neurology and neurophysiology, with application in the study of central conduction in multiple sclerosis, diagnosis of psychogenic disorders, study of facial paralysis, diagnosis and monitoring of spine injury, or as an operating room monitoring system. Likewise its application for basic research has grown exponentially in the last decade, given it is a suitable tool for exploring visual system physiology, language, learning, memory, and of course, motor system, for which it was originally developed. However, the effectiveness of TMS as a therapeutic tool is an issue of debate. Some researchers have stated that no treatment based on rTMS has consistently improved any disorder in the last decade. Despite, it is clear the technique is very attractive, given the possibility of stimulating cerebral circuitry through a non-invasive way with proved effect on functional re-organization, which can become of clinical utility. The technique has been used in several neurological and psychiatric diseases, like depression, stroke, migraine, or tinnitus. Results have been variable. Part of scepticism around the therapeutic effectiveness of TMS might be due to a wrong choice of the disorders to be treated; probably these are still to be determined. Wassermann and Lisanby (2001) reviewed the therapeutic use of rTMS, Pascual-Leone et al. (2002) and Hallet and Chokroverty (2005) have edited excellent monographs also. Some disorders have been the subject of research applying rTMS at low frequency with the aim of depressing neuronal activity of abnormally hyperexcitable cerebral circuits. This is the case, for instance, of focal dystonia, epilepsy, or auditory hallucinations present in schizophrenia, which might be good targets to be treated with rTMS (Lefaucheur et al., 2004, Poulet et al., 2005; Allam et al., 2007; Joo et al., 2007). On the other hand,

in some other disorders which affect (diffusely) larger areas of the brain, the hope of succeeding should be more modest. An intermediate example is Parkinson’s disease, where histological and neurochemical deficits are subcortical (beyond the direct TMS pulses influence), but with concomitant cortical dysfunctions well documented. Some of the affected cortical areas are the dorsolateral prefrontal cortex, the supplementary motor area, the lateral premotor cortex, the parietal cortex, the sensorimotor cortex, and also the motor cortex. Therefore, it seems conceivable that some of these areas can represent a target for rTMS application with the aim to relieve parkinsonian symptoms.

TMS over the Motor Cortex (Area 4) This area was the first target for TMS application in PD and some other movement disorders. Several of the parameters that can be evaluated with this technique are impaired in PD: •

•

Motor threshold: The minimum stimulation intensity that can produce a motor output of a given amplitude from a muscle at rest (RMT) or during a muscle contraction (AMT). It reflects excitability within the corticospinal system (Rossini et al. 1994). It is usually registered by electromyography recordings of the Motor Evoked Potential (MEP). Some works report normal threshold in PD; though some other reports RMT to be reduced in PD (Cantello et al. 2002). MEP amplitude: size (peak-to-peak) of MEP which increases in direct proportion to stimulus intensity. Most of the published works have reported that MEP is enlarged at rest; this may reflect an excitation-inhibition unbalance in the corticospinal system and desinhibition would be increased (Cantello et al. 2002).

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Transcranial Magnetic Stimulation (TMS) as a Tool for Neurorehabilitation in Parkinson’s Disease

Figure 5. Cortical silent period obtained after stimulation of M1. After the MEP, a period without electrical activity is observed

• •

•

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Cortical silent period: this parameter has become a key element of the use of TMS in PD. If a TMS pulse is delivered over the motor cortex whilst a muscle is voluntary contracted, a period of silent EMG activity is displayed following the muscular evoked potential (Figure 5) (Cantello et al. 2002). Its duration reflects the functionality of the cortical inhibitory mechanisms; it is shortened in PD, and increased by antiparkinsonian drugs. Neurophysiological evaluation of cortical physiology by mean of paired pulse stimulation: Stimulation with two distinct stimuli through the same coil over the cerebral cortex at a range of different intervals; the intensities can be varied independently. In this technique, a subthreshold conditioning stimulus (CS) is applied prior to a suprathreshold test stimulus (TS). The effect of the conditioning stimulus depends critically on the interstimulus interval (ISI) between CS and TS but also on CS strength. The motor-evoked response (MEP) elicited by the test stimulus is reduced if CS precedes TS by approximately 1–4 ms, while facilitation of the MEP results in ISIs of 7–20 ms (Ziemann et al., 1996). This phenomenon is commonly referred to as intracortical inhibition (and facilitation, since it is thought that either inhibitory or excitatory intracortical connections to the pyramidal tract neurones are activated in an

ISI-dependent way. More recently, this kind of inhibition has been termed shortinterval intracortical inhibition to distinguish it from another kind of cortically mediated inhibition observed with longer (50–200 ms) interstimulus intervals (VallsSolé et al., 1992) both altered in PD (Cantello et al. 2002). Repetitive TMS (rTMS) in PD: Alvaro Pascual Leone and collaborators in 1994 were the first to describe the effects of rTMS on PD signs. They showed how stimulation the motor cortex at 5 Hz, with an intensity of 90% of the RMT sped up movements in a hand dexterity test (Grooved Pegboard). However, other authors using similar protocols have not been able to reproduce those results (Ghabra et al. 1999).

Some other stimulation paradigms, more complex from a methodological point of view, have yielded promising results. In a placebo controlled trial by Siebner et al., (1999), rTMS with an intensity of 90% RMT resulted in significant improvement of hand movements. Stimulation was applied by means of 15 trains of 30 seconds each at 5 Hz, for a total of 2250 pulses over the motor cortex contralateral to the evaluated hand. In the task, patients had to perform balistic movements to a target in front of them. Movement time, accuracy, and velocity variability improved after real stimulation, but not in the case of those patients receiving sham stimulation. Though the experiment was carefully designed, it has been criticised given real stimulation facilitates ongoing movement, a fact that has not been achieved in the case of sham stimulation. Based on studies showing long lasting effects on parkinsonian sings obtained with just one session of rTMS, it seems conceivable to look for cumulative effects after different sessions. Recently, data obtained in a well controlled study have shown an improvement in clinical signs in PD

Transcranial Magnetic Stimulation (TMS) as a Tool for Neurorehabilitation in Parkinson’s Disease

after several sessions of rTMS at high frequency applied bilaterally over the motor cortex. The effects lasted for several days (Kherd et al., 2006).

Stimulation over Some Other Areas Involved in Motor Control Some other areas than the motor cortex have been also stimulated in order to ameliorate motor and non-motor sings in PD. •

•

Supplementary motor area (SMA): This area is involved in sequencing complex movements along with motor planning. When SMA was stimulated at 5 Hz (intensity 110% AMT) delivered by a figure of eight coil with a slight improvement in clinical signs was found (Hamada et al., 2008). Dorsolateral prefrontal cortex (DLPFC): This region mediates cognitive processes, like decision making or working memory. It is also related to movement given it connection to the basal ganglia-SMA loop. DLPFC has become a target for stimulation in PD given that stimulation of this area induces dopamine release in the caudate nucleus. Also some reports have shown improvement in symptoms of depression, which are common in PD. Although early reports indicate that motor sings might be improved after stimulation of DLPFC, a later placebo controlled study has pointed out that placebo effect could be responsible for the observed improvement in several motor task (arm movements, grip strength, and gait) after DLPFC stimulation of the contralateral hemisphere for several days (del Olmo et al. 2007).

DLPFC and Motor Cortex rTMS stimulation delivered over these areas may be beneficial and with long-lasting effects.

Stimulation (25 Hz at 100% RMT) was applied twice per week, for 4 weeks, resulting in a significant improvement in gait, and bradykinesia in the PD receiving real stimulation, but not in the sham group. This was a double-blind study with a positive outcome which reinforces the view that TMS might be a promising tool to treat PD (Lomarev et al. 2006).

Cerebellum Recently the use of rTMS has been explored with promising results in order to control some side effects of antiparkinsonian medication. Kock et al., 2009 have shown how rTMS applied over the cerebellum under continuous theta-burst protocol reduced levodopa induced diskynesias in the patients, as well as modified some altered cortical excitability parameters in the PD.

CONCLUSION Any kind of therapeutic approach must fulfil at least three main requirements in order to be consider useful from a clinical perspective: (i) the effect must last, hours or days, (ii) the improvement must be measurable and, finally (iii) benefit/risk ratio must be clearly positive. rTMS (even with the existing discrepancies) seems to match all requirements to be taken into account as a therapeutic tool in order to treat PD, the topic keeps on being matter of research. Some reports indicate improvements in clinical sings lasting several days after the last rTMS session with minor side effects, which are chiefly restricted to headache just after stimulation, which lasts a few minutes and appears seldom. In spite of the promising results, some questions remain open. What is it mechanism of action? What is the best protocol of stimulation? Is it suitable for all PD or is it phenotype dependent? Is the effect observed really different to that obtained by placebo? So far, the main conclusions to be drawn

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are: the technique allows a better knowledge of the disease from a neurophysiological point of view and it gives hope to find a new way to ameliorate parkinsonian symptoms.

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Transcranial Magnetic Stimulation (TMS) as a Tool for Neurorehabilitation in Parkinson’s Disease

Hovey, C., & Janilous, R. (2008). The guide to magnetic stimulation. The Magstims Company UK. Jalinous, R. (1991). Technical and practical aspects of magnetic nerve stimulation. Journal of Clinical Neurophysiology, 8(1), 10–25. doi:10.1097/00004691-199101000-00004 Joo, E. Y., Han, S. J., Chung, S. H., Cho, J. W., Seo, D. W., & Hong, S. B. (2007). Antiepileptic effects of low-frequency repetitive transcranial magnetic stimulation by different stimulation durations and locations. Clinical Neurophysiology, 118(3), 702–708. doi:10.1016/j.clinph.2006.11.008 Kammer, T., Beck, S., Thielscher, A., Laubis-Herrmann, U., & Topka, H. (2001). Motor thresholds in humans: a transcranial magnetic stimulation study comparing different pulse waveforms, current directions and stimulator types. Clinical Neurophysiology, 112(2), 250–258. doi:10.1016/ S1388-2457(00)00513-7 Kaneko, K., Kawai, S., Fuchigami, Y., Morita, H., & Ofuji, A. (1996). The effect of current direction induced by transcranial magnetic stimulation on the corticospinal excitability in human brain. Electroencephalography and Clinical Neurophysiology, 101, 478–482. doi:10.1016/ S0013-4694(96)96021-X

Koch, G., Brusa, L., Carrillo, F., Lo Gerfo, E., Torriero, S., & Oliveri, M. (2009). Cerebellar magnetic stimulation decreases levodopa-induced dyskinesias in Parkinson disease. Neurology, 73(2), 113–119. doi:10.1212/WNL.0b013e3181ad5387 Lefaucheur, J. P., Fénelon, G., Ménard-Lefaucheur, I., Wendling, S., & Nguyen, J. P. (2004). Low-frequency repetitive TMS of premotor cortex can reduce painful axial spasms in generalized secondary dystonia: a pilot study of three patients. Neurophysiologie Clinique, 34(3-4), 141–145. doi:10.1016/j.neucli.2004.07.003 Lomarev, M. P., Kanchana, S., Bara-Jimenez, W., Iyer, M., Wassermann, E. M., & Hallett, M. (2006). Placebo controlled study of rTMS for the treatment of Parkinson’s disease. Movement Disorders, 21(3), 325–331. doi:10.1002/mds.20713 Machii, K., Cohen, D., Ramos-Estebanez, C., & Pascual-Leone, A. (2006). Safety of rTMS to non-motor cortical areas in healthy participants and patients. Clinical Neurophysiology, 117(2), 455–471. doi:10.1016/j.clinph.2005.10.014 Mally, J., & Stone, T. W. (1999). Improvement in Parkinsonian symptoms after repetitive transcranial magnetic stimulation. Journal of the Neurological Sciences, 162(2), 179–184. doi:10.1016/ S0022-510X(98)00318-9

Khedr, E. M., Rothwell, J. C., Shawky, O. A., Ahmed, M. A., & Hamdy, A. (2006). Effect of daily repetitive transcranial magnetic stimulation on motor performance in Parkinson’s disease. Movement Disorders, 21(12), 2201–2205. doi:10.1002/mds.21089

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Patton, H. D., & Amassian, V. E. (1954). Single and multiple-unit analysis of cortical stage of pyramidal tract activation. Journal of Neurophysiology, 17, 345–363. Poulet, E., Brunelin, J., Bediou, B., Bation, R., Forgeard, L., & Dalery, J. (2005). Slow transcranial magnetic stimulation can rapidly reduce resistant auditory hallucinations in schizophrenia. Biological Psychiatry, 57(2), 188–191. doi:10.1016/j. biopsych.2004.10.007 Ridding, M. C., & Rothwell, J. C. (2007). Is there a future for therapeutic use of transcranial magnetic stimulation? Nature Reviews. Neuroscience, 8(7), 559–567. doi:10.1038/nrn2169 Rossi, S., Ferro, M., Cincotta, M., Ulivelli, M., Bartalini, S., & Miniussi, C. (2007). A real electro-magnetic placebo (REMP) device for sham transcranial magnetic stimulation (TMS). Clinical Neurophysiology, 118(3), 709–716. doi:10.1016/j. clinph.2006.11.005 Rossi, S., Hallett, M., Rossini, P. M., & PascualLeone, A. (2009). The Safety of TMS Consensus Group. Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clinical Neurophysiology, 120(12), 2008–2039. doi:10.1016/j.clinph.2009.08.016 Rossini, P. M., Barker, A. T., Berardelli, A., Caramia, M. D., Caruso, G., & Cracco, R. Q. (1994). Non-invasive electrical and magnetic stimulation of the brain, spinal cord and roots: basic principles and procedures for routine clinical application. Report of an IFCN committee. Electroencephalography and Clinical Neurophysiology, 91(2), 79–92. doi:10.1016/0013-4694(94)90029-9 Roth, Y., Amir, A., Levkovitz, Y., & Zangen, A. (2007). Three-dimensional distribution of the electric field induced in the brain by transcranial magnetic stimulation using figure-8 and deep Hcoils. Journal of Clinical Neurophysiology, 24(1), 31–38. doi:10.1097/WNP.0b013e31802fa393

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Rothwell, J. C. (1997). Techniques and mechanism of action of transcranial stimulation of the human motor cortex. Journal of Neuroscience Methods, 74(2), 113–122. doi:10.1016/S01650270(97)02242-5 Rothwell, J. C., Thompsom, P. D., Day, B. L., Boyd, S., & Marsden, C. D. (1991). Stimulation of the human motor cortex through the scalp. Experimental Physiology, 76(2), 159–200. Shimamoto, H., Morimitsu, H., Sugita, S., Nakahara, K., & Shigemori, M. (1999). Therapeutic effect of repetitive transcranial magnetic stimulation in Parkinson’s disease. Rinsho Shinkeigaku. Clinical Neurology, 39(12), 1264–1267. Siebner, H. R., Mentschel, C., Auer, C., & Conrad, B. (1999). Repetitive transcranial magnetic stimulation has a beneficial effect on bradykinesia in Parkinson’s disease. Neuroreport, 10(3), 589–594. doi:10.1097/00001756-199902250-00027 Siebner, H. R., Rossmeier, C., Mentschel, C., Peinemann, A., & Conrad, B. (2000). Short-term motor improvement after sub-threshold 5-Hz repetitive transcranial magnetic stimulation of the primary motor hand area in Parkinson’s disease. Journal of the Neurological Sciences, 178(2), 91–94. doi:10.1016/S0022-510X(00)00370-1 Sommer, M., Alfaro, A., Rummel, M., Speck, S., Lang, N., Tings, T., & Paulus, W. (2006). Half sine, monophasic and biphasic transcranial magnetic stimulation of the human motor cortex. Clinical Neurophysiology, 117(4), 838–844. doi:10.1016/j. clinph.2005.10.029 Tormos, J. M., Catalá, M. D., & Pascual-Leone, A. (1999). Estimulación magnética transcraneal. Revista de Neurologia, 29(2), 165–171. Valls-Solé, J., Pascual-Leone, A., Wassermann, E. M., & Hallett, M. (1992). Human motor evoked responses to paired transcranial magnetic stimuli. Electroencephalography and Clinical Neurophysiology, 85(6), 355–364. doi:10.1016/01685597(92)90048-G

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KEY TERMS AND DEFINITIONS AMT: Motor threshold (MT) during a muscle contraction. Dorsolateral Prefrontal Cortex (DLPFC): Part of the cerebral cortex involved in cogni-

tive processes, like decision making or working memory. It is also related to movement given it connection to the basal ganglia-SMA loop. Interstimulus Interval (ISI): Time between consecutive stimulus. Motor Cortex (Area 4): Part of the cerebral cortex involved in controlling muscle contraction. Motor Evoked Potential (MEP): Electrical activity recorded from muscles following direct stimulation of motor cortex. Motor Threshold (MT): The minimum stimulation intensity that can produce a motor output of a given amplitude from a muscle at rest (RMT) or during a muscle contraction (AMT). PD: Parkinson’s disease. Repetitive TMS (rTMS): Modality of TMS in which pulse trains are delivered with a maximum frequency of 50 Hz during tens, hundreds or thousands of milliseconds. RMT: Motor threshold at rest. Subthreshold Conditioning Stimulus (CS): Stimulus with an intensity below the motor threshold. Supplementary Motor Area (SMA): In sequencing complex movements along with motor planning. Supra-Threshold Test Stimulus (TS): Stimulus with an intensity above the motor threshold. Transcranial Magnetic Stimulation (TMS): Stimulation technique based on the generation of magnetic fields to induce currents in the cortical tissue (or any excitable tissue) interfering, therefore, with the electrical activity of neurons.

This work was previously published in Handbook of Research on Personal Autonomy Technologies and Disability Informatics, edited by Javier Pereira, pp. 131-143, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 3.15

Modeling of Porphyrin Metabolism with PyBioS Andriani Daskalaki Max Planck Institute for Molecular Genetics, Germany

ABSTRACT Photodynamic Therapy (PDT) involves administration of a photosensitizer (PS) either systemically or locally, followed by illumination of the lesion with visible light. PDT of cancer is now evolving from experimental treatment to a therapeutic alternative. Clinical results have shown that PDT is at least as efficacious as standard treatments of malignancies of the skin and Barrett’s esophagus. Hemes and heme proteins are vital components of essentially every cell in virtually all eukaryote organisms. Protoporphyrin IX (PpIX) is produced in cells via the heme synthesis pathway from the DOI: 10.4018/978-1-60960-561-2.ch315

substrate aminolevulinic acid (ALA). Exogenous administration of ALA induces accumulation of (PpIX), which can be used as a photosensitiser for tumor detection or photodynamic therapy. Although the basis of the selectivity of ALA-based PDT or photodiagnosis is not fully understood, it has sometimes been correlated with the metabolic rate of the cells, or with the differential enzyme expressions along the heme biosynthetic pathway in cancer cells. An in silico analysis by modeling may be performed in order to determine the functional roles of genes coding enzymes of the heme biosynthetic pathway like ferrochelatase. Modeling and simulation systems are a valuable tool for the understanding of complex biological systems. With PyBioS, an object-oriented mod-

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Modeling of Porphyrin Metabolism with PyBioS

elling software for biological processes, we can analyse porphyrin metabolism pathways.

INTRODUCTION The use of PDT for curative treatments of superficial tumors of the skin and for palliative treatments of disseminated tumors of skin and oral mucosa is well known (Daskalaki 2002). PDT also is efficacious as treatment of malignancies of Barrett’s oesophagus (Foroulis and Thorpe 2006). PDT is based on a photochemical process, where photosensitizers (PS) act cytotoxic by generation of 1O2 after laser irradiation. The use of fluorescence measurements as quantitative indicators for PpIX accumulation after exogenous ALA administration is suitable for differentiating neoplastic, necrotic and inflammatory tissues from normal tissues. The modulation of ALA-induced PpIX accumulation and expression will provide more diagnostic information and more accuracy for the diagnosis of unhealthy tissue, especially in border-line cases. The modulation of fluorescence characteristics of ALA-induced PpIX with NAD has been used for differentiation between fibroblast and fibrosarcoma (Ismail et al. 1997). The flow of substrates into the porphyrin pathway is controlled by tsynthesis of d–aminolevulinic acid (ALA), the first committed precursor in the porphyrin pathway. Although light is required to trigger ALA synthesis and differentiation of chloroplasts (Reinbothe and Reinbothe, 1996), a feedback inhibition of ALA synthesis by an end product of the porphyrin pathway is thought to be involved in the regulation of influx into the pathway (Wettstein et al., 1995; Reinbothe and Reinbothe, 1996). Both the nature of the product and the mechanism involved in effecting feedback inhibition remain unknown, probably because there have been no porphyrin pathway mutants identified so far that affect both chlorophyll and heme biosyntheses. Thus, modelling of porphyrin

pathway may fill this gap and allow researchers to address these questions. Downey (2002) tried to show how the porphyrin pathway may be an integral part of all disease processes through a model. Analytical techniques capable of measuring porphyrins in all cells are needed. Data gathered from plant and animal studies need to be adapted to humans where possible. An inexpensive, accurate and rapid analysis needs to be developed so porphyrins can be measured more routinely. The committed step for porphyrin synthesis is the formation of 5-aminolevulinate (ALA) by condensation of glycine (from the general amino acid pool) and succinyl-CoA (from the TCA cycle), in the mitochondrial matrix. This reaction is catalyzed by two different ALA synthases, one expressed ubiquitously (ALAS1) and the other one only expressed in erythroid precursors (ALAS2) (Ajioka, 2006). Heme inhibits the activity of ALA synthetase, the first and rate-limiting enzyme of the biosynthetic pathway, thereby preventing normal cells from drowning in excess production of its own porphyrins. This negative feedback control can be bypassed in certain malignant cells exposed to an excess amount of ALA, which is metabolised leading to overproduction of PpIX. Excess accumulation of PpIX occurs because of the enzyme configuration in malignant cells (Kirby 2001). The enzyme ferrochelatase (FECH) catalyzes insertion of an iron atom into PpIX forming heme which is not photoreactive. However, cancer cells have a relatively low activity of ferrochelatase which leads to an excess accumulation of PpIX (Schoenfeld 1988). Another factor leading to augmented PpIX synthesis is an increased activity of the rate-limiting enzyme porphobilinogen deaminase in various malignant tissues (Wilson 1991). Kemmner W et al. (2008) recently showed that in malignant tissue a transcriptional downregulation of FECH occurs causeing endogenous PpIX accumulation. Furthermore, accumulation of intracellular PpIX because of FECH small

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Modeling of Porphyrin Metabolism with PyBioS

interfering RNA (siRNA) silencing provides a small-molecule-based approach to molecular imaging and molecular therapy. Kemmner W et al (2008) demonstrated accumulation of the heme precursor PpIX in gastrointestinal tumor tissues. To elucidate the mechanisms of PpIX accumulation expression of the relevant enzymes in the heme synthetic pathway has been studied. Kemmner W et al (2008) described a significant down-regulation of FECH mRNA expression in gastric, colonic, and rectal carcinomas. Accordingly, in an in vitro model of several carcinoma cell lines, ferrochelatase down-regulation and loss of enzymatic activity corresponded with an enhanced PpIX-dependent fluorescence. Silencing of FECH using siRNA technology led to a maximum 50-fold increased PpIX accumulation. Bhasin G et al. (1999) investigated the hypothesis that inhibition of ferrochelatase will cause in situ build up of high PpIX concentrations which may act as a putative agent for photodestruction of cancer cells. The parenteral administration of lead acetate, a known inhibitor of ferrochelatase, to mice bearing cutaneous tumors (papillomas and carcinomas) caused a six-fold enhancement in the concentration of PpIX in tumors within a period of one month. A significant reduction in tumor size was observed starting as early as day one following the treatment. 5-Aminolevulinate synthase (ALAS) is a mitochondrial enzyme that catalyzes the first step of the heme biosynthetic pathway. Mitochondrial import as well as synthesis of the nonspecific ALAS isoform (ALAS1) is regulated by heme through a feedback mechanism (Munakata 2004).

Mutations A deficiency of FECH activity underlies the excess accumulation of protoporphyrin that occurs in erythropoietic protoporphyria (EPP). In some patients, protoporphyrin accumulation causes liver damage that necessitates liver transplanta-

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tion. Mutations of the codons for 2 of [2Fe-2S] cluster ligands in patients with EPP support the importance of the iron-sulfur center for proper functioning of mammalian FECH and, at least in humans, its absence has a direct clinical impact (Schneider-Yin X. et al. 2000). Fu-Ping Chen et al.(2002) studied patients who developed liver disease with mutations in the FECH gene. Recent attempts to increase the efficacy of ALA-mediated PDT include the use of iron chelators to decrease the amount of PPIX converted to heme by FECH in removing the free iron that is necessary for the enzyme to work (Curnow, 1998. Ferreira et al., 1999). X-linked sideroblastic anemias (XLSAs), a group of severe disorders in humans characterized by inadequate formation of heme in erythroblast mitochondria, are caused by mutations in the gene for erythroid eALAS, one of two human genes for ALAS (Astner et al., 2005). Cloning and expression of the defective gene for deltaaminolevulinate dehydratase (ALAD) from patients with ALAD deficiency porphyria (ADP) were performed by Akagi et al.(2000). Mitchel et al.(2001), studied Escherichia coli and human porphobilinogen synthase (PBGS) mutants.

MODELING OF PORPHYRIN METABOLISM Quantitative modeling studies of pathways have been successfully applied to understand complex cellular processes (Schoeberl 2002, Klipp 2005). Particular attention has been paid to the way, in which PpIX is distributed and accumulated in cells under the effect of ALA (Gaullier et al. 1995). For induction of a clinical effect it is important to recognize the kinetics of PpIX accumulation in cells, as influenced by the applied ALA dose. Cellular content of tis photosensitizer precursor should be optimal for induction of the photodestructive effect, following light exposure of targeted neoplas-

Modeling of Porphyrin Metabolism with PyBioS

tic lesions. The kinetics of PpIX formation under the effect of exogenous ALA is thought to result from circumvented bottle-neck linked to synthesis of endogenous ALA, the level of which remains under control of free heme (Kennedy and Pottier (1992). Considering that these problems may not only be of theoretical significance, but also have a practical value for establishing conditions of a photodynamic therapy,we have to define kinetics of PpIX accumulation in different cells under the effect of various concentrations of ALA.

Pathway Databases Pathway databases can act as a rich source for such graphs, because a reaction graph is simply a pathway. The reactome pathway database (Vastrik et al., 2007) has been used as a key starting point for kinetic modelling since it entails detailed models for reaction graphs, which describe the series of biochemical events involved in the models and their relationships. The graphs establish a framework for the models and suggest the kinetic coefficients that should be obtained experimentally. Kamburov et al. (2006) developed ConsensusPathDB (Figure 1), a database that helps users to summarize and verify pathway information and to

enrich a priori information in the process of model annotation. The database model allows integration of information on metabolic, signal transduction and gene regulatory networks. Cellular reaction networks are stored in a PostgreSQL database and can be accessed under http://pybios.molgen.mpg.de/CPDB. By forward modelling we integrate all interactive properties of molecular components to understand systems behavior (Westerhoff et al. 2008). The forward-modeling approach supports the formulation of hypothesezing for e.g. in silico knock-out experiments. Thus, to construct a model of the porphyrin metabolism pathway, one should consider one enzymatic or transport step at a time, should comb the literature for information about this enzyme, its cofactors and modulators, and should translate this information into a mathematical rate law which could be a Michaelis-Menten, among a wide variety of possibilities. The collection of all rate laws governs the dynamics of this model. Comparisons of model responses with biological observations support the validity of this appointed model or suggest adjustments in assumptions or parameter values. This forward process may lead to model representations of the pathway exhibiting the same features as reality, at least qualitatively, if not quantitatively.

Figure. 1. Schematic illustration of PpIX biosynthesis. One part of the synthesis is localized in mitochondrium the other part in cytoplasm. In this scheme not all biosynthesis steps are to be seen.

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Modeling of Porphyrin Metabolism with PyBioS

The porphyrin metabolism model was assembled, simulated and analysed by PybioS. PyBioS is an object-oriented tool for modeling and simulation of cellular processes. This tool has been established for the modelling of biological processes using metabolic pathways from databases like KEGG and the Reactome database. Modeling and simulation techniques are valuable tools for the understanding of complex biological systems. The platform is implemented as a Python-product for the Zope web application server environment. PyBioS acts as a model repository and supports the generation of large models based on publicly available information like the metabolic data of the KEGG database. An ODE-system of this model may be generated automatically based on pre- or user-defined kinetic laws and used for subsequent simulation of time course series and further analyses of the dynamic behavior of the underlying system.

Modeling with PyBioS A model of a disease-relevant pathway, such as porphyrin metabolism, has been employed to study the relationship between basic enzymes and products in the biosynthetic pathway. Visualization of the porphyrin metabolism interaction network (Figure 2) was enabled by automatically generated graphs that include information about the objects, reactions and mass- and information-flow. The model includes a total of 16 reactions, and 42 objects. It is composed of an ordinary differential equations system with 14 state variables and 16 parameters. The law of mass-action has been applied to describe the rate of porphyrin metabolism.Time-dependent changes of the concentration of participating proteins and protein complexes are determined by a system of differential equations.

Figure 2. CPDB database. ConsensusPathDB assists the development, expansion and refinement of computational models of biological systems and the context-specific visualization of models provided in SBML.

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Figure 3. A part of the porphyrin metabolism pathway is illustrated as a network diagram in PyBioS. Catalysis of heme formation by the enzyme ferrochelatase is illustrated in the network graphic in PyBioS.

Mutations related to genes FECH and ALAS have been analyzed by simulating knockouts of these genes by using a mathematical model in order to study mutation effects in the concentration of heme.

CONCLUSION The modeling and simulation platform PyBioS has been used for the in silico analysis of porphyrin metabolism pathway. This model of a porphyrin metabolim pathway should be used for hypotheses generation by forward modeling. Also the model should be

Figure 4. Ferrochelatase (FECH) catalyzes the terminal step of the heme biosynthetic pathway. Graphical illustration of the time course of the PpIX concentration, heme and ferrochelatase (FECH). FECH catalyzes the production of heme by PpIX.

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Table 1. Parameters in the model Parameter symbol

Biological meaning

massi_k (FECH homodimer)

Heme production rate constant

deg_degradation_k (heme [mitochondrial matrix])

Heme degradation rate constant

massi_k (ALAS homodimer)

5-aminolevulinate production rate constant

massi_k (PPO homodimer (FAD cofactor)

Protoporphyrin IX production rate constant

massi_k (CPO homodimer)

Protoporphyrinogen IX production rate constant

ComplexFormationReversible_kf (ALAD homooctamer)

Formation of complex rate constant

massi_k (ALAD homooctamer (Zinc cofactor)

Porphobilinogen production rate constant

massi_k (Porphobilinogen deaminase)

Hydroxymethylbilane (HMB) production rate constant

massi_k (UROD homodimer)

Coproporphyrinogen III production rate constant

Degradation_degradation_k (ALAD homooctamer (Pb and Zn bound)

Degradation rate constant

Degradation_degradation_k (Coproporphyrinogen I

Degradation rate constant

massi_k (Uroporphyrinogen-III synthase)

Uroporphyrinogen III production rate constant

massi_k (Protoporphyrin IX)

Export rate constant for Protoporphyrin IX

massi_k (Coproporphyrinogen)

Export rate constant for Coproporphyrinogen III

massi_k (ALAD)

Formation of ALA Dehydratase inactive complex

massi_k Hydroxymethylbilane

Dissociation rate constant for Hydroxymethylbilane

Table 2. State variables (proteins) in the model Parameter symbol

Biological meaning

5-aminolevulinate [cytosol]

5-aminolevulinate in the cytosol

5-aminolevulinate [mitochondrial matrix]

5-aminolevulinate in the mitochondrial matrix

ALAD homooctamer (Pb and Zn bound) [cytosol]

ALA Dehydratase inactive complex in the cytosol

ALAD homooctamer (Zinc cofactor) [cytosol]

ALA Dehydratase in the cytosol

ALAS homodimer [mitochondrial matrix]

ALA Synthetase in the mitochondrial matrix

Coproporphyrinogen I [cytosol]

Coproporphyrinogen I in the cytosol

Coproporphyrinogen III [cytosol]

Coproporphyrinogen III in the cytosol

Coproporphyrinogen III [mitochondrial intermembrane space]

Coproporphyrinogen III in the mitochondrial intermembrane space

CPO homodimer [mitochondrial intermembrane space]

Coproporphyrinogen oxidase in the mitochondrial intermembrane space

FECH homodimer (2Fe-2S cluster) [mitochondrial matrix]

Ferrochelatase in the mitochondrial matrix

heme [mitochondrial matrix]

Heme in the mitochondrial matrix

PPO homodimer (FAD cofactor) [mitochondrial intermembrane space]

Protoporphyrinogen oxidase in the mitochondrial intermembrane space

UROD homodimer [cytosol]

Uroporphyrinogen III-Decarboxylase in the cytosol

Uroporphyrinogen-III synthase [cytosol]

Uroporphyrinogen-III synthase in the cytosol

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Figure 5. A diagram summarizing the heme production. Illustration of (A) heme production and (B) PPIX time course in case of mutation of ferrochelatase. FECH inhibition is indicated by a blunted line. The simulation analysis of the model indicates that FECH inhibition caused a (B) decrease of heme.

Figure 6. Illustration of 5-aminolevulinate production time course of ALAS. ALAS inhibition is indicated by a blunted line. The simulation analysis of the model indicates that ALAS inhibition because of ALAS mutation caused a decrease of 5-aminolevulinate.

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disturbed to test tinfluences of gene knock-outs, mutations and performance of this model system. Knock-out experiments can be performed in order to determine the functional roles of genes coding enzymes of the heme biosynthetic pathway like ferrochelatase by studying the defects caused by the resulting mutation. A next step should be integration of experimental data into the kinetic model of this pathway. The results of the in silico experiments have to be compared with the experimental data to decide, which kind of perturbation caused the phenotype of the investigated system. Thus, we should be able to test mutations of enzymes playing an important role in the heme biosynthetic pathway.

REFERENCES Ajioka, R. S., Phillips, J. D., & Kushner, J. P. (2006). Biosynthesis of heme in mammals, biochimica et biophysica acta (BBA). Molecular Cell Research, 1763(7), 723–736. Akagi, R., Shimizu, R., Furuyama, K., Doss, M. O., & Sassa, S. (2000, March). Novel molecular defects of the delta-aminolevulinate dehydratase gene in a patient with inherited acute hepatic porphyria. Hepatology (Baltimore, Md.), 31(3), 704–708. doi:10.1002/hep.510310321 Astner, I., Schulze, J. O., van den Heuvel, J., Jahn, D., Schubert, W.-D., & Heinz, D. W. (2005). Crystal structure of 5-aminolevulinate synthase, the first enzyme of heme biosynthesis, and its link to XLSA in humans. The EMBO Journal, 24, 3166–3177. doi:10.1038/sj.emboj.7600792 Bhasin, G., Kausar, H., & Athar, M. (1999, November). December). Ferrochelatase, a novel target for photodynamic therapy of cancer. Oncology Reports, 6(6), 1439–1442.

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Chen, F.-P., Risheg, H., Liu, Y., & Bloomer, J. (2002, February). Ferrochelatase gene mutations in erythropoietic protoporphyria: Focus on liver disease. Cell Mol Biol (Noisy-le-grand), 48(1), 83–89. Daskalaki, A. (2002). The use of photodynamic therapy in dentistry. Clinical and experimental studies. Diss. Berlin: FU. Downey, D. C. (2002). The porphyrin pathway: The final common pathway? Medical Hypotheses, 59(6), 615–621. doi:10.1016/S03069877(02)00115-9 Ferreira, G. C., Franco, R., & José, J. (1999). Ferrochelatase: A new iron-sulfur center containing enzyme. 3.3 Steady - State kinetic properties of ferrochelatase. Iron metabolism. Wiley-VCH. Foroulis, C. N., & Thorpe, J. A. C. (2006). Photodynamic therapy (PDT) in Barrett’s esophagus with dysplasia or early cancer. European Journal of Cardio-Thoracic Surgery, 29, 30–34. doi:10.1016/j.ejcts.2005.10.033 Gaullier, J. M., Geze, M., Santus, R., Sa, M. T., Maziere, J. C., & Bazin, M. (1995). Subcellular localization of and photosensitization by protoporphyrin IX in human keratinocytes and fibroblasts cultivated with 5-aminolevulinic acid. Photochemistry and Photobiology, 62, 114–122. doi:10.1111/j.1751-1097.1995.tb05247.x Ismail, M. S., Dressler, C., Strobele, S., Daskalaki, A., Philipp, C., & Berlien, H.-P. (1997). Modulation of 5-ALA-induced PplX xenofluorescence intensities of a murine tumour and non-tumour tissue cultivated on the chorio-allantoic membrane. Lasers in Medical Science, 12, 218–225. doi:10.1007/BF02765102

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Kamburov, A., Wierling, C., Lehrach, H., & Herwig, R. (2006, December 1-2). ConsensusPathDB - Database for matching pathway annotation. Systems Biology, Proceedings of Computational Proteomics Joint Annual RECOMB 2005 Satellite Workshops on Systems Biology and on Regulatory Genomics, San Diego, CA, USA. Kemmner, W., Wan K., Rüttinger S., Ebert B., Macdonald R., Klamm U., & Moesta K.T. (2008, February). Silencing of human ferrochelatase causes abundant protoporphyrin-IX accumulation in colon cancer. FASEB J., (2), 500-9. Epub 2007, Sep 17. Kennedy, J. C., & Pottier, R. H. (1992). Endogenous protoporphyrin IX, a clinical useful photosensitizer for photodynamic therapy. J Photoresponse Chem Photobiol, 14, 275–292. doi:10.1016/1011-1344(92)85108-7 Kirby, I., Bland, J., & Daly, M. Constantine, & Karakousis, P. (2001). Surgical oncology: Contemporary principles and practice. New York: McGraw-Hill. Klipp, E., Herwig, R., Kowald, A., Wierling, C., & Lehrach, H. (2005). Systems biology in practice. Concepts, implementation and application. WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Klipp, E., Nordlander, B., Kruger, R., Gennemark, P., & Hohmann, S. (2005). Integrative model of the response of yeast to osmotic schock. Nature Biotechnology, 23, 975–982. doi:10.1038/nbt1114 Mitchell, L. W., Volin, M., Martins, J., & Jaffe, E. K. (2001) Mechanistic implications of mutations to the active site lysine of porphobilinogen synthase. J Biol Chem, 12, 276(2), 1538-44. Munakata, H., Sun, J-Y., Yoshida, K., Nakatan, T., Honda, E., Hayakawa, S., Furuyama, K., & Hayashi, N. (2004). Role of the heme regulatory motif in the heme-mediated inhibition of mitochondrial import of 5-aminolevulinate synthase. J Biochem, 136, 233-238, 136(2), 233.

Reinbothe, S., & Reinbothe, C. (1996). The regulation of enzymes involved in chlorophyll biosynthesis. European Journal of Biochemistry, 237, 323–343. doi:10.1111/j.1432-1033.1996.00323.x Schneider-Yin, X., Gouya, L., Dorsey, M., Rüfenacht, U., & Deybach, J.-C. (2000). Mutations in the iron-sulfur cluster ligands of the human ferrochelatase. Blood, 96, 1545–1549. Schoeberl, B., Eichler-Jonsson, G., Gilles, E. D., & Muller, G. (2002). Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nature Biotechnology, 20, 370–375. doi:10.1038/ nbt0402-370 Schoenfeld, N., Epstein, O., Lahav, M., Mamet, R., Shaklai, M., & Atsmon, A. (1988). The heme biosynthetic pathway in lymphocytes of patients with malignant lymphoproliferative disorders. Cancer Letters, 43, 43–48. doi:10.1016/03043835(88)90211-X Vastrik, I., D‘Eustachio, P., Schmidt, E., JoshiTope, G., Gopinath, G., & Croft, D. (2007). Reactome: A knowledge base of biologic pathways and processes. Genome Biology, 8, R39. doi:10.1186/ gb-2007-8-3-r39 Von Wettstein, D., Gough, S., & Kannangara, C. G. (1995). Chlorophyll biosynthesis. The Plant Cell, 7, 1039–1057. Westerhoff, H. V. et al. (2008). Systems biology towards life in silico: Mathematics of the control of living cells. J Math Biol. Wierling, C., Herwig, R., & Lehrach, H. (2007). Resources, standards and tools for systems biology. Briefings in Functional Genomics and Proteomics, 10, 1093/bfgp/elm027. Wilson, J.H.P., van Hillegersberg, R., van den Berg, J.W.O., Kort, W.J., & Terpsta, O.T. Photodynamic therapy for gastrointestinal tumors. Scand J Gastroenterol, 26(Suppl), 188: 20-25.

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KEY TERMS AND DEFINITIONS ALA: The first committed precursor of the porphyrin pathway Ferrochelatase: Also known as FECH is an enzyme involved in porphyrin metabolism, converting protoporphyrin IX into heme. Forward Modeling: Modeling approach to study the interaction of processes to produce a response. Heme: Iron-containing porphyrin (heme-containing protein or hemoprotein) that is extensively found in nature ie. hemoglobin. Knockout-Experiment: A experiment, where an organism is engineered to lack the expression and activity of one or more genes.

Mutation: Changes to the nucleotide sequence of the genetic material of an organism. Mutations can be caused by copying errors in the genetic material during cell division, by exposure to ultraviolet or ionizing radiation, chemical mutagens, or viruses. Porphyrin: Porphyrins are heterocyclic macrocycles, consisting of four pyrrole subunits (tetrapyrrole) linked by four methine (=CH-) bridges. The extensive conjugated porphyrin macrocycle is chromatic and the name itself, porphyrin, is derived from the Greek word for purple. PyBioS: PyBioS is a system for the modeling and simulation of cellular processes. It is developed at the Max-Planck-Institute for Molecular Genetics in the department of Prof. Lehrach.

This work was previously published in Handbook of Research on Systems Biology Applications in Medicine, edited by Andriani Daskalaki, pp. 643-654, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 3.16

AI Methods for Analyzing Microarray Data Amira Djebbari National Research Council Canada, Canada Aedín C. Culhane Harvard School of Public Health, USA Alice J. Armstrong The George Washington University, USA John Quackenbush Harvard School of Public Health, USA

INTRODUCTION Biological systems can be viewed as information management systems, with a basic instruction set stored in each cell’s DNA as “genes.” For most genes, their information is enabled when they are transcribed into RNA which is subsequently translated into the proteins that form much of a cell’s machinery. Although details of the process for individual genes are known, more complex interactions between elements are yet to be discovered. What we do know is that diseases can result DOI: 10.4018/978-1-60960-561-2.ch316

if there are changes in the genes themselves, in the proteins they encode, or if RNAs or proteins are made at the wrong time or in the wrong quantities. Recent advances in biotechnology led to the development of DNA microarrays, which quantitatively measure the expression of thousands of genes simultaneously and provide a snapshot of a cell’s response to a particular condition. Finding patterns of gene expression that provide insight into biological endpoints offers great opportunities for revolutionizing diagnostic and prognostic medicine and providing mechanistic insight in data-driven research in the life sciences, an area with a great need for advances, given the urgency

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

AI Methods for Analyzing Microarray Data

associated with diseases. However, microarray data analysis presents a number of challenges, from noisy data to the curse of dimensionality (large number of features, small number of instances) to problems with no clear solutions (e.g. real world mappings of genes to traits or diseases that are not yet known). Finding patterns of gene expression in microarray data poses problems of class discovery, comparison, prediction, and network analysis which are often approached with AI methods. Many of these methods have been successfully applied to microarray data analysis in a variety of applications ranging from clustering of yeast gene expression patterns (Eisen et al., 1998) to classification of different types of leukemia (Golub et al., 1999). Unsupervised learning methods (e.g. hierarchical clustering) explore clusters in data and have been used for class discovery of distinct forms of diffuse large B-cell lymphoma (Alizadeh et al., 2000). Supervised learning methods (e.g. artificial neural networks) utilize a previously determined mapping between biological samples and classes (i.e. labels) to generate models for class prediction. A k-nearest neighbor (k-NN) approach was used to train a gene expression classifier of different forms of brain tumors and its predictions were able to distinguish biopsy samples with different prognosis suggesting that microarray profiles can predict clinical outcome and direct treatment (Nutt et al., 2003). Bayesian networks constructed from microarray data hold promise for elucidating the underlying biological mechanisms of disease (Friedman et al., 2000).

BACKGROUND Cells dynamically respond to their environment by changing the set and concentrations of active genes by altering the associated RNA expression. Thus “gene expression” is one of the main determinants of a cell’s state, or phenotype. For example, we can investigate the differences between a normal cell and a cancer cell by examining their relative gene expression profiles. Microarrays quantify gene expression levels in various conditions (such as disease vs. normal) or across time points. For n genes and m instances (biological samples), microarray measurements are stored in an n by m matrix where each row is a gene, each column is a sample and each element in the matrix is the expression level of a gene in a biological sample, where samples are instances and genes are features describing those instances. Microarray data is available through many public online repositories (Table 1). In addition, the Kent-Ridge repository (http://sdmc.i2r.a-star.edu. sg/rp/) contains pre-formatted data ready to use with the well-known machine learning tool Weka (Witten & Frank, 2000). Microarray data presents some unique challenges for AI such as a severe case of the curse of dimensionality due to the scarcity of biological samples (instances). Microarray studies typically measure tens of thousands of genes in only tens of samples. This low case to variable ratio increases the risk of detecting spurious relationships. This problem is exacerbated because microarray data contains multiple sources of within-class variability, both technical and biological. The high

Table 1. Some public online repositories of microarray data Name of the repository

URL

ArrayExpress at the European Bioinformatics Institute

http://www.ebi.ac.uk/arrayexpress/

Gene Expression Omnibus at the National Institutes of Health

http://www.ncbi.nlm.nih.gov/geo/

Stanford microarray database

http://smd.stanford.edu/

Oncomine

http://www.oncomine.org/main/index.jsp

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levels of variance and low sample size make feature selection difficult. Testing thousands of genes creates a multiple testing problem, which can result in underestimating the number of false positives. Given data with these limitations, constructing models becomes under-determined and therefore prone to over-fitting. From biology, it is also clear that genes do not act independently. Genes interact in the form of pathways or gene regulatory networks. For this reason, we need models that can be interpreted in the context of pathways. Researchers have successfully applied AI methods to microarray data preprocessing, clustering, feature selection, classification, and network analysis.

MINING MICROARRAY DATA: CURRENT TECHNIQUES, CHALLENGES AND OPPORTUNITIES FOR AI Data Preprocessing After obtaining microarray data, normalization is performed to account for systematic measurement biases and to facilitate between-sample comparisons (Quackenbush, 2002). Microarray data may contain missing values that may be replaced by mean replacement or k-NN imputation (Troyanskaya et al., 2001).

Feature Selection The goal of feature selection is to find genes (features) that best distinguish groups of instances (e.g. disease vs. normal) to reduce the dimensionality of the dataset. Several statistical methods including t-test, significance analysis of microarrays (SAM) (Tusher et al., 2001), and analysis of variance (ANOVA) have been applied to select features from microarray data. In classification experiments, feature selection methods generally aim to identify relevant

gene subsets to construct a classifier with good performance (Inza et al., 2004). Features are considered to be relevant when they can affect the class; the strongly relevant are indispensable to prediction and the weakly relevant may only sometimes contribute to prediction. Filter methods evaluate feature subsets regardless of the specific learning algorithm used. The statistical methods for feature selection discussed above as well as rankers like information gain rankers are filters for the features to be included. These methods ignore the fact that there may be redundant features (features that are highly correlated with each other and as such one can be used to replace the other) and so do not seek to find a set of features which could perform similarly with fewer variables while retaining the same predictive power (Guyon & Elisseeff, 2003). For this reason multivariate methods are more appropriate. As an alternative, wrappers consider the learning algorithm as a black-box and use prediction accuracy to evaluate feature subsets (Kohavi & John, 1997). Wrappers are more direct than filter methods but depend on the particular learning algorithm used. The computational complexity associated with wrappers is prohibitive due to curse of dimensionality, so typically filters are used with forward selection (starting with an empty set and adding features one by one) instead of backward elimination (starting with all features and removing them one by one). Dimension reduction approaches are also used for multivariate feature selection.

Dimension Reduction Approaches Principal component analysis (PCA) is widely used for dimension reduction in machine learning (Wall et al., 2003). The idea behind PCA is quite intuitive: correlated objects can be combined to reduce data “dimensionality”. Relationships between gene expression profiles in a data matrix can be expressed as a linear combination such

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that colinear variables are regressed onto a new set of coordinates. PCA, its underlying method Single Value Decomposition (SVD), related approaches such as correspondence analysis (COA), and multidimensional scaling (MDS) have been applied to microarray data and are reviewed by Brazma & Culhane (2005). Studies have reported that COA or other dual scaling dimension reduction approaches such as spectral map analysis may be more appropriate than PCA for decomposition of microarray data (Wouters et al., 2003). While PCA considers the variance of the whole dataset, clustering approaches examine the pairwise distance between instances or features. Therefore, these methods are complementary and are often both used in exploratory data analysis. However, difficulties in interpreting the results in terms of discrete genes limit the application of these methods.

Clustering What we see as one disease is often a collection of disease subtypes. Class discovery aims to discover these subtypes by finding groups of instances with similar expression patterns. Hierarchical clustering is an agglomerative method which starts with a singleton and groups similar data points using some distance measure such that two data points that are most similar are grouped together in a cluster by making them children of a parent node in the tree. This process is repeated in a bottomup fashion until all data points belong to a single cluster (corresponding to the root of the tree). Hierarchical and other clustering approaches, including K-means, have been applied to microarray data (Causton et al., 2003). Hierarchical clustering was applied to study gene expression in samples from patients with diffuse large B-cell lymphoma (DLBCL) resulting in the discovery of two subtypes of the disease. These groups were found by analyzing microarray data from biopsy samples of patients who had not been previously treated. These patients continued to be studied after

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chemotherapy, and researchers found that the two newly discovered disease subtypes had different survival rates, confirming the hypothesis that the subtypes had significantly different pathologies (Alizadeh et al., 2000). While clustering simply groups the given data based on pair-wise distances, when information is known a priori about some or all of the data i.e. labels, a supervised approach can be used to obtain a classifier that can predict the label of new instances.

Classification (Supervised Learning) The large dimensionality of microarray data means that all classification methods are susceptible to over-fitting. Several supervised approaches have been applied to microarray data including Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and k-NNs among others (Hastie et al., 2001). A very challenging and clinically relevant problem is the accurate diagnosis of the primary origin of metastatic tumors. Bloom et al. (2004) applied ANNs to the microarray data of 21 tumor types with 88% accuracy to predict the primary site of origin of metastatic cancers with unknown origin. A classification of 84% was obtained on an independent test set with important implications for diagnosing cancer origin and directing therapy. In a comparison of different SVM approaches, multicategory SVMs were reported to outperform other popular machine learning algorithms such as k-NNs and ANNs (Statnikov et al., 2005) when applied to 11 publicly available microarray datasets related to cancer. It is worth noting that feature selection can significantly improve classification performance.

Cross-Validation Cross-validation (CV) is appropriate in microarray studies which are often limited by the number of instances (e.g. patient samples). In k-fold CV,

AI Methods for Analyzing Microarray Data

the training set is divided into k subsets of equal size. In each iteration k-1 subsets are used for training and one subset is used for testing. This process is repeated k times and the mean accuracy is reported. Unfortunately, some published studies have applied CV only partially, by applying CV on the creation of the prediction rule while excluding feature selection. This introduces a bias in the estimated error rates and over-estimates the classification accuracy (Simon et al., 2003). As a consequence, results from many studies are controversial due to methodological flaws (Dupuy & Simon, 2007). Therefore, models must be evaluated carefully to prevent selection bias (Ambroise & McLachlan, 2002). Nested CV is recommended, with an inner CV loop to perform the tuning of the parameters and an outer CV to compute an estimate of the error (Varma & Simon, 2006). Several studies which have examined similar biological problems have reported poor overlap in gene expression signatures. Brenton et al. (2005) compared two gene lists predictive of breast cancer prognosis and found only 3 genes in common. Even though the intersection of specific gene lists is poor, the highly correlated nature of microarray data means that many gene lists may have similar prediction accuracy (Ein-Dor et al., 2004). Gene signatures identified from different breast cancer studies with few genes in common were shown to have comparable success in predicting patient survival (Buyse et al., 2006). Commonly used supervised learning algorithms yield black box models prompting the need for interpretable models that provide insights about the underlying biological mechanism that produced the data.

Network Analysis Bayesian networks (BNs), derived from an alliance between graph theory and probability theory, can capture dependencies among many variables (Pearl, 1988, Heckerman, 1996).

Friedman et al. (2000) introduced a multinomial model framework for BNs to reverseengineer networks and showed that this method differs from clustering in that it can discover gene interactions other than correlation when applied to yeast gene expression data. Spirtes et al. (2002) highlight some of the difficulties of applying this approach to microarray data. Nevertheless, many extensions of this research direction have been explored. Correlation is not necessarily a good predictor of interactions, and weak interactions are essential to understand disease progression. Identifying the biologically meaningful interactions from the spurious ones is challenging, and BNs are particularly well-suited for modeling stochastic biological processes. The exponential growth of data produced by microarray technology as well as other highthroughput data (e.g. protein-protein interactions) call for novel AI approaches as the paradigm shifts from a reductionist to a mechanistic systems view in the life sciences.

FUTURE TRENDS Uncovering the underlying biological mechanisms that generate these data is harder than prediction and has the potential to have far reaching implications for understanding disease etiologies. Time series analysis (Bar-Joseph, 2004) is a first step to understanding the dynamics of gene regulation, but, eventually, we need to use the technology not only to observe gene expression data but also to direct intervention experiments (Pe’er et al., 2001, Yoo et al., 2002) and develop methods to investigate the fundamental problem of distinguishing correlation from causation.

CONCLUSION We have reviewed AI methods for pre-processing, clustering, feature selection, classification and

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mechanistic analysis of microarray data. The clusters, gene lists, molecular fingerprints and network hypotheses produced by these approaches have already shown impact; from discovering new disease subtypes and biological markers, predicting clinical outcome for directing treatment as well as unraveling gene networks. From the AI perspective, this field offers challenging problems and may have a tremendous impact on biology and medicine.

Brenton, J. D., Carey, L. A., Ahmed, A. A., & Caldas, C. (2005). Molecular classification and molecular forecasting of breast cancer: ready for clinical application? Journal of Clinical Oncology, 23(29), 7350–7360. doi:10.1200/ JCO.2005.03.3845

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Causton, H. C., Quackenbush, J., & Brazma, A. (2003) Microarray Gene Expression Data Analysis: A Beginner’s Guide. Oxford: Blackwell Science Limited.

Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., & Rosenwald, A. (2000). Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, 403(6769), 503–511. doi:10.1038/35000501 Ambroise, C., & McLachlan, G. J. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data. Proceedings of the National Academy of Sciences of the United States of America, 99(10), 6562–6566. doi:10.1073/ pnas.102102699 Bar-Joseph, Z. (2004). Analyzing time series gene expression data. Bioinformatics (Oxford, England), 20(16), 2493–2503. doi:10.1093/bioinformatics/bth283 Bloom, G., Yang, I. V., Boulware, D., Kwong, K. Y., Coppola, D., & Eschrich, S. (2004). Multiplatform, multi-site, microarray-based human tumor classification. American Journal of Pathology, 164(1), 9–16. Brazma, A., & Culhane, A. C. (2005). Algorithms for gene expression analysis. In Jorde LB., Little PFR, Dunn MJ., Subramaniam S. (Eds.) Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics., (3148 -3159) London: John Wiley & Sons.

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Buyse, M., Loi, S., Van’t Veer, L., Viale, G., Delorenzi, M., & Glas, A. M. (2006). Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. Journal of the National Cancer Institute, 98, 1183–1192.

Dupuy, A., & Simon, R. M. (2007). Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. Journal of the National Cancer Institute, 99(2), 147–157. doi:10.1093/jnci/djk018 Ein-Dor, L., Kela, I., Getz, G., Givol, D., & Domany, E. (2004). Outcome signature genes in breast cancer: is there a unique set? Bioinformatics (Oxford, England), 21(2), 171–178. doi:10.1093/ bioinformatics/bth469 Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the United States of America, 95, 14863–14868. doi:10.1073/ pnas.95.25.14863 Friedman, N., Linial, M., Nachman, I., & Pe’er, D. (2000). Using Bayesian networks to analyze expression data. Journal of Computational Biology, 7(34), 601–620. doi:10.1089/106652700750050961 Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., & Mesirov, J. P. (1999). Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science, 286(5439), 531. doi:10.1126/ science.286.5439.531

AI Methods for Analyzing Microarray Data

Guyon, I., & Elisseff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182. doi:10.1162/153244303322753616

Quackenbush, J. (2006). Microarray Analysis and Tumor Classification. The New England Journal of Medicine, 354(23), 2463–2472. doi:10.1056/ NEJMra042342

Hastie, T., Tibshirani, R., & Friedman, J. (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer Series in Statistics.

Simon, R., Radmacher, M. D., Dobbin, K., & McShane, L. M. (2003). Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. Journal of the National Cancer Institute, 95(1), 14–18.

Heckerman, D. (1996). A Tutorial on Learning with Bayesian Networks. Technical Report MSRTR-95-06. Microsoft Research. Inza I., Larrañaga P., Blanco R., & Cerrolaza A.J. (2004). Filter versus wrapper gene selection approaches in DNA microarray domains. Artificial Intelligence in Medicine, special issue in “Data mining in genomics and proteomics”, 31(2), 91-103. Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1-2), 273–324. doi:10.1016/S00043702(97)00043-X Nutt, C. L., Mani, D. R., Betensky, R. A., Tamayo, P., Cairncross, J. G., & Ladd, C. (2003). Gene Expression-based Classification of Malignant Gliomas Correlates Better with Survival than Histological Classification. Cancer Research, 63, 1602–1607. Pe’er, D., Regev, A., Elidan, G., & Friedman, N. (2001). Inferring subnetworks from perturbed expression profiles. Bioinformatics (Oxford, England), 17(S1), S215–S224. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, San Mateo: Morgan Kaufmann Publishers. Quackenbush, J. (2002). Microarray data normalization and transformation. Nature Genetics, 32, 496–501. doi:10.1038/ng1032

Spirtes, P., Glymour, C., & Scheines, R. Kauffman, S., Aimale, V., & Wimberly, F. (2001). Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data. Proceedings of the Atlantic Symposium on Computational Biology, Genome Information Systems and Technology. Statnikov, A., Aliferis, C. F., Tsamardinos, I., Hardin, D., & Levy, S. (2005). A comprehensive evaluation of multicategory classification methodsfor microarray gene expression cancer diagnosis. Bioinformatics (Oxford, England), 21(5), 631–643. doi:10.1093/bioinformatics/bti033 Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., & Tibshirani, R. (2001). Missing value estimation methods for DNA microarrays. Bioinformatics (Oxford, England), 17(6), 520–525. doi:10.1093/bioinformatics/17.6.520 Tusher, V. G., Tibshirani, R., & Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences of the United States of America, 98(9), 5116–5121. doi:10.1073/ pnas.091062498 Varma, S., & Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics, 7, 91. doi:10.1186/1471-2105-7-91 Wall, M., Rechtsteiner, A., & Rocha, L. (2003). Singular value decomposition and principal component analysis. In D.P. Berrar, W. Dubitzky, M. Granzow (Eds.) A Practical Approach to Microarray Data Analysis. (91-109). Norwell: Kluwer. 883

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KEY TERMS AND DEFINITIONS Curse of Dimensionality: A situation where the number of features (genes) is much larger than the number of instances (biological samples) which is known in statistics as p >> n problem.

Feature Selection: A problem of finding a subset (or subsets) of features so as to improve the performance of learning algorithms. Microarray: A microarray is an experimental assay which measures the abundances of mRNA (intermediary between DNA and proteins) corresponding to gene expression levels in biological samples. Multiple Testing Problem: A problem that occurs when a large number of hypotheses are tested simultaneously using a user-defined α cut off p-value which may lead to rejecting a nonnegligible number of null hypotheses by chance. Over-Fitting: A situation where a model learns spurious relationships and as a result can predict training data labels but not generalize to predict future data. Supervised Learning: A learning algorithm that is given a training set consisting of feature vectors associated with class labels and whose goal is to learn a classifier that can predict the class labels of future instances. Unsupervised Learning: A learning algorithm that tries to identify clusters based on similarity between features or between instances or both but without taking into account any prior knowledge.

This work was previously published in Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, Julian Dorado and Alejandro Pazos, pp. 65-70, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization Guang Li National Cancer Institute, USA

Boris Mueller Memorial Sloan-Kettering Cancer Center, USA

Deborah Citrin National Cancer Institute, USA

Borys Mychalczak Memorial Sloan-Kettering Cancer Center, USA

Robert W. Miller National Cancer Institute, USA

Yulin Song Memorial Sloan-Kettering Cancer Center, USA

Kevin Camphausen National Cancer Institute, USA

INTRODUCTION Image registration, segmentation, and visualization are three major components of medical image processing. Three-dimensional (3D) digital medical images are three dimensionally reconstructed, often with minor artifacts, and with limited spatial resolution and gray scale, unlike common digital pictures. Because of these limitations, image filtering is often performed before the images DOI: 10.4018/978-1-60960-561-2.ch317

are viewed and further processed (Behrenbruch, Petroudi, Bond, et al., 2004). Different 3D imaging modalities usually provide complementary medical information about patient anatomy or physiology. Four-dimensional (4D) medical imaging is an emerging technology that aims to represent patient motions over time. Image registration has become increasingly important in combining these 3D/4D images and providing comprehensive patient information for radiological diagnosis and treatment.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization

3D images have been utilized clinically since computed tomography (CT) was invented (Hounsfield, 1973). Later on, magnetic resonance imaging (MRI), positron emission tomography (PET), and single photon emission computed tomography (SPECT) have been developed, providing 3D imaging modalities that complement CT. Among the most recent advances in clinical imaging, helical multislice CT provides improved image resolution and capacity of 4D imaging (Pan, Lee, Rietzel, & Chen, 2004; Ueda, Mori, Minami et al., 2006). Other advances include mega-voltage CT (MVCT), cone-beam CT (CBCT), functional MRI, open field MRI, time-of-flight PET, motioncorrected PET, various angiography, and combined modality imaging, such as PET/CT (Beyer, Townsend, Brun et al., 2000), and SPECT/CT (O’Connor & Kemp, 2006). Some preclinical imaging techniques have also been developed, including parallel multichannel MRI (Bohurka, 2004), Overhauser enhanced MRI (Krishna, English, Yamada et al., 2002), and electron paramagnetic resonance imaging (EPRI) (Matsunoto, Subramanian, Devasahayam et al., 2006). Postimaging analysis (image processing) is required in many clinical applications. Image processing includes image filtering, segmentation, registration, and visualization, which play a crucial role in medical diagnosis/treatment, especially in the presence of patient motion and/ or physical changes. In this article, we will provide a state-of-the-art review on 3D/4D image registration, combined with image segmentation and visualization, and its role in image-guided radiotherapy (Xing, Thorndyke, Schreibmann et al., 2006).

ing anatomic or physiologic information in 3D space. The smallest element of a 3D image is a cubic volume called voxel. A 4D medical image contains a temporal series of 3D images. With a subsecond time resolution, it can be used for monitoring respiratory/cardiac motion (Keall, Mageras, Malter et al., 2006). Patient motion is always expected: faster motion relative to imaging speed causes a blurring artifact; whereas slower motion may not affect image quality. A multislice CT scanner provides improved spatial and temporal resolution (Ueda et al., 2006), which can be employed for 4D imaging (Pan et al., 2004). Progresses in MRI imaging have also been reported, including parallel multichannel MRI (Bodurka, Ledden, van Gelderen et al., 2004). Because PET resolution and speed are limited by the physics and biology behind the imaging technique, some motion suppression techniques have been developed clinically, including patient immobilization (Beyer, Tellmann, Nickel, & Pietrzyk, 2005), respiratory gating (Hehmeh, Erdi, Pan et al., 2004), and motion tracking (Montgomery, Thielemans, Mehta et al., 2006). Motion tracking data can be used to filter the imaging signals prior to PET image reconstruction for reliable motion correction. Motion blurring, if uncorrected, can reduce registration accuracy. Visual-based volumetric registration technique provides blurring correction (filtering) before registration, by defining the PET volume with reference to the CT volume, causing blurred PET surface voxels to be rendered invisible (Li, Xie, Ning et al., 2007).

BACKGROUND

Medical image segmentation defines regions of interest used to adapt image changes, study image deformation, and assist image registration. Many methods for segmentation have been developed including thresholding, region growing, clustering, as well as atlas-guided and level sets

3D/4D Medical Imaging A 3D medical image contains a sequence of parallel two-dimensional (2D) images represent-

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Image Segmentation and Visualization

3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization

(Pham, Xu, & Prince, 2000; Suri, Liu, & Singh et al., 2002). Atlas-guided methods are based on a standard anatomical atlas, which serves as an initial point for adapting to any specific image. Level sets, also called active contours, are geometrically deformable models, used for fast shape recovery. Atlas-based level sets have been applied clinically for treatment planning (Lu, Olivera, Chen et al., 2006a; Ragan, Starkschall, McNutt et al., 2005;) and are closely related to image registration (Vemuri, Ye, & Chen, et al., 2003). Figure 1 shows automatic contours. Depending on how the 3D image is segmented, it can be either 2D-based or 3D-based (Suri, Liu, Reden, & Laxminarayan, 2002). 3D medical image visualization has been increasingly applied in diagnosis and treatment (Salgado, Mulkens, Bellinck, & Termote, 2003), whereas 2D-based visualization is predominantly applied clinically. Because of the demand on computing power, real-time 3D image visualFigure 1. Orthogonal 2D-views of CT images and comparison of automatic recontours (solid-lines) and manual contours (dash-lines) in different phases (A&B) of a radiotherapeutic treatment (courtesy of Dr. Weiguo Lu)

ization is supported by specialized graphics hardware (Terarecon, Inc.) (Xie, Li, Ning et al., 2004) or high-end consumer graphics processors (Levin, Aladl, Germanos, & Slomak, 2005). 3D image visualization has been applied to registration of four imaging modalities with improved spatial accuracy (Li, Xie, Ning et al., 2005; Li et al., 2007). Figure 2 shows 3D volumetric image registration using external and internal anatomical landmarks.

Rigid Image Registration Rigid image registration assumes a motionless patient such that the underlying anatomy is identical in different imaging modalities for alignment. Three approaches to rigid registration are: coordinate-based, extrinsic-based, and intrinsicbased (Maintz & Viergever, 1998). Coordinatebased registration is performed by calibrating the coordinate system to produce “co-registered” images. Multimodality scanners, such as PET/CT and SPECT/CT, are typical examples. Extrinsic-based image registration relies on the alignment of extrinsic objects placed in/on a patient invasively/noninvasively. Such objects can be fiducials or frames that are visible in all imaging modalities and serve as local coordinate markers (sets of points) for rigid registration. Examples are gold seeds for prostate localization in radiotherapy and head frame for stereotactic radiosurgery. Intrinsic-based image registration uses a patient’s anatomy (anatomic landmarks, segmented geometries, or intact voxels) as the registration reference. Alignment of visual landmarks or segmented geometries requires user interaction, so the registration is manual or semi-automatic. The statistical similarity of the intact voxels (grayscale) of two images, such as mutual information (Viola & Wells, 1995), has been widely used for fully automated registration (Pluim, Maintz, & Viergever, 2003).

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Figure 2. 3D-views of CT (A, B, and C: head) and MR (D, E, and F: segmented brain) phantom images. The homogeneity of color distributed on an anatomic landmark is used as the registration criterion. From (A) to (C), three images (red, green, and blue) are approaching registration with shifts from 5.0mm to 0.5mm and 0.0mm, respectively. From (D) to (E and F), four images (grey, blue, green, and red) are 5.0mm apart from each other (D) and registered in front (E) and side (F) views.

Automatic image registration requires three key elements: a metric function, a transformation, and an optimization process. One common voxelbased image registration uses mutual information as the metric, a rigid or nonrigid transformation and a maximization algorithm. Recently, the homogeneity of color distributed on a volumetric landmark has been used as quantitative metric, assisted by the ray-casting algorithm in 3D visualization (Li et al., 2007). Figures 3 and 4 show clinical examples using the 3D visualization-based registration technique.

Deformable Image Registration Deformable image registration contains a nonrigid transformation model that specifies the way to deform one image to match another. A rigid image registration is almost always performed to determine an initial position using rigid transformation with six variables (3 translations and 3 rotations). For nonrigid transformation, the

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Figure 3. 3D-views of before (A, B, and C) and after (D, E, and F) 3D volumetric image registration, using homogeneity color distribution as registration criterion. Voluntary patient head movement is corrected in three MR images, T1 (green), FLAIR (red), and T2 (blue), which are acquired in the same scanner with time interval of 3 and 20 minutes.

Figure 4. 3D-views of before (A, B, and C) and after (D, E, and F) rigid volumetric image registration of PET/CT images, correcting patient movement.

number of variables will increase dramatically, up to three times the number of voxels. Common deformable transformations are spline-based with control points, the elastic model driven by image similarity, the viscous fluid model with region growth, the finite element model using rigidity classification, the optical flow with motion estimation, and free-form deformation (Chi, Liang, & Yan, 2006; Crum, Hartkens, & Hill, 2004; Lu, Olivera, Chen et al., 2006b).

3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization

The image similarity measures are ultimately the most important criteria for determining the quality of registration. They can be feature-based and voxel-based (Maintz & Viergever, 1998). The former is usually segmentation/classification based, adapting changes in shape of anatomical landmarks, while the latter is based on statistical criteria for intensity pattern matching, including mutual information. Most deformable registrations are automated. Combining the two methods can improve registration accuracy, reliability, and/ or performance (Hellier & Barillot, 2003; Liu, Shen, & Davatizikos, 2004; Wyatt & Noble, 2003). Figure 5 shows one example of deformable registration.

Figure 5. Orthogonal 2D-views of before (A) and after (B) deformable image registration of two 3D images (red and green) in a 4D CT image (courtesy of Dr. Weiguo Lu)

A Challenge from 3D/4D Conformal Radiotherapy: Deformable Image Registration Broadened Concept of 4D Medical Imaging The 4D imaging concept has been broadened to cover various time resolutions. The common 4D image has subsecond temporal resolution (Pan et al., 2004), while a series of 3D images, reflecting patient changes over a longer time span, should be also qualified as a 4D image with sufficient resolution to assess slower changes, including tumor growth/shrinkage and weight gain/loss during a course of treatment. Registration of a 3D image to a 4D image involves a series of deformable registration. Because patient motion/change is inevitable, deformable image registration is the key to combining these images for clinical use. Clinically, MVCT images can be acquired daily and used for patient daily setup via rigid registration to the reference planning image, assuming minimal patient changes. Within a treatment, 4D CT imaging has shown dramatic anatomical changes during respiration (Keall et al., 2006). Image-guided frameless cranial and extra-cranial stereotactic radiosurgery has been

performed clinically (Gibbs, 2006). Rigid image registration provides the best current solution to these clinical applications. Ultimately, deformable image registration will improve the registration accuracy substantially (Lu et al., 2006b), permitting highly conformal 3D/4D radiotherapy (Barbiere, Hanley, Song et al., 2007; Mackie, Kapatoes, Ruchala et al., 2003).

Challenges in Deformable Image Registration For deformable image registration, the underlying anatomy changes, therefore voxel mapping among images is a challenge. First, the deformable transformation handles a large number of positioning variables that must be determined for every voxels within the anatomy. This can be abstracted as a multiple variable optimization problem in mathematics, limiting the performance of deformable image registration for many years (Crum, 2004). Second, the deformable registration is extremely difficult to be validated as there is

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lack of absolutes with respect to the location of corresponding voxels. Therefore, the accuracy and reliability of deformable registration should be evaluated on a case specific basis (Sanchez Castro, Pollo, Meuli et al., 2006; Wang, Dong, O’Daniel et al., 2005). Regardless the limitations above, progress has been made by combining image registration and segmentation/classification to provide intrinsic simplification and cross verification. It remains a challenge, however, to develop a fully automated deformable registration algorithm because image segmentation often requires human interaction. Deformable image registration is generally a “passive” mapping process. It does not anticipate how patient anatomy might deform. An example is whether superficial 3D contour information detected by a real-time infrared camera can be used to predict the motion of internal organs (Rietzel, Rosenthal, Gierga et al., 2004). Anatomically, the correlation between superficial and internal organ motion should exist, although as a complex relationship. Therefore, an anatomic model based image registration with motion estimation can provide an “active” mapping process, but is beyond the current scope of deformable image registration.

Gaps between Frontier Research and Clinical Practice Despite of the advances in 3D and 4D imaging and image registration, 2D-based rigid registration techniques are predominantly used in the clinic; although automatic rigid registration methods exist in most commercial treatment planning software. Two reasons are primarily responsible for this disconnect: First, the user must visually verify the final registration using the 2D-based visualization tools available for image fusion in most commercial software. Second, most clinical images have some degree of pre-existing defor-

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mation so that automatic rigid registration can prove unreliable but manual methods allow the user to perform local organ registration. Some recent commercial software has recognized this problem and provides the option of selecting the region-of-interest to ease the deformation problem. This method, however, is only a partial solution to cope with image changes. The gap between the clinical research and routine practice can be reduced by translational research and development. Recently, open-source medical image processing and visualization tool kits have become available for public use. Many recently published algorithms in medical image processing are implemented in a generic, objectoriented programming style, which permits reusability of such toolkits.

FUTURE TRENDS 3D rigid image registration will dominate clinical practice and will remain essential as more specialized complementary 3D imaging modalities become clinically relevant. Although the simplicity of automatic image registration is more attractive, manual image registration with 2D/3D visualization is irreplaceable because it permits incorporation of medical knowledge for verification and adjustment of the automatic registration results. As awareness of the problems of the patient motion and anatomic changes increases, further research on 4D imaging and deformable registration will be stimulated to meet the clinical demands. Motion correction in the PET/CT and SPECT/CT will continue to improve the “coregistration” of these images. Interdisciplinary approaches are expected to offer further improvements for the difficult registration problem. With advances in hybrid registration algorithms and parallel computing, more progresses are expected, resulting in improved accuracy and performance.

3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization

CONCLUSION Higher dimensional deformable image registration has become a focus of clinical research. The accuracy, reliability, and performance of 3D/4D image registration have been improved with assistance of image segmentation and visualization.

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Suri, J. S., Liu, K., & Singh, S. (2002). Shape recovery algorithms using level sets in 2-D/3-D medical imagery: A state-of-the-art review. IEEE Transactions on Information Technology in Biomedicine, 6(1), 8–28. doi:10.1109/4233.992158

KEY TERMS AND DEFINITIONS

Ueda, T., Mori, K., & Minami, M. (2006). Trends in oncological CT imaging: Clinical application of multidetector-row CT and 3D-CT imaging. International Journal of Clinical Oncology, 11, 268–277. doi:10.1007/s10147-006-0586-1 Vemuri, B. C., Ye, J., Chen, Y., & Leonard, C. M. (2003). Image registration via level-set motion: Applications to atlas-based segmentation. Medical Image Analysis, 7, 1–20. doi:10.1016/ S1361-8415(02)00063-4 Viola, P., & Wells, W. M., III. (1995). Alignment by maximization of mutual information. In Int. Conf. Computer Vision, IEEE Computer Society Press, Los Alamitos, CA (pp. 16-23). Wang, H., Dong, L., & O’Daniel, J. (2005). Validation of an accelerated demons algorithm for deformable image registration in radiation therapy. Physics in Medicine and Biology, 50, 2887–2905. doi:10.1088/0031-9155/50/12/011 Wyatt, P. P., & Noble, J. A. (2003). MAP MRF joint segmentation and registration of medical images. Medical Image Analysis, 7, 539–552. doi:10.1016/S1361-8415(03)00067-7 Xie, H., Li, G., Ning, H., et al. (2004). 3D voxel fusion of multi-modality medical images in a clinical treatment planning system. In R. Long, S. Antani, D.J. Lee, B. Nutter, M. Zhang (Eds.), Proc. Computer-Based Med. Sys., IEEE Computer Science Society (pp. 48-53).

3D Medical Imaging: A process of obtaining a 3D volumetric image composed of multiple 2D images, which are computer reconstructed using a mathematical “back-projection” operation to retrieve pixel data from projected image signals through a patient, detected via multichannel detector arrays around the patient. 4D Medical Imaging: A process of acquiring multiple 3D images over time prospectively or retrospectively, so that patient motions and changes can be monitored and studied. Imaging Modality: A type of medical imaging technique that utilizes a certain physical mechanism to detect patient internal signals that reflect either anatomical structures or physiological events. Image Processing: A computing technique in which various mathematical operations are applied to images for image enhancement, recognition, or interpretation, facilitating human efforts. Image Registration: A process of transforming a set of patient images acquired at different times and/or with different modality into the same coordinate system, mapping corresponding voxels of these images in 3D space, based on the underlying anatomy or fiducial markers. Image Segmentation: A process in which an image is partitioned into multiple regions (sets of pixels/voxels in 2D/3D) based on a given criterion. These regions are nonoverlapping, homogeneous with respect to some characteristics such as intensity or texture. If the boundary constraint of the region is removed, the process is defined as classification.

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3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization

Image Visualization: A process of converting (rendering) image pixel/voxel into 2D/3D graphical representation. Most computers support 8-bit (256) grayscale display, sufficient to human vision that can only resolve 32-64 grayscale. A common 12/16-bit (4096/65536 grayscales) medical image can be selectively displayed based on grayscale classification. Window width (display range in grayscale) and linear level function (center of the window width) are frequently used in adjusting display content.

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, pp. 1-9, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 3.18

Time-Sequencing and ForceMapping with Integrated Electromyography to Measure Occlusal Parameters Robert B. Kerstein1 Tufts University School of Dental Medicine, USA

ABSTRACT Computerized Occlusal Analysis technology records, and quickly displays for clinical interpretation, tooth contact timing sequences of .003 second increments, and each tooth contacts’ fluctuating force levels which occur during functional jaw movements. These measurements are recorded intraorally with an ultra-thin, mylar-encased sensor that is connected to a computer via a USB interface. This sensor is placed between a patients’ teeth during functional jaw movements to record DOI: 10.4018/978-1-60960-561-2.ch318

changing tooth-tooth interactions. The displayed occlusal data aids in the examination and treatment of occlusal abnormalities on natural teeth, dental prostheses, and dental implant prostheses. The software can be linked to an Electromyography software program that simultaneously records the electromyographic potential of 8 head and neck muscles. This combination of dynamic tooth contact force and time data, and functional muscular data, affords a dentist detailed, precise, and unparalleled diagnostic and treatment information, with which to address many differing clinical occlusal pathologies.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

INTRODUCTION Computerized Occlusal Analysis technology (TScan III for Windows®, Tekscan Inc., S. Boston, MA, USA) records, and quickly displays for clinical interpretation, tooth contact Time-Sequences while simultaneously Force-Mapping each tooth contacts’ fluctuating relative occlusal force levels which occur during functional jaw movements (known as Occlusal Events) (Maness 1993, Montgomery and Kerstein 2000, Kerstein 2001, Kerstein and Wilkerson 2001). These occlusal event measurements are recorded intraorally, with an ultra-thin, electronically charged, mylarencased sensor that is connected to a computer via a USB interface. By measuring Relative Force, the T-Scan III system can detect whether an occlusal force on one set of contacting opposing teeth is greater, equal to, or less than, the occlusal forces occurring on other contacting teeth all throughout the dental arches. Determining relative force is important to the user-Dentist, as relative force illustrates measured differences of varying applied loads within all contacting tooth locations. The T-Scan III software displays the relative occlusal force, at any instant within a recorded occlusal event on all contacting teeth, as a percentage of the maximum occlusal force obtained within the recording. Detected relative force variances can be employed clinically to: •

•

Precisely balance an unbalanced occlusal force distribution with targeted time-based and force-based occlusal adjustments Diagnose excessively high occlusal load present in one area of the occlusion, while simultaneously diagnosing where there is little, moderate, or no occlusal load in other areas of the same occlusion

After various occlusal events are recorded, the retrieved occlusal contact time-sequence data and

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relative force data are mapped for analysis in 4 different quantifiable ways: •

•

• •

By real-time increments that measure the sequential order of each individual tooth contacts’ loading occurrence. The realtime sequence can be separated into increments as short as .003 seconds duration By force location on the contacting surfaces of many teeth individually and collectively within the 2 dental arches By relative changing force percentage values on all individual contacting teeth By a changing, moveable occlusal force summation icon and trajectory, that describes the changing concentrated force position within the 2 dental arches of all the collective tooth contacts’ relative occlusal forces

The mapped contact time-sequence and relative occlusal force data, when analyzed by a user-Dentist, aids in the clinical determination of, and occlusal treatment of, many time premature contacts, and occlusal force abnormalities, which occur during occlusal events on natural teeth, dental prostheses, and dental implant prostheses (Kerstein Chapman and Klein 1997, Kerstein 1997, Kerstein and Wilkerson 2001, Kerstein and Grundset 2001,). This technology’s ability to isolate timepremature tooth contacts, and excessive occlusal contact forces, is vastly superior to the commonly utilized, non-technology based occlusal indicators which dentists routinely employ (articulating paper, wax imprints, silicone imprints, and articulated stone dental casts). None of these dental materials have any scientifically proven capability to time-sequence tooth contacts, or force-map occlusal forces. Additionally, they all necessitate the user-Dentist to “subjectively interpret” their occlusal representations. The most commonly employed occlusal indicator is dental Articulating Paper. It is comprised of

Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

ink-impregnated strips of either Mylar, or various thicknesses and forms of papers. To date, no study has shown Articulating Paper to be reliably accurate when it is used to describe occlusal forces (Reiber Fuhr and Hartmann 1991, Millstein and Maya 2001, Carossa Lojacono Schierano and Pera 2001, Saraçoğlu and Ozpinar 2001, Carey Craig Kerstein and Radke 2007, Saad Weiner and Ehrenberg 2008). Therefore, user-Dentist’s subjective interpretation of the size and shape, and color intensity of various Articulating Paper markings, can lead to incorrect clinical determinations of where in the dental arches occlusal problems exist, and where occlusal treatment should be performed or not performed. These incorrect clinical determinations occur because the user-Dentist is taught (incorrectly), to perceive widespread and uniform shaped markings’ appearance on the teeth as representative of “time simultaneous” tooth contact sequences with “normal and balanced” occlusal forces present within the dental arches.

EVOLUTION OF TIME SEQUENCING AND RELATIVE OCCLUSAL FORCE-MAPPING BY COMPUTERIZED TECHNOLOGY The evolution of pressure sensitive ink-Mylar encased sensor technology that could time-sequence and force-map relative occlusal forces, was introduced as the T-Scan® I computerized occlusal analysis system (Maness Benjamin Podoloff and Bobick and Golden 1987). Occlusal data was obtained by instructing patients to occlude through an intraoral recording sensor that was connected to a stand-alone computer. The computer screen displayed a Force Movie (Maness 1988), which illustrated a dynamic columnar time and force display of the recorded occlusal contacts for playback and analysis. The original T-Scan I Recording Sensor was comprised of a flexible laminated epoxy matrix surrounding a pressure sensitive ink grid, which

was formed in the shape of a dental arch. When inserted intraorally, and occluded into and through by a patients teeth, the sensor relayed occlusal contact real-time and relative force-mapping information to compatible software that was capable of interpreting 16 levels of intraoral relative force in approximately .01 second time increments. The resultant occlusal analysis was displayed in two or three dimensions, as a Snapshot (Maness 1988), or as a continuous Movie (Maness 1988) of the entire occlusal contact event the patient made. The data could be played forwards, backwards, or in individual .01 second time increments. In 1998, the entire T-Scan I system was redeveloped as the T-Scan II Occlusal Analysis System for Windows®. The changes included hardware, software, and further sensor advances. T-Scan II was a Microsoft Windows® (Microsoft Corp, Seattle WA, USA) compliant system that was integrated into a clinical diagnostic computer workstation. An IBM compatible PC with a Pentium processor, and a minimum of 4-8 megabytes of RAM, were required to properly operate the system (Figure 1 and Figure 2). The graphical interface used familiar Windows® toolbar icons, to display the software features that analyzed recorded occlusal contact information. In 2006, the T-Scan II hardware was altered to interface with the PC via a USB plug that replaced a serial parallel port interface. Then, in 2008, the T-Scan III USB with Turbo Mode Recording was introduced. A recording handle hardware improvement resultant from increased speeds with which computer chips process commands, made it possible to significantly increase the recording speed of a given occlusal event. The Evolution Handle of the T-Scan III USB System, when operated in Turbo Mode, can record timesequences and map relative occlusal force data in increments of .003 seconds, thereby capturing 3 times more occlusal data for analysis than was possible with the T-Scan III USB. Turbo Mode data acquisition provides a user-Dentist with an increased ability over the conventional non-

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Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

Figure 1. T-Scan III Evolution Handle with sensor

Turbo Mode recording, to locate more non-simultaneous tooth contact sequences and aberrant occlusal force concentrations within a patient’s occlusion.

T-SCAN III SENSOR DESIGN The most current available recording sensor for the T-Scan III system represents the 4th generation of T-Scan system sensors. It is known as the High Definition sensor (HD). The HD sensor is dental arch-shaped such that it fits between a patient’s maxillary and mandibular teeth during the recording of occlusal events. When correctly positioned between a patients 2 maxillary Central Incisor teeth, and held stable by a hard plastic Sensor Support (Figure 1), the sensor is then clenched upon, and chewed over by a patient, so as to record and precisely map changing toothto-tooth occlusal force interactions in real-time. A number of different occlusal events can be captured by the recording sensor:

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Figure 2. T-Scan III Evolution Handle connected to a personal computer

• • •

• •

Single or multiple patient-self closures into complete tooth intercuspation Dentist-assisted patient closure into the Centric Relation mandibular position Patient self-closure into complete tooth intercuspation followed by a mandibular excursion to the left, right, or forward, out of the complete intercuspated position Patient clenching and grinding motions in and around complete tooth intercuspation Habitual Occlusal Force Patterns

The HD sensor’s structural design consists of two layers of Mylar encasing a grid of resistive ink rows and columns printed between them (Figure 3 and Figure 4). The high definition design has both larger Sensels (the Tekscan proprietary force recording element within each sensor; a sensel is 1.61 mm2) and less space between the sensels than all of the previous T-Scan I and II sensor designs. Within the HD sensor, the sensels are packed closer together within the recording grid, resulting in

Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

Figure 3. Sensor schematic

Figure 4. HD sensor

mean force variances were significantly larger than for the same 6 occlusal contact locations measured with the HD sensor. Additionally, the HD sensor exhibited consistent force reproduction in at least 20 in-laboratory crushing cycles (Kerstein Lowe Harty and Radke 2006).

SENSOR RECORDING DYNAMICS

increased recording surface area available for teeth to occlude upon. When compared to all pervious sensor designs, the HD configuration specifically limits the amount of inactive, non-recording grid areas between the sensels. A published performance study (Kerstein Lowe Harty and Radke 2006) comparing Generation-3 (G3) sensors against HD sensors revealed that the HD sensor reproduced more consistent force levels than the older G-3 sensor. The G-3 sensor

When tooth contact is made with a T-Scan III sensor, the applied force on individual sensels changes their resistance. Larger applied forces produce larger resistance changes and lower occlusal forces produce lesser resistance changes. The electronics in the Recording Handle of the T-Scan III system excites the sensor by applying voltage to each column of recording sensels in succession, Then, as a T-Scan III sensor is occluded upon, a change in the applied force at various tooth contacts results in a change in the resistance of the resistive ink at each of the contacted sensels. This resistance change is measured by the system’s hardware electronics as a change in Digital Output Voltage (DO). Higher forces on contacting teeth result in larger decreases in the resistance of the loaded sensels, and therefore, a higher measured output voltage from the loaded sensels. The greater the applied force to the contacting teeth then the higher the output voltage that is measured.

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The electronics in the recording handle reads the output voltage of each row of sensels in a process called Scanning. The output signal is conditioned and converted to an 8-bit digital value so that the measured resistance change is proportional to force applied to each sensel with a range between 0 and 255 raw counts. The raw count for an individual sensel can be conceptualized as the minimum pressure that is on that sensel. And, because each sensel has an area of 1.61 mm2, the raw count can also be conceptualized as a force. A map in the software receives from the recording handle, the stream of changing sensel digital voltage output values, and organizes them for display to the user-Dentist into the same orientation pattern that the sensels have on the sensor. Within the software, the user-Dentist can raise or lower the Recording Sensitivity across 16 differing levels to compensate for varying occlusal strengths of individual patients. This allows the user-Dentist to modify the sensor recording behavior to select a pressure range which will record optimum individual patient occlusal data for analyses, regardless of a patients’ absolute occlusal strength.

RELATIVE FORCE MEASUREMENT VS. ABSOLUTE FORCE MEASURED IN ENGINEERING UNITS As stated in the Introduction, the T-Scan III system records relative force. It therefore, does not provide absolute occlusal force in engineering units (calibrated force numbers) such as in Newtons (n) or pounds per square inch (lb/in2). To obtain accurate numeric force values in engineering units, a T-Scan sensor would need to undergo pre-use Equilibration, some surface preparationand thenCalibration: •

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Sensor Equilibration is accomplished by applying a uniform load all over a sensor at three levels (low, near the load of interest,

•

•

and at a high load) to determine that each sensel has a uniform digital output from a uniform applied load. This can be accomplished by using an air bladder with the sensor being compressed on a flat surface. Next, the recording sensor would need to be covered with a shim material – The shim would prevent sensor crinkling when loaded, while at the same time, distribute the load amongst a greater sensor surface area than the surface areas of individual tooth contact points. The shim helps avoid individual sensor saturation. This shim material increases the sensor thickness from approximately 0.1 mm (no shim) to about 3 mm (with shim). This is a “clinical use problem” for the user-Dentist because a modified sensor thickness increase does not allow a patient’s teeth to completely and fully intercuspate against each other. Lastly, the sensor must undergo sensor with shim Calibration - This would be performed on a set of articulated dental casts made from a patient’s dental arches, by applying 1 or 2 representative occlusal loads to a modified sensor that are within the patient’s occlusal force range. Calibration is performed to correlate the digital output of the sensels to engineering units (force or pressure) and confirms and improves the uniformity of output from sensel to sensel throughout the entire recording grid.

Because individual patients have variable occlusal strengths, and the dental community continually resists the use of a thickened sensor which interferes with complete tooth contact intercuspation, and there are no commercially available dental calibration devices with which to apply known test loads to a modified T-Scan sensor, it is not possible to use the T-Scan III system to obtain force values in engineering units. Therefore, throughout the remainder of this chapter, all references made forward from

Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

here regarding occlusal force are to be describing relative occlusal force. For any measurement of force in engineering units, the I-Scan system is commercially available (I-Scan for Windows®, Tekscan Inc. S Boston, MA. USA). I-Scan is a general purpose research product designed for industrial applications that lacks many software features which aid in the understanding of dental occlusion (such as those related to time-sequencing analyses). Alternatively, the I-Scan’s software features include equilibration, calibration, general purpose analysis graphs, and the ability to export arrays of X–Y sensel pressure data into ASCII format into other applications. These features make the I-Scan a more versatile absolute force analysis system than is the T-Scan III system.

RECORDED T-SCAN III MOVIE ANALYSES A T-Scan III recording of an occlusal event is captured in a dynamic Movie (Maness 1988) which reveals all the varying occlusal contact forces, as time evolves within a given occlusal event. Once recorded, the Movie is retrieved for review in a 2-Dimensional View window, a 3-Dimensional View window, and two Force vs. Time Graph windows (Figure 5). The mapped occlusal force data is converted into colors so each frame of the Movie is an image of the pressure at varying tooth contact locations at that instant of time. This pressure results from the tooth contact forces that dissipate between opposing teeth as they interact during a recorded occlusal event. By advancing the Movie or going back in time, all force changes that occur between sets of interacting teeth are observed in the exact order with which they occurred throughout the entire recorded occlusal event. A Legend with Color Spectrum (Figure 5) depicts a scale of relative occlusal forces. Darker, cooler colors, such as dark blue through light blue,

Figure 5. Legend with color spectrum

represent lower forces. Brighter, warmer colors such as yellow, orange and red, indicate higher occlusal contact forces. Pink indicates that the sensel has saturated, or reached its maximum digital output. Pink sensels may have an unknown amount of greater force than red sensels. In 2 or 3 dimensions, the occlusal contact timing sequence can be played forwards or backwards continuously, or in either .01-second increments when recorded non-Turbo Mode, or in .003 second increments when recorded in Turbo Mode. In the 3 dimensional (3-Dimensional ) playback window the force columns change both their height and color designation. In the 2 dimensional (2-D) contour view, the color-coded force concentration zones change size, shape, and color, as occlusal forces change. Below the 2-Dimensional view and 3-Dimensional view windows are 2 Force vs. Time Graph windows (Figure 6). It is here where TimeSequencing is mapped incrementally, through changing Red and Green colored lines that represent the changing force percentages found in both dental arch halves over the entire recorded occlusal event. 4 additional moveable, vertical, hyphenated lines denote key Time-Regions within a recorded occlusal event. Lastly, a Data Window to the far

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Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

Figure 6. T Scan III playback windows

right describes the occlusal Time Parameters that can be calculated from the recorded occlusal event. The Occlusal Time Parameters that can be displayed for analyses within the graph Data Window are: •

•

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Occlusion Time (OT): The elapsed time from 1st tooth contact to the complete intercuspation of all occluding teeth. <.2 seconds duration is considered optimal bilateral tooth contact simultaneity (Kerstein and Grundset 2001) Disclusion Time (DT): The elapsed time required for a patient to exit their complete intercuspated position, and move their mandible either right, left, or forward, so as to disclude (completely separate) all posterior teeth, including and behind the 1st premolar tooth, so that only Canines and/or Incisors are making tooth contact. <.4 seconds duration is considered optimal so as to reduce long periods of tooth in-

teraction friction, and lessen masticatory muscle hyperactivity (Kerstein and Wright 1991) Warning Icons located next to the Time Parameters inform a user-Dentist whether the recorded occlusal Time Parameters are clinically physiologic (green check mark), borderline physiologic (yellow triangle) or non-physiologic (pink triangle) occlusal function. The tolerance settings for these warning thresholds can be altered by the user-Dentist within the software’s User Preference settings. The lower left graph is a Zoom Graph, which shows .75 seconds of occlusal function from before and after the Movie frame that is being displayed in the 2-Dimensional and 3-Dimensional windows above. The Zoom Graph affords a user-Dentist greater visual clarity in comparison to the regular Force vs. Time Graph (Figure 7), of the subtleties present within all the changing colored force lines.

Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

Figure 7. Force vs. time graph

All 4 playback windows can be kept together to move as a unit around the desktop as a connected group. Or, they can be separated into individual windows for comparison to other similar type playback windows that come from other Movies recorded on the same patient when treatment is rendered to improve their occlusion (Figure 8). Figure 8. 2D playback window and 4 quadrant force vs. time graph

OCCLUSAL PARAMETER TIME CALCULATION USING THE FORCE VS. TIME GRAPH Occlusal Parameter time calculations can be precisely accomplished by using the Force vs. Time Graph. There are 3 distinct graph types that illustrate 3 different diagnostic occlusal event types that can be recorded from a patient’s occlusal scheme. They are: •

•

•

Closure Graphs: A patient occludes into complete tooth intercuspation, holds their teeth together firmly and fully intercuspated for 1-3 seconds, then opens to disarticulate their teeth completely Excursive Graphs: A patient occludes into complete tooth intercuspation, holds their teeth firmly and fully intercuspated for 1-3 seconds, and then slides their lower teeth across and over their maxillary teeth to either the left, right, or forward by leaving their complete intercuspated position Multi-Bite Graphs: A patient occludes into complete tooth intercuspation, holds their teeth together firmly and fully intercuspated for 1-3 seconds, then opens to disarticulate their teeth, then re-closes their teeth back together into complete intercuspation, holds their teeth firmly together

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Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

again for 1-3 seconds, and then reopens to disarticulate all occluding teeth. This can be repeated again and again in 1 continuous recording. A recorded occlusal event contains certain key Time-Regions, which are graphically depicted with colored portions on the x-axis of a Force vs. Time Graph. The different time regions displayed directly relate to specific mandibular movements within a recorded occlusal event. Time Regions are mapped within each graph by 3 non-vertical force lines, and 5 vertical moveable time position lines (Figure 9). The distances between all these lines both vertically and horizontally, describes the time-sequencing and force-mapping of specific parts of a recorded occlusal event. The 3 non-vertical Force Lines are: •

A non-vertical Red line: This line denotes the Right arch-half force changes that are mapped throughout the entire recorded event • A non-vertical Green line: This line denotes the Left arch-half force changes that are mapped throughout the entire recorded event

•

The 5 Time Position Lines are: •

•

A non-vertical Black line: This line denotes the Total Force changes that are mapped throughout the entire recorded event

Figure 9. Pre operative and post operative COF trajectory

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A Black vertical Time Line: This line denotes how much time has elapsed within the recorded occlusal event and marks the time location of the Movie frame being displayed in the 2-D and 3-Dimensional windows 4 Black vertical hyphenated A, B, C, & D Lines: The A-B line separation, and the C-D line separation, can be used to calculate the Occlusion Time (A-B) and the Disclusion Time (C-D) of the recorded event. The separation between these linepairs is color coded along the x-axis of the

Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

graph; OT (Occlusion Time) is denoted in blue; Intercuspation (IP) when teeth are held firmly together is denoted in green; and DT (Disclusion Time) is denoted in orange. Figure 7 depicts an Excursive Graph. During the A-B increment (OT; x-axis blue area), teeth in the right and left arch halves are occluding sequentially so the Red and Green lines change from 0% left (no occlusal contact) and 100% right (1st and only contact) to 40% left and 60% right. The Total Force line (non-linear black line) rapidly rises to the top of the graph, peaks near 100%, and then slightly lessens in the IP period (x-axis green area) as the patient firmly holds their teeth together at 95% total force for 1.69 seconds duration. The calculated OT is .237 seconds duration, which is marked in the graph data box as “physiologic” by a green check mark. The green IP period denotes the distance from the B line to the C line, while the patient firmly holds their teeth together fully intercuspated. The Total Force line and the Red and Green lines stay horizontal and unchanging during the IP period, as there are no occlusal force changes during this time period of the recorded event. At C, the Total Force line sharply declines when the mandibular excursion to the left begins because there is a major force drop when the patient disarticulates their teeth to commence moving to their left. After C, the Red line rises indicating forces on the right arch-half are increasing while the left side forces drop (Green line declines). Then later in the excursion, the Green and Red lines cross each other because the forces reverse between the arch-halves. During the excursion, low total occlusal force is generated when the teeth are no longer intercuspated and move over each other. Therefore, the Total Force line stays near the x-axis. Throughout the entire C-D increment, the x axis is orange. The DT is extremely prolonged at 4.961 seconds duration, hence a pink Warning Triangle in the graph data box denotes

this time duration as “non-physiologic” for the user-Dentist. This DT warrants correction to reduce tooth frictional engagement of the posterior teeth, and decrease applied muscular hyperactivity to the teeth and Temporomandibular Joints during left excursive function.

4-QUADRANT FORCE VS. TIME GRAPH The same Force vs Time Graph can display 4 quadrants (instead of only 2 quadrants) for even more detailed time-sequencing and occlusal force-mapping. A button on the 2-Dimensional view window tool bar divides the force plot into 4 quadrants instead of 2. As a result, within a 4-quadrant graph, 2 new lines are created by the software; •

•

Purple line: This right posterior quadrant line denotes the force changes that are mapped in the posterior right region of the dental arch (from right 3rd molar up to the right 1st premolar) throughout the entire recorded event Aqua line: This left posterior quadrant denotes the left arch-half force changes that are mapped in the posterior left region (from left 3rd molar up to the left 1st premolar) throughout the entire recorded event

The Red and Green lines now denote force changes in the anterior right and anterior left arch-halves, respectively (from canine to central incisor). Figure 8 depicts a 4-quadrant graph that describes the left excursion seen in figure 7. As this patient attempts to move their mandible to her left, the posterior right teeth engage thereby limiting the patient’s ability to easily move left. This occlusal phenomenon is denoted in the 4-quadrant graph after the C line, where the posterior right Purple line rises highest from horizontal while

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Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

the left anterior Green line struggles to rise. Only much later in the movement to the left does the Purple line decline and the Green line rise. This indicates a transfer of movement control from the earlier controlling right posterior teeth to the later controlling left anterior teeth near to D.

FORCE-MAPPING SOFTWARE FEATURES In addition to the playback windows, there are several force-mapping software features that enhance the user-Dentists’ ability to locate problem occlusal force concentrations: •

The Center of Force Trajectory (COF): The history of changing total occlusal force summation can be mapped positionally throughout the dental arches by a software feature known as the Center Force Trajectory. As forces evolve on individual teeth in sequence, the force summation will move towards higher force concentrations and away from lesser concentrations throughout the occlusal event. These force summation changes are graphically displayed during playback within the 2-Dimensional view window, by a red and white diamond-shaped icon followed by its’ colored-line trailer (Figure 6). Each leg of the trailer represents .01 seconds (in non-Turbo Mode recording) or .003 seconds (in Turbo Mode recording). The incremental positions of the movement of the COF icon creates its’ trailer.

The COF icon pinpoints the location of the sum of the total force of the occlusal contacts by the software’s calculation of the sum of the medio-lateral and antero-posterior force moments of the recorded occlusal contacts. This data is presented to the user-Dentist by superimposing the COF marker within the tooth contact data. The shorter in length, and the less number of individual

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legs in total that constitute the COF trajectory, the more simultaneous is the time-sequence of a closure occlusal event. The more centered along the midline of the 2-Dimensional view window the COF trajectory travels, the more balanced are the occlusal forces between the right and left halves of the dental arch. An off-center COF trajectory, which indicates poor occlusal force balance, can be rerouted and centered within the occlusal contacts by the user-Dentist performing both time-sequence and force-based correction occlusal adjustments. In figure 9, the left-positioned 2-Dimensional view window details a right-sided COF that begins posteriorly and moves anteriorly (physiologic abnormal). The overall force summation is skewed to the right side of the dental arch. This represents a right side early-left side late time-sequence of non-simultaneous occlusal contacts which demonstrate very poor occlusal balance. After T-Scan III guided occlusal adjustments are accomplished on both the pre-operative locations of time-sequence early contacts, and the areas of occlusal force excess, the mapped direction and position of the new COF has been improved in the right-positioned 2-Dimensional view window. The total occlusal force summation is shared equally between the right and left arch halves, and the COF moves from anterior to posterior (physiologic normal). This clinical rerouting of the COF indicates improved contact time simultaneity, and equally shared occlusal force evolution between the right and left arch halves. Another unique T-Scan III force analysis software feature is: •

Force Percentage per Tooth: During both recording and playback, the percentage of changing force per individual tooth is mapped in the 2-Dimensional view window in either .01 or .003 seconds,. Force % per Tooth describes unequal, or equal, individual tooth loading between each arch half as time elapses. Corrective occlusal

Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

adjustments can be accomplished to equalize force % per tooth discrepancies, which are based upon the right % per tooth comparison to its left % per tooth counterpart (i.e. % 1st molar right - % 1st molar left). Figure 10, Figure 11 and Figure 12 illustrate the differing force percentages per tooth being displayed as a Movie of a patient self closure into intercuspation evolves. These 3 sequential individual 2-Dimensional Movie frames map the changing percentage per tooth of the occlusal forces on all 14 individual teeth involved within the occlusion. Note that the COF trajectory moves towards the teeth with the most force percentage at different stages of the patient self closure.

3-DIMENSIONAL SIMULTANEOUS TIME-SEQUENCING AND FORCEMAPPING To observe changing occlusal forces across passing time, a user-Dentist can employ the 3-Dimensional view window with its’ changing heights of force columns. This columnar view displays the same

data as the 2 dimensional window, but in a forcewise, more visually descriptive fashion. Figure 13, Figure 14 and Figure 15 are the same 3 time moments depicted in Figure 10, Figure 11 and Figure 12. Here they are represented in the 3-Dimensional columnar view. The progression of rising forces on the closure occlusal contacts is displayed as differing column heights in different regions of the dental arch beginning in Figure 13 (early contact forces), thru Figure 14 (mid–closure contacts), to Figure 15 (complete intercuspated contacts). Note how the posterior right teeth demonstrate consistently taller column heights, indicating they are absorbing more occlusal force, than their left side counterparts during each stage of the patient’s closure.

INTEGRATED ELECTROMYOGRAPHY SYNCHRONIZED WITH COMPUTERIZED OCCLUSAL ANALYSIS Computerized Occlusal Analysis and Electromyography technologies have been synchronized together so that a user-Dentist can record, and

Figure 10. Early closure contact force % per tooth

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Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

Figure 11. Mid closure contact force % per tooth

Figure 12. Late closure contact force % per tooth

playback, their separate diagnostic data simultaneously (Kerstein 2004). The T-Scan III system can be linked together thru software integration with the BioPAK Electromyography System (Bioresearch, Assoc., Milwaukee, Wisconsin. USA.).

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The T-Scan III/BioEMG Integration uniquely combines the simultaneous mapping of timesequencing, force-mapping, and muscle activity. The integration of these two technologies allows the user-Dentist to analyze and correlate

Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

Figure 13. 3D columnar view early closure contacts

Figure 15. 3D columnar view late closure contacts

Figure 14. 3D columnar view mid closure contacts

specific time-sequences and occlusal event contact force moments to specific electromyographic changes that result from these occlusal moments. Their combined desktops can be seen in Figure 16, which places the recorded occlusal event data beside the recorded electromyographic data of up to 8 head and neck muscles. The Time Line in the T-Scan III is synchronized with a Time Line in the EMG playback window (vertical grey line) such that the displayed occlusal data and displayed electromyographic data are mapped simultaneously. This T-Scan III/BioEMG synchronization affords the user-Dentist a unique method to ascertain the relationship of the muscle activity levels resultant from any occlusal contact time-sequence, and/or contact force-map

BIOPAK ELECTROMYOGRAPHY SYSTEM The BioPAK electromyography system records electrical (bio-potential) activity from eight

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Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

Figure 16. T-Scan III/BioEMG desktop with long disclusion time and hyperactive EMG data

muscles simultaneously. Bilaterally, the anterior temporalis, massetter, digastric, and sternocleidomastoid muscles, can be measured. The userDentist can choose which muscle combinations are to be analyzed. Surface EMG measurements are obtained by means of conductive adhesive gel-based bipolar electrodes. The electrodes are attached to the skin, over the palpated main bulk of the contracted muscle, and oriented parallel to the general direction of the muscle fibers (Figure 17). An IBM/ PC-compatible computer (or Macintosh with

Intel Processor) samples the individual muscular activity level signals sequentially through a USB interface at the rate of 1000-6000 samples per second (with a resolution of 12 or 16 bits). Before analysis, the filtered raw myoelectric signals are amplified to 5000 times their original levels. The signals are displayed on a computer as real-time time-domain waveforms and off-line as frequency-domain waveforms, as average levels that disclose contraction patterns and relative muscle contraction intensities. After the data are recorded, they are digitally filtered to remove

Figure 17. T-Scan III Handle and Bio/EMG leads placed on 8 head and neck muscles

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Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

virtually all (99%) of any 50/60-cycle noise content from the electromyographic signals. NOTE: A reduction of 40 dB (on a logarithmic scale) is equivalent to a 99% reduction in noise amplitude. Subsequently, specific occlusal event muscle contraction actions (clench, swallow, excursion, etc.) can be quantified in microvolts. An optional, special module, is available that is specifically designed for the analysis of masticatory function.

The time synchronization of the 2 systems insures that transitory occlusal contact variances in time-sequencing and force-mapping, which occur throughout a recorded Movie, can be timecorrelated to specific changes in muscle activity levels that are the result of these variances. This is an occlusal “first” as at no prior time has it been possible to record simultaneously dynamic occlusal contact information and its corresponding muscle activity information.

SYSTEM INTEGRATION DYNAMICS

EXAMPLE OF CLINICAL OCCLUSAL PARAMETER OPTIMIZATION

The integration and synchronization of these 2 separate systems affords the user-Dentist real-time recording of occlusal contacts and the electrical potential of selected masticatory muscles in a dynamic “Movie Form”. The T-Scan III system initiates the acquisition of both types of data when its recording function is activated. At that exact moment, the electromyographic recording initiates, as well. As tooth contact time-sequence and force data is recorded, so is the muscle activity recorded (Kerstein 2004). In the Biopak EMG playback windows, the recorded electromyography data is displayed as a real-time EMG Movie that is time-stamped to match the occlusal contact data time-stamps recorded by the T-Scan III system. 2 EMG playback windows are present; one that shows the recorded muscle activity in wave-form (above) with a vertical time curser that moves through the wave forwards or backwards (time-synchronized with the T Scan). The other window below is a horizontal bar graph which contains changing bar lengths that illustrate the changing quantitative muscle activity levels in micro-volts. Both the wave-form and bar graphs can be played forwards and backwards continuously, or as .01 second frames (non-Turbo Mode) or in .003 second frames (Turbo Mode) in stop-action, at the same time, and in the same manner as the T-Scan III Movie playback.

Because of the precision simultaneous data capture of the T-Scan III and the Bio EMG integration module, crucial occlusal optimization procedures and their impact on muscle function can all be quantified, understood, and manipulated through computer-guided occlusal adjustments. Computer-guidance insures the treated occlusion is measurably optimized. For example, when a userDentist performs an occlusal adjustment procedure known as a Disclusion Time Reduction (Kerstein and Wright 1991, Kerstein 1992, Kerstein 1993, Kerstein 1994, Kerstein 1995, Kerstein Chapman and Klein 1997, Kerstein and Radke 2006) the recipient patient will undergo Clinical Occlusal Parameter Optimization, which is designed to shorten the elapsed time that all posterior teeth engage during functional jaw movements. Disclusion Time Reduction reduces the frictional forces that are shared between teeth as they mill together during chewing, and clenching and Bruxism. Less frictional force applied to the engaging teeth, reduces their potential for long-term tooth wear, while simultaneously lessening masticatory muscle hyperactivity (Kerstein and Wright 1991, Kerstein 2004, Kerstein 1995, Kerstein Chapman and Klein 1997) that is often a factor in muscular overload of the Temporomandibular Joints. Within the pre operative EMG data (Figure 12), note that to the direct right of the vertical Time

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Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

Line, there is excursive muscle hyperactivity in the right temporalis and left massetter muscles, while the left temporalis and right massetter muscles demonstrate almost no elevated muscle activity. The observed elevated muscle activity is the result of prolonged tooth socket compressions that occur when the patient’s Disclusion Time is prolonged (2.256 seconds). The longer is the Disclusion Time, the longer the opposing occluding posterior teeth compress each other into their respective tooth sockets (Kerstein 1993). Because of the unique neuroanatomy of the molar periodontal mechanoreceptors, which synapse directly with muscles of mastication within the Central Nervous System (Haines 2000) prolonged tooth socket compressions can trigger an equally prolonged time-duration of masticatory muscle contraction. In Figure 12, when the patient is moving their mandible to their right, the prolonged Disclusion Time present within this patient’s occlusion, contracts the right temporalis and left massetter muscles for a equal time duration of 2.256 seconds. This observed hyperactivity can be minimized therapeutically by shortening the Disclusion Time, which directly shortens the tooth socket compression time. Figure 18 illustrates the optimized right excursive Disclusion Time after the pre-operative

prolonged Disclusion Time is shortened to .279 seconds duration. Within the EMG data (Figure 18), note that just to the right of the vertical Time Line post-treatment, there is almost no hyperactivity remaining in the right temporalis and left massetter muscles. This muscular effect is observed because of the dramatically shortened compression time of the contacting opposing teeth within their respective tooth sockets. This therapeutic action is resultant from shortening the pretreatment prolonged Disclusion Time to <.4 seconds (Kerstein and Wright 1991) on the occlusal surfaces of the involved teeth. A comparison of Figure 19 and Figure 20 shows that the pre-operative excessive muscle activity visible to the right of the vertical Time Line (Figure 19), is absent in the post-treatment corrected occlusion (Figure 20).

CHAPTER SUMMARY Time-sequencing and relative occlusal forcemapping with Computerized Occlusal Analysis technology displays for clinical interpretation, tooth contact timing sequences of .003 second increments, with each tooth contacts’ fluctuating relative occlusal force levels that occur during

Figure 18. Short disclusion time post treatment of the long disclusion time depicted in Figure 16

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Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

Figure 19. EMG data of pre operative long disclusion time depicted in Figure 16

Figure 20. EMG data of post operative short disclusion time depicted in Figure 18

functional jaw movements. The displayed occlusal time-sequence and force data aids in the examination and treatment of occlusal abnormalities on natural teeth, dental prostheses, and dental implant prostheses. The software can also be linked to an Electromyography software program that simultaneously records the electromyographic potential of 8 head and neck muscles. This combination of dynamic tooth contact time-sequencing and force-mapping, with functional muscular electromyographic data affords a user-Dentist detailed, precise, and unparalleled diagnostic and treatment information, with which to diagnose and treat many differing clinical occlusal pathologies.

REFERENCES Carey, J.P., Craig, M., Kerstein, R.B., Radke, J. (2007). Determining a relationship between applied occlusal load and articulating paper mark area. The Open Dentistry Journal, (1), 1-7

Carossa, S., Lojacono, A., Schierano, G., & Pera, P. (2000). Evaluation of occlusal contacts in the dental laboratory: influence of strip thickness and operator experience. The International Journal of Prosthodontics, 13(3), 201–204. Haines, D. E. (2000). Neuroanatomy: An Atlas of Structures, Sections, and Systems, (5th Ed.) (pp. 175, 193). Baltimore, MD: Lippincott, William and Wilkins. Kerstein, R. (2001). Current Applications of Computerized Occlusal Analysis in Dental Medicine. General Dentistry, 49(5), 521–530. Kerstein, R. B. (1992). Disocclusion time-reduction therapy with immediate complete anterior guidance development to treat chronic myofascial pain-dysfunction syndrome. Quintessence International, 23(11), 735–747. Kerstein, R. B. (1993). A Comparison of Traditional Occlusal Equilibration and Immediate Complete Anterior Guidance Development. Cranio, 11(2), 126–140.

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Kerstein, R. B. (1994). Disclusion Time Measurement Studies: Stability of disclusion time. a 1 year follow - up study. The Journal of Prosthetic Dentistry, 72(2), 164–168. doi:10.1016/00223913(94)90075-2 Kerstein, R. B. (1994). Disclusion time measurement studies, Part 2: A comparison of disclusion time length of 49 chronic myofascial pain dysfunction syndrome patients to 40 non - patients. A population analysis. The Journal of Prosthetic Dentistry, 72(5), 473–480. doi:10.1016/00223913(94)90117-1 Kerstein, R. B. (1995). Treatment of myofascial pain dysfunction syndrome with occlusal therapy to reduce lengthy disclusion time - a recall study. Cranio, 13(2), 105–115. Kerstein, R. B. (1999). Computerized Occlusal Management of a Fixed/ Detachable Implant Prosthesis. Pract Periodontol Aesthet Dent, 11(9 November/December), 1093-1102. Kerstein, RB Grundset, K. (2001). Obtaining Measurable Bilateral Simultaneous Occlusal Contacts with Computer-Analyzed and Guided Occlusal Adjustments. Quintessence International, January, 32(1), 7-18. Kerstein, R. B. (2004). Combining Technologies: A Computerized Occlusal Analysis System Synchronized with a Computerized Electromyography System. Cranio, 22(2), 96–109. Kerstein, R. B., Chapman, R., & Klein, M. (1997). A Comparison of ICAGD (Immediate Complete Anterior Guidance Development) to Mock ICAGD for Symptom Reductions in Chronic Myofascial Pain Dysfunction Syndrome Patient. Cranio, 5(1), 21–37. Kerstein, R.B., & Lowe, M., Harty, Radke, J. (2006). A Force reproduction analysis of two recording sensors of a computerized occlusal analysis system. Cranio, 24(1), 15–24.

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Kerstein, R. B., & Radke, J. (2006). The effect of Disclusion Time Reduction on maximal clench muscle activity level. Cranio, 24(3), 156–165. Kerstein, R. B., & Wilkerson, D. (2001). Locating the Centric Relation Prematurity with a Computerized Occlusal Analysis System. Compend Contin Educ Dent, 22(6), 525–532. Kerstein, R. B., & Wright, N. (1991). An electromyographic and computer analysis of patients suffering from chronic myofascial pain dysfunction syndrome; pre and post - treatment with immediate complete anterior guidance development. The Journal of Prosthetic Dentistry, 66(5), 677–686. doi:10.1016/0022-3913(91)90453-4 Maness, W. L. (1988). Force Movie: A Time and Force View of Occlusal Contacts. Compend Contin Educ Dent, 10(7), 404–408. Maness, W. L. (1993). Computerized Occlusal Analysis, Hi-Tech, Hi-Care. Dental Journal, 59(8). Maness, W. L., Benjamin, M., Podoloff, R., Bobick, A., & Golden, R. F. (1987). Computerized occlusal analysis: a new technology. Quintessence International, 18(4), 287–292. Millstein, P., & Maya, A. (2001). An evaluation of occlusal contact marking indicators descriptive quantitative method. The Journal of the American Dental Association, 132(9), 1280–1286. Montgomery, M., & Kerstein, R. B. (2000). Mapping Occlusal Forces on Rebuilt Anterior Guidance. Contemporary Esthetics, 4(4), 68–72. Reiber, T., Fuhr, K., & Hartmann, H. (1989). Recording pattern of occlusal indicators, influence of indicator thickness, pressure, and surface morphology. Deutsche Zahnarztliche Zeitschrift, 44(2), 90–93.

Time-Sequencing and Force-Mapping with Integrated Electromyography to Measure Occlusal Parameters

Saad, M.N., Weiner, Ehrenberg D., et al. (2008). Effects of load and indicator type upon occlusal contact markings. Journal of Biomedical Materials Research. Part B, Applied Biomaterials, 85(1), 18–22. doi:10.1002/jbm.b.30910 Saraçoğlu, A., & Ozpinar, B. J. (2002). In vivo and in vitro evaluation of occlusal indicator sensitivity. The Journal of Prosthetic Dentistry, 88(5), 522–526. doi:10.1067/mpr.2002.129064

KEY TERMS AND DEFINITIONS T-Scan III System: This is a Microsoft Windows® compliant clinical occlusal diagnostic computer workstation. An IBM compatible PC with a Pentium processor and a minimum of 4-8 megabytes of RAM are required to properly operate the system. The graphical interface uses Windows® toolbar icons, to display the software features that analyze recorded occlusal contact information. Occlusal Force: Force that is generated between the occlusal (bite) surfaces of contacting teeth Relative Occlusal Force: Describes different occlusal force levels as greater to, equal to, or less than each other Absolute Force: Force measured in Engineering Units. I-Scan System: An alternative Tekscan computer program that measures absolute force in engineering units Occlusal Event: A functional movement of the mandible, like chewing, opening and closing, or sliding the mandible across the maxilla, during which opposing teeth from the mandible and maxilla frictionally generate occlusal forces over fractions of seconds of elapsed time Time-Sequence: The order of individual tooth contacts that occur between sets of upper and lower interacting occlusal surfaces when recorded over elapsed time and measured in .001-003 second real-time increments as a patient makes functional mandibular movements

Force-Mapping: The spatial locating and displaying of relative occlusal force levels that result from the frictional interaction of the occlusal surfaces of sets of upper and lower contacting teeth as a patient makes functional mandibular movements when recorded over elapsed time. Timed-Based Occlusal Adjustments: The technique of correcting prolonged occlusal contact time engagement abnormalities by utilizing the measured and elongated occlusal contact time durations to guide the dentist as to where to reshape affected occlusal surface regions of the dental arches Force-Based Occlusal Adjustments: The technique of correcting relative occlusal force abnormalities by utilizing the location of measured and excessive relative occlusal force levels as a guide for the dentist to where to reshape specific affected occlusal surface regions of the dental arches. Articulating Paper: Colored ink-impregnated strips of Mylar or paper that is employed by Dentists to mark occlusal contact locations between occluding teeth. Scientific studies have repeatedly determined that Articulating Paper cannot measure or map occlusal forces, or describe occlusal contact time-sequences. Recording Sensor: The dental arch-shaped recording element of the T-Scan system. It is comprised of rows and columns of “sensels” surrounded by rows conductive ink. When compressed by occluding teeth, the individual sensels register a voltage output that is proportional to the applied load HD Sensor: 4th generation T-Scan III sensor design with the most active recording surface area and the least inactive non-recording surface area Force Movie (or Movie): The playback of a digital recording of numerous simultaneous and non-simultaneous changing occlusal forces that evolve over elapsed time during Occlusal Events. It can be displayed in 2 or 3 dimensions and played forwards or backwards continuously, or in individual time increments of .01-.003 seconds in duration Recording Sensitivity: Variable levels of sensor “activeness” that can be selected by the

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user-Dentist, to optimize the quality of the occlusal force recording so as to compensate for varying individual patient occlusal force strength capacity Occlusal Time Parameters: The measured elapsed time of opposing tooth contact interactions that occur during recorded Occlusal Events. Occlusion Time (OT) and Disclusion Time (DT) are 2 occlusal time parameters Time Regions: Colored portions on the time axis (x-axis) of a Force vs. Time Graph that quantify tooth contact durations that occur between interacting teeth within a recorded occlusal event. These regions are defined within the Graph between 2 sets of hyphenated vertical lines (A-B and C-D lines). Electromyography: The recording of the electrical activity in muscles using conductive adhesive gel-based bipolar surface electrodes. The BioPAK Electromyography system can be synchronized with the T-Scan III System so that the 2 data sets (muscle activity levels, and tooth contact force-mapping and time-sequencing) can be recorded simultaneously. Occlusal Parameter Optimization: The improvement of prolonged pre treatment non-ideal Occlusion Times and/or Disclusion Times to more physiologic durations through corrective computerguided occlusal adjustments Disclusion Time Reduction: A computerguided occlusal adjustment procedure that shortens prolonged durations of frictional occlusal surface engagement shared between opposing molars and

premolars during a mandibular excursive movement (right working, left working or protrusive). Prolonged occlusal surface engagement creates an equal time duration of prolonged molar and premolar compressions into their respective tooth sockets and Periodontal Ligaments. The objective of the “Disclusion Time Reduction” procedure is to shorten the pre-treatment prolonged frictional interaction of the involved posterior teeth such that, there is an equal time reduction of the time duration that these molar and premolar compress their respective Periodontal Ligaments. By reducing the compression time of the Periodontal Ligaments, excess masticatory muscle hyperactivity created in the masticatory muscles from the prolonged compressions of the periodontal Ligament, is physiologically lessened. Muscular symptoms of Myofascial Pain Dysfunction Syndrome (MPDS) can be effectively and rapidly treated with Disclusion Time Reduction (average published treatment times are 3 -5 treatment visits spread over 1-2 months of total treatment time). User-Dentist: A dental clinician who employs the T-Scan System to map occlusal forces and timesequence occlusal contacts

ENDNOTE 1



Practice limited to prosthodontics and computerized occlusal analysis

This work was previously published in Informatics in Oral Medicine: Advanced Techniques in Clinical and Diagnostic Technologies, edited by Andriani Daskalaki, pp. 88-110, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 3.19

Ultrasound Guided Noninvasive Measurement of Central Venous Pressure Vikram Aggarwal Johns Hopkins University, USA Aniruddha Chatterjee Johns Hopkins University, USA Yoonju Cho Johns Hopkins University, USA Dickson Cheung Johns Hopkins Bayview Medical Center, USA

INTRODUCTION Central venous pressure (CVP) is a measure of the mean pressure within the thoracic vena cava, which is the largest vein in the body and responsible for returning blood from the systemic circulation to the heart. CVP is a major determinant of the filling pressure and cardiac preload, and like any fluid pump, the heart depends on an adequate preload to function effectively. Low venous reDOI: 10.4018/978-1-60960-561-2.ch319

turn translates into a lower preload and a drop in overall cardiac output, a relationship described by the Frank-Starling Mechanism. CVP is an important physiological parameter, the correct measure of which is a clinically relevant diagnostic tool for heart failure patients. In addition to other vitals such as heart rate and mean arterial pressure, accurate measures of central venous pressure through simple diagnostic instrumentation would provide physicians with a clear picture of cardiac functionality, and allow for more targeted treatment. Recent literature has also

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Ultrasound Guided Noninvasive Measurement of Central Venous Pressure

shown that measuring CVP can be an important hemodynamic indicator for the early identification and treatment of more widespread conditions, such as sepsis (Rivers, Nguyen, Havstad, & Ressler, 2001). With over five million patients (American Heart Association, http://www.americanheart. org/presenter.jhtml) in the U.S. presenting with heart failure-like symptoms annually, a current challenge for physicians is to obtain a quick and accurate measure of a patient’s central venous pressure in a manner that poses minimum discomfort. A novel noninvasive method to estimate CVP is to use ultrasound imaging in conjunction with a surface pressure measurement. Ultrasound is first used to visualize the internal jugular (IJ) vein below the skin on a patient’s neck, and a custom pressure transducer is then used to detect the surface pressure required to collapse the IJ. The collapsing pressure is correlated to the central venous pressure and reported back to the operator. This proposed measurement procedure is suitable for emergency situations or primary care settings where rapid diagnosis of a patient’s CVP is required, and mitigates the need for further invasive and costly procedures.

BACKGROUND

VV CV



(1)

From this relationship, it is clear that an increase in blood volume within the thoracic vena cava will lead to greater pressure exerted on the vessel walls, and that a vessel with low compliance will experience greater pressure from an increase in blood volume than a vessel with high compliance. A variety of physiological conditions may

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•

•

•

•

Central Venous Pressure is dependent on the ratio of venous blood volume (Vv) to venous compliance (Cv), as described in (1): CVP =

influence CVP, due to changes in blood volume and/or compliance, and a few are presented here: Hypervolemia: Condition where overall blood volume is too high. Hypervolemia can arise from high salt intake or a failure in the kidney’s ability to excrete salt and water from the body. Excess fluids seep into body tissue leading to edema (swelling), which can cause headaches and respiratory difficulties. The increase in blood volume increases central venous pressure. Hypovolemia: Condition where overall blood volume is too low. A common cause of hypovolemia is blood loss due to trauma, which leads to weakness and, in extreme cases, organ failure. The drop in venous blood volume decreases central venous pressure. Heart Failure: Condition where the heart is no longer able to maintain adequate blood circulation. Some causes of heart failure including coronary artery disease, valvular disease, and myocardial infarction. Low cardiac output leads to a backup of blood in the venous compartment, and increases central venous pressure. Sympathetic Activation: General increase in the activity of the sympathetic nervous system. Sympathetic activation, characterized as the “fight or flight” response, leads to increased heart rate, inhibition of the digestive system, and increased vascular tone. The latter decreases venous compliance, and increases central venous pressure.

Due to the high compliance of the thoracic vena cava and its inconvenient physiological location, direct measurement of CVP using a sphygmomanometer has long been considered impractical. Physicians have instead relied on direct measurements of intraluminal venous pressure, or crude

Ultrasound Guided Noninvasive Measurement of Central Venous Pressure

noninvasive techniques for estimating CVP. This section explores both the traditional and more novel diagnostic approaches for measuring CVP, as a prelude to our discussion of the proposed measurement procedure and device.

Traditional Methods of Measuring Central Venous Pressure a. Lewis (1930) describes one of the oldest methods to estimate CVP using jugular vein distension. When hypervolemic or cardiac failure symptoms manifest as increased venous blood volume, the extra blood pools and increases the size of the patient’s jugular veins. The jugular vein’s proximity to the surface of the neck makes it possible for a clinician to visually identify the swelling of neck veins, but this technique is not a true measurement of CVP since it is quite subjective. For one, the jugular veins are not always visible, especially when obscured by layers of fat, and venous distension is only readily apparent in cases of extremely high CVP. Therefore, this technique is useful for rapid diagnosis of a major increase in CVP, but not suitable for an objective measurement, rendering it largely inadequate for continuous monitoring. b. Another noninvasive measurement technique that attempts to quantify CVP readings involves treating the jugular veins as manometer tubes to the right atrium, implying that higher pressure leads to a greater column of blood in the jugular veins (Lipton, 2000). The selected zero-reference point is the angle of Louis, located at the junction of the manubrium and the sternum 5 cm above the right atrium, because venous pressure at this point changes very little with posture. The height of the blood column can be determined by visually identifying jugular vein pulsations, which are oscillations in pressure caused by the cardiac cycle, and visible where the vein

transitions from the distended (filled with blood) to the collapsed (no blood) state. The hydrostatic pressure that corresponds to the height difference between the point of fluctuations and the angle of Louis is used as a measure of CVP. In practice, this quantitative approach is often impractical, because the venous pulsations are only visible when the top of the blood column is in an area of the neck where the jugular passes close to the skin surface. If not, the clinician must raise the patient slowly upward from a supine position until the pulsations become visible, a process that is both physically taxing and time-consuming. Furthermore, if CVP is too high, then the top of the blood column may lie above the mandible, even with the patient sitting upright, making the fluctuations invisible. As with the previous method, layers of fat may also hinder visualization of the venous pulsations on the neck. c. Numerous studies have shown that noninvasive methods fare poorly in quantifying CVP, because subjective measurements are difficult to standardize. Factors such as posture are hard to control in heart failure patients that must be lifted upright to observe jugular pulsations, which leads to highly variable and inaccurate measurements when compared to invasive recordings (McGee, 1998). Therefore, situations necessitating accurate CVP readings for prompt and correct treatment rely on invasive catheterbased measurements as the standard-of-care. Central venous access is the process of placing a venous catheter inside a major vein, which allows clinicians to obtain pressure readings from directly within the vessel lumen. A common insertion method is intravenous cannulation, which requires a larger insertion point in the vein to fit the cannula, and may lead to complications from excessive bleeding (Mitchell & Clark, 1979). A modified approach that uses a smaller inser-

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tion point is the Seldinger technique, which threads a small guide wire to the appropriate location, and then pushes the catheter along the wire (Seldinger, 1953). Although routinely performed, central venous accesses are susceptible to a variety of complications, such as difficulty locating the vein after the initial incision, overinsertion of wires and cannula leading to accidental puncture of an artery or lungs (Mitchell & Clark, 1979), and neurological complications from nerve lesions (Defalque & Fletcher, 1988). Due to the difficulty inherent in these techniques and the risk of complications, central venous access procedures require skilled medical personnel in a hospital setting.

Alternative Methods of Measuring Central Venous Pressure a. Bloch, Krieger, and Sackner (1991) describe a method where an inductive neck plethysmograph is used to detect and record the small changes in neck dimensions that correspond to venous and arterial pulsations. In certain positions, such as the head-down position, the venous pulsation dominates in strength, whereas in the recumbent position, a mixed venous-arterial pulsation is detected. The device relies on measuring the height with respect to the phlebostatic axis, at which the waveform level changes from a venous to a venous-arterial or arterial configuration. Measurement of this height is a measure of CVP in cm H2O. Their study indicates that this noninvasive method is accurate, compared to invasive catheter measurement. However, the measurement procedure is time-consuming and requires specialized equipment, like the plethysmograph, which is not as common as an ultrasound device. b. Lipton (2000) describes a method where an ultrasound machine is used to locate the jugular venous pulsations. An ultrasonic

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probe is applied to the internal jugular vein (IJ), which lies very close to the neck surface, and provides a straight path to the right atrium. The IJ is a prime location for viewing jugular pulsations, and it is easily located by referencing the sternocleidomastoid muscle. This method allows the clinician to more easily determine the location of these pulsations, especially in situations where they are not visible on the surface of the neck. The vertical distance between this location and the right atrium is used as a measure of CVP. c. Baumann, Marquis, Stoupis, Willenberg, Takala, and Jakob (2005) describe a method where an ultrasonic probe is custom-fitted with a quartz pressure-transducer. The probe is pressed against the IJ, until the vessel first starts to collapse on the ultrasound display, and this pressure is recorded as the bearing pressure. The probe is then pressed until the IJ fully collapses, and the final pressure is recorded. The difference between collapsing pressure and bearing pressure is the pressure needed to directly collapse the vessel. This quantity is equal to the pressure within the vessel lumen if the vein is assumed to be an ideal vessel following the Law of Laplace. While the assumption is imperfect, it is still a useful approximation compared to existing noninvasive approaches. The study noted high inter-operator variability and low precision, which may be improved by better design of the force transducer and standardized training.

A NOVEL NONINVASIVE APPROACH A novel noninvasive method to estimate central venous pressure using ultrasound-guided surface pressure measurement is discussed here. Specifically, the device works in conjunction with an ultrasound machine and probe that is used to visualize the internal jugular (IJ) vein below the

Ultrasound Guided Noninvasive Measurement of Central Venous Pressure

surface of the skin on a patient’s neck. Once the vein is located, the device detects the pressure on the skin required to collapse the IJ, and correlates this value to a central venous pressure reading reported to the operator. This quick and noninvasive measurement is suitable for emergency situations or primary care settings where rapid diagnosis of a patient’s CVP is required, and prevents the need for further invasive and costly procedures. The measurement procedure is also simple enough to be performed by operators without extensive medical training.

Measurement Principle The measurement principle assumes that the IJ vein can be modeled as an ideal vessel according to the Law of Laplace as described in (2) and shown in Figure 1: T =

P ×R M

(2)

where T is wall tension, P is transmural pressure (difference between internal and external pressure), R is vessel radius, and M is wall thickness. When transmural pressure is zero, the tension falls to zero, and the vessel collapses. Conversely, if the vessel is directly collapsed, it can be inferred that the external pressure required to collapse the IJ is equal to the CVP. A custom handheld probe

is responsible for measuring this applied external pressure, and is used alongside an ultrasound probe capable of visualizing the IJ. The internal jugular vein is located underneath the sternocleidomastoid muscle, and anteriolateral to the common carotid artery. The IJ is targeted because it provides a direct pipeline to the heart, and because of its anatomical proximity to the skin surface, making it easier to see on an ultrasound (Lipton, 2000). After locating the IJ on the ultrasound monitor, the operator applies pressure with the handheld probe until the IJ first begins to collapse, and records this as the initial pressure required to collapse the superficial tissue between the skin and the vein. Since the tissue surrounding the IJ is not homogenous, the pressure applied at the skin surface will not directly collapse the IJ, but will be affected by factors like skin compliance and the presence of layers of obstructive tissue between the skin and vein. This step helps factor out the effects of surrounding soft tissue and varying initial skin compliance on the pressure measurement. The operator then continues to apply pressure until the IJ is completely collapsed, and records this final collapsing pressure. The central venous pressure is estimated as the difference between the final collapsing pressure and the initial pressure, as described in (3). In this manner, one can dynamically account for the surrounding soft tissue without need for calibration on each use. CVP = Pcollape − Pinitial

Figure 1. Model of ideal vessel described by wall tension T, internal pressure Pi, external pressure Po, and vessel radius R (Klabunde, 2004)

(3)

This device utilizes the same principle of operation described in Baumann et al. (2005), but differs in its integration of a highly sensitive pressure transducer with a custom-designed probe that can be used in conjunction with any available ultrasound. Operation of the device is illustrated more clearly in Figure 2, with the probe and actual ultrasound images of the IJ during each stage of the measurement procedure.

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Figure 2. Left column shows progression of probe during each step of the measurement procedure; right column depicts actual ultrasound images visible to operator

Device Design As shown in Figure 3, the device consists of a handheld probe with a pressure transducer and a control unit. The handheld probe is cylindrical in shape, and has a high-precision 5-lb load cell on the tip that detects the applied pressure and transmits this reading to the control unit. The probe also has a button along the longitudinal side of the probe body, which is used to initiate and end the CVP recordings. The probe tip (see inset of Figure 3) is designed to address how the collapsing force at the surface of the skin is transmitted to the IJ. Since the IJ is not exactly an ideal vessel, vessel compliance disperses the force somewhat. In order to minimize this, the probe tip is curved with a contact surface area of approximately 2 cm2 that helps localize the applied force appropriately, and is an effective contact profile for compressing the IJ. The control unit is battery-powered and consists of a microprocessor that analyzes the incom-

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Figure 3. Handheld probe (left) and control unit (right). Inset details design of custom probe tip, which takes distributed force from skin surface and channels it to the force transducer

ing load cell data, and an LCD that outputs the CVP readings. The probe and control unit are meant to work in conjunction with an ultrasound machine and transducer for visualization of the internal jugular vein. The choice of ultrasound machine is up to the operator, since the described device is self-contained and does not require a particular ultrasound machine model. To aid in emergency diagnosis, a portable ultrasound is recommended; to aid in visualization accuracy, a high-frequency linear ultrasound transducer with low penetration depth is recommended.

Future Trends Ultrasound-guided therapies have gained significant momentum in the last decade, and are expected to continue to revolutionize health care technology. The U.S. ultrasound market is forecasted to grow at a compound annual growth rate (CAGR) of 5.9%, and reach over $1.89 billion by 2010 (Frost & Sullivan, 2004). This growth is driven primarily by the demand for portable units, as well as the increasing adoption of ultrasound technology among new end users such as cardiologists. Ultrasounds for cardiology, specifically,

Ultrasound Guided Noninvasive Measurement of Central Venous Pressure

have a market with a CAGR of 7.6%, and will reach over $638 million by 2010—34% of the total market (Frost & Sullivan, 2004). And with manufacturers rapidly releasing smaller portable ultrasound machines that are easily transported and deployed in emergency settings, the future of medicine may see compact ultrasound machines replace stethoscopes as the diagnostic instruments of choice for physicians. The fast pace of research in ultrasound technology has brought faster and more accurate devices that are also equipped with features such as color flow Doppler mode, used commonly to detect blood flow velocity and direction in large vessels and cardiac chambers (Perry, Helmcke, Nanda, Byard, & Soto, 1987). Doppler ultrasound is one example of an imaging strategy that might support the proposed measurement procedure by providing operators with a less subjective means of determining complete collapse of the IJ during application of the probe. Considering the advances in ultrasound technology, our CVP measurement device fits well in this rapidly growing market, and may lead to widespread adoption of ultrasound devices by cardiologists and emergency room doctors, to primary care physicians and nurses. The incorporation of a noninvasive measurement of central venous pressure into standard practice will enable clinical practitioners to use this vital parameter more often in their assessment of patient health. The benefits are clear in cases where patients present with heart problems and central venous access is a routine procedure. However, physicians have also begun using central venous pressure, along with other hemodynamic indicators, to help treat a wide variety of disorders. Hocking (2000) recommends that CVP should be measured in patients that are hypotensive, unresponsive to basic clinical management, or those requiring infusions of inotropes; Rivers (2001) used regular CVP measurements, among other indicators, as part of a goal-directed therapy to manage patients in septic shock; and Wang, Liang, Huang, and Yin (2006) report that patients

who had their CVP lowered suffered less blood loss during liver resection. As CVP becomes an increasingly vital parameter for patient treatment, the advent of a reasonably accurate approach to noninvasively measure CVP can lead to significant improvements in standard of care in fields outside cardiology.

CONCLUSION Clearly, there is a need for a system that provides a reliable noninvasive measure of a patient’s central venous pressure. The real impact of the proposed measurement procedure and device will be felt in the 6,000 ICUs (NJHA, 2004) and 35,000 primary care facilities (HRSA, 2006) across the country, either as a necessary step in a life-saving procedure, or during measurement of key vitals during a standard exam. Critics who consider the price of an ultrasound too high for simply measuring CVP should also consider the numerous additional uses of ultrasound, including guidance in central venous access procedures (Denys, Uretsky, & Reddy, 1993; Troianos, Jobes, & Ellison, 1991) and visualization of organs such as the heart, liver, and kidneys. Another benefit of the proposed noninvasive approach is to mitigate the costs and risks associated with central venous access, a procedure that would be performed less often if a cheaper and safer alternative to measure CVP were available. The novelty of the technology may be a deterrent to its adoption by medical practitioners, but clinical studies of the technology and measurement procedure should be pursued to provide hard evidence of its utility. If the data is encouraging, industrial partners would be free to pursue product development and marketing in an open and highly profitable field. In conclusion, the direct ultrasound-guided noninvasive surface pressure measurement of CVP is a significant improvement over previous diagnostic techniques, and has the potential to make CVP measurements quicker, easier, and safer—thus more suitable

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for rapid diagnosis in emergency situations and primary care environments.

REFERENCES Baumann, U., Marquis, C., Stoupis, C., Willenberg, T. A., Takala, J., & Jakob, S. M. (2005). Estimation of central venous pressure by ultrasound. Resuscitation, 64, 193–199. doi:10.1016/j. resuscitation.2004.08.015

Lipton, B. (2000). Estimation of central venous pressure by ultrasound of internal jugular vein. The American Journal of Emergency Medicine, 18(4), 432–434. doi:10.1053/ajem.2000.7335 McGee, S. R. (1998). Physical examination of venous pressure: A critical review. American Heart Journal, 136(1), 10–18. doi:10.1016/ S0002-8703(98)70175-9 Mitchell, S. E., & Clark, R. A. (1979). Complications of central venous catheterization. Journal of Roentgenology, 133(3), 467–476.

Bloch, K. E., Krieger, B. P., & Sackner, M. A. (1991). Noninvasive measurement of central venous pressure by neck inductive plethysmography. Chest, 100, 371–375. doi:10.1378/chest.100.2.371

NJHA Quality Institute. (2004). Quest: News and updates from NJHA Quality Institute. New Jersey Hospital Association, 3(2).

Defalque, R. J., & Fletcher, M. V. (1988). Neurological complications of central venous cannulation. Journal of Parenteral and Enteral Nutrition, 12(4), 406–409. doi:10.1177/0148607188012004406

Perry, G.J., Helmcke, F., Nanda, N.C., Byard, C., & Soto, B. (1987). Evaluation of aortic insufficiency by Doppler color flow mapping. Journal of American Colleges of Cardiology, 9952–9959.

Denys, B. G., Uretsky, B. F., & Reddy, P. S. (1993). Ultrasound-assisted cannulation of the internal jugular vein. A prospective comparison to the external landmark-guided technique. American Heart Association, 87, 1557–1562.

Rivers, E., Nguyen, B., Havstad, S., & Ressler, J. (2001). Early goal-directed therapy in the treatment of severe sepsis and septic shock. The New England Journal of Medicine, 345(19), 1368–1377. doi:10.1056/NEJMoa010307

Frost & Sullivan. (2004). U.S. ultrasound market – A675-50. Palo Alto, CA.

Seldinger, S. I. (1953). Catheter replacement of the needle in percutaneous arteriography: A new technique. Acta Radiology, 39(5), 368–376. doi:10.3109/00016925309136722

Health Resources and Services Administration. (2006). HRSA geospatial data warehouse – primary care service areas. U.S. Department of Health and Human Services. Retrieved February 1, 2008, from http://datawarehouse.hrsa.gov/ pcsa.htm Hocking, G. (2000). Central venous access and monitoring. Update on Anaesthesia, 12(13), 1–6. Klabunde, R. (2004). Cardiovascular physiology concepts. Lippincott Williams & Wilkins. Lewis, T. (1930). Early signs of cardiac failure of the congestive type. British Medical Journal, 1, 849–852.

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Troianos, C. A., Jobes, D. R., & Ellison, N. (1991). Ultrasound-guided cannulation of the internal jugular vein. A prospective, randomized study. Anesthesia and Analgesia, 72(6), 823–826. doi:10.1213/00000539-199106000-00020 Wang, W. D., Liang, L. J., Huang, X. Q., & Yin, X. Y. (2006). Low central venous pressure reduces blood loss in hepatectomy. World Journal of Astroenterology, 12(6), 935–939.

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KEY TERMS AND DEFINTIONS Cardiac Preload: Initial stretch of cardiac muscle before the systolic contraction phase; preload directly affects stroke volume and cardiac output through the Frank-Starling mechanism. Cardiac Output: Total volume of blood ejected from left side of the heart into systemic circulation per minute. Central Venous Pressure (CVP): Mean pressure inside the thoracic vena cava, near the right atrium of the heart; primary determinant of cardiac preload, and sensitive to changes in blood volume and venous compliance. Compliance: Ratio of change in blood volume to change in intraluminal pressure; highly compliant vessels will respond to increased blood flow by distending, whereas low compliance vessels are more rigid, and will have increased pressure with more blood flow; compliance is regulated by the sympathetic nervous system, and affected by collagen build-up and short-term changes in vascular tone. Frank-Starling Mechanism: Pioneering work of Otto Frank and Ernest Starling that provides a framework for relating cardiac muscle

length, tension, and activation to parameters such as preload and cardiac output. Heart Failure: Pathological condition whereby the heart has reduced cardiac output, leading to backup of blood volume in the venous system and increased central venous pressure; causes include coronary artery disease, valvular disease, and myocardial infarction. Intravenous Cannulation: Insertion of a flexible tube or cannula into the venous compartment to allow for passage of a catheter; a procedure commonly performed when inserting a central intravenous line. Plethysmograph: Instrument used to measure changes in volume of a vessel or organ. Sphygmomanometer: An instrument for measuring arterial blood pressure, also known as a blood pressure cuff. Ultrasound Imaging: Procedure by which high-frequency sound waves are reflected off internal tissues or organs to produce echoes, which are analyzed to form an image of body tissues, called a sonogram; ultrasound is commonly used in fetal monitoring, and more recently used in cardiology and radiology.

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, pp. 1331-1337, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Software Support for Advanced Cephalometric Analysis in Orthodontics Demetrios J. Halazonetis National and Kapodistrian University of Athens, Greece

ABSTRACT Cephalometric analysis has been a routine diagnostic procedure in Orthodontics for more than 60 years, traditionally employing the measurement of angles and distances on lateral cephalometric radiographs. Recently, advances in geometric morphometric (GM) methods and computed tomography (CT) hardware, together with increased power of personal computers, have created a synergic effect that is revolutionizing

the cephalometric field. This chapter starts with a brief introduction of GM methods, including Procrustes superimposition, Principal Component Analysis, and semilandmarks. CT technology is discussed next, with a more detailed explanation of how the CT data are manipulated in order to visualize the patient’s anatomy. Direct and indirect volume rendering methods are explained and their application is shown with clinical cases. Finally, the Viewbox software is described, a tool that enables practical application of sophisticated diagnostic and research methods in Orthodontics.

DOI: 10.4018/978-1-60960-561-2.ch320

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Software Support for Advanced Cephalometric Analysis in Orthodontics

INTRODUCTION Diagnostic procedures in Orthodontics have remained relatively unaltered since the advent of cephalometrics in the early 30’s and 40’s. Recently, however, the picture is beginning to change, as advances in two scientific fields and dissemination of knowledge and techniques to the Orthodontic community are already making a discernible impact. One field is the theoretical domain of geometric morphometrics (GM), which provides new mathematical tools for the study of shape, and the other is the technological field of computed tomography (CT), which provides data for three-dimensional visualization of craniofacial structures. This chapter is divided into three main parts. The first part gives an overview of basic mathematical tools of GM, such as Procrustes superimposition, Principal Component Analysis, and sliding semilandmarks, as they apply to cephalometric analysis. The second part discusses the principles of CT, giving particular emphasis to the recent development of cone-beam computed tomography (CBCT). The final part reports on the Viewbox software that enables visualization and measurement of 2D and 3D data, particularly those related to cephalometrics and orthodontic diagnosis.

GEOMETRIC MORPHOMETRICS Geometric morphometrics uses mathematical and statistical tools to quantify and study shape (Bookstein, 1991; Dryden & Mardia, 1998; Slice, 2005). In the domain of GM, shape is defined as the geometric properties of an object that are invariant to location, orientation and scale (Dryden & Mardia, 1998). Thus, the concept of shape is restricted to the geometric properties of an object, without regard to other characteristics such as, for example, material or colour. Relating this definition to cephalometrics, one could consider

the conventional cephalometric measurements of angles, distances and ratios as shape variables. Angles and ratios have the advantage that they are location- and scale-invariant, whereas distances, although not scale-invariant, can be adjusted to a common size. Unfortunately, such variables pose significant limitations, a major one being that they need to be of sufficient number and carefully chosen in order to describe the shape of the object in a comprehensive, unambiguous manner. Consider, for example, a typical cephalometric analysis, which may consist of 15 angles, defined between some 20 landmarks. It is obvious that the position of the landmarks cannot be recreated from the 15 measurements, even if these have been carefully selected. The information inherent in these shape variables is limited and biased; multiple landmark configurations exist that give the same set of measurements. A solution to this problem (not without its own difficulties) is to use the Cartesian (x, y) coordinates of the landmarks as the shape variables. Notice that these coordinates are also distance data (the distance of each landmark to a set of reference axes), so they include location and orientation information, in addition to shape. However, the removal of this ‘nuisance’ information is now more easily accomplished, using what is known as Procrustes superimposition.

Procrustes Superimposition Procrustes superimposition is one of the most widely used methods in GM (Dryden & Mardia, 1998; O’Higgins, 1999; Slice, 2005). It aims to superimpose two or more sets of landmarks so that the difference between them achieves a minimum. There are various metrics to measure the difference between two sets of landmarks, but the most widely used is the sum of squared distances between corresponding points, also known as the Procrustes distance. Therefore, Procrustes superimposition scales the objects to a common size (various metrics can be used here as well, but centroid size (Dryden & Mardia,

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1998) is the most common) and orientates them to minimize the Procrustes distance. The remaining difference between the landmark sets represents shape discrepancy, as the nuisance parameters of orientation and scaling have been factored out. In Orthodontics, superimposition methods are widely used for assessment of growth and treatment effects. When comparing a patient between two time points, the most biologically valid superimposition is based on internal osseous structures that are considered stable, or on metallic implants (Björk & Skieller, 1983). However, this is not possible when comparing one patient to another. Although such a comparison may, at first sight, be considered a rare event, or even pointless, it is essentially the basis of every cephalometric analysis performed for diagnostic evaluation at the start of treatment. Measuring angles and distances and comparing these to average values of the population is equivalent to superimposing our patient to the average tracing of the population and noting the areas of discrepancy. The problem of finding the most appropriate superimposition is not easy and GM can offer a new perspective (Halazonetis, 2004). Figure 1 shows cephalometric tracings of 4 patients superimposed by the traditional cranial base Sella-Nasion superimposition and the Procrustes superimposition. The cranial base method makes it very difficult to arrive at a

valid interpretation of shape differences between these patients, because the location of points S and N within the structure is the only factor driving the superimposition. Apparent differences in the position of other points may be due more to the variability of points S and N within the shape than to variability of the other points. The Procrustes distance of a patient to the average of the population is an overall measure of the distinctiveness of the patient’s shape. It can be used to judge the extent of craniofacial abnormality and it can give a measure of treatment success; if the Procrustes distance after treatment is smaller than before, then the patient has approached the population average (assuming this is our target). Similarly, it can be used in treatment planning to evaluate various treatment alternatives by creating shape predictions and comparing their Procrustes distances. The prediction with the smallest Procrustes distance relative to the average of the population may be selected as the best treatment choice. This method of treatment planning is not diagnosis-driven but predictiondriven and could be a solution in those cases where diagnostic results are conflicting or difficult to interpret.

Figure 1. Cephalometric tracings of 4 patients. Left: superimposed on Sella-Nasion line. Right: superimposed by Procrustes superimposition.

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Shape Variables and Principal Component Analysis Assume that we use Procrustes superimposition to superimpose a cephalometric tracing of a patient on the average of the population. Each cephalometric point will not coincide exactly with the corresponding point of the average tracing but will be a distance away in the x and y direction. These small discrepancies constitute the shape variables and are used for calculation of the Procrustes distance, as explained above. For each point of the shape we will have two such variables (dx and dy), giving a rather large total number of variables to deal with in statistical tests, but, most importantly, to get a feeling of the underlying patterns in our data. However, since all the points belong to the same biological entity, it is expected that there will be correlations between the positions of the points, due to structural and functional factors. Using the statistical tool of Principal Component Analysis (PCA) we can use these correlations to transform our original shape variables into new variables that reveal the underlying correlations and their biological patterns (O’Higgins, 1999; Halazonetis, 2004; Slice, 2005). The variables produced by PCA (Principal Components, PC) can be used for describing the shape of our patient in a compact and quantitative manner. A few principal components are usually sufficient to describe most of the shape variability of a sample, thus constituting a compact and comprehensive system of shape description that could be used for classification and diagnosis.

Semilandmarks The discussion on shape assessment has thus far made the implicit assumption that the landmarks used for defining the shape of the patients are homologous, i.e. each landmark corresponds to a specific biological structure, common between patients. Although we define most landmarks to follow this rule, sometimes landmarks are placed

along curves (or surfaces) that do not have any discerning characteristics to ensure homology. The landmarks merely serve the purpose of defining the curve and their exact placement along the curve is not important. In such cases, the landmarks are considered to represent less information and are called semilandmarks (Bookstein, 1997). Since the exact placement of semilandmarks is arbitrary to some extent, differences in shape between patients may appear larger than actual. In such cases, the semilandmarks can be adjusted by sliding them on the curve or the surface they lie on, until differences are minimized (Bookstein, 1997; Gunz et al., 2005).

COMPUTED TOMOGRAPHY Computed tomography was invented in the early 1970’s by Godfrey Hounsfield, who later shared the Nobel Prize in Medicine with Allan Cormack, developer of the mathematical algorithms for reconstruction of the data. The development of computed tomography (CT), which revolutionized medical diagnosis in the 70’s and 80’s, had a barely noticeable effect in Dentistry, mainly because of the cost of the procedure and the high amount of radiation. However, the recent development of cone-beam computed tomography (CBCT) and the manufacturing of ‘dental’ CBCT machines is beginning to make a large impact in all areas of dental practice, including implant placement and orthodontic diagnosis (Sukovic, 2003; Halazonetis, 2005). Orthodontic practices and university clinics in the US and other countries are phasing out the conventional radiographic records, consisting of a lateral cephalogram and a panoramic radiograph, and substituting CBCT images. Although radiation to the patient is higher, many believe that the higher diagnostic information more than compensates.

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Data The data from a CT examination can be though of as many 2-dimensional digital images stacked one on top of the other, to produce a 3-dimensional image, or ‘volume’. Each image has pixels that extend in 3-dimensions and are called ‘voxels’. The whole ‘volume’ is typically 512x512 in the x- and y-directions and can extend to 300 or more slices in the z-direction, giving a total count of more than 80 million voxels. Because each voxel represents x-ray attenuation, the voxels do not have colour information; data are represented by an 8-bit or 12-bit number, so a voxel value ranges from 0 to 255 or from 0 to 4095. The higher the value, the more dense the tissue represented by the voxel. Voxel values are sometimes converted to Hounsfield units (HU). In this scale, air is assigned a value of -1000 HU and water a value of 0 HU. Values of other materials are assigned by linear transformation of their attenuation coefficients. Bone has a HU value of 400 and above.

Cone-Beam Computed Tomography in Orthodontics Advantages and Limitations There are two main questions to consider when assessing CBCT imagining in Orthodontics. One is whether CBCT is preferable to the conventional records of a lateral cephalogram and a panoramic radiograph, and second, whether CBCT is advantageous relative to a medical CT examination. Various factors come into mind for both of these questions, including quantity and quality of diagnostic information, radiation hazard, cost, acquisition time, ease of access to the machine and ease of assessment of data. Although some of these factors may be determinative in some circumstances (e.g. no CT machine available in area of practice), the most important ones are related to diagnostic information, radiation concerns and data evaluation.

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Diagnostic Information Three-dimensional information is undoubtedly better than the 2-D images of the conventional cephalogram and panoramic radiographs. There is no superposition of anatomical structures and the relationship of each entity to the others is apparent in all three planes of space. The superiority of 3D images has been demonstrated in cases of impacted teeth, surgical placement of implants and surgical or orthodontic planning of patients with craniofacial problems, including clefts, syndromes and asymmetries (Schmuth et al., 1992; Elefteriadis & Athanasiou, 1996; Walker et al., 2005; Cevidanes et al., 2007; Cha et al., 2007; Van Assche et al., 2007; Hwang et al., 2006; Maeda et al., 2006). However, the fact that 3D images are superior does not imply that they should substitute 2D images in every case (Farman & Scarfe, 2006). The clinician should evaluate whether the enhanced information is relevant and important on a case-by-case basis, just as the need for a cephalometric or panoramic radiograph is evaluated. One factor that may be limiting in some cases is the restricted field of view of CBCT machines. The first models could image a severely limited field, just enough to show the mandible and part of the maxilla, up to the inferior orbital rims. Newer models allow large fields, but it is still not possible to image the entire head (Figure 2 and Figure 8). Additionally, the time taken to complete the scan may be more than 30 seconds, a factor that could introduce blurring and motion artifacts. Another limiting factor is the resolution of the images. A periapical radiograph can give a very clear view of the fine bony trabeculae in the alveolar process. Panoramic radiographs and cephalograms can also show such details, but CBCT data have a voxel size of approximately 0.4 mm in each direction resulting in a comparatively blurred picture, not to mention a multitude of artifacts that reduce image quality severely.

Software Support for Advanced Cephalometric Analysis in Orthodontics

Figure 2. Restricted field of view in CBCT image, even though machine was set to widest possible. In this instance, the extent towards the back of the head barely includes the mandibular condyles. Data rendered in Viewbox.

of the exposure involved in medical diagnosis.” (UNSCEAR, 2000) Figure 3 reports the equivalent dose from various medical examinations, including conventional CT, CBCT and cephalometric and panoramic radiography.

Data Evaluation

Radiation Exposure Numerous investigations have been conducted to measure radiation exposure to CT examinations. One of the most widely used measures is the equivalent dose (or effective dose), which measures the biological effect of radiation. The equivalent dose is calculated by multiplying the absorbed dose by two factors, one representing the type of ionizing radiation and the other mainly representing the susceptibility of the biological tissue to the radiation. The unit of measurement is the sievert (Sv). Natural background radiation incurs about 2400 μSv per year. According to the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) 2000 Report to the General Assembly, “the average levels of radiation exposure due to the medical uses of radiation in developed countries is equivalent to approximately 50% of the global average level of natural exposure. In those countries, computed tomography accounts for only a few per cent of the procedures but for almost half

An aspect that is seldom discussed in relation to the advent of CBCT in orthodontic diagnosis is data evaluation. The assessment of the data obtained by a CBCT examination represents some difficulties compared to conventional examinations. These difficulties arise because the dentist or orthodontist may not be trained for this task and because extra facilities are needed (computers and software). Furthermore, normative data may not be available, making it difficult to differentiate the normal from the pathological, or, to assess the degree of discrepancy from the average of the population. 3D Cephalometrics The rather fast introduction of CBCT imaging in Orthodontics seems to have taken the field unprepared. The more than 70 years of 2D conventional cephalometrics seems so ingrained that recent papers in the literature concentrate on evaluating methods that create simulations of 2D cephalograms from the 3D CBCT data (Moshiri et al., 2007; Kumar et al., 2008), thus trying to retain compatibility with old diagnostic methods instead of seeking to develop something new. Very little thought seems to have been invested into recognizing and assessing the capabilities of this new medium as well as the significant differences between it and 2D cephalometrics. Consider, for example, the ANB measurement, which aims to assess anteroposterior discrepancy between the maxilla and mandible. A direct transfer of this measurement to 3D seems without problems until one realizes that an asymmetry of the mandible will move point B laterally, thus increasing the

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ANB angle, without there being any change in anteroposterior mandibular position in relation to the maxilla. Similar problems crop up with other measurements. A 3D cephalometric analysis should be developed starting from a complete overhaul of current practices and should probably incorporate geometric morphometric methods for assessment of shape. Currently no such analysis exists, although efforts have been made, mostly in the lines described previously (Swennen et al., 2006). Thus, whereas CBCT imaging is increasingly used, most of the available information remains unexploited; evaluated either in a qualitative manner, or by regressing to 2D. A major difficulty hindering progress, besides the conceptual problems of the third dimension, is the lack of normative data. The standards of the historical growth studies are of little use and ethical considerations do not allow such studies to be carried out with the ease there were done in the early years of cephalometrics. However, the large number of CT examinations done all over the world for other medical and diagnostic reasons constitute a pool of data that could provide invaluable information if they could be gathered and systematically analysed. Image Interpretation and Artifacts in CBCT A significant difficulty in the clinical application of CBCT images is the lack of training in their interpretation. Dental Schools and Orthodontic Departments are starting to add courses in computed tomography but it will be many years until the knowledge and skills permeate to faculty members and practicing clinicians. Some of the most common methods of viewing CT data are described in the next section of this chapter. Below we mention a few of the most important artifacts that occur in all forms of CT imaging but are most apparent in CBCT (Barrett & Keat, 2004). The difference in artifact level between medical CTs and dental CBCTs is large and image quality is considerably lower in CBCTs.

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Noise Noise can be produced by many factors including stray and scatter radiation and electromagnetic interference. The lower the radiation level, the higher the noise will be. Thus, CBCT images usually have more noise than medical CTs. Noise can be reduced by the application of various smoothing filters, but at the expense of loss of image detail. Streaking Artifacts Streaking artifacts are caused by very dense materials, usually dental amalgams, restorations or metal crowns and bridges (Figure 3). They are due to a complete absorption of x-ray radiation, thus allowing no signal to reach the detectors. Various algorithms exist to reduce such artifacts but it is very difficult to abolish them. Ringing Artifacts These appear as concentric circles centred at the centre of the image (Figure 4). They are due to differences in detector sensitivity and can be reduced by calibration of the machine.

Figure 3. Streaking artifacts due to highly radiopaque metal prosthesis. Notice that streaks radiate from the metal source and extend to the edges of the image (arrows).

Software Support for Advanced Cephalometric Analysis in Orthodontics

Figure 4. Ringing artifacts. Image from a microCT machine showing rat tooth embedded in fixing material. Note concentric rings due to detector mis-calibration.

Beam Hardening - Cupping Artifacts X-ray beams are composed of photons of a wide range of energies. As an x-ray beam travels through the patient, its intensity is reduced due to absorption, but this reduction is not uniform over the energy range, because lower energy photons are absorbed more rapidly than high energy photons. The result is a change in energy distribution of the beam (also known as beam hardening). Therefore, a beam that passes through a thick portion of the patient’s body will have proportionately more of its low energy photons absorbed and will appear to

the detectors to be more energetic than expected. A more energetic beam is interpreted by the machine as a beam having passed through less dense material. Thus, the internal portion of the patient’s body will appear darker, producing a characteristic ‘cupping’ profile of voxel values along the line of the beam (Figure 5). Cupping artifacts widen the range of voxel values that correspond to the same tissue type and make volume segmentation and rendering more difficult. Partial Volume Averaging Voxels are not of infinitesimal size but extend in spatial dimensions, usually having a size of 0.3 to 0.6 mm in each direction. If a voxel happens to be located at the interface between two (or more) different tissues, then its value will be the average of those tissue densities. Depending on the relative proportion of each tissue, the voxel could have any value between the values of the two tissues. The partial volume averaging effect (PVAE) is thus a problem of resolution; the larger the voxel size the more the effect. The voxel size commonly used in CT imaging is large enough to create artifacts in numerous areas of the craniofacial complex. The paper-thin bone septa of the ethmoid bone may completely disappear, leaving an image of soft tissue surrounding empty spaces with no osseous support. Similarly, the cortical bone covering the roots of teeth may be too thin

Figure 5. An axial slice of a CBCT image. The profile of voxel values along the line shows the characteristic cupping artifact due to beam hardening. The profile is not smooth due to noise.

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and be confused with soft-tissue, thus giving the impression of dehiscence. Pseudo-foramina are sometimes seen on calvarial bones, especially in infants, whose bones are very thin. PVAE is especially significant when taking measurements, because measurements entail the placement of landmarks on the interface between anatomical structures, the area that PVAE affects most. The Partial Volume Averaging effect should not be confused with the Partial Volume Effect (PVE). This artifact occurs when the field of view is smaller than the object being imaged, so it is seen predominantly in CBCTs. The parts of the object outside the field of view absorb radiation and through shadows on the detectors, but this happens only for part of the image acquisition (otherwise the whole object would be visible). This extraneous information cannot be removed by the reconstruction algorithm and shows as artifacts, usually manifesting as streaks or inconsistent voxel densities. PVE artifacts are also known as projection data discontinuity–related artifacts (Katsumata, 2007) and are particularly troublesome in limited field of view CBCT images (Figure 6).

Figure 6. Axial slice of anterior part of the maxilla showing impacted canine. PVE artifacts are evident.

Artifact Effect on Voxel Value Distributions As explained above, each voxel represents the density of the tissue at the voxel’s position. The voxel value is used for a multitude of purposes, from rendering (explained below) to segmentation and measurements. Volume segmentation is the process of subdividing the volume into tissue types so that anatomical structures can be identified and measurements taken. Depending on its value, a voxel can be classified as belonging to a particular tissue type such as bone, muscle, skin, etc. However, tissues are not completely homogeneous, ‘noise’ may be present and artifacts (e.g. PVE and cupping) may shift voxel densities from their true value. Thus, each tissue type does not contain voxels that have exactly the same value. Instead, voxels of a particular tissue span a range of values. Volume segmentation requires that the density ranges of the various tissue types do not overlap, so that cut-off points (thresholds) can be established that will divide the voxels without misclassification. This requirement is frequently violated and the distribution of bone density values and soft-tissue values overlap each other. As Figure 7 shows, there is no threshold value that can separate the two tissues without any misclassifications. This problem is especially apparent in CBCT imaging compared to medical CT and affects volume rendering as well (see below).

VOLUME RENDERING Volume rendering is the process of visualizing the volume data as an image on the computer screen (Halazonetis, 2005). The volume data constitutes a rectangular three-dimensional grid of voxels, the value of each voxel representing the radiographic density of the tissue at the corresponding position. For this discussion, it helps to consider the whole volume as an object in 3-dimensional space, floating behind the computer screen, the screen being a window into this space. We know the density of the object at specific coordinates and we wish

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Figure 7. A CBCT axial slice showing a section of the mandible. Due to artifacts, the soft-tissues on the buccal side of the mandible have comparable densities to the bone on the lingual side. The green line is the iso-line for a threshold value that is appropriate for segmenting the lingual part of the mandible but not for the labial part. Conversely, the red line represents a higher threshold, appropriate for the denser bone of the outer mandibular surface, but not for the lingual. Voxel size is 0.42 x 0.42 x 0.60 mm.

to reconstruct an image of the object from this information. There are two main methods to do this, direct volume rendering, where the values of the voxels are directly converted into colour values for the pixels of the computer screen, and indirect volume rendering, where the voxel values are first converted into data describing a geometrical object, which is then rendered on the screen, usually with the help of dedicated graphics hardware.

Direct Rendering: Transfer Function Direct volume rendering can be accomplished in a multitude of ways (Marmitt, 2006) but the easiest to describe and understand is ray-casting. Ray-casting uses the analogy of an object floating behind the computer screen. Since the screen is our window to the 3-dimensional world, the

colour of each pixel will depend on the objects that lie behind it. A straightforward approach assumes that from each screen pixel starts a ray that shoots towards the object. As the ray pierces the object and traverses through it, it takes up a colour that depends on the tissues it encounters along its way. The colours are arbitrary and are usually assigned according to the voxel values. For example, to show bone in white and soft tissue in red we could assign white to the pixels whose ray encounter voxels of a high value and red to the pixels whose ray encounter voxels of a low value. To show bone behind soft tissue we additionally assign opacity according to voxel value. Voxels of low value could have a low opacity, so that the ray continues through them until it encounters high-value voxels. In this way, the final colour assigned to the pixel will be a blend of red and white. This method of ray casting can produce high quality renderings of CT data (Figure 8A). There are a few details that need to be mentioned. First, the value of a voxel represents the value at a specific point in space. Mathematically, this point has no spatial extent, so the rays that are spawn from the screen pixels may penetrate the volume without encountering a voxel. The solution to this problem is that the ray is sampled at regular intervals along it. At each sampling point on the ray, the required value is interpolated from the neighbouring voxel points. There are numerous ways of interpolating a voxel value. The fastest is tri-linear interpolation, where only the 8 immediate neighbours are taken into account, but other methods, using a larger neighbourhood, may produce better results. In any case, the calculated value is only an approximation of the true value. Another factor that may lead to artifacts in the rendered image is the frequency of sampling along the ray. Too large a sampling distance may result in skipping of details and loss of smooth gradients (Figure 9). As was mentioned above, the colour and opacity at each sampling point along the ray is arbitrarily set, usually dependent on the calculated

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Figure 8. Rendering of a CBCT dataset. (a) Ray casting using a transfer function. (b) Iso-surface rendering of two iso-surfaces (soft-tissues transparent). (c) Average intensity ray casting (simulation of conventional radiograph). (d) MIP (maximum intensity projection). Data from NewTom 3G, rendered in Viewbox.

voxel value or tissue density. In general, the mapping of the voxel values to colour and opacity is called the transfer function. Most commonly this is a one-dimensional transfer function, meaning that colour and opacity are a function of one variable only, voxel value. However, more complex transfer functions are possible (Kniss et al., 2002). A two-dimensional transfer function can map tissue density and gradient of tissue density (dif-

ference of density between neighbouring voxels), making it possible to differentiate areas at the interface between tissues (Figure 10).

Direct Iso-Surface Rendering This method also uses ray casting, but instead of accumulating colours and opacities as the ray traverses through the volume, it detects the posi-

Figure 9. Artifacts produced from too large a sampling step during ray casting. Left: large sampling step leading to slicing artifacts. Right: high-quality rendering using 4 times smaller step size. Medical CT data.

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Figure 10. (a) Two-dimensional histogram of CT volume data. Horizontal axis is voxel density, increasing from left to right. Vertical axis is voxel gradient. Arches are characteristic of low-noise data and represent voxels that lie on tissue boundaries. (b) and (c) Transfer functions mapping voxel densities and gradients to colours and opacities. The transfer function of (c) was used for rendering of Figure 9.

tion where the ray crosses a specific threshold of voxel density (Parker et al., 1998). The threshold has been set by the user and corresponds to the boundary between two tissues (e.g. soft-tissue and bone, or air and soft-tissue). Such boundaries that represent a specific voxel density are called iso-surfaces. Figure 8B shows two iso-surfaces rendered by ray casting. The skin iso-surface has been rendered semi-transparent, in order to show the skeletal structures underneath.

Average Intensity Ray Casting This method calculates the average density of the voxels that each ray passes through and creates an image that approximates the image that would be produced by conventional radiographic techniques (Figure 8C). In Orthodontics, average intensity rendering can create simulated cephalograms from CBCT data, to be used for conventional cephalometric analysis.

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Maximum Intensity Projection Maximum Intensity Projection (MIP) is a raycasting technique that shows the densest structures that each ray encounters as it travels through the CT volume (Figure 8D). MIP rendering can be useful to locate and visualize dense objects, such as metal foreign objects or blood vessels infiltrated with radio-dense enhancing material, or to identify areas of bone perforation and fractures. Indirect Rendering Indirect rendering uses the CT data to create a geometric object that is then rendered on the screen. The object is, most commonly, an iso-surface, i.e. a surface that represents the interface between areas of a higher density value and areas of a lower density value. The points that lie on the iso-surface have all a density equal to a specified threshold, called the iso-value. It can be shown mathematically that an iso-surface is continuous (except at the edges of the volume) and closes upon itself. Such a surface can be approximated by a large number of triangles, usually calculated from the voxel data using the Marching Cubes algorithm or one of its variants (Lorensen & Cline, 1987; Ho et al., 2005). The resulting triangular mesh can be rendered very quickly using the graphics hardware of modern personal computers (Figure 11). Advantages of indirect rendering include the speed of rendering and the capability to easily place points on the mesh for measurements or to compute volume and area, and to splice and ma-

nipulate the mesh in order to simulate surgical procedures. Meshes can also be used for computer-aided manufacturing of 3D objects, either biological structures for treatment planning, or prostheses and implants. However, meshes do not represent the biological structures as well as direct rendering because the iso-surface that is used to construct them does not necessarily represent the boundary of a tissue, due to artifacts of CT imaging.

THE VIEWBOX SOFTWARE The Viewbox software (www.dhal.com) (Figure 12) started as conventional 2-dimensional cephalometric analysis software for orthodontists in the early 1990’s (Halazonetis, 1994). Recently it has been updated for 3-D visualization and analysis, including volume rendering and geometric morphometric procedures. Viewbox has been designed as a flexible system that can work with various kinds of 2D and 3D data, such as images, computed tomography data, surface data and point clouds. The software is built upon a patient-centric architecture and can handle various types of virtual objects, as detailed below. Initially, when Viewbox development started, the only way to get cephalometric data into a computer was through an external digitizer tablet. The data were x and y point coordinates that were used for calculating the cephalometric measure-

Figure 11. Indirect rendering of volume by creation of triangular mesh. Left: Mesh rendered as a wireframe object. Middle: Mesh rendered as a faceted triangular surface. Right: Smooth rendering.

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Figure 12. The viewbox software

ments. Scanners and digital cameras were not widely available, nor did they have the specifications required for precise measurements. Therefore, objects such as digital images and CT data were not even considered. The focus was placed on developing a flexible user-defined system that could handle almost any type of measurements that might be required, either in conventional cephalometric analysis or any 2D measurement of diagnostic or experimental data (e.g. data from animal photographs or radiographs, measurement of dental casts, panoramic radiographs etc.). This system was based on the definition of Templates that incorporated all the data structures and information necessary to carry out the measurements and analyses specified by the user. A Template would include information on the points that were digitized, the measurements based on these points, the analyses (groups of measurements), types of superimpositions available, and so on. When analyzing a new cephalometric radiograph (or any other diagnostic record), the user would create a new Dataset based on a selected Template. The Dataset would consist of a collection of x and y coordinate data, mapped to the points of the Template. The coordinate data would be filled by the user by digitizing the radiograph and the measurements would be calculated using the

functions specified in the Template’s definition. This architecture enabled a system that was completely user-definable with no rigid built-in restrictions. The user could specify measurement types, normal values, types of superimpositions, names and number of digitized points and other such data, making it possible to build completely customized solutions to any 2D analysis task. Datasets contained nothing more than the coordinates of the digitized points, making it easy and fast to store and retrieve data, as the main bulk of information was in the Template structure, which needed to be loaded only once. With the progress in imaging devices and the fall in price of transparency scanners, digitization of radiographs on-screen soon became an attractive alternative. Viewbox was updated to be able to load digital images and communicate with scanners. Digitization could be performed by clicking on the points of the digital image on screen. Thus, a new object, the Image, was added to the Viewbox inventory. Images were accompanied by functions for image enhancement and manipulation. Soon afterwards, functions that would aid the user in locating the landmarks on the images were added (Kazandjian et al., 2006), as it was evident in the literature that landmark identification errors were probably the largest source of numerical errors

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in cephalometric analysis (Baumrind & Frantz, 1971; Houston et al., 1986). The latest step in Viewbox development came with the increasing use of CBCT machines in the orthodontic practice and the realization that 3D data will dominate clinical diagnosis and treatment planning in the future. This is now evident by the steady penetration of 3D models in orthodontic practices, the increasing use of 3D facial photographs and the use of 3D CT data and stereolithography models for diagnosis and treatment planning of challenging cases (Halazonetis, 2001). Thus, Viewbox has been redesigned by adding more internal objects, such as meshes, and volumes, and a 3D viewer for the visualization of these objects. The 2D viewer has been retained for backward compatibility, but it may be removed in the future when the 3D viewer inherits all its capabilities, as it will serve no real use. Viewbox is now a patient-centric system and includes the types of ‘objects’ described in the part “key terms and definitions”.

Images can be viewed both in a 2D viewer and a 3D viewer. Images can be adjusted to enhance perception of difficult to see structures. In addition to basic capabilities such as image inverse, brightness, contrast and gamma adjustment, Viewbox includes more sophisticated histogram techniques, such as adaptive histogram stretching, adaptive histogram equalization and contrast limited adaptive equalization (Figure 13). Furthermore, using a combination of such techniques, together with transparent blending of two images, it is possible to do structural superimposition of two radiographs, as proposed by Björk & Skieller (1983), in order to assess growth and treatment effects (Figure 14). When digitizing points on images, Viewbox can detect brightness levels and assist in accurate landmark placement by locking on abrupt brightness changes, which usually correspond to bony or soft-tissue outlines (Kazandjian et al., 2006), (Figure 15).

Figure 13. Image enhancement. (a) Original image. (b) Gamma adjustment. (c) Adaptive histogram equalization. (d) Contrast limited adaptive histogram equalization (CLAHE)

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Figure 14. Structural superimpositioning using blending of transparent images. Left: Before superimposition, one radiograph is rendered in red and the other in green, after a CLAHE filter has been applied to both. Right: After manual registration most of the cranial base structures are yellow, signifying a good fit.

Figure 15. Edge detection feature in Viewbox. Red circle is the mouse position. Green line is the direction along which edge detection is attempted. Blue line shows the detected edge. This corresponds to a zero-crossing of the second derivative of brightness, as shown in the inset graph.

the face and objects created from CT scans (Figure 16, Figure 17, Figure 11). Meshes are drawn using OpenGL routines and they can be rendered as a faceted surface, a wireframe object (showing only the outlines of the triangles) or a smooth surface (Figure 11). Viewbox contains basic mesh manipulation routines such as cropping, smoothing and decimation (reducing the number of triangles). Additionally, meshes can be registered on each other by using best fit methods.

Digitizing Points and Curves The multiple objects supported by Viewbox make it a flexible system for digitizing patient records

Mesh

Figure 16. A laser-scanned plaster model imported as a triangular mesh. The holes in the model are areas that were not visible to the laser light.

A mesh is a surface composed of triangular elements, each element consisting of 3 vertices and a face. A mesh has connectivity information so it is possible to determine if the surface is composed of separate detached objects. Meshes can be loaded from files (common file types are supported, including OBJ, PLY and STL) or can be created from volumes using a variant of the marching cubes algorithm (Lorensen & Cline, 1987). In orthodontics, meshes are used for rendering digital dental models, 3D digital photographs of

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Figure 17. 3D photograph imported as a mesh, rendered as a wireframe model, a smooth surface and a textured surface.

as well as data from other sources such as scanned bones, fossils, animal specimens, individual teeth, etc. Points can be digitized by clicking on the object on-screen; this method is supported for images, meshes, points clouds, slices and volumes rendered with iso-surface rendering. Additionally, curves can be placed on objects. Curves are smooth lines (cubic splines), usually located on edges of images or ridges of a 3D object, whose shape is controlled by points placed along them. Curves can be used for measuring the length of a structure or the surface area, in cases of a closed flat curve. However, the main usage is for automatic location of landmarks. For example, if a curve is drawn along the outline of the symphysis on a lateral cephalogram, Viewbox can locate points Menton, Gnathion, Pogonion, B point and other such landmarks, by using the definitions of these points. For instance, Menton will be

automatically located on the most inferior point of the curve, ‘inferior’ being a direction specified either absolutely (i.e. along the y-axis of the coordinate system) or relative to another cephalometric plane of the patient (e.g. perpendicular to Frankfurt horizontal, as defined by points Porion and Orbitale). Automatic point location can be helpful for reducing error in point identification, especially for points that are defined relative to a particular reference direction, such as points defined by phrases like ‘the most anterior’, ‘the most superior’, etc. Also, points defined relative to the curvature of a structure can be located in the same way. An example of this type is point Protuberance Menti (Ricketts, 1960), defined as lying on the inflection point (point of zero curvature) of the outline of the alveolar process between B point and Pogonion.

Figure 18. Left: A slice cutting through a CBCT volume. The slice can be moved along a user-adjustable path, shown here by the green line. Right: The slice image.

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Curves are also used for placing semilandmarks and letting them slide. As described previously, semilandmarks do not encode biological information by their position along the curve, because there are no discernable features on the curve. The biological information is in the shape of the curve; as long as this shape reflects the underlying biological structure, the semilandmarks’ precise location is irrelevant. However, since the semilandmarks will be used for shape comparison, we need to ensure that discrepancies in their position between patients or specimens does not affect our results. This can be accomplished by first automatically distributing them along the length of a curve, at predefined intervals, and then sliding them on the curve, until the Procrustes distance, or some other metric of shape similarity relative to a reference template, is reduced to a minimum. This ensures that no extraneous shape variability is introduced by incorrect semilandmark placement. Viewbox supports sliding semilandmarks on both curves and surfaces (meshes) in 3D (Figure 19). In addition to digitized points and curves, Viewbox provides for geometrically derived landmarks such as midpoints, projections of points on lines, extensions of lines, centroids, etc.

on the position of points (e.g. various distances and angles), compound measurements (e.g. sum, difference, product of other measurements), measurements between datasets (e.g. the distance between one point of one dataset to a point of another dataset, useful for measuring changes due to growth or treatment) and basic morphometric measurements (e.g. Procrustes distance). For each measurement the user can define normal values (average and standard deviation) for arbitrary age ranges. This enables Viewbox to show measurements colour-coded, so that it is immediately apparent which measurements are outside the normal range of the population (Figure 20).

Measurements

Figure 20. Cephalometric analysis on radiograph using colour-coded measurements to signify deviations from average.

There are more than 40 measurement types available. These include direct measurements based

Research Tools and Clinical Use Viewbox is a comprehensive system incorporating features useful for research projects but also for clinical applications. For research, the most significant is the ability to define points and measurements to suite almost any 2D or 3D task. Measurements and raw point coordinates can be exported to a text file for statistical evaluation in

Figure 19. Points placed on mesh created from CT data. Most of the points are sliding semilandmarks.

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any statistics software. The average of a population can be computed, using any conventional superimposition or Procrustes. Morphometric functions give access to basic GM tools, including sliding semilandmarks on curves and surfaces in 2D and 3D. In addition to the commonly found features of conventional cephalometric software, clinically valuable are the 3D rendering capabilities for CT data. These allow CT visualization on personal computers at a quality equal to that attainable on dedicated hardware of CT machines (although at a significantly lower speed). Such visualization can be very valuable for treatment planning of patients with craniofacial anomalies, for locating impacted teeth or for assessing the position of teeth within the jaws and to each other (Figure 21). For quantitative assessment, landmarks can be placed on such data and measurements taken, thus enabling 3D analyses. Surface data from laser-scanners or other devices can be imported, allowing the simultaneous visualization of dental casts, facial scans and CT images. The combined data can be used for virtual surgical treatment planning. Because the data are 3D, this method produces more accurate and representative predictions, both of the bony relationships and of the soft-tissue shape.

software to import CBCT data, produce various visualizations, including renderings simulating conventional cephalograms and panoramic radiographs, and take measurements in 3D. Medicim (www.medicim.com) have a 3D cephalometric module and a surgical treatment planning module that enables prediction of soft-tissue response to surgical procedures using a mathematical model. Digital dental models have established themselves as an alternative to plaster. The three main companies that provide the service of converting impressions or plaster casts to digital data are Geo-Digm Corp. (www.dentalemodels.com), OrthoCAD (www.orthocad.com) and OrthoProof (www.orthoproof.nl). Each company provides its own software that enables viewing, measurements, and virtual setups. Facial scans are the third data type that completes the picture. Various technologies are used, including laser scanning and stereophotogrammetry (Halazonetis, 2001). Companies actively working in this field include Breuckmann (www. breuckmann.com), Dimensional Imaging (www. di3d.com), Canfield Scientific (www.canfieldsci. com) and 3dMD (www.3dmd.com).

Other Software and Hardware

As computed tomography and sophisticated shape measuring tools permeate the fields of Orthodontic diagnosis and treatment, software support will become more and more important in everyday clinical practice. Development of such software

Software for 3D cephalometric analysis are in rapid development. Dolphin Imaging (www.dolphinimaging.com) has extended its cephalometric

CONCLUSION

Figure 21. Impacted canine as seen from three different views of a 3D rendering of a CBCT scan

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is slow and difficult. Efforts should be applied to produce user-friendly systems so that our patients can benefit. However, software development may not be the critical factor in dissemination of these methods. Postgraduate Orthodontic programmes should place more emphasis on teaching geometric morphometrics and the techniques of computed tomography and research needs to be conducted for gathering normative 3D data as well as for developing new 3D cephalometric analyses.

ACKNOWLEDGMENT Thanks to Alexandros Stratoudakis, Panos Christou, Christos Katsaros and Michael Coquerelle for providing CT data and meshes for the images. This work was supported by the EVAN project (European Virtual Anthropology Network) MRTN-CT-2005-019564, a Marie Curie Research Training Network. All images in this chapter were produced by the Viewbox software.

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Mah, J. K., Danforth, R. A., Bumann, A., & Hatcher, D. (2003). Radiation absorbed in maxillofacial imaging with a new dental computed tomography device. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontics, 96, 508–513. doi:10.1016/S1079-2104(03)00350-0 Marmitt, G., Friedrich, H., & Slusallek, P. (2006). Interactive Volume Rendering with Ray Tracing. Eurographics State-of-the-Art Report 2006, Vienna. Retrieved April 12, 2008, from http://graphics.cs.uni-sb.de/Publications/2006/ eg2006star_vrt.pdf Moshiri, M., Scarfe, W. C., Hilgers, M. L., Scheetz, J. P., Silveira, A. M., & Farman, A. G. (2007). Accuracy of linear measurements from imaging plate and lateral cephalometric images derived from cone-beam computed tomography. American Journal of Orthodontics and Dentofacial Orthopedics, 132, 550–560. doi:10.1016/j. ajodo.2006.09.046 Ngan, D. C., Kharbanda, O. P., Geenty, J. P., & Darendeliler, M. A. (2003). Comparison of radiation levels from computed tomography and conventional dental radiographs. Australian Orthodontic Journal, 19, 67–75. O’Higgins, P. (1999). Ontogeny and phylogeny: some morphometric approaches to skeletal growth and evolution. In M.A.J. Chaplain, G.D. Singh, J.C. McLachlan (Ed.), On growth and form. Spatiotemporal pattern formation in biology (pp. 373-393). New York: John Wiley & Sons. Parker, S., Shirley, P., Livnat, Y., Charles Hansen, C., & Sloan, P. P. (1998). Interactive ray tracing for isosurface rendering. In IEEE Visualization ’98, pp. 233–238. Ricketts, R. M. (1960). A foundation for cephalometric communication. American Journal of Orthodontics, 46, 330–357. doi:10.1016/00029416(60)90047-6

Schmuth, G. P. F., Freisfeld, M., Koster, O., & Schuller, H. (1992). The application of computerized tomography (CT) in cases of impacted maxillary canines. European Journal of Orthodontics, 14, 296–301. Shrimpton, P. C., Hillier, M. C., Lewis, M. A., & Dunn, M. (2003). Doses from Computed Tomography (CT) examinations in the UK – 2003 Review. National Radiological Protection Board. Retrieved March 16, 2008, from http://www. hpa.org.uk/radiation/publications/w_series_reports/2005/nrpb_w67.pdf Slice, D. E. (2005). Modern morphometrics. In D. E. Slice (Ed.), Modern morphometrics in physical anthropology (pp. 1-45). New York: Kluwer Academic / Plenum Publishers. Sukovic, P. (2003). Cone beam computed tomography in craniofacial imaging. Orthodontics & Craniofacial Research, 6(Suppl 1), 31–36. doi:10.1034/j.1600-0544.2003.259.x Swennen, G. R. J., Schutyser, F., & Hausamen, J. E. (2006). Three-dimensional cephalometry. A color atlas and manual. Berlin: Springer-Verlag. Tsiklakis, K., Donta, C., Gavala, S., Karayianni, K., Kamenopoulou, V., & Hourdakis, C. J. (2005). Dose reduction in maxillofacial imaging using low dose Cone Beam CT. European Journal of Radiology, 56, 413–417. doi:10.1016/j. ejrad.2005.05.011 UNSCEAR, United Nations Scientific Committee on the Effects of Atomic Radiation. (2000). Sources and effects of ionizing radiation, Report to the General Assembly, with scientific annexes. Report Vol. I. Retrieved February 18, 2008, from http://www.unscear.org/unscear/en/publications/2000_1.html

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Van Assche, N., van Steenberghe, D., Guerrero, M. E., Hirsch, E., Schutyser, F., Quirynen, M., & Jacobs, R. (2007). Accuracy of implant placement based on pre-surgical planning of threedimensional cone-beam images: a pilot study. Journal of Clinical Periodontology, 34, 816–821. doi:10.1111/j.1600-051X.2007.01110.x Walker, L., Enciso, R., & Mah, J. (2005). Threedimensional localization of maxillary canines with cone-beam computed tomography. American Journal of Orthodontics and Dentofacial Orthopedics, 128, 418–423. doi:10.1016/j. ajodo.2004.04.033

KEY TERMS AND DEFINTIONS Dataset: A dataset is a collection of points and curves that have been placed on an object. The dataset contains the coordinates of the digitized points and the coordinates of the controlling points of the curves (cubic splines). Image: Digital images have become the most common method of entering diagnostic data for digitization and measurement. The most prevalent file formats in dentistry include JPEG, TIFF and DICOM. Viewbox can communicate directly with scanners using the TWAIN interface. Patient: This is the fundamental object, representing a patient or specimen. It holds the

name, gender and date of birth of the patient as well as the ‘child’ objects, such as datasets, images or CT data. Point Cloud: Point clouds are collections of 3D points. Point clouds can be acquired from a surface digitizer or can be created from meshes. Point clouds contain no information regarding the shape of the surface from which they were collected and for this reason their usefulness is limited. Slice: A slice is used to create a 2D image by taking an arbitrary cross-section through a volume or a mesh (Figure 18). Template: Datasets are based on Templates. A template can be thought of as an exemplary dataset, containing all the information required to measure and analyze the object. The most common dataset in orthodontics is related to analysis of a lateral cephalogram and contains the conventional cephalometric points and measurements. However, templates are completely user-definable, so they can be created for whatever purpose is desired. Examples include templates for measuring dental casts, facial photographs, osseous structures from CTs, etc. Volume: Volumes contain data from CTs or MRIs. They are regular rectangular 3D arrays of voxels. Each voxel holds a single value representing the density of the tissue at that location. Volumes can be rendered in various ways, as previously described.

This work was previously published in Dental Computing and Applications: Advanced Techniques for Clinical Dentistry, edited by Andriani Daskalaki, pp. 1-27, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 3.21

Quantitative Analysis of Hysteroscopy Imaging in Gynaecological Cancer Marios Neofytou University of Cyprus, Cyprus Constantinos Pattichis University of Cyprus, Cyprus Vasilios Tanos Aretaeion Hospital, Nicosia, Cyprus Marios Pattichis University of New Mexico, USA Eftyvoulos Kyriacou Frederick University, Cyprus

ABSTRACT The objective of this chapter is to propose a quantitative hysteroscopy imaging analysis system in gynaecological cancer and to provide the current situation about endoscopy imaging. Recently works, involves endoscopy, gastroendoscopy, and colonoscopy imaging with encouraging results. All the methods are using image processing using texture and classification algorithms supporting the physician diagnosis. But none of the studies DOI: 10.4018/978-1-60960-561-2.ch321

were involved with the pre-processing module. Also, the above studies are trying to identify tumours in the organs and no of the are investigates the tissue texture. The system supports a standardized image acquisition protocol that eliminates significant statistical feature differences due to viewing variations. In particular, the authors provide a standardized protocol that provides texture features that are statistically invariant to variations to sensor differences (color correction), angle and distance to the tissue. Also, a Computer Aided Diagnostic (CAD) module that

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Quantitative Analysis of Hysteroscopy Imaging in Gynaecological Cancer

supports the classification of normal vs abnormal tissue of early diagnosis in gynaecological cancer of the endometrium is discussed. The authors investigate texture feature variability for the aforementioned targets encountered in clinical endoscopy before and after color correction. For texture feature analysis, three different features sets were considered: (i) Statistical Features, (ii) Spatial Gray Level Dependence Matrices, and (iii) Gray Level Difference Statistics. Two classification algorithms, the Probabilistic Neural Network and the Support Vector Machine, were applied for the early diagnosis of gynaecological cancer of the endometrium based on the above texture features. Results indicate that there is no significant difference in texture features between the panoramic and close up views and between different camera angles. The gamma correction provided an acquired image that was a significantly better approximation to the original tissue image color. Based on the texture features, the classification algorithms results show that the correct classification score, %CC=79 was achieved using the SVM algorithm in the YCrCb color system with the combination of the SF and GLDS texture feature sets. This study provides a standardized quantitative image analysis protocol for endoscopy imaging. Also the proposed CAD system gave very satisfactory and promising results. Concluding, the proposed system can assist the physician in the diagnosis of difficult cases of gynaecological cancer, before the histopathological examination.

INTRODUCTION In the United States, in 2007, it is estimated that over 39,080 new cases will be diagnosed with gynaecological cancer of the endometrium resulting to approximately 7,400 deaths (American Cancer Society). Within the female population, gynaecological cancer accounts for the second highest mortality rate. Early diagnosis and treat-

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ment of gynaecological cancer are essential for better quality of life and longer life. The development of minimally invasive surgery has presented the possibility of new approaches to certain longstanding problems in gynaecology. The initial efforts with hysteroscopy, transabdominal/transvaginal laparoscopy operations have already demonstrated the advantages of endoscopic techniques over traditional open and endovascular approaches. The advantages of laparoscopic/hysteroscopic methods are especially significant in patients with a low risk factor when the operation is usually prophylactic (Cohen et al, 2003). The objective of this chapter is to provide a standardized protocol for eliminating significant differences in texture feature analysis of endoscopy images that is also used for classifying ROIs into normal and abnormal tissue. For gynaecological cancer, we show that the proposed approach eliminates significant statistical different due to sensor variations (color correction), distance from the tissue (panoramic vs close up) and camera angle. We validate the approach for texture features extracted at difference viewing conditions from: calf endometrium chosen for its resemblance to human tissue, chicken cavities chosen for providing a more realistic laparoscopy/hysteroscopy operation environment, and also verify the findings for human subjects. The structure of the chapter is as follows. In section II, a brief sections on overview of hysteroscopy/laparoscopy imaging is given. This is followed by methodology, results, discussion, and concluding remarks.

BACKGROUND In laparoscopic/hysteroscopic imaging, the physician guides the telescope inside the uterine or abdominal cavity investigating the internal anatomy, in search of suspicious, cancerous lesions (Bankman et al, 2000). During the exam, the

Quantitative Analysis of Hysteroscopy Imaging in Gynaecological Cancer

experience of the physician plays a significant role in identifying suspicious regions of interest (ROIs), where in some cases, important ROIs might be ignored and crucial information neglected (Sierra et al, 2003). The analysis of endoscopic imaging is usually carried out visually and qualitatively (Fayez et al, 1991, based on the subjective expertise of the endoscopist. Therefore, this procedure suffers from interpretational variability, lack of comparative analysis and it is time consuming. To the best of our knowledge, there are no other studies proposing a standardized quantitative image processing and analysis procedure for the laparoscopic/hysteroscopic imaging for gynaecological cancer. Several endoscopic studies have been reported related to standardisation, that focused on comparing different treatment methods in clinical gynaecological laparoscopy. On the other hand, several CAD systems with most of them based on texture analysis have been reported mainly for colonoscopy with highly promising results (Shi et al, 2006; Bankman et al, 2000; Tjoa et al, 2003; Karkanis et al, 1999; Karkanis et al, 2003), laryngoscopy, (Haralick et al, 1973) and other endosopic imaging modalities. In (Shi et al, 2006), 47 computed tomographic (CT) colonography data sets were obtained in 26 men and 10 women (age range, 42–76 years). Results shown that the use of 3D viewing improves classification accuracy for the three readers and increases significantly the area under the receiver operating characteristic curve. Features maximum polyp width, polyp height, and preparation significantly affected the true positive measure, whereas features like colonic segment, attenuation, surface smoothness, distention, preparation, and true nature of candidate lesions significantly affected the false positive measure. In (Tjoa et al, 2003), A hybrid system was developed for the assessment of colon tissue. It was shown that a slightly higher classification accuracy was achieved when combining texture and color features, versus texture only, or color only features. In (Karkanis et al, 1999) it was shown that the texture spectrum

maintains the important structure of different texture classes and also preserves the textural information in the original endoscopic images. In (Karkanis et al, 2003), a CAD system was presented for the detection of tumours in colonoscopic video based on color wavelet covariance (CWC) analysis features. The performance in the detection of abnormal colonic regions corresponding to adenomatous polyps was very high, reaching 97% specificity and 90% sensitivity. In this chapter, a standardized procedure based on color imaging correction and texture feature extraction, analysis and classification proposed by our group for the analysis of gynaecological tissue is presented (Neophytou et al, 2005; Neophytou et al, 2004; Neophytou et al, 2004; Neophytou et al, 2006; Neophytou et al, 2007). The gamma correction algorithm which is used extensively in many applications for correcting the camera images is applied for correcting the endoscopy images. The usefulness of gamma correction was also demonstrated on endoscopic video processing. In a medical endoscopy video contrast improvement method that provides intelligent automatic adaptive contrast control was presented. The method was based on video data clustering and video data histogram modification, that allowed defining the automatic gamma control range from 0.5 to 2.0. Applying gamma correction on the images, will also limit the variability when analyzing images captured with different cameras, telescopes and endoscopic hardware. Several textural features were computed in this work based on Statistical Features (SF) (Wu et al, 1992), Spatial Gray Level Dependence Matrices (SGLDM) (Haralick et al, 1973), and Gray level difference statistics (GLDS) (Wenska et al, 1976). Furthermore the diagnostic performance of the texture features was evaluated with two different classifiers: the Probabilistic Neural Network (PNN), and the Support Vector Machine (SVM) (Wenska et al, 1976; Christodoulou et al, 1999; Specht et al, 1990; Joachims et al, 1999; Ebrchart et al, 1990). These classifiers were trained to clas-

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Quantitative Analysis of Hysteroscopy Imaging in Gynaecological Cancer

Figure 1. A standardized quantitative image acquisition and analysis protocol in hysteroscopy imaging

sify the texture features into normal or abnormal ROIs, with promising success.

METHODOLOGY We summarize our integrated approach in Figure 1. We break our method into four parts. First, we perform color correction to compensate for sensor variations. Second, we acquire clinical images while carefully controlling the camera angle and distance to the tissue. Third, we perform texture analysis through statistical analysis of the extracted texture features. Fourth, classification analysis is carried out in classifying tissue as normal or abnormal, and comparing the results with the medical expert.

Recording of Endoscopic Video For image acquisition, we used the medical telescope provided by Wolf (The company Richard 952

WOLF GmbH) (2,8 mm diameter and 30 degrees viewing angle). Endoscopy video was captured using the Circon IP4.1 RGB video camera (The ACMI Corporation). All videos were captured at clinically optimum illumination and focusing. The camera was white balanced using a white surface (white color of the palette) as suggested by the manufacturer. The light source was a 300 Watt Xenon Light Source from ACMI Corporation (The ACMI Corporation). The analog output signal from the camera (PAL 475 horizontal lines) was digitized at 720x576 pixels using 24 bits color and 25 frames per second at two resolutions: (i) approximately 15 pixels/mm for the panoramic view and at (ii) approximately 21 pixels/mm for the close up view. The video was saved in AVI format. Digitization was carried out using the Digital Video Creator 120 frame grabber (The Pinnacle Systems company) that was connected to the PC through the IEEE 1394 port. The capturing conditions were controlled by the physician reflecting the clinical conditions of an operation.

Quantitative Analysis of Hysteroscopy Imaging in Gynaecological Cancer

Recording of Testing Targets

Rout

The testing targets were obtained from the Edmund Industrial Optics Company (The Edmund Optics company). The general purpose of a test pattern is to correct for variations in the camera sensors. The target contained 24 color squares. Testing images were captured at optimum illumination and focusing based on the experience of the physician, using the camera and the telescope under investigation. Following the above procedure we captured and saved the medical video (AVI format) of the testing palette and then extracted TIFF images of the 24 color squares. The corresponding targets were digitally generated based on the data given by the Edmund Optics Company (The Edmund Optics company) as the ground truth of the experiment.

Color Correction Algorithm Most of the cameras have a nonlinear relationship between the signal voltage and the light intensity (Haeghen et al, 2000; Jung et al, 2005, Grossberg et al, 2004). We assume that the recorded image intensity is a function of a simple linear model prior to separable non-linear gamma distortion (see general model reported in Fig. 1 of (Grossberg et al, 2004)). We write: R  a      p   11 a12 a13  Rin  k1      G  = a  p   21 a22 a23  Gin  + k2         Bp  a 31 a 32 a 33  Bin  k3 

(1)

where [Rin Gin Bin]T denotes the red (Rin), green (Gin), and blue (Bin) components of the target image intensity and [Rp Gp Bp]T denotes the colors of the acquired image. We let A denote the transformation matrix and k denote the constant offset vector. We then have a gamma model for the non-linear gamma relationship to the recorded image (components Rout Gout Bout)

Gout Bout

 R γR  = 255  p   255  γ  G  G  p  = 255    255  γ  B  B  p  = 255   .  255 

(2)

To compute all the parameters of the model, we use non-linear least squares (see lsqnonlin function in MATLAB (The MathWorks company for software)) by solving Equations (1) and (2) for known target images. We estimate matrices A, k and the gamma values, γR, γG, γB for each color component. To recover the original, target image color components, we invert the color transformations given in Equations (1) and (2).

Capturing Video from Experimental Tissue in Panoramic vs. Close Up Views A total of 40 images (20 panoramic, and 20 close up) were captured from experimental tissue from two calf endometria at distances of 3 and 5 cm for panoramic and close up views respectively (see Figures. 2a and 2b). A similar experiment was repeated using tissue from a chicken cavity. A total of 200 images (100 panoramic, and 100 close up) were captured from 10 chickens under the same viewing conditions as above.

Capturing Video from Tissue at Two Different Consecutive Angle Views Similar to the previous experiment, a total of 40 images (20 at angle 1 and 20 at angle 2, with 3 degrees of difference) were captured from two calf endometria (see Figures. 2c and 2d). The same experiments were carried out for the chicken cav-

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Quantitative Analysis of Hysteroscopy Imaging in Gynaecological Cancer

Figure 2. ROIs from the calf endometrium under different viewing conditions: (a) panoramic, (b) close up, (c) angle 1 and (d) angle 2.

ity where a total of 200 images from 10 chicken cavities were captured at two different angles.

V S

Capturing Video from the Endometrium H

The physician guides the telescope connected to a camera inside the uterus in order to investigate suspicious lesions of cancer. First, he/she investigates the anatomy of the organ and second, in panoramic view, he/she searches for suspicious areas. When a suspicious area is identified the physician switches to close up mode. This procedure is considered to be the standard procedure for identifying ROIs. A total of 40 videos were recorded from 40 subjects from the endometrium. From these videos, 418 ROIs of 64x64 pixels were cropped and classified into two categories: (i) normal (N=209) and (ii) abnormal (N=209) ROIs based on the opinion of the physician and the histopathological examination.

RGB Transformation to Gray Scale, HSV, YCrCb Systems All ROIs of 64x64 pixels were extracted from the tissue videos by the physician, for the purpose of clinical endoscopy imaging. The RGB images were transformed to gray scale using Y = (0.299R + 0.587G + 0.114B) (3) where Y is the intensity image. For transforming to HSV, we used:

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= max (R,G, B ), max(R,G, B ) − min(R,G, B ) , = max(R,G, B )   G −B  3− , for R ≥ G, B   max(R, G, B ) − min(R, G, B )    B −R , for G ≥ R, B =  1 −  max(R,G, B ) − min(R,G, B )    R −G  , for B ≥ R,G . 5 − max( R , G , B ) − min(R,G, B ) 



(4)

For transforming to Y, Cr, Cb, we used:    0.114  R   Y  0.299 0.587 Cr  = 0.596 −0.275 −0.321 G            0 . 212 − 0 . 523 0 . 311 Cb    B 

(5)

Feature Extraction Texture features were extracted from the segmented ROI images in order to characterize tissue captured under different viewing conditions, as well as to differentiate between normal and abnormal tissue. A total number of 26 texture features were extracted from endoscopic images (described next). These feature sets were also successfully used in numerous previous works in texture analysis (Petrou et al, 2006). Some of the features used capture complementary textural properties, however, features that were highly dependent or similar with features in other feature sets, were identified through statistical analysis and eliminated. The ROI color images were trans-

Quantitative Analysis of Hysteroscopy Imaging in Gynaecological Cancer

formed into gray scale images and the following texture features were computed: Statistical Features (SF): SF features describe the gray level histogram distribution without considering spatial dependence (Wu et al, 1992). Spatial Gray Level Dependence Matrices (SGLDM): The spatial gray level dependence matrices as proposed by (Haralick et al, 1973) are based on the estimation of the second-order joint conditional probability density functions that two pixels (k, l) and (m, n) with distance d in direction specified by the angle θ, have intensities of gray level (i) and gray level (j). Based on the estimated probability density functions, texture measures were extracted, proposed by Haralick et al. (Haralick et al, 1973). For a selected distance d (in this work d=1 was used), and for angles θ = 0o, 45o, 90o and 135o we computed four values for each of the texture measures. The features were calculated for displacements δ=(0,1), (1,1), (1,0), (1,-1), where δ=(Δx, Δy), and their range of values were computed. Gray level difference statistics (GLDS): The GLDS algorithm (Wenska et al, 1976) is based on the assumption that useful texture information can be extracted using first order statistics of an image. The algorithm is based on the estimation of the probability density pδ of image pixel pairs at a given distance δ=(Δx, Δy), having a certain absolute gray level difference value. For any given displacement δ=(Δx, Δy), let fδ (x , y ) = f (x , y ) − f (x + ∆x , y + ∆y ) . Let pδ be the probability density of fδ(x,y). If there are m gray levels, this has the form of an m-dimensional vector whose ith component is the probability that fδ(x,y) will have value (i). If the picture f is discrete, it is easy to compute pδ by counting the number of times each value of fδ(x,y) occurs, where Δx and Δy are integers. Coarse texture images result in low gray level difference values, whereas, fine texture images result in inter-pixel gray level differences with great variances. Features were estimated for the following distances:

δ=(d,0), (d,d), (-d,d), (0,d). A good way to analyze texture coarseness is to compute, for various magnitudes of δ, some measure of the spread of values in pδ away from the origin.

Statistical Analysis The Wilcoxon rank sum test was applied (Shapiro et al, 1965) to investigate if the texture features exhibited significant statistical difference for different viewing conditions and between texture features extracted before and after gamma correction at a≤0.05. The Wilcoxon test returns a p-value, which represents the probability of observing the given data by chance if the medians are equal. Small values of p imply that the null hypothesis should be rejected (Gibbons, 1965).

Classification Algorithms The diagnostic performance of the texture features was evaluated with two different classifiers: the Probabilistic Neural Network (PNN), and the Support Vector Machine (SVM). These classifiers were trained to classify the texture features into two classes: i) normal ROIs or ii) abnormal ROIs. The PNN (Specht, 1990) classifier basically is a kind of Radial Basis Function (RBF) network suitable for classification problems. This classifier was investigated for several spread radius in order to identify the best for the current problem. The SVM network was investigated using Gaussian Radial Basis Function (RBF) kernels; this was decided as the rest of the kernel functions could not achieve so good results. The SVM with RBF kernel was investigated using 10-fold cross validation in order to identify the best parameters such as spread of the RBF kernels (Joachims, 1999). The leave-one-out method was used for validating all the classification models. A total of 418 runs were carried out for training the classifiers, and the performance of the classifiers was evaluated on the remaining one subset (Ebrchart et al, 1990).

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The performance of the classifier systems were measured using the parameters of the receiver operating characteristic (ROC) curves: true positives (TP), false positives (FP), false negatives (FN), true negatives (TN), sensitivity (SE), specificity (SP), and precision (PR). We also computed the percentage of correct classifications ratio (%CC) based on the correctly and incorrectly classified cases.

RESULTS Color Correction Algorithm The Circon IP4.1 endoscopy camera was used for capturing video from both the testing targets and tissues. In these experiments, the color correction algorithm was run using the recorded test targets and the ground truth images as supplied by Edmund Optics Company. The computed

color correction parameters were then used for correcting the images. Table 1 tabulates the A, k and γ values of the R, G, B channels for three different experiments as well as their median values. It is clearly shown that a variability exists between the A, k, and γ values for these experiments. The variability documented in Table 1 motivated us to investigate it further. A database of 209 normal and 209 abnormal ROIs of the endometrium recorded from 40 women was analysed. Images were corrected, using different combinations of the A, k, and γ values and their corresponding texture features were computed. Neural network models were trained to classify 100 normal and 100 abnormal endometrium images. The rest of the cases were used for evaluating the performance of the models. The percentage of correct classifications score was computed for the evaluation set. It was found that the texture features computed with the median values of A, k and γ for the three experiments

Table 1. Gamma correction parameters A, k and γ for three different experiments and their median values. (Copyright BioMedical Engineering OnLine, 2007, (Neofytou et al, 2007)) A matrix

No Correction

Exp 1

Exp 2

Exp 3

Median values for Exps 1, 2, 3

a11

1

0.827

0.927

0.975

0.927

a12

0

0.065

0.011

0.105

0.065

a13

0

0.042

0.004

0.104

0.042

a21

0

0.065

0.011

0.105

0.065

a22

1

0.780

0.935

0.895

0.895

a23

0

0.071

0.062

0.134

0.071

a31

0

0.042

0.004

0.104

0.042

a32

0

0.044

0.032

0.023

0.032

a33

1

0.868

1.011

1.044

1.011

k matrix k11

0

7.693

1.101

-1.673

1.101

k21

0

10.083

2.090

0.528

2.090

k31

0

-8.161

1.598

-5.689

-5.689

γR

1

1.285

1.078

1.038

1.078

γG

1

1.220

1.046

0.999

1.046

γB

1

1.180

0.971

1.040

1.040

γ matrix

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Quantitative Analysis of Hysteroscopy Imaging in Gynaecological Cancer

gave the highest score. The results of these experiments are reported in detail in another study (Neofytou et al, 2007). It was thus decided to use, the median values of A, k and γ in this study as well. The median gamma values for the three channels (γR=1,078; γG=1,046; γB=1,040) were very close to unit values.

Capturing Video from Experimental Tissue in Close Up vs. Panoramic Views The results of the statistical analysis in the close up vs the panoramic view (using experimental tissues) indicates the variability due to the use of different viewing conditions. For this experiment, we use calf endometria, in an environment that is similar to actual operating conditions. Prior to gamma correction, we have found that there was no significance difference between features computed from the panoramic and close-up views (Neofytou et al, 2007). We also repeated these experiments using the chicken cavities, under the same viewing conditions and the same medical equipment. The results were very similar as for the calf endometria (Neofytou et al, 2004).

Capturing Video from Experimental Tissue in Two Different Consecutive Angle Views We now present statistical analysis results for texture feature values extracted from different angles. Here, we note that gamma correction did not seem to affect the results. There was no significant difference between the texture features values. It is clear that there are no significant differences between texture feature values from different angles, whether we apply gamma correction or not (Neofytou et al, 2004). As before, we also repeated these experiments using the chicken cavities, under the same viewing conditions and the same medical equipment, and the results were similar.

Analysis of Images of Human Endometria In this subsection we present results from the statistical analysis of ROIs extracted from human endometria. The results are summarized in Table 2. The non-parametric Wilcoxon rank sum test was used to decide if there is a significant difference between normal and abnormal ROIs at a≤0.05. The results indicate that there is a significant difference. Furthermore, as we can see in Table 2, after gamma correction, the entropy values are preserved. From the table, it is clear that the median SGLDM contrast for abnormal cases is dramatically larger than the corresponding median value for the normal ROIs.

Classification Results Table 3 presents the performance of the different PNN and SVM classification models investigated using the texture features. It is clearly shown that the SVM classifier performed better than the PNN classifier. For the SVM classifier, the best performance was achieved with the SF+SGLDS followed by the SGLDM+GLDS, and SF+SGLDS+GLDS with a percentage of correct classifications (%CC) of 79%, 76%, and 77%, respectively. Similar classification performance to the corrected ROIs was also obtained for all models for the uncorrected ROIs. Moreover, similar performance for all models as given in Table 3 was obtained when the feature sets were transformed using PCA without increasing the %CC.

DISCUSSION OF THE RESULTS For better and consistent classification performance, we limited our study to a distance of 3 cm for close up examinations and a distance of 5 cm for panoramic examinations. We also limited our camera angle variations to remain within 3 degrees. Furthermore, we recommend that the

957

958

1,37

1,18

Entropy

Mean

0,37

Homogeneity

0,17

13,02

Variance

2,54

0,85

Correlation

Energy

2,54

Contrast

Contrast

╯

0,37

2,66

Entropy

SGLDM

Homogeneity

0,02

Energy

4,47

1,94

Kurtosis

╯

-1,01

Skewness

GLDS

3,02

109,95

Mode

Entropy

0,03

110,18

Median

1,33

1,45

0,24

3,09

0,45

╯

4,93

0,45

28,83

0,93

3,1

╯

2,26

-0,46

135,75

138,83

29,44

13,23

Variance

138,44

P25%

110,11

P5%

Mean

SF

Normal ROIs

1,44

1,54

0,25

3,81

0,48

╯

5,31

0,48

53,97

0,96

3,82

╯

3,34

0,04

2,64

-0,14

156

156,44

54,63

156,06

P50%

1,63

1,64

0,27

4,86

0,5

╯

5,78

0,5

126,41

0,98

4,87

╯

3,68

0,06

3,09

0,12

175

174,42

127,94

173,91

P75%

2,45

2

0,3

12,24

0,53

╯

6,28

0,53

284,3

0,99

12,27

╯

4,09

0,09

4,39

0,56

201,05

203,53

286,63

204,36

P95%

1,19

1,37

0,14

2,55

0,31

╯

5,01

0,31

30,89

0,91

2,55

╯

3,11

0,02

1,82

-1,14

98

98,2

31,33

98,48

P5%

1,62

1,63

0,18

4,81

0,38

╯

5,49

0,38

65,53

0,95

4,82

╯

3,44

0,02

2,24

-0,47

124

127,92

66,9

129,37

P25%

1,89

1,77

0,21

7,03

0,42

╯

5,93

0,42

120,85

0,97

7,04

╯

3,74

0,03

2,62

-0,14

146,5

143,75

124,39

144,65

P50%

2,31

1,96

0,24

10,97

0,46

╯

6,28

0,46

221,38

0,98

10,99

╯

3,99

0,04

3,16

0,18

176

171,43

223,33

170,48

P75%

Abnormal ROIs

3,16

2,24

0,3

21,89

0,53

╯

6,65

0,53

488,55

0,99

21,94

╯

4,32

0,06

4,85

0,62

211,4

207,7

492,3

206,06

P95%

1

1

1

1

1

1

1

1

1

1

1

1

0

0

1

1

1

1

H

Normal vs Abnormal ROIs

1

1

1

1

1

1

1

1

1

1

1

1

0

0

1

1

1

1

H

Original vs Corrected Images For Normal ROIs

1

1

1

1

1

1

1

1

0

1

1

1

0

0

1

1

1

1

H

Original vs Corrected Images For Abnormal ROIs

Table 2. Percentile values of the texture features and statistical analysis for normal (N=209) vs abnormal (N=209) ROIs of the endometrium extracted from 40 subjects. Statistical analysis was carried out after gamma correction but also between the normal/abnormal ROIs before and after gamma correction at a≤0.05. (Copyright BioMedical Engineering OnLine, 2007, (Neofytou et al, 2007))

Quantitative Analysis of Hysteroscopy Imaging in Gynaecological Cancer

Quantitative Analysis of Hysteroscopy Imaging in Gynaecological Cancer

Table 3. Classification performance of the SVM (PNN) models for the classification of normal and abnormal ROIs of the endometrium based on texture features for the RGB, HSV and YCRCB systems. SVM (PNN) classifier

%CC

%FP

%FN

%SE

%SP

%PR

RGB SF

75 (66)

21 (18)

28 (49)

71 (50)

78 (81)

77 (72)

SGLDM

72 (67)

23 (25)

31 (40)

68 (59)

76 (74)

74 (70)

GLDS

69 (63)

3 (18)

27 (55)

72 (44)

66 (81)

68 (70)

SF+SGLDM

70 (67)

34 (24)

25 (40)

75 (59)

65 (75)

68 (71)

SF+GLDS

73 (68)

30 (14)

22 (49)

77 (50)

69 (85)

71 (77)

SGLDM+GLDS

74 (67)

22 (21)

29 (43)

70 (56)

77 (78)

76 (72)

SF+SGLDM+GLDS

73 (68)

22 (19)

31 (43)

68 (56)

77 (80)

75 (74)

SF

72 (70)

30 (22)

25 (37)

75 (62)

69 (77)

71 (73)

SGLDM

74 (70)

27 (21)

23 (37)

76 (62)

72 (78)

73 (74)

HSV

GLDS

69 (67)

24 (26)

37 (39)

62 (60)

75 (73)

72 (69)

SF+SGLDM

74 (70)

36(21)

15 (37)

84 (62)

63 (78)

69 (74)

SF+GLDS

76 (70)

20 (17)

26 (42)

73 (57)

79 (82)

77 (76)

SGLDM+GLDS

72 (71)

31 (20)

24 (37)

75 (62)

68 (79)

70 (75)

SF+SGLDM+GLDS

75 (69)

30 (21)

19 (39)

80 (60)

69 (78)

72 (73)

SF

73 (68)

21 (11)

31 (51)

68 (48)

78 (88)

76 (81)

SGLDM

74 (69)

28 (14)

23 (47)

76 (52)

71 (85)

72 (78)

YCrCb

GLDS

75 (68)

24 (13)

25 (50)

75 (49)

75 (86)

75 (78)

SF+SGLDM

76 (70)

25 (13)

22 (46)

77 (53)

75 (86)

75 (79)

SF+GLDS

79 (69)

25 (12)

16 (48)

83 (51)

74 (87)

76 (81)

SGLDM+GLDS

76 (69)

25 (15)

23 (46)

76 (53)

75 (84)

75 (77)

SF+SGLDM+GLDS

77 (70)

25 (15)

20 (44)

79 (55)

74 (84)

75 (78)

camera should be color corrected. When the proposed standardized protocol is followed, we show that there are no significant differences between texture features extracted from the same type of tissue (normal or abnormal), but under different viewing conditions. On the other hand, even for the same type of tissue, significant differences arise from large variations in the viewing conditions that do not conform to the protocol (as shown in (Neofytou et al, 2007)). More importantly, after applying the proposed protocol, a large number of texture features show significant differences between ROIs extracted

from normal versus abnormal tissues. Findings of this work were published in (Neofytou et al, 2005; Neofytou et al, 2004; Neofytou et al, 2004; Neofytou et al, 2007). To the best of our knowledge, although there are guidelines for performing the endoscopy examination, there are no guidelines for the quantitatively interpretation of the results (American Society for Gastrointestinal Endoscopy), (European Society for Gynaecological Endoscopy). Standardization efforts for reporting endoscopy examinations have been proposed (Yokoi et al, 2006).

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Recording of Endoscopic Video Recent efforts are focused on producing guidelines for gynaecological endoscopy such as gynaecological endoscopy and hysteroscopy (European Society for Gynaecological Endoscopy). These efforts will help the gynaecologist in standardizing the procedure for capturing endoscopic video and will enable the quantitative analysis of tissue pathology. Similar efforts exist in other endoscopic procedures such as gastrointestinal endoscopy and colonoscopy (American Society for Gastrointestinal Endoscopy). Quantitative analysis in these areas is still under investigation. In this study, a complete framework for capturing and analyzing gynaecological endoscopic video was proposed (Scarcanski et al, 2006).

Color Correction Algorithm Although the importance of the gamma color correction algorithm is widely recommended in the literature, it is rarely used (relevant exceptions are (Neofytou et al, 2007) and (Cohen et al, 2003). We recommend that the gamma color correction algorithm should be used routinely for endoscopic images. This will facilitate the standardised analysis of endoscopic images.

Image Analysis from Experimental Tissue for Different Viewing Conditions It is shown that there was no significant difference in the texture features for panoramic vs close up views and for small consecutive angles in normal, experimental tissue. Gray scale median, variance and entropy were higher in the close up view compared to the panoramic view, whereas contrast and homogeneity were essentially the same in both views. When comparing two consecutive angles, variance was higher in the smaller angle, whereas median, entropy, contrast and homogeneity were in the same range.

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In this study, the close up and panoramic view distances were 3 cm and 5 cm respectively. Another study was carried out by our group where the conditions similar to laparoscopy examination were investigated. In that study the close up and panoramic view distances were 4 cm and 7 cm respectively and similar results to this study were obtained (Neophytou et al, 2004). Similar results were also obtained for texture features obtained from different angles (with a difference of 2 degrees). However, when the distance between the close up vs panoramic views was higher than 6 cm, significant differences in some texture features were obtained. We have also found that some texture feature values exhibited significant differences when the angle differences were more than 5 degrees.

Human Images from the Endometrium We have found that a standardized protocol is necessary in order to eliminate any significant differences that may arise due to the lack of color correction. When the proposed standardized protocol is applied, significant differences in texture features are only due to the desired difference between normal versus abnormal tissue. The standardized protocol is essential for subsequent use of texture features in a CAD system in gynaecological cancer. The protocol is also expected to contribute to increased accuracy in difficult cases of gynaecological cancer. We hope that the proposed standardized protocol will serve as a starting point for allowing comparisons between different medical centers and images acquired using different medical equipment. Table 4 tabulates the texture characteristics of normal vs abnormal ROIs as these were obtained by interpretation of the texture features values given in Table 2.

Quantitative Analysis of Hysteroscopy Imaging in Gynaecological Cancer

Table 4. Texture characteristics of normal vs abnormal ROIs of the endometrium as these were obtained by interpretation of the texture features values given in Table 2. Normal

Abnormal

Gray level

High

Slighthly darker

Variance

Low

Very High

Contrast

Low

High

Homogeneity

Normal range

Slighthly lower

Entropy

Normal range

Slighthly higher

Classification Performance There was a significant difference in the SF, SGLDM, and GLDS features investigated between the normal and abnormal ROIs as documented in previous sections. Based on the CAD system developed, the highest percentage of correct classifications score was 79% and was achieved for the SVM classifier for the SF+GLDS feature sets. These results support the application of texture analysis for the assessment of difficult cases of normal and abnormal ROIs for gynaecological cancer. The proposed CAD system can be proved to be a useful tool for the gynaecologist during the operation so as to identify suspicious ROIs that should be investigated with histopathological examination.

FUTURE TRENDS Future work will focus on investigating the usefulness of the proposed methodology in other gynaecological organs, i.e. cervix, and ovary. Moreover, the standardized protocol will be used for investigating if there is variability in the computed texture feature sets between different clinics, as well as in collecting and analyzing more cases. The protocol could also be applied for the provision of telediagnosis or teleconsultation services to

rural health centers by experienced clinics during a regular laparoscopy/hysteroscopy examination. More work is needed towards automating the pre-processing component of the protocol, and more specifically in the automated segmentation of normal and abnormal tissue, not only in freezed laparoscopy/hysteroscopy images, but also in video. It is noted that this is a very difficult task. Multi scale texture analysis, and multi classifier classification should also further be investigated towards differentiating between normal and abnormal tissue. will be explored in the cases of endometrium and ovary cancer increasing the physician diagnosis in difficult cases of gynaecological cancer. It is hoped that the above mentioned technologies, will help the physician in detecting the disease at its onset, thus offering a better service to the citizen, via the CAD standardized and quantitative analysis. These will facilitate increased prevention, and better monitoring and management of the patient.

CONCLUSION The use of a standardised protocol for capturing and analyzing endoscopic video will facilitate the wide spread use of quantitative analysis as well as the use of CAD systems in gynaecological endoscopy. The proposed standardized protocol suggests the use of color correction and the use of specific viewing conditions so that there will be no significant differences in texture feature values extracted from the same type of tissue (normal or abnormal). On the other hand, when either color correction is not applied or the standardized viewing conditions are not used, significant differences in texture features can arise, even when they come from the same type of tissue. This implies that the proposed standardized protocol cannot be further simplified by reducing any of its requirements. When the proposed protocol is applied, we have found that several texture features can be used to

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discriminate between normal and abnormal tissue since they exhibit significant differences for the two types of tissue investigated. Furthermore, a CAD system was developed based on texture features and classification models for classifying between normal and abnormal endometria with very satisfactory and promising results. Future work will focus on investigating the usefulness of the proposed methodology in other gynaecological organs and clinics, as well as in comparing the findings between the different clinics. Finally, we hope that the proposed system can also be applied to other endoscopic modalities such as colonoscopy and gastroscopy.

ACKNOWLEDGMENT This study is funded through the Research Promotion Foundation, Cyprus, PENEK 2005, Program for the Financial Support of New Researchers, through the project entitled: Intra-operative Computer Assisted Tissue Image Analysis (CATIA), April 2006 - April 2008. Furthermore this study is partially funded by the program for High Tech Companies of the Ministry of Commerce, Industry and Tourism, Cyprus, through the project Computer Aided Diagnosis system in Gynaecological Endoscopy (CAD_Gen), July 2008 - July 2010.

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Christodoulou, C. I., Pattichis, C. S., Pantziaris, M., Tegos, T., Nicolaides, A., Elatrozy, T., et al. (1999). Multi-feature texture analysis for the classification of carotid plaques. Presented at the Int. Joint Conf. Neural Networks IJCNN ‘99, Washington DC. Cohen, R. V., Schiavon, C. A., Pinheiro, F. J. C., & Correa, J. L. (2003). Laparoscopic Bariatric Surgery: New Technologies, Trends and Perspectives. Revista do Hospital das Clinicas, 58(5), I–VIII. Ebrchart, R. C, & Dobbins, R. W. (1990). Neural Networks PC Tools A Practical Guide. (Academic Pr., 1990). European Society for Gynaecological Endoscopy [http://www.esge.org/] Fayez, J. A., & Vogel, M. F. (1991). Comparison of Different Treatment Methods of Endometriomas by Laparoscopy. Obstetrics and Gynecology, 78, 660–665. Gibbons, J. D. (1985). Nonparametric Statistical Inference. 2nd Ed., M. Dekker. Grossberg, M. D., & Nayar, S. K. (2004). Modelling the Space of Camera Response Functions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(10). doi:10.1109/ TPAMI.2004.88 Haeghen, V. Y., Naeyaert, J., Lemahieu, I., & Philips, W. (2000). An Imaging System with Calibrated Color Image Acquisition for Use in Dermatology. IEEE Transactions on Medical Imaging, 19(7). doi:10.1109/42.875195 Haralick R.M., Shanmugam K., & Dinstein I. (1973). Texture Features for Image Classification. IEEE Trans. Nov., SMC-3, 610-621. Ilgner, J., Palm, C., Schutz, A., Spitzer, K., Westhofen, M., & Lehmann, T. (2003). Colour Texture Analysis for Quantitative Laryngoscopy. Acta Oto-Laryngologica, 123, 730–734. doi:10.1080/00016480310000412

Quantitative Analysis of Hysteroscopy Imaging in Gynaecological Cancer

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Neophytou, M. S., Pattichis, C. S., Pattichis, M. S., Tanos, V., Kyriacou, E., & Koutsouris, D. (2006). Texture-Based Classification of Hysteroscopy Images of the Endometrium. 28th Annual International conference of the IEEE Engineering in Medicine and Biology Society, 30-3 September, New York, USA, (pp. 3005-3008). Neophytou, M. S., Pattichis, C. S., Pattichis, M. S., Tanos, V., Kyriacou, E., Pavlopoulos, S., & Koutsouris, D. (2004). Texture Analysis of the Endometrium During Hysteroscopy: Preliminary Results. 26th Annual International conference of the IEEE Engineering in Medicine and Biology Society, 2, 1483-1486. 1-5 September, San Francisco, California, USA. Petrou, M., & Sevilla, G. P. (2006). Image Processing, Dealing with Texture. John Wiley and Sons. Plataniotis, K. N., & Venetsanopoulos, A. N. (2000). Color Image Processing and Applications. Springer Verlag. Berlin. Scarcanski, J., Gavião, W., Cunha, S., & João, F. (2005). Diagnostic Hysteroscopy Video Summarization and Browsing. 27th Annual International conference of the IEEE engineering in Medicine and Biology Society, Shanghai, China, (pp. 56805683). Shapiro, S. S., & Wilk, M. B. (1965). An Analysis of Variance Test for Normality (complete samples). Biometrika, 52, 3, & 4, 591-611. Sheraizin, S., & Sheraizin, V. (2005). Endoscopy Imaging Intelligent Contrast Improvement. 27th Annual International conference of the IEEE engineering in Medicine and Biology Society, 1-4 September, Shanghai, China, (pp. 6551-6554). Shi, (2006). CT Colonography: Influence of 3D Viewing & polyp Candidate Features on Interpretation with Computer-Aided Detection. Radiology, 239, 768–776. doi:10.1148/radiol.2393050418

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Wenzl, R., Lehner, R., Vry, U., Pateisky, N., Sevelda, P., & Husslein, P. (1994). Three-Dimensional Video-endoscopy: Clinical use in Gynaecological Laparoscopy. Lancet, 344, 1621–1622. doi:10.1016/S0140-6736(94)90412-X Wu, C. M., Chen, Y. C., & Hsieh, K. S. (1992). Texture Features for Classification of Ultrasonic liver Images. IEEE Transactions on Medical Imaging, 11, 141–152. doi:10.1109/42.141636 Yokoi, H., & Fujino, M. A. (2006). Activities for Endoscopy Information Systems Standardization in Japan. 28th Annual International conference of the IEEE engineering in Medicine and Biology Society, New York, USA, (pp. 5667-5670).

KEY TERMS AND DEFINITIONS Automatic Classifiers: Mathematical functions that can classify events based on several features and previously known cases. Computer Aided Diagnosis: Diagnosis supported by computer methods, usually by using automatic classifiers in order to get an estimation on the exact diagnosis. Hysteroscopy Examination: The physician guides the telescope connected to a camera inside the endometrium in order to investigate the cavity.

This work was previously published in Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications, edited by Themis P. Exarchos, Athanasios Papadopoulos and Dimitrios I. Fotiadis, pp. 181-196, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 3.22

Myoelectric Control of Prosthetic Devices for Rehabilitation Rami N. Khushaba University of Technology, Australia Adel A. Al-Jumaily University of Technology, Australia

INTRODUCTION Bio-signals patterns analysis problems have enjoyed a rapid increase in popularity in the past few years. The electromyography (EMG) signal, also referred to as the Myoelectric signal (MES), recorded at the surface of the skin, is one of the biosignals generated by the human body, representing a collection of electrical signals from the muscle fibre, acting as a physical variable of interest since it first appeared in the 1940s (Scott, 1984). It was considered to be the main focus of scientists, and was advanced as a natural approach DOI: 10.4018/978-1-60960-561-2.ch322

for the control of prosthesis, since it is utilising the electrical action potential of the residual limb’s muscles remaining in the amputee’s stump (which still has normal innervations, and thus is subject to voluntary control) as a control signal to the prosthesis—in other words, it allows amputees to use the same mental process to control their prosthesis as they had used in controlling their physiological parts; however, the technology in that time was not adequate to make clinical application viable. With the development of semiconductor devices technology, and the associated decrease in device size and power requirements, the clinical applications saw promise, and research and development increased dramatically.

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Myoelectric Control of Prosthetic Devices for Rehabilitation

This control approach referred to as myoelectric control has found widespread use for individuals with amputations or congenitally deficient limbs. The methodologies used in this approach of control spanned the range from classical multivariate statistical and syntactic methods, to the newer artificial intelligence (symbolic and connectionist) approaches to pattern processing. In these systems, voluntarily controlled parameters of myoelectric signals from a muscle or muscle group are used to select and modulate a function of a multifunction prosthesis. The essential elements of a myoelectric control of prosthesis devices are shown in the block diagram schematic of Figure 1 (Merletti & Parker, 2004). The Myoelectric control system is based on the noninvasive interfaces designed for casual wear. In the following sections, the details and related works to the research problem are given; after that, the methodology adopted is explained and discussed, followed by future work, and finalised with conclusion.

BACKGROUND AND RELATED WORK Continuous myoelectric-controlled devices are one of the challenging research issues, in which the Figure 1. Normal and myoelectric control system

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prosthetic is controlled in a manner proportional to the level of myoelectric activity. Although the success of fitting these systems for single device control is apparent, the extension to control more than one device has been difficult (Hudgins, Parker, & Scott, 1993), but, unfortunately, it is required for those with high-level (above the elbow) limb deficiencies, and the individuals who could stand to benefit from a functional replacement of their limbs (Englehart, Hudgin, & Parker, 2001). It has been proved that the MES signal exhibits a deterministic structure during the initial phase of muscle contraction, as shown in Figure 2 for four types of muscle contractions measured using one surface electrode. Based on this principle, a continuous myoelectric control strategy based on the use of pattern classifiers was the main focus during the last years for most scientists in related fields. The MES patterns exhibit distinct differences in their temporal waveforms. Within a set of patterns derived from the same contraction, the structure that characterizes the patterns is sufficiently consistent to maintain a visual distinction between different types of contraction. Hudgins et al. (1993), and Lighty, Chappelly, Hudgins, and Englehart (2002) aligned the patterns using a cross-correlation technique, and showed that the ensemble average of patterns within a class pre-

Myoelectric Control of Prosthetic Devices for Rehabilitation

Figure 2. Patterns of transient MES activity recorded using a single bipolar electrode pair, placed over the biceps and triceps

serves this structure. The myoelectrical signal is essentially a one-dimensional pattern, and the methods and algorithms developed for pattern recognition can be applied to its analysis. The myoelectric control, system-based pattern classifier consists of four broad system components, which are (Ciaccio, Dunn, & Akay, 1993; Lusted & Knapp, 1996): •

• • •

Myoelectric signal acquisition using surface or implanted electrodes, mostly surface electrodes. Signal conditioning and features extraction. Pattern recognition algorithms to classify the signal into one of multiple classes. Mapping of classified patterns to interface actions that control external devices.

A general look onto the myoelectric control system components reveal that it operates at a few stages of machine pattern recognition or interpretation for biosignals that were proposed in Ciaccio et al. (1993), and Lusted and Knapp (1996). Also, specific features are extracted usually because the motivation is toward the evaluation of myoelectric signal features in ways which are not tied to accurate estimates of signal charac-

teristics, but rather to the intrinsic quality of the features as control signals for a desired device, which have been usually a prosthetic hand robot for the purpose of rehabilitation. The features extraction is considered as the most important part, because the success of any pattern classification system depends almost entirely on the choice of features used to represent the continuous time waveforms (Hudgins et al., 1993). Although the literature includes many papers which explore the extraction of features from the MES for controlling prosthetic limbs, there have been few works which make quantitative comparison of their quality. Overall, a high-quality MES feature space should have the following properties (Boostani & Moradi, 2003): •

•

•

Maximum class separability: A highquality feature space is that which results in clusters that have maximum class separability or minimum overlap. This ensures that the resulting misclassification rate will be as low as possible. Robustness: The selected feature space should preserve the cluster separability in a noisy environment as much as possible. Complexity: The computational complexity of the features should be kept low, so that the related procedure can be implemented with reasonable hardware, and in a real-time manner

Raw MES offers us valuable information in a particularly useless form. This information is useful only if it can be quantified. Various signalprocessing methods are applied on raw EMG to achieve the accurate and actual MES. Most of the researches on myoelectric control focused on the extraction of features that will best serve the purpose of controlling the prosthetic arm; the first attempts were made since the 1980s, when Doerschuk (Doerschuk, Gustafson, & Willsky, 1983) and Graupe (Graupe, Salahi, & Zhang,

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1985) (that were both based on Graupe and Cline (1975)), used the parameters of some stochastic models, such as an autoregressive (AR) model, or auto regressive moving average (ARMA) model as features set for the myoelectric control system. Auto-regressive parameters (AR) were used as features for the MES signal (Karlik, Pastaci, & Korurek, 1994; Kocyigit, Karlik, & Korurek, 1996), as AR parameters can accurately estimate the power spectrum of the signal. It is worthwhile to observe some important advantages of modelling the signal in this way (Lamounier, Soares, Andrade, & Carrijo, 2002): •

•

Variations in the positioning of the electrodes on the surface of the muscle do not severely affect the AR-coefficients. The amount of information to be presented to the classifier is greatly reduced. Therefore, the total processing time is also reduced.

Recently, all attempts to extract features from the MES can be classified, in general, into two categories (Vuskovic, Pozos, & Pozos, 1995), although there are still many using the AR model for features extraction, and those two categories are: •

•

Temporal approach: Identifies the attributes of the raw MES signal that characterize its temporal structure relative to a specific muscular function. Spectral approach: Uses information contained in frequency domain, which leads to a better solution for encoding the MES signal.

It was shown through literature that the spectral approach results were superior to that of the temporal approach, and that is why we adopted a spectral technique for features extraction.

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METHODOLOGY Due to the nonstationary behaviour of biosignals, the use of time frequency representations is highly desirable with the view to deriving meaningful features. These representations provide the means to regard the phenomenon simultaneously under two different points of view. The frequency characteristics, as well as the temporal behaviour, can be described with respect to the uncertainty principle. With multiresolution analysis, fast wavelet transform (FWT) leads to dyadic pyramidal implementation using filter banks and the corresponding Mallat algorithm (Mallat, 1989). FWT develops the two channel filter banks, through which the signal is split into two subspaces, and, which are orthonormally complementary to each other, with being the space that includes the low-frequency information about the original signal, and includes the high-frequency information. We keep repeating the decomposition of the low frequency subspace. Compared to FWT, the wavelet packet (WP) method is a generalization of wavelet decomposition that offers a richer range of possibilities for signal analysis. The wavelet packet transform (WPT) not only decomposes the approximation coefficients, but also the details coefficients as shown in Figure 3, in which the level of decomposition is to scale Index 3. The WPT provides a selective subbanding, which is the best for a given part of signals selected, such as in the case of the MES in which the most signal energy is positioned in between 35Hz–250Hz (Chan, Yang, Lam, Zhang, & Parker, 2000; Hudgins et al., 1993). The selective banding in WPT is determined by the order of low-pass and high-pass filtering from a tree-like structure using these filters (5). Our proposed approach uses wavelet packet transform (WPT) for features extraction, with four levels of decomposition of the same database that is used by (Sijiang & Vuskovic, 2004) from the San Diego State University (focusing on the synergetic control of groups of finger joints that

Myoelectric Control of Prosthetic Devices for Rehabilitation

Figure 3. Tree-like structure of digital signal processing by

correspond to the basic prehensile motion), in which the MES signals of the four channels are sampled and recorded as the raw signal data. There are six categories of grasp types: small cylinder (SC), large cylinder (LC), small ball (SB), large ball (LB), small disk (SD), and a key (SK) as shown in Figure 4. Each of the six categories has 30 recorded grasp instances, respectively. There are 180 total recorded grasp instances, and the recorded data are used for feature extraction. Each instance generates a feature vector as the result of feature extraction by the WPT method. Four

types of grasps are considered in this database, those are: cylindrical grasp, precision grasp, lateral key grasp, and spherical grasp, as shown in Figure 4. The input feature vectors are computed in two simple steps—first, each record of the database is decomposed using WPT with the fourth level (using two wavelets families: Daubechies, and Symmlet); second, features are extracted by simply determining every component of the feature vector (f1,f2,…,fN) using the Euclidean norm. Assuming the number of nodes in the fourth level is simply, and that each node contains ele-

Figure 4. Four grasp types

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ments, representing a row vector rj for j=1,2,…,N the Euclidean norm is given by:   f := r   j

j

M

2

=

∑v i =1

2 i

   

(1)

This means that each feature fi is determined as the square root of the energy of the wavelet coefficients in the corresponding node, representing time and frequency information of the specific signal. A single feature especially describes a certain frequency range, which is equal to that described by wavelet coefficients underlying this feature. The method of computing the energy of wavelet coefficients was introduced in Pittner and Kamarthi (1999), but they used the FWT instead of WPT. Because WPT possesses the useful property of identifying distinguishing characteristics from the original signals, we map the original signal into many WPT feature space. The number of valid decompositions increases exponentially with the number of decomposition level, so a problem found here is to compute the optimal decomposition. In our experiment, we found that the fourth level was very appropriate to deal with such a problem, as further decomposition is not necessary for our work. The number of features selected at the fourth decomposition level was also a matter of study for us, were we started with 12 features, and gradually increased the number of features extracted per channel (till 16 feature/channel), reaching to optimal results. The other part of our system was the classifier—to distinguish the six classes we have of movements in the database, we simply choose the Linear Discriminant AnalyFigure 5. Block diagram of the proposed system

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sis (LDA), and achieved a high accuracy from the proposed system. The block diagram of the proposed system for classifying six categories of movements is shown in Figure 5, simply containing the extraction of features from the energy of WPT and followed by the LDA classifier. The proposed system is different from that proposed in Englehart et al. (2001) by Englehart and his colleagues, where they used records of data each with 256 sample, and later extracted features by WPT, due to the size of their feature vectors they used Principle Component Analysis to reduce the feature set dimension, and followed that was an LDA part for classification, achieving an average error rate of 0.5% for four classes of motion, and 2% for six classes problem. In our case, we used the records from SDSU, each containing 400 samples, and extracted a number of features from the Euclidean norm of wavelet coefficients, achieving 64 features for the whole four channels, thus not requiring PCA, and conveyed those to an LDA classifier, and the results were very successful, as shown in Table 1, consisting of the number of features per channel, the error rate, and the wavelet families that we used in our experiments. It is obvious from the table of experimental results that the case of 16 features per channel for Symmlet family of WPT, and Daubechies of WPT gave the best results; also, increasing the decomposition level to five gave 100% accuracy for the both families of wavelet. Our design was used to classify six classes of motion, achieving an error rate of 0.56% using Symmlet family, whereas in Englehart et al. (2001), the six classes problem reached only 2% of error rate.

Myoelectric Control of Prosthetic Devices for Rehabilitation

Table 1. Practical results from our experiment Features/Channel

Error %

Wavelet

16

0.56%

Symmlet

14

0.56%

Symmlet

12

1.11%

Symmlet

16

0.52%

db4

14

1.67%

db4

12

2.78%

db4

FUTURE TRENDS The fields of human-computer interaction and robotics emphasise the necessity of humanizing machine interaction, thus calling for more intuitive interfaces. The biocontrol systems technology had long served to provide the support required for people with paralysis, resulting from many causes, by focusing on noninvasive interfaces (those designed for casual wear). The EMG signal was utilised mainly in two applications in the last few years: those wearing controlling prosthetics devices for rehabilitations of patients, and in speech recognition that emerged recently. In both of the applications, the EMG signals were used to build a successful natural controller with accurate results. Work in this field is a continuing one to achieve optimum results. Various methods have been utilised in literature in this field, but no one yet have used Type 2 fuzzy sets in myoelectric control; that is expected to achieve great results, as fuzzy set Type 2 is the extension to the fuzzy logic that has been used in this field, providing more accurate results than those based to statistical methods.

CONCLUSION In this article, a simple and very effective wavelet-based approach has been explained for the problem of MES classification for the purpose

of rehabilitation of patients after stroke. A simple method of extracting features from the wavelet coefficients was adopted and proven to be very effective in increasing the classification accuracy of the designed system. Although being simple, the accuracy of the classification system reached 99.44% for the case of six classes of motion, using the Symmlet family of the order five. The design described in this article represented a new promising approach for practical implementation of Myoelectric-controlled prosthetics, which we will be further enhancing for more applications, where we are currently investigating fuzzy approaches in feature selection as future work, and the application of artificial intelligence in pattern recognition rather than the LDA classifier.

ACKNOWLEDGMENT The authors would like to acknowledge the support of Dr. Marko Vuskovic from the San Diego State University for supplying the database.

REFERENCES Boostani, R., & Moradi, M. H. (2003). Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiological Measurement, 24, 309–319. doi:10.1088/0967-3334/24/2/307 Chan, F. H. Y., Yong-Sheng, Y., Lam, F. K., Zhang, Y. T., & Parker, P. A. (2000). Fuzzy EMG classification for prosthesis control. IEEE Transactions on Rehabilitation Engineering, 8(3), 305–311. doi:10.1109/86.867872 Ciaccio, E. J., Dunn, S. M., & Akay, M. (1993). Biosignal pattern recognition and interpretation systems. Engineering in Medicine and Biology Magazine, IEEE, 12(3), 89–95. doi:10.1109/51.232348

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Doerschuk, P. C., Gustafson, D. E., & Willsky, A. S. (1983). Upper extremity limb function discrimination using EMG signal analysis. IEEE Transactions on Bio-Medical Engineering, BME30(1), 18–29. doi:10.1109/TBME.1983.325162

Lighty, C. M., Chappelly, P. H., Hudgins, B., & Englehart, K. (2002). Intelligent multifunction myoelectric control of hand prostheses. Journal of Medical Engineering & Technology, 26(4), 139–146. doi:10.1080/03091900210142459

Englehart, K., Hudgin, B., & Parker, P. A. (2001). A Wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Transactions on Bio-Medical Engineering, 48, 302–311. doi:10.1109/10.914793

Lusted, H. S., & Knapp, R. B. (1996). Controlling computers with neural signals. Scientific American, 82–87.

Graupe, D., Salahi, J., & Zhang, D. (1985). Stochastic analysis of myoelectric temporal signatures for multifunction single-site activation of prostheses and orthoses. Journal of Biomedical Engineering, 7(1), 18–29. doi:10.1016/01415425(85)90004-4 Graupe, J., & Cline, K. (1975). Functional separation of EMG signal via ARMA identification methods for prosthesis control purposes. IEEE Transactions on Systems, Man, and Cybernetics. SMC, 5, 252–259. Hudgins, B., Parker, P., & Scott, R. N. (1993). A new strategy for multifunction myoelectric control. Biomedical Engineering. IEEE Transactions on, 40(1), 82–94. Karlik, B., Pastaci, H., & Korurek, M. (1994). Myoelectric neural networks signal analysis. In Proceedings of the 7th Mediterranean Electrotechnical Conference (Vol. 261, pp. 262–264). Kocyigit, Y., Karlik, B., & Korurek, M. (1996). EMG pattern discrimination for patient-response control of FES in paraplegics for walker supported using artifical neural network (ANN). Paper presented at the 8th Mediterranean Electrotechnical Conference, MELECON ‘96. Lamounier, E., Soares, A., Andrade, A., & Carrijo, R. (2002). A virtual prosthesis control based on neural networks for EMG pattern classification. 6th IASTED International Conference on Artifical Intelligence and Soft Computing (ASC) (pp. 425–430). Banff, Alberta, Canada. 972

Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The wavelet representation. Pattern Analysis and Machine Intelligence. IEEE Transactions on, 11(7), 674–693. Merletti, R., & Parker, P. (2004). Electromyography physiology, engineering, and noninvasive applications. IEEE Press Engineering in Medicine and Biology Society. Pittner, S., & Kamarthi, S. V. (1999). Feature extraction from wavelet coefficients for pattern recognition tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(1), 83–88. doi:10.1109/34.745739 Scott, R. N. (Ed.). (1984). An introduction to myoelectric prosthesis. The Bioengineering Institute, University of New Brunswick. Sijiang, D., & Vuskovic, M. (2004). Temporal vs. spectral approach to feature extraction from prehensile EMG signals. In Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration (pp. 344–350). Vuskovic, M. I., Pozos, A. L., & Pozos, R. (1995, October 22–25). Classification of grasp modes based on Electromyographic patterns of preshaping motions. Paper presented at the Proceedings of the Internet, Conference on Systems, Man and Cybernetics, Vancouver, B.C., Canada.

Myoelectric Control of Prosthetic Devices for Rehabilitation

KEY TERMS AND DEFINITIONS Biocontrol System: A mechanical system that is controlled by biological signals—for example, a prosthesis controlled by muscle activity. Electromyogram (EMG): These are electrical potentials arising from muscle movements. They originate from the motor cortex when the brain sends action potentials along appropriate nerve tracts. They then transmit to the muscle groups, resulting in contractions of the muscle fibers, and are accessible as bioelectric signals under direct volitional control. Grasp: To take hold of or seize firmly with, or as if with the hand. Myoelectric Control: A man-machine control scheme in which myoelectric signals are used as control signals. Noninvasive Interfaces: Those interfaces that acquire signals transcutaneously, using surface electrodes, which are preprocessed to reduce noise content.

Rehabilitation: The process of restoration of skills by a person who has had an illness or injury, so as to regain maximum self-sufficiency, and function in a normal—or as near normal—manner as possible. For example, rehabilitation after a stroke may help the patient walk again and speak clearly again. Wavelet Transform: In mathematics, wavelets, wavelet analysis, and the wavelet transform refers to the representation of a signal in terms of a finite length or fast-decaying, oscillating waveform (known as the mother wavelet). This waveform is scaled and translated to match the input signal. In formal terms, this representation is a wavelet series, which is the coordinate representation of a square integrable function with respect to a complete, orthonormal set of basis functions for the Hilbert space of square integrable functions.

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, pp. 965-971, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Med-on-@ix:

Real-Time Tele-Consultation in Emergency Medical Services – Promising or Unnecessary? In-Sik Na University Hospital Aachen, Germany

Stefan Beckers University Hospital Aachen, Germany,

Max Skorning University Hospital Aachen, Germany

Harold Fischermann University Hospital Aachen, Germany

Arnd T. May University Hospital Aachen, Germany

Nadja Frenzel University Hospital Aachen, Germany

Marie-Thérèse Schneiders RWTH Aachen University, Germany

Tadeusz Brodziak P3 Communications GmbH, Germany

Michael Protogerakis RWTH Aachen University, Germany

Rolf Rossaint University Hospital Aachen, Germany

ABSTRACT The aim of the project Med-on-@ix is to increase the quality of care for emergency patients by the operationalisation of rescue processes. The currently available technologies will be integrated into a new emergency telemedical service system. The aim is to capture all the necessary information DOI: 10.4018/978-1-60960-561-2.ch323

comprising electrocardiogram, vital signs, clinical findings, images and necessary personal data of a patient at the emergency scene and transmit this data in real time to a centre of competence. This would enable a “virtual presence” on site of an Emergency Medical Services physician (EMSphysician, the German Notarzt). Thus, we can raise the quality of EMS in total and counter the growing problem of EMS-physician shortage by exploiting the existing medical resources. In addition, this

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Med-on-@ix

system offers EMS-physicians and paramedics consultation from a centre of competence. Thereby referring to evidence-based medicine and ensuring the earliest possible information of the hospital.

INTRODUCTION At present, emergency calls are condensed to certain keywords, such as respiratory distress, and dispatched to both paramedics as well as the EMS-physician. Typically, the ambulance staffed with paramedics is the first on scene and the EMS-physician arrives a few minutes later. In rural areas the arrival period can exceed 30 minutes as there are more ambulance bases than EMS-physician bases. The distances from the bases to the scene are much shorter for ambulances than for the EMS-physician. As a consequence drug free interval vary significantly, depending on whether the emergency is taking place in an urban or rural area. Additionally, the lack of EMS-physicians in times of general shortage of physicians impedes the 24 hours occupation of EMS-physician bases in rural areas (Deutscher Bundestag, 2006) (Behrendt & Schmiedel, 2004). The Med-on-@ix-project aims at the broad implementation of a telemedical system in EMS enabling support up to a real-time teleconsultation. The basis of the system is a centre of competence with an experienced emergency medical physician (further called Telenotarzt in accordance to the German equivalent for EMS-physician, the Notarzt). Advanced mobile data transmission enables the real time transmission of all vital parameters of a patient and a video live stream from the ambulance and the scene to the competence centre, where the information is visualized on large screens. He uses specially designed software, including maps, and he has a documentation software as well as databases. The competence centre is responsible for the contact and data transfer to the hospital and the consultation with other institutions (for

example family physician, cardiology, poisoning centre, and so forth). Thus, the rescuers on scene can focus on their main task, which is the manual patient care. German paramedics are restricted in their clinical actions due to legal limitations, for example drug therapy at the emergency scene. Therefore, the advantage of this system is, that measures reserved normally only for physicians could be begun on the order of the Telenotarzt until the EMS physician arrives on site. Thus, the treatment free interval may be shortened. In cases where the EMS-physician is not present in the foreseeable future, the Telenotarzt could even replace the EMS physician in part. In most cases it is mainly the expertise and decision-making of the physician that is needed, rather than the practical skills (Gries, Helm & Martin, 2003). The main aim of this research is to improve the quality of medical care and patient safety while speeding up the whole process. Since 2004, this project has been created by the emergency medicine section at the Department of Anaesthesiology, University Hospital of Aachen. In 2006, the merger of the existing consortium of industry and research was completed. The project Med-on-@ix is funded by the Federal Ministry of Economics and Technology funding programme SimoBIT (secure use of mobile information technology to increase value creation in small business and government) The main partners in this consortium listed below: 1. Department of Anaesthesiology at the University HospitalAachen↜EMS-physicians are recruited from the Department of Anaesthesiology↜The department operates a simulation centre (AIXTRA) with a “FullScale Simulator” to practice various real emergency situations . 2. Department of Information Management in Mechanical Engineering of the RWTH Aachen University, Germany, (ZLW/ IMA)↜This department focuses on system

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integration, customer-driven technology development and design of human-technology interfaces.↜Within the Med-on-@ix project, the ZLW / IMA is responsible for the implementation of scenarios, the design of human-technology interfaces, as well as the conduct of simulation studies and their evaluation. 3. Philips Healthcare, Hamburg↜Philips develops and manufactures products for patient monitoring systems for the global market. In the Med-on-@ix project, Philips is involved with the Cardiac & Monitoring Section in Boeblingen and the marketing section in Hamburg. 4. P3 communications GmbH, Aachen (Telecommunications)↜P3 communications includes the telecommunication activities since 1999 of the P3 SOLUTIONS GmbH Consulting Company for Management & Organisation. P3 is now a Europe-wide company which has specialized in the measurement, analysis and optimisation of mobile networks and services. In our project, P3 is responsible for transmission techniques and hardware devices as well as software development.

BACKGROUND The trade-off between the best possible care and controllable costs is compounded by other problems in EMS. One of these problems is the Working Hours Act, which took effect in 2007, and is now being implemented for physicians. The act limits the working hours of physicians to twelve hours a day. Hereby, a greater number of EMS-physicians is needed in times of general shortage of physicians (Rieser, 2005). The consequences of the lack of EMS-physicians, especially in rural areas, are a deterioration of quality management and deficiencies in medical care. Additionally, the number of EMS-physician stations will be reduced, and the

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time of arrival of the emergency patient will be extended (Bundesärztekammer, 2004). However, arrival times have already become prolonged, as shown by a direct comparison of data from the survey periods of 2000/2001 and of 2004/2005, which is the most recent data available. In 95% of the emergency missions, service arrived within 16.3 minutes (previously 15.9 minutes). An EMSphysician arrived in 95% of cases, at the latest, after 22.3 minutes (Deutscher Bundestag, 2006). In the years 1994/1995, the mean arrival time was 18.6 minutes, in 1998/1999, it was 20.2 minutes and in 2000/2001 it increased to 21.9 minutes (Behrendt & Schmiedel, 2003). These numbers demonstrate the successive deterioration of arrival time. This leads to serious consequences, such as a prolonged drug-free interval and an overall delayed emergency medical care by the physician, especially in rural areas. In this context, a comparison with other European Rescue Systems in other countries (for example the UK and the Netherlands) is remarkable. In most other European countries the outof-hospital emergency medical care is exclusively maintained by well qualified paramedics and without physicians, which has not led to poorer results overall. However, there is evidence that a physician-led EMS is advantageous for invasive measures, such as respiratory protection (Jemmet, Kendal, Fourre & Burton, 2003) (Jones, Murphy & Dickson, 2004) (Katz & Falk, 2001) (Silvestri, Ralls & Krauss, 2005) (Timmermann, Russo, Eich & 2007). The health sector is one of the most important economic factors in Germany with expenditures of 224.94 billion Euros every year. Of this, 2.39 billion Euros are needed just for the short period of EMS care, which is more than 1%. In particular, the costs for EMS have increased by leaps and bounds in recent years (Statistisches Bundesamt, 2006). The EMS also faces other major challenges. The total quantity of emergency cases has increased from year to year. In Germany, 10.2 millions of rescue missions have been carried out per year

Med-on-@ix

according to the most recent survey data, and 4.7 millions of these have been categorised as emergency missions (Deutscher Bundestag, 2006). In parallel with the total number of ambulance calls, the number of emergency cases with involvement of EMS-physicians has also increased. In 1985, the proportion of EMS cases involving EMSphysicians was 33%. In Germany, in 2000, it was already 50%, which corresponded to 1.7 million emergency missions involving physicians (Gries et al., 2003). More recent nationwide data on the incidence of emergency medical interventions are not available, but a further increase is most likely. This increase also means that there are increases in failed emergency missions, which account for 8.9% of all emergency missions involving physicians (Gesundheitsberichterstattung des Bundes, 2007). This would also entail an increase in emergency missions not requiring EMS-physicians. Moreover, only a small proportion of emergency medical interventions actually require the manual skills of an EMS-physician on scene. For most provided care, only the expertise and decision-making authority of an EMS-physician is required. EMS-physicians are for example needed for intubation, resuscitation, anaesthesia induction and so forth. Gries et al. (2003) reported that the sum of incidences of individual medical procedures that required an experienced EMS-physician on site was only 14.3% of all assignments. The actual proportion would still be much lower if one considers that many of these measures were performed on the same patients. Messelken, Martin & Milewski (1998) state that the EMS-physicians themselves retrospectively assess that in only 51.5% of their missions, the patient is in a life-threatening (36.4%) or acute life-threatening (15.1%) situation. In at most 4.0% of these missions, resuscitation measures must be taken, but successful resuscitation as the primary goal represents only 1.7% of emergency missions (Messelken, Martin & Milewski, 1998).

Nationally, an emergency operation takes an average of 50.1 minutes to claim (Behrendt & Schmiedel, 2004), and during this time, the EMS-physician is attached to the patient and is not available for other missions. This is despite the fact that the EMS-physician’s actual therapeutic activity is often limited to a few minutes (for example during the subsequent transportation to the hospital), and in many cases, his primary liability is only to manage the situation. The duration of the emergency mission is correlated primarily to the transportation time to the nearest suitable hospital and thus is longer in rural areas.

MAIN FOCUSES OF THE CHAPTER The project Med-on-@ix consists of a multidisciplinary team of researchers that all have a distinctive perspective on telemedicine. Therefore, the project has a medical, technical, socio-technical as well as ethical focus. These different perspectives will be elaborated in the following chapter.

Issues, Controversies, Problems Medical Focus The Department of Anaesthesiology at the University Hospital Aachen is responsible for all medical aspects of the project Med-on-@ix and elaborates on all clinical issues that arise due to telemedicine in the German EMS. In international emergency medicine systems, telemedicine applications have been examined for many years and have partly been established. The telemedicine systems for prehospital emergency medicine have their seeds in the United States, where teleconsultation between the physician in the hospital and the paramedics on scene via radio has become standard. In this way, the quality of primary care can be improved and assessed more exactly. In addition, in-hospital procedures could be optimized through better and faster transfer of

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information (Augustine, 2001) (Erder, Davidson & Cheney, 1989) (Wuerz, Swope, Holliman & Vazquez, 1995). For example, the transfer of a 12-lead electrocardiogram (ECG) to the receiving cardiology department has also become a standard in some German EMS, and it has been shown in international studies to lead to a significant reduction in ischaemia time during ST-elevation myocardial infarction. Contact-to-balloon time and door-to-balloon time could be reduced through target-oriented confinement of the patient in a centre with cardiac catheterisation and prior information of the hospital staff (Adams, Campbell & Adams, 2006) (Dhruva, Abdelhadi & Anis, 2007) (Sejersten, Sillesen & Hansen, 2008). But such innovative procedures are carried out mainly by using second-generation mobile phones, without any additional encryption mechanisms and without suitability for larger amounts of data and an accelerated transmission. The approach of the project Med-on-@ix is the broad implementation of a telemedicine system in the EMS. All mission- and patient-related data,

such as ECGs, pulse oximetry, blood pressure, various vital signs, auscultation information, and video live-streams of the rescue unit, can be exchanged in real time between the working on-site rescue personnel and the physician at the centre of competence. The on-site rescue personnel may consists of paramedics and EMS-physician. These competence centres are staffed with highly qualified specialists who determine further treatment on site and during transportation based on the information at the scene and the relevant guidelines. These competence centres will also have extensive opportunities to contact other health care providers in order to obtain comprehensive organizational and patient-related information. The competence centre will transmit all relevant information to the designated hospital in advance of the patient arrival. Therefore, the patient benefits from a timely adequate therapy. Figure 1 shows how an emergency mission would proceed with and without the Med-on-@ ix system.

Figure 1. The Process of an emergency mission with Med-on-@ix

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Normally, there is no EMS physician on the ambulance in the EMS in the city of Aachen. The physician has his own car and meets the ambulance on the scene in a rendezvous system, so he is much more flexible. In our third year, the trial run, we placed an EMS physician on the ambulance, as a backup system. 1. Phase: Incoming emergency call into the control centre, the dispatcher alarms the ambulance and the EMS physician. Depending on the location of the scene, the ambulances start from different bases, while the EMS physician car is placed on the main fire fighter base.↜With Med-on-@ix: Dispatcher also alarms the Telenotarzt 2. Phase: Ambulance and EMS physician car are on the direction to the scene.↜With Med-on-@ix: Telenotarzt in the Competence centre gets in contact with the paramedics and the EMS physician who is on the ambulance. There is no separate EMS-physician car on direction to the scene in our trial run. 3. Phase: Ambulance reach the scene, the EMS physician arrives a few minutes later, depending on the location of the scene.↜With Med-on-@ix: Paramedics and the EMS physician arrive at the scene. Please notice that normally there is no physician on the ambulance. In our research trial, we placed the physician as a backup system for the paramedics on the ambulance. 4. Phase: Paramedics will ask for past medical history and the actual disorders, get the blood pressure and other vital parameters, like ECG and so forth. If extended therapy is necessary they have to wait for the EMS physician for further therapy which are reserved only for physicians (for example application of special medicaments) In this phase or in one of the following phases the paramedics or the physician must inform the hospital, give them the information about the disorders, vital signs or other problems,

maybe ask for a free place on the intensive care unit. During this time the paramedic or the physician is not focused to the patient.↜With Med-on-@ix: Paramedics starts also with asking for past medical history, the actual disorders but starts parallel with the extended therapy, ordered and supported by the Telenotarzt in the Competence Centre. If he has all important information about the patient, he could inform the hospital and organize everything. If the manual skill of the physician is needed, the backup physician could help. (In the future, the system is professional implemented in the EMS, the EMS physician must be reordered in this case, if he was not alarmed parallel to the ambulance) 5. Phase: Transport to the ambulance. While the team is carrying the patient to the ambulance it might not be so much focused on the patient.↜With Med-on-@ix: During the transport to the ambulance, the Telenotarzt could control the vital signs of the patient, while the team is concentrated on the transport. 6. Phase: The therapy will go on in the ambulance. In the case that the condition of the patient gets worse, he is not available for a new emergency case, because his expertise and decisions is probably needed.↜With Medon-@ix: The Telenotarzt in the Competence Centre could support the paramedics with his expertise and decisions and the EMS physician would be free for a new emergency. 7.& 8. Phase: Transport to the hospital and handover. If the patient`s condition deteriorated, the paramedic or the physician is occupied with the therapy, to stabilize the patient, but maybe cannot inform the hospital early enough about the new situation. The consequence could be, that the procedure in hospital could take much longer, because they were not well prepared.↜With Med-on-@ix: There is a constant exchange of information between hospital and paramedics or EMS physician

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by the Telenotarzt, so the hospital is up to date about the situation of the patient and the therapy, therefore the hospital could better prepare their workflow. The strategy was to implement the concept above within three years. In the first year, we identified the requirements for a comprehensive telemedicine system and defined the specifications. In close cooperation with our industrial partners, operational systems have been re-evaluated and assessed on how well the system is available and how it can be combined with up-to-date technical equipment. The project started with a technical pre-release (mock-up), which showed the transfer functionality in a simulator environment, but lacked the required mobility and security in its entirety. The second year of the project was mainly used for studies on the Emergency and Anaesthesia Simulator Centre, AIXTRA of the University Hospital Aachen. Here, the application of the system was prospectively tested by paramedics and EMS-physicians. In September 2008, the first simulator study, SIMI I, was carried out successfully. In this study, 29 teams, each consistFigure 2. Data transmission in Med-on-@ix

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ing of one EMS-physician and two paramedics, were confronted with two different emergency scenarios (ST-elevation myocardial infarction and brain trauma), for which there are primary care guidelines. We randomised the Telenotarzt support for each scenario. The teams’ treatments of the patients were assessed with video-based evaluations in terms of the differences in adhering to emergency treatment guidelines, conformity and the time requirement, with and without the Telenotarzt. The second simulator study, SIMI II, which was conducted in May 2009, examined to what extent drug therapy by paramedics with telemedicine guidance and control can be performed safely. The quality of the diagnosis and treatment recommendations of the Telenotarzt was compared with those of an EMS-physician treating the patient on scene. In the third project year, the trial run was carried out in the EMS of the City of Aachen, for which an ambulance of the City of Aachen Fire Department has been fully equipped with telemedical devices. Figure 2 shows how the patient data (for example vital parameters, 12 lead-ECG, videos) are transmitted from the emergency scene to the competence

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centre and to the hospital. It also shows the communication pathways. This makes the system Med-on-@ix ready for application and testing in real operations in the evaluation phase. Medical assignments in this phase take place only in the presence and supervision of an experienced EMS-physician, so that maximum safety for the patient and legal certainty for the paramedics is guaranteed. Legal aspects have been a key issue in this context. Extensive legal advice was obtained from Professor Christian Katzenmeier, who is specialist in medical law and director of the Institute of Legal Medicine at the University of Cologne, and Professor Karsten Fehn, who is specialist in medical law, criminal law and professor at the Cologne University of Applied Sciences. They discussed the issues of privacy and patient data protection as well as the liabilities including criminal liabilities in case of damage. Legal problems can also result from the interaction of the Telenotarzt working at the competence centre with the staff working on scene. Questions arose on how the authority and responsibility should be allocated between the EMS-physician on the scene and the Telenotarzt in the competence centre. Another scenario had to be considered in which paramedics had to be delegated by the Telenotarzt in the physical absence of a physician on site, for example in the administration of certain intravenously applied medications. In summary, the Telenotarzt is responsible for medical decisions and instruction to the paramedics, unless an EMSphysician is on site. The on-site EMS-physician has the final responsibility for making decisions.

Technical Focus In this section, the requirements for the telematic support system will be outlined. These requirements have led to a technical system summarised separated into the hardware and software system architecture. Ancillary to an appropriate system architecture a methodology to reduce technical

risk in the development and the realisation is needed (Müller, Protogerakis & Henning, 2009) Requirements. The condensed functional and non-functional requirements concerning the system design, which were based on discussions from two expert workshops with physicians and paramedic staff, are presented below. Functional Requirements. Table 1 shows the necessary signals to be transmitted from the emergency site that would serve as the basis for decisions by the physician at the centre of competence for classification of cases into continuous live or intermittent transmission and for classification of cases into high or low priority for transmission. No need could be identified by the experts for a more dynamic prioritisation or control of these priorities by the centre of competence.

Table 1. Signals to be transferred from site and their priorities Signal

Continuous / Intermittent Transmission

Priority

12-Lead-ECG

Intermittent (5-30 min)

1A

Voice Communication

Continuous

1B

Rhythm ECG

Continuous

2

Non-Invasive Blood Pressure

Intermittent (1-5 min)

3

Pulse Oximetry

Intermittent Continuous

4 10

Kapnometry

Intermittent Continuous

5 8

Defibrillator

Intermittent

6

Invasive Blood Pressure

Intermittent Continuous

7 9

Central Venous Pressure

Continuous

11

High Resolution Pictures

Intermittent

12

~30 sec Video Sequences

Intermittent

13

Video

Continuous

14

Stethoscope

Intermittent

15

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All vital parameters shown in Table 1 are measured under optimal circumstances by one single monitor/defibrillator device. Because we were limited by technical factors, we had to work with a single monitor and extra defibrillator. The device supports the real-time export of all signals except the 12-lead ECG. An electronic stethoscope allows the live wireless transmission of auscultation. The ambulance is equipped with at least one fixed remote control camera to allow the transmission of live video stream and high quality pictures. A portable camera provides a video live stream and high-resolution steady pictures from the place of emergency. Up to three wireless headsets are used for voice communication at the place of emergency. The microphones of the on-site staff can be activated by a central control device on site. The quality of the headsets is of diagnostic quality for auscultation. As an important step towards better quality control, the system features the documentation of the medical and mission data by software on site and by the software at the competence centre (Bergrath, 2006). The documented data from the place of emergency is displayed in real-time at the centre of competence. The documentation software receives and displays textual commands from the centre of competence, for example for the application of drugs. The documentation software allows an automatic data import of patient data from the medical devices. Pictures taken with a camera device are embedded in the documentation. Commercial mobile radio networks are well established and available at reasonable prices in most countries. If they are used redundantly, they can also be utilised for critical safety applications to take advantage of the huge capabilities in data transfer, which contrasts with the Professional Mobile Radio Standards, such as the Terrestrial Trunked Radio (TETRA). Global System for Mobile Communications (GSM) and TETRA can be used for voice communication where their high availability can be utilized. All transmitted data from the place of emergency is archived for later evaluation and

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quality management. The latter access to anonymised archive data is restricted with the use of an appropriate authentication system. Non-functional Requirements. To be easily adapted to the heavily varying local conditions for EMS, the system architecture must be configurable in a modular way. This applies especially to medical devices and to mobile communication technologies. The main issues concerning safety, security and reliability that have to be ensured in the system design are 1. the safety from interception of patient related data, 2. the prevention of unauthorised access to the system and 3. the data privacy of staff and patient related data. Thus, all wireless communication must be secured by signing and encrypting of the data. A mobile device, such as a Tablet PC, is used as the central control unit and for the documentation software on site. The audio transmission of auscultation from the stethoscope is of diagnostic quality. Therefore, the stethoscope allows for wireless transmission e.g., via Bluetooth with the Advanced Audio Distribution Profile (A2DP) standard. The video cameras feature a resolution of at least 640x480 pixels and a frame rate of at least fifteen frames per second. A change of the frame rate must be possible during online operation. For energy and weight efficiency reasons, a fixed camera in the ambulance vehicle features a hardware compression of the video stream. A portable camera is either mounted on a small telescope tripod that is fixed onto one of the other units or be a head mounted model. Bluetooth 2.0 is used for the connection between the communication unit and the headsets. The headsets features the Headset-Profil (HSP) and Handsfree-Profil (HFP) as well as the A2DP profile to allow for listening to the stethoscope signal. The system provides a middleware solu-

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tion to ensure its extensibility and adaptability. It re-establishes lost connections due to problems on the underlying communication channels. It supports the abstraction of hardware vendor specific interfaces, such as the real-time interface of the ECG/defibrillator unit, by means of adapters between the vendor specific device driver and a middleware interface. In the case of insufficient bandwidth, it must feature the prioritisation of the data according to Table 1. The system must ensure the synchronicity of signals in configurable groups. The communication unit offers a dedicated Internet Protocol (IP) based packet tunnel and a separate voice service to the middleware. It supports the use of the Professional Mobile Radio services (PMR), such as TETRA and a Circuit Switched GSM mode for voice communication. Common mobile network technologies, such as General Packet Radio Service (GPRS) with Enhanced Data Rates for GSM Evolution (EDGE), and Universal Mobile Telecommunications System (UMTS) with High Speed Packet Access (HSPA), support through a generic interface for the IP-based communication. The unit offers one tunnel through the parallel channels of different providers. It uplinks bandwidth information to the middleware. Hardware Network Architecture. The distribution of hardware components and the physical network architecture is shown in Figure 2. The components on site are connected to the communication unit according to the requirements described above. The communication unit is an embedded computer that hosts the middleware, the networking logics and the hardware network interfaces. It connects to the ambulance vehicle via an 802.11 network or directly to the Public Switched Telephone Network (PSTN) and the Internet through GSM/TETRA and GPRS/UMTS. Multiple GPRS/UMTS data connections to different providers ensure higher mobile network coverage and/or higher bandwidth. Bundling the different connections is a task to be realised in the network layer of the system’s software. The com-

munication unit in the ambulance connects to the mobile networks in the same way. The connection between the On-Site Communication Unit and the Ambulance Vehicle Communication Unit is for redundancy only. Additional peripheral devices such as a printer are connected to the Ambulance Vehicles Communication Unit by Ethernet. On the centre of competence’s side, the servers are connected to the PSTN via ISDN and to the Internet. The Clients in the centre of competence connect to the Servers through a local network. Software Layer Architecture. As for most communication systems, a layered architecture was also chosen for the software architecture of the telematic support system. The software architecture of the telematic support system is divided into the Application Layer, the Session Layer and the Network Layer as shown in Figure 3. The Application Layer encapsulates the medical devices and other sensors as well as the clients and servers in the centre of competence. The interfaces between the session layer and the application layer is clearly described. That makes it easy to exchange single components of the application layer to meet the different demands of different emergency medical services. When using components with proprietary interfaces adapters can be implemented to adapt them to the defined interface. The Session Layer consists of the Middleware and is responsible for the session management and the conditioning of all outgoing data. The session management holds the state of the actual connection. That makes it possible to use stateless applications as components on the application layer. For example an ECG sender application on the application layer sends data to a not specified receiver. The middleware decides about the routing of this data to the appropriate receiver. That approach supports loser couplings of applications into the architecture. The conditioning of the outgoing data by the session layer is mainly the prioritization of the signals to be transferred according to Table 1 and the adaptive compres-

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Figure 3. Hardware distribution and network architecture overview

Figure 4. Overview of the different logistical layers in the system

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sion of streams like video, according to the actual available bandwidth resulting from the network coverage. In the Network Layer multiple encrypted VPN Tunnels in the Network Layer are established over multiple concurrent physical links and bonded to one virtual tunnel interface. While techniques for vertical handovers like Mobile IP and the Session Initiation Protocol (SIP) in mobile networks have been well established over the last few years (Good & Ventura, 2006), the parallel use of multiple links to increase bandwidth is still a challenge (Li & Brassil, 2006). The resulting networking properties of the bonded tunnel are determined by the fact that the order of arriving packets on the receiver side is non-deterministic due to the different behaviours of the underlying communication channels. That makes it useless to have protocols like User Datagram Protocol (UDP) that lack robustness against the reordering of packets. The Transmission Control Protocol (TCP) is much more robust against packet reordering. Bohacek, Hespanha, Lee, Lim and Obraczka (2006) described a new TCP with high resistance against packet reordering. The RealTime Transport Protocol (RTP) extends UDP with a reordering robustness and is well suited for real-time streaming applications (Iren, Amer & Conrad, 1999) (Schulzrinne, Casner, Frederick & Jacobson, 2006). Streaming Services. To achieve the integration of services with real-time and synchronicity requirements, the Streaming Part must balance different directives for the streaming services. It must ensure the synchronicity of different signals in “synchronisation groups” such as the different ECG leads. In the case of an insufficient total available bandwidth, it should prevent the “stuttering” of the signal output on the receiver side (continuity). The minimal length of continuous data is determined by the minimal length needed for diagnostic purposes. The Middleware should not present data that is too old to the receiver side (timeliness). Only the most recent parts of a

minimum useful length must be transmitted from the sender’s side. Therefore, in order to achieve a trade-off between both continuity and timeliness, streams have been broken down into smallest segments that are still medically interpretable. To achieve this, the Streaming Part design contains two buffers and a resampler or recoder in between. A controller calculates the parameters to achieve the described balance for the buffer and resampling/recoding parameters. There is one Packer for each synchronisation group of streaming signals. It fetches data from outgoing buffers of all signal paths in one synchronisation group and multiplexes them.

Socio-Technical Focus Socio-technical aspects are relevant in a telemedical context. The Department of Information Management in Mechanical Engineering of the RWTH Aachen University has elaborated on the implementation of scenarios, the design of humantechnology interfaces, the conduct of simulation studies and their evaluation. Evaluation of User Acceptance within Med-on-@ix To ensure a user oriented development of the telematic support system, the researchers relied on an iterative development process characterized by a consequent dialogue between developers and users of the system. To illustrate the user integration some of the evaluation results gathered so far within the project are described within the following lines. Until now findings derived from simulation studies realised within Med-on-@ix can provide an indication on the acceptance of the system by the different involved user groups and constituted an important basis for a user-centred technical development. The user acceptance is one decisive factor to guarantee a successful development and implementation of the telematic support system. Various theories of user acceptance had been developed,

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describing user satisfaction as one major key to success in the development of new technologies. Davis (1993) describes acceptance in line with the technology acceptance model (TAM), correlating perceived usefulness, perceived ease of use, attitude towards use and actual system use. Generally, user acceptance reflects whether a technology fits the requirements of the user as well as the characteristics of the task. The process-oriented approach of Kollmann (1998) focuses on acceptance developed within three phases, distinguishing the attitude, the acting and the long-term user acceptance. Regarding the arrangement of a user-centred development strategy, this theoretical approach offers the possibility to evaluate acceptance at different stages within the research project. Usability aspects and user acceptance were therefore reflected in the technical development in keeping with the holistic research strategy of Med-on-@ix (Skorning et al., 2009). Besides various technical and medical requirements, economical, judicial and safety-related specifications were analysed to set up the broad trial operation in Aachen. At different stages of development of the telematic support system, simulation studies were conducted at the Interdisciplinary Medical Simulation Centre (AIXSIM) of the University Hospital Aachen, Germany. In September 2008 and in June 2009, the simulation studies were carried out to validate the identified requirements by involving EMS workers as well as a full-scale patient simulator. EMS Teams and EMS-physicians had the opportunity to test the telematic support system in different simulated rescue scenarios. The researchers focused on the effect of using a telematic support system in EMS missions on the quality of medical care. Following the scenarios performed by EMS Teams, different social research methods were applied to examine the factors of acceptance and utilisation issues amongst EMS. The results from both studies were partly published within the Proceeding of the International Conference on Successes &

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Failures in Telehealth 2009, which took place in Brisbane, Australia. A questionnaire with both open-ended and closed-ended questions were designed and used to gather feedback from the different stakeholders – EMS-physicians, paramedics and the Telenotarzt. The questions referred to the general attitude towards the application of telemedicine in EMS, the evaluation of the scenarios with and without the support of the telematic system, the support by the remote EMS-physician and especially the impact of the telemedical assistance on communicationor work-related processes, the perception of the role of the team members as well as the perceived quality of care and guideline conformity of the treatment processes. The respondents specified their level of agreement on a six-point Likert scale. The semi-structured interviews were carried out by using an issue-focused interview guide. Gathering data by moderated group discussions offered the interviewer the possibility to react individually to the issues that the respondents wanted to focus on. Following the methods described by Mayring (2000), the transcript video-documented interviews underwent a rule-guided qualitative content analysis. The approach is advantageous because it combines the methodological strengths of quantitative content analysis and the concept of qualitative procedure. The textual data was coded into thematic categories representing the different aspects of interpretation in terms of the underlying textual material. In the first study, 87 subjects from 29 EMSteams (one EMS-physician and two paramedics) took part in the interview, whereas 48 test persons (16 EMS-physicians and 32 paramedics) took part in the second run in 2009. In both studies, the test subjects were divided into half, with one half assigned to the control group without the telematic support system to allow a comparison of the quality of treatment with and without the application of telematics. The first simulation study addressed the support of an EMS-physician, who was informed about the scenarios. The second simulation study

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tackled the support of an EMS Team consisting of two paramedics by a trained EMS-physician in the competence centre who was uninformed about the course of the scenarios. Another decisive difference between both studies was the number and type of the simulated scenarios: in the first run, only two standardised scenarios (ST-elevation myocardial infarction and severe traumatic brain injury) were performed, while in the second study, five different scenarios (a diving accident, a renal colic, a second-degree burn, an intoxication and a hypoglycaemia) were performed by actors as patient simulators, and they offered a wide scope of scenarios for testing (Schneiders, Protogerakis & Isenhardt, 2009). The first survey showed a positive attitude of the respondents towards the use of a telematic support system in the emergency care. For 83% of the EMS-physicians and almost 93% of the questioned paramedics, the implementation of an always available EMSphysician in a competence centre was viewed as a great support in their everyday work. The interviewees especially approved of the support by the telematic system in case of doubt or rare conditions as well as for inexperienced physicians. Critics were concerned about the encroachment on usual operational procedures by the telematic support system. In particular, experienced EMSTeams had difficulties handling the interventions from the EMS-physician in the competence centre and complained about the fact of not knowing the physician and hence having some problems of communication within the team. It was significant that the unusual working situation had a crucial impact on the information exchange between the medical services as well as with the patient. The feedback and results from questionnaires and interviews were integrated into further development of the system itself and the organisational implementation into the EMS. For example, the project aims to provide the needed confidence towards the EMS-physician in the competence centre by the rotation of physicians working on site and the physicians working in the competence

centre. The teamwork on site provides the required routine and trust towards the involved co-workers. All subjects called for continued training with the system to get used to the new working situation. To guarantee efficient teamwork, some crucial rules, concerning the timing and modalities of communication, were defined and disseminated in line with necessary training courses for the user groups. Those defined rules underwent an in-depth analysis within the second simulation study in 2009 and had a positive impact on user acceptance. Although the critical voices constituted 83% in the first study, only 45% of the test subjects criticised the communication during the scenarios in the second run (Schneiders et al., 2009). The acceptance of the telematic support systems was 91.6% in the second study, highlighting all the positive aspects of the system on the diagnostic confidence and the operational procedures on site – like the preparation of hospitalisation or the management of additional or specialised rescue units. Most of the interviewed persons estimated that the use of such a system would reduce the pressure of work on site. A significant positive development of the teamwork and the communication between the participants was observed over the course of the five scenarios. The interviewees confirmed a training effect and underscored the needed routine in working with the telematic support system (Schneiders et al., 2009). The intensified exchange between researchers, developers and potential users of the described studies offered a wide range of feedback and points of criticisms from the user groups. The consideration of the documentation requirements during the development process was crucial to improve the system and strengthen the acceptance of the telematic support system by the EMS teams. A high involvement of the user had a positive impact on earning their trust in the research activities and the developed system. To achieve user-centred development and an acceptable result, the project has to take into account the requirements and suggestions for improvement. A user- and market-focused design of new technolo-

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gies for the healthcare sector can be achieved by using an iterative and cooperative user-centred developing strategy, which incorporates acceptability factors, criticisms and statements of distrust on the part of test users to overcome possible market barriers in an early stage of development.

Ethical Focus Telemedicine and especially the Telenotarzt enable a higher standard of care with a shorter time for treatment from paramedic care for patients. The principle of care and beneficence for the patient is enhanced by a trained EMS-physician who supports the EMS-physician at the emergency scene via telemedicine equipment. The teamwork between the EMS-physician at the emergency scene and the Telenotarzt has to be trained in order to achieve optimal medical competence for the patient. In a traditional patient-doctor relationship, it is preferable that the physician sees the patient in a face-to-face consultation. In contrast to the on-scene EMS-physician, the Telenotarzt’s impression of the situation of the patient is limited to the information he gets via telemedicine. The Telenotarzt only gets in contact with other professionals such as the EMS-physician or trained paramedics. The direct communication of the patient and the Telenotarzt is not a focus of the project. Even if an EMS-physician is not present, the patient would presumably seek telemedicine consultation because even the limited communication of the patient with an off-scene EMS-physician is preferable to the situation where only paramedics are involved. The organizing principle of emergency medical service in Germany is still to send an EMS-physician to the emergency scene to support the paramedics. In cases where the EMS-physician is expected to arrive at the emergency scene after the paramedics due to traffic or distance, then the telemedicine system reduces the time that a physician is not involved to a minimum. The principle of beneficence of

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Beauchamp and Childress (2009) requires agents to provide benefits to others. In the absence of a clearly formulated and known wish of the patient, the paramedics and the EMS-physician are in charge to provide the best possible medical treatment. Therefore, it is a moral obligation to involve an EMS-physician via telemedicine to shorten the time without involvement of a physician. It is a moral duty to reduce the time of treatment if the telemedicine system is available because the alternative would be a longer period of time of not being treated by a physician. The telemedicine system in Aachen comes into effect if a person is in danger, and therefore, the best possible medical treatment is in the interest of the patient. The patient can expect the best possible treatment, and it is a strong ethical imperative that physicians act for the benefit of their patients. The involvement of the telemedicine system in emergency medical service in Germany is a moral obligation if the system is available, and no other form of involvement of an EMS-physician is possible. The involvement of an EMS-physician via telemedicine is demanded by the patient because a competent patient interacts with the physician in a special way. The principle of informed consent requests a communication of the patient with the physician. For the interpretation and acknowledgment of an Advance Directive, it is necessary to have the qualification of a physician. In such a situation, an EMS-physician via telemedicine can make treatment decisions that paramedics are not allowed to make. For the situation where there is an absence of an emergency physician on the scene, the involvement of an EMS-physician via telemedicine secures the respect for autonomy of the patient. The principle of justice is a focus of the telemedicine system for emergency treatment in Aachen, and the telemedicine system values efficiency in allocating the on-duty personnel. On the other hand, the possibility of involving an emergency physician via telemedicine reduces the risk of accidents on the way to the patient. Not all patients need the treatment of an EMS-physician,

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and the physician at the hospital can take care of the patient when there is no life-threatening situation. Paramedics can be directed via telemedicine support by an emergency physician, the use of this needed competence is fair, equitable and appropriate for the patient, and more patients can benefit from the higher availability of EMS-physician competence. The activation of the EMS-physicians by the coordinating fire department strictly follows medical principles and depends on the need of the patient and is not determined by other factors, such as insurance status or neighbourhood of the patient. It would be unethical to send paramedics only, if it is clear by the information provided in the emergency call that the help of an EMS-physician is needed. Another situation would be when all on-duty EMS-physician are busy in treating patients, and the support of an EMS-physician via telemedicine for a colleague is stopped when a paramedic orders support of an EMS-physician via telemedicine. The quality of the treatment of a patient by an EMS-physician, if substituted with an EMS-physician via telemedicine, might slightly decline, but the quality of treatment for the patient with only paramedics at the scene will considerable improve. The principle of justice demands the support of an EMS-physician via telemedicine for patients who need a physician.

FUTURE RESEARCH DIRECTIONS The Med-on-@ix systems may be used in other medical applications as well as in non-medical areas. Due to the open interfaces and the individual components of the system, Med-on-@ix could be further broadened or supplemented and thus could be adjusted to individual needs. The use of the specific system components for less complex applications is also possible. In particular, the application of the secure transmission technology, which was developed in the framework of the Medon-@ ix project, could be of use for many other industries. Wherever rapid expert consultation in

an emergency is necessary, the patient sensor and the transmission technology of Med-on-@ix can be used. For example: 1. Emergency care provided through the general practitioner by the Resident Doctors` Association 2. Emergency care provided in commercial airliner or on cruise ships 3. Emergency care in remote areas, e.g., on expeditions 4. Emergency care in difficult terrain, such as in a mountain rescue Additionally, the concept of Med-on-@ix could be transferred to other sectors of health care, such as developing interfaces to home monitoring, digital health recordings or nursing homes. To implement the technology in clinical practice, one goal must be further miniaturisation and weight reduction of mobile technologies for data transmission, which may also allow the launching of these technologies to the commercial market. Another interesting aspect is the extension of the evaluation phase and the involvement of more ambulances equipped with telematic techniques from the Med-on-@ix project, for evaluating not only the situation in an urban environment but also in rural regions. It would be of particular interest to assess long distance teleconsultations to see if it is possible to compensate for the lack of EMS-physicians and to assess what kinds of technical problems may arise compared to an urban environment. Another conceivable area of application for the Med-on-@ix technology would be during a major crisis with mass numbers of injured people, in which there might be a shortage of EMS-physicians, especially at the beginning of such as crisis. Here, the Telenotarzt could perform mainly organizational tasks, such as alerting hospitals and asking for free intensive care unit capacities, even in hospitals at a larger radius from the incident. Easy implementation, continuous updating of all relevant medical guidelines,

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usability and teaching concepts in combination with telematic techniques remain major important themes, especially in the EMS sector, where the shortage of well qualified EMS-physicians is challenged by the steadily increasing number of missions. Therefore, a continuing discourse is needed in society about the quality of care. Also, a continuing dialog between governmental policies, health authorities and medical industries is mandatory to allow the economisation of the health system as well as the optimisation of the quality of care within the EMS. Projects such as the EU projects “MyHeart” (MyHeart, n.d.) “6WINIT” (Rall et al., 2004), and “MobiHealth” (MobiHealth, 2010), which consist of several individual projects on telemedicine (van Halteren et al. 2004), or the “Tele-trauma system” project (Chu & Ganz, 2004) show how much potential can be found in the area of telemedicine and how challenging it is to explore the opportunities and the complexity of the interactions between men and machines.

CONCLUSION There is a need for new strategies to face the current and future problems in the German Emergency Medical Services. The lack of quality management and the increasing costs in the presence of a shortage of EMS-physicians are typical challenges, resulting in an increasingly inadequate medical care. In addition, the information and communication technology used in the German EMS is out of date. The physician-powered EMS has to be modernized to increase the quality and to show measurable evidence of its effectiveness. Otherwise, its future existence is at serious risk. Therefore, the project Med-on-@ix was created by the Department of Anesthesiology at the University Hospital Aachen, Germany. After the three-year project period, we expect that there will be a safe functioning telemedicine system that is available for use in the German EMS.

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A comprehensive telemedicine system needs to solve the above mentioned problems through the optimisation of the emergency process in the German physician-led EMS. This system offers EMS-physicians and paramedics an additional consultation by a specialized centre of competence, thus assuring medical therapy that is compliant with evidence-based guidelines. Several prospective studies have been conducted to analyse this system in comparison to the conventional EMS. The efficiency and quality of emergency care can only be improved by taking the telemedicine approach for the entire process of emergency care from receiving a distress call to care for the patient. Therefore, the Med-on-@ix project includes in its action plan, a modeling system that connects technical challenges with organizational and human aspects. First, all partners of the consortium, including the emergency physicians, lawyers and engineers, helped to define all the requirements for a telemedical emergency assistance system. These various requirements included security, speed and reliability of data transmission, but also the ergonomics of the controls. Telematic assistance in the safety of EMS can be achieved with the already established Professional Mobile Radio in combination with commercial mobile network technologies. The parallel use of multiple physical links adds reliability and can be secured by using VPN mechanisms. A middleware architecture based on this technology is restricted in the choice of usable transport protocols. It must offer different channels for message-oriented communication and streaming-oriented communication with real-time and synchronicity requirements. In the future, the architecture will be extended to fulfill the needs for the interoperability with other centers of competence and for the interconnection with hospital information systems. Med-on-ix @ ensures integration of all relevant actors in the EMS, including the emergency physicians, paramedics, control room staff and patients.

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We are now in the third year of the project and testing the system in real time in real operations. The results of the evaluation phase will be presented in the future.

Bundesärztekammer (2004). Tätigkeitsbericht der Bundesärztekammer: Entschließungen zum Tagesordnungspunkt VI. Deutsches Ärzteblatt, 101 (22), A1584, B1313, C1266.

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Adams, G. L., Campbell, P. T., & Adams, J. M. (2006). Effectiveness of prehospital wireless transmission of to a cardiologist via handheld device for patients with acute myocardial infarction. The American Journal of Cardiology, 98, 1160–1164. doi:10.1016/j.amjcard.2006.05.042 Augustine, J. J. (2001). Medical direction and EMS. Emergency Medical Services, 30, 65–69. Beauchamp, T. L., & Childress, J. F. (2009). Principles of Biomedical Ethics. New York: Oxford University Press. Behrendt, H., & Schmiedel, R. (2003). Ausgewählte Ergebnisse der Leistungsanalyse 2000/2001. In K. Mendel. Handbuch des Rettungswesens. Witten, Germany: Mendel Verlag. Behrendt, H. & Schmiedel, R. (2004). Die aktuellen Leistungen des Rettungsdienstes in der Bundesrepublik Deutschland im zeitlichen Vergleich (Teil II). Notfall- & Rettungsmedizin. 7, 59-70. Bergrath, S. (2006). Retrospektive Analyse der Datenqualität eines kommerziellen Datenbanksystems für den Notarztdienst und der Dokumentationscompliance des Notarztdienstes der Stadt Aachen. Universitätsklinikum der RWTH Aachen: Ph. D. Dissertationen. Bohacek, S., Hespanha, J. P., Lee, J., Lim, C., & Obraczka, K. (2006). A new tcp for persistent packet reordering. IEEE/ACM Transactions on Networking, 14(2), 369–382. doi:10.1109/ TNET.2006.873366

Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioural impacts. International Journal of Man-Machine Studies, 38, 475–487. doi:10.1006/imms.1993.1022 Deutscher Bundestag. (2006). Unfallverhütungsbericht Straßenverkehr 2004/2005. Retrieved from www.dip.bundestag.de Dhruva, V. N., Abdelhadi, S. I., & Anis, A. (2007). ST-Segment analysis using wireless technology in acute myocardial infarction (STAT-MI) trial. Journal of the American College of Cardiology, 50, 509–513. doi:10.1016/j.jacc.2007.04.049 Erder, M. H., Davidson, S. J., & Cheney, R. A. (1989). Online medical command in theory and praxis. Annals of Emergency Medicine, 18, 261–268. doi:10.1016/S0196-0644(89)80411-1 Gesundheitsberichterstattung des Bundes. (2007). Ad-hoc-Tabellen. Retrieved 06.03.2007 from www.gbe-bund.de Good, R., & Ventura, N. (2006). A multilayered hybrid architecture to support vertical handover between ieee802.11 and umts. In IWCMC ’06: Proceedings of the 2006 international conference on Wireless communications and mobile computing(pp.257-262). New York: ACM. Gries, A., Helm, M., & Martin, E. (2003). Zukunft der präklinischen Notfallmedizin in Deutschland. Der Anaesthesist, 52, 718–724. doi:10.1007/ s00101-003-0548-1

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Iren, S., Amer, P. D., & Conrad, P. T. (1999). The transport layer: tutorial and survey. ACM Computing Surveys, 31(4), 360–404. doi:10.1145/344588.344609 Jemmet, M. E., Kendal, K. M., Fourre, M. W., & Burton, J. H. (2003). Unrecognized misplacement of endotracheal tubes in a mixed urban to rural emergency medical services setting. Academic Emergency Medicine, 10, 961–965. doi:10.1111/j.1553-2712.2003.tb00652.x Jones, J. H., Murphy, M. P., & Dickson, R. L. (2004). Emergency physician-verified out-ofhospital intubation: miss rates by paramedics. Academic Emergency Medicine, 11, 707–709. Katz, S. H., & Falk, J. L. (2001). Misplaced endotracheal tubes by paramedics in an urban emergency medical services system. Annals of Emergency Medicine, 37, 32–37. doi:10.1067/ mem.2001.112098 Kollmann, T. (1998). Akzeptanz innovativer Nutzungsgüter und -systeme. Wiesbaden, Germany: Gabler. Li, J., & Brassil, J. (2006). On the performance of traffic equalizers on heterogeneous communication links. QShine, Proceedings of the 3rd international conference on Quality of service in heterogeneous wired/wireless networks. New York: ACM. Mayring, P. (2000). Qualitative Content Analysis. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research. Retrieved 2000 from http://nbnresolving. de/urn:nbn:de:0114fqs0002204 Messelken, M., Martin, J., Milewski, P. (1998). Ergebnisqualität in der Notfallmedizin. Notfall-& Rettungsmedizin. 1, 143-149. MobiHealth. (2010). MobiHealth – shaping the future of Healthcare. Retrieved 29.03.10 from http://www.mobihealth.org/

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Müller, M., Protogerakis, M., & Henning, K. (2009). A methodology to reduce technical risk in the development of telematic rescue assistance systems. Proceedings of the second international conference on computer and electrical engineering. Dubai: UAE. MyHeart. (n.d.). Forschungsprojekt im 6. EURahmenprogramm. Retrieved 30.11.2009 from www.hitech-projects.com/euprojects/myheart/ Rall, M., Reddersen, S., Schädle, B., Zieger, J., Christ, P., Scheerer, J., & Liang, Y. (2004). Das Schutz-Engel- System - Telemedizinische Unterstützung in Echtzeit. InA. Jäckel, Telemedizinführer Deutschland (pp. 51–64). Witte, Germany: Mendel Verlag. Rieser, S. (2005). Arbeitsbedingungen schrecken viele ab. Deutsches Arzteblatt, 102, C629. Schneiders, M., Protogerakis, M., & Isenhardt, I. (2009). User acceptance as a key to success for the implementation of a Telematic Support System in German Emergency Medical Services. Proceedings of The International Conference on Successes & Failures in Telehealth 2009: SFT-09 Australia. 2009. Schulzrinne, H., Casner, S., Frederick, R., & Jacobson, V. (2003). RTP: A Transport Protocol for Real-Time Applications RFC 3550 (Standard). Retrieved 30.11.2009 from http://www.ietf.org/ rfc/rfc3550.txt Sejersten, M., Sillesen, M., & Hansen, P. R. (2008). Effect on treatment delay of prehospital teletransmission of 12-lead electrocardiogram to a cardiologist for immediate triage and direct referral of patients with ST-segment elevation acute myocardial infarction to primary percutaneous coronary intervention. The American Journal of Cardiology, 101, 941–946.

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Silvestri, S., Ralls, G. A., & Krauss, B. (2005). The effectiveness of out-of hospital use of continuous endtidal carbon dioxide monitoring on the rate of unrecognized misplaced intubation within a regional emergency medical services system. Annals of Emergency Medicine, 45, 497–503. doi:10.1016/j.annemergmed.2004.09.014 Skorning, M., Bergrath, S., Rörtgen, D., Brokmann, J., Beckers, S., & Protogerakis, M. (2009). E-Health in der Notfallmedizin – das Forschungsprojekt Med-on-@ix. Der Anaesthesist, 58, 285–292. doi:10.1007/s00101-008-1502-z Statistisches Bundesamt. (2006). Gesundheit – Ausgaben, Krankheitskosten und Personal 2004. Retrieved 16.08.2006 from www.destatis.de Timmermann, A., Russo, S. G., & Eich, C. (2007). The out-of-hospital esophageal and endobronchial intubations performed by emergency physicians. Anesthesia and Analgesia, 104, 619–623. doi:10.1213/01.ane.0000253523.80050.e9 van Halteren, A., Bults, R., Wac, K., Dokovsky, N., Koprinkov, G., & Widya, I. (2004). Wireless body area networks for healthcare: the MobiHealth project. Studies in Health Technology and Informatics, 108, 181–193. Wuerz, R. C., Swope, G. E., Holliman, C. J., & Vazquez-de Miguel, G. (1995). Online medical direction: a prospective study. Prehospital and Disaster Medicine, 10, 174–177.

ADDITIONAL READING Arntz, H. R., & Somasundaram, R. (2008). Internistische Notfallmedizin. Intensivmedizin, 45, 212–216. .doi:10.1007/s00390-008-0879-x

Heidenreich, G., & Blobel, B. (2009). IT-Standards für telemedizinische Anwendungen - Der Weg zum effizienten Datenaustausch in der Medizin. Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 52, 316–323. doi:10.1007/ s00103-009-0788-6 Norgall, T. (2009). Fit und selbstständig im Alter durch Technik - Von der Vision zur Wirklichkeit? Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 52, 297–305. doi:10.1007/ s00103-009-0789-5 Perlitz, U. (2010) Telemedizin verbessert Patientenversorgung. Deutsche Bank Research. Retrieved 31.01.2010 from www.dbresearch.de Protogerakis, M., Gramatke, A., & Henning, K. (2009a). A Software Architecture for a Telematic Rescue Assistance Systerm. InIFMBE Proceedings Vol. 25, WC2009. Hrsg. v. Dössel, O.; Schlegel, W., 1–4. www.springerlink.com Röhrig, R., & Rüth, R. (2009). Intelligente Telemedizin in der Intensivstation-Patientennaher Einsatz von Medizintechnik und IT in der Intensivmedizin. Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 52, 279–286. Schmidt, S., & Grimm, A. (2009). Versorgungsforschung zur telemedizinische Anwendung. Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 52, 270–278. doi:10.1007/ s00103-009-0794-8 Thomas, E. J., Lucke, J. F., & Wueste, L. (2009). Association of Telemedicine for Remote Monitoring of Intensive Care Patients With Mortality, Complications, and Length of Stay. Journal of the American Medical Association, 302(24), 2671–2678. doi:10.1001/jama.2009.1902

Gries, A., Zink, W., Bernhard, M., Messelken, M., & Schlechtriemen, T. (2005). Einsatzrealität im Notarztdienst. Notfall und Rettungsmedizin, 8, 391–398. .doi:10.1007/s10049-005-0756-0

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KEY TERMS AND DEFINITIONS Telenotarzt: An experienced Emergency Medical Services (EMS) physician who is situated at the competence centre and support the paramedics and the EMS physician on scene in medical and organizational questions. Telemedicine: Support in medical therapy and diagnosis by advanced mobile data transmission between the medical personal on scene and a highly expert physician who is not on site. Competence Centre: The competence centre is responsible for the contact and data transfer to the hospital, the consultation with other institutions (for example family physician, cardiology,

poisoning centre, and so forth) and is staffed with the Telenotarzt. Teleconsultation: Consultation between the Telenotarzt and paramedics or EMS physician at the emergency scene. Emergency Medical System (EMS): German Emergency Medical System is physician based Live Vital Data Transmission: Real time transmission of all vital parameters of a patient and a video live stream from the ambulance and the scene to the Telenotarzt in the competence centre. Quality Management: The Telenotarzt can initiate a shorter drug free interval if the paramedics are the first on scene and can also support a better guideline compliant therapy.

This work was previously published in E-Health, Assistive Technologies and Applications for Assisted Living: Challenges and Solutions, edited by Carsten Röcker and Martina Ziefle, pp. 268-288, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 3.24

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Using Social Web and Semantic Technologies to Monitor Diseases in Limited Environments Ángel Lagares-Lemos Universidad Carlos III de Madrid, Spain Miguel Lagares-Lemos Universidad Carlos III de Madrid, Spain Ricardo Colomo-Palacios Universidad Carlos III de Madrid, Spain Ángel García-Crespo Universidad Carlos III de Madrid, Spain Juan M. Gómez-Berbís Universidad Carlos III de Madrid, Spain

ABSTRACT Information technology and, more precisely, the internet represent challenges and opportunities for medicine. Technology-driven medicine has changed how practitioners perform their roles in DOI: 10.4018/978-1-60960-561-2.ch324

and medical information systems have recently gained momentum as a proof-of-concept of the efficiency of new support-oriented technologies. Emerging applications combine sharing information with a social dimension. This paper presents DISMON (Disease Monitor), a system based on Semantic Technologies and Social Web (SW) to improve patient care for medical diagnosis

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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in limited environments, namely, organizations. DISMON combines Web 2.0 capacities and SW to provide semantic descriptions of clinical symptoms, thereby facilitating diagnosis and helping to foresee diseases, giving useful information to the company and its employees to increase efficiency by means of the prevention of injuries and illnesses, resulting in a safety environment for workers.

INTRODUCTION Nonfatal workplace injuries and illnesses among private industry employers in 2008 occurred at a rate of 3.9 cases per 100 equivalent full-time workers (U.S. Bureau of Labor Statistics, 2009). Due to these sick leaves the companies are forced to hire new workers in order to fill the temporary vacancies, often employing the new members of the staff in short time-frames. Thus, it drives the company to carry out a quick selection process which entails a bad performance of this task and finally it can conclude with a no proper decision. In addition in most of the cases the incorporation of the new employees comprises an in-service training period or/and an adaptation period, therefore an efficiency loss. On the other, hand if the company does not hire new workers, it presents a worse scenario. Hence all these issues can imply loss of money by the company; unsatisfied clients; loss of partners; and unhappy stakeholders. This paper proposes an automatic system for monitoring and helping to foresee the diseases using the social web and semantic technologies, giving useful information to the company and its employees in order to increase the efficiency by means of the prevention of injuries and illnesses, resulting in a safety environment for the workers. The system by means of analyzing the different information exchanged in the social web, will detect the diseases that are suffering the employees of a given company in a particular time. Furthermore the system will inform to the

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company about the spreading of the different illnesses and the recognized patterns. The aim of DISMON is to prevent massive infections and by means of the recognized patterns inferring which environmental conditions of the working place could have been the cause of a given disease, for instance temperature of the offices, contaminated air or water. In addition the system will report to each employee how probably is for them to be infected, based on the profile of the user, as age, location, previous illnesses or allergies in order to compare them with the characteristics of the previous infected workers, obtaining a percentage of the possibilities of the workers to get an illness.

STATE OF THE ART Social Web In latest years, the number of Social Web Sites has increased very quickly; these webs allow the knowledge to be generated just by using the contributions of the users via blogs, wikis, forums, online social networks, and so forth (Kinsella et al., 2009). The Web 2.0 phenomenon made the Web social, initiating an explosion in the number of users of the Web, thus empowering them with a huge autonomy in adding content to web pages, labeling the content, creating folksonomies of tags, and finally, leading to millions of users constructing their own web pages (Breslin & Decker, 2007). Therefore the user participation is the key and the main value of the Social Web. This participation concludes in a “collective intelligence” or “wisdom of crowds” where the opinion taking into account is the one expressed by a group of individuals rather than single or expert opinions answering a question. The concept of collective intelligence, or “wisdom of the crowds” (Surowiecki, 2004), stands that when working cooperatively and sharing ideas, communities can be significantly more productive than individuals working in isolation. Moreover,

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the ability of multitudes to generate accurate information from diverse data sets has been well documented elsewhere and is not unique to Web 2.0 (Surowiecki, 2004). That’s why social web has demonstrated its success with efforts like the Wikipedia, in which the “wisdom of the crowds” is creating and maintaining world’s largest online encyclopedia. The Social Web can be used by anybody with internet connection, but for the Social Web to work properly, the web developers have to provide websites with the capability of being social. This is becoming easier because the costs of gathering and computing the user’s contributions have decreased and today, even the companies with very modest budgets can offer to the users social websites (Gruber, 2007). With the purpose of summarize, the evolution of the web has brought about the Social Web which is based on dynamic public content that is changing depending on the people’s input. The communication inside this web is not just between the machine and the person, but between all the people that is using the web application (Porter, 2008). And it is very important to remark how important has been the mind change into the users, that used to enter into the Internet just to read the webs and at the present time they are involved in the web creation process converting the web in a Social Web.

Semantic Technologies Semantic Technologies have emerged as a new and highly promising context for knowledge and data engineering (Vossen, Lytras, & Koudas, 2007; Fensel & Munsen, 2001), being pointed to out as the future of the Web (Benjamins et al., 2008) and the next step in the evolution of the World Wide Web (Lukasiewicz & Straccia, 2008). The fields under Semantic Technologies are applied allowing support knowledge comprise a wide range of domains (Lytras & García, 2008), including medicine. Moreover, according to Ding

(2010), semantic web is fast moving in a multidisciplinary way. Semantic Technologies, based on ontologies (Fensel, 2002), provide a common framework that enables data integration, sharing and reuse from multiple sources. Durguin and Sherif (2008) portrays the semantic web as the future web where computer software agents can carry out sophisticated tasks for users. Ontologies (Fensel, 2002) are the technological cornerstones of the Semantic Technologies, because they provide structured vocabularies that describe a formal specification of a shared conceptualization. Ontologies were developed in the field of Artificial Intelligence to facilitate knowledge sharing and reuse (Fensel et al., 2001). An ontology can be defined as “a formal and explicit specification of a shared conceptualization” (Stude, Benjamins, & Fensel, 1998). Ontologies provide a common vocabulary for a domain and define, with different levels of formality, the meaning of the terms and the relations between them. Knowledge in ontologies is mainly formalized using five kinds of components: classes, relations, functions, axioms and instances (Gruber, 1993). Languages such as Resource Description Framework (RDF) and Ontology Web Language (OWL) have been developed; these languages allow for the description of web resources, and for the representation of knowledge that will enable applications to use resources more intelligently (Horrocks, 2008). These languages, and the tools developed to support them, have rapidly become de facto standards for ontology development and deployment; they are increasingly used, not only in research labs, but in large scale IT projects (Horrocks, 2008). The Semantic Web consists of several hierarchical layers, where the Ontology layer, in form of the OWL Web Ontology Language (recommended by the W3C), is currently the highest layer of sufficient maturity (Lukasiewicz & Straccia, 2008). Due to the increasing maturity of semantic technologies, the adoption of these technologies in industrial and corporate environments is becom-

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ing closer (Lytras & García, 2008). There have been numerous applications of the Semantic Web in industrial service environments. Applications have been seen in human resources management (e.g. Gómez-Berbís et al., 2008; Colomo-Palacios et al., 2010a; Colomo-Palacios et al., 2010b), software development (Colomo-Palacios et al., 2008; García-Crespo et al., 2009a), tourism (García-Crespo et al., 2009b), learning (Naeve, Sicilia, & Lytras, 2008; Collazos & García, 2007) to cite some of the most relevant and recent cases.

Using Social Web in Medicine According to Giustini (2006), Web 2.0 is changing medicine. Several authors have identified tools that highlight the usefulness of the social web as a modifying element for future medical practice and education (McLean et al., 2007). In the education field, many works have been produced that recommend the usage of social web tools for freshmen education (Kamel-Boulos & Wheeler, 2007; Sandars & Schroter, 2007). In the working domain, the utility of Web 2.0 has been demonstrated in professional organization and collaboration (Schleyer et al., 2008; Eysenbach, 2008), volunteer recruitment (Seeman, 2008), patient self care and self-empowerment and research (Seeman, 2008), remote patient monitoring (Czejdo & Baszun, 2010) among other application areas. On the other hand, some researchers have also identified threats regarding the usage of Web 2.0 in the medical environment, such as unwanted behaviors in patients – for example, the avoidance of consultations with physicians (Hughes et al., 2008) or inaccurate online information (Seeman, 2008; Hughes et al., 2008). In relation to the latter case, some initiatives have recently emerged to approve medical web content, using both supervised and automatic methods (Karkaletsis et al., 2008). Moreover, since the internet is crucial for developing nations (Khasawneh, 2009), this can be a way to improve medical services in these nations.

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Recently, two new terms have come into use: Medicine 2.0 and Health 2.0. According to Hughes et al. (2008), Medicine 2.0 is the broader concept and umbrella term which includes consumerdirected “medicine” or Health 2.0. Medicine 2.0 is the use of a specific set of Web tools (blogs, podcasts, tagging, wikis, etc) by entities in health care including doctors, patients, and scientists, using principles of open source and generation of content by users, and the power of networks in order to personalize health care, collaborate, and promote health education. However, Web 2.0 capacities can be extended by the use of SW. Its usefulness has been identified in medical environments from a generic point of view (Giustini, 2007) as well as in its application (Karkaletsis et al., 2008; Falkman et al., 2008). DISMON, the approach presented in this paper combines Web 2.0 capacities and SW in order to provide semantic descriptions of clinical symptoms, thereby facilitating diagnosis and helping to foresee the diseases, giving useful information to the company and its employees in order to increase the efficiency by means of the prevention of injuries and illnesses, resulting in a safety environment for the workers.

Using Semantic Technologies in Medicine The evolution of the semantic web and knowledge management technologies in the last years set a new context for the exploitation of patient-centric strategies based on well-defined semantics and knowledge (Lytras, Sakkopoulos, & Ordóñez de Pablos, 2009). According to Fuentes-Lorenzo, Morato, and Gómez-Berbís (2009), semantic technologies can be exploited to reveal machinereadable latent relationships within information in the medical field, where the homogeneity of terminology is particularly problematic. In this way, modern formal ontology facilitates the creation of knowledge-based systems such as

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those required for managing medical information (Sicilia et al., 2009). Ontologies are currently being used in the medical domain as an integration of heterogeneous sources (e.g. Deogun & Spaulding, 2010; Dogac et al., 2006; Orgun & Vu, 2006), as a tool to engineer formal knowledge descriptions from existing and diverse medical terminologies (Lee, Supekar, & Geller, 2006) or to enable differential diagnosis (García-Crespo et al., 2010a). A recent review of the use of ontologies in the medical domain can be found in Schulz, Stenzhorn, Boeker, and Smith (2009). In addition, the adaptation of clinical ontologies to real world scenarios is depicted in Stenzhorn, Schulz, Boeker, and Smith (2008).

State of the Art Conclusion The social web comprises an environment to share information. The users employ the social web to post comments regarding personal information, current status, opinions, links to relevant information, etc. The social web offers a place where the medical knowledge can be shared. According to Lytras, Sakkopoulos, and Ordóñez de Pablos (2009), semantic web provides an excellent scientific domain capable of developing sound propositions and applications for the health domain. Moreover, the use of semantic technologies in social webs allows the possibility of process the amount data by means of a computer, offering interoperability between the users and the system. This is why the use of medical ontologies in limited environments can be used for detecting injuries and illnesses which are taking place within an organization. This is the case of the intranet of a given company. It can be considered as a corporative social web where the semantic technologies can be applied. Following the path described in García-Crespo et al. (2010a, 2010b), this paper presents DISMON, a tool that combines Web 2.0 capacities and SW in order to provide semantic descriptions of clinical

symptoms, thereby facilitating diagnosis in limited environments.

ARCHITECTURE AND INTERNALS The architecture has been designed to offer the best flexibility and usability for the development and the maintenance of the system, dividing it in three layers with the required elements in each layer to cover all the functionalities (Figure 1). The first layer is the closest to the user. It contains the DISMON interface to organizational applications. This component provides to the user the availability of interact with the system using existing applications. This layer also contains the crawler. This component is in charge of exploring the intranet of the company surfing for symptoms. The second layer contains the main part of the system, in this layer all the data is processing using the four subsystems designed to work with the data provided by the crawler of the first level and the data obtained from the third layer. The third layer contains the data correspondent to the diseases and the employees of the company, this data will be processed by the second layer.

Interface Layer This layer contains two elements, the Enterprise Application Interface and the Crawler. The Company Intranet is located at the same level in the graphic of the architecture but it is not a part of the system. This layer is connected with three external elements, the rest of the applications of the company, the Intranet and it is connected with the second layer too. The first element is the crawler. A crawler (also called a spider or robot) is a program controlled by a crawl control module that “browses” the Web. It collects documents by recursively fetching links from a set of start pages (Pokorny, 2004). In this case, this element crawls a given set of

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Figure 1. DISMON Schema of the architecture

pages edited by employees in search of relevant information about diseases. The intranet is the principal resource to look for information related with diseases suffered by the personnel. The relevant parts of the intranet for the DISMON system are the opinions of the employees, where the information about the diseases inside the company will be extracted. This opinions are inserted into the intranet by the employees via blogs, microblogs, forums and suggestion boxes, therefore the system will search for information about diseases in all the public places of the intranet where the users has the ability to publish. The web crawler of DISMON system scans the intranet to look for the opinions mentioned. Once detected, the obtained URLs will be processed in the Logic layer. This component populates an ontology using RDF triples obtained from HTML obtained from the Crawler using wrappers based on predefined patterns.

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The second element in this layer is the Enterprise Applications Interface. This component allows the communication with external solutions using a web service.

Logic Layer This layer is between the other two layers that conform the system and it is the one that contains the main functionality of the software because it is dedicated to process all the information coming in from the other two layers, then this layer will process and analyze the information and finally will return the results to be shown to the user. The Natural Language Processing Engine module entails a Natural Language Processing (or NLP) that provides with intelligence to the whole system. NLP is in charge of processing all the comments, posts, status and any other text information updated from the employees to the intranet. The objective of processing all this

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information is to give a computer understanding to all the data shared within the workers of the company. This processing will be carried out making use of medical knowledge by means of medical and regular dictionaries. For the extraction of comprehensive information, the system uses of several subtasks: natural language understanding, named entity recognition and relationship extraction. The main objective of these subtasks is to identify text related with medical information and to recognize the context of the situation, the participants involved and their relationships. The natural language understanding deals with the contextualization of a given information, in order to understand what the information is related with. Then it can be inferred if the data submitted by the worker is about any medical situation, therefore taking in account by the system. The named entity recognition allows identifying different concepts, mapping them in categories, with the goal of obtaining classified information. Whether it is possible the NLP module will extract the information related with the persons involved and the current context of the situation. Finally the relationship extraction will infer the possible links between employees, places and tasks. In this module several well known tools are implemented, including GATE (General Architecture for Text Engineering) to annotate in a syntactic way noun phrases and JAPE (Java Annotation Patterns Engine) to extract all phrases related to symptoms. Once symptom evidence is found, the Disease Mapping Engine will be responsible of its classification and storage. The second element in this layer is the Disease Mapping Engine. This engine will match the words found in the URLs returned by the crawler with the diseases in the ontology, therefore this engine provides the system with the capability of recognize if the opinions found in the intranet are related to diseases or they are related to any other topic. It is also responsible of dealing with disease ontology and stores information of symp-

tom evidences in the persistence layer. It hides competence complexity to other components of the system. The last element in this layer is the Pattern Recognition System. With the information of every employee in the database, the system will match the current disease employee characteristics with the similar characteristics of other employees, concluding a percentage of probability for the similar employees to become ill. These characteristics are: • • • • • • •

Age Sex Previous diseases Work places Family members Race Feeding (Vegetarian, Carnivorous, etc.)

Persistence Layer There are two databases used in order to be able to map the ontologies. These two databases refer to the employees of the company and the different injuries and illnesses. The database of the employees comprises the different profiles of all the workers of the company. It contains any relevant information of the persons involved in the organization breakdown structure. The data managed are: • • • •

Demographic information. Place of work inside the organization. Role within the organization. Relationships.

The database of the different injuries and illnesses consists on all the medical terminology employed for each injury or sickness and the any relevant information to take in account when the injury or the illness could be produced by environment conditions or it could entail any infection to other people. Then the data managed are:

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• • • • • •

Medical name/s of the injury or illness. Common name/s of the injury or illness. Possible causes. Infectious level. At risk group. Possible days away from work.

The diseases ontology allows establishing the relations between the diseases. Given these relations implemented, DISMON is able to group diseases. Different diseases in the database can have almost the same symptoms and the same treatment, the ontology adds the intelligence to the database and permits to consider these two diseases together. Both the ontology and some of the intelligence given to the systems inherits directly from García-Crespo et al. (2010a).

USE CASE To explain the realization of DISMON in a functional environment, as referred to in the previous section, a use case will be included. GreenHR is an organization devoted to human resources consultancy that implements DISMON. The company has an intranet where its 3,000 employees consult and add information, using the company’s internal blog, forums and microblogs. The headquarters of the company is placed in Sydney, where a total of 1,500 work. There are several branches of GreenHR in the cities of Paris, London and New York. The director of human resources and the marketing manager, working in the Sydney headquarter, are planning a trip to the branch of London. The human resources director is 45 years old and is a healthy person; he has no children and is single. The marketing director is 58 years old, has had pneumonia nine months ago and has a wife and two children. At the branch of London two days ago it has been detected an outbreak of flu, according to user’s comments about common symptoms of this disease (Fever, Aches, Chills,

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Tiredness, etc.). This detection is performed by ODDIN, an ontology-driven medical diagnosis system (García-Crespo et al., 2010a). Once the system interacts with scheduling system it detects that HR and Marketing managers are planning to visit London and, given that the latter suffered from pneumonia, this trip is not desirable. In such circumstance, DISMON warns the other system to ask for cancelling the trip to both directors. With the impending visit of the directors, DISMON assess potential risks, detecting that the director of marketing is among the risk groups and thus advising against his trip. The director of human resources is warned by DISMON that there is an outbreak of flu in London, but he is not considered within the risk groups. Besides all this, DISMON has alerted to all risk groups who work at headquarters in London to take precautions and avoid contact with the departments most affected. Given this example, DISMON has prevented several aspects: •

•

•

The possible infection of the director of marketing, given their background and condition. The transportation of the virus from London to Sydney, perhaps preventing the spread of many employees working at the headquarters of Sydney and are easily infected by the influenza virus. There will be partially mitigated infections in the London headquarters.

CONCLUSION AND FUTURE WORK It is a fact that systems that allow foreseeing and preventing contagion in an automated manner improve the quality of life, welfare, and in this particular case, the efficiency and functioning of companies. Moreover, the advent of the information age represents both a challenge and an opportunity for knowledge management and employee relationship management. In this new scenario,

DISMON

counting on with tools to prevent diseases in the scope of the corporation could improve personnel efficiency and this improved efficiency could be a competitive advantage for the company. DISMON, following the path of some previous works (García-Crespo et al., 2010a), is an innovative system that is based on semantic and social web technologies along with NLP. The combination these technologies allow creating a system that provides the user with a tool that seeks to foresee any medical problems in the workplace. This tool is not only relevant to health workers. Counting on a tool that enables the diagnosis of diseases to improve employee performance could be a competitive advantage for the firm. Limitations on the system can be found in two main areas. On the one hand, confidentiality issues and other matters relative to the acceptation of both users and organizations. DISMON is based on the analysis of personal blogs, nanoblogs and other Web 2.0 tools. Given the fact that many of these resources are personal, the overall acceptation of the system depends on the capacity of acquiring information from this sources. On the other hand, NLP techniques still suffer from imprecision and dealing with aspects as ironies is not easy as reveled in previous studies (e.g. García-Crespo et al., 2010c). Future work will focus on the expansion of the system in order to read data from any resource available in the internet. By doing this, DISMON will be able to detect diseases in broader contexts such as cities, regions or countries.

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Breslin, J. G., & Decker, S. (2007). The Future of Social Networks on the Internet: The Need for Semantics. IEEE Internet Computing, 11(6), 86–90. doi:10.1109/MIC.2007.138 Collazos, C. A., & García, R. (2007). Semanticssupported cooperative learning for enhanced awareness. International Journal of Knowledge and Learning, 3(4/5), 421–436. doi:10.1504/ IJKL.2007.016703 Colomo Palacios, R., García Crespo, A., Gómez Berbís, J. M., Casado-Lumbreras, C., & Soto-Acosta, P. (2010b). SemCASS: technical competence assessment within software development teams enabled by semantics. International Journal of Social and Humanistic Computing, 1(3), 232–245. doi:10.1504/IJSHC.2010.032685 Colomo-Palacios, R., Gómez-Berbís, J. M., García-Crespo, A., & Puebla-Sánchez, I. (2008). Social Global Repository: using semantics and social web in software projects. International Journal of Knowledge and Learning, 4(5), 452–464. doi:10.1504/IJKL.2008.022063 Colomo-Palacios, R., Ruano-Mayoral, M., SotoAcosta, P., & García-Crespo, Á. (2010a). The War for Talent: Identifying competences in IT Professionals through semantics. International Journal of Sociotechnology and Knowledge Development, 2(3), 26–36. Czejdo, B. D., & Baszun, M. (2010). Remote patient monitoring system and a medical social network. International Journal of Social and Humanistic Computing, 1(3), 273–281. doi:10.1504/ IJSHC.2010.032688 Deogun, J. S., & Spaulding, W. (2010). Conceptual Development of Mental Health Ontologies. In Ras, Z. W., & Tsay, L.-S. (Eds.), Advances in Intelligent Information Systems. Studies in Computational Intelligence (Vol. 265, pp. 299–333). Berlin: Springer.

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Ding, Y. (2010). Semantic Web: Who is who in the field — a bibliometric analysis. Journal of Information Science, 36(3), 335–356. doi:10.1177/0165551510365295 Dogac, A., Laleci, G. B., Kirbas, S., Kabak, Y., Sinir, S., & Yidiz, A. (2006). Artemis: Deploying semantically enriched Web services in the healthcare domain. Information Systems, 31(4-5), 321–339. doi:10.1016/j.is.2005.02.006 Durguin, J. K., & Sherif, J. S. (2008). The semantic web: a catalyst for future e-business. Kybernetes, 37(1), 49–65. doi:10.1108/03684920810850989 Eysenbach, G. (2008). Medicine 2.0: Social Networking, Collaboration, Participation, Apomediation, and Openness. Journal of Medical Internet Research, 10(3), e22. doi:10.2196/jmir.1030 Falkman, G., Gustafsson, M., Jontell, M., & Torgersson, O. (2008). SOMWeb: A Semantic Web-Based System for Supporting Collaboration of Distributed Medical Communities of Practice. Journal of Medical Internet Research, 10(3), e25. doi:10.2196/jmir.1059 Fensel, D. (2002). Ontologies: A silver bullet for knowledge management and electronic commerce. Berlin: Springer. Fensel, D., & Munsen, M. A. (2001). The Semantic Web: A Brain for Humankind. IEEE Intelligent Systems, 16(2), 24–25. doi:10.1109/ MIS.2001.920595 Fensel, D., van Harmelen, F., Horrocks, I., McGuinness, D. L., & Patel-Schneider, P. F. (2001). OIL: An ontology infrastructure for the semantic web. IEEE Intelligent Systems, 16(2), 38–45. doi:10.1109/5254.920598 Fuentes-Lorenzo, D., Morato, J., & GómezBerbís, J. M. (2009). Knowledge management in biomedical libraries: A semantic web approach. Information Systems Frontiers, 11(4), 471–480. doi:10.1007/s10796-009-9159-y

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García Crespo, A., Chamizo, J., Rivera, I., Mencke, M., Colomo Palacios, R., & Gómez Berbís, J. M. (2009b). SPETA: Social pervasive e-Tourism advisor. Telematics and Informatics, 26(3), 306–315. doi:10.1016/j.tele.2008.11.008 García-Crespo, A., Colomo-Palacios, R., Gómez-Berbís, J. M., Chamizo, J., & MendozaCembranos, M. D. (2010b). S-SoDiA: a semantic enabled social diagnosis advisor. International Journal of Society Systems Science, 2(3), 242–254. doi:10.1504/IJSSS.2010.033492 García-Crespo, A., Colomo-Palacios, R., Gómez-Berbís, J. M., & Mencke, M. (2009a). BMR: Benchmarking Metrics Recommender for Personnel issues in Software Development Projects. International Journal of Computational Intelligence Systems, 2(3), 257–267. doi:10.2991/ ijcis.2009.2.3.7 García-Crespo, A., Colomo-Palacios, R., GómezBerbís, J. M., & Ruiz-Mezcua, B. (2010c). SEMO: a framework for customer social networks analysis based on semantics. Journal of Information Technology, 25(2), 178–188. doi:10.1057/jit.2010.1 García-Crespo, A., Rodríguez, A., Mencke, M., Gómez-Berbís, J. M., & Colomo-Palacios, R. (2010a). ODDIN: Ontology-driven differential diagnosis based on logical inference and probabilistic refinements. Expert Systems with Applications, 37(3), 2621–2628. doi:10.1016/j. eswa.2009.08.016 Giustini, D. (2006). How Web 2.0 is changing medicine. British Medical Journal, 333, 1283– 1284. doi:10.1136/bmj.39062.555405.80 Giustini, D. (2007). Web 3.0 and medicine. Make way for the semantic web. British Medical Journal, 335, 1273–1274. doi:10.1136/bmj.39428.494236. BE

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Gómez Berbís, J. M., Colomo Palacios, R., García Crespo, A., & Ruiz Mezcua, B. (2008). ProLink: a semantics-based social network for software projects. International Journal of Information Technology and Management, 7(4), 392–405. doi:10.1504/IJITM.2008.018656 Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199–220. doi:10.1006/knac.1993.1008 Gruber, T. R. (2007). Collective knowledge systems: Where the Social Web meets the Semantic Web. Web Semantics: Science. Services and Agents on the World Wide Web, 6, 4–13. Horrocks, I. (2008). Ontologies and the Semantic Web. Communications of the ACM, 51(12), 58–67. doi:10.1145/1409360.1409377 Hughes, B., Joshi, I., & Wareham, J. (2008). Health 2.0 and Medicine 2.0: tensions and controversies in the field. Journal of Medical Internet Research, 10(3), e23. doi:10.2196/jmir.1056 Kamel-Boulos, M. N., & Wheeler, S. (2007). The emerging Web 2.0 social software: an enabling suite of sociable technologies in health and health care education. Health Information and Libraries Journal, 24, 2–23. doi:10.1111/j.14711842.2007.00701.x Karkaletsis, V., Stamatakis, K., Karmapyperis, P., Svátek, V., Mayer, M. A., Leis, A., et al. (2008, July 21-25). Automating Accreditation of Medical Web Content. In Proceedings of the 18th European Conference on Artificial Intelligence (ECAI 2008), 5th Prestigious Applications of Intelligent Systems (PAIS 2008), Patras, Greece (pp. 688-692). Khasawneh, A. (2009). Arabia online: internet diffusion in Jordan. International Journal of Society Systems Science, 1(4), 396–401. doi:10.1504/ IJSSS.2009.026511

Kinsela, S., Passant, A., Breslin, J. G., Decker, S., & Jaokar, A. (2009). The Future of Social Web Sites: Sharing Data and Trusted Applications with Semantics. Advances in Computers, 76(4), 121–175. doi:10.1016/S0065-2458(09)01004-3 Lee, Y., Supekar, K., & Geller, J. (2006). Ontology integration: Experience with medical terminologies. Computers in Biology and Medicine, 36(7-8), 893–919. doi:10.1016/j. compbiomed.2005.04.013 Lukasiewicz, T., & Straccia, U. (2008). Managing uncertainty and vagueness in description logics for the Semantic Web. Web Semantics: Science. Services and Agents on the World Wide Web, 6(4), 291–308. doi:10.1016/j.websem.2008.04.001 Lytras, M. D., & García, R. (2008). Semantic Web applications: a framework for industry and business exploitation - What is needed for the adoption of the Semantic Web from the market and industry. International Journal of Knowledge and Learning, 4(1), 93–108. doi:10.1504/ IJKL.2008.019739 Lytras, M. D., Sakkopoulos, E., & Ordóñez-De Pablos, P. (2009). Semantic Web and Knowledge Management for the health domain: state of the art and challenges for the Seventh Framework Programme (FP7) of the European Union (20072013). International Journal of Technology Management, 47(1-3), 239–249. doi:10.1504/ IJTM.2009.024124 McLean, R., Richards, B. H., & Wardman, J. I. (2007). The effect of Web 2.0 on the future of medical practice and education: Darwikinian evolution or folksonomic revolution? The Medical Journal of Australia, 187(3), 174–174. Naeve, A., Sicilia, M. A., & Lytras, M. D. (2008). Learning processes and processing learning: from organizational needs to learning designs. Journal of Knowledge Management, 12(6), 5–14. doi:10.1108/13673270810913586

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Orgun, B., & Vu, J. (2006). HL7 ontology and mobile agents for interoperability in heterogeneous medical information systems. Computers in Biology and Medicine, 36(7-8), 817–836. doi:10.1016/j.compbiomed.2005.04.010 Pokorny, J. (2004). Web searching and information retrieval. Computing in Science & Engineering, 6(4), 43–48. doi:10.1109/MCSE.2004.24 Porter, J. (2008). Designing for the Social Web. Berkeley, CA: New Riders. Sandars, J., & Schroter, S. (2007). Web 2.0 technologies for undergraduate and postgraduate medical education: an online survey. Postgraduate Medical Journal, 83, 759–762. doi:10.1136/ pgmj.2007.063123 Schleyer, T., Spallek, H., Butler, B. S., Subramanian, S., Weiss, D., & Poythress, M. S. (2008). Facebook for Scientists: Requirements and Services for Optimizing How Scientific Collaborations Are Established. Journal of Medical Internet Research, 10(3), e24. doi:10.2196/jmir.1047 Schulz, S., Stenzhorn, H., Boeker, M., & Smith, B. (2009). Strengths and limitations of formal ontologies in the biomedical domain. Electronic Journal of Communication Information & Innovation in Heath, 3(1), 31–45. Seeman, N. (2008). Web 2.0 and chronic illness: New Horizons, New Opportunities. Healthcare Quarterly (Toronto, Ont.), 11(1), 104–110.

Sicilia, J. J., Sicilia, M. A., Sánchez-Alonso, S., García-Barriocana, E., & Pontikaki, M. (2009). Knowledge Representation Issues in Ontology-based Clinical Knowledge Management Systems. International Journal of Technology Management, 47(1-3), 191–206. doi:10.1504/ IJTM.2009.024122 Stenzhorn, H., Schulz, S., Boeker, M., & Smith, B. (2008). Adapting Clinical Ontologies in Real-World Environments. Journal of Universal Computer Science, 14(22), 3767–3780. Studer, R., Benjamins, V. R., & Fensel, D. (1998). Knowledge engineering: Principles and methods. Data & Knowledge Engineering, 25(1-2), 161–197. doi:10.1016/S0169-023X(97)00056-6 Surowiecki, K. (2004). The wisdom of crowds. New York: Doubleday. U.S. Department of Labor, Bureau of Labor Statics (BLS). (2009). Nonfatal occupational injuries and illnesses from the Survey of Occupational Injuries and Illnesses. Retrieved from http:// www.bls.gov/iif/ Vossen, G., Lytras, M., & Koudas, N. (2007). Editorial: Revisiting the (Machine) Semantic Web: The Missing Layers for the Human Semantic Web. IEEE Transactions on Knowledge and Data Engineering, 19(2), 145–148. doi:10.1109/ TKDE.2007.30

This work was previously published in Journal of Information Technology Research (JITR), Volume 4, Issue 1 edited by Mehdi Khosrow-Pour, pp. 48-59, copyright 2011 by IGI Publishing (an imprint of IGI Global).

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Section IV

Utilization and Application

This section discusses a variety of applications and opportunities available that can be considered by practitioners in developing viable and effective clinical technologies programs and processes. This section includes over 20 chapters reviewing certain utilizations and applications of clinical technologies, such as artificial intelligence and social network analysis in healthcare. Further chapters show case studies from around the world, and the applications and utilities of clinical technologies. The wide ranging nature of subject matter in this section manages to be both intriguing and highly educational.

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Chapter 4.1

Speech-Based Clinical Diagnostic Systems Jesús Bernardino Alonso Hernández University of Las Palmas de Gran Canaria, Spain Patricia Henríquez Rodríguez University of Las Palmas de Gran Canaria, Spain

INTRODUCTION It is possible to implement help systems for diagnosis oriented to the evaluation of the fonator system using speech signal, by means of techniques based on expert systems. The application of these techniques allows the early detection of alterations in the fonator system or the temporary evaluation of patients with certain treatment, to mention some examples. The procedure of measuring the voice quality of a speaker from a digital recording consists of quantifying different acoustic characteristics of speech, which makes it

DOI: 10.4018/978-1-60960-561-2.ch401

possible to compare it with certain reference patterns, identified previously by a “clinical expert”. A speech acoustic quality measurement based on an auditory assessment is very hard to assess as a comparative reference amongst different voices and different human experts carrying out the assessment or evaluation. In the current bibliography, some attempts have been made to obtain objective measures of speech quality by means of multidimensional clinical measurements based on auditory methods. Well-known examples are: GRBAS scale from Japon (Hirano, M.,1981) and its extension developed and applied in Europe (Dejonckere, P. H. Remacle, M. Fresnel-Elbaz, E. Woisard, V. Crevier-Buchman, L. Millet, B.,1996), a set of

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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perceptual and acoustic characteristics in Sweden (Hammarberg, B. & Gauffin, J., 1995), a set of phonetics characteristics with added information about the excitement of the vocal tract. The aim of these (quality speech measurements) procedures is to obtain an objective measurement from a subjective evaluation. There exist different works in which objective measurements of speech quality obtained from a recording are proposed (Alonso J. B.,2006), (Boyanov, B & Hadjitodorov, S., 1997),(Hansen, J.H.L., Gavidia-Ceballos, L. & Kaiser, J.F., 1998),(Stefan Hadjitodorov & Petar Mitev, 2002),(Michaelis D.; Frohlich M. & Strube H. W., 1998),(Boyanov B., Doskov D., Mitev P., Hadjitodorov S. & Teston B.,2000),(Godino-Llorente, J.I.; AguileraNavarro, S. & Gomez-Vilda, P., 2000). In these works a voiced sustained sound (usually a vowel) is recorded and then used to compute speech quality measurements. The utilization of a voiced sustained sound is due to the fact that during the production of this kind of sound, the speech system uses almost all its mechanisms (glottal flow of constant air, vocal folds vibration in a continuous way, …), enabling us to detect any anomaly in these mechanisms. In these works different sets of measurements are suggested in order to quantify speech quality objectively. In all these works one important fact is revealed; it is necessary to obtain different measurements of the speech signal in order to compile the different aspects of acoustic characteristics of the speech signal.

• • • •

Time Domain Spectral Domain Cepstral Domain Inverse Model Domain

Most works in digital speech signal processing are based on these domains. However, other works use new domains derived from the former ones. In the following section the most important features of each domain are described.

Time Domain A high quality speech signal possesses a more regular envolope than a low quality speech signal. This fact is more evident in short time intervals. The main phenomena that enable us to distinguish between high quality speech and low quality speech are: The energy of the speech signal in a short time interval changes considerably between two consecutive intervals in low quality speech whereas in high quality speech there is a less change in energy. (Figure 1) In low quality speech unperiodicity (without perdiodicity) intervals during voiced sustained speech appear. Figure 1. Speech signal in time domain: the five Spanish and sustained vowels are illustrated. The upper figure is a speaker with high quality speech. The lower figure is a speaker with low quality speech.

BACKGROUND A speech recording gives different characteristics of the speech quality of a speaker. The recorded speech signal can be represented in different domains. Each domain shows some of the speech characteristics in a preferential way. The main domains studied in speech processing are:

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Figure 2. Sustained voiced sound during a short time interval from a high quality speech (left) and from a low quality speech (right).

Spectral Domain A low quality speech (a voiced sustained sound) has the following characteristics: • • • •

Less regularity of the spectral envelope, mainly in low frequencies. More percentage of energy in low frequencies with regard to the total energy. Energy blocks in high frequencies. These blocks are caused by glottal noise. A great change of the power spectrum from a frame with regard to contiguous frames. (Figure 2)

High quality speech concentrates its energy around certain formants, mainly the first and the third formants, whereas low quality speech has noise components around the formants. High quality speech has great spectral wealth. However, low quality speech has a little amount of harmonic component, mainly concentrated in very low frequencies. (Figure 3) The amount of spectral wealth is a characteristic of the voice of a certain speaker. However, the spectral wealth variation in time (during the production of a sustained voiced sound) is indeed an indicator of low quality speech. Another characteristic in low quality speech (during the production of a sustained voiced sound)

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is its variations in vibration rhythm of vocal folds, i.e. frequency variation in pitch frequency. Figure 3. Estimated specgram from a high quality speech (top) and from a low quality speech (bottom). The five Spanish vowels are pronounced. The sample frequency is 22050 Hz.

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Cepstral Domain Characteristics to evaluate the speech quality can be identified in the cepstral domain: envelope of the spectrum, spectral wealth, harmonics and noise components identification, etc. A sustained voiced sound with three times the pitch period in length is used in the cepstral domain.(Figure 4) The spectral wealth of a speech signal can be quantified by the amplitude and width of the cepstral component of the pitch frequency. The existence of a peak with great amplitude indicates the presence of notorious energy in that harmonic component. This is a characteristic of high quality speech. A reduced width of the cepstral peak corresponding to the pitch indicates the high stability of the pitch frequency for three consecutive periods of pitch. This is also a characteristic of high quality speech. Characteristics such as amplitude and width of cepstral peak corresponding to the second harmonic can be also used to distinguish between high and low quality speech. High quality speech possesses a cepstral peak corresponding to the first harmonic narrower than the cepstral peak corresponding to the second harmonic. Glottal noise in speech signal can be estimated by means of the relationships among different

regions in the cepstral domain: the harmonic component (ceptrals components of pitch and its harmonics) and noise component (the remaining ceptrals components).

Inverse Model Domain In this domain, the waveform of air pulse produced by the vocal folds during the production of a sustained voiced sound is estimated. The air pulse is also called residual signal or glottal flux. The estimatation is obtained with an inverse filter applied to the speech signal in order to eliminate the vocal tract effect and lips radiation effect. The quality of speech can be quantified by some features of the glottal signal such as values of amplitude, time in which vocal folds start to open, time in which vocal folds are completely open, time in which the closing phase of vocal folds starts and different relationships between different times in the glottal cycle: open quotient, speed quotient, closing quotient, etc.

Non Linear Domain The main comercial systems to evaluate speech quality from a recording objectively (Dr Speech (Tiger Elemetric),SSVA (System for Sigle Voice

Figure 4. A high quality speech (top) and a low quality speech (bottom) in the corrected power cepstrum.

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Analysis), MDVP (Multi-Dimensional Voice Program), EVA (Evaluation Vocal Assistee), CSL (Computerized Speech Laboratory) PRAAT, VISHACSRE (Computerized Speech Research Environment), MEDIVOZ, etc) do not assess nonlinear characteristics in speech signal. The most popular model of characterization of the production voice system is a time-variant system, based on linear acoustics theories. It consists of a source/filter model. The existence of variations in spectral amplitude of speech signal in the fundamental frequency leads us to cvonsider a nonlinear behaviour of speech signal. A fundamental frequency (fs) and a subharmpnic (fs/2) have been identified in the speech signal (Xuejing Sun & Yi Xu, 1995). A subharmonic effect is the amplitude modulation or/and the frequency modulation. Other authors indicate that 31% of speakers with pathological speech have subharmonics in speech. However, the existence of subharmonics has also been identified in high quality speech (Haben, Kost & Papagiannis, 2003),. It is estimated that 10.5% of speakers with healthy speech have subharmonics, not being a symptom of an anomalous speech. There exist two theories to justify the presence of subharmonics: •

•

Titza theory (TItze, IR., 1994): subharmonics are due to mechanical or geometric asymmetries between vocal folds. Svec theory (Svec JG, Schutte HK, Miller DG, 1996): subharmonic frequency is due to the combinations of two vibrational modes (biphonation: the presence of two main frequencies) whose frequencies have a 3:2 relation.

Nevertheless, both theories are the same according to (Neubauer J., Eysholdt P., Eysholdt U., Herzel H., 2001), where the authors point out that biphonation is due to asymmetry between the left and right-hand vocal folds or to desynchronization in the back-forth vibration, (Haben C.M., Kost

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K. & Papagiannis G., 2003). Assymmetry and desynchronization are caused by differences in masses and viscoelastic properties between the vocal folds. This can be modelled by nonlinear phonation. In (Ayache S., Maurice Ouaknine, Dejonkere P., Prindere P. & Giovanni A., 2004) nonlinear models are suggested in order to explain the effect of mucus viscosity of vocal folds (mucus in vocal folds surface generates superficial tension and causes adhesion). In the traditional model of the vocal tract, sound wave propagation is assumed to be plain wave propagation. However, sound pressure measurements and volume variation measurements are better fitted to a nonlinear model of dynamics fluid. This stems from turbulences (or even periodic turbulences) produced by cavities between the vocal folds and the false vocal folds. This turbulence excites the vocal tract in the closing phase of vocal folds. Fractal dimension has been studied by some authors. They conclude that high quality speech signal has a low dimensionality. It is stated (Orlikoff R.F., Baken R. J., 2003), that the amount of alinearities in the speech system is an indicator of anormal phonation and it has been suggested that phase space dimensionality, used for the attractor characterization, could be related to the amount of mass of vocal folds.

QUALITY SPEECH QUANTIFICATION In the previous section a description of the different features of speech signal in different domains has been given. Theses features permit us to evaluate the speech quality. Each feature characterizes a physical phenomenum that is involved in voice production. A physical phenomenum can appear in different domains. In this work a set of physical phenomena to make a correct documentation of voice quality has been identified. The four physical phenomena identified are:

Speech-Based Clinical Diagnostic Systems

•

•

•

•

Voice stability: this is the ability of a speaker to create a constant intesity air flux in order to excite the vocal folds (during a sustained voiced sound). This physical phenomenum is quantified from measurements of speech stability. Spectral wealth: this is the ability to generate a periodical movement in the vocal folds (during a sustained voiced sound) and produce a voiced excitation of the vocal tract with a great amount of spectral components. This physical phenomenum is quantified computing the pitch frequency stability and by the number of harmonics with high energy in different frequency bands. Presence of noise: this is related to the presence of glottal noise in speech signal during the phonation of a sustained voiced sound. The presence of glottal noise is due to problems in the closing phase of vocal folds. This physical phenomenum is quantified by measuring the presence of nonstationary noise in speech signal. Vocal folds irregularities: alinearities in speech system are caused by an anomalous working of vocal folds. This is due to irregularities in masses involved in closing phase of vocal folds, asymmetric movement of vocal folds, and factors related to vocal folds mucus. These phenomena are quantified by means of nonlinear behaviour of speech signal.

An anormal speech shows unless one of the values corresponding to the quantification of the four physical phenomena is out of the normal

range. This procedure of quality speech quantification permits us to identify anomalous speech qualities from diverse origins. These four kinds of physical phenomena can be quantified in different domains, existing different objective measurements of speech quality which are capable of quantifying with more or less accuracy a single physical phenomenum.

FUTURE TRENDS In general, it is impossible to identify pathology in the fonator system using only a speech recording. This is stated by various authors. This stems from the fact that the acoustic characteristics of two speakers with different pathologies in the fonator system can be similar. Even in a visual inspection of the larynx the identity of the pathology cannot be determined. Furthermore, the coexistence of more pathology of the fonator system is also frequent. Nevertheless, several works have focused on identifying the presence of anomalies in the fonator system. An automatic detection system of anomalies in speech system has the same diagram as a voice recognition system (see Figure 5). The “Voice acquisition” block digitalizes speech signal. In this block, a discrimination between speech signal and noise is usually made and the segmentation of speech signal in frames is also carried out. In the “Parameterization” block the speech quality is quantified using diverse quality measurements for each frame into which the speech signal is divided. The quantification enables us to identify differential characteristics among the different classification units. In our case, the classi-

Figure 5. Diagram block of the automatic detection system of anomalies in the fonator system

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fication units are healthy and pathological speech. In this block, each speech frame is turned into a characteristics vector (or measurements vector). Some measurements average certain quantifications of an acoustic characteristic or evaluate its time evolution during the phonation. An automatic classification of the characteristics vector is made in the “classification” block. The classification systems include Support Vector Machines, Neural Networks, etc.. In our case, the classification for each characteristics vector is between healthy speech and pathological speech. We propose carrying out clinical studies in order to assess the usefulness of speech quality quantification automatic systems in speech therapy, otolaryngology and phoniatry. These studies will permit the application of the proposed protocol to measure the speech quality in fields such as the assessment of a surgical operation, documentation of a treatment evolution, medicallegal documentation and telemedicine. It will be possible to implement automatic classification systems between healthy and pathological speech from databases with different qualities of speech, or even systems capable of automatically giving a measurement of the level of disphonia. These systems can be used in a screening evaluation or in a speech therapist evaluation.

CONCLUSION In this work, the different physical phenomena which characterize voice quality have been identified. These phenomena have been quantified in order to obtain a correct documentation of voice quality. The quantification of nonlinear behaviour in signal speech has been introduced to describe in a more realistic way the vocal folds behaviour. Voice quality quantification allows for the implementation of systems to help in pathologies diagnosis in the fonator system by means of supervised automatic recognition systems such

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as Support Vector Machines (SVM) or Neural Networks (NN). Advances in voice quantification applied to the voice synthesis field will improve naturality in the production of synthetic voices. The development of automatic mood detection is a possibility (for example, detection of sadness, anger or happiness) with the application of the knowledge acquired in measurements of voice quality. With these systems it will be possible to perceive no verbal language. These systems can be applied to new generations of human-computer interfaces.

REFERENCES Ayache, S., Ouaknine, M., Dejonkere, P., Prindere, P., & Giovanni, A. (2004). Experimental Study of the Effects od Surface Mucus Viscosity on the Glottic Cycle. Journal of Voice, 18(1), 334–340. doi:10.1016/j.jvoice.2003.07.004 Boyanov B., Doskov D., Mitev P., Hadjitodorov S. & Teston B. (2000), New cepstral parameters for description of pathologic voice, Comptes Rendus de L’Academie Bulgare des Sciences (Ann. of Bulgarian Academy of Sciences), 53(3), 41-44. Boyanov, B., & Hadjitodorov, S. (1997). Acoustic analysis of pathological voices. A voice analysis system for the screening of laryngeal diseases. IEEE Engineering in Medicine and Biology Magazine, 16(4), 74–82. doi:10.1109/51.603651 Dejonckere, P. H., Remacle, M., Fresnel-Elbaz, E., Woisard, V., Crevier-Buchman, L., & Millet, B. (1996). Differentiated perceptual evaluation of pathological voice quality: reliability and correlations with acoustic measurements. Revue de Laryngologie - Otologie - Rhinologie, 117(2), 219–224.

Speech-Based Clinical Diagnostic Systems

Godino-Llorente, J. I., Aguilera-Navarro, S., & Gomez-Vilda, P. (2000), Non supervised neural net applied to the detection of voice impairment. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP ‘00. vol. 6, pp.3594-3597. Haben, C. M., Kost, K., & Papagiannis, G. (2003). Lateral Phase Mucosal Asymmetries in the Clinical Voice Laboratory. Journal of Voice, 17(1), 3–11. doi:10.1016/S0892-1997(03)00032-8 Hadjitodorov, S., & Mitev, P. (2002). A computer system for acoustic analysis of pathological voices and laryngeal diseases screening. Medical Engineering & Physics, (24): 419–429. doi:10.1016/ S1350-4533(02)00031-0 Hammarberg, B., & Gauffin, J. (1995), Perceptual and acoustic characteristics of quality differences in pathological voices as related to physiological aspects, in O. Fujimura & M.Hirano (eds.), Vocal Fold Physiology,283-303. Hansen, J. H. L., Gavidia-Ceballos, L., & Kaiser, J. F. (1998). A nonlinear operator-based speech feature analysis method with application to vocal fold pathology assessment. IEEE Transactions on Bio-Medical Engineering, 45(3), 300–313. doi:10.1109/10.661155 Hirano, M. (1981), Clinical Examination of Voice, New York, Springer-Verlag.Dejonckere, P. H. Remacle, M. Fresnel-Elbaz, E. Woisard, V. Crevier-Buchman, L. Laver, J. (1991), The Gift of Speech, Edinburgh University Press Michaelis, D., Frohlich, M., & Strube, H. W. (1998). Selection and combination of acoustic features for the description of pathologic voices. Acoustical Society of America, 103(3), 1628–1640. doi:10.1121/1.421305

Millet, B. (1996). Differentiated perceptual evaluation of pathological voice quality: reliability and correlations with acoustic measurements. Revue de Laryngologie - Otologie - Rhinologie, 117(2), 219–224. Neubauer, J., Eysholdt, P., Eysholdt, U., & Herzel, H. (2001). Spatio-temporal analysis of irregular vocal fold oscillations: Biphonation due to desynchronization of spatial models. The Journal of the Acoustical Society of America, 110(6), 3179–3192. doi:10.1121/1.1406498 Orlikoff, R. F., & Baken, R. J. (2003), Curing Diagnosis: Improving the Taxonomy of Phonatory Dysfunction, Sixth Conference on Advances in Quantitative Laryngology. Hamburg, Germany. Robert, F. Orlikoff & R. J. Baken (2003), Curing Diagnosis: Improving the Taxonomy of Phonatory Dysfunction, Sixth Conference on Advances in Quantitative Laryngology. Hamburg, Germany. Svec, J. G., Schutte, H. K., & Miller, D. G. (1996). A Subharmonic vibratory pattern in normal vocal folds. Journal of Speech and Hearing Research, 39(1), 135–143. Titze, I. R. (1994), Principles of Voice Production, Englewood Cliffs, NJ: Prentice-Hall, Inc. Xuejing Sun & Yi Xu. (1995). Perceived Pitch of Synthesized Voice with Alternate Cycles. Journal of Voice, 16(4), 443–459.

KEY TERMS AND DEFINITIONS Characterization or Representation Domains: These are the different spaces into which a signal can be transformed, where certain characteristics of this signal (levels of regularity, levels of noise, similarities, etc) are pronounced of preferential form. Diagnosis Automatic Intelligent Systems: These are systems which enable the identification

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Speech-Based Clinical Diagnostic Systems

of pathological states without the presence of a clinical expert. These systems are oriented to preventive medicine or first screening. Disphonia: This is the alteration of voice quality. It is mainly caused by laryngeal pathologies. Other different motives to those of a medical nature can produce changes in voice quality, such as, for example, factors related to mood. GRBAS: Objective measures of speech quality by means of multidimensional clinical measurements based on auditory methods. Help Systems For Diagnosis: These are systems that help the clinical professionals to identify certain situations that need special attention. They are used generally in tasks of clinical monitorization.

Laryngeal Pathology: Due to different organic injuries (such as malformations, benign injury, inflammations, infections, precancerous and cancerous injuries, traumatisms, or endocrine, neurological and auditive injuries), different functional disphonies (in spoken and sung voice) and of psychiatric origin. Pitch: Vibration frequency of vocal folds. In fact, there is not complete periodicity in the vibration of vocal folds. That is why it is said that vocal folds have a quasiperiodic movement.

This work was previously published in Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, Julian Dorado and Alejandro Pazos, pp. 1439-1446, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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Chapter 4.2

The Use of Artificial Intelligence Systems for Support of Medical Decision-Making William Claster Ritsumeikan Asia Pacific University, Japan Nader Ghotbi Ritsumeikan Asia Pacific University, Japan Subana Shanmuganathan Auckland University of Technology, New Zealand

ABSTRACT There is a treasure trove of hidden information in the textual and narrative data of medical records that can be deciphered by text-mining techniques. The information provided by these methods can provide a basis for medical artificial intelligence and help support or improve clinical decision making by medical doctors. In this paper we extend previous work in an effort to extract meaningful information from free text medical records. We discuss a methodology for the analysis of mediDOI: 10.4018/978-1-60960-561-2.ch402

cal records using some statistical analysis and the Kohonen Self-Organizing Map (SOM). The medical data derive from about 700 pediatric patients’ radiology department records where CT (Computed Tomography) scanning was used as part of a diagnostic exploration. The patients underwent CT scanning (single and multiple) throughout a one-year period in 2004 at the Nagasaki University Medical Hospital. Our approach led to a model based on SOM clusters and statistical analysis which may suggest a strategy for limiting CT scan requests. This is important because radiation at levels ordinarily used for CT scanning may pose significant health risks especially to children.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

The Use of Artificial Intelligence Systems for Support of Medical Decision-Making

INTRODUCTION Text-mining is applied in various fields to extract useful and previously unknown information contained in databases and texts. In the field of bioinformatics significant efforts are being made in genome sequencing, protein identification, medical imaging, and patient medical records. This study continues the efforts to mine patient medical records that consist of clinician notes in the form of free text. Harris et al (2003) developed a system to extract terms from clinical texts. Using natural language processing techniques, i.e., a parser, the MedLEE system, Jain et al (1996) turned free-text from patient records into an output with structured information. For example, this system may identify patients with tuberculosis based on chest radiographs. To do this it uses a corpus of controlled vocabulary developed from a collection of medical reports. This, as well as similar work discussed below such as BIRADS UMLS, SNOMED, are useful in converting clinician notes as free text into some form of structured codes for medical diagnosis purposes. They use natural language parsers and a domain vocabulary (knowledge base) developed either using a corpus or stored expert knowledge. The gold standard in text mining is natural language processing, which aims to include semantic information in the text mining task. The full realization of this goal is still on the distant horizon however serious efforts have already achieved some success. These include AQUA, PROTEUS-BIO, and SemRep. AQUA (A Query Analyzer) is an underspecified semantic interpreter that was originally formulated for processing MEDLINE queries. PROTEUS-BIO applies to web documents on infectious disease outbreaks. It mines semantic predications relevant to this domain and stores them in a database. The database is then available to users. SemRep is being developed to recover semantic propositions from biomedical research literature. It again focuses on MEDLINE citations. SemRep utilizes

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underspecified syntactic analysis and structured domain knowledge. In addition to investigations that consider semantics, non semantic approaches are yielding significant progress as well. Vector space models, neural networks, kernel methods, decision trees and rule induction, and probabilistic models are all being used for classification without strong emphasis on semantic characteristics and are yielding promising and interesting results as applied to text mining.

BACKGROUND The advent of computed tomography (CT) has revolutionized diagnostic radiology (Figure 1). Since the inception of CT in the 1970s, its use has increased rapidly. It is estimated that more than 62 million CT scans per year are currently obtained in the United States, including at least 4 million for children (Brenner & Hall, 2007). The increase in the use of medical radiation, especially in diagnostic CT scanning has raised many concerns over the possible adverse effects of procedures conducted in the absence of any serious risk/benefit analysis, especially where these procedures are carried out on Children (UNSCEAR, 2000). According to a survey conducted in 1996 (White, 1996) the number of CT scanners per 1 million population was 26 in the United States and 64 in Japan. The growth of CT use in children has been driven primarily by the decrease in the time needed to perform a scan — now less than 1 second — largely eliminating the need for anesthesia to prevent the child from moving during image acquisition (Frush et al, 2003). Overuse of diagnostic CT radiation, which deliver radiation doses 50 to 200 times higher than most X-rays, can lead to an increased risk of cancer. Additionally, it may lead to an unnecessary rise in health care costs (Roebuck, 1999; Ghotbi et al, 2005; Walsh, 2004).

The Use of Artificial Intelligence Systems for Support of Medical Decision-Making

Figure 1. The basics of CT

In this system a motorized table moves the patient through the CT imaging system. At the same time, a source of x-rays rotates within the circular opening, and a set of x-ray detectors rotates in synchrony on the far side of the patient. The x-ray source produces a narrow, fan-shaped beam, with widths ranging from 1 to 20 mm. In axial CT, which is commonly used for head scans, the table is stationary during a rotation, after which it is moved along for the next slice. In helical CT, which is commonly used for body scans, the table moves continuously as the x-ray source and detectors rotate, producing a spiral or helical scan. The illustration shows a single row of detectors, but current machines typically have multiple rows of detectors operating side by side, so that many slices (currently up to 64) can be imaged simultaneously, reducing the overall scanning time. All the data are processed by computer to produce a series of image slices representing a three-dimensional view of the target organ or body region (White, 1996). Originally, prior to our investigation into the use of ANN based approaches, researchers at Nagasaki Hospital attempted to reevaluate the efficiency of CT scanning in both the diagnosis of acute appendicitis and also when used to detect

possible injuries after acute head trauma using conventional methods (Deboeck & Kohonen, 1998a). As a result of that study a recommendation was made to the two departments studied. The recommendation was to employ guidelines and algorithms which present a stepwise set of clinical diagnostic methods and tools. The intention of this recommendation being that CT scans be reserved for patients that may be expected to benefit from them. However, in other departments, due to the lack of such a stepwise approach to diagnosis, many unnecessary CT scan have been and continue to be undertaken, and sound clinical judgment is generally postponed until viewing the results of a CT scan. This was the initial impetus for our current work. The standard procedure adopted for requesting a scan at the Nagasaki Medical University Hospital as well as other medical practices and classification by field expert is outlined in Figure 2. In the past much concern has focused on the lack of digitalized patient records, but with the digital revolution in full sway this is becoming a non-issue (Walsh, 2004). Using medical records obtained from Nagasaki Medical University Hospital Radiology Department’s CT scanning database we preprocessed these records and

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Figure 2. Schematic diagram showing the standard procedure followed at Nagasaki Medical University Hospital and the expert classification on the necessity of a CT scan

emerged with a dictionary of 900 features (i.e., words) which were then used to search for clusters which could represent factors with predictive significance. In our previous work (Claster et al, 2007) we employed Kohonen Self Organizing Mapping to analyze a sample of 50 of the free text medical records (clinician notes) out of a collection of 982 records obtained from the Nagasaki University Hospital. In the present study all 982 records were considered. In both the original study and the present study because of the free text nature of the data, the use of conventional analysis techniques became impracticable, as there were 900 words or features involved. In the prior 50 record study we sought to look deeper into factors which might indicate unnecessary scanning. In the present study, in addition to extending our work to the full 982 patient records we also develop a methodology for testing the analysis. We have used a k-fold validation design to test the proposed methodology. Finally we apply a novel approach to decision tree algorithms by focusing on the clusters discovered at the SOM layer of the analysis.

CT SCAN DATA AND SOM BASED TEXT MINING A thorough understanding of the indicators for the request to do a CT examination requires the

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analysis of huge amounts of text data in the medical records. Because of the unstructured form of these records, use of traditional statistical methods showed limited promise in isolating factors that could accurately predict the patterns of CT scan usage. Unstructured text is a candidate for SOM text mining. A SOM is a feed forward neural network (Figure 3), which uses an unsupervised training algorithm to perform non-linear regression. Through a process called self-organization the network configures the output data into a display of topological representation, where similar input data are clustered near each other. At the end of the training SOM enables analysts to view any novel relationships, patterns or structures in the input vectors. The topology preserving mapping nature of SOM algorithm is highly useful in projecting multi dimensional data sets into low dimensional displays, generally into one- or two-dimensional planes. Thus SOMs can be used for clustering as well as visualization of multi dimensional data sets (Deboeck & Kohonen, 1998b). The SOM techniques are successfully applied to visualize and cluster large volumes of complex statistical data sets of many real world problems such as pattern recognition, image analysis, process monitoring and control, and fault recognition. As SOM methods are based on an unsupervised training algorithm, they could be used for data clustering without knowing the class membership of the input data (Simula & Vesanto, 1999). Traditional methods (i.e. simple statistical methods)

The Use of Artificial Intelligence Systems for Support of Medical Decision-Making

Figure 3. Simplified diagram of a SOM

that are useful in summarizing low-dimensional data sets (mean value, smallest and highest values), are seen to be less effective in visualizing multidimensional (i.e. multivariate) data sets (Deboeck, 1998; Deboeck & Kohonen, 1998b). The self-organizing map (SOM) algorithm, first introduced by Tuedo Kohonen in 1982 was developed from basic modeling information of the human brain’s cortical cells, as they were known from the neuro-physiological experiments of the late twentieth century. The processing of synaptic connections between the cortex cells in the human brain is based upon the nature of the sensorial stimuli; hence different patterns of sensorial signals converge at different areas within the brain’s cortex cells. Because of this different individual neurons or groups of neurons become sensitive to different sensorial stimuli, and neighboring neurons also learn to respond to similar patterns of signals (i.e. visual, auditory, somatosensory, etc). Despite this realization our knowledge on the associative areas of signals and the other different tasks involved with the rest of the cortical area is relatively poor. Only ten percent of the total cortical area is described as involved with primary sensorial signals. The planning of actions is assumed to take place in the frontal lobe. Kohonen’s SOM applications to real world problems range across many disciplines, mainly in the field of knowledge discovery. SOM ability to discover implicit knowledge from numerical data displaying the input vectors on low dimensional grid structures is significant. The following are some of the major identified areas of SOM applications (Deboeck & Kohonen, 1998a):

1. Classification, clustering, and/or data reduction; 2. Visualization of the data; 3. Decision-support; 4. Hypothesis testing; 5. Monitoring system performance; 6. Lookup of (missing) values; 7. Forecasting. Traditional statistical methods consist of limited abilities for revealing structures, relationships and novel patterns in low dimensional data sets. Two to three dimensional data sets can be visualized by using simple two- to three-dimensional graphs. But with multi dimensional data sets, plotting a vector or analyzing the relationships between different vectors by simple graphs is not possible. Thus other methods are needed to visualize such multi dimensional data sets. In existing data visualization methods, the different components contributed by each and every dimension are integrated into the one final result. The major drawback experienced with conventional methods is that they are unable to reduce the amount of data within large sets, as processing becomes incomprehensible. However, they can be used to display simple summaries of data sets (Deboeck & Kohonen, 1998c). Data clustering is one such operation through which similar data items are categorized or grouped together and it is one of the best possible methods available for reducing large volumes of data for visualization purposes. Clustering is described as similar to information processing in humans and preferred over projection methods. The goal of projection

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methods is to represent the input data in a chosen low dimensional space, where certain properties of the structure of original data are preserved as faithfully as possible to the original values. Thus these projections can be used to visualize a high dimensional data set if a sufficiently enough low dimensionality is chosen for output display. Clustering can be automated to classify different categories and automation also reduces bias and errors in the grouping process. Traditional clustering methods can be classified into two basic types: hierarchical and (ii) non-hierarchical. On the other hand, projection methods can also be classified into two basic types: linear and (ii) non-linear. In our example a commercial software package called Viscovery SOMine was employed to model the data. This provides a visualization tool that maps high dimensional inputs onto a twodimensional map for easy visualization of the inputs that enhance detection of new knowledge in the form of patterns.

CT Scan Data Preprocessing Medical records were examined by breaking each patient record into its constituent words. We used a standard method of weighting the words which gives consideration to the frequency at which a word occurs in a document and also the overall frequency that the word occurs within the entire corpus. This method is known as tf-idf (see below). This allows us to recode text data as numerical data and thus makes it amenable to analysis with Kohonen mapping procedures. Our hypothesis is that information contained within the narrative text of medical records may determine whether a particular medical procedure (CT scans) would prove to be unnecessary. Of the 1024 pediatric patient records extracted from the Nagasaki University Hospital, clinician notes and their outcome classified as either necessary or unnecessary scan by a medical expert from the radiologist comments were used in this SOM based clustering. The original clinicians’ notes

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in Japanese were initially translated into English for this purpose. We then removed standard stop words (i.e. ‘a’, ‘able’, ‘about’, ‘above’ etc.,) as well as common medical terms identified by a physician (i.e., ‘abduction’, ‘advance’, ‘vessel’) from the clinician notes. The 42 records that came out blank through this process were removed altogether from this analysis. Consequently, a matrix of word x record no. was created in which the rows consisted of all the words in the text corpus of patient records, to apply the tf x idf formula discussed in the methodology section. For further details on the process and formula see Claster et al (2007). From this matrix, words that were found to be useless in the analysis were again removed and a new matrix of word x patient record numbers was created. The records that lost all the words in the process were labeled as ‘no comments’. The records that had radiologist notes unclassifiable either as necessary or unnecessary by the medical expert were also tagged with ‘no comments’. Using the final matrix we created a 2000 node SOM (Figures 4 and 7). The validation performed on the SOM clustering is discussed in the next section.

Figure 4. SOM of physician’s CT referral rationale (text being mined) segregated into positive versus negative outcome clusters

The Use of Artificial Intelligence Systems for Support of Medical Decision-Making

METHODOLOGY: CLUSTER EXPLORATION, MODEL DEVELOPMENT AND TESTING An expert in the medical field (in this case a physician) indicated whether each particular scan was necessary or unnecessary and thus we had a classification for the data. Viscovery SOM discovered clusterings on the dataset ranging from 2 clusters to more than 30. When we explored the 2-cluster SOM (Figure 4) we were able to identify a cluster of records that were 98% necessary (C1) and a cluster of records that were 97% unnecessary (C2) - where necessary and unnecessary refer to whether a CT scan was deemed necessary or not by the expert. The two cluster profiles are shown in Figure 4. We analyzed each cluster and developed a methodology (described below) to weight a word in the text according to how often it occurred in either cluster of the 2 cluster SOM (Figure 2; note: this does not refer to tf-idf but a weighting used for another purpose described below). In short this can be described as: a word appearing only in the cluster C1 was given a high positive weight and a word appearing only in the cluster (C2) a high negative weight. Then the word weights for all the words contained in a particular record were summed to determine a record classification value (“rc”) for that record. This record classification value was then used to predict whether a particular record belonged to the class of necessary scans or to the class of unnecessary scans. A K-fold cross-validation procedure was later employed as a means of model verification (Table 1). Table 1. Averaged results of K-fold cross validation €€€Predicted Classification Actual Classification

necessary

unnecessary

necessary

€€€506

€€€105

unnecessary

€€€108

€€€253

Model The tf–idf weight (term frequency–inverse document frequency) is a statistical measure used to evaluate how important a word is to a document in a collection or corpus. The term frequency is given by: tfi =

ni

∑

n k k



with ni being the number of occurrences of the considered term, and the denominator is the number of occurrences of all terms. The inverse document frequency is given by: idfi = log

D {d : d ∋ ti }



with |D| the total number of documents in the corpus and|{d:d∍ti}|: the number of documents where the term ti appears (that is ni ≠0). Then tfidf=tf∙idf. Now define a word prediction vector for word wi by defining jth component of the word prediction vector (“wpvij”) for word wi in cluster j as: wpvij =

(t f − idf )

∑

all records in cluster j

ni , j



In other words, wpvij is the mean of the tf-idf over all the records in the jth cluster for word wi. Next letting Ki be the cluster with the maximum wpvij for word wi (over all j clusters), define the word prediction weight (“wpwi”) for word wi over all clusters to be: wpwi = wpviK − i

∑ wpv

∀j ≠Ki

ij



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The Use of Artificial Intelligence Systems for Support of Medical Decision-Making

Figure 5. C5.0 chart for cluster C1 (expanded view). For example, node 2 indicates that when tumor <= 0 then 55.4% of the records were necessary and 44.6% were unnecessary

(note, that in our case there are just two clusters 1 and 2, and therefore this becomes just wpvi1 wpvi2 which will be either positive or negative). These word prediction weights, wpwi, are used to establish an overall value for any record. This gives us a way of taking a new document and assigning a value (and therefore a classification as a necessary or unnecessary CT scan) based on the following scheme. Define the record value v as:

1024

v=

∑

∀wi in the record

wpwi

Then define rc to classify a record as necessary or unnecessary by necessary rc =  unnecessary 

if v ≥ 0 if v < 0

The Use of Artificial Intelligence Systems for Support of Medical Decision-Making

Model Testing The above classification model was tested in a K-fold cross-validation procedure. We subdivided the data into 3 subsets and the cross validation process was carried out 3 times, after which the results of the folds were averaged.

series of keywords within the CT scan referral rationale. The statistical strength assigned to the keywords led to their separation into three sets which had a strong association with a positive finding by radiologists, a strong association with a negative finding by radiologists, or a weak association with both a positive and a negative finding (Figure 6).

RESULTS

Classifying and Testing

In previous work we were able to identify through the methods of text mining explained earlie, a

We conducted a 3-fold cross validation procedure and arrived at Table 1. This table shows a false-

Figure 6. C1 (necessary) word weights (mean, minimum, maximum and sum). C2 (unnecessary) word weights (mean, minimum, maximum and sum)

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Figure 7. Positive records further subdivided into 25 clusters and negative records subdivided into 9 clusters

positive error rate of 11% and a false-negative error rate of 11% and an overall error rate of 22%. Although this accuracy is substantial we may improve upon it by the elimination of words which occur in higher frequencies in both clusters. Modification of the wpvij weight assignment may also contribute to a reduction in either false positives and/or false negatives. Additional preference was given to have the positive and negative notes clustered together. Cluster C1 contains 98%P (necessary scans) and Cluster C2 contains 95% N (unnecessary scans).

Clusters We investigated the subgroups within C1 (necessary) and C2 (unnecessary) clusters to see whether these subgroups could be developed into a C5.0 charts. Based on SOM clustering suggestions (generated with Viscovery commercial software) C1 and C2 clusters were further divided into 25 and 9 subgroups respectively (Figure 7) and then their word groupings (Figure 8) were analyzed for any possible scenarios of developing a C5.0 chart. Most of the groupings appeared to consist

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of a uniform and unique set words relating to a particular type of disease (ENT, pulmonary) accident (involving a vehicle, fall from a tree, etc) or birth defects. For example, clusters 30 and 31 of C1 have words relating to neural disorders (see Figure 8). Similarly, cluster 32 words relate to orthopedic (lower facial). We ran a C5.0 algorithm to produce a decision tree (Figures 5 and 9). C5.0 is a commercial classification algorithm used to generate a decision tree using the idea of information entropy. It is not restricted to producing binary trees. It is hoped that a tree could provide feedback to a medical practitioner that may be included as one factor in the decision to request a CT scan and we will explore this in future work.

CONCLUSION This discussion shows that medical doctors may be able to consider some pre-test factors as predicted strength of the test, before requesting a CT scan for children. Our analysis gives a preliminary methodology for predictive record classification.

The Use of Artificial Intelligence Systems for Support of Medical Decision-Making

Figure 8. Constituent words (including record number, word, weights, and necessary/ unnecessary classification) within clusters 30 to 34

The 11% false-negative rate is large but there are strategies for pruning this error rate.

Future Work Further research is underway to study an additional 10,000 records. With these records we hope to explore other weighting systems and by including time series analysis to develop an expanded hybrid

methodology to include seasonal effects in order to achieve improved accuracy. Using the same data, we will compare the current methodology with a neural network classification scheme as well as modifying the SOM clustering to a Kmeans clustering algorithm. Additional research is underway to measure the effectiveness of SOM based decision charts. We believe it is possible to design a form of text mining system that helps

Figure 9. C5.0 chart for subgroup cluster number 30. 1 indicates the predication of a necessary scan and 2 indicates the prediction of an unnecessary scan

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with such decision making when a medical doctor is considering whether or not a CT scan may be helpful in reaching a diagnosis. This text mining system can be fed with the hospital’s own data so that patterns of association between clinical information and radiological findings are determined, and help with decision-making further on.

Deboeck, G., & Kohonen, T. (1998a). Best Practices in Data Mining using Self-Organizing Maps. In Deboeck, G., & Kohonen, T. (Eds.), Visual Explorations in Finance with Self-organizing Maps (pp. 203–229). New York: Springer.

ACKNOWLEDGMENT

Deboeck, G., & Kohonen, T. (1998c). Introduction. In Deboeck, G., & Kohonen, T. (Eds.), Visual Explorations in Finance with Self-organizing Maps (p. xxix). New York: Springer.

The authors wish to express their sincere gratitude to Professor Monte Cassim who has been a tremendous inspiration for all of us here at Discovery Laboratory, especially, into text mining clinical data. Nagasaki University Hospital clinical staff members are acknowledged for permission to use their data in this study.

REFERENCES Brenner, D. J., & Hall, E. J. (2007). Computed Tomography — An Increasing Source of Radiation Exposure. The New England Journal of Medicine, 357, 2277–2284. doi:10.1056/NEJMra072149 Claster, W., Shanmuganathan, S., & Ghotbi, N. (2007) Text Mining in Radiological Data Records: An Unsupervised Neural Network Approach., In Al-Dabass, D., Zobel, R., Abraham, A., & Turner, S. (Eds) Proceedings of the First Asia International Conference on Modeling & Simulation (AMS2007) held in conjunction with Thailand’s 11th Annual National Symposium on Computational Science and Engineering (ANSCSE-11) and published by the IEEE Computer Society. Phuket: Prince of Songkla University, 27-30 March, 329-333. Deboeck, G. (1998). Software Tools for SelfOrganizing Maps. In Deboeck, G. & Kohonen, T. (1998) Visual Explorations in Finance with Selforganizing Maps (p. 188). New York: Springer.

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Deboeck, G., & Kohonen, T. (1998b). Visual Explorations in Finance with Self-organizing Maps. New York: Springer.

Friedman, C., Liu, H., & Shagina, L. (2003). A vocabulary development and visualization tool based on natural language processing and the mining of textual patient reports. Journal of Biomedical Informatics, 36(3), 189–201. doi:10.1016/j. jbi.2003.08.005 Frush, D. P., Donnelly, L. F., & Rosen, N. S. (2003). Computed tomography and radiation risks: what pediatric health care providers should know. Pediatrics, 112, 951–957. doi:10.1542/ peds.112.4.951 Ghotbi, N., Morishita, M., Ohtsuru, A., & Yamashita, S. (2005). Evidence-based Guidelines Needed on the Use of CT Scanning in Japan, Japan Medical Association Journal, 48(9), September. Jain, N. L., Knirsch, C., Friedman, C., & Hripcsak, G. (1996). Identification of suspected tuberculosis patients based on natural language processing of chest radiograph reports [supplement]. Journal of the American Medical Informatics Association, 3, 542–546. Roebuck, D. J. (1999). Risk and benefit in pediatric radiology. Pediatric Radiology, 29, 637–640. doi:10.1007/s002470050666

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Simula, O., & Vesanto, J. (1999). Analysis and modeling of complex systems using the selforganizing map. In Kasabov, N., & Kosma, R. (Eds.), Studies in Fuzziness and Soft Computing Neuro-Fuzzy Techniques for Intelligent Information Systems (pp. 3–22). Berlin: Physica-Verlag. UNSCEAR. (2000). Sources and effects of ionizing radiation: United Nations Scientific Committee on the Effects of Atomic Radiation. UNSCEAR 2000 Report to the General Assembly. New York: United Nations.

Walsh, S. H. (2004). The Clinician’s Perspective on Electronic Health Records. British Medical Journal, 328, 1184–1187. doi:10.1136/ bmj.328.7449.1184 White, K. S. (1996). Helical/spiral CT scanning: a pediatric radiology perspective. Pediatric Radiology, 26, 5–14. doi:10.1007/BF01403695

This work was previously published in Biomedical Knowledge Management: Infrastructures and Processes for E-Health Systems, edited by Wayne Pease, Malcolm Cooper and Raj Gururajan, pp. 18-30, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.3

Persistent Clinical Encounters in User Driven E-Health Care Rakesh Biswas Manipal University, Malaysia

Shashikiran Umakanth Manipal University, Malaysia

Joachim Sturmberg Monash University, Australia

Edwin Wen Huo Lee Kuala Lumpur, Malaysia

Carmel M. Martin Northern Ontario School of Medicine, Canada

Kevin Smith National Digital Research Centre, Ireland

A. U. Jai Ganesh Sri Sathya Sai Information Technology Center, India

ABSTRACT This chapter discusses the role of e-health in creating persistent clinical encounters to extend the scope of health care beyond its conventional boundaries utilizing social networking technology to create what the authors’ term ‘user driven health care’. It points out the necessity to direct

the development of health information systems such that they serve as important vehicles between patient and health professional users in communicating and sharing information other than their role in automated alerts and responses. A project is described that plans to create a system of online sharing of health information in a user driven manner that necessarily becomes persistent due to being stored in electronic health records.

DOI: 10.4018/978-1-60960-561-2.ch403

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Persistent Clinical Encounters in User Driven E-Health Care

INTRODUCTION In the pre-globalization motorization era when physical distances mattered, the practice of medicine was confined to individual health care experts who seemed to know all about their individual patients. In the absence of specialists (if at all the only competition was the barber-surgeon) the wise community physician’s knowledge was not just confined to pills and potions (which came in various colors), but s/he also knew all about how her/his patients led their lives. With time all that changed, and now the present day expert dispenses advice to a global consumer and uses findings that may be generalized to all humans (on the basis of quantitative research which is considered to be evidence-objective, absolute and rational). The resulting fragmentation – resulting from specialization and sub-specialization – characterizing ‘modern medicine’ has reduced each health professional’s knowledge about individual patients to only those fragments he needs to know and that match his area of expertise. In many instances the life of present day health care giver is often far removed from the lives of his patients unlike his know all, do all predecessor who used to share the lives of his patients in more ways than one. It is unlikely that the present day expert can shed a tear for his patient when he dies because he doesn’t know his patient to merit as much but it is still possible with the practitioner who values her knowledge of individual patients derived from her local settings. The wise physicians’ anecdotal wisdom although negligible in a global society was of immense value in their local communities where they were seeped in information about the details of their patient’s lives that gave them a non mathematical but perhaps a grounded narrative and equally fair impression of what suited their individual patient needs (Biswas et al, 2007). The traditional patient and health professional clinical encounter has evolved into a series of

fragmented exchanges of information, often between several professionals. The information exchange between professionals often excludes the patient and is usually limited to a synthesized ‘factual’ written account. The synthesized ‘factual’ written account however fails to convey much of the subtleties gained through the information exchanges in the encounter, and which ultimately build a most valuable tacit knowledge base about a patient. (Sturmberg, 2007) Yet the encounter could actually evolve into an informational collaborative process, persistent in virtual space and time. A persistent clinical encounter has immense potential advantages for the patient as well as health professional. Medicine is in fact a collaborative effort in problem solving between individual patients and their health professionals. This collaboration also involves others who are directly or indirectly related to the patient and health professional (for example, the patient’s relatives, the practice staff and the physicians’ institutions etc) who provide the necessary support to the two primary collaborators. In this Chapter we suggest viewing such an integrated approach to health care as ‘User driven health care’ that may be defined as: ‘Improved health care achieved with concerted collaborative learning between multiple users and stakeholders, primarily patients, health professionals and other actors in the care giving collaborative network across a web interface’ (Biswas, 2007). And needs to be differentiated from the presently more ubiquitous ‘consumer driven health care’, which is essentially a strategy for users/ consumers to decide how they may pay for their own health care through multiple stakeholders like employers who provide the money and insurance companies who receive the premiums.

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PERSISTENT CLINICAL ENCOUNTERS AND CONTINUITY OF CARE An individual patient who sees her/his physician for a chronic problem like diabetes for example may be called back on an average for a follow up after 2-3 months. In this interval between physician visits, the patient on his/her own is expected to continue a judicious diabetic diet, maintain an optimal exercise schedule and dutifully consume all his/her medicines on time (and presumably also have an understanding of correct dosage). Any confusion or queries on the patient’s part would be solved on the next visit unless it can be prescheduled (which is not always easy). Of course, apart from these usual information needs that are compromised there may be other emotional information needs that would go unexpressed. As information needs keep being suppressed there may be a gradual build up of patient dissatisfaction from sources other than the worsening of the disease. Timely answering of patient informational needs that would have resulted in a better-educated diabetic may have prevented this. Patient education has long been a recognized positive factor in successful management of diabetes and good education may result from problem based experiential learning that begins with addressing patient’s information needs (Biswas, 2007). Therefore, to understand a given individual patient’s information needs we require an

informational continuity about the person and his/ her disease. Documented information tends to focus on the medical condition but knowledge about the patient’s preferences, values, and context is equally important for bridging separate care events and ensuring that services are responsive to needs. This type of knowledge is usually accumulated in the memory of providers who interact with the patient (Haggerty, 2003). A persistent clinical encounter would involve accumulation of knowledge not just in the human memories of individuals (that is inherently perishable and worse corruptible) but also in a shared web space that can be accessible for asynchronous interaction between the individual actors in the clinical encounter. Such a well-documented clinical encounter for each individual patient can serve as a foundation to continuity of care and learning. Continuity of care, in its traditional form, presumably consisted of the same doctor looking after a patient from the beginning to the end of an illness. It would have been even better if the same doctor was involved in any subsequent illnesses, a sort of cradle to grave care package. Patients would get to know and trust their doctor, forming a comfortable relationship for both. The nearest any doctor has come to this model is the general practitioner, but with the introduction of large group practices and realistic working hours it is no longer always possible to see the same doctor (Sturmberg, 2007). At the other end of the spectrum, anesthetists, radiologists, and accident

Figure 1. From current limited clinical encounter to an ideal persistent clinical encounter

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and emergency doctors have always accepted the single consultation or intervention as the rule rather than the exception. Continuity of care in general practice – though it is a defining characteristic of the discipline (McWhinney, 1989; Starfield, 1998; Sturmberg, 2007) - is under threat; while for consultants in hospitals it has probably not existed for some time, if ever. In the case of the acutely sick patient, where a clear perspective of the developing situation may be crucial to the management of the patient, it might seem that there could be no substitute to continuity of care (Bulstrode, 1995). Haggerty et al (2003) in a review have described the concept - and reality - of continuity of care that crosses disciplinary and organizational boundaries. Continuity is the degree to which a series of discrete healthcare events is experienced as coherent and connected and consistent with the patient’s medical needs and personal context. Continuity of care is distinguished from other attributes of care by two core elements - care over time and the focus on individual patients. Continuity of care is achieved by bridging discrete elements in the care pathway -whether different episodes, interventions by different providers, or changes in illness status - as well as by supporting aspects that endure intrinsically over time, such as patients’ values, sustained relationships, and care plans. For continuity to exist, care must be experienced as connected and coherent. For patients and their families, the experience of continuity is the perception that providers know what has happened before, that different providers agree on a management plan, and that a provider who knows them will care for them in the future, (Haggerty et al, 2003).

Health Information Sharing Systems for Continuity of Care For care givers, the experience of continuity relates to their perception that they have sufficient knowledge and information about a patient to

best apply their professional competence and the confidence that their care inputs will be recognized and pursued by other providers (Haggerty et al, 2003). At present the stage is already set for health information sharing (HIS) to grow in India as in the west with a large number of hospitals (including those run by charitable trusts) increasingly getting interlinked by web-based HIS. A hospital information system (HIS) can be defined as an integration of administrative, financial and clinical information systems of a hospital, with specialty or department specific sub-systems also known as modules. Author JG’s hospital Health Information Systems includes the following modules: System Manager, Application Master, Master Patient Index, Duplicate Registration Check, Inpatient Management, Outpatient Management, Appointment Scheduling (includes wait listing), Accident & Emergency, Medical Records, Patient File Tracking, Order Entry & Results Reporting, Pharmacy, Radiology Information system, Operation Theatre Management System, Laboratory Information System, Blood Transfusion Services & Blood Donor Management, and Clinician Access. Of particular interest is the Clinician Access module which provides users with a single point access to all the information residing in the electronic medical record (EMR). It facilitates viewing the list of inpatients under the doctor’s care, patients waiting for consultation in the OPD, history of the patient chronologically listing out each encounter (patient’s visit and consultation) with hyperlinks to the clinical details for each of those visits. The user can update/view the pre-consultation observations, place orders for diagnostic imaging procedures and lab tests. All these features are available to the users based on their roles/responsibilities and access privileges. Doctors place orders for lab investigations, imaging procedures, look up the reports released by say the radiologists/pathologists in the HIS, wait list patients for surgery, request for blood, nursing/ ward staff assign beds for patients, transfer them, perfusion details recorded at the OT’s, discharge

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summaries printed out, signed and given to the patient. All these activities/transactions right from the registration, admission to discharge are through the HIS. From a functional perspective, the HIS consists of components that support the following distinct purposes: 1. 2. 3. 4. 5.

Patient management; Departmental management; Care delivery and clinical documentation; Financial and resource management; and Managed care support.

HIS also harbor logic modules with sufficient knowledge to make a single clinical decision. The health knowledge base may contain contraindication alerts, management suggestions, data interpretations, treatment protocols, diagnosis scores etc. For example, when a doctor prescribes the patient penicillin, the logic module will check for contraindications. If the patient has a recorded allergy to penicillin, an alert is generated. This decision support system in clinical institutions has been in use for many years. However these systems emphasize increasing automation through human computer interaction whereas health and healing require human to human interaction (that just needs to be augmented by computers). The human interaction aspect, with a focus on the person and his unique illness experience, is fundamental to medical care, to healing and to cost efficiency, and cannot be overemphasized. This is particularly important for the rapidly rising number of patients with chronic illness who have most to gain from well directed personal and technical care (Martin, 2007). This importance of human to human interaction and its augmentation by technology is illustrated below with a single day hospital ward scenario from a hospital physician consultant’s e-diary (Note that care giver logs are often just telegraphic information):

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1. Relooked Bed 11…student’s request. 50 yr Male, Nephrotic syndrome with diabetes mellitus and with his diabetic foot dripping pus on the bed. No dressing since yesterday? Blood sugar values? 2. Bed 10, 60 yr Male, collapsed suddenly, cirrhosis with end stage, didn’t want to resuscitate but had to for protocol as we had missed out on the DNR earlier. Students had an exercise. Another note a month later: 3. This was a month back I think…can’t be sure…time flies so fast on the daily ward rounds. Most of my E-logs remain unutilized. In fact I first started making e-logs on my PDA chiefly to identify my information needs as a physician before I gradually started realizing even patients had similar needs and we needed to have an integral solution for both.

Relevance of Daily E Logs to solve Individual Patient/Health Professional User Needs The academic physician-consultant (author of these real log examples) had seen the diabetic man in Bed 11 earlier on his morning consultation rounds but he realized that this patient’s blood sugar control had been overlooked only after a medical student requested him to have another re-look. He made a point in his mind to inform this to his junior colleague, the medical officer who would remain in the wards (also at the same time making the telegraphic note about the patient in Bed 11 in his personal diary). Suddenly, at that point in time, the patient in Bed 10 collapsed and he had to participate in his CPR that was emotionally and physically draining and he was relieved to escape to the out patient department (OPD) for the day.

Persistent Clinical Encounters in User Driven E-Health Care

SHARING AND COMMUNICATING VALUABLE INDIVIDUAL PATIENT DATA

Individual Patient Learning and Communicating from Global Experiences

If this data were on a web portal (a kind of virtual hospital filing system) as soon as the physician entered it into his personal digital assistant (PDA), the data would have matched with his other colleague’s data for the day regarding this particular patient. His junior colleague (Medical Officer/ Senior Resident) doing just a file review on his PDA would have noticed the note and acted on the diabetic man’s blood sugars if it was high. Controlling it better may have benefited the wound more than the systemic antibiotics that he was already on (and which had doubtful local benefit although again it is an issue that may be debated). This technology offering a convenient local solution to improving hospital communication among inhouse health professionals is evolving at present in many hospitals.

We need a global solution for all varieties of individual users. Perhaps a diabetic patient in another part of the world on keying his concerns about his own foot ulcer would find a match in this incident and drop a word of sympathy for our diabetic in bed 11. Another person in another part of the world could become aware of the importance of a do not resuscitate (DNR) form for his dying father with end stage disease. Another diabetic with a mild foot ulcer on reading bed 11’s note could become aware of the need for better sugar control in his own situation and may remind his physician about his increasing sugars (perhaps the emotional appeal in narratives may make for better patient learning?). Not that his/her physician is not knowledgeable about the importance of controlling blood sugars in foot ulcers but perhaps it is sometimes human for physicians to make errors of omission (especially under pressure) and unfortunately it is perhaps human to even want to cover up our errors (covering up is incidentally another word for creating privacy). We also need to increase more transparency in patients and physicians so that both parties can benefit continuing to learn from each other. Having said that it would still be important to maintain patient anonymity and there are methods of anonymous information sharing where patient identity would remain secure. In fact, most health care professionals/individual patients can generate a variety of beneficial examples in shared e-logging in health care regularly maintaining their own daily process logs for which one may still have time if it is done on the job and the information needs generated from these may be identified by web based user driven solutions. For each and every individual patient who suffers it is possible to electronically document her/his clinical encounter with the entire social network that supports her/his healthcare. This persistent

Medical Students as a Vital Force in E Learning and Improvement of Patient Care The government generally thinks that it spends too much money in Undergraduate Medical training perhaps as these student doctors apparently do not serve while they learn. However it is the medical student who has the time to listen in detail to their chosen individual patient (they do not have to see and are not responsible for all the ward patients unlike their overworked houseman/resident seniors). Medical student logs on their individual patients can be a vital source of detailed narrative data on individual patients which their consultant might often enjoy reading and also benefit from daily. The medical student who pulled the consultant to the bedside may as well have entered his thoughts about his patient on his PDA-elog that would have automatically been reviewed by the consultant or his Medical officer (Senior Resident).

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documentation in individual personal health records (PHRs) made accessible to all stakeholders (that include innumerable patients and caregivers) would serve as a valuable learning resource that may enable improved decision-making utilizing meaning derived from multiple dimensions of the clinical encounter. (Pauli & White, 1998; Sturmberg, 2007; Martin, 2007; WHO, 2007)

CREATING PERSISTENT CLINICAL ENCOUNTERS THROUGH USER DRIVEN E- HEALTH CARE TO ANSWER MULTIDIMENSIONAL INFORMATION NEEDS IN INDIVIDUAL PATIENTS A web-based solution to integrate healthcare Elearning needs could lie in a simple forum model already in use at present in various web sites using what is loosely termed as Web 2.0 technology. In web sites using this technology user-generated tags allow the site to evolve, enabling individual users to conduct more precise searches, make previously unacknowledged associations between

facts, and explore a diverse undercurrent of themes to synthesize learning. It has been recently named Health 2.0 with reference to health care and has been described to be all about Patient Empowered Healthcare whereby patients have the information they need to be able to make rational healthcare decisions (transparency of information) based on value (outcomes over price). The Four Cornerstones (Connectivity, Price, Quality, and Incentives) of the Value Driven Healthcare movement begin to create a virtuous cycle of innovation and reform. Transparency serves as a key catalyst in this process by creating positive sum competition that can deliver better outcomes at a lower cost (Shreeve et al, 2007). As more information becomes available as a result of increased transparency, there will be a wave of innovation at all points along the full cycle of care (Fig 2), which includes phases where health care providers Educate, Prevent, Diagnose, Prepare, Intervene, Recover, Monitor, and Manage the various disease states (Shreeve et al, 2007) Each and every human has the capacity and likelihood of performing both roles of caregiver

Table 1. This section has been separated as it is speculative but it has been included for curious readers who may want to browse through for remotely testable ideas EHRs (Electronic health records) need to start as a simple free text entry platform for the individual user with minimal interference from the software which may irritate health professionals as well as patients who are utilizing EMRs mainly for data storage and communication particularly in their own style with which they are used to and which need not necessarily always follow a standard. The maximum value of EMRs lie in its ability to foster knowledge sharing and learning and this can be achieved with a simple free text storage that can be updated by the user be it the patient, patient’s relative, health professional. The present dissatisfaction with EHRs among practicing health professional users may lie in its software’s automated interference with individual user’s capacity to create meaningful data in their own terms through free text (or sketches). Free text open ended entries promote creativity in the user whereas drop down developer designed windows stifle it. As currently conceptualized, evidence-based medicine cannot adequately accommodate concepts that resist quantitative analysis and therefore cannot logically differentiate complex human beings with natural intelligence from machines with artificial intelligence. Our present day computer based clinical decision support systems suffer due to the same reasons. Medicine needs a more robust knowledge based system capable of recognizing patients and clinicians as persons. One that is more likely to be driven by natural intelligence than artificial. A truly person-centered medical epistemology requires a revised conception of medical uncertainty and recognition that clinician–patient interactions are central to medicine. (Henry et al, 2007) It is necessary to debate if we really need logic modules for automated drug allergy alerts or we need to be alerted by a care giving collaborative network of users that may be accessing and modifying an individual patient user’s EHR? Perhaps we need a judicious mixture of both but at present the emphasis unduly lies on automated software that require universal standards to tackle interoperability. Humans have their own standards in communication and their own tacit ways of understanding each other. We need to utilize this potential human base of dedicated users (patients, health professionals) instead of discouraging them with software that force them to conform to automated standards.

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Figure 2. Explaining Health 2.0 (Copyright 2007 by Scott Shreeve, MD. Made available under the Creative Commons Non-Commercial Attribution 2.5 License)

and care seeker (patient) in their lifetimes. The illness experience posts would automatically generate related posts depending on the keywordtags they use to represent their posts and this would enable every user posting his/her individual experience to go through similar relevant lived experiences of other individuals. This would be a tool delivered remotely, often anonymously, and yet may foster a sense of belonging and intimacy. In this way any individual user feeding input into the net can receive automatic feedback that can grow as individual users for this web based solution grow as they keep feeding their own data regularly. This may function purely on the power of human collaborative intelligence or distributed knowledge arising from the wisdom of the crowd (Surowiecki, 2004) rather than pure

artificial intelligence and yet may prove to be much more efficient. Each and every individual may be the author of his own destiny (as well as his own web log) that reflects his experiential life processes and decisions that can shape his future. User driven health care is an attempt to help make those decisions. It is a proposal to document valuable individual experiences of patients, physicians and medical students in a practicably feasible grassroots manner that has till now regularly gone undocumented and has been lost to the medical literature that may have actually benefited from it. The present version of Pub-Med, a popular evidence based resource (http://www.ncbi.nlm.nih.gov/sites/entrez) utilizing the Web 2.0 technology displays related publications as soon as a user clicks on to read an article abstract after entering his/her search terms.

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However this evidence based resource is chiefly concerned with empirical collective experimental data and even the case reports available on the site do not offer the richness of the individual lived experience that addresses our previously discussed dimensions of the clinical encounter. Pub-Med is a limited information source as most of the articles have restricted access to the full text version.

Introducing and Sustaining the Conceptual Model in Routine Clinical Practice Regular experiential informational input may be posted on to a web based forum along with a copy to the individual user’s password protected web account that would function as an E-portfolio if s/ he were posting as a caregiver and one to his/her private personal health record if s/he is posting as a patient. The individual user could even do this through email and every post made by mail could easily open a new post on to the forum. All this information sharing could be optionally kept anonymous, as usernames could be made impersonal (again depending on user choice). Once individual users key in their unique experiential information that could also reflect pathophysiologic rationale, patient values and preferences, system features including resource availability, societal and professional values they would be presented with related individual experiences mashed up with empirical data immediately at the click of a mouse. Later it would be up to the individual to derive meaning from these multiple dimensions of information – though for patients this may only be possible in conjunction with their doctor. To date this experiential information may exist in difficult to search web logs but most parts of our day-to-day innumerable clinical encounters remain undocumented. As a result the present day user is far from optimal satisfaction with the information on the net. A few physician led companies have already made considerable headway in this direction.

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SERMO is a web based US company that uses a forum model to develop experiential knowledge networks among US physicians while ‘ Patient opinion ‘ is a UK based setup that aims to access the collective wisdom of patients using Web 2.0 tools where patients can share their stories and rate the care they receive down to ward and department level. This patient opinion aspect of user driven health care is about conversations, democracy and improving services by making the individual voice more audible. Curbside.MD is another US based company that goes one step beyond Pub-Med in data mining and presentation by utilizing semantic fingerprinting to let users key in natural language queries in their search engine, which returns a variety of useful empirical evidence arranged as, all research, systematic reviews, guidelines, review articles, images etc. It also has begun fingerprinting of individual health profiles and matching similar profiles. Even if one could collate a larger variety of experiential information on the net, it would still be confined to PC literate individuals and to bridge the digital divide we along with our technical collaborators (see operational model) are trying to develop a mobile SMS (short messaging system) portal for data entry into the web repository. An individual at his leisure or even while waiting in queue to meet his/her physician may SMS their thoughts and queries about their disease onto the forum that could be responded to by anyone on the web. All this information sharing could be optionally kept anonymous, as usernames could be made impersonal (again depending on user choice). SMSes to the web may display only the individual user’s mobile numbers.

CREATING PERSISTENT CLINICAL ENCOUNTERS THROUGH USER DRIVEN HEALTH CARE PROJECTS This section describes a project to create persistent clinical encounters through user driven E health

Persistent Clinical Encounters in User Driven E-Health Care

care underway in Malaysia, in which the authors are involved. An operational model was created to plan a trial on a sample diabetic population utilizing a randomized control trial (RCT) design, assigning one randomly selected group of diabetic patients to receive electronic information intervention and analyze if it would improve their health outcomes in comparison to a matched diabetic population who would only receive regular medical intervention. Diabetes was chosen for this particular trial, as it is a major chronic illness in Malaysia as elsewhere in the world.

The Role of Individual Patients, Health Professionals and IT Collaborators in the Trial As this was a multidisciplinary user driven project it was necessary to clearly define certain user roles although there is scope for these to evolve further after the pilot phase.

Role of Patients Participating in the Project Patients who have consented to be willing participants in the trial period will be randomly allocated to either receive the mobile phone intervention with current standard therapy or receive current standard therapy alone. All participants in the study trial will have their baseline characteristics entered in a structured manner into a web database designated as personal health record (PHR). The patients receiving mobile intervention would receive weekly SMSs enquiring about their present symptom status, self monitored blood pressure, blood glucose values if any, current diet, exercise patterns and any other complaints they would like to convey. All responses and interaction would be automatically recorded into the non-structured portion of their web-based record. Patients may communicate either through text or voice messages.

Role of Physicians Involved in the Project Patient entries to the mobile web database will be reviewed by one of the physicians involved in the study and s/he will make appropriate adjustments to the patient’s management according to standardized diabetes management protocol (ADA, 2007; Malaysian Clinical Practice Guidelines, 2004). S/ He will also be instrumental in supervising entry of all the individualized data into a standard format to be later entered into a web-based repository that can serve as the individual patient user’s standardized personal health profile that may be accessed by appropriate health care givers and controlled by the patient and his/her health care provider. S/he will also monitor the non-structured patient generated health profile for day-to-day patient queries, thoughts etc. S/he will respond to patient queries and comments to the best of her/ his expertise and refer appropriate information needs to medical information specialists/other health professionals who will try to enrich each individual patient profile with the addition of appropriate evidence based data at relevant areas in the profile. During each visit of the patient s/he will access the patient’s personal record on her/ his computer and provide a print out of the PHR (latest version with changes) if necessary.

Role of the Research Assistant/ Clinical Care Co-Coordinator S/he will be instrumental in entry of all the individualized data into a standard format supervised/ guided by the physician after ensuring a record of proper informed consent. S/he will be an important liaison between patient and health care providers, which includes physicians, diabetic educators, dieticians and even mobile web support staff. S/he will arrange for mobile phone based continuity of care by ensuring appropriate weekly and monthly, individualized phone and SMS reminders and discussion. These reminders and discussions with

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patients are primarily aimed at assessing patient compliance to treatment of which diet, exercise and drugs are equally important components. S/he will ensure this assessment is done weekly in mobile phone users apart from the standard 3 monthly face-to-face assessments on hospital visits, which would be same as for the matched control group. S/he may help the other health care providers to address patient queries sent by patients to the mobile phone support network by collecting their answers and giving them back to the patient with the help of the mobile network support staff. S/ he will also help the mobile network web support staff to create and maintain the individual patient’s personal health profiles by ensuring appropriate entry of proper and relevant data.

Role of Mobile Phone Based Web Support Staff The proper functioning of mobile phone intervention in the lives of the selected patients will be developed and maintained by web support staff who regularly monitor and update the individual patient web profiles based on the data provided

by the patient, health care provider and research assistant. S/He will ensure that authorized users only with valid personal identification, user name, passwords etc use the system.

Prototype Development The structured and non-structured information log for patient centric support to manage and control disease allows patients to overcome limitations in the time spent with physicians. This system provides continuous virtual connection with physicians and support group. The diabetes support system is designed to provide care to patients in the areas depicted in Figure 3. Patients will have access to support and care through their mobile phones at all times. This is to allow patients to monitor, manage and control their own health at anytime and place. Support and alert messages are sent to mobile phones to disseminate and collect information from patients as depicted in Figure 3. The collected information is published in an online web log anonymously to provide support and help to patients who are suffering from the same disease and have similar disease related

Figure 3. The Role of Various Collaborators in answering Multidimensional Informational Needs (AMIN)

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problems. The organizing of the structured and non-structured information is done based on the patient centric model. There are other benefits. This system allows getting connected with users who share the same common goals and challenges (but would be otherwise difficult to locate), along with care givers who can contribute usefully at their own time. It also maintains privacy and confidentiality of patient and empowers them to manage their own health. Search and other utilities will be added to facilitate fast access to information.

and feedback. This “information need” is particularly poorly explored, and yet it may well be the most important need and the biggest stumbling block to a technical solution’ (Smith, 1996).

Network Architecture

1. Thought partner matching for a learning community creation—Thought partnerships in different diabetic patients with similar needs as expressed in their e-logged thoughts could be identified by web based matching using text tags. This aims to promote shared learning in individual diabetics with similar needs gradually leading to an improved learning community of diabetics; 2. Informational needs on SMS, conveyed explicitly as health queries, could be manually responded to by health professional monitors. Medical informational specialists (previously designated Medical library scientists) other than physicians could monitor the discussion between patient-patients and patient to physician with valuable evidence based inputs to the gradually developing structure on the individual health record.

The prototype mobile application is designed to interact with Mobile Internet Platform (MIP) to send and receive short messages. The architecture shown in Figures 4 and 5 is applicable to all mobile phone users irrespective of mobile services providers. The above initial prototype was further modified to introduce the unified communication engine that would support both voice and data inputs especially for structured data. Users could choose to use phone, mobile phone, PC/laptop or an embedded system to work with the proposed health portal.

ANSWERING MULTIDIMENSIONAL INFORMATIONAL NEEDS (AMIN) Patient needs: To address the problem of multidimensional information needs we plan to use a Web based-learning solution being perhaps tested for the first time (Figure 6). The following quote may explain the background to the term, multidimensional information needs and our enthusiasm to address it: ‘…the need for information is often much more than a question about medical knowledge… are looking for guidance, psychological support, affirmation, commiseration, sympathy, judgment,

To address this problem we propose maintenance of non-conventionally structured personal disease logs. Regular short messaging services (SMSs/Emails) from individual diabetics conveying their daily thoughts on their disease would be kept in a personalized repository in the web for those in the intervention group:

CAREGIVER NEEDS All personalized data generated from the patient’s regular SMS/Email interaction with the mobile web support system would be structured into a personalized health record (PHR) with the following components:

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Figure 4. Interaction between Patient and Care Provider models

Figure 5. Mobile web log network architecture

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1. Structured summary of the patient’s health status (mostly monitored/maintained/modified by health professionals) based on a. Basic information on identification, insurance, allergies, advance directives etc. (format); b. Latest Problem list along with patient’s care plan (Investigations and treatment listed serially according to priority of action to be taken); c. Hospital admission discharge summaries in the past; d. Present hospital record (if admitted at present) 2. Non-structured evolving narratives inserted by the patient, thought partners or care givers at various points of time (with date). This structure would simulate thediscussionstructure of a wiki at present; 3. Non-structured PHRs (Personal health profiles) created /modified either by patient/thought partner or personal physician could also be labeled and stored here for future needs. This structure would simulate thearticlestructure of a wiki at present.

Persistent Clinical Encounters in User Driven E-Health Care

Figure 6. AMIN (answering multidimensional informational needs) Solution Framework

Personal Health Records (PHR) are a medical knowledge-based characterization of a user of a medical information service. Such a technology facilitates convenient and personalized access to knowledge produced by medical practice--the primary knowledge construction process. Therefore, a personal health record enables exchange, debate, and reasoning about personal experiences with disease and the health care system, as a secondary knowledge construction process (Sittig & Hazelhurst, 1999). The PHR as described above must reflect the following attributes (Personal Health Working Group Summary, 2003): 1. Each person controls his or her own PHR; 2. PHRs contain information from one’s entire lifetime including information from all health care providers; 3. PHRs are accessible from any place at any time; 4. PHRs are private and secure; 5. PHRs are transparent. Individuals can see who entered each piece of data, where it was transferred from and who has viewed it; 6. PHRs permit easy exchange of information across the health care system.

The initial elaborate entry for the individual users PHR can be made by the patient user through his/her mobile phone with a direct telephonic interaction with a web support staff/research assistant who would fill up relevant details into a structured online database that could be downloaded into the mobile as per need. At present the health record structures in the Malaysian health system are predominantly paper based with the attendant disadvantages of information of a single patient in multiple paper files that are difficult to trace and maintain. The patient often doesn’t carry any substantial information about his past medical history. All this is expected to change soon with implementation of electronic health records (EHRs) in most Malaysian health care set-ups. Complimentary to this, our introduction of the proposed mobile phone based PHRs in diabetics (to start with) aims at trying to eliminate the present problem of information tracing confounding medical decision-making. These PHRs would not only be in the mobile phones of individual users but would also remain safe in a web-based individual health record bank (IHRB) (Shabo, 2005)

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Persistent Clinical Encounters in User Driven E-Health Care

Figure 7. The overall approach to AMIN

FUTURE DIRECTIONS

CONCLUSION

On completion of the test phase this web-based solution to integrate healthcare e-learning needs can be opened to the world in a simple forum model. Regular experiential informational input may be posted on to the forum along with a copy to the individual user’s password protected web account that would function as an E-portfolio if s/he were posting as a caregiver and a private personal health record if s/he is posting as a patient. The individual user could even do this through email and every post made by mail could easily open a new post on to the forum. Most PC users in recent times spend their Internet time predominantly in their mailbox and integrating this solution into the mailbox would target this population. Finally the digital divide would only be effectively bridged as the basic mobile phone is phased out and the personal digital assistant (PDA) combined with mobile phone and PC functionality takes over boosted with WIMAX (Worldwide Interoperability for Microwave Access) technology for continuous easy online access).

This chapter discussed the role of e-health in creating persistent clinical encounters to extend the scope of health care beyond its conventional boundaries and strengthening the foundations of healing through a long-term trusted professionalpatient relationship utilizing social networking technology to create what the authors’ term user driven health care. It pointed out the necessity to direct the development of health information systems such that they serve as important vehicles between patient and health professional users in communicating and sharing information other than their role in automated alerts and responses. A project was described that plans to create a system of online sharing of health information in a user driven manner and which necessarily becomes persistent due its getting stored in electronic health records. This is an operational model of user driven health care developed in an attempt to optimally answer multidimensional needs, utilizing post EBM approaches, in individual patients and health professionals. This may allow them to achieve better health outcomes through inter individual collaboration between multiple

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stakeholders in the care giving and care seeking collaborative network (see conceptual model). AMIN, which is an acronym for answering multidimensional informational needs, is a Web 2.0 enabled and moderated forum to support users’ unstructured queries, thoughts and journals by returning related thoughts and text with each entry made by users. It is an integrated system to simplify and streamline the diabetes monitoring process and support users’ unstructured queries, thoughts and journals. This operational prototype, which still continues to evolve, has been shared with other future stakeholders particularly in the government healthcare system and is expected to considerably improve communication and transparency between multiple users and stakeholders, primarily patients, health professionals and other actors in the care giving collaborative network across a web interface.

ACKNOWLEDGMENT The first author assumes complete responsibility for the views expressed in this Chapter. We acknowledge the technical contributions of Waqar Aziz in developing the software interface for the solution discussed and the technical liaison maintained by Lim, Chia Ni, Tan, Eng Hoo, and Tan, Eng Hoo, Jia Yu, HonX and Ang, Koon Liang. We are thankful to the Ministry of Health, Malaysia for having gone through our proposal demonstration and at this point eagerly wait for the next step in this collaborative venture that is executing the operational model into a validation model. This write up has been shared extensively in emails and web portals and ideas contained within it are being considered for publication in completely different contexts (for example as an illustrative process description for a chapter on collaborative E-learning). The appendices mentioned in this article may be obtained from the corresponding author by email.

REFERENCES Biswas, R. (2007) User driven health care model to answer present day patient physician needs. Paper presented at the meeting of the IEEE P2407 working group, London. Biswas, R., Umakanth, S., Strumberg, J., Martin, C. M., Hande, M., & Nagra, J. S. (2007). The process of evidence-based medicine and the search for meaning. Journal of Evaluation in Clinical Practice, 13, 529–532. doi:10.1111/j.13652753.2007.00837.x Bulstrode, C. (1995). Continuity of care--sacred cow or vital necessity? British Medical Journal, 310(6987), 1144a–1145. Haggerty, J. L., Reid, R. J., & Freeman, G. K. (2003). Continuity of care: a multidisciplinary review. British Medical Journal, 327(7425), 1219–1221. doi:10.1136/bmj.327.7425.1219 Henry, S. G., Zaner, R. M., & Dittus, R. S. (2007). Moving Beyond Evidence-Based Medicine. Academic Medicine, 82, 292–297. doi:10.1097/ ACM.0b013e3180307f6d Martin, C. M. (2007). Chronic disease and illness care: Adding principles of family medicine to address ongoing health system redesign. Canadian Family Physician Medecin de Famille Canadien, 53(12), 2086–2091. McWhinney, I. (1989). A Textbook of Family Medicine. New York: Oxford University Press. Pauli, H., & White, K. (1998). Scientific Thinking, Medical Thinking and Medical Education: Questions derived from their Evolution in the 20th Century. Human Resources Development Journal, 2(3). Retrieved 14 April, 2008, from http://www. who.int/ hrh/en/ HRDJ_ 2_3_02.pdf. Shabo, A. (2005). The implications of electronic health records for personalized medicine. Personalized Medicine, 2(3), 251–258. doi:10.2217/17410541.2.3.251

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Shreeve, S., Holt, M., & O’Grady, L. (2007). Health 2.0 Definition. Retrieved 14 April, 2008, from http://health20.org/ wiki/Health_ 2.0_Definition Sittig, D. F., & Hazlehurst, B. L. (1999). Personalized Health Care Record Information on the Web, The Informatics Review, Oct. Retrieved 16 February, 2007, from http://www.informatics- review. com/ thoughts/ personal.htm. Smith, R. (1996). What clinical information do doctors’ need? British Medical Journal, 313, 1062–1068.

Starfield, B. (1998). Primary Care. Balancing Health Needs, Services, and Technology. New York, Oxford: Oxford University Press. Sturmberg, J. (2007). The Foundations of Primary Care: Daring to be Different. Oxford, San Francisco: Radcliffe Medical Press. Surowiecki, J. (2004). Wisdom of the Crowds. London: Random House. WHO - Western Pacific Region. (2007). People at the Centre of Health Care: Harmonizing mind and body, people and systems. Geneva: WHO Western Pacific Region.

This work was previously published in Biomedical Knowledge Management: Infrastructures and Processes for E-Health Systems, edited by Wayne Pease, Malcolm Cooper and Raj Gururajan, pp. 101-117, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.4

Remote Patient Monitoring in Residential Care Homes:

Using Wireless and Broadband Networks Tanja Bratan Brunel University, UK Malcolm Clarke Brunel University, UK Joanna Fursse Brunel University, UK Russell Jones Chorleywood Health Center, UK

ABSTRACT The UK’s National Health Service (NHS) is undergoing great reform. Driven by a demand for higher quality health care provision, information and communication technologies (ICT) are increasingly being used as tools to realize this change. We have investigated the use of remote patient monitoring (RPM), using wireless and broadband networks, in three community care DOI: 10.4018/978-1-60960-561-2.ch404

homes between July 2003 and January 2006. The aim of the project was to determine for what conditions and in which setting the RPM was most useful and to establish an organizational and clinical infrastructure to support it. Evaluation of the project demonstrated clinical benefits such as the early detection of cardiac events, allowing prompt intervention and routine monitoring of other conditions. A change in work practices resulted in a more collaborative approach to patient management and led to an increase in communication between health care professionals from different

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sectors, as well as the establishment of protocols for seeking advice. Technically, the equipment largely met the users’ needs. In conclusion, the monitoring proved a useful tool for the management of chronic diseases and has great potential to contribute to the reform of the NHS.

INTRODUCTION The UK’s National Health Service (NHS) has been and continues to be subject to major change. Driven by an increase in expectations, together with an aging population and the availability of new medical technologies (Department of Health, 2006), the government presented its vision for reform of the NHS in the NHS Plan in 2000. The main themes of this plan were to: (1) develop a service that would offer prompt and convenient care; (2) enable rapid access to diagnosis and treatment in modern facilities; and (3) give patients the choice over the time, place, and personnel involved in their treatment (Department of Health, 2001). Central to the plan was the creation of the National Program for IT (NPfIT), the largest IT program in the world. A national data spine and a national broadband network (N3) were designed to connect health care providers to a central secure system (Calkin et al., 1999) and support many e-health initiatives. These initiatives include the electronic patient record, repeat prescription, choose and book, as well as help with profiling, clinical governance, and reuse of data (Department of Health, 2002). e-health is an umbrella term and can be defined as “the application of information and communications technologies (ICT) across the whole range of functions which, one way or another, affect the health of citizens and patients” (Maheu et al., 2001). This ranges from medical applications such as telemedicine, remote patient monitoring, and electronic patient records to telecare and beyond to tools that empower patients such as health Web sites.

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The objectives of this chapter are to: • • •

Inform the reader about the potential of ICTs to reform health care Demonstrate the application of ICTs in form of a specific e-health case study Discuss future trends in the area

The case study, e-Vital, was a feasibility and market validation project providing remote patient monitoring (RPM). The UK element of the project investigated the use of RPM in two residential care homes and one nursing home. The work was novel in that it exploited new forms of technology, wireless and broadband networks, to provide the communication infrastructure to small health care organizations.

BACKGROUND As the number of elderly people in society continues to grow, so do the health care costs associated with this section of the population and the need to find a means for efficient and effective provision of health care. Continued aging of the population is inevitable during the first half of this century, as the relatively large number of people born after the Second World War and during the 1960s baby boom become older (British National Statistics: Ageing, 2005). Chronic disease is more prevalent among the elderly, with almost 75% of the over 65 year-olds suffering from at least one chronic disease, while nearly 50% have two or more (Calkins, Boult, & Wagner, 1999). Many require frequent medical attention, both in a clinical environment and at home. As a result of cost-issues, over-crowded hospitals, and the preference of elderly people to remain in their normal environment, there has been a trend to move away from hospital-based health care to home-based health care (Maheu, Whitten, & Allen, 2001). The UK government has been actively promoting home-based health care as part of its program

Remote Patient Monitoring in Residential Care Homes

to move services into the community (Wistow, 2000). It has recently made available 80 million pounds for preventative technologies over a 2 year period (TeHIP, 2005). It is speculated that with the provision of home care services, patients can live in their usual environment for longer, thus avoiding the hotel costs of hospital; the patients’ own care-givers can provide no-cost nursing; and the actual costs of primary care are often lower than the equivalent service provided from hospital (Hersh et al., 2002).

Remote Patient Monitoring RPM can be defined as the monitoring of physiological measurements in a setting other than a hospital, using ICTs to transfer data over geographical distances. It has the potential to offer a convenient and cost-effective solution for the problems described. Information such as blood pressure, respiration, temperature, weight, blood oxygen saturation (SpO2), glucose monitoring, peak expiratory flow, and remote ECG monitoring can be collected and transmitted (Bratten & Cody, 2000). Currently, most RPM takes place in the patient’s own home. There are three main reasons for remote patient monitoring: (1) the prevention of hospital admissions through early detection of deterioration, (2) prompt hospital admission where indicated, and (3) the provision of hospital-like services in the home, which might facilitate keeping patients at home and out of hospital or early discharge from hospital and the follow-up care in the home. The aim of each is to triage cases so that resources can be allocated according to need and that patients receive the most appropriate form of care for their condition at the time they need it. Benefits include: •

Early discharge of patients from hospital, as it can provide an alternative to hospital monitoring

•

•

Help in identifying deterioration early and thus either expedition of hospitalization or prompt treatment resulting in a reduced number of unnecessary admissions, and enabling of prompt emergency response (Bratan et al., 2005) Allowing for health care professionals to look after a greater number of patients as they can do so from their offices (e.g., Demeris, 2004)

There are three distinct types of RPM: (1) chronic disease management which might also include other regular monitoring for example during pregnancy, (2) acute monitoring, and (3) ambulatory monitoring. Diagnostics, which have a different purpose, for example, Holter ECG monitoring, will not be covered in this chapter. 1. Chronic and other disease management: This is currently the most common form of RPM. Measurements are taken on a periodic or regular basis, for example, daily, and data is forwarded to the data store usually without processing. The purpose is to detect a significant change in a parameter over a period of time, which may be an early indicator of deterioration and would indicate that intervention in therapy may be required. The most important aspect of designing such a service is to define the clinical response and the intervention in the pathway of care, and, importantly, how it may fit within the existing organizational structures. The most commonly measured data includes blood pressure, weight, and oxygen saturation of the blood (SpO2). Data are frequently measured by the patients themselves. Chronic heart failure (CHF) and chronic obstructive pulmonary disease (COPD) are the most commonly monitored diseases. 2. Acute: The purpose is to monitor a patient in situations where there might be a sudden or rapid deterioration in condition which must

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receive prompt medical intervention. This can include acute exacerbation of a chronic disease, cardiac conditions, stroke, or post operative care. Data from vital signs will have to be sent on a near continuous basis and would include ECG, SpO2, temperature, and blood pressure. The requirement in design is to ensure that the clinical response is capable of rapid intervention and would include provision of professional medical personnel close to hand. 3. Ambulatory: In this form of monitoring, the goal is to identify a critical and sudden change in the vital sign in an otherwise reasonably healthy person, for example a prolonged ST segment change (a part of the ECG wave) in an ischemic patient that might be the early indicator of imminent myocardial infarction. This requires that the signal be monitored continuously and algorithms be used to detect an event. The system must be capable of sending an alarm, and, ideally, data representative of the event. The system should allow the patient mobility and be designed to operate within a range of environments, such as within the home or the community, and so is likely to be based on wireless communication.

E-VITAL CASE STUDY The e-Vital project established a monitoring service in three community care homes (two residential homes and one nursing home) and investigated its use in this environment. Community care homes are characterised by having a large proportion of dependent and semidependent residents who often suffer from multiple chronic diseases and frequently require medical attention. They also have residential staff members who are either medically qualified (in the case of the nursing home) or have some medical training (in the case of the residential care home). RPM was seen as supporting the staff members in the manage-

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ment of their residents. They are responsible for their care and well-being. With training, they are able to apply and use the monitoring system to gather data for onward transmission to a health care professional to provide support and advice. The first phase of the e-Vital project was to evaluate the practical feasibility and acceptability of the monitoring. The second phase determined the impact of the monitoring on clinical outcomes, organizational and human factors, and technical issues. This chapter discusses the outcomes of both phases, although it focuses on the second phase.

Set-Up The three community care homes within Watford and Three Rivers Primary Care Trust (PCT) to the northwest of London were equipped with a telemonitor from RGB Medical Devices, S.A/ Spain. The telemonitors were able to measure several parameters including 7-lead ECG, blood pressure, oxygen saturation (SpO2), heart rate, temperature, and respiration, and were designed to support monitoring of conditions that commonly occur in a community care home setting, including cardiac and pulmonary diseases. As such, they were to be operated in a nonclinical environment by nonmedical personnel. If one of the residents gave cause for concern in their condition, physiological measurements would be recorded and securely transmitted over the Internet to the data server. Residential staff members would then contact the health care professional to inform them of the situation and that data was available to be viewed, which was via a secure Web site. Figure 1 shows a patient being monitored. The overall network architecture for the system is shown in Figure 2. Monitoring devices were connected to the network in each community care home using a wireless local area network (WLAN 802.11 b/g) to give freedom and flexibility of placing the devices according to the needs of the resident or staff. Recordings were most often made in the seclusion of the bedroom, although some

Remote Patient Monitoring in Residential Care Homes

Figure 1. Patient being monitored with the telemonitor

routine measurements such as blood pressure were made in communal areas when convenient for the resident. The wireless network connected through an access point to an asymmetric digital subscriber line (ADSL) broadband router to provide access to the public Internet. Data was transferred through a virtual private network (VPN) connection to the data server. The data was viewed by medical staff at the associated health center or local hospital through their connection to NHSNet, the regional health network of the UK’s National

Health Service. Being a private managed network, this allowed no external access, and it was necessary to place the server outside of NHSNet. The community care homes were privately run and therefore not allowed access to NHSNet. Although innovative in its use of technology and designed as a feasibility study, it was important not to concentrate solely on the technical aspects but to also consider the processes, interactions, and impacts on the various stakeholders of the system. Failure to consider this has often resulted in unsuccessful implementations of information systems in the past (see Lyytinen & Hirschheim, 1987; Sauer, 1993).

Process of Care The community care homes normally have a contract for care with the local general practitioner (GP) and the residents of each home are registered with the local health center. Routine health care, such as reviewing medication, chronic care follow up, and dealing with acute illness, is provided by the GP visiting the home on a regular basis. The GP would also make unscheduled visits to the home to deal with urgent problems.

Figure 2. The network architecture in the pilot sites

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We have used the term community care home to refer to all institutions providing residential and in some cases also nursing care. The homes having individual bedrooms but share communal and living facilities and all meals are provided. However, the type of resident in such homes may vary from the very well, but usually aged, through to high dependency, with the latter referred to as nursing homes and having several well qualified and experienced nursing staff. This population of the homes are characterized by individuals who become ill relatively frequently and whose health, often during an illness, can deteriorate rapidly. As a result, they require a relatively high level of medical intervention. In the case of sudden deterioration, there will be great concern over the well-being of the resident, and it is normal practice to seek medical assistance from the local GP on an emergency basis. However, help may often be sought out of hours for the GP service or at busy periods, and is not immediately available. This often results in the person being sent to the hospital by ambulance, often with accompanying stress and ordeal. One of the main aims of the project was therefore to reduce these unnecessary admissions to the hospital. As part of this, we investigated for which conditions and in which setting the monitoring would be most useful.

Design of the Project A qualitative evaluation was used to assess the success of the monitoring in each home. This was based on semistructured interviews with key members of staff: two doctors, four managers, one carer, one nurse, and the technical director. We developed a theoretical model and framework for factors important for successful implementation of an ICT project in health care and in particular related to e-health. This was used to underpin the design of the project and to direct the interview questions.

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Evaluation: Findings from the Literature It has been suggested that the evaluation of e-health projects is highly complex. Research teams tend to underestimate the technical and organizational complexity of the task, which leads to problems selecting and installing equipment and difficulties in interpreting the data collected (May et al., 2002). Wootton and Hebert (2001) state that before evaluating a telehealth project, an understanding of what constitutes success is necessary. Listed below are evaluation criteria adapted from the authors. It is emphasized, however, that telehealth projects must be considered in relative, not absolute terms, that is, “success cannot be judged in isolation” (Wootton & Hebert, 2001, p.6). This means that success depends on the perspective and on the available alternatives. If no other health care is available for instance, telehealth will always be preferable, even if it is clinically inferior to conventional health care. Success can also depend on the perspective adopted, such as that of the clinician, the patient, the health care provider, or society as a whole, and so forth. Consequently, there is “no single criterion for success,” but the most relevant criteria should be applied (May et al., 2002). As an overall guideline, it is suggested that success in telehealth can be measured in the extent to which it has contributed to the provision and maintenance of a health service (Hailey, 2001). Wootton and Hebert (2001) identified the following indicators for success in e-health projects: • • • • • • • •

Routine operation High activity levels Clinical efficacy Cost-effectiveness Adequate financing (no special funding arrangements required) Acceptance by clinicians Acceptance by patients Improved access to health care, particularly in rural and remote areas

Remote Patient Monitoring in Residential Care Homes

•

Reduction of travel

Although these apply to service programs rather than pilot services, many indicators are still useful for evaluating pilots and can give an indication of the “maturity” of the project. In the next section, e-Vital will be compared to the above indicators, and other findings from the evaluation will be presented. The findings are divided into clinical, organizational, human, and technical issues.

Findings from the Case Study Clinical Outcomes Thirty residents were monitored for various conditions and individual specific cases of respiratory disorders, cardiac problems, high blood pressure, diabetes, and renal problems. Generally, the monitoring was used to investigate residents who were unwell, and in most cases the outcome was that no immediate action was required. However, in these cases it did reassure the staff whether a resident needed to be seen by a doctor or not. In three cases (10%), a significant cardiac event (two asymptomatic myocardial infarctions and one pericardial effusion) was found and permitted a prompt response, and, in one case, controlled admission to hospital. We are unable to comment on the outcome of such patients without RPM; however, response time can be critical for the outcome for the patient. We are also unable to quantify how many admissions may have been avoided, as the effect of RPM is not always direct. But use and experience of the technology, improved awareness, and closer professional relationships also impact on how patients are managed in an RPM environment. Staff members learn to be more proactive, take increased responsibility, and become more autonomous (Aas, 2000) and this will have an impact on admissions. E-Vital demonstrated that RPM in residential homes has value. We envisioned that it could

reduce unnecessary admission to hospital, saving cost and discomfort for the resident. In the event, our system diagnosed several cases of silent myocardial infarction, which otherwise would have gone undiagnosed and untreated. Incidence of this type is to be expected in the environment of the community care home with its significant population of elderly residents having existing disease, and with increased experience of use we would expect such systems to have greater value. Furthermore, we would also expect such a system in the future to include the capability to perform routine daily measurements on patients to manage residents with chronic disease.

Clinical Organization Patient care pathways were used as a methodology to plan the introduction of the technology. The pathway before the introduction of the RPM system shows how residents giving concern were normally immediately sent to hospital (Figure 3). Many such admissions were unnecessary and resulted in residents spending long periods waiting in the accident and emergency department. This could cause distress to the resident and their family, and might also lead to the unnecessary occupation of hospital beds. Introducing RPM into the pathway, as shown in Figure 4, allowed staff members to triage cases and so determine those needing admission to hospital or those who could safely remain in the home. In this system only the information moved, not the patient or the GP.

Organizational Factors The effects of introducing technology to an organization are well known (Aas, 2001) and must be well planned and considered. Staff members must adopt new working practices, be introduced to and trained on new equipment, learn new skills, and relate to new people or in new ways. In this project, staff members in the community care homes were expected to have more direct communication

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Figure 3. Existing clinical pathway

with staff at the health center regarding medical conditions and their management. They were to apply medical monitoring devices to take physiological measurements, which were, for many, new skills. Staff members in the health center had to access medical data from which decisions had to be taken without having direct access to the patients and on types of data to which they may not previously have been accustomed. New diagnostic and interpretive skills may have been required and access to expert skills and support found. Other changes, such as daily routine could be affected. This might include learning how best to manage a situation such as when to review data in the busy schedule of a morning clinic, how to liaise with the staff of the community care home to elicit physical symptoms and condition, how to manage the patient, and learning the limits and capabilities of the system so that each health care professional felt safe and secure in later decisions. Users reported that they found their time expenditure “manageable,” but commented that they considered the support and encouragement from higher management to be critical and felt

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Figure 4. E-vital pathway of care

that the RPM system was more valuable when supported by their superiors. Meeting user requirements is one of the most important factors for a successful information system (Currie & Brown, 1997). The technical director therefore arranged two meetings with the staff before implementation and two during implementation in order to receive feedback and comments on the RPM system. This user involvement in setting up and conducting the trial proved to be very beneficial and created the necessary trust and understanding between the medical and care staff as well as the research team. The system also built upon the existing relationship between each home and the GP. The time required to train the staff to use the equipment was relatively short and built upon existing skills. The staff members were quick to recognize the potential advantages of the system to improve patient care. Initially, key members of the staff were identified and trained, using the concept of the champion (see the next section). Once established, training for other members of the staff was arranged. Although the staff members were shown how to access the data on the server in order to check the data was available, we had to stress that it was not their role to interpret the data; rather, that they should inform the GP that data was available and seek advice on management. Some confusion and

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concern about this role was found. We determined that ensuring each individual had a clear understanding of their role and how, when, and where to seek support was very important. Legal issues were encountered. During a routine inspection by the Commission for Social Care Inspection (CSCI) of one of the homes, it was decided that the staff did not possess the necessary skills to operate the RPM system. Use of such equipment was not considered normal. This decision was subsequently reversed after lengthy discussions.

Human Factors Communication between the staff of the community care homes and the health care professionals was key, as a greater level of communication than normal was required. We therefore took specific actions to develop the relationship between the parties. Confidence in the use of the system by the home staff was important, and thus was affected by technical problems experienced early in the project. Staff members were sometimes reluctant to report problems, assuming that they were major and insurmountable. However, often, they were due to simple issues and quickly resolved. Having a clinical champion at each site was vital. We tried to find a person who was enthusiastic about the monitoring and had sufficient authority to influence other members of staff. The home where the monitoring proved most successful was the one with the most enthusiastic champion. The champion demonstrated the benefits of the monitoring to the home’s management and enthusiasm convinced others to use it. The response to using the system was generally very positive. Most members of staff embraced the system readily once they had been trained in its use. This can be explained by a shared interest in providing the highest level of care for the residents.

However, resistance to the RPM system was also encountered. In this case, one of the GPs was uncooperative and this appeared to be as a result of a reluctance to change work practices. Specifically, the GP refused to use a Web browser and insisted on the data being printed out and faxed. Good project management was essential. This included having clearly defined phases of functional user requirements and system specification, procurement, implementation, training, and support (Beynon-Davies, 1999). Conflicts with the suppliers were not encountered. This was attributed to the trust that had been formed between the technical director and supplier in the early stages of the project and had been built on an existing relationship.

Technical Issues The monitoring equipment chosen met most of the needs of the users. Overall, use was considered to be “easy” or “very easy” by all the community care home staff interviewed. This included setting up the monitor, attaching the sensors to the patient, taking a measurement, and transmitting the data. Data transmission was found to be unreliable on occasion, which was because of a number of wireless dead spots. This was resolved by extending the wireless network in the homes. Data access and presentation were considered acceptable, although suggestions for changes were made. Although the telemonitor was considered easy to use, some commented that it was cumbersome to move around due to its size and weight. We observed that the staff could quickly and easily become demoralized in use of the equipment following technical problems. It could take significant effort to restore trust. We would advocate expending effort early in a project to ensure reliability of equipment, training, and familiarizing users with the equipment and establishing good support mechanisms.

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Comparison Against the Indicators for Success The project may be evaluated against the indicators for success of Wootton and Hebert (2001), as shown in Table 1. Five of the eight indicators for success were met. However, the project was designed as a pilot, and so was not integrated into routine health care or financially sustainable. It showed good acceptance and good clinical results, which were the objectives for the project.

Comparison of Sites The nursing home differed from the residential homes by having nursing staff. They were better able to recognize situations when monitoring could be of value. They were also often able to interpret the data and take responsibility for managing the condition with the support of the distant doctor. In contrast, the staff in the residential homes were hesitant to make recordings and would delay contacting the doctor. In part, this could be explained by the cumbersome size of the equipment and reluctance to set it up in a resident room unless there was clear need. The role of the home would also affect usage. The nursing home, with its greater proportion of high dependency patients and nursing staff, had

a greater need to use the system and opportunity for use was better recognized. This resulted in more than 70% of total use being carried out in the one nursing home.

BROADBAND AND RPM In any RPM application, there are several important aspects of the network characteristics that will influence performance and behavior. Most important is bandwidth as this ultimately dictates which applications will be possible and the measure of their success. Furthermore, it is essential to consider the asymmetric bandwidth of most current implementations of broadband, as RPM applications generally will demand the greater bandwidth in the upstream direction, and where it is most limited, often by a significant degree (e.g., 512 kbps upstream, 8 Mbps downstream). The high bandwidth of broadband will support well a large variety of RPM applications including not only simple data transmission but also streaming of vital signs, video conferencing, and good access to Web resources for the user. RPM applications may also need to be permanently connected with their server, so that the remote server may configure the RPM or check its status at all times. Most RPM applications will tolerate delay and jitter in delivery of packets, except for

Table 1. Indicators for success Indicator

Achieved

Routine operation

No

Clinical efficacy

Yes

Cost effectiveness

Not evaluated

Adequate financing (no special funding arrangements required):

No

Acceptance by clinicians

Yes

Acceptance by patients

Yes

Improved access to health care

Yes

Reduction of travel

Yes

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Comment The project was a pilot and did not become a routine part of health care delivery.

The project was funded by the European Union, and therefore relied on external funding.

Remote Patient Monitoring in Residential Care Homes

video conferencing. All RPM applications will demand low error rates. Broadband provides excellent support for RPM over other residential connection methods such as dial up and delivers qualities that match well to the desired level of quality—it has inherent high speed, low delay, is reliable, and is continuously available with fixed cost. It will support all typical RPM applications, which have modest bandwidth needs that are well within the upstream bandwidth limitations, except for high quality video conference applications. One aspect of normal residential broadband needs to be discussed. Usually broadband will be implemented by providing the user with a single dynamically allocated IP address each time they connect to the ADSL service, with the address being retained for the duration of the connection. A NAT router will be installed to provide connection for any computers located in the premises. Each of these computers is allocated a “private” address that may not be directly connected from the public Internet; the computers may only connect outwards to the public. This is done to “share” the single IP address. The NAT router maintains a mapping between the internal IP address of the computer and the external public IP address whenever a connection is made. This mapping is retained as long as data is transmitted or received, but is lost if there is no activity for some period (necessary in case a computer should be switched off). Although the connection can be easily restored by the internal computer, the route from the remote server will be lost and cannot be restored. Steps therefore must be taken to ensure that the TCP/ IP connection between the RPM and the distant server is maintained against loss of connection through the reset of the route mapping in any NAT routers, easily accomplished by keep-alive traffic. Using the NAT router has the advantage of allowing many computers to be connected using the same broadband connection, within the limits of sharing the bandwidth between applications.

Furthermore the internal private network may then incorporate a wireless LAN to add flexibility and convenience and can be well protected from outside intrusion from the public Internet by the NAT router.

FUTURE TRENDS While early detection and intervention should prevent unnecessary admissions, treatment, and expense, at present there is still some debate over the cost benefits that could be achieved from using remote patient monitoring technology. While some purport significant savings through the use of RPM, it has been suggested that 15% of all home visits in the UK could be replaced by RPM technology, saving £1.26 million in the first year (Celler et al., 1999). Others argue that the scale of the projects implemented to date—most projects have not continued past the initial pilot stage and have not been integrated fully into the healthcare system (May et al., 2003)—has meant that it has been impossible to truly assess the cost benefits that such a service would achieve. This has created somewhat of a paradox, with health professionals reluctant to invest in a technology that has so far failed to prove its cost effectiveness. Yet for the technology to effect the cost savings, there is a need to reach economies of scale and a critical mass of users. In order to combat this, the UK government has recently put out to tender for three demonstrator projects which will cover a population base of over one million each. It is hoped that this will provide the evidence-base required for the cost effectiveness for RPM. However, despite this uncertainty, the need for RPM looks promising. It is to be expected that as use and experience increase, so will the capabilities of the systems. At present, current solutions provide the basic functionality to support simple management of chronic disease. Systems are wired in the home to the telephone and take

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a limited range of measurements (blood pressure, oxygen saturation, blood glucose, weight, peak flow) on a daily basis. However, the increasing availability of extra functionality such as ECG, temperature, and continuous oxygen saturation will allow systems to monitor the same patient in the home during an acute phase of an illness, provided that the clinical infrastructure to support this is available. Wireless transmission of alarms and data would allow monitoring of patients with conditions such as angina, who, although relatively well, may deteriorate very rapidly. Such systems would support well the work in community care homes. Systems are getting more powerful by enabling remote real-time configuration by the clinician, so that reliable, sophisticated alarms can be set and modified based on the condition of the patient. Data could be recorded and sent automatically based on pre-configured schedules. This might mean that a system monitoring a person with angina would send an alarm if ST depression for last 10 minutes and 10 seconds of the ECG. If there was cause for concern, the clinician could configure the device to send the ECG every few minutes until recovery was seen or action taken. Such systems do exist in prototype and pilot form. An example is the Telecare project (Clarke, 2004), which produced a system having all the above capabilities that GPRS to provide wireless connectivity for ambulatory monitoring. The system would also manage blood pressure and weighing scales for chronic disease, and fall detectors, smoke alarms for simple in home monitoring, so that a single unified architecture could support the full range of monitoring modalities. Work also must be undertaken on how these systems become integrated into mainstream health care delivery. This would include how and where data are managed, how sectors of health care interrelate, which patients might most benefit, and best strategies for use of equipment and medical intervention.

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CONCLUSION This chapter has discussed acceptability and feasibility issues of an RPM system piloted in three community care homes in the UK. This included detailed consideration of clinical, organizational, and human factors as well as technical issues. A number of factors were found to be critical for the success of the system. The project demonstrated feasibility and was well accepted by patients and staff. Clinical benefits included early detection of cardiac events, allowing prompt intervention, and routine monitoring of other conditions, particularly cardiac and respiratory chronic diseases. The monitoring system allowed staff to triage cases and determine the most appropriate action. The evaluation showed that the monitoring proved very effective in the population of a high dependency nursing home, but showed fewer benefits in the relatively well residents of a residential care home. The nursing home also benefited from having a clinical champion. The new approach required increased communication and collaboration between different sectors of the health care system involved in the care of the residents of the homes. Closer relationships between the staff at the residential care homes and the staff at the health centers fostered a team approach that was important for successful use of the technology. At the same time, staff gained a greater sense of autonomy. Rather than immediately requesting a visit or calling an ambulance in case of a problem, they were able to do more for the patients. This did require that the staff of the community care homes learn to seek support when reporting and requesting assistance for a problem and that the staff at the health centers respond appropriately. Patient pathways may be used to analyze how to best redesign the system to support the new method of working. In this case, the telemonitor enables the movement of information rather than of the patient or health care professional and allows cases to be triaged remotely. The continu-

Remote Patient Monitoring in Residential Care Homes

ous involvement of users prior to and during the implementation of the system, as well as the reliance on existing relationships between the homes and the health centers, created a positive atmosphere between the participants in the project. This allowed a relatively smooth introduction of the system. Both the support from management of the homes and local clinical champions played an essential role in establishing the project, and its continued use. Wireless networks were key to the success of the pilot, as they allowed use of the telemonitors everywhere in the large old buildings of the care homes without the need for rewiring. Broadband enabled speedy data transmission. The equipment chosen was found to largely meet the users’ needs. The overall conclusion is that the wireless and broadband based RPM system offers much potential for improving and providing excellent health care delivery, both in terms of reducing unnecessary hospital admissions and expediting admission where indicated. It is expected that such mobile health care applications will become an integral part of health care once the current challenges have been resolved.

Bratten, R. L., & Cody, C. (2000). Telemedicine applications in primary care: A geriatric patient pilot project. Mayo Clinic Proceedings, 75, 365–368. doi:10.4065/75.4.365 Brennan, S. (2005). The NHS IT project, the biggest computer program in the world…ever! Oxon: Radcliffe Publishing. British National Statistics. Ageing. (2005). Retrieved July 21, 2007, from http://www.statistics. gov.uk./ cci/ nugget. asp?ID=949 Calkins, E., Boult, C., & Wagner, E. (1999) New ways to care for older people. Building systems based on evidence. New York: Springer. Celler, B. G., Lovell, N. H., & Chan, D. K. Y. (1999). The potential impact of home tele-care on clinical practice. The Medical Journal of Australia, 171, 518–521. Clarke, M. (2004). A reference architecture for RPM. In L. Bos, S. Laxminarayan & A. Marsh (Eds.), Medical and care compunetics: Volume 103 studies in health technology and informatics (pp. 374-380). Amsterdam: IOS Press.

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Aas, I. H. M. (2001). A qualitative study of the organizational consequences of telemedicine. Journal of Telemedicine and Telecare, 7, 18–26. doi:10.1258/1357633011936093

Demeris, G. (2004). Electronic home healthcare: Concepts and challenges. International Journal of Electronic Healthcare, 1(1), 4–16. doi:10.1504/ IJEH.2004.004655

Beynon-Davies, P. (1999). Human error and information systems failure: The case of the London ambulance service computer-aided dispatch system project. Interacting with Computers, 11, 699–720. doi:10.1016/S0953-5438(98)00050-2

Department of Health. (2001). The NHS plan: A progress report. Retrieved October 14, 2006, from http://www.dh.gov.uk/ en/Publications And Statistics/ Publications/ Annual Reports/ DH_4064826

Bratan, T., Clarke, M., Paul, R., & Jones, R. (2005). Evaluation of practical feasibility and acceptability of home monitoring in a residential home setting. Journal of Telemedicine and Telecare, 11(S1), 29–31. doi:10.1258/1357633054461796

Department of Health. (2002). Developing the information systems – national service frameworks: A practical implementation in primary care (Home Page). Retrieved July 21, 2007, from http://www.dh.gov.uk/ assetRoot/ 04/ 06/ 06/ 42/ 04060642.pdf

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Department of Health. (2006). Health reform in England; update and commissioning framework. Policy & Strategy Directorate, Department of Health. Retrieved July 21, 2007, from www. dh.gov.uk/ assist root/ 04/ 13/ 72/ 27/04127227.pdf European Commission. (2006). What is e-health? Information Society. Retrieved July 21, 2007, from http://europa. eu.int/ information_ society/ eeurope/ ehealth/ what is ehealth/ index_en.htm Hailey, D. (2001). Some successes and limitations with telehealth in Canada. Journal of Telemedicine and Telecare, 7(S2), 73–75. doi:10.1258/1357633011937218 Hersh, W., Helfand, M., Wallace, J., Kraemer, D., Patterson, P., & Shapiro, S. (2002). A systematic review of the efficacy of telemedicine. Journal of Telemedicine and Telecare, 8, 197–209. doi:10.1258/135763302320272167 Lyytinen, K., & Hirschheim, R. (1987). Information systems failure: A survey and classification of the empirical literature. Oxford Surveys in Information Technology, 4, 257–309. Maheu, M., Whitten, P., & Allen, A. (2001). Ehealth, telehealth and telemedicine-a guide to start-up and success. San Francisco: Josey-Bass. May, C. R., Harrison, R., MacFarlane, A., Williams, T., Mair, F., & Wallace, P. (2003). Why do telemedicine systems fail to normalize as stable models of service delivery? Journal of Telemedicine and Telecare, 9(S1), 52–26. doi:10.1258/135763303322196222 May, C. R., Williams, T. L., Mair, F. S., Mort, M. M., Shaw, N. T., & Gask, L. (2002). Factors influencing the evaluation of telehealth interventions: Preliminary results from a qualitative study of evaluation projects in the UK. Journal of Telemedicine and Telecare, 8(S2), 65–67. doi:10.1258/135763302320302073

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Sauer, C. (1993). Why information systems fail: A case study approach. Henley-on-Thames: Alfred Waller Ltd. TeHIP. (2005). The impact of e-health and assistive technologies in health care. The E-Health Innovation Professionals Group of IHM, ASSIST and BCS HIF. Retrieved July 21, 2007, from www. health- informatics.org/ tehip/ tehipstudy.pdf Wistow, G. (2000). Home care and the reshaping of acute hospitals in England – an overview of problems and possibilities. Journal of Management in Medicine, 14(1), 7–24. doi:10.1108/02689230010340354 Wootton, R., & Hebert, M. A. (2001). What constitutes success in telehealth? Journal of Telemedicine and Telecare, 7(S2), 3–7. doi:10.1258/1357633011937245

KEY TERMS AND DEFINITIONS Community Care Home: Facility in which residential and sometimes nursing care is provided to disabled or elderly people. Chronic Disease: A disease that lasts for a long time (at least three months) and cannot be prevented or cured. E-Health: The use of emerging information and communication technology, especially the Internet, to improve or enable health and health care, thereby enabling stronger and more effective connections among patients, doctors, hospitals, payers, laboratories, pharmacies, and suppliers. ICTs: Information and communications technology, a broad subject concerned with technology and other aspects of managing and processing information. NHS: The UK’s National Health Service.

Remote Patient Monitoring in Residential Care Homes

Remote Patient Monitoring: The monitoring of physiological measurements in a setting other than a hospital, using ICTs to transfer data over geographical distances.

Wireless Network: A computer network that is not connected by wires but by radio frequencies.

This work was previously published in Handbook of Research on Global Diffusion of Broadband Data Transmission, edited by Yogesh K. Dwivedi, Anastasia Papazafeiropoulou and Jyoti Choudrie, pp. 604-617, copyright 2008 by Information Science Reference (an imprint of IGI Global).

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Chapter 4.5

Telemedicine Consultations in Daily Clinical Practice: Systems, Organisation, Efficiency

Anton V. Vladzymyrskyy Association for Ukrainian Telemedicine and eHealth Development & Donetsk R&D Institute of Traumatology and Orthopedics, Ukraine

ABSTRACT This chapter introduces usage of telemedicine consultations in daily clinical practice. Author has describe process of teleconsultation, sample schemes of systems, parties of this process and its roles. Also, main steps of clinical teleconsultation (determination of necessity for teleconsultation, preparation of medical information, observance of ethics and law conditions, preparation of conclusion) are shown. Special part is dedicated to efficiency of teleconsultation – author has proDOI: 10.4018/978-1-60960-561-2.ch405

pose own complex method for estimation of it. Furthermore, the authors hope that understanding of teleconsultations’ process will make it more accessible and easy-to-use for medical practitioners.

INTRODUCTION In this chapter the author describe approaches to usage of telemedicine consultation in daily clinical practice. First teleconsultations described at 1910s (published in JTT, 1997). Since that time telemedicine is use wide range of technologies – TV, satellite, Internet, cellular etc – for discussion

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Telemedicine Consultations in Daily Clinical Practice

of serious clinical cases at distance (Bashshur et al,1997, Kamaev et al, 2001, Nerlich et al,1999, Vladzymyrskyy,2004). Annually in the world are spent thousands teleconsultations. It is possible to say that this procedure is most wide spread telemedicine service. Teleconsultation (telemedicine, remote consultation) – remote discussion of the clinical case via special computer information and telecommunication system to get answers to precisely formulated questions for the help in clinical decisions (Vladzymyrskyy,2003). Author has propose classification of teleconsultation by 3 main classes: terms of teleconsultation leading, sort (kind) of organisation and technical platform.

Informal: Free discussions of clinical cases in professional Internet societies (via mailing lists, Web-forums); ◦⊦ Second opinion: Teleconsultations for patients who contacted a medical organization by email or via a special online form/forum. 3. By technical basis: ◦⊦ Systems at the base of Internet and its services (e-mails, Web-platforms, mailing lists, IP-phones, IPvideoconferences, chats, messengers etc); ◦⊦ Systems at the base of special links (satellite, ISDN, ftn-protocol, computer health networks etc);

1. By term: ◦⊦ Synchronous: All parties use the same telemedicine system in the same time (in real time); ◦⊦ Asynchronous: All parties use the same telemedicine system with time delay (sequential use). 2. By sort: ◦⊦ Formal: Two or more organizations were involved under a previously signed contract/protocol/agreement;

Systems at the base of cellular phoniness and its services (mobile Internet, SMS, MMS, voice etc).

◦⊦

SYSTEMS OF TELECONSULTATION As we can see in Figure 1, there are 4 main participants of teleconsultation process (Vladzymyrskyy,2003): 1. Inquirer: Legal or physical person representing a clinical case for the teleconsulta-

Figure 1. Sample scheme of teleconsultation

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tion. Most frequently inquirer is the “faceto-face” physician/nurse, also - patient or relatives (in case of self-reference for teleconsultation, “second opinion”). Functions of the Inquirer: ◦⊦ Granting of the clinical case for teleconsultation, formulation of questions; ◦⊦ Registration of the medical documents according to the requirements of the adviser (digitalization, translation into foreign language etc.); ◦⊦ Granting the additional information by inquiry of the adviser; ◦⊦ Participation in synchronous procedures. 2. Adviser: Expert or group of the experts considering the clinical case which was presented for the teleconsultation. Functions of the Adviser: ◦⊦ Consideration and consultation of the clinical case in the stipulated terms; ◦⊦ Granting the conclusion with use of the standard medical terminology; ◦⊦ Participation in synchronous procedures. 3. Coordinator (dispatcher): Physician/ nurse, expert in the field of computer technologies and telemedical procedures, which provides uninterrupted work on realization of telemedical procedures. Functions of the Coordinator: ◦⊦ Primary estimation of the quality of the medical data which were received from inquirer; ◦⊦ Check of the data on conformity by the requirements of adviser’s medical establishment; ◦⊦ Additional communications with the inquirer (in case of data discrepancy); ◦⊦ Choice of the establishment or/ and personal adviser for the teleconsultation;

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◦⊦

Sending electronic case history to the adviser or/and in other telemedical centre; ◦⊦ Decision of organizational and financial questions of the telemedical network. 4. Assistant: Technical expert serving telemedical system. Functions of the Assistant: ◦⊦ Maintenance of technical readiness for telemedical system; ◦⊦ Elimination of technical failures and malfunctions; ◦⊦ Technical support and consultation of other participants of the telemedical procedure. Technically teleconsultations realized by use of telemedicine work stations which linked by any telecommunication protocol. Telemedicine work station (TWS) – complex of the hardware and software (multitask workplace) with opportunities of digitalization, input, processing, transformation, conclusion, classification and archiving of the any kinds of the medical information and realization of telemedical procedures (teleconsultation) (Vladzymyrskyy,2003).

Basic Components of Clinical TWS: • • •

Computer (PC, notebook, tablet, PDA etc); Devices for digitalisation of medical information; Communication line (Internet, satellite, cellular etc). There are three kinds of clinical TWS: 1. Room unit: Not moving TWS within the limits of one room (office). 2. Rollabout unit: Moving version of TWS mounted on a mobile table. Such TWS can easily be moved from one room to another (from physician’s office to patients’ yard etc). 3. Mobile unit: Moving TWS for outside/ outhospital telemedical procedures.

Telemedicine Consultations in Daily Clinical Practice

In daily clinical practice practitioners most often use next systems for teleconsultations: 1. TWS+Internet/Satellite/ISDN link. 2. TWS + special digital diagnostic equipment + Internet/Satellite/ISDN link. 3. T W S + s p e c i a l d i g i t a l d i a g n o s tic equipment + kit for wide format videoconferences+Internet/Satellite/ISDN link. 4. TWS at the base of cellular phone/ communicator+Mobile Internet /MMS link. As an example of decision for clinical teleconsultation system author can propose own Best practice model, which was recognized by International Society for Telemedicine and eHealth (ISfTeH) in 2005.

Best Practice Model for Telemedical Equipment Background: for any kind of telemedical procedures it’s necessary: (1) to create an effective telemedical work station (TWS) with adequate free or/and licensed software, (2) connect TWS to some kind of telecommunication line. The basic requirements for telemedical equipment: an opportunity of processing of any kind of the medical information, cheapness, standardisation, availability, simplicity and reliability of use, technical and information safety. Main goals: • • •

Equipment for telemedical work station, Telecommunication lines, Software.

Decisions We are propose a few sets for telemedicine work station. Classical set for TWS: basis PC, SVGA monitor, multimedia equipment, CDROM/CDRW/

DVD, network adapter, high quality scanner, digital photocamera, digital videocamera, Webcamera, high quality colour printer, microphone, dynamics, modem, connector to hospital information system, sets of special digital equipment for diagnostic and treatment, auxiliary equipment. Clinical set for TWS: basis PC, SVGA monitor, multimedia equipment, CDROM, network adapter, digital photocamera, printer, modem, auxiliary equipment. Minimal set for TWS: basis PC, SVGA monitor, high quality scanner or digital photocamera, modem. Optimal clinical set for TWS: basis PC, SVGA monitor, multimedia equipment, CDROM or CDRW, high quality scanner, digital photocamera, Web-camera, printer, modem, auxiliary equipment. Example: in daily clinical practice we use TWS: PC (1000 Mhz and more) with multimedia equipment, scanner (1200 dpi and more), digital photocamera (1,3-3 mpx), printer (laser, 600x600 dpi and more), film-viewer, Web-camera. Telecommunication lines - best ways: • •

Direct and dial-up Internet lines for any kind of telemedical procedures; Mobile phones services for emergency teleconsultations.

Direct Internet lines (256 Kb and more, for videoconferences 512 Kb at least) intercity, interregional communications, communications between big regional hospitals and medical universities. Dial-up Internet lines (56 Kb and more) – for intraregional, intracity, rural-city communications. SMS/MMS services – for any kind of emergency teleconsultations.

Software Its possible to use special or non-special software for telemedical procedures. We prefer:

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• •

•

Standard licensed software from Microsoft™; Web-application designed with open code (optimal for medical establishments with low financial support); Special telemedical application on the base on Internet (for example, “Regional Telemedical System” ™).

Thus, in general any system for teleconsultation consist from telemedicine work stations, telecommunication lines and human factors (Inquirer, Adviser, Coordinator, Assistant). Gathering of work stations and selection of communication kind should be evidence-based and can be grounded at best practice models of worldwide eHealth organizations.

ORGANISATION OF TELECONSULTATION Teleconsultation is the process, which consist of two main points: 1. Sending remote expert digital medical information about patients with maximal high diagnostic accuracy and with minimal volume. 2. Organization of an effective feedback. In Figure 2, you can see the main steps of teleconsultation: digitalization of medical information, information exchange, notification about results. As we can understand that for making medical information digital we are need equipment (some telemedicine work station); for digital information exchange we are need telecommunication (some data bearer); and for notification about results of teleconsultation we are need methods for estimation of efficiency. Thus, it is possible to use almost any computer and telecommunication technology/ tool for teleconsultation. Only one condition: this

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Figure 2. Main steps of sample teleconsultation

technology/tool should support digitalization, data transfer and feedback. So, there are 4 main steps for choice of the most effective technology (Figure 3). For example, early author had developed the algorithm which enables quick choice of the most suitable telemedical technique for a present clinical situation (Figure 4) (Vladzymyrskyy,2005). Synchronous teleconsultations are most suitable where dynamic types of medical information prevail, e.g. in psychiatry (direct communication between physician and the patient is of importance) and emergency surgery. Asynchronous teleconsultations are most suitable where static types of medical information prevail, e.g.

Figure 3. Main steps for choice of the most effective technology for teleconsultation

Telemedicine Consultations in Daily Clinical Practice

Figure 4. Algorithm for choice of the telemedical technology

trauma surgery, orthopaedics, dermatology, cytology, pathology. In clinical practice teleconsultation include four main steps: 1. D e t e r m i n a t i o n o f n e c e s s i t y f o r teleconsultation. 2. Preparation of medical information. 3. Observance of ethics and law conditions. 4. Preparation of conclusion. For determination of necessity for teleconsultation it is possible to use list of iindications for teleconsultation in daily clinical practice (Vladzymyrskyy,2005): • • •

• •

Determination (confirmation) of diagnosis; Determination (confirmation) of treatment; Determination of diagnosis and treatment of rare, severe diseases or diseases with a non-typical course; Determination of complication prevention methods; Need for a new and/or infrequent surgery (for treatment or diagnosis) or procedure;

•

• •

•

•

• •

Lack of immediate specialists in the necessary or adjacent medical field or lack of sufficient experience for diagnosis or treatment of the disease; The patient doubting diagnosis, treatment and its results, complaint analysis; Decrease of diagnostics and treatment cost without impairment of quality and efficiency; Search and selection of medical establishment most suitable for urgent and planned treatment of the patient, coordination of terms and conditions of hospitalization; Medical care for patients located at considerable distance from medical centres, when geographical distance between the patient and health-care provider cannot be overcome; Search for alternative solutions for clinical tasks; Obtaining of additional knowledge and skills concerning a given medical problem.

For preparation of medical information practitioner needs equipment and rules for telemedicine

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case records. As you can see before telemedicine work station is use for digitalisation and exchanging of relevant medical information. Digitalisation could be done by to ways:

The basic requirements for the telemedicine case record:

•

•

•

Initial reception of medical data in the digital kind (from special computer aided/digital diagnostic equipment); “Manual digitalization” of medical data (by cameras, scanners, digitizers etc).

In clinical practice digital photo/video cameras, scanners, document cameras and film digitizers are widely adopted for preparation of medical information for teleconsultation. After digitalization special processing should be perform, which include: elimination of not informative areas; reduction of volume without loss of diagnostic value; correction of images without loss of diagnostic value; anonymity. In short, main targets of such processing – to make medical information with: maximal accuracy, minimal volume, safe. When we have all necessary information about patient in digital form we should to prepare telemedicine case record, which consist from: • Short case text (identifier of the patient, date, age, sex, diagnosis, brief anamnesis) - text format; •

•

•

• •

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Relevant visual data (x-rays, CTs, MRIs, clinical photos, videoclips etc) - graphic files or dicom; Explanation data (accompanying text, anatomic area, projection, method of colouring, increase, date of research etc.) - text format; Relevant text data (clinical tests, opinions of “face-to-face” advisers etc) - text format; Questions to the adviser (“diagnosis?”, “tactics of treatment?” etc) - text format; Additional information - any format.

•

• • • •

Information and methodical conformity of the standard paper/electronic case record; As it is possible the smaller size of files with minimal losses of diagnostic value; Conformity to standards; Flexibility (an opportunity of use at any technological decisions); Safety. Observance of ethics and law conditions could be done in frame of telemedicine deontology.

Telemedicine Deontology is a professional etiquette and a complex of moral requirements for the persons practising a telemedicine, principles of behaviour for medical, technical and support personnel. Main problems of telemedicine deontology: 1. Observance of laws and ethical norms 2. Preservation of Medical Information 3. Attitudes — doctor-patient-information system” 4. Attitudes — inquirer-coordinator-adviser”, “doctor-technician” 5. Physical and information safety of telemedicine systems 6. Standardisation and documentation 7. Information consent For teleconsultation practice we should understand and adopt next rules: 1. Teleconsultation is used for the help in acceptance of the clinical decision. 2. Last decision must be accepted by the attending physician (inquirer). 3. The attending “face-to-face” physician should bear all responsibility for the patient due to using or not using of recommendations of the distant expert.

Telemedicine Consultations in Daily Clinical Practice

4. Information consent and signed agreement of patient should be best practice. 5. Main ways for teleconsultation safety: patients consent, anonymisation, login/ password access for all telemedical work stations, digital signature system. So, after remote discussion expert should prepare formal conclusion, which consist from: 1. General part (identifier of the patient, date and time of inquiry, date and time of conclusion, advisers’ full name, place of work, degrees, posts) - text format. 2. Conclusion (answers to questions of the inquirer, additional information) - text format 3. Appendix (explaining Figures, example of the similar clinical case, the references to the literature and Internet etc.) - any format (text, graphic files, video etc). Note that the Appendix is most important part, because opinion of expert should have evidencebased background. Thus, choice of technology for teleconsultation (e-mail, IP-phone, videoconference etc) should be first of all grounded at medical targets, available recourses and clinical situation. Author has propose approaches and decisions for main steps of daily clinical telemedicine, include indications for teleconsultation, ways and methods for digitalisation of medical information, ethical rules, telemedicine case record description.

EFFICIENCY OF TELECONSULTATION Usually researchers consider financial benefits (Bergmo,1997, PalaninathaRaja,2006), changes of clinical parameters (Chan,2000, Lambrecht,1997, Vladzymyrskyy,2004), moral, technical aspects and management improvements (Aoki,2003, Rosser,2000, Siden,1998, Rendina,1998) of tele-

consultations. The complex estimation of quality of telemedical consultation is extremely actual question. Such method should be reliable, simple and accessible for any researcher (scientists, medical doctor, decision maker etc.). It should be the set of objective criteria which it would be possible to use for statistical processing with the purpose of comparison, studying of different kinds of telemedical consultations etc. Author has propose three groups of parameters: relevance, economic feasibility, quality indicators. Relevance. In some sources for the characteristic of efficiency of telemedicine the term “relevance” is used. In modern informatics relevance in information retrieval, measures a document’s applicability to a given subject or query. For telemedicine I am offered the following formulation.(Table 1) Relevance of teleconsultation: Conformity of the distant adviser’s answer to information and medical needs of the attending physician (inquirer). There are two kinds of an estimation of relevance (Rel): subjective and objective. In the clinical practice for value judgment we are use an approximate individual estimation on 3 mark scale: discrepancy of answers to questions - 1 point; incomplete conformity of answers to questions, an illegibility of formulations and recommendations - 2 points; full conformity of answers to questions, presence of the additional confirming information (articles, links, references, similar clinical cases etc) - 3 points. By the given scale it is possible to define quantity and relative density of high, average and low relevant answers in group of homogeneous teleconsultations (by pathology, by technical system etc). For an objective estimation author had developed the questionnaire (see table). The questionnaire for definition of relevance includes 7 questions with several variants of answers. Each answer is estimated from 1 up to 3 points. The score within the limits of 17-21 points shows on high, 12-16 - average, 7-11 - low relevance of the

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Table 1. Scale for objective judgment of teleconsultation’s relevance 1. Terms. Teleconsultation is lead: Before the necessary terms

3

In the necessary terms

3

After necessary terms

2

In terms of full loss of the urgency

1

2. Conformity of answers: Full conformity

3

Incomplete conformity of answers to questions, an illegibility of formulations and recommendations

2

Discrepancy of answers to questions

1

3. Presence of the additional confirming information (articles, links, references, similar clinical cases etc), evidence-based recommendations: Yes

3

No

1

4. Influence on the clinical tactics: Tactics of the adviser is completely accepted

3

Essential change of own tactics

2

Acknowledgement of own tactics

2

Refusal of adviser’s recommendations

1

5. Inquiry for additional diagnostic tests: No/ Accessible tests

3

Accessible tests with an investment of significant expenses (work, money)

2

Inaccessible tests

1

6.Expert has propose: One clinical program

3

A few clinical programs

2

Preconditions for formation of the program

1

7. A few distant experts take a part: Yes

3

No

1

8. Transportation after teleconsultation Yes

1

No

3

lead teleconsultation. Also, there is an opportunity to define relevance for telemedical system (Relsys) for the some period of time: Rel sys =

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TKrel TK

,

TKrel - quantity of teleconsultations with specific relevance (high and/or average), ТК - total quantity of teleconsultations. Accordingly, in ideal situation this parameter aspires to 1. Economic feasibility. Most often define the prime price (Stk) and profitability (Rtk) of teleconsultations. Definition of one teleconsultation’s cost can be spent proceeding according laws and

Telemedicine Consultations in Daily Clinical Practice

instructions accepted in the given state (at the base of calculation of the cost price of medical service). For example, calculation of cost of simple medical service: S=S1+S2=Z+H+M+I+O+P, S1 - direct charges, S2 - indirect charges, Z - salary, H - taxes, M - charges for medicines, equipment etc, I - deterioration of materials, O - deterioration of equipment, P - miscellaneous costs. Also, it is possible to use already developed methods. For example, the cost price of telemedical service (teleconsultation) by Kamaev et al, 2001:

are five quality indicators: parameter of presence/ absence of the expert’s answer (A); parameter of average duration (T); average quantity of the experts’ answers (Aq); timeliness of teleconsultations (Pt); quality of teleconsultations (Pq). First three indicators are most simple. The A-parameter can have two values: 0 - absence of the answer, 1 - presence of the answer. It is possible to define a parity of taken place and not taken place teleconsultations by A-parameter. The T-parameter estimate for sample of teleconsultations as an arithmetical mean (in numerator - the sum of durations of all teleconsultations, in denominator - quantity of teleconsultations):

Stk=(S1+S2+S3) * (1+S)+A+D+C+G+SO+P, S1- salary of the medical personnel; S2 - salary of the technician personnel; S3 - salary of the other personnel (administrative, auxiliary); S - deductions in social funds; A - amortization of the equipment; D - deterioration of equipment; C - cost of materials; G - general charges of establishment, SO - services of other organizations (providers etc); P- profit. Profitability (Rtk) of telemedicine services of hospital, clinics etc is defined by the formula (where С – price of rendered services, S – prime cost of rendered services): Rtk =

! −S . C

After calculation of Stk and Rtk researcher can compare results with other servicies. For example, there is an opportunity to economically compare telemedical consultation and traditional consultation.

Quality Indicators Quality indicators estimate for certain sample of teleconsultations. For example, lead in the certain period of time or by specific technology. There

n

T =

∑T i =1

i

n

.

The Aq-parameter estimation, at the base of [14] (in numerator - quantity of answers (experts), in denominator - quantity of teleconsultations): n

Aq =

∑ Aq i =1

n

i

.

Timeliness of teleconsultations (Pt) estimate on the basis of method by [4] (in numerator - quantity of duly received teleconsultations during certain time, in denominator - total quantity of teleconsultations for the same period of time): Pt =

m(t ≤ tcert ) nt

.

Quality of teleconsultations (Pq) also estimate on the basis of method by [4] (m - quantity of teleconsultations of admissible quality, n - total quantity of teleconsultations):

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Telemedicine Consultations in Daily Clinical Practice

m Pq = . n

Figure 5. Algorithm for an estimation of teleconsultations’ efficiency

It is possible to understand “quality of teleconsultation” as relevance or other certain estimation, for example, the quantity of teleconsultations with more than one answer so on. With help of two last criteria we can calculate probability of effective teleconsultation - (Ptk) (Gerasimov et al,2006): Ptk = Pt * Pq, In ideal situation this parameter aspires to 1. By Ptk- parameter researcher can estimate activity of telemedical system in general and, moreover, predict efficiency of teleconsultation after introduction of some technical, clinical, organizational, economical decision for telemedicine. In Figure 5 you can see an algorithm for an estimation of teleconsultations’ efficiency on the basis of the offered complex method. Thus, there are three groups of parameters in complex method for investigation of efficiency (quality) of telemedicine consultations: relevance (Rel, Relsys), economic feasibility (compare of prime price (Stk) and profitability (Rtk)), quality indicators (A-parameter of answers, T-parameter of duration, Aq-parameter of answers’ quantity, Pt-parameter of timeliness, Pq-parameter of quality, and also Ptk - probability of effective teleconsultation). In clinical practice it is better to use given method by special algorithm.

CONCLUSION Thus, clinical teleconsultation is the process which include gathering of telemedicine work station, choice of telecommunication kind, participation of four parties (Inquirer, Adviser, Coordinator and Assistant - everyone of them has own special aims and functions), digitalisation of medical information, generation of telemedicine case re-

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cord, information exchange, estimation of results and outcomes. Wide improvement of an effective telemedicine consultation systems in daily clinical practice open new ways for increasing of health care quality and capacity, “bring on” special care to rural and remote areas, makes clinical decisions easy and better.

REFERENCES Aoki, N., Dunn, K., Johnson-Throop, K. A., & Turley, J. P. (2003). Outcomes and methods in telemedicine evaluation. Telemedicine Journal and e-Health, 9(4), 393–401. doi:10.1089/153056203772744734 Bashshur, R. L., Sanders, J. H., & Shannon, G. W. (1997). Telemedicine: Theory and practice. springfield. ILL. C.C. Thomas Publisher, Ltd.

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Bergmo, T. S. (1997). An economic analysis of teleconsultation in otorhinolaryngology. Journal of Telemedicine and Telecare, 3, 194–199. doi:10.1258/1357633971931156 Chan, F. Y., Soong, B., & Lessing, K. (2000). Clinical value of real-time tertiary fetal ultrasound consultation by telemedicine: preliminary evaluation. Telemedicine Journal, 6, 237–242. doi:10.1089/107830200415171 (1997). Classic episodes in telemedicine. Treatment by telegraph (1917). Excerpt from the obituary of John Joseph Holland (1876-1959). JTT, 3, 223. Gerasimov, B., Oxijuk, A., & Gulak, N. (2006). Estimation of efficiency of the systems for support of decision-making. In System of Support of Decision Making Theory and Practice.Kiev, 25-7. (rus.)

Rosser, J. C. Jr, Prosst, R. L., Rodas, E. B., Rosser, L. E., Murayama, M., & Brem, H. (2000). Evaluation of the effectiveness of portable low-bandwidth telemedical applications for postoperative followup: initial results. Journal of the American College of Surgeons, 191, 196–203. doi:10.1016/ S1072-7515(00)00354-9 Siden, H. B. (1998). A qualitative approach to community and provider needs assessment in a telehealth project. Telemedicine Journal, 4, 225–235. Vladzymyrskyy A., Chelnokov A., (2006). Relevance of telemedicine consultation. Ukr.z.telemed. med.telemat, 1(4), 99-100. Vladzymyrskyy, A. V. (2003). Klynycheskoe teleconsultirovanie. Rukovodstvo dlya Vrachei [Clinical Teleconsultation. Manual for Physicians]. Sevastopol: OOO “Veber”, 125 (rus.)

Kamaev I., Levanov V., Sergeev D., (2001). Telemedicine: Clinical, organisational, law, technological, economical aspects. N. Novgorod: NGMA,. (rus.)

Vladzymyrskyy, A. V. (2004). The Use of Teleconsultations in the Treatment of Patients with Multiple Trauma. European Journal of Trauma, 6(30), 394–397. doi:10.1007/s00068-004-1346-4

Lambrecht, C. J. (1997). Telemedicine in trauma care: description of 100 trauma teleconsults. Telemedicine Journal, 3, 265–268.

Vladzymyrskyy, A. V. (2005). Four years’ experience of teleconsultations in daily clinical practice. JTT, 6(11), 294–297.

Nerlich, M., & Kretschmer, R. (1998). The impact of telemedicine on health care management. In Proceedings of the G 8 Global Health Care Applications Project (GHAP) Conference. Regensburg, Germany, 21 November. Studies in Health Technology and Informatics, 1999(64), 1–281. PalaninathaRaja M., Wadhwa S., Deshmukh S. G. (2006). Cost-benefit analysis on eHealthcare services. Ukr. z. telemed.med.telemat, 1(4), 53-64. Rendina, M. C., Downs, S. M., Carasco, N., Loonsk, J., & Bose, C. L. (1998). Effect of telemedicine on health outcomes in 87 infants requiring neonatal intensive care. Telemedicine Journal, 4, 345–351.

KEY TERMS AND DEFINITIONS Adviser: Expert or group of the experts considering the clinical case which was presented for the teleconsultation. Assistant: Technical expert serving telemedical system. Coordinator: Physician/nurse, expert in the field of computer technologies and telemedical procedures, which provides uninterrupted work on realization of telemedical procedures. CT: Abbreviation of computer tomography. Inquirer: Legal or physical person representing a clinical case for the teleconsultation. Most

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frequently inquirer is the “face-to-face” physician/ nurse, also - patient or relatives (in case of selfreference for teleconsultation, “second opinion”). IP: Abbreviation of Internet Protocol. ISDN: Abbreviation of Integrated Service Digital Network. MRI: Abbreviation of magnetic resonance imaging. MMS: Abbreviation of multimedia messaging system. Relevance of Teleconsultation: Conformity of the distant adviser’s answer to information and medical needs of the attending physician (inquirer). Teleconsultation (Telemedicine, Remote Consultation): Remote discussion of the clinical case via special computer information and

telecommunication system to get answers to precisely formulated questions for the help in clinical decisions. Telemedicine Deontology: Is a professional etiquette and a complex of moral requirements for the persons practising a telemedicine, principles of behaviour for medical, technical and support personnel. Telemedicine Work Station (TWS): Complex of the hardware and software (multitask workplace) with opportunities of digitalization, input, processing, transformation, conclusion, classification and archiving of the any kinds of the medical information and realization of telemedical procedures (teleconsultation).

This work was previously published in Handbook of Research on Distributed Medical Informatics and E-Health, edited by Athina A. Lazakidou and Konstantinos M. Siassiakos, pp. 260-272, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.6

The Use and Evaluation of IT in Chronic Disease Management Julia Adler-Milstein Harvard University, USA Ariel Linden Linden Consulting Group & Oregon Health & Science University, USA

ABSTRACT This chapter describes the broad array of information technologies currently used in programs that manage individuals with chronic diseases and discusses evaluation strategies to assess the impact of implementing programs that incorporate such technologies. More specifically, it describes the three components of a chronic disease management program and then details the technologies commonly used in each component. Three evaluation designs well-suited to measure DM program effectiveness are also discussed. The intent of this chapter is to educate readers on the DOI: 10.4018/978-1-60960-561-2.ch406

range of approaches to incorporating information technology into chronic disease management and the most appropriate evaluation designs that will strengthen the understanding of which approaches are most successful.

INTRODUCTION This chapter examines the use and evaluation of a broad array of information technology that is used in the outpatient management of individuals with chronic diseases such as diabetes, heart failure, and asthma. While chronic diseases are typically managed in traditional health care settings alongside acute conditions and preventive

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

The Use and Evaluation of IT in Chronic Disease Management

care, there is growing consensus that chronic diseases merit special focus. This has led to the establishment of disease management as a distinct and systematic approach to managing chronic diseases (Ofman et al., 2004). Disease management has been defined as “an approach to patient care that emphasizes coordinated, comprehensive care along the continuum of disease and across health care delivery systems” (Ellrodt et al., 1997). In practice, an array of activities falls under the broad term “disease management”. Nonetheless, well accepted approaches to disease management exist; one such approach, the chronic care model, integrates community resources, health system organizations, and self-management with the aid of decision-support and clinical-information systems (Wagner et al., 2001). This model delivers multidisciplinary, evidence-based care and offers patients the education and tools required to manage their disease(s). The chronic care model is rooted in the assumption that care for chronic diseases can only be improved by systems of care and that information technology plays a critical role in supporting these systems. Thus, a comprehensive understanding of the tools that are currently incorporated into disease management and strategies for their evaluation are essential for any effort to improve care for those with chronic diseases.

CHRONIC DISEASE MANGEMENT AND INFORMATION TECHNOLOGY As the burden of chronic disease escalates, it has become clear that health care systems designed to deliver acute care do not meet the needs of the chronically ill population (McGlynn et al., 2003). Chronic conditions require ongoing, multi-disciplinary care that includes more frequent patient contact to assess changes in clinical status. Chronic conditions also require unique forms of care, such as patient education on self-management. In addition, assessments take place at both the patient level to inform the management of individual

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cases, and at the population level to identify trends that should be addressed systematically. These three characteristics create substantial opportunity for information technology (IT) to improve the efficiency and effectiveness of chronic disease management efforts. Many stakeholders have responded to the evidence that chronic care requires improvement. In the United States, the system with which the authors’ are most familiar, a billion-dollar commercial industry has emerged, which offers stand-alone programs designed to address current shortcomings in chronic care; commercial health insurers as well as the Medicare and Medicaid programs have contracted with these companies to manage their members with chronic illnesses (Linden & Adler-Milstein, 2008). In parallel, traditional health care delivery systems are rethinking how to deliver care that meets the needs of the chronically ill population. As a result, a variety of stakeholders currently sponsor disease management programs. An individual care provider or medical group can initiate a program to manage the chronically ill patients in their panel. A private or public/government payer can implement a program to manage their chronically ill members or beneficiaries; this can be done by the payer itself or by contracting with a third party vendor to provide this service. Finally, integrated delivery systems and staff model Health Maintenance Organizations like the US-based Kaiser system can offer disease management. The type of sponsor influences the program structure and the associated IT tools that are incorporated. For example, a provider-sponsored program can leverage an electronic medical record system to support the program while a payer-sponsored program may not have access to this clinical technology. The variety of stakeholders pursuing disease management has fueled the development of IT tools used to support these efforts. In disease management, IT tools both facilitate the use of large volumes of data in everyday patient management and assist in the data collection process

The Use and Evaluation of IT in Chronic Disease Management

itself. IT tools can also be customized to support specific components of disease management. We first describe the three components common to most chronic disease management programs and then, in the following section, we discuss the supporting technologies in detail. While different types of sponsors offer disease management programs, the approach to managing chronic disease in a population can be generalized and typically involves three components. In the first component, the target population is engaged in the intervention. Initially, the target population must be identified and then their chronic disease status must be confirmed. If the intervention varies by disease severity, engagement also involves riskstratification. The last dimension of engagement involves enrolling the patient in the intervention, which can happen explicitly or by default. The second component of a disease management program, the intervention itself, varies according to the focus of the program. Interventions may focus on patient education, care coordination, or directly supporting the treatment of chronic disease (e.g., by promoting adherence to clinical guidelines). The third and final component of disease management is monitoring. This encompasses feedback loops for direct patient follow-up as well as an outcomes assessment to identify the areas in which the program is successful and the areas that require modification. Within each of the three components, there are many opportunities

for IT tools to lend support. For example, IT can (a) promote practice guideline compliance by presenting disease-specific recommendations to physicians at the point-of-care; (b) help identify patients overdue for disease-specific care and assist providers with proactively reaching out to them, and (c) support patients with managing their chronic disease through online education and communication tools that allow them to receive individual feedback.

OVERVIEW OF TECHNOLOGIES In designing an IT-enabled chronic disease management program, there is a wide array of options. This section will describe: (1) the types of IT that can be included in a disease management program, (2) the different degrees of sophistication of each technology, (3) how each technology supports the disease management components described above and (4) by whom each technology is typically used (e.g., providers, patients). Figure 1 summarizes the disease management components and the types of IT that support each component. Designed specifically for use in the engagement phase, predictive modeling software is an analytic tool that stratifies a population according to medical need and risk of future medical service utilization. Less sophisticated predictive models rely on basic linear regression with predictors

Figure 1. Overview of technologies by disease management component

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limited to historical medical utilization. More sophisticated predictive models rely on multiple regression and artificial intelligence to inform predictions. The performance of any predictive model is determined by the quality and breadth of the data on which it runs. Thus, a predictive model that is able to draw on medical, pharmacy, and lab claims data as well as clinical data (e.g., lab values) is better positioned to make more accurate predictions. Predictive models also vary according to the rules they use to organize and group input data with different clinical groupers built into the models (e.g., ACG, DCG). Predictive models support the stratification process, one of the subcomponents of engagement. Since they predict the members of a chronically ill population who are most likely to incur high medical service utilization in the near future, they enable a program to intervene before this occurs and avoid the associated cost. Further, they allow a program to be tailored to or restricted to specific risk groups. DM program sponsors differ in the data they have available to run a predictive model; for example, providers have more complete clinical data than payers. In turn, this impacts the performance of predictive models. (See Winkelman & Mehmud, 2007 for a comparison of predictive modeling software performance based on type of input data.) Personal assessment tools (PATs), such as health risk assessments (HRAs), are most often used during engagement, but can also be employed in the intervention and monitoring components. They are survey-based instruments that assess health history and current health factors to determine health status and risk of future medical service utilization. These tools typically include psychometrically validated questions and groups of questions vary in their focus (e.g., behavioral, clinical). While such tools do not require technology (i.e., they can be administered on paper), they have become IT-enabled to improve the efficiency of data collection and the customizability. While a basic tool will simply ask the respondent a series

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of questions, more sophisticated versions rely on branching logic and deliver automated feedback based on responses. PATs support various engagement subcomponents; they can be administered to an entire population to identify candidates suitable for a disease management intervention or administered to a subset of chronically ill patients to assist in stratification. PATs can also support other components. When these tools include participant feedback, they become part of the intervention by attempting to modify behaviors. For example, a diabetic patient who reports a high-sugar diet may be shown a set of recommendations for diet modification. Finally, when these tools are administered on a regular basis, they become part of the monitoring component and enable assessment of changes in participants’ health status over time. As with predictive models, PATs are used by a range of sponsors. We now turn to two technologies that support the intervention phase. Self-management tools (SMTs) are used by patients while clinical decision-support tools (CDSTs) are used by providers. SMTs seek to improve chronic disease status by supporting patient self-care and, when needed, encouraging appropriate utilization of medical services. With such a broad goal, there is a range of technologies that can be classified as SMTs, but the two primary subcategories include (1) educational tools -- topic-based health information designed to educate users on symptoms, conditions, behaviors, and treatments and (2) tools that enable patients to capture and review biometric data. Educational tools, which can be paper based, are increasingly IT-enabled to allow for a more interactive learning experience. Simple tools will convey health information on a static website or via a software program while more advanced tools include interactive content. For example, a DM program may have a website that allows participants to log-in and view content customized to their disease and severity. Patient forums or “ask-an-expert” functionalities

The Use and Evaluation of IT in Chronic Disease Management

may also be available. Tools that enable patients to upload biometric data (e.g., blood sugar for diabetics or weight for individuals with heart conditions) to a device or website can stand-alone or be integrated with educational tools. While less sophisticated tools require patients to enter data manually, more sophisticated tools collect data directly from scales, blood pressure cuffs, or glucose monitors. Once biometric data is entered, the tools track trends over time and give patients automated feedback or direct them to relevant educational modules. Closely associated with SMTs are personal health records (PHR). While PHRs were not specifically developed for chronic disease management, as a repository of patient-entered health-related data, patients with complex health conditions may receive the most benefit from a PHR. PHRs can include many of the SMT functionalities but are differentiated in that they are not focused on chronic diseases. SMTs typically support a patient-focused intervention but they can also assist with engagement if they identify potential participants. Often PATs will be included on a patient education website, which are linked to specific educational content. Any sponsor may incorporate SMTs into their DM program but they are most commonly used by payer and vendor efforts that rely on remote patient management (i.e., patients use these tools from home). Providers, especially large delivery systems may offer SMTs, and in some instances, tools may be made available to patients on kiosks or tablet PCs in the practice waiting room. Conceptually, there are different roles that a patient can take in their self-management and these are reflected in the types of SMTs offered by a program. The California HealthCare Foundation proposes a framework in which self-management technologies support four patient roles: (1) subordinate when technologies “provide modest patient discretion within a strong supervisory context”; (2) structured when technologies “involve more active but limited patient participation”; (3) collaborative when technologies “involve patients

using their own knowledge and making decisions jointly with clinicians”; and (4) autonomous when technologies “help patients take health matters in hand without major participation by clinicians” (Barrett, 2005). Most SMTs in DM fall into the latter two categories; for example, a tool that enables patients to upload and manage biometric data would be autonomous. If this functionality were combined with online “ask-an-expert” consultation, the solution would be collaborative. While some DM programs focus on patients, other programs focus on providers and seek to improve their care of patients with chronic diseases. Clinical decision-support tools generate alerts and reminders for clinicians during a patient visit. They may offer information that helps providers navigate a complex array of treatment options or remind them of recommended disease-specific guidelines. More basic tools allow providers to pull information when needed while more advanced tools automatically push content to providers based on specific patient data or actions. CDSTs are commonly incorporated into electronic medical records (EMRs), which will be discussed in more detail below. CDSTs are designed to support the intervention component of a DM program and, due to the clinical focus, are typically limited to provider-sponsored programs. Remote telemonitoring (RTM) supports the intervention and monitoring components of DM programs by enabling a patient’s provider or other clinical staff to obtain symptom and biometric data between visits. Patients periodically submit structured electronic data about their condition remotely— or example via a telephone’s touchtone keypad—and they may receive feedback and instructions based on their data. More advanced remote-monitoring solutions use automated channels to upload data directly from glucometers, scales, blood pressure cuffs, and other remote devices via phone lines or the web. Some remote monitoring tools also deliver educational content, such as self-care advice in recorded phone messages, to patients when they submit data. As

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opposed to self-management tools that capture biometric data for patient use, via RTM the signs or symptoms of an impending acute exacerbation triggers an alert to a clinical staff member who can respond immediately and triage the patient to the appropriate ambulatory care setting. For example, daily monitoring of congestive heart failure patients catches symptoms including weight gain, lower extremity edema, and increasing dyspnea that are typically present in the 8 to 12 days prior to hospitalization (Schiff et al., 2003). While RTM supports the monitoring component of a DM program, it can also support the intervention. RTM can be used by a provider who wants to obtain patient clinical data between visits. Alternatively, RTM may be included as part of a payer sponsored DM program in which the RTM vendor hires the clinical staff to receive and respond to patient data. Typically, RTM is reserved for patients who are most likely to have an acute exacerbation that may require hospitalization. While the prior five technologies were designed to support specific components of a DM program, the final three technologies are more comprehensive. As repositories of clinical data, they can be used to support all components of a DM program. The first tool, a disease registry, is differentiated from the second, an electronic medical record, and the third, a chronic disease management system, by the scope of clinical data it contains; disease registries are typically intended to support the management of a specific disease while EMRs are tools that are used to support comprehensive care for all patients. Chronic disease management systems (CDMS) occupy the middle ground between registries and EMRs (Jantos & Holmes, 2006). They are more comprehensive than registries in that they assist providers in managing patients with multiple chronic diseases but are more limited in scope of data as well as functionalities compared to an EMR. Since CDMS are much less common than registries and EMRs, we will only discuss the

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latter two technologies. (For readers interested in CDMS please see Jantos & Holmes, 2006.) Disease registries are databases that contain condition-specific information for a group of patients (Metzger, 2004). At the point-of-care, registries may generate concise patient reports that highlight areas requiring clinician attention during the office visit. Registries may also aggregate information across the population to generate “report cards” that assess how well the population is managed. For example, a diabetes registry may include the proportion of diabetic patients who had foot exams during the prior six months and a list of patients with hemoglobin A1c (HbA1c) levels above 9%. Registries can be simple databases that require manual data entry or sophisticated software applications that are updated in real-time via claims-feeds and contain algorithms to flag patients requiring attention. Registries can support each of the three intervention components. They support engagement by aggregating data on the chronically ill members in a population, which can be analyzed for identification and stratification. Registries are also useful in the intervention and monitoring components. As described above, advanced registries can be used at the point-of-care to highlight care gaps. Registries are most often used for monitoring as they enable a population-level view of chronic disease status to track progress of a DM program over time as well as individual-level assessments to identify specific problems. Registries are widely used by payers and providers. For payers, a registry (a) enables the identification those who should be targeted for enrollment, (b) informs the opportunities for intervention, and (c) tracks progress over time. For providers, a registry can play similar roles, but it can also be used at the point-of-care. For providers with electronic medical records, integrated registries allow them to view outcomes for their chronically ill patients as a group, a feature not consistently available in EMRs. Electronic medical records are comprehensive repositories of health-related patient data that can

The Use and Evaluation of IT in Chronic Disease Management

be accessed in real-time to support care delivery. While registries are often limited to claims data, EMRs contain clinical data (e.g. laboratory test values), which enables a more accurate assessment of chronic disease status and, if used at the pointof-care, facilitates provider-targeted interventions. EMRs can support engagement by identifying patients with chronic illnesses and assessing their severity. They can support provider-targeted interventions, such as compliance with evidencebased guidelines by programming automated reminders at the point-of-care. Finally, they can support monitoring by reporting individual or population-level process or outcome measures. There are many different EMR products on the market—some are basic repositories of clinical data that can be accessed by the provider and others include sophisticated functionalities that may be specifically geared towards managing chronically ill populations. Given their clinical focus, EMRs are typically restricted to providersponsored programs. However, as a comprehensive repository of clinical data, other sponsors would benefit from access to such data (though third-party access would have to be negotiated to ensure patient privacy safeguards). There are other types of IT that are typically included in disease management programs but these tools are less specific to disease management and therefore not discussed in detail. Telephony is commonly used to enable DM program staff to make outbound calls to patients or receive inbound calls. Sophistication can range from manual call dialing and routing to automated call routing linked to an IT platform, and may include associated technologies such as interactive voice response (IVR). Telephony is an example of a broader group of communication tools that are often used as part of a disease management program to facilitate information flow between patients, providers, and programs. Reporting applications are a second type of IT commonly used by disease management programs. These primarily support the monitoring component as

they use program data to generate metrics that measure the effectiveness and/or efficacy of a program. In general, advanced tools generate reports automatically while less sophisticated tools require manual report generation. In practice, the IT tools described in this section are often used in combination or integrated into a comprehensive solution. For example, an electronic medical record may have a registry application along with clinical decision support tools for providers; this could be integrated with a patient portal that offers personal assessment tools. Thus, this section has outlined a menu of technologies that can be combined in different ways to support a chronic disease management program. It is important to note that this menu may be incomplete as it includes a set of technologies available at one point in time and based on the authors’ experience with disease management in the United States. With these limitations in mind, the choice of technologies in a given program should ultimately depend on the goal of the program and the best available evidence that the use of a specific technology is associated with the desired outcome in a similar population. The industry is in the early stages of compiling the research to be able to answer this set of questions. The most comprehensive effort to assess the impact of incorporating a range of IT tools in chronic disease management was limited to a single condition, diabetes, and found evidence for only a subset of tools (Bu et al., 2007). A summary of study results can be found in Figure 2. The study concluded that there was best evidence for the impact of disease registries, but that more research was needed to support a rigorous meta-analysis. Other studies have evaluated specific technologies for specific conditions. For example, several studies are available on the effectiveness of remote telemonitoring (Fleishman & Sclar, 2004; Pare, Jaana, & Sicotte, 2007). With such a wide-range of technologies available and an incomplete evidence base, rigorous evaluations of the effectiveness of all technologies are needed. Thus, we now turn to

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Figure 2. Overview of ten-year cumulative results for IT-enabled diabetes management with select 95% confidence intervals. (Adapted from Bu, 2007)

a discussion of methodologies for evaluating the use of IT in chronic disease management.

EVALUATION OF TECHNOLOGIES USED IN DISEASE MANAGEMENT While IT tools can be assessed on many dimensions, the program sponsor will likely determine the focus of the evaluation. If the sponsor seeks to lower the cost of providing a given DM program, replacing manual processes with an IT solution typically lowers the per person program cost. In turn, for the same total cost, more people can be included in the program. IT may also allow a sponsor to offer DM program features that would not be feasible without that technology (e.g., pointof-care reminders to clinicians). In other words, IT may either allow current practices to operate

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more efficiently, or it may improve program effectiveness by employing new IT-enabled approaches that were previously unavailable. In the former scenario, the evaluation would likely focus on whether efficiencies gained from IT result in lower per participant cost, a straightforward evaluation that we will not discuss further; in the latter scenario, the evaluation would more likely focus on whether program effectiveness improved, the focus of this section. While IT tools can directly lower the cost of providing a given program, IT tools do not directly alter behaviors or disease states. Thus an evaluation of the effectiveness of an IT-enabled program reflects the technology and the context in which it is used. This limits the generalizability of individual studies and argues for multiple evaluations of the same technology in different settings. With the rapid nature with which most new technologies are developed and

The Use and Evaluation of IT in Chronic Disease Management

diffused, there are many challenges to conducting randomized controlled trials (RCT) before a technology is either enhanced or replaced. Therefore, researchers typically rely on rigorous observational evaluation approaches suitable for examining the technology’s effectiveness in the context of a given program. With these two points in mind, this section of the chapter will discuss three observational evaluation designs that can be used to assess the effectiveness of a given DM program. As described earlier in the chapter, DM programs are typically comprised of three components: (1) patient engagement - which consists of identifying those individuals with the chronic illness, stratifying them, and then enrolling them into the program; (2) the intervention – which may focus on patient education, care coordination, or directly supporting chronic disease care; and (3) monitoring these activities to provide systematic feedback on whether the program is meeting its goals. Regardless of which program component is being evaluated, all rigorous evaluations must control for factors that may influence the study results outside of the program itself. These factors (i.e., biases) may cause the results to look better or worse than what was actually achieved by the program. Therefore, the choice of evaluation strategy must consider which biases are likely to be present and which technique can best mitigate their effect. The choice of evaluation strategy is also guided by the program component under evaluation, as components are assessed against different types of measures. In evaluating the identification component, the assessment would logically focus on accuracy. An evaluation of the intervention may assess a wider variety of outcomes such as behavioral, clinical, process, quality of life, satisfaction, and economic. For example, an intervention could be evaluated on whether participants achieve improved clinical outcomes, but an intervention targeting provider guideline compliance would likely want to directly

measure changes in compliance rates, a process measure. As will be discussed below, some of the evaluation techniques are better suited to particular types of outcome measures. Finally, while the monitoring component is treated distinctly in the preceding section, for evaluation purposes it makes more sense to evaluate it together with the intervention given that the two components are closely integrated. The following subsections will discuss evaluation strategies specific to the engagement component and to the intervention/ monitoring components.

Engagement Since the evaluation of the enrollment subcomponent of engagement is typically assessed as the percent of targeted participants successfully enrolled, the challenging part of evaluating engagement is assessing the success of the identification and stratification subcomponents. The technologies used in these subcomponents supplement a basic identification and stratification approach that relies solely on diagnosis codes found in medical claims or EMRs. Predictive models are intended to improve upon a claims-based approach by incorporating a broader range of data into a more sophisticated model. Self-assessment tools like HRAs are administered when no prior medical care data is available or to gain supplemental information about the patient’s psycho-social behaviors. Thus, the evaluation of a given approach (e.g., predictive model) assesses how accurately individuals with the chronic disease in a population are identified and stratified. It is also valuable to know whether there is an incremental benefit in combining two or more approaches to improve accuracy. The most common method for assessing accuracy treats the outcome measure as a binary variable (i.e., has disease or does not have disease) in which the result either does or does not occur (i.e., identified or not identified), and presents the analytic model in a two-by-two table. These data

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allow the calculation of the proportion of patients whose diagnosis (or lack thereof) was correctly predicted by the model (true positives and true negatives), along with sensitivity (the proportion of true positives that were correctly predicted by the model as having the diagnosis) and specificity (the proportion of true negatives that were correctly predicted by the model as not having the diagnosis). There are several limitations to this method that may introduce bias, including; (1) the reliance on a single defined criterion or cutoff for determining a true-positive result, (2) the lack of standardization of the measurement instrument used for defining the disease, and (3) the sensitivity of the predictive values of the test or model to the prevalence rate of the observed outcome in the population (Linden, 2006). The receiver operator characteristic (ROC) analysis is a more useful and valid technique for assessing diagnostic and predictive accuracy than the standard two-by-two table method. Its advantages are that (1) it tests accuracy across the entire range of scores and thereby does not require a pre-determined cut-off point, (2) visual and statistical comparisons across tests or scores are easily examined, and (3) it is not influenced by the prevalence of the outcome (Linden, 2006). ROC analysis involves first calculating the sensitivity and specificity of every individual in the sample group (i.e., both subjects with and without the diagnosis or chosen outcome) and then plotting sensitivity versus 1-specificity across the full range of values. A test that perfectly discriminates between those with and without the outcome passes through the upper left hand corner of the graph (indicating that all true positives were identified and no false positives were detected). Conversely, a plot that passes diagonally across the graph indicates a complete inability to discriminate between individuals with and without the chosen diagnosis or outcome. The true value of the ROC is evident when comparisons are made between two or more models or tests because multiple curves can be plotted on the same graph and the

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Figure 3. A comparison of the areas under the curve for two receiver operator characteristic (ROC) curves. (Adapted from Linden, 2006a)

better approach can be visually identified. Figure 3 displays a comparison between two hypothetical identification models. As illustrated, Model A is better at identifying individuals with and without the outcome than Model B. So that one does not have to rely on visual inspection to determine how well the model or test performs, it is possible to assess the overall accuracy by calculating the area under the curve (AUC). A model or test with perfect discriminatory ability will have an AUC of 1.0, while a model unable to distinguish between individuals with or without the chosen outcome will have an AUC of 0.50. Therefore, if a choice was to be made between selecting Model A and Model B in Figure 3, Model A is the better choice because the AUC is greater (.761 vs. .584). (Linden, 2006) While this description focuses on assessing identification accuracy, the same approach can be followed to assess stratification accuracy.

Intervention/Monitoring The evaluation of the intervention/monitoring components is distinct from the evaluation of the engagement component. In prospective experimental studies (e.g. the RCT), changes

The Use and Evaluation of IT in Chronic Disease Management

in the outcomes of interest (behavioral, clinical, economic, etc.) are assumed to be causally associated with the intervention. By virtue of pre-program randomization, participants in all groups are considered equivalent, and therefore any difference in outcomes is attributed to the intervention. Conversely, causality is not assured in non-randomized studies (either prospective or retrospective); the assignment of individuals to the intervention is not random (in DM programs individuals typically choose whether to participate), and thus treatment and control groups are likely to be systematically different in ways that may influence the outcomes. Consequently, in observational studies the evaluator must determine which evaluation design is best able to control for the most likely biases that would invalidate evaluation results. Since each observational design has flaws, the inclusion of multiple designs in order to triangulate results bolsters confidence in the conclusion. In the following sections, we briefly describe three observational research designs that are particularly well-suited to evaluating the effectiveness of IT-supported DM interventions. Each model’s strengths and weaknesses are also discussed.

Design 1: Regression Discontinuity (RD) Given the practical and ethical limitations that can preclude the use of the RCT in evaluating DM programs, the regression discontinuity (RD) design offers the most suitable alternative (Linden, Adams, & Roberts, 2006a). In contrast to the RCT in which individuals are randomly assigned to treatment or control groups, the RD design utilizes a cutoff score on a baseline measure or test (using a continuous or ordinal scale) to determine who will subsequently be assigned to the intervention or control group. The defining characteristic of the RD design is in identifying whether a difference is found in the relationship between the assignment variable and outcome occurring exactly at the

cutoff score, where individuals in the treatment and non-treatment groups are most similar. If a statistically significant difference is found, we can then conclude that there was a program effect. Strict adherence to the cutoff when assigning patients to treatment is critical to the validity of this design. Thus, this design cannot be employed after the program has been initiated if the assignment criterion was not followed. A criterion necessary for obtaining an unbiased estimate of a treatment effect is to have an assignment process that is completely known and perfectly measured (Shadish, Cook, & Campbell, 2002). The underlying premise of the RD design is that subjects located immediately adjacent to the cutoff are the most similar and therefore provide the best comparison units for assessing treatment effect. To demonstrate the practical application of the RD design, assume that all employees of a firm are asked to complete an online HRA that is scored from 0 to 100, with a lower score indicating higher risk. For the purpose of this example, all individuals scoring below 50 (the cutoff score) are classified as “high risk” and will be eligible to receive the intervention and everyone above that score is considered “low risk” and will serve as controls. The post-test measure will be the number of hospitalizations in the program year. Figure 4 illustrates the results of this hypothetical evaluation. All individual pre-test and post-test values are charted on the scatterplot as single X-Y coordinates and a separate regression line is then drawn through each group’s data. As shown, the two lines do not overlap at the cutoff score (i.e., discontinuity), and the intervention group (high risk) line intersects with the cutoff at a lower level than the control group (low risk) line. In other words, the DM intervention appears to have reduced hospitalizations by an average of 1 hospitalization per patient per year in the intervention group compared to the control group. While visual inspection of the data gives a general sense of the structural form of the RD model, statistical modeling is still required to verify whether the

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Figure 4. Graphic display of the regressiondiscontinuity (RD) design. Individuals are assigned to the intervention if their HRA score is lower than the cutoff score of 50 (vertical line). Regression lines are fitted to intervention and control group data (dotted lines). The graph shows a classic discontinuity at the cutoff, indicating a treatment effect.

discontinuity is statistically significant. If so, we can be confident that a discontinuity at the cutoff point is a result of the treatment effect, and not due to a bias, because the likelihood that a bias would occur at exactly the cutoff point is very low.

Design 2: Propensity-Score Matching A second robust observational study design is to pair each participant with a non-participant who has similar observable baseline characteristics. Outcome measures between the two groups are then compared statistically. Typically a propensity score is used to conduct the matching process; a propensity score is “the probability of assignment to the treatment group, conditional on covariates” (Dehejia & Wahba, 2002). Propensity scores are derived from a logistic regression equation that reduces each participant’s set of covariates into a single score, making it feasible to match on multiple variables simultaneously. The general

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ease of implementation makes this the preferred matching technique over more labor intensive methods, such as case-control in which matching occurs on each variable sequentially. To ensure that the propensity matching technique is successful in creating equivalent groups, baseline characteristics of the two groups should be compared. There should be no statistically significant differences between groups on any pre-program variable. If there are significant pre-program differences in the outcomes of interest, the propensity score can be used to control for these. Outcomes are assessed in a manner similar to any pre-post study design with a control group. The underlying assumption for propensity score matching is that enrollment in the program is associated with observable pre-program variables such as age, sex, utilization, cost, etc. (Linden, Adams, & Roberts, 2005a). The most significant threat to validity (as with any observational study) is that selection bias may occur because propensity scores are based solely on observable confounding variables and not on unknown or “hidden” sources of variation. A sensitivity analysis should be performed to estimate the odds of subjects assigned to the program intervention having hidden bias. This analysis allows the researcher to determine if the results are sensitive enough to bias to alter conclusions about a causal relationship between the intervention and the outcome. (See Linden, Adams, & Roberts, 2006b for a detailed explanation of this procedure.) A second consequence of matching on specific observed baseline characteristics is that generalizability is limited. However, this issue can be partially mitigated by matching on the broadest array of characteristics (Linden, Adams, & Roberts, 2004). The size of the non-participant pool available for matching is a third limitation of the propensity scoring method (Linden et al, 2005a). For example, assume that that an interactive educational website is offered to all DM program participants. As more individuals access the website, the pool of suitable controls diminishes. In situations such as

The Use and Evaluation of IT in Chronic Disease Management

these, matching current participants with historic controls (untreated individuals from a prior period) may be the best alternative; however, adjustments are required to account for secular trends. If there are significantly fewer concurrent controls than cases available for matching, the researcher may choose to use the “matching with replacement” technique in which a given control may be matched to more than one case. This introduces additional statistical issues, but they can be managed via the non-parametric analytic method of bootstrapping (Linden, Adams, & Roberts, 2005b).

Design 3: Time Series Analysis A time series is defined as a variable that undergoes repeated observation or measurement. The variable can be either at the patient level (e.g., EKG readings, respiratory rates, blood pressure), or at an aggregate level (e.g., hospitalization rates, mammography rates). The time period between measurements may be as short as a fraction of a second or as long a century, but the study must be long enough to collect enough measures to establish a valid pattern. In general, time series analyses are used to characterize a pattern of behavior occurring in the natural environment over the measurement period, analyze fluctuations of the variable along the continuum, infer the impact of an intervention introduced during the measurement period, and forecast future direction of the variable (Linden, Adams, & Roberts, 2003). An important feature of time series is serial dependence. Any variable measured over time has the potential to be influenced by prior observations (i.e., autocorrelation). To take advantage of these relationships, time series models use previous observations as the basis for predicting future behavior, which in turn enables an assessment of whether an intervention altered the predicted result. This is the essential difference between time series analyses and traditional statistical tests for measuring change, such as regression analysis, which rely on variation in independent variables

to explain changes in the outcome (Linden et al., 2003). In other words, time series analysis allows the DM program evaluator to predict future behavior of the observed variable based on past observations alone, without attempting to measure independent relationships that influence it. This is an important advantage since there are countless factors that may govern the behavior of the time series variable that cannot be identified or accurately measured (Linden et al., 2003). Implementing a time series analysis first requires the identification of a pattern present in the data and then selection of the appropriate model for that pattern. The three most prevalent time series patterns in health care data include: (a) Trends - the long-term movement of a data series that slopes either upward or downward, (b) Seasonality - a pattern that emerges as spikes at regular intervals that reflects a temporal factor (usually monthly, quarterly or annually) and, (c) Stationarity - a constant variance around a constant mean (i.e., relatively horizontal along the x-axis) with no linear trend or seasonal component (Linden et al., 2003). While a visual inspection of a time series plot may clearly identify a linear trend or a seasonal effect, most health care data contain enough variability to produce unreliable results using this method. Thus, this task can be performed empirically using a statistical tool called the autocorrelation function (ACF). An ACF indicates how each observation is correlated to prior observations and allows pattern detection. For example, an ACF that corresponds with a time series showing a trend is indicated by a significant autocorrelation with the two most recent observations. Intuitively, this makes sense as a trend is not a random occurrence but a movement in a specific direction. Therefore, we expect to see a correlation between subsequent observations. Similarly, an ACF corresponding to a seasonal time series is indicated by a significant autocorrelation with every twelfth observation (when the data are presented as monthly observations the seasonal effect appears one month every year). When the

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time series does not indicate the existence of a trend or seasonality, the ACF shows no significant autocorrelation and can be considered stationary. There are several different time series models available to the DM program evaluator and the best model depends on the pattern observed in the data. When the data appear stationary, Simple Exponential Smoothing (SES) is the preferred method (Linden et al., 2003). Double Exponential Smoothing (DES) is appropriate if there is a noticeable trend in the data but no obvious seasonality (Holt, 1957). The Holt-Winters design (Winters 1960) includes variables to account for randomness, trend, and seasonality component of the time series. The Autoregressive Integrated Moving Average (ARIMA) design (Box & Jenkins, 1976) is currently the most widely used time series methods in the health services literature. It is intended to mathematically describe the changes in a series over time, which makes it the most complicated and theoretically based time series model available. Irrespective of which time series model is chosen, there are four steps to execute the analysis: (1) choose an outcome variable that minimizes measurement bias (e.g., utilization measures in

lieu of cost), (2) include sufficient observations to allow the model to accommodate any patterns in the data that may impact the fitting parameters (for a chronically ill population in which there is seasonality, at least 4 years of monthly data leading up to the month prior to the commencement of the intervention is suggested), (3) limit the forecast period to no more than 12 observations to ensure an accurate forecast horizon, and (4) compare actual versus forecasted values (Linden et al., 2003). Figure 5 illustrates a time series analysis of hospital admissions in a hypothetical population in which 30 historical monthly observations were used to produce forecasts with 95% confidence intervals for the first 12 months of a DM program. Upon visual inspection, the observed values cross over the lower confidence interval during several months of the program. Although it must be confirmed via statistical analysis, it appears that the decreasing admission rate is associated with the intervention. While the time series method controls for most threats to validity, the large number of observations makes time series analysis particularly susceptible to secular trends and other external influences occurring during long evaluation pe-

Figure 5. Graphic display of a time series analysis. Thirty historic monthly observations were used to create forecasts and confidence intervals for the first program year. The observed hospital admission rate in the program period appears to decline relative to the predicted values, and even crosses over the lower confidence interval at several points along the continuum.

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riods. For example, the introduction of a new diabetes drug that dramatically lowers HbA1c levels in a population would make a DM program employing remote telemonitoring look effective if it was measured on percent of diabetics with HbA1c below 7%. These potential confounders, once documented, can provide the necessary explanation for any unexpected spikes in the data set. Given this concern, it is important to monitor secular trends as well as carefully select measures that are likely to be directly impacted by the intervention (Linden et al., 2003).

Choosing the Appropriate Model Each evaluation design has strengths and weaknesses that must be weighed along with other factors including (a) the outcome measure(s) under study, (b) the availability of data, and (c) the biases that are likely to be present. Nonetheless, there are considerations that favor the selection of a particular study design. Frequently observed measures that have been collected over a meaningful period prior to the commencement of the intervention are well suited to time series analysis. For example, clinical data collected from remote telemonitoring devices or monthly hospital admission rates at the population level meet these criteria. The approach to program assignment is also influential in selecting the optimal evaluation design. Programs that assign participants to the treatment based on a cutoff score on a risk survey or other scalar measure are well suited to the regression discontinuity design. On the other hand, when participants self select into the intervention, a matched pairs design such as propensity-score matching is likely to be the most suitable design. The type of bias that is likely to be present also weighs heavily into the choice of design. If there is a concern about regression to the mean1 then time series analysis should be considered, since the multiple measurements nullify the effect (Linden, 2007). When selection bias is the primary threat

to validity, the regression discontinuity design is preferred assuming the assignment criteria is met. The propensity score matching technique is more flexible and quite robust when coupled with a sensitivity analysis. If there are multiple threats to validity or a more rigorous evaluation approach is sought, the best solution is to implement multiple designs (if possible) and triangulate the results. If different evaluation designs elicit results in the same direction (i.e., all indicate a program effect), the evaluator can have greater confidence that biases have been adequately controlled.

CONCLUSION Chronic conditions require ongoing, multi-disciplinary care that includes more frequent patient contact to assess changes in clinical status as well as unique forms of care, such as patient education on self-management. Further, the evidence base is growing rapidly, which requires efficient diffusion of new knowledge to patients and providers. Information technology solutions both facilitate the use of large volumes of data in everyday patient management and assist in the data transfer process itself. With a broad array of activities that can be included in chronic disease management, an equally broad array of tools is available to support these activities. This calls for rigorous evaluations of the effectiveness of all technologies. While the choice of evaluation design is often limited in practice, there are several robust observational designs that can be employed to strengthen the understanding of which approaches are most successful. While individual studies may be limited in their ability to infer a causal relationship, the growing use of IT to support disease management programs should offer multiple opportunities to assess a given technology and build a more robust evidence-base that identifies successful IT-enabled disease management programs.

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REFERENCES Barrett, M. (2005). Patient self-management tools: An overview. California HealthCare Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. San Francisco, USA: Holden Day. Bu, D., Pan, E., Walker, J., Adler-Milstein, J., Kendrick, D., & Hook, J. M. (2007). Benefits of information technology-enabled diabetes management. Diabetes Care, 30(5), 1137–1142. doi:10.2337/dc06-2101 Dehejia, R. H., & Wahba, S. (2002). Propensity score-matching methods for nonexperimental causal studies. The Review of Economics and Statistics, 84, 151–161. doi:10.1162/003465302317331982 Ellrodt, G., Cook, D. J., Lee, J., Cho, M., Hunt, D., & Weingarten, S. (1997). Evidence based disease management. Journal of the American Medical Association, 278, 1687–1692. doi:10.1001/ jama.278.20.1687 Fleishman, V., & Sclar, D. I. (2004). Remote physiological monitoring: Innovation in the management of heart failure. New England Healthcare Institute. Retrieved on February 11, 2008, from http://www.nehi.net/ CMS/ admin/ cms/ _uploads/ docs/ HF% 20Report.pdf Foundation. Retrieved on February, 11, 2008, from http://www.chcf.org/ documents/ chronic disease/ Patient Self Management Tools Overview.pdf Holt, C.C. (1957). Forecasting seasonal and trends by exponentially weighted moving averages. Office of Naval Research, Research Memorandum No. 52. Jantos, L., & Holmes, M. (2006, July). IT tools for chronic disease management: How do they measure up? California HealthCare Foundation. Retrieved on February 11, 2008, from http://www. chcf.org/ documents/ chronic disease/ IT Tools For Chronic Disease Management.pdf 1090

Linden, A. (2006). Measuring diagnostic and predictive accuracy in disease management: An introduction to receiver operating characteristic (ROC) analysis. Journal of Evaluation in Clinical Practice, 12(2), 132–139. doi:10.1111/j.13652753.2005.00598.x Linden, A. (2007). Estimating the effect of regression to the mean in health management programs. Disease Management & Health Outcomes, 15(1), 7–12. doi:10.2165/00115677-200715010-00002 Linden, A., Adams, J., & Roberts, N. (2003). Evaluating disease management program effectiveness: An introduction to time series analysis. Disease Management, 6(4), 243–255. doi:10.1089/109350703322682559 Linden, A., Adams, J., & Roberts, N. (2004). The generalizability of disease management program results: Getting from here to there. Managed Care Interface, 17(7), 38–45. Linden, A., Adams, J., & Roberts, N. (2005a). Using propensity scores to construct comparable control groups for disease management program evaluation. Disease Management & Health Outcomes, 13, 107–127. doi:10.2165/00115677200513020-00004 Linden, A., Adams, J., & Roberts, N. (2005b). Evaluating disease management program effectiveness: An introduction to the bootstrap technique. Disease Management & Health Outcomes, 13(3), 159–167. doi:10.2165/00115677200513030-00002 Linden, A., Adams, J., & Roberts, N. (2006a). Evaluating disease management program effectiveness: An introduction to the regression-discontinuity design. Journal of Evaluation in Clinical Practice, 12(2), 124–131. doi:10.1111/j.13652753.2005.00573.x

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Linden, A., Adams, J., & Roberts, N. (2006b). Strengthening the case for disease management effectiveness: Unhiding the hidden bias. Journal of Evaluation in Clinical Practice, 12(2), 140–147. doi:10.1111/j.1365-2753.2005.00612.x Linden, A., & Adler-Milstein, J. (2008). Medicare disease management in policy context. Health Care Financing Review, 29(3), 1–11. McGlynn, E. A., Asch, S. M., Adams, J., Keesey, J., Hicks, J., & DeCristofaro, A. (2003). The quality of healthcare delivered to adults in the United States. The New England Journal of Medicine, 348(26), 2635–2645. doi:10.1056/NEJMsa022615 Metzger, J. (2004). Using computerized registries in chronic disease care. California HealthCare Foundation. Retrieved on February 11, 2008, from http://www.chcf.org/ documents/ chronic disease/ Computerized Registries In Chronic Disease.pdf Ofman, J. J., Badamgarav, E., & Henning, J. M. (2004). Does disease management improve clinical and economic outcomes in patients with chronic diseases? A systematic review. The American Journal of Medicine, 117(3), 182–192. doi:10.1016/j.amjmed.2004.03.018 Pare, G., Jaana, M., & Sicotte, C. (2007). Systematic review of home telemonitoring for chronic diseases: The evidence base. Journal of the American Medical Informatics Association, 14(3), 269–277. doi:10.1197/jamia.M2270 Schiff, G. D., Fung, S., & Speroff, T. (2003). Decompensated heart failure: Symptoms, patterns of onset, and contributing factors. The American Journal of Medicine, 114, 625–630. doi:10.1016/ S0002-9343(03)00132-3 Shadish, S. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin.

Wagner, E. H., Austin, B. T., & Davis, C. (2001). Improving chronic illness care: Translating evidence into action. Health Affairs, 20(6), 64–78. doi:10.1377/hlthaff.20.6.64 Winkelman, R., & Mehmud, S. (2007). A comparative analysis of claims-based tools for health risk assessment. Society of Actuaries. Schaumburg, IL. Retrieved on August 6, 2007, from http://www. soa.org/ files/ pdf/ risk- assessmentc.pdf Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6, 324–342. doi:10.1287/mnsc.6.3.324

KEY TERMS AND DEFINITIONS Clinical Decision-Support Tools: Applications that support clinical staff in delivering evidence-based care to patients with specific conditions and symptoms via reminders, alerts, and access to related clinical information Disease Management: A coordinated, comprehensive, and systematic approach to managing chronic disease-related behaviors and care by engaging patients and/or their providers Disease Registry: Databases that capture and provide access to condition-specific information for a group of people with a given condition to support organized care management Electronic Medical Record: Electronic repositories of all health-related patient data that can be accessed in real-time by clinical staff members to assess clinical status, health history, etc. Personal Assessment Tools: Survey-based tools that assesses health history and current health factors to determine health status and risk for chronic disease Predictive Modeling Software: Analytic tool that applies available data to stratify people according to medical need and risk of future medical service utilization

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Remote Telemonitoring: Frequent electronic measurement and reporting of symptoms and clinical indicators by patients outside of a medical setting to assess health status and risk of near-term health episode Self-Management Tools: Topic-based health information applications designed to educate users on symptoms, conditions, behaviors, and treatments to improve health status and encourage appropriate utilization of medical services

ENDNOTE 1



Regression to the mean occurs when a person with a high value on the first measurement has a lower value on a second measurement, and vice-versa, which is not due to a program effect.

This work was previously published in Handbook of Research on Information Technology Management and Clinical Data Administration in Healthcare, edited by Ashish N. Dwivedi, pp. 1-18, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.7

Safe Implementation of Research into Healthcare Practice through a Care Process Pathways Based Adaptation of Electronic Patient Records V. G. Stamatopoulos Biomedical Research Foundation of the Academy of Athens, Greece & Technological Educational Institute of Chalkida, Greece G. E. Karagiannis Royal Brompton & Harefield NHS Trust, UK B. R. M. Manning University of Westminster, UK

ABSTRACT Concern has been expressed over the lack of evidence of the effective transfer of new research findings, guidelines and protocols into front-line clinical use. This book chapter outlines an approach which focuses on establishing and then using existing end-to-end care process pathways DOI: 10.4018/978-1-60960-561-2.ch407

as initial benchmarks against which to evaluate the effects of changes in clinical practice in response to new clinical knowledge inputs. Whilst the prime focus of this approach is to provide a robust means of validating improvement in best practice processes and performance standards as the basis of good clinical governance, it will also seek to identify potential risks of adverse events and provide the basis for preventive measures, further backed-up by training within the relevant

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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specialist domains. The objective is to close this loop by monitoring incremental change in care processes through a rolling analysis of input to Electronic Patient Record integrated into the clinical governance process. A pathway-linked knowledge service approach that could radically simplify and speed up the record updating process that supports and does not impinge on the professional autonomy.

INTRODUCTION Despite the introduction of multi-disciplinary team working, the healthcare professions still maintain much of their traditional ‘silo’ centric approach to knowledge exchange across their specialist domain boundaries (Pirnejad, Bal, Stoop, & Berg, 2007). Whilst Continuing Professional Development requirements should ensure that appropriately validated new knowledge is properly disseminated within these domains, and clinical decision quality is improved, procedures to confirm its adoption in practice and quality improvement are missing. There also tends to be little in the way of formalised interdisciplinary exchanges, reinforcing the ‘silo’ effects. Currently this effect is fairly well mitigated by clinical management use of procedures, guidelines and recommended best practice processes and pathways to establish performance standards as the basis of good governance. However, problems and adverse events have been shown by forensic studies to arise out of variability at the detailed level and in particular where there are hidden or unrecognised interdependencies between treatment practices by different professions, as well as at the interface of different healthcare settings (Kohn, 2001). Healthcare providers also exhibit the same ‘silo’ approach displayed as by their constituent professions, due to the need for all organisations to set boundaries that clearly delineate the limits of operations with obvious implications in continuity

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of care (Pirnejad et al., 2007). The unfortunate consequence for patients is that their end-to-end care process threads its way across these domain boundaries with resultant changes both in organisations and professionals in responsible for their treatment. The overall effect is that there is a lack of clarity of the likely sequence of events involved in resolving or ameliorating the patient’s presenting conditions, especially when new procedures and guidelines are adapted. The prevailing culture appears to consider that it is pointless to have to have even an approximate plan of action in view of the innate variability in the progression of either the illness concerned or its treatment. However, this is tantamount to saying that in other potentially hazardous domains, the use of navigation charts and flight plans are unnecessary, as they will not be followed to the letter – which is patently wrong and would be highly unsafe! The key issue is that whilst such charts fulfil the need for the point of reference for decision making in these domains, their use is noticeably absent in the clinical domain. This defect is even more evident in the context of the complete cross-border ‘patient journey’ from one end of the overall process to the other. This transit not only crosses primary, hospital and community care organisational boundaries, but also many treatment stages, which may well be ‘fenced-in’ within departmental sub-domains settings, as shown in Figure 1. These stage boundary conditions involve achievement of target organisational or treatment outcome goals that act as ‘gateways’ to the next stage. These could be organisational transfers from Intensive Care Unit (ITU) – to High Dependency Unit (HDU) – towards; or clinical assessment – diagnosis – treatment outcomes/s – discharge – community needs assessment – community care. Case complexity adds a further dimension in the level of treatment complexity in in-stage processes required. These follow the same pat-

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Figure 1. The cross border patient journey through the different healthcare settings

tern of minor, intermediate, major, and major plus sets used in surgery. These severity sets act as further domains, where transfers are driven by the level of care response required by changes in the patient’s condition. Finally, implementation of new knowledge in the form of new recommendations/guidelines/ procedures may complicate matters even further as it requires a number of steps to translate the knowledge contained in guideline text into a computable format and to integrate the information into clinical workflow (Shiffman, Michel, Essaihi, & Thornquist, 2004).

BACKGROUND Care Process Pathways (CPP) High-level care pathways (Manning, 2003) are generally used to identify all the major steps across the spectrum of complexity that are potentially involved in the treatment of a given condition, such as shown below in Figure 2 for Acute Myeloblastic Leukaemia (Schey, 2001), including latest evidence based guidelines. Whilst one of the more complicated conditions to diagnose and treat, it illustrates at high-level only just how wide a variety of routes the ‘patient journey’ treatment sequence may take. Whilst this

Figure 2. Acute Myeloblastic Leukaemia care pathways

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provides the starting point for gathering statistical data to support clinical governance and control procedures, it is not sufficient to provide the necessary insights into actual routes taken – both formal and experience-led - or the decision criteria used. As ever the ‘devil is in the detail’, and emphasises the need to map formal in-stage processes at various levels of detail, dependent on the level of case complexity and the options available. This will provide a sound foundation on which to tackle the problem of coping with experienceled process variability that invariably occurs in decision-making. Tacit knowledge derived from

years of professional activity invariably leads to idiosyncratic process evolution around the formal norm, which generally remains hidden and yet contributes to variations in clinical outcomes. The complexity of potential formal treatment options is well illustrated by the chemotherapy/ bone marrow transplant CPP options shown below in Figure 3 for Acute Myeloblastic Leukaemia (Schey, 2001). Here the four main routes each have major variability in-built in the initial two stages with ‘switch-out/over’ decision points where complete remission has not been achieved. Whilst the potential for dosage variability is immediately obvious from the first route shown

Figure 3. Chemotherapy/bone marrow transplant care process pathway options and potential for dosage variability

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below in Figure 3, it also demonstrates the potential to attach and index knowledge to a process map. This knowledge-indexing approach (McKeon Stosuy, Manning, & Layzell, 2005), together with appropriate version release controls would be central to providing the latest validated clinical information to support decision-taking directly to those in the ‘front-line’ of service delivery. However, provision of such information alone will not ensure that it is used appropriately or at all. Its adoption into the everyday clinical working culture is ultimately dependent on delivering real, clinically desired benefits to professional practice and to resultant patient outcomes. As a result, evidence of its latent worth and subsequent delivered value is essential to secure a successful transformation in attitude and uptake by all the professions concerned. This evidence can only be gathered through much improved detailed knowledge of the current processes in use together with their limitations, deficiencies and attendant risks of errors or error producing conditions. The key to this lies in integrating a widely acceptable feedback mechanism that makes negligible downside impact on clinical activities, and does not adversely affect or limit the use of professional judgement and experience

Electronic Health Record implementation of Care Process Pathways This can be achieved through an adaptation of the way the Electronic Patient Record (EPR) is created. The proposed approach centres on the use of well-structured CPP maps as a means of semi-automatically logging events into the EHR. Its aim is to use the relevant pathway step together with the knowledge indexed to it as a means of updating the record. The essence of the approach is that updating would either entail adopting the recommended action and uploading its ‘as is’, adapting it to suit the circumstances, or choosing an alternative approach - effectively

ignoring the recommendation - and entering the action taken as per current practice. Automatically categorising all record updates in terms of the three types of action taken: adopted; adapted; or ignored, would provide a direct profile of the clinician’s responses to newly available knowledge. More importantly it could radically reduce record input workloads, provided the pathway correctly reflected the bulk of current practice, whilst not constraining or impacting professional autonomy.

Combating Adverse Events The introduction of the proposed CPP approach would provide much greater transparency not only in governance terms, but also provide the basis for early identification of potential hazards and latent exposure to adverse event risks. The risk of accidents or other incidents almost invariably result from a combination of clinical error producing conditions and/or violations of guidelines/procedures together with unidentified points of failure in defence mechanisms. These are frequently compounded by a chain of unforeseen events that are often set in motion by a mixture of corporate and clinical cultural stresses. However, another major contributory factor that lies behind this problem is the general lack of any clear visibility either of the processes and their interaction with the resources that enable them to proceed. This is particularly surprising when compared with sectors, where high stress-levels and threats to life are inherent, all base their risk management defence procedures on process-based modelling and analysis. As healthcare is centrally reliant on experienced clinical human resources to deliver its care delivery processes, it almost inevitable that most adverse events stem from errors and omissions in the various supply chain processes and their interaction. Care delivery is naturally the core process enabled in turn by other sets of supply chains. Whilst the series of alternative routes, discussed

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earlier, outline the broad treatment process, they in turn are likely to involve a mixture of inter-related parallel processes carried out by care professionals from different disciplines. Even though they may be operating as part of a multidisciplinary team, a combination of the lack of a shared interdisciplinary pathway plans, plus the professional ‘silo’ effect can result in a serious disconnect between the activity streams. At best, this may result in wasted effort; at worst it may negate the work of the other professions involved and trigger an adverse event. The key to avoiding these circumstances lies in identifying these points of interdependence and putting in place procedures to obviate or mitigate such potential disconnects. Since the trigger for most adverse events result from human errors, often made under stressful circumstances, minimising and mitigating their effects, is paramount. Whilst the deployment of appropriately skilled and experienced clinicians, is central to enabling consistent high quality care to be delivered, ensuring that continuously evolving new clinical knowledge is absorbed into their working culture is vital. The success of the ‘rolling’ change process is ultimately determined by a well-balanced input from three main supply chains. Whilst subtly delivered professional development through effective knowledge transfer contributing to the growth of individual clinician’s expertise is central to this, so is a controlled exposure to situations that help build up their experiential knowledge. The second chain delivers a supporting framework to this development through the provision of guidelines and procedures, which provide the control mechanism needed to ensure best practice consistency. However, at present this control process suffers from lack of day-to-day visibility of performance quality; no point of reference to identify significant deviations from currently evolving norms; little or no basis for timely, proactive identification of treats or triggers of adverse events; and of necessity, reliance on post-event preventative action.

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The final chain is more amorphous in that it delivers a wide range of different types of information – and by definition knowledge – in a variety of forms, ranging from the readily understandable though to the highly complex, difficult to assimilate and potentially error producing. Almost more importantly, there are relatively few controls in place to ensure validity of content, or reduce latent ambiguities in its presentation. Whilst reducing the error potential both within these individual chains and where they join to interact at the point of delivery of care is a decidedly non-trivial task, much can be achieved through the development of process mapping and monitoring techniques proposed earlier. The emergence of a far clearer picture of clinical practice processes will in turn provide the basis for identifying potential adverse event triggers and putting in preventive measures.

Care Process Change Cycle The impact of changes in clinical practice as a result of the dissemination and take-up of new knowledge inputs will be seen in changes to care processes, as shown above in Figure 4 for a typical care stage. Evidence of this will be derived jointly from regular checks for changes in process practice from that of the opening benchmarks, and from analysis of adaptations or alternative ‘off pathway’ treatments found in EHR updates. As distinct changes in the patterns of treatment practice, are established – and confirmed as valid improvements by the expert medical panel – process pathways will be up-issued under conventional version control. Similarly revisions to the knowledge base are reflected in the clinical content, indexed to the relevant steps in the CPP.

Implementing AND Managing Change Human nature inherently abhors change, especially if it is imposed. The remedy is to engage

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Figure 4. Changes in clinical practice as a result of the dissemination and take-up of new knowledge as seen in changes to care processes

all sides in identifying the defects and discrepancies of the ‘As Is’ situation together with the benefits carefully managed change could deliver and resulting in a benefits ‘roadmap’. Exploiting the full potential of newly available clinical knowledge requires the combination of process mapping methodology together with supporting technology to deliver the operational environment needed to enable changes in clinical practice to take place. However, for it to take root the benefits must be seen by all to substantially outweigh any perceived risks and disadvantages. Whilst the motivational issues are central to securing commitment and on-going involvement of users to their developmental programmes, they are equally relevant to service providers both at the individual professional as well as at all levels throughout their organisations. However, from the service provider perspective this needs to take full account of the additional influencing factors (benefit, value, cost, risk) and their inter-actions. Inevitably service costs will tend to be the dominant issue, followed closely by its relationship to perceived deliverable benefits. The problem that this poses is that by polarising interest on these

two factors alone it is all too easy to lose sight of the more subjective aspects of risk and value. Ignoring these two latter elements can all too easily destabilise any consensus for change and with it any chance of achieving anything approaching an optimal solution. In particular, value tends to be wrongly equated to cost incurred, rather than as the expected or actual return on the investment made. This difficulty usually arises out of the subjective nature of value, especially when viewed from different standpoints and often associated entrenched positions. Viewed as an inter-related whole, rather than as two sets of dissociated of pairs, suggests a far more subtle judgement criteria should generally be considered – balancing each aspect in turn against the three others in the combined set. Testing this from the various perspectives involved in such a complex and multifaceted problem area as outlined above, would add considerable insight into the options available.

Measuring Change In general terms, measurement splits broadly between quantitative and qualitative aspects of

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the issue under study – albeit sometimes using an intermediate approach either ranking selected qualitative criteria or by scoring them against an agreed subjective scale. Where the effects of process change are concerned its effects can be measured in part by counting the number of given attributes and specific effects. These can be collected, and the results assembled into one or more aggregation hierarchies – as dimensions in a relational database structure, as shown Figure 5 in simplistic tabular form. This would allow the effects of change to be assessed as variances – typically as changes in the number of stages and steps in comparison with those of the initial benchmark within a defined period. Similarly, occurrence of actual or prevented adverse events – as indicated by the triggering Adverse Events Countermeasure [AECs] - can be monitored. One of the limitations of any purely quantitative analytical approach is that the numbers alone invariably lead to a point where qualitative interpretation and perceptive input or reasoning is needed to add value and insight into the results obtained. A useful bridge between these tabular and purely textual domains is a range of Cognitive Mapping techniques – shown in simplistic outline form above right. The essence of the approach is to breakdown the mass of qualitative input into short concept statements in the form – ideally as polar arguments framed as “this rather than that” and expressed as x // y. However of necessity

this is often unipolar where a particular aspect is dominant. These are then linked as consequential or reasoned chains to capture the essence of the knowledge obtained. This is illustrated above in terms of aspects of the starting benchmark conditions – Process Tb; Tb AECs; Process Quality Tb – in reality an extended set of concepts surrounding these focal topics. These typically then focus on the effects of change that centre around the node Process T1// Process Tb and its evolution through variants such as Process T1v to Process T1+, etc. The source of this qualitative information will be derived as a by-product of the proposed simplification of the EHR updating process. These changes in practice will be evident where decisions to adopt a process step in a newly authorised option will be a positive confirmation of it acceptance. As significant changes in clinical practices, as a result of new knowledge are regulated by ethical committees or similar bodies, their authorisation procedures should ensure that current clinical process maps are updated though an appropriate change control process. A similar procedure would be followed under local clinical governance control in response to frequently used process adoptions. Whilst an assessment of the quality of clinical decision-making must remain in the hands of experts from the clinical professions concerned, the provision of evidence of the effects of change will be available as outputs from this study. Where a qualitative assessment of the relative worth of the impacts of changes in clinical practice is re-

Figure 5. Effects of process change assembly as dimensions in a relational database structure

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Figure 6. Radar plot methodology for assessment of the impacts of changes in clinical practice

quired, the “radar plot” methodology (Benton & Manning, 2006), shown in Figure 6 can be used. This uses a radial set of multiple assessment criteria with a common scale range. The area enclosed by linking the initial settings gives an immediate view of the scale and segmentation of the deficits. Regular re-assessment plots reveal progress made towards an end goal as a result of any change process. Whilst the example used, demonstrates the principle in terms of an elderly patient’s presenting circumstances and subsequent improvement as a result of multi-agency care, its applicability to a wide range of applications is evident. Its application to the impact of knowledge enabled process change merely requires the definition and agreement on an appropriate set of assessment criteria and any required segmentation. Although its implementation would primarily be based on case group governance reviews, it has the potential to be a semi-automatic derivative of the proposed EHR updating process.

Identifying Risk Potential Inevitably, potential risk is spread throughout any care process, both within and between interacting elements (Vincent, Taylor-Adams, & Stanhope, 1998). No individual element is risk-free – a clinician can inadvertently be carrying a transmittable infection; a process stage can fail; information may be incomplete or wrong. Equally interaction between them can be equally risk-prone.

A rigorous in-depth risk assessment review of the complete end-to-end process sequence that covers every element, their actual and potential interactions, and all supply chains – e.g sterile supplies, information – is essential. However, although risk management techniques are now an integral part of healthcare service provision, little attempt has been made to map in detail all aspects involved in service delivery – no doubt due in particular to the inherent complexity and variability in treating each diagnostic condition. Once the clinical process has been mapped, it provides the ideal basis for assessing the likely sources of risk threatening the achievement, the functional goals of each step along the pathway, as shown above right in Figure 7. This centres on identifying the key dependencies, whose failure would prevent or seriously disable achievement of each functional goal. This in turn leads to identifying threats and their damage potential and probability of occurrence. Blocking or mitigating these effects leads directly to the design and implementation of appropriate countermeasures. Once countermeasures are in place as part of a clinical risk reduction strategy, the triggering of any defence process will provide an indication of the occurrence – and hopefully prevention of an adverse event. Whilst each of these ‘attacks’ will obviously need to be investigated and followed up by any necessary additional countermeasures, the trigger action can provide a measurable indicator of Adverse Event Countermeasure response.

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Figure 7. Schematic diagram of pathways for adverse event risk sources/countermeasures

Educational Aspects Whilst one of the main thrusts of this approach is acquiring knowledge and its addition to the knowledge base, which in turn is indexed to each of the relevant steps in the pathway, it can also be the basis of a web-based e-Learning Service. The focus of this would be to ‘drip-feed’ information to frontline clinical staff as part of their Continued Professional Development training. The process would be built upon a Clinical Decision Support Service. Its aim will be to deliver a sequence of modular ‘bite-sized’ packages using a limited-access web service. These would be assembled using well-proven e-Learning methodologies set in the context of a careful assessment of the prevailing clinical cultures and motivational issues to ensure acceptance as a valued aid, rather than an imposed inconvenience. Of necessity, its output would have to be validated by an independent expert panel not only in terms of content, but also as fitness to fulfil training needs.

Technical Solution Description and Customisation Technology As mentioned earlier, an Electronic Health Record should be implemented with the use of well-structured CPP maps as a means of semiautomatically logging events into the EHR. Its

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aim is to use the relevant pathway step together with the knowledge indexed to it as a means of updating the record. The essence of the approach is that updating would either entail adopting the recommended action and uploading its ‘as is’, adapting it to suit the circumstances, or choosing an alternative approach - effectively ignoring the recommendation - and entering the action taken as per current practice. Automatically categorising all record updates in terms of the three types of action taken, viz: adopted; adapted; or ignored, would provide a direct profile of the clinician’s responses to newly available knowledge. More importantly it could radically reduce record input workloads, provided the pathway correctly reflected the bulk of current practice, whilst not constraining or impacting professional autonomy.

Advance that Care Process Pathways Would Bring Beyond the State of the Art To date Integrated Clinical Pathways based projects have almost invariably been focused on gaining a high-level view of the optimal process sequence, often centred on establishing management information datasets. This has not been helped by the lack of any generic process mapping standard, which has led to a profusion of different and highly idiosyncratic approaches

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The proposed CPP mapping approach is in part derived from initial ISO TC215 and CEN TC251 Working Group activities on Strategic Architecture development, coupled with that for Security and Safety. Its ultimate objective would be to form the basis of a generic standard within the ISO 9000 set, which could be supplemented if necessary by healthcare specific guidelines as in the case of ISO 27799. The integration of Clinical Knowledge access with CPP is novel – as is the application of dependency modelling and mapping techniques to Clinical Risk Management. Similarly, the potential simplification and reduction in clinical administrative effort inherent in the ‘adopt, adapt or alternative’ approach to updating the EHR is a further innovative feature. Whilst analysis of EHR updates will inevitably involve a variety of knowledge acquisition methods dependent of facilities available at each of the hospital sites, the long term aim would be to access relevant information, under appropriate security controls from a back-up service. Such an approach would ultimately be based on access to pseudonymised information from a dynamic archive running as a back-up to the main EHR, as currently under discussion within ISO and CEN. The use of e-learning methods appears to be somewhat novel as means to disseminate new

clinical knowledge especially in the context of Continuing Professional Development, although it has been pioneered in other environments. However, the proposed ‘drip-feed’ of mini, or even micro modules designed specially to meet clinical interests and provided as an interactive web service is likely to be an attractive innovation – as well as a training aid to support the introduction of CPP. The technical infrastructure, although new to the healthcare environment, should be constructed from as set of inter-operable software applications that have been used to provide similar functionality, albeit in different economic sectors. Overall this approach combines a number of novel concepts which will not only provide a robust methodology to track the successful dissemination and usage of new knowledge, but also develop a radically new approach both to giving easy access to clinical knowledge and simplifying clinical reporting routines.

CONCLUSION The primary focus of the proposed CPP methodology is to track the transfer of newly validated evidence-based clinical knowledge through to its application in frontline application, and to determine the degree and timescales involved in its

Figure 8. Conceptual overview of the main elements of the proposed CPP approach

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incorporation into mainstream treatment, together with its effects on outcome quality. Basing the approach on a detailed analysis of the CPP, their variability and the knowledge used in clinical decision-making should provide a sound platform from which to establish an adverse event risk management system. Moreover, the innovative combination of several analytical tools and techniques will not only provide insights and actions to counter adverse events and improve quality of care delivery, but also establish the basis for enhanced clinical decision support systems development to underpin this. Inherent in this approach is the use of these systems to provide data for clinical governance and comparative analysis to local and international benchmarks. The development of adverse event countermeasure triggers will aim to initiate timely preventive action and contribute to enhancing patient safety. In addition to providing the basis for providing rapid frontline access to newly validated evidencebased clinical knowledge, a further aim will be to develop sets of easily absorbed incremental e-learning packages that can be accessible via controlled-access web services not only to clinicians but also to support self-management of disease by patients where appropriate.

REFERENCES Benton, S., & Manning, B. R. M. (2006). Assistive Technology - Behaviourally Assisted. In Bos, L., Roa, L., Yogesan, K., O’Connell, B., Marsh, A., & Blobel, B. (Eds.), Medical and Care Compunetics (Vol. 3, pp. 7–14). Amsterdam: IOS Press.

Kohn, L. T. (2001). The Institute of Medicine report on medical error: Overview and implications for pharmacy. American Journal of Health-System Pharmacy, 58(1), 63–66. Manning, B. R. M. (2003). Clinical Process Maps as an indexing link to knowledge and Records. In Healthcare Digital Libraries Workshop, 7th European Conference on Research and Advanced Technology for Digital Libraries, Trondheim, Norway. McKeon Stosuy, M., Manning, B. R. M., & Layzell, B. R. (2005). E-care co-ordination: An inclusive community-wide holistic approach. Boston: Springer. Pirnejad, H., Bal, R., Stoop, A. P., & Berg, M. (2007). Infrastructures to support integrated care: connecting across institutional and professional boundaries: Inter-organisational communication networks in healthcare: centralised versus decentralised approaches. International Journal of Integrated Care, 7, 14. Schey, S. (2001). Guidelines for Haemo-oncology Treatment Processes [Internal Document]. London: Guys and St Thomas’ Hospitals NHS Trust. Shiffman, R. N., Michel, G., Essaihi, A., & Thornquist, E. (2004). Bridging the Guideline Implementation Gap: A Systematic, DocumentCentered Approach to Guideline Implementation. Journal of the American Medical Informatics Association, 11(5), 418–426. doi:10.1197/jamia. M1444 Vincent, C., Taylor-Adams, S., & Stanhope, N. (1998). Framework for analysing risk and safety in clinical medicine. British Medical Journal, 316, 1154–1157.

This work was previously published in E-Health Systems Quality and Reliability: Models and Standards, edited by Anastasius Moumtzoglou and Anastasia Kastania, pp. 73-85, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.8

Towards Computer Supported Clinical Activity:

A Roadmap Based on Empirical Knowledge and Some Theoretical Reflections Christian Nøhr Aalborg University, Denmark Niels Boye Aalborg University, Denmark

ABSTRACT The introduction of electronic health records (EHRs) to the clinical setting has led healthcare professionals, policy makers, and administrators to believe that health information systems will improve the functioning of the healthcare system. In general, such expectations of health information system functionality, impact, and ability to disseminate have not been met. In this DOI: 10.4018/978-1-60960-561-2.ch408

chapter the authors present the findings of three empirical studies: (1) the structured monitoring of EHR implementation processes in Denmark from 1999–2006 by the Danish EHR observatory, (2) a usability study based on human factors engineering concepts with clinicians in artificial but realistic circumstances—a “state of the art (2005)” for Danish CPOE (computerized physician order entry system), and (3) user reactions to a conceptual “high level model” of healthcare activities—the Danish G-EPJ model in order to better understand the reasons for health information system failures

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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and to suggest methods of improving adoption. The authors suggest that knowledge handling as a science seems immature and is not in line with the nature of clinical work. The prerequisites for mature knowledge handling are discussed in the second part of this chapter. More specifically, the authors describe one way of improving knowledge handling: the development of a more true digital representation of the object of interest (OOI) or the virtual patient/citizen that interacts with computer based healthcare services on behalf of and for the benefit of the citizen’s health.

INTRODUCTION In 1968, the Danish journal of engineering science “Ingeniøren” published an article about a hospitalbased computer system. The article described a computer system that was being used to support administrative and clinical tasks at the largest hospital in Denmark “Rigshospitalet.” The article provided the reader with a picture of a desk with a computer terminal and a telephone. The text under the picture read: “This is how the doctor’s desk will look in a few years: No paper, the patient’s record will be retrieved on the computer screen within

fractions of a second” (see Figure 1) (Jda, 1968). Almost 40 years later we are able to retrieve patient data, but not the entire record, and the predicted response time suggested in the article remains wishful thinking. The Danish example is not an exception. International studies report that up to 75 percent of all large IT projects in healthcare fail (Littlejohns, Wyatt, & Garvican, 2003), and according to Michael Rigby, evaluation is still a “Cinderella science” where information and communication technology (ICT) is concerned (Rigby, 2001). A commonly held notion among the international electronic health record (EHR) community is that the failure of numerous IT projects is due to instances of bad programming and poor implementation that can be easily avoided the next time around (Wears & Berg, 2005). Results from a number of studies in Denmark, which the authors have been involved in, indicate that the difficulties associated with implementing ICTs in healthcare or health information systems (HIS) can be traced back to the perspectives and theories that computer scientists and systems developers hold about medical work and how these theories influence HIS development and implementation processes.

Figure 1. Perception of how a doctor’s office would look from 1968

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In this chapter the authors will present the results from a number of Danish studies involving a group of ICTs (i.e., HIS). The studies do not evaluate the promised benefits of HIS in terms of their outcomes. Instead, they focus on the practical use of HISs in clinical work situations. Based on our cross-study experiences, the authors then examine the future merits of information technology (IT) from a clinical point of view. Prior to beginning our discussion, they will first provide some background information about the Danish healthcare system to provide the context for our research work.

EMPIRICAL STUDIES

BACKGROUND

THE EHR-OBSERVATORY: SIX YEARS OF MONITORING THE IMPLEMENTATION PROCESS OF EHRS IN DENMARK

In the Danish healthcare system, policy making is the responsibility of the Ministry of Health and Prevention. Decision making involving healthcare system functioning is a county level activity. Until January 1, 2007 Denmark had 14 counties which now are merged into five regions that are responsible for healthcare system decision making. Health coverage is public and is tax-financed. Overall health expenditures in Denmark are 9.2% of gross national product (GNP) (in 2004) (USA 15.3%) (Source: OECD health data). Currently, health coverage is provided to approximately 4.2 million people for the price of 200 € a month per citizen. Dental services, physiotherapy and some pharmaceutical costs are not covered. These costs average 50 € a month (250 € ≈350 US$). Citizens can also obtain additional health coverage from private insurance companies in order to obtain faster access to treatments in private clinics. Private insurers provide faster access to treatment in private clinics. Alternatively, the private sector provides only a relatively small number of hospital beds (i.e., 1-2%) to the citizens of Denmark.

The empirical studies that are presented in this chapter include the following: 1. The EHR-Observatory study in which the authors monitored the EHR implementation process in Denmark over a six year period. 2. A usability study involving a medication module. 3. A study of users’ reactions to common conceptual domain models for Danish EHRs (GEPJ).

Since 1996 a national strategy has guided Danish EHR-projects in the development and implementation of EHRs (Danish Ministry of Health, 1996). Denmark’s national EHR strategy has been revised a number of times. The 1999 version of the strategy initiated the formation of an independent monitoring group, “the EHR-Observatory,” with participants drawn from research institutions: Aalborg University, Danish Institute for Health Services (until 2001), Center for Health Telematics (until 2003) and a consultancy company “MEDIQ” (Danish Ministry of Health, 1999). The purpose of the EHR-Observatory is support the advancement of Denmark’s national HIS strategy by monitoring and assessing the development, implementation and application of EHR systems in Danish hospitals. Since 1999, the Observatory has collected data on various aspects of national EHR work including the following: 1. Implementation and dissemination issues: ◦⊦ Diffusion and diffusion rates of EHR systems.

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◦⊦

Experience gained among the different stakeholders. ◦⊦ Factors that increase diffusion and use of EHR-systems. 2. Issues related to a common frame of reference for EHR systems: ◦⊦ Differences and compatibilities between regional data models. ◦⊦ Communication consequences of using incompatible data models. ◦⊦ Institutional demands for a common frame of reference. In Denmark, it is the responsibility of the Ministry of Health, through the Danish National Board, to issue guidelines and common standards for EHRs. The Ministry of Health also publishes national strategies and revises the Danish national strategy every two to four years. The Danish National Board of Health houses an office for clinical information systems use and implementation that offers advice to county (regional) administrations about EHRs. Over the past five years, the Ministry of Health has worked on the development of a common conceptual domain model for Danish EHRs, which is expected to be implemented in Denmark in the next few years. County administrations are the “owners” of public hospitals, and have the political and administrative responsibility for running hospitals in Denmark. The implementation and financing of EHRs is the responsibility of the counties. The EHR-Observatory has sent survey questionnaires each year for the last five years (i.e., 2001-2006) to each of the county administrations (n = 15) in order to monitor EHR work. The results of these surveys are reported annually (in Danish) (Andersen, Nøhr, Vingtoft, et al., 2002; Bernstein, Rasmussen, Nøhr et al., 2001, 2006; Bruun-Rasmussen, Bernstein, Vingtoft et al., 2003; Nøhr, Andersen, Vingtoft et al., 2004; Vingtoft, Bruun). Rasmussen, Bernstein et al., 2005). Survey questionnaires are sent to county administrations

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Figure 2. Response rate

electronically in April of each year (one electronic reminder is provided in addition to the original electronic invitation to participate). Response rates have been close to optimal (see Figure 2). The questionnaire itself consists of a kernel of 12 questions. Survey questions address general aspects of EHR strategy, diffusion, economy, benefits, and barriers. These questions have remained the same throughout the 6 year period during which survey has been conducted. The survey questionnaire also asks a number of detailed questions that focus on specific topic areas. These questions vary from year-to-year and have specific themes. There are several survey questions that focus on diffusion. Diffusion questions ask county administrations about the number of beds in their hospitals and the number of people covered by their EHR system (i.e., clinical documentation and medication). Furthermore, counties are asked to provide information about the number of beds that they are planning to cover with the EHR in the upcoming three years (these questions were not asked in 2001 as the counties were asked for the amount of beds covered at that point in time). Data from the survey on actual and expected national EHR diffusion appears in Figure 3. The data clearly shows that the number of beds covered by an EHR system is steadily increasing and will continue to increase over the next few years, but that expectations show a systematic overestimation of expected EHR bed coverage by county administrators.

Towards Computer Supported Clinical Activity

Figure 3. Actual and expected national EHR diffusion

County estimates for the year immediately following the survey are reasonably accurate— counties overestimate by only a few per cent. Alternatively, county estimates about EHR coverage become more optimistic with projections into the future. This finding is consistent with optimistic EHR policies at the national level. Such a tendency to be optimistic about EHR implementation or coverage is also present internationally. In a publicly financed healthcare system, to a large extent controlled by politics, there will always be a tendency to adhere to policy goals, even if they seem unrealistic. The hospital administrators were also asked what they perceived as barriers to implementing EHRs. The responses to these questions are shown in Figure 4. (Each respondent could choose three barriers).

The 2001-2006 data suggest there are no trends in terms of perceived barriers to EHR implementation. It must be noted that county administrations have not systematically surveyed for EHR barriers. However, the belief among county administrators is that users are resisting change and this resistance has increased significantly over the last three years (see Figure 4). Unfortunately, the questionnaire does not explore this finding further. County administrators identify that poor integration and missing or lack of EHR standards are the second largest barriers to EHR implementation (as illustrated in Figure 4). The issue of lack of EHR standards must be understood in terms of the local, Danish context. The National Board of Health and The Ministry of the Interior and Health have not subscribed to any international EHR standards, for example, HL7. Instead, the govern-

Figure 4. Barriers to implementing EHR in Danish hospitals in percent of the total responses

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ment’s strategy has been to develop a national, basic EHR structure (GEPJ) for all the counties to follow (briefly mentioned in the introduction to this section as the Danish common conceptual model, and further explained in Figure 4). This policy has given rise to widespread discussions about the relevance and robustness of developing a basic EHR structure independent to international standards such as HL7. Poor EHR integration has also emerged as an issue. Poor EHR integration is linked to missing or a lack of EHR standards. For example, a single sign-on as a standard EHR implementation principle is absent in systems that have been implemented.1 The absence of a single sign on has created a number of practical problems for local IT departments. It has also fueled users’ dissatisfaction with the EHR. Anecdotal user reports suggest clinical staff are spending up to 20 minutes of every morning logging or “signing on” to various HIS and applications they plan to use to do their work during the course of a typical day. The questionnaire also asks hospital administrators about how they evaluated their EHR implementations and what specific variables were measured as part of those evaluations. The majority of county administrations stated they evaluated their HIS implementations, but when asked about how they evaluated their implementations. Answers were remarkably unclear, leaving researchers with the impression that evaluation is a “want-to-do issue.” For example, one of the chief information officers (CIO) in a county replied that the hospital was undertaking continuous formative evaluation. The hospital had built in continuous formative evaluation into their internal meetings, as a standing point in their meeting agendas (i.e., meeting participants were asked to report on EHR developments since their last meeting). The EHR-Observatory is a monitoring project. It does not perform any evaluation of local EHR progress. The consequence is that EHR evaluation at the county level is not possible and therefore there is little data available that could possibly be

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used to fully understand the possible reasons for continuous delays in EHR implementation plans and the constant tendency to be overly optimistic in terms of county ability to implement EHRs. The EHR-Observatory has only been able to establish the fact that there are delays in EHR implementations, overly optimistic beliefs about county capacity to implement EHRs, and the presence of barriers in the form of user resistance to change and poor integration of HIS systems.

A USABILITY STUDY OF A CPOE MODULE Some of the reasons for user dissatisfaction could be explained by usability issues. However, most usability tests performed in traditional usability laboratories do not adequately replicate the complexities associated with HIS use in the clinical setting. Also, due to ethical issues traditional field studies cannot be carried out in the clinical setting. The study outlined in the following describes the advantages associated with using a combination of a traditional laboratory and field study approach. In the usability study we undertook, we retained a large degree control over the setting while at the same time creating a realistic, rich, clinical situation and context (Lilholt et al., 2006). The components of the study include: “think aloud,” video recording, screen capturing, and debriefing. These study components were selected because they provided the authors with experimental data that could be triangulated to validate the results.

Study Materials and Methods The usability studied employed “think aloud.” The think aloud method is a simple method of collecting the users’ immediate thoughts and reflections throughout a test. The user simply verbalizes his thoughts and reflections while using the system. The think aloud method was considered vital for later data analysis. Video recording of the contex-

Towards Computer Supported Clinical Activity

tual work situation as well as screen capture of the computerized physician order entry (CPOE) system2 under study was used as a method for collecting data from the test. These recordings are essential as they provide documentation of the occurrence of usability problems during the test. Directly after the test, a debriefing with the user was conducted, to obtain additional information about the test. Since the main objective of the study was to evaluate usability using a number of methods and not to conduct a full usability evaluation, only two CPOE, experienced physicians participated as users. A scenario was scripted based on an authentic, complex patient trajectory to ensure the use of a wide range of functionalities of the CPOE system. The scenario consisted of a 39-year-old female patient suffering from cancer, admitted to a long-term medical treatment. The case involved several health professional stakeholders and multiple drug prescriptions. In the evaluation laboratory, Skej-Lab, at the Aarhus University Hospital, Skejby Sygehus, a hospital ward was simulated (i.e., hospital room and office). The hospital room consisted of a bed, table, chair, and medicine chest and various other accessories. In addition to the hospital room, an office was set up with a computer from which the user could access the CPOE system. An actress played the role of the patient, partly due to ethical issues, but also to secure a

consistent patient simulation for each test. Since the test facility was physically located at the hospital, it was possible to use a normal production IT-system instead of a test version of a prototype. This contributed to the realism of the test exposing the user to such authentic EHR system attributes as system response time. After the usability test, the video and screen recordings were analyzed and log files were prepared. These log files were then analyzed with the purpose of identifying usability problems. The data analysis of the log files identified a number of usability problems. These were classified according to Molich’s rating scale for classifying the severity of a usability problem using the following categories: cosmetic, severe, and critical (Molich, 2000). Inspired by Skov and Stage (Skov & Stage, 2005) a diagram to assist with the quantification of usability problems according to Molich’s rating scale was developed using an iterative process. The diagram shown in Figure 5 is not generic, but specifically targeted at usability problems associated with the CPOE system. An example of a serious problem regarding response time is the following: the time it takes to right click on a medication in a medication order list to the appearance of an ordering window that allows for medication prescribing takes anywhere from nine to 20 seconds to occur. This is a very frequently used function of CPOE. The

Figure 5. The classification scheme, the number of occurrences of a problem and in brackets the number of work functions where the problem occurred

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wait time associated with this functionality creates a lot of user annoyance; hence it is classified as critical. The following is an example of a serious problem in graphical user interface (GUI) design: The user chose the wrong route of medication administration when ordering the drug “Fragmin” (Dalteparin, a low molecular weight heparinanalogue that almost exclusively is administered subcutaneously), because the default dosage in the system was not standard. The following is an example of a cosmetic functionality problem: The user finds it annoying that they must discontinue a drug and then order it again just to change the dosage. During usability testing, one of the central servers went down. Several error messages appeared during the study on the users screen. When the database server controlling the drug database went down the user received an error message: “Error when accessing information about the drug.” When the user clicked the “OK” button in the error window a new window popped up with the text: “No drug has been chosen.” The two error messages appeared because there was a system error in a central computer. The user believed he had forgotten to choose a drug. As a result, he repeated the sequence several times. This was classified as a serious error because the user was delayed in their work by close to a minute. The authors believe these usability problems arose because of the authenticity of the scenario (i.e., the usability testing was performed on a production system, instead of a test version of a prototype). The authors argue that the problems discussed above occurred because of limited developer insight into clinical reasoning and practice.

USERS’ REACTIONS TO THE COMMON CONCEPTUAL DOMAIN MODEL FOR DANISH EHRS (GEPJ) To comply with the objectives of the National Strategy for IT in Health Care, the Office for Clinical

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Information Systems Use and Implementation of the National Board of Health developed a Common Conceptual Domain Model for Danish EHR systems or GEPJ. The common conceptual domain model (GEPJ) will be implemented in the years to come in Denmark. Currently, GEPJ is present in version 2.2. It specifies the structure, relation, and formalization of data necessary to create a coherent clinical documentation system. GEPJ is a model that is: problem oriented, focuses on the patient, is used by differing health professionals, and promotes the interdisciplinary use of the EHR. GEPJ does not specify patient record composition, but rather it specifies the documentation process and the relation between patient information and the EHR system (Sundhedsstyrelsen (SeSI), 2005). In GEPJ, clinical processes consist of four main processes: diagnostic considerations, planning, execution, and evaluation. The model is outlined in Figure 6. The circles represent processes in the model. Processes in the model lead to the creation of information elements (see the boxes between the circles in Figure 6). The information elements produce documentation elements, as illustrated by four text boxes in the corners of the Figure. The four clinical processes are outlined in greater detail:

Diagnostic Consideration During the diagnostic consideration process the healthcare provider collects information about the patient’s health condition in order to develop an understanding of the patient’s health state. This process involves learning about the patient’s expression of health and disease and evaluating all possible relationships between the patient and his or her health conditions. During the diagnostic consideration process, the health professional must document the patient’s health condition and information associated with the health condition when using the EHR. The diagnostic considerations may also be further elaborated upon in a diagnostic note. The following documentation elements exist in

Towards Computer Supported Clinical Activity

Figure 6. GEPJ model

GEPJ: focused information, applied guidelines, complicating healthcare activities, external causes, and diagnostic notes.

is. The following documentation elements exist for the process of execution: executed activity, applied guidelines, and execution note.

Planning of the Intervention

Evaluation

In the planning of the intervention process, the provider plans relevant healthcare activities for the patient. This includes the definition of specific plans for care, treatments, observations, diagnostic tests, and so forth. Simultaneously, the healthcare provider plans and sets out operational objectives and expresses these plans as expected outcome for the patient. Planning considerations can be further documented in a planning note. The following documentation elements exist in the planning of the intervention: healthcare objective, indication, applied guideline, and planning note.

In the evaluation process the healthcare provider compares the expected and obtained results of the execution. Here, the healthcare provider evaluates whether the achieved outcome is acceptable. This process involves two sub-processes: comparison and new diagnostic considerations. The result of the evaluation can be used as focused information for documenting about a new health condition. The following documentation elements exist for evaluation: evaluation note. Two prototype GEPJ based documentation systems were built for evaluation purposes. The systems were implemented in two different departments: a geriatric department at Amager Hospital in Copenhagen, and a department for internal medicine at Århus University Hospital. The process of building systems that were useful during daily, clinical routines turned out to be more complicated than expected (Vingtoft, 2004). The prototype systems never achieved an adequate level of practical, daily or routine use by healthcare providers. Hence, it was impossible

Execution In the execution process the health provider implements the plan. This activity should improve the patient’s condition or produce new knowledge about the patient’s health condition. The outcome is the result of the intervention. The outcomes should be clear, state exactly what has been done, for how long, by whom and what the exact result

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to obtain data of an adequate quality to draw any significant conclusion. It was impossible to determine whether user statements about the prototype system were about its underlying domain model or the immature interface. However, users made a number of significant, positive statements about the prototype when straightforward and uncomplicated cases were considered (i.e., cases where patients were suffering from trivial or well known problems). In contrast, users made more negative statements about the EHR when the situations or cases they had to deal with were complicated (i.e., patients with multiple diagnoses, or rare diseases). The GEPJ model was intended to be used as a documentation model—a model that ensures coherence in the data gathered, by structuring the work procedures associated with health provider documentation (mainly doctors and nurses). However, when applied to health provider documentation practices associated with clinical work, the model heavily influenced work practices. Health providers found the model restrictive. The core of the model promotes a hypotheticaldeductive thinking process, which is quite common in scientific research, quality assurance, clinical work, and so forth. Hence, the model was easily recognized and understood by most clinicians. However, not all clinical work is performed using a hypothetical deductive thinking approach. Therefore, the authors will discuss the model in terms of computer-assisted clinical work and present some theoretical reflections on the model.

HUMAN CLINICAL REASONING: USING KNOWLEDGE IN AN INCONGRUENT CONTEXT Biomedical research or the root of medical knowledge arises from the fields of applied biochemistry, genetics, physics, and mathematics (mathematics is also the “mother” of computer science). This means that ontologies and models

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in medicine are built upon concepts that arise from the domain of the natural sciences. During the provision of healthcare, health knowledge is applied or activated for the benefit of the individual patient. During this activation process, knowledge rooted in the human science domain is used by healthcare providers. In the human sciences scales are not as exact as those used in the domain of the natural sciences. The process of selecting and activating applied natural science knowledge in the human science domain is individual in nature and is probably the most unique and abstract feature of medical practice as compared to work in other domains. The foundations of clinical decision making are tangible and measurable, but they do not easily transform into proper or individualized health related decisions. Therefore clinical decisions are difficult to formalize using a digital system. The patient record documents a patient’s subjective and measurable problems, the signs and symptoms of their disease, and the procedures undertaken to alleviate the patient’s health problems. The patient record may also contain patient specific considerations. Patient specific considerations appear as text in EHRs. The authors are not aware of any EHR research that has documented the EHRs ability to adequately formalize or extend clinical decision making. The EHRs inability to adequately formalize or extend clinical decision making may arise from the shared “genes” of bio-medical knowledge and computer-science, the historic reasons for record content (i.e., an indirect way of representing the subject/object of care), and a lack of a genuine formalization for clinical decisions. Overall, it makes the EHR a poor surrogate for the patient in a digital universe. The object of work, object of interest (OOI) or the patient is not truly represented in an operational manner in a clinical computer system. Therefore, case consideration is more easily undertaken using computer systems in other business domains than medicine (i.e., “an entry in the record is not a cure”).

Towards Computer Supported Clinical Activity

If one accepts the argument that “one is not cured by entries into medical records,” how does one proceed towards real computer-supported clinical activity (CSCA)” while taking into account the dualistic nature of the natural and humanistic properties of clinical work? “Activity,” instead of “work,” occurs in the acronym CSCA for two reasons: (1) the acronym CSCW stands for computer-supported co-operative work (actually also an issue in clinical computer supported medicine), and (2) “activity” implies that professionals, patients, and relatives could benefit from computer support in care, self-care, and the care of their loved ones.

REPRESENTING OBJECTS OF INTEREST (OOI) IN THE COMPUTER SYSTEM Every computer program has: (1) a primary mission (e.g., recoding or supporting clinical activity), (2) a conceptual model to represent the domain of interest (roles, organization and activities), and (3) models of the objects of interest. These three abstract components should fit with the reality (of clinical work), and together they should determine what a human operator can achieve using the software. “Killer applications” developed in the areas such as finance, airline booking, construction, architecture, and so forth can be characterized as having an evident fit between the three abstract components outlined above and therefore an ability to “do the job.” Rich and adequate representations of objects of interest (e.g., money, passengers and seats, piping systems, houses, etc.) ensure there is a strong fit. We have seen no “killer applications” in healthcare as yet. Currently, there are three different uses of computer power in connection with advanced clinical activities: •

Financial, administrative, logistic and communication purposes. This category of

•

•

use also includes EHRs used in the general practitioner’s office. Embedded in diagnostic and therapeutic equipment. This has probably been the most successful use of computer power in the clinical domain so far. The mission, (limited) domain model, semantics and representation of the OOI in the software supporting specific clinical hardware are always clear and relatively simple (mono-modal). General clinical use (mainly EHRs). EHRs typically record and quantify clinical activity. EHRs often use weak, incoherent, conceptual models, and the OOI can only be represented as within or close to—the natural sciences domain. Furthermore, the object of interest is only indirectly represented though the signs, symptoms, measurements and acts carried out in relation to the patient’s health status. The use of computer power for general clinical purposes demands multimodality, which in fact is a feature of the paper record that has not yet been mimicked by the EHR. This multimodality is based on a flexible formalization (as is currently possible). In the paper record this flexible, formalization provides room for narrative as well as arithmetic data entry and retrieval.

The construction of contemporary clinical software is based on the capabilities of the paper record combined with the main hard and software features of office automation, including the “personal computer” and sequential long sessions of desk-situated work as one “case” is executed by one logged-in human at a time. Often the model and representation of OOI in office automation software is advanced enough for the human operator to actually complete the tasks assigned. As outlined above the office context differs from clinical work. The clinical reality is that users are often nomadic and shifting, are involved in

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Table 1. Differences between office and hospital work Office work (administrative purposes)

Hospital work (pure clinical purposes)

Desk situated

Nomadic

Quiet surroundings

Noisy surroundings

Long sessions on one case

Short interactions on individual cases

One person—one case; fixed teams

Many persons—many cases; changing teams

Predominantly sequential processing

Predominantly parallel processing

The object of interest (OOI) is represented sufficiently in the computer

The object of interest is not represented sufficiently in the computersystem

co-operative work with many disciplines, need to review several patient cases in parallel, and require their core professional functions to be supported not just recorded (Bardram & Bossen, 2005; Bossen, 2006). Some of these differences are summarized in Table 1.

TOWARDS A NEEDED CONCEPTUAL THEORETICAL FRAMEWORK The mission of future clinical software should be to clearly support all clinical activities not just tasks such as computerized physician order entry (i.e., CPOE) or acting as a “bridge” between clinical activity and administrative or financial systems. This would seem to be already implied in the EHR concept, but as shown empirically in the first part of this chapter, this seems to not be the case in real life. To approach a more coherent model and a better representation of the OOI in software development for general clinical use, one must understand the abstract nature of healthcare activity and of the OOI, including human (biological) and individual variation.

WHAT ARE THE COMPONENTS OF HEALTHCARE ACTIVITY? An analysis of the components of healthcare activity supports the construction of computer-

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supported work that addresses four abstract components, which are rendered by an adaptation of a historical and artistic example displayed in Figure 7. The activities in the clinical domain consist of providing healthcare using the four components. In the next section the knowledge component is discussed.

KNOWLEDGE REPRESENTATION The representation of knowledge remains an abstract art from a theoretical as well as clinical point of view. Other chapters in this book will deal with the challenges and methods associated with implementing knowledge representation schemes and decision supports using a computer system. Medical knowledge can be stratified into three inter-dependent layers (Figure 8). At the top are abstract theories that form a coherent explanation and foundation for activity and research in the medical domain. The main theory in the medical domain is the patho-anatomical disease model. According to this model, every disease-entity has a specific lesion of anatomical, biochemical, genetic, microbiological, social, or psychological origins. Lesions give rise to symptoms, signs and problems and to some extent predict a “standard” disease course, trajectory or a general prognosis for a disease (i.e., This is sometimes referred to as the heuristic layer). Disease course or trajectory

Towards Computer Supported Clinical Activity

Figure 7. Detail from a painting (1882) of Robert Hinckley showing the first public demonstration of anesthesia done on the 16th of October 1846 at Massachusetts General Hospital, Boston (Harvard Museum, Cambridge, Boston). Annotations are done by the authors showing the abstract components of the provision of healthcare. The three main components are: (1) technology utilization (in the example sulfur ether in a glass flask) (2) Manual skills and (3) Knowledge. The fourth component—teamwork—is present in many advanced healthcare organizations and “teamwork” will include the patient in the team. One could include organization as the fifth component in providing healthcare.

can be modified by an individual’s own defense mechanisms, life-style and/or healthcare treatments or interventions. When employing medical knowledge in clinical encounters healthcare professionals are

intuitively aware of the stratification of health knowledge. Alternatively, computer systems are not. Currently, intuitive awareness of the stratification of health knowledge is not currently built into HISs. Instead, knowledge is only utilized in

Figure 8. The knowledge structure employed in the discussion

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the phenomenological layer. We know that even in similar situations knowledge used in clinical decision making is different for differing individuals. We know that “clinically context sensitive” knowledge from the layers outlined in the diagram in Figure 8 are activated during clinical work. Therefore, it is essential that healthcare professionals have a deep understanding of the upper two layers of knowledge to be used in medical work (i.e., philosophical and heuristic). Informed patients may also benefit from such knowledge of their disease. For example, patient heuristic knowledge about the management of disease is necessary for the patient to engage in daily problem-solving in the phenomenological layer especially when managing a chronic disease or illness.

REPRESENTING THE OBJECT OF INTEREST (OOI) As discussed previously, the OOI is only indirectly represented in the documentation model of EHRs. EHR documentation models are inherited from paper records. We need a documentation model that is clinically, context sensitive such as the “virtual patient” model. A “virtual patient” model

should include knowledge about disease trajectory (thereby replacing the traditional chronology of the paper record). A “virtual patient” trajectory could be cross-sectional in nature, multi-stakeholder in content, and appear as a narrative, formatted record that balances health agonists and antagonists (as seen from the perspective of the patient) (Figure 9). The “virtual patient” model may to some extent replace the EHR. It could certainly replace personal health records that are currently found on the Internet. Health care organizations may choose to continue using their own corporate “bookkeeping” systems that are inter-operable with a collaborative health grid (as defined in the pervasive healthcare chapter). Alternatively, a “virtual patient” could be part of a health collaboration grid and a virtual trajectory could act as a digital knowledge proxi, thereby enabling the patient’s ability to customize and operationalize abstract, general and specific knowledge to their own individual context.

CONCLUSION In order to improve HISs, increase their adoption, usability, cross-societal diffusion as well as improve citizen use of such systems, we need

Figure 9. Individual human related knowledge and information is represented as a trajectory in the virtual patient conceptual framework—a trajectory that is balanced between the health agonist and antagonist

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to develop more coherent models of OOI in healthcare. The authors argue that the current lack of clinically context sensitive models such as the “virtual patient” may account for many of the adoption and dissemination issues described in the first part of the chapter. Historically, the development of clinical systems has been modeled on the industrial and office automation paradigm. Clinical hardware and software need to be modeled after clinical work, decision making and patient disease processes to improve their clinical impact and enhance their dissemination speed. In the past, the focus of clinical hardware and software development has been on “manual” procedures and acts, which may not be equivalent to the knowledge content and individual service needs associated with those medical acts in the clinical setting. Improvement in knowledge handling and a more “true” digital representation of patients or objects of interest should be developed. Health informaticians need to develop prerequisites for integrating digital health knowledge into societal activities that patients engage in. Furthermore, researchers need to explore the relationships between patient health related knowledge pervasive healthcare (as described in chapter 5 of this book).

ACKNOWLEDGMENT The authors wish to thank the partners in the EHRObservatory: Søren Vingtoft, Knut Bernstein, Morten Bruun Rasmussen and Stig Kjær Andersen for their contribution to the study on EHR diffusion in Denmark. This study was supported by The Ministry of the Interior and Health, The County Council, and The Copenhagen Hospital Cooperation.

REFERENCES Andersen, S. K., Nøhr, C., Vingtoft, S., Bernstein, K., & Bruun-Rasmussen, M. (2002). EHR-observatory annual report 2002. Aalborg: EPJ-Observatoriet. Bardram, J. E., & Bossen, C. (2005). Mobility work: The spatial dimension of collaboration at a hospital. [CSCW]. Computer Supported Cooperative Work, 14(2), 131–160. doi:10.1007/ s10606-005-0989-y Bernstein, K., Rasmussen, M. B., Nøhr, C., Andersen, S. K., & Vingtoft, S. (2001). EHR observatory. Annual report 2001. Odense: The County of Funen. Bernstein, K., Rasmussen, M. B., Nøhr, C., Andersen, S. K., & Vingtoft, S. (2006). EHR Observatory. Annual Report 2006. Aalborg: EHR-Observatory. Bossen, C. (2006). Evaluation of a computerized problem-oriented medical record in a hospital department: Does it support daily clinical practice? International Journal of Medical Informatics, 76(8), 592–600. doi:10.1016/j. ijmedinf.2006.04.007 Bruun-Rasmussen, M., Bernstein, K., Vingtoft, S., Andersen, S. K., & Nøhr, C. (2003). EHRobservatory annual report 2003. Aalborg: EPJObservatoriet. Danish Ministry of Health. (1996). Action plan for electronic patient records—strategy report. Copenhagen: Danish Ministry of Health. Danish Ministry of Health. (1999). National IT strategy for the hospitals 2000-2002. Copenhagen: Danish Ministry of Health.

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Jda. (1968, March 15). Avanceret databehandlingsanlæg med “fjernsynsskærme” til Rigshospitalet. Ingeniørens Ugeblad, Lilholt, L.H., Pedersen, S.S., Madsen, I., Nielsen, P.H., Boye, N., Andersen, S.K., et al. (2006). Development of methods for usability evaluations of EHR systems. In A. Hasman, R. Haux, J. van der Lei, E. De Clercq, F. Roger-France (Eds.), Ubiquity: Technologies for better health in aging societies (pp. 341-346). Amsterdam: IOS Press. Littlejohns, P., Wyatt, J. C., & Garvican, L. (2003). Evaluating computerized health information systems: hard lessons still to be learnt. BMJ (Clinical Research Ed.), 326(7394), 860–863. doi:10.1136/ bmj.326.7394.860 Ministry of Health and Prevention. (n.d.). Retrieved on February 11, 2008, from http://www. sum.dk

Vingtoft, S., Bernstein, K., Bruun-Rasmussen, M., From, G., Nøhr, C., Høstgaard, A. M., et al. (2004). GEPKA-projektet. Klinisk afprøvning. Aalborg: EPJ-Obseervatoriet. Vingtoft, S., Bruun-Rasmussen, M., Bernstein, K., Andersen, S. K., & Nøhr, C. (2005). EHRobservatory annual report 2005. Aalborg: EPJObservatoriet. Wears, R. L., & Berg, M. (2005). Computer technology and clinical work: Still waiting for Godot. Journal of the American Medical Association, 293(10), 1261–1263. doi:10.1001/ jama.293.10.1261

ENDNOTES 1



2



Molich, R. (2000). Brugervenligt webdesign. København: Teknisk Forlag. Nøhr, C., Andersen, S. K., Vingtoft, S., BruunRasmussen, M., & Bernstein, K. (2004). EHRobservatory annual report 2004. Aalborg: EPJObservatoriet. OECD. (2007). OECD Health Data 2007. OECD Rigby, M. (2001). Evaluation: 16 powerful reasons why not to do it—and 6 over-riding imperatives. Medinfo, 10(Pt 2), 1198–1202. Skov, M., & Stage, J. (2005). Supporting problem identification in usability evaluations. In Proceedings of the Australian Computer-Human Interaction Conference 2005 (OzCHI’05). ACM Press.

Single sign-on (SSO) is a method of access control that enables a user to authenticate once and gain access to the resources of multiple software systems i.e., booking module, lab. result system, medication system, etc. In general computerized physician order entry (CPOE), is a process of electronic entry of physician instructions for the treatment of patients. These orders are communicated over a computer network to the medical staff (nurses, therapists or other physicians) or to the departments (pharmacy, laboratory, or radiology) responsible for fulfilling the order. In the Danish context focus has been on the medication process as other proprietary systems have taken care of the communication to laboratories, radiology etc.

Sundhedsstyrelsen. (SeSI). Beskrivelse af GEPJ - på begrebsniveau. (2005). Sundheds-styrelsen.

This work was previously published in Human, Social, and Organizational Aspects of Health Information Systems, edited by Andre W. Kushniruk and Elizabeth M. Borycki, pp. 67-83, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.9

Nursing Documentation in a Mature EHR System Kenric W. Hammond VA Puget Sound Health Care System, USA Charlene R. Weir George W. Allen VA Medical Center, USA Efthimis N. Efthimiadis University of Washington, USA

ABSTRACT Computerized patient care documentation (CPD) is a vital part of a Patient Care Information System (PCIS). Studying CPD in a well-established PCIS is useful because problems of system adoption and startup do not interfere with observations. Factors interfering with optimal nursing use of CPD are particularly challenging and of great concern, given today’s shortage of nursing manpower. We describe problems and advantages of CPD usage identified by nurses in a series of research interviews. The chief advantages of CPD for nurses found were better accessibility and reli-

ability of patient care documentation. The main disadvantage was an awkward fit between current input technology and nursing workflow. A second disadvantage was difficulty in translating portrayal of the nursing process into readable documentation that is useful to all members of the clinical team. We interpret these findings to show that explicit consideration of nursing workflow constraints and communication processes is necessary for development of effective nursing documentation systems. Some findings point to a PCIS reconfiguration strategy that is feasible in the short term. Other findings suggest the value of considering mobile and team-oriented technologies in future versions of the PCIS.

DOI: 10.4018/978-1-60960-561-2.ch409

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Nursing Documentation in a Mature EHR System

INTRODUCTION Patient care information systems do not exist in a vacuum. Inevitably, these systems articulate with the cognitive, social and practical dynamics of teams of information workers in a health care environment. To the extent that workers’ tasks and the functionality of the information systems they use mesh well, these systems are accepted by their users and may demonstrate benefits measurable in terms of efficiency, safety and clinical effectiveness. Present computerized systems do not meet this challenge uniformly (Chaudhry et al., 2006) and are particularly poor at meeting the complex, multi-faceted information needs of nursing. Much of the nursing role in health care delivery is at the final delivery point of care. Nemeth, et al. (2005) call this aspect of health care delivery the “sharp end” of care. As these authors note “. .. sharp end knowledge is dense, complex, changes rapidly, and is embedded in a complex social setting that resists scrutiny by those who are considered to be outsiders.’’(2005, p. 19) One important aspect of a patient care information system (PCIS) is input and retrieval of electronic text, which we refer to as computerized patient care documentation or CPD. In this chapter, we will review CPD as practiced by direct providers of health care, focusing on nursing documentation. We will report on an ongoing investigation of CPD as it exists in a mature, complete and widely-deployed patient care information system, the United States Department of Veterans Affairs (VA) Computerized Patient Record System, known as CPRS. We will offer a preliminary analysis of these findings, taking a cognitive work analysis perspective, and illustrate our points with excerpts from individual and group interviews conducted in VA settings. Our findings will show important advantages of CPD for nursing in the VA and important problems, especially with the “task-technology fit” (Goodhue & Thompson, 1995) of CPD and nursing practice.

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We will review applicable medical informatics literature and present hypotheses about the origins of the current state of affairs. We will discuss the scope and purpose of nursing documentation activity, and make some short and long term suggestions for improving the “fit” of CPD with nursing work, especially in inpatient care. Improving the “fit” of the CPD system with inpatient nursing care is important. Patient safety has been shown to be highly correlated with nurse (especially RN) staffing intensity, as reviewed by Hinshaw (2008). It is generally accepted that availability to patients is important to safety. In addition, numerous studies of medical errors suggest that effective inter-staff communication is also crucial (Bhasale, Miller, Reid, & Britt, 1998; Coiera, 2000; Wilson, Harrison, Gibberd, & Hamilton, 1999). In hospitals, ward nurses play a critical role in both areas. The more time nurses spend being pulled away from patient and staff contact to work with CPD the less time is available for safety-promoting activities of communication and patient monitoring. Nurses’ work with CPD should be as productive and relevant as possible. While our present scope is limited to examining nursing usage of the VA’s CPD system in a single facility, many of our most important observations address fundamental cognitive and workflow issues that would apply to nursing documentation in any health care setting using a PCIS.

BACKGROUND CPD and its Place in the PCIS The term “Patient Care Information System” refers more or less to the generation of health care information systems that succeeded departmental information systems serving various operational sectors in health care settings, typically: patient registration, appointment scheduling, pharmacy, laboratory, radiology and finance. Three transitions characterized emergence of the PCIS from

Nursing Documentation in a Mature EHR System

these systems: integrated data access, provider order entry, and computerized patient care documentation, or CPD. Free-form narrative documentation is necessary informational “glue” that connects and coordinates health care work. Narrative, because it is immediately accessible to human thought, serves to supplement, integrate, interpret and communicate the structured data in an organization’s information system. In the 1980s, when putting this narrative component on line was first contemplated, resistance from users, especially those reluctant to type, was anticipated. With time, increased computer skills in the population, and establishment of the norm that health care professionals type, resistance abated. Full integration of structured data, narrative and order entry creates powerful synergy that brings the PCIS “to life”. The impact of having all documentation rapidly accessible at workstations, simultaneously viewable by many users cannot be understated. An information system lacking access to clinical documentation cannot be considered a “complete” PCIS, and a PCIS lacking a high penetration of direct user CPD entry cannot be considered optimal.

The VA CPRS: A Mature PCIS The VA CPRS system originated in the late 1970’s, driven by small groups of users with special interests united in a vision of an integrated hospitalbased system that supported business and clinical needs (Kolodner & Douglas, 1997, pp. 39-56). By the mid-1980’s, this system, based on relatively inexpensive “mini-computers”, written in the MUMPS (later M and Caché), and deployed with a standardized, portable data base management program, was established in over 160 VA hospitals across the United States. The initial “core” version served basic business functions: laboratory, patient registration and appointing, and the pharmacy. Extended “core” functionality addressed clinical needs in Medicine, Surgery, Mental Health and

Nursing. The earliest implementation of CPD functionality was found in a “Progress Notes” section of the Mental Health package. In the early 1990’s it was recognized that order entry and general clinical access to a documentation system was needed, and a text integration utility was developed. Nursing package development included a Vital Signs utility and a Care Plan. At this stage, the CPRS was implemented on “dumb” ASCII terminals. In the mid-1990’s policy, fiscal, and technological forces converged to accelerate general clinical adoption of a paperless PCIS. In 1997, in exchange for complying with Medicare requirements, the VA was permitted to bill private insurance carriers and facilities were allowed to recoup some of these billings. This drove documentation to support billing, and track diagnoses and procedures. This was initially accomplished via paper forms. Soon, though, plain text terminals gave way to client-server architecture and workstations and a graphical user interface (GUI) version of the CPRS was deployed. The GUI allowed viewing multiple data sources together on the same screen and offered a friendlier text editor. By the late 1990’s, billing and workload tracking policy had evolved to require documentation of all outpatient clinic visits. For inpatient care, the discharge summaries and procedure notes were the primary documents used for billing. By 2002, most VA Medical Centers and clinics had implemented paperless systems. A national data repository architecture allowed access to patient records at any VA facility in the country from any VA workstation. This system, with 180,000 clinical employees, including 61,000 nurses, provides care for approximately 4.5 million veterans annually. This rapid summary glosses the challenge faced by all VA clinical staff changing over from paper to computer documentation, but the challenge was met. VA has a highly computer-literate workforce that uniformly values the satisfactions of delivering care with this powerful tool. The VA has received repeated recognition for its accom-

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plishment, and regularly leads other large systems in indices of care quality (Perlin, Kolodner, & Roswell, 2004). It is recognized as a leader in deployment of patient care information systems, and may possess the world’s largest PCIS network in a single organization. Influenced by incentives in fiscal reimbursement policy and a corollary workload and resource allocation structure, the last 15 years of CPRS and CPD development evolved to provide system support for practitioners – not nurses. In the present arrangement, only practitioner services can be directly billed. Notwithstanding, nurses in clinics and wards adopted this CPD system as well, because it had enough flexibility to address basic documentation needs, and VA nurses mastered the computer skills to use the system regularly.

Background on CPD CPD includes electronic progress notes, discharge summaries, consult reports and other text documents that until recently were on paper. In addition to direct clinical use, CPD serves important administrative functions, including billing, quality review and information exchange between institutions. Input methods for CPD vary and include dictation, input assistance through templates, direct typing of narrative, and insertion of stored patient data. In the VA system, dictated entry accounts for less than 1% of CPD. VA data bases currently store approximately 1 billion patient care documents nationwide.

Background on Nursing Documentation and the Nursing Process Inpatient nursing documentation consists of assessments, treatment and response flow sheets, periodic interval notes (every shift, daily, etc) and care plans. In some settings, these documents resided in a nursing care section of the record, but in others, such as the VA, electronic nursing

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documents are mingled with the document from all authors. Outpatient nursing documentation includes patient screenings, telephone contacts and follow-up notes, and these are typically interspersed in the general “progress notes” record. Our focus will be on inpatient nursing documentation. Since the 1980s, documentation of “the nursing process” has been influenced by nursing theory. Most recent efforts to systematize nursing documentation incorporate specialized language associated with nursing theory to describe identification of clinical problems, treatment goals and interventions managed by nurses. This effort has not been altogether popular with nurses or other health professions, and considerable difficulty implementing documentation based in nursing theory has ensued. Not the least of these is that the specialized language incorporated in the nursing documentation is difficult for other professionals to understand.

Nursing Document Creation Tasks As noted in the introductory section, document creation tools used by nurses were the same as those developed to support office based practice. Documents in the VA system are stored as ASCII text. Customized nursing content is accomplished by using document templates to build blocks of text that are then inserted as unstructured text into a document data structure which can be edited before saving the note. The initial implementation of nursing documents (assessments, care plans and other charting) involved templates consisting of editable checklists and boilerplate presented to authors. Later versions of the template tool supported selecting items from a dialog box.

Nursing Assessment Patient assessment documentation occurs with each inpatient episode. In the first 7 years of use, assessments consisted of checklists customized to each unit. The limitations of the checklist ap-

Nursing Documentation in a Mature EHR System

Figure 1. Nursing assessment template

proach are evident: meeting the requirement to use a “complete” assessment with standardized problem-specific language faithful to nursing terminology required a lengthy checklist. Applicable items are marked with an “X”, but unchecked inapplicable items often remain in the resulting documents, as shown in Figure 1(a). A great disadvantage of the resulting documents was that the “dumped” checklists were unreadable and generally ignored. Documents met administrative and theoretical requirements, but were not

perceived to improve nursing performance in rendering care. The most useful parts of these documents were the nurses’ free text, which tended to be buried in the boilerplate, as shown below in an “Assessment” paragraph (Figure 2.) from the same patient.

Nursing Care Planning Documentation of nursing care plans has evolved, and in the setting we studied, written care plans are

Figure 2. Nursing assessment free text

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a much less prominent part of nursing documentation. Efforts to develop support for a computerized structured nursing care plan in the 1990’s were largely dropped because of difficult usability of the system. Figure 1(b) shows the intervention section of a care plan created in 2003. Our nurse informants told us that while care planning still occurs, its documentation is less formalized. The diminished role of formal written nursing care plans is noteworthy. For at least a generation, in pursuit of a professional identity for nurses distinct from physicians, academic nursing has emphasized development of the “Nursing Process” and attendant terminology systems of nursing diagnoses, interventions and outcomes. Nursing classification systems have been prominent, if not predominant in the nursing informatics literature. Numerous computerized nursing care planning systems have been developed, including one for the VA that was largely abandoned a decade ago. The preceding figures illustrate the streamlining that has occurred in recent years. The more

streamlined version contains more free text and less white space.

RESEARCH FINDINGS Motivation The goal of our ongoing research has been to increase understanding of computerized documentation in order to identify “valued” characteristics of patient care documents that might facilitate their retrieval for the benefit of users. A secondary goal is to use insights gained to shape recommendations for improving current and future versions of the VA’s CPD system. An important part of this research effort has been a qualitative investigation of CPD users’ experiences using the VA CPRS system, specifically its practitioner, nursing and administrative stakeholders. We believed that determining document quality from an information theory perspective

Figure 3. January 2003 assessment document

Figure 4. December, 2007 Version of assessment document

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requires understanding what health care workers experience when creating and using CPD in their daily work. Accordingly, our investigation focused on users’ experiences conducting typical work tasks. Our analytic perspective is that of cognitive work analysis. (Cam, Efthimiadis, & Hammond, 2008; Rasmussen, 1994; Weir et al., 2006). We are reporting initial findings from this work, focusing most on what we learned from nurses at the first of five VA sites we will study.

as a general approach to help information system designers analyze and understand the complex interaction between (a) the activities, organizational relationships and constraints of work domains, and (b) users’ cognitive and social activities and their subjective preferences during task performance (Vicente, 2003). The framework is the result of generalization of experiences from field studies that led to the design of support systems for a variety of modern work domains, such as process plants, manufacturing, hospitals, and libraries. The CWA work-centered approach to the evaluation and design of information systems assumes that information interaction was determined by a number of dimensions. To facilitate an evaluation, a framework for cognitive work analysis is constructed. This analysis addresses dimensions such as: task situation in terms of work domain, decision making, and mental strategies that can be used; the organization in terms of division of work and social organization; user characteristics, resources and values. CWA in health care is not new. Over the past fifteen years the methodology has been used in the design of clinical displays, clinical information systems, medical equipment, and modeling intensive care unit patients (J. Effken, Loeb, Johnson, Johnson, & Reyna, 2001; J. A. Effken, 2002; Miller, 2004; Rasmussen, 1994; Vicente, 2003; Weir et al., 2006).

Methods We began interviewing staff (practitioners, administrators and nurses) at the VA Puget Sound Health Care System individually and in separate focus groups in May, 2007. Nurse participants (described by role and location in Table 1) were recruited by asking for volunteers with the assistance of the chief nurse executive’s office, and thus represented a non-random convenience sample. 24 nurses were interviewed individually or in focus groups. We also interviewed 25 practitioners and 12 administrative staff at the same site. 83% were inpatient nurses, a proportion similar to that found in the medical center. Compared to outpatient care, inpatient care is more document-intensive. Patients who only receive outpatient care typically accumulate ten to thirty documents per year. Inpatients usually accumulate more than 100 documents per week. Our methodology was informed by the Cognitive Work Analysis (CWA) conceptual framework for work-centered evaluation and design (Rasmussen, 1994). This framework has been developed

Procedures We began interviewing VA staff (practitioners, administrators and nurses) individually and in

Table 1. Roles and locations of nurses interviewed Inpatient

Outpatient

Direct care

Support

NP

RN

LPN

Focus Group

17

3

17

3

2

17

2

Individual

3

1

3

1

1

2

20

4

20

4

3

19

83%

18%

83%

18%

12.5%

80%

17%

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focus groups in 2007 to learn about usage of computerized patient care documentation. Subject recruitment was assisted by the chief nurse executive’s office, and thus represented a non-random convenience sample. Of 19 nurse participants, 80% were inpatient nurses, a proportion similar to the nurse distribution in the medical center. Of the non-inpatient nurses, one worked in interventional radiology, two in a same day surgery unit and one provided administrative and clinical support to line nurses. Missing were the perspectives of outpatient, community care and telephone care nurses. The skewing of the sample toward inpatient care is also reflected in document statistics. In our document data set we found that patients who only receive outpatient care typically accumulate ten to thirty documents per year. When admitted to a ward, patient documents accumulate at a rate of over 100 documents per week. All subjects gave informed consent to be interviewed. 30-minute individual interviews were conducted to pilot the focus group procedure. These interviews were recorded, transcribed and anonymized. From the individual inteviews, we developed a, semi-structured interview script for the focus groups which asked participants to identify key tasks associated with using CPD; to discuss benefits obtained and barriers encountered; and to describe the work strategies used to overcome the barriers. One-hour focus groups of 8-15 participants each were conducted, recorded, transcribed, anonymized and analyzed.

Analysis Analysis consisted of reading transcripts to identify key nursing tasks and common themes emerging in the interviews. Preliminary theme identification was achieved by discussions held by the investigators.

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Results We found that the VA’s CPD system was valued and accepted by all the stakeholder groups. Users were well-trained and had excellent computer and typing skills. Most had four or more years of experience using the system. Direct service providers reported using documentation functions between 30 and 40% of their total working day. A striking difference found was that practitioners and administrators found the CPD system to fit much better with their work flow than nurses, especially nurses providing inpatient care. Perhaps the most useful way to present these preliminary results is to tell a series of “stories” about the nursing experience with CPD.

Nursing CPD Tasks Identified The topic and scripting method yielded rich and affectively open responses about nurses’ use of CPD system. Eight sub-tasks in three categories emerged. The first group of tasks concerned the creation of documents. Sub-tasks identified included writing: (1) Assessments, (2) Care Plans, (3) periodic progress notes and (4) summaries (discharge, transfer, and interval notes) documents, all of which corresponded closely to traditional Nursing Process Documentation. The second group of responses involved processing information. Sub-tasks identified included (5) reading documents written by core members of the clinical team and peripheral sources (consultants and off-unit therapists) as patient care episodes unfolded, and (6) reviewing, as time permitted, documents from previous inpatient and outpatient care episodes. The third task category identified involved sharing information: (7) integrating document information with other patient care process information and (8) communicating (primarily verbally, but occasionally as a written addendum to a note) processed document information with

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other members of the care team, especially, but not exclusively, with other nurses. Nurses reported spending about 30% of their shift performing these tasks related to documentation. We estimated that they spent two thirds (20% overall) of this time writing documentation, and one-third (10% overall) reading, reviewing, integrating and communicating information in the continuously flowing narrative stream characteristic of an inpatient ward. Below we will describe details we learned about these tasks.

Care Planning Care plan documentation followed a similar procedure, as shown above (b). Documents produced at the time care planning was mandated used standardized nursing language, but did not have an “individualized look and feel”. Nurses, at the time we interviewed them, did not hesitate to criticize this: Nurse:There’s been a bit of a controversy of what sort of notes nurses should use… There’s been an informal expectation that nurses should all use the same format… But as practitioners, unless it’s mandated (like you won’t have your job), practitioners aren’t going to do that. …They find the part of the system that works best for them, with that individual patient…So, you can’t cookie-cut: a patient that’s first-day post-op, and a person who’s tenth-day post-op, have very different needs, and different documentation needs as well… One unanticipated finding was that all members of the care team, including the nurses expressed lack of interest in reading nursing notes. Administrative personnel, such the coder below, also reported little use for nursing documentation. Coder:Typically, to look for procedures I won’t initially click on a nursing note; mainly I’m going to go to – for example, here’s a medicine attending note: I’ll kind of want to glance at that, to see if there’s anything there that I might want to

note… Sometimes nursing notes do give me what I need – they’ll insert a foley into a patient – and I will look for that from time to time, but for now I’m just going to click on the notes where I see the physicians for now – I might need to go back and look at nursing notes later, but for now I’m going to click on physicians and look at those… The awkwardness of the template approach to nursing documentation was eventually recognized. Notes containing checklists with empty items were especially difficult to read and users of all categories soon learned to ignore them. About six years after introduction of the paperless system a more compact and readable version of nursing notes and plans was implemented. At the time of our investigation, the VA center we studied had used a paperless system for 10 years. One striking finding was that written nursing care planning, conventionally considered the core of the Nursing Process, played a diminished role in this “mature” system. The only units where formal written care plans persisted were psychiatric programs, where multidisciplinary treatment planning remained mandated by an accreditation agency. On medical and surgical units, nurse informants reported that formal written treatment plans were no longer the norm, but but created by exception, when necessary. The following remarks trace a nurse’s perception of how written treatment planning became streamlined. Interviewer: Do you all do daily charting and treatment planning? Nurse 1: Not so much treatment planning. Interviewer: OK, so explain more about that. Nurse 1: I think last year they actually had on one of the menus you could bring up ‘care plan’. Nurse 2: That’s the way we did it three years ago.

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Nurse 1: That sort of disappeared. And so they actually have one of the books… (laughter) Nurse 2: They actually have one of the books in the references where we can pull it up, look at care plans and hopefully get to use that - copy and put it in if we need to - but we are basically just making it up. Interviewer: So would that be part of your daily charting? Nurse 1: It used to be, and in a lot of facilities it’s still the standard that you do have an active care plan and you put these things in your notes saying ‘this is what we’re working on’. Nurse 2: There used to be a nursing standard that you always had a care plan. Always. And then about 3 years ago we were going through and cleaning up the notes because there were thousands and thousands in there and they weren’t being used and so at that point we had another little group get together and we went through the care plans and we said OK let’s go and change the formatting and everything and so they took them away and never put them back. [Author emphasis]. Interviewer: But you’re not doing that anymore? Nurse 1: Occasionally, because I’ll sit there and I’ll type it out, but it’s a lot of work to sit there and type these things out.

Charting and Summaries Nurse informants did not reject all standardized approaches to documentation. They valued the mnemonic properties of the checklist approach because it encouraged completeness in a variety of critical situations.

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Nurse: We have an admission/assessment template; we have a discharge template; we have a transfer template – those would all be considered when patients are changing not location as much as they’re changing clinical status. So, if someone’s being admitted, they’re just starting they’re pre-op work-up. But if they’re being transferred out of the unit, their condition has changed – you’ve gone to surgery, gone to critical care, coming back to the ward; or they’ve had surgery, they’ve been here, and they’re going into SICU – so, those three there’s a definite template to use. Nurse: As far as a patient who might be twodays post-operative, a nurse has a variety of notes she can choose from to document. She can document on a daily assessment, or a progress note – we’re trying to standardize it, but nurses still have choices. Despite the awkwardness of the Patient Assessment tool, they used it consistently for guidance, even if the notes created no longer resembled the structure of the parent template. Interviewer: … All right, we’ve heard about the problems. How is it helpful? Nurse 1: Well sometimes there are so many things we have to cover and so many details that it helps jog your mind on all the areas that you to cover. You can’t forget anything. It won’t let you. You can’t sign it if you miss them. Nurse 2: So, when you go into the nursing progress note, you can cancel out the SOAP format, and make it a narrative – all systems drop off. Which is a nurse’s choice. They also found ways to bypass limitations of system design inherited from office practice, such as a twenty-minute time-out for completing a note.

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Nurse: Well, one thing that’s not helpful, I mean the admission template. Once you start it you can’t really pause – or you’ll lose your information that you wrote…Lose the entire document…. Interviewer: So, if you’re called away to do something or if you have to communicate with someone or something like that it… Nurse 3: And even if you want to explore something further with the patient – say, for example, even in extended care admissions – part of admitting a patients …is allowing the patient the opportunity to … just be able to tell their story. And it’s amazing the healing process occurs in just being able to get that out. And having someone hear them and listen to them. You don’t have time. The computer will cut you off. Nurse 4: It’s very difficult with the admissions. The template kicks; it won’t really let you out. You have to finish that.You have to finish any template to completion. You cannot stop and go do anything else. You must complete a template in one sitting. Nurse 4: That’s why we have this copy in Extended Care Home. That we have this blank copy hooked up to a pseudopatient. And when you’re done writing it down personally for a patient, you go back and now it’s safe. [Author emphasis]. Interviewer: Were there other shortcuts or ways that you- strategies you use so that you can try to get your work done? Nurse 5: Well sometimes you can put, what I do, for admission assessments is open two CPRS sessions at the same time. You have to flip back and forth.

Barriers: Document Creation Burden The 20% of the day spent creating documents was universally addressed with feelings of frustration

and even sorrow at having to spend time away from patients. One nurse remarked: Nurse: For us… being on the floor as the charge nurse you’re all night – an octopus with lots of tentacles… Then you have only 8 hours … and financially what the VA is saying ‘you have too much overtime!’ … That’s our problem also, we have to … be at all the patients’ bedside. We need to get all. But it’s known, we have to put it on the computer…. And I didn’t even learn how to type long time ago! It’s really hard to put all those hours in that little table. Without going to overtime. Or we don’t, really, and you feel guilty if you are not able to do, to put everything that is there. Because they will say ‘ooh, that’s only one patient, you put your 8 hours there?’ Oh my God! Interviewer: You feel guilty? Nurse: You feel guilty because you didn’t put everything in that you’re supposed to. You know what you did at your patient’s bedside. One consequence of the document burden is that it steals time from other traditional nursing tasks. In the VA we studied, one consequence of work reallocation was the disappearance of the nursing treatment record. The nurse below pleads for computer support integrating documentation of treatment with the treatment action plan found in the “Kardex”, a capability yet unsupported in the VA computer system. Interviewer: Can you explain the Kardex…? Nurse 1: Sure. The Kardex, first of all, is not part of the medical record. It’s a pre-printed document that nurses write-on in pencil, about the daily changes in a person’s care plan, driven by the order set. So, a person is coming to 3 east, and the nurse takes their order set, and pencils in categories of those orders onto the cardex. So, for example: diet, activity, respiratory treatment, IV fluids, consult,

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procedures, labs. All the stuff you see in the order set is transcribed via pencil onto the cardex, to be used as a guide and report guide…

what’s going on with the patient. And how you see his behavior when he comes to the ward. If he’d be dangerous. All those important details.

Nurse 1: The other part is that there’s no treatments that you can document – nursing treatments are still done on paper as well. Well, what happens is – I’m supposed to be telling the truth – we don’t put it on paper. [Author emphasis] So, things that used to be documented, when there was no computer and we were a paper chart, we had a thing called treatment sheets, and things on there were foot care, wound care… Well, we don’t have anything for example that exists to document those things in the computer. [Author emphasis]

Interviewer: So, does that make you feel safer? More powerful to have this information?

So, what’s happened is those have all fallen-off, because everyone is doing everything in the computer, except the Kardex, the treatment sheets… the treatment sheets have fallen totally into a black hole. [Author emphasis] I’ve given up – I’ve tried for a couple of years to get that on the top of the list, we need to get this in the computer so people are actually compliant, but now I take the energy that I had to divide through all these various duties and what I want people to continue doing on paper is the Kardex, because that’s driving the report system.

Reading and Processing Notes One unexpected finding was strong voicing by nurses that the entire patient care documentation record was of immense value to pursuing their work, despite general readability challenges of boilerplate, inserted data and checklists. Nurses described the power of being able to access a large volume of patient documentation. Nurse: We do chart review; not just when the patient is being admitted. When they’re being admitted to us, we go back on discharge summary, way back until, you know years later, or months later and then we see how long is the history of

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Nurse 1: I think so (several voices agreeing). Yes. You know what to expect. Nurse 2: The other thing is that we see something in there that doesn’t look right in the note we’re going to talk among the staff and discuss it with them and say “you know, in your note you said this patient blah blah this and we saw something totally different”. So if you bring that around where everybody else can see to that there’s two different things going on that everybody else can see. And your seeing one side and a lot of times you do. Because of the shifts – the time of day. Nurse 1: [what] you read in the ER … it’s not the story they give us when they’re on our unit. They want to come to our unit. So that what they said over there is not what they tell us when we sit down with them. Nurse 2: And in the same thing, what they say to me, or to her can be completely different from what they say to the physician in order to buy more time to stay on the ward. Interviewer: So is that useful that all that information makes it into the record? Several voices:Yes. Nurse 3: Because it creates more balance. Both nurses and physicians noted that authors’ “original thoughts”, often difficult to locate, were valuable. A nurse stated:

Nursing Documentation in a Mature EHR System

But what’s interesting as you look at the record as a whole, even though some people have kind of an aversion to the narrative, the narrative often captures more of the data – all they say is what’s happening; you don’t have a lot of the miscellaneous stuff. A physician nicely summarized the cognitive task of reading documentation: Yeah, so going through the template, you’re editing the template in your eyes and in your mind to get all the boilerplate out of the way and actually see the two or three lines that might be in the note. The boilerplate gets in the way. You’ll find that some people have 98% boilerplate and 2% fresh thoughts… Searching a note for fresh input is very challenging.

Sharing Information The above quotations begin to illustrate that with sufficient nurse-nurse and nurse-team contact, insights from readings of the documentation are eagerly shared. When we began our series of interviews our expectation was that the typical document-related tasks for nurses would consist of creating and reading documents. We soon heard repeatedly that seeking, processing and sharing the information in documents were equally important. Interviewer: Do you use the documentation system for preparing for rounds? How do you prepare for rounds? Nurse 1: Basically, by the time the teams are making rounds you know your patients. When we first come in at the start of the shift we’ll review almost all of the patients records. We’ll review the last note or so, we’ll review our medication, we’ll look at their orders and get to know it that way. Plus we do a face to face handoff when we do our reports. We sit there, we talk and we tell: “this is what’s going on. This is the history” and

that kind of thing. Because for a lot of the patients in the Intensive Care Unit they have a huge number of notes for their stay. That for us to sit there and read through everything in order to get the patient we’d never get the patient. So we do a really brief face to face handoff we don’t use CPRS for that. We use it when we admit a patient. We get a call saying somebody’s coming in. We pull up usually the most recent history/physical notes by the surgical team - why this patient’s coming in - that type of a thing. Interviewer: How would you describe that activity? Nurse 1: Most of us work 12 hr shifts. So by the end of your 12 hr shift you know your patient fairly well, and so we don’t refer back to CPRS during the course of the shift. We’ll go in and we’ll read patient history, to understand how long has this been happening…. Nurse 2: …We use it as a reference. It’s a reference tool. We read it to go in and find out about the patient. What kind of meds he’s on, how in the heck this happened to him, histories of things like bedsores, IV’s and procedures that have been done. Other nurses commented: Interviewer: Do you also read – you’re writing the admission notes – do you read others? Do you read the admission note from the prior shift or… Nurse: Oh yeah… Interviewer: So you end up reading the admission note for your patients even if you’re not the one that’s writing it. How efficient do you find the notes when you’re reading them. The admission notes or other notes. How efficient or effective are they? Nurse: I think it’s very good because you get some information that you might not otherwise

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have time to get or you’re not there to get it or the next day you’re there, I mean, you want to know something about that patient that was admitted all you have to do is go back and read the note. Interviewer: Do you have time to sit down and do that? Nurse 1: You have to make time. (voices agreeing). Most days. Nurse 2: Because, you’re thinking of the patient. Nurse 1: If I’d been off for two days and we had an admission on my day off, I’m not going to get a complete detailed report that I would get if they had just been admitted and I was just there and I was in report to hear it. So, if I want to know what’s going on, I’m going to have to go back and read the progress notes. Nurse 2: Because she’s caring for the patient she’s accountable for that information whether she’s there or not.

Using the Document System to Aid Communication Another unexpected aspect of communication was nurses’ adapting a capability in the documentation system to draw others’ attention to a particular document. Designed to support multi-authored documents, the tool is now more frequently used as a communication device. N: What I find really helpful is that if something important happens over the weekend or on an off shift staff will add team members, people that are on the team as an additional signer. And so when you get into the system it comes up as a note that needs your attention. The nurses also speak of using the “addendum” feature, originally designed to allow correction

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of errors in notes, to coordinate team knowledge and planning. Interviewer: What about addendums? Is that like a little messaging back and forth? Is that something you use? Nurse 1:Yes Nurse 2: We use it a lot. Nurse 1: Nursing staff use it. …if an LPN has assessed a patient and has called my attention to the patient assessment: “see what you think about this.” I will make an addendum to the LPN’s note that says “met with patient concur with above note and assessment”. To let the reader know that an RN is also involved in the care of this situation. And that the LPN’s assessment is correct. Nurse 2: Also to let the LPN know that you’ve been in and that your taking an action.

DISCUSSION Our original research goal was to increase understanding of the documentation process from the perspective of the practitioner, nursing and administrative stakeholders in order to identify “valued” characteristics of individual patient care documents in order to facilitate their retrieval for the benefit of users. A second goal was to use the insights gained from stakeholders to make recommendations for areas of design improvements in a future version of the CPRS system. Serendipitously, our initial investigation of user work experience with CPD reveals clues that warrant recommendations about how to think about the current CPD process, and suggests improvements that can be made using the configuration capabilities of the existing system in its present form. Perhaps not surprisingly, these recommendations have more to do with how and

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why the CPRS CPD system is used than changing the system greatly. In some ways, it would be easier to effect such changes under the cover of a major software change. Lacking that, we will doubtless confront the organizational currents that have fostered the present philosophy driving CPD usage. In the following discussion, we hope to demonstrate that nursing documentation is an especially apt area for incremental, but helpful, change in the VA CPRS system we have examined. Certain opportunities and dynamics in the nursing arena make this so. Not the least is that nursing manpower is a scarce and precious resource that must be optimally managed. Our research is ongoing, and the work reported here is in the pilot stage, but some strong themes identified permit useful discussion now. We anticipate that as our study proceeds we will identify important areas where technology change can further enhance nursing performance and won’t hesitate to make recommendations. We hope that keeping our focus on the real work of nurses will avoid the distractions of technological innovation. We assert, and hope to demonstrate with the results of our investigation so far, that a great portion of nurses’ work is cognitive and social, and not well captured in the documentation that the VA nurses create. Further, we expect that the principles we have identified are applicable across all settings, especially, to those deploying nursing informatics and nursing documentation systems using patient care information systems other than the CPRS. One consequence of examining a well-established, rather well-functioning integrated system is that the “noise” of a new system deployment has dissipated, and that some of the key issues that remain have more to do with management philosophy and policy than technology. These same issues, because they arise from nursing practice and human behavior, are likely to be evident in other patient care information systems, and may benefit from a similar review of the goals and costs of deployment decisions.

Issues Related to “TaskTechnology Fit” Our findings show that nurses have adopted a system not designed for them, but they have nudged its evolution toward greater functionality through creative workarounds, and no small measure of computer skill and sophistication. In so doing, they participate as peers in the information flux that surrounds an inpatient care episode and add value, by contributing their perspective to the treatment team in a variety of ways. Their experience also demonstrates Varcoe’s point (Varcoe, 1996) that the technology for documentation is still not a good fit with many aspects of the nursing process. This stems, at least in the VA case from the absence of documentation tools (such as the capability to document and track treatments) that are specifically needed for nursing. It would seem that they have done about as good a job as they could in adapting a system designed for office practice, with computers at the practitioner’s desk and five minutes between patients to write an outpatient note. Our interviewees have commented poignantly on the difficulty of having to pull away from the patient’s bedside to do their documentation. A second consequence of using system designed for office work is that the VA CPD system forces nursing documents into the general document space (a “Notes” section of the CPRS). By default, this space (figure below) displays notes chronologically, with title and author information. Clicking allows only one note at a time to be opened. VA users in all work categories report that browsing to track the flow of care or find a needed piece of information is difficult. This creates difficulty when a user wants to focus on a particular theme. One physician complaining about the difficulty of finding consultation reports remarked: All these consults, they get buried in the notes section – the progress notes. Everyone writes a

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progress note – the nurses write 2-3 notes a day; the physicians write, if they’re in the hospital, they write at least a note a day; PT, OT, everyone who has something to do with the patient writes a note! Nurses experience difficulty with aggregation of documents as well, because it makes tracking the flow of nursing care challenging. Despite the barriers, nursing notes are valued by other caregivers. More than one nurse commented on positive feedback from physicians and others about the value of the nursing persepective in understanding the patient. In inpatient care, where nursing care is so important to the patient’s safety and survival and nurses have the most contact with patients, the evolving flow of nursing observations should be especially supported. A separate section of the chart devoted to the flow of inpatient nursing care would avoid diluting the nursing documentation contribution, and make it easier for nurses to follow as well. In a non-VA setting, Campion et al. (2007) analyzed usage of a tool built to produce a group of documents selected from the note space for the purpose of handing off the patient to another provider. The tool was designed for practitioner use, but unanticipated use by nurses and other staff was frequent. Among all the categories of staff who selected documents, the authorship of the selected documents was similar. Practitioners authored 90% of the documents they printed and Nurses authored only 8% of the documents they printed. Campion concluded, that for the purposes of sign-out, practitioners were “producers”, and that nurses and others were “consumers” of signout content. A broader view of using the “document space” was offered by a nurse we interviewed. Interviewer: “Do you use the documentation system for preparing for rounds? How do you prepare for rounds?”

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Nurse: Basically, by the time the teams are making rounds you know your patients. When we first come in at the start of the shift we’ll review almost all of the patients’ records. We’ll review the last note or so, we’ll review our medication, we’ll look at their orders and get to know it that way. Plus we do a face to face handoff when we do our reports. We sit there, we talk and we tell: “this is what’s going on. This is the history” and that kind of thing. Because for a lot of the patients in the they have a huge number of notes for their stay. That for us to sit there and read through everything in order to get the patient we’d never get the patient. So we do a really brief face to face handoff. We don’t use CPRS for that. We use it when we admit a patient. We get a call saying somebody’s coming in. We pull up usually the most recent history/physical notes by the surgical team - why this patient’s coming in - that type of thing. We found this to be a recurring theme among inpatient nurses interviewed. The documents and other data in the PCIS provide a rich information resource which ward nurses absorb, verbally summarize and integrate into the care process. This constitutes the “juice” of nursing care information processing. It is efficient, fluent and current. We propose that “nursing documentation” serves more as a shorthand acknowledgement of care processes than a definitive journal or formulation of the care given. Berg (2002, p. 34) anticipated this “undocumented” aspect of clinical documentation when commenting on furnishing full access of the electronic health record to patients: Health care work requires continuous communication and coordination between professionals around and about patients; they order their thoughts in the record, and discuss potential hypotheses and next steps to take in the record. In this sense, records are working notes, written for a small group of peers or even only for oneself…

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Consequently, it can be easily predicted that any directive to really give patients complete access to the record will immediately result in a ‘cleaning up’ of these records, and in the emergence of a new layer of ‘informal’, ‘grey’ record keeping, for personal usage only. Berg takes a physician’s viewpoint here, but it appears that VA ward nursing practice has finessed the problem of lay people misunderstanding documentation by producing “cleaned-up” documents and communicating much of the essence vital care information verbally. In a previous paper, (Berg & Goorman, 1999, p. 51) he and Goorman remark on the “ (im)possibilities of the utilization of primary health care data for secondary purposes such as research and administration.” He makes the strong point that “work is required to make data suitable for accumulation. We might phrase this as a ‘law of medical information’… the more active the accumulation, the more work needs to be invested.” Our research observations of the documentation process show the impact of institutional demands on clinical documentation to support budgets, billing, quality reviews and compliance with government regulation. Of all document authors, nurses may experience the greatest pull between meticulously journaling patient progress for a variety of institutional demands and attending to patients. In the VA setting we studied, it seems the pendulum has nudged slightly toward institutional recognition that nursing documentation is but a part of a broader, vital communication process. We see hints that the stored nursing “documents” are regarded by most caregivers, including nurses, as impoverished “footprints” of nursing care. Recognizing and adapting to this has taken most of a decade. Was anything been lost when formalized nursing care planning was abandoned? Given the importance of care planning and nursing terminology to the academic side of the nursing profession, one might think so. Curiously, instead, we now see

many examples of nursing concepts persisting in nursing documentation. Nurses, when they decide that it is important, still write care plans. Trained to use a contemporary conceptual framework, they document care plans when they are helpful, and seek on-line references for special situations. In the VA Nursing care planning continues as part of nursing care, but no longer consistently appears in nursing documentation. In the next section we will further discuss the forces that brought this situation about, and discuss directions our thoughts on how nursing could be more effectively integrated into nursing care processes.

Issues Related to the Nursing Process and CPD Nurses also criticize nursing process-based documentation as burdensome. Varcoe (Varcoe, 1996) makes the point that the problem with theory-based nursing documentation may not be with the theory, but with its implementation. Lest nursing feel singled out, we should point to similar difficulties for physicians in strictly adhering to Weed’s guidelines for a Problem-Oriented medical record, and similar efforts in mental health documentation based on the Diagnostic and Statistical Manual of the American Psychiatric Association. In all cases, it seems that a completely reasonable theoretical framework translates poorly to the information management tasks required for patient care. Goosen, et al. (1997)summarized numerous reports of problems with introduction of computerized Nursing Process oriented documentation. Some of these included the possibility that information systems negatively affect nursing practice and nursing identity by creating tension between the aspiration to deliver “individualized” patient care and the goal of using “objective” language. Vivid terms used by Harris (1990) to describe this effect are “de-autonomisation”, “de-individualization” and “de-professionalization” and “one size fits all”. Harris’s qualitative findings from 1990

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anticipated many of the themes we heard in 2007. One of the nurses Harris interviewed stated: The only nursing diagnosis that we can come up with for GI Bleed is … Loss of Body Fluids. That’s not quite it, but that’s the idea. And that doesn’t describe a GI Bleed. And yet, if you put that in there, “GI Bleed,” that’s a medical diagnosis [which is not acceptable] (Harris, 1990, p. 70) Another observation Harris made was that nurses perceived the computer process to facilitate supervisory surveillance of work output and control of personal expressiveness. Laduke (2001)noted problems with nurses using and understanding the language to describe nursing issues and the complexity of the concept space, stating, “While the created documentation system was well-grounded academically, we soon discovered it was too complex for bedside nurses… The large number of care plans made it difficult to become familiar with the contents and distinguish and the nursing diagnoses and interventions in one care plan from another.” Other complaints noted were the time-burden of document entry and a lack of nurses’ conviction that the documents they created were useful to others or clinically relevant. Allan and Englebright (2000) reported an effort to improve acceptance of computerized nursing documentation by simplifying tasks and lists. At baseline, “Nurses were frustrated that, although they were spending more time on documentation, chart audits continued to find discrepancies and omissions in the documentation. Physicians were complaining that nurses were spending more time with the computer than with patients. Managers and administrators were concerned about rising overtime costs and the lack of a reliable medical record” (Allan & Englebright, 2000, p. 91). LaDuke (2001) reported that nurses were as appreciative of efforts to simplify the system and there was a decrease in overtime. Ammenwerth et al. (2003) reported a controlled longitudinal study of acceptance of nursing

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process documentation on four wards. Two were psychiatry wards, one was a pediatrics ward, and one a dermatology unit. Over the period of the study, acceptance of the theoretico-professional “nursing process” was unchanged by users. Acceptance of computers in the clinical setting was also essentially unchanged. Acceptance of the computerized document system was stable and “good” on three wards, but in the pediatric ward was lower, and worsened with time. The author noted that the pediatric ward required nurses to spend much more time at ill infants’ bedsides, and that the required detail of computerized document was incompatible with the work pattern. The author also noted that computerized documentation required nurses to remove themselves to an office to complete their work. On the pediatric ward, nurses complained of doing double documentation, once on paper notes they kept with them in their rounds, and once again transferring this information to the computer. The burden of “double entry” was mentioned in numerous reported studies. The most complete review to date of the impact of nursing process-based care documentation on nursing practice and outcome concludes that these documentation systems completely fail to demonstrate evidence of health outcome benefits. Currell and Urquhart (2003) conclude: It would seem that the relationship between information, information systems and practice is poorly understood by practitioners and researchers. The questions addressed by the excluded studies and those rejected on methodological grounds were predominantly concerned with administration rather than clinical practice. The studies demonstrate the tension for practitioners between meeting the needs of clinical practice and the needs of management and administration. The results of the review suggest that the design of record systems to actively support nursing practice is a complex task, requiring synthesis of the knowledge of nursing practice, nursing organisation and nursing informatics. … Nurses in practice now

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need to be ready to share information systems as well as information, with their patients and with their medical and health care colleagues.

FUTURE TRENDS Considering the bumpy progress of computerized nursing process documentation, we are cautious about making sweeping recommendations. The current picture suggests the following changes in current CPRS CPD use can be made now. 1. Allow as much free text as possible in nursing documents 2. Keep nursing process language available to nurses as an on line reference but be parsimonious about its use in documents frequently viewed by non-nursing treatment team members. 3. Consider separating nursing documentation from general documentation to improve viewing the flows of care from both of these important perspectives. Changes to consider for the future include: 1. Improved placement of computer equipment in patient rooms dictated by workflow ergonomics. For example, nurses viewing monitors should also be able to face the patient and instrument displays for the patient. 2. Develop mobile “smart clipboards” to capture treatment actions at the point of care. This almost certainly awaits deployment of portable mobile devices and software so that tracking treatments does not “steal time” from vital nursing activity. 3. Develop team based, multi-patient displays (“whiteboards”) to assist nursing handoff and planning activity (Xiao, Schenkel, Faraj, Mackenzie, & Moss, 2007).

4. Develop mobile voice input and playback capabilities to allow asynchronous verbal communication among members of the nursing team.

CONCLUSION Nursing documentation is important but can be improved, as our research in a mature setting has shown. More effort is needed to understand and exploit the benefits of staff to staff safetypromoting documentation that CPD in a PCIS provides. Consideration of the social aspects of nursing team coordination is important. In the PCIS studied, documentation of nursing treatments is haphazard and probably awaits better data capture at the point of care. Given the nursing manpower shortage, it is likely that more resources will be devoted to improving the efficiency of nursing process documentation.

ACKNOWLEDGMENT This work was supported in part by the United States Department of Veterans Affairs Health Services Research and Development Service, Investigator Initiated Research Grant IIR 05-019. The opinions and conclusions in this chapter are the authors’ own, and do not reflect official U.S. Department of Veterans Affairs Policy. All research was conducted under the auspices of the University of Washington Institutional Review Board, Committee V. We also acknowledge support of the University of Washington Information School and the following individuals who made valuable individual contributions to this work: Kenneth M. Cam, Ashley N. Hedeen, Peter J. Embi, and Stephen M. Thielke.

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REFERENCES Allan, J., & Englebright, J. (2000). Patientcentered documentation: An effective and efficient use of clinical information systems. The Journal of Nursing Administration, 30(2), 90–95. doi:10.1097/00005110-200002000-00006 Ammenwerth, E., Mansmann, U., Iller, C., & Eichstadter, R. (2003). Factors affecting and affected by user acceptance of computer-based nursing documentation: Results of a two-year study. Journal of the American Medical Informatics Association, 10(1), 69–84. doi:10.1197/ jamia.M1118 Berg, M. (2002). Patients and professionals in the information society: what might keep us awake in 2013. International Journal of Medical Informatics, 66(1-3), 31–37. doi:10.1016/S13865056(02)00033-3

Chaudhry, B., Wang, J., Wu, S., Maglione, M., Mojica, W., & Roth, E. (2006). Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Annals of Internal Medicine, 144(10), 742–752. Coiera, E. (2000). When conversation is better than computation. Journal of the American Medical Informatics Association, 7(3), 277–286. Currell, R., & Urquhart, C. (2003). Nursing record systems: effects on nursing practice and health care outcomes. Cochrane Database of Systematic Reviews (Online : Update Software), (3): CD002099. Effken, J., Loeb, R., Johnson, K., Johnson, S., & Reyna, V. (2001). Using cognitive work analysis to design clinical displays. Medinfo. MEDINFO, 10(Pt), 127-131.

Berg, M., & Goorman, E. (1999). The contextual nature of medical information. International Journal of Medical Informatics, 56(1-3), 51–60. doi:10.1016/S1386-5056(99)00041-6

Effken, J. A. (2002). Different lenses, improved outcomes: a new approach to the analysis and design of healthcare information systems. International Journal of Medical Informatics, 65(1), 59–74. doi:10.1016/S1386-5056(02)00003-5

Bhasale, A. L., Miller, G. C., Reid, S. E., & Britt, H. C. (1998). Analysing potential harm in Australian general practice: an incident-monitoring study. The Medical Journal of Australia, 169(2), 73–76.

Goodhue, D. L., & Thompson, R. L. (1995). “TaskTechnology Fit and Individual Performance”. MIS quarterly: management information systems., 19(2), 213.

Cam, K. M., Efthimiadis, E. N., & Hammond, K. W. (2008, January 7-10, 2008). An Investigation on the Use of Computerized Patient Care Documentation: Preliminary Results. Paper presented at the 41st Annual Hawaii International Conference on System Sciences, Waikoloa, Big Island, Hawaii.

Goossen, W. T., Epping, P. J., Dassen, T. W., Hasman, A., & van den Heuvel, W. J. (1997). Can we solve current problems with nursing information systems? Computer Methods and Programs in Biomedicine, 54(1-2), 85–91. doi:10.1016/S01692607(97)00037-0

Campion, T. R., Weinberg, S. T., Lorenzi, N. M., & Waitman, L. R. (2007). Analysis of a computerized sign-out tool: Identification of Unanticipated Uses. Paper presented at the 2007 National Library of Medicine Fellowship Conference.

Harris, B. L. (1990). Becoming deprofessionalized: one aspect of the staff nurse’s perspective on computer-mediated nursing care plans. ANS. Advances in Nursing Science, 13(2), 63–74.

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Hinshaw, A. S. (2008). Navigating the perfect storm: balancing a culture of safety with workforce challenges. Nursing Research, 57(Supplement 1), S4–S10. doi:10.1097/01. NNR.0000280649.99062.06

Vicente, K. J. (2003). Less is (sometimes) more in cognitive engineering: the role of automation technology in improving patient safety. Quality & Safety in Health Care, 12(4), 291–294. doi:10.1136/qhc.12.4.291

Kolodner, R. M., & Douglas, J. V. (1997). Computerizing large integrated health networks the VA success. New York: Springer.

Weir, C. R., Nebeker, J. J. R., Hicken, B. L., Campo, R., Drews, F., & Lebar, B. (2006). A cognitive task analysis of information management strategies in a computerized provider order entry environment. Journal of the American Medical Informatics Association: JAMIA., 14(1), 65. doi:10.1197/ jamia.M2231

LaDuke, S. (2001). Online nursing documentation: finding a middle ground. The Journal of Nursing Administration, 31(6), 283–286. doi:10.1097/00005110-200106000-00003 Miller, A. (2004). A work domain analysis framework for modelling intensive care unit patients. Cognition Technology and Work, 6, 207–222. doi:10.1007/s10111-004-0151-5 Nemeth, C., Nunnally, M., Connor, M. O., Klock, P. A., & Cook, R. (2005). Getting to the point: developing IT for the sharp end of healthcare. Journal of Biomedical Informatics, 38(1), 18–25. doi:10.1016/j.jbi.2004.11.002 Perlin, J. B., Kolodner, R. M., & Roswell, R. H. (2004). The Veterans Health Administration: quality, value, accountability, and information as transforming strategies for patient-centered care. The American Journal of Managed Care, 10(11), 828–836. Rasmussen, J., Pejtersen, A. M., & Goodstein, L. P. (1994). Cognitive Systems Engineering. New York: Wiley. Varcoe, C. (1996). Disparagement of the nursing process: the new dogma? Journal of Advanced Nursing, 23(1), 120–125. doi:10.1111/j.1365-2648.1996.tb03144.x

Wilson, R. M., Harrison, B. T., Gibberd, R. W., & Hamilton, J. D. (1999). An analysis of the causes of adverse events from the quality in Australian health care study. The Medical Journal of Australia, 170(9), 411–415. Xiao, Y., Schenkel, S., Faraj, S., Mackenzie, C. F., & Moss, J. (2007). What whiteboards in a trauma center operating suite can teach us about emergency department communication. Annals of Emergency Medicine, 50(4), 387–395. doi:10.1016/j.annemergmed.2007.03.027

KEY TERMS AND DEFINITIONS CPD: Computerized Patient Care Documentation. CPRS: The PCIS used in the VA. Computerized Patient Record System. LPN: Licensed Professional Nurse. NP: Nurse Practitioner. PCIS: Patient Care Information System. RN: Registered Nurse. VA: United States Department of Veterans Affairs.

This work was previously published in Nursing and Clinical Informatics: Socio-Technical Approaches, edited by Bettina Staudinger, Victoria Höß and Herwig Ostermann, pp. 73-93, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.10

Applying Social Network Analysis in a Healthcare Setting Salvatore Parise Babson College, USA

INTRODUCTION The people-to-people relationships where knowledge work actually gets performed in organizations are called social networks, and they may be in complete contradiction to the information flows expected, based on looking at the organizational chart of formal roles or titles. These informal or social networks are playing an increasingly important role in the healthcare industry, as medical and clinical knowledge needs to be shared effectively between people within and among healthcare organizations. Social network analysis (SNA) is a research methodology to analyze networks beDOI: 10.4018/978-1-60960-561-2.ch410

tween people, groups, organizations, and systems within and across organizations (Wasserman & Faust, 1994). The results of the analysis inform the researcher of both the structure of the network, as well as the positions of nodes or people in the network. This article provides a description of how SNA can be applied in a healthcare setting. Organizations are increasingly relying on networks of people, groups, and other institutions collaborating with each other to perform knowledge-based work. This is especially true in knowledge-intensive industries, such as business consulting, technology, research, and petroleum, where work is project-based in structure and sharing information within and between teams is essential to performance. Also, much of the

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critical work in businesses today requires tacit knowledge, which, by definition, is difficult to codify and resides in the key experts of the organization (Polanyi, 1983). Therefore, connections to these experts are required in order to leverage their expertise, past experiences, and institutional memory. These informal or social networks are playing an increasingly important role in the healthcare industry. With the continued advancements in medical treatments, the rapid dissemination and sharing of this information becomes vital. While treatment descriptions can be codified and put into a database that physicians can access, often physicians will call on their personal relationships to understand the relevance, risks, and benefits of the latest medical knowledge. Physicians are turning to Web-based communities, such as sermo.com, to exchange, comment on, and rate others’ postings on medical insights. Social networks are also vital to understanding the communication patterns, both between and within healthcare organizations. While traditionally operating as silos, these organizations recognize the need for improved access and sharing of clinical and medical research knowledge across their organizational boundaries. Within healthcare providers, social networks can reveal who are the key decision-makers or influencers pertaining to medical treatments, and the adoption of medical technology and information technology (IT), as well as determining if certain physicians are a bottleneck (or overburdened) when it comes to answering questions and disseminating information. Therefore a network perspective is vital to understanding how work gets performed in the healthcare industry.

BACKGROUND There has been a dramatic rise recently in social network research in the management field (Borgatti & Foster, 2003). Network research has

grown as a result of the importance of connections between people, groups, organizations, and IT systems. Network research has been used to study leadership (Brass & Krackhardt, 1999), entrepreneurship (Baron & Markman, 2003), knowledge management (Parise, Cross, & Davenport, 2006), individual performance (Mehra, Kilduff, & Brass, 2001), and team performance (Hansen, 1999). Social network analysis (SNA) is a structured methodology to analyze networks within and across organizations. The results of the analysis inform the researcher of both the structure of the network as well as people’s positions within the network. Based on these findings, organizations can then develop interventions to produce the desired network effects. There has been limited research in the healthcare setting, using SNA as a methodology. SNA has been used to study the interaction patterns in primary care practices (Scott, Tallia, Crosson, Orzano, Stroebel, DiCicco-Bloom, O’Malley, Shaw, & Crabtree, 2005), identify influential individuals who are critical to the successful implementation of medical informatics applications (Anderson, 2005), and study the relationship between communication density and the use of an electronic medical record system by nurse practitioners and physicians’ assistants (Tallia, Stange, McDaniel, Aita, Miller, & Crabtree, 2003). SNA allows us to analyze the relationships among actors or nodes in a network. Nodes can be people, organizations, and IT systems. Connections between nodes are called links and determine the relationships between nodes. Two common representations of networks are bounded networks and ego networks (Wasserman & Faust, 1994; Scott, 2000). In a bounded network, the nodes in the network are predetermined. When using bounded networks, the researcher will often look for silos or fragments in the network. This occurs when there is a breakdown in communication between structures, such as between departments or divisions. Attribute data, such as current job tenure, company tenure, job level, location, and

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functional area are often collected for each node in the network. This allows the researcher to analyze the structure of the bounded network, and determine if any of these dimensions are having a significant impact on its structure. Ego networks are those generated based on the perspective of an individual node or person in the network. Typically, in an ego network analysis, each respondent is asked to list nodes or actors pertaining to them specifically. The SNA researcher can then derive the network, based on the nodes listed by all respondents, as well as study any biases or gaps in an individual’s personal network. SNA researchers will typically analyze both the structure of the network as well as the role of individual nodes within the network. Density and centralization are two common metrics used to describe the entire network. Density is defined as the ratio of observed links or connections in a network to the maximum number of links possible if every node in the network was connected (Wasserman & Faust, 1994). Centralization refers to the degree to which links are concentrated in the network. The centralization metric is very useful in analyzing the degree to which there are “dominant” nodes in the network. In addition to analyzing the network structure, the other major insight from doing an SNA analysis is to understand a node’s role. Certain nodes in the network may be more central resulting in a more advantageous position. Individuals who are more central in the network have been found to have greater influence (Brass, 1984; Brass & Burkhardt, 1992), and have greater access and control of relevant information (Brass & Burkhardt, 1993; Krackhardt, 1990; Umphress, Labianca, Brass, Kass, & Scholten, 2003). In terms of identifying nodes with power or influence, central degree, and central betweeness metrics can be used. Degree centrality refers to the number of links going into (“in-degree”) or coming out of (“out-degree”) a node in a network (Freeman, 1979). Brokers in the context of SNA are used to describe nodes in the network that connect different subgroups. They

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may not have the most direct links, but they act as a conduit between departments, locations, and hierarchies. One common way to identify brokers is the betweeness centrality metric. Betweeness centrality for a particular node refers to the number of links that each other node in the network needs to go through that particular node to reach any other node in the network (Freeman, 1979).

SNA USE IN HEALTHCARE Researchers can apply both a network perspective and the SNA methods to better understand the healthcare setting. In particular, there are two areas in healthcare that can benefit from a network approach: (1) analyzing medical and clinical information flows within and between healthcare organizations, and (2) identifying key “influencers” with respect to adopting medical treatments, medical technologies, and IT, or recommending referrals. SNA can be used as an analytical tool to study interaction patterns within medical practices. It is critical to understand who the medical staff turn to for help regarding medical treatments, unstructured and complex problems, or decision-making. Great variations in organizational design exist in primary care practices (Tallia et al., 2003). Scott et al. (2005), for example, used SNA to study the decision-making patterns in two different medical practices, and found two vastly different organizational structures: one practice had a hierarchical structure where the practice leader had final decision-rights, while the other practice had a much more collaborative decision-making process where staff had the authority to make decisions on their own. Also, physician-to-physician communication is critical to the success of outpatient referral (Gandhi, Sittig, Franklin, Sussman, Fairchild, & Bates, 2000). This process requires the transfer of relevant clinical information in both directions between the physician and specialist. Furthermore, primary care physicians are often

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regarded as an obstacle or gatekeeper with respect to referring patients to specialist care (Scherger, 2002). An SNA could determine if any bottlenecks exist between the primary care physician and the specialist in the referral process. SNA can be very effective at identifying influential individuals in the implementation of medical informatics applications and in the patient referral process. Suppliers of medical equipment face new challenges as new players are having a more active role in the assessment and purchase decision process (IBM E-Health Transformation report, 2002). Also, in terms of assessing the effectiveness of the referral process, one would need to know if there are only a few physicians making most of the referral decisions. For example, Anderson (2005) found that a group of physicians controlled the referral process, and were then enlisted in the planning and implementation of a new IT system.

CASE STUDY: SNA AT A MEDICAL CENTER An SNA study was conducted at a medical center to analyze the collaboration between primary care physicians and specialist physicians. The center provided comprehensive adult and adolescent primary care. It consisted of several departments and practice areas, including primary care and major specialty areas, such as cardiology and gastroenterology. Most of the staff resided in two buildings located a few hundred feet apart in a campus setting. The research sample consisted of two practice areas: the primary care practice and a large specialist practice. A total of 57 respondents took the survey, including primary care physicians (PP), specialist physicians (SP), office managers (OM), administrative staff (ADM), nurses (N), physician assistants (PA), and lab personnel (LAB). All survey network data was analyzed using UCINET VI (Borgatti, Everett, & Freeman, 2002).

Three bounded networks were used that focused on information sharing, advice regarding complex medical problems, and the referral process. The questions used in the survey include: •

•

•

Information:Please indicate the extent to which the people listed below provide you with information you use to accomplish your work. A “0” response indicated the respondent did not know the person. A “1” (very infrequent) to “6” (very frequent) likert scale was used. Advice:Please indicate the extent to which the people listed below provide you with advice regarding unstructured, complex medical problems. A “1” (very infrequent) to “6” (very frequent) likert scale was used. Referrals:Please indicate the extent to which the people listed below provide you with patient referrals. A “1” (very infrequent) to “6” (very frequent) likert scale was used.

The information, advice, and referral networks were dichotomized at responses greater than “4,” so as to only use strong responses as links in the networks. All 57 respondents answered the Information network question, and only the 35 physicians were given the Advice and Referral network questions. Ego networks were also examined to get a sense of where physicians obtained critical knowledge and what expertise areas were critical to perform their job. Each physician respondent was asked to: •

•

Identify (up to 10) critical sources of information that help you with your job. These sources can be individuals (inside or outside the medical center) as well as IT systems (such as on-line medical communities). Identify the major (up to 10) expertise areas that are critical for you to perform your job. For each expertise area, the respondent

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was asked to associate any of the sources of information to the expertise area. Table 1 shows the overall network densities, as well as the dominant employees in the information and advice networks. The information network had an overall density of 12.7%. The top central connectors were PP4 and PP8, as 23 people in the network sought out PP4 for information, while 20 respondents relied on PP8 for information. The top brokers in the information network (as measured by betweeness scores) were the two office managers (OM1 and OM2) and PP4. The most surprising find was the fragmentation caused by building location. Figure 1 depicts the information network, and there is a clear divide in information sharing among employees between the two buildings. Of the 670 total links, 592 were within buildings, and only 78 links existed across the two buildings. Figure 2 depicts the advice network in solving complex medical problems. The arrows in the network point to the source of the advice. The advice network was much less dense (8.5%) than the information network, and was much more

Table 1. Key influencers () indicate in-degree scores Network

Density

Central Connectors

Brokers

Information

12.7%

PP4 (23) PP8 (20) SP3 (16) OM1 (15)

OM1 OM2 PP4 SP10

Advice

8.5%

SP3 (17) SP12 (13) PP8 (11)

SP12 PP8 SP14 SP3 PP18

concentrated with 43% centralization, compared with 28% for the information network. In other words, many of the respondents relied on three physicians to help solve complex problems: two specialists (SP3 and SP12), and one primary care physician (PP8). Figure 3 depicts the referral network. The arrows in the network point to the source of the referrals. The source of the referrals was dominated by three primary care physicians: PP4, PP8, and PP17. These three physicians knew most of the specialists and their specific areas of expertise. Specialists indicated all three were effective in

Figure 1. Information network (the arrows in the network point to the source of work-related information)

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Applying Social Network Analysis in a Healthcare Setting

Figure 2. Advice network (the arrows in the network point to the source of advice regarding complex medical problems)

both deciding when a referral was needed, as well as providing effective content in the referral letter itself. Other primary care physicians often relied on PP4, PP8, and PP17 for advice regarding referrals. Follow-up interviews clearly revealed other physicians were not as effective in the referral process and most referred patients to only a single specialist with whom they were somewhat familiar and comfortable communicating, but was not necessarily the ideal specialist, based on the patient’s condition.

Finally, the ego network yielded interesting findings, especially when comparing the personal network of the well-connected physicians with those that were on the periphery. Several of the well-connected physicians had listed many diverse sources of information, and for each of their expertise needs, they had several knowledge sources from which to choose (including physicians from other hospitals). Meanwhile, many of the peripheral physicians had personal networks that resembled primary care physician PP12’s personal network. PP12 had been with the medi-

Figure 3. Referral network (the arrows in the network point to the source of the referrals)

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cal center for just about one year. She listed the following expertise areas as critical to her job: • • • • • •

Latest medical treatment knowledge IT systems and medical technology knowledge Referral process understanding Regulations/legal issues knowledge Awareness of other physicians’ expertise areas Managing staff

As seen in Figure 4, her personal network was very limited and insular, as she relied too heavily on PP5, SP5, and OM1 to get her work done. She indicated in interviews that she was frustrated with doing too much “busy work,” and did not feel well-connected with other physicians at the center. She admitted she was unaware of the specific areas of expertise of many of the physicians, so as a result, she relied heavily on only a few people at the center. From these SNA findings, the office managers developed some interventions to improve collaboration and information sharing among the staff:

Figure 4. Physician PP12’s personal network

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•

•

It was clear from the SNA study that PP4, PP8, SP3, and SP12 were very influential at the medical center, since each of them held very central positions in the information, advice, and referral networks. All four of these physicians had over 10 years of tenure at the center. They all had built relationships with doctors throughout the industry, and they were well represented at professional conferences, and were members of professional groups and communities. However, they indicated that one source of frustration was they thought too many physicians and nurses came to them for routine questions or medical information, which could easily be handled by other staff members. Therefore, one action item was to free-up time for these physicians by delegating decision-rights for certain procedures and tasks to other physicians and nurses. In this way, they could focus on the complex medical problems. Another way to leverage these four influencers (PP4, PP8, SP3, and SP12) was to help them develop staff, especially new doctors coming into the center. Periodic meetings (e.g., once a month) could be scheduled between a newcomer and an ap-

Applying Social Network Analysis in a Healthcare Setting

•

pointed influencer physician. The meetings could be used as a forum for the newcomer physicians to: ask any questions about medical treatments and procedures; help build their awareness of the other doctors and their expertise areas; better understand the referral process, specifically which specialists should be contacted for what procedures and how to best communicate with certain specialists; help build their personal network by having experienced physicians introduce them to their own personal contacts and professional groups/ communities; and create a trusting and sharing community with other doctors at the center. The office managers also realized they needed to leverage the influencer physicians in terms of planning and implementing their IT plans for the center. The goal was to have an interactive Web site and intranet for the center. The center staff would use the intranet to send emails, to do prescription refills, to schedule patient appointments, to access medical databases and communities, and to create expertise profiles for each of the staff members. The hope was that physicians could also use the intranet to develop their own content and to share their own experiences regarding the latest medical technologies and treatments. The office managers also wanted to make the center a “paperless” office by digitizing clinical records and making all patient information available either through the intranet or handheld computers. But, to achieve this vision, the office managers now realized they needed to get the influencer physicians to be a strong proponent of their IT plans, so they were invited to be part of this IT steering committee. This would also be a good opportunity to get some of the new doctors (who were more

•

•

familiar with using IT) to be part of the IT committee. The referral network clearly showed the need for better communication between the primary care physicians and specialists, as that network was dominated by only three of the 20 primary care physicians. Part of the problem stemmed from a lack of awareness of what each of the specialists did, and the other part of the challenge was an understanding of what clinical information was expected to be transferred between the primary care and specialist physician. The hope was that IT could aid the referral process. Having an expertise profile for each of the physicians on the intranet could help build awareness. Specialists could also post information about their expertise areas, including information regarding a disease, its treatment, and caring for the patient. IT could also help automate the referral process by generating referral letters that could be sent via email. Finally, one of the most surprising findings was how much fragmentation there existed in the information network as a result of the staff’s building location. One idea was to rotate work assignments across the two buildings, especially for the primary care physicians and their staff. For example, primary care physicians (especially newcomers) could work certain days of the week in each of the two buildings. The physical proximity would help the physicians build awareness and relationships with other doctors, including specialists.

FUTURE TRENDS SNA continues to be a popular research methodology in understanding information flows among people in knowledge-intensive organizations. Going forward, there are many opportunities to

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incorporate a network perspective, and to leverage SNA in the healthcare setting. One specific area is to understand the information flows between healthcare organizations, such as hospitals, medical centers, pharmaceutical companies, and insurers. As the healthcare industry continues to evolve and change to be more efficient and effective in delivering patient care, both medical and clinical information need to be more transparent and accessible across organizations. SNA could be an effective means to map collaboration and information flows at an industry-level. Another area that could benefit from incorporating a network perspective is to understand how physicians are using online medical communities to gain access to medical knowledge. Physicians today are able to tap into a larger network of physicians through blogs, wikis, and discussion threads on specific medical topics. These communities can exist both within and outside organizational affiliations. An ego network could be used to analyze a physician’s personal network in terms of using electronic medical communities as a source of their medical knowledge. In this way, popular or effective communities could be shared with other physicians. Or, a network map could be created that identifies physicians as nodes, and comments or responses to physicians in these communities as links between physicians. In this way, central physicians could be identified based on their frequency of postings, and to which physicians they are responding to. The effectiveness of the postings (based on a user rating system) could also be incorporated as part of the network map.

CONCLUSION This article has provided a description on how a network perspective, and in particular, a social network analysis (SNA) methodology could be applied in a healthcare setting. As illustrated in the case study, SNA allows the researcher to identify areas of fragmentation in the network as well

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as central people in the network. Based on this analysis, interventions can then be developed to improve the network structure and network roles, ultimately resulting in more effective collaboration. Going forward, incorporating a network perspective to understand the healthcare setting is important as collaboration among different stakeholders and sharing of medical and clinical knowledge remains a critical strategy.

REFERENCES Anderson, J. G. (2005). Evaluation in health informatics: Social network analysis. In J.G. Anderson, & C.E. Aydin (Eds.), Evaluating the Organizational Impact of Healthcare Information Systems (pp. 189–204). London: Springer. Baron, R. A., & Markman, G. D. (2003). Beyond social capital: The role of entrepreneurs’ social competence in their financial success. Journal of Business Venturing, 18(1), 41–60. doi:10.1016/ S0883-9026(00)00069-0 Borgatti, S. P., Everett, M., & Freeman, L. (2002). UCINET 6 for Windows. Natick, MA: Analytic Technologies. Borgatti, S. P., & Foster, P. C. (2003). The network paradigm in organizational research: A review and typology. Journal of Management, 29(6), 991–1013. Brass, D. J. (1984). Being in the right place: A structural analysis of individual influence in an organization. Administrative Science Quarterly, 29, 518–539. doi:10.2307/2392937 Brass, D. J., & Burkhardt, M. E. (1992). Centrality and power in organizations. In N. Nohria, & R.G. Eccles (Eds.), Networks and organizations: Structure, form and action (pp. 191–215). Cambridge: Harvard Business School Press.

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Brass, D. J., & Burkhardt, M. E. (1993). Potential power and power use: An investigation of structure and behavior. Academy of Management Journal, 36, 441–470. doi:10.2307/256588 Brass, D. J., & Krackhardt, D. (1999). The social capital of 21st century leaders. In J.G. Hunt, & R.L. Phillips (Eds.), Out-of-the-box leadership (pp. 179–194). Stamford, CT: JAI Press. E-Health Transformation. (2002, October). Managing healthcare in a networked world. IBM Business Consulting Services report, G510-9212-00. Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215–239. doi:10.1016/0378-8733(78)90021-7 Gandhi, T. K., Sittig, D. F., Franklin, M., Sussman, A. J., Fairchild, D. G., & Bates, D. W. (2000). Communication breakdown in the outpatient referral process. Journal of General Internal Medicine, 15, 626–631. doi:10.1046/j.1525-1497.2000.91119.x Hansen, M. T. (1999). The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits. Administrative Science Quarterly, 44(1), 82–111. doi:10.2307/2667032 Krackhardt, D. (1990). Assessing the political landscape: Structure, cognition, and power in organizations. Administrative Science Quarterly, 35, 342–369. doi:10.2307/2393394 Mehra, A., Kilduff, M., & Brass, D. J. (2001). The social networks of high and low self-monitors: Implications for workplace performance. Administrative Science Quarterly, 46(1), 121–146. doi:10.2307/2667127 Parise, S., Cross, R., & Davenport, T. H. (2006). Strategies for preventing a knowledge-loss crisis. MIT Sloan Management Review, 47(4), 31–38. Polanyi, M. (1983). The tacit dimension. New York: Doubleday.

Scherger, J. E. (2002). Challenges and opportunities for primary care in 2002. Medscape Family Medicine. Primary Care, 4(1). Scott, J. (2000). Social network analysis: A handbook. London: Sage. Scott, J., Tallia, A., Crosson, J. C., Orzano, A. J., Stroebel, C., & DiCicco-Bloom, B. (2005). Social network analysis as an analytical tool for interaction patterns in primary care. Annals of Family Medicine, 3(5), 443–448. doi:10.1370/afm.344 Tallia, A. F., Stange, K. C., McDaniel, R. R., Aita, V. A., Miller, W. L., & Crabtree, B. F. (2003). Understanding organizational designs of primary care practices. Journal of Healthcare Management, 48(1), 45–61. Umphress, E. E., Labianca, G., Brass, D. J., Kass, E., & Scholten, L. (2003). The roles of instrumental and expressive social ties in employees’ perceptions of organizational justice. Organization Science, 14, 738–753. doi:10.1287/ orsc.14.6.738.24865 Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Boston: Cambridge University Press.

KEY TERMS AND DEFINITIONS Broker: In the context of SNA, brokers are used to describe nodes in the network that connect different subgroups. Degree Centrality: Refers to the number of links going into (“in-degree”) or coming out of (“out-degree”) a node in a network. Information Network: Information networks map who people turn to for information regarding work-related activities. Network Fragmentation: In the context of SNA, network fragmentation occurs where silos exist in the network and information is not be-

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ing transferred across functional, hierarchical, cultural, or geographic boundaries. Social Network: The people-to-people relationships in organizations where knowledge work actually gets performed. The social network may be in complete contradiction to the information flows expected, based on the organizational chart of formal roles or titles.

Social Network Analysis: A research methodology to analyze networks between people, groups, organizations, and systems within and across organizations. Tacit Knowledge: Knowledge that is difficult to codify, and often resides in the key experts of the organization.

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, pp. 93-101, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.11

The Graphic Display of Labour Events Olufemi T. Oladapo Olabisi Onabanjo University Teaching Hospital, Nigeria

ABSTRACT This chapter introduces the partograph as an essential tool of labour management. It describes the concept of the partograph from its historical perspective and highlights its benefits, practical application in contemporary clinical practice, and current challenges to its universal implementation. It also explores the feasibility of design and incorporation of electronic partograph into teleobstetrics to facilitate remote but skilled birth attendance as one of the ways to tackle the problems of prolonged labour resulting from inequitable distribution of maternity specialists DOI: 10.4018/978-1-60960-561-2.ch411

in underserved populations. The author hopes that understanding of the basic concept of the partograph, its practical application, and barriers to its global implementation would reveal research priorities in the subject of partography and guide interested information technologists in the development of appropriate design and usage of partograph in the electronic form.

INTRODUCTION Globally, the quality of health care provided for women during childbirth largely determines their pregnancy outcomes. Poor quality and lack of adherence to evidence based interventions in labour

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

The Graphic Display of Labour Events

care have been blamed for the gross disparities in the maternal health profile between developed and developing nations. Abnormality in the course of labour, especially the prolongation of its duration and consequent complications such as obstructed labour, uterine rupture, postpartum haemorrhage and infection are leading causes of maternal mortality and morbidity particularly in low income countries (AbouZahr & Wardlaw, 2001; Adamu et al., 2003). Prolongation of labour is often the result of inefficient force of uterine contractions or disproportion between a woman’s pelvis and the leading part of her baby. Since these causes can be addressed by appropriate and timely medical interventions, this complication does not arise in well supervised labour. Its high incidence has persisted in low income countries as a result of lack of antenatal care, inadequate health care facilities and large proportion of unskilled birth attendance among the population (Bartlett et al., 2005; Onah et al., 2005). One of the proven and widely accepted technologies that help reduce maternal and perinatal morbidity and mortality from prolonged labour and its complications is the use of an inexpensive obstetric tool called the partograph (or partogram). This is a simple chart for continuous recording of information about the progress of labour and the conditions of the unborn baby and mother during labour. Along with a standard labour management protocol, this visual representation of labour helps providers recognize abnormal labour and know when intervention becomes necessary. Timely intervention during labour often makes a difference between life and death for the mother and baby when this tool is appropriately employed. For over two decades, this obstetric tool has been promoted by international and developmental health agencies as an essential part of labour care within the context of the Safe Motherhood Initiative. However, available evidence indicates lack of universal implementation of the partograph especially in countries where prolonged labour is common. Among other factors, this trend has been

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attributed to criticisms that reflect lack of understanding of the evolutionary history of partograph and the basis for adoption of its World Health Organization (WHO) model for Safe Motherhood interventions. Unless these gaps are bridged, the clinical benefits of the partograph are unlikely to be fully realized on a global scale and technological advancement in this area of intrapartum care would remain slow and uninteresting. This chapter serves to pave way for bridging these gaps by providing labour attendants, researchers, health planners and information technologists with comprehensive and unbiased information on the subject of partography. It provides a detailed description of the fundamental concept of the partograph from its historical perspective, its practical application in contemporary clinical practice, benefits to parturients and caregivers and barriers to its universal implementation. In addition, the chapter aims to reveal research priorities in its clinical application and stimulate innovative ideas among information managers on its prospect for electronic transformation.

SEARCH STRATEGY FOR IDENTIFICATION OF STUDIES An initial electronic search of MEDLINE from inception to January 2008 was conducted to identify potentially relevant studies. The following algorithm was applied both in MeSH and free text words: partograph OR partogram OR labour curve, cervicograph OR cervicogram, alert line AND action line. The Cochrane Library (Issue 4, 2007) was also searched using the same MeSH and free text words as for MEDLINE. Handsearches of proceedings of major conferences on labour complications, bibliography of relevant articles, reviews and chapters in standard textbooks of obstetrics were also conducted. The list of titles and abstract of articles identified by electronic search were examined and full articles of those considered relevant to the subject of partography

The Graphic Display of Labour Events

were retrieved. All relevant articles were critically appraised irrespective of study methodology and no attempt was made to synthesize findings of studies.

HISTORICAL BACKGROUND The concept of the partograph was first introduced by Emmanuel Friedman who provided the basis for the scientific study of the progress of labour in his paper, ‘the graphic analysis of labour’ at New York in 1954 (Friedman, 1954). This followed his study on the cervical dilatation of 100 African primigravidae in labour at term. Although the design of the study had some inherent flaws, its result has remained the basis for assessment and clinical evaluation of the progress of labour until today. The simplest partograph as designed by Friedman had cervical dilatation in centimetres plotted in a linear fashion against time in hours (Figure 1). This was then called cervicograph (or cervicogram) while the process of assessment was referred to as cervimetry. Friedman’s reliance on cervical dilatation as a predictive index of the progress of labour was based on his observation that among all major events that can be monitored during the course of labour (e.g. characteristics of uterine contractions, descent of the presenting part, cervical effacement and dilatation), only cervical

effacement and dilatation appeared to best reflect the overall progress of labour. The graph presented by Friedman was sigmoid in nature and it described a slow early ‘latent phase’, a rapid and progressive ‘active phase’ and a late ‘deceleration phase’ as full cervical dilatation is approached. According to Friedman, the first phase (latent phase) lasts between 8 and 10 hours and it is the period that the cervix dilates from 0 to 3 cm. The second phase (active phase) lasts approximately two hours and is depicted by steepness of the slope as the cervix dilates from 4 cm to 9 cm. Unlike the first two phases, the phase of deceleration was less accepted as it was not supported by the findings of subsequent workers on the subject. Hendricks et al (1970) showed that in the active phase of labour, the rate of dilatation in primigravidae and multigravidae varies very little and that there was no deceleration phase. However, the establishment of the ‘normal curve’ (or nomogram) of cervical dilatation against time by Friedman permitted various deviations in labour to be identified and examined in retrospect. Although there has been a significant change in the timing of latent and active phase of labour introduced by Friedman and the findings indicated by later studies, the principles of cervimetry in assessing the progress of first stage of labour has remained unchallenged until today. While it may be argued that descent of the presenting part

Figure 1. The ‘sigmoid’ labour curve of Friedman

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of the fetus and not cervical effacement and dilatation best reflects the progress of labour, the wide acceptance and application of cervimetry in contemporary obstetric practice is related to its ease of assessment, replicability and relative objectivity compared to the descent of the presenting part which is occasionally influenced by both physiologic and pathologic alteration during the course of labour. Many years after the concept of the labour graph was introduced by Friedman, other workers reported the rates of cervical dilatation among different ethnic population of parturients who experienced normal labour with the aim of establishing different reference curves for different ethnic groups. These studies, however, showed no evidence of any difference in the rates of cervical dilatation among different tribes and all the graphs derived were only variants of the Friedman’s curve (Duignan et al., 1975). Nevertheless, the curve proposed by Friedman did not include another major event that occurs during labour, the descent of the presenting part, which obviously is the expected result of sustained uterine contractions and subsequent dilatation of the cervix in the first place. This shortcoming and the need to practice active management of labour (AML) while maximizing the efficiency of midwives in peripheral

units in Zimbabwe (then Rhodesia) informed the modification made by Philpott and Castle (1972a & 1972b) following their prospective study of 624 consecutive primigravid women. They included the assessment of the descent of the presenting part alongside that of cervical dilatation and in addition proposed an ‘Alert’ and ‘Action’ lines (Figure 2). The Alert line was a straight line that indicated a progress rate of 1 cm per hour and which reflected the mean rate of cervical dilatation of the slowest 10% of studied primigravid women in active phase of labour. Whenever the labour of a woman progressed slower than this rate, the Alert line would be crossed and preparations would then be made to transfer her to a centre where prolonged labour can be appropriately managed. The Action line was another straight line drawn four hours to the right of Alert line and was designed to identify primary inefficient uterine activity for prompt correction with appropriate intervention. These developments in the subject of partography were essentially informed by the earlier works of O’Driscoll and colleagues on AML with some modifications to suit resource poor areas of the world (Orhue, 2008). Specifically, action such as labour augmentation with oxytocin was, unlike in that proposed by

Figure 2. The ‘Alert’ and ‘Action’ lines proposed by Philpott and Castle (1972a & b)

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O’Driscoll, delayed for four hours to allow transfer of parturients by midwives at the peripheral centres to referral hospitals where such intervention can be performed. The transit time between the peripheral and referral hospitals was thus the basis of the time interval between Alert and Action lines proposed by Philpott and Castle. It is widely acknowledged that the introduction of the Alert and Action lines allows prompt recognition of deviation from normal and provided the scientific basis for early intervention that is necessary to prevent prolonged labour. Since then, the partograph became a practical tool of decisionmaking value during the course of labour. Following the contributions of Philpott and colleagues, various adaptation and modifications of the partograph were presented ranging from very simple to extremely complex, but all tailored to satisfy local expectations and circumstances. Many research works in the 1970s and 1980s indicate the usefulness of the partograph in labour management. The lessons learned from these previous works and the need to encourage the practice of AML even in low resource settings formed the foundation for the World Health Organization (WHO) model of the partograph, which was produced and promoted as a universal standard in 1988 after the launch of the Safe Motherhood Initiative a year earlier. The WHO model contains not only the cervical changes and descent of the presenting part but in addition, many other parameters related to labour events and maternal and fetal condition that could potentially influence the course of labour management. All these parameters are arranged sequentially and in relation to one another on a single sheet of paper. The ‘composite’ WHO partograph, as it is often called, has three main sections for recording observations on fetal condition, maternal condition and progress of labour (Figure 3). On this model of the partograph, the latent phase extends to 8 hours and an active phase begins when the cervical dilatation reaches 3 cm. The active phase contains an Alert line, drawn from the level of 3 cm to 10 cm

dilatation with a slope of 1 cm cervical dilatation per hour while an Action line is drawn parallel to, and four hours to the right of the Alert line. Both lines are distinctly and boldly pre-printed on the graph. In an effort to reduce the difficulty faced by health care providers in the completion of this model of partograph, it was later modified by the WHO in the disseminated guidelines for doctors and midwives involved in the provision of emergency obstetric care (WHO, 2000). In the ‘simplified’ model (Figure 4), the latent phase of labour is removed and plotting on the partograph begins in the active phase when the cervical dilatation is 4 cm. Mathews et al (2007) showed that compared to the composite partograph, the simplified partograph was more user friendly and associated with better labour outcomes. In spite of the concerted efforts to maintain a universal standard, many other institutional partographs exist although all has retained the original concept proposed by Friedman.

BENEFITS OF THE PARTOGRAPH The primary objective of the early partograph was to identify abnormal pattern of labour by comparing the progress of labour with a standard labour curve. This enabled prompt interventions necessary to forestall prolonged labour and its consequences. The partograph therefore represents an early warning system which facilitates the prompt recognition of poor labour progress and has been an effective tool in preventing prolonged labour for decades. However, as time went on, other advantages of the partograph were being realized. The largest study to show the importance of the partograph in contemporary obstetric practice was the WHO multicentre trial conducted in three South East Asian countries (Thailand, Indonesia and Malaysia) over a period of 15 months among 35, 484 women (WHO, 1994). The study showed that the introduction of the partograph with an agreed labour management protocol significantly

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Figure 3. The ‘composite’ WHO partograph. © 2006, World Health Organization. Used with permission.

reduced the incidence of prolonged labour (>18 hours) from 6.4% to 3.4% and the proportion of labour requiring augmentation from 20.7% to 9.1%. The rate of intrapartum stillbirths decreased from 0.5% to 0.3% and rate of emergency caesarean section from 9.9% to 8.3%. There was also a 59% reduction in the cases of intrapartum infection which was attributed to a reduction in the mean number of vaginal examinations during labour. Although the improvement in maternal and perinatal morbidity and mortality were generally recorded in both nulliparous and multiparous

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women, the improvement in the outcome was even more marked among singleton pregnancies without any complicating factors with caesarean section rate falling from 6.2% to 4.5%. These findings formed the basis for the WHO recommendation of the use of the partograph in all labour wards for all categories of parturients as part of the efforts to promote Safe Motherhood in all countries. Besides the reported positive obstetric outcomes, the routine application of the partograph in labour management obviously reduces the

The Graphic Display of Labour Events

Figure 4. The ‘simplified’ WHO partograph. © 2006, World Health Organization. Used with permission.

volume of documentation and stationeries used during labour as all labour events are recorded on a page of a single sheet of paper. Therefore it frees more time for the midwives to practice the recommended companionship in labour which on its own contributes to better labour outcomes (Hodnett, 2007). Because of its simplicity and pictorial value, it facilitates teaching of junior health care providers and ease the ‘handing over’ of patients by labour attendants. It also permits early identification and correction of omissions and improper labour ward practice. A quick visual scan through a partograph used during an ongoing labour can easily bring to notice of a supervisor or

senior personnel what has not been properly done and quick restitution made before significant and irreversible damage occurs. Awareness of this fact by labour attendants also improves the quality of observations made and recorded during labour. The partograph is also important in clinical audit for quality assurance purposes by allowing quick and detailed retrospective assessment of how labour was managed as against review of case notes of observation recorded. It aids the practice of AML as it helps to identify the optimum timing of interventions such as artificial rupture of membranes, augmentation of labour with oxytocin, emergency caesarean section or referral to a

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specialist institution all which form the integral part of AML. Finally, the partograph permits early recognition of other labour related events that are not directly reflective of the progress of labour but which can significantly influence the outcome for both mother and child and thus have the potential to determine the course of labour management. For example, abnormality in fetal heart rate pattern in the presence of meconium staining of liquor may suggest fetal hypoxia which may necessitate immediate delivery (emergency caesarean section or instrumental vaginal delivery as the case permits) if confirmed by pH assessment following fetal scalp blood sampling or fetal heart rate recording. It should be noted that such complication of labour may occur in spite of good progress of the labour process as indicated by the uterine contractions, cervical dilatation and descent of the presenting part. Similarly, reduction in maternal blood pressure coupled with elevation of pulse rate during labour is a pointer to conditions associated with internal haemorrhage such as uterine rupture which requires immediate surgical exploration of the abdomen.

COMPONENTS OF MODERN PARTOGRAPH The initial partograph contained only the cervicograph which was essentially for assessing the progress of labour, most of the later models have three main sections for observation of fetal condition, progress of labour and maternal condition. The components of the composite WHO partograph which formed the template for most of the recent modifications will be described in this section. This model of the partograph is a single page pre-printed graph with maternal biodata: name, gravidity (number of pregnancies including the current one), parity (previous number of deliveries), hospital number, date of admission, time of admission, whether and for how long the amniotic membranes were ruptured. The fetal

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heart rate chart ranges from 100 to 180 beats per minutes. This is followed by the nature of the amniotic fluid once the membranes are ruptured and assessment of the fetal head for the degree of overriding of fetal skull bones (clinically described as moulding). In the middle part is the cervicograph (or cervicogram) which shows the latent phase and the active phase of labour. The latent phase component extends from 0 to 8 hours while the active phase extends from 9 to 24 hours. It also includes already printed Alert and Action lines. The Alert line starts at 3 cm cervical dilatation and extends to the point of expected full cervical dilatation (10 cm) at the rate of 1 cm per hour. The Action line is a similar line drawn parallel and four hours to the right of the Alert line. It should be noted that the Alert and Action lines are not pre-printed on some partographs but are constructed by the midwives once the diagnosis of labour is established with the Alert line starting from the point of cervical dilatation on admission. Another important component of the cervicograph is the descent of the presenting part of the fetus scored from 5 down to 0 as it descends down the birth canal. Also in this section of the partograph is the chart for uterine contractions rated from 0 to 5. The next set of rows is for noting the strength of oxytocin used for augmentation and any drugs given during labour. The bottom section of the partograph is for recording maternal observations and these include pulse rate, blood pressure, temperature, urinary output and urine analysis.

PRACTICAL APPLICATION OF THE PARTOGRAPH The partograph is meant to be a simple tool that can be employed by those with minimum literacy level and basic midwifery training. Although the minimum level of education needed to use the partograph is yet to be determined, an ability to perform vaginal examination for assessment of

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cervical dilatation is a mandatory prerequisite. Operationally, all the details of labour are recorded on the partograph. It needs to be realized that it is essentially for the management of low risk labour, where normal progress and vaginal delivery is anticipated without complications. Therefore, it should not serve as a substitute for adequate risk assessment or cause any delay in appropriate intervention. High risk cases and those in whom complications are anticipated demand immediate referral if at peripheral centre or intervention if in a specialized facility.

Labour Ward Admission When a woman presents with features suggestive of labour, the first and one of the most important step before a partograph is started is to confirm that labour is truly established. This is essential to forestall unnecessary interventions that may follow misdiagnosis of labour onset. The effacement and dilatation of the cervix are essential components of established labour and thus cervical dilatation is a central feature of the partograph. At admission, it is mandatory that all indications for immediate referral or intervention must be excluded before the partograph is ‘opened’.

Documentation of Fetal Condition The ability of the fetus to withstand the stress of labour as reflected by its vital signs is paramount to good neonatal outcome. Changes in the fetal condition during labour may therefore influence the management decisions. Monitoring of the fetal well being during labour is primarily by regular auscultation of the maternal abdomen to determine its heart rate but in addition by observation of the characteristics of liquor (when the membranes are ruptured) and degree of compression of fetal skull bones. The normal fetal heart rate ranges between 120 and 160 beats per minute. On the composite WHO partograph, this normal range is depicted by thick horizontal lines to permit quick recognition

of abnormal heart rates and reference or reminder to labour attendants at peripheral health centres. The fetal heart rate is counted with the aid of a fetal stethoscope over a full minute every half hour with the woman in the left lateral position preferably after uterine contraction to determine any deceleration. It could, however, be recorded as frequently as quarter hourly or less when there is evidence of fetal jeopardy. The recorded fetal heart rate is noted on the partograph as dots connected to one another as they are being plotted to give a visual tracing of the fetal heart rate. The fetal heart rate also needs to be determined before and after artificial rupture of membranes for early detection of cases of cord prolapse and compression. There is need to intervene if the fetal heart rate is less than 120 beat per minute or greater than 160 beats per minute to quickly identify the likely cause and rectify it as appropriate. Sometimes, a change in maternal position may be all that is necessary to correct such abnormality. However, when the fetal heart rate is less than 100 beats per minute especially with meconium staining of liquor, it becomes mandatory that the fetus is delivered through the fastest possible means (either by caesarean section if remote from full cervical dilatation or assisted vaginal delivery if close to or at full dilatation). In the next column of the fetal section is recorded the nature of the amniotic fluid. This is usually noted as I if the membranes are still intact, C if the membranes are ruptured and clear, B if it is ruptured but blood stained, M if the liquor is meconium stained and A if liquor is absent as in cases of prolonged rupture of membranes. The presence of meconium may suggest fetal distress and thus require vigilant observation of the fetal heart rate and institution of appropriate intervention. When possible and where the facilities exist, it becomes imperative to carry out fetal scalp blood sampling for determination of its pH. The next parameter on the partograph deals with an indirect way of accessing the relationship between the fetal head and the maternal pelvis –

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moulding. This refers to the compression of fetal skull and subsequent reduction in its diameter as it maneuvers itself down the birth canal. At every vaginal examination, the degree of compression of the fetal skull is assessed and recorded as figures 0, 1, 2, and 3 corresponding to no moulding, mild, moderate and severe moulding respectively. While degrees 1 and 2 are compatible with good fetal outcome as they may represent physiologic phenomenon, severe moulding suggests varying degree of obstruction that needs to be relieved immediately to avoid poor perinatal and maternal outcome.

Progress of Labour Latent and Active Phase of First Stage of Labour In most instances, unfavourable changes in fetal and maternal conditions are preceded by abnormality in the progress of labour as indicated by the cervical dilatation and the descent of the presenting part on the cervicograph. The first stage of labour is divided into two phases; the latent phase and the active phase and these are indicated on the partograph. The latent phase of labour begins from labour onset and extends to 3 cm dilatation and may last up to 8 hours. As a result of the problem of identifying when labour actually starts and its mimicry by false labour, the importance of including this phase of labour in the partograph has been questioned (Orhue, 2005). However, when the timing of the onset of labour is certain, prolongation of this phase beyond 8 hours warrants transfer of a woman in a peripheral health centre to a specialized unit. At such referral centre, no intervention is provided when the woman is not in labour and partographic recordings are abandoned. Other options may include recourse to caesarean section especially with features of fetal distress or associated maternal health problems necessitating discontinuation of the labour process; artificial rupture of membranes and augmentation

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of labour with oxytocin if cervical dilatation and uterine contraction patterns suggest continuing labour. However, poor progress of latent phase of labour is infrequent and while monitoring of progress of labour starts as soon as a patient is admitted, more attention is paid to the duration of this phase than the rate of cervical dilatation. On the composite WHO partograph, cervical dilatation plotted against time in the latent phase of labour must be transferred to the Alert line in the active phase portion once the cervical dilatation exceeds 3 cm. The clinical importance of the partograph is reflected mostly in the active phase of labour when the cervical dilatation is more than 3 cm and its progress is expected to be at least 1 cm per hour. In the active phase section, an Alert line is drawn from 3 cm to 10 cm. Satisfactory progress of labour is indicated when cervical dilatation remains on the left of this line against time. Therefore, cervical dilatation which falls on the right of the Alert line indicates that labour is slower than expected and a full assessment of the possible cause of the delay and the conditions of the mother and the fetus becomes necessary. It therefore keeps the labour attendant in specialized centres on ‘alert’ while it indicates the need for transfer of a woman from a peripheral health unit to specialized centre. The Action line is drawn 4 hours parallel to and to the right of the Alert line. Whenever cervical dilatation reaches this line, some form of action must be instituted immediately. This may be emergency caesarean section or augmentation by use of oxytocin and or amniotomy. Under no circumstances should the cervical dilatation of women undergoing labour in rural or peripheral maternity centres be allowed to reach the Action line before transfer to specialized unit. The four hours interval between the Alert and Action lines was the period considered necessary for transportation of referred women from peripheral maternity units to specialized centres without severe damage to the fetus and mother.

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Descent of the Fetal Head Descent of the fetal head is assessed by abdominal palpation and it represents the portion of the head above the pelvic brim in ‘fifths’. Descent of the head is assessed before each vaginal examination which is usually performed every four hours. This is plotted using the symbol O against time on the cervicograph. The descent is recorded at the same time as cervical dilatation starting from level 5 (on the cervical dilatation axis) and down towards zero and the head descends. The level of descent recorded against time is the proportion of the head in fifth palpable above the pelvic brim. Therefore level 5 of descent on the partograph represents 5/5, level 4 indicates four-fifth of the head palpable, level 3 indicates three-fifth palpable and so on. The symbol for descent is connected to one another by a straight line to indicate the rate of descent in a line graph fashion. It is expected that as the cervix dilates, there should be a corresponding descent of the presenting part and such line should be approaching zero towards full cervical dilatation. Failure of the head to descend may be the first indication of impending labour obstruction. It is also important to correlate the descent of the head with the degree of moulding as they both reflect the degree of disproportion between the fetal head and the maternal pelvis.

Uterine Contractions The frequency and duration of the uterine contractions are recorded below the cervicograph. In this section, there are columns of five blocks numbered 1 to 5 and each corresponding to half an hour on the time line above and representing the number of contractions in 10 minutes. The number of uterine contractions in 10 minutes is recorded every half an hour and the number of corresponding blocks are shaded. The texture of the shaded blocks indicates the strength and duration of contractions. By convention, dotted blocks represent mild contractions lasting less than

20 seconds, diagonal lines represents moderate contractions lasting between 20 and 40 seconds while completely blacked out blocks represent strong contractions lasting over 40 seconds.

Maternal Condition Maternal vital signs are closely monitored during labour and are also recorded at the bottom part of the partograph. The pulse rate is recorded every half an hour and the temperature and blood pressure are measured every four hours. The pulse rate and blood pressure however need to be monitored more frequently if discovered to be abnormal at any time during labour. The woman is also encouraged to urinate every 2-4 hours and urine is tested for protein and ketones each time. Beneath the section for recording uterine contractions is that for recording the strength of oxytocin in international unit per litre of fluid and the rate of infusion per minute. Any drugs and intravenous fluid administered during labour e.g. pethidine can also be charted within this column on the partograph.

CHALLENGES IN UNIVERSAL IMPLEMENTATION OF PARTOGRAPH Many modifications of the early labour curves have made it difficult to have a universal graph for monitoring labour. For various reasons, the WHO recommended partograph is not being followed worldwide. Some of the reasons are related to reservations expressed by end-users on the format of the WHO partograph and the questions raised about the reliability and applicability of the multicentre study that formed the basis of its recommendation (Neilson et al., 2003). Orhue (2005) questioned the absence of the space to record false labour and prolonged latent phase on the composite WHO partograph, which in his opinion, are also important prodromal aspects of

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the first stage of labour. In the USA, the Friedman labour curve is still popular while in Europe, the composite partograph is commonly used with no pre-printed Alert and Action lines. There is also no universally accepted protocol for managing labour with the partograph. Orhue traced this lack of consensus protocol to the poor understanding of the evolutionary history of the partograph that has generated many criticisms which are focused on time separation of Alert and Action lines; whether artificial rupture of membranes at the Action line is compatible with AML principle; and whether augmentation performed four hours after deviation from normal course is still compatible with the prevention of prolonged labour. He noted that these criticisms, which arose from the attempt to use the WHO partograph to implement O’Driscoll version of AML, have constituted the barriers to universal application of the partograph as caregivers made different modifications according to their position on these criticisms. Thus, there is no uniform action proposed for situations where the labour curve crosses between Alert and Action line and when it also crosses the Action line. Some care providers have argued that plotting of the Action line four hours to the right of the Alert line in specialist hospital settings contradicts the basis for which this time interval was proposed by Philpott and Castle and that actions centred on such interval may be too long to reverse the causal factors of slow labour progress. They therefore suggested shorter number of hours between Alert and Action lines. Consequently, other Action line times of 2 and 3 hours from Alert lines for initiating and guiding active intervention were proposed and subjected to research. A randomized trial of 3000 primigravid women in spontaneous labour at term comparing outcomes of labour managed with Action lines at 2 and 4 hours to the right of the Alert lines indicates that intervention is increased with the 2 hour Action line (52% versus 42%) (Lavender et al., 2006). However, the caesarean section rates of the two comparison groups were similar. A South African study, however, shows

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a reduction in the caesarean section rate with the 2 hour-Action line (16%) compared to 23% for the 4-hour Action line (Pattinson et al., 2003). It is possible that using shorter Action time line may reduce the caesarean section rate in practices where the caesarean section rate is already high. While it is generally agreed that the Alert line serves as an early warning signal in less equipped hospital and signifies the point to transfer a woman to better equipped hospital, its significance at the tertiary care setting where quick and adequate intervention are possible is less clear. It should be noted that the lack of a well tested and universally accepted labour management protocol may result in disparity in the benefits associated with the use of the partograph from one centre to the other. The use of the partograph without a mandatory labour management protocol had been shown to have no effect on the rates of caesarean section or other intrapartum interventions in healthy primiparous women suggesting that an appropriate labour management protocol may be more important than the partograph itself (Windrim et al., 2007). A study in Tanzania showed that lack of written guidelines on how to use the partograph for recording and management of labour was associated with incomplete entry of important maternal and fetal parameters and subsequent poor fetal outcome (Nyamtema et al., 2008). Since the partograph was primarily designed for the management of low risk labour, its use in the management of ‘abnormal’ labours and in special circumstances has also raised a lot of questions. For instance, its use for the management of breech labour pose some challenge as parameters such as moulding of the presenting part does not apply. Besides, various protocols guide the management of breech presentation in various centres around the world. However, experience with the WHO partograph suggests that the protocol for management of breech presentation can be the same as that used for a cephalic presentation and Alert and Action lines

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are equally important. In the WHO multicentre trial, use of the partograph in the management of the breech labour was associated with reduction in the incidence of prolonged labour and among multigravida reduced caesarean section rate and improved fetal outcome (Lennox et al., 1998). In spite of this evidence, it would be unwise to attempt to use the partograph for management of breech labours in centres without the capacity for emergency caesarean section. In the same vein, the use of the partograph should be with caution for women with previous caesarean section. This is because the Alert and Action lines during the first stage of labour have been derived from data in patients without previous uterine surgery, in whom the risk of uterine rupture is low. Application of the management protocol for labour in scarred uteri may result in major catastrophe if applied to women with previous uterine surgery. An analysis of the partograph used for women who experienced uterine rupture in Ghana indicated that 16% of such patients (all of whom were undergoing vaginal birth after caesarean section) had ruptured uterus prior to their labour reaching the Alert line (Seffah, 2003). Thirty five percent of the women had a ruptured uterus when their labour was between the Alert and Action lines; a third of whom were undergoing vaginal birth after caesarean section. An investigation of the value of the partograph in predicting the risk of scar rupture shows that the partographic zone 2-3 hours after the Alert line represents a time of high risk of uterine rupture and suggests that an Action line in this time zone would be more appropriate to help reduce scar rupture rate without an unacceptable increase in the rate of caesarean section (Khan and Rizvi, 1995). Another major challenge to the implementation of the partograph is the reluctance of health care providers to employ it in the labour management especially in developing countries. Even in some peripheral hospitals where it is used, it is applied inappropriately. Studies in Nigeria, Benin, Ecuador, Jamaica and Rwanda showed that the ability

to correctly use the partograph by the health care providers was low (Azandegbe et al., 2004; Harvey et al., 2007; Oladapo et al., 2006; Umezulike et al., 1999). Factors attributed to this include care providers’ perception of its association with restriction in their clinical practice, reduced autonomy, increased obstetric intervention and limited flexibility to treat women as individuals (Lavender & Malcolmson, 1999; Walraven, 1994). In addition, the partograph have been introduced in many countries without supporting guidelines, training workshops, follow up and supervision. As indicated by the experiences in Nigeria, Angola and Indonesia, education, in-service training and regular supervision and monitoring of the maternity care providers have the tendency to improve providers’ knowledge of the partograph and thus its quality (Fahdhy & Chongsuvivatwong, 2005; Fatusi et al., 2008; Pettersson et al., 2000) Lastly, the adoption and universal use of the partograph faces a greater challenge with the growing interest in the concept of evidence-based medicine. Although the partograph is widely accepted as an essential part of labour care, there is a general lack of adequate randomized controlled trials comparing its use to non-use. The landmark WHO multicentre trial on the partograph was a ‘before and after’ study which has inherent methodological flaws. Events may occur in between the pretest and the posttest that may influence the result of the posttest, thus challenging the research questions that changes in posttest are actually due to the intervention. A few well conducted randomized trials comparing partograph use to non-use are low powered and outcomes still need to be synthesized in a systematic review. The result of a systematic review is presently being awaited in the Cochrane Database of Systematic Reviews (Lavender et al., 2005). While awaiting the best evidence, current available evidence of the value of the partograph may suffice for its implementation especially in developing countries. Besides the documented effects on obstetric outcomes, other advantages of the partograph such as reduction in the bulk

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of documentation and creation of more time for companionship in labour are relevant issues that would continue to promote its use especially in less privileged countries.

THE FUTURE PARTOGRAPH In spite of the relevance of the partograph to contemporary obstetric practice, it is yet to be incorporated into teleobstetrics, a growing field of telemedicine. There is no doubt that there is inequitable distribution of maternity care specialists in developing countries and among underserved populations of developed countries, as most concentrate in the main cities where they contribute to the focalized excess of supply in places where the latest technology and high quality are available and leaving the remote and the rural areas uncovered. Unfortunately, maternal mortality from prolonged labour is highest in the remote and rural areas and efforts need to be made to produce low cost means of bridging the gap.

Electronic Partograph There is no doubt that the development of an electronic partograph and its incorporation into the concept of telemedicine will be a positive step towards reducing the problems created by short supply of specialists in peripheral maternity centres. An electronic partograph machine should be a portable device with capability to record multiple partographs at the same time. The control panel should include buttons that can convert input figures to visual representation on an electronic graph. By default, the electronic partograph should be able to notify the attendants of the time to record observations using various symbols and colour codes in order to improve the completeness and quality of data entry. The system should be equipped with automated indicators of abnormality such that attendants with minimum

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knowledge of the partograph can be alerted of oncoming danger. Abnormality in the fetal heart rate should be indicated by red colour codes. The Alert and Action lines on the electronic partograph should be electronically mobile such that they are plotted once the first cervical examination in the active phase of labour is recorded. The Alert and Action lines can also be represented with blue and red lines respectively. The machine should be equipped with automated alarm system that can be triggered when the cervical dilatation hits the Action line. Entries on fetal heart rates, cervical dilatation, descent of the presenting part and maternal vital signs should automatically be connected in a line drawing version as they are being entered such that deviations from normal can be noted as soon as possible. In addition, the system should have an inbuilt decision-support mechanism which should depend on the input made by a central (supervisory) unit that is based on agreed management protocol in order to help labour attendant decide next line of action for abnormal labour. Data from these machines should be transferable to a central unit. This way a specialist doctor on call duty can be monitoring several labour cases either through real time electronic transfer or intermittent transfer of the electronic graph from peripheral units. This remote labour management can be augmented with telephone conversation between the labour attendant and the supervisor. Provision of appropriate referral mechanism and linkage system between the remote and specialized units also needs to be established to maximize the efficiency of this arrangement. Plotted electronic partograph should be storable for retrieval at any time necessary for auditing purposes. In places where electronic documentation of management is the norm, the use of paper documentation may be phased out completely. It is unlikely that such machine would require specialist training as for electronic fetal monitor but rather minimum ability to use computer software.

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However, the cost effectiveness of such project will have to be subjected to case studies as done in other areas of telemedicine.

CONCLUSION The partograph is a visual representation of events in low risk labour that guides labour supervision along the expected course and alerts the caregiver of deviation from normal. In practical terms, it can be described as the pictorial representation of a structured protocol for the management of first stage of labour that reflects the basic principle of AML in a modified version. It is not a substitute to, but rather complements, appropriate labour protocol, without which it is of little significance. In spite of its evolution over several decades, the full meaning of its concept and clinical implications are yet to be fully understood and accepted by all. This has led to many modifications of the format and lack of a consensus protocol for its practical application. Presently, the emphasis has been on the promotion of the WHO partograph which includes other useful labour parameters besides the progress of labour making it a comprehensive labour record. It is apparent from the various modifications in current use that this model is not embraced by all due to certain reservations by health workers and researchers. As it stands, the concept that formed the foundation of the WHO partograph supports its use in the peripheral units where specialist obstetric interventions are lacking. It is apparent from the aforementioned that the partograph and its components need further evaluation if its clinical significance is to be fully realized. Firstly, in the interest of evidence based health care, concrete evidence from multicentre randomized controlled trials (of the magnitude of the WHO multicentre trial) are still needed to demonstrate that introduction of the partograph into labour management has more clinical benefits

than non-use. Secondly, the most appropriate time separation between Alert and Action lines that is associated with reduced obstetric interventions and optimal maternal and perinatal outcome for different levels of healthcare should be determined in larger randomized trials. Thirdly, the most suitable evidence based guidelines for labour management when the partograph is used requires further study and lastly, the development and integration of an electronic partograph into the growing field of teleobstetrics should be explored and subjected to case studies in areas where telemedicine is already in existence. A recognized limitation of this review is the non-systematic method used for gathering information although this is justifiable on the grounds of paucity of randomized controlled trials on partography. Although the electronic search for relevant information was limited to two major databases, it is unlikely that any study that could influence the inferences made in this chapter was excluded considering the retrieval rates of these databases for intervention studies.

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Bartlett, L. A., Mawji. S., Whitehead, S., Crouse, C., Dalil, S., Ionete, D., Salama, P., & Afghan Maternal Mortality Study Team. (2005). Where giving birth is a forecast of death: Maternal mortality in four districts of Afghanistan, 1999-2002. Lancet, 365(9462), 864–870. doi:10.1016/S01406736(05)71044-8 Bosse, G., Massawe, S., & Jahn, A. (2002). The partograph in daily practice: It’s quality that matters. International Journal of Gynaecology and Obstetrics: the Official Organ of the International Federation of Gynaecology and Obstetrics, 77, 243–244. doi:10.1016/S0020-7292(02)00004-8 Duignan, N. M., Studd, J. W. W., & Hughes, A. O. (1975). Characteristics of labour in different racial groups. British Journal of Obstetrics and Gynaecology, 82, 593–601. Fahdhy, M., & Chongsuvivatwong, V. (2005). Evaluation of World Health organization partograph implementation by midwives for maternity home birth in Medan, Indonesia. Midwifery, 21, 301–310. doi:10.1016/j.midw.2004.12.010 Fatusi, A. O., Makinde, O. N., Adeyemi, A. B., Orji, E. O., & Onwudiegwu, U. (2008). Evaluation of health workers’ training in use of the partogram. International Journal of Gynaecology and Obstetrics: the Official Organ of the International Federation of Gynaecology and Obstetrics, 100, 41–44. doi:10.1016/j.ijgo.2007.07.020 Friedman, E. A. (1954). The graphic analysis of labour. American Journal of Obstetrics and Gynecology, 68, 1568–1571. Harvey, S. A., Blandón, Y. C. W., McCaw-Binns, A., Sandino, I., Urbina, L., Rodríguez, C., Gómez, I., Ayabaca, P., & Djibrinaf, S., & the Nicaraguan maternal and neonatal health quality improvement group. (2007). Are skilled birth attendants really skilled? A measurement method, some disturbing results, and a potential way forward. Bulletin of the World Health Organization, 85, 783–790. doi:10.2471/BLT.06.038455

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Hendricks, C. H., Brenner, W. E., & Kraus, G. (1970). Normal cervical dilatation pattern in late pregnancy and labor. American Journal of Obstetrics and Gynecology, 106, 1065–1082. Hodnett, E. D., Gates, S., Hofmeyr, G. J., & Sakala, C. (2007). Continuous support for women during childbirth. Cochrane Database of Systematic Reviews (Online : Update Software), 18(3), CD003766. Khan, K. S., & Rizvi, A. (1995). The partograph in the management of labour following caesarean section. International Journal of Gynaecology and Obstetrics: the Official Organ of the International Federation of Gynaecology and Obstetrics, 50, 151–157. doi:10.1016/0020-7292(95)02431-B Lavender, T., Alfirevic, Z., & Walkinshaw, S. (2006). Effect of different partogram action lines on birth outcomes: A randomized controlled trial. Obstetrics and Gynecology, 108, 295–302. Lavender, T., & Malcolmson, L. (1999). Is the partogram a help or a hindrance? An exploratory study of midwives’ views. The Practising Midwife, 2, 23–27. Lavender, T., O’Brien, P., & Hart, A. (2005). Effect of partogram use on outcomes for women in spontaneous labour at term. Cochrane Database of Systematic Reviews (Online : Update Software), 3, CD005461. Lennox, C. E., Kwast, B. E., & Farley, T. M. M. (1998). Breech labour on the WHO partograph. International Journal of Gynaecology and Obstetrics: the Official Organ of the International Federation of Gynaecology and Obstetrics, 62, 117–127. doi:10.1016/S0020-7292(98)00083-6 Matthew, J. E., Rajaratnam, A., George, A., & Mathai, M. (2007). Comparison of two World Health Organization partographs. International Journal of Gynaecology and Obstetrics: the Official Organ of the International Federation of Gynaecology and Obstetrics, 96, 147–150. doi:10.1016/j.ijgo.2006.08.016

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Neilson, J. P., Lavender, T., Quenby, S., & Wray, S. (2003). Obstructed labour. British Medical Bulletin, 67, 191–204. doi:10.1093/bmb/ldg018 Nyamtema, A. S., Urassa, D. P., Massawe, S., Massawe, A., Lindmark, G., & Van Roosmalen, J. (2008). Partogram use in the Dar es Salaam perinatal care study. International Journal of Gynaecology and Obstetrics: the Official Organ of the International Federation of Gynaecology and Obstetrics, 100, 37–40. doi:10.1016/j. ijgo.2007.06.049 Oladapo, O. T., Daniel, O. J., & Olatunji, A. O. (2006). Knowledge and use of the partograph among healthcare personnel at the peripheral maternity centres in Nigeria. Journal of Obstetrics & Gynaecology, 26, 538–541. doi:10.1080/01443610600811243 Onah, H. E., Okaro, J. M., Umeh, U., & Chigbu, C. O. (2005). Maternal mortality in health institutions with emergency obstetric care facilities in Enugu State, Nigeria. Journal of Obstetrics & Gynaecology, 25, 569–574. doi:10.1080/01443610500231484 Orhue, A. A. E. (2006). Normal labour. In A. Agboola (Ed.), Textbook of obstetrics and gynaecology for medical students (pp. 283-302). Ibadan, Nigeria: Heinemann Educational Books Plc. Orhue, A. A. E. (2008, May). Barriers to universal application of the partograph for labour management. Paper presented at the Strategic Dialogue to Reduce Maternal and Newborn Deaths in Nigeria, Dialogue between Policy Makers and Researchers, Sagamu, Nigeria. Pattinson, R. C., Howarth, G. R., Mdluli, W., Macdonald, A. P., Makin, J. D., & Funk, M. (2003). Aggressive or expectant management of labour: A randomised clinical trial. British Journal of Obstetrics and Gynaecology, 110, 457–461.

Pettersson, K. O., Svensson, M., & Christensson, K. (2000). Evaluation of an adapted model of the World Health Organization partograph used by Angolan midwives in a peripheral delivery unit. Midwifery, 16, 82–88. doi:10.1054/ midw.1999.0206 Philpott, R. H., & Castle, W. M. (1972b). Cervicographs in the management of labour in the primigravida II. The action line and treatment of abnormal labour. Journal of Obstetrics and Gynaecology of the British Commonwealth, 79, 599–602. Philpott, R. M., & Castle, W. M. (1972a). Cervicographs in the management of labour in the primigravida I. The alert line for detecting abnormal labour. Journal of Obstetrics and Gynaecology of the British Commonwealth, 79, 592. Seffah, J. D. (2003). Ruptured uterus and the partograph. International Journal of Gynaecology and Obstetrics: the Official Organ of the International Federation of Gynaecology and Obstetrics, 80, 169–170. doi:10.1016/S0020-7292(02)00307-7 Umezulike, A. C., Onah, H. E., & Okaro, J. M. (1999). Use of the partograph among medical personnel in Enugu, Nigeria. International Journal of Gynaecology and Obstetrics: the Official Organ of the International Federation of Gynaecology and Obstetrics, 65, 203–205. doi:10.1016/S00207292(98)00168-4 Walraven, G. E. (1994). WHO partograph. Lancet, 344, 617. doi:10.1016/S0140-6736(94)92004-4 Windrim, R., Seaward, P. G., Hodnett, E., Akoury, H., Kingdom, J., & Salenieks, M. E. (2007). A randomized controlled trial of a bedside partogram in the active management of primiparous labour. Journal of Obstetrics and Gynaecology Canada, 29(1), 27–34.

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World Health Organization. (2000). Managing complications in pregnancy and childbirth: A guide for midwives and doctors. IMPAC. WHO/ RHR/00.7. World Health Organization, Geneva. World Health Organization and Maternal Health and Safe Motherhood Programme. (1994). World Health Organization partograph in management of labour. Lancet, 343, 1399–1404.

KEY TERMS AND DEFINITIONS Action Line: A straight line on the active phase section of the cervicograph, drawn parallel to, and a number of hours (usually 4 or less) to the right of the Alert line. Active Phase of Labour: The second part of the first stage of labour during which the cervix dilates from 3 to 10 cm (full dilatation).

Alert Line: A straight line on the active phase section of the cervicograph that starts at 3 cm cervical dilatation (or more) and extends to the point of expected full cervical dilatation (10 cm) at the rate of 1 cm per hour. Cervicograph (or Cervicogram): The portion of the partograph that displays cervical dilatation in centimetres plotted against time in hours. Latent Phase of Labour: The initial part of the first stage of labour during which the cervix dilates from zero to 3 cm. Partograph (or Partogram):A graphic record of all the observations made on a woman in labour, the central feature of which is the display of cervical dilatation against time. Prolonged Labour: Any labour extending beyond 12 hours in the active phase of its first stage regardless of age, parity and race.

This work was previously published in Handbook of Research on Information Technology Management and Clinical Data Administration in Healthcare, edited by Ashish N. Dwivedi, pp. 447-464, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.12

The MOBEL Project:

Experiences from Applying UserCentered Methods for Designing Mobile ICT for Hospitals Inger Dybdahl Sørby Norwegian University of Science and Technology, Norway

Pieter Toussaint Norwegian University of Science and Technology, Norway

Line Melby Norwegian University of Science and Technology, Norway

Øystein Nytrø Norwegian University of Science and Technology, Norway

Yngve Dahl Telenor Research & Innovation, Norway

Arild Faxvaag Norwegian University of Science and Technology, Norway

Gry Seland Norwegian University of Science and Technology, Norway

ABSTRACT This chapter presents results and experiences from the MOBEL (MOBile ELectronic patient record) project at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway. MOBEL was a multidisciplinary research project established in 2000. The problem area of the project was communication and information needs in hospital wards, and the aim of the project was to develop and explore methods and prototypes for point of care clinical information

systems (PoCCS) that support clinicians in their patient-centered activities. The chapter summarizes four sub studies performed during the project. Each study presents different approaches to usercentered design of PoCCS. Findings from these studies confirm the need for mobile information and communication technology (ICT) in hospitals. Furthermore, the studies demonstrate how more user involvement and complementary approaches to traditional requirements engineering (RE) and system development methods can be useful when developing mobile information and communication systems for clinicians.

DOI: 10.4018/978-1-60960-561-2.ch412

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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INTRODUCTION The Information and communication systems that are employed in today’s health organizations are still to a high extent designed according to what can be called the desktop computer interaction model. The underlying assumption of this model is that support must be given to an individual user at a stable location. This model works well in most office environments, where users interact with information systems at their office desk, performing mostly isolated and well-demarcated tasks. However, as will be argued in this chapter, the model does not readily apply to clinical work. Clinical work is inherently different from office work in a number of important aspects. This implies that different types of systems are needed to support clinical work and moreover that different approaches and methods are used for designing these systems. This insight motivated the MOBEL (MOBile ELectronic patient record) project that will be presented in this chapter. The MOBEL project consisted of four separate studies (denoted here as Study A to D). They have each pursued a set of different research questions, but seen together they form a continuum of user-centered design approaches. The main research focus of Study A was to obtain in-depth understanding of information- and communication practices amongst physicians in a mobile clinical setting. Study B aimed at developing a method for performing structured observation and analysis of clinical work as a basis for empirical based requirements engineering. In Study C, the focus was on involving users more actively in the system design process, by including health personnel in role playing workshops. And finally, the main focus of Study D was to investigate evaluation criteria for sensor-based interaction techniques providing mobile health care personnel point of care access to medical information. This chapter summarizes the objectives, research approaches and findings of the MOBEL studies. The objective of the chapter is to contribute

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to the body of knowledge that is relevant for designing mobile ICT for hospitals. Findings from the project confirm the aforementioned characteristics of clinical work, and stress the need for mobile ICT in hospitals. Furthermore, the studies demonstrate how more user involvement and complementary approaches to traditional requirements engineering (RE) and system development methods can be useful when developing mobile information and communication systems for clinicians. The next section of the chapter briefly presents the problem area and an overview of the project. In the following sections studies A to D are described in more detail, and finally, a summary and some concluding remarks are given.

THE MOBEL PROJECT The MOBEL project was established in 2000 as an interdisciplinary research project under the strategic area of medical technology at the Norwegian University of Science and Technology (NTNU).1 The basic insight motivating the project was that clinical work has some specific characteristics that distinguish it from what can be referred to as ‘office work’. The most prominent of these characteristics are: •

•

•

It is inherently mobile: Clinicians have to move between patients who are physically distributed and interact with colleagues at different locations. There is not one, stable workplace. It is information intensive: Doing clinical work requires a lot of information. This involves information about the nature and stages of the patient’s disease(s), his clinical condition, the actions taken and the actions planned. Furthermore, a lot of information is produced as a result of clinical work. It is highly collaborative: Clinicians work in teams. Their work involves shared re-

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•

sponsibilities, collective planning, team decision making, multidisciplinary work, and continuous negotiation of responsibilities for actions. It is inherently multi-tasking: A clinician is normally involved in providing care for more than one patient at a time. Often they are interrupted in their work and have to change their attention to another patient or clinical task.

Information and communication systems that support clinical work must take into account these characteristics. This does not only require different systems than the ones currently in place, but it also requires different approaches to designing these systems. The focus in the design process must not so much be on isolated clinical tasks performed by individual clinicians, but more on the communication and coordination that take place in point of care situations. The main objective of the MOBEL project was to develop and explore methods and prototypes for point of care clinical information systems (PoCCS) that support clinicians in their patientcentered activities. Three research issues were central to the project: 1. How to gain insight at work practices at the point of care? 2. How to derive requirements for PoCCS? 3. How to evaluate prototypes of PoCCS in a controlled and feasible way? The four MOBEL-studies presented in this chapter have each addressed different aspects relevant for understanding and designing systems for mobile clinical work. They are methodologically related, in the sense that they address the different phases of what literature refers to as user-centered design. User-centered design is a philosophy and a collection of methods where knowledge about users and their involvement in the design process

is central. The extent to which users are involved varies with different methods. In some cases users are consulted at specific times during the design process. Other methods imply a more continuous involvement of users, by adopting them as partners throughout the entire design process. Approaches in which users have deep impact on the design process are often referred to as participatory design (Ehn, 1988; Greenbaum & Kyng, 1991), or the Scandinavian approach (Kyng, 1994). User-centered design was first applied as a large-scale software design methodology to get feedback on the usability of the graphical interfaces designed for the Xerox “Star” workstation in late 1970s. Norman and Draper (1986) popularized the term through their book User-Centered System Design: New Perspectives on Human-Computer Interaction. Norman (1988) drew further on the concept in The Psychology of Everyday Things (POET). Figure 1 shows how the different approaches of the studies presented in this chapter are related. Although the project was based on user-centered approaches, the different studies involved users in different ways and in different stages. Studies A and B are based on field studies; observing real users (i.e. physicians and nurses) in real clinical settings. Hence, they were mostly involved with research issue 1 and 2 identified above. Study C moved the users out of the hospital wards and actively involved them in role play, developing new situations and solutions of problems that they found relevant. This study was involved with research issues 2 and 3 mostly. This also holds true for Study D in which prototypes of messaging systems were developed and evaluated with real users in a usability laboratory configured as a small hospital ward. The following sections provide a more detailed presentation of the objectives, research approaches and findings of Study A to D.

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Figure 1. Research approaches and connections between the MOBEL studies

STUDY A: UNDERSTANDING CLINICAL INFORMATION AND COMMUNICATION PRACTICES IN MOBILE WORK: AN ETHNOGRAPHIC APPROACH The motivation behind Study A was that computer systems in Norwegian hospitals predominantly have been stationary, meaning that clinicians receive little computer support at the point of care. This led to a desire of investigating clinical work in hospital wards in more detail, particularly focusing on how clinicians communicate and work with information. The study had an explicit focus on mobile work (Melby, 2007).

Objectives: Study A The main objective of Study A was to obtain in-depth understanding of information and communication practices amongst physicians in a mobile clinical setting, by conducting participant observation in a hospital ward. More specifically, the following research questions were pursued:

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How do clinicians collect, transfer and store information? What are their information needs and how are these needs solved? How do clinicians communicate, e.g. what mediums are used? Due to the focus on mobile work situations, the study domain was limited to the investigation of clinical work performed outside physicians’ offices.

Research Approach: Ethnographic Study The theoretical approach in Study A was a combination of concepts from medical sociology and Science and Technology Studies (STS), or what we may call a socio-technical approach. Such an approach makes it pertinent to understand how the organization works; how work practices are carried out, how routines are established and sustained, and how actors organize and reorganize their work in a constantly changing environment. The research has drawn on concepts like articulation work (Strauss, Fagerhaug, Suczek, & Wiener, 1997), coordination (Berg, 1999), standardization (Timmermans & Berg, 2003) and boundary

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objects (Star & Griesemer, 1989). Using an STS perspective implies applying a broad definition of technology. Investigating health personnel’s use of technologies for mediation of information and knowledge thus includes studying the use of mundane technologies, like a piece of paper, to the use of sophisticated technologies, like advanced clinical information systems (Timmermans & Berg, 2003). Since the technologies are embedded in relations to other tools, people, and working practices, it is not possible to study the technology in itself, it must be studied in practice. In Study A, it was thus studied how a number of technologies play a part in the mediation of information and knowledge, and how the social and material artifacts mutually constitute each other in a hospital ward. Furthermore, the study focused on physicians’ expressed information needs, the use of procedures and guidelines in clinical mobile settings, and finally on how certain objects may function as communicative devices or ‘boundary objects’ between the different groups of health personnel. Methodologically, the study has been drawing on ethnographic data collected from one hospital department during two periods of fieldwork. Ethnography or participant observation denotes a research method where the researcher takes part in peoples’ everyday life and everyday activities over a period of time. The aim is to obtain firsthand knowledge about social processes in a real life setting (Dingwall, 1997; Silverman, 2001). Participant observation is a method that may be useful in different kinds of research projects, including projects that aim at providing input to design. First of all, participant observation provides knowledge about what people actually do, and not what they say they are doing. By using participant observation we avoid problems of people not remembering what they have done, or people not telling the truth, or becoming normative. Secondly, it is a useful method for investigating how people are interacting with their environment. The interplay between social and material actors is

often an important focus in observational studies, and it was a core theme in Study A. The first period of fieldwork took place in 2001 and lasted for one month, while the second period took place during a four-month period in 2002. In addition to participant observation, informal interviews with health personnel were conducted. The observational data was documented through field notes. In addition, health personnel’s presentation of patient histories, discussions of test- and laboratory results and discussions of ward patients were recorded and subsequently transcribed and analyzed. In total, 30 different meetings were recorded. Ethnography has been criticized for taking too long time and to only offer snapshots of current practice. Ethnographic data may, however, be analyzed and used in different ways and may thus be tailored to provide input to requirements (Randall, Harper, & Rouncefield, 2007). In Study A the main objective was to obtain knowledge of current practice. However, in collaboration with Study B, there was also an objective to investigate ways of utilizing observational data as basis for design.

Findings: Study A Study A had a broad and mainly explorative objective, but was confined to the study of clinical mobile work, especially looking into the informaFigure 2. Discussion of ward patients’ test and laboratory result

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tion and communication practices taking place in the ward. The clinicians were not actively involved in the study, except for being observed while conducting their daily work, e.g. they were not explicitly providing input to requirements or design. Only on a few occasions during the interviews, clinicians directly expressed needs for specific kinds of computer support. One example of this was the request for a screen on the wall, where clinicians jointly could see patient information, e.g. from the patient record, while discussing a particular patient. The main findings of Study A underline how coordination and mediation of knowledge and information are core tasks for health personnel. The study also shows that medical knowledge is a product of negotiations between the personnel, meaning that medical work is very much collaborative work. Furthermore, the study shows that clinical work to a great extent is characterized by flexible and improvised practices, even though standards and guidelines do exist. The flexible character of clinical work – and the need to keep clinical work flexible – implies that applying standard procedures are challenging and that such standards must be subject to discussions and tinkering. The study also emphasizes the need for mobile systems in hospitals that support collaborative and flexible work. Study A also showed that there are no seamless information and communication technology in the ward. On the contrary, health personnel are using a number of different and complementary technologies in a skilful way. The content in these different information and communication technologies overlap and provide information redundancy. This redundancy could be seen as an advantage since important information could be found in different sources, but it could also be a disadvantage if the information sources are not updated simultaneously. The analyses of the observations show that sources of information have to be easily accessible

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to be used. At the same time health personnel assess the credibility of the sources, and credibility seems to be put higher than easy access. Furthermore, the study shows that hospital physicians demand several types of information. In the study the need for seven different information types were assessed, and the information types most in demand were patient data and medical knowledge. This kind of information is by far also the easiest to integrate into computer systems. The information parallels the information found in the patient record or in medical reference works. Even though there exist a myriad of information sources at the hospital, physicians quite frequently still experience a lack of information. This may in turn affect how they perform their work. The study shows that paper-based mediums still are important, and documentation, collection and planning happened primarily through paper mediums. The electronic patient record was seldom used. The study shows that there is a great need for common spaces where health personnel may negotiate, discuss and coordinate clinical information. The great challenge is to design good places or spaces where health personnel can meet. Such a place or space may take a virtual form, or it may take the form of a mobile computer system brought into the different clinical situations.

STUDY B: REQUIREMENTS ENGINEERING FOR MOBILE HEALTH INFORMATION SYSTEMS As described in the previous section, the main objective of Study A was to obtain knowledge of current practice by using participant observation, but also to investigate ways of utilizing observational data as basis for design. Study B was also based on understanding current clinical practice, but the focus of the research performed in Study B was to explore how observation and analysis of clinicians’ information and communication behavior could be used as an approach to requirements

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engineering (RE) for mobile health information systems (Sørby, 2007).

Objectives: Study B The main objective of Study B was to investigate how knowledge about clinical information and communication practice can be transformed into specific requirements for PoCCS. The study focused on exploring a method for identifying and recording knowledge about actors, roles, situations, procedures, information, communicative acts, and other rich context information in real hospital ward situations. The ultimate goal of the research was to elicit and produce requirements to context-aware user interfaces for clinicians (i.e. physicians, nurses, and other allied healthcare professionals working with hospital patients) (Coiera, 2003).

Research Approach Characterizing Cooperation in the Ward In order to get a general understanding of the domain and a view of how the current information sources were used in the hospital wards, an initial observational study was conducted in collaboration with Study A. Five days of observations in two hospital wards were supplemented with informal interviews with the health personnel, in addition to insights from the more extensive participative observational study performed in a third ward (described in Study A). During the study, the observers followed physicians and nurses in their daily patient-centered work, taking free-text notes. Based on the notes and supplementary information, 11 example scenarios were extracted. The scenarios included meetings, ward rounds, medication administering and other important ward situations. Subsequently, the scenarios were characterized by means of a previously developed framework, consisting of attributes with corresponding predefined values. The attributes were

grouped in three main sections: process attributes, input attributes, and outcomes, see (Sørby, Melby, & Nytrø, 2006) and (Sørby, Melby, & Seland, 2005) for details. The main attributes were related to the produced or exchanged information; e.g. information type, amount, medium, information flow, and time perspective. Other important attributes concerned contextual information such as participants/actors, and planning, delegation, and decision-making issues. Table 1 shows an example scenario abstracted from the observations with corresponding characterization.

Structured Observations The initial study outlined above revealed a need for more extensive and detailed observations of clinicians’ information and communication behavior. This led to the development of a method for structured, focused observation of clinicians’ communication and information behavior. Structured observation can be defined as the planned watching and recording of behavior and/or events as they occur within a well-known/predefined environment. The method was developed iteratively through several observational studies consisting of a total of more than 400 hours of observation. It is based on manual observation of clinicians in hospital wards in their daily, patient-centered work such as pre rounds meetings, ward rounds, and patient discharge. The field data is recorded by means of note-taking forms, which can be adapted according to current focus of interest. The observation forms include fields consisting of predefined codes with primary emphasis on information sources and information types, but the forms also include free-text fields, which enables recording of context information such as situation or event trigger, reason for admission, illness history, the purpose of the act being performed, and the outcome of the act. This rich context data allows for further investigation of findings in the initial analyses. Figure 3 shows an example form and extract from observational data recorded at

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Table 1. Example scenario (S3) and characterization S3: Medication - per patient One of the nurses in the patient care team uses information from the patient chart to put today’s medications for the ward patients onto a medicine tray. Later, the nurse in charge inspects the medicine tray to ensure that the medicines correspond to the recorded information on the patient chart Facet

Process

Information input

Output/ Produced outcome

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Attribute

Value(s) S3

Number of participants

2-4

Number of roles

Two

Number of role levels

Two

Composition

Predetermined

Decomposition

Yes

Scenario nature

Formal

Regularity

Daily

Scheduling

On the spot

Variance of required info.

Somewhat

Location(s)

Predetermined, fixed

Spatiality

One place

Temporality

Asynchronous

Information exchange

One-to-many

Initiation

On demand, Precondition

Delay tolerance of scenario start

None

Novelty

To some

Recorded

Patient record

Longevity

Short term

Medium/mode

Text

Scope

All

Delay tolerance of input info.

None

Explicit

Yes

Shared

Yes

Novelty

To some

Recorded

Patient record

Longevity

Long term

Type of produced Information

Cooperative Constructive

Medium/mode

Text

Scope

Patient care team members

Delegation of responsibility

Predefined

Delegation of tasks

Predefined

Delay tolerance

None

Outcome type known in adv.

Yes

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Figure 3. Example form and excerpt from observational data – Dept. of Cardiology (translated from Norwegian). Main actor: Resident 9 (R9). Information sources are Patient list (PATLIST), Nurse (NUR), the Electronic Patient Record (EPR), the Patient Chart (PC), and Patient (PAT). ‘I/O’ indicates the direction of the information exchange (In (I)/ Out (O)), and the main information types are Changes (NEW), Findings and examination results (FINDEX), and Medications (MED).

the Department of Cardiology during summer 2005 (translated from Norwegian).

Analysis of Communicative Behaviour In order to analyze data obtained through the structured observations a goal has been to develop a technique for transforming observational field data to visualizations of profiles of the communicative behavior of individual physicians or nurses, roles, or other groups of healthcare personnel. Visualization of communicative behavior provides the opportunity to elucidate variations among individual clinicians, as well as between different clinical

roles and activities in various hospital wards. We have used the narratives in the observations, background knowledge, and the codified information to classify communicative activities according to a limited set of predefined communicative acts (e.g. ‘inform’, ‘prescribe’ and ‘navigate into common understanding (NCU)’). The approach is described in detail in (Sørby & Nytrø, 2007). Figure 4 shows examples of communicative acts profiles for two residents working in different wards; Department of Cardiology (Resident9) and Division of Gastroenterology (Resident1). Figure 4a shows the profile of Resident9 during 22 pre rounds situations (total number of comm. acts:

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192), while Figure 4b shows the corresponding profile for Resident1 during 37 pre rounds situations (total number of comm. acts: 225). The figure shows the percentage distribution of the different communicative acts for the two residents. The unbroken lines show the communicative acts performed by means of paper-based sources (e.g. the patient chart and personal notes); the dotted lines show the use of electronic sources (e.g. the EPR) and the stippled lines show the use of human sources (e.g. colleagues and nurses). The figure reflects the variations in the communicative patterns of the two residents and indicates how new information and communication systems must be flexible and adaptive to the different users’ needs.

Findings: Study B The research performed in this part of the MOBEL project has mainly concentrated on the development of an approach to structured, focused observation and analysis of clinicians’ communication and information behavior. The main strength of such an approach is that it enables efficient field data recording at appropriate abstraction levels, and the recorded data requires a minimum of transcription before further analysis. The technique combines structured, predefined coding of field data with free-text fields, which enables both

quantitative and qualitative analysis of the data. This is useful for understanding the context of the communication and information behavior of the clinicians being observed, and leads to valuable insight into clinical work which is necessary when designing new information systems. The approach can also be used after the implementation of a new system in order to compare information and communication behavior before and after a new system has been introduced. Medical students were found to be particularly suited for performing observations in the hospital wards. Both clinicians and patients are used to being followed by medical students, and they are found less intrusive than system developers and computer science students. Due to their domain knowledge and their role as apprentices and future users of the information systems they can also function as mediators between the end users and the system developers, both during the data collection and the analysis phases of the studies. Combining the structured observation approach with knowledge and insights from in-depth observational studies as described in Study A provides a strong foundation for creating empirically grounded use cases and scenarios. These use cases and scenarios can then be used as a basis for further requirements engineering and development of new clinical information systems.

Figure 4. Example of comm. acts profiles for two residents in different wards during pre rounds situations. a: Resident9 (Dept. of Cardiology), # Comm. Acts: 192 (22 pre rounds situations), b: Resident1 (Div. of Gastroenterology), # Comm. Acts: 225 (37 pre rounds situations)

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STUDY C: USER INVOLVEMENT WITH ROLE PLAY AND LOWFIDELITY PROTOTYPING While Study A and Study B were based on field studies and observations of health care workers’ current information and communication practice, Study C ’Creative system development through dramatization of use scenarios’ had a different approach. In this study we wanted to investigate whether users (health care workers) are able to envision mobile, ergonomic and contextual aspects of clinical systems that do not yet exist, i.e. is it possible to involve users more actively in the design of future clinical information systems? The overall goal of Study C was to explore and develop role play as a method to involve users in the process of developing and understanding requirements for mobile clinical systems in hospitals. Based on current practice scenarios identified in Studies A and B, health care workers were invited to participate in workshops aiming at improving and developing the scenarios into future solutions.

Objectives: Study C Role play with low fidelity prototyping was chosen as the method in focus in this study for several reasons. First of all, role playing brings users and developers ‘out of the chair’ and into the physical, social, and embodied reality of mobile computing (Svanæs & Seland, 2004). This is important for development of clinical IT systems to support health care personnel’s mobile or nomadic work (Bardram & Bossen, 2005). Secondly, role play is a promising method for exploring current and future work practices and to understand hospital work without disturbing patients and clinicians. Thirdly, acting out everyday work situations and drawing screen sketches on paper require no technical skills. The role play and the prototyping enable users and system developers to achieve a common understanding about work processes, IT-

systems and possible solutions and thus become a tool for communication. And finally, even though role play has been used by several designers during the last decade, little empirical data is gathered on role play as a design method. Role play is not yet fully understood as a system development method, nor has it been methodically applied (Kuutti, Iacucci, & Iacucci, 2002). The research on role play with low fidelity prototyping in this study has mainly focused on understanding 1) how role play workshops can be organized and facilitated to enhance user involvement, and 2) strengths, weaknesses and when to use the method in a system development process.

Research Approach The empirical material for this study was gathered through eight role play workshops, where five were directly related to mobile devices in hospitals (Seland, 2006; Svanæs & Seland, 2004). The participants in the health related workshops were nurses and physicians from the local hospital. These workshops were held in a 1:1 architectural model of a future hospital ward, containing several patient rooms, meeting rooms, laboratory, and reception area. For each workshop we added and evaluated a new element, such as “involving a theatre instructor”, “involving real users”, “involving system developers” and “starting the day with field data”, and in this way the workshop format was gradually refined. After our first attempts to systematize role play as a method, we ended up with a general twostep format focusing on current and future work practice. In each workshop the participants were divided into two groups, and each group started brainstorming on current work practice. Some of the ideas from the brainstorming session were elaborated into a typical work scenario, where the participants either had to search for or register some information. As workshop organizers we encouraged the participants to focus on situations that were slightly complicated and that preferably

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involved several people. After the initial agreement on the scenario, the participants rehearsed their scenario, and showed their performance for the other group. In the second step of the workshop, the groups transformed their current practice scenario into a future practice scenario. The change from current to future practice was done by a process called ‘design-in-action’, where the participants improvised how they would use new information technology, services and mobile devices while acting. Each time a participant or an observer found a need for some information, the role play was stopped and the participants discussed whether they needed it or not. If the

Figure 5. Workshop description

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answer was ‘yes’ the idea was sketched down and the role play continued. A typical course of a workshop is presented in Figure 5. Each workshop day was closed by a discussion among the participants about ideas they had developed and their view on the applicability of role play as a system development method. In addition there was an evaluation session with seven health informatics developers, who discussed limitations and advantages of role play with low fidelity prototyping as a system development method, and in which phases of the system development process such methods could be useful. These discussions were video taped and transcribed, and

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the main results from these evaluations are presented in the next section.

Findings: Study C The main outcomes from the series of workshops were related to the organization and the format of such activities (for more detail see (Seland, 2006)). The system developers evaluating the workshops perceived the method as valuable for involving nurses and physicians in actively sharing information needs and developing requirements for new solutions. For example, being able to ‘freeze’ a play and ask “What type of information do you need now?” was considered useful since it was possible to get immediate feedback on suggested solutions. We as role play organizers observed that role playing was very natural for the health care professionals when they acted themselves. They required very little theatre warm up exercises. In fact, some nurses even started to improvise as if they were in their roles when they were asked to brainstorm a typical everyday scenario. One nurse asked another: “How long have you been ill?” etc. and a second nurse responded as if she were a patient. From this we concluded that role play is a natural means for health care professionals to describe their stories, and share their system requirements. When improvising with low-fidelity prototypes, the nurses and the physicians had many useful ideas. Some ideas appeared because the role play and the context of the play acted as cues, while other suggestions had been thought through in advance. In the second case, the concreteness of the role play situations and the possibility of sketching user interfaces on paper made it easy for the participants to present and to share their ideas with the other workshop participants. Role play with low-fidelity prototyping was perceived as useful for fast exploration of new ideas and for establishing a common ground for the system developers and end users. The approach

has also proven beneficial when system developers have little or no knowledge of the domain and when it is inconvenient to perform field studies. We believe that role play is a useful complementary technique to other system development methods to enhance user participation in early phases of the system development process. However, role play is not perceived as sufficient to create an overall understanding of a system and must be supplemented with other methods (such as those presented in Studies A and B) when designing mobile systems for hospitals.

STUDY D: CONDUCTING LABORATORY-BASED USABILITY EVALUATIONS OF MOBILE ICT IN SIMULATED CLINICAL ENVIRONMENTS The introduction of mobile and context-aware ICT into clinical work has raised new challenges related to how we evaluate this type of technology. While the human-computer interaction community has contributed to develop a well-established understanding of how to evaluate desktop computer systems, less is known about how to conduct usability evaluations of mobile ICT. In clinical settings, in particular, mobile ICT is likely to be used in rapidly changing use situations with different physical and social characteristics. In order to produce valid results, the research settings in which usability evaluations are conducted need to reflect these dynamic aspects of use.

Objectives: Study D This Part of the MOBEL project aimed to provide empirically grounded guidelines for how to conduct formative usability evaluations of mobile ICT supporting clinical work at the point of care (Dahl, 2007).

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Research Approach Evaluating mobile ICT for hospitals is challenging. In particular, conducting evaluations in realistic environments can raise difficulties. Because of patient safety and information confidentiality it is often not feasible to conduct such evaluations in the field (i.e. in actual hospital wards). In the field it can also be challenging to achieve a sufficient degree of control over the research setting, which is often required to conduct systematic evaluations of IT solutions. Likewise, conducting evaluations of mobile designs for clinical care in conventional usability laboratories is generally not an appropriate option. Standard usability laboratories are intended for evaluation of desktop applications, and generally not suited for recreating aspects like mobility and interaction in physical space (Kjeldskov & Skov, 2007). To assess the user-perceived usability of the different candidate solutions (see below) supporting mobility, we invited nurses and physicians to test prototypes as part of simulated point of care scenarios acted out in the same full-scale ward model that was used in Study C.

Supporting Point of Care Access to Medical Information with Ubiquitous Computing Much of the current part of the MOBEL project has been inspired by, and builds on principles, associated with context-aware and ubiquitous computing (UbiComp). A central notion in the ubiquitous computing paradigm is to integrate computers into the physical environment, thereby allowing places and objects that surround us to become physical interfaces through which we can interact with computers (Weiser, 1991). By enabling computers embedded into the physical environment to automatically sense and adapt to their use context, ubiquitous computing seeks to seamlessly integrate computer technology with activities taking place in the real world.

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Ubiquitous computing builds on the premise of changing use situations. Mobility and interruptions are often the cause of such change. This has made hospitals attractive candidates for ubiquitous computing solutions (Bardram, 2004).

Supporting Point of Care Access to Medical Information with Ubiquitous Computing: Prototypes Earlier work on interaction in digitally augmented space suggests that different configurations of the elements that form the physical interface of ubiquitous computing solutions might have an impact on how such solutions are experienced by users in situ (Svanæs, 2001). To further explore the dynamic relationship between physical entities and the role they play in interaction with point of care systems, we designed four candidate solutions. Each solution combined implicit or explicit (location or token-based) sensor input provided by users, with mobile or fixed (PDA-based or bedside terminal) output. Figures 6-9 show the evaluated design solutions. In Figure 6, information from a patient’s medical record is automatically retrieved and presented on the hospital worker’s handheld device as he comes within a predefined range (indicated by the white dashed line) of a given patient. In Figure 7, patient information retrieval is also based on the physical location of the hospital worker, but in contrast to the previous solution the information is automatically presented on a fixed bedside terminal. Figure 8 and Figure 9 show interaction design solutions in which user input is provided explicitly via physical tokens (i.e. barcode cards). In Figure 8, the hospital worker uses a mobile device with a token reader to scan a patient’s ID tag attached to her bed. Scanning the patient’s ID tag causes the corresponding medical information to be presented on the hospital worker’s device. Lastly, in Figure 9, the tokens and the computer device have changed roles. In this setup the hos-

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Figure 6. The hospital worker’s position in physical space is used to automatically retrieve medical information concerning a patient who is physically close. The information is presented on a portable device

Figure 7. Fixed bedside terminals replace the mobile device used in the interaction design solution shown in Figure 6

Figure 8. The location-aware zones surrounding each patient bed in Figures 6 and 7 are replaced by physical tokens (cards) containing bar codes. The tokens act as physical links to the patients’ medical record. Scanning the barcode on a token with a mobile device retrieves the corresponding information

Figure 9. Tokens and devices switch roles vis-à-vis Figure 8

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pital worker carries the tokens identifying each patient with him. Medical information concerning a specific patient is presented on fixed bedside terminals when his or her corresponding token is scanned. As the current study focused on what constitutes the physical interface of UbiComp solutions for point of care access to medical information, only simplified graphical user interface representations of medical records were used as terminal output.

Findings: Study D The test design and results are described in detail in Dahl and Svanæs, (2008). The main contribution of the laboratory evaluations and the major methodological implications of the study are summarized below.

Usability Criteria for Mobile ICT for Hospitals Based on observations of how the prototypes were used during the evaluation and feedback from the participants, we found that the usability of the evaluated prototypes were to a large extent determined by factors related to the immediate physical and social use situation. This included ergonomic aspects such as the ability to have both hands free for physical interaction with patients (e.g., examinations, assistance). It also included aspects relevant for the communication between the caregiver and the patient at the point of care. Examples of the latter included the extent to which a particular configuration of I/O devices had an interruptive effect on the conversation, and to which degree the configuration facilitated caregiver-patient communication (e.g., the possibility of using the terminal as a shared visual display during consultations).

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Methodological Implications Based on the findings above we will briefly discuss the methodological implications that usability evaluations of mobile ICT for hospitals raise. Realistic Test Environments can Help Detect Subtle Design Flaws and Qualities Feedback from the hospital workers participating in the evaluation suggested that the perceived usability of the different techniques was subject to factors beyond the terminal interface and the technical design as such. Often, this was related to subtle qualities of the designs, such as the possibility to share screen content with patients, or hide it from casual bystanders, and the ability to keep face-to-face contact with the patient. Generally, these are examples of aspects that are not captured with summative evaluations performed in conventional usability laboratories. In essence, this highlights the need for conducting usability evaluations of mobile ICT for clinical work in the physical environments and care scenarios that closely simulate actual work situations. Simple Prototypes Tested in Realistic Settings can Promote Reflections Among End Users From our experiences from the laboratory evaluations we also found that practical usability experiments, with simplified candidate solutions and end users performing simulated tasks in realistic settings, encourage participants to relate the testing to their own work experiences, and comment on the usability of the prototypes as if the testing was conducted in their actual work environment. We found that during experiments, simple prototypes can act as vehicles for reflection for participants. Such qualitative feedback from end users is often highly valuable in early phases of design, when functionalities and features to implement are not yet set in stone.

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The Contextual Nature of the Usability of Mobile ICT Raises the Need for Evaluating Multiple Design Solutions The current approach has contributed to identify subtle contextual factors that can affect the usability of the mobile designs. It has also highlighted that for mobile ICT and UbiComp the total user experience does not separate physical aspects of a system (ergonomic flexibility) from software aspects. Lastly, the current study has shown that approximating at design time the contextual factors that affect the usability in an immediate situation is difficult. This can be viewed as an incentive for designers of mobile ICT and point of care systems to consider a number of complementary solutions, and then implement the set of solutions found most suitable.

furthermore, it is unclear how the obtained insights into health care work actually do inform design. The studies presented in this chapter focus on this issue, or, in other words, on the transition from knowledge about the clinical workplace to specified requirements for supporting information systems. The transition is achieved as follows (see Figure 10): Extensive workplace studies result in empirically grounded scenarios. These scenarios can be played out in role playing workshops that result in functional specifications of supporting technologies. These functional specifications are turned into prototypes that will be assessed in experimental clinical settings in a laboratory environment. It is clear that the presented studies are in-depth investigations into important aspects of the transition from work practice to system requirements.

SUMMARY AND CONCLUDING REMARKS

Figure 10.

Of the different phases traditionally distinguished in requirements engineering, the elicitation phase has proved to be more problematic. The name of this phase suggests that requirements are out there, waiting to be elicited or discovered. It is clear that this is an oversimplification. Work practices, actors, communicative behavior and the like, are out there to be observed and analyzed. Requirements for information systems that support and are embedded in these work practices must be derived from these observations and analyses. Exactly how this derivation takes place is still very much an unresolved issue, and can be seen as one of the major challenges faced by systems development and requirements engineering. In health informatics this issue is very prominent. Successful applications that support real clinical work are few. An important reason for this could be that we still lack the knowledge as to which aspects of health care work should be taken into account in order to inform design. And,

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Linking the results into an integrated method for requirements elicitation and specification is the next step. The concept of ‘clinical scenario’ is pivotal in this. It bridges the workplace study to the role playing workshops and the prototype development. The exact specification of a scenario is, however, still an open question. It is clear that the Unified Modeling Language (UML) approach that restricts use case scenarios to manmachine interaction sequences is much too restrictive. Purely narrative fragments, relating sequences of events, are probably too unrestrictive. There has not yet been any substantial theory proposed about the exact information that must be contained in these scenarios and the format in which this information should be framed. Future research will focus on the role of the clinical scenario in bridging work practices and requirements. Major questions that will be addressed are: • • •

How are workplace observations accounted for in clinical scenarios? How are clinical scenarios specified? How do clinical scenarios inform system requirements?

ACKNOWLEDGMENT The research presented in this chapter was financed by the NTNU Innovation Fund for Business and Industry (Næringslivets Idéfond for NTNU), Allforsk Research, and Telenor Research and Innovation (R&I).

REFERENCES Bardram, J. E. (2004). Applications of contextaware computing in hospital work: Examples and design principles. In Proceedings of the 2004 ACM Symposium on Applied Computing (pp. 1574-1579). Nicosia, Cyprus: ACM.

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Bardram, J. E., & Bossen, C. (2005). Mobility work: The spatial dimension of collaboration at a hospital. [CSCW]. Computer Supported Cooperative Work, 14(2), 131–160. doi:10.1007/ s10606-005-0989-y Berg, M. (1999). Patient care information systems and healthcare work: A sociotechnical approach. International Journal of Medical Informatics, 55(2), 87–101. doi:10.1016/S13865056(99)00011-8 Coiera, E. (2003). Guide to health informatics, second ed. London: Hodder Arnold. Dahl, Y. (2007). Ubiquitous computing at point of care in hospitals: A user-centered approach. Doctoral Thesis, Norwegian University of Science and Technology, 2007:147. Dahl, Y., & Svanæs, D. (2008). A comparison of location and token-based interaction techniques for point-of-care access to medical information. Personal and Ubiquitous Computing, 12(6), 459–478. doi:10.1007/s00779-007-0141-8 Dingwall, R. (1997). Accounts, interviews, and observations. In G. Miller & R. Dingwall (Eds.), Context and method in qualitative research. London: Sage Publications. Ehn, P. (1988). Work oriented design of computer artifacts. Arbetslivscentrum (Swedish Center for Working Life). Greenbaum, J., & Kyng, M. E. (1991). Design at work: Cooperative design of computer systems. Hillsdale, NJ: Lawrence Erlbaum. Kjeldskov, J., & Skov, M. B. (2007). Studying usability in sitro: Simulating real world phenomena in controlled environments. International Journal of Human-Computer Interaction, 22(1-2), 7–37. doi:10.1207/s15327590ijhc2201-02_2

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Kuutti, K., Iacucci, G., & Iacucci, C. (2002). Acting to know: Improving creativity in the design of mobile services by using performances. Paper presented at The Fourth Conference on Creativity & Cognition, Loughborough, UK. Kyng, M. (1994). Scandinavian design: Users in product development. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems: Celebrating interdependence (pp. 3-9). New York & Boston: ACM Press. Melby, L. (2007). Talk, pen, and paper: A sociological analysis of the use of medical information and communication practices among hospital physicians (in Norwegian). Doctoral Thesis. Norwegian University of Science and Technology, 2007:80. Norman, D. (1988). The psychology of everyday things. New York: Basic Books. Norman, D., & Draper, S. W. (1986). User centered system design: New perspectives on human-computer interaction. Lawrence Erlbaum Associates Inc. Randall, D., Harper, R., & Rouncefield, M. (2007). Fieldwork for design. Theory and practice. London: Springer. Seland, G. (2006). System designer assessments of role play as a design method: A qualitative study. In Proceedings of the 4th Nordic Conference on Human-Computer Interaction: Changing roles (pp. 222-231). New York: ACM Press. Silverman, D. (2001). Interpreting qualitative data. London: Sage Publications. Sørby, I. D. (2007). Observing and analyzing clinicians’information and communication behaviour: An approach to requirements engineering for mobile health information systems. Doctoral Thesis. Norwegian University of Science and Technology, 2007:220.

Sørby, I. D., Melby, L., & Nytrø, Ø. (2006). Characterizing cooperation in the ward: Framework for producing requirements to mobile electronic healthcare records. International Journal of Healthcare Technology and Management, 7(6), 506–521. Sørby, I. D., Melby, L., & Seland, G. (2005). Using scenarios and drama improvisation for identifying and analysing requirements for mobile electronic patient records. In J. L. Maté & A. Silva (Eds.), Requirements engineering for sociotechnical systems (pp. 266-283). Hershey, PA: Information Science Publishing. Sørby, I. D., & Nytrø, Ø. (2007). Analysis of communicative behaviour: Profiling roles and activities. Studies in Health Technology and Informatics, 130, 111–120. Star, S. L., & Griesemer, J. R. (1989). Institutional ecology, translations, and boundary objects: Amateurs and professionals in Berkley’s Museum of Vertebrate Zoology 190739. Social Studies of Science, 19, 387–420. doi:10.1177/030631289019003001 Strauss, A. L., Fagerhaug, S., Suczek, B., & Wiener, C. (1997). Social organization of medical work. New Brunswick: Transaction Publishers. Svanæs, D. (2001). Context-aware technology: A phenomenological perspective. Human-Computer Interaction, 16(2), 379–400. doi:10.1207/ S15327051HCI16234_17 Svanæs, D., & Seland, G. (2004). Putting the users center stage: Role playing and low-fi prototyping enable end users to design mobile systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2004) (pp. 479-486). New York: ACM Press. Timmermans, S., & Berg, M. (2003). The gold standard. The challenge of evidence-based medicine and standardization in healthcare. Philadelphia: Temple University Press.

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Weiser, M. (1991). The computer for the 21st century. Scientific American, 265(3), 66–75. Westbrook, J. I., & Gosling, A. S. (2002). The impact of point of care clinical systems on healthcare: A review of the evidence and a framework for evaluation. Sydney, Australia: Centre for Health Informatics, University of New South Wales.

KEY TERMS AND DEFINITIONS Low-Fidelity Prototype: A prototype that is sketchy and simple and often is created with foam models and drawings on paper. It has some characteristics of the target product, but may differ in interaction style, visual appearance or level of detail. Low-fidelity prototypes are often developed to visualize or test early design ideas. Point of Care Clinical Systems (PoCCS): Information systems that support clinicians by allowing easy input and retrieval of information as close as possible to where care takes place (Westbrook & Gosling, 2002). Role Play: In this chapter, role play is defined as the acting of relevant and representative scenarios from everyday work situations by prospective end users. The users either play their usual role or a role they know very well. As soon as the role play participants have agreed on setting, main actors, and most important plot, a scenario is improvised. Socio-Technical Approach: An approach that recognizes the interrelatedness of social and technical aspects of an organization. Socio-technical

insights may be used to inform the design of computer systems. Structured Observation: The planned watching and recording of behavior and events as they occur within a well-known or predefined environment Ubiquitous Computing: A human-computer interaction model introduced by Mark Weiser (Weiser 1991), where computer technology to a high degree allows itself to reside silently into the background of the users’ attention. A central notion in the ubiquitous computing paradigm is to integrate computers seamlessly into our everyday activities and physical environments. By enabling these embedded computers to automatically sense and adapt to their use context, ubiquitous computing seeks to render the computer “invisible” in use. Usability: The extent to which a product can be used by specific users to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use (ISO 9241). User-Centered Design Approach: A collection of methods where knowledge about users and their involvement in the design process is central.

ENDNOTES 1



The MOBEL project included participants from Department of Sociology and Political Science, Department of Linguistics, Department of Electronics and Telecommunication, Department of Computer and Information Science, and Faculty of Medicine at The Norwegian University of Science and Technology (NTNU).

This work was previously published in Handbook of Research on Advances in Health Informatics and Electronic Healthcare Applications: Global Adoption and Impact of Information Communication Technologies, edited by Khalil Khoumbati, Yogesh K. Dwivedi, Aradhana Srivastava and Banita Lal, pp. 52-73, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Processing and Communication Techniques for Applications in Parkinson Disease Treatment Álvaro Orozco-Gutiérrez Universidad Tecnológica de Pereira, Colombia Edilson Delgado-Trejos Instituto Tecnológico Metropolitano ITM, Colombia Hans Carmona-Villada Instituto de Epilepsia y Parkinson del Eje Cafetero – Neurocentro, Colombia Germán Castellanos-Domínguez Universidad Nacional de Colombia, Colombia

ABSTRACT This chapter deals with processing and communication techniques for Parkinson’s disease treatment applications. First, the authors summarize the background of physiological dynamics related to degenerative disorders of the central nervous system and common clinical procedures using microelectrode recordings (MER) for detecting brain areas. This summary is followed by a discussion of different aspects related to the inclusion of a DOI: 10.4018/978-1-60960-561-2.ch413

communication platform for specialized assistance by expert neurologists to remote hospitals. Next, the authors present different techniques derived from biomedical signal processing for analyzing non-stationary and complexity components, with the aim of developing an automatic recognition system that will support computer-based clinical decisions in detecting brain areas. In addition, they explain each component of medical teleconsult. Finally, they discuss the whole integrated system, including the advantages, limitations and viability of this clinical procedure based on modern technology resources.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Processing and Communication Techniques for Applications in Parkinson Disease Treatment

INTRODUCTION Parkinson’s disease is a degenerative disorder of the central nervous system that often impairs a person’s motor skills and speech. It is characterized by muscle rigidity, tremor, a slowing of physical movement (bradykinesia) and, in extreme cases, a loss of physical movement (akinesia). Deep brain stimulation can be applied to the thalamus, the pale or the nucleus subthalamicus. The success of neurosurgery mainly depends on the ability of the specialist in detecting the area where the microelectrode is found. In most cases, the target area is the globus pallidus internus (GPI) or the nucleus subthalamicus (STN), where the neuro-stimulators are located. These treatments use a stereotaxic apparatus and imaging techniques to guide the electrode implantation into the correct area of the brain. The microelectrode crosses through regions with shooting characteristics (action potentials), and its amplified and filtered output is heard by a neurophysiologist using a speaker. Thus, the specialist identifies the brain area by listening to the rhythm created by the action potentials from nearby neurons. These signal types are not stationary due to the presence of action potentials, thus the detection of each area becomes a very complex task. Moreover, it is known that information contained in biological signals is highly dependent on numerous biological aspects with nonlinear structure, which means that the biological signal is not the sum of its components. Automatic procedures have several advantages over clinical perception because computer-based clinical support offers more information and evidence, high objectivity and different communication possibilities, which are frequently used in decision committees of specialists with the aim of evaluating the case from different points of view. Ever since technical advances have allowed the transfer of electronic information, the possibility of remote diagnosis has been studied in order to enable access to distant resources. Telemedicine has become an important alternative for isolated

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populations and for medical centers that lack specialists or require the assistance of experts (Field, 1996). There are two things that form the base of medicine practiced at a distance; these are telepathology and teleconsult. Telepathology is the interaction between signals (1D or 2D) and clinical reports where the primary diagnosis is given by a doctor in a remote location. Teleconsult is the interaction between signals and medical records where the primary diagnosis is given by the doctor at the medical center. The purpose of teleconsult is to provide a second opinion by a specialist to either confirm the diagnosis or to help the local doctor make a correct diagnosis (Norris, 2002). Further development of telepathology and teleconsult is limited by the large file sizes generated by medical equipment. For this reason, the development of a compression tool is necessary for transferring information in less time. Moreover, with the aim of facilitating the remote perception processes, it is very important to create an audio and visualization application in order to provide the remote specialist with the tools for generating an accurate and timely clinical verdict. This kind of collaborative procedures can be analogically taken as a knowledge network (Cataño et al, 2008).

PARKINSON’S DISEASE: TREATMENT AND SURGICAL PROCEDURE The basal ganglia are part of a comprehensive cortico-basal- thalamic-cortical brain circuit and they are anatomically and functionally separated in parallel.

Normal Physiology In the most accepted model, the neurons of the GPI (Globus Pallidus Internus) and NS (Substantia Nigra) have a pattern of similar high-tone discharges. The activation of the direct path causes

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the inhibition of GPI and NS, which translates into thalamic desinhibition, providing the excitation for the thalamic motor pre-central areas. The effect of this is a positive feedback for the initiated crustal movement. Indirect activation produces the inhibition of GPE (Globus Pallidus external) and second facilitation of SN (Subthalamic Nucleus), which sends excitatory impulses toward GPI / NS and inhibition of the thalamus and brain stem occurs. The end effect of this route is a negative feedback for the movement (inhibition of unwanted movements or as a signal to stop moving). Dopamine modulates the effects of glutamatergic projections from the cortex, which exerts a dual effect on striatal neurons: D1 excites on the direct track and by inhibiting D2 in the indirect track.

Pathophysiology in Parkinson’s Disease In this disease it is postulated that there is a hyperactivity of the SN and increased activity of neuronal GPI / NS that leads to an excessive inhibition of the thalamus and cortex of the brain stem engine system. The reduction in the activity of dopamine receptors caused by the dopaminergic deficiency reduces the inhibition of the indirect route and diminishes the excitation of the direct route. The final result is an excess activation of basal ganglia output that leads to an overinhibition of the thalamus and motor systems. Excessive tonic inhibition on the thalamus and the reduced cortical activation is responsible for the hypokinesia. Fluctuations in the physical and hypersyncronic discharges of the thalamus, SN, GPI and GPE, are related to the state and possibly to Parkinsonian tremor. The Nigrostriatal dopaminergic system operates the internal circuitry of the basal ganglia and is responsible for maintaining the stable system of motor control. The dopaminergic decrease leads to an increase in synchronization and oscillation of neuronal activity. When there is a dopaminergic decrease, intermittent administration of L-Dopa

can act as a destabilizer when not exposed to regulated concentrations. These oscillations between periods of extremely low and high dopaminergic activity can force the basal ganglia to an abnormal adaptation. This pattern leads to a pulsating dysregulation of genes and proteins and altered patterns of neuronal discharge, inducing the emergence of secondary dyskinesias to the use of L-Dopa.

Epidemiology and Medical Treatment of Parkinson’s Disease Parkinson’s syndrome is a degenerative disease of the central nervous system that progresses slowly, and is often intractable to medication. This disease is characterized by a variety of movement disorders such as tremor, rigidity, slowness in gait, postural instability, and so on. Multiple etiologies have been proposed for this disease and the disease is probably multifactorial. It is said that about 1-2% of the world population over 65 years old suffer from this disease and 15% of those were diagnosed before 50 years of age (Aminoff, 1998). The symptoms of Parkinson’s disease begin to appear when approximately 50-80% of dopaminergic neurons of the substantia nigra have died. Therefore, an investigation has begun of various drugs such as antioxidants, and anti-NMDA antagonists, which have a neuroprotective role by preventing neuronal death. However, the progress of the symptoms with the drugs has not, to this day, given encouraging results or conclusions on the matter. When there are already symptoms and there is confidence in the diagnosis of the disease, it is recommended to start treatment of symptoms with a dopamine agonist, because it has been found to have the lowest rate of complications, which are more severe when the drug levodopa is chosen as an initial treatment and the patient has taken large doses. If monotherapy with such drugs does not provide a satisfactory control of symptoms, a combination therapy of levodopa plus an inhibi-

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tor of catechol-O-methyltransferase (COMT) is recommended, which increases its elimination half-life. Around 40-80% of levodopa users will develop dyskinesias after 5 to 10 years of treatment, with different intensities, but that sometimes can be very disabling due to loss of efficiency and motor fluctuations (Juri et al, 2005). Surgery is considered when the Parkinsonism cannot be adequately controlled with medical therapy or complications related to treatment with levodopa appear.

Surgical Techniques for Parkinson’s Disease Surgical treatment for Parkinson’s disease dates back to the 1930’s, and consisted of complex procedures such as resection of the primary cortex, lenticular nucleus, caudate nucleus and pallidus. The surgical technique was a breakthrough in the 1990’s, a period in which they conducted numerous studies on the pathophysiology of the disease, which described an independent motor circuit involved in controlling the movement and was associated with motor symptoms of Parkinson’s disease. The first surgeries were injuries in the motor circuit and were refined through studies in rats. Years later they began to perform these techniques in humans and the results were an improvement especially in the motor symptoms of the disease. These surgical techniques are summarized below. a. The thalamotomy using radiofrequency to injure several nuclei of the thalamus, especially Vim (Ventral Intermediate Nucleus) is a procedure that has proved to be very effective in decreasing the tremors in most patients; however, improvement was not shown in bradykinesia and normal running. The bilateral thalamotomy that is needed to relieve the bilateral symptoms causes serious cognitive problems and difficulty in speech. For these reasons and based on new surgeries

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with more beneficial results for patients, the thalamotomy is no longer performed as an elective treatment for Parkinson’s disease, and is restricted to the treatment of tremors when they are predominant in Parkinson’s disease or essential tremor (Linazasoro et al, 1997). b. The unilateral pallidotomy improves up to 86% of levodopa-induced dyskinesias, an improvement of approximately 45% in the UPDRS scale in the pharmacological off state (without medication), and has long-lasting effects, up to 8 years, but with progression of the hemibody that does not benefit from the injury (Linazasoro et al, 2002). After the bilateral pallidotomy, hypotonia, disastria and negative effects on cognition and gait have been reported. For these reasons at present this procedure is done only unilaterally for the treatment of severe dyskinesias produced by chronic use of Levodopa in Parkinson’s disease. c. Deep Brain Stimulation (DBS). It is currently the preferred surgical procedure for treating Parkinson’s disease as it provides a significant improvement in symptoms such as tremor, rigidity, slow movement and gait. The area that can be stimulated varies according to the criteria described above in the thalamus and the globus pallidus in the subthalamic nucleus. The latter, in all the studies, is the one that shows the best results in the control of the disease’s motor symptoms. The electrode that is in the brain communicates through an insulated wire, which passes under the skin of the head, neck and shoulder, to the neurostimulator or generator implanted under the collarbone. In some cases, the neurostimulator or generator may be implanted below the chest or under the skin of the abdomen. The neurostimulator is programmed with the parameters that have been tested to make the proper adjustments

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whenever necessary according to the clinical improvement of the patient. The main advantages of the DBS in the SN is the large number of studies that have reported the success of this technique as a significant improvement of all symptoms, improvement of dyskinesias, and reduction of up to 40% of the daily requirements of Levodopa (Linazasoro et al, 2002). Another advantage of using the DBS is the absence of a permanent brain injury. Consequently, bilateral procedures can be performed with relatively little risk and changes to the parameters of stimulation and places of contact electrode to improve Parkinsonian symptoms are still possible in the future. The main side effects associated with this technique are likely to be bleeding in about 2.5% of the surgeries and complications in about 2%, related to the stimulation system because of infections and mechanical problems (Linazasoro et al, 2002). In our environment, the main drawback of this procedure is its high cost, the largest limitation being the high cost of neurostimulators. Smaller costs include the need for a second procedure under general anesthesia in order to implant the neurogenerator on the pectoral region, changes of the neurogenerator after a few years due to depletion of the battery and a postoperative hardware failure rate that varies in the different studies from 10 to 23%. d. Subthalamotomy was introduced as a surgical technique in the International Neurological Restoration Center (CIREN) from Havana Cuba in 1995, beginning with the unilateral implementation, then the bilateral subthalamotomy in two-times and in one-time (Pedroso et al, 2006). Subsequently, other researchers have performed this technique around the world. In the medical literature the results of the subthalamotomy have been reported as an alternative to the use of deep brain stimulation in the SN. In

addition, there is no need for a second surgery under general anesthesia and an absence of complications of the stimulator system such as infections, broken electrodes, the patient living with external devices, and so on. The main disadvantages of this technique are the lack of experience and few studies with a large number of patients to evaluate the procedure, which makes it more difficult to define the ideal size and location of the lesion as well as the risk of side effects due to the permanent injury such as heart attacks, bleeding, cognitive side effects, and so on (Linazasoro et al, 2002). It has been found that the highest percentage of improvement in patients with Parkinson’s disease was 50% (Walter & Vitek, 2004). It was shown that the bilateral injury not only improves rigidity, tremor and dyskinesia of the legs and arms, but also improves axial motor, postural stability and gait (Tseng et al, 2007), leading in some patients to complete elimination of motor fluctuations and to a reduction in the consumption of drugs (Alvarez et al, 2003), confirming that the procedure is one of the best in the treatment of this degenerative brain disease. When talking about the cognitive aspect, impairment as a result of bilateral injuries (Tseng et al, 2007), (Alvarez et al, 2003) was not observed; on the contrary, there was an unexpected improvement in the outcome of the neuropsychological test. One of the most frequent complications of subthalamotomy is the hemichorea-ballism. The occurrence of this complication depends on the location and size of the lesion and has a spontaneous and gradual resolution (Alvarez et al, 2003), and it is rarely presented as a difficult problem to handle. The benefit of subthalamotomy lasted for about 3 years (Tseng et al, 2007), (Alvarez et al, 2003). After this period it was found that some patients showed tremor when resting.

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Surgical Procedure for Parkinson’s Disease Before the surgery the patient is evaluated by Neurology, Neurosurgery, Neuropsychology, and Psychiatry, by the coordinator of the abnormal movement board and the Medical Board, who define if the patient is a candidate for surgery. Some days before the surgery, the patient undergoes a three-dimensional brain nuclear magnetic resonance (NMR), with and without contrast, with more axial cuts in T1-T2 and coronal in T2, IR, in order to obtain an anatomical location of the brain and anterior commissure nucleus of the base, on the axis line AC - PC (anterior commissure – posterior commissure) in the third ventricle. The anti-parkinsonian medication is suspended 12 or 24 hours before surgery. The time in which it has to be withdrawn depends on the degree and severity of the patient’s symptoms, in order to assess the patient in surgery in a pharmacological off state. In addition to this, prior to surgery the patient must have blood, glucose, and coagulation tests, creatinine, ECG and uroanalysis. On the day of surgery, the patient is placed in a stereotactic frame, then a brain-dimensional contrast computerized tomography (CT-TAC) is performed for the location of the Cartesian coordinates X, Y and Z and the subsequent fusion of images with the previously taken NMR. The neuroimages are conducted in such a way that there is no space between cut and cut, as thin cuts approximately 1.25 mm thick are preferred. As a result, approximately 256 cuts are performed in the NMR and 126 cuts performed in the CT-TAC. Once the information is entered into a program specially designed for the exact location of the point where the procedure is needed, in this case the subthalamic nucleus, the program blends the images acquired by CT-TAC and NMR for a more clear and accurate picture of each of the brain structures. The target is chosen according to established coordinates, taking as its starting point the mid-commissural line.

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For the core subthalamic nucleus X = ±12, Y = −4, Z = −4 For the Globus Pallidus internus X = ±19, Y = 2, Z = −6 For the Vim of the thalamus: X = ±13, Y = −4, Z = +1 When the point is located, the next step is to define the entry point and the journey in encephalon, where ridges should be avoided as this is where arteries are located. When the patient arrives at the operating theater, a mild sedative (Midazolam) is given, which permits waking the patient required. Furthermore, local anesthetic is given and an incision is made, through which electrodes are first inserted that allow recording and intraoperative stimulation. The electrical brain recording equipment (ISIS MER - Inomed or Lead-Point-Medtronic), records and recognizes the electrical signals coming from the basal nucleus, correlating them with 2D and 3D images of the electrode trajectory. In this way, an exact location of the nucleus is provided, to injure or to stimulate (Macías et al, 2005). A modification of neuronal activity in response to passive movement or the movement of the limbs, face, neck and trunk are used to locate the sensoriomotor region, located in the dorsolateral portion of the subthalamic nucleus. Using electrical stimulation equipment, stimulation is performed with pulses of current (0.1 and 5 mA, 0.3 ms pulse width, frequency of 60 to 180 Hz), while assessing the therapeutic effects, the possible induction of dyskinesias and the relationship with surrounding structures (Flores et al, 2004). The best place to make the injury is decided when the patient has noticeable improvement in their symptoms by stimulating this point. In the case of subthalamotomy, it necessary to then change the electrodes to the injury instrument, which uses high temperatures. The region which is going to be injured is first heated to temperatures of 42, 50 and 60 °C during 10 second periods separated by intervals of 1 or 2 minutes. Subse-

Processing and Communication Techniques for Applications in Parkinson Disease Treatment

quently, the lesion is heated to a temperature of 70 °C during approximately 60 seconds (Alvarez et al, 2003). In the literature it is found that the injury may be produced with temperatures of 60 to 80 °C, with an area of approximately 3 mm, which can vary among patients. After inducing the injury or after placing an electrode in a DBS position, the stereotactic frame is withdrawn, the patient leaves the operating theater, is observed in recovery for several hours, and is then moved to a standard room. Patients are permanently monitored for 24 hours and are then placed in ambulatory care for approximately 7 days. A TAC control is suggested in the first 48 hours after surgery and a NMR at 6 months and 1-2 years after surgery. In some cases the size of the lesion cannot be assessed by these means (Alvarez et al, 2003). The patient must attend check ups with the neurosurgeon and neurologist in order to be evaluated with the CAPSIT-PD protocol, to assess the patient’s response to surgery and consider a reduction of levodopa dosage.

DIGITAL PROCESSING OF MER SIGNALS FOR BRAIN ZONE RECOGNITION The brain tissue of all organisms is a highly interconnected network of neurons that generates pulse train sequences as a result of the temporal and spatial aggregation of their action potentials. These pulse trains are characteristic of areas, functions and phenomenological states in the white and gray matter around the brain. Several surgical procedures require the precise location of these brain areas, as is necessary in Parkinson’s disease (Guridi & Rodríguez-Oroz, 2004). Usually, in addition to the use of some imaging techniques and planning of the trajectory to the target, the interpretation of the signals provided by a record of microelectrodes is used for the location of a brain area. Such activity picked up

by microelectrodes is known as MER signal. The non-stationary nature of these signals presents high frequency components, also known as spikes which appear in short moments of time, and low frequency components with longer duration. Due to these characteristics, it is necessary to use computational techniques that conform to this strong dynamic in order to allow feature extraction for the identification of brain areas. A methodology for localizing the four brain areas (thalamus, subthalamic nucleus, and zona incerta and substantia nigra pars reticulata) from the extraction of characteristics that describe the dynamics of MER signals is shown throughout this section. First there is a brief description of the electrical nature of MER signals and of the database used in this document, which motivates the use of computational techniques for timefrequency analysis. Then, we study the theory of Hidden Markov Models (HMM) that will be useful for the subsequent classification of the dynamic characteristics previously obtained. The final section presents some results and comments. MER signals are non-stationary signals composed by neuronal activity, artifacts, neuronal background noise and electronic noise. These signals can be described by the mathematical model of Equation (1) and the frequency characteristics shown in Figure 1. ξ(t ) = λ1 (k, t )ξ1 (t ) + λ2 (k, t )ξ2 (t ) + η(t )

(1)

Database Specifications The developments described in this chapter were obtained using the Polytechnic University of Valencia’s database (UPV-BD). The UPV-BD consists of MER signals that were recordings of 16 surgeries on 10 patients with Parkinson’s disease, 6 bilateral and 4 unilateral in the STN. The surgeries were carried out in the General Hospital of the University of Valencia, and labeled by specialists

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Figure 1. MER signals from brain zones, thalamus (Tal), subthalamic nucleus (STN), zona incerta (Zi) and substantia nigra pars reticulata (SNr). (©2008 Used with permission).

in both neurophysiology and electrophysiology, according to the affected region. The equipment used in the acquisition was the LEADPOINTTM Medtronic. The sampling frequency is 24 kHz with 16-bit resolution. Each recording lasts approximately 10 s. In total there are 177 records discriminated into sets of 43 thalamus signals, 25 subthalamic nucleus signals, 24 signals from the substantia nigra reticulate zone and 85 signals from the zona incerta. It should be noted that in bilateral surgical procedures, records obtained by each side are statistically independent and not correlated. Figure 2 shows a MER signal from this database with the different components of the model (1).

Procedure Outline The methodology developed for the identification of brain areas follows the scheme of Figure 3.

From records of MER signals, a pre-processing stage aims to reduce electronic noise and artifacts. The signal is analyzed with mathematical transformations that allow the extraction of timefrequency information using two approaches: quasi-stationary and non-stationary analysis. Later, a dynamic classifier for discrimination of the brain area is used.

Preprocessing A reduction of artifacts, due to patient movements, that generate high values on the acquisition compared with the typical signal is carried out. Similar to the spikes detector proposed by (Quiroga et al, 2004) an event detector is used to eliminate the artifacts. The noise level of the signal is estimated, then all the events that exceed ± 10 times the noise level are suppressed (the values of the samples are zero).

Figure 2. MER signal’s components, a) subthalamic nucleus’ raw signal, b) background noise, c) local neural activity

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Figure 3. Methodology for the identification of four brain zones by using MER signals processing

Regarding the reduction of the electronic background noise from the MER signal, threshold techniques based on wavelet transform are used with the hypothesis that the noise has less energy than the useful signal. This is a characteristic reflected in the different coefficients of the transform, which allow elimination or change. The wavelet transform performs better approximations for signals with noise modeled as a white Gaussian process with μ = 0 and variance σ = 1, so for our purposes we assume this noise model. Different methods based on the wavelet coefficients threshold (Donoho & Johnstone, 1994) like soft and hard thresholding, and methods for threshold with cyclical schemes have been used. Let x(t) be a MER signal. The process of noise reduction involves the reconstruction (implemented by the inverse wavelet transform) of the signal from the remainder of the coefficients obtained by applying the wavelet transform directly to the signal and the γ that corresponds to the contribution of the noise in each coefficient (Figure 4 shows this representation). Here, W{y(t)} are the wavelet coefficients of the signal with noise, x(t) is the signal ideally free of noise and γ is the estimation of noise in the signal. The threshold value reflects a compromise between accuracy and smoothing. A small threshold value results in an output signal with shape

near the input signal so that noise removal is still insufficient. By contrast, a very high threshold value produces an output signal with many wavelet coefficients equal to zero, that is, too much distortion and loss of information when performing the reconstruction for our application (Jansen, 2001).

Characterization The first strategy is to highlight the spectral information of the MER signal using the Short Time Fourier transform (STFT) is described in Figure 5. Where g(t − t0) is a sliding window that locates the analysis within a specific time where the signal can be considered quasi-stationary, allowing the extraction of information from both the time and frequency spaces. The important parameters to consider are precisely those that involve the window, for example, the width and type of window, taking into account the features you want emphasize and also considering that the interaction between windows should be quasi-stationary (Delgado et al., 2009). For example, a thin window will highlight the high frequency components while a wide window will highlight the low frequency components, at the expense of losing time localization. A special case is when this window is a Gaussian distribution described in Figure 6,

Figure 4. Block diagram related to noise reduction

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Processing and Communication Techniques for Applications in Parkinson Disease Treatment

Figure 5. Block diagram related to the extraction of spectral information

which is known as the Gabor transform and introduces a new parameter α that represents the standard deviation of the distribution. The larger this value α the narrower the window. A systematic analysis of these parameters found that using a 80 ms Hanning type window gave the higher scores (70%) in the identification of the brain area. This was the best approach to quasi-stationary segments of the MER signal compared within lengths [50 ms, 1 s] and different window types (Hanning, Hamming and Blackman). In the case of the Gabor transform, the systematic analysis of the values for α in a range 0.1 to 10 resulted in a best error value of 4.55%, improving the average identification accuracy obtained with the STFT. However, the width of this window is the paradox of this transform (established by the principle of Heissengber). Once a fixed time window length is chosen the spectral components that are highlighted in the frequency are also limited to a fixed bandwidth, and vice versa. This is a drawback if you want to represent the strong dynamism of the MER signal

Figure 6. Block diagram related to the Gabor transform

where a fixed-width window in the time-frequency space is not adequate as we shall see later. In order to overcome this limitation the next step is the use of the wavelet transform (WT) described in Figure 7. Where Ψ(t) is called wavelet function, translated by τ and scaled by s, so that any function of square-integral energy can be fully represented on a basis of orthogonal vectors Ψ(t). This transform allows extraction of several characteristics of the signal in the time-frequency space (t-f space) that is better suited to the qualities of the MER signal than the STFT. The discrete version of this transform (DWT) is commonly used because it can be implemented efficiently using filter banks and the dyadic algorithm of Mallat. Before continuing with the analysis of the DWT, the t-f space should be analyzed in detail. The measures obtained from this analysis are well suited for the discrimination of brain zones. Feature extraction from the t-f space can be done through the contours of time and frequency as shown in Figure 8. In the spectral case, the location of the WT around the instantaneous frequency (IF) of each component is of great interest in order to estimate the dynamism of the frequency contour. It is defined as the average of the frequencies in an instance of time or also called time-conditional average frequency. In the case of the time contour, the power spectrum is used as a measure of the dynamic behavior of the signal, resulting in an

Figure 7. Block diagram related to the wavelet transform

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Processing and Communication Techniques for Applications in Parkinson Disease Treatment

Figure 8. Time-frequency space and time and frequency contours

approximation of the dynamics of the amplitude of the signal. Due to the behavior of MER signals, which change according to the patient and cerebral area, it cannot be easily determined which wavelet is best suited for each case, because certain wavelets with different properties perform well or poorly depending on the brain region. The selection of wavelets was conducted by a systematic study of the three families of functions called Coiflet, Daublet, and Symlet. These families share an excellent relationship between support and van-

ishing moments, which are characteristics that will determine the representation obtained by the transform. Figure 9 shows that wavelet Coiflet of order 1 presents the highest score (78%) in the recognition of four brain areas, using a method of identifying a linear Bayesian classifier. A major modification of our analysis compared to others is to dynamically change the parameters of wavelets (adaptive wavelet transform AWT) as the signal changes (e.g. vanishing moments). This modification also reveals certain characteristics by making an approximation of the structure of the signal (Jansen, 2001), (Piella & Heijmans, 2002). The implementation of these adaptive schemes is made by lifting schemes (Piella & Heijmans, 2002), (Sweldens, 1998). These schemes not only reduce computing time over the traditional filter banks but also allow changing the filter order as the signal passes through (Jansen & Oonincx, 2005). The construction of the wavelet transform through the lifting schemes developed in (Sweldens, 1996) and shown in Figure 10 is the iteration of the following three steps: •

Split: Split the original signal into two different sets: even points xe[n] = x[2n] and

Figure 9. Average percentage of success yielded by a linear Bayesian classifier for three wavelet functions a) Daublet 1, b) Symlet 4, c) Coiflet 1. The higher score was presented by the Coiflet 1

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Figure 10. Adaptive lifting scheme

•

odd points xo[n] = x[2n − 1]. This operation is also called wavelet lazy Prediction: The coefficients of detail d[n] are generated as the error in predicting x[n] using information from xe[n] and the operator of prediction P

(

)

xH n  = xo n  − Pdn xe n  •

(6)

Update: Combining xe[n] and xH[n] obtains xL[n], which corresponds to the lower frequency components and represents an approximation of the original signal x[n]. This is achieved by applying an update operator to the coefficients of wavelet plus xe[n].

(

)

xL n  = xe n  − Udn xH n 

(7)

The decision operator D determines, at the sample n, the adaptability criteria that must be used. These criteria are based on local characteristics of the signal, which perform a transformation from RN to R. To obtain the decision dn it is necessary to determine whether the value delivered by the adaptability criteria is within the range given by the thresholds γ and γ′. The adaptability criteria are calculated within an analysis window that can be of fixed or variable size. The size of this analysis window depends on the order of the filters used in the current sample. To obtain a measurement of the signal’s local characteristics both vector gradient and statistical moments are taken into account.

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Estimation of the thresholds can be done from the value of the signal calculated on the analysis window in each sample in an online fashion; in which case it is not necessary to calculate these thresholds before implementing the adaptive lifting scheme. The methods used in this paper for calculating the threshold were the universal value and the adaptive threshold of Stein. The biorthogonal filters that were used in this document were those proposed by DeslauriersDubuc (Deslauriers & Dubuc, 1987), which have an acceptable relationship between the support size of the function and the order of approximation (order of the filter). The support of these filters is given in the interval [1 - 2r, 2r - 1].

Dynamic Classification The dynamic classification is based on Hidden Markov Models. A Hidden Markov Model is basically a Markov Chain (Huang et al., 2001), where the observations upon leaving a random variable are generated according to a probability associated with each state (Huang et al, 2001). The strength of Hidden Markov Models is their ability to model sudden changes in the dynamics of the data, a situation that is often present in physiological signals. Another crucial factor is its ability to segment observations on statements of information in a completely unsupervised way. Formally, a Hidden Markov Model of N states is defined by: A = {aij} A probability transition matrix that denotes the probability of making the transition from state i to state j, B€=€{bi(k)} is an output matrix of probability, where bi(k) is the

Processing and Communication Techniques for Applications in Parkinson Disease Treatment

Figure 11. Discrete hidden Markov model structure

on the minimum distance from the new model to the class models.

General Performance

probability of emitting the symbol Vk when comes from state i. Define X = {xn} as the output seen from the hidden Markov chain. The sequence of states S = s1,s2,s3,…, is not observed. Since the observation does not come from a discrete space but from a continuous space, the discrete distribution of the output can be replaced by a continuous probability density function bi(k). Among several alternatives, a combination of multivariate Gaussian density functions is usually employed. π = {πi} is an initial distribution to the states. For simplicity, the set of parameters is denoted as λ€=€(A,B,π). A discrete hidden Markov model λ, is shown in Figure 11. Figure 12 shows the general structure of a classifier based on HMM where a model is generated for each class, then an unknown sample is modeled by HMM and the decision is made based

The first experiment consisted in selecting the wavelet function, also known as wavelet mother, for the reduction of noise using threshold methods. It is done by comparing the average percentage of success yielded by a linear Bayesian classifier after processing the MER signal with the noise reduction technique. Table 1 shows the classification results for MER signals using different orders of the mother wavelet Coiflet, Daublet, and Symlet. The best index rating (66%) was obtained with the wavelet Daublet of order 1. However, Table 2 shows that higher percentages are obtained without filtering. This is because the neural activity that corresponds to the component of the MER signal is most affected by filtering, and is part of the information necessary for proper identification of some areas of the brain. Thus, signals are not passed through the noise reduction stage with the aim of preserving the information present in the neural activity. The time-frequency analysis techniques were compared to the results obtained by the classification of brain zones. For this experiment the STFT used 80 ms Hanning-type window, while the DWT used a mother wavelet Daublet of order 1 and a second level decomposition tree. The AWT used filters Deslauriers-Dubuc of order 2, 4 and 6, decomposing the signal to the level 2. Table 3

Figure 12. The general structure of the classifier based on HMM

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Table 1. Average percentage of success yielded by a linear Bayesian classifier using noise reduction by threshold of the wavelet coefficients Wavelet Function

% Tal

% STN

% SN

% ZI

% Total

Coiflet 2

38.33

94.29

31.43

78.43

64.90 ± 5.88

Daublet 1

49.17

94.29

34.29

75.60

66.27 ± 3.43

Symlet 8

40.83

97.14

28.57

79.20

65.69± 5.41

Table 2. Average percentage of success yielded by a linear Bayesian classifier using different methods of noise reduction: hard and soft threshold of the wavelet coefficients, cyclic scheme, and without reduction Noise Reduction Method

% Tal

% STN

% SN

% ZI

% Total

Without reduction

86.67

81.43

62.86

65.60

72.35 ± 4.75

Soft threshold

53.33

90.00

35.71

83.20

70.59 ± 5.99

Hard threshold

40.83

87.14

41.43

82.40

67.65 ± 7.91

Cyclic scheme

54.17

91.43

18.57

88.80

71.37 ± 2.11

shows the results obtained using a linear Bayesian classifier for identification with both the quasistationary and the non- stationary approach. The results show that the adaptive filter banks describe the dynamic characteristics of MER signals by changing the vanishing moments of the filters. It is also shown that time-frequency analysis of this MER signal taking into account its non-stationary nature provides better results in the identification of brain areas (about 12% higher) compared with the quasi-stationary analysis. This result reflects the strong dynamism of these signals and encourages the use of stochastic classifiers for the identification task instead of the linear Bayesian classifier that is best suited for static characteristics.

Table 4 shows the average percentage of success using a Discrete Hidden Markov Model (DHMM) and a Continuous Hidden Markov Model (CHMM) as dynamic classifiers, using the non-stationary characteristics (t-f contours) from time-frequency analysis as input to the classifier system.

General Considerations This section presented a methodology for identifying brain areas from the dynamic characterization of MER signals, taking into account both their strong dynamism and their non-stationary nature. Implementation of the wavelet transform through adaptive filter banks (AWT), where the

Table 3. Average percentage of success yielded by a linear Bayesian classifier using different methods of time-frequency analysis. The higher score is achieved using the adaptive filter banks (AWT) and the non-stationary approach. t-f analysis (type)

% Tal

% STN

% SN

% ZI

% Total

STFT frequency contour (quasi-stationary)

75.0

58.6

82.9

72.8

72.8 ± 4.9

DWT Daublet 1 (quasi-stationary)

73.3

72.9

92.2

64.0

71.4 ± 3.2

AWT adaptive filter bank (non stationary)

84.1

80.0

77.1

87.2

84.1 ± 2.8

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Table 4. Average percentage of success yielded by a dynamic classifier based on Markov chains using different methods of time-frequency analysis (DWT, AWT) Classifier used (contour used)

% Tal

% STN

% SN

% ZI

% Total

DHMM DWT time contour

46.7

75.7

35.7

77.2

64.1 ± 7.4

CHMM DWT time contour

77.5

91.4

71.4

72.4

76.1 ± 5.8

DHMM AWT time contour

95.8

67.1

34.3

100.0

85.5 ± 4.3

CHMM AWT time contour

92.5

98.6

78.6

100.0

95.1 ± 3.9

time of their vanishing moments (filter order) change as the signal’s energy gradient changes, presented the highest (95.1%) average percentage of success in the identification of the areas of the brain. These results are an improvement compared to results carried out by others using time-frequency techniques. These results were possible due to the location of the energy that makes the wavelet transform in the time-frequency space and to the adaptability of lifting schemes that describe the dynamics of the signal through multivariate analysis (Delgado et al., 2008). This methodology has allowed, through Hidden Markov Models, modeling of the dynamic structure of the MER signals, where a stochastic profile represents the physiological changes in the dynamics of the signal.

MEDICAL TELECONSULTATION SYSTEM The popular applications and developments of telecommunication networks are Telemedicine and Teleconsultation, because health experts can solve problems by using electronic and communication technologies without the limitation of distance. Telemedicine or e-medicine is the use of communication tools for the remote transmission of information related to medicine. It is a cost-saving health care mode which can be applied in clinics by using electronic information and communication technologies and is an effective method for distant consultation and

discussion. Indeed, the term Telemedicine is used to cover many topics, Videoconferencing (audio and video), Teleradiology, Telepathology and Telemedecine. Teleconsultation is the evaluation of a patient, or his data, without direct physical interaction. It is a very vast domain which includes prioritizing urgent cases, and an eventual transfer agreement, but is also the primary health service if a doctor is not available. The use of information technology to deliver health care from one location to another has the potential of increasing the quality and access to health care and of lowering costs (Perednia & Allen, 1995). In the United States, at least 35 federal organizations have been involved in telemedicine projects, and between 1994 and 1996 the federal government provided over $600 million to fund telemedicine projects. Over 400 rural health care facilities in 40 states were involved in telemedicine projects in 1996, and another 500 facilities are expected to offer telemedicine services over the next few years Office of Rural Health Policy (1997). Patients, doctors, staff, and administrators agree that advanced technology in health care is a viable option. However, statistically, utilization of technology is not just a viable option; it is a necessary and cost-effective solution. A study done in 2005 states that US$140 billion a year will be saved by the year 2014 or six percent of the $1.7 trillion dollar industry if technology is implemented and used. Furthermore, the 98,000 deaths each year due to hospital errors would decrease due to computational and network “proofreading” of the doctor’s prescriptions and other types of errors (Deng

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& Poole, 2003; Mullaney & Weintraub, 2005). Technology and the ability to improve quality of life due to telemedicine revolve around the fundamental policies of standardization, patient acceptance, and the continued increase in adaptive technologies. Telemedicine in surgery is sometimes referred to as Telesurgery, which is the use of telemedicine for the care of surgical patients as well as for training of surgeons. This technology has been successfully used in developed countries for some time and is now being used in developing countries. Health care services, and especially management of surgical patients, are not well developed in small cities and peripheral hospitals meaning that patients from these areas have to seek treatment in tertiary care hospitals situated far from their home. Most elective surgeries require extensive pre-operative work and post operative follow up, which entails multiple visits to the hospital. These are time consuming and prove too expensive for poor patients (Alia et al, 2007). Teleconsultation is a means of telecommunication through networks and is one of the most important applications in telemedicine. Real-time consultations use videoconferencing technology and allow interaction between medical experts and clients. During consultation, both sites need to communicate with each other and synchronously manipulate images and documents (Zhang et al, 2000). Two widely used types of telemedicine technology are forward technology and two-way interactive television. The practice of store-and-forward telemedicine often involves a digital image that is taken, stored, and then sent (or forwarded) to another location. This technology may have an application in radiology, where radiographic images need to be transferred, or in dermatology, where visually examining skin lesions is crucial. Two-way interactive television may be utilized when both locations have the necessary equipment (such as cameras and monitors) to complete the interaction. Applications of two-way interactive television may be found in numerous medical

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specialties such as cardiology, neurology, and gynecology (Choi et al, 2006).

Standardization Through Information Management The solution to standardization issues in telemedicine is the development of timely and acceptable standards. A standard is defined as “a document, established by consensus and approved by a recognized body, that provides, for common and repeated use, rules, guidelines or characteristics for activities or their results, aimed at the achievement of the optimum degree of order in a given context” Office of Rural Health Policy(1997). The American National Standards Institute (ANSI) accredits organizations to develop standards in the health care arena. For example, Health Level 7 (HL7) is one of such organizations, whose mission is “to provide standards for the exchange, management, and integration of data that support clinical patient care and the management, delivery, and evaluation of health care services” (Deng & Poole, 2003). Standards are developed in order to ensure the safety and integrity of the information (audio or visual) that is sent via telecommunications and to ensure that the information may be sent effectively from one location to another.

Technical Applications Computer-Supported Cooperative Work (CSCW) applications should conform to a set of strict specifications to ensure user acceptance and service efficiency (Kopsacheilis et al, 1997). The following categories of requirements can be identified: interoperability, connectivity, quality of service (QoS) management, use of graphical user interfaces, and cost effectiveness. Interoperability and connectivity imply the capability of effective end-to-end communication of two or more applications running in different machines over a set of subnetworks. They imply that all communicating stations use a common end

Processing and Communication Techniques for Applications in Parkinson Disease Treatment

encoding and accessing scheme that must include the communication protocol at the application level, the method for data representation, etc. Interoperability is served using protocols conforming to international standards. In particular, connectivity means that the users should have the means of accessing the communication network. This requirement is met, for example, by applications using the spreading public ISDN infrastructure. The need for interoperability exists also at higher levels. For example, the DICOM standard is used mainly as a common inter-manufacturer image format. General use of DICOM-compatible software architectures may in the near future obviate time-consuming translations of image formats. QoS management is the capability to request, negotiate, and achieve a minimum quality during the communication session. The concept of QoS was first introduced in 1984 with the CCITT X.200 series standards for open system interconnection or OSI reference model (OSI-RM). As the name implies, QoS refers to the end-to-end quality of communication services. QoS can be measured by a set of performance criteria (Henshall & Shaw, 1988). These criteria cover each of the phases of a typical session between remote applications over a network, and they mainly involve the communication speed and accuracy/reliability characteristics of the interchange. Graphical user interfaces are increasingly used in practice since they allow easy access of non-specialized users to the system resources and services. They are recommended especially for applications designed to support emergency cases since they provide easy to understand guidance to the user. Cost effectiveness is clearly one of the major factors for success and user acceptance for a distributed multimedia application system. It is affected both by the cost of the required user equipment and the communication cost, which depends on the allocated bandwidth. This leads to the conclusion that all applications should be designed for optimum bandwidth utilization.

Effective compression methods should be used for better exploitation of existing bandwidth capabilities. Compression is essential for distributed multimedia applications since the volume of the communicated information is large and the bandwidth is limited. Effective algorithms have been developed for still image compression, compression of data files, voice, and video. For multimedia-distributed applications, compression of still images and video is of particular importance due to the very high volume of these data types. CSCW research has led to several important findings that might help improve the design of real-time conferencing (Kleinholtz et al, 1995). Understanding how people use existing media is important for the success of conferencing software. Adequate support for gesture-using telepointers, minimal conference management, and good system performance is important for all implementations of telemedicine applications (Makris et al, 1998). a. Vi d e o a n d a u d i o c o n f e re n c i n g : Conferencing consists of the transmission of video and synchronized audio signals between two or more stations. Video conferencing is rapidly becoming common place, and person-to-person desktop conferencing systems are beginning to appear. b. Shared workspace and telepointing: Participants must be able to view and manipulate a common set of multimedia structures. For diagnosis or therapy planning, physicians typically view many images. The software must provide workspace management and allow arbitrary iconification, stacking, moving, and selection of individual images and text documents. Facilities that convey gesture are essential. As a minimum, telepointers should be provided to ensure efficient work on shared diagnostic images. Tools for measurement support (for

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Processing and Communication Techniques for Applications in Parkinson Disease Treatment

morphometry measurements, for example) should accompany the marking facilities. c. Data security: Medical applications must guarantee the privacy and integrity of patient data and the authenticity and authorization of the conference partner. d. Bandwidth management: Medical teleconsultation and telemedicine applications generally require lossless transmission of at least the portion of the medical image (region of interest: ROI) that is of diagnostic interest. Since lossless coding achieves typically a 3:1–4:1 maximum compression ratio, this requirement necessitates careful design of smart bandwidth management procedures to accelerate as much as possible the transmission of the medical image records and avoid annoying delays. An effective system should also be characterized by bandwidth scalability, meaning that the system should operate correctly over different network data rates. This is required as some remote users are connected via low-capacity lines, while central users are connected using higher capacity lines. Bandwidth management may include different priority levels for the communicated data types, provision for automated prefetching of bulk data in deferred time, flexible bandwidth allocation for data, voice and video in real time, etc. e. Simplicity: In emergency situations, timepressed physicians do not want to navigate superfluous menu hierarchies. The conference setup or registration procedure should be automated. All observations have shown the importance of speed and fluency in presentations.

Surgery and Telesupervision Computer-assisted surgery premiered in the mid1990s and was the next step toward the goal of remote surgery. The computer interface aids decision committees of specialists who get together

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with the aim of evaluating a case from different points of view and giving expert advice. Two- and three-dimensional visualization of the operating field is possible. The surgeon can perform a maneuver on the console, review it to be sure of its safety and efficacy, then instruct the remote device to perform the task. In some countries the telecommunication infrastructure is well developed. Not only analog telephone lines, but also more sophisticated ISDN, asynchronous transfer mode (ATM) and satellite links are available. They offer a much broader transmission band, with real-time, simultaneous, multidirectional, high quality voice and video transmission (Wysocki et al, 2005). As a consequence of ongoing subspecialisation in medicine, medical doctors at the highest professional level are dispersed all over the world. This dispersion is an aspect of teleconsulting in medicine that is particularly important in the multimodal approach in modern treatments of Parkinson’s disease. Telecommunication with fast, live voice, image and patients’ data transmission, without a compromise in quality, offers a unique possibility of bringing medical authorities together in a virtual space. Usually, there are not enough pathologists, and some hospitals hire only one, or have no pathologist at all (Dunn et al, 1999). Sending the specimen away would be too time consuming. Telemedicine offers a unique opportunity for a pathologists’ second opinion. This can be obtained by: (1) sending tissue photographs by e-mail, (2) remotely accessing a video camera by a consultant and (3) remotely steering local motorized equipment of vital signs by a consultant. A technician can prepare tissue and the diagnosis can be established at a distance, using telepathology equipment.

Communication Platform for Surgical Treatment of Parkinson Disease Several surgical procedures, such as in Parkinson disease, require the precise localization of various

Processing and Communication Techniques for Applications in Parkinson Disease Treatment

cerebral zones. Usually, as well as employing the appropriate imaging techniques and planning the trajectory for localizing a cerebral zone, the interpretation of the signals provided by a microelectrode (MER signal) that explores such a zone is also taken into consideration. A group of specialist doctors, involved in the surgical procedure, discriminate between the different zones using the auditory records from the neuronal signals and their experimental knowledge too. Thus, this identification depends completely on the capability of the specialists, which is subjective and increases the possibility of errors that can cause irreversible injuries in the patient. In order to obtain the best results and diminish the risks to the patient during surgery for Parkinson’s disease, the neurosurgeon uses, and is supported by, different technological equipment in the operating theater. For acquiring and recording MER signals it is necessary to have a micro-registry machine, which is made up of an amplification system and digital filtering that lets through frequencies in the range of 10 Hz to 20 kHz, while rejecting the electric components (60 Hz). This equipment must have a high fidelity sound reproduction system, an information storage system and a monitor. The equipment must allow the observation of the signals detected by the microelectrode tips, which are generally 5 μm diameter. Other electrodes with thicker tips (25 μm) are considered to be semi-microelectrodes. A microdrive, like the one shown in Figure 13, is used for supporting the microelectrodes with the stereotaxic frame, which is spatially located in a coordinate system given by the AC-PC line and supported on the patient’s head by screws. The coordinate system has been previously established in agreement with the planning software developed for the communication platform (Figure 14) and the fusion of MR and CR images. Using this software, the neurosurgeon can establish the pathway for the microelectrode from the cerebral cortex to its objective in the basal ganglia, taking great lengths to ensure that it does not pass

Figure 13. Stereotaxic frame and microdrive for holding the microelectrodes to the patient’s head (©2008 Used with permission)

through internal structures like blood vessels or ventricles, which can result in complications or irreversible damage to the patient.

Figure 14. Software for planning the pathway followed by the microelectrode from the cerebral cortex to the basal ganglia objective (©2008 Used with permission)

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An X-ray machine is also used to confirm the location of the microelectrode once it has reached its objective, to minimize the risk of error during the ablation phase with radio frequency or deep stimulation. Deep brain stimulation is an important step in the surgical procedure for Parkinson’s disease and generally the MER signal recording equipment has a neurostimulation unit, which produces an electrical current that travels through the microelectrode to the cerebral zone, allowing the neurosurgeon to evaluate functional reactions in the patient. The use of automatic systems based on pattern recognition by computer processing and telecommunication techniques allows the information to be shared online with other specialists who can provide assistance from a distance and interact with each other by dialogue and visualization windows (See Figure 15), this way the identification of the cerebral zones is more objective and precise, potentially improving the outcome. Figures 13, 15 and 16 are images from a surgical procedure performed in Colombia at the

Institute for Epilepsy and Parkinson (Instituto de Epilepsia y Parkinson del Eje Cafetero - Neurocentro) where communication techniques, such as telemedicine and telesupervision, are applied in the treatment of Parkinson disease. These images were used with the permission of Neurocentro.

CONCLUSION This pilot study demonstrates that interactive telemedicine may be useful in the management of neurosurgical patients in the perioperative period. Cooperation between the on-site physician and the remote physician was successful. As with most clinical studies in telemedicine our study design is prospective and observational. This may not reflect the true accuracy and capabilities of telemedicine as well as a randomized, prospective controlled trial in which patients would actually be managed by telemedicine and then compared with a control group managed by on-site care. Certainly patient

Figure 15. Telemedicine procedures applied to Parkinson’s surgery a) Interaction with other specialized surgeons by telecommunication, b) visualization tools (©2008 used with permission)

Figure 16. Telemedicine procedures applied to Parkinson’s surgery a) telesupervision, b) surgical procedure for Parkinson’s disease (©2008 used with permission)

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satisfaction and acceptance may be considerably different under these circumstances. This study shows the high accuracy of a working diagnosis established at a distance, even on medium-bandwidth (i.e., integrated services digital network, ISDN) connections. Recent studies in hospitals and future trends show that technology in health care leads to better services, while also demonstrating the potential to improve the lives of health care professionals and make transactions more efficient. The implementation of current standards and continued progress in Parkinson’s disease treatment will make a permanent and longlasting positive effect on the health care industry.

FUTURE TRENDS As future work, we propose Robotics and Mechatronics procedures for the position control of electrodes in the acquisition stage. On the other hand, the success of telemedicine as demonstrated in clinical studies will determine the role of this technology in the future. Scientifically valid clinical studies will allow meaningful evaluation of cost effectiveness, expanding the use of telemedicine where patients will benefit, while defining its weaknesses prior to widespread application.

ACKNOWLEDGMENT MER signals used in all the developed procedures were obtained within the project titled “Assisted system for decision-making during surgery for Parkinson’s disease” grant PI031546, from the Department of Health and Consumption, Carlos III Institute of Health, Medical Research Fund, Spain. Additionally, the authors also want to thank the condonable credit program of COLCIENCIAS in Colombia and to Sarah Röthlisberger for her contributions.

REFERENCES Alia, Q., Murada, F., Abdullahb, M., & Malik, A. Z. (2007). The Role of Telemedicine for Pre and Post Operative Evaluation of Elective Surgical Patients. In 9th International Conference on e-Health Networking, Application and Services, (pp. 267-269). Alvarez, L., Macias, R., Lopez, G., Alvarez, E., Pavon, N., & Rodriguez-Oroz, M. C. (2005). Bilateral subthalamotomy in parkinson’s disease: initial and long-term response. Brain, 128, 570–583. doi:10.1093/brain/awh397 Aminoff, M. J. (1998). Parkinson’s disease and other extrapyramidal disorders. Harrison’s Principles of Internal Medicine (14th ed. pp. 23562452). New York: McGraw Hill. Cataño, G., Botero, P., Vanegas, J. G., Castro, J., & Ibarra, A. (2008). Redes de conocimiento en sistemas regionales de innovación. Medellín, Colombia: Fondo Editorial Instituto Tecnológico Metropolitano. Choi, Y. B., Krause, J. S., Seo, H., & Capitan, K. E. (2006). Telemedicine in the USA: Standardization through Information Management and Technical Applications. IEEE Communications Magazine, 41–48. doi:10.1109/MCOM.2006.1632648 Delgado, E., Castellanos, G., Sánchez, L. G., & Suárez, J. F. (2008). Feature Selection in Pathology Detection Using Hybrid Multidimensional Analysis. In N. Wickramasinghe & E. Geisler (Ed.). Encyclopedia of Healthcare Information Systems, (2nd Ed., pp. 587-593). Delgado, E., Quiceno, A. F., Godino, J. I., Blanco, M., & Castellanos, G. (2009). Digital auscultation analysis for heart murmur detection. Annals of Biomedical Engineering, 37(2), 337–353. doi:10.1007/s10439-008-9611-z

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Jansen, M. (2001). Lecture notes in Statistics: Noise reduction by wavelet thresholding. Berlin: Springer Verlag. Jansen, M., & Oonincx, P. (2005). Second generation wavelets and applications. Berlin: Springer. Juri, C., Rodríguez-Oroz, M. C., & Obeso, J. A. (2005). Tratamiento de la Enfermedad de Parkinson en Estadio Inicial. Cuadernos de neurología, 29. Kleinholtz, L., Virchow, R., & Ohly, M. (1995). Supporting cooperative medicine: The Bermed project. IEEE Multimedia Magazine, 44–53. Kopsacheilis, V., Kamilatos, I., Strintzis, M. G., & Makris, L. (1997). Design of CSCW applications for medical teleconsultation and remote diagnosis support. Medical Informatics, 22(2), 121–132. doi:10.3109/14639239709010885 Linazasoro, G., Guridi, J., Vela, L., Gorospe, A., Rodríguez-Oroz, M. C., & Aguilar, M. (1997). Cirugía estereotáctica en la Enfermedad de Parkinson. Neurologia (Barcelona, Spain), 12(6), 343–353. Linazasoro, G., Van Blercom, N., & Ramos, E. (2002). ¿Nuevos vientos en el tratamiento quirúrgico de la enfermedad de Parkinson? Neurologia (Barcelona, Spain), 17(1), 1–6. Makris, L., Kamilatos, I., Kopsacheilis, E. V., & Strintzis, M. G. (1998). Teleworks: A CSCW Application for Remote Medical Diagnosis Support and Teleconsultation. IEEE Transactions on Information Technology in Biomedicine, 2(2), 62–73. doi:10.1109/4233.720524 Mullaney, T. J., & Weintraub, A. (2005). The Digital Hospital. Business Week, 77–84. Norris, A. C. (2002). Essentials of Telemedicine and Telecare. Chichester, UK: John Wiley & Sons.

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Walter, B. L., & Vitek, J. L. (2004). Surgical treatment for Parkinson’s disease. The Lancet Neurology, 3(12), 719–728. doi:10.1016/S14744422(04)00934-2

Perednia, D. A., & Allen, A. (1995). Telemedicine technology and clinical applications. Journal of the American Medical Association, 273(6), 483–488. doi:10.1001/jama.273.6.483 Piella, G., & Heijmans, H. (2002). Adaptive lifting schemes with perfect reconstruction. IEEE Transactions on Signal Processing, 50(7), 2204–2211. doi:10.1109/TSP.2002.1011203 Quiroga, R. Q., Nadasdy, Z., & Ben-Shaul, Y. (2004). Unsupervised spike superparamagnetic clustering. Neural Computation, 16, 1661–1687. doi:10.1162/089976604774201631 Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257–286. doi:10.1109/5.18626 Sweldens, W. (1996). The lifting scheme: A custom-design construction of biorthogonal wavelets. Applied and Computational Harmonic Analysis, 3(2), 186–200. doi:10.1006/acha.1996.0015 Sweldens, W. (1998). The lifting scheme: A construction of second generation wavelets. SIAM Journal on Mathematical Analysis, 29(2), 511–546. doi:10.1137/S0036141095289051 Teijeiro, J., Macías, R. J., López, G., Álvarez, L., & Maragoto, C. (2005). Sistema computarizado de registro cerebral profundo como guía neuroquirúrgica en trastorno del movimiento. Revista Mexicana de Neurocirugía, 6(5), 393–398.

Wysocki, W. M., Komorowski, A. L., & Aapro, M. S. (2005). The new dimension of oncology Teleoncology ante portas. Critical Reviews in Oncology/Hematology, 53, 95–100. doi:10.1016/j. critrevonc.2004.09.005 Zhang, J., Stahl, J. N., & Huang, H. K. (2000). Real-time teleconsultation with high-resolution and large-volume medical images for collaborative healthcare. IEEE Transactions on Information Technology in Biomedicine, 4(2), 178–185. doi:10.1109/4233.845212

KEY TERMS AND DEFINITIONS Deep Brain Stimulation (DBS): It is currently the preferred surgical procedure for treating Parkinson’s disease, where the area that can be stimulated varies according to the criteria described above in the thalamus and the globus pallidus in the subthalamic nucleus. MER Signals: Micro-Electrode Recordings for detecting brain areas. Non-Stationary Signals: It is quite common in bio-medical time series (and elsewhere) that otherwise harmless looking data once in a while are interrupted by a singular event, for example a spike. It is now debatable whether such spikes can be generated by a linear process by nonlinear rescaling. Parkinson’s Disease: It is a degenerative disorder of the central nervous system that often impairs a person’s motor skills and speech.

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Teleconsult: It is the interaction between signals and medical records where the primary diagnosis is given by the doctor at the medical center. The purpose of a teleconsult is to provide a second opinion by a specialist to either confirm the diagnosis or to help the local doctor make a correct diagnosis. Telepathology: It is the interaction between signals (1D or 2D) and clinical reports where

the primary diagnosis is given by a doctor in a remote location. Telesupervision: Telecommunication with fast, live voice, image and patients’ data transmission, without a compromise in quality, offers a unique possibility of bringing medical authorities together in a virtual space.

This work was previously published in Handbook of Research on Developments in E-Health and Telemedicine: Technological and Social Perspectives, edited by Maria Manuela Cruz-Cunha\, Antonio J. Tavares and Ricardo Simoes, pp. 194-218, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.14

Informatics Applications in Neonatology Malcolm Battin National Women’s Health, Auckland City Hospital, New Zealand David Knight Mater Mother’s Hospital, Brisbane, Australia Carl Kuschel The Royal Women’s Hospital, Melbourne, Australia

ABSTRACT Neonatal care is an extremely data-intensive activity. Physiological monitoring equipment is used extensively along with web-based information tools and knowledge sources. Merging data from multiple sources adds value to this data collection. Neonatal databases assist with collecting, displaying, and analyzing data from a number of sources. Although the construction of such databases can be difficult, it can provide helpful support to clinical practice including surveillance of infectious diseases and even medical error. Along with recording outcomes, such systems are extremely useful for the support of audit and quality improvement as well as research. Electronic information sources are often helpful in education and communication with parents and others, both within the unit and DOI: 10.4018/978-1-60960-561-2.ch414

at a distance. Systems are beginning to be used to help with decision making – for example in the case of weaning neonates from ventilators, and this work is likely to become more important in the future.

INTRODUCTION This chapter will outline the potential value of and barriers to the use of an informatics approach in neonatology. The term neonatology refers to the branch of medicine concerned with the care, development, and diseases of newborn infants 1. Although the term neonatal strictly defines the newborn period from birth to four weeks of age, we will refer to neonatology in a broader sense. This may include everything from routine care of the normal newborn infant with the mother, on the postnatal ward or at home, through to provision

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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of intensive care for the smallest and sickest of infants. In many cases this will involve premature infants who are often older than four weeks of chronological age but less than 44 weeks corrected gestational age. This type of care is complex and generates large amounts of clinical, monitoring and laboratory or imaging data. However, it also has some information requirements that are in common with that of an uncomplicated term infant on the postnatal wards. Specifically, there is a need to link infant details with the antenatal history, maternal demographic data and clinical coding. The five main areas where neonatology and informatics relate well are the provision of clinical care, including physiological monitoring and computer based clinical guidelines; data collection and management incorporating quality or benchmarking issues; education and training of staff; support of parents, including providing clear accessible information; and research.

CLINICAL CARE An informatics approach has much to offer in terms of both efficiency and clinical safety. Firstly, it may

serve as a web-based resource or repository for information. Secondly, it can provide web-based tools, such as drug calculators or nomograms, which aid the clinician with procedures such as estimation of length of insertion of catheters or endotracheal tubes 2, 3. Thirdly, informatics may include the provision of a portal or web-based interface with other applications giving up-todate access to clinical information stored elsewhere such as radiology, lab results, and clinical documents. Fourthly, data from physiological monitoring can be analyzed, albeit largely after a clinical event requiring review. Finally once data has been collected it can then be used to generate an automated discharge summary that includes physiological parameters, radiology and laboratory results, as well as clinical information. In the screenshot above, an example is given of the clinical workstation interface in place in our institution. The menu to the left provides the user with access to clinical information for specific patients (Figure 1). Electronic results can be “signed off” by the clinician once the results have been acknowledged or acted on. Other results, such as radiology reports, can also be viewed. Radiology images can be viewed directly for this

Figure 1. Clinical workstation interface in our institution

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patient by choosing the logo resembling an x-ray of the hand. Similarly, the electronic clinical record used within our institution can be accessed directly through this interface. The triangle logo with an exclamation mark alerts the user to the presence of important specific information such as a drug reaction, child protection issues, or infection

with an organism that may have infection control implications. (Figure 2 and Figure 3) In its simplest form, physiological review may take the form of a perusal of data stored within a monitor, evaluating trends in parameters such as heart rate, respiratory rate, oxygen saturation, and blood pressure over time. This may be helpful

Figure 2. Trend graph of pulse oximeter saturation in a newborn infant with congenital cyanotic cardiac disease

Figure 3. Output from a bedside EEG monitor demonstrating both real time raw EEG data (upper two boxes) and trend amplitude integrated summary data (bottom two boxes) to assess response to treatment

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when evaluating the effect of an intervention or interventions over time as demonstrated in the two examples given below. In the first trend graph saturation data are displayed before and after intervention with prostaglandins then ventilation. In the second graph output from a bedside amplitude-integrated EEG (aEEG) monitor demonstrate the value of both real-time and trend analysis in diagnosis and determining the response to a treatment. Another typical use for trend analysis would include ventilator parameters and lung compliance data following administration of surfactant. Data have been downloaded from the cardio respiratory monitor, via a network server where data are stored every 60 seconds. The graph shows an initial period of acceptable oxygenation for the first 90 minutes, followed by a decline. At 3 hours of age, prostaglandin E1 is commenced at a dose of 5 nanograms(ng)/kg/min (PG 5) to maintain patency of the ductus arteriosus to assist with oxygenation. However, the baby does not improve and the dose is increased further to 10 (PG 10) and then 15 ng/kg/min (PG 15). The saturations are maintained largely between 70 and 85%, but there are increasing periods of desaturation. The prostaglandin dose is increased further to 20 ng/kg/min (PG 20) and then 50 ng/kg/min (PG 50), following which the baby is artificially ventilated in preparation for a modified BlalockTaussig shunt. In the above example the output from a bedside amplitude-integrated EEG (aEEG) monitor demonstrates the value of both real-time and summary aEEG data in diagnosis and determining the response to a treatment. The user is able to highlight events on the monitor, such as clinical events, handling, and treatments. For this infant, clinical seizures were suspected and are marked as events 1 to 7. A review of the aEEG data demonstrates a raised baseline voltage strongly suggestive of bilateral electrical seizures - indeed, the baseline voltages are high between the time that the EEG is commenced (10:57) and some two hours later. Closer inspection of the raw EEG data

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between events 3 and 4 shows high-voltage rhythmic electrical discharges over the right cerebral hemisphere, consistent with electrical seizures. No seizures are seen in the 10 second epoch of raw EEG from the left cerebral hemisphere. A loading dose of phenobarbitone is administered (event 8), following which the baseline returns to a normal level around 5 uV. No further electrical seizures are obvious on the aEEG data up until 14:28hr. As well as data available from cardio respiratory monitoring and EEG systems, trend data can also be viewed and downloaded from ventilators, showing changes in ventilation requirement. For example, our NICU uses Drager Babylog 8000plus ventilators which have a companion computer attached that analyses data collected from the ventilator. Graphical analysis of extracted data using the accompanying software (VentView) allows the clinician to look at a considerable amount of data related to direct patient care, such as the oxygen requirement, tidal volume delivered, and the ventilator pressures required by the baby. Although clinicians may use this information to integrate the support that the baby is receiving with their own clinical analysis of the baby’s progress, it is not clear that this amount of information alters clinical outcomes. In a randomized trial of respiratory function monitoring of 35 ventilated newborn infants, where one arm of the study allowed clinicians to view the output of respiratory function monitoring whilst the other arm masked the output, there were no differences in ventilation parameters between the two groups 4. However, clinicians who were able to see the information provided by the respiratory function monitor did sometimes make clinical decisions around ventilation based on the information that was provided by the monitor, and most clinicians thought that the information was very useful, especially in those infants who were more difficult to ventilate. There are potentially several major advantages, particularly with regard to safety, associated with the use of the types of informatics tools outlined above. Notably, a web-based data source avoids

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the need for paper-based copies, which ensures the latest version is the only version available and thus is used at all times. New guidelines and protocols can be updated quickly and efficiently, and multiple copies do not need to be disseminated widely to staff or other stakeholders. However, other systems to inform staff of changes in practice or protocols are useful, such as an email distribution list or a highlighted “update” section on the website. Furthermore, web-based calculators or nomograms assist the clinician in calculations but also may decrease errors and identify areas in which systems and processes may improve efficiency. Examples of applications that are commonly used in neonatal care include an up-to-date drug formulary and calculators for drugs and clinical guidelines. Although such applications are very useful in saving the clinician time and effort there are important considerations with regard to safety. With the increasing use of computers in medicine, with access to web-based tools or applications based on other platforms, one issue to consider is the use of hand-held devices (for example, personal data assistants, or PDAs) versus networked personal computers. Within our NICU, increasing reliance on web-based applications has seen a marked increase in the availability of personal computers. Therefore, web-based applications have been designed with this platform in mind. Although hypertext pages can be converted to a format for PDAs, the same issues arise around updating webpages and accessing the only version in use within a clinical unit. In addition, some applications that work appropriately on a hypertext markup language web page may not render appropriately in a PDA format, which may lead to problems with the use of the electronic document. Although the role of PDAs in the provision of healthcare is expanding, the use of technology to assist delivery requires caution. One example of poor rendering with conversion of an application from a PC format to a PDA has been reported 5. In this example, output from a

previously performed calculation could not be protected, and a dose of adrenaline seven-times that intended was administered when the previous information was overwritten. It is critical that any information technology system responds safely and reliably in clinical scenarios, and it is the obligation of those developing the software to ensure that the systems are extensively tested by a range of users prior to the widespread adoption of the programs. It is possible to utilize web-based technology or medical information system to decrease medical errors; a particularly good example of this is the ordering of intravenous nutrition (IVN). Nutritional support is an essential part of neonatal intensive care. Sick or preterm infants often require IVN to meet their metabolic and growth demands. Ordering IVN is a significant task particularly when it needs to be performed on a regular/daily basis. However, it is often left to the most junior member of the team despite the fact that it is a complex activity requiring adjustment of constituents and has potential for errors. A computer system for ordering provides the opportunity to build in safeguards. This could include limits on individual constituents (such as protein and fat), total osmolality, and ensuring that components are kept below levels that may cause precipitation. Our NICU has adopted this approach, and places some absolute limits on composition (for example, no more than 4 grams/ kg of protein per day), but also provides alerts to the person completing the order when intended constituent quantities exceed the usual amounts. Apart from our anecdotal experience with an IVN calculator, the literature includes examples where a low-cost, pragmatic approach using such technology has been introduced and the effect monitored. Prior to the introduction of such as system an average of 10.8 errors were detected per 100 IVN orders. However after the introduction of an online IVN calculator the rate had fallen to 4.2 per 100 orders. Moreover the system under went a rapid cycle development and after some

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redesign a further decrease to 1.2 per 100 orders was achieved. Furthermore, the users were positive about use of the system and considered it have merit over the previous paper-based system. One challenge with the use of extensive information technologies is the integration of data from multiple sources. Whilst a clinical workstation interface, such as that shown above (Figure 1) integrates data from a number of sources, linking multiple data repositories can pose difficulties. Ideally, patients should have a unique identifier and systems should cross-reference this ID so that correct data is pulled across from platform to platform. This allows data be stored in one place (for example, demographic data such as address, contact details, ethnicity, family practitioner) yet used in many documents. Another challenge with such a large amount of data is determining which data is important enough to be included in any documentation or new database. It is all too easy to import data, yet for there to be inadequate interpretation of the meaning of the data. For example, although clinically important data such as the results of a full blood count may give a result such as a hemoglobin of 75 g/L, interpretation of the importance of this result (such as, “The baby is anemic with a hemoglobin of 75g/L. However, she remains asymptomatic and is receiving iron supplementation – we recommend a follow-up full blood count in one week”) is often lacking.

CLINICAL NEONATAL DATABASES Databases have proliferated throughout hospitals over recent years. The best funded, first introduced and best developed ones that tend to be orientated towards hospital finances and management. These are usually followed by systems related to support services such as radiology and laboratories. Databases on clinical units are often developed by the clinicians involved and may or may not integrate with the hospital systems. More recently standardized databases have become available

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and uptake has been widespread, such as with the Badger system in the UK. A neonatal database generally includes both demographic and clinical data. Items such as maternal age and ethnicity plus information on pregnancy clinical course and complications would ideally be available from the antenatal record. Postnatal infant data will need to be collected prospectively. Admission data includes gestational age, birth weight, Apgar scores, resuscitation details and initial clinical examination findings and treatment details. Subsequently data can be collected on clinical course, investigations and clinical monitoring such as blood pressure, heart rate, oxygen saturation and blood gas analysis. Clinical databases are of use in analyzing the workload of a neonatal unit, tracking changes in demographics and clinical presentations, looking at short term outcomes, identifying areas of practice that need reassessing, conducting audits of practice as well as research. Many are well established so giving the potential to review data collected over 20 years previously. They also are used to generate the data to be sent to neonatal networks. Many databases are fairly limited in their capabilities. For instance many hospital-wide databases will track admissions and discharges and be able to give information on length of stay. However frequently they will not have information on birth weight or gestational age. Such simple information is essential in order to understand workload and interpret length of stay. The lack of basic clinical data limits the usefulness of hospital-wide databases to clinicians. Neonatal services have tended to develop their own databases using propriety software available on personal computers. The most common system at present is the use of MS Access™, possibly using an SQL server as a data repository. These have advantages of being ‘owned’ and developed by the clinicians involved. This makes them responsive to clinician needs and they can be adapted quickly as circumstances change. For instance a new field can be added and reports can be gener-

Informatics Applications in Neonatology

ated at short notice. However, the development of the database takes clinician time and effort. Often those involved have limited programming skills. The systems are often not integrated with the hospital systems. Other clinical databases are set up as web-based databases and are shared by many neonatal units across different hospitals. An example of this is the Pediatrix Medical Group of 220 hospitals in 32 US states and Puerto Rico. Data is entered daily and is used for generating progress notes and billing information. It also provides a valuable repository for generating audit and research 7 . Another example is the database currently being developed for the NICUS Network of all Neonatal Intensive Care Units in New South Wales, Australia. These web-based systems have the advantage of being part of networks, enabling information to be easily exchanged between units and having standardized definitions. They operate in real time with the information from them being available in a timely manner. However, they will often not be linked to hospital systems. Therefore, some data will need to be double-entered, some will be out of date (for instance family doctor addresses or patient demographics). The data will not be available to hospital management or other clinical departments. Two major issues with all databases are: a) who puts the data in; and b) how easy is it to get data out. The old axiom of ‘rubbish in, rubbish out’ needs emphasizing. For data input, the choice is generally between clinicians (often residents and nurses, the busiest staff on the Neonatal Unit) or data-entry clerks. Clinicians understand the clinical conditions and the definitions of different diagnoses. However, they often have little motivation or incentive to enter data. To overcome this, it is necessary for them to get some return for entering data. For instance, if the database generates the discharge summary and daily problem list, resident staff will want to enter data in order to avoid prolonged periods in a dictation booth trolling through thick clinical notes to produce

the discharge summary. Any database that is being designed for a clinical unit needs to consider who is to input data. If it is to be clinicians, the database must be easy to use, need little training and be considered by them to be useful for them in doing their jobs. Paradoxically, as databases are now so easy to set up many have the fault of collecting too much data. Clinicians in particular tend to ask the developer to add fields that may never be analyzed. It is better to start with the question ‘what data and reports do we need to get out’ of the database rather than ‘what information can we put in’. Even if data is entered by clinicians, there needs to be a system for checking its accuracy and collecting missing data. It is seldom satisfactory to rely sole on multiple clinicians entering data accurately. There is often missing or inaccurate data. Therefore, any setup that is to be used to report important outputs (for instance for hospital planning, reporting to networks or research publications) needs to have regular checking and audit of the data. Any repository of data requires key identifying features that are unique to the individual, such as a unique hospital identification number, so that data are not erroneously collated for the wrong patient. Allowing for the sensitive nature of clinical data that may be linked to identifying features, security is a critical factor in any system that integrates clinical and demographic data. Notwithstanding these issues for programmers, the portal should ensure availability of clinical data to enable efficiencies in time and integration of health care data. Finally a computerized discharge summary will combine data collection and communication. This allows easy linkage with electronic health records and/or can be distributed by email.

Data Management Efficient data management is essential in order to provide effective clinical care. Although this is an important aspiration for all fields of clinical care there are some characteristics of neonatol-

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ogy that are particularly relevant to this concept. In common with other intensive care specialties neonatologists often use scoring systems such as CRIB, CRIB II and SNAP scores 8-10 in order to predict outcome or to a lesser extent use in benchmarking exercises. Also like other intensive care specialties neonatology focuses on very unwell individuals and clinical decisions are frequently based on a mixture of clinical and monitoring data. However, in neonatology there is characteristically a more homogeneous population with narrower range of presenting conditions, greater patient numbers and a longer mean stay in the unit 11. Furthermore, prematurity in general rather than an organ specific clinical condition, such as pulmonary or cardiovascular disease, is the most common indication for neonatal intensive care. Such characteristics increase the need for and utility of a “population” approach to data analysis when compared with adult or pediatric intensive care.

Impact of Data on Clinical Care Decisions in intensive care are made based on the available clinical information in conjunction with laboratory and imaging data. In addition to the earlier examples reviewing physiological or monitoring data from an individual patient, it may be important to appraise data collected from a larger group or cohort of infants. A classic example of this would be rationalization of antibiotic choice based on microbiological sensitivity data obtained from a single unit or group of units. An example of a very basic data collection system would be a simple spread sheet such as Microsoft Excel with recording of clinical events such as blood-borne infection or pneumothorax in ventilated infants. Simple analysis would include calculating rates for standardized patient groups or identifying trends in organisms and antibiotic sensitivity. Increasing sophistication could be obtained by also collecting data on risk factors and/or associated morbidities and by using either automatic data collection from

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laboratory systems or the use of computer generated prompts at the point of care. Measures such as these could lead to decreased rates of acquired infection; thus well formatted and accessible data have potential to inform and significantly improve clinical care. A recent example of how surveillance of this sort has contributed to clinical care is the published work on altered rates of early onset sepsis with ampicillin-resistant Escherichia coli associated with the wide spread introduction of antibiotic prophylaxis for newborns at risk of group B streptococcus infection 12,13. Another way that data can inform and improve clinical care is in the area of medical error. Error is not uncommon in health care 14 and is responsible for a significant amount of morbidity and mortality in hospitalized patients 14. Newborn infants, especially those in the neonatal intensive care unit are particularly at risk of drug-related adverse events 15. One major risk factor is the inability to vocalize or comprehend drug orders, a situation in common with the elderly who are another high risk group. Other contributing factors include the fact that drugs may be used for off label indications; drug doses need to be calculated based on weight, with frequent adjustment for weight gain or loss; and the need for pharmacists to make special dilute preparations of drugs, due to small doses 15. Many adverse events are potentially preventable and better monitoring and education 16 plus more appropriate choice of medication may have a role. However, increasing use of technology in the form of computerized ordering, with appropriate inbuilt checks, is another common prevention strategy suggested 17. Ideally a system to identify errors and potential errors should be specific to the individual specialty and encourage voluntary reporting of errors by health care professionals. It may also be helpful if the system allowed anonymous reporting as an important method of getting more complete ascertainment. One study examined the feasibility of an Internet-based reporting system for medical errors in neonatal intensive care to identify errors that affect high-risk neonates and their families

Informatics Applications in Neonatology

. A total of 1230 reports were received from 739 health professionals from 54 hospitals in the Vermont Oxford Network. Errors in medication included: wrong dose; schedule; or infusion rate, including nutritional agents and blood products. Other incidents were errors in administration of treatment; patient misidentification; other system failure; error or delay in diagnosis; and error in the performance of an operation, procedure, or test. The contributing factors that were identified were failure to follow policy or protocol (47%), inattention (27%), communications problem (22%), error in charting or documentation (13%), distraction (12%), inexperience (10%), labeling error (10%), and poor teamwork (9%). The study concluded that in addition to identifying a wide range of errors in neonatal intensive care the specialty-based, voluntary, anonymous Internet reporting system promoted multidisciplinary collaborative learning 18. 18

Role of Data from Neonatal Networks in Quality or Benchmarking Issues One of the primary reasons to collect and analyze neonatal data is examine variation in both clinical practice and outcomes between centers or services for the purposes of examining quality of the care provided 19. A common mechanism to do this is subscribing to a neonatal network, which is defined as a collaboration involving more than one clinical site where a common protocol is used for a randomized trial, observational study, or quality improvement project 20. Data is collected and checked by the individual unit then regularly submitted to the network administration, which collates and processes the data to form a report in a format that allows comparison between the whole network and the individual units. The approach has a proven utility in improving clinical care 21 and has several inherent advantages, particularly, as the quality of data is assured, due to it being checked both at source and by the network. Furthermore, it allows the individual unit to perform

comparison over a number of standard outcomes and, using the data from other network centers, can identify any centers that are outliers for each of these outcomes. Finally the combined data from the whole network will have a greater power to examine important questions on the quality of clinical care. One clinical outcome with important potential adverse long term developmental consequences, which has been examined within a network context, is intraventricular hemorrhage 19, 22, 23. The incidence of this potentially serious complication of preterm birth may be influenced by both facets of clinical care and case mix; thus adjustment in raw data should be made for this and some disquiet has occurred around creating league tables using data that is not fully adjusted or only recorded over a limited time period 24. Nevertheless, analysis of network data does suggest that some variation in IVH incidence between units may be dependent on NICU characteristics 22. Once such data is available then it can be used to improve outcomes. Although both philosophically and in practice it may be preferable to focus on shifting all centers towards the better centers rather than concentrating on the worst performing hospitals 19 . Examples of further important outcomes that may be studied using large network data sets are chronic lung disease 25, retinopathy of prematurity 26 and death 27. An example of the benefit of pooled data in a network having an increased power to examine important questions on the quality of clinical care is the topic of sepsis rates, where network data demonstrated the importance of certain intravenous lines as a risk factor 28. Other important studies based on network outcome data have evaluated the influence of hospital type 29 and time of day when the infant was admitted to neonatal intensive care units at night 30. Importantly, the first of these studies demonstrated worse outcomes for outborn infants admitted to pediatric hospitals in terms of mortality, nosocomial infection and oxygen dependency at 28 days of age compared to those

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admitted to perinatal centers. The second study reported a higher neonatal mortality for inborn infants admitted at night. Such information can be used in optimize service provision and clinical outcomes. One way that services may optimize care is in the appropriate early adaptation of new technologies and proper audit of such developments. Shared network data may be usefully employed to both assess uptake and evaluate the impact on related clinical outcomes from such practices. An example of this is a study of the use of high frequency ventilation in Australia and New Zealand 31. Data from 3270 infants who received high frequency ventilation between 1996 and 2003 were examined. It revealed that use had doubled over this period but outcome had not improved; in fact there was a higher mortality in the infants receiving this form of ventilation. A further example of new technology assessment is the Vermont Oxford Network of early nasal CPAP (continuous positive airways pressure) 32. In this study lower rates of adverse outcomes including chronic lung disease and retinopathy of prematurity were associated with CPAP use and associated early respiratory management. The data generated by this type of review of practice are thus useful in informing the collective clinical experience and can generate hypotheses that may be subsequently examined in large randomized multicenter trials.

Provision of Information and Support for Parents Parents of a sick newborn infant admitted to an intensive care nursery are subjected to a considerable number of potential stresses. Although this fact is well recognized 33,34, the stresses vary between parents, and over time this may make it harder for the health professional to address them fully in one off conversations. Each family will have their own specific fears and worries but there are a number of common and recurring themes. These include uncertain outcome, anxiety about

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procedures and treatments, financial worries, unfamiliar staff, abnormal appearance of their baby, difficulty understanding complex medical problems, major acute changes in condition and perceived lack of information 33. Such worries are frequently mixed with feelings of anger and guilt plus concern about the potential adverse impact on the family dynamics. In addition, the situation may be made more difficult if the admission is to a hospital away from the family supports such as transfer to another tertiary neonatal unit due to either bed shortages or the need for specialized services. Finally, the smallest and sickest preterm infants will require prolonged hospital stay often for several months but the mother is often discharged from hospital after a few days resulting in an inevitable separation between the mother and baby that may be quite prolonged. Although visiting is essential for infant attachment or “bonding” and is encouraged by a “family based” approach to neonatal care, it can in itself produce problems for the parents as it is time consuming, tiring, and expensive 33. Potential ways that informatics may be used to support parents include harnessing web-based technology to “demystify” the NICU environment. This could include the provision of web pages with information on the neonatal unit such as staff names and roles; data on neonatal outcomes, including mortality and morbidity; information on common conditions that may affect their baby, or procedures that may be performed; or may cover more prosaic information such as visiting policies and parking. There could also be the opportunity provide feedback or ask questions of the staff. In addition, particularly where there is geographical separation for any reason, activities that help to form memories such as writing a diary for their baby 35 can be supported by web-based technologies. In the early days of the NICU admission, despite the efforts of the staff to be supportive and approachable, the parents will be unfamiliar with the potentially complex problems. Thus may find

Informatics Applications in Neonatology

the neonatal unit often “high tech” and intimidating. At this stage the parents are particularly in need of information and a vital aspect of support for the family is orientation to the NICU environment. Although some parents may have had a visit to the neonatal unit as part of routine antenatal education most parents of preterm infants will not have started antenatal classes and so will not have been on a routine visit. In many cases there will have been very little time to prepare the family for a preterm birth. Conversely in some complex pregnancies the mother may be confined to bed rest in hospital for a long period but not be able to visit the NICU. In these situations, one particularly helpful development is the ability to enable a virtual tour of the neonatal nursery. In our institution two short video tours are available. One deals with level 3 (intensive care) and the other focuses on level 2 (special care). They are useful to familiarise families, who may potentially use the Newborn Service, and may be viewed by potential parents, on the antenatal or postnatal wards at a mutually convenient time. In many cases a physical tour of NICU is no longer required; although it is not intended that this service replaces fully either medical consultation or orientation to the unit for families who have had a baby admitted to NICU. Once the infant has been admitted for neonatal intensive care they will receive “a package” of measures that may include: respiratory support, such as ventilation; cardiovascular support, including inotropes; antibiotics; intravenous nutrition and in selected cases anticonvulsants or muscle relaxing agents. This type of care is complex and it is not appropriate for written consent to be obtained for each and every therapeutic maneuver or for every medication change to be explained in detail; this would very quickly over burden the families. However, it is very important to include the parents in the care of their baby and the process of consent to neonatal intensive care is based on sharing information. This incorporates explanation of the types of interventions that may be required and the range of outcomes that may be expected.

As the family become more familiar with intensive care and understanding increases their need for information may change and they will often require more detailed or complex answers. Thus, providing information that can be accessed by the family in their own time and at their own pace is a vital part of the overall process of consent. In our unit we have a number of resources for families including web-based information sheets covering common conditions, outcome data and procedures performed on the neonatal unit.

Informatics to Support Families An important principle regarding the provision of information is that it should be available to the intended audience. It is accepted that some information may be needed in printed format such as information sheets and that some items will need to be available in different languages. Furthermore, it would not be helpful to provide all the information required by families of infants undergoing neonatal care on a website if only a small proportion had regular and easy access to a computer or the internet. One study from Canada examined access and parent expectations from computer based information and reported that in 100 parents of NICU infants 79% owned a computer, 86% had internet access, and 76% regularly spent more than one to two hours per day on the computer. Of note there were some important socioeconomic factors associated with access. For instance, higher rates of Internet access were associated with higher level of education and fluency in reading English 36. Similar findings are reported from a US survey of families who have children with congenital heart disease presenting for cardiac surgery 37. Of 275 families surveyed 160 (58%) had Internet access either from home or work and (93/160) 58% used it to obtain information about their child’s cardiac diagnosis. Of the families, who accessed the Internet for material about congenital cardiac disease, 82% found locating relevant information

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easy and 95% considered the information to be helpful or very helpful in understanding of their child’s condition. Notwithstanding the fact that most families were able and willing to access the internet, and in one study over 90% felt the information was helpful 38, many still considered the information as potentially unreliable 36, too distressing or not specific enough 38. One area of parental information that has been systematically studied is retinopathy of prematurity 39. Sites were identified using two search engines (MetaCrawler and MSN) and the terms: “retinopathy of prematurity,” “premature eye,” “premature retina,” and “ROP”. Each website was classified as academic, organizational, or commercial then graded for readability, general quality of the website and quality of the content specific to ROP. Overall the majority of sites were graded as fair or poor. Several authors 36, 38, 39 have commented that the best way to ensure that parents have access to good quality and accurate information about their child’s condition or procedure is for the clinician to provide it. It is important that clinicians should also provide some time to review and discuss selected material identified by the family. The burgeoning use of internet technologies and increasing acceptability of use in many aspects of society does not necessarily mean that all families will be confident in the technology or will use it. Thus, it is important to consider these aspects with respect particularly to relatively disadvantaged families when providing internet based family supports. Attempts to provide web-based educational resources for low literacy families in the NICU include the use of touch-screen platform with voice-recorded messages to accommodate varying levels of literacy and computer experience 40 . Compliance with internet based information for families is generally good. Current evidence suggests that if information on a specific webpage, sometimes called an information prescription, is provided to a patient or family on how to help manage a health problem then almost two thirds

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of the families will log onto that web site 41. Also the probability of logging on is further increased by 45% if an e-mail reminder is sent 41. As mentioned above separation is a major source of stress and web-based technology has been utilized to attempt to decrease this; using multimedia communication based on internet technology to allow parents to view their baby in neonatal intensive care via the family home computer is a viable option. Two good examples of such innovative projects are the Telebaby from University Medical Centre Utrecht and Baby CareLink from Harvard. Experience of using these systems has been published 42,43. The Telebaby project used Standard Internet technology and allowed parents to virtually “visit” their infant. This system was easily adopted by the parents and was reported to give parents the feeling of control. The Baby CareLink system was evaluated in the role of providing “enhanced medical, informational, and emotional support to families of very low birth weight (VLBW) infants during and after their neonatal intensive care unit (NICU) stay”. The system was described as being able to allow virtual visits and distance learning from home during the period of hospitalization plus virtual house calls and remote monitoring after discharge. It also allowed confidential and efficient sharing of patient-based data between authorized hospital and community users. In a randomized trial conducted in a cohort of VLBW infants, born between November 1997 and April 1999, eligible infants were recruited within 10 days of birth and a computer including internet browser and videoconferencing equipment was installed in the family home within three weeks of birth. The families of intervention group infants were given access to the Baby CareLink telemedicine application and the control group received standard NICU care. Evaluation was by using a standardized family satisfaction survey and assessment of the effect on hospital stay, family visitation and interaction with infant and staff. A total of 30 control and 26 study patients were enrolled from

Informatics Applications in Neonatology

176 VLBW infants admitted to the unit during the study period. The two groups were similar in terms of morbidity and family characteristics. There were no differences in frequency of family visits, telephone calls to the NICU, and infant holding between the groups. However, based on the survey data the system was considered to significantly improve family satisfaction including satisfaction with the unit’s physical environment and visitation policies. Although the time to discharge home was similar in the two groups it was suggested that the system may potentially lower costs as the infants born weighing <1000 g had a trend toward shorter lengths of stay but further study would be required to prove this as there were differences in the process of discharge between the groups 43.

INFORMATICS TO SUPPORT RESEARCH IN NEONATOLOGY Informatics may support research and audit in a number of ways. A very simple method is an internet based survey. This could be a satisfaction survey for parents or a simple survey of clinicians on treatments/techniques available in a unit or approaches to medical care 44. Another example could be analysis of locally held records extracted from a neonatal database specifically set up to prospectively collect data on a given procedure or condition. One centre, from Australia, reporting their experience from 2186 percutaneously inserted silicone central venous catheters over an 18 year period highlighted the value of keeping good prospective records 45. In reviewing their experience they were able to illustrate the safety of these catheters particularly with reference to catheter tips in the right atrium 45. However, a real advantage of an internet-based survey over a paper-based survey is that supporting clinical data may be included in an appropriate format. A case in point would be where high quality imaging data is provided. This may then

closely mimic the clinical situation and be used to investigate the ability of the subject to interpret the data. There are good examples of this in the neonatal literature. A local study 46 used a series of radiographs demonstrating neonatal long lines and Australian and New Zealand Neonatal Network members and National Women’s Hospital NICU staff to identify not just the likely anatomical position but also the desired action. The study provided data on interpretation, including intra and interobserver variation and action taken for a given line tip location. Interobserver and intraobserver reliability using the radiographs was poor and the major determinant of line repositioning was the perceived location. A further example is a web-based assessment of the ability to interpret neonatal cranial ultrasound scans 47. High resolution scanned images of six important neonatal cranial ultrasound abnormalities were posted as a questionnaire to the 59 neonatal units. The mean accurate identification of cerebral abnormalities was only 59% (range 45-71%). However, it was noted that there was a difference in experience and training, with only 44% of the neonatal registrars, compared with nearly all the consultant staff, have had any formal training in cranial ultrasonography. Other potential uses of informatics in neonatal research include the use of web-based technology to record and transmit images for central interpretation. An example of this is a prospective, multicenter, masked, internet-based clinical trial of Retinopathy of Prematurity Study 48. Pilot data have been collected to evaluate the utility of digital wide-angle photographic fundus screening for retinopathy of prematurity as compared to bedside indirect ophthalmoscopy in premature infants <31 weeks postmenstrual age at birth and <1000 g birth weight. The technology employed in this study is currently available and could be incorporated in to other research studies. Finally, in common with other fields of medicine, web-based research support is vitally important for conducting large multi-centre randomized trials. The initial screening of patients and ensur-

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ing eligible status can easily be supported on a web-based interface. Similarly the randomization process may be performed using this interface and appropriate clinical and demographic data collected without recourse to paper based data entry. This will help to minimize mistakes and also facilitate rapid data analysis. This can also facilitate collaboration between study groups who share similar protocols (such as the BOOST2 group of studies evaluating oxygen saturation in preterm infants), and can allow for a prospective meta-analysis to be performed.

Education Informatics has considerable potential with regard to education. Electronic textbooks and journals can be made available on a desktop computer. Other electronic educational resources such as interactive clinical cases using high quality images of x-rays or dermatology slides can be distributed to large groups, viewed on a personal computer and then appropriate interpretation and management discussed, so encouraging interaction. Grand rounds and lectures can be recorded and presented as web casts or Pod casts, so staff unable to attend the original presentation are still able to use the material for learning. Staff training and orientation may include review of material on web pages and training log books may be web-based. However, none of these applications are specific to neonatology so will not be discussed further here. One area of training and education that is particularly pertinent to neonatology clinical care is the echocardiographic assessment of the newborn infant with congenital cardiac disease 49. This is a specialized skill usually the domain of highly trained technicians and clinicians and would normally fall to a cardiologist or a neonatologist with specific training in the morphology of congenital heart disease. However, with a widely dispersed population there may be problems in the provision of a nation-wide echocardiographic service. In this case studies may be transmitted by tele-

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echocardiography and reviewed by a specialist in a referral centre.

Assessment of the Impact of WebBased Information on Neonatology There is no doubt that the provision of informatics support is an important development for neonatology. One aspect of this is the ability for a tertiary unit to develop guidelines then share their experience by publishing them on the internet. This activity is likely to improve care not only in the centre developing the clinical guideline but also in the greater neonatal community due to the shared knowledge. Our neonatal unit has maintained a website, with open access to several sections including clinical guidelines and education pages, since 2002. There has been considerable growth in the amount of activity seen over time such that in one month in 2007 there were 1.3 million total hits with 44,600 hits per day on average. The most common country of origin for the 79,600 visitors per month was USA, followed by UK, Canada, New Zealand and Australia. However, there were up to 800 visits per day from developing countries; this illustrates the important contribution to the wider community. The most popular pages were educational, particularly those illustrating neonatal dermatological conditions, with 30,000 pages accessed per day. Other popular educational pages deal with neonatal respiratory conditions, including radiographs. There are also frequent requests to use this type of material in other formats such as books or lectures.

FUTURE DIRECTIONS As described in this chapter, there is already a strong interrelationship between informatics and neonatology. However, it is likely that in the future we will see a further expansion of infor-

Informatics Applications in Neonatology

matics applications so that these become integral to providing care to newborn infants. There is rich potential to both improve the care delivered and make life for the clinician easier. Key areas for development include the refinement of data analysis to produce appropriate outputs that can be utilized to influence clinical decisions and measures that improve patient safety. Attempts have already been made to harness informatics systems and data management to provide real time “expert systems” to guide neonatal care. Increased data storage capacity and computer power are likely to be associated with further increases in the data warehousing and mining abilities with which to interrogate data. Important applications of this knowledge would include alteration of respiratory support systems (e.g. ventilator settings) based on physiological monitoring and blood gas parameters. A published example 50 of a rule based expert system, derived from the knowledge of two consultant pediatricians, aimed to provide ventilator settings to maintain the arterial blood gas tensions within an acceptable range. In order to minimize barotrauma the rules were designed to reduce pressures if feasible and increase pressures only as a last resort. Overall, it was able to provide advice on ventilator adjustments that was accepted on 83% of occasions 50. In another study 51the artificial ventilation expert system for neonates (AVES-N) made recommendations which were then compared to the decisions made by the expert-physician in 320 newborn infants. Overall, the best agreement (approximately 70% of the time) was for the parameters: positive end expiratory pressure, inspired oxygen fraction and peak inspiratory pressure. In contrast, there was poor agreement for time related parameters such as frequency (54%), inspiration time (46%) and time of next blood gas analysis (15%). In this study differences were attributed to a) different therapeutic strategies in the two NICU’s, b) missing data regarding complications in the database that therefore could not be taken into account by the expert system. Further work is likely re-

quired before such systems can be incorporated in to clinical practice and some of this work will need to explore the utility of expert systems with recent developments in respiratory support such as volume guarantee, continuous positive airway pressure and noninvasive ventilation. A third study examined the potential role of expert systems in the stabilization of neonates prior to transport 52. In this task the computer was considered to be safe, and recommend measures within the guidelines of neonatal clinical practice. Overall, these studies have shown great potential for “expert systems”. However, there are still some barriers in practice and further work is required before an automated analysis is likely to be used as the sole basis for a clinical decision; although currently it may comfortably be used to inform that decision. There is also enormous potential for the development of algorithms in physiological monitoring systems that can detect subtle changes in physiological╯measures that may herald a significant alteration in the status of the infant prior to a clinically detectable event. One such important clinical event in the newborn is pneumothorax, which has a significant mortality and morbidity. If an early diagnosis were possible this would likely improve the outcome. In a study of 42 infants with pneumothorax over a 4 year period the mortality before discharge was 45% 53. Moreover, the median time (range) between onset of pneumothorax and clinical diagnosis was over two hours and in many cases measures such as suctioning the endotracheal tube or reintubation were taken that delayed appropriate action. Computer analysis of data from transcutaneous CO2 measurements showed good discrimination for a pneumothorax (area under the receiver operating characteristic curve, 89%) and it was concluded that trend monitoring using reference centiles could form the base for automatic decision support. It is likely that in the future we will see more sophisticated forms of monitoring with decision support capability become more frequent in the care of the smallest and sickest infants.

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A further area where technology may be utilized to improve infant safety is in identification. Due to their inability to speak, babies are at risk for misidentification errors particularly if there are similarities in standard identifiers such as names or identification/medical record number 54. There have been frequent calls for increasing the use of information technology to reduce medication errors. It is likely that simple measures such as bar codes for identification and drugs administration purposes will increase. This has already been reported to reduce, and prevent bedside medication errors and to be well accepted by nurses 55. This type of measure can also potentially be of use in the labeling of expressed breast milk as exposure to the wrong mothers milk can carry risk of disease transmission such as Cytomegalovirus. There are some general issues including potential barriers that would need to be addressed in order to increase utilization of informatics to the full potential. Some of these issues are similar to other fields of health informatics including alleviation of manual data entry or labor intensive scanning procedures and difficulty with coding of specific clinical conditions. Other general matters are particularly pertinent to neonatology and child birth, where issues around data privacy need to be handled with sensitivity. Apropos the technology, the systems need to be flexible to allow for changes in practice related to new information or local practices. Although there are advantages to proprietary software (in terms of development and support), instituting changes to meet new demands can be challenging. The use of relatively simple software programs (e.g. Microsoft Excel™ or Access™) or programming languages (e.g. JavaScript in web-based applications) may assist with relatively easy modification and development of IT systems. Frequently, a local “champion” may be required to develop and support local systems. Basic IT skills will be developed over time, but

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this runs the inherent risk that the support of the system is dependent on the presence, commitment, and activity of a single individual. Although neonatology is by nature a technology friendly specialty care must be taken to ensure that the first duty of the doctor is care of the baby and their family. If this is not borne in mind, the role of the doctor has the potential to become less humanistic when there is an emphasis on technology! Although heavy dependence on information technology means that medical staff spend less time on the telephone gaining the information they require, it also means that they spend more time in front of a computer screen and less time in direct patient care (Figure 4). At times it is easy to overlook the many developments in informatics that have become part of everyday neonatal care, perhaps because both specialties are relatively young and new developments are quickly and often seamlessly adopted. However, there remains much that can be learnt not just from other branches of medicine but also other industries, such as aviation which has a strong safety focus. Informatics and neonatology already have a close relationship but in order to see the full benefits of informatics in neonatal care this relationship needs to be nurtured and the role of new applications in clinical care systematically studied.

Figure 4. The changing work role of medical staff in a NICU

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Association, 281(9), 799–805. doi:10.1001/ jama.281.9.799 Stenson, B. (1996). Promoting attachment, providing memories. BMJ (Clinical Research Ed.), 313(7072), 1615. Dhillon, A. S., Albersheim, S. G., Alsaad, S., Pargass, N. S., & Zupancic, J. A. (2003). Internet use and perceptions of information reliability by parents in a neonatal intensive care unit. Journal of Perinatology, 23(5), 420–424. doi:10.1038/sj.jp.7210945 Ikemba, C. M., Kozinetz, C. A., Feltes, T. F., Fraser, C. D. Jr, McKenzie, E. D., Shah, N., & Mott, A. R. (2002). Internet use in families with children requiring cardiac surgery for congenital heart disease. Pediatrics, 109(3), 419–422. doi:10.1542/peds.109.3.419 Sim, N. Z., Kitteringham, L., Spitz, L., Pierro, A., Kiely, E., Drake, D., & Curry, J. (2007). Information on the World Wide Web--how useful is it for parents? Journal of Pediatric Surgery, 42(2), 305–312. doi:10.1016/j. jpedsurg.2006.10.003 Martins, E. N., & Morse, L. S. (2005). Evaluation of internet websites about retinopathy of prematurity patient education. The British Journal of Ophthalmology, 89(5), 565–568. doi:10.1136/bjo.2004.055111 Choi, J., Starren, J. B., & Bakken, S. Webbased educational resources for low literacy families in the NICU. AMIA Annu Symp Proc. 2005;2005:922. Ritterband, L. M., Borowitz, S., Cox, D. J., Kovatchev, B., Walker, L. S., Lucas, V., & Sutphen, J. (2005). Using the internet to provide information prescriptions. Pediatrics, 116(5), e643–e647. doi:10.1542/ peds.2005-0404 Spanjers, R., & Feuth, S. (2002). Telebaby videostreaming of newborns over Internet. Studies in Health Technology and Informatics, 90, 195–200. Gray, J. E., Safran, C., Davis, R. B., PompilioWeitzner, G., Stewart, J. E., Zaccagnini, L.,

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& Pursley, D. (2000). Baby CareLink: using the internet and telemedicine to improve care for high-risk infants. Pediatrics, 106(6), 1318–1324. doi:10.1542/peds.106.6.1318 Taylor, R. S. (2003). Changes in thresholds for prescribing postnatal corticosteroids between 2000 and 2002: are we better educated? Pediatric Research, 53(4), 103A. Cartwright, D. W. (2004). Central venous lines in neonates: a study of 2186 catheters. Archives of Disease in Childhood. Fetal and Neonatal Edition, 89(6), F504–F508. doi:10.1136/adc.2004.049189 Odd, D. E., Battin, M. R., & Kuschel, C. A. (2004). Variation in identifying neonatal percutaneous central venous line position. Journal of Paediatrics and Child Health, 40(9-10), 540–543. doi:10.1111/j.1440-1754.2004.00459.x Reynolds, P. R., Dale, R. C., & Cowan, F. M. (2001). Neonatal cranial ultrasound interpretation: a clinical audit. Archives of Disease in Childhood. Fetal and Neonatal Edition, 84(2), F92–F95. doi:10.1136/fn.84.2.F92 Photographic Screening for Retinopathy of Prematurity (Photo-ROP) Cooperative Group. (2006). Balasubramanian M, Capone A Jr, Hartnett ME, Pignatto S, Trese MT. The Photographic Screening for Retinopathy of Prematurity Study (Photo-ROP): study design and baseline characteristics of enrolled patients. Retina (Philadelphia, Pa.), 26, S4–S10. doi:10.1097/01.iae.0000244291.09499.88 Groves, A. M., Kuschel, C. A., & Skinner, J. R. (2006, Aug). International Perspectives:

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The Neonatologist as an Echocardiographer. NeoReviews, 7, e391–e399. doi:10.1542/ neo.7-8-e391 Snowden, S., Brownlee, K. G., & Dear, P. R. (1997). An expert system to assist neonatal intensive care. Journal of Medical Engineering & Technology, 21(2), 67–73. Michnikowski, M., Rudowski, R., Siugocki, P., Grabowski, J., & Rondio, Z. (1997). Evaluation of the expert system for respiratory therapy of newborns on archival data. The International Journal of Artificial Organs, 20(12), 678–680. Heermann, L. K., & Thompson, C. B. Prototype expert system to assist with the stabilization of neonates prior to transport. Proc AMIA Annu Fall Symp. 1997;213–7. McIntosh, N., Becher, J. C., Cunningham, S., Stenson, B., Laing, I. A., Lyon, A. J., & Badger, P. (2000). Clinical diagnosis of pneumothorax is late: use of trend data and decision support might allow preclinical detection. Pediatric Research, 48(3), 408–415. doi:10.1203/00006450-200009000-00025 Gray, J. E., Suresh, G., Ursprung, R., Edwards, W. H., Nickerson, J., & Shiono, P. H. (2006). Patient misidentification in the neonatal intensive care unit: quantification of risk. Pediatrics, 117(1), e43–e47. doi:10.1542/peds.2005-0291 Anderson, S., & Wittwer, W. (2004). Using bar-code point-of-care technology for patient safety. Journal for Healthcare Quality, 26(6), 5–11.

This work was previously published in Medical Informatics in Obstetrics and Gynecology, edited by David Parry and Emma Parry, pp. 130-150, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.15

Digital Pathology and Virtual Microscopy Integration in E-Health Records Marcial García Rojo Hospital General de Ciudad Real, Spain Christel Daniel Université René Descartes, France

ABSTRACT In anatomic pathology, digital pathology integrates information management systems to manage both digital images and text-based information. Digital pathology allows information sharing for diagnosis, biomedical research and education. Virtual microscopy resulting in digital slides is an outreaching technology in anatomic pathology. Limiting factors in the expansion of virtual microscopy are formidable storage dimension, scanning speed, quality of image and cultural change. DOI: 10.4018/978-1-60960-561-2.ch415

Anatomic pathology data and images should be an important part of the patient electronic health records as well as of clinical datawarehouses, epidemiological or biomedical research databases, and platforms dedicated to translational medicine. Integrating anatomic pathology to the “healthcare enterprise” can only be achieved using existing and emerging medical informatics standards like Digital Imaging and Communications in Medicine (DICOM®1), Health Level Seven (HL7®), and Systematized Nomenclature of MedicineClinical Terms (SNOMED CT®), following the recommendations of Integrating the Healthcare Enterprise (IHE®).

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Digital Pathology and Virtual Microscopy Integration in E-Health Records

INTRODUCTION: WHAT IS DIGITAL PATHOLOGY? Several socioeconomic factors have accelerated the dissemination of information technology in anatomic pathology. The spread in the use of Internet, the presence of computers in doctor’s offices, and the increasing demand of second opinion from the patient has facilitated the adoption of local, national, and international governmental initiatives to explore and adopt digital pathology and telepathology programmes, and to support the developing of patient electronic health (e-Health) records. Patient electronic health records (EHR) should contain a full range of data set, including the digital images of all types of imaging studies ever performed on the patient. The concept digital pathology comprises the information technology that allows for the management of information, including data and images, generated in an anatomic pathology department. Anatomic pathology information system and digital imaging modalities are two main components of digital pathology. Anatomic pathology information systems manage computerized orders of anatomic pathology examination of specimen collected from patients as well as the fulfilment of these orders. In most anatomic pathology departments, images are more and more being used in a digital format. Photographic images are digitized during specimen macroscopic study and microscopic images during microscopy evaluation and molecular pathology documentation. Nowadays most of the anatomic pathology departments using digital imaging in microscopy, which is the essential tool of pathologists, are equipped with imaging modalities producing only still images captured from a selected field under the microscope. The use of still microscopic images in the diagnostic process is time consuming and results in frustration for clinicians and pathologists. In the last 5 years, slide scanners have become very popular since they allow a complete digitization

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Figure 1. Using virtual microscopy systems, pathologists can review complete microscopy slides simulating the operation of an optical microscope.

of tissue and cytology slides, a process termed whole slides imaging (WSI), to create digital slides. Since this disruptive innovation allows a realistic simulation of the work performed with the conventional optical microscope (Figure 1), it is known as virtual microscopy (Ferreira, 1997; Afework, 1998). Conventional glass slides are fragile, and they are non permanent, since stain will fade over time, especially in immunofluorescence. Also, in cytology, it is not possible to distribute copies of the slides. These disadvantages of conventional slides can be overcome with digital slides, which also have additional advantages, like having lower magnification pictures with extraordinary quality, a dynamic map of the slide, tracking and playing back the path followed by the pathologist during the examination of the slide, images are permanently stored, and it is possible to make annotations that can also be recorded. Additional advantages of digital slides over glass slides are the possibility to distribute multiple copies on a DVD of exactly the same slide, and the high quality always on focus and well illuminated images that decreases eye fatigue, and other ergonomic advantages by reducing back and neck fatigue (Zeineh, 2005).

Digital Pathology and Virtual Microscopy Integration in E-Health Records

Virtual microscopy is already being applied in anatomic pathology for primary histopathology diagnosis, primary cytopathology diagnosis, cytological cancer screening. The use of digital images is been validated for diagnostic applications in surgical pathology (Gilbertson, 2006), cytopathology (Dee, 2007), and immunohistochemistry (O’Malley, 2008). There is no statistically significant difference in the diagnostic accuracy either between virtual microscopy and conventional light microscopy. Some automated image analysis algorithms that are being used on digital slides have been U.S. Food and Drug Administration (FDA) 510(k)-cleared for assessing the level or certain immunohistochemical markers. However, there is little experience in the use of virtual microscopy in diagnostic pathology daily practice, and there in no slide scanner that is FDA approved for primary or initial diagnosis. The consequences of the full digitalization of pathology departments are hard to foresee, but short term issues have arisen that imply interesting challenges for health care standardization bodies. Digital pathology in an anatomic pathology department also includes biomedical equipments, like laboratory autostainers control software, automated image analysis tools, quality assurance program management systems, etc, that are used during the fulfilment of the orders. These equipments need to be properly interfaced to the anatomic pathology information system. Digital pathology also addresses information technology used for inter-laboratories exchanges for collaborative diagnostic processes including telepathology for second opinion. Telepathology is the practice of pathology at a distance using some of the elements that compose digital pathology (still images, digital slides, and less frequently video microscopy). It can be aimed to primary diagnosis or to second opinion teleconsultation (Kayser, 1999). Telepathology developed in some countries as a response to a shortage of pathologists (Sawai, 2007a). It was classically divided into static tele-

pathology (store and forward, generally sending still images files of representative areas by e-mail or FTP) and dynamic or real time telepathology (video transmission) (Cowan, 2005). Digital or virtual slides can be considered a special type of store and forward telepathology where the whole slide is scanned and saved, to be transmitted under user-demand, with the advantage of sending only requested areas that can be sent using conventional Internet bandwidth. Due to this dynamic interaction between users and the digital slide, some users classify virtual microscopy as digital dynamic telepathology systems (Afework, 1998). In Japan, since 2000, telepathology is included as an insured healthcare service (Sawai, 2007a). At last, digital pathology is not only dedicated to patient care and also addresses the use of information systems during research and teaching activities. Education libraries, biobanks managing systems, or cancer registry databases are part of digital pathology. Virtual microscopy, for example, is already being widely applied in undergraduate teaching, distance learning and continuous medical education, proficiency testing, pathology quality assurance programs, and research (tumour banking). Digital pathology comprises the use of information technology for all the different activities of the pathologists and should be integrated to the “Healthcare Enterprise” information system for patient care (integration to electronic healthcare record or personal healthcare record) and research activities (integration to biomedical or epidemiological research databases, to cancer registries, to tissue or images banks, to clinical datawarehouses, to translational medicine platforms, etc).

DIGITAL ORDERS AND REPORTS IN ANATOMIC PATHOLOGY Typical anatomic pathology cases represent the analysis of (all) tissue removed in a single col-

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Digital Pathology and Virtual Microscopy Integration in E-Health Records

lection procedure (e.g., surgical operation/event, biopsy, scrape, aspiration etc.). With regards to the diagnostic activities, digital pathology addresses the management of both digital (or computerized) orders for anatomic pathology examination of specimen and digital reports, delivered in fulfilment of these orders. Like in clinical laboratory, the diagnostic process in anatomic pathology is specimen-driven (Figure 2). However, the diagnostic process in anatomic pathology differs from that in the clinical laboratory since it relies on image interpretation. It also differs from that in radiology since, in the current daily practice, digital imaging is not systematically performed.

DIGITAL IMAGING IN ANATOMIC PATHOLOGY Digital imaging is one major component of digital pathology. Digital imaging solutions include image acquisition modalities, post processing stations, image viewers and picture image and Figure 2. Overview of anatomic pathology workflow

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distribution systems (PACS). There are different types of digital image acquisition modalities in anatomic pathology. Scanning speed, quality of image during capture and viewing processes, automated image analysis or computer-aided decisions, adoption of health care standards, and internal organizational issues, are some of the important factors that need to be addressed in the integration of digital slides in EHR.

Acquiring Digital Images in Anatomic Pathology Digital images in anatomic pathology consist in photographic gross images or microscopic images. Photographic gross images capture relevant information about specimen at a macroscopic level (photographs of specimen or derived specimen (e.g. paraffin blocks), etc). The acquisition modality for macroscopic (or gross) imaging is photo camera. Microscopic images capture relevant information about specimen at a microscopic level. The main objective of virtual microscopy system or

Digital Pathology and Virtual Microscopy Integration in E-Health Records

whole slide imaging (WSI) devices is building digital slides. They are capable of digitizing slides at high magnifications. Two main groups of acquisition modalities for microscopic images (WSI devices) can be distinguished: modalities using both a motorized microscope and a camera and slide scanners (Rojo, 2006). Multispectral microscopy is considered an alternative to the red-green-blue (RGB) imaging used by current automated imaging systems, since RGB sensors have shown to be insufficient to distinguish between similar chromogens above all if they overlap spatially. These problems can be solved acquiring a stack of images at multiple wavelengths, and obtaining the determination of precise optical spectra at every pixel location. Slide scanners are closed systems that include components similar to those used in motorized microscopes (an objective to optically magnify the specimen, digital camera, and motorized stage) but with some modifications, such as absence of eyepieces and absence of position and focus control. There are many different slide scanning solutions available for pathology slides (table 1) (Rojo, 2006; Yagi, 2007). A virtual microscopy system has three essential elements: slide scanner, image storage, and viewer (Tofukuji, 2007b). The main barriers to the shift to digital imaging are the storage capacity, the scanning speed and the quality assessment of digital slides in different contexts of use.

Storage Dimension Digital slides carry a huge amount of data. A single glass slide size is 76 mm x 26 mm and includes a label area and an area of interest with the specimen (tissue or cytology) of about 25 mm x 50 mm. When scanned using a 40x objective, this specimen area can be converted into an up to 220,000 x 90,000 pixels size image, over 55 GB uncompressed data. A typical specimen of 15 x 15 mm digitized with a 20x objective at a resolution of 0.3 µm/

pixel contains 50,000 x 50,000 pixels. For each pixel containing 3 bytes colour information, the total one single slide size is 50,000 x 50,000 x 3 = 7.5 GB of uncompressed data (Tofukuji, 2007b). These figures are related to one single X-Y plane, and they can be increased by several orders of magnitude in those 3D scanning systems capable of saving multiple planes in the Z-axis. Specimens (tissue slices or cytology smears) usually doesn’t occupy the complete specimen area, and since most digital slides are used in compressed format (generally JPEG2000 or JPEG), we can estimate an average of 1 GB per slide (100 times an X-ray film) when scanned at 40x. A medium-sized anatomic pathology department manages over 100,000 slides per year, which means that a storage capacity of 100 TB is needed every year, only for the microscopic images of the anatomic pathology department. The price of magneto-optical disks and new storage devices decreases every year. In 1999, the price of 1 TB on magnetic hard disks was 10,000 US dollars (Kayser, 1999); ten years later, the price has dropped to about 100 US dollars.

Scanning Speed In a slide scanner, specimens are highly magnified optically to obtain desired information. In some cases, digitizing pathology slides using a 20x lens, instead of a 40x one, is enough. This saves time and disk space, but most pathologists feel more confident having their specimens scanned with the highest resolution available, even more if those files are considered for a long-term use. A 10 mm x 10 mm area can be scanned at 20x magnification in 6 min (Claro Vassalo™), in 2 minutes (Aperio ScanScope® XT) or in less than 50 seconds (DMetrix® DX-40 array-microscope). With these scanning times, it is possible to use digital slide and virtual microscopy systems for quick frozen intra-operative telepathology diagnosis (Tsuchihashi, 2008).

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Digital Pathology and Virtual Microscopy Integration in E-Health Records

Table 1. Pathology digital slide scanners System

Vendor

Objective/NA(1)

# slides

Light path

File format

ACIS® III

Dako http://www.dako.com/

Nikon 4x, 10x, 20x, 40x, 60x

100

Halogen lamp

JPEG, TIFF, BMP

Applied Imaging Ariol®

Genetix http://www.genetix.com/

Motorized microscope. 1.25x, 5x, 10x, 20x (0.36 μm/pixel), 40x (0.18 μm/ pixel)

50

Fluorescence available

JPEG

dotSlide™

Olympus http://www.olympus.co.uk/ microscopy/22__slide.htm

Motorized microscope. 2x plapon, 10x, 20x, 40x uplsapo

1 (50)

Halogen lamp. Fluorescence, polarisation available

Olympus VSI, JPEG, TIFF, JPEG2000

DX40®

Dmetrix http://www.dmetrix.com/

Array of 80 objectives (20X)

40

Bright-field,

DMetrix format. Export to JPEG,TIFF

GoodSpeed®

Alphelys http://www.alphelys.com/

Motorized microscope

4 or 8

Dark field and fluorescence available

JPEG

iSan™

BioImagene http://www.bioimagene.com/

20x/0.50 Plan Fluor 40x/0.75 Plan Fluor

160

Bright-field, integrated LED

JPEG2000, JPEG, TIFF

Leica SCN400

Leica Microsystems http://www.leica-microsystems. com/

x40 (0.25 µm/pixel)

4 (384)

Bright-field,

Leica format, JPG, PNG, MS Silverlight

NanoZoomer® HT

Hamamatsu http://www.hamamatsu.com/

20x/0.7. Modes: x20 (0.46 µm/pixel) x40 (0.23 µm/pixel)

210

Fluorescence available

JPEG, TIFF

NanoZoomer® RS

Hamamatsu http://www.hamamatsu.com/

20x/0.75. Modes: x10 (0.92 µm/pixel) x20 (0.46 µm/pixel) x40 (0.23 µm/pixel)

6

Fluorescence available

JPEG, TIFF

Pannoramic 250®

3D Histech http://www.3dhistech.com/

20x/0.80 (0.23 μm) Plan Apo. 40x/0.95 Plan Apo

250

Fluorescence available

Mirax format, JPEG, TIFF

Panoramic Desk®

3D Histech http://www.3dhistech.com/

20x/0.80 (0.23 μm) Plan Apo. 40x/0.95 Plan Apo

1

Bright-field, halogen lamp

Mirax format, JPEG, TIFF

Panoramic Midi®

3D Histech http://www.3dhistech.com/

20x/0.80 (0.23 μm) Plan Apo. 40x/0.95 Plan Apo

12

Fluorescence available

Mirax format, JPEG, TIFF

Panoramic Scan®

3D Histech http://www.3dhistech.com/

20x/0.80 (0.23 μm) Plan Apo. 40x/0.95 Plan Apo

150

Fluorescence available

Mirax format, JPEG, TIFF

ScanScope® CS

Aperio http://www.aperio.com/

20x/0.75 Plan Apo

5

Bright-field, halogen lamp

TIFF (Aperio SVS), CWS

ScanScope® GL

Aperio http://www.aperio.com/

20x/0.75 Plan Apo

1

Bright-field, halogen lamp

TIFF (Aperio SVS), CWS

ScanScope® GL-E

Aperio http://www.aperio.com/

20x/0.75 Plan Apo

1

Bright-field, halogen lamp

Composite WebSlide (CWS)

ScanScope® OS

Aperio http://www.aperio.com/

60x/1.35 Plan Apo

1

Oil immersion

TIFF (Aperio SVS), CWS

ScanScope® XT

Aperio http://www.aperio.com/

20x/0.75 Plan Apo

120

Bright-field, halogen lamp

TIFF (Aperio SVS), CWS

Toco™

Claro http://www.claro-inc.jp/

Carl Zeiss Aplan 20x, 40x

1

Bright-field, flat LED

Claro format, JPEG

(2)

continues on following page

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Digital Pathology and Virtual Microscopy Integration in E-Health Records

Table 1. continued System Vassalo™

Vendor Claro http://www.claro-inc.jp/

Objective/NA(1) Carl Zeiss Aplan 20x,40x

# slides

Light path

80 or 20

Bright-field, flat LED

File format Claro format, JPEG

(1) NA: Numerical Aperture (2) In March 2009, Carl Zeiss MicroImaging, Inc. acquired ACIS® and Trestle® Instrument Systems from Clarient, Inc.

When higher resolutions and quality are needed, for instance using oil immersion with an objective 100x, the same area needs about 20 minutes to be scanned (Aperio ScanScope® OS). In our experience, average scanning time with an objective 40x for each slide is 15 minutes with Aperio ScanScope® XT. However, when using large tissue areas (e.g. cytology smears) that need to be scanned at 40x, with optical quality including multiple focusing points, scanning time of a single slide can be over 1 hour. At the moment, because of the slowness of current slide scanners a selection of glass slides to be scanned seems necessary. An anatomic pathology department generates about 500 slides per day in the routine workload. In an integrated environment, where digital slides are sent to a central server, scanning speed may be affected by the network traffic. Image compression time should also be considered when computing slide scanning throughput. With current technology, a full-digitalization approach would require multiple scanners running in parallel to avoid a significant delay in anatomic pathology service. For that reason, users are currently demanding faster or ultrarapid scanners, integrated with PACS.

Compression Since digital slide files are large by current standards, it is highly desirable to compress those files. JPEG image compression at ratios less than 20:1 do no compromise diagnostic accuracy. JPEG2000 provides higher image quality than JPEG for the same compression rations (Soenksen, 2005).

The pyramidal image file format, used by some systems, stores the image in several resolutions (e.g. 40x, 10x and 2.5x) simultaneously. This increases the efficiency of viewing, since only the appropriate resolution level is sent to the digital slide viewer. TIFF and JP2 file formats support multiple pyramidal images within one file.

Quality of Digital Slides The main quality factor in microscopic images is spatial resolution in micrometers per pixel, determined by the numerical aperture (NA) of the objective, and the quality of the lenses. An objective 20x with a 0.70 NA objective is frequently found inside many current slide scanners. It can yield a 0.23 µm/pixel resolution. Higher NA objectives, like 40x (NA 0.95) increases spatial resolution, although it offers less depth of field and correct focusing of thicker sections or cytological smears can become a problem. The camera used by the scanner is another essential element that limits the quality of obtained images. They can be charge-coupled devices (CCD) or complementary metal oxide semiconductor (CMOS). According to the scanning method, these capturing devices can be area sensors, area transference sensor, linear sensors or array microscope sensors. Digital cameras in scanners have been reviewed elsewhere (Rojo, 2006). Microscopy specimens are difficult to be scanned because they contain relevant information not only in X and Y axis, but also in Z axis. This is especially true in cytology specimens. For that reason, most manufacturers offer an enhanced focusing mode to digitize multiple Z-axis planes

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Digital Pathology and Virtual Microscopy Integration in E-Health Records

(z-stacks), with the possibility to obtain as a result either a single plane collecting the best focused areas of each of these planes, or a stack of images with the possibility to navigate in the Z-axis, at least in a small portion of the digital slide. Almost every current digital slide contains at least small out-of-focus areas, and, less frequently, stitching or seams artefacts can be found. Colour reproducibility, illumination problems, or compression effects are other described problems.

Using Digital Images in Anatomic Pathology Human Interpretation of Digital Images Viewers Due to the special characteristics of large digital slides images, a specific slide viewer is required in most cases to show special file formats and display them efficiently on the screen. Viewing should support fast panning and zooming. To improve user’ experience some viewers use pre-fetching technology to load neighbour areas of the image in the computer memory. Adequate viewing tools, including annotation and image processing are also essential. Hardware must also be considered. The ideal virtual microscopy workstation should be fast in graphical performance, since users are demanding being able to read large amount of data on the monitor without having the pixelating or mounting effect of the current systems. Special high performance graphics card and mainboard buses may be needed to solve these problems. Most pathologists have the impression that digital slides are slower than glass slides to review. Optical microscopes cannot be surpassed on image viewing speed because one cannot form an image faster then the speed of light, but some other movements and actions taken when reviewing a slide can be better using digital slides. For instance, in well focused digital slides, focus change is not neces-

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sary, and changing objective is faster and without illumination variations (Zeineh, 2005). Monitor quality and colour calibration should also be taken into account. Many scanning systems are distributed with a 24” high-resolution LCD (1920 x 1200 pixels). In our experience 3 Mpixels 20.8” (52.8 cm) monitors with a pitch of 0.207 mm and a screen size of 2048 x 1536 pixels and brightness of 500(Dicom)/800 cd/m2 are excellent for routine diagnosis. These medium sized monitors are more efficient than large 30” monitors, which generally have lower brightness and, due to large size (2560 x 1600 pixels), need more time to show images. Existing viewing software are generally developed to work efficiently only with the corresponding proprietary format. Some companies are developing web-base solutions in open platforms (J2EE) that can be the basis of the future ubiquitous medical imaging for pathology images (figure 3). Some authors claim that the ideal digital pathology workstation would have three monitors: a small monitor to display clinical data and image metadata; a large, high-resolution monitor with the same aspect ratio as the slide, mounted horizontally for navigation and to provide an uninterrupted, low to middle resolution image of the entire slide; and a high quality, 17–21 inch monitor for high-resolution evaluation of specific areas. In most studies, the mouse and keyboard are felt to be suboptimal for controlling the system (Gilbertson, 2006). Most digital slide viewers are able to store annotations in an Extensible Markup Language (XML)-based format, describing all parameters such as coordinates, contours, associated label, and magnification, amongst others. However there is no standard in digital slide annotations XML format and annotations are not interchangeable between different manufacturers. Viewing Digital Images through Internet Mobile technology in the pathology scenario can be used to securely access medical information without delays from a remote location (Sato, 2007).

Digital Pathology and Virtual Microscopy Integration in E-Health Records

Figure 3. Small bowel biopsy stained with haematoxylin-eosin, showing parasitic infestation by Schistosoma haematobium. Aurora mScope® viewer is able to read multiple virtual slide formats.

Digital slides can be viewed with an Internet connection. After reviewing three different digital viewing systems for web browsers we found that Aperio® viewer (Flash™ player) was very efficient in overview images, but Aurora mScope® viewer (Java applet) was especially efficient in lower magnifications (10x). For larger magnifications (20x and 40x) no significant differences were found between different vendors. Olympus® (Flash™ player) was found to be the most user-friendly interface (Rojo, 2008). Generally, these web-based viewers are less efficient than client-server applications. Two main complains from pathologists are that reading slides at low or medium magnification is too painful and slow, and it is difficult to find areas of interests using overview or low resolution images, which forces to review too many mid or high power fields (Gilbertson, 2006). A broadband optic fibre network with a capacity of 100 Mbps may be needed in some telepathol-

ogy applications, when high throughput is needed (Tsuchihashi, 2008). Different Contexts of Use Virtual microscopy manufacturers should take into account the main application that different users will make of theses systems. User needs are different if the scanner is being utilized for rapid frozen section intraoperative studies or for education purposes. In Japan, the primary use of digital imaging and telepathology is frozen section intraoperative studies (63%) whereas in Europe, most telepathology services are used for second opinion consultation and conferences. It is expected that the use in cytodiagnosis will increase in a significant proportion during the next years (Tofukuji, 2007a). New business models of healthcare services will arise around digital pathology. The current US LABS® (LabCorp, Irvine, California) model focuses on the application of digitized whole

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slide imaging for the interpretation of immunohistochemical stains and image analysis. This 24hour operation shared expertise service of virtual immunohistochemistry offers 300 antibodies or combination stains and Web-based image analysis programs for breast cancer and prostate cancer evaluation. This centralized solution proposes access to more subspecialty practitioners by having consultative and collaborative review of cases to prevent misdiagnosis, and can lead to an elevation of the standard of care (O’Malley, 2008). The University of Arizona College of Medicine and the Arizona Cancer Center in Tucson designed a rapid throughput breast clinic using telepathology for immediate readout of cases by telepathologists as stained slides come off the automated immunostainers. The main benefit is reducing the time it takes a woman with a breast lesion to obtain a tissue diagnosis (Weinstein, 2005). Diagnostic Accuracy with Digital Slides With current technology, are digital slides good enough for anatomic pathology diagnosis? Most existing studies are based in a limited amount of cases and do not reproduce the same conditions in daily anatomic pathology practice with conventional microscopes (e.g. difficulty of cases, pathologist’s experience, and special techniques available). In 2002, a critical analysis of 32 published trials of telepathology, some of them about virtual microscopy, including some large prospective studies, concluded that there was strong published evidence for a diagnostic accuracy comparable with glass slide diagnosis, that there was a clear-cut economic argument in favour of telepathology, and that the technique should be integrated into mainstream diagnostic histopathology (Cross, 2002). In routine diagnosis, images produced by existing virtual microscopy devices have enough image quality to allow pathologists to produce accurate, complex, and detailed diagnostic reports, even for difficult and complex cases. Furthermore, pathologists seem

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to get better at digital slide interpretation with experience (Gilbertson, 2006). Concordance rate is more related to the inherent difficulty of each case than to differences between conventional microscope and digital slides, and it may range from 96% in low difficulty cases (superficial spreading melanoma) to 63% in medium difficult cases (palisaded encapsulated neuroma). Clinical correlation and special stains can be essential for the diagnosis of certain cases, and they should be provided for distant diagnosis in the same way they are provided in daily practice with conventional microscopy (RCPA, 2008). Randomized controlled studies of internetbased virtual microscopy have also been performed in external quality assessment programs. The results of these studies encourage for the implementation of digital slide-based telepathology systems, although equivalence of diagnostic accuracy has not been proved, and financial, legal, professional and ethical factors should also be considered (Furness, 2007). Similar diagnostic accuracy results have been described with images obtained from array microscope (DMetrix®) scanning systems and lineal (Aperio®) scanners (Weinstein, 2005). The Royal College of Pathologists of Australasia carried out a pilot survey to determine if virtual microscope images were suitable for use as survey material for the external quality assurance program in anatomic pathology in 2005. The main complaints were related to the poor resolution of fine detail on high power, the difficulty in making a diagnosis from a computer screen compared with a microscope, and some difficulties to launch viewer. The percentage of participants that achieved the target diagnosis with the digital virtual microscope image was similar to that obtained using conventional glass slides, except in some difficult cases, such as biopsies of lung Wegener’s granulomatosis (RCPA, 2006). For teaching activities, virtual microscopy has shown to be at least as effective as traditional

Digital Pathology and Virtual Microscopy Integration in E-Health Records

microscopy in teaching undergraduate students (Scoville, 2007).

Automated Image Analysis and Computer-Aided Decisions Anatomic pathology diagnosis is a very complex process that cannot be easily automated or simulated; however, in this process there are some steps that can be addressed in an automated way, to facilitate the decision-making process. Some of the applications that digitalization of microscopic images allow are the selection of areas of interest, quantification of biomarkers, and measuring structures. Different algorithms can be applied to conventional slides, TMA slides, chromogenic in situ hybridization (CISH), fluorescent in situ hybridization (FISH), polarized light and mutispectral images. For example, measuring and counting is crucial in the evaluation of certain tumours, like melanoma, and this task can be easier with the use of measuring tools available in digital slide viewers. Other modes of microscopy, like inverted microscopy of living cells and reflected light microscopy can also benefit from virtual microscopy (Soenksen, 2005). The main advantages of using digital slides in automated image analysis are: • • • • • • •

Providing a mechanism to share results of experiments with other researchers. Unattended automated quantitative analysis Removing observer bias Providing standards for quality control High-throughput analysis Software tools for automated analysis (e.g. TMA localization) Can be applied to frozen sections, haematoxylin-eosin, special strains, immunohistochemistry (IHQ), FISH, RNA in situ hybridization

Current pharmacology research in fields like oncology is mainly based in the validation for translation of tissue biomarkers from the research lab to the clinical lab. This effort will probably rely heavily on the combination of tissue microarray technology with automated quantitative analysis, but well-designed, thorough validation studies are still needed. Currently, scanning and image analysis solutions are mainly dedicated to research and training purposes. Some automated image analysis algorithms are USA FDA 510(k)-cleared applications for assessing the level or certain immunohistochemical markers, being the most frequent ones oestrogen receptors, progesterone receptors, and Her-2 membrane staining. In Europe, scanning devices are requested to be compliant with Directive 98/79/EC on in vitro diagnostic medical devices, but this directive is focused on safety requirements, electromagnetic compatibility, software life cycle, information supplied by the manufacturer, performance evaluation, and other related aspects.

Integrating Anatomic Pathology Digital Images into PACS Digital medical images consist in massive amounts of information that Hospital Information Technology departments should be capable of managing in central repositories. The financial hurdle of this high demand for storage can be optimized by having a single enterprise medical image repository consisting in a well dimensioned picture archiving and communication system (PACS) based on standards. In radiology, the diagnostic process is patientdriven, an examination (study) usually involves a single image acquisition modality and all images of the study are related to one and only one patient. Digital imaging in anatomic pathology differs from that in radiology resulting in specific aspects in acquiring, archiving and distributing digital im-

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Digital Pathology and Virtual Microscopy Integration in E-Health Records

ages in anatomic pathology. These specific aspects are key elements to take into account. In radiology, digital imaging is the main procedure requested by the ordering clinician who is expecting both images and a report. In anatomic pathology, the ordering clinician is mainly expecting a report. It is the initiative of the pathologists to perform digital macroscopic or microscopic images and to make them available to the clinician. Indeed, digital imaging in anatomic pathology is a procedure step ordered by pathologists within the anatomic pathology department. This is an important issue while defining how to integrate acquisition modalities to anatomic pathology information systems. Moreover, unlike radiology, whenever performed, digital imaging in anatomic pathology usually results in images of different types (gross images, microscopic still images, digital slides, etc) finally gathered in the same imaging study. Many types of imaging acquisition modalities (photo camera, motorized microscope with camera, slide scanners, motorized multispectral microscope, etc) may be involved for a single examination. While radiologic examination is a patient-centric process, digital imaging in anatomic pathology is a specimen-centric process and images of the same study may be related to different specimen (parts and/or slides) from one or even different patients (e.g. Tissue Micro Array) (Le Bozec, 2007). This point has required main changes in standards in order to integrate properly identified anatomic pathologic images into PACS. Moreover, unlike in radiology, the “subjects” of digital imaging in anatomic pathology (e.g. slides) are always available to acquire more images, if needed. Last but not least, the main issue of digital imaging in anatomic pathology is the very large size of virtual slides so that current PACS cannot handle the massive amounts of data being generated by anatomic pathology departments. Besides the storage capacity and network band-

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width required managing virtual slides, the first barrier is that the current standards cannot handle such images.

Defining Relevant DICOM Objects for Anatomic Pathology DICOM had several information object definitions (IODs) useful in anatomic pathology including the visible light photographic image for gross specimens and the visible light slide-coordinates microscopic image for slide-based microscopic imaging. In 1998, minor additions to DICOM Supplement 15 (Visible Light Image) in two existing data types, Pixel Data Type and Curve, were proposed to permit DICOM to be the standard for analytical cytology and its two modalities, digital microscopy and flow cytometry (Leif, 1998). Some limitations of the DICOM information object definitions (IOD) dedicated to anatomic pathology are currently addressed by the specific DICOM pathology working group (WG26) created in December 2005. First of all, some types of anatomic pathology digital image (whole-slide images, multispectral images, flow cytometry, etc) do not have applicable DICOM information object definitions. One of the immediate aims of DICOM WG 26 was creating a mechanism to store, retrieve and view microscope whole slide images within the DICOM framework. Whole-slide images can be larger than 200,000 x 80,000 pixels and are too large in both pixels and byte size to be handled by current DICOM objects. In DICOM there is a limit for maximum values of rows and columns and an image pixel matrix cannot be greater than 64.000 x 64,000. Pixel data attribute is also limited in length. Some workarounds are under development, with the challenge to maintain backwards compatibility of all PACS installed base. One approach could be allowing attributes with a long value representation to be used instead of the existing attributes rows and columns, but only when the value exceeds the existing limits.

Digital Pathology and Virtual Microscopy Integration in E-Health Records

Another solution is dividing large images into tiles that can be managed with current DICOM object definition. An alternative approach is using JPEG2000 and JPEG2000 Interactive Protocol (JPIP) transfer syntaxes that are known to support large images. Currently, the DICOM standard includes the basic parts of the JPEG2000 standard in Supplements 61 and 105. Supplement 106 (JPEG 2000 Interactive Protocol) describes two JPIP-based Transfer Syntaxes as methods of delivering image pixel data apart from patient data: the noncompressed JPIP Referenced Transfer Syntax and the Deflate-compressed JPIP Referenced Deflate Transfer Syntax. With the JPIP Transfer Syntaxes, the DICOMbased Picture Archiving and Communication System (PACS) server gives its client a Uniform Resource Locator (URL) string that refers to the virtual slide pixel data provider (i.e., a JPIP server), together with the image name, which can be arbitrary and unrelated to patient data (as shown in figure 4). Upon receiving the pixel data provider reference, the PACS workstation either uses a built-in JPIP viewer or invokes an external one for retrieving the virtual slide from the specified JPIP server (Tuominen, 2009).

In the regional project of telepathology in Castilla-La Mancha, Spain, it was decided to use DICOM Supplement 106 (JPEG2000 Interactive Protocol) to integrate digital slides with PACS (DICOM WG4, 2006). With this solution, we can send a DICOM object to the PACS, containing all DICOM IOD attributes, and a URL that links this object to the JPIP server. A drawback for this approach is the need to implement two storage servers (PACS and JPIP servers). The Institute of Medical Technology also explored the feasibility of using JPIP to link JPEG2000 WSIs with a DICOM-based Picture Archiving and Communications System (PACS) at the Tampere University Hospital in Finland (Tuominen 2009). Besides these initiatives, most DICOM systems cannot manage TIFF files with a tiled organization, and/or TIFF files with JPEG or JPEG2000 compression, that are used by some slide scanners manufacturers. Moreover, although some digital slide viewers, like Aura mScope® (http://www. auroramsc.com/en_/Solutions/PatientReord. html), Alphelys Inaveo® (http://www.alphelys. com/site/us/pIMG_InaveoVS.htm) or PathFind® (Goode, 2008) are scanner vendor-neutral and are able to show many different formats, including the support of ImageJ image analysis macros, an

Figure 4. Using virtual slides within a DICOM-based PACS over JPIP. (Adapted from Tuominen, 2009)

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Digital Pathology and Virtual Microscopy Integration in E-Health Records

additional effort should be done by manufacturers and standards bodies to have a universal DICOM viewer able to show digital slides. Another immediate aim of DICOM WG 26 was updating and extending the data model used to describe anatomic pathologic specimens which are the subject of imaging procedures, The DICOM model did not initially describe specimens in sufficient detail or associate images with specimens with enough precision for the complexity of anatomic pathology practice. In anatomic pathology, a single diagnostic study may involve multiple images of multiple related specimens (parts, blocks, and/or slides) and in some cases, an image can include more than one specimen or even tissue from more than one patient (e.g., tissue microarrays). While the relationship between patient and image is straightforward and accurately captured by DICOM objects, in anatomic pathology there was a need for a new specimen module that formally defines specimen attributes at the image level. The DICOM WG26 defined the specimen module in a new DICOM supplement (number 122). The specimen module defines formal DICOM attributes for the identification and description of specimens when said specimens are the subject of a DICOM image. In supplement 122, the “DICOM Model of the Real World” has been extended for specimen with the addition of the objects “specimen,” “container,” “component” and “preparation step.” Attributes of the specimen, container, component, and preparation step objects are represented in the specimen module, which is focused on critical specimen information necessary to interpret the image. Specimen attributes include attributes that (1) identify the specimen (within a given institution and across institutions); (2) identify and describe the container in which the specimen resides as well as each component of the container if required (e.g., a “slide” is a container that is made up of the glass slide, the coverslip, and the “glue” that binds them together); (3) describe specimen collection, sampling, and

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processing; and (4) describe the specimen or its ancestors when these descriptions help with the interpretation of the image (Daniel, 2009). The specimen module distinguishes the container ID and the specimen ID, making them different data elements to allow maximal flexibility for different situations. Even though the full history of specimen processing is not required in every instance, specimen attributes allow that processing history to be encoded. However, a unique specimen identifier is useful to link a subject specimen to its processing history. Attributes that convey diagnostic opinions or interpretations are not within the scope of the specimen module. The DICOM specimen module does not seek to replace or mirror the pathologist’s report. Although old Specimen Identification Module is retired from all Information Object Definitions (IODs), minimal problems are expected since there were few, if any, actual implementations that used the old specimen identification module (DICOM WG26, 2008). Besides data related to the specimen, series of important data, including patient data, clinical data and data describing the technical aspects of the image acquisition context must be associated with the digital slides and available for the pathologist (Tofukuji, 2007b). Integrating clinical information and usual anatomic pathology diagnostic and prognostic data with digital slides including annotations, 2-dimensional (2-D) image analysis, and 3-dimensional (3-D) tumour reconstructions and volumetric analysis, can be useful to create, for instance, an effective cancer evaluation system (Petushi, 2008). Some additional efforts are required so that the DICOM Information Definition Objects dedicated to anatomic pathology include all these relevant information. In practice, current imaging devices in anatomic pathology departments (gross station cameras, digital cameras on conventional microscopes, slide scanners) are not DICOM compliant devices and do not accept DICOM messages or worklists.

Digital Pathology and Virtual Microscopy Integration in E-Health Records

Also, conventional colour monitors do not include DICOM colour curves or gamma correction.

Specifying Anatomic Pathology PACS PACS offers reliable long term solution to ensure storage, retrieval, and delivery without error of many types of medical images. The heart of PACS is the image database manager, that must be able to retrieve images for a given patient o specimen when required by hospital or departmental information systems (RIS-radiology information system or PIS-pathology information system) or other systems. These queries are defined by the Digital Imaging and Communications in Medicine (DICOM) standard (Smith, 2006). The main advantages of using DICOM in pathology are organizational (one central repository of medical images for all medical specialties: PACS), facilitating the integration with EHR, the use of one viewer for all medical images, and the independence of devices manufacturers and proprietary file formats. Some authors describe the term “pathology PACS” as a storage system to save the results of laboratory tests, including images of patients’ laboratory specimens such as digital slides (Weinstein, 2005). The term PACS should only be utilized in those systems having an image archive, a database manager, a workflow/control software (image manager) and an interface with other information system (e.g. radiology information system (RIS), pathology information system(PIS)), and it is able to respond to the queries defined by the Digital Imaging and Communications in Medicine (DICOM) standards. Image manager is an essential part of PACS since it is responsible to ensure that images are stored correctly once received from imaging devices or modalities (Smith, 2006). With new demands to store images coming from pathology, endoscopy and many other clinical departments, hospitals should establish or redesign their PACS strategic business plan, since these demands will have a significant impact in

operational, technical, and financial sections of the PAC strategic plan (Levine, 2006). In the operational section of the strategic plan, according to Levine (2006) the following components should be considered: •

•

•

•

•

•

Alignment to strategic goals and objectives: The institution should decide the alignment of including pathology images in the PACS to its strategic goals and objectives. In this sense, some institutions need to move to digital archives because they are running short of room for glass slides, in other cases, the strategic goal may be directed towards educational objectives. PACS readiness assessment: According to technical infrastructure, departmental culture, and available personnel, a decision should be made if the hospital is ready to implement and manage the change process associated with PACS storage of anatomic pathology images. In most cases, the radiology department of the hospital is already working with PACS, and this model can help to make easier the transitions for pathology. Implementation plan: The phases of the implementation plan, including primary objectives and benefits of each phase, and plans to reduce expenses (e.g. reduction in sign-out time, or mailing costs reduction). PACS impact analysis: Improved workflow, productivity, and clinical care at each phase of PACS deployment. Pathology market share analysis: A description of competitors and the status of their PACS implementation. Meeting future demands needs: A potent argument for PACS strategic plan is to compare capacity and productivity gains that will make possible future increases in demands for pathology services

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There are two technical components of the PACS strategic plan. The first one is the assessment of information technology readiness, including storage technology, clustering design, network infrastructure, installed PC base, and integration interfaces. The second technical component is the discussion of the human resources and contingency plan to successfully support a PACS dimensioned to store large pathology images. The financial analysis should include a return on investment (ROI) analysis. We must realize that digital slides are not expected to replace conventional glass slides in diagnostic pathology in the medium term, but it may help to reduce turnaround time for pathology reports, optimize archives, decrease errors, reduce transportation costs, decrease teaching costs, and lower quality assurance expenses. The financial analysis should be projected for at least 5 years (Levine, 2006). Architectural redesign, evolution of technologies underlying PACS, adaptation of existing standards to a broad range of medical imaging and a good PACS strategy (Nagy, 2007) are essential for pathologists to enjoy the benefits of multidepartmental PACS in the near future.

INTEGRATING DIGITAL PATHOLOGY TO THE HEALTHCARE ENTERPRISE Electronic health records increase the efficiency and quality of medical services, preventing errors and promoting the implementation of clinical guidelines. Anatomic pathology data and images should be an important part of the patient EHR dedicated to patient care. Anatomic pathology also plays a major role in the research activities and anatomic pathology data and images should be also largely integrated to clinical datawarehouses, epidemiological or biomedical research databases and platforms dedicated to translational medicine. Integrating anatomic pathology to the “healthcare enterprise”, as part of e-Health records, can only be achieved with standardization of processes,

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messages and data following the recommendations of Integrating the Healthcare Enterprise (IHE) Anatomic Pathology Technical Framework. There are several standardization bodies providing complementary efforts to standardize clinical information systems, including DICOM for medical images, Health Level Seven (HL7) for managing data and documents, and SNOMED CT® for terminology (Tofukuji, 2007).

HL7: Health Level Seven The mission of the HL7 Anatomic Pathology Working Group is to develop and review implementation guides of HL7 standards and to enhance existing HL7 standards to support anatomic pathology use cases. HL7-Anatomic Pathology Working Group contributed to the DICOM WG26 efforts to define the specimen module in a new DICOM supplement (number 122). The specimen module has been harmonized with the HL7 v2 SPM segment and the HL7 v3 specimen domain information model. HL7-Anatomic Pathology recent activities have been related to structuring anatomic pathology reports, in order to provide a Clinical Document Architecture (CDA) template for the anatomic pathology report (HL7, 2009). Another important work item is reporting anatomic pathology to cancer registries and public health repositories, in collaboration with North American Association of Central Cancer Registries (NAACCR) and College of American Pathologists in the United States, and the Association pour le Développement de l’Informatique en Cytologie et Anatomo-Pathologie (ADICAP) in France. This group is working in the alignment of IHE and NAACCR pathology electronic reporting implementation guides, and a study of terminology, including Logical Observation Identifiers Names and Codes (LOINC®), Systematized Nomenclature of Medicine, Clinical Terms (SNOMED CT®), NAACCR Search Term List, International

Digital Pathology and Virtual Microscopy Integration in E-Health Records

Classification of Diseases for Oncology 3rd Edition (ICD-0-3), and ADICAP coding system.

IHE: Integrating the Healthcare Enterprise The Integrating the Healthcare Enterprise (IHE) initiative was started in the United States in 1999, promoted by healthcare professionals and industry, to address the correct use of standards to integrate multivendor electronic medical record systems, initially focused on radiology. IHE is a process based on technical frameworks for the implementation of established messaging standards to achieve specific clinical goals, rigorous testing processes for the implementation of those technical frameworks at a connect-a-thon, and educational sessions and exhibits at major meetings of medical professionals. IHE promotes the coordinated use of established standards such as DICOM and HL7 (Health Level 7) to address specific clinical needs in support of optimal patient care. Systems developed in accordance with IHE communicate with one another better, are easier to implement, and enable care providers to use information more effectively. IHE activity in the domain of pathology was started in Japan by the Japanese Association of Healthcare Systems Industry (JAHIS) in January 2004 (Tofukuji, 2007). The first European IHE-Pathology meeting was held at the European Congress of Pathology in Paris, on September 5th, 2005 (Rojo, 2009). The encouraging integration efforts in pathology from multiple international groups to define a general workflow in pathology information systems materialized in the publication of the IHE Anatomic Pathology Technical Framework, for trial implementation, at the end of 2008 (http://www.ihe.net/Technical_Framework/index. cfm#pathology). The Anatomic Pathology Technical Framework aims at the integration of the anatomic pathology laboratory department in the health-

care enterprise. The diagnostic process requires tight consultation and close cooperation between different healthcare providers: pathologists and technicians, surgeons, oncologists, clinicians, radiologists, etc. The ultimate goal is a comprehensive digital pathology record for the patient, of which images are a significant part. The primary focus will be digital formats for clinical patient management, but digital imaging for research applications may also be addressed as appropriate (dealing with Tissue Micro Arrays (one slide for hundreds of patients) with a link to patient information or dealing with animal experimentation, etc). The aim is to progressively cover all specialties of anatomic pathology: surgical pathology, clinical autopsy, cytopathology, biopsies and all special techniques (gross examination, frozen section, immunohistochemistry (including TMAs), molecular pathology, flow cytometry, special microscopy techniques (confocal laser scanning, multispectral microscopy), etc. The IHE Anatomic Pathology Technical Framework comprises two volumes. Volume 1 provides a high-level view of IHE functionality, showing the transactions organized into functional units called integration profiles that highlight their capacity to address specific information technology requirements. Therefore, it is underlined that the diagnostic process in anatomic pathology differs from that in the clinical laboratory since it relies on image interpretation, and it also differs from the process in radiology since pathology is a specimen-driven activity, and a single examination may involve many types of imaging equipments (e.g. gross imaging, microscopic still imaging, whole slide imaging, multispectral imaging) (IHE, 2008a). Volume 2 of the Anatomic Pathology Technical Framework provides detailed technical descriptions of each IHE transaction or messages used in the existing anatomic pathology profile (IHE, 2008b). Until now one integration profile (Anatomic Pathology Workflow) have been defined. The

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Anatomic Pathology Workflow (APW) Integration Profile establishes the continuity and integrity of basic pathology examination data and images exchanged between the systems of the ordering care units, those of the performing anatomic pathology laboratory, and the systems producing, archiving, communicating or presenting images. In a PACS environment, acquisition modalities (e.g. slide scanners) should be able to receive patient and study-related data from the hospital information system (“Order Placer”) and from the anatomic pathology information system (“Order Filler”). DICOM Modality Worklist is the link that transfers this critical information between the acquisition modalities and the HIS or PIS (Gale, 2000). To have the anatomic pathology information system and digital imaging systems interoperate, it is critical that they all be able to reference a particular specimen accordingly to the same model before associating it with one or more reports or images. The Anatomic Pathology Workflow (APW) Integration Profile includes 10 transactions (PAT1 Placer Order Management, PAT-2 Filler Order

Management, PAT-3 Order Results Management, PAT-4 Procedure Scheduled and Updated, PAT5 Query Modality Worklist, RAD-8 Modality Images Stored, RAD-10 Storage Commitment, RAD-14 Query Images, RAD-16 Retrieve Images, and RAD-43 Evidence Document Stored) between nine actors (Acquisition Modality, Department System Scheduler/Order Filler, Evidence Creator, Image Archive, Image Display, Image Manager, Order Placer, Order Filler, and Order Result Tracker) (Figure 5). Daniel et al (2009) have described how IHE Anatomic Pathology technical framework can support the basic diagnostic workflow in anatomic pathology departments. Pathology imaging devices can be adapted to generate DICOMized images that can be sent to the PACS using middleware or integration engines. Non-DICOM compliant imaging devices in pathology departments can be connected to an intermediate information system that transforms original image formats (TIFF, JPEG, or proprietary digital slide files) in to a DICOM object, performs messaging with PACS and achieves storage commitment.

Figure 5. Schematic representation of the anatomic pathology workflow (APW) profile

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IHE Anatomic Pathology Technical Framework has been used as a reference to implement standards in some telepathology projects (Rojo, 2009). Even so, we have not been able to implement all transactions defined by IHE since we have to adopt high level rules governing IT infrastructure in our region. For instance, some problems were found to adopt transaction PAT-3 (Order Results Management) in which the PIS should send the pathology report to a report repository using an HL7 ORU^R01 message. However, in the design SESCAM, our public health service, does not agree with sending a copy of the pathology report to a central repository, instead SESCAM prefers sending a message including patient identification, report identification, and report status; and pathology reports are sent directly from the PIS, only when it is requested by the user. This allows having a single instance of the report, always updated. As multiple actors appear on scene (hospital information system, pathology information system, image repository, and automated immunostainers) a protocol for designing databases is necessary to create a logical model adapted to business requirements. This will avoid inefficiencies like saving the same information in different systems when it is not necessary (Cowan, 2005). Nowadays, the main efforts of IHE anatomic pathology are focused on how to structure and encode anatomic pathology reports and how to report anatomic pathology results to public health or research repositories. In conclusion, the use of medical digital image standards (DICOM), compression standards (JPEG2000), messaging and document standards (HL7), are the ground in order to create interoperable solutions. In addition current anatomic pathology information system vendors are only beginning to consider the standard-based integration between the reports generated by the pathologist and its corresponding images, and the role of those standards in virtual microscopy.

Terminology and Data Representation Standards Terminology standards need to be part of the interoperability of pathology information systems, including digital slides and the rest of the e-Health records. The Systematized Nomenclature of MedicineClinical Terms (SNOMED CT®) is a comprehensive clinical terminology currently adopted as a terminology standard in electronic health information exchange. The January 2009 international release includes more than 310,000 active concepts, 794,000 active English-language descriptions, and over 947,000 relationships. The U.S. National Library of Medicine Unified Medical Language System (UMLS®) is a complete system to facilitate the development of computer systems for managing biomedicine and health language. The three main components of UMLS are the Methathesaurus, which contains over one million biomedical concepts, their various names, and the relationships among them, from over 100 source vocabularies, including over five million multi-lingual terms or names; the Semantic Network, which defines categories and relationships between them; and the Specialist Lexicon & Lexical Tools, which provide lexical information and programs for language processing (UMLS, 2009). SNOMED CT® was originally created by the College of American Pathologists (CAP), and as of April 2007, it is owned, maintained, and distributed by the International Health Terminology Standards Development Organisation (IHTSDO) (http://www.ihtsdo.org/). SNOMED CT® (both English and Spanish versions) is included in UMLS Metathesaurus®, where it is linked to many other biomedical terminologies and natural language processing tools (UMLS, 2009). German and Spanish translations are available. SNOMED CT® is not only the most comprehensive clinical vocabulary available, but also is

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a well-formed, machine-readable terminology, concept-oriented, with an advanced structure. SNOMED CT® is more that a controlled vocabulary. It is defined as a controlled clinical terminology with characteristics of interface terminology and reference terminology. As a clinical terminology, it is designed to register clinical data in EHR. As an interface terminology it allows the utilization of users preferred terms, and it has an intuitive and efficient approach in clinical data grouping. Relationships between terms, hierarchies, and mapping to other nomenclatures are valuable tools. Terminology experts as well as institutional policies do not recommend binding directly clinical statements to reference terminologies, such as SNOMED CT® and support using standard reference terminology concepts - and possibly semantic linkages - for representing commonly used interface terminologies (Rosenbloom, 2006). Rosenbloom et al. investigated how to use SNOMED CT® to represent two interface terminologies (MEDCIN and CHISL) (Rosenbloom, 2009). They encourage terminology developers to create or enrich interface terminologies by using reference terminologies concepts as a starting point but stress out that assembling clinically meaningful compositions, appropriate synonyms and linkage between concepts (related concepts or modifiers) is a labour-intensive approach. Daniel et al propose functional requirements of terminology services for coupling interface terminologies to reference terminologies (Daniel, 2009). In our experience, user should be offered only a subset of terms that may be useful, and they must be translated into the language of use, without losing meaning. Since different clinical departments have different needs, the creation of a local interface terminology to SNOMED CT® is recommended also by other groups. With this approach, local classifications and concepts subsets can be mapped to SNOMED CT® (Lopez Osornio, 2007). A significant work is needed to adapt SNOMED CT® in all health areas and specialties,

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and calls for collaboration and harmonisation are under way in order to achieve multilingual interoperability (Donnelly, 2008). New diseases are described every year and new techniques are being applied continuously; for that reason, an extensibility mechanism must be considered to include new terms that may not be available in SNOMED CT®. The IHTSDO has set a high priority on the development of a SNOMED CT® concept request submission system, but this system is still under development. A synoptic reporting system must be part of the pathology information system. This is especially important in the management of cancer. The College of American Pathologists (CAP) Cancer Protocols and Checklists© were created in response to the mandate of the American College of Surgeons Commission on Cancer to use cancer protocols in 2002 (Robert, 2008; Tobias, 2006). Encoding structured pathology protocols or checklists with SNOMED CT® allows enhancing the value of the pathology report, saving user valuable time and reducing coding errors. In order to facilitate and standardize tissue banks, three main sets of data standards can be used: the College of American Pathologists (CAP) Cancer Checklists©, the protocols recommended by the Association of Directors of Anatomic and Surgical Pathology (ADASP) for pathology data, and the North American Association of Central Cancer Registry (NAACCR) elements for epidemiology, therapy and follow-up data. Combining them it is possible to create a core set of annotations for banked specimens, organized as a set of International Standards Organization (ISO)-compliant Common Data Elements (CDEs) for tissue banking (Mohanty, 2008). Multidimensional business process modelling can be considered a first step in the interoperability effort. This normalization effort can also be extended to research efforts, as has been proposed in the tissue microarrays (TMA) data exchange specification (TMA DES).

Digital Pathology and Virtual Microscopy Integration in E-Health Records

ORGANIZATION IN DIGITAL PATHOLOGY In a digital imaging environment, the greatest changes in workflow organization involve three tasks: automation, integration, and simplification (Reiner, 2006). An example of automation in pathology is image analysis software that is able to count with precision the number of oestrogen receptors positive cells in an immunohistochemistry digital slide. Integration between PIS and PACS eliminates the time-consuming steps of transferring gross images from grossing station camera to another computer. Simplification allows converting complex, time-consuming tasks into more straightforward ones. With digital images, pathologists may measure the depth of invasion of a tumour with just a click of a mouse. Digital pathology includes solutions for pathology primary diagnosis and second opinion, and also includes telepathology solutions. Most authors agree that pathology images (macroscopic images, digital slides, molecular pathology images) will become an integral component of EHR as part of pathology reports (Weinstein, 2005). The organization must consider digital pathology as a global integrated effort and provide the adequate infrastructure, including telecommunications. Therefore, it will be necessary not only acquiring all the necessary imaging devices and computer equipment needed in a digital pathology department, or to develop an information system that allows pathology departments to storage and manage the reports and its images, but also they must consider developing web applications that allows the communication between the different pathology departments, sharing reports and images, facilitating the collaborative work between hospitals. The development of the digital pathology project must be based on medical standards in accordance with the guidelines described in the IHE Anatomic Pathology Technical Framework.

Since pathology slide images are very large files, a detailed analysis of those cases in which those files must travel through the hospital network should be performed. A different approach to radiology is needed, but the large central storage model delivering images through a very fast network, already used in radiology (Siegel, 2006), can be a good option for pathology. Access to pathology images for diagnostic and screening purposes requires very fast access to many different areas of the slide. Using the proposed model of central image servers, although current digital slide viewers reduce significantly the size of transmitted data in the network since only the request part of the image to be shown is retrieved from the server, this process may result in a delay in the image to be shown sharp on the monitor. According to local network and server capabilities, each institution should decide if they prefer proprietary solutions versus JPIP standard solutions, or client-server viewers versus web-based viewers to manage digital slide image requests. Worklists and pre-fetching can also be applied to pathology images. Implementation of DICOM modality worklists management software has been reported to reduce input errors up to 6% in radiology (Siegel, 2006). The advantage of worklists for pathologists are filtering images according to multiple criteria (e.g. accession number, type of image, or date and time), having access to all unread images of all the department, the security improvement eliminating confusion in selecting a wrong slide from a another case, and the ability for multiple pathologists to share responsibility or reading similar types of specimens. Pre-fetching in radiology is used to store new and historic examinations locally at a workstation to optimize transfers, mainly if they are saved in long term storage (Siegel, 2006). The pathologist may have ready the digital slide files to be read in his/her workstation or in a departmental server before he/she begins reading slides.

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A temporal repository of proprietary format files may be needed when essential tools, like image analysis algorithms can only be run using those proprietary files. Digital slide scanners should be available in small hospitals mainly in those institutions with only one pathologist or without pathologists. Non-specialists technicians and physicians may take the responsibility to put the slide into the scanner. Virtual microscopy avoids the need to select representative images. In primary diagnosis, the diagnosis responsibility should be on the pathologists. If misdiagnosis occurs as a result of the limits of the telepathology system employed by the client side, some responsibility can be relieved of the client institution. The amount of effort put into overcoming telepathology limitations must be considered in assigning responsibilities. In second opinion, the final diagnostic responsibility lies with the sender, and in return for providing free advice the consultant may be protected from any liability (Ito, 2007). The use of digital slides in rapid frozen intraoperative studies is expected to increase significantly, not only in hospitals without a pathology department but also in small and medium sized pathology departments due to the special difficulties of these studies that may require an expert opinion. About 5% of surgical procedures require rapid diagnosis (Sawai, 2007b). Since current scanning systems are not able to expedite the workload of most pathology departments, in our institution, we select those cases that need to be scanned according to the following priority criteria: 1. Before pathology report is available a. Frozen section evaluation (consultation to reference hospital) b. Cytological smear material adequacy in fine needle aspiration procedures c. Second opinion in cytology

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d. Second opinion in histopathology where diagnosis can be made in haematoxylin-eosin stain (e.g. skin biopsies) e. Measuring is necessary (e.g. melanomas, distance to borders, or area calculation) f. Automated biomarkers counting or image analysis of immunohistochemical stains g. Differential diagnosis orientation (counselling for best immunohistochemistry panel to be applied) h. Clinicopathological sessions 2. After pathology report sign-out a. Identified cases i. Patient’s request (a copy in DVD) ii. Hospital cancer committees iii. Second opinion to/from other institutions iv. Legal actions b. Anonymous cases i. Quality assurance program in immunohistochemistry programme (Spanish Society of Pathology) ii. Quality assurance programme in diagnosis (Spanish Society of Pathology) iii. Collections of interesting cases iv. Education v. Conferences vi. Research In summary, the technical implementation of a digital pathology project can be divided into two strongly interconnected parts. The first important information system is the Pathology Information System (PIS) deployed in each hospital, and its main aim is to cover the daily workflow of a pathology department. Included functionalities of this system are: •

Integration with the Hospital Information System (HIS). This allows obtaining the patient demographics data, the scheduled

Digital Pathology and Virtual Microscopy Integration in E-Health Records

•

•

•

•

patient appointments and all the order requests associated to the pathological examination performed at the hospital. Integration with all image acquiring devices (called modalities) using the DICOM worklist. Each modality will receive a worklist, which contains the information about the specimens that will be digitized. Storage all the images generated by the pathology department in the PACS, including digital slides. A unique viewer that allows showing, at the same time, gross and microscopic images, including digital slides. A reporting system that allows the management of autopsy, biopsy and cytology reports. In addition, it can retrieve from the PACS all the images associated with a report.

The second Information System is a centralized system that consists of a telepathology web portal. Its goal is to offer the following collaborative tools: •

•

•

Second opinion: that allows a pathologist to make a consultation about a case with other colleague to obtain a second opinion of the case. Consultation forum: the forum is composed of several topics, like skin pathology or soft tissue pathology. The forum allows a pathologist to make a consultation with a group of pathologists belonging to a topic, and then have a discussion about the case. Pedagogical public library: A pathology atlas with studies that are of high scientific or teaching interest. All the images and reports showed are anonymous. This tool will be very useful to improve the training of medical students and, in the future, other specialties can be included in the atlas, like dermatology or haematology.

CONCLUSION The digitalization of anatomic pathology images will allow including them in the electronic health record. This solution will allow other specialists, like dermatologist or haematologist, to have access to this kind of images, as well as improving the training of students and medical residents. By using teleconsultation and distance diagnosis through digital imaging will offset the shortage of specialists in some countries. Telepathology services will rapidly grow when all pathology images become available in a digital and standard format. Reviewing pathology reports of patients with certain diseases will allow validation of new physiopathological concepts and therapies. Digital slides are an important challenge to existing standards. A DICOM standard for whole slide imaging in pathology is needed for interoperability and long term solutions. By using medical informatics standards in the development of a digital pathology project will provide, in the future, an interconnection with others telepathology networks for accessing, exchanging, and upgrading electronic medical records through the Internet. Nowadays, the use of digital slides is limited by quality factors and the limited 2-D information of the obtained images, which although they are suitable for diagnosis in most situations, in some difficult cases those limitations in current technology can avoid reaching the correct diagnosis. An additional effort should also be made in obtaining 3-D information of the slide, and improving digital slide navigation viewer and hardware, since current software and mouse device are uncomfortable for most pathologists. Internet-based virtual microscopy needs ubiquitous broadband infrastructure. Digital pathology allows for an unprecedented flexibility in reporting and documenting results, and it is possible to generate different types and formats of reports for different users with different levels of understanding.

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Virtual microscopy is an efficient solution for consultation, under- and postgraduate education, clinico-pathological conferences, and it is under study in rapid intraoperative pathology diagnosis and cytological cancer screening. With the degree of similarity of virtual microscopy with conventional optical microscope most pathologists would be able to transition to digital solutions. Current experts’ opinion is that glass slides will remain part of anatomic pathology practice into the predictable future, although pathologists might stop using light microscopes when virtual microscopy becomes a sufficiently advanced technology. Integrated Digital Pathology solutions will improve the cooperative working relationship between the clinicians and the pathologists in order to increase patient safety, quality of health services and translational medicine

ACKNOWLEDGMENT This work has been carried out with the support of COST Action IC0604 “EURO-TELEPATH” and the BR-CCM-2006/03 grant from the Foundation for Health Research in Castilla-La Mancha, FISCAM.

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Tofukuji, I. (2007c). Electronic medical records and the Integrating the Healthcare Enterprise initiative. In T. Sawai (Ed.), Telepathology in Japan. Development and practice (pp. 235-238). Morioka, Iwate, Japan: Celc, Inc. Tsuchihashi, Y., Takamatsu, T., Hashimoto, Y., Takashima, T., Nakano, K., & Fujita, S. (2008). Use of virtual slide system for quick frozen intra-operative telepathology diagnosis in Kyoto, Japan. Diagnostic Pathology, 3(Suppl 1), S6. doi:10.1186/1746-1596-3-S1-S6 Tuominen, V. J., & Isola, J. (2009). Linking wholeslide microscope images with DICOM by using JPEG2000 Interactive Protocol. [Epub ahead of print]. Journal of Digital Imaging, (May): 5. U.S. National Library of Medicine. (2009). Unified Medical Language System, Bethesda, MD: National Institutes of Health. Retrieved February 20, 2009, from http://www.nlm. nih.gov/ research/ umls/ Weinstein, R. S., Descour, M. R., Liang, C., Richter, L., Russum, W. C., Goodall, J. F., et al. (2005). Reinvention of light microscopy. Array microscopy and ultrarapidly scanned virtual slides for diagnostic pathology and medical education. In J. Gu, R.W. Ogilvie, R.W. (Eds.), Virtual microscopy and virtual slides in teaching, diagnosis, and research. Series Advances in Pathology, Microscopy & Molecular Morphology. (pp. 10-34). Boca Ratón, FL: Taylor & Francis. 1261

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Yagi, Y. (2007). Critical comparison of digital pathology systems. Retrieved February 16, 2009, from http://www.bio imagene.com/ pdfs/ Study% 20Yagi% 20et% 20al% 20070501.pdf Zeineh, J. (2005). Reinvention of light microscopy. (2005). The Trestle Digital Backbone. In J. Gu, R.W. Ogilvie, R.W. (Eds.), Virtual microscopy and virtual slides in teaching, diagnosis, and research. Series Advances in Pathology, Microscopy & Molecular Morphology. (pp. 77-96). Boca Ratón, FL: Taylor & Francis.







ENDNOTES 1



DICOM® is the registered trademark of the National Electrical Manufacturers Association for its standards publications relating to digital communications of medical information. HL7® is a registered trademark of Health Level Seven (HL7), Inc. IHE® is a registered trademark of Integrating the Healthcare Enterprise (IHE) International. LOINC® is a registered trademark of Regenstrief Institute, Inc. UMLS® and Metathesaurus® are registered trademarks of the National Library of Medicine.

IHTSDO®, SNOMED® and SNOMED CT® are registered trademarks of the International Health Terminology Standards Development Organization.

This work was previously published in Ubiquitous Health and Medical Informatics: The Ubiquity 2.0 Trend and Beyond, edited by Sabah Mohammed and Jinan Fiaidhi, pp. 457-484, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.16

Clinical Decision Support System to Prevent Toxicity in Patients Treated with Digoxin Asunción Albert University Hospital Dr. Peset/University of Valencia, Spain Antonio J. Serrano University of Valencia, Spain Emilio Soria University of Valencia, Spain Nicolás Victor Jiménez University Hospital Dr. Peset/University of Valencia, Spain

ABSTRACT Digoxin (DGX) is drug used to control signs and symptoms involved in congestive heart failure and atrial fibrillation. Due to its narrow therapeutic range more than 10% of the patients treated with DGX can suffer toxic effects, but it is estimated that half of the cases of digitalis toxicity could be prevented. Two multivariate models were developed to prevent digitalis toxicity, with data of DOI: 10.4018/978-1-60960-561-2.ch416

125 patients monitored at the Pharmacy Service of the University Hospital Dr. Peset (Valencia, Spain). One hundred and six patients were used to develop the models and the other (55) to validate them. The logistic regression model achieved a 90.9% of sensitivity and 81.8% of specificity in the validation set with only 2 variables that are statistical significant (cardiomegaly and digoxin plasmatic concentration). The feed-forward multilayer network model achieved 100% of sensitivity and 90.9% of specificity equal in the validation set but all 14 variables studied are used as input

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Clinical Decision Support System to Prevent Toxicity in Patients Treated with Digoxin

in this model. A software for identifying patients at risk in order to implement preventive measures to avoid signs and symptoms of digitalis toxicity was developed.

INTRODUCTION Digoxin is one of the most widely prescribed drugs for the treatment of congestive heart failure and atrial arrhythmia. Despite current guidelines relegate digoxin use to patients that remain symptomatic after receiving optimum doses of antihypertensive and the utility of digoxin in atrial dysrhythmia is only superior to other agents when the patient has associated heart failure. Digoxin use has decreased significantly from 31.4% (late 2001) to 23.5% in (late 2004) (p<0.00001), the number of toxic or potentially toxic exposures to digoxin requiring hospitalization has not decreased (Hussain, Swindle, & Hauptman, 2006), specially in geriatric patients who uses about 80% of the global consumption of digitalis drugs (Kernan, Castellsague, Perlman, & Ostfeld, 1994; Warren, McBean, Hass, & Babish, 1994). The Institute for Safe Medication Practices classifies digoxin as a “high alert medication”; that is drugs that bear a heightened risk of causing significant patient harm when they are used in error (High-alert medication list.). Therefore, digoxin intoxication represents about a fourth of the adverse drug events (ADEs) detected in the geriatric population, which frequently results in hospitalization (Gurwitz et al., 2000). Digoxin is the fourth drug class (7.7%) most frequently involved in potentially preventable adverse drug events at the emergency room (Otero et al., 2006), and one of the ten drugs most frequently associated with medication that demand interdisciplinary (physicians, pharmacists and nurses) actions (Llopis, Albert, Sancho, Calipienso, & Jiménez-Torres, 1999; Markota, Markota,

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Tomic, & Zelenika, 2009; Mutnick et al., 1997; Rupawala, Kshirsagar, & Gogtay, 2009). The wide pharmacokinetic and pharmacodynamic variability of digoxin due to its narrow therapeutic range, together with the multiple factors that can affect the organism response to digoxin and the diversity of clinical manifestations of digitalis toxicity (DT), results in digoxin being frequently associated to drug related problems. It is estimated that half of the cases of DT could have been prevented; this gives an opportunity to improve the treatment that patients receive and reduce medical costs (Gandhi, Vlasses, Morton, & Bauman, 1997). In order to improve efficiency, with the available resources, it is necessary to identify those patients that are most susceptible to profit from pharmaceutical care actions; that is to say, to identify patients with either potential or real drug related problems, in order to prevent the former and resolve the latter. Multivariate statistical methods, as logistic regression and neural networks, are useful tools to develop prediction models of morbidity (toxicity or therapeutic failure) associated to drug use, particularly when factors that influence the therapeutic response are analyzed. Neural networks technology does not require “a priori” models and represents an important improvement to the multivariate analysis. Artificial neural networks stand out for being able to find complex relations between dependent variables and the independent ones, in agreement with their superior ability to discover non linear relations, a representative characteristic of pharmacologic variability. The application of these models presents advantages over the actual statistical methods, although this doesn’t mean not to recognize their limitations (Soria, Jiménez, & Serrano, 2000).

Neural Networks in Clinical Medicine Artificial neural networks have been applied with marked success in classification problems, modelling, signal processing and time series prediction in

Clinical Decision Support System to Prevent Toxicity in Patients Treated with Digoxin

as scattered fields as engineering, economics and telecommunications. Experts in other fields have recognized the new technology potential and they are incorporating it into their work. However, in health sciences, statistical methods are still mainly used (Michie, Spiegelhalter, & Taylor, 1994), such as logistic regression applied to data analysis. In a study about 1341 articles published in The New England Journal of Medicine between 1986 and 1990, 68% they used logistic regression, falling behind other conventional statistical techniques: t-Student, Chi-square test, analysis of variance and Fisher test. The mathematical basis on which this methodology rests, its relative simplicity, and especially its widespread popularity among medical practitioners, justify still for many researchers its continued use. An important thing about the statistical techniques commonly used is that they are linear in parameters, that is to say, assumes that the relationship between the covariates in the problem is not excessively complex. In contrast, in health science disciplines, studies are conducted that involve many factors for which, in many cases, interactions are obviated or simply assessed with insufficient rigor. The problem simplification actually makes the analysis easier but, by the way, involves losing generality and precision in the study. Neural networks are able to capture the nuances that are beyond simple statistical methods. Indeed, there are some good results evidences provided by well-defined tasks where the problem covariates interactions are significant (Lisboa, 2002). It has been established that neural networks are equivalent to statistical techniques, parametric and nonparametric (Sarle, 1994). However, the neural networks are located in an intermediate and really privileged placement, they act as semiparametric techniques, that is to say, they are more flexible than parametric methods but they require fewer parameters than non-parametric methods (Dybowski & Gant, 2001). Other advantages are inherent in its constitution and neurobiological underpinnings (Haykin, 1999):

In general, using neural networks is justified in problems where we can exploit their potential analyzing nonlinear information, its role as a distributed associative memory as a way to avoid the difficulties in acquiring expertise knowledge, noise tolerance, provided by its inherently parallel architecture, and its adaptability to accommodate new manifestations of the disease. These features make neural networks preferable to other mathematical methods in problems which (Soria, 2000): • • • • •

It is unable to find a systematic set of rules which describes entirely the problem. There are a reasonable amount of representative examples. You have to work with imprecise or inconsistent data. There are many variables defining the problem (high problem dimensionality). Changing conditions problem.

These conditions reflect faithfully the work environment of clinical practitioners. The clinical decision making is performed under uncertainty in the information conditions, sometimes vague and sometimes incoherent (Weinstein, 1980). The complex situations analysis usually involves a huge factors amount which, unfortunately, is not always known well enough to make a decision. Neural networks, learning from real examples, are able to process large data amounts to extract data relevant and useful features. This will reduce the complexity problem level and raises the abstract level in information, closer to the doctor and therefore easier to assimilate and use to determine the action line.

Applications in Clinical Medicine The medical field has not been immune to the trend of applying neural networks. The attraction key has been the improvement in results in complex problems, usually approached with classical statistical techniques. Searching for better data

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Clinical Decision Support System to Prevent Toxicity in Patients Treated with Digoxin

processing or exploiting large medical databases to extract evidences has justified increasing in neural networks applications. Between 1993 and 2000, in a literature searching in MEDLINE, there were 3101 articles related to neural networks in a clear increasing trend. In a neural network applications comparative review about clinical trials and randomized controlled trials the medicine field (Dybowski & Gant, 2001; Lisboa, 2002; Westenskow, Orr, Simon, Bender, & Frankenberger, 1992), comparisons were made between neural networks and other approximation methods previously employed. The comparison mainly concerns the mostly used techniques by both sides, the multilayer perceptron and logistic regression obtaining comparable results or even superior with the neural network in almost every study. Also comparisons were made between neural networks and other different methodologies. The best known is the Statlog project, in which a neural networks extensive comparison with traditional statistical decision trees over 22 data sets was made, including 3 medical cases and it showed that the network worked better using just a dataset (Michie et al., 1994). However, it is accepted that the networks gave the lowest error and therefore generated the best predictive models in almost every case.

Aiding Clinical Diagnosis Diagnosis is the process of determining a disease status considering the patient nature and its circumstances. It is a process based on hypothesis formed on the patient evidences which are finally accepted or rejected. The diagnosis is also a classification problem simply by assigning each scenario to a class. Thus, the decision making process is to identify the most likely scenario depending on the symptoms and signs observed in the patient. The neural networks characteristics make them ideal treating classification problems. The learning rules based on minimizing the squared error, provided a networks interpretation as conditional probability

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distribution estimators and its output values as a posteriori class probabilities. Adding to this the ability approximating any function with an arbitrary accuracy degree, then it can be inferred that, given sufficient data, computational resources and time, it is possible, using a forward propagation network, to estimate the optimal Bayes classifier, directly and without any assumptions about the probabilistic data structure (Bishop, 2006). In almost every medical field neural networks have been applied in order to provide support to the diagnosis: In cardiology, neural networks have been used for prevention through early myocardial infarction diagnosis using patient history data (Wang, Ohno-Machado, Fraser, & Kennedy, 2001) or from biometric markers (Ellenius, Groth, & Lindahl, 1997) and sudden death after infarction (ZoniBerisso, Molini, Viani, Mela, & Delfino, 2001), detection of ischemic (Papaloukas, Fotiadis, Likas, & Michalis, 2002) and even auscultation assistance (Cathers, 1995). In Oncology neural networks have been used on prostate cancer early detection obtaining better results than logistic regression and biomedical parameters (Djavan et al., 2002), also have been used to diagnose breast cancer (Naguib et al. 1997; Setiono, 1996). In addition, survival studies were performed on patients with cancers located in neck and head (Bryce, Dewhirst, Floyd, Hars, & Brizel, 1998) showing neural networks potential in medical intervention in the cancer treatment. The anemias diagnosis (Birndorf, Pentecost, Coakley, & Spackman, 1996) or identifying low risk patients at cancer recurrence (McGuire et al., 1992). In Urology, neural networks have been used to aid determining male infertility diagnosis through testicular biopsies and to predict metastasis and mortality in patients with renal cancer by improving results on linear and quadratic discriminant analysis (Niederberg & Golden, 2001) or to detect patients at high risk of developing kidney problems

Clinical Decision Support System to Prevent Toxicity in Patients Treated with Digoxin

(Geddes, Fox, Allison, Boulton-Jones, & Simpson, 1998). In neurology, neural networks have been used to distinguish between Alzheimer’s disease and vascular dementia (deFigueiredo et al., 1995).

Modelling and Prediction The ability to model systems from examples allow to address system studies that involve multiple factors. A field in which is being implemented successfully is pharmacokinetics. Predicting drug concentrations in blood is a complex problem caused by the strong dependence on the patient’s metabolism. Neural networks, tracking or monitoring, capture the relationship between plasma drugs levels and patients characteristics. Modelling in this way, the drugs kinetic in the body can be used to make personalized predictions (Chow, Tolle, Roe, Elsberry, & Chen, 1997). This ability is important on drugs with narrow therapeutic ranges, especially those drugs with toxicity to overcome certain plasma concentrations. In this field, neural networks have been applied successfully to Gentamicin plasma concentrations prediction in patients with serious infections (Brier, Zurada, & Aronoff, 1995), Tacrolimus blood levels prediction after liver transplant (Chen, Chen, Min, Fischer, & Wu, 1999), predicting Cyclosporine blood concentration in kidney transplant patients (Camps, Soria, & Jimenez, 2000), Erythropoietin right dose prediction in anemic patients (Martin et al ., 2000.) or blood urea concentrations after haemodialysis (Fernandez, Valtuille, Willshaw, & Perazzo, 2001). The neural networks are not only used on modelling and predicting pharmacokinetics. In general, are also being applied to modelling human body organs to study their behaviour and interaction with the rest of the body as well as in rehabilitation (Graupe & Vern, 2001; OhnoMachado & Rowland, 1999).

Analysis Laboratory The new automatic systems development for clinical laboratories allows to extract and analyze more and more data from patient samples and to do it more accurately. This information explosion is creating a demand for systems capable of analyzing and interpreting data to produce increasingly comprehensive reports to support clinicians in the diagnostic process. In this context, neural networks play an important role in these systems because of their estimation advantages without models assumptions, generalization and ability to organize data in non-linear way. For instance, in Microbiology, neural networks are used to analyze mass spectra obtained by pyrolysis (chemical separation using heat) to identify bacteria. Examples are tuberculosis bacteria identification (Freeman et al., 1994) or detection of streptomyces (Chun et al., 1993). They are also being used in genetics, in the genetic code classification and prediction. New techniques sequencing DNA and RNA generate many protein sequences that need to be analyzed (Baldi, Brunak, & ebrary, 2001). Neural Networks have also been applied predicting the second structure in amino acid proteins or in the protein mutations among others predictions (Cross, 2001). Any discipline, generally used laboratory reports to add information to the patient clinical history or to confirm a particular hypothesis. Blood tests, urine test or spinal ones provide extra information that neural networks can leverage better than clinical practitioners because of neural networks power establishing non-linear numerical relationships. Two examples of this are laboratory data interpretation to distinguish malignant and benign tumor breast (Astion & Wilding, 1992) and improving death predictions in alcoholic patients with severe liver disease (Lapuerta, Rajan, & Bonacini, 1997).

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Medical Images Analysis Medical images play, in many cases, an important role describing the patient’s condition. X-ray machines, CT scanners, MRIs, etc. provide valuable information that the specialist must unravel. Sometimes images contain many elements that hinder analysis. Thus, in cytological tests, images composed of many cells must be reviewed looking for abnormalities. This workload can be reduced using neural networks parallel and nonlinear processing. In cytology neural networks have been used to search for cancer cells (Wolberg & Mangasarian, 1993) or to detect thyroid and stomach harms (Karakitsos, Cochand-Priollet, Guillausseau, & Pouliakis, 1996). Neural networks are also being implemented in histology (tissue image analysis) as a diagnostic aid (Stotzka, Manner, Bartels, & Thompson, 1995). A system that has been commercially successful using neural networks is PAPNET ®. The system detects suspicious cells by examining Papanicolaou stains. It uses two neural networks, one to recognize suspicious cells and another to recognize abnormal cells groups. The elimination of the search process, about 98% of the workload, PAPNET improves diagnostic accuracy. However, the system has been evaluated in many occasions and it has shown similar results to conventional diagnosis (Doornewaard et al., 1999). It is the only one example of a technology based on neural networks that has been evaluated in both prospective and retrospective trials and with commercial success.

Monitoring and Intensive Care The clinical parameters monitoring and the warning signs inspection in intensive care units is usually done by the clinical practitioner. An alarm system which is able that to assess changes and interactions between physical, chemical and thermodynamics parameters describing the patient condition, seems ideal. For example, in anesthesia,

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physicians require expertise to evaluate the different patients monitoring signs. Neural networks can support doctors to process faster such information (Papik et al., 1998). It has been found that response time can be reduced from 45 seconds on average by the clinical practitioners to 17 provided by the networks (Westenskow et al., 1992). In intensive care areas, neural networks have been applied to classify the sedation level based on an electroencephalogram analysis (Veselis, Reinsel, Sommer, & Carlon, 1991), activity increasing detection in EEG (Pradhan, Sadasivan, & Arunodaya, 1996) or to predict intracranial haemorrhage in neonates (Zernikow et al., 1998).

NEURAL NETWORKS OVERVIEW IN HEALTH SCIENCES The good networks performance and clinical information powerful processors ensure a future widespread use. However, there aren’t many evidences from the clinical practitioner part that prototypes obtained from research will develop further. The rigor lack in some works done with neural networks, which does not detail the population used in the study or does not describe the models developing process or does not compare it with other approaching methods is generating criticisms that discredit neural networks value. So if neural networks should be actually accepted as a clinic tool, it is essential that studies be conducted based on randomized controlled trials (Lisboa, 2002). Neural networks, despite providing better results than other approaching methods, have many detractors because they imagine them as black boxes. The lack of understanding caused by the difficulty explaining the result provided by the network in simple terms is, in many cases, sufficient reason to discredit them. As in other methodologies, the desirable model properties are accuracy and interpretability. Accuracy ensures that the estimate model is close to real model

Clinical Decision Support System to Prevent Toxicity in Patients Treated with Digoxin

and interpretability provides an understanding between input and output relations in the model. Interpretability allows to discover mistakes in the model when the explanations are in contradiction with the facts. Interpretability lack makes unacceptable a model depending on the purpose. Clearly, if you wish to use it on educational purposes is not acceptable, but if used for prediction and its behavior is evaluated rigorously, should not have any trouble being accepted (Lisboa, 2002).

MULTIVARIATE PREDICTION MODEL OF DIGITALIS TOXICITY Prevention is the best treatment of digitalis toxicity; therefore, providing a model for predicting digitalis toxicity allows identifying the population at risk and implementing strategies for avoiding toxicity. To develop a predictive model to identify the patients at risk of presenting DT, a sample of patients with digoxin plasmatic concentrations monitored at the University Hospital Dr. Peset in Valencia, Spain, has been used. 125 patients (77.6%, CI 95%: 71.2 to 84.1%) did not show any toxicity criteria, and 36 patients (23.6%, CI 95%: 15.5 to 31.7%) presented sings and symptoms compatible with digitalis toxicity. Patients were randomly distributed in two subsets, 106 patients in the study or training set, and 55 patients in the validation or generalization set. Applied statistical analyses indicate that randomization created two homogeneous subsets with no statistically significant differences between the variables included in this study (Albert, 2002). Table 1 summarizes the anthropometric, clinical, and pharmacokinetic and pharmacotherapeutic variables included in multivariate analysis. These variables are prognostic factors associated with an increased risk of DT with good theoretical justification in previous studies and practical or clinical relevance. Continuous variables are introduced as such in the model and dichotomous variables were categorized into two levels (0 and

1). The statistical processing has been conducted with statistics package SPSS® 10.0. Two probabilistic multivariate models were developed to predict digitalis toxicity: a logistic regression and artificial neural network model. The development of the logistic regression model was conducted following Hosmer and Lemeshow (Hosmer & Lemeshow, 1989) guidelines. The final model selected is the one that offers the best reliability and accuracy in estimating the parameters. The exploratory process of the different possible models with the independent variables was performed using the backward elimination Table 1. Variables included in the multivariate analysis Variable

Type

DEPENDENT Digitalis Toxicity

Categorical (2 categories)

INDEPENDENT Clcr (mL/min)

Continuous

Cpdgx initial (ng/mL)

Continuous

Cpk (mEq/L)

Continuous

Age (years)

Continuous

Number of tables per week

Continuous

Number of chronic disease

Continuous

Weight (Kg)

Continuous

Height (cm)

Continuous

Amiodarone use

Categorical (2 categories)

Oral antibiotics use

Categorical (2 categories)

Anti ulcerants use

Categorical (3 categories)

Cardiomegaly use

Categorical (2 categories)

Gender

Categorical (2 categories)

IECAS use

Categorical (2 categories)

FA

Categorical (2 categories)

Obesity

Categorical (2 categories)

* All categorical variables were coded as 0 = absent condition, 1 = present condition; Clcr: creatinine clearance; Cpdgx: digoxin plasmatic concentration; Cpk: potassium plasmatic concentration; IECAS: Inhibitors of angiotensin converting enzyme; FA: atrial fibrillation.

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and forward selection methods, available in the statistics package, showed that the variables that were retained in the models using these methods were the digoxin plasmatic concentration (Cpdgx) and cardiomegaly. Thus, the final logistic model selected included a continuous variable (Cpdgx) and a dichotomy one (cardiomegaly), whose parameters are shown in Table 2, as odds ratio (OR), 95% confidence interval and the significance achieved by applying the test of likelihood. Cpdgx is expressed as ng/mL and cardiomegaly takes the value of 1 if present in the patient and 0 otherwise. The expression to calculate the probability of presenting digitalis toxicity for each individual patient, depending on the explanatory variables included in the logistic model obtained, is presented in Equation 1 (Albert, 2002): Pr(Y = 1/X) =

1 1+ e

−(−5.62+2.37 ⋅Cpdgx+1.28 ⋅cardiomegaly )



(1)

These results allow confirming previous studies using multivariate models in which the digoxin plasmatic concentration is the most significant variable associated with DT (Abad-Santos, Carcas, Ibanez, & Frias, 2000). So decreasing the digoxin plasmatic concentrations, by means of reducing dosage, decreases the cardiac and non cardiac events in patients at risk of developing digitalis toxicity (Miura et al., 2000). Cardiomegaly is a variable that cannot be modified.

Table 2. Multivariate logistic regression analysis of toxicity factors: Final model β

OR

CI 95% OR

P

2.37

10.64

3.82-29.64

< 0.001

Cardiomegaly

1.28

3.59

1.08-11.94

0.030

Constant

-5.62

Cpdgx (ng/mL)

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The predictive capability of this model has been evaluated by constructing the ROC curve, (Figure 1, left) and selecting the best cut-off point to stratify the patients into two categories (intoxicated and non-intoxicated) according with McNeil criterion (McNeil, Keller, & Adelstein, 1975) (Figure 1, right). The optimal cut-off point is established for a probability value of 0.226. Thus, when the estimated toxicity probability with the described logistic model is equal to or greater than 0.226, the patient is considered intoxicated. The agreement between the prediction model and the clinical outcome (Table 3), real toxicity or no toxicity, as assessed by Kappa statistic using this cut off point is 0.52 (CI 95%: 0.35 to 0.70), p<0.001, indicating a moderate agreement degree (0.40-0.75: moderate concordance) (Seigel, 1992). For the chosen cut off point, sensitivity is 80.0% (CI 95%: 63.5 to 96.5%) and specificity is 80.2% (CI 95%: 71.5 to 88.9%). The area under the ROC curve (AUCROC) is 86.3% (CI 95%: 78.3 to 92.2%). The validity of the model to predict the DT toxicity in new patients has been studied using the validation set according to the probability calculated by the DT logistic model developed, for the cutoff point selected (0.226). The sensitivity of the logistic regression as a diagnostic test is 90.9% (95% CI: 58.7 to 99.8%) and specificity is 81.8% (95% CI: 70.4 to 93.2%). In developing the model of artificial neural networks the multilayer perceptron was chosen for being able to tackle large-scale classification problems in an effective and relatively simple way. The activation function chosen for the nodes in the hidden and output layers was the hyperbolic tangent; the “backpropagation” was the training algorithm used (Bishop, 2006). In the neural analysis the quadratic error function has been used as cost function. The neural networks training programs were designed using Matlab®. The node number selected for the input layer corresponds to the total number of independent variables to study (14 variables). In the output

Clinical Decision Support System to Prevent Toxicity in Patients Treated with Digoxin

Figure 1. ROC curve (left) and sensitivity, specificity and efficiency (right) of logistic model.

Table 3. Contingency table in the study subpopulation for the logistic model (cut off: 0.226) Toxicity prediction Yes Clinic Toxicity

No

Total

Yes

20

5

25

No

16

65

81

36

70

106

Total Kappa=0.523, p<0.001

layer, it a single neuron has been chosen, because in this model when a situation (DT) is identified the other is automatically set. 1000 iterations have been fixed to train networks with only one hidden layer, where the neuron number varies between 9 and 20. Learning adaptation constant used have taken the following values: 0.001, 0.01, 0.03, 0.05, 0.1, 0.2 and 0.5. For the momentum constant the following options have been explored: 0, 0.1, 0.5 and 0.9. To avoid saturation the initial weights were randomly selected with values ranging from +3 to -3 to avoid the overfitting phenomenon, the stop in training is conducted using the cross-validation technique, using the summation of sensitivity and specificity generated by the network on the validation set. The optimal network consists of 20 neurons, and learning and momentum constants of 0.001 and 0.9 achieve the best results in the validation set with 100.0% sensitivity and 90.9% specificity (95% CI: 82.4 to 99.4%), with a 95.5% of correct classification rate. In the training group the

network sensitivity is maintained at 100.0% and the sensitivity increases to 98.8% (95% CI: 93.3 to 100.0%). The ROC curve and efficiency for the selected artificial neural network model is showed in Figure 2. The agreement degree between model prediction and the real clinical outcome in the patient, evaluated by the Kappa statistic is 0.97 (95% CI 0.92 to 1.00), p <0.001, indicating an excellent agreement degree (> 0.75) (Table 4). The AUCROC in the training population is 98.9% (95% CI: 94.6 to 99.9%), and represents a curve with an almost perfect discrimination capability. The agreement degree between the neural network prediction and the clinical outcome in patients who constitute the population of generalization (validation), evaluated using the Kappa statistic is 0.80 (95% CI: 0.62 to 0.98), p <0.001. This value indicates an excellent degree of concordance between the neural network classification and the documented patient’s toxicity.

COMPARATIVE ANALYSIS OF MULTIVARIATE MODELS DEVELOPED TO PREDICT DIGITALIS TOXICITY In Medicine and Pharmacy is common to consider the agreement degree or clinical concordance between different observers or diagnostic methods, to take a decision based on a categorical answer. 1271

Clinical Decision Support System to Prevent Toxicity in Patients Treated with Digoxin

Figure 2. ROC curve (left) and sensitivity, specificity and efficiency (right) of artificial neural network model.

Table 4. Contingency table in the study set for the artificial neural network model Toxicity prediction

Clinic Toxicity

Yes

Yes

No

Total

25

0

25

No

1

80

81

Total

26

80

106

Kappa=0.97, p<0.001.

The concordance between the logistic model and the artificial neural network, to classify the patients if presenting or not DT, has been evaluated by the degree of simple agreement and kappa statistic. As for the simple agreement degree, in 81.1% (95% CI: 73.7 to 88.6%) cases the classification of DT of both models agree on the study set subpopulation. This concordance is higher on the negative results (specificity) than on the positive, 77.0% and 67.7%, respectively. When the concordance is adjusted by chance, through the kappa statistic, the agreement in prediction between logistic regression and artificial neural network in the study set population, shows statistically significant differences (p <0.001). That is to say, in the absence of chance, 55% (95% CI: 38 to 72%) of the logistic model’s predictions agree with the neural model to classify clinical toxicity in patients (Table 5). This indicates a moderate concordance between the two models, when there

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are discrepancies in classification between the models; the neural network coincides with the actual toxicity described in the patient at all times. The Mc Nemar test (comparison of proportions in paired samples) indicates statistically significant differences (p = 0.041) in classifying patients according to the model used in the study set, thereby logistic regression classified the largest number of patients at risk of presenting digitalis toxicity, that have non-real clinical toxicity (false positives) than the neural network. In the subpopulation of unseen cases, validation or generalization group, the rate of simple agreement reached 90.9% (95% CI: 83.3 to 98.5%), maintaining the superiority in concordance for the negative versus positives results (93.5% and 84.8%), as shown in Table 6. The concordance in the Kappa statistic in this subset between logistic regression and neural network is 0.78 (95% CI 0.61 to 0.96), p <0.001. As in the study set, where Table 5. Concordance between the logistic model and the artificial neural network on the study set Toxicity prediction Neural Network

Logistic model Yes

No

Total

Yes

21

5

26

No

15

65

80

Total

36

70

106

Kappa=0.55, p<0.001.

Clinical Decision Support System to Prevent Toxicity in Patients Treated with Digoxin

Table 6. Concordance between the logistic model and the artificial neural network on the validation set Toxicity prediction

Logistic model Yes

No

Total

Yes

14

1

15

No

4

36

40

Total

18

37

55

Neural Network

likelihood-ratio, on positive and negative results, for both models in both subpopulations. The ideal diagnostic test for predicting digitalis toxicity must present values of sensitivity and specificity as close as possible to 100%. Basically test values below 80% should be doubted, in general the proposal is that both must exceeded 75% (Doménech, 1999). In order to consider the total percentage of right classification as a summary index for classification power both values should be acceptable (Doménech, 1999). The sensitivity represents the percentage of patients with DT who presented a positive test result. The sensitivity in the logistic regression model is superior to 80.0%, but for the networks it reaches 100.0% in both sets, showing the greatest clinical utility potential of the second model. The specificity represents the percentage of patients without toxicity with a negative test result; in this case also the neural networks superiority is evident with values above 90%. The results predicting positive or negative depend on the prevalence of DT, and they are crucial when clinical decision-making time arrives, and they show clearly neural networks superiority. So when the model based on logistic regression predicts toxicity (probability 0.226), only 55.6% patients are actually intoxicated, this happens in both subset, study and validation. Using clinical terms, almost half of the patients that the model indicates at risk of toxicity are not actually intoxi-

Kappa=0.78, p<0.001.

there are discrepancies in the prediction of DT between the two models, in all cases the prediction with neural networks represents a correct classification with the real toxicity in the patient. The Mc Nemar statistic indicates that there are no statistically significant differences (p = 0.375) in classifying patients according to the model used for this subpopulation. The comparison in the classification of the patients provided by the two multivariate analysis models (logistic and neuronal), and the diagnostic tests behavior are interpreted through the parameters sensitivity, specificity, positive and negative predictive values, likelihood-ratio on positive and negative results, as well as visual and statistical analysis of the ROC curves for each model. Table 7 summarizes the parameters of internal validity of a diagnostic test, sensitivity and specificity, as well as predictive values and the

Table 7. Summary table of logistic regression and artificial neural network Logistic regression Parameter

Artificial Neural Network

Study

Validation

Study

Validation

Sensitivity (%)

80.0

90.9

100.0

100.0

Specificity (%)

80.2

81.8

98.8

90.9

VPP (%)

55.6

55.6

96.2

73.3

VPN (%)

92.9

97.3

100.0

100.0

RVP

4

5

83.33

10.98

RVN

0.3

0.1

0

0

VPP: Positive predictive value; VPN: Negative predictive value; RVP: Likelihood-ratio positive; RVN: Likelihood-ratio negative

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cated, that means that this model overestimates toxicity (false positives). When the outcome of the model is negative, in more than 90.0% of situations, the model matches the clinical situation (real classification) of the patient. Neural networks classify correctly all patients with toxicity, especially in the study set, where the superiority of artificial neural networks versus logistic regression shows statistically significant differences. That is, the neural network model classifies better than the logistic regressions one, in both situations, patients with digitalis toxicity and non-intoxicated ones. If this model does not predict toxicity in 100.0% of cases the patient will not develop signs or symptoms compatible with DT. The superiority of the negative predictive value suggests that the models developed are much more effective discarding than confirming digitalis toxicity. In our prediction to classify “a priori” a patient as intoxicated who will not develop toxicity, i.e. a false positive, do not represent any trouble for the patient, as clinical pharmacokinetic drug monitoring will be increased and will be provided with health education for DT prevention. The greatest risk is to classify incorrectly as a “non-intoxicated patient” a patient at risk of toxicity, which is a false negative result, because clinical manifestations of digitalis toxicity will not be prevented in the patient, who may even require immediate or early hospitalization. In these situations, the

proposed tests must have good negative predictive value, ideally with values of 100%, as obtained by neural networks. The likelihood-ratio value also reflects the increased classification ability of neural networks. The odds ratio for a positive result allows knowing the accuracy of a diagnostic test. Thus the probability of a positive outcome in the logistic model in an intoxicated patient, is proportionately 4 or 5 times higher than the probability of a positive result in a patient without toxicity, suggesting a diagnostic test classified as regular (2-5: regular) (Doménech, 2001). For the neural network model developed the likelihood-ratio value of a positive result is over 10, therefore it is an excellent diagnostic test, the greater value over the unit the higher contribution of a positive result on the diagnosis of DT. The likelihood-ratio on negative values, opposite to positive ones, is better when approaching zero (Doménech, 2001). The likelihood-ratio on negative values has no significant differences between both models. For the neural network model, the likelihood-ratio on negative values outcome reaches zero, again has to be classified as an excellent test. The graphical comparison of the ROC curves in the subpopulation of validation (Figure 3), allows to show a higher discrimination power for the artificial neural network compared to the logistic regression (greater approximation to the upper left

Figure 3. ROC curve from logistic regression (left) and neural network (right) in validation set.

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Clinical Decision Support System to Prevent Toxicity in Patients Treated with Digoxin

corner or curve with a larger area, indicating greater global accuracy) in the validation subpopulation. The area under the curve (AUCROC) in the validation subset for the logistic model is 88.8% (95% CI: 77.4 to 95.7%) and 94.6% (95% CI: 84.9 to 98.9%) in the artificial neural networks model. In this design the two models we studied using the same patients. This design is very effective in controlling the patient-patient variation, so that the observed differences in prediction results are real differences between the models and not due to differences for sampling variation or bias selection between the study subpopulation. The differences between the classification power of the logistic regression and neural networks are more evident on the study or training subset than on the validation one, which could indicate a network specialization in the population used to develop the model (overtraining). The most direct way to avoid this bias is to control the number of parameters, or freedom degrees, in the model. The simplest and with the most future alternative is from models with the highest predictive ability (and hence with high overtraining probability) use pruning algorithms that analyze the weights of the network, using predefined criteria, and eliminate those providing less information to its predictive ability. Thus we get a model with the best predictive capacity / number of weights ratio, which makes easy its interpretation and simplifies its use (Bishop, 2006).

Web Computer Application to Clinical Decision Support The most effective strategy to reduce morbidity associated with digitalis toxicity is, beyond doubt, its prevention and the avoidance of serious events that result in patient hospitalization or even death. Prediction models allow identifying “a priori” patients at risk of presenting DT and implementing actions aimed at preventing its development. The logistic model provides the probability of developing DT with a single and simple

manual calculation (Equation 1). The complexity of the neural network model makes not feasible a similar calculation without computer aid, in order to simplify the process of obtaining the network classification result after entering the independent variables. The neural network model requires the use of all 14 independent variables studied to predict DT in the individual patient, but with the advantage of being able to redirect the patient’s diagnosis to other different causes than the presence of digoxin plasmatic concentrations potentially toxic. With the developed models a flexible computer application has been designed, with fast handling and easy introduction in different work areas such as the outpatient cardiology consult, emergency room and even at community pharmacies. If the final model is used as a diagnostic test its usefulness has to be assessed based on the setting prevalence and the optimal cut-off point has to be adapted to the population to be applied. The design of a specific software application allows that these models be available thanks to the Internet system to practically all health professionals. The computer application PredTox-DGX (http://www.uv.es/ajserran/predtoxdgx.htm) has been developed in PHP, a scripting language that is especially suited for Web development with MySQL database manager, resulting an -friendly environment application (Figure 4). This computer application once the independent variables to predict a priori the risk of digitalis intoxication in the individual patient have been registered shows the probability of toxicity calculated by the logistic regression and the multilayer perceptron classification outcome (Figure 5). The criteria for the final decision is based on both models coincidence, if any discrepancy the risk is set using the outcome provided from the neural network as it is the model that gets better classifications results. To facilitates the visualization of the result in areas of clinical work, the risk is indicated using a colour code; green predicts

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Clinical Decision Support System to Prevent Toxicity in Patients Treated with Digoxin

Figure 4. Main window of the application PredTox-DGX

low risk of digoxin intoxication, orange moderate risk (it indicates difference between the two models), and red colour means high risk (Figure 6). Pharmacotherapeutic actions recommended to be implemented have been developed co-ordinately in the health-care team (health professionals from the Cardiology Service and the Short Stay Unit) based on the predicted patient’s outcome (Albert, 2002). In low risk situation, educational actions, verbal and written, about the rational use of digoxin on patients or family are recommended. In moderate and high risk cases, this information will be also notified to the doctor in charge of the patient whit the calculated risk,

Figure 5. Decision detail window

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recommending an individualized dosage adjustment, as well as drug pharmacokinetics and electrocardiographic monitoring. This information is included in the Pharmacotherapeutic report prepared into the Clinical Pharmacokinetic Unit in the Pharmacy Department of the University Hospital Dr. Peset in Valencia, Spain. The educational activities on drug therapy are designed to prevent serious complications and unnecessary hospitalizations (Borron, Bismuth, & Muszynski, 1997). It has been estimated the benefit (potentially avoided cost) of the early identification of DT risk and the implementation of multidisciplinary

Clinical Decision Support System to Prevent Toxicity in Patients Treated with Digoxin

Figure 6. Window of the application for predicting the risk of digitalis toxicity

actions for its prevention, considering a success rate in preventing DT of 75%. The comparison of the benefits (potentially DT costs avoided) and costs (time of health professionals) results in a benefit / cost ratio of 3.85 and 285% return of investment (ROI). Both indexes indicate that in economic terms the implementation of this program to prevent digitalis toxicity generates a benefit for the hospital (Albert, 2002). The software for identification patients with drug related problems become valuable tools in daily practice (Climente & Albert, 1998). The program designed in this study is a pilot project to identify patients likely to develop digitalis toxicity, in order to reduce the incidence of morbidity and mortality associated with drug use (Cipolle, Strand, & Morley, 2004; Gallagher, Ryan, Byrne, Kennedy, & O’Mahony, 2008).

to model. The obtained results in classification of patients in this study confirm the artificial neural networks superiority compared with the logistic regression method to identify patients at risk of developing digitalis toxicity, both in training set and in general population. The identification of patients who at risk of drug related morbidity that can be prevented implementing pharmacotherapy measures is a key strategy proposed by different health-care organizations to improve patient safety. Multivariate models are powerful tools to support clinical decision to achieve this goal, but they are still little applied in the setting of pharmaceutical care in which the search for models is a constant to predict potential drug adverse events. Therefore collaboration among various disciplines of health science and statistics is needed.

CONCLUSION

ACKNOWLEDGMENT

The neural network produces an improvement in the rating power between 5 and 15% compared to traditional methods. This is a logical result because the model based on a logistic regression is equivalent to use a network consisting on one single neuron with sigmoid activation function; so that artificial neural networks expand the ability

This work has been partially supported by the Spanish Inter-Ministry Committee of Science and Technology under project TIN2007-61006.

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ADDITIONAL READING Alpaydin, E. (2004). Introduction to machine learning. Cambridge, MA: MIT Press. Arbib, M. A. (2003). The handbook of brain theory and neural networks (2nd ed.). Cambridge, MA: MIT Press. Bindoff, I. K., Tenni, P. C., Peterson, G. M., Kang, B. H., & Jackson, S. L. (2007). Development of an intelligent decision support system for medication review. Journal of Clinical Pharmacy and Therapeutics, 32(1), 81–88. doi:10.1111/j.13652710.2007.00801.x

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Haykin, S. S. (2007). New directions in statistical signal processing: From systems to brain. Cambridge, MA: MIT Press. Haykin, S. S., & Haykin, S. S. (2009). Neural networks and learning machines (3rd ed.). New York: Prentice Hall. Isaksen, S. F., Jonassen, J., Malone, D. C., Billups, S. J., Carter, B. L., & Sintek, C. D. (1999). Estimating risk factors for patients with potential drug-related problems using electronic pharmacy data. IMPROVE investigators. The Annals of Pharmacotherapy, 33(4), 406–412. doi:10.1345/ aph.18268 Jang, J. R., Sun, C., & Mizutani, E. (1997). Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Upper Saddle River, NJ: Prentice Hall. Lodise, T. P., Patel, N., Lomaestro, B. M., Rodvold, K. A., & Drusano, G. L. (2009). Relationship between initial vancomycin concentration-time profile and nephrotoxicity among hospitalized patients. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America, 49(4), 507–514. Principe, J. C. (1997). Neural networks for dynamics modeling. Signal Processing Magazine, IEEE, 14(6), 33–35. doi:10.1109/MSP.1997.637300 Reggia, J. A. (1993). Neural computation in medicine. Artificial Intelligence in Medicine, 5(2), 143–157. doi:10.1016/0933-3657(93)90014-T

Clinical Decision Support System to Prevent Toxicity in Patients Treated with Digoxin

Ripley, B. D. (1996). Pattern recognition and neural networks. New York: Cambridge University Press. Terrell, K. M., Perkins, A. J., Dexter, P. R., Hui, S. L., Callahan, C. M., & Miller, D. K. (2009). Computerized decision support to reduce potentially inappropriate prescribing to older emergency department patients: A randomized, controlled trial. Journal of the American Geriatrics Society, 57(8), 1388–1394. doi:10.1111/j.15325415.2009.02352.x Zou, J., Han, Y., & So, S. S. (2008). Overview of artificial neural networks. Methods in Molecular Biology (Clifton, N.J.), 458, 15–23.

KEY TERMS AND DEFINITIONS Adverse Drug Event: Patient harm associated with the use of a drug or lack of use. Atrial Fibrillation: Cardiac abnormal heart rhythm that involves the upper chambers (atria) of the heart. Cardiomegaly: Heart enlargement, heart hypertrophy.

Drug Related Problem: Any undesirable event experienced by the patient that involves or is suspected to involve drug therapy and that actually or potentially interferes with a desired patient outcome. Morbidity: Patient pharmacotherapy outcome with no clinical, negative or suboptimal effect. Multilayer Perceptron: It is the most widely used neural network. It can be applied to modeling, classification and time series problems. Its name comes from the peculiar arrangement of neurons in different layers. Neural Networks: Non-linear processing models that carry out a mapping of an input space onto an output space using a set of parameters (synaptic weights). Synaptic weights vary their value according to a certain learning algorithm. Pharmacodinamics: Branch of Pharmacology that study the therapeutic or toxic effect of drugs to the organism. Pharmacokinetics: Branch of Pharmacology that study of the mechanisms of liberation, absorption, distribution, metabolism and elimination of drugs administered.

This work was previously published in Intelligent Medical Technologies and Biomedical Engineering: Tools and Applications, edited by Anupam Shukla and Ritu Tiwari, pp. 1-21, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.17

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering Founded on a NonSpeech Etiologic Paradigm Manuel Prado-Velasco University of Seville, Spain Carlos Fernández-Peruchena University of Seville, Spain

ABSTRACT Persistent Developmental Stuttering affects 1-2% of the world adult population. Its etiology is still unknown, although modern neuroimaging techniques have shown a new and exciting perspective of earlier ideas and hypotheses. However, it is now clear that a new approach to understand the true DOI: 10.4018/978-1-60960-561-2.ch417

nature of the disorder is needed. We present a new etiological model of persistent developmental stuttering based on a deep analysis of earlier models and on the stuttering phenomenology, described in basic, clinical, and even ethnographic sources. One of the more stimulating conclusions has been the suggestion that stuttering is a non-speech based disorder, in opposition to the accepted belief. The implications of this model have guided the design of a new adaptive AAF device for prosthetic and

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

therapeutic functions. It is supported by a wearable multimodal intelligent system, which evolves from a preliminary proposal presented in (Prado & Roa, 2007).

INTRODUCTION Persistent Developmental Stuttering (PDS) is a disorder that affects 1 – 2% of world adult population. It is disorder recognized from the first civilizations and recurrently cited by Greek and other classic authors. Unfortunately, the complexity and associated strange behaviors guided the research on the etiology through strange and superficial roads since the starting of orthodox medicine. References to several of these hypotheses can help to understand this asseveration. Hippocrates hypothesized that body humors were perturbed in these subjects, and thus prescribed the delivery of sticking blistering substances to the tongue (4th century BC). That causal model could be justified in that period, but it is more difficult to understand the prescription of surgical resections of tongue’s chunks with the aim of avoiding the hypothetical spasms that cause the speech blocks, at half of 19th century, in Germany, England and France. That is, a century after the explosion of advances in the mechanistic vision of life that led the definition of physiology (Bobrick, 1996). Modern treatments and causal explicative models acquired a more scientific methodology at 20th century. Current causal models defend an integrative conception of stuttering. The origin of this perspective is usually assigned to the demands and capacities model of Starkweather (C. W. Starkweather, 1987). That model is based on the diagnosogenic theory of Johnson (Johnson, 1942), and it considers some type of innate inability of child to respond to external demands. Therefore, this model combines environmental (external) factors with biological (internal) factors. The tendency to join different types of factors

has continued up to now, justifying the definition of developmental stuttering as a consequence of bio-psycho-social elements (Rodríguez Carrillo, 2002). In our opinion this definition does not throw light in its etiology either helps to design therapies or prosthetic systems. In agreement with the integrative causal models of PDS, current therapies combine logopedic (direct) treatments with psychological (indirect) ones. Studies show a high rate of relapse in adults (nearly 100%) (Craig, 1998; Hayhow, Cray, & Enderby, 2002). The scenario is even more complex for children because the difficulty to set apart the spontaneous recovery (Rogere J. Ingham & Bothe, 2001). However, there is a lack of significant and longitudinal studies. The consequences of the failure of biomedicine for understanding and treating PDS include the deficit of speech language professionals (SLPs) specialized in PDS, and the proliferation of a high number of stuttering therapies, which are frequently selected as a function of the subjective preferences of professionals (Limongi, et al., 2005). There is also a serious and relevant reaction among PDS subjects featured by the negation of the PDS as a disorder (Loriente Zamora, 2006). Stuttering is a disability with a very variable gradation and strong social and personal consequences. This disorder impedes that PDS subjects can communicate ideas in the manner they wish. An important characteristic of PDS is the emergence of blocking qualia at the same time that words appear to the consciousness. This leads to an exhausting fight for avoiding or overcoming these words, producing anxiety and fear. The true problem of PDS subjects is not the dysfluency perceived by the listener, but the collateral reactions and suffering that corrupt the communication and damage the person life. PDS subjects try to hide the disorder because the exclusion and ignorance of societies regarding this disability. Governmental actions for assisting this population are just emerging, like the “Spanish Cabinet Council Agreement on October 21 of 2005

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to remove the stuttering as an exclusion cause in the calls for accessing to public administration”. However, there is still a lack of legal norms that build a proper framework for this disability. It is well established that people who stutter improves their fluency when singing or talking in unison. This fact was attributed to the influence of speech signals on the speaker (Ward, 2006). AAF devices are an artificial approach to simulate such exogenous speech signals, according some researchers. Among AAF techniques, the most popular ones are Delayed Auditory Feedback (DAF), Frequency Altered Feedback (FAF) and Masked Auditory Feedback (MAF). The application of altered auditory feedback to improve speech communication has been ongoing for decades, although the mean in which such devices inhibit stuttering remains undetermined (Kehoe, 2008). Initial stuttering treatments began in the late twenties, and they were focused on accepting stuttering, reducing stutterers’ speechrelated fears and anxieties, and helping stutterers to communicate better despite stuttering. AAF prosthetic devices improve the fluency of persons who stutter, changing how stutterers hear their voices (singing, choral speaking, masking, delayed or frequency altered feedback) (Reitzes & Snyder, 2006; Ryan, 2005). It has been reported the enhancement of speech naturalness with AAF devices (without a reduced speech rate) (R.J. Ingham, Moglia, Frank, Ingham, & Cordes, 1997; Kalinowski, Guntupalli, Stuart, & Saltuklaroglu, 2004). Early altered auditory feedback devices were large and thus confined to the laboratory or therapy room, but advances in electronics have permitted increasingly the development of wearable devices, including self-contained ear-level AAF devices. This work presents our recent advances in the development of an assistive and therapeutic system based on embedded computing for treating the stuttering. This system is supported by a novel causal model about PDS. The model is mathematically implemented and linked to the

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subject by means of a technology that allows validating and refining the underlying hypotheses. A first and brief description about the system was presented in (Prado & Roa, 2007). The study has two major goals. Firstly, a description of a causal model of persistent developmental stuttering able to explain the phenomenology of the disorder, considering the last findings on image neuronal patterns (Watkins, Smith, Davis, & Howell, 2008), as well as the more recent neuronal approaches to this problem (Alm, 2005; Dodge, 2009; Watkins, et al., 2008), together with a review of relevant causal models from 16th century. Our studies point to the stuttering as a disorder produced by an operant conditioning of the inhibition reactive system, mediated by the amygdala, which is able to induce an involuntary block of complex motor supervised sequential tasks. This hypothesis shares some concepts with several causal models that are reviewed, but it claims singular differences with all of them. These differences simplify the explanatory framework that is necessary to understand the stuttering phenomenology. Moreover, it extends the concept of persistent stuttering to other supervised sequential tasks, like writing, sign language communication, or playing an instrument, in agreement with some few observational studies (Snyder, 2006). Secondly, starting from this model, a wearable embedded computing system, founded on an adaptive altered auditory feedback (A-AAF), has been conceived. This chapter presents a description of the system, emphasizing new advances with respect to an earlier proposal (Prado & Roa, 2007), and the project stages.

FOUNDATION OF A NONSPEECH ETIOLOGY According to the International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM) from the World Health Organiza-

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

tion (WHO) the stuttering is a disturbance in the normal fluency and time patterning of speech that is inappropriate for the individual’s age. This disturbance is characterized by frequent repetitions or prolongations of sounds or syllables. Various other types of speech dysfluencies may also be involved including interjections, broken words, audible or silent blocking, circumlocutions, words produced with an excess of physical tension, and monosyllabic whole word repetitions. Stuttering may occur as a developmental condition in childhood or as an acquired disorder which may be associated with BRAIN INFARCTIONS and other BRAIN DISEASES. (From DSM-IV, 1994). We address only the persistent developmental stuttering, whose phenomenology is well-known, it starts during the childhood and it develops during the evolution of the subject. This is the most usual type of stuttering. Certainly, the above definition from WHO is unfortunate, since it is limited to the description of a perturbed pattern as it is perceived by an external listener and it neglects the internal perception of the stutterer. Another serious deficiency is to mix simplistically PDS with other types of stuttering disorders, like those acquired after an accident. Indeed, and in agreement with (Büchel & Sommer, 2004), the term stuttering refers to the symptom, despite it is frequently used to refer to both the disorder and symptom. PDS is an idiopathic stuttering with a well-known evolution since the childhood, whereas neurogenic or acquired stuttering is directly related to a brain damage, as for example stroke or head trauma. Phenomenology of PDS is also different from acquired stuttering, although a better analysis exceeds the scope of this chapter. It can be reviewed in (Büchel & Sommer, 2004; Craig, 2000; Loriente Zamora, 2006).

A Review of Current Causal Models We have selected a model’s taxonomy based on the nature of the causal mechanism underlying the disorder. The models are not developed with

detail because this task has been successfully performed in other works (Andrews, et al., 1983; Büchel & Sommer, 2004; Craig, 2000; Hahn, 1956; Loriente Zamora, 2006) and it exceeds our objectives. We focus the review on the evolution of the models’ foundations from scientific revolution in 16th century until now, with the goal of extracting clues that help us to understand better them and the phenomenology that they try to embrace. Hypotheses have fallen on three major types: organic, psychological and linguistic - semiotic. The Figure 1 shows a conceptual tree with the most relevant explicative models of stuttering starting in the 16th century and organized according to the nature of their causal mechanisms (etiology). Directed edges reflect secondary influences between models or groups of models. The edge’s color is equal to the color of the starting block, and indicates the type of influence that is carried. For example, the Starkweather’s model is a logopedics-based model, but it uses concepts from the Wendell Johnson’s model and the group of linguistic models, as shown. Three markers have been used along the tree to point those models or group of models that put emphasis in the fear emotion, those which have reached a singular relevance, and finally, those characterized by some key concepts according to our causal model (following Section). The linguistic – semiotic type claims that persistent development stuttering is caused by a deficit in linguistic abilities of subjects. This hypothesis was based on a significant increment of stuttering in conceptual words with respect to function words and on the worse scores of stuttering children in grammatical and listening tasks according to several authors (Bloodstein, 1981; Santacreu & Fernandez-Zúñiga, 1991). This research hypothesis started in 30’s (Bluemel, 1931) and it is already active, as proved a recent article that seems to support the hypothesis (Bloodstein, 2006). The EXPLAN theory is a very recent model based on the assumption that language (PLAN)

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An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

Figure 1. Conceptual tree with the most relevant causal models and hypotheses concerning PDS from 16th century

and motor (EX) processes constitute the basis of speech control (Peter Howell, 2002). Stuttering emerges when an element in the speech sequence is delayed due to the difficulty of linguistic system to generate it, thus the speech-motor system cannot continue the process. Howell explains classical PDS speech features assuming this assumption. The importance of this model is due to its relationship with current organic hypotheses based on neuroimaging. Another interesting aspect of EXPLAN is the attempt to provide explanation of the PDS subject behavior to AAF. According to the independence between planning and execution in EXPLAN, the improvement of fluency produced by AAF is not associated to the feedback monitoring loop, but to the reduction in the speech rate associated with a change in rate control. A secondary mechanism proposed by Howell is the increase of the sensitivity to alerts (alerts due to fluency failures) of the timekeeper, due to the addition of load imposed by AAF, which compels the timekeeper to remove the excess of

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alerts that induce its saturation in a PDS subject (Peter Howell, 2002). The demands and capacities model of Starkweather considers the delay to acquire linguistic capacities a major cause that promotes the developmental stuttering, starting a strong tendency to integrate external (environmental) with internal (innate or biological) factors (C. W. Starkweather, 1987). It is considered the origin of current integrative conception of stuttering. More details of this model are exposed in the organic models (see Figure 1). There are important doubts regarding the linguistic nature of the disorder. A recent study based on a qualitative exploration examining verbal-descriptive data from structured interviews on 23 male children who stutter against 23 fluent male peers, indicated that stuttering children have a bad conception regarding their own communicative abilities, appointing to a low self-esteem in this area (Bajaj, Hodson, & Westby, 2005). This result suggests that stuttering itself could cause the

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

more frequent grammatical errors in these children as a consequence of their low self-esteem in linguistic area. Other authors claim that stutterers and fluent people have the same linguistic capacities, in opposition to previous results (Rodríguez Carrillo, 2002). In 80’s decade this model evolves to suggest that stuttering is a semiotic disturb. This concept extends the communication disability to the whole communicative task, including vocal and kinesic elements (Rodríguez Carrillo, 2002). Vocal elements include linguistic and paralinguistic issues. Paralinguistic alterations include distortion in rhythm, pauses, and intonation. Kinesics complements paralinguistic behaviors, and embraces respiration and gestural movements. It is a matter of fact that vocal and kinesics is altered in stutterers, supporting the semiotic hypothesis and the current holistic vision of the disorder. However, this model fails to explain what causes that semiotic disorder. The model from Rodríguez Carrillo claims a holistic biopsicosocial perspective, connecting the three types of hypotheses as shown in Figure 1. The models based on psychological hypotheses define an important line of research in 20th century. However they started in 16th century with the hypothesis of Mercurialis (1530 – 1606), which assumes the fear as one of the two causes of stuttering (Charles Van Riper, 1973). The other cause was the brain hyperactivity. Accordingly, he mixes two different types of therapeutic approaches: organic and behavioral. It has been linked to organics hypotheses in Figure 1 according to the weight given to these ones. He suggested not treating children under 7 years and to use the Avicena’s method (deep breath) instead of previous surgical aggressive methods (Bobrick, 1996). Three major groups of hypotheses have been developed within the psychological foundation: learned behavior, approach-avoidance conflict, and psychoanalysis. The learned behavior concept was first mentioned in 1701 by the Swiss physician Johann Amman (Bobrick, 1996). The

approach-avoidance conflict emerged in the 18th century from Erasmus Darwin, father of the Charles Darwin’s uncle and severe stutterer (Bobrick, 1996). It is interesting to remark that the advances in the comprehension of life by scientific mechanistic and chemical approaches in the 18th century concurred with the explosion of psychologist explanation of stuttering. Other psychological-type hypothesis of that century that has been preserved in the popular wisdom was proposed by Moses Mendelssohn, which assumed that stuttering is caused by the collision of too many ideas concurrently in the mind (Bobrick, 1996). This concept underlies several speech therapies pertaining to the logopedics, as shown in Figure 1. The third group of hypotheses, psychoanalysis, emerged mainly at the end of the 19th century, pushed by the abortion of surgical treatments associated with several organic proposals. The most of them pointed to strange hypotheses without any kind of solid basis: repression of sexual emotions, fixation of oral and anal stages, conflict between superego and ego, etc. Freud assumed that stuttering, tics, and asthma share a similar neurosis of organ fixation (García Mila, 1987). The learned behavior group of models takes one of the most influential expressions with the diagnosogenic theory (Johnson, 1959). Wendell Johnson suggests that stuttering is caused by the intention of the child to avoid infantile dysfluencies that are normal during the development (Johnson, 1942). The reasons for this behavior could be, for example, a non-proper reaction of parents to these ones. The model claims that the disorder appears after the diagnostic by parents or others. The diagnosogenic theory is one of the most cited and studied psychologist models of stuttering. A key aspect of this theory, according to Johnson and subsequent researchers, are the environmental responses to spontaneous dysfluencies which could disappear without treatment (Rogere J. Ingham & Bothe, 2001; Salgado, 2005; Yairi & Ambrose, 1999). Therapies based on this

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model seek removing the fear and the strategies to avoid stuttering. It is accepted that the diagnosogenic theory of Johnson is the starting of the modern conception of stuttering as a learned behavior. However, many speech therapists have misunderstood the true basis of the diagnosogenic theory. What Johnson’s theory stated is not that labeling stuttering to a speaker that repeat sounds and syllables causes developmental stuttering, but chronic stutterers were taught (even by themselves) to become frightened or ashamed when they stuttered, inducing attempts to avoid stuttering and this way inducing a feedback that reinforces the stuttering. Key words in this feedback are the anticipatory, apprehensive and hypertonic avoidance reactions of the subjects (Gateley, 2003). As a consequence, the diagnosogenic theory is not opposed to the successful outcomes of the Lidcombe program of early stuttering intervention (M. Jones, et al., 2005). This program was performed in New Zeland with 47 randomized selected participants with 3 – 6 years old (once discarded 7 participants that did not complete the trial), and showed a difference in the mean proportion of syllables stuttered nine months after the beginning of 2.3% (95% confidence interval 0.8 to 3.9) between treatment and control arms. Therapy was based on verbal contingencies provided by parents to children for periods of free speech and stuttering, by conversational exchanges in the child’s natural environment. A key aspect of the program is that indications of parents to children did not transmit any type of reject to them. However, the outcomes are very limited by size, time, and even methods to measure the improvement of fluency. Diagnosogenic theory uses operant conditioning as a causal mechanism for stuttering (Gateley, 2003). Others authors have presented similar conditioning, supported by different environmental responses. This is the case of (Shames & Sherrick, 1963) who proposed the imposition of wishes and demands as the operant mechanism for conditioning (Demostenes complex), connecting this type

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of models to the Psychoanalytic model of Barbara (Barbara, 1954). The latter is described below. The classic conditioning has also been claimed as a causal model for stuttering: traumatic stress associated to speech during the childhood could induce an altered fluency (Bloodstein, 1981). Experimental studies associating electric shocks with a signal of speech starting support this hypothesis (Hill, 1954). Some authors associated the stuttering associated with classic conditioning with muscular spasms in vocal cords (Schwartz, 1977), connecting to the group of organic hypotheses. As it could be expected, other subsequent authors in 70’s tried to integrate classic and operant conditioning giving a new paradigm or causal model. Other models derived from the concept of learned behavior, classic and operant, emerged in 20th century. It is interesting to mention the model of Wischner, which considers that the avoidance behavior of stutterer is due to the intention of eluding the original consequences of the initial dysfluencies, as guilt or shame, consolidating the avoidance behavior and even de avoided consequences (Wischner, 1950, 1952). Van Riper has been one of the most cited researchers in stuttering. He developed a therapy that is currently applied by speech therapists and discussed among many authors (Charles Van Riper, 1949, 1973, 1982). Van Riper considers that origin of stuttering is a muscle disorder that induces delays or interruptions associated with the bad regulation of speech movements, although the persistence of the disorder and thus the developmental stuttering in adults was associated with the fight or avoidance. Therefore, he pointed to learned behavior as the main causal mechanism. This one fills the gap between the childhood stuttering and the persistent developmental stuttering. The kernel of the therapy based on this model is to learn stuttering without fight and avoidance. This therapy may be considered the pathfinder in the stuttering modification approaches. The approach-avoidance conflict started by Erasmus Darwin in 18th was developed by

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

Sheehan, which wrote: “Stuttering is a result of approach-avoidance conflict, of opposed urges to speak and to hold back from speaking. The “holding back” may be due either to learned avoidances or to unconscious motives; the approach-avoidance formulation fits both” (Reed, Tomkins, & Alexander, 1958; Sheehan & Voas, 1957). Sheehan based on earlier models of Miller (1951) and Donald and Miller (1951), who had formulated the nature of conflicts, especially those related to the concurrent approach and avoidance tendencies. He also took into account the theories of Johnson (Johnson, 1942) and Van Riper (Charles Van Riper, 1949), among others, who stressed the implication of non-avoidance and fear (mainly Johnson) in the disorder. He also signals to other researchers that discussed the stuttering in terms of performing two mutually opposed tasks simultaneously: talk and not to stutter. He tried to answer to two essential questions in the stutterer’s behavior: what makes him stop and what makes him to continue. These lead to the two central hypotheses of the Sheehan model of stuttering (Reed, et al., 1958): •

•

The conflict hypothesis. The position of stutterer is just the equilibrium point of those opposed drivers, approach (talk) and avoidance (non to stutter). The fear-reduction hypothesis. Occurrence of stuttering reduces the fear that drives the avoidance, so that during the block it is possible release the blocked word.

This issues lead to therapies that reduce fear, since this is the origin of conflict according to Sheehan. Combined therapies with indirect (psychological) and direct (logopedic) treatments have been used with this objective. Psychoanalytic theories are characterized by a crowd of frameworks, many of them separated and opposed. Following the selection of (Loriente Zamora, 2006) we distinguish three main models. According to the Coriat model, stuttering is a

psycho-neurosis due to persistence of oral, sadistic, and anal elements (Wingate, 1988). A more detailed description of this “theory”, supported by many published articles during 1928 – 1958, exceeds the scope of this work. Fenichel disagrees with Coriat considering that the “key conflict” is not an oral fixation but anal (Fenichel, 1945; Charles Van Riper, 1973). In opposition to those works, the model of Barbara (Barbara, 1954) focuses to the speech blocks and assumes more diverse conflicts that can be addressed by classical psychoanalytical sessions. The Demosthenes complex was his more known share to this field, justifying the stuttering as a mechanism by which the subject seeks more special attention. We consider finally in the psychoanalytic group a variant of Bloodstein logopedics-based model focused to the psychological field, which was proposed by Friedman. She asseverates that PDS is a cognitive state induced by a lack of self-confidence of the person who stutter (S. Friedman, 1990). Models based on organic hypotheses are classified in this work on two types: brain (encephalon) disorders and non-brain nervous disorders. However, in many cases organic causal models point to a diffuse flaw (nervous etiology, bad regulation of sound emission…) that originates the childhood stuttering that develops as adult persistent developmental stuttering if it does not disappear spontaneously. These models have been grouped in a set of logopedics-based models. They are not certainly complete causal models, but there are a high number of models and associated-therapies that have been developed in this way due to the lack of knowledge regarding the disorder. Logopedic conceptions point to nervous system, breath affections, and even linguistic disabilities as the major and original causes that impede a fluent speech. Treatments are oriented to improve the speech abilities by training, better control of articulatory and emission techniques, and reduction of stress due to the increase of self-confidence.

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The hypothesis of brain hyperactivity of Mercurialis (1530 – 1606) can be considered the first organic hypothesis from our review from the 16th century. This author was earlier presented, although he has classified in logopedics hypotheses (Figure 1) according to the prescription of using breathing techniques to improve fluency. Antoine Rullier proposed in 1829 a model based on the Mendelssohn hypothesis, focused to the inability of phonation muscles to reach the necessary temporal resolution. Notwithstanding, one of the first precursors of classic logopedics was Columbat (1831), who prescribed breath exercises and rhythm speech by a basic metronome (Le Huche, 2000). The method was completed in a second stage with syllabication, which was one of the most used logopedics techniques in 20th century. A subsequent logopedics technique was presented in (Chervin, 1896), who pointed to four key signs to diagnostic PDS: childhood beginning, breath disruptions, intermittence, and total disappear when singing. The method of Chervin was widely adopted in Spain in the late 19th. The work of (Rius, 1897) continued with Chervin concepts but added the fear feeling as a particular feature of stutters that should be mitigated. Logopedics approaches in 20th, such as that proposed in (George Andrew Lewis, 1906) share similar concepts with the abovementioned, with slight variants. For example, the basis of Lewis’s model is the nervous weakness of the stutterer. Therefore, he combined breathing and syllabication with relaxation. Two classic authors should be mentioned because of their influence in Spain. A basic book for physicians and speech therapists in 20th century was (Ordoñez, 1956). He emphasizes that stuttering is a nervous disorder that must be corrected by syllabic reading. A more recent Spanish author considered that the nervous disorder was characterized by an acute interior stress that induces spasms and speech haste (Parellada, 1978). He promoted a variant of logopedics methods with emphasis on keeping fill the lungs and disguising the voice (Parellada, 1978). A common concept of

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logopedics methods is the projection of guilty to the stutterer if the therapy is unsuccessful, that is “therapies fail due to the lack of effort of subjects” (Loriente Zamora, 2006). Despite the aforementioned problems and inconsistencies with regard to the linguistic disability in stutterers, this causal hypothesis has been used also as support to logopedics (Pichon & Borel-Maisonny, 1973). These authors assume that linguistic disability impedes that sentences are timely built, which induces disturbances in the respiratory rhythm, and other paralinguistic deficiencies, in agreement with logopedics-based speech therapy. The demands and capacities model (C. W. Starkweather, 1987) combines the influence of the environmental responses of the diagnosogenic model, in terms of speech demands, with the speech abilities (motor, linguistic, cognitive) available to satisfy them. It considers that stuttering appears when demands exceed the abilities. This model starts the tendency to integrate external (environmental) factors with internal (biological) ones, and justifies the current integrated and holistic treatments (Salgado, 2005). Other more recent models delimitate better the type of biological limitations of the Starkweather model. For example, the model of Guitar takes into account the differences in neuronal patterns between fluent and stuttering subjects, to propose a neurobiological deficiency (Guitar, 1998). Another integrative logopedics-based theory was proposed by Bloodstein (Bloodstein, 1981). The main difference with respect to the Starkweather model is the assumption that the slight neuromuscular dysfunctions associated with stuttering are induced by the environmental responses and subject’s experiences, once the anticipation and fear are established in the subject. Bloodstein assigns an important role to the self-concept as a stutterer. This role is associated with a system of beliefs that is built by the child and promotes the subsequent dysfunctions. Moreover, Bloodstein and other subsequent authors (Rodríguez

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

Morejón, 2003; Salgado, 2005) suggest that these dysfunctions can be reverted if the stuttering role is changed. This concept is strongly related to the causal model that we develop in the following Section. Models of stuttering based on brain disorders are mainly of 20th century. Many of them are based on neuroimaging techniques. In opposition to other models that are referred in Figure 1 by the author’s name, brain disorders’ based models are referred by the causal mechanism because there are many authors working currently in each one. Notwithstanding, we have used the author’s name when he is well identified and causal mechanisms are complex. The cerebral dominance theory or model emerged as a consequence of several stuttering observations in left handed children that are required to shift to the right hand (Orton, 1927; Travis, 1978; Travis & Herren, 1929). It was considered a deficit of hemispheric dominance, which was assumed necessary for the proper hemispheric coordination (Hardyck & Petrinovich, 1977). Other authors met also a relationship between left-handedness and stuttering (Bryngelson & Clark, 1933). Experiments performed administering sodium amytal to 4 severe developmental stuttering subjects (Wada test), both in right and left carotids, showed that they have “bilateral control” of language (R. K. Jones, 1966). Other authors obtained results suggesting also problems with auditory control dominance (Curry & Gregory, 1969). Several results in 50’s and 60’s pointed to some type of feedback disorder (auditory, bony, or kinesthetic) in the speech control mechanisms, as cause of stuttering (Goldiamond, Atkinson, & Bilger, 1962; Lee, 1951; May & Hackwood, 1968; Ronald L. Webster & Lubker, 1968). Many of these first and the subsequent works showed the strong influence of the delayed auditory feedback (DAF) and other altered auditory feedback (AAF) mechanisms, such as frequency auditory feedback (FAF), masking, or chorus reading on the fluency, disrupting this one in non-stutterers (Goldiamond,

et al., 1962; Lee, 1951), and improving fluency in stutterers (Alm, 2005; Roger J. Ingham, Warner, Byrd, & Cotton, 2006; Kehoe, 2008; Andrew Stuart, Kalinowski, Saltuklaroglu, & Guntupalli, 2006). We include masking and chorus reading as types of AAF. Although the degree of fluency achieved by AAF depends on each person who stutters, the improvement is a general feature that should be explained by any valid causal model. Stuttering reductions under AAF are important, but the most dramatic are those achieved with choral speech, which is highly robust (Kiefte & Armson, 2008). Choral speech has proved to keep its effectivity despite changes in speech rate, familiar and unfamiliar speeches, and even when accompanist’s voice is temporally lagged (Kiefte & Armson, 2008). DAF and FAF present different effectiveness as a function of the stress, environment and long-term behavior (Kehoe, 2008). It is interesting to emphasize that AAF, for example chorus reading, is able to produce not only stutter free (or reduced) speech, but also significant reduction in speech effort (Roger J. Ingham, et al., 2006) and natural sounding (Andrew Stuart, et al., 2006). These outcomes agree with the amelioration of neuronal abnormalities found in the prime work of Fox et al., where stuttering and fluent speech among PDS and non-PDS adults were compared by means of positron emission tomography (PET) (Fox, et al., 1996). Authors found an underactivation of the frontal-temporal system implicated in speech production (left-side) and an overactivation of the premotor cortex, operculum and insula, with right cerebral dominance, in PDS with respect to non-PDS subjects. Chorus reading remediated the neuronal pattern difference associated with speech tasks. The effect of DAF was explored on fluent subjects by means of functional magnetic resonance imaging (fMRI), concluding that bilateral superior temporal gyrus (STG), together with middle temporal gyrus and supramarginal gyrus, is correlated with the degree of DAF effect, suggesting that temporo-parietal

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regions act as a conscious self-monitoring system in speech production (Hashimoto & Sakai, 2003). The Fox et al. study boosts a research line about stuttering based on neuroimaging (Brown, Ingham, Ingham, Laird, & Fox, 2005; Fox, 2003). Although current discoveries by neuroimaging agree with the theory of cerebral dominance or deficit of hemispheric dominance, mentioned previously (Orton, 1927; Travis, 1978; Travis & Herren, 1929), researchers do not agree concerning the causes and their nature (compensatory or primary). One of the first models of stuttering based on neuroimaging was proposed in (Foundas, Bollich, Corey, Hurley, & Heilman, 2001). It suggests that persistent developmental stuttering is a consequence of the anomalous anatomy of speechlanguage areas. Authors found that right and left planum temporale were significant larger and the magnitude of the planar asymmetry was reduced in adults with PDS. In general, volumetric MRI images showed more gyral variants in perisylvian cortical areas in PDS adults than controls. An alternative more developed model was subsequently proposed to account for cortical anatomy anomalies. This one suggests that stuttering is due to a flaw in the speech-dominant left hemisphere. It is supported by timing disturbances between areas involved in language preparation and execution in dominant hemisphere (based on magnetoencephalographic images), and by the difference of white matter connecting motor-speech areas with temporal and frontal areas, between PDS and fluent subjects (diffusion tensor imaging) (Sommer, Koch, Paulus, Weiller, & Buchel, 2002). As a consequence, authors suggest that the right hemisphere overactivation is a compensatory mechanism. A subsequent study based on fMRI showed that activation on the right frontal operculum (RFO) observed in PDS subjects was negatively correlated with the severity of stuttering (Preibisch, et al., 2003). The same negative correlation was detected during baseline task consisting on passive viewing of meaningless

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signs. Both outcomes point, according to authors, to compensatory instead of primary dysfunction. A second experiment involving covert semantic decision task also showed that RFO activation. A more recent study assessed the effect of fluency shaping techniques in the neuronal patterns of PDS subjects (Neumann, et al., 2005). It showed a widespread activation in frontal speech and language regions, and in temporal areas of both hemispheres, after therapy. The differences among post-treatment left-side activations occurred close to the white matter area detected by Sommer et al., which was considered by the authors a suggestion that therapy is able to remodel brain neuronal networks near the dysfunction source. The results point to a flaw in the speech-dominant left hemisphere, according to authors. A very recent and detailed study using functional MRI and diffusion tensor imaging agrees with this causal model, according to authors (Watkins, et al., 2008). They give explicit support to the linguistic – semiotic type model EXPLAN, previously cited (Peter Howell, 2002). Authors found some common differences during speech production between PDS and control groups that were not related to the type of auditory feedback. These ones could reflect an invariant and characteristic pattern of activity of the stuttering brain. They do not consider choral speech in their study, but only DAF and FAF. It is key to mention the overactivity observed in the midbrain, close to the substantia nigra pars campacta, caudal to the red nucleus and subthalamic nucleus (STN) and to the putative location of the pedunculopontine nucleus (PPN), in PDS subjects. These nuclei are integrated in the circuitry of basal ganglia, and present reciprocal connections PPN – cortex, STN – globus pallidus, and outputs from PPN to cortex, striatum and substantia nigra, among others. In addition, authors refer to a recent study that correlates negatively stuttering severity and activity in substantia nigra, both before and after therapies. Authors do not provide any suggestion regarding this finding, with the exception of the

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

hypothesis that a flaw in cortico-striatal-thalamic loops could be associated with the cortical abnormalities, due to the relationship among basal ganglia, the outputs to premotor system, and the inhibition of motor programs (Watkins, et al., 2008). This flaw hypothesis is supported, according to authors, by the improvement in fluency that follows administration of dopamine antagonists, as risperidone, and the deterioration that follows with the administration of dopamine agonists, such as levodopa. Neuroimaging results and the behavior of PDS subjects have led to propose that stuttering is mainly due to a problem in the sensorimotor control of speech (Max, Guenther, Gracco, Ghosh, & Wallace, 2004). This model is essentially different of the causal model based on timing disturbances above presented. It is considered that central nervous system (CNS) performs complex command-to-motor output transformations during the planning of movement by means of control schemes, as well as the feedforward and feedback mechanisms involved in sensorimotor control and learning. Mach et al. propose two hypotheses for persistent developmental stuttering. The first one assumes an unstable or insufficiently activated internal representation of transformations associated with the conversion of commands in speech. The second one considers the possibility that the motor control uses afferent signals with considerable time lags. Authors base both hypotheses on data related to differences on speech and non-speech movements between stutterers and non-stutterers. However, perhaps the most solid basis is the combination of MEG, PET and fMRI data showing that own speech modulates auditory cortex in a different manner that external auditory stimuli. The first hypothesis gives an explanation to AAF fluency enhancement based on the activation of the internal control models (so, improving the feedback controllers prediction), used to monitor efference copies of motor commands rather than

through actual auditory afferent signals. Max et al. presented a mathematical model that provides the error signal during speech in certain situations. This model implements some aspects of their hypothesis, although it is still immature. Alm presented a causal model of stuttering that puts emphasis in the tangled effect of AAF, including choral speech, on the fluency (Alm, 2005). Alm had the ability to extricate some psychodynamic interpretations of the PDS behavior by means of neuro-physiological analysis. For example, he opposed to the conclusion that PDS subjects reduce stress when stuttering appears, based on the lower heart rate and blood pressure in comparison with non-stuttering persons (WIlliams H. Perkins, 1996). Instead, he argues that reduction of heart rate is a common response in mammals associated to sudden fear where sympathetic and parasympathetic nervous systems are co-activated. This is a well-known physiological mechanism denoted as “freezing response”. According to his model, stuttering is caused by an impairment of the medial premotor system, located along the midline of the brain, whose main parts include basal ganglia and supplementary motor area (SMA) (Alm, 2005). Basal ganglia have an important role on motor dysfunction disorders such as Parkinson’s disease, and SMA is a part of cortex with a role in providing go-signals messages for movement. The model is completed considering that speech control is shift to the lateral system (lateral premotor cortex and cerebellum) when timing is linked to external stimuli or when there is an increased level of conscious attention of speech, such during change of role, imitation, etc. This mechanism is able to support the fluency enhancement of PDS subjects in many situations, including AAF. The model associates stuttering to a freezing state, in which the brain amygdale, located in the medial temporal lobes near to basal ganglia, has a key role. This way, the model allows relating emotional state to stuttering degree, although in a complex and unknown way. The model relates “involuntary” movements and

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increased muscular tension associated with stuttering events to a type of dystonia, in agreement with the role of basal ganglia in automated sequential motor tasks, although clearly distinguishes stuttering from dystonia. It is one of the first models that mention the similitude among task-specific dystonia associated with basal ganglia in tasks like walking, writing, or playing, and point to the metaphoric expressions of them as “stammering with instruments”, etc., used by Bluemel (1930) (Alm, 2005). Finally, the author discusses many of the phenomenology of PDS, for example the increment of basal ganglia type D2 dopamine receptors at 2-3 years is hypothesized an important cause of the high rate of childhood recovery in that age, in agreement with his model. A last and important model of stuttering based on brain causes that is considered relevant in this review was published by Dodge in 2003 in a personal web page and updated two times since then, to best of our knowledge, until the present version (Dodge, 2009). Current version attends to recent research results aforementioned, like the white matter tracts differences between PDS subjects and fluent subjects. The model is based on the assumption that persistent developmental stuttering originates from increasingly conditioned reactions of the amygdala to developmental dysfluencies, which may be associated with several types of temporary or permanent speech-languages impairment in childhood. Therefore stuttering emerges as an inhibition of the speech-language motor system, which induces disruption or freezing when some components of the speech mechanisms return an impaired and unexpected response. Behaviors like loss of speech control, compensatory movements (e.g. tension of speech musculature), etc., are compensatory reactions. Summarizing, Dodge’s model hypothesizes that stuttering is not a neurological flaw but an extreme reaction to fear and threat conditioning to initial and slight impairments. The model seems to suggest that initial speech errors are greater in PDS subjects, who have a higher vulnerability to inhibition. It

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assumes also that the extreme inhibitory reaction in PDS may come from a genetic predisposition. Dodge discusses with detail how the model supports the complex behavior of the PDS, including therapy relapse, and AAF. It is worth to mention the citation to current studies that show how rapid inputs from the sensory thalamus connect directly with amygdala, bypassing the slower sensory cortex representation (Phelps & LeDoux, 2005). This fast circuit must have had an important role in survival, but it could lead to inappropriate activation and even conditioning. For example, auditory stimuli can reach the lateral nucleus of amygdale via thalamus, and promoting freezing through the output from central nucleus in amygdale to the central gray. The latter is a neuron group within the midbrain with a key role in defensive behavior. The midbrain overactivity observed in PDS subjects (Watkins, et al., 2008) agrees with this hypothesis. Several explicative models of stuttering that point to non-brain nervous disorders have been developed since 16th century. Certainly, they have not too relevance in recent days, but the most important should be cited in order to complete the image shown in Figure 1. They provided a better perspective of classical wrongs and misinterpretations of the PDS behavior in popular and even professional environments nowadays. Several ideas emerged in early 19th century regarding the possible impairment of the nerves that enervate tongue and larynx in PDS. These concepts lead to the theory of stuttering as an spasm in the glottis that migrates up to the tongue, causing lingual cramps (Bobrick, 1996). This hypothesis justified the interruption of neurological impulse from glottis by surgeries techniques: cutting out a triangular wedge across the tongue. This technique started in 1841 by Dieffenbach. Other surgeons joined to this “brilliant idea”, adding some variants and creating three schools in German, France, and England. Therapies were retired after several deaths, without any fluency improvement. However, muscular spasms are

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

considered the underlying mechanism of stuttering by some professionals nowadays (Santacreu & Fernandez-Zúñiga, 1991). The different EMG pattern between PDS and fluent subjects is used to support this hypothesis. A different organic model points to tha lack of coordination between respiratory and phonatory systems, with important developments from (W. Perkins, Rudas, Johnson, & Bell, 1976) and (Adams & Hayden, 1976). This assumption was supported by experimental works and was taken into account by several researchers (Charles Van Riper, 1982), although many methodological doubts emerge from a critical analysis (Santacreu & Fernandez-Zúñiga, 1991). In summary, despite neuroimaging techniques are helping to know better the neuronal correlates of persons who stutter, the nature of stuttering remains elusive. Some authors claim the necessity of a new view (Fox, 2003). The complex perspective that shows Figure 1 have led to the sensation that persistent developmental stuttering has multiple causes. The idea of a multiplicity of causes is shared by many researchers, and it is written in popular sources like Wikipedia (http:// en.wikipedia.org/wiki/Stuttering). However, integrative and multidimensional therapies for stuttering have not achieved significant improvements in PDS subjects (Blomgren, Roy, Callister, & Merrill, 2005).

A Non-Speech Etiology: Basis and Discussion A careful analysis of the state of the art shows that no model is able to provide a clear and non-forced explanation of the well-known phenomenology of persistent developmental stuttering. One of the most entangled behaviors is associated with AAF. Recent brain disorders’ models based on neuroimaging suggest several flaws as the underlying cause, but there are many doubts regarding the causal or compensatory (or indeed any other) nature of the neuronal patterns measured.

We present in this Section a novel model of stuttering that provides a more natural explanation of the phenomenology and agrees with neuroimaging and other experimental findings. This model cannot be developed with a great neurophysiologic detail in this Chapter, but the description should clarify the key mechanisms and justify a new etiological paradigm. We have marked several models or group of models that are particularly relevant, according to our knowledge, in Figure 1. These are the models of Johnson (1959), Van Riper (1973), and Sheehan (1957) with psychological nature, the Starkweather’s model (1987) with logopedics nature, and models of cerebral dominance (1929), dominant hemisphere timing disturbances (2002 -2008), and Alm (2005), with a nature founded on brain disorders. The relevance of these models is justified on their wide scientific diffusion, references, and use as basis of therapies. We are aware that this is not the case for the models of Alm (2005) and timing disturbances (2002-2008). However, we are placed all of them together because these two last models, published in relevant journals, collect the most important conclusions reached using modern neuroimaging data nowadays. A second important mark of Figure 1 points to those models that take into account the fear as an emotion that plays a key role in stuttering. We strongly agree with the influence that this quale has in the development of stuttering. Several of the marked relevant models, as those of Johnson, Sheehan, and Alm, put emphasis on this feeling. It has been a factor considered also by other models, starting with the hypothesis of Mercuriales in 1583 (with logopedics basis and psychological influences), models of Rius (1987) and Bloodstein (1981) (logopedics nature), and the model of reactive inhibition of Dodge (2003 – 2009). The latter has not been published in a scientific journal, to best of our knowledge, but in our opinion it is very close of the true nature of the disorder.

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A final mark is necessary before presenting and discussing our model. According to our research, several models in Figure 1 are characterized by some key concepts (besides the fear quale) that are strongly associated with the true causal mechanism of developmental stuttering. These are the group of models based on learned behavior (psychological nature), the model of Bloodstein (1981) (logopedics nature), and the models of cerebral dominance (1929), Alm (2005), and Reactive Inhibition from Dodge (2003 – 2009). Our agreement with those different key concepts is compatible with a single causal mechanism for stuttering. We hypothesize that persistent developmental stuttering is consequence of the blocking of complex motor supervised sequential tasks, programmed in the parallel and unidirectional poly-synaptic loops through basal ganglia and several thalamic nuclei, by means of operant conditioning mediated by amygdala in response to fear emotions (fear conditioning). The Figure 2 shows a grossly simplified diagram of these motor loops, according to (Barker & Barasi, 1999; Edelman & Tononi, 2000; Jhou, 2005; Phelps & LeDoux, 2005; Takakusaki, Saitoh, Harada, & Kashiwayanagi, 2004). Despite the diagram is very incomplete, it tries to put emphasis on the long and non-reentrant pathways that go out the thalamocortical system, passing through the basal ganglia to return to the thalamocortical system. Many studies point to the key role of these pathways in the control of several types of behavioral expressions. This role is related to the contribution of the basal ganglia and brainstem structures (substantia nigra pertains to basal ganglia and brainstem) to the automatic control of movements, with emphasis in the emotionally triggered motor behavior (Takakusaki, et al., 2004). However, many cells within the basal ganglia are not clearly sensory or motor in terms of their response properties. This way, it seems that this cortical appendage takes highly processed

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information to convert it into some type of motor programme (Barker & Barasi, 1999). An important characteristic of these loops is that they are constituted by projections which form parallel pathways, in such a way that this architecture seems to support independent neuronal subroutines (Barker & Barasi, 1999; Edelman & Tononi, 2000). This concept is well known and the experimental evidence is solid. As shown in Figure 2, the most important outflow from the basal ganglia is via GPi and Substantia Nigra reticulata (SNr) to the Thalamus (ventroanterior and ventrolateral nuclei), as well as to the brainstem, mainly to PPN. Polysynaptic loops return to the cortex via Thalamus projection. There are also important back-projections from SN to striatum (dopaminergic nigrostriatal tract) and from other nuclei of the brainstem to basal ganglia. The Substantia Nigra compacta (SNc) is a well-known region because its implication in Parkinson disease. Basal ganglia are strongly involved in functions of amygdala. Indeed and anatomically, the centromedial nucleus of amygdala can be considered part of the basal ganglia. Figure 2 shows the lateral nucleus (LA) and the central nucleus (CE), which in turn projects mainly to the Periaqueductal Gray (PAG). It is also shown that the stimuli (auditory or somatosensory) can reach the PAG via projections from thalamus to LA, without cortex processing (Phelps & LeDoux, 2005). The existence of these fast pathways has deep implications on the mechanism underlying the fear conditioning and the associated freezing behaviors, which are usually associated to the PAG region. There is an overwhelming evidence of the role of amygdala in conditioned freezing, and it is well-known the relevance of fear in that process. However, the knowledge about basal ganglia an amygdala functions in human is still poor. In spite of this, several recent discoveries can throw important clues with regard to the

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

Figure 2. Simplified parallel poly-synaptic pathways through basal ganglia and several thalamic nuclei, with emphasis on feedback to the thalamocortical system and the involvement of the amygdala and several zones of brainstem. The different neuroanatomic zones are marked by different colors

causal mechanisms of stuttering according to our model. Some researchers suggest the possibility that a region within the brainstem near to Median Raphe Nucleus (MRN) and Ventral Tegmental Area (VTA) regulates freezing via projections to the midbrain dopamine neurons (Jhou, 2005). That zone seems to integrate aversive information from multiple sources into a common pathway to inhibit dopamine neurons during learned aversive behaviors. Interestingly, the cited neuron group, within the medial portion of brainstem, has a strong dopaminergic projection to the SNc and VTA (Jhou, 2005). This is shown in Figure 2. This brief description of some major underlying pathways and neuronal regions involved in

stuttering, according to our model, allows developing some important derived issues that clarify the scope of the model. These are subsequently discussed. •

Point 1. PDS is not restricted to speech. It is a fear-mediated learned behavior associated with supervised sequential motor tasks (expressive communication), like speech, writing, sign language communication, or even playing an instrument. Fear is associated with the supervision (potential) by other subjects, particularly during the developmental childhood stage.

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•

•

•

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Point 2. PDS is not due to any type of lack or innate disorder. Fear conditioning can be modulated by a set of internal parameters of the subject, like the sensitivity to external threats or the synaptic plasticity involved in learning, and external conditions, like the reactions of parents, teachers, or other culture and social issues. Point 3. PDS is not associated with a particular behavioral subject profile in agreement with the previous point. Point 4. The variability of stuttering severity for a subject is due to two factors: the possibility of supervision and the subject’s role. The first one induces reactive inhibition or freezing, which causes the emergence of blocks in complex sequential motor tasks. This is a learned process in a PDS subject (adult). The second one is related to the self-concept of the PDS subject. It is psychologically associated with a type of conscious state or experience, which in turn has a correlation with neuronal activity. The two factors are necessary to induce blocks and so stuttering. Point 5. Although the complex processes involved in the building of the semantic elements of a sentence (in any expressive communicative task), like words in speech or writing, do not pertain to the conscious experience, these final elements emerge to the consciousness. Similarly, the complex motor tasks involved in the communicative execution do not pertain to the conscious experience, with the exception of blocking quale in PDS subjects. It is interesting that these quale emerge together with the built word and compel these persons to avoid (or fight with) that word in order to produce the communicative expression. This mechanism was denoted by previous authors as anticipation and it is poorly known. It is the origin of the observed disturbance in vocal and kinesic elements.

•

•

Point 6. Stuttering aid systems should work through the modulation of factors that control blocks: supervision and subject’s role. Point 7. Current therapies for stuttering act similarly to stuttering aid systems, although they are oriented to achieve fluency at medium term instead of real time. The percentage of relapse in those therapies (near 100%) is due to the fact that fearmediated learned behavior associated with expressive communication is kept. A longterm therapy should be able to modify the learned behavior, by means of synaptic depression in projections that cause inhibition of basal ganglia programmed motor tasks. However, as the subject role is a key factor in the induction of blocks, the ability of the subject to keep a different role, as occurs with some actors, professors, or professional speakers, can inhibit stuttering. Notwithstanding, the disorder persists in these persons.

The extension of PDS to other supervised sequential motor tasks claimed in point 1 implies a change in the paradigm of stuttering. It was firstly proposed in (Prado & Roa, 2007; Snyder, 2006). The earlier publication has also been presented recently in (Snyder, 2009). Snyder proposed the possibility of a new paradigm non-based on speech as a consequence of the interview with a student that stuttered both in speech and sign language. This student had analyzed the analogies between spoken and sign language stuttering in his Master Thesis for graduation (Whitebread, 2004). After some resistance to consider the validity of this phenomenon, Snyder reviewed the literature and found a list of studies from 1937 pointing to several cases and data regarding “some type of stuttering” in sign language. This manner, a study by Backus in 1938 found 0.4% of 13.691 students with deaf or hard hearing (HH) exhibiting stuttering either verbally or in sign. Other study by Silverman & Silverman in 1971 revealed stuttering

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

signing in deaf or HH students, and showed that this behavior was more frequently associated with finger-spelling and often resulting in the repetition of a sign or initial letters of finger-spelling. The study of Montgomery & Fiftch in 1988 involving 77 participant schools for hearing impaired students shown that 0.12% of the sampled population exhibited stuttering behavior in speech, sign, or during the simultaneous use of speech and sign (SimCom). The half of this stuttering population demonstrated stuttering-like behavior only in sign communication. The behaviors included blocking on sign production. There are other studies that document blocking or stuttering in other expressive communications modalities such as piano, violin playing, and handwriting (Snyder, 2006). Although those studies present some distinctive features in these behaviors, they are not well-known, and thus diagnosis is difficult. As a study case, a subject with speech stuttering and “trumpet stuttering” reported that he felt exactly the same block qualia and avoidance behavior in both expressive communicative tasks (Snyder, 2006). Those studies have been ignored or disregarded because of the preconception of stuttering as a speech pathology, but they provide support to the point 1 derived from our causal model. This manner, our model provides a single and common basis for stuttering in agreement with the Occam’s razor. It is relevant to emphasize that the semantic atoms of speech and other types of expressive communication, like playing music have different nature and they are related to different cortex neuronal activities patterns (Callan, et al., 2006), which disagrees with linguistic/semiotic hypotheses for PDS. Despite the interest of the studies of (Snyder, 2006) and (Whitebread, 2004), they did not present a new causal model. They suggested a shift of paradigm based on the analysis of previous studies. Moreover, Snyder wrote that stuttering behaviors are symptomatic responses relative to

their corresponding expressive modality, which in turn may be a response to errors in formulation, processing, and/or execution of expressive output in a central level, such as language. This manner Snyder keeps the idea that stuttering is based on some innate flaw, which disagrees with our hypothesis started in (Prado & Roa, 2007) and developed in this work. Another important asseveration of point 1 is that fear is a key issue in the development of stuttering. Fear-conditioning has a well- known implication on operant conditioning and it has been pointed by a relevant group of causal models reviewed and presented in Figure 1. Finally, the point 1 agrees with the concept of learned behavior, which underlies a group of psychological models of Figure 1 marked with the exclamation mark (key issue). The absence of any innate flaw is asseverated in point 2. It is a key aspect of our model. However, we must answer some neuroimaging findings aforementioned. The bilateral activation of brain hemispheres, found experimentally in (R. K. Jones, 1966) using the Wada test in PDS subjects, and shown repeatedly by different neuroimaging techniques in many studies subsequent to the leading work (Fox, et al., 1996), seems to support the cerebral dominance model. It has been better developed afterwards to propose the existence of timing disturbances between areas involved in language preparation and execution, due to a flaw in the speech-dominant hemisphere (Sommer, et al., 2002). The difference of white matter connecting motor-speech areas with temporal and frontal areas between PDS and fluent subjects has been also used to support this model. However, there are other explanations of these data. According to our model, the overactivation of several zones in right hemisphere should be caused by the anticipation and avoidance behaviors associated with the emergence of blocking qualia. This can be supported by studies that shown a correlation between conscious self-monitoring in speech production and temporo-parietal regions, using

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fMRI in DAF experiments with fluent subjects, as described in the previous Section (Hashimoto & Sakai, 2003). The negative correlation between the severity of stuttering and the overactivation of the RFO in PDS subjects shown in (Preibisch, et al., 2003) agrees with our hypothesis. Moreover, the RFO activation observed under covert semantic decision in (Preibisch, et al., 2003) is in agreement with the factors that control blocking, described in point 4. With the aim of clarifying this asseveration, it must be taken into account that it is not necessary a true supervision of the expressive communicative task to trigger stuttering, and thus the conscious anticipation and avoidance mechanisms. These factors are better discussed below. The effect of fluency shaping techniques in leftside activations in PDS subjects was considered by (Neumann, et al., 2005) in agreement with the flaw in the speech-dominant hemisphere, but that interpretation could be erroneous. It should be expected that if a therapy is able to reduce stuttering, the neuronal activity associated to the conscious activity of avoidance should be modified. In addition, the timing disturbance model does not provide any convincing explanation of the overactivity observed in the midbrain, close to SNc in PDS subjects (Watkins, et al., 2008). There is still a poor knowledge with regard to the effect, increase or decrease of dopamine release during aversive stimuli. However, the overactivity observed in the midbrain, close to SNc, and the effect of dopamine antagonists and agonists in PDS subjects are in agreement with the existence of a strong dopaminergic projection from a region within brainstem (near to MRN and VTA) that seems to integrate aversive information implicated in fear conditioning (Jhou, 2005). Therefore, the cited overactivity in midbrain is in agreement with our model of stuttering. With respect to the reduction in white matter integrity underlying underactive areas in PDS subjects, it is well-known that the myelination process is active in human brain and it is modulated by

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experience, affecting the process of information by regulating velocity and synchrony between distant cortical regions (Fields, 2008). This manner, it could be expected that differences in white matter integrity were induced by differences in cortex pattern activations. The model of hemisphere timing disturbances has been criticized in (William H. Perkins, 2001). The main argument of the author was the too slow cognitive function rates of Broca and Wernicke areas, which compels to discard them in the control of timing of high-speed phonatory and articulatory synchronization. According to (Rosenfield & Viswanath, 2002) this asseveration is not true, on the basis of the lack of knowledge concerning the manner that neurons process information in these areas. Moreover, the responding authors emphasize that there is not neuroscientific data indicating that we think at the rate that we talk, finishing with a brief mention to the hard problem of thought (Rosenfield & Viswanath, 2002). The cognitive function rate mentioned by Perkins is related to the binding (cortical integration) problem, which points to the mechanisms that underlying the rapid integration of distributed and functionally specialized neuronal groups (Giulio Tononi & Edelman, 1998; Giullo Tononi, Sporns, & Edelman, 1992). Many studies shown that the integration of distributed differentiated neuronal regions through reentrant interactions in the thalamocortical system is required for conscious experience (Edelman & Tononi, 2000; Giulio Tononi & Edelman, 1998). In addition, it is required 100 - 150 ms to reach this binding, called unified neural process, which agrees with the psychological refractory period necessary to discriminate two sequential conscious states. This way, in our opinion, the criticism of Perkins is well founded, but it has a mistake. It must be hypothesized that the activity of Broca and Wernicke areas during speech occurs within a unified neural process. Moreover, recent theories concerning neuronal dynamics suggest that degeneracy and redun-

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

dancy has an influence greater than earlier was believed, in agreement with the theory of neural group selection (Edelman & Tononi, 2000). This agrees with the lack of histological differentiation in many regions and explains why the nondominant hemisphere can perform functions of the dominant hemisphere, including language functions, especially but not solely in childhood. Our major conclusion is that the neuronal patterns differences observed is a consequence of a conscious experience related to the subject’s role and strongly associated with the avoidance behavior. This is addressed with new details when discussing points 4 and 5. The role that genetic factors play in the development of stuttering is another key issue that seems faced with the lack of an innate disorder that we claim. There are two major elements that contribute to this misleading concept. The first one is that sometimes PDS is confounded with others dysfluencies in cluttering, spastic dysphonia and even Tourette’s disorder. The second one is due to the misinterpretation of linkage studies results. A recent study in the genetics of stuttering from (Suresh, et al., 2006) carried out with 100 families of European descent ascertained in the United States, Sweden, and Israel, can help us to clarify these elements. A synthesis of this work could be that the susceptibility locus for developmental stuttering is on chromosome 9 (LOD = 2.3 at 60 cM) if both persistent and recovered stuttering are considered, and on chromosome 15 (LOD = 1.95 at 23 cM) if only persistent stuttering is taken into account. However, when sex is considered in the population selection, evidence for linkage is on chromosome 7 (LOD = 2.99 at 153 cM) in the male-only subset, and on chromosome 21 (LOD = 4.5 at 34 cM) for female-only data set. When conditional linkage is analyzed, there is evidence for linkage on chromosome 12 (in agreement with previously reported signal in families ascertained in Pakistan), conditional on the evidence for linkage at chromosome 7. Moreover, there is also a region in chromosome 2 that

increased the evidence for linkage conditional on either chromosome 9 (positive) or chromosome 7 (negative). Thus, chromosomes and loci associated to stuttering vary with the criterion used to select the population sample along six chromosomes. In addition, the evidence score (LOD) depended on the sample size. In our opinion, these data do not allow pointing to an innate disorder linked to genetic in stuttering. In addition, even in the case that the genetic profile will be considered as a risk factor to stuttering, the plausible relationship genetic – stuttering predisposition does not involve an innate disorder. The point 3 of our model derives from point 2 and it is supported by several review studies (Bloodstein, 1981; Loriente Zamora, 2006). The point 4 derives directly from our model, and takes into account the behavior of the conscious experience as an array of “simpletons”, which go in and out, as a consequence of demands of environment, providing variability to the self (Ornstein, 1991). This concept is well-accepted in evolutionary psychology and agrees with the neurophysiological hypotheses of the conscious experience as a “dynamic core” based on the aforementioned thalamocortical unified process (Edelman & Tononi, 2000; Giulio Tononi & Edelman, 1998). It is not possible to develop here completely this issue in connection with our model, notwithstanding we need to refer several key concepts. The first one refers to the AAF phenomena described in the previous Section. Some works suggest that AAF promotes a conscious attention of speech, which in turns improves fluency by means of some mechanism. A relevant example was presented by the causal model of (Alm, 2005). It assumes that the conscious attention induces the shift of speech control from the medial premotor system to a hypothesized lateral system, which is not impaired, inducing thus the improving of fluency. However the Alm’s model does not provide a clear explanation concerning the improvement of PDS subjects when talk without supervision

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An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

or whisper. It uses the connection between basal ganglia and limbic system to propose that the cause is a low level of conflicting impulses (emotions or thoughts). In summary, although it is a very interesting model that emphasizes the key role of fear, basal ganglia and limbic system, it assumes an innate flaw and requires different and forced mechanisms to explain partly the phenomenology of stuttering. In opposition, our model predicts the effect of AAF on stuttering severity on the basis of a change of the subject’s role. The theory of dynamic core provides a very interesting physiological basis to this concept, since it relates the activity of the integrated and distributed neuronal regions in the thalamocortical system, to the activations of input and output ports, from and towards polysynaptic parallel loops, through cortical appendages like basal ganglia (Edelman & Tononi, 2000; Giulio Tononi & Edelman, 1998). Therefore, it is able to link conscious states (i.e. cortex neuronal patterns) associated with subject’s roles to cognitive and motor tasks programs in those parallel pathways. With the proper conscious state (and thus subject’s role), the possibility to be supervised activates the blocking mechanisms, mediated by operant conditioning, and causes stuttering. The combination of the two factors of point 4 provides a more simple explanation of the stuttering phenomenology than other models. This includes also the elimination of stuttering in patients where some surgical operation modified strongly the cortex pattern activation, as occurred in the four patients of (R. K. Jones, 1966). A major difference with the other relevant models presented in Figure 1 is that it is not based on any innate flaw. Moreover, the independence or lack of correlation among parallel and unidirectional polysynaptic pathways, through cortical appendages, justifies the specificity of the stuttering behaviors. That is, a subject can suffer only speech stuttering and not other type of expressive communication

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stuttering, in agreement with our current knowledge (Snyder, 2006). The dynamic core hypothesis provides also a new perspective concerning the well-known emergence of blocking quale to the conscious experience of PDS subjects, referred in point 5. This quale would be acquired through the input ports associated to the cognitive-motor programmed tasks, linking the subject’s role with learned blocking mechanisms, mediated by amygdala, in basal ganglia. This new point of view is very important according to the importance of the anticipationavoidance mechanism in the stuttering behaviors, in agreement with many relevant models presented in the previous Section (see Figure 1). The point 6 derives directly from point 4, and it provides an explanation for the effect of the many types of electronic devices for stuttering, including DAF, FAF, Metronome, Masked Auditory Feedback (MAF), etc. These devices modulate both supervision perception and modification of the subject’s role in different ways, depending on the subject experiences. For example, the MAF device seems oriented to the modulation of the supervision perception, whereas FAF seems to act mainly on the role. Indeed, the change in the auditory feedback achieved by means of a simple cotton earplug can induce a role shift that improves greatly the fluency (non-published self-experience of the first author, which is stutterer). The point 7 derives from the presented model and it is in agreement with the lack of effectiveness of therapies, which a percentage of relapses close to 100%. However, the achievement of a complete fluency by a PDS subject does not require unlearning the blocking mechanisms, but a good control of the subject’s role. This issue explains why the stuttering severity uses to increase in familiar environments. Finally, it is relevant to show that some of the conclusions of our model agree with other two important models previously addressed (Bloodstein, 1981; Dodge, 2009). These ones have flagged with two marks in Figure 1. The earlier is based on the

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

self-concept as a stutterer, and hypothesizes that dysfunctions are induced by subject’s experiences, in agreement with our model. The second one assumes that PDS is induced by conditioned reactions of amygdala and presents the mechanism of freezing as a key piece in the disorder, in agreement with us. However, it assumes the existence of some initial slight impairment and does not clarify the other regions or mechanisms that cooperate with amygdala in the stuttering development, despite discuss some cortical appendages that should be implied: “”implicit” learning of the amygdala is accompanied by more situational (“explicit”) and spatially cued learning of the hippocampus and associated rhinocortical areas, as well as by conditioned “implicit” learning in the basal ganglia (striatum) and the cerebellum.” (Dodge, 2009).

Implications on Treatments and Wearable Prosthetic Devices According to point 4 of our model of stuttering, the degree of fluency can be modulated by masking the speech or in general the expressive communication task, and by changing the subject’s role. The first one seeks providing the subject the feeling: “there is not supervision of my communicative task”, which avoids triggering the learned reactive inhibition. The second one is correlated with the dynamic core, which is implied in the activation of inputs /outputs associated with the parallel pathways programmed with that learned behaviour. A stuttering aid device has as main goal the reduction of blocks to facilitate the communications, and it is not interested in keeping the fluency after its use. In such a case, an approach based on a role shift could be very efficient. Techniques like frequency altered frequency (FAF) or any other that helps the subject with this mission are adequate. It is interesting to note that these techniques should be dependent on the expressive communication. This conclusion should be validated in future works.

In opposition, the major objective of a therapy for stuttering should be the reduction of external (perceived by listener) and internal (qualia) blocks, in a long-term and context-independent manner. The ultimate objective is the cure. If our model is correct, then the only way to achieve this goal is to assure than parallel loops through cortical appendages that support the learned behaviour are not activated. Another possibility is that they could be modified. Aside from aggressive techniques that avoid the cortical integration associated with the stutterer’s role (R. K. Jones, 1966), the modification of the synaptic weights related to those complex motor tasks by means of behavioral methods is an exciting possibility. Some clinical studies concerning AAF and other altered auditory feedback techniques (Kehoe, 2008), as well as works regarding the basic mechanisms associated with learning (Gruart, Munoz, & Delgado-Garcia, 2006; Sporns, Almassy, & Edelman, 2000) suggest that this possibility is feasible. The development of a wearable device that combines stuttering aid with a therapeutic objective could be based on the selection of proper weights in supervision masking and role shifts techniques. The accommodative character of sensory system, particularly the auditory system (Sussman & Winkler, 2001), together with the effect that the environmental changes have in the stuttering severity (because its influence on role and supervision feeling), justify that the effect of AAF on fluency wears off in many subjects. This fact led us to design an adaptive AAF technique, based on the feedback of speech and other relevant biosignals, which was briefly presented by one of the authors in (Prado & Roa, 2007). The underlying methodology and the new advances are addressed in the following Section. A relevant feature of the technology that we present is the ability to acquire and integrate speech and other biosignals used for feedback, into a personalized mathematical model, under a distributed architecture developed earlier (Prado, Roa, Reina-Tosina, Palma, & Milán, 2002). This

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An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

approach allows validating several of the derived consequences of our causal model of stuttering. We will take into account definitions of neural complexity and other related measures proposed in (Giulio Tononi & Edelman, 1998), for analyzing quale, role’s shift, and other mental states, in terms of neural dynamics.

person (Armson, et al., 2006; R.J. Ingham, et al., 1997; Lincoln, et al., 2006). Among the most popular AAF approaches, we can highlight the following ones: •

ALTERED AUDITORY FEEDBACK TECHNOLOGY UNDER A NON-SPEECH PARADIGM A Review of Altered Auditory Feedback Prosthetic Devices AAF devices reduce stuttering by 40-80% in reading tasks (Lincoln, Packman, & Onslow, 2006; Van Borsel, Reunes, & Van den Bergh, 2003; Ward, 2006). However, there is only evidence of the short-term effect (Armson, Kiefte, Mason, & De Croos, 2006; Pollard, Ellis, Finan, & Ramig, 2009). Several works have reported a reduction in stuttering, including the enhancement of the speech naturalness by means of AAF, keeping the normal speech rate (R.J. Ingham, et al., 1997; Kalinowski, et al., 2004). It has also been reported that the effect of AAF in reduction of stuttering can decrease (Ward, 2006), even after a few minutes of exposure in some subjects (Armson & Stuart, 1998). Stutterers report improved fluency and better confidence in speaking, together with less stuttering. Contradictorily, satisfaction reports from subjects of studies are not highly correlated with benefits obtained on measures of fluency (O’Donnell, Armson, & Kiefte, 2008; Pollard, et al., 2009). Other studies reported a difficult in the use of devices in noisy situations and the need of some acclimatization for using them (Pollard, et al., 2009). In general, the effects of altered feedback can wear off over time and vary from person to

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•

MAF (masked auditory feedback): masking provides a synthesized sound, generally a noise of some sort, in user’s headphones. This covers up the own auditory feedback. It has demonstrated a reduction in stuttering (Dewar & Dewar, 1979; Kalinowski, Armson, Roland-Mieszkowski, Stuart, & Gracco, 1993; Lincoln, et al., 2006), though that reduction faded with time in many cases (Ward 2006). Furthermore, other AAF approaches, as delayed auditory feedback and frequency altered feedback, seem more effective (Kalinowski, et al., 1993; Lincoln, et al., 2006). The earliest portable MAF device was presented in (Derazne, 1966; Parker & Christopherson, 1963; C. Van Riper, 1965). The Edinburgh Masker was presented afterwards (Dewar & Dewar, 1979). DAF (delayed auditory feedback): this approach reproduces the user’s voice in his headphones delayed a fraction of a second. It has proved its effect in reducing stuttering since 1950s (Molt, 2005; Nessel, 1958). Normally, delays ranges from 50 to 200 milliseconds (Lincoln, et al., 2006). DAF has been used to improve user’s fluency at a low speaking rate, reducing subsequently the delay in stages, until the user can speak fluently with normal speaking rate (Bothe, Finn, & Bramlett, 2007; Ward, 2006). It has been found that speakers can maintain fast rates and achieve increased fluency at 50-75 millisecond delays (Kalinowski, et al., 1993; Kalinowski & Stuart, 1996; Lincoln, et al., 2006; Zimmerman, Kalinowski, Stuart, & Rastatter, 1997). DAF has been applied to correct different disorders including apha-

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

•

sia, dysarthria, dyspraxia, Parkinson’s disease and vocal tremor (Armson & Stuart, 1998; Hargrave, Kalinowski, Stuart, Armson, & Jones, 1994; Saltuklaroglu & Kalinowski, 2006; A. Stuart, Kalinowski, Rastatter, Saltuklaroglu, & Dayalu, 2004). Nowadays there are a number of commercial portable devices that provide DAF. FAF (frequency-shifted auditory feedback): FAF is the most recent AAF approach. The first work that demonstrates its effect on decreasing the frequency of stuttering was published in 1987 (P. Howell & El-Yaniv, 1987). FAF reproduces user’s voice in headphones shifted in frequency. As this approach achieved a reduction of stuttering both at normal and faster-than-normal speaking rates, some authors suggested that altered auditory feedback may correct the neurological abnormality involved (Kalinowski, et al., 1993). However, our model proposes a different causal mechanism. In short reading tasks, variations in pitch ranging from quarter to full octave resulted in 55-74% decrease in stuttering (Armson, Foote, Witt, Kalinowski, & Stuart, 1997; Kalinowski, et al., 1993; Kalinowski, Stuart, Wamsley, & Rastatter, 1999; Zimmerman, et al., 1997). In longer exposures to FAF, only few subjects experience a significative reduction in stuttering (Armson & Stuart, 1998; R.J. Ingham, et al., 1997). FAF has been extensively studied at various speaking rates, shifting frequency in either direction, and combined with other techniques (P. Howell & El-Yaniv, 1987; P. Howell, Wingfield, & Johnson, 1988; Kalinowski, et al., 1993; Kalinowski, et al., 1998).

Among commercial FAF devices, it is worth noting the Fluency Master (R. L. Webster, 1991) developed at HCRI (Hollins communication Research Institute).

•

AAF Combination. It has been analyzed the use of DAF and FAF in a combined manner, to improve their effect in reduction of stuttering (Kalinowski, et al., 1998; MacLeod, Kalinowski, Stuart, & Armson, 1995). In fact, the so-called AAF combination category is currently the most used in assistive devices (Molt, 2005).

Casa Futura Technologies, the first company which offered a device which combined AAF approaches, has recently presented the Pocket Speech Lab (PSL), an upgrade of his Pocket Fluency System. PSL provides both DAF and FAF independently or simultaneously, and it can also deliver MAF (www.casafuturatech.com/Catalog/ psl.shtml). It is worth to note here the Digital Speech Aid (DSA), a device which delivers FAF, MAF and a combination of DAF and FAF. Firstly presented in 1992 by Marek Roland-Mieszkowski, Andrzej Czyzewski, and Bozena Kostek, it is now in its 5th generation (www.digital-recordings.com/dsa/ dsa.html). Furthermore, we must cite also the Speech easy device (www.speecheasy.com), developed in 1999 by Michael Rastatter, Joseph Kalinowski and Andrew Stuart among others. There are three versions of Speech easy: BTE (behind-the-ear), ITC (in-the-canal) and CIC (completely-in-thecanal), and all of them provide DAF and FAF in a simultaneous manner. Other devices which belong to this category are shown in Table 1. There is not a consensus regarding the more effective approach in inhibiting stuttering. Some works have shown that DAF and FAF are more effective than MAF (P. Howell & El-Yaniv, 1987; Kalinowski, et al., 1993). Howell and colleagues reported a better effectiveness of FAF than DAF (P. Howell & El-Yaniv, 1987), whereas other authors showed similar effectiveness in DAF and FAF (P. Howell & El-Yaniv, 1987; Kalinowski, et al., 1993).

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An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

Table 1. Commercial AAF devices (adapted from (NHSC, 2007)) Company Janus Development Group Casa Futura Technololgies

product SpeechEasy

Pocket speech lab

SmallTalk

Telephone fluency system

VoiceAmp

A.S. Genstar Limited Digital Recordings Inc

Kay Elemetrics

technology DAF

Up to 220 ms, programmed, but not user adjustable

FAF

500, 1000 or 2000 Hz

DAF

30-200 ms, user adjustable

FAF

+0.4 to -1.2 octaves in 0.2 octave steps

MAF

Manual control button

DAF

30-200 ms, user adjustable

FAF

+0.4 to -1.2 octaves in 0.2 octave steps

4.100 – 4.900 $

3.495 $

2.495 $

DAF

50 ms, fixed +0 to -1.2 octaves in 0.2 octave steps

School DAF

DAF

30-200 ms, user adjustable

995 $

VoiceAmp 601

DAF

30-200 ms, user adjustable

1.850 $

FAF

+0.4 to -1.2 octaves in 0.2 octave steps

Defstut Digital speech aid III & Digital speech aid V

Facilitator model 3500

DAF

-

MAF

-

DAF

-

1.495 $

350 $ -

FAF

-

-

MAF

-

-

DAF

-

-

MAF

-

-

Devices for the alteration of speech motor production patterns

Alteration of speech production patterns can cause a high degree of fluency in stuttering subjects: talking faster or slower than normal, in a softer (louder) voice, with a monotone (monoloudness) pattern, etc. The earliest one of these devices was presented by Jean Marc Gaspard Itard in the first years of

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Cost (estimated)

FAF

It is worth mentioning here some logopedicbased devices for reduction of stuttering, despite most of them were completely unsuccessful, because these ones complete the perspective of prosthetic and also therapeutic techniques. •

Features

19th century. Itard developed different strengthening exercises and utilized a gold or silver “fork” placed under the tongue, holding it in a higher position in the oral cavity (Bobrick, 1996). The Bates Appliance was patented in 1851, designed to deal with different types of stuttering. It consisted of three parts: a metal plate to keep the lips apart, a hollow tube prevented stuttering on dental phonemes, and a laryngeal compressor (a leather collar) prevented stuttering on guttural phonemes (Molt, 2005). The Idehara “StutterCure” appliance, presented in 1937, was a small whistle placed in the oral cavity and sitting against the hard palate. Whistle provided feedback of the air flux (Charles Van Riper, 1973).

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

•

Feedback of physiological status or production patterns

This class of devices provides visual and (or) auditory feedback of physiological states or speech production patterns. We consider the following categories: Respiratory kinematics. It has been shown respiratory irregularities in stuttering patients (Bloodstein, 1995). Therefore, some devices were focused on respiratory kinematics to enhance fluency (Molt, 2005). For example, the “Breathing Monitor” (basically, a type of induction plethysmograph) was included in the “Dr. Fluency” computerized stuttering treatment program (A. Friedman, Fetterman, & Jolson, 1999). The CAFET (Computer Aided Fluency Establishment Trainer) is a computer program that tries to improve the control of respiratory and voice production systems. According to authors, the user can coordinate his breathing with the onset of speech and maintain the speech flow (Goebel, 1986). The PFSP (Precision Fluency Shaping Program, http://fluentspeech.com) uses a voice monitor that provides acoustic analysis of sound/speech onset and other parameters, and a “respiration monitor” (abdominal induction plethysmograph) for providing feedback of respiratory kinematics. Phonatory and articulatory kinematics. Phonation and articulation have received attention as a possible cause of stuttering, as they are altered in many stuttering patients (Molt, 2005). Its modification has been the basis of several therapy approaches: •

•

Training of gentle/gradual phonatory onset. CAFET and Dr. Fluency programs use acoustic monitoring devices for providing real-time feedback of voice onset and characteristics: patterns, duration and amplitude. Training of sustained (continual) phonation. The “Vocal tactile Feedback Method and Associated Apparatus” (US Patent

•

•

4685448, 1987) places a vibrotactile apparatus around the neck for helping to keep a continuous phonation pattern, thanks to the vibration of the device. Recently, transcutaneous electrical nerve stimulation has replaced vibratory signal in the EZ-Speech Sustained Phonation Unit (SPU). We can cite also the Stutter-Free Program (Shames & Florance, 1980). Articulatory tension, timing, and (or) duration. As stuttering is often accompanied by muscle tension in different structures, EMG measurements provide information which can be used in the training to reduce such tension. Several studies have reported a reduction in frequency of stuttering and tension in different structures, as laryngeal area, lips and jaws. Pacing/Timing devices. Rhythmic speaking techniques have been focused as an important point of therapy since more than a century (G. A. Lewis, 1897). The orthophonic lyre was commercialized in 1830. This is a mechanical metronome which allows the regulation of the speech rhythm. Electronic metronomes became very popular as therapy devices for stuttering in the 60’s and 70’s. They provide a pacing and rhythmic effect which slows patient’s speech to the rhythmic beat, increasing fluency. Several works support the device effectiveness (Beech & Fransella, 1968; Berman & Brady, 1973; Wohl, 1968). Tactual metronomes have been used in stuttering therapy, providing rhythmic vibrotactile pulses instead of auditory signals (Azrin, Jones, & Flye, 1968).

As mentioned in the previous section, there is no evidence that the improvements achieved by these approaches are truly caused by logopedic corrections. In fact, such improvements are probably due to an increase of self-confidence.

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An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

An Adaptive Version of AAF: A Bridge to Control Technologies In the section concerning the implications of our model on treatments was justified the adaptive AAF (A-AAF)) approach because of the accommodative character of sensory system, together with the dependence of stuttering severity on environmental changes. The adaptive version of AAF attempts to improve the effectiveness of the AAF algorithm through the modification of its parameters, according to subject state information (Prado & Roa, 2007). This adaptation pursues an increase in subject’s fluency, both external (perceived by the listener) and internal. This new approach takes in consideration the variable character of the stuttering severity for a given subject, in different surroundings, situations, etc., and would led to a substantial advance respect to current devices. The achievement of a quality real-time quantification of stuttering severity is a key point in the reliability of the A-AAF algorithm. The concept of stuttering severity is not new, and it has been analyzed and discussed in different works (Ludlow, 1990). We can apply different methods to assess stuttering severity. Among these methods, probably the most common is the measurement of stuttering frequency through the quantification of the percentage of stuttered syllables (%SS). However, this measurement just provides information about the number of instances of blocks, and such frequencies are not highly correlated with the internal tension (Charles Van Riper, 1982) and blocking qualia. Another method to assess stuttering severity is the duration of stuttering, i.e., the percentage of dysfluent speaking-time (C.W. Starkweather, Gottwald, & Halfond, 1990), but this measurement is difficult and time-consuming (Alm, 2005). Several rating scales have been proposed to classify stuttering severity. A scale with a 7-point with four separate concept groups (frequency of stuttering, level of struggle or tension,

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mean duration of instances of stuttering and level of postponement-avoidance) was proposed in (Charles Van Riper, 1982). A simpler scale with points labelled “mild” and “severe” was proposed in (Martin, Haroldson, & Woessner, 1988). Other method to quantify the stuttering severity is the Stuttering Severity Instrument (SSI) (Riley, 1972). This method evaluates three aspects on a 5-point scale: the percent of stuttered syllables, the estimated length of the three longest blocks and the percent rating of associated behaviours. However, its validity and reliability has been questioned (K. E. Lewis, 1995; Mowrer, 1991). Probably, the most accurate method is the combination of two or more aspects of stuttering. For example, (Alm, 2005) hypothesized that the severity of stuttering can be described by the proportion of speech time with symptoms of stuttering and the instance of the highest level of superfluous muscular activity of speech, on a rating scale ranging from 0 to 7. Signal-processing techniques have been successfully applied in screening speech disorders in the last two decades. Voice analysis, EEG analysis and EMG analysis, separately or combined, are appropriate for the assessment of stuttering severity. Furthermore, these techniques can be complemented with kinematic analysis, taking into account the presence of some motor tics suffered by stuttering subjects in severe phases. •

Voice analysis

Acoustic measurement is a non-invasive technique. As a consequence, it is often used for screening and monitoring speech disorders. Furthermore, its effectiveness in speech processing has been demonstrated in a number of works (Baken & Orlikoff, 2000; Eskenazi & Childers, 1990; Klingholz, 1990; Parsa & Jamieson, 2001). The control of delay and frequency in a A-AAF defined by a combination of DAF and FAF requires the recognition of words and phrases, which can be achieved through a multimodal architecture

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

as presented in (Prado & Roa, 2007). To extract such acoustic characteristics from speech there are two classical approaches: the sustained vowels (Parsa & Jamieson, 2000; Umapathy & Krishnan, 2005) and the continuous speech segments (Hammarberg, Fritzell, Gauffin, Sundberg, & Wedin, 1980; Qi, Hillman, & Milstein, 1999). Signals involved in the sustained vowel method are controllable and well structured, and their processing is easier. Its main drawback is the loss of the dynamics. In the continuous speech method, on the other hand, it is usually required the segmentation of signals because of its non-stationary nature. However, this difficult has been recently overcome through various computationally efficient time-frequency techniques. Continuous speech approach is expected to achieve an accurate and realistic assessment of pathological speech. A number of approaches that promise the recognition of voice disorders have been proposed in recent years. Among these, we can cite a neural network based classifier (Godino-Llorente & Gomez-Vilda, 2004), or a classification of pathological speech signals using a local discriminant bases (LDB) algorithm (Umapathy & Krishnan, 2005). Wavelet transform technique has also been proposed for signal analysis in various speech applications with success. Its multi-resolution capability is appropriate for speech and audio applications. Several works using wavelet approaches for speech coding, enhancement, and classification have been reported (Hsieh, Lai, Chen, & Wan, 2002; Najih, Ramli, Ibrahim, & Syed, 2003; Veselinovic & Graupe, 2003). However, the optimum choice of a wavelet (bases functions) for speech applications remains inconclusive.

neocortical dynamics function. Robust correlations among EEG signal and different cognitive processes associated with mental calculations, working memory and selective attention, have been observed (Nunez & Srinivasan, 2006). The EEG activity of people who stutter can provide invaluable information about the severity of subject’s stuttering. Effect of stuttering in evoked potentials and EEG was investigated in (Khedr, El-Nasser, Haleem, Bakr, & Trakhan, 2000), finding a reduction of amplitude of visual evoked potentials (P100), as well as a significant prolongation of wave (I, III, V) and interpeak (I-III and I-V) latencies in brainstem auditory evoked potentials compared with the control group. Furthermore, they found the dominant EEG rhythm was slower in stutterers with a significant interhemispheric asymmetry. The discoveries of that study correlate EEG recordings with stuttering severity, and point to the possibility of evaluating the stuttering severity of a subject in real-time. Achim and colleagues placed EEG electrodes at inferior frontal, precentral, and tempo-parietal locations on each hemiscalp of both five righthanded dysfluent speakers and fluent speakers. They found that dysfluent speakers presented overall smaller contingent negative variations (CNVs1) that were more marked over the right hemisphere than left (Achim, Braun, & Collin, 2008). The main drawback of the EEG analysis is the presence of artefacts during stuttered speech that mask the signal of interest. Their sources are mainly due to speech musculature, ocular activity, or face tension. Some recent works have shown the possibility of removing them during stuttering (Tran, Craig, Boord, & Craig, 2004).

•

•

EEG

The electroencephalogram (EEG) technique records the oscillations of brain electric potential recorded from electrodes placed on the human scalp. It provides large-scale measurements of

EMG

Electromyography (EMG) is the study of muscle function through its electrical activation. EMG signal can be analyzed to detect neuromuscular functions and diseases or analyze the biomechanics

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An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

of human movement (including vocal cords), so it can be applied in areas such as computer human interface and in developing intelligent prosthetic devices. Usually their measurements are carried out by surface contact sensors and the electrical signals are transformed into patterns. Several authors provided the first approaches on the use of EMG to analyze stuttering in the early 70’s (Cross, 1977; Guitar, 1975; Hanna, Wilfling, & McNeil, 1975; Lanyon, 1977). A work of Kumar and colleagues performed the recognition of human speech through an artificial neural network based on EMG recordings acquired from three articulatory facial muscles. The reported results showed an accuracy of 88% (Kumar, Kumar, Alemu, & Burry, 2004). EMG signals have also been applied in subauditory (or unvoiced) speech recognition, interpreting the nervous system signals sent to muscles of vocal tract and providing valuable information comple-

mentary to other techniques, such as EEG (C. C. Jorgensen, Lee, & Agabon, 2003). Subauditory speech recognition is targeted for use in acoustically noisy environments (C. Jorgensen & Betts, 2006), and may be helpful for stuttering patients. These results show the feasibility to extract information about stuttering severity through EMG. For example, EMG signals could assist to detect silent block events, which can be removed then by MAF (Kehoe, 2008).

A Multimodal Wearable Sensor: Actuator Computational Architecture The A-AAF device is based on the computational architecture depicted in Figure 3. This is an evolution of the technological system presented previously (Prado & Roa, 2007). The system has been designed on the basis of our experience with multimodal intelligent wearable systems for

Figure 3.Simplified block diagram of an A-AAF device. IHS and ISs are shown to illustrate the functional devices. In the clockwise, starting above the WPAN label: “In the canal (ITC)” DAF-FAF device from SpeechEasyTM, a SmartPhone, a commercial MEMS accelerometer sensor, hybrid EEG sensor adapted from http://www.sensorsmag.com/, and a multiuse EMG electrode

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telehealthcare (Prado-Velasco, del Río-Cidoncha, & Ortiz-Marín, 2008). A multimodal intelligent wearable system is composed by a Smartphone that acts as the main node of a wireless personal area network (WPAN) and several intelligent sensors and actuators that act as wireless nodes in WPAN. Intelligent sensors (IS) acquire biosignals, perform a personalized pre-processing of them, and send results towards the Smartphone. Intelligent actuators (IA) implement personalized and adaptive algorithms on the basis of the inputs from Smartphone, and execute actuations on the target. Any IS or IA device includes sensors or effectors, a microcontroller for operation and signal processing, a wireless transceiver and the associated antenna, and the auxiliary circuitry that depends on their particular functions. The ability of IS and IA to be adapted to the subject and context evolution depends on the processing capacity of the microcontroller. The operation methodology of this type of multimodal intelligent wearable system is able to reduce the dimensionality of the data to be processed by the Smartphone, making easier to adopt a real time processing approach. It is also an efficient architecture with the aim of reducing power consumption, circuit sizes, resources allocation, and communication channel capacity requirements (Prado, Reina-Tosina, & Roa, 2002). It allows also the fusion of multimodal data and the design of prosthetic and therapeutic systems that require feedback from multimodal sources. Our A-AAF is a particular intelligent wearable system composed by the Smartphone, an intelligent headset (IHS) and optionally several ISs to acquire EEG, EMG, and body kinematics through accelerometers based on micro-electromechanical systems (MEMS). The IHS implements the A-AAF algorithm according to the parameters sent by Smartphone. It comprises an intelligent sensor (microphone) and actuator (speaker) in one autonomous node of WPAN. We have a preliminary design based on a programmable single-chip Bluetooth 2.1 (EDR

compliant) solution with on-chip and specialized Digital Signal Processor (DSP), stereo CODEC, and Flash, on-chip lithium battery charger, and DCDC converter (BlueCore5-Multimedia integrated circuit, http://www.csr.com/products/bc5range. htm). This is a low cost integrated circuit designed especially for high-end headsets. The IHS architecture has been defined with the aim of separating the execution of the more basic AAF algorithm from the more advanced features of our device, which are the reactivity to the subject’s state through the adaptive parameters of the algorithm, and the possibility to turn off/on some of them (e.g. masking), as a function of the stuttering degree and state. This manner, the IHS is able to perform the basic AAF, whereas the adaptive function is implemented in the Smartphone and supported by data acquired from ISs, included the IHS. Figure 3 shows, as an example, an IHS from SpeechEasyTM (http://www. speecheasy.com/). That is an “In the canal (ITC)” DAF-FAF device cited in the previous Section. The minimal A-AAF solution is composed by the IHS and the Smartphone, since the technology of these elements allows a discrete design that can be used by the PDS user in any place. However, there is an intense research to achieve wearable and discrete EEG and EMG electrodes, promoted by the last European framework and national programs. As an example, Figure 3 shows an image of a hybrid EEG electrode from Quatum Applied Science & Research (QUASAR). That sensor addresses the problem of triboelectric effect in capacity electrodes by means of a set of “fingers” that reach through hair the scalp. Several relevant international projects are addressing other issues concerning the wearable ability of EEG and EMG electrodes (Casson, Smith, Duncan, & Rodriguez-Villega, 2008). At the moment, the more complex A-AAF, including EEG and EMG intelligent sensors, is thought to be experimented in a laboratory environment. These ISs will provide relevant information concerning the stuttering severity,

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associated with neuronal correlates and muscular activity of speech, together with body kinematics in selected segments. That information will optimize the feedback and this way the performance of the adaptive function of the A-AAF. The adaptive function of the A-AAF device is supported by a multilevel mathematical model that describes the neuronal dynamics of the regions involved in stuttering according to the hypothesis developed in the earlier Section. This model will be developed following an approach similar to that presented by (Izhikevich & Edelman, 2008). However, we will extract a personalized reducedform of the main model that will be able to run in real-time into the Smartphone, using proper techniques (Bassingthwaighte, Chizeck, & Atlas, 2006). This methodology was designed with the aim of generating customized knowledge of a chronic patient on a telehealthcare system (Prado, Roa, et al., 2002). It is characterized by the presence of a computational component, called Patient Physiological Image (PPI), which comprises a systemic mathematical model of a patient, and evolves together with him by means of monitored biosignals and other biomedical parameters. Intelligent sensors and actuators communicate with the Smartphone in the WPAN by two types of wireless technologies: Bluetooth (BT) standard and free Media Access Control (MAC), as a function of the power consumption requirement and autonomy that is required. Current BT technology (v 2.1) allows data transmission up to 3 Mbps, with a typical power consumption of 100 mw, whereas other well-known transmission technology for personal area networks, Zigbee, provides only 0.2 Mbps with 30 mw. Moreover, the specification BT v3.0+HS (based on a UWB modulation technology), is near to be released. This will provide up to 54 Mbps in the same radio frequency band (2.4 GHz), including also a cleaner band (5 GHz) (http://bluetooth.com/). Power consumption in the later will be also low. Interestingly, the Bluetooth Special Interest Group

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(SIG) is working since 2007 to develop an ultralow-power version of BT with consumptions like Zigbee, but able to communicate with other BT devices. This technology, previously denoted as Wibree, was considered in (Prado & Roa, 2007). Moreover, BT has been approved by the Continua Health Alliance, an industry coalition of health care and technology companies charged with establishing a system of interoperable personal telehealthcare solutions, as the standard for device interoperability. All these facts compelled us to select Bluetooth for data transmission within the WPAN of A-AAF, for audio or other signals whose frequency sampling overpass clearly 1 - 10 KS/s. This is the case of IHS and some accelerometer sensors (ACC) with specialized functions (see Figure 3). We have selected a free MAC technology for signals whose sampling frequencies are lower than 102 S/s. Zigbee has been rejected, in opposition to our initial proposal in (Prado & Roa, 2007), because the ratio data rate / power consumption is poor (Jin-Shyan, Yu-Wei, & Chung-Chou, 2007). This way EEG, EMG, and the most ACC intelligent sensors use Free MAC in our current architecture (see Figure 3). Accordingly, ISs with free MAC communication technology are based on the new CC981H Sensium family (previously denoted TZ1030). This is an ultralow power system on chip, including a digital subsystem with the 8051 microprocessor, a very efficient (4 mw) integrated transceiver with 50 kbps data rate (ISM bands), program and data memory, and an 11-bit sigma delta ADC. This IC was designed by ToumazTM for multimodal monitoring in body area networks applications, and it was also selected by our group (as TZ1030) for a new falling detector based on a triaxial accelerometer sensor (Prado-Velasco, et al., 2008). The cellular link of Figure 3 is an optional feature of our A-AAF device, which allows sending and even taking feedback from a services provider, where data obtained from the intelligent sensors can feed a more complex multilevel mathematical

An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering

model. This function extends the A-AAF to be a module of a telehealthcare system, providing richer information and knowledge to professionals and facilitating the development of research studies. The A-AAF architecture is being developed in two stages. In the first one, a standard headset will be selected and the A-AAF algorithm will be implemented in the Smartphone, together with the adaptive functions. This first prototype pursues the analysis of the current limitations of headsets due to wireless technology, operating systems in the Smartphone (windows mobile, Symbian, Palm, etc.), and processing capacity of the core. The complete architecture will be developed in a second stage, including the IHS and the others ISs.

SUMMARY AND CONCLUSION Despite centuries of research the etiology of persistent developmental stuttering is still unknown. Current discoveries based on modern neuroimaging techniques have shown a new and exciting perspective of earlier ideas and hypotheses, but it seems now clear that it is needed a new approach to understand the nature of the disorder (Fox, 2003). This work presents a new etiological model of persistent developmental stuttering, based on a deep analysis of earlier models and on the stuttering phenomenology, described in basic, clinical, and even ethnographic sources. Our model was developed taking the Occam’s razor as criterion for avoiding unnecessary intricacies. One of the more stimulating conclusions has been the suggestion that stuttering is a non-speech based disorder, in opposition to the accepted belief. This is supported by a number of works concerning different expressive communication tasks, including sign language, playing an instrument or handwriting (Snyder, 2006). The model has guided the design of our new prosthetic and therapeutic device. It is based in the hypothesis of the dynamic core (Edelman &

Tononi, 2000; Giulio Tononi & Edelman, 1998), which facilitates the development of the adaptive AAF technology, using neural complexity and other measures over the Patient Physiological Image (PPI) approach (Prado, Roa, et al., 2002). The PPI is a computational component founded on a multilevel systemic mathematical model that it is customized for a user, and executed in an autonomous manner, evolving together with him. A relevant feature of the PPI approach is the ability to integrate the feedback data into the multilevel mathematical model to generate knowledge, which is used in turn to improve, validate, or reject the hypothesized causal mechanisms. This methodology fills the gap between the therapeutic and prosthetic techniques to treat the stuttering, and the scientific basis on which they must be based, and supposes a new perspective to throw light on the nature of the disorder. We have presented the preliminary design and computational architecture of the adaptive AAF device. It is being developed within a research project with two technological stages, above described, plus a third line devoted to the research and development of the mathematical model of a neuronal system that embraces the regions involved in stuttering. With that objective, we will take into account the last large-scale neuroanatomic model of (Izhikevich & Edelman, 2008) as reference, together with modern techniques for building reduced-form models (Bassingthwaighte, et al., 2006).

ACKNOWLEDGMENT We are grateful to Dr. José Ignacio Piruat Palomo, senior researcher of the Institute for Biomedical Research of Seville (IBiS) of the University Hospital Virgen del Rocio, for his valuable comments concerning neuroanatomic details of our stuttering model.

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KEY TERMS AND DEFINITIONS (Stuttering) Etiology: Causal mechanisms underlying the stuttering. This chapter is devoted

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to the cause of the persistent developmental stuttering. Expressive Communication: It refers to a supervised sequential motor task like speech, writing, sign language communication, or even playing an instrument. Mathematical Model: Mathematical description of a physical system. In the framework of this work mathematical models pursue the descriptions of mechanisms underlying stuttering, putting emphasis in the dynamics of neuronal regions involved in the disorder. Multimodal Intelligent System: Computational system that processes signals from heterogeneous sources and it has the ability to modify its behavior for adapting to the environment or associated subject in an autonomous mode. It is used as a basis for multimodal smart sensors in this work. Persistent Developmental Stuttering: Disorder that affects between 1 – 2% of world adult population. It impedes that subjects can communicate with others in the manner they wish. From the perspective of the listener, the disturbance is characterized by frequent repetitions or prolonga-

tions of sounds and syllables, although it includes also other types of speech dysfluencies, broken words, audible or silent blockings or circumlocutions, among others. Prosthetic (AAF) Device: Devices designed to improve the fluency of persons who stutter by changing how they hear their voice (speech feedback). There are several well-known distortions to the feedback of speech, like delayed or frequency altered feedback, although the concept is not restricted to them. Therapeutic (AAF) Device: Devices designed to treat the stuttering disorder and achieve a persistent improvement or even cure. There is not consensus with regard the potential application of AAF as therapy for PDS subjects, but the same occurs with any other current therapy.

ENDNOTE 1



CNV: electrical event associated with an anticipated response to an expected stimulus, and thus indicative of a state of readiness or expectancy

This work was previously published in Handbook of Research on Personal Autonomy Technologies and Disability Informatics, edited by Javier Pereira, pp. 76-118, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.18

Reporting Clinical Gait Analysis Data Raymond White Sunderland University, UK Robert Noble The Robert Gordon University, Scotland

ABSTRACT Gait analysis is a special investigation that can assist clinical staff in the decision making process regarding treatment options for patients with walking difficulties. Interpretation of gait analysis data recorded from 3D motion capture systems is a time consuming and complex process. This chapter describes techniques and a software program that can be used to simplify interpretation of gait data. It can be viewed with an interactive display and a gait report can be produced more quickly with the key results highlighted. This will allow referring clinicians to integrate the relevant gait DOI: 10.4018/978-1-60960-561-2.ch418

measurements and observations and to formulate the patient treatment plan. Although an abbreviated analysis may be useful for clinicians a full explanation with the key features highlighted is helpful for movement scientists. Visualisation software has been developed that directs the clinician and scientist to the relevant parts of the data simplifying the analysis and increasing insight.

INTRODUCTION Gait analysis is the systematic measurement, description and assessment of those quantities thought to characterise human bipedal locomotion (Davis et al, 1991). Kinematic, kinetic, electro-

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Reporting Clinical Gait Analysis Data

myographic (EMG) and temporo-spatial data of the subject is acquired and analysed during the gait analysis process. Human gait is a cyclical event and by convention the gait cycle begins and ends at initial foot contact of the same leg. That is, it represents 2 steps. The purpose of clinical gait analysis is to communicate reliable, objective data on which to base clinical decisions. It can also be used to quantify outcomes and may ultimately predict the effects of various interventions for a patient with ambulatory difficulties. This requires a multi-disciplinary team to synthesize, analyse and evaluate the large number of variables used to describe a person’s gait and to provide a clear course of surgical, orthotic, prosthetic, pharmaceutical or therapeutic intervention. Clinical gait analysis techniques using 3D motion capture systems have several inherent sources of errors due to limitations of the modelling software used, skin movement artefacts and marker placement errors [Kadaba et al, 1989]. The current cost of gait analysis for a patient is high and is estimated to be equivalent in cost to 6 MRI scans. This is an important limitation of these techniques. Also, the current method of presentation of clinical gait analysis data is not readily understood by many clinicians and this also inhibits the widespread use of these techniques. At present there are no universally accepted techniques for interpretation of clinical gait analysis data but a consensus has developed in some areas, notably in cerebral palsy gait. Clinical gait analysis begins to provide a scientific basis to determine how neuromuscular impairments correspond to abnormal movements. Traditionally, clinicians report changes in joint angles following surgery as an outcome measure of the effectiveness of the surgical procedure e.g. increased knee flexion following hamstring lengthening. This does not give any indication of functional improvements to the patient’s gait and therefore gait analysis data can be an important adjunct to the clinical examination. For gait data to be useful

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in the management of the patient both the clinical examination data (a static, passive assessment) and the gait data (a dynamic or functional assessment) should correspond. In most gait laboratories clinicians normally require only summary information from the gait recording provided by the gait analysis team (movement scientists). The movement scientists carry out a comprehensive investigation of the data and therefore need detailed information to support the quality of their reporting to the clinical team. However, both the clinician and the movement scientist would benefit from the data processing being automated and the reporting to be simplified and presented in an easily comprehensible, standardised format. This will lower the cost of a gait assessment and may improve the consistency of the reporting. This paper describes the development of a software package that shows how clinical gait analysis data can be presented in alternative ways and can offer simplifications that may aid understanding and reduce costs. Three dimensional motion capture systems such as Vicon (Oxford Metrics, Oxford, UK) offers accurate, high resolution, recordings of human locomotion. These systems acquire, and display three dimensional motion data on patients whilst walking and are able to integrate analogue data to enable simultaneous acquisition of force plate, EMG and video data. The Vicon system has a specifically designed software package, Polygon (Oxford Metrics, Oxford, UK) to generate interactive multimedia reports from the captured 3D data. This software produces the graphical data used in clinical reports.

BACKGROUND Bipedal gait represents each person’s unique solution of how to get from one place to another. There is a natural variability to a person’s gait as motor tasks cannot be repeated identically from walking trial to walking trial. Therefore, intra

Reporting Clinical Gait Analysis Data

and inter-subject variability are natural elements of movement patterns associated with functional tasks [Manal et al, 2004]. This natural variation is an important consideration when analysing the gait of patients with walking disabilities. Diagnosis and rehabilitation of patients with locomotor disorders is increasingly supported by gait analysis data [Barton et al, 1995]. Cerebral palsy is a locomotor disorder that can prevent or inhibit walking and cause a lack of muscle coordination and spasms due to an injury to the brain prior to or shortly after birth. Children with cerebral palsy are the most commonly assessed patient group using gait analysis techniques. This is due the unpredictable nature of surgical outcomes and other forms of intervention intended to improve their gait [Gage, 1991]. Gait analysis data is usually reported as a time history of kinematic and kinetic variables expressed as a percentage of the gait cycle in the form of line graphs (see Figures 1- 4). Normal movement implies that the time history of the pattern lies within a range of values that are distributed about a time varying mean. That is, a normative range is established for comparison with a patient’s gait. A clinical gait report may contain over 50 graphs when kinematic and kinetic data of the pelvis, hip knee and ankle are presented in the 3 orthogonal planes. The clinical team visually assess deviations and interprets the patient’s gait by comparison with the normative data range [Manal et al, 2004]. The data from the

Figure 1. Sagittal plane motion of the knee

gait analysis is also compared with the data from the clinical examination. Many studies have recognised the challenging nature of analysing gait data and the need for simplification of the graphical output of the 3D motion capture systems (Chau, 1991a: Manal et al, 2004). A variety of approaches have been tried to aid the analysis of gait data including fuzzy logic, neural networks, artificial intelligence and multivariate statistical techniques have been described (Chau, 1991 a,b). Manal et al (2004) developed a type of thermometer scale for colour coding the magnitude and direction system of movement pattern deviations relative to normative kinetic and kinematic values. However, our approach differs from that previously described in gait studies as we have employed visualisation techniques. Spence (2001) states that the use of iconic displays can be an aid to the understanding of complex data. Card (1999) describes the techniques of visualisation as using vision to think. The software developed in this study therefore uses these principles to represent gait data.

METHOD Interrogation of the kinetic (forces, moments and powers) and kinematic (joint rotation angles) output graphs from a multi-media authoring tool such as Polygon (Oxford Metrics, Oxford, UK) can be automated using simple analytical techniques. This can save time in the analysis process if only the graphs showing out of range data are displayed and salient features highlighted. Graphs of kinematics and kinetics are assessed in terms of 3 properties: their pattern, range and timing (PRT). The pattern refers to the general shape of the graph and the number and height of specific peaks and troughs. The range refers to the total spread in the angle from minimum to maximum. The timing refers to where particular peaks, troughs or crossing points occur in the cycle.

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Therefore, software was developed as an aid to automating the analysis of the graphical output of the gait data. Visualization techniques were used to aid the interpretation of this data. Individual cerebral palsy patient graphs (see Figure 1, 2 and 3) were compared to normative curves to detect whether any of the following occurred: 1.out of range data at initial foot contact 2.out of range data greater than the mean ± 2SD 3.out of range peak and trough values 4.out of phase peak and trough values 5.discrepancies in the total range of the data 6.incorrect slope of the graph at a selected point in the cycle Each curve is stored as a dataset of 51 points, representing one gait cycle. The point in the gait cycle at which a given stance phase event occurs is read in from the system (i.e. initial contact IC, opposite foot off OFO, opposite foot contact OFC, toe-off, TO). Finding a value from the graph at one of these events is simply a matter of reading from the corresponding curve.

Figure 2. Delayed knee flexion

At each point, the mean value and standard deviation (SD) of the normative curve is known. An out of range value, at a given point in the cycle, is then taken to be one which is more than two standard deviations from the normative value. The ± 2 SD value was arbitrarily selected since there are currently no guidelines. An alternative choice could have been to select the mean ± 1SD. The patient data may show variations from the normal range (± 2 SD) such that the peak amplitude may be high or low or its phase may be early or late. The following paragraphs describe how amplitude and phase deviations are calculated. If the peak value is missing in the subject’s data, the method still gives an answer, although its meaning is uncertain. To find the phase difference of the peak value the subject’s curve is moved horizontally until it gives the best match to the normal curve. For example, in Figure 1 sliding the subject’s right knee curve to the left by about 5% lines up with the peak value in the normal curve. The program therefore calculates the root mean square (RMS) value of these differences in the patient data compared to the normative range. It is important to limit the part of the curve which is being compared to the peak of the normal curve. If too little of the curve is compared, the result may be affected by noise. It is therefore not sufficient to simply look for the maximum value of the curve. If too much of the curve is used, the Figure 3. Pelvic tilt example of rule 1

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result may be inaccurate. In principle the normal curves should be inspected for each joint and a suitable value chosen per curve and per peak. In practice two positions have been used on the normal curve, for example the toe off frame to initial contact frame. It is also important to limit the shift being applied to the subject’s curve. As a match could probably be found (erroneously) by shifting the curve half a cycle. The horizontal shift is therefore limited to a specified amount. This is read in from a data file and is currently 12 steps in either direction, although this is self selected. The peak value is found by taking the selected part of the subject’s curve and shifting it one time increment from the maximum shift to the left to the maximum shift to the right. At each step a difference from the normal curve is computed and the position with the least difference is taken to be the best match. The shift at the best match is the required phase shift of the peak. The difference at each step is computed as follows. The selected part of the subject’s curve has a number of points (N) and a time shift of a given number of time steps (Tshift). Suppose the part of the normal curve selected as having the peak runs from time step Tstart to Tend, where:

up. The total difference for the curve is given by summing over the N points of the curve:

Tend + 1 – Tstart = N

a) Reduction of graphs and simplified display: This involves interrogation of the graphs for out of range data and eliminating data within pre-set limits. Methods of highlighting out of range data are described below. b) Clinically relevant data from gait analysis: The kinetic and kinematics graphs are assessed for their pattern, range and timing. A rule based method is described for assessing the graphs. c) Clinical verification of gait data: Use of hypothesis buttons to observe the dynamic effects of the clinical examination data d) Icon Interface: Presentation of the graphical results is made using an icon interface.

Calling the subject curve at time step T, Subject [T] and the normal curve Normal [T], we are comparing Subject [T + Tshift] with Normal [T] from T = Tstart to T = Tend. The comparison at each point seeks to find the difference and these differences add up to give a total difference. The difference is therefore calculated at point T as: difference [T] = (Normal [T] – Subject [T + Tshift]) 2 Subtracting gives the difference and squaring makes the difference positive, so all the errors add

Total difference (Tshift) = Tshift = -12, .., 12

Tend

∑ difference

Tstart

 T ,  



This is then calculated for each value of Tshift and the minimum resulting total difference is chosen as the position of best fit. The value of Tshift then gives the amount by which the peak is early or late: Time difference = Tshift for minimum total difference Having found the required time shift the peak can then be moved vertically to line up with the normal curve. A similar method is used to find the minimum difference between the shifted subject curve and the normal curve as the subject curve is shifted vertically. Having identified peak and trough values, the range is simply the difference. This is compared to the value obtained for the mean curve. The slope of a graph was estimated by using the range from the adjacent trough to peak values. The 4 main components to the software are:

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These components are combined into the integrated display screen showing the outputs of the software (see section 3). They can be displayed collectively or individually for greater visualisation.

REDUCTION OF GRAPHS AND SIMPLIFIED DISPLAY There may be in excess of 50 graphs to analyse if kinematic (joint rotation angles) and kinetic (forces, moments and powers) and EMG data are interrogated and only limited parts of certain graphs may be clinically important. The software was developed to eliminate graphs that are within the normal range (mean ± 2 SD) so that the user can concentrate on the more important out of range graphs. The out of range features of the displayed graphs are highlighted using different techniques to assist the user. Figure 1 shows sagittal plane motion of the knee. The black lines are the mean value for normal knee motion ± 2 standard deviations (SD). The left leg data for the patient is shown by the red line and the right leg is shown by the green line. The patient data shows increased knee flexion in early stance, hyperextension in terminal stance and delayed flexion in swing. The out of range difference is shown by a purple horizontal line and is only flagged if the joint is out of range for more than 3 consecutive frames, so minor excursions are ignored. Along the top of Figure 1 there is a thermometer scale that shows an alternative method of encoding out of range values (Manal et al, 2004). This uses a scale from red (below) to blue (above) the normal range. The further the value is below (or above) the range, the brighter the red (or blue). The graphical output is supported by text (not shown in Figure 1) explaining what has been found. For example, the average deviation from the mean of left knee flexion = - 10º and the range is 15 º less than expected.

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The normal curve can be divided into the ranges of interest and similar analysis can be carried out on these ranges. If swing phase knee flexion (2nd peak) is of interest its peak value and whether it occurs early or late can be highlighted as shown in Figure 2. The horizontal position of the curve is offset by 8% (4 frames) to the right indicating a delay in knee flexion.

CLINICALLY RELEVANT DATA FROM GAIT ANALYSIS For gait data to be useful in the management of the patient both the clinical examination data and the gait data should correlate well. Clinicians report changes in joint angles or limb segment position following surgery as an outcome measure of the effectiveness of the procedure. This does not however, give any indication of functional improvements to the patient’s gait. In the previous section parts of graphs outside the normal range (mean ± 2SD) were highlighted. To facilitate analysis of pathological data a rule based method was developed using 20 clinical features reported by Gibbs (2000). Three of these have been used to illustrate the software. Many more rules exist and can also be included in the software. Rules applied: Rule 1 states that a patient with spasticity of the hip flexors may have increased pelvic tilt prior to toe off. It would be useful to highlight the pelvic tilt graph prior to toe off to see if this has increased and by how much. The rules can also be used to confirm the static examination findings and quantify the dynamic effects on the patient’s gait. This is called the “hypothesis” mode in the software. Several rules however may have the same gait deviation. Rule 2. Delayed maximum knee flexion in swing and a reduced rate of flexion prior to and after toe-off may be associated with spasticity of rectus femoris.

Reporting Clinical Gait Analysis Data

Rule 3 states that knee extension at toe off is associated with spasticity of rectus femoris. An illustration of the design of the software is shown in the following 2 examples. Example of Rule 1. Increased anterior tilt of the pelvis prior to toe off of the right leg may be due to spasticity of the hip flexors. Comparing the patient values with the normal data (mean + 2 SD), increased pelvic tilt has occurred (lordosis) if the average difference over the 4 frames is more than 2 SD (see Figure 3). The following features can be seen on the graph: 1) The horizontal brown line at the bottom shows the range over which the rule applies. 2) The brown arrow runs from the mean value of a normal curve to the right leg curve of the patient 3) Thickened areas of the right and left curves show the 4 frames prior to toe off for each curve 4) Text also appears stating the conclusions of the rule Example of Rule 2. Delayed maximum knee flexion in swing and a reduced rate of flexion prior to and after toe-off may be associated with spasticity of rectus femoris. The normal graph of sagittal plane knee flexion is shown by the black dotted line in figure 2 with a peak in mid-swing. A best fit can be found for the peak and hence the delay in the peak obtained. This reduced rate of flexion prior to and after toe off, corresponding to a slope on the graph at the appropriate point is interpreted as a reduced range of flexion. The figure shows the display that appears if this rule is activated. Various indicators on the graph show why it has been triggered: 1) The curves are highlighted at the peaks and an arrow pointing left shows the peak is delayed.

2) The range is indicated at the peak value and the frames around toe-off (at which the slope is considered reduced) is indicated. 3) A text message lists the possible problems connected with the rule. Optionally, text could be added to explain why the rule fired. A graph, similar to Figure 2 or 3, is displayed automatically for each rule that is initiated, when data is presented to the program. The annotations highlight the reason why the graph has been presented. The text explains the possible associated problems. In this way, the user is quickly directed to important parts of the data.

Clinical Verification of Gait Data If the clinical examination of the patient reveals that they have spasticity of the hip flexors then the gait analysis graphs would be expected to show an increase at the anterior tilt prior to toe off (Rule 1). If several rules have the same suggested problem as one of their outputs, there will be several parts of different graphs to be considered. A series of hypothesis buttons are available so that when the clinical examination reveals a musculo-skeletal problem, all the rules that have the associated problem as outputs are run. Those that are triggered produce graphs as shown in Figures 2 and 3. Those that are not triggered also produce graphs, but without the blue arrows. This allows the user to see all the rule-related data associated with this problem, with significant out of range data highlighted. Figure 4 shows a typical example. The hypothesis button for rectus femoris spasticity generates graphs for rules 1, 2 and 3. Rule 3 states that knee extension at toe off is associated with spasticity of rectus femoris. Rule 3 is activated and has the blue indicators on it. Rules 1 and 2 do not and only have the thickened line showing the point of the graph the rule is looking at and the event markers.

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Figure 4. Knee flexion only rule 3 is activated

Icon Interface The volume of data produced from motion capture systems make the analysis process very time consuming. Furthermore, clinicians are not always familiar with analysing this type of graphical data. For these reasons, an icon interface has been developed that presents the useful information to the user, extracted from the graphs. There are two features of the graphical outputs that characterise the gait cycle, the foot to ground events (IC and TO) and the pattern, range and timing (PRT) of the graphs. Rule 1 was typical of the former, where the critical issue was the pelvic tilt prior to toe off. Rule 2 is typical of the latter, referring to a delayed peak and a reduced range (Figure 2). Section 3a discussed general properties of individual graphs related to PRT. Using the normal curve, the peaks, ranges and troughs of each graph can be identified. Values can be calculated for the delay or excess height of a peak or trough and whether a range is too large or small. This analysis can be input to an icon interface as follows.

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Figure 5 shows a screen shot of the icon interface. The top row shows the temporo-spatial data. It could also include features such as the normalcy index Schutte et al (2000), the energy cost, or the asymmetry index, Herzog et al (1989). Below this, a table of joint angles at various critical events in the gait cycle and (PRT) data is shown. The four icons down the left hand side shows whether the pelvis, hip, knee, and ankle are being analysed and is highlighted in red (left) or green (right). The icons along the top show the four critical events in the gait cycle followed by specific PRT properties, such as initial value, first peak and range. Not all values will necessarily apply to all graphs. The display can reflect data for the sagittal (S), frontal (F) and transverse (T) views and angle, force, moment or power measurements. The rest of the table has arrows indicating out of range joint angles (degrees) at the given event, or an empty square if it is in range. The small triangles indicate that the feature described by this square relates to a rule which has run. Rules which run and fire bring up a solid triangle, those which run but do not fire bring up an outline triangle.

Reporting Clinical Gait Analysis Data

Figure 5. The icon interface

1) simplifying individual graphs in a meaningful way (section 3a). 2) using a rule based analysis to highlight significant features on the graphs (section 3b). 3) comparing clinical examination data with gait data, using the hypothesis mode, (section 3c). 4) presenting an icon interface, that summarises the relevant facts from each graph (section 3d). To be useful as a diagnostic aid, these four parts must act together in an integrated environment that also includes the clinical examination data. A screen shot of the complete interface is shown in Figure 6, as it would appear after the rectus femoris hypothesis button of section 3c has been selected. There are four areas:

Values are coded by colour (purple is high or early) and direction (up arrow for high, right arrow for early). A series of buttons at the bottom allow the user to select the view, sagittal (S), frontal (F) or transverse (T) as shown by the icons. The next button on this row allows the left or right leg to be selected. Graphs of joint rotation angles, ground reaction forces, joint moments and powers can be selected. Familiarity with the layout of the interface will allow the user to quickly scan it to decide which features are important.

RESULTS: THE INTEGRATED DISPLAY SCREEN Successful interpretation of motion capture data depends upon finding the pertinent information from a large amount of data and making a correct deduction. This paper has discussed four ways in which the computer can help identify relevant items of data by;

a) the icon display that allows an overview of the data, b) the button area that contains the various options needed to run the rule based analysis and check hypotheses etc., c) the graph area to display the current graphs including the EMG data if available and d) the text area for clinical examination data, rule output and single graph analysis reports. When new patient data is entered into the software the corresponding indicative arrows are added to the icon interface, showing the out of range values, for example, the pelvic tilt is increased by 10 degrees. The graph and text area will initially be empty. This gives the clinician an overview of the subject and indicates possible areas of interest. Different views or the other leg may be selected. The display can be switched from angle to force, moment or power as required. Selecting a square containing one of the joint icons adds the corresponding graph for that joint to the graph area. This also displays the corresponding analysis for the graph in the text area. All computed values for peaks, troughs, ranges and out of range sequences

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Figure 6. The integrated display screen

are listed. This allows a clinician to identify any parts of the graph which are out of range and their variation from normal. Selecting the rules button resets the display to its initial state, runs the rules and brings up graphs (as for example in Figure 6) for any rule (s) that is triggered. Messages derived from the output of any rule that is triggered are also listed, pointing towards possible areas of investigation. Selecting a hypothesis button resets the display and brings up all graphs related to that hypothesis. Visualisation techniques have been used in the integrated display in two ways; in the icon interface and in the interrogation of the graphical display. The integrated display therefore is designed to follow the users thought patterns and to assist in their analysis (Noble et al, 2005). The next stage in development of reporting gait analysis data is to achieve agreement amongst the gait analysis community regarding the establishment of a set of rules that can be programmed into an expert system. That is, features on the graphical display along with clinical examination (and imaging data) can be used to identify the cause and severity of the gait disability. This work therefore represents a starting point for much further development in the interpretation of gait data.

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CONCLUSION We have introduced some innovative ideas in an attempt to address some of the key issues that prevent gait analysis from being more widely used. Gait analysis is currently the only objective method for recording and understanding the complexity of human gait. However, other imaging modalities, such dynamic MRI and cine fluoroscopy may potentially be developed to overcome some of the major limitations of marker based video systems. In order for the current clinical gait analysis techniques to be more widely accepted the data produced needs to be simplified and automated so that the process can become more cost effective. Interpretation of gait analysis data for clinical use currently requires a multidisciplinary team. To make the process more cost effective a sophisticated internal expert system could be used. This requires substantial development using a rule-based system of the type described in this paper. Ultimately more patients will then be able to benefit from these techniques and clinicians will have a reliable tool to assess their work. Gait data is of necessity complex but simpler methods of display need to be developed. The use of icons as described in this paper is a potential solution to this problem. In future the gait analysis process should provide a treatment programme for the patient and ultimately, it is hoped that the

Reporting Clinical Gait Analysis Data

gait analysis process will be able to predict likely outcome of various interventions.

FUTURE RESEARCH DIRECTIONS The present camera based systems using reflective markers for 3D motion capture will be superseded by marker-less tracking systems. Hence some of the technical issues regarding the errors due to marker placement and movement artefacts will not be pertinent. Developments in computer modelling techniques will allow prediction of the effects of wearing an orthotic device, or how changes in muscle tone may affect gait. However, the synthesis and interpretation of the data produced by 3D motion capture systems is the biggest challenge facing clinical gait analysis. If the analysis of the data is automated and described in clinical terms this will make dynamic assessment of the locomotor system more clinically useful and therefore of greater benefit to patients as well as being more cost effective. This project takes cyclical data representing the pathological walking patterns of cerebral palsy children and seeks to find the clinically relevant parts of it by comparing these patterns with normative data. This could be greatly enhanced if diagnostic rules were available to help in identifying the parts of interest in the graphs such as limited or excessive ranges of joint motion. Interpretation of gait data would be quicker and simpler if a set of rules could be established that explain the clinically relevant features of the graphs. That is, with an extensive set of rules the use of artificial intelligence or an expert system could be employed to automatically interpret those rules. The software could run in either of two modes: to apply the rules and state the clinical interpretation, e.g. increased plantarflexion of the ankle occurs in the swing phase of gait if there is weakness in tibialis anterior, or the user could ask questions which the expert system would answer in the light of the rules and the data.

The software package currently looks at cyclical walking data and could in principle be extended to any cyclical function of the body, from heart rate data to the evaluation of a golf swing. The system needs both normal data and a set of rules for interpretation. For new applications, normal data could be collected and stored. Eventually it might be built into a shared database where all users contribute. Rules could be built up from experience in the community. A more exciting research direction would be to include diagnostics with the data (once an expert had studied the subject) and to apply machine learning (e.g. neural networks, case based reasoning) to the data to build up rules automatically. An extension of the system might be used in other medical applications. For example, following anaesthesia, a patient might do simple repetitive tests until the system verified they were fit to drive. Analysis of cyclical data is not limited to the medical field. Other areas include condition monitoring of rotating machinery. Rules relating to the machine’s condition could be written, or derived by the system and relevant performance highlighted by the system to alert the user as to a pending wear condition.

ACKNOWLEDGMENT The authors wish to acknowledge the contribution to this work made by Richard Longair, during his honours year computing project.

REFERENCES Barton, G., & Lees, A. (1997). An application of neural networks for distinguishing gait patterns on the basis of hip-knee joint angle diagrams. Gait & Posture, 5, 28–35. doi:10.1016/S09666362(96)01070-3

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Card, S. K., Mackinlay, J. D., & Shneiderman, B. (Eds.). (1999). Readings in Information Visualization: Using vision to think. San Francisco, Morgan Kaufmann.

Noble, R., & White, R. Visualisation of Gait Analysis Data. (2005, July). Paper presented at the International Conference on Information Visualisation, London.

Chau, T. (2001a). A review of analytical techniques for gait data. Part 1, Fuzzy, statistical and fractal methods. Gait & Posture, 13, 49–66. doi:10.1016/ S0966-6362(00)00094-1

Schutte, L. M., Narayanan, U., Stout, J. L., Selber, P., & Gage, J. R. (2000). An index for quantifying deviations from normal gait. Gait & Posture, 11, 25–31. doi:10.1016/S0966-6362(99)00047-8

Chau, T. (2001b). A review of analytical techniques for gait data. Part 2, Neural networks and wavelet methods. Gait & Posture, 13, 102–120. doi:10.1016/S0966-6362(00)00095-3

Spence, R. (2001).Information Visualization, Essex: ACM Press.

Davis, R. B., Ounpuu, S., Tyburski, D., & Gage, J. R. (1991). A gait analysis data collection and reduction technique. Human Movement Science, 10, 575–587. doi:10.1016/0167-9457(91)90046-Z

ADDITIONAL READING

Gage, J. R. (1991). Gait analysis in cerebral palsy. London, Mac Keith Press. Gibbs, S. (2000) Guide to the Interpretation of Gait Deviations, The Dundee Royal Infirmary, Scotland. Herzog, W., Nigg, B.M., Read, L.J., & Olsson, E. Asymmetries in Ground Reaction Force Patterns in Normal Human Gait.(1989). Medicine and Science in Sports and Exercise, 21,110 - 114. doi:10.1249/00005768-198902000-00020

Awad, E. M. (1996). Building Expert Systems: principles, procedures and applications. St. Paul, Minnesota, West Publishing Company. Darlington, K. (2000). The essence of expert systems. Essex, England, Prentice Hall. Goodwill, C. J. Chamberlaine M.A. & Evans C. (1997) Rehabilitation of the Physically Disabled Adult. London: Stanley Thornes. Gurney, K. (1997). An introduction to neural networks. London, UCL Press. Haupt, R. L. (1998). Practical Genetic Algorithms, New York, John Wiley & Sons.

Kadaba, M. P., Ramakrishnan, H. K., Wootten, M. E., Gainey, J., Gorton, G., & Cochran, G. V. B. (1989). Repeatability of kinematic, kinetic, and electromyographic data in normal adult gait. Journal of Orthopaedic Research, 7, 849–860. doi:10.1002/jor.1100070611

Inman, V. T., Ralston, H. J., & Todd, F. (1981) Human Walking. Baltimore, Williams & Wilkins.

Manal, K., & Stanhope, S. J. (2004). A novel method for displaying gait and clinical movement analysis data. Gait & Posture, 20, 222–226. doi:10.1016/j.gaitpost.2003.09.009

Kirtley, C. (2005) Clinical gait analysis: theory & practice Oxford, Churchill Livingstone.

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Inselberg, A. (1997). Multidimensional detective. Phoenix, Arizona, IEEE Symposium on Information Visualization, (InfoVis ‘97).

Michalewicz, Z., Schmidt, M., Michalewicz, M., & Chiriac, C. (2006). Adaptive Business Intelligence. New York, Springer Verlag.

Reporting Clinical Gait Analysis Data

Negnevitsky, M. (2002). Artificial Intelligence: A Guide to Intelligent Systems. New York, Addison Wesley.

Russell, S., & Norvig, P. (2002). Artificial Intelligence: A Modern Approach. Essex, England, Prentice Hall.

Nigg, B. M., & Herzog, W. (1999). Biomechanics of the musculo-skeletal system, Chichester, John Wiley & Sons.

Smidt, G. L. (1990). Gait in Rehabilitation. New York, Churchill Livingstone.

Perry, J. (1992) Gait Analysis. Normal and Pathological Function, Slack Inc. Petrovski, A., & McCall, J. (2005). (Ed.). Smart Problem Solving Environments for Medical Decision Support in H-G. Bayer et al Genetic and Evolutionary Computation Conference (GECCO) Proceedings of the Workshops on Genetic and Evolutionary Computation, Washington DC, ACM Press. Pirolli, P., & Rao, R. (1996). (Ed.). Table Lens as a tool for making sense of data. In Catarci, T., Costabilem, M. F., Levidaldi, S. & Santucci, G. Workshop on advanced visual interfaces. Washington DC, ACM. Robertson, D. G. E. (1997). Introduction to Biomechanics for Human Motion Analysis, Waterloo,Waterloo Biomechanics. Robertson, D. G. E., Caldwell, G. E., Hamill, J., Kamen, G., & Whittlesey, S. N. (2004). Research Methods for Biomechanics, Human Kinetics, Rose, J. & Gamble, J.G. (1994) Human Walking. Baltimore, Williams & Wilkins.

Soderberg, G. L. (1986). Kinesiology–Application to Pathological Motion. Baltimore,Williams & Wilkins. White, R., Agouris, I., Selbie, R. D., & Kirkpatrick, M. (1999). The variability of force platform data in normal and cerebral palsy gait. Clinical Biomechanics (Bristol, Avon), 14(3), 185–192. doi:10.1016/S0268-0033(99)80003-5 Whittle, M. (1996). Gait Analysis: An Introduction. Butterworth-Heinemann, Oxford. Williams, M., & Lissner, H. R. (1977). Biomechanics of Human Motion. Philidelphia, Saunders. Winter, D. A. (1990). The Biomechanics and Motor Control of Human Gaits. Waterloo, University of Waterloo Press. Zatsiorsky, V. M. (1998). Kinematics of Human Motion, Human Kinetics, Zajac, F.E. & Neptune, R.R. & Kautz, S.A.(2002). Biomechanics and muscle coordination of human walking Part I: Introduction to concepts, power transfer, dynamics and simulations. Gait & Posture, 16, 215–232.

This work was previously published in User Centered Design for Medical Visualization, edited by Feng Dong, Gheorghita Ghinea and Sherry Y. Chen, pp. 58-72, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.19

Variational Approach Based Image PreProcessing Techniques for Virtual Colonoscopy Dongqing Chen University of Louisville, USA Aly A. Farag University of Louisville, USA Robert L. Falk Jewish Hospital & St. Mary’s Healthcare, USA Gerald W. Dryden University of Louisville, USA

ABSTRACT Colorectal cancer includes cancer of the colon, rectum, anus and appendix. Since it is largely preventable, it is extremely important to detect and treat the colorectal cancer in the earliest stage. Virtual colonoscopy is an emerging screening technique for colon cancer. One component of virtual colonoscopy, image pre-processing, is important for colonic polyp detection/diagnosis, feature extraction and classification. This chapter

aims at an accurate and fast colon segmentation algorithm and a general variational-approach based framework for image pre-processing techniques, which include 3D colon isosurface generation and 3D centerline extraction for navigation. The proposed framework has been validated on 20 real CT Colonography (CTC) datasets. The average segmentation accuracy has achieved 96.06%, and it just takes about 5 minutes for a single CT scan of 512*512*440. All the 12 colonic polyps with sizes of 6 mm and above in the 20 clinical CTC datasets are found by this work.

DOI: 10.4018/978-1-60960-561-2.ch419

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

INTRODUCTION Colorectal cancer includes cancer of the colon, rectum, anus, and appendix. Colorectal cancer can develop from the mucosa located throughout the colon or rectum. Most colorectal cancers progress through a series of mutations which confer a growth advantage, leading to polyp formation and eventually an invasive tumor. In most cases, colorectal cancers develop slowly and normally take a period of several years to grow from the earliest lesion to an advanced cancer. Adenocarcinomas account for about 95 percent of colorectal cancers, arising from the intestinal epithelial cells that line the colon and rectum. It is the second leading cause of cancerrelated death and the third most common form of cancer in the United States (Abbruzzese, 2004). Colorectal cancer is largely preventable. Several screening tests, including the digital rectal exam, fecal occult blood test (via guaiac or fecal immunochemical test), flexible sigmoidoscopy, double-contrast barium enema, and colonoscopy are recommended for all people age 50 and over. Currently, optical colonoscopy is considered the gold standard for colorectal cancer screening. During optical colonoscopy, a thin flexible video endoscope is inserted into the patient’s rectum and advanced to the cecum. Inspection of the colon for polyps generally occurs during the withdrawal phase of the colonoscopy. Although a colonoscopy can detect more than 90% of colorectal cancers, it is invasive, sometimes uncomfortable, the preparation is inconvenient, and inability to the cecum results in an incomplete exam (Macari, 1999). On the other hand, computed tomographic colonography (CTC), also known as virtual colonoscopy (VC), is a computer-based alternative to optical colonoscopy. It has evolved rapidly over the past decade due to advances in manufacturing of high-resolution helical spiral computed tomography scanners. Ferrucci (2001) comprehensively analyzed the promise, polyp detection and politics

of colon cancer screening with VC. The accuracy (mainly the sensitivity and specificity of polyp detection) is comparable to the conventional colonoscopy for the significant size greater than 10 mm with few false-positive. VC has many advantages, including relative lack of invasiveness, lower incidence of complications and side effects, patient tolerability and preference (Juchems, 2005). VC is not intended to replace traditional optical colonoscopy (Summers, 2002; Zalis, 2005; Chen, 2008), but rather to complement it by providing an additional mechanism for providing CRC screening. Benefits of VC include visualization of neighboring structures outside the colon, visualization of difficult anatomical locations (i.e. behind flexures), the ability to bypass high grade stenoses, and providing an alternative to colonoscopy in those patients who either refuse optical colonoscopy or cannot tolerate it due to severe illness. The process for computer aided diagnosis (CAD) assistance for colorectal cancer screening involves: 1) colon segmentation, 2) colon isosurface generation and rendering, 3) 3D centerline extraction for navigation, 4) colonic polyp detection, 5) color coding for polyp candidate visualization, 6) features extraction, and 7) benign/ malignant polyp classification. Accurate and reliable colon segmentation are important, since any incorrect segmentation, for example, missing colonic segments, containing non-colon tissue (e.g. small bowel) or reconstructing colon wall of poor quality, impairs interpretation of 3D visualization. Moreover, inaccurate 3D colon segmentation and visualization diminish the perception of polyp detection, classification and the whole performance of CAD system. The 3D colon segmentation normally suffers from the following difficulties. First, CTC sometimes contains disconnected regions of the colon because collapsed segments result from different reasons such as colon spasm, insufficient distension, etc. Second, in clinical practice, oral contrast agent may be given to patients for CTC, however, oral contrast

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Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

in colon can cause leakage into small bowel. Third, opacified liquid is generated by ingesting iodine and barium of the oral contrast, and then some polyps may submerge under the liquid, which causes hard colonic polyp detection. Fourth, the segmentation accuracy and speed are greatly affected by the complicated colon structure and topology. The literature has introduced several sophisticated image segmentation procedures. Most are based on two concepts: thresholding and connectivity (Franazek, 2006). Tagged CT colonography uses thresholding for digital stool subtraction. Opacified liquid is generated by ingesting iodine and barium. When contrast enters the colon and tags residual fluid, electronic cleaning can occur. Electronic cleaning (Zalis, 2004; Chen, 2001) served as a very useful technique to balance CT values in fluid-filled lumen parts and others filled with air, and to remove the contrast fluid. However, some artifacts were generated which affected image interpretation. Yoshida and Nappi (2001) designed a CAD framework to detect and classify the colonic polyps by using shape index and curvedness. The first stage of their CAD system involved colon segmentation, consisting of two major steps: 1) anatomy based extraction and 2) colon based analysis. Recently, Franazek et al. (2006) proposed an entire framework for hybrid segmentation of colon tissue in CT colonoscopy. It achieved good accuracy for the segmentation results; however it consisted of eight different steps, so the average processing time was very long. For a single CT scan 512*512*400, it took 18 minutes on 1.8 GHz PC (even without I/O operations). This chapter aims at an accurate and fast colon segmentation algorithm and a general variational-approach based framework for image pre-processing techniques, which include 3D colon isosurface generation and 3D centerline extraction for navigation. A statistical model of regions is explicitly embedded into partial differential equations, which describes the evolution of the level sets. The probability density function for

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each region (bi-model in this work, colon tissue and non-colon tissue) is modeled by a Gaussian function with adaptive parameters. These parameters include the mean values and variances, which are estimated by the maximum likelihood estimation (MLE) method. The prior probabilities of two regions are automatically re-estimated during each iteration. Finally, the pixel candidate is classified into colon tissue or non-colon tissue by using Bayesian decision theory (Duda, 2001). The designed level set model depends on these density functions. The region information over the image is also taken into account. The proposed framework has been validated on 20 real CTC datasets. The average segmentation accuracy has achieved 96.06%, and it just takes about 5 minutes for a single CT scan of 512*512*440. The rest of the chapter is organized in the following manner: Section 2 presents the mathematical and theoretical background of curvature formulation, curvature based curve evolution, and variational calculus, Section 3 introduces variational methods for colon tissue segmentation, Section 4 discusses colon isosurface generation and 3D colon centerline extraction for navigation. The proposed framework has been validated in Section 5. Two future directions are introduced in Section 6. Section 7 concludes the book chapter.

MATHEMATICAL BACKGROUND Three Dimension Differential Geometry (Manfredo, 1976) Let p(x, y, z) denote a voxel on a 3D surface S, and (u, v) is an orthogonal basis on the plane tangent to S at p shown in Figure 1. An isosurface S with intensity level I in a 3D space R3 is given as S = {p(x, y, z) ∈ R3 | h(p) = I} In a small neighborhood around each point p, z can be expressed as a function of (x, y), say r(x, y). Then, the iso-surface S can be represented by denoting (x, y) as (u, v).

Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

Figure 1. 3D surface patch parameterized by (u, v) ∈ [0,1] × [0,1]

p(u,v) = {(u,v) ∈ R2 | h(u, v ,r(u, v)) = I}

Now, consider the distance ds between p: r(u, v) and q: r(u + du, v + dv) inside the neighborhood of p: r(u, v), and it can be expressed as         (ds )2 = dr • dr = (rudu + rvdv ) • (rudu + rvdv )       = ru • ru (du )2 + 2ru • rvdudv + rv • rv (dv )2 (1) If Equation (1) is rewritten as (ds)2 = Eu(du)2 + Fdudv + G(dv)2

(2)

  Let us define ru , rv the first order partial de   rivatives, ruu , ruv , rvv the second order partial derivatives of S at point p, the first fundamental   forms E, F and G can be expressed as: E = ru • ru     , F = ru • rv and G = rv • rv , where • is the dot product. The first fundamental forms denote the coefficients of the differential quadratic norm that gives the length of a differential arc on the surface. If we continue to consider the perpendicular distance ± between q: r (u + du, v +dv) and the tangential plane to the 3D surface S at p: r(u, v).    δ = (r (u + du, v + dv ) − r (u, v )) • n

(3)

 where, n is the normal vector of surface S at p. Ta k i n g t h e Ta y l o r e x p a n s i o n t o  r (u + du, v + dv ) , then     r (u + du, v + dv ) = r (u, v ) + ru (u, v )du + rv (u, v )dv   1  + ruu (du )2 + 2ruvdudv + rvv (dv )2 + ο(du, dv ) 2

(

)

 Substituting r (u + du, v + dv ) in Equation (3), we can get   δ = (ru (u, v )du + rv (u, v )dv    1  + ruu (du )2 + 2ruvdudv + rvv (dv )2 + ο(du, dv )) • n 2

(

)

(4)

  Since(ru , rv ) is the tangential vector of surface S, and if the residual o(du,dv) is ignored, Equation (4) is represented by δ=

   1  ruu (du )2 + 2ruvdudv + rvv (dv )2 • n 2

(

)

(5)

Similarly, the equation above can take the form δ=

1 L(du )2 + 2Mdudv + N (dv )2 2

(

)

(6)

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Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

The second fundamental forms L, M, and N     are expressed as: L = ruu • n , M = ruv • n , and   N = rvv • n . The second fundamental forms give the coefficients of a local tangent quadratic approximation to the surface.

Curvature Formulation Given the first fundamental forms E, F and G, and the second fundamental forms L, M and N of surface S, the maximum principal curvature κ1 and the minimum principal curvature κ2 are defined as the two roots λ1 and λ2, which satisfy the following identity: Eλ − L Fλ − M

Fλ − M = 0 Gλ − N

(7)

As a result, Gaussian curvature K and mean curvature H can be defined as: K = κ1κ2 = λ1λ2 =

H =

LN − M 2 EG − F 2

(8)

1 1 EN − 2FM + GL (κ + κ2 ) = (λ1 + λ2 ) = 2 1 2 2(EG − F 2 )

(9) Then the two principal curvatures can be computed as: κ1 = H + (H 2 − K )

(10)

κ2 = H − (H 2 − K )

(11)

The principal curvatures κ1 and κ2 represent the inverses of the maximum and minimum radii of curvature of all the curves passing through a given point of the surface. The mean curvature is just the average of κ1 and κ2. The Gaussian curvature is 1344

the product of κ1 and κ2which is thus independent of the orientation of the surface. In general, two approaches are used to compute the curvatures for isosurfaces from a volume dataset. The first category works directly on gray value information by exploiting the local differential structure of the image, while the second approach estimates curvatures from isosurfaces created by the Marching Cube algorithm (Lorensen, 1987). The geometry of the deformation (evolution) of C(X, t) is only dependent of the normal component of the velocity field, where C(X, t) denote a family of 2D closed curves, where t parameterizes the family and X = (x, y) parameterizes the curve. The general geometric deformation for curve C is given by (Sapiro, 2001; Chen, 2008).  ∂C (X , t ) = β(X , t )N (X , t ) ∂t

(12)

where, β(X,t) is the geometric velocity on the direction of the 2D normal. A well-known problem with the parametric representation of curves is that during evolution the points on the curve bunch close together at certain regions and they space out elsewhere. It increases error in numerical approximation of curves measures like tangent and curvature. This may lead to formation of discontinuities even explosion of in the curve.

Variational Calculus and Euler-Lagrange Formula Variational calculus is a field of mathematics that deals with functionals, as opposed to ordinary calculus which deals with functions. Such functionals can be formed as integrals involving an unknown function and its derivatives. The functional could attain a maximum or minimum value. The Gâteaux differential is often used to formalize the functional derivative, which is commonly adopted in the calculus of variations. Unlike other forms of derivatives, the Gâteaux

Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

differential of a function may be nonlinear. If the Gâteaux differential is linear and continuous, then the resulting linear operator is called the Gâteaux derivative (wiki web, 2008). Given a one dimensional function u(x):[0,1]→R, the basic problem is to minimize a given energy E, which satisfies the following equation: E (u(x )) =

∫

1 0

F (u, u ′)dx

with the given boundary conditions u(0) = a and u(1) = b, and F:R2→R. The solution to the above equation is the first order Euler-Lagrange formula (Sapiro, 2001). ∂F d  ∂F   = 0 −  ∂u dx  ∂u ′ 

(13)

For the 2D problem, give a 2D function u(x, y) and an energy E, where E (u(x , y )) =



∫∫ F u, ∂u ∂x, ∂u ∂y, ∂ u ∂x , ∂ u ∂y 2

2

2

2

  dxdy 

The Euler equation is written as (Sapiro, 2001) ∂F d  ∂F  d  ∂F  d 2  ∂F  d 2  ∂F   −  + =0 −  +  ∂u dx  ∂ux  dy  ∂uy  dx 2  ∂uxx  dy 2  ∂uyy 

(14)

VARATIONAL METHOD BASED IMAGE SEGMENTATION

model, which is supported by the explicit deformable models (e.g. snake (Kass, 1987), balloon force model (Cohen, 1991) and gradient vector flow (GVF) model (Xu, 1998)) and the implicit deformable models (e.g. geodesic active contour (Caselles, 1997), level sets (Osher & Sethian, 1988), and prior shapes based model (Abd El Munim, 2007). This section will briefly go over the snakes and GVF deformable models. The governing equations of deformable models are dependent on Lagrangian continuum mechanics formulations, in a volume dedicated to the Eulerian formulations, which are associated to level set methods introduced in Section 3.2. The deformable models mimic different generic behaviors of natural non-rigid materials in response to forces, e.g. continuity, smoothness, and elasticity (Osher, 2003).

Snake Snake (Kass, 1987) as a deformable model, is defined as a parametric contour embedded in the image plane (x, y). The contour is required to cover the desired object in the image I(x, y) and minimize a certain energy function defined as follows: E(C) = Einternal(C) + Eexternal(C)

(15)

where, C is the 2D contour (closed curve). The first term is defined as the internal energy which characterizes the deformation of the contour: 1

1 ω (p) | C p |2 +ω2 (p) | C pp |2 dp 2 ∫0 1

Traditional Active Contour Method for Image Segmentation

E int ernal (C ) =

Variational method based deformable modeling is a breakthrough of image processing, especially in image segmentation. A famous application of curve or surface evolution is the deformable

where, Cp and Cpp are the first and second order partial derivatives of closed curve C with parameterization point p, and p∈[0,1].

(

)

(16)

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Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

The non negative values ω1 and ω2 have the important rule in controlling the stretching and bending of the contour. The length of the contour is reduced by increasing ω1 which removes the ripples and loops. Big value of ω2 increases the bending and makes the contour smoother. The external energy represented by the second term in Equation 17. It is derived from the input image I (x, y), where the typical external energy functions are listed as follows: 2

Eexternal (x , y ) = − ∇I (x , y ) and, ∇ is the gradient operator. Or

(17)

2

Eexternal (x , y ) = − ∇(I (x , y ) * Gσ (x , y ))

(18)

where, G(x, y) is a 2D Gaussian function with the standard deviation σ. Using the variational calculus, the contour evolves in term of the following vector-based PDE. Ct =

where, t1 and t2 are two constant, and 0< t1, t2<1, t1+t2=1. Ct is the partial derivative of C with respective to t, governing the contour evolution, and ∇ is the gradient operator.

+r (uin+1, j + uin, j +1 + uin−1, j + uin, j −1 − 4uin, j ) + ci1, j ∆t (23)

GVF is a bi-directional external force field that moves active contours in highly concave regions (Xu, 1998). The GVF is the vector field V(X)= [u(X), v(X)]T, which minimizes the following energy function,

1346

+ | ∇v(X ) |2 )+ | ∇f (X ) |2 | V (X ) − ∇f (X ) |2 dX

To set up the iterative solution, let indices i, j and n correspond to x, y, and t, respectively, the numerical solutions to Equations 21 and 22 are given as follows: uin, j+1 = (1 − bi, j ∆t )uin, j

Gradient Vector Flow

2

+∆t(µ∇2u(X , t ) − (u(X ) − fx (X ))) | ∇f (X ) |2 (21)

+∆t(µ∇2v(X , t ) − (v(X ) − fy (X ))) | ∇f (X ) |2 (22)

(19)

∫∫ µ(| ∇u(X ) |

u(X , t + 1) = u(X , t )

v(X , t + 1) = v(X , t )

 ∂ 1  ∂ − (t1 | C p |2 ) − − (t2 | C pp |2 ) − ∇Eexternal 2   2  ∂P ∂P 

E (V ) =

where X=(x, y), μ is a regularization parameter, and f(X) is an edge map derived from the image I(X). For a binary volume, I(X) = -f(X). The interpretation of Equation 20 is that if |∇f(x)| is small, E(V) is dominated by the sum of squares of the partial derivatives of the vector field, yielding a slowly varying field. On the other hand, if |∇f(x)| is large, E(V) becomes dominated by the second term, and can be minimized when V(x)=∇f(x). This produces a vector field V(X) that is nearly equal to the gradient of the edge map ∇f(x) when it is large and slowly varying in homogeneous regions. V(X) can be computed iteratively by solving the following decoupled PDE in u and v (Xu, 1998).



(20)

vin, j+1 = (1 − bi, j ∆t )vin, j

+r (vin+1, j + vin, j +1 + vin−1, j + vin, j −1 − 4vin, j ) + ci2, j ∆t (24)

where, b(x, y) = fx(x, y)2 + fy(x, y)2, c1(x, y) = b(x, y) µ∆t fx(x, y), c2(x, y)2 = b(x, y) fy(x, y), and r = . ∆x ∆y The iterative process is guaranteed to converge when the Courant–Friedrichs–Lewy (CFL) step-

Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

size r≤1/4, given that b, c1, and c2 are bounded. So, the time-step ∆t on 2D case is: ∆t ≤

∆x ∆y 4µ

(25)

where ∆x and ∆y are the data spacing of a given dataset. Figure 2 shows one result of real colonic polyp segmentation using gradient vector flow active contour. The initial contour, the contour after five iterations, the contour after ten iterations and the final result are shown in Figure 2 (a), (b), (c) and (d). The evolving speed is fast, and it averagely takes 15 iterations to obtain the results on a computer with 4GB RAM and Intel Pentium 4 CPUs at 2.6 GHz.

Problems of Classical Deformable Models The deformable models efficiently apply to many applications. However they have some disadvantages:

1). It is almost impossible to handle the topology changes, for example, merging and splitting; 2). It is difficult to avoid self-intersection of the parametric curve or surface; 3). It is impractical to choose a very small value of time interval; and 4). Initialization needs to be near the steady state solution which represents a big problem in 3D. Figure 2(e) to 2(h) shows two examples of selfintersection of the traditional active contour in (e) and (g), respectively, when GVF is used to segment the two colonic polyps. The final segmentation results are given in (f) and (h), respectively. From the results, the self-intersections are indicated by the red contours.

Level Sets Method Level sets representation was proposed (Osher and Sethian 1988; Sethian 1989) to overcome the problems of the classical deformable models. As mentioned before, the topology change problem,

Figure 2. GVF based active contour for colonic polyp segmentation. (a) initial contour, (b) after 5 iterations, (c) after 10 iterations, and (d) final result. Two colonic polyps in (e) and (g), while the segmentation results using GVF active model are shown in (f) and (h)

1347

Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

for example, merging or splitting, is almost impossible to be handled by the conventional explicit deformable models. However, it is done naturally by implicit level sets, and the demo is as shown in Figure 3. The surface function evolves with time and then the evolution front is always represented as the zero level, the following equation can be written as a general description: Φ(X,t)=0

(26)

Taking derivative on both sides of the above equation, it leads to:

∂Φ(X , t ) ∂Φ(X , t ) ∂X + • = 0 ∂t ∂X ∂t Φt + ∇Φ(X , t ) •V = 0 where, ∇Φ and V represent the gradient of © and the velocity field, respectively. The velocity vector which can be set in terms of the tangent and normal vectors as   V = VTT +VN N resulting in:   Φt + ∇Φ(X , t ) • (VTT +VN N ) = 0

∂Φ(X , t ) = 0 ∂t

 Then substituting for ∇Φ =| ∇Φ | N gives the following:

In terms of chain rule, the above equation can be written as follows.

Φt + | ∇Φ(X , t ) | VN = 0

Figure 3. Topology changes, for example, merging and splitting, are dealt naturally with the implicit representation. This figure shows two curves during the merge procedure: the initial (a), intermediate (b) and final (c) positions. 3D models are represented on the first row and 2D curves on the second row

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Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

Letting VN=F, the basic level sets function is given as follows. Φt + F | ∇Φ(X , t ) |= 0

(27)

Equation 27 can be interpreted as follows. If Φt is written as: Φt =

∂Φ(X , t ) Φ(X , t + ∆t ) − Φ(X , t ) = ∂t ∆t

then it follows Φ(X , t + ∆t ) − Φ(X , t ) + F | ∇Φ(X , t ) |= 0 ∆t Multiplying ∆t on both sides, we can get Φ(X , t + ∆t ) − Φ(X , t ) + ∆t • F | ∇Φ(X , t ) |= 0

By switching the second and third terms to the right hand side, the above equation becomes as follows. Φ(X , t + ∆t ) = Φ(X , t ) − F • ∆t | ∇Φ(X , t ) | (28) If F>0, the original curve shrinks, while it expands when F<0. And the curve remains unchanged, if F=0. Normally, F=±1-εκ, where κ is the curvature. ε is the parameter controlling the bending of the curve. If the curve is very sharp (high degree bending), ε normally takes a small value, for example, 0.01, 0.001, etc. Otherwise, ε needs to be assigned a relatively big value to the curve with low degree bending in order to keep the smooth evolution. The numerical algorithm implementation for Level Sets is summarized as follows.

1) Initialize using signed distance function (SDF) (1) Detect the edge points of the original figure using Canny detector (Canny, 1986); (2) Compute the Euclidean distances between a given point and all the edge points, to find the minimum distance dmin, if dmin > 0, the given point is located inside the region, otherwise (3) Belong to the outside region. 2) Control the curve or surface evolution using Equation 26; 3) Find and mark all the points with Ф > 0; 4) Mark all the contour points by applying Canny detection; 5) During each iteration, re-initialization; and go back to Step 2, until iterations stops.

Adaptive Level Sets Technique A segmented image consists of homogeneous regions characterized by statistical properties. For the bi-model system, we use Gaussian distributions for the colon (foreground) and other tissue (background). If we only know the general knowledge of the bi-model system, maximum-likelihood estimation (MLE) is classical in statistics to estimate the unknown probabilities and probability densities (Duda, 2001). For the Gaussian distribution, the unknown mean μ and variance σ are estimated by MLE as: 1  µ= n

n

1  ∑ x k and σ 2 = n k =1

n

∑ (x k =1

k

 − µ)2

where, x is the sample, and n is the number of samples. For each class i (i=1, 2) in this work, in accord to the estimation method in (Vese & Chan, 2002), the mean μi, variance σi, and the priori probability πi are updated during each iteration as follows.

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Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

µi =

∫ H (Φ )I (x )dx ∫ H (Φ )dx i

α

i

α

2 i

σ

∫H =

πi =

α

(Φi )(µi − I (x ))2 dx

∫

∫H

α

H α (Φi )dx

(Φi )dx

2

∑∫ H i =1

(29)

α





(30)

(31)

(Φi )dx

where, H α (•) is the Heaviside step function as a smoothed differentiable version of the unit step function. I(x) is the input image. The Bayesian decision theory (Duda, 2001) is a fundamental statistical approach to quantify the tradeoffs between classification decision using probability and the cost. We let ω denote the state of nature in this work, with ω= ω1 for colon tissue and ω= ω2 for noncolon tissue. Since it is unpredictable, ω is considered to be a variable, which must be probabilistically described. We assume the priori probability π1 that next pixel candidate is colon, and some prior probability π2 which is non-colon tissue

2

∑π i=1

i

= 1.

In seems logical to use the decision rule: decide ω1 if π1 >π2; Otherwise, decide ω2. Finally, the classification decision at pixel x is based on the Bayesian criteria as follows. i * (x ) = arg(max(πi pi (I (x )))) i =1,2

(32)

For further details, please refer to our work in (Abd El Munim 2004; Chen, 2008). An example of simple level sets function is the signed distance function (SDF) to the curve. However, its initialization needs not to be close to the desired solution, and one level sets func-

1350

tion could only represent two phases or segments in the image. Compared with the simple level sets function, the adaptive level-sets function represents boundaries with more complicated topologies, for example, triple junction. In the piecewise constant case, it only needs log2(n) level sets functions for n phases. Moreover, under the piecewise smooth case, only two level sets functions are sufficient to represent any partition. In this work, only one level-sets function has been used for colon tissue segmentation. This function was successfully applied for multi-modal (3 or more tissues) images, which could be found in (Abd El Munim, 2004).

COLON ISOSURFACE GENERATION & VARATIONAL SKELEON EXTRACTION 3D Colon Isosurface Generation Due to the existence of oral contrast, which generates opacified liquid, then three layers: air-liquid, air-mucosa (colon inner wall), and liquid-mucosa, after colon segmentation by using adaptive level sets method, sometimes there are very few liquid dots inside the colon tissue, we apply 3D median filter to remove these tiny sparkles and 3D Gaussian filter for smoothness. The 3D colon object is reconstructed by searching its isosurface using Marching Cubes, and it is visualized by surface rendering. The Marching Cubes algorithm (Lorensen, 1987) was proposed to create a constant density surface from a 3D array of data, especially the medical datasets. The main idea of the Marching Cubes is summarized as follows: 1) creation of a triangular mesh which will approximate the isosurface, and 2) calculation of the normals to the surface at each vertex of the triangle. The Marching Cubes Algorithm Implementation is summarized as follows:

Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

1) Read four slices into memory; 2) Create a cube from four neighbors on one slice and four neighbors on the next slice; 3) Calculate an index for the cube; 4) Look up the lists of edges from a pre-created table; 5) Find the surface intersection visa linear interpolation; 6) Calculate a unit normal at each cube vertex and interpolate a normal to each triangle vertex; 7) Output the triangle vertices and vertex normals. This procedure is accomplished by Visualization ToolKit (VTK) 5.0 under Windows XP. The VTK is an open source and freely available software system for 3D computer graphics and medical image visualization (http://www.vtk.org).

3D Colon Centerline Extraction The 3D centerline, also known as curve skeleton CS, is considered as the optimal flying path for navigation inside the colon lumen. All the 3D centerlines in this work are generated by using the previous work in (Hassouna 2007, 2008) based on the gradient vector flow (GVF) (Xu, 1998) algorithm. This book chapter assumes that there only exists a single 3D centerline/flying path, which connects between the cecum and rectum inside the colonic lumen. Consider the minimum-cost path problem that finds the path C(s):[0,∞)→Rn that minimizes the cumulative travel cost from a starting point A to some destination X. If the cost U is only a function of the location X in the image domain, the cost function is called isotropic, and the minimum cumulative cost at X is defined as X

T (X ) = min ∫ U (C (s ))ds A

(33)

The path that gives the minimum integral is the minimum cost path. The solution of Equation 33 satisfies the solution of a nonlinear partial differential equation known as the EikonalEquation 34, where F(X)=1/U(X), and T(X) is the time at which the front crosses X. |∇T(X)|F(X)=1.0

(34)

Let A and B be medial voxels. Assume that A is a point source Ps that transmits a high speed front as given by Equation 9, where λ(X) is a medialness function that distinguishes medial voxels from others and α controls the curvature of the front at medial voxels. F(X)=eαλ(X)

(35)

Because x is intrinsic to the image, only X cannot change the speed of the front of the propagated wave from the point source Ps. By multiplying by α, the speed varies and hence the curvature. Since the GVF does not form medial surfaces in 3D, we propose the following medialness function as given by Equation 36, where the magnitude of the GVF goes to zero at medial voxels. λ(X)=max(|V(X)|q)-|V(X)|q

(36)

where 0
(37)

1351

Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

The proposed curve skeleton extraction framework can be summarized as follows: 1) Construct the distance transform D(x); 2) Compute the point source Ps and Identify extreme nodes; 3) Construct the new GVF based medial function; 4) Propagate an α-front from Ps and solve for a new distance field D(x); 5) Extract those curve skeletons that originate from extreme nodes and end at Ps.

VALIDATION, RESULT AND DISCUSSION This book chapter validates the proposed framework on 20 CTC datasets. One has been provided by the 3DR Inc., Louisville, KY, and the remaining 19 CTC data were received from the Virtual Colonoscopy Center at the Walter Reed Army Medical Center, Washington, DC, U.S.A. The patients underwent standard 24-hour colonic preparation by oral administration of 90 ml of sodium phosphate and 10 mg of bisacodyl; then consumed 500 ml of barium (2.1 percent by weight) for solid-stool tagging and 120 ml of Gastrogra_n to opacify luminal fluid (Pickhardt, 2003). A four-channel or eight-channel CT scanner was either GE LightSpeed or LightSpeed Ultra. The CT protocol included 1.25 mm to 2.5 mm collimation, 15 mm/second table speed, and 100 mAs and 120 kVp scanner settings. Each dataset contains 400~500 slices. The spatial resolution for each dataset is 1.0×1.0×1.0mm3. All the experiments have been carried out on a computer with 4GB RAM and Intel Pentium 4 CPUs at 2.6 GHz. The software programs are developed by using Visual C++ under Microsoft Windows XP, and Visualization Toolkit (VTK) 5.0 as Visualization libraries.

Colon Tissue Segmentation An original CTC slice as shown in Figure 4(a) typically consists of colon lumen including opacified liquid-filled part and air-filled part, small intestine, and some other tissues, for example: bones having the similar image intensity with liquid part. Firstly, the opacified liquid is removed by using a simple thresholding method to equalize image intensity inside the colon as shown in Figure 4(c). During this process, most opacified liquid is roughly removed, and iso-dense tissues such as boney structures on the same slice are removed as well. However, threshold subtraction does not affect colon segmentation, since we manually implant the initial seeds totally inside the colon region, which guarantees the segmentation results. Results following 10, 20 and 30 iterations of level sets evolutions after seed implantation are shown in Figure 4 (c), (d), (e), and (f), respectively. The final result of zero level sets convergence to the lumen-air boundaries is shown in (g). During the iterations, every candidate pixel is automatically classified as either colon associated or background based on its probability density function (PDF) by using Bayesian decision criteria. After each iteration, the average values and variances for both foreground and background are estimated by using the MLE. The iteration procedure stops when the level sets curve converges with the lumen-air boundaries, which means that the total number of newly classified colon tissue pixels does not change. In order to evaluate the segmentation accuracy, one CTC data set containing 461 slices was manually segmented under expert guidance. Then, the accuracy η is calculated by computing the overlap between the results by manual and algorithm segmentations using Equation (38) (Yao, 2004). η=

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Sa ∩ S m Sa ∪ S m



(38)

Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

Figure 4. Segmentation results of colon in tagged CTC data: (a) A CTC slice containing opacified liquid-filled part and air-filled part, (b) original CTC slice with oral contrast agent, (c) manual seed initialization inside colon after thresholding, intermediate results after (d) 10, (e) 20, (f) 30 iterations, and (g) final result

where, and denote the results by manual and algorithm segmentation, respectively. Due to the space limitation, only 3 examples are listed in Figure 5(a) to (f). The manual segmentation results are shown on the first row, while automated segmentation by the proposed algorithm is listed on the second row. The comparisons below demonstrate that the proposed method works well on these CTC slices. This is verified by the accuracy curve (only represents the first 30 slices) in Figure 5(g), which shows the overlap ranging from 94.5% up to almost 97%. The maximum and minimum overlaps are given in Table 1. The average accuracy of total 461 slices dataset has achieved 96.063%±0.56%. Figure 6 shows how the 3D segmentation algorithm works. Figure 6 (a) and (b) show the early stage results just after 20 and 40 iterations, respectively. From the segmented results, it is

clearly observed that most of the parts have not reached the desired lumen air-tissue boundaries, especially in (a), some of the colon parts are not connected due to the insufficient iterations. Compared with those in (a) and (b), the result in (c) shows that most of the colon parts have achieved the boundaries. Figure 6 (d) shows nice final results with smooth colon surface and clear haustral folders.

Table 1. Average overlap and its standard deviation (Std) Overlap

Maximum

Minimum

Average

STD

96.99%

94.48%

96.06%

±0.56%

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Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

Figure 5. Comparison results by manual (a), (b), and (c), and algorithm segmentation (d), (e), and (f). Accuracy curve (g) of calculating overlaps of the segmentation results with the average at 96.06%±0.56%

Figure 6. An Example of 3D colon segmentation by the proposed algorithm after (a) 20, (b) 40, (c) 60 iterations, and (d) final result

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Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

3D Colon Isosurface Generation and Centerline Extraction After colon segmentation, all the isosurfaces are generated by using the Marching Cubes algorithm. All the isosurfaces are triangulated to generate mesh surfaces in order to calculate the 3D centerlines for navigation. Table 2 shows the volume size, vertices number and centerline execution time for each dataset. It is found that all the vertices numbers range from 2.5 Million to about 5.5 Million. All the centerlines are generated and superimposed for demonstration as shown in Figure 7. For each dataset, it takes from 6 to 12 minutes to load in the segmented data, generate and render the 3D colon object, and extract 3D Table 2. Volume size, vertices number and execution time

1*

Volume Size

Number of Vertices

Execution Time

394*305*541

5,048,836

9 min 57 sec

2

512*512*432

3,262,143

7 min 6 sec

3

512*512*497

4,568,371

8 min 16 sec

4

512*512*431

5,395,522

12 min 35 sec

5

512*512*496

3,438,940

9 min 10 sec

6

512*512*399

5,438,468

12 min 50 sec

7

512*512*437

3,272,356

7 min 10 sec

8

512*512*413

4,551,929

9 min 49 sec

9

512*512*428

2,928,778

7 min 14 sec

10

512*512*438

2,502,288

6 min 29 sec

11

512*512*468

6,319,156

13 min 9 sec

12

512 *512*387

4,893,086

9 min 37 sec

13

512*512*432

2,988,630

7 min 37 sec

14

512*512*405

3,694,700

8 min 08 sec

15

512*512*430

2,877,740

6 min 56 sec

16

512*512*380

3,902,101

7 min 30 sec

17

512*512*417

4,893,901

11 min 31 sec

18

512*512*461

5,724,714

11 min 38 sec

19

512*512*470

3,060,831

8 min 2 sec

20

512*512*484

3,618,593

7 min 26 sec

centerline, depending on the presence of complicated shapes and larger numbers of vertices. We have compared the results by the proposed 3D colon segmentation with those by 3D region growing (Gonzalez, 2003). From three locally zoomed-in results in Figure 8(a), the colon surface generated by the proposed method has been improved greatly. A significant colonic polyp is defined as one larger than 5 mm in diameter (Kreeger 2002). The polyps no less than 6 mm are only focused. For the 12 colonic polyps with sizes of 6 mm and above in the 20 clinical CTC datasets, they are all found by the proposed framework, and compared with the reported ground truth. Two examples in Figure 8 show that this work can find and identify colonic polyps. The first one is located in the descending segment (c), and is Figure 7. 20 colon objects and their centerline

(* Provided by 3DR Inc. Louisville, KY, USA).

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Figure 8. Comparison results by 3D region growing (a) and the proposed method on the right (b). All the three local surface regions have been improved. (c) polyp 1: size-9 mm in descending segment, (d) found during 3D navigation, (e) polyp 2: size-6 mm submerged under the opacified liquid in the rectum segment, (f) found during 3D navigation

successfully found during both 3D supine and prone navigation (d). Moreover, even the second polyp submerged under the opacified liquid in the rectum segment (e), it is identified by the proposed method and visualized during 3D virtual navigation (f).

FUTURE RESEARCH DIRECTIONS Given the fact that colorectal cancer is a largely preventable disease through routine detection and removal of adenomatous polyps, colon cancer

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prevention has now moved to the forefront. Most colorectal cancers begin as polyps. As polyps enlarge, they are more likely to develop into a cancer, which has the ability to disseminate through the body. It is clearly demonstrated that the percentage of polyps containing cancer increases from 1% to 30%~50%, when comparing polyps ranging from 5mm to 22mm. Therefore, polyp size is considered to be one of the most important factors in distinguishing benign polyps from cancerous ones. After detection and classification, accurate polyp segmentation could provide an easy way to measure polyp size, enhancing and improving the detection of significant lesions. CAD systems for colorectal neoplasia depend on image preprocessing of clinical colon datasets, colonic polyp detection and visualization, and polyp features extraction. The next step will involve potential classification of benign or malignant polyps. We are currently investigating whether polyp architectural features could be analyzed to generate reliable classification into benign or malignant lesion. We must conduct prospective trials to validate the clinical applicability of these methods. The corresponding evaluation of the images will occur within a two hour period, and the results will be placed into five envelopes, corresponding with five recognized segments of the colon. An additional reading by a second study radiologist (blinded to the results of the initial reading) will be obtained prior to proceeding with the follow up colonoscopy, and will be combined with the initial radiologist’s reading, to prevent possible missing of colonic lesions. After the scanning and reading process is complete, the subject will be transported to the endoscopy area in University of Louisville Hospital for the colonoscopy. After reaching the cecum or small intestine, the scope will be slowly withdrawn in the usual manner. At this point, each of the segmental results from the CT colonoscopy will be revealed after the endoscopist has completed the pertinent segment. If a polyp was discovered on the CTC, a thorough

Variational Approach Based Image Pre-Processing Techniques for Virtual Colonoscopy

search of the segment will be initiated until the lesion has been found. Characteristics of each polyp will be recorded (size, as determined by measuring catheter), segmental location, growth characteristic (sessile vs pedunculated), and histology will be matched up for each polyp.

CONCLUSION This book chapter describes a general variational framework of image pre-processing techniques which have been proposed as the basis for a colorectal neoplasia CAD system. It relies upon adaptive level sets based colon segmentation, colon isosurface generation, and 3D centerline extraction. The proposed framework has been successfully validated on fifteen real CTC datasets. Future work will involve combining the proposed framework with other colonic polyp detection and visualization techniques recently developed by our group.

REFERENCES Abbruzzese, J. L., Pollock, R. E., Ajani, J. A., Curley, S. A., Janjan, N. A., & Lynch, P. M. (2004). Gastrointestinal Cancer. Verlag, Berlin, Heidelberg: Springer. Abd El Munim, H. H., & Farag, A. A. (2004). Adaptive segmentation of multimodal 3d data using robust level set techniques. In Proceedings of the 8th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI’04) (pp.143-150), MICCAI Press. Abd El Munim, H. H., & Farag, A. A. (2007). Curve/surface representation and evolution using vector level sets with application to the shapebased segmentation problem. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 945–958. doi:10.1109/TPAMI.2007.1100

Canny, J. F. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698. doi:10.1109/TPAMI.1986.4767851 Caselles, V., Kimmel, R., & Sapiro, G. (1997). Geodesic active contours. International Journal of Computer Vision, 22(1), 61–79. doi:10.1023/A:1007979827043 Chen, D., Abd El Munim, H. H., Farag, A. A., Falk, R. L., & Dryden, G. W. (2008) Adaptive Level Sets Based Framework for Segmentation of Colon Tissue in CT Colonography. In H.Yoshida (Ed.), Workshop on Computational &Visualization Challegences in the New Era of Virtual Colonoscopy with MICCAI’08 (pp.108-115), MICCAI Press. Chen, D., Farag, A. A., Hassouna, M. S., Falk, R. L., & Dryden, G. W. (2008). Curvature flow based 3D surface evolution model for polyp detection and visualization in CT colonography. In T. Smolinski (Ed.), Computational Intelligence in Biomedicine and Bioinformatics (pp. 201-222), Verlag, Berlin: Springer Publishing. Chen, D., Liang, Z., Wax, M., Li, L., Li, B., & Kaufman, A. (2000). A novel approach to extract colon lumen from CT images for virtual colonoscopy. IEEE Transactions on Medical Imaging, 19(12), 1220–1226. doi:10.1109/42.897814 Cohen, L. D. (1991). On active contour models and balloons. Computer Vision, Graphics, and Image Understanding, 53(2), 211–218. Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern Recognition, JohnWiley & Sons, Inc. 2nd Edition Ferrucci, J. T. (2001). Colon Cancer Screening with Virtual Colonoscopy: Promise, Polyps, Politics. AJR. American Journal of Roentgenology, 177(11), 975–988.

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Franazek, M., Summers, R., Pickhardt, P., & Choi, J. (2006). Hybrid segmentation of colon filled with air and opacified fluid for ct colonography. IEEE Transactions on Medical Imaging, 25(3), 358–368. doi:10.1109/TMI.2005.863836

Malladi, R., Sethian, J. A., & Vemuri, B. C. (1995). Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(2), 158–175. doi:10.1109/34.368173

Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2003). Digital Image Processing Using MATLAB, Prentice Hall

Manfredo, D. C. Differential Geometry of Curves and Surfaces (1976). Prentice Hall

Hassouna, M. S., & Farag, A. A. (2007), On the extraction of curve skeletons using gradient vector flow. In Eleventh IEEE International Conference on Computer Vision (pp. 694-699). IEEE Press. Hassouna, M. S., & Farag, A. A. (in press). Variational Curve Skeletons Using Gradient Vector Flow. IEEE Transactions on Pattern Analysis and Machine Intelligence. Juchems, M. S., Ehmann, J., Brambs, H. J., & Aschoff, A. J. (2005). A retrospective evaluation of patient acceptance of computed tomography colonography (“virtual colonoscopy”) in comparison with conventional colonoscopy in an average risk screening population. Acta Radiologica, 46(7), 664–670. doi:10.1080/02841850500216277 Kass, M., Witkin, A., & Terzopolos, D. (1987). Snakes: Active contour models. International Journal of Computer Vision, 1(4), 321–331. doi:10.1007/BF00133570 Kreeger, K., Dachille, F., Wax, M., & Kaufman, A. E. (2002). Covering all clinically significant areas of the colon surface in virtual colonoscopy. In SPIE medical imaging, (pp. 198–206). SPIE Press. Lorensen, W. E., & Cline, H. E. (1987). Marching cubes: A high resolution 3d surface construction algorithm. Computer Graphics, 21(4), 163–169. doi:10.1145/37402.37422 Macari, M., Berman, P., Dicker, M., Milano, A., & Megibow, A. J. (1999). Usefulness of CT colonography in patients with incomplete colonoscopy. Journal of Roentgenol, 173, 561–564.

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Osher, S., & Paragios, N. (2003). Geometric Level Set Methods in Imaging, Vision and Graphics, Springer. Osher, S., & Sethian, J. A. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on hamilton-jacobi formulations. Journal of Computational Physics, 79(1), 12–49. doi:10.1016/0021-9991(88)90002-2 Pickhardt, P. J., Choi, J. R., Hwang, I., Butler, J. A., Puckett, M. L., & Hildebrandt, H. A. (2003). Computed tomographic virtual colonoscopy to screen for colorectal neoplasia in asymptomatic adults. The New England Journal of Medicine, 349(23), 2191–2200. doi:10.1056/NEJMoa031618 Sapiro, G. R. (2001). Geometric Partial Differential Equations and Image Processing. New York, NY: Cambridge University Press. Sethian, J. A. (1989). Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. Cambridge University Press. Summers, R. M., Jerebko, A. K., Malley, J. D., & Johnson, C. D. (2002). Colonic polyps: Complementary role of computer-aided detection in CT colonography. Radiology, 225(2), 391–398. doi:10.1148/radiol.2252011619 Tikhomirov, V. M. (2001), “Gâteaux variation”, Encyclopaedia of Mathematics, Kluwer Academic Publishers. Retrieved April 9, 2009, from http:// en.wikipedia.org/ wiki/G% C3% A2 teaux_ derivative

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Vese, L., & Chan, T. (2002). A multiphase level set framework for image segmentation using the mumford and shah model. International Journal of Computer Vision, 50(3), 271–293. doi:10.1023/A:1020874308076

Yoshida, H., & Nappi, J. (2001). Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE Transactions on Medical Imaging, 20(12), 1261–1274. doi:10.1109/42.974921

Xu, C., & Prince, J. L. (1998). Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing, 7(3), 359–369. doi:10.1109/83.661186

Zalis, M., Perumpillichira, J., & Hahn, P. (2004). Digital subtraction bowel cleaning for CT colonography using morphological and linear filteration methods. IEEE Transactions on Medical Imaging, 23(2), 1335–1443. doi:10.1109/TMI.2004.826050

Yao, J., Miller, M., Franazek, M., & Summers, R. M. (2004). Colonic polyp segmentation in CT colonography-based on fuzzy clustering and deformable models. IEEE Transactions on Medical Imaging, 23, 1344–1352. doi:10.1109/ TMI.2004.826941

Zalis, M. E., Barish, M. A., Choi, J. R., Dachman, A. H., Fenlon, H. M., & Feeucci, J. T. (2005). CT colonography reporting and data system: A consensus proposal. Radiology, 236(1), 3–9. doi:10.1148/radiol.2361041926

This work was previously published in Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques, edited by Fabio A. Gonzalez and Eduardo Romero, pp. 78-101, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.20

Verification of Uncurated Protein Annotations Francisco M. Couto Universidade de Lisboa, Portugal

Evelyn Camon European Bioinformatics Institute, UK

Mário J. Silva Universidade de Lisboa, Portugal

Rolf Apweiler European Bioinformatics Institute, UK

Vivian Lee European Bioinformatics Institute, UK

Harald Kirsch European Bioinformatics Institute, UK

Emily Dimmer European Bioinformatics Institute, UK

Dietrich Rebholz-Schuhmann European Bioinformatics Institute, UK

ABSTRACT Molecular Biology research projects produced vast amounts of data, part of which has been preserved in a variety of public databases. However, a large portion of the data contains a significant number of errors and therefore requires careful verification by curators, a painful and costly task, before being reliable enough to derive valid conclusions from it. On the other hand, research in biomedical information retrieval and information extraction are nowadays delivering Text Mining solutions DOI: 10.4018/978-1-60960-561-2.ch420

that can support curators to improve the efficiency of their work to deliver better data resources. Over the past decades, automatic text processing systems have successfully exploited biomedical scientific literature to reduce the researchers’ efforts to keep up to date, but many of these systems still rely on domain knowledge that is integrated manually leading to unnecessary overheads and restrictions in its use. A more efficient approach would acquire the domain knowledge automatically from publicly available biological sources, such as BioOntologies, rather than using manually inserted domain knowledge. An example of this approach is GOAnnotator, a tool that assists the

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Verification of Uncurated Protein Annotations

verification of uncurated protein annotations. It provided correct evidence text at 93% precision to the curators and thus achieved promising results. GOAnnotator was implemented as a web tool that is freely available at http://xldb.di.fc.ul.pt/ rebil/tools/goa/.

INTRODUCTION A large portion of publicly available data provided in biomedical databases is still incomplete and incoherent (Devos and Valencia, 2001). This means that most of the data has to be handled with care and further validated by curators before we can use it to automatically draw valid conclusions from it. However, biomedical curators are overwhelmed by the amount of information that is published every day and are unable to verify all the data available. As a consequence, curators have verified only a small fraction of the available data. Moreover, this fraction tends to be even smaller given that the rate of data being produced is higher than the rate of data that curators are able to verify. In this scenario, tools that could make the curators’ task more efficient are much required. Biomedical information retrieval and extraction solutions are well established to provide support to curators by reducing the amount of information they have to seek manually. Such tools automatically identify evidence from the text that substantiates the data that curators need to verify. The evidence can, for example, be pieces of text published in BioLiterature (a shorter designation for the biological and biomedical scientific literature) describing experimental results supporting the data. As part of this process, it is not mandatory that the tools deliver high accuracy to be effective, since it is the task of the curators to verify the evidence given by the tool to ensure data quality. The main advantage of integrated text mining solutions lies in the fact that curators save time by filtering the retrieved evidence texts in comparison to scanning the full amount

of available information. If the IT solution in addition provides the data in conjunction with the evidence supporting the data and if the solutions enable the curators to decide on their relevance and accuracy, it would surely make the task of curators more effective and efficient. A real working scenario is given in the GOA (GO Annotation) project. The main objective of GOA is to provide high-quality GO (Gene Ontology) annotations to proteins that are kept in the UniProt Knowledgebase (Apweiler et al., 2004; Camon et al., 2004; GO-Consortium, 2004). Manual GO annotation produces high-quality and detailed GO term assignments (i.e. high granularity), but tends to be slow. As a result, currently less than 3% of UniProtKb has been confirmed by manual curation. For better coverage, the GOA team integrates uncurated GO annotations deduced from automatic mappings between UniProtKb and other manually curated databases (e.g. Enzyme Commission numbers or InterPro domains). Although these assignments have high accuracy, the GOA curators still have to verify them by extracting experimental results from peer-reviewed papers, which is time-consuming. This motivated the development of GOAnnotator, a tool for assisting the GO annotation of UniProtKb entries by linking the GO terms present in the uncurated annotations with evidence text automatically extracted from the documents linked to UniProtKb entries. The remainder of this chapter starts by giving an overview on relevant research in biomedical information retrieval and extraction and exposing which contribution GOAnnotator brings to the current state-of-the-art. Afterwards, the chapter describes the main concepts of GOAnnotator and discusses its outcome and its main limitations, as well as proposals how the limitations can be solved in the future. Subsequently, the chapter provides insight into how approaches like GOAnnotator can change the way curators verify their data in the future, making the delivered data more complete and more reliable. For achieving this, the chapter

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will describe the issues that need to be addressed leading into valuable future research opportunities. The chapter ends by giving an overview of what was discussed and by presenting the concluding remarks.

BACKGROUND A large amount of the information discovered in Molecular Biology has been mainly published in BioLiterature. However, analysing and identifying information in a large collection of unstructured texts is a painful and hard task, even to an expert.

BioLiterature The notion of BioLiterature includes any type of scientific text related to Molecular Biology. The text is mainly available in the following formats: 1. Statement: a stretch of text that describes the protein similar to comment or description field of a database entry (e.g., in UniProtKb). 2. Abstract: a short summary of a scientific document. 3. Full-text: the full-text of a scientific document including scattered text such as figure labels and footnotes. Statements contain more syntactic and semantic errors than abstracts, since they are not peer-reviewed, but they are directly linked to the facts stored in the databases. The main advantage of using statements or abstracts is the brief and succinct format on which the information is expressed. However, usually this brief description is insufficient to draw a solid conclusion, since the authors have to skip some important details given the text size constraint. These details can only be found in the full-text of a document, which contains a complete description of the results obtained. For example, important details are sometimes only present in figure labels.

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The full-text document contains the complete information for the presented research. Unfortunately, full-text documents are not yet readily available, since up to now publishers’ licensing agreements restrict access to most of the full-text content. In addition, the formats and structures of the full-text document tend to vary according to the needs of the journal in where the document has been published leading to unnecessary complications in processing the documents. Furthermore, processing of full-text documents in comparison to document summaries also increases the complexity for text-mining solutions, leading to the result that the availability of more information is not necessarily all-beneficial to text-mining tools. Some of the information may even induce the production of novel errors, for example, the value of a fact reported in the Results section has to be interpreted differently in comparison to a fact that has been reported in the Related Work section. Therefore, the use of full-text will also create several problems regarding the quality of information extracted (Shah et al., 2004). Access to BioLiterature is mainly achieved through the PubMed Web portal, which in 2008 delivered more than 17 million1 references to biomedical documents dating from the 1950s (Wheeler et al., 2003). It is the main task of PubMed to make it easier for the general public to find scientific results in the BioLiterature. The users can search for citations by author name, journal title or keywords. PubMed also includes links to full-text documents and other related resources. More than 96% of the citations available through PubMed are from MEDLINE, a large repository of citations to the BioLiterature indexed with NLM’s controlled vocabulary, the Medical Subject Headings (MeSH). Besides the bibliographic citations, PubMed also provides the abstracts of most documents, especially of the newer ones. The articles from 1950 through 1965 are in OLDMEDLINE, which contains approximately 1.7 million citations (Demsey et al., 2003). These old citations do not contain the

Verification of Uncurated Protein Annotations

abstract and certain fields may contain outdated or erroneous data. MEDLINE was designed to deal with printed documents, but nowadays many journals provide the electronic version of their documents. Moreover, some of them became Open Access Publications, which means that their documents are freely available and can be processed and displayed without any restrictions. These documents have been exploited by tools, such as Google Scholar2, Scirus3 or EBSCO4, which can be used to search and locate scientific documents. One of the major free digital archives of life sciences full-text documents is PMC (PubMed Central), which aims at preserving and maintaining access to this new generation of electronic documents. Presently, PMC includes over 1.3 million documents. The availability of full-text documents offers new opportunities for research to text-mining tool providers, who were up to now often restricted to analysing only the abstracts of scientific documents.

Text Mining An approach to improve the access to the knowledge published in BioLiterature is to use Text Mining, which aims at automatically retrieving and extracting knowledge from natural language text (Hearst, 1999). The application of text-mining tools to BioLiterature started just a few years ago (Andrade and Bork, 2000). Since then, the interest in the topic has been steadily increasing, motivated by the vast amount of documents that curators have to read to update biological databases, or simply to help researchers keep up with progress in a specific area (Couto and Silva, 2006). Thus, Bioinformatics tools are increasingly using Text Mining to collect more information about the concepts they analyse. Text-mining tools have mainly been used to identify: 1. Entities, such as genes, proteins and cellular components;

2. Relationships, such as protein localisation or protein interactions; 3. Events, such as experimental methods used to discover protein interactions. One of the most important applications of text-mining tools is the automatic annotation of genes and proteins. A gene or protein annotation consists of a pair composed by the gene or protein and a description of its biological role. The biological role is often a concept from a BioOntology, which organises and describes biological concepts and their relationships. Using a BioOntology to annotate genes or proteins avoids ambiguous statements that are domain specific and context dependent. The best-known example is the gene ontology (GO) that is a well-established structured vocabulary that has been successfully designed and applied for gene annotation of different species (GO-Consortium, 2004). To understand the activity of a gene or protein, it is also important to know the biological entities that interact with it. Thus, the annotation of a gene or protein also involves identifying interacting chemical substances, drugs, genes and proteins. Very early on the text-mining system AbXtract was developed to identify keywords from MEDLINE abstracts and to score their relevance for a protein family (Andrade and Valencia, 1998). Other systems have been developed in recent years to identify GO terms from the text: MeKE identified potential GO terms based on sequence alignment (Chiang and Yu, 2003) and BioIE uses syntactic dependencies to select GO terms from the text (Kim and Park, 2004). Furthermore, other approaches use IT solutions where GO terminology is applied as a dictionary (Koike et al., 2005; Müller et al., 2004; Pérez et al., 2004; RebholzSchuhmann et al., 2007; Jaeger et al., 2008). However, none of these systems have been integrated into the GOA curation process. Moreover, only Perez et al. make use of the hierarchical structure of GO to measure the distance between two terms based on the number of edges that separate them

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(path length). However, incorrect annotations can be caused by neglecting the semantics of the hierarchical structure of GO causes. For example, if a large number of GO terms from the leaves or the deep levels in GO are assigned then the system tends to generate over-predictions, and if general GO terms from the top levels of the hierarchy are produced then the annotations tend to be useless. The performance of state-of-the-art textmining tools for automatic annotation of genes or proteins is still not acceptable by curators, since gene or protein annotation is more subjective and requires more expertise than simply finding relevant documents and recognising biological entities in texts. To improve their performance, state-of-the-art text-mining tools use domain knowledge manually inserted by curators (Yeh et al., 2003). This knowledge consists of rules inferred from patterns identified in the text, or on predefined sets of previously annotated texts. The integration of domain knowledge improves overall the precision of predictions, but it cannot be easily extended to work on other domains and demands an extra effort to keep the knowledge updated as BioLiterature evolves. The selection of pieces of text that mention a GO term was assessed as part of the first BioCreAtIvE competition (Hirschman et al., 2005). This competition enabled the assessment of different text mining approaches and their ability to assist curators. The system with the best precision predicted 41 annotations, but 27 were not correct, which lead to a 35% precision (14 out of 41) (Chiang and Yu, 2004). The main problem is that the terms denoting GO concepts were never designed to support text-mining solutions. Terms in the vocabulary are ambiguous and could not be easily deciphered by automatic processing and sometimes even by humans (Camon et al., 2005). Without improvements to the precision, such automatic extractions are unhelpful to curators. This reflects the importance of designing more efficient tools to aid in the curation effort.

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GOAnnotator uses publicly available biological data sources as domain knowledge for improving the retrieval and extraction tasks requiring minimal human intervention, since it avoids the complexities of creating rules and patterns covering all possible cases or creating training sets that are too specific to be extended to new domains (Shatkay and Feldman, 2003). Apart from avoiding direct human intervention, automatically collected domain knowledge is usually more extensive than manually generated domain knowledge and does not become outdated as easily, if the originating public databases can be automatically tracked for updates as they evolve. The most important data resource used by GOAnnotator is GO.

GENE ONTOLOGY (GO) The GO project is one of the long-lasting and successful resource building efforts in Molecular Biology constructing a BioOntology of broad scope and wide applicability (Bada et al., 2004). GO provides a structured controlled vocabulary denoting the diversity of biological roles of genes and proteins in a species-independent way (GOConsortium, 2004). GO comprised 24,397 distinct terms in September 2007. Since the activity or function of a protein can be defined at different levels, GO is composed of three different aspects: molecular function, biological process and cellular component. Each protein has elementary molecular functions that normally are independent of the environment, such as catalytic or binding activities. Sets of proteins interact and are involved in cellular processes, such as metabolism, signal transduction or RNA processing. Proteins can act in different cellular localisations, such as the nucleus or membrane. GO organises the concepts as a DAG (Directed Acyclic Graph), one for each aspect. Each node of the graph represents a concept, and the edges represent the links between concepts (see example in Figure 1). Links can represent two relationship

Verification of Uncurated Protein Annotations

Figure 1. Sub-graph of GO

types: is-a and part-of. The content of GO is still evolving dynamically: its content changes every month with the publication of a new release. Any user can request modifications to GO, which is maintained by a group of curators who add, remove and change terms and their relationships in response to modification requests. This prevents GO from becoming outdated and from providing incorrect information. (Figure 1)

GOAnnotator GOAnnotator is a tool for assisting the GO annotation of UniProtKb entries by linking the GO terms present in the uncurated annotations with evidence text automatically extracted from the documents linked to UniProtKb entries. Initially, the curator provides a UniProtKb accession number to GOAnnotator. GOAnnotator follows the bibliographic links found in the UniProtKb database and retrieves the documents. Additional documents are retrieved from the GeneRIF database or curators can add any other text (Mitchell et al., 2003). GOAnnotator prioritizes the documents according to the extracted GO terms from the text and their similarity to the GO terms present in the protein uncurated annotations (see Figure 2). Any extracted GO term is an indication for the topic of the document, which is also taken from the UniProtKb entry. The curator uses the topic as a hint to potential GO annotation. The extraction of GO terms is based on FiGO, a method used for the BioCreAtIvE competition (Couto et al., 2005). FiGO receives a piece of text and returns the GO terms that were detected in the given text. To each selected GO term, FiGO

Figure 2. List of documents related with a given protein. The list is sorted by the most similar term extracted from each document. The curator can use the Extract option to see the extracted terms together with the evidence text. By default GOAnnotator uses only the abstract, but the curator can use the AddText option to replace or insert text.

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assigns a confidence value that represents the terms’ likelihood of being mentioned in the text. The confidence value is the ratio of two parameters. The first parameter is called local evidence context and is used to measure the likelihood that words in the text are part of a given GO term. The second parameter is a correction parameter, which increases the confidence value when the words detected in the text are infrequent in GO. In BioCreAtIvE, FiGO predicted 673 annotations but 615 were not correct, which lead to a 8.6% precision (58 of 673). The low performance is the result of the finding that GO terms are not explicitly used in the literature to annotate proteins. As a consequence, contextual information has to be taken into consideration to extract the correct annotations. It is advantageous to this process if additional domain knowledge can be integrated. For instance, experiments performed as part of the KDD2002 Cup challenge (bio-text task) have shown that the statistical text classification systems generate low performance if they refrain from integrating domain knowledge into their reasoning (Yeh et al., 2003). A more effective approach has been used by GOAnnotator. Here the necessary domain knowledge has been acquired from publicly available resources and significantly improves the precision to the annotation process for yet uncurated resources. GO terms are considered to be similar if they are in the same lineage or if they share a common parent in the GO hierarchy. To calculate a similarity value between two GO terms, we decided to implement a semantic similarity measure. Research on Information Theory proposed many semantic similarity measures. Some of them calculate maximum likelihood estimates for each concept using the corpora, and then calculate the similarity between probability distributions. Semantic similarity measures take into consideration a combination of parameters linked to the structure of an ontology as well as information content based on statistical data from corpora (Rada et al., 1989). The information content of a

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concept is inversely proportional to its frequency in the corpora. Concepts that are frequent in the corpora have low information content. In case of GO the corpora used to derive the statistical information is the annotations provided by GO, i.e. the information content of a GO term is calculated based on the number of proteins annotated to it. For example, GO terms annotated to most of the proteins normally provide little semantic information. Many semantic similarity measures applied to ontologies have been developed. We implemented a measure based on the ratio between the information content of the most informative common ancestor and the information content of both concepts (Lin, 1998). Recent studies have explored on the effectiveness of semantic similarity measures over the GO (Couto et al., 2006; Lord et al., 2003; Gaudan et al., 2008). The results have shown that GO similarity is correlated with sequence and family similarity, i.e., they demonstrated the feasibility of using semantic similarity measures in a biological setting. GOAnnotator displays a table for each uncurated annotation with the GO terms that were extracted from a document and were similar to the GO term present in the uncurated annotation (see Figure 3). The sentences from which the GO terms were extracted are also displayed. Words that have contributed to the extraction of the GO terms are highlighted. GOAnnotator gives the curators the opportunity to manipulate the confidence and similarity thresholds to modify the number of predictions.

RESULTS The GOA team curated 66 proteins out of a list of 1,953 uncurated UniProtKb/SwissProt proteins that GOAnnotator analysed. This 66 proteins were selected by applying a similarity threshold of 40% and a confidence threshold of 50%. In other words, GOAnnotator identified evidence texts

Verification of Uncurated Protein Annotations

Figure 3. Identified and proposed GO terms. For every selected uncurated database resource, GOAnnotator identifies and proposes GO terms extracted from the selected document and displays the sentence that contains the evidence for the GO term. If the curator accepts the evidence as a correct annotation then he can use the Add option to store the annotation together with the document reference, the evidence codes and any comments in the database.

with at least 40% similarity and 50% confidence to selected GO terms for all these 66 proteins. These proteins had 80 uncurated annotations that GOAnnotator analysed, providing 89 similar annotations and the respective evidence text from 118 MEDLINE abstracts. The 80 uncurated annotations included 78 terms from different domains of GO (see Table 1). After curating the 89 evidence texts, GOA curators found that 83 were valid to substantiate 77 distinct uncurated annotations (see Table 2), i.e. 93% precision. Table 3 shows that 78% (65 out of 83) of the correct evidence texts confirmed the uncurated annotations, i.e. the extracted annotation and the Table 1. Distribution of the GO terms from the selected uncurated annotations through the different aspects of GO GO Aspect

GO Terms

uncurated annotation contained the same GO identifier. In cases where the evidence text was correct, it did not always contain exactly any of the known variations of the extracted GO term. In the other cases the extracted GO term was similar: in 15 cases the extracted GO term was in the same lineage of the GO term in the uncurated annotation; in 3 cases the extracted GO term was in a different lineage, but both terms were similar (share a parent). In general, we can expect GOAnnotator to confirm the uncurated annotation using the findings from the scientific literature, but it is also obvious that GOAnnotator can propose new GO terms.

Table 2. Evaluation of the evidence text substantiating uncurated annotations provided by the GOAnnotator Evidence Evaluation

Extracted Annotations

molecular function

54

biological process

18

correct

cellular component

6

incorrect

6

total

78

total

89

83

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Table 3. Comparison between the extracted GO terms with correct evidence text and the GO terms from the uncurated annotations GO Terms

Extracted Annotations exact

65

same lineage

15

different lineage

3

total

83

Examples GOAnnotator provided correct evidence for the uncurated annotation of the protein “Human Complement factor B precursor” (P00751) with the term “complement activation, alternative pathway” (GO:0006957). The evidence is the following sentence from the document with the PubMed identifier 8225386: “The human complement factor B is a centrally important component of the alternative pathway activation of the complement system.” GOAnnotator provided a correct evidence for the uncurated annotation of the protein “U4/U6 small nuclear ribonucleoprotein Prp3” (O43395) with the term “nuclear mRNA splicing, via spliceosome” (GO:0000398). From the evidence the tool extracted the child term “regulation of nuclear mRNA splicing, via spliceosome” (GO:0048024). The evidence is the following sentence from the document with the PubMed identifier 9328476: “Nuclear RNA splicing occurs in an RNA-protein complex, termed the spliceosome.” However, this sentence does not provide enough evidence on its own, the curator had to analyze other parts of the document to draw a conclusion. GOAnnotator provided a correct evidence for the uncurated annotation of the protein “Agmatinase” (Q9BSE5) with the term “agmatinase activity” (GO:0008783). From the evidence the tool extracted the term “arginase activity” (GO:0004053) that shares a common parent. The evidence was provided by the following sentence

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from the document with the PubMed identifier 11804860: “Residues required for binding of Mn(2+) at the active site in bacterial agmatinase and other members of the arginase superfamily are fully conserved in human agmatinase.” However, the annotation only received a NAS (Non-traceable author statement) evidence code, as the sentence does not provide direct experimental evidence of arginase activity. Papers containing direct experimental evidence for the function/subcellular location of a protein are more valuable to GO curators. GOAnnotator provided a correct evidence for the uncurated annotation of the protein “3’-5’ exonuclease ERI1” (Q8IV48) with the term “exonuclease activity” (GO:0004527). The evidence is the following sentence from the document with the PubMed identifier 14536070: “Using RNA affinity purification, we identified a second protein, designated 3’hExo, which contains a SAP and a 3’ exonuclease domain and binds the same sequence.” However, the term “exonuclease activity” is too high level, and a more precise annotation should be “3’-5’ exonuclease activity” (GO:0008408).

Discussion Researchers need more than facts, they need the source from which the facts derive (RebholzSchuhmann et al., 2005). GOAnnotator provides not only facts but also their evidence, since it links existing annotations to scientific literature. GOAnnotator uses text-mining methods to extract GO terms from scientific papers and provides this information together with a GO term from an uncurated annotation. In general, we can expect GOAnnotator to confirm the uncurated annotation using the findings from the scientific literature, but it is obvious as well that GOAnnotator can propose new GO terms. In both cases, the curator profits from the integration of both approaches into a single interface. By comparing both results, the curator gets convenient support to take a decision

Verification of Uncurated Protein Annotations

for a curation item based on the evidence from the different data resources. GOAnnotator provided correct evidence text at 93% precision, and in 78% of these cases the GO term present in the uncurated annotation was confirmed. These results were obtained for a small subset of the total number of uncurated annotations, but it represents already a significant set for curators. Notice that manual GO annotation covers less than 3% of UniProtKb. Over time, proteins tend to be annotated with more accurate uncurated terms and bibliography. Thus, the percentage of uncurated proteins satisfying the 40% similarity and 50% confidence thresholds will grow, and therefore make GOAnnotator even more effective. Sometimes, the displayed sentence from the abstract of a document did not contain enough information for the curators to evaluate an evidence text with sufficient confidence. Apart from the association between a protein and a GO term, the curator needs additional information, such as the type of experiments performed and the species from which the protein originates. Unfortunately, quite often this information is only available in the full text of the scientific publication. GOAnnotator can automatically retrieve the abstracts, but in the case of the full text the curator has to copy and paste the text into the GOAnnotator interface, which only works for a limited number of documents. BioRAT solves this problem by retrieving full text documents from the Internet (Corney et al., 2004). In addition, the list of documents cited in the UniProtKb database was not sufficient for the curation process. In most cases, the curators found additional sources of information in PubMed. In the future, GOAnnotator should be able to automatically query PubMed using the protein’s names to provide a more complete list of documents. GOAnnotator ensures high accuracy, since all GO terms that did not have similar GO terms in the uncurated annotations were rejected. Using this 40% similarity threshold may filter out meaningful potential annotations that are not similar

to known curated annotations. However, without this restriction the results returned by the text mining method would contain too much noise to be of any use to curators, as it was demonstrated in the BioCreAtIvE competition. GOAnnotator meets the GOA team’s need for tools with high precision in preference to those with high recall, and explains the strong restriction for the similarity of two GO terms: only those that were from the same lineage or had a shared parent were accepted. Thus, GOAnnotator not only predicted the exact uncurated annotation but also more specific GO annotations, which was of strong interest to the curators. MeKE selected a significant number of general terms from the GO hierarchy (Chiang and Yu, 2003). Others distinguished between gene and family names to deal with general terms (Koike et al., 2005). GOAnnotator takes advantage of uncurated annotations to avoid general terms by extracting only similar terms, i.e. popular proteins tend to be annotated to specific terms and therefore GOAnnotator will also extract specific annotations to them. The applied text-mining method FiGO was designed for recognizing terms and not for extracting annotations, i.e. sometimes the GO term is correctly extracted but is irrelevant to the actual protein of interest. The method also generated mispredictions in the instances where all the words of a GO term appeared in disparate locations of a sentence or in an unfortunate order. Improvements can result from the incorporation of better syntactical analysis into the identification of GO terms similar to the techniques used by BioIE (Kim and Park, 2004). For example, a reduction of the window size of FiGO or the identification of noun phrases can further increase precision. In the future, GOAnnotator can also use other type of text-mining methods that prove to be more efficient for extracting annotations.

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FUTURE TRENDS Recent publications on the improvements by using information retrieval and extraction tools are promising and encourage the research community to make an effort to improve their quality and expand their scope. However, the performance of most tools is still highly dependent on domain knowledge provided through experts. Integration of the expert knowledge is time-consuming and imposes limitations whenever services have to be extended to other domains with different user requirements. On the other side, the domain of molecular biology draws profits from publicly available databases containing a significant amount of information. In our opinion, better use of such domain knowledge and automatic integration of the data from these biological information resources will be the key to develop more efficient tools and will thus contribute to their wider acceptance among curators in the biological domain. Apart from avoiding direct human intervention, automatic collection of domain relevant information is usually more comprehensive than any manually generated representation of domain knowledge and does not become outdated, since public databases can be automatically tracked for updates as they evolve. Domain knowledge is only available thanks to the research community efforts in developing accurate and valuable data resources and by making them publicly available. These data resources are continually being updated with more information. However, they are still too incomplete, too inconsistent and/or too morpho-syntactically inflexible to efficiently be used by automatic tools. For example, GO started by adding generic terms and simple relationships to provide a complete coverage of the Molecular Biology domain. Thus, the main limitation of GO is the lack of specific terms that, for example, represent precise biochemical reactions like EC numbers. However, as different research communities understand the importance of adding their domain knowledge to

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GO, it will expand its coverage and improve its interoperability with other data sources. While BioOntologies are traditionally used mainly for annotation purposes, their ultimate goal should be to accurately represent the domain knowledge so as to allow automated reasoning and support knowledge extraction. The establishment of guiding principles, as in OBO, to guide the development of new BioOntologies is a step in this direction, by promoting formality, enforcing orthogonality, and proposing a common syntax that facilitates mapping between BioOntologies. This not only improves the quality of individual BioOntologies, but also enables a more effective use of them by information retrieval and extraction tools.

CONCLUSION This chapter introduced the biomedical information retrieval and extraction research topics and how their solutions can help curators to improve the effectiveness and efficiency of their tasks. It gives an overview on three important aspects of these research topics: BioLiterature, Text Mining and BioOntologies. It has presented GOAnnotator, a system that automatically identifies evidence text in literature for GO annotation of UniProtKb/SwissProt proteins. GOAnnotator provided evidence text at high precision (93%, 66 sample proteins) taking advantage of existing uncurated annotations and the GO hierarchy. GOAnnotator assists the curation process by allowing fast verification of uncurated annotations from evidence texts, which can also be the source for novel annotations. This document discusses the results obtained by GOAnnotator pointing out its main limitations. This document ends by providing insight into the issues that need to be addressed in this research area and represent good future research opportunities. The approach developed here constitutes a small and relatively early contribution to the advance of biomedical information retrieval and

Verification of Uncurated Protein Annotations

extraction topics, but the main idea presented here seems promising and the results encourage further study. Still, despite all the limitations presented here, many relevant biological discoveries in the future will certainly result from an efficient exploitation of the existing and newly generated data by tools like GOAnnotator.

ACKNOWLEDGMENT

Camon, E., Barrell, D., Dimmer, E., Lee, V., Magrane, M., & Maslen, J. (2005). An evaluation of GO annotation retrieval for BioCreAtIvE and GOA. BMC Bioinformatics, 6(Suppl 1), S17. doi:10.1186/1471-2105-6-S1-S17 Camon, E., Magrane, M., Barrell, D., Lee, V., Dimmer, E., & Maslen, J. (2004). The Gene Ontology Annotations (GOA) database: sharing knowledge in UniProt with Gene Ontology. Nucleic Acids Research, 32, 262–266. doi:10.1093/nar/gkh021

This work was supported by the Marie Curie Training Sites scheme of the European Commission’s Quality of Life Programme (Contract no. QLRI-1999-50595) and by FCT through the Multiannual Funding Programme.

Chiang, J., & Yu, H. (2003). MeKE: discovering the functions of gene products from biomedical literature via sentence alignment. Bioinformatics (Oxford, England), 19(11), 1417–1422. doi:10.1093/bioinformatics/btg160

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Corney, D., Buxton, B., Langdon, W., & Jones, D. (2004). BioRAT: Extracting biological information from full-length papers. Bioinformatics (Oxford, England), 20(17), 3206–3213. doi:10.1093/ bioinformatics/bth386 Couto, F., & Silva, M. (2006). Mining the BioLiterature: towards automatic annotation of genes and proteins. In Advanced Data Mining Techonologies in Bioinformatics, Idea Group Inc. Couto, F., Silva, M., & Coutinho, P. (2005). Finding genomic ontology terms in text using evidence content. BMC Bioinformatics, 6(S1), S21. doi:10.1186/1471-2105-6-S1-S21 Couto, F., Silva, M., & Coutinho, P. (2006). Measuring semantic similarity between gene ontology terms. DKE - Data and Knowledge Engineering, Elsevier Science (in press). Demsey, A., Nahin, A., & Braunsberg, S. V. (2003). Oldmedline citations join pubmed. The NLM Technical Bulletin, 334(e2).

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Devos, D., & Valencia, A. (2001). Intrinsic errors in genome annotation. Trends in Genetics, 17(8), 429–431. doi:10.1016/S0168-9525(01)02348-4 Gaudan, S., Jimeno, A., Lee, V., & RebholzSchuhmann, D. (2008). (accepted). Combining evidence, specificity and proximity towards the normalization of Gene Ontology terms in text. EURASIP JBSB. GO-Consortium. (2004). The Gene Ontology (GO) database and informatics resource. Nucleic Acids Research, 32(Database issue), D258–D261. doi:10.1093/nar/gkh036 Hearst, M. (1999). Untangling text data mining. In Proc. of the 37th Annual Meeting of the Association for Computational Linguistics. Hirschman, L., Yeh, A., Blaschke, C., & Valencia, A. (2005). Overview of BioCreAtIvE: critical assessment of information extraction for biology. BMC Bioinformatics, 6(Suppl 1), S1. doi:10.1186/1471-2105-6-S1-S1 Jaeger, S., Gaudan, S., Leser, U., & RebholzSchuhmann, D. (2008). Integrating ProteinProtein Interactions and Text Mining for Protein Function Prediction. Data Integration in The Life Sciences (DILS 2008), Evry June 25-27 2008 (accepted for publication in BMC Bioinformatics). Kim, J., & Park, J. (2004). BioIE: retargetable information extraction and ontological annotation of biological interactions from literature. Journal of Bioinformatics and Computational Biology, 2(3), 551–568. doi:10.1142/S0219720004000739 Koike, A., Niwa, Y., & Takagi, T. (2005). Automatic extraction of gene/protein biological functions from biomedical text. Bioinformatics (Oxford, England), 21(7), 1227–1236. doi:10.1093/ bioinformatics/bti084 Lin, D. (1998). An information-theoretic definition of similarity. In Proc. of the 15th International Conference on Machine Learning.

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Lord, P., Stevens, R., Brass, A., & Goble, C. (2003). Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation. Bioinformatics (Oxford, England), 19(10), 1275–1283. doi:10.1093/bioinformatics/btg153 Mitchell, J., Aronson, A., Mork, J., Folk, L., Humphrey, S., & Ward, J. (2003). Gene indexing: characterization and analysis of NLM’s GeneRIFs. In Proc. of the AMIA 2003 Annual Symposium. Müller, H., Kenny, E., & Sternberg, P. (2004). Textpresso: an ontology-based information retrieval and extraction system for biological literature. PLoS Biology, 2(11), E309. doi:10.1371/journal. pbio.0020309 Pérez, A., Perez-Iratxeta, C., Bork, P., Thode, G., & Andrade, M. (2004). Gene annotation from scientific literature using mappings between keyword systems. Bioinformatics (Oxford, England), 20(13), 2084–2091. doi:10.1093/bioinformatics/ bth207 Rada, R., Mili, H., Bicknell, E., & Blettner, M. (1989). Development and application of a metric on semantic nets. IEEE Transactions on Systems, 19(1), 17–30. Rebholz-Schuhmann, D., Kirsch, H., & Couto, F. (2005). Facts from text - is text mining ready to deliver? PLoS Biology, 3(2), e65. doi:10.1371/ journal.pbio.0030065 Rebholz-Schuhmann, D., Kirsch, H., Gaudan, M. A. S., Rynbeek, M., & Stoehr, P. (2007). EBIMed - text crunching to gather facts for proteins from Medline. Bioinformatics (Oxford, England), 23(2), e237–e244. doi:10.1093/bioinformatics/btl302 Shah, P., Perez-Iratxeta, C., Bork, P., & Andrade, M. (2004). Information extraction from full text scientific articles: Where are the keywords? BMC Bioinformatics, 4(20).

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Shatkay, H., & Feldman, R. (2003). Mining the biomedical literature in the genomic era: An overview. Journal of Computational Biology, 10(6), 821–855. doi:10.1089/106652703322756104 Wheeler, D., Church, D., Federhen, S., Lash, A., Madden, T., & Pontius, J. (2003). Database resources of the national center for biotechnology. Nucleic Acids Research, 31(1), 28–33. doi:10.1093/nar/gkg033

ENDNOTES 1



4 2 3

http://www.nlm.nih.gov/bsd/licensee/baselinestats.html http://scholar.google.com/ http://www.scirus.com/ http://www.epnet.com/

Yeh, A., Hirschman, L., & Morgan, A. (2003). Evaluation of text data mining for database curation: Lessons learned from the KDD challenge cup. Bioinformatics (Oxford, England), 19(1), i331–i339. doi:10.1093/bioinformatics/btg1046

This work was previously published in Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration, edited by Violaine Prince and Mathieu Roche, pp. 301-314, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.21

Relationship Between Shrinkage and Stress Antheunis Versluis University of Minnesota, USA Daranee Tantbirojn University of Minnesota, USA

ABSTRACT Residual stress due to polymerization shrinkage of restorative dental materials has been associated with a number of clinical symptoms, ranging from post-operative sensitivity to secondary caries to fracture. Although the concept of shrinkage stress is intuitive, its assessment is complex. Shrinkage stress is the outcome of multiple factors. To study how they interact rEquationuires an integrating model. Finite element models have been invaluable for shrinkage stress research because they provide an integration environment to study shrinkage concepts. By retracing the advancements in shrinkage DOI: 10.4018/978-1-60960-561-2.ch421

stress concepts, this chapter illustrates the vital role that finite element modeling plays in evaluating the essence of shrinkage stress and its controlling factors. The shrinkage concepts discussed in this chapter will improve clinical understanding for management of shrinkage stress, and help design and assess polymerization shrinkage research.

WHAT IS SHRINKAGE STRESS? The concept of shrinkage stress is intuitive. The notion that stresses arise if a composite filling shrinks inside a tooth is not difficult to convey. The actual assessment of shrinkage and its effects, however, has turned out to be complex and

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Relationship Between Shrinkage and Stress

is often a source of confusion. Shrinkage became an issue in dentistry when resin-based composites were introduced as restorative materials (Bowen, 1963), and has remained a persistent concern since. The main difference between resin-based composite and the classic amalgam restorations was adhesion to the tooth structure. Bonding of composite restorations enabled more conservative cavity designs that no longer relied on undercuts for retention and thus preserved more tooth tissue. Unlike unbonded amalgam fillings, an adhesive composite restoration becomes an integral part of the tooth structure. This allows recovery of the original distribution of masticatory loads in a treated tooth. Hence, a composite “filling” actually restores structural integrity lost by decayed tooth tissue and during the process of cavity preparation (Versluis & Tantbirojn, 2006). However, by becoming a more integral part of the load-bearing tooth structure, changes in the composite, such as shrinkage, will also be transferred into the tooth structure causing so-called residual stresses. The origin of shrinkage is the formation of a polymer network during the polymerization reaction. This process results in a denser material, where the density change is manifested in volumetric contraction or shrinkage. Where the composite is bonded to the tooth, the tooth structure will confine the dimensional changes. This leads to the generation of shrinkage stresses in both the restoration and tooth. Under normal clinical conditions, shrinkage movement is so small that it is practically imperceptible. However, the effects of shrinkage may show up indirectly in clinical symptoms. Shrinkage stress has been associated with enamel crack propagation, microleakage and secondary caries, voids in the restoration, marginal loss, marginal discrepancy, and post-operative sensitivity (Jensen & Chan, 1985; Eick & Welch, 1986; Unterbrink & Muessner, 1995). It should be pointed out that none of these clinical symptoms are actually stresses, and their correlation with shrinkage stresses, if any, is not obvious. Further-

more, clinical symptoms are seldom caused by only one factor. Therefore, the precise correlation of these symptoms with polymerization shrinkage is debatable and remains an area of investigation. To understand the contributions of polymerization shrinkage under clinical conditions, the nature of shrinkage stress has to be studied. The scientific method for studying any subject is creating a theoretical “model” of reality that expresses the current understanding. SubsEquationuently, such models are tested with precise observations and measurements, and if needed, amended or replaced based on new insights. In this chapter, a progression of shrinkage models will be used to discuss advances in dental shrinkage stress research.

SHRINKAGE STRESS MODEL: CORRELATION WITH SHRINKAGE The most simple model for shrinkage stress is that the residual stress is related to shrinkage. This intuitive model is based on the observation that without shrinkage there will be no shrinkage stress. The observation is subsEquationuently extrapolated into a positive correlation between shrinkage and stress, because it appears credible that when shrinkage increases, shrinkage stress increases too. This intuitive model is probably the most common and practical expression of our understanding of the nature of polymerization shrinkage stress. Manufacturers, clinicians, and researchers alike have the same initial response when confronted with shrinkage data: “a composite with higher shrinkage values must cause higher stresses”. To validate this model, the relationship between shrinkage and stress will be considered more closely. It is important to clearly distinguish between shrinkage and stress. A shrinkage value α is a dimensional change which is defined as the amount of change divided by the original dimension:

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Relationship Between Shrinkage and Stress

α = (d1 - d0) / d0 = Δd / d0

(1)

where d0 is the original dimension, d1 the dimension after shrinkage, and Δd is the dimensional change. Note that α is dimensionless, and often reported as percentage. The amount of shrinkage can be determined in a physical experiment in which a material dimension before and after polymerization are measured. If it were true that shrinkage stress is directly correlated with shrinkage, shrinkage stress can be assessed by ranking the shrinkage values. In effect, this assumption implies that shrinkage stress would be a material property independent of the restoration environment or geometry. Unlike shrinkage, stress cannot be measured directly in an experiment. Stress is a calculated engineering factor that expresses how a force is distributed inside a material. However, stress is related to physical deformation or strain, which can be measured experimentally. The most simple relationship between stress σ and strain ε is a uniaxial (i.e., one-dimensional) linear elastic correlation with the elastic modulus E: σ = E ε [N/m2]

(2)

An elastic modulus is a material property, which means that it has a constant value for each material, and expresses the inherent stiffness of a material. Since shrinkage can be considered as a form of strain, this relationship illustrates that shrinkage stress is not only a function of the shrinkage but also depends on the elastic modulus. Applying the insight that shrinkage stress depends on both shrinkage and elastic modulus is not intuitive anymore. It rEquationuires a calculation that involves another mechanical property. Still, in a new shrinkage stress model based on Equation (2), residual stress may seem to remain a simple expression, that is easily calculated by the product with the elastic modulus and again can be ranked as if it were a material property. Unfortunately, simple Equation (2) is only valid

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for one-dimensional conditions. In reality, shrinkage and shrinkage stress are three-dimensional. Hence, the definition of stress rEquationuires a set of six Equationuations (three normal stresses σ and three shear stresses τ), which under isotropic conditions becomes: σxx = E / ((1+ν)(1-2ν)) ((1-ν)εxx+ν(εyy+εzz)) σyy = E / ((1+ν)(1-2ν)) ((1-ν)εyy+ν(εxx+εzz)) σzz = E / ((1+ν)(1-2ν)) ((1-ν)εzz+ν(εxx+εyy))

(3a) (3b) (3c)

τxy = E / (2 (1+ν)) γxy

(3d)

τyz = E / (2 (1+ν)) γyz

(3e)

τzx = E / (2 (1+ν)) γzx

(3f)

where the x-y-z indices denote x-y-z directions, γ are the shear strains, and ν is the Poisson’s ratio representing the lateral-axial strain ratio. Note that isotropic conditions mean that the mechanical properties (i.e., E and ν) are the same in all directions, which is a reasonable assumption for a particle-filled resin-based composite. Clearly, the three-dimensional reality makes predicting shrinkage stresses much more difficult. Taking into account six stress components simultaneously cannot be considered intuitive anymore. Solving these expressions to obtain the shrinkage stress will rEquationuire considerably more effort. The most comprehensive solution to calculate these complex stress conditions is by using finite element analysis. This engineering tool provides a system to solve the necessary constitutive Equationuations and continuity rEquationuirements. Finite element analysis solves stress and strain patterns in a complex structure by dividing it into smaller “elements” that are much simpler to calculate. By calculating those elements all

Relationship Between Shrinkage and Stress

together simultaneously, the whole structure is solved. This simultaneous solution of many elements rEquationuires considerable computing power. With the availability of powerful computer systems and improvements in user friendly graphical interfaces, finite element analysis has become widely accessible to dental researchers (Korioth & Versluis, 1997). Finite element analysis has played a driving role in the current state of polymerization shrinkage research and thinking. This chapter will use finite element analysis to test and discuss shrinkage stress models, and show the powerful and unique insight a finite element analysis approach offers when trying to resolve the effects of polymerization shrinkage stress in dentistry. Using finite element analysis, the original intuitive shrinkage stress model can be easily tested. Figure 1 shows a finite element model of a buccal-lingual cross-section in a premolar with a deep Class II MOD (mesio-occluso-distal) restoration. The tooth is restored with a composite for which the shrinkage and elastic modulus values were varied. Figure 1A was filled with a typical restorative composite with a shrinkage value of 1.8%. The resulting residual shrinkage stresses in the restoration are shown according to a linear color scale. Yellow and orange colors are high stresses, purple and blue are low stresses. In Figure 1D the cavity was restored with a composite with only half the amount of shrinkage (1.0%). Such impressive improvement in shrinkage can, for example, be achieved by increasing the filler content and thus reducing resin volume. Since fillers do not shrink during polymerization, a lower resin content generally decreases shrinkage values. A practical analogy of this example is the comparison between a conventional and packable composite, where the increased filler content in a packable composite reduces shrinkage values. The intuitive shrinkage stress model would anticipate lower shrinkage stresses for the composite that shrinks less. However, comparison between Figures 1A and 1D shows that the shrinkage stresses are virtually the same because, along

with a reduction in shrinkage, the elastic modulus increased. Increasing filler content generally increases elastic modulus. Therefore, the benefit of decreased shrinkage may be cancelled out by the increase in elastic modulus. Figures 1B and 1C show the effect on shrinkage stresses when one property at the time is varied. This example illustrates that shrinkage stress cannot be assessed based only on shrinkage, but must include other composite properties as well in the evaluation. It also shows that even a very simple finite element model can test common shrinkage stress behavior, and can integrate different material properties in a multi-dimensional condition to determine the relationship between shrinkage and stress.

SHRINKAGE STRESS MODEL: C-FACTOR Finite element analysis has only relatively recently become an integral part of shrinkage stress Figure 1. Residual shrinkage stress distributions in composite restorations with different shrinkage (α) and elastic modulus (E) values. Colors show stress levels (modified von Mises Equationuivalent stress) according to a linear scale.

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research. The acceptance of numerical analysis has been slow in a dental materials research environment that has traditionally been the domain of laboratory methods. Moreover, clinical relevance has also been an important quality for dental research. It is therefore not surprising if there is a strong desire for simple shrinkage stress models that can be assessed experimentally and are easily applied to clinical conditions. One of the models that has become very popular in dentistry is the so-called C-factor (Feilzer et al., 1987). A C-factor or “configuration” factor is the ratio of bonded to free surface area. This model states that a composite restoration with a high C-factor (e.g., Class I or Class V) will have higher shrinkage stresses than a restoration with a lower C-

factor (e.g., Class II or Class IV). The model is based on experimental observations in which cylindrical composite samples were attached to a load-cell and cured. Given a constant diameter, increasingly higher shrinkage forces will be recorded when the cylinder length is reduced (Figure 2A). This observation was explained by the relative increase of bonded to free surface area, which was subsEquationuently extrapolated to cavity configurations where a higher ratio of bonded surfaces should also increase shrinkage stresses. It is true that shrinkage stress is affected by the amount of shrinkage constraints, as was predicted by Bowen who stated that restorations with large free surfaces were likely to develop less shrinkage stress (Bowen, 1967). The simplic-

Figure 2. C-factor model and shrinkage stress prediction. (a) Cylindrical test specimens to measure axial forces. (b) Shrinkage and stress distribution in cylindrical specimens. (c) Analogy of C-factors in stylized cavities. (d) Shrinkage and stress distribution in cavities assuming rigid walls. (e) Shrinkage and stress distributions in cavities assuming non-rigid walls. Dashed curves in graphs are stress distributions of the cylindrical specimens for comparison. Shrinkage deformation shown 100-times enlarged.

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ity and practicality of the C-factor model is certainly attractive. Firstly, because it implies that stress can be measured experimentally, basically using the relationship: σ = F / A

(4)

where F is the load measured by the load-cell and A is the cross-sectional area of the cylindrical test sample. Note that a load-cell does not measure shrinkage stress, but rather a reaction force in axial direction. Secondly, because the C-factor model assumes that stress measured in simple shaped test specimens (cylinders) can be applied to more complex restorations, merely using the ratio between the bonded and unbonded surface areas. Using finite element analysis the C-factor model is easily tested. The graph in Figure 2A shows the axial reaction forces that a load-cell measures for different lengths of cylindrical shaped composite samples. The graph shows an increasing reaction force with decreasing cylinder length (i.e., increasing C-factor). The graph also shows the nominal stress, calculated using Equation (4). However, the finite element analysis offers a more comprehensive three-dimensional stress solution. The graph in Figure 2A shows the average stress determined as the average of all axial stress components in the whole cylindrical specimen. The average stress level turns out to be about twice as high as the nominal stress value. Although the nominal and average axial stress values are significantly different, they follow the same trend as the axial force output of the loadcell, which increases when the cylinder length is reduced. This appears to validate the C-factor model, at least qualitatively. However, the assumption of uniaxial stress is only valid if a cylinder is infinitely long and if the shrinkage would take place only in axial direction (i.e., along the longitudinal cylinder axis). In reality, stress conditions are not one-dimensional due to the finite cylinder length and the threedimensional nature of shrinkage itself. According

to Equation (3), there are six stress components that should be considered. These six stress components are calculated by the finite element analysis, but it is not convenient to plot all indivually for each cylinder length. Since it is the combined effect of these six components that determine the overall stress state in the composite, they are often integrated into one value using a specific criterion. A popular criterion is the von Mises criterion. Figure 2B shows the stress distribution in the cylinders using a modified von Mises criterion. It modifies the original von Mises criterion to take into account the difference between compressive and tensile strength (de Groot et al., 1987; Versluis et al., 1997). The figure shows a gradient of stress values in the composite cylinders rather than one nominal “shrinkage stress”. The yellow and orange colors indicate that there are high stress concentrations close to the bonded areas and the blue and purple colors indicate lower stress values along the free surfaces. Clearly, shrinkage stress in the composite is not a single or nominal value as implied by a C-factor shrinkage stress model. To further test the validity of the C-factor shrinkage stress model, shrinkage stresses were simulated in cubical cavity shapes, where an increasing number of bonded walls represent different clinical preparation types (Figure 2C). Since the bonding conditions and shapes are different, direct comparison of stress patterns between the cavity configurations and between the cavities and cylinders is problematic. However, stress values themselves can be compared since in this case the objective is to test the validity of the C-factor model for extrapolation of shrinkage stresses between different configurations. Stress values are compared by collecting all stress values from the cylindrical and simulated restoration configurations, and plotting them in ascending order in the graphs of Figures 2B and 2D. The graphs show that for exactly the same composite properties, the cylindrical and cubical composite geometries generate different shrinkage stresses for identical C-factors. Apparently, stress is not only determined by mechanical properties

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and bonded surface area, but geometry also affects shrinkage stresses. Stresses calculated with one geometry can therefore not be simply extrapolated to another geometry. Additionally, the C-factor model only distinguishes between “bonded” and “unbonded” areas. Since it does not identify the substrate to which the composite is bonded, an infinitely stiff substrate or undeformable cavity wall is implied. Figure 2E shows the stress curves if the simulated restoration would have been bonded to a deformable cavity wall, such as enamel and dentin. The figure shows that the stresses again change substantially when the bonding substrate changes. Stress thus not only depends on the composite attributes, but also on the properties of the substrate. Again, using a simple finite element analysis, a very popular shrinkage stress model that extrapolates one physical aspect of shrinkage stress generation to predict stress values, could be easily tested. The finite element model demonstrated some crucial shrinkage stress insights. First, shrinkage stress is not a single nominal value, but a location dependent distribution consisting of a whole range of values. Secondly, this location dependent distribution of shrinkage stresses depends on geometry. Thus, changing the geometry changes the shrinkage stresses. Thirdly, even if one could determine every quality of a composite restoration, such as composite properties and restoration configuration, it would still not provide all necessary information to determine the shrinkage stresses in a restoration. Shrinkage stress cannot be determined without consideration of the response of the structure to which a composite is bonded. To understand shrinkage stresses, it is imperative to consider the whole restored tooth structure.

SHRINKAGE STRESS MODEL: RESTORATION SIZE A third shrinkage stress model to be discussed here is the assumption that large restorations shrink

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more than small restorations, implying that large restorations will generate more shrinkage stresses. This shrinkage stress model has often crept into shrinkage stress discussions. It is another example of a mostly intuitive shrinkage concept, which is usually incorrectly rationalized by the perception that large restorations “shrink more”. Unlike the first intuitive concept that correlated shrinkage and stress, and the second C-factor model that based shrinkage stresses on one configuration measure, discussion of this model rEquationuires consideration of multiple facets. Because the size of a restoration affects multiple factors concerning the whole tooth structure, the implication of this model is more complex than the former simple deduction models. The first facet to examine is the definition of shrinkage. Shrinkage is a dimensionless ratio (Equation 1), often reported as a percentage. Shrinkage is thus per definition independent of size. Given the same geometry and boundary conditions, 5% shrinkage in a large restoration causes the same shrinkage stresses as 5% shrinkage in a small restoration. This can be simply illustrated using finite element analysis by scaling a stylized tooth-restoration model up or down in Figure 3A. The figure shows no change in shrinkage stress values and distribution. Although this example decisively demonstrates that shrinkage and thus shrinkage stress is not higher in larger restorations, what happens to the shrinkage stresses when a small and large restoration are compared in a same sized tooth? In such case the mechanical response of to the restoration shrinkage will be different. Cutting a large cavity means a larger loss of tooth tissue, and hence more weakening of the tooth crown than from a small cavity. As a result, the larger restoration is bonded to a less rigid structure compared to a smaller restoration. A weakened tooth structure will be less capable of constraining composite shrinkage and thus the restoration will experience lower shrinkage stresses than if the tooth would have retained more rigidity, like with a smaller restoration (Figure 3C). Contrary

Relationship Between Shrinkage and Stress

Figure 3. Effect of restoration size on residual shrinkage stresses. (a) Auto-curing composite. (b) Light-activated composite. (c) Quarter view of molar restored with small and large Class I restoration. Von Mises Equationuivalent stress is shown.

to the popular notion, it can thus be concluded that, given the same shrinkage properties, a larger restoration will have lower shrinkage stresses in the restoration itself than a smaller restoration. But this is not the final word about this shrinkage stress model, because there is another facet to shrinkage stresses in a restoration. Observing the stress distributions in Figure 3C shows that shrinkage stresses are not confined to the restoration and its interface with the tooth, there are also shrinkage stresses generated inside the restored tooth. Compared to the smaller restoration, the tooth with the larger restoration has lost more of its rigidity, i.e., its resistance to deformation. Therefore, the tooth with the larger restoration will deform more easily and consEquationuently will be higher stressed than it would have been with a smaller restoration. Shrinkage stress is thus not only in the restoration, but also applies to the

tooth. It cannot be emphasized enough that when shrinkage stresses are evaluated, both sides of the tooth-restoration complex should be taken into consideration, because lower stresses in the restoration may go along with higher stresses in the tooth. The example above assumed the same shrinkage properties in the large and small restorations. This is a reasonable approximation for auto-curing systems. Such systems could be assumed to be independent of restoration size if temperature effects are neglected. Shrinkage behavior for light-activated composites, on the other hand, may not be independent of restoration size. Due to light attenuation effects that cause diminishing light intensities deeper inside a light-cured material, the polymerization reaction at the restoration surface will develop more rapidly and result in a higher degree of cure compared to composite deeper inside a restoration. The consEquationuences of this effect will be more pronounced in large restorations than in small ones. As a result, compared to a large restoration, a small restoration will cure faster and more thoroughly, resulting in a higher overall shrinkage (Figure 3B). Therefore, again contrary to the popular notion, a large restoration of light-activated composite material may shrink less under the same conditions and thus generate less shrinkage stresses rather than more. Not because large restorations “shrink less” but because a large restorations may not be cured as thoroughly as smaller ones. This example demostrates that determination of accurate shrinkage values is a prerEquationuisite for accurate residual stress calculations.

SHRINKAGE VALUE Until now, a generic shrinkage term has been used to describe the amount of polymerization contraction. Although shrinkage stress may not be linearly correlated to shrinkage, the shrinkage value is still the most important factor for

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polymerization shrinkage stress because without shrinkage there would be no shrinkage stress. Assessing shrinkage stresses thus rEquationuires determination of shrinkage values. This section will examine shrinkage values and how they relate to shrinkage stresses. According to Equation (1), shrinkage can be measured as the change in dimensions. Shrinkage can be determined in both volume and distance measurements. These provide volumetric and linear shrinkage, respectively. Given the reasonable assumption that composites shrinkage is isotropic, linear shrinkage L can be converted into volumetric shrinkage V: V = 3 L - 3 L2 + L3

(5a)

If the shrinkage value is low, the higher order terms become small enough to be neglected, resulting in the convenient expression: V = 3 L

(5b)

ConsEquationuently, linear shrinkage can be converted into volumetric shrinkage: L=V/3

(5c)

For 9% volumetric shrinkage, 3% linear shrinkage is obtained with Equation (5c), where the correct value is about 3.1%. Since most composites shrink considerably less, the simple conversion is a good approximation for dental applications. Shrinkage values have been measured in many ways. One common technique is to measure displaced volume, in which a composite sample is immersed in a liquid and cured (Penn, 1986). The shrinkage volume can be calculated by measuring the displaced liquid. A shrinkage value obtained with such method can be called the total shrinkage, because it measures the entire amount of dimensional change of the composite during polymerization. It should be noted that any technique that restricts free contraction can-

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not be considered a pure measurements of total shrinkage, because some of its shrinkage movement was prevented. The origin of shrinkage is the formation of a polymer network. Therefore, total shrinkage is primarily a function of degree of cure. Furthermore, effective density can also be affected by network organization. It has been suggested that curing regimens can affect the network distribution, and thus network structures are not necessarily comparable at the same degree of cure. However, the effects on the total dimensional change seem to be small. Therefore, good rule of thumb remains that the same composite with the same degree of cure should produce similar values for total shrinkage. While the shrinkage value is the origin of shrinkage stresses, calculation of the stress using this factor has to be carried out with caution. Figure 4A shows a premolar with a composite restoration where stress and deformation been calculated using a typical 3% total shrinkage value. The figure shows how the composite shrinkage within the cavity has deformed the tooth structure, pulling the buccal and lingual cusps together by about 200 μm. Such a large deformation, which exceeds the thickness of a human hair, is unlikely to be realistic since it should be visible by the naked eye and there would have been many sightings in clinical practice. A shrinkage model in which shrinkage stresses are calculated using the total shrinkage value is thus likely incorrect. Bowen has pointed out that not all polymerization shrinkage causes shrinkage stresses (Bowen, 1963). When a composite cures, it is transformed from a viscous paste to an essentially solid material. In the viscous state, flow of the composite can relax shrinkage stresses without a buildup of residual shrinkage stresses (Bausch et al., 1982). Residual stresses will only be generated when the composite material cannot timely relax anymore, which happens when a more rigid polymer network structure has developed. In other words, shrinkage stress is generated when the composite material behaves solid enough to transfer stresses. The

Relationship Between Shrinkage and Stress

Figure 4. Residual polymerization stress distributions and deformation calculated with three different shrinkage models: (a) elastic, (b) viscoelastic, and (c) pre/post-gel. Green outline is the original contour before polymerization shrinkage.

problem with the calculation used in Figure 4A was that the material was considered elastic (i.e., no plastic flow) throughout the polymerization reaction, completely disregarding viscous and viscoelastic effects. Figure 4B shows the stress and deformation calculated with a viscoelastic composite model. It shows the same amount of total shrinkage but significantly lower stress and a tooth deformation (approximately 40 μm intercuspal distance change) that is within a realistic range of cusp-flexure values (Pearson & Hegarty, 1989; Suliman et al., 1993; Panitvisai & Messer, 1995; Meredith & Setchell, 1997; Alomari et al., 2001; Tantbirojn et al., 2004). Calculating the shrinkage stress is thus not a linear elastic calculation, where the end result is determined independently of the polymerization process by the final shrinkage and modulus values. The unrealistic assumption of an elastic model is easily exposed by finite element analysis, which demonstrates that shrinkage stress assessment must include the time-dependent shrinkage and modulus development as they evolve during a polymerization process. The dynamic process is a significant complication for studying polymerization shrinkage. Most test methods for material property determination are designed for steady-state materials.

However, properties of composite that describe their mechanical response change rapidly during the polymerization reaction. To record such changes rEquationuires methods that can capture complete material behavior instantaneously and continuously. Viscoelastic behavior goes through fundamental changes during polymerization. Simply said, its response shows initially a Maxwell type behavior (spring and dashpot in series) and transforms to a predominantly Kelvin-Voight type behavior (spring and dashpot parallel) behavior during polymerization (Dauvillier et al., 2001, 2003). Since there is often not sufficient experimental data available to accurately calculate this complex transformation, alternative methods that measure effective shrinkage values have been proposed for shrinkage stress assessment.

SHRINKAGE STRESS MODEL: EFFECTIVE SHRINKAGE The concept of an effective shrinkage value divides the total shrinkage into a pre- and post-gel contribution (Bausch et al., 1982; Davidson & de Gee, 1984): αtotal = αpre-gel + αpost-gel

(5)

The pre-gel shrinkage αpre-gel can be considered the amount of shrinkage during polymerization when the composite is still viscous, and thus represents the shrinkage that does not generate shrinkage stresses. Post-gel shrinkage αpost-gel is the shrinkage component after a composite has become solid enough to generate shrinkage stresses. Shrinkage stress therefore originates only from the post-gel shrinkage portion. Post-gel shrinkage can thus be considered the effective shrinkage with regard to the shrinkage stress. The transition point between the pre- and post-gel shrinkage has been called the gel-point. Note that this gel-point term is defined to describe a mechanical response, and

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Relationship Between Shrinkage and Stress

does not necessarily coincide with similar terms used in polymer science. The formulation of the pre-and post-gel shrinkage model has led to a search for the gel-point. However, the accurate determination of the gel-point has remained elusive. It should be noted that the pre- and postgel model is a simplification of the viscoelastic process. It is unlikely that the development of viscoelastic properties will show a well-defined point, as has been discussed in detail by Versluis et al., (2004a). Nevertheless, the general concept of an effective shrinkage value has turned out to be very illustrative and practical. Effective shrinkage has been measured using a strain gauge method (Sakaguchi et al., 1997). In this method, a composite sample is attached to a strain gauge and cured. Strain gauges are sensors consisting of a very thin metallic wire-frame encapsulated in a glass-fiber reinforced epoxyphenolic resin film. Strain gauges are based on the principle that the electrical resistance of the metal wire increases with increasing strain (deformation). When the attached composite shrinks, it deforms the embedded wire-frame in the strain gauge. The resulting change in resistance can be measured and shrinkage strain is obtained. Since the composite can only deform the strain gauge when it is solid enough to generate stresses, a strain gauge measures per definition the effective shrinkage or post-gel shrinkage. Application of the pre- and post-gel shrinkage model in a finite element analysis (Figure 4C) shows that, while the total shrinkage is the same as in Figure 4A, the calculated shrinkage stress and resulting tooth deformation is similar to the viscoelastic model (Figure 4B). The post-gel shrinkage value thus includes the viscoelastic history during polymerization. It has been shown that using post-gel shrinkage values, tooth deformation and thus likely shrinkage stress can be closely approximated (Versluis et al., 2004b). The advantage of using this method is that the analysis becomes quasi-linear, simplifying the measurement and

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solution of an otherwise fully nonlinear analyses of viscoelastic composite properties. Another advantage of the pre- and post-gel shrinkage model is that it makes it easier to conceptualize effects of reaction kinetics on the shrinkage stress. An example is the discussion in the dental literature about the question if shrinkage stress is affected by the curing light intensity. The reason that this issue has not been resolved may be due in part to experimental data that show no effect of curing light intensity on the shrinkage value, from which some have concluded that there will also be no effect on the shrinkage stress. As discussed above, light curing to the same degree of cure will cause a similar polymer network density and thus total shrinkage value, irrespective of the used light intensity. However, total shrinkage does not predict the shrinkage stress, because it is the amount of effective or post-gel shrinkage that generates the residual stresses. The amount of viscous flow, and thus the pre- and post-gel shrinkage, is determined by the kinetics of the polymerization process. It is conceivable that a high light intensity increases the rate at which a rigid network is formed, and thus accelerates the occurrence of the “gel-point”. Because the total shrinkage for a particular composite is constant at the same degree of cure, a gel-point that occurs earlier in the polymerization process reduces pregel shrinkage and increases post-gel shrinkage. Higher curing light intensities, especially during the early polymerization reaction, increases the effective shrinkage and therefore shrinkage stresses. Using the strain gauge method it has been confirmed that curing light-intensity affects the value of the effective shrinkage (Versluis et al., 1998, 2004a; Sakaguchi & Berge, 1998). Lightcuring to a similar degree of cure at different lightintensities resulted in different effective shrinkage values. Curing at lower light-intensities decreased the effective shrinkage values, which will reduce shrinkage stresses if other conditions are comparable, while higher light-intensities increase the

Relationship Between Shrinkage and Stress

effective shrinkage. Note that experimental observations like these have led to the introduction of ramp-cure or step-cure regimens on commercial curing light sources (Kanca & Suh, 1999; Suh et al., 1999; Bouschlicher & Rueggeberg, 2000). The post-gel shrinkage concept has turned out to be a useful factor to develop an understanding of the development and calculation of shrinkage stresses. From the examination of shrinkage values, it can be concluded that (a) not all shrinkage generates stresses and (b) not all curing regimens generate the same amount of residual stresses. The calculation of shrinkage stresses has become more complex since the evolving viscoelastic effect must be taken into account. While the final shrinkage may be a constant value in a thoroughly cured restoration, the effective or post-gel shrinkage can vary depending on local reaction kinetics. While the consEquationuences of this complexity may be overwhelming for an intuitive approach, finite element analysis is perfectly suited to integrate and apply comprehensive local, time-dependent, and nonlinear behavior into one shrinkage stress analysis. This section about the shrinkage value showed that finite element analysis is not only for calculating stresses, but its use also advances understanding of the shrinkage value itself. Although total shrinkage remains the most commonly reported property to characterize a composite’s shrinkage stress quality, finite element analysis has clearly demonstrated its limitations. Total shrinkage does not reflect shrinkage stress behavior, which is much better characterized by the effective or post-gel shrinkage.

AFTER THE SHRINKAGE: RELAXATION Most of a polymerization reaction in a lightactivated composite typically takes place within the first 10 seconds, after which it continues at a decreasing rate. Shrinkage strain development has been shown to continue for more than 24

hours. Although elastic behavior dominates in a cured composite resin, the material still exhibits some viscoelastic behavior after it has been fully cured. Viscoelastic effects in cured composites are thought to relax residual stresses over an extended period of time. This process is enhanced by viscoelastic properties of the tooth tissues, which in effect also reduce residual shrinkage stresses over time (Kinney et al., 2003). Furthermore, environmental conditions may cause stress relaxation due to water sorption and consEquationuently hygroscopic expansion, while the composite modulus may undergo a slow deterioration due to solubility and matrix softening effects (Momoi & McCabe, 1994; Oysaed & Ruyter, 1986). It is not known to which extend such stress relaxation happens under clinical conditions. Figure 5 shows cuspal deformation in an in vitro experiment for a molar with a Filtek Supreme (3M ESPE, St Paul, MN) Class II MOD restoration after polymerization, and after 4 and 8 weeks in water. The initial cuspal deformation due to polymerization shrinkage stresses substantially decreased after 4 weeks in water, and had continued the decrease 8 weeks later for this composite material. Shrinkage stresses calculated for composite restorations are therefore probably most acute immediately after curing. The fact that any harmful stresses may relax over time does not diminish the rEquationuirement that the tooth-restoration complex must survive the initial period, probably spanning more than a week for restorative composites, because debonding caused by initially high stresses is not restored when residual stresses relax. Consideration and management of shrinkage stresses therefore remains necessary.

THE ROLE OF SHRINKAGE EXPERIMENTS The previous sections discussed the relationship between polymerization shrinkage and shrinkage stresses. It was demonstrated that shrinkage stress

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Figure 5. Residual shrinkage stress relaxation due to hygroscopic expansion, shown as reduction in tooth deformation. (a) Digital image of an Mesial-Occlusal-Distal slot preparation in an extracted upper molar. (b) Deformation pattern at buccal and lingual cusps due to residual shrinkage stresses from the composite restoration immediately after curing. (c,d) Cuspal deformation decreased after 4 and 8 weeks immersion in water. Negative values represent inward movement of cuspal surface.

is not a composite or restoration property, but is a local value determined by a number of factors involving both the composite and the surrounding environment. It was stated that stress cannot be measured experimentally, but is a calculated engineering factor. It was shown that when all contributing factors are taken into account, the calculation of shrinkage stress has become so complex that the only comprehensive solution is using finite element analysis. This brings up the question if there is any role left for experimental shrinkage research. Finite element analysis must be considered an integration system. It offers an environment in which basic material properties are integrated according to universal physical laws. It cannot predict polymerization shrinkage, but calculates shrinkage stress based on external input of physical material behavior obtained from

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experimental measurements using a shrinkage model designed by the operator. The validity of any finite element model of shrinkage must also be checked against reality using experimental measurements, because using finite element analysis does not ensure validity of a shrinkage model. Clearly, experiments remain a vital component of any shrinkage research. To better understand the role of experiments in research, it is important to distinguish between two types of experiments: basic experiments to provide input for the shrinkage model and simulation experiments for validation of the shrinkage model. Basic experiments must determine properties independent of geometry. This allows their application in other systems than the one in which they were measured. For example, elastic modulus is a basic property while cusp flexure is not. Cusp flexure involves the overall stiffness of a tooth, which includes various material properties, geometry, boundary conditions, and even the location where the flexure was measured. It is only valid for that tooth under the exact same conditions. However, elastic modulus can be applied to calculate the flexure in any tooth shape, and is thus considered a basic property. The measuring of cuspal flexure is an example of a simulation experiment. It can be used to test the shrinkage model, because instead of isolating a single property, flexure is the result of multiple interacting properties. If the shrinkage model recreates those interactions correctly, it will replicate the flexure in a similar tooth system. If that is the case, the model is considered validated. Based on the previous discussions, it is clear that the ultimate value of these two categories of shrinkage experiments is accomplished if used in conjunction with finite element analysis. Without a system to integrate basic shrinkage properties, such properties have little value beyond arbitrary material ranking, because they cannot predict shrinkage stress by themselves. Similarly, without finite element analysis, simulation experiments

Relationship Between Shrinkage and Stress

also have limited predictive value, because they only apply to the one test condition and even in those unique conditions they cannot provide actual shrinkage stress values. Still, the dental literature shows various examples of alleged shrinkage stress experiments. Measuring techniques in those publications typically involve load cells, strain gauges, or cusp flexure measurements. However, none of those techniques actually measures stress but rather an arbitrary deformation from which a nominal load value can be derived at best. For example, measuring shrinkage “stresses” using load cells assumes a homogeneous uniaxial stress distribution. Such nominal stress value does not do justice to the spectrum of actual stress values, and has limited application because it cannot be associated with other shapes or conditions since stress is not a material property. Shrinkage measurement, on the other hand, can be considered a basic property, that can be determined independent of shape as a change in density. One may notice that light-activated composites do have a size dependency, simply because they depend on curing light attenuation. However, this means that shrinkage should be determined locally as a function of curing conditions, such as the applied light-intensity. Similarly, it can been argued that shrinkage depends on strain rate, where a heavily constrained composite results in a different shrinkage development. Determination of basic properties is obviously not synonymous with a “simple” property or experiment. Shrinkage behavior may rEquationuire a strain rate dependent property determination, where the transient properties must be determined as a function of the strain rate. Clearly, any meaningful utilization of such increasingly complex material model reconfirms the necessity of using numerical methods such as finite element analysis, which in return are completely dependent on experimental shrinkage research for input and validation.

HOW CAN POLYMERIZATION SHRINKAGE BE MODELED? Finite element analysis of polymerization shrinkage can be achieved at different levels of complexity. Finite element modeling of shrinkage can be as accurate and complex as the available input data allows. The limitations for modeling shrinkage and its effects are determined by the abilities of the modeler, the details of the supporting experimental data, and the access to computational and software resources. It is not the intention of this chapter to teach finite element analysis. However, a few comments will be made regarding the modeling of shrinkage. Polymerization shrinkage is basically densification, which results in a volumetric change. Few finite element programs offer specific polymerization shrinkage options but all should support an option for thermal expansion. Shrinkage can be modeled using the thermal expansion option, applying the linear shrinkage value as a coefficient of thermal expansion. Material contraction is achieved by lowering the temperature in the model. To accommodate time-dependent shrinkage, temperature changes can be applied as a function of time. During polymerization not only a dimensional change takes place, but there is also a change in viscoelastic properties. These can be applied simultaneously along with the density changes. Modeling the viscoelastic changes is not trivial. It has been discussed in previous sections that there is a fundamental transformation in viscoelastic response. Since shrinkage stresses depend on the history of shrinkage and viscoelastic development, only a correctly modeled transformation can accurately determine the shrinkage stress outcome. Unfortunately, due to the complexity of the experimental determination, there is still little data available to describe the complete time-dependent constitutive behavior during polymerization for all dental composites. Furthermore, not all numerical software is easily adapted for modeling the

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transformation, because it has not been a common concern in general engineering where the software is developed. However, it has been shown that a pre- and post-gel shrinkage model can serve as a simple method to estimate polymerization shrinkage stresses. Using pre- and post-gel shrinkage values approximates the nonlinear behavior in a quasi-linear analysis with a minimum of two time increments. During pre-gel increments, a very low elastic modulus and high Poisson’s ratio can simulate the nearly stress-free mostly-plastic flow, while the final elastic modulus and Poisson’s ratio in combination with the post-gel shrinkage are applied to generate the effective residual shrinkage stresses. Since the composite deformation during the pre-gel increments is simulated in an elastic model, it is imperative to ensure its viscous stress relaxing effect when progressing to post-gel modeling. There are various options to accomplish this. For example, by using an element switch. In this method, pre- and post-gel behavior is modeled by two different but overlapping elements that share the same nodes. The elements can be activated/ deactivated accordingly. This method ensures that the post-gel elements inherit the pre-gel shrinkage deformation without their elastic memory. Since the contribution of the pre-gel shrinkage simulation on the shrinkage stresses calculated using this method is negligible, it may sometimes be unnecessary to model the pre-gel shrinkage for the calculation of shrinkage stresses. However, this assumes a homogeneous polymerization reaction inside the composite material, which occurs in an auto-curing composite system. For light-activated composites, shrinkage development is not homogeneous. Time and location of pre- and post-gel shrinkage development will affect the outcome of the calculated shrinkage stresses. To simulate those conditions, each location (i.e., each element, or better yet, each element integration or gauss point) should be given its own time-dependent pre- and post-gel material response based on the local light intensity.

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The results of finite element analysis should be validated, especially when shrinkage models become more intricate. Validation is not to prove the validity of the proven concept of numerical analysis, but rather the modeling performance of the operator. Results of a finite element analysis should be critically tested against other models (convergence and alternative condition testing to ensure that final conclusions do not depend on the mesh distribution or other anomalies) and other data, preferably from simulation experiments, to assure that the shrinkage model and consEquationuently the results are realistic. It should be noted that validation is not based on exact duplication of experimental values. Even experimental values do not replicate exactly. However, it is the similarity in behavior and magnitude that indicates a positive validation.

SUMMARY: THE ROLE OF FINITE ELEMENT ANALYSIS IN SHRINKAGE RESEARCH The role of finite element analysis in polymerization shrinkage stress research was highlighted in this Chapter. Tracing some of the most popular shrinkage models, it was shown that the use of even very simple finite element models can prevent simplistic and potentially misleading shrinkage stress concepts that may hamper progress. Application of finite element analysis does not only facilitate better insight in shrinkage stress concepts, it also helps designing research experiments and provides a system in which the experimental polymerization shrinkage data can be integrated. A finite element approach does not replace experimental shrinkage research, but instead completes it. Individual composite properties such as shrinkage or nominal stress values derived from simulation experiments cannot be extrapolated to shrinkage stresses. However, in combination with finite element analysis, such experiments

Relationship Between Shrinkage and Stress

contribute vital information for polymerization shrinkage stress research. It was demonstrated that the more is learned about shrinkage stresses, the more complex its assessment becomes. Starting from a simple initial concept in which shrinkage stress was simply considered a property of the composite, it was shown in a number of shrinkage model refinements that stress depends on a large number of factors. These factors include the composite restoration as well as the tooth structure to which it is attached and the interfacial conditions. Beyond the modeling of polymerization shrinkage effects discussed in this chapter, the finite element approach can be seamlessly extended to study how such residual stresses interact with other clinical conditions, such as thermal and masticatory load cycles, and to design and test shrinkage stress management actions such as cavity design and incremental filling techniques. It is important for continued progress in shrinkage research that shrinkage stress is not considered by itself, but is applied within more comprehensive systems. Actions undertaken to control shrinkage stresses may impact other aspects. For example, liners have been advocated to reduce shrinkage stresses. Bonding to a compliant (“soft”) liner material “absorbs” the shrinkage deformation, reducing resistance encountered by the shrinking composite and thus generating less residual stresses. However, the long-term success of a composite restoration is ultimately determined by its ability to survive the oral function. Lining a cavity with compliant materials may reduce shrinkage stresses, but it could also prevent recovery of a tooth’s original stiffness and thus mechanical performance. Finite element analysis is perfectly suited to accommodate the study of such aggregated conditions, integrating the shrinkage model with other challenges that are encountered in an oral environment. The final chapter about polymerization shrinkage stress has not been written. Ongoing research continues to contribute corrections and

refinements of our current insight of the process and clinical impact. Due to its versatility as a powerful integration system, finite element analysis is most likely to remain a vital analysis tool that will continue to drive this progress and apply new insights along the way that improve clinical decisions.

REFERENCES Alomari, Q. D., Reinhardt, J. W., & Boyer, D. B. (2001). Effect of liners on cusp deflection and gap formation in composite restorations. Operative Dentistry, 26, 406–411. Bausch, J. R., de Lange, K., Davidson, C. L., Peters, A., & de Gee, A. J. (1982). Clinical significance of polymerization shrinkage of composite resins. The Journal of Prosthetic Dentistry, 48, 59–67. doi:10.1016/0022-3913(82)90048-8 Bouschlicher, M. R., & Rueggeberg, F. A. (2000). Effect of ramped light intensity on polymerization force and conversion in a photoactivated composite. Journal of Esthetic Dentistry, 12, 328–339. doi:10.1111/j.1708-8240.2000.tb00242.x Bowen, R. L. (1956). Use of epoxy resins in restorative materials. Journal of Dental Research, 35, 360–369. Bowen, R. L. (1963). Properties of a silica-reinforced polymer for dental restorations. The Journal of the American Dental Association, 66, 57–64. Bowen, R. L. (1967). Adhesive bonding of various materials to hard tooth tissues. VI. Forces developing in direct-filling materials during hardening. The Journal of the American Dental Association, 74, 439–445. Dauvillier, B. S., Aarnts, M. P., & Feilzer, A. J. (2003). Modeling of the viscoelastic behavior of dental light-activated resin composites during curing. Dental Materials, 19, 277–285. doi:10.1016/ S0109-5641(02)00041-6

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Dauvillier, B. S., Hübsch, P. F., Aarnts, M. P., & Feilzer, A. J. (2001). Modeling of viscoelastic behavior of dental chemically activated resin composites during curing. Journal of Biomedical Materials Research, 58, 16–26. doi:10.1002/10974636(2001)58:1<16::AID-JBM30>3.0.CO;2-7 Davidson, C. L., & de Gee, A. J. (1984). Relaxation of polymerization contraction stresses by flow in dental composites. Journal of Dental Research, 63, 146–148. de Groot, R., Peters, M. C. R. B., de Haan, Y. M., Dop, G. J., & Plasschaert, A. J. M. (1987). Failure stress criteria for composite resin. Journal of Dental Research, 66, 1748–1752.

Korioth, T. W. P., & Versluis, A. (1997). Modeling the mechanical behavior of the jaws and their related structures using finite element (FE) analysis. Critical Reviews in Oral Biology and Medicine, 8, 90–104. doi:10.1177/10454411970080010501 Meredith, N., & Setchell, D. J. (1997). In vitro measurement of cuspal strain and displacement in composite restored teeth. Journal of Dentistry, 25, 331–337. doi:10.1016/S0300-5712(96)00047-4 Momoi, Y., & McCabe, J. F. (1994). Hygroscopic expansion of resin based composites during 6 months of water storage. British Dental Journal, 176, 91–96. doi:10.1038/sj.bdj.4808379

Eick, J. D., & Welch, F. H. (1986). Polymerization shrinkage of posterior composite resins and its possible influence on postoperative sensitivity. Quintessence International, 17, 103–111.

Oysaed, H., & Ruyter, I. E. (1986). Composites for use in posterior teeth: mechanical properties tested under dry and wet conditions. Journal of Biomedical Materials Research, 20, 261–271. doi:10.1002/jbm.820200214

Feilzer, A. J., De Gee, A. J., & Davidson, C. L. (1987). Setting stress in composite resin in relation to configuration of the restoration. Journal of Dental Research, 66, 1636–1639.

Panitvisai, P., & Messer, H. H. (1995). Cuspal deflection in molars in relation to endodontic and restorative procedures. Journal of Endodontics, 21, 57–61. doi:10.1016/S0099-2399(06)81095-2

Jensen, M. E., & Chan, D. C. N. (1985). Polymerization shrinkage and microleakage. In G. Vanherle, & D.C. Smith (Eds.), Posterior composite resin dental restorative materials (pp. 243-262). Utrecht, The Netherlands: Peter Szulc Publishing Co.

Pearson, G. J., & Hegarty, S. M. (1989). Cusp movement of molar teeth with composite filling materials in conventional and modified MOD cavities. British Dental Journal, 166, 162–165. doi:10.1038/sj.bdj.4806750

Kanca, J. III, & Suh, B. I. (1999). Pulse activation: Reducing resin-based composite contraction stresses at the enamel cavosurface margins. American Journal of Dentistry, 12, 107–112. Kinney, J. H., Marshall, S. J., & Marshall, G. W. (2003). The mechanical properties of human dentin: a critical review and re-evaluation of the dental literature. Critical Reviews in Oral Biology and Medicine, 14, 13–29. doi:10.1177/154411130301400103

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Penn, R. W. (1986). A recording dilatometer for measuring polymerization shrinkage. Dental Materials, 2, 78–79. doi:10.1016/S01095641(86)80056-2 Sakaguchi, R. L., & Berge, H. X. (1998). Reduced light energy density decreases post-gel contraction while maintaining degree of conversion in composites. Journal of Dentistry, 26, 695–700. doi:10.1016/S0300-5712(97)00048-1

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Sakaguchi, R. L., Versluis, A., & Douglas, W. H. (1997). Analysis of strain gauge method for measurement of post-gel shrinkage in resin composites. Dental Materials, 13, 233–239. doi:10.1016/ S0109-5641(97)80034-6

Versluis, A., Tantbirojn, D., Pintado, M. R., DeLong, R., & Douglas, W. H. (2004b). Residual shrinkage stress distributions in molars after composite restoration. Dental Materials, 20, 554–564. doi:10.1016/j.dental.2003.05.007

Suh, B. I., Feng, L., Wang, Y., Cripe, C., Cincione, F., & de Rijk, W. (1999). The effect of the pulse-delay cure technique on residual strain in composites. The Compendium of Continuing Education in Dentistry, 20(2Suppl), 4–12.

KEY TERMS AND DEFINITIONS

Suliman, A. A., Boyer, D. B., & Lakes, R. S. (1993). Interferometric measurements of cusp deformation of teeth restored with composite. Journal of Dental Research, 72, 1532–1536. Tantbirojn, D., Versluis, A., Pintado, M. R., DeLong, R., & Douglas, W. H. (2004). Tooth deformation patterns in molars after composite restoration. Dental Materials, 20, 535–542. doi:10.1016/j.dental.2003.05.008 Unterbrink, G. L., & Muessner, R. (1995). Influence of light intensity on two restorative systems. Journal of Dentistry, 23, 183–189. doi:10.1016/0300-5712(95)93577-O Versluis, A., & Tantbirojn, D. (2006). Filling cavities or restoring teeth? Journal of Dental Sciences, 1, 1–9. Versluis, A., Tantbirojn, D., & Douglas, W. H. (1997). Why do shear bond tests pull out dentin? Journal of Dental Research, 76, 1298–1307. doi :10.1177/00220345970760061001 Versluis, A., Tantbirojn, D., & Douglas, W. H. (1998). Do dental composites always shrink toward the light? Journal of Dental Research, 77, 1435–1445. doi:10.1177/00220345980770 060801 Versluis, A., Tantbirojn, D., & Douglas, W. H. (2004a). Distribution of transient properties during polymerization of a light-initiated restorative composite. Dental Materials, 20, 543–553. doi:10.1016/j.dental.2003.05.006

Elastic Modulus: A material property that plays an important role in the amount of residual shrinkage stress because it describes the relationship between stress and strain (deformation). Finite Element Analysis: A numerical method developed to compute physical responses, based on subdivision of a (structural) system into smaller, simple shaped, interconnected elements. Polymerization Shrinkage: Material contraction that takes place during a polymerization reaction. Resin-based composites shrink when conversion of monomers results in a polymer network with a more compact structure. Shrinkage Model: A theoretical model that expresses the relationship between polymerization shrinkage and shrinkage stress based on scientific observations. Shrinkage models are used to assess shrinkage stresses and develop clinical procedures to minimize their effects. Shrinkage Relaxation: The decrease in initial residual shrinkage stresses over time due to material and environmental effects such as viscoelasticity and hygroscopic expansion. Shrinkage Stress: Stresses that are generated as a result of polymerization shrinkage. These can leave residual stresses in a restored tooth structure, in the composite material, and/or at the tooth-material interface. Shrinkage Value: Quantification of polymerization shrinkage. The amount of shrinkage can been expressed in various terms, such as volumetric, linear, total, pre-gel, post-gel, and effective shrinkage.

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Stress Concentration: A local area with increased stress level, e.g., at sharp corners or where materials with different elastic moduli meet. Stress concentrations are areas of increased risk for failure initiation because those stresses are most likely to exceed material strength properties first.

This work was previously published in Dental Computing and Applications: Advanced Techniques for Clinical Dentistry, edited by Andriani Daskalaki, pp. 45-64, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 4.22

Predicting Ambulance Diversion Abey Kuruvilla University of Wisconsin Parkside, USA Suraj M. Alexander University of Louisville, USA

ABSTRACT The high utilization level of emergency departments in hospitals across the United States has resulted in the serious and persistent problem of ambulance diversion. This problem is magnified by the cascading effect it has on neighboring hospitals, delays in emergency care, and the potential for patients’ clinical deterioration. We provide a predictive tool that would give advance warning to hospitals of the impending likelihood of diversion. We hope that with a predictive instrument, such as the one described in this paper, hospitals DOI: 10.4018/978-1-60960-561-2.ch422

can take preventive or mitigating actions. The proposed model, which uses logistic and multinomial regression, is evaluated using real data from the Emergency Management System (EM Systems) and 911 call data from Firstwatch® for the Metropolitan Ambulance Services Trust (MAST) of Kansas City, Missouri. The information in these systems that was significant in predicting diversion includes recent 911 calls, season, day of the week, and time of day. The model illustrates the feasibility of predicting the probability of impending diversion using available information. We strongly recommend that other locations, nationwide and abroad, develop and use similar models for predicting diversion.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Predicting Ambulance Diverson

BACKGROUND A majority of Emergency Departments (EDs) across the United States perceive they are at or over capacity (Lewin Group, 2002). As ED visits have been on the rise, the number of hospital EDs and beds available at hospitals has decreased (Nawar, Niska, & Xu, 2007; U.S. General Accounting Office [GAO], 2003) In literature, several authors discuss factors contributing to ED saturation, ranging from high patient acuity and bed shortages (Derlet, Richards, & Kravitz, 2001) to lab delays and nursing shortages (Richards, Navarro, & Derlet, 2000). When EDs reach their capacity, ED staff is unable to promptly care for new arrivals, and services within the hospitals are unable to accommodate the specific needs of new ambulance arrivals; hence ambulances must be diverted to other facilities that can provide critical care. This situation, referred to as “Ambulance Diversion,” not only results in delays in emergency care (Redelmeier, Blair, & Collins, 1994), but could also contribute to patients’ clinical deterioration (Glushak, Delbridge, & Garrison, 1997). We attempt to develop a mathematical model whereby hospitals/EMS agencies in a region can use 911 calls and diversion status of hospitals to predict the likelihood of the occurrence of diversion.

LITERATURE REVIEW A study of the literature shows that the rising trend in ambulance diversions started causing concern during the late 1980s (Richardson, Asplin, & Lowe, 2002), resulting in reports, position papers and task forces studying this problem from the early 1990s (Frank, 2001; Vilke, Simmons, Brown, Skogland, & Guss, 2001; Pham, Patel, Millin, Kirsch, & Chanmugam, 2006). However, owing to the elevated utilization level of EDs, ambulance diversion continues to be an issue today

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and is a common and increasing event that delays emergency medical care (Redelmeier et al., 1994). A wide range of literature exists, discussing the problem and various solutions have been suggested. A U.S. General Accounting Office survey (2003) found that while about two of every three EDs reported going on diversion at some point in fiscal year 2001, a much smaller portion—nearly 1 of every 10 hospitals—was on diversion more than 20 percent of the time. A cohort of twenty-two master’s degree candidates from the University of Virginia (2001) did a detailed study on diversion at Richmond hospitals, and outlined problems and solutions, analyzed via a simulation model. A government study (U.S. House of Representatives, 2001), quoting instances of diversion from the local press in all states, reported that ambulance diversions have impeded access to emergency services in the metropolitan areas of 22 states. Vilke et al., (2001) tested the hypothesis that, if one hospital could avoid ED diversion status, need for bypass could be averted in the neighboring facility. They concluded that reciprocating effects can be decreased with one institution’s commitment to avoid diversion, thus decreasing the need for diversion at a neighboring facility. Neely, Norton, and Young (1994) found that ambulance diversions increase transport times and distances. One community served by four hospitals reduced ambulance diversion during a year, by 34% (Lagoe, Kohlbrenner, Hall, Roizen, Nadle, & Hunt, 2003). This was accomplished by sending daily diversion statistics to hospital chief executive officers and ED directors and managers, along with each hospital individually implementing its own measures to reduce diversion hours. Schull, Mamdani, and Fang (2004) found that there was an increase of diversion hours during the months of November and December and correlated it to the effect of flu on diversion. Only two papers in medical literature referred to 911 calls being used in a transport decision. Anderson, Manoguerra, and Haynes (1998) explored the effect of diverting poison calls to a poison center and Neely et

Predicting Ambulance Diverson

al. (1994) found that diversion of 911 patients correlated strongly with unavailability of specific categories of beds. Several communities have also produced policies to honor patient requests regardless of diversion status and to limit the total time of diversion for each hospital. Some large metropolitan areas have established oversight task forces to study and track the diversion issue in their communities (GAO, 2003). Some communities have addressed this issue with political mandates by non-medical personnel banning the use of diversion (Anderson, 2003). One city’s solution (Lagoe & Jastremski, 1990) was the installation of an ambulance diversion system, whereby ambulances carrying patients with relatively minor injuries were diverted, when necessary, from the city’s busy emergency departments to less crowded ones in neighboring counties. A model that is able to predict the likelihood of a hospital or a combination of hospitals going on ambulance diversion would give advance warning to hospitals and allow them to take proactive steps to prevent it. The region itself would be better served by, for example, rerouting ambulances to alternate medical establishments. This would reduce transportation time and free up ambulances quicker for the next emergency. A survey of the literature yielded only one model for predicting an impending diversion. This model used the “work score” of an ED to predict ambulance diversion (Epstein & Tian, 2006). However, the model was developed for individual hospitals and does not consider the diversion status of other hospitals in the region; also, the workscore is not an easily available independent variable. The model presented in this paper defines the joint probability of the diversion status of a collection of hospitals, based on readily available data, such as 911 calls and current status of hospitals. The objective of this paper is to propose a methodology and evaluate its feasibility for predicting the likelihood of ambulance diversion using readily available data, such as 911 calls and

the diversion status of hospitals. Its purpose is to encourage metropolitan areas to develop similar models to predict ambulance diversion.

SELECTION AND EVALUATION OF METHODOLOGY Since the purpose of this paper is to encourage metropolitan areas to develop similar models to predict the likelihood of ED diversion, we used multinomial logistic regression, a widely accepted statistical methodology that relates independent variables to the likelihood of a dependent event. The model developed uses data such as 911 calls and the diversion status of hospitals in the region, which is readily available from health/EMS agencies. 911 calls have been used for transport decisions (Anderson et al., 1998), and there is a strong association between the number of ambulance patients and diversion (Schull, Lazier, Vermeulan, Mauhenney, & Morrison, 2003). Since the first call made by, or for, a potential ED patient is to “911,” the number of calls for transport to hospitals in the region is an obvious leading indicator of the patient load at an ED. Other variables that could affect the number of patients at an ED include the time of year or season (such as flu season), the day of week, and time of day; hence, these variables are also used as independent variables in the model. The proposed methodology was evaluated through a retrospective examination of real ambulance diversion and emergency 911 call data for the Kansas City metropolitan area. 911 call data was obtained for the Kansas City metropolitan area for the year 2003 and first 6 months of 2004. Twenty-nine of the 36 hospitals in the region were on diversion at some point during that period. The emergency 911 call data for the Kansas City metropolitan area was obtained from the Metropolitan Ambulance Services Trust (MAST), the ambulance service authority for the area. The data included the time a call was received, the type of

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emergency, and the hospital to which the patient was initially transported. The diversion data was obtained from “EMSystems,” a program that tracks diversion status of hospitals. The system helps emergency departments report their status and thus coordinate and communicate with the other hospitals/EMS agencies in the region. EMSystems includes the time at which a hospital went on diversion, the reasons for going on diversion, and how long it stays on diversion. Three hospitals with the most instances of diversion from the region were selected for analysis. The number of variables used in the model increases exponentially as the number of hospitals increases. Also, preliminary experiments indicated that a logistic model was useful in determining the probability of diversion at each of the hospitals based on the 911 calls, particularly for hospitals where diversion was most frequent. For these reasons, this study focuses on the three hospitals with the highest incidence of diversion. The preliminary logistic model was modified to account for the correlations among the diversion statuses of hospitals within a region, because the state of any one hospital affects the impending state of other hospitals. For instance, consider a region with two hospitals A and B, and Hospital A is similar to Hospital B with respect to any specialized types of patients it accepts, such as trauma patients. The current state of Hospital A clearly affects the future state of Hospital B because Hospital B has responsibility for a larger patient population when Hospital A is on diversion and vice-versa (Vilke et al., 2001). In order to simultaneously consider the current ED state at each of the hospitals to predict the probability of diversion at any one of the hospitals, a multivariate multinomial model was constructed. While the individual logistic models provided valuable information on their own, the collective valid are coded as 0, 1,…, K - 1; the vector x denotes the p covariates. The logit functions are represented as:

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 P (Y = k | x )  = x ' β, k = 1..., K − 1. gk (x ) = ln   P (Y = 0 | x ) 

where β is a vector of unknown coefficients Thus, the conditional probability that a hospital is in a specific state, that is on diversion or not, is given by the following equations: P (Y = 0 | x ) = P (Y = k | x ) =

1 K −1 g (x ) i

1 + ∑ i =1 e e

g k (x )

K −1 g (x ) i

1 + ∑ i =1 e

, k = 1,..., K − 1.

One of the key assumptions of the multinomial model is that the observations are independent. Thus for each of the models, only one out of every d observed values of the response variable is used, so that no variable representing 911 calls in a period is used twice. If all the observations of the response variable were used, nearly all observed 911 data would be used d times (t-d+1 to period t), in establishing the model, which would make the assumption of independence less plausible. This approach also significantly decreased the computation time and improved the overall fit of the models. The data processing and statistical analysis were accomplished using Microsoft Excel and the statistical packages R and SAS. The data on ambulance diversion and 911 calls is partitioned into bins of length 1 hour. For each bin, the response variable gives the conditional probability of the future state of the three hospitals. There are eight possible outcomes that could occur in the next period which can be denoted by O, A, B, C, AB, AC, BC, and ABC. These are coded as k = 0, 1, …, 7, respectively. The letters denote which hospitals are on diversion. For example, AC represents the state where hospitals A and C are on diversion, but hospital B is not. The

Predicting Ambulance Diverson

state O represents the situation where none of the hospitals are on diversion. There are several explanatory variables that are represented in the vector x. This includes the state of diversion during the current hour, which is a categorical variable with the same eight possible states as the response variable. Also, included is another categorical variable representing the period of the day, which is defined by partitioning the day into early morning (midnight–6 a.m.), morning (6 a.m.–noon), afternoon (noon–6 p.m.), and evening (6 p.m.–midnight). Additionally, indicator variables enable distinguishing between different years, weekdays and weekends, and seasons, such as flu season (which we have specified as being the months of November and December). In addition, three variables ct, ct-1, and ct-2 that represent the number of 911 calls during the current hour, the previous hour, and the hour before the previous hour, are included.

The explanatory variables included in the model are shown in Table 1. The multinomial logistic regression model is defined using the explanatory variables and the historical data on the state of the hospital EDs. The statistical significance of the variables is shown in the analysis of effects table, Table 2. As indicated in the table, only the variable for weekend is not statistically significant at a 5% level; however, it is a natural variable to include in the model. The Deviance and Pearson goodness-of-fit statistics also give no evidence against the model. The odds ratios are shown in Tables 3, 4, and 5.

DISCUSSION The results indicate that the most recent 3 hours of the overall number of 911 calls are significant

Table 1. Codes for the data set Variable Name

Description

Codes

Current

The current state of diversion.

O = no hospitals on diversion A = only A is on diversion B = only B is on diversion C = only C is on diversion AB = A and B are on diversion AC = A and C are on diversion BC = B and C are on diversion ABC = all hospitals are on diversion

Period

The period of the day.

0 = early morning (midnight–6am) 1 = morning (6am–noon) 2 = afternoon (noon–6pm) 3 = evening (6pm–midnight)

Weekend

An indicator variable for the weekend.

0 = weekday 1 = weekend

Year

An indicator variable for the year 2003.

0 = year 2004 1 = year 2003

Flu

An indicator variable for the flu season.

0=not flu season 1=flu season

The number of 911 calls during the current hour.

Non-negative integer

The number of 911 calls during the previous hour.

Non-negative integer

The number of 911 calls during the hour before the previous hour.

Non-negative integer

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Table 2. Effect table for factors considered Effect

df

Chi-Square

P-Value

CURRENT

49

13590.9257

<.0001

PERIOD

21

154.9376

<.0001

WEEKEND

7

12.5411

.0841

YEAR

7

46.2325

<.0001

FLU

7

35.9705

<.0001

7

110.5531

<.0001

7

31.6680

<.0001

7

17.9957

.0120

in predicting whether hospitals will go on diversion. Beyond that, the calls do not seem to be significant in predicting diversion. Also, other factors like the current state of diversion, indicator variables for flu, and quarter of day and the year were highly significant. The logistic model that has been developed illustrates the feasibility of predicting the probability of diversion. Since a number of EMS agencies are currently using online technologies with the data necessary for prediction readily available, this type of analysis might be timely. An early warning of impending diversion provided by this model could enable hospitals and EMS agencies to take appropriate actions to prevent and mitigate the effects of diversion. Such actions would include reallocation of resources and directing the transport of patients by EMS. The multinomial model presented in this paper is shown to be able to effectively predict the conditional probability of diversion at hospitals that are frequently on diversion. The model illustrates that the number of 911 calls and other factors are more closely linked with the occurrence of diversion, than with the duration of diversion. Hospitals have internal factors they take into consideration when they decide to go on diversion, such as average waiting time, number of patients in the waiting room, or number of ambulances waiting. These policies and decisions are currently hospital specific and need to be integrated to derive maximum benefits from models, such as those presented in

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Table 3. Odds ratios for weekend, year, and flu effects Odds Ratio Estimates

Effect

Future

Point

95% Wald

Estimate

Confidence Limits

weekend 0 vs 1

ABC

1.091 0.725 1.642

weekend 0 vs 1

ABO

0.700 0.494 0.990

weekend 0 vs 1

AOC

0.898 0.677 1.190

weekend 0 vs 1

AOO

0.835 0.662 1.052

weekend 0 vs 1

OBC

1.042 0.750 1.448

weekend 0 vs 1

OBO

0.788 0.617 1.007

weekend 0 vs 1

OOC

1.087 0.906 1.304

yr 0 vs 1

ABC

1.949 1.268 2.993

yr 0 vs 1

ABO

1.249 0.873 1.786

yr 0 vs 1

AOC

1.618 1.219 2.147

yr 0 vs 1

AOO

1.129 0.905 1.409

yr 0 vs 1

OBC

1.640 1.188 2.264

yr 0 vs 1

OBO

1.036 0.817 1.313

yr 0 vs 1

OOC

1.617 1.373 1.906

flu 1 vs 0

ABC

1.621 0.964 2.725

flu 1 vs 0

ABO

0.575 0.338 0.977

flu 1 vs 0

AOC

1.218 0.841 1.764

flu 1 vs 0

AOO

0.820 0.600 1.122

flu 1 vs 0

OBC

1.561 1.082 2.254

flu 1 vs 0

OBO

1.329 0.993 1.779

flu 1 vs 0

OOC

1.520 1.241 1.860

this paper, that signal impending diversion. The model presented in this paper could probably be improved by including additional causal factors like downstream inpatient bed availability to predict diversion. Also, the model is specifically developed using Kansas City EM system and 911 call data. The authors encourage the development of similar models for other regions in the United States. Similar models could also be developed in metropolitan areas of Canada or UK or where similar systems exist for collecting data on ambulance arrivals as well as emergency call data and where diversion is a public health issue.

Predicting Ambulance Diverson

Table 4. Odds ratios for different periods Odds Ratio Estimates

Effect

Future

Table 5. Odds ratios for calls during different times Odds Ratio Estimates

Point

95% Wald

Estimate

Confidence Limits

period 1 vs 0

ABC

4.359 1.899 10.005

period 1 vs 0

ABO

1.752 1.053 2.915

period 1 vs 0

AOC

4.662 2.794 7.778

period 1 vs 0

AOO

2.020 1.458 2.799

period 1 vs 0

OBC

1.604 0.926 2.776

period 1 vs 0

OBO

0.795 0.574 1.100

period 1 vs 0

OOC

1.305 0.997 1.707

period 2 vs 0

ABC

6.403 2.967 13.819

period 2 vs 0

ABO

1.654 0.861 3.175

period 2 vs 0

AOC

5.153 3.053 8.697

period 2 vs 0

AOO

1.188 0.794 1.776

period 2 vs 0

OBC

1.500 0.827 2.719

period 2 vs 0

OBO

0.751 0.485 1.162

period 2 vs 0

OOC

1.215 0.905 1.631

period 3 vs 0

ABC

1.017 0.516 2.002

period 3 vs 0

ABO

0.599 0.348 1.032

period 3 vs 0

AOC

1.019 0.641 1.621

period 3 vs 0

AOO

0.527 0.372 0.749

period 3 vs 0

OBC

1.381 0.859 2.220

period 3 vs 0

OBO

1.028 0.723 1.462

period 3 vs 0

OOC

1.045 0.815 1.339

ACKNOWLEDGMENTS The authors wish to thank Todd Stout, President, First Watch Solutions; Jonathan D. Washko, President, Washko & Associates; Gil Glass, Director of Operations and the Metropolitan Ambulance Services Trust at Kansas City, Missouri, for providing the impetus and data for this study; Ryan Gill, Assistant Professor, University of Louisville, for assisting with data analysis; the Editor-in-Chief of the journal, Professor John Wang; and reviewers for their invaluable input in developing this paper.

Effect

Future

Point

95% Wald

Estimate

Confidence Limits

Calls during time t Xt1

ABC

1.120 1.058 1.186

Xt1

ABO

1.072 1.015 1.131

Xt1

AOC

1.073 1.031 1.116

Xt1

AOO

1.029 0.994 1.065

Xt1

OBC

1.139 1.086 1.195

Xt1

OBO

1.089 1.049 1.131

Xt1

OOC

1.130 1.102 1.159

Calls during time t-1 Xt2

ABC

1.065 1.004 1.129

Xt2

ABO

1.029 0.973 1.088

Xt2

AOC

1.088 1.044 1.133

Xt2

AOO

1.067 1.029 1.105

Xt2

OBC

1.056 1.007 1.108

Xt2

OBO

1.000 0.962 1.040

Xt2

OOC

1.058 1.031 1.086

Calls during time t-2 Xt3

ABC

1.012 0.956 1.072

Xt3

ABO

0.961 0.909 1.017

Xt3

AOC

1.004 0.966 1.044

Xt3

AOO

0.967 0.934 1.002

Xt3

OBC

1.058 1.009 1.110

Xt3

OBO

1.011 0.972 1.051

Xt3

OOC

1.026 1.000 1.053

REFERENCES Anderson, B. (2003, February 25). Fresno County bans diversion of ambulances. Sacramento Bee. p. A1. Anderson, B. D., Manoguerra, A. S., & Haynes, B. E. (1998). Diversion of 911 calls to a poison center. Prehospital Emergency Care, 2(3), 176–179. .doi:10.1080/10903129808958867

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Cohort. (2002). Metropolitan Richmond hospital diversions: A system analysis and change proposal. Project report, Systems and Information Engineering Executive Master’s Program. Charlottesville, VA: University of Virginia. Derlet, R. W., Richards, J. R., & Kravitz, R. L. (2001). Frequent overcrowding in U.S. emergency departments. Academic Emergency Medicine, 8(2), 151–155. .doi:10.1111/j.1553-2712.2001. tb01280.x Epstein, S. K., & Tian, L. (2006). Development of an emergency department work score to predict ambulance diversion. Academic Emergency Medicine, 13(4), 421–426. .doi:10.1111/j.1553-2712.2006.tb00320.x Frank, I. C. (2001). ED crowding and diversion: Strategies and concerns from across the United States. Journal of Emergency Nursing: JEN, 27(6), 559–565. .doi:10.1067/men.2001.120244 Glushak, C., Delbridge, T. R., & Garrison, H. G. (1997). Ambulance diversion. Standards and Clinical Practices Committee, National Association of EMS Physicians. Prehospital Emergency Care, 1(2), 100–103. .doi:10.1080/10903129708958797 Hosmer, D., & Lemeshow, S. (2000). Applied Logistic Regression (2nd ed.). New York: John Wiley & Sons. doi:10.1002/0471722146 Lagoe, R. J., & Jastremski, M. S. (1990). Relieving overcrowded emergency departments through ambulance diversion. Hospital Topics, 68(3), 23–27. Lagoe, R. J., Kohlbrenner, J. C., Hall, L. D., Roizen, M., Nadle, P. A., & Hunt, R. C. (2003). Reducing ambulance diversion: A multihospital approach. Prehospital Emergency Care, 7, 99–108. .doi:10.1080/10903120390937184 Lewin Group. (2002). Emergency department overload: A growing crisis. Chicago: American Hospital Association.

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Nawar, E. W., Niska, R. W., & Xu, J. (2007). National hospital ambulatory medical care survey: 2005 Emergency department summary (Advance data from vital and health statistics No. 386). Hyattsville, MD: National Center for Health Statistics. Retrieved March 30, 2009, from http:// www.cdc.gov/ nchs/ data/ ad/ ad 386.pdf Neely, K. W., Norton, R. L., & Young, G. P. (1994). The effect of hospital resource unavailability and ambulance diversions on the EMS system. Prehospital and Disaster Medicine, 9(3), 172–177. Pham, J. C., Patel, R., Millin, M. G., Kirsch, T. D., & Chanmugam, A. (2006). The effects of ambulance diversion: A comprehensive review. Academic Emergency Medicine, 13(11), 1220–1227. .doi:10.1111/j.1553-2712.2006.tb01652.x Redelmeier, D. A., Blair, P. J., & Collins, W. E. (1994). No place to unload: A preliminary analysis of the prevalence, risk factors, and consequences of ambulance diversion. Annals of Emergency Medicine, 23(1), 43–47. .doi:10.1016/S01960644(94)70006-0 Richards, J. R., Navarro, M. L., & Derlet, R. W. (2000). Survey of directors of emergency departments in California on overcrowding. The Western Journal of Medicine, 172(6), 385–388. .doi:10.1136/ewjm.172.6.385 Richardson, L. D., Asplin, B. R., & Lowe, R. A. (2002). Emergency department crowding as a health policy issue: Past development, future directions. Annals of Emergency Medicine, 40(4), 388–393. .doi:10.1067/mem.2002.128012 Schull, M. J., Lazier, K., Vermeulan, N., Mauhenney, S., & Morrison, L. J. (2003). Emergency department contributors to ambulance diversion: A quantitative analysis. Annals of Emergency Medicine, 41(4), 467–476. .doi:10.1067/mem.2003.23

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Schull, M. J., Mamdani, M. M., & Fang, J. (2004). Community influenza outbreaks and emergency department ambulance diversion. Annals of Emergency Medicine, 44(1), 61–67. .doi:10.1016/j. annemergmed.2003.12.008 U.S. General Accounting Office. (2003). Hospital emergency departments: Crowded conditions vary among hospitals and communities (GAO03–460). Washington, DC: Author. Retrieved March 30, 2009, from http://www.gao.gov/ new. items/ d03460.pdf

U.S. House of Representatives. (2001). National Preparedness: Ambulance Diversions Impede Access to Emergency Rooms. Washington, DC: Special Investigative Division, Minority Staff for Representative Henry A. Waxman, Committee on Government Reform, U.S. House of Representatives. Vilke, G. M., Simmons, C., Brown, L., Skogland, P., & Guss, D. A. (2001). Approach to decreasing emergency department ambulance diversion hours. Academic Emergency Medicine, 8(5), 526.

This work was previously published in International Journal of Information Systems in the Service Sector (IJISSS), Volume 2, Issue 1, edited by John Wang, pp. 1-10, copyright 2010 by IGI Publishing (an imprint of IGI Global).

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Section V

Organizational and Social Implications This section includes a spacious range of inquiry and research pertaining to the behavioral, emotional, social and organizational impact of clinical technologies around the world. From demystifying human resources to privacy in mammography, this section compels the humanities, education, and IT scholar all. Section 5 also focuses on hesitance in some hospital members’ integration with clinical technologies, and methods therein. With more than 10 chapters, the discussions on hand in this section detail current and suggest future research into the integration of global clinical technologies as well as implementation of ethical considerations for all organizations. Overall, these chapters present a detailed investigation of the complex relationship between individuals, organizations and clinical technologies.

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Chapter 5.1

Demystifying E-Health Human Resources Candace J. Gibson The University of Western Ontario, Canada H. Dominic Covvey University of Waterloo, Canada

ABSTRACT The introduction and use of information and communication technologies (ICT) in health care, particularly the electronic health record (EHR), may be seriously hampered or delayed by the lack of available human resources with the necessary skills and competencies in e-health. A number of different types of professionals are needed, and an appropriate mix of skills and workers who can complement one another in the final deployment of the EHR and in the appropriate and best use and management of the health information it contains. These include health informatics (HI)

professionals or health informaticians, health information management (HIM) professionals, and others, with not only knowledge of ICT, but also knowledge of the health system, data standards, and interoperability across platforms; privacy and security of health records; human factors and process engineering; project management and technology adoption; and user-supporting mechanisms. A human resources strategy is needed to address the current shortage of skilled workers and to develop a long term strategy for education and training of e-health personnel necessary to ensure the continued quality of health data collected, its security and confidentiality, and to manage and maintain the systems and data in the future.

DOI: 10.4018/978-1-60960-561-2.ch501

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Demystifying E-Health Human Resources

INTRODUCTION Healthcare systems across the world have undergone massive transformations in the past decade, including the restructuring of hospital, physician, and home care services; the consolidation of health services; and the introduction of digital technologies and electronic information systems. Increasingly, health information is being recognized as the key to more efficient healthcare delivery. Information and communications technologies (ICT)-based information systems and longitudinal electronic health records (EHRs) are being implemented to assist in maintaining continuity of care across institutions and regions. These systems are intended to provide access by geographically-dispersed authorized users, make available decision support tools, support access to knowledge bases of evidence-based clinical practice, and increase the productivity and quality of health care. They are also seen as providing greater margins of safety by preventing, detecting, and assisting in the management of adverse events that occur due to the lack of the right information at the right time and place. Most of the effort to date has been spent on putting the necessary enabling (e-health) infrastructure in place – the technological apparatus and services serving institutions, a region or a nation, integrated into a shared network of health information systems. Little effort has been invested in developing the necessary human resources – informatics professionals to design and develop or define and deploy the needed technological capabilities, to ensure the quality of health data collected, to manage and maintain the systems and data, and to ensure their productive use and evaluation. Although the e-health infrastructure has been touted as a means of connecting health care professionals across the globe and providing needed training, the infrastructure itself may not exist if health informatics professionals are not available to implement it (Hersh & Wright, 2008; Ozbolt,

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2008). Unless professionals with the requisite competencies are available, we will fail in our efforts to manage, understand, and appropriately interpret and apply the growing flood of health data and information. Unfortunately, in many countries calls for a national strategy to address the significant health informatics (HI) and health information management (HIM) capacity gaps have gone unheeded. Several healthcare industry surveys have indicated that competent people are in short supply and that thousands of HI/HIM jobs go unfilled or are filled by less-than-qualified personnel. In some jurisdictions, e.g., the U.K. and Australia, national strategies to address HI needs have been suggested (Australia Dept of Health and Aging, 2003a; 2003b; UK-NHS Connecting for Health, 2002; 2009). No such strategy has been undertaken at any level in Canada or the United States1. The implementation of a national e-health infrastructure requires professionals with many core competencies, including, but going well-beyond, knowledge of ICT. These professionals must also deeply understand the health system; methods of re-engineering and project management; data standards and interoperability across platforms; privacy and security of records; human factors and process engineering; and technology adoption and user-supporting mechanisms, as examples (Covvey, Zitner & Bernstein, 2001). Defining a human resources (HR) development strategy to address this need is a significant challenge.

THE E-HEALTH HUMAN RESOURCES CHALLENGE As integrated, electronic health information systems become commonplace in the health system, the traditional roles of health records technicians, information managers, data analysts, and information services staff will also change. New roles for these already highly trained health system workers are emerging, as well as the need for a new type

Demystifying E-Health Human Resources

of professional with new competencies. These e-health (electronic health) workers comprise professionals like health informaticians, health information management professionals, technical specialists, project managers and others. These workers will take on new roles that enable unprecedented improvements in the health system through ICT and complementary factors that support its delivering desired benefits. Developing a human resources strategy for the e-health worker of the future is difficult because it does not fall easily into the traditional model of supply and demand used for determining the complement and distribution of physicians, nurses, and other regulated health professionals. Other than certified health information management professionals (CHIM in Canada, RHIA or RHIT in the United States), there is no certifying body in most countries, nor regulation/legislation that requires certification, licensure or registration of any of the variety of “e-health” professional. In other words defining and finding those individuals with the skills to form part of the necessary e-health workforce is problematic. An HR strategy for e-health does not fit well with models of population health needs-based planning either. Although e-health and the electronic health record are intuitively felt to be necessary for the future and for improvement in the safety and quality of health care, the long term benefits of an electronic record have still not been completely proven, and certainly, in the short term, the costs of implementation, especially on a national scale, can be enormous (e.g., the NHS in the UK has committed £12.4 billion over 10 years to implement its national program for information technology – the largest civil information technology project in the world (Coeira, 2007)). Virtually all countries are experiencing an aging demographic, with an increasing number of chronic illnesses, and a significant number of health professionals approaching retirement age. These factors impact the demand for health services, access to care, the cost of care, and our

concerns about patient safety. Moreover, they will continue to drive regional and national governments towards the introduction of ICT and the shared electronic record to deliver increasingly dispersed healthcare services and to provide accessible and affordable quality health care. What we do know is that, in all jurisdictions that have begun the journey towards e-health, the lack of skilled human resources is cited as a barrier to implementation, and the demand for skilled health workers is high, but the supply is low. The U.K.’s National Health Service (NHS) Connecting for Health project has been “hampered by a workforce that lacks experience in largescale IT implementation and familiarity with health services” (Coeira, 2007, p.3). In Canada it’s estimated that at least $7 to $10 billion is needed to implement a pan-Canadian EHR and that roughly 9,000 health informatics professionals (although “e-health professionals” would be a better descriptor) are required (Smith, 2005). The lack of healthcare IT resources is slowing the rate of progress in health informatics projects across the country. Although funding of e-health projects has increased, the supply of skilled human resources has not, and a shortage of HI professionals is “a significant risk to successful electronic health record initiatives” (LaFleche & Gardner, 2008; Seaton, 2008). Within Canada three major professional associations, COACH (the Canadian Health Informatics Association), CHIMA (the Canadian Health Information Management Association), ITACHealth (the health division of the Information Technology Association of Canada - ITAC), along with Canada Health Infoway, and the Information and Communications Technology Council of Canada (ICTC) have joined together to initiate the first national sector study of e-health/health informatics/health information management professionals to assess the numbers of available e-health professionals (LaFleche & Gardner, 2008). In the United States an analysis of about 5,000 hospitals using the HIMSS Analytics Database

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Demystifying E-Health Human Resources

(Hersh & Wright, 2008) found an overall staffing ratio of 0.142 IT full time equivalents per hospital bed and, extrapolating to all hospital beds in the US, an estimated workforce size of 108,390 FTE. It was estimated that, to achieve a fully electronic medical record with shared data contributing to an electronic health record, a total of 149,174 IT FTE would be needed – or an increase in supply of 40,784 HIT personnel. An additional study, looking at the estimated workforce for deployment of the nationwide health information network and implementation of electronic records for the 400,000 practicing physicians who do not have them, estimated the need for another 7,600 FTEs. For the 4,000 hospitals still needing EHRs, another 28,600 specialists and about 420 other professionals are needed to build the infrastructure in communities to interconnect all these systems (DHHS-NHIN, 2007). A workforce survey of health information management (HIM) professionals, those individuals who work with these health information and health records systems, showed HIM professionals moving out of the traditional in-patient hospital settings (about 53%) and into outpatient, clinic, physicians’ offices, and consulting firms (about 19%) (Wing, Langelier, Continelli & Armstrong, 2003). US Department of Labor Statistics indicates that HIM is one of the fastest growth areas and that an additional 49% of practitioners will be needed by 2010.

THE NATURE OF E-HEALTH PROFESSIONALS: THEIR COMPETENCIES, THEIR ROLES, AND THEIR WORK Eysenbach (2001, para. 3) has defined e-health as “an emerging field in the intersection of medical informatics, public health and business, referring to health services and information delivered or enhanced through the Internet and related technologies. In a broader sense, the term characterizes not only a technical development, but also a

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state-of-mind, a way of thinking, an attitude, and a commitment for networked, global thinking, to improve health care locally, regionally, and worldwide by using information and communication technology”. Thus e-health refers to the applications of informatics concepts, methods, and tools to the health system and the people (providers, administrators, patients, families) involved in it. As such, e-health represents the results of the work of Research and Development Informaticians, HIM researchers and developers, and researchers and developers in other disciplines being applied by Applied Health Informaticians, HIM professionals, technical professionals and professionals from other disciplines (Covvey et al., 2001; Covvey et al., 2009; Hersh, 2006).

Some Definitions and Clarification of Terms E-Health Professionals E-health Professionals are those individuals who deploy and use informatics concepts, methods and tools in support of health system processes. E-health Professionals include: Health Informatics Professionals, Health Information Management Professionals, technical professionals, and an almost unlimited variety of other professionals who work together in the e-health team to apply informatics concepts, methods and tools to improving the efficiency and effectiveness of the health system.

Health Informaticians (or Health Informaticists) Health Informaticians are e-health professionals who have become competent in the discipline of health informatics. Health Informatics is the discipline that researches, formulates, designs, develops, implements and evaluates informationrelated concepts, methods, and tools (e.g., ICT)

Demystifying E-Health Human Resources

to support clinical care, research, health services administration and education. There are two major types of health informaticians: •

•

Applied Health Informaticians are professionals who understand the fundamental concepts of health informatics and deploy methods (including planning, management, analytic, procedural, etc.) and tools (e.g., information and communications systems) in support of health system processes. The competencies expected of these professionals are documented in “Pointing the Way” (Covvey et al, 2001: Section A on Applied Health Informatics). Generally speaking, applied health informaticians qualify for this designation by completion of a bachelor’s degree in health informatics or another field, and/or an advanced degree in health informatics, or by achieving professional certification. Research and Development Health Informaticians are (typically academic or highly innovative) professionals who design, develop and evaluate health informatics innovative concepts, methods and tools. The competencies expected of these professionals are documented in “Pointing the Way” (Covvey et al, 2001: Section B on Research and Development Health Informatics). Generally speaking, research and development health informaticians qualify for this designation by completion of a bachelor’s degree in health informatics or another field and/or an advanced degree in health informatics. When based at an academic institution, these informaticians will generally have a Ph.D. degree or equivalent.

In addition to these professional informaticians, there are users, such as clinicians and administrators who have learned sufficient health informatics to be modern, fully capable clinical

or administrative professionals. We have called these “informatics-enabled professionals”. There are also specialists of both types of professional informatician: clinical informaticians, and public health, nursing, telehealth, health policy, and medical imaging informaticians, to name a few.

Technical Professionals Technical Professionals are members of the ehealth team who are trained in one or more technical areas. They include those trained in system (i.e. operating system, database, programming languages, software engineering and development) and applications software (i.e. productivity tools, departmental information systems, office systems, etc.), hardware, communications and networking, biomedical engineering, and a variety of methods or procedures, such as project management, security, risk management, training, documentation, and process engineering.

Health Information Management Professionals Health Information Management Professionals are members of the e-health team who are trained in the discipline of health information management. The Canadian Health Information Management Association (CHIMA) defines health information management as the discipline that focuses on healthcare data and the management of healthcare information, regardless of the medium and format. Research and practice in HIM address the nature, structure and translation of data into usable forms of information for the advancement of health and health care of individuals and populations. “Health Information Management professionals provide leadership in all aspects of clinical information management at both the micro and macro levels. At the micro (or individual record level) HIM professionals support the collection,

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Demystifying E-Health Human Resources

use, access and disclosure, to the retention and destruction of health information regardless of format. HIM professionals perform qualitative analysis on the documentation within the health record and are responsible for the access to and security of health records. HIM professionals are advocates of the individual’s right to private, secure and confidential health information. At the macro (or aggregate data level), HIM professionals deal with the information through the health system, analyze statistics, manage complex information systems including registries and work with public, private and key stakeholders in understanding and using health data to improve the health of Canadians” (https://www.echima. ca/about-us). The American Health Information Management Association (AHIMA) defines health information management as the body of knowledge and practice that ensures the availability of health information to facilitate real-time healthcare delivery and critical health-related decision making for multiple purposes across diverse organizations, settings, and disciplines. HIM roles are described as health information managers, clinical data specialists, patient information coordinators, data quality managers, information security manager, data resource administrator, research and decision support specialist. The domains of HIM practice on a broad level include planning (administration, policy development, strategic planning), informatics, and health information technology.

Other Professionals Many other types of professionals from many disciplines may be members of the e-health Team. Examples of these include: ICT systems management, business, management, human resources, privacy and industrial engineering professionals.

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THE PRODUCTION OF E-HEALTH PROFESSIONALS Training programs for e-health and/or health informatics professionals are relatively new and, at best, producing well under the number of graduates needed to address the projected demand for e-health professionals. Roughly 100 students graduate every year from HI programs and 200 HIM professional graduates from HIM programs across Canada – not enough to provide the thousands needed for EHR implementation in the short term. Short term strategies involve providing training for practitioners in other disciplines (e.g. HIM, nursing, clinicians) to take on some of these e-health roles; increasing retention of current HI professionals by increasing the profile of the profession and offering opportunities for professional certification; marketing the e-health sector jobs to computer science and engineering graduates; and easing the transition of foreigntrained HIs and HIMs into positions in Canada (LaFleche & Gardner, 2008). Over the long term a coordinated HR strategy is needed linking training in e-health at all levels (undergraduate and graduate) to industry and community needs.

DEFINING AND EXECUTING A HUMAN RESOURCES STRATEGY FOR E-HEALTH: A SURVEY OF E-HEALTH HR STRATEGIES United Kingdom: National Health Service’s Connecting for Health Health Information Workforce Capacity Building The National Health Service (NHS) in the United Kingdom has taken on one of the largest information technology projects in the world to improve delivery of services and the quality of

Demystifying E-Health Human Resources

care across its acute care hospitals, primary and community care organisations, and mental health trusts (Coeira, 2007; NHS, Connecting for Health, 2002; NHS, 2009). The UK health system has certain advantages over other national efforts in that it is a single, public health system and has been able, to a certain extent, to mandate uniform adoption of systems and standards. The IT platform consists of a secure, national IT network with an electronic prescription service, messaging service, and a central link or “Spine” to a patient register, that will also contain a Summary Care Record (the SCR is a minimum data set of patient data available anywhere, anytime). The path to implementation has not been completely smooth with cost overruns, many delays, clinician dissatisfaction, and backpedalling on information governance issues and informed consent models (i.e., patient data is automatically included on the Spine and individuals must ‘opt out’ of the SCR). The project has been slowed by lack of a workforce that has experience in large scale IT implementation and the lack of computer technologists who are familiar with health services (Coeira, 2007). It has been suggested that a staged approach to implementation with opportunities to increase the skills base and capacity of the HI workforce might have been more desirable and sustainable. There are lingering questions about the capacity of system suppliers to be able to deploy “their best and brightest” to other parts of the world (Coeira, 2007). HI workforce capacity building is described within the context of one main employer – the NHS. The NHS can more easily define and promote health informatics by categories where workers can progress within or between these categories within virtually a single main employer (and where the staff member has seniority). In Canada and other countries without a truly national health service, the career progression may not appear as seamless, since health care is planned provincially (or by state or territory). National qualification standards would assist in portability

and clear progression within the profession and would assist in attracting students. In much of the literature (and within the NHS surveys), the term “health informatics”, as a more inclusive term, has been used to define the roles, skills, and competencies that will be required of these individuals and encompass this field of work and inquiry, roughly the equivalent to our use of the term e-health professional in this chapter. In its broadest terms health informatics was defined by the NHS as: “The knowledge, skills and tools which enable information to be collected, managed, used and shared to support the delivery of healthcare and to promote health”(Making Information Count: A Human Resources Strategy for Health Informatics Professionals, UK- NHS, 2002, p.3). This definition emphasizes the uses of health informatics and, in a further breakdown of who is working in HI, the NHS identifies information and communication technology staff (who develops, manages, and supports the ICT infrastructure – about 37%) and the health records staff (who collates, organizes, retrieves patient information – about 26%) as the first and second largest groups, respectively. Additionally they identify knowledge management staff (e.g., health sciences librarian), clinical coders, information management staff (who retrieves, analyzes, interprets, and presents data and information for planning and delivery of services), health informatics senior managers and directors of services, and clinical informatics staff (doctors and other clinical professionals involved in the development and implementation of electronic tools to support the cycle of clinical information) as HI professionals (ASSIST, 2006). The Health Informatics Workforce Survey commissioned by ASSIST (the Association for Informatics Professionals in Health and Social Care) in 2006 recommended the establishment of a formal professional body for Health Informatics and the creation of a meaningful strategic work-

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force plan in light of projected staffing shortfalls (ASSIST, 2006). HI workforce numbers were estimated to be about 25,000 or roughly 2% of the total NHS workforce. The NHS has followed up with a national strategy to develop, recruit, and retain a skilled HI workforce. Their strategy includes the mapping of function and organizational roles of HI workers; projections on needs and numbers of workers at local levels;collaboration with educational institutes and universities for the development of courses/programs in health informatics that can be delivered at the local level; career progression from entry level HI skills through HI specialists to HI managers and directors that is accompanied by increasing levels of education and work experience; and the development of recognition and awards for HI leaders and workers (see NHS Connecting for Health: Professionalising HI). In an earlier 2002 survey along with insufficient HI workforce capacity, low morale and lack of capability in the current NHS health informatics workforce were identified (NHS, 2002). The capability – education, training and development of the workforce was found to be greatly lacking with a huge competency shortfall across all subject areas and all staff groups. Educational providers in the UK have been reluctant to invest scarce resources in health informatics subjects when they are not specified as a requirement. The NHS has developed a health informatics professional’s framework with associated educational and training benchmarks to address the problem, but there is no nationally recognized funding structure for HI education and training in the UK. Gaining qualifications that prove competence has not been linked to career progression or a pay increase. Recruitment, retention, and staff morale of health informatics practitioners in the NHS also presents challenges. There is difficulty at all levels of need in recruiting and retaining health informatics staff especially recruiting into senior posts and retaining Health Records and ICT staff; 43% of HI staff moved from a similar post to the

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private sector and 29% moved to different NHS or healthcare (non-NHS) posts. Health Records positions in the NHS are mostly part-time with low pay and poor working conditions resulting in high turnover. The ICT labour market is described as volatile in general, responding to fluctuations in organizational investment, and new technology. Entry level and middle HI positions present less recruitment problems; however, once staff does gain knowledge and experience, they leave in search of greater reward and development. Senior HI positions and all managerial job levels pay 30-50% less than the private sector, which is a factor contributing to the difficulty of recruiting into these roles. In general, pay is not the sole reason HI staff consider leaving their jobs. Rather, the top three reasons relate to: (1) lack of training and development, (2) poor work environment (i.e. stress, lack of feedback on performance and inflexible hours preventing a good home and work balance), and (3) low salary (NHS, 2002). Following the NHS blueprint to successfully build the e-health workforce capacity we can look at recommendations under several categories: •

Securing the health informatics workforce: ◦⊦ Develop effective recruitment policies based on understanding of the work required of the job holder, and how their job fits into the whole function it supports. ◦⊦ Create career information that portrays Health Informatics in a positive and integrated way with career advancement both within a specialist area (health records as well as across other specialist areas such as information management, information services, etc). ◦⊦ Promote career pathways that attract external entrants (i.e. from other clinical health roles or the possibility of

Demystifying E-Health Human Resources

•

•

foreign-trained immigrants with the necessary skills). Developing the health informatics workforce: ◦⊦ Create a lifelong learning framework. ◦⊦ Promote a skills escalator to attract a wider range of people into HI by offering a variety of ‘step-on’ and ‘stepoff’ points. ◦⊦ Develop Health informatics or ehealth training programs (at a variety of levels) for continuing knowledge, skills and experience that includes both the current role and career advancement. Retaining the health informatics workforce: ◦⊦ Provide a decent work environment including equitable pay structures. ◦⊦ Promote HI (e-health) as relevant to patient care. ◦⊦ Celebrate success (excellence award). ◦⊦ Professional recognition (e.g. voluntary professional regulatory process; continuous professional development/education and require CPE credits to maintain registration; establish infrastructure for UK-wide professional body; and note that in some countries this may include certification of HI or e-health professionals).

AUSTRALIA Health Information Workforce Capacity Building The Australian healthcare system is similar to that of Canada with responsibilities for health care divided between the federal and state governments (eight state and territorial governments), and both the public and the private sectors play a role. Medicare is funded out of general tax revenue, and pays for hospital and medical services. Medicare covers all Australians, pays the

entire cost of treatment in a public hospital, and reimburses for visits to doctors. Australia’s workforce capacity building report provides anecdotal evidence of a shortage of HI professionals and a lack of HI and IM skills in the general healthcare workforce (Australia Dept of Health and Aging, 2003). It also indicates that worldwide availability of health sector workers with HIM/HI skills is a critical issue and this is the case not only in Australia, but also in the United Kingdom, the United States, and Canada. Australia’s health care system is most similar to Canada’s; however, Canada’s provinces have greater autonomy in making healthcare policy, service delivery, and allocating funding indicating a national strategy for Canada would need to be flexible, adaptive, inclusive and responsive to each province to increase the likelihood of participation in a national human resources strategy. A national approach to health information workforce capacity building in Australia was chosen to prevent substantial duplication, wasted effort, and expense; for the use of common standards to permit the flow of information, people, and skills; to utilize work from national information/health informatics groups; and for the sharing of current workforce planning and capacity building among the Australian federal, state and territory governments. It was recognized that information about the current workforce needs and skills base must be collected to enable capacity building, plan the workforce, and project needed skill levels. Australia developed their workforce plan in collaboration with many different professionals across the healthcare sector for a comprehensive identification of issues, priorities, objectives, and action plans (Australia Dept of Health and Aging, 2003a; 2003b). Based on the Australian experience we can learn to: •

Define issues: (1) who needs training and why; (2) what skills are needed?

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•

•

Identify factors contributing to the shortage of skilled professionals: lack of educational programs; lack of educational funding; HI/HIM is a new field with a low profile resulting in an unclear career path that makes it difficult to attract students; low pay rates; lack of professional status; and health IT, and IM require different skill sets that in combination are in short supply. Identify obstacles to solutions: lack of coordination and leadership; lack of a business case for health information; the need for research; and the need for a stronger health IT and IM education sector. Additionally, it was noted that no uniform curricula or qualifications exist for health IM and IT. It was unclear if the variety of courses was a problem or an asset.

Their recommendations fell under six priorities that were prioritized and strategies suggested to put them in place (Australia Dept of Health & Aging, 2003b). •

•

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Priority 1: Leadership: Leadership from HIM/HI professional organizations and educators is needed to build a persuasive case to justify planning and funding a national Human Resources plan and to provide direction. Actions from the Australian proposal included releasing their Think Tank Report (Australia Dept of Health & Aging, 2003a), forming a steering group to link inter-government and inter-departmental communication; and learning from government workforce planners the methodologies, processes, strategies, projects, and current data. Priority 2: Business Case and Priority 4: Planning. Utilize HI/HIM survey results to establish a body of data that would support the business case for workforce capacity planning and building; inform funding, and provide evidence based policy.

•

•

•

Priority 3:Support for Research: To promote research for policy development and IT and IM usage in the health sector. A research plan needs to be established in consultation with stakeholders, and presented to relevant and appropriate funding bodies. Priority 5: Education. The Australian report recommends a national curriculum with agreed upon core competencies to inform education and professional development in health informatics. A definition of health informatics is not provided, nor how it compares to health information management, or other types of professionals who may be needed to implement e-health. Priority 6: Development and Retention of Staff: Improved access to health informatics education and continued professional development will create support for career progression and ensure competencies are up-to-date so that workers are not overwhelmed by ongoing technical advances. Training courses could be offered online for easy access through universities, colleges, or professional associations. Career progression and a variety of opportunities with required education could also be presented.

NORTH AMERICA: CANADA AND THE UNITED STATES Health Information Workforce Capacity Building Health care in the U.S. is very fragmented and most is delivered by the private sector, unlike Canada’s quasi-national, largely publicly-funded and provincially/territorially planned and delivered health system guided by the federal Canada Health Act (Health Canada, 2001). Provincial fragmentation exists. However, the problem is much greater in the U.S. where different hospital corporations can compete for business even within the same city.

Demystifying E-Health Human Resources

Canada’s health care focus is a non-profit service for all citizens and as such does not have substantial funding support of private industry to build the e-health workforce and support research. Canada has greater potential than the U.S. of securing cooperation across the country if federal funding and standards are equitable and adaptable to the provinces. However, without private industry support, Canada will have greater financial constraints. Involvement of private industry at the government level in Canada may not be acceptable to Canadians who want a public system. And since Canada is a smaller market, attracting private industry funding may be difficult. But as the main suppliers of the infrastructure for the electronic health record, industry has a vested interest in promoting its successful implementation. Since many of these vendors also operate on a global scale, they can bring their “best and brightest” to work on projects anywhere in the world. Similar to Canada’s pan-Canadian EHR, spearheaded by the Canada Health Infoway (http:// www.infoway-inforoute.ca/) and various sector partners, the U.S. is supporting a national health information infrastructure or network (NHIN) and EHR for each citizen (in 2004, George W. Bush promised an electronic health record for most Americans by 2014). Reasons for the implementation of a national health infrastructure and health information network in both nations are similar and include: improved patient safety (through medication error alerts, listing of drug allergies and current medications); improved healthcare quality (availability of complete medical records, test results and X-rays at point of care, integrating health information from multiple sources); disease surveillance and public health; provision of consumer health information; and to increase understanding of healthcare costs. A National Coordinator for Health Information Technology within the department of Health and Human Services was established “to provide leadership for the development and nationwide implementation of an interoperable health information technology

infrastructure to improve the quality and efficiency of health care” (DHHS - ONC, 2008). Progress on implementing a U.S. national strategy has been moving forward slowly, mainly due to lack of adequate funding for the office. Within the strategic plan published by the Office of the National Coordinator in 2008 a shortage of e-health human resources is scantily addressed with no concrete remedies offered. Strategy 1.3.5: Develop the workforce for health IT product development and use. “Health IT development and implementation require an appropriately trained, highly-skilled workforce to play a wide range of specialized and essential supporting roles. Currently, the numbers of trained health IT professionals are only adequate to support the relatively low rate of health IT adoption. To enable the adoption rate to increase, and thus enable attainment of the 40 percent adoption objective by 2012 (and over 50 percent by 2014), the size of this trained health IT workforce will need to increase substantially” (DHHS- ONC, 2008, p. 21). The American Health Information Management Association (AHIMA) has estimated approximately 4,000 HIM positions go unfilled in the United States each year due to the worker shortage and an insufficient supply of HIM graduates (AHIMA-AMIA, 2006). This report refers to the 2001 AHIMA workforce study series, which discovered that all positions and roles requiring HIM competencies lacked adequate numbers of certified professionals to fill them (Wing et al., 2003). According to almost 75% of survey respondents, there are insufficient certified professionals to fill open HIM positions in their organizations. And at least an additional 41,000 HIT professionals are needed to implement the national health infrastructure model and the dream of a shared electronic record by 2014 (Hersh & Wright, 2008).

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The HIM/HI workforce shortage issues have been addressed jointly by AHIMA and the American Medical Informatics Association (AMIA) with substantial financial support from private industry. Their joint report warns that implementing a national health infrastructure without a workforce trained to innovate, implement, or use the technology, would cause the plan to fail or even result in harm if the systems cannot work correctly to improve safety, quality, efficiency, and effectiveness (AHIMA-AMIA, 2006). AMIA believes that strengthening the breadth and depth of the health informatics workforce is a critical component in the transformation of the American health care system. As the national informatics organization it is committed to the education and training of the needed biomedical and health informaticians and to leading the transformation of the American health care system through the deployment and use of advanced clinical computing systems of care. The AMIA 10x10 Program’s goal is to train 10,000 healthcare professionals in applied health and biomedical and health informatics by the year 2010 (Hersh & Williamson, 2007).

AHIMA/AMIA Report Recommendations The AHIMA and AMIA national workforce building report (2006) is the result of a strategy summit held in 2005 where participants from academia, professional associations, provider organizations, business professionals, and government officials collaborated to produce recommendations that address the national shortage of health information and technology skills in the general health workforce and specialist HI workers in the U.S. The recommendations are targeted to specific groups that include: healthcare employers, employees, vendors, government, and professional organizations to prepare the current health workforce for technology use and to ensure an adequate supply of qualified health information specialists.

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The workforce identified as needing training are divided into two groups: (1) health information specialists (HIM, applied clinical informatics, and information technology resource management); and (2) those who must use EHRs and health information technology to perform their duties.

Global E-Health Capacity The lack of a skilled e-health workforce extends globally. As information and communications technologies are brought to countries in the “global south” (e.g. Latin America, Africa, the Middle East, South Asia, and the Pacific) a shortage of people who know how to select, implement, use, and support appropriate technologies is apparent (Hersh, Margolis, Quiros & Otero, 2008; Ozbolt, 2008). The challenge in these countries is to educate a health informatics/e-health workforce locally that is familiar with the cultures and systems and limitations of scarce resources and pressing healthcare demands. In December of 2008 AMIA received a $1.2 million grant from the Bill & Melinda Gates Foundation for the development of a program to address this global workforce shortage and to support global biomedical and health informatics education and training with a focus in low resource countries. Low-income countries have limited health budgets, and e-health tools can help them provide the most cost-effective care. One of the biggest challenges to e-health capacity building is the growing demand for, and shortage of, qualified health care professionals and technicians trained to develop and support these e-health tools locally. The program will train leaders by linking them and their institutions to partner institutions affiliated with AMIA to build capacity for managing and improving high-quality, low cost health care in these less developed economies. Training will emphasize the use of informatics to support health and health care delivery and evaluation by implementing EHRs and effective information management, and assuring their stability over

Demystifying E-Health Human Resources

time through a local, trained health informatics workforce (AMIA Press Release, 2008).

A STRATEGY FOR E-HEALTH HUMAN RESOURCES The shortage of e-health professionals is evident world-wide and in many nations is limiting the effective transformation of health care and healthcare delivery. Within Canada the work has just barely begun with the initiation of the first e-health sector scan study in which stakeholders from the HI and HIM professional associations, industry, and government (both health and human resources) are participating in a workforce survey to document the roles, functions and numbers of current e-health workers. In Canada, formalized education in health informatics dates back to 1981 with the founding of the School of Health Information Science at the University of Victoria with programs offered now at both the undergraduate and graduate levels. Over the intervening years other programs have emerged at both the undergraduate and graduate levels (e.g. a health informatics stream in the Bachelor of Informatics degree program at Dalhousie University, an applied BHSc degree in health informatics management offered at Conestoga College, and at the graduate level a Master’s in Medical Informatics at Dalhousie University and new Master’s programs in Health Informatics at the University of Toronto and eHealth at McMaster University). In addition to these health informatics programs, a number of new health information management (HIM) programs have arisen (e.g. a health information management degree at the baccalaureate level at the University of Ontario Institute of Technology). These programs are accredited by the Canadian Health Information Management Association (CHIMA) and are intended to prepare individuals for careers in working with the health record (whether paper-based, electronic or a ‘hybrid’) and

health information management field. Thus, there are a variety of options for training, each program with a somewhat different nature and emphasis, available across the entire country. Even with the accelerating number of new programs these programs will still not be able to produce the number of e-health practitioners needed to implement and work with the electronic record in the near future. These programs (and their counterparts in other countries around the world), in the long term, will help to build a critical mass of health informatics and e-health professionals and to build the research and development capacity needed for sustainable growth (Covvey, Kushnirik & Fenton, 2006; for a listing of HI/HIM programs in Canada see http:// hi.uwaterloo.ca/hi/HI-HIMProgramSurvey.pdf). All of these human resources strategies depend upon defining the skills and competencies needed for these new health care workers, linking training and education to human resources, enlisting the aid of all stakeholders (i.e., government, industry and vendors, educators), and recognition and elevation of the profession in order to recruit and retain workers. These are all strategies that are aimed towards a long term sustainable e-health workforce – one that has adequate capacity or enough properly trained professionals with demonstrable competencies in the broad spectrum needed to make e-health a reality. These aren’t just the technical competencies (e.g., hardware, software, networks, etc); rather these professionals must have adequate knowledge, skills, and experience, as well as positive attitudes, in topics such as the nature and structure of health information and the health system; health system processes and roles; health data and data standards; information retrieval; the psycho-social aspects of systems; re-engineering and management of change; telehealth; and many other areas (Covvey et al, 2001; Covvey & Abrams, 2006). Beyond competent individuals, we need people who will stay within organizations, understand their objectives and culture, participate in their processes, communicate effectively within and

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outside of the organization, and become a part of the fabric that makes them vital entities (Covvey & Abrams, 2006). But in the short-term, additional strategies need to be exploited to address the immediate needs. Solutions in one country may be rapidly adapted and applied in others, and benefits globally can be achieved by building on what has been seen to work successfully in other countries, and by sharing of existing expertise and personnel. Competency gaps in existing professionals, be they computer scientists, HIM professionals, clinicians, or other specialists, can be assessed and addressed by providing on-line courses, intensive boot camps or advanced diplomas or certificates (Covvey & Fenton, 2007). In the long-term, this information will feed-back to inform the continued development of the HI and HIM programs at the university level that will produce the e-health workforce of the future, as well as produce those researchers and educators who will continue to strengthen and develop the discipline. The ultimate goal is to develop a blueprint for long-range e-health human resources planning that will be the key element to the success of the new health information infrastructure and the electronic record.

REFERENCES American Health Information Management Association and American Medical Informatics Association. (2006). Building the work force for health information transformation. AHIMA: Chicago. Bethesda, MD: IL and AMIA. American Medical Informatics Association. (2008 December). Press Release- AMIA receives grant from Bill & Melinda Gates Foundation to develop a global biomedical and health informatics fellowship program. Retrieved from http://www.amia. org/files/Gates_GlobalFellowshipProgramPR.pdf

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ASSIST. (2006 August). NHS Informatics workforce survey. London: Tribal Consulting, UK. Retrieved from http://www.bcs.org/upload/pdf/ finalreport_20061120102537.pdf. ASSIST. (2008 June). NHS Informatics workforce survey 2007/08. London: Tribal Consulting, UK. Australia Department of Health and Aging. HealthConnect. (2003a, July 28). Report on the health information workforce capacity think tank. Retrieved from http://www.health.gov.au/ internet/hconnect/publishing.nsf/Content/774 6B10691FA666CCA257128007B7EAF/$File/ july03think.pdf Australia Department of Health and Aging. HealthConnect. (2003b, November 27). Health information workforce capacity building national action plan. Retrieved from http://www.health. gov.au/internet/hconnect/publishing.nsf/Conten t/7746B10691FA666CCA257128007B7EAF/$ File/wcbnap03-04.pdf Coeira, E. W. (2007). Lessons from the NHS national programme for IT. The Medical Journal of Australia, 186(1), 3–4. Covvey, D., Gibson, C., Crook, G., Abrams, K., Fenton, S., & Irving, R. (2009, May). A time for clarity: eHealth human resources-related terminology. [HCIMCC]. Healthcare Information Management and Communications Canada, 23(1), 36–43. Covvey, H. D., & Abrams, K. (2006). Do HIM professionals get HI? [HCIMCC]. Healthcare Information Management and Communications Canada, 20(3), 52–54. Covvey, H. D., & Fenton, S. (2007). Promoting Applied Health Informatics: The AHI Bootcamp. [HCIMCC]. Healthcare Information Management and Communications Canada, 21(2), 59–62.

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Covvey, H. D., Kushnirik, A., & Fenton, S. (2006). The state of health informatics education in Canada. [HCIMCC]. Healthcare Information Management and Communications Canada, 20(2), 44–47. Covvey, H. D., Zitner, D., & Bernstein, R. M. (2001). Pointing the way: Competencies and curricula in health informatics. Retrieved from http://hi.uwaterloo.ca/hi/Resources.htm Department of Health and Human Services. (2007, September 19). Nationwide Health Information Network (NHIN) workforce study: Final report. Retrieved from http://aspe.hhs.gov/sp/ reports/2007/NHIN/NHINReport.shtml Department of Health and Human Services. Office of the National Coordinator for Health Information Technology. (2008). The ONC-Coordinated Federal Health IT Strategic Plan: 2008-2012. Retrieved from http://www.hhs.gov/healthit/ Eysenbach, G. (2001). What is e-health? Journal of Medical Internet Research, 3(2), e20. Retrieved from http://www.jmir.org/2001/2/e20/. doi:10.2196/jmir.3.2.e20 Health Canada. (2001). Tactical plan for a panCanadian health infostructure. Retrieved from http://www.hc-sc.gc.ca/hcs-sss/pubs/ehealthesante/2001-plan-tact/index_e.html Health Canada. (2006, August 30). Canada’s health infostructure. Retrieved from http://www. hc-sc.gc.ca/hcs-sss/ehealth-esante/infostructure/ index_e.html Hersh, W. (2006). Who are the informaticians? What we know and should know. Journal of the American Medical Informatics Association, 13, 166–170. doi:10.1197/jamia.M1912

Hersh, W., Margolis, A., Quiros, F., & Otero, P. (2008). Determining health informatics workforce needs in developing economies. In Making the ehealth Connection Conference, Bellagio, Italy, July 13-August 8, 2008. New York: Rockefeller Press. Hersh, W., & Williamson, J. (2007). Educating 10,000 informaticians by 2010: the AMIA 10X10 program. International Journal of Medical Informatics, 76, 377–382. doi:10.1016/j.ijmedinf.2007.01.003 Hersh, W., & Wright, A. (2008). Characterizing the health information technology workforce: Analysis for the HIMSS Analytics™ database. Retrieved from http://medir.ohsu.edu/~hersh/ hit-workforce-hersh.pdf LaFleche, C., & Gardner, N. (2008 October). Hitting the health informatics (HI) wall: A call to collaborative action on human resources. Healthcare Information Management & Communications Canada (HCIM&C), 22(3), 16-18. Ontario Ministry of Health and Long-Term Care – Information Management. (2005 December). Provincial report on the state of health records departments in Ontario hospitals. Retrieved September 20, 2006, from http://www.health. gov.on.ca/transformation/providers/information/ data_quality/healthrecords_depts.pdf Ozbolt, J. (2008). An environmental scan: educating the health informatics workforce in the global South. In Making the ehealth Connection Conference, Bellagio, Italy, July 13-August 8, 2008. New York: Rockefeller Press. Seaton, B. (April 2008). A great time to be a Health Informatics Professional… (and not so great if you need to hire one). Healthcare Information Management & Communications Canada (HCIM&C), 22(2), 16-17.

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Smith, J. (2005 June). Wanted: Cyber clinicians to transform the nation’s healthcare system bit by bit. Canadian Healthcare Manager, 13- 14. United Kingdom, National Health Services. (2002 October). Making Information Count: A Human Resources Strategy for Health Informatics Professionals. United Kingdom, National Health Services Connecting for Health. (2009). Retrieved from http:// www.connectingforhealth.nhs.uk/ United Kingdom, National Health Services Connecting for Health: Capability and Capacity. (2009). Retrieved from http://www.connectingforhealth.nhs.uk/systemsandservices/capability United Kingdom, National Health Services Connecting for Health: Professionalizing Health Informatics (PHI). (2009). Retrieved from http:// www.connectingforhealth.nhs.uk/systemsandservices/capability/phi

Wing, P., Langelier, M., Continelli, T., & Armstrong, D. (2003). Data for decisions: The HIM workforce and workplace 2002 member survey. Chicago: American Health Information Management Association (AHIMA).

ENDNOTE 1



As we go to press with this chapter the US government through its American Recovery and Reinvestment Act, 2009 has released $84 million in the short term to universities and colleges to train 50,000 health IT professionals as part of a $2 billion strategy to achieve widespread meaningful use of healthcare IT and provide for the use of an electronic health record (EHR) for each person in the United States by 2014. (Monegain, E. Government releases millions to train ‘cadre’ of health IT professionals April 05, 2010, Healthcare IT News. Retrieved from: http://www.healthcare itnews.com/news/ government- releases- millions- train- cadre -health-it- professionals).

This work was previously published in Human Resources in Healthcare, Health Informatics and Healthcare Systems, edited by Stéfane M. Kabene, pp. 212-227, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 5.2

Multi-Agent Systems in Developing Countries Dean Yergens University of Manitoba, Canada and University of Calgary, Canada Julie Hiner University of Calgary, Canada Jörg Denzinger University of Calgary, Canada

ABSTRACT Developing countries are faced with many problems and issues related to healthcare service delivery. Many factors contribute to this, such as a lack of adequate medical resources, a shortage of skilled medical professionals, increasing clinical demands due to infectious diseases, limited technological systems and an unreliable telecommunications and electrical infrastructure. However, the potential for multi-agent systems and multi-agent simulations to address some of these issues shows great promise. Multi-agent simulations have already been applied to modelDOI: 10.4018/978-1-60960-561-2.ch502

ing infectious diseases such as HIV and Avian Flu in the developing world. Furthermore, groups of smart agents, by their very design, can function autonomously and act as a distributed service, which greatly enables them to successfully operate in the kind of environments encountered in developing countries.

INTRODUCTION Developing countries possess an ideal environment for multi-agent systems (MAS). However, most of the published literature around multi-agent systems seems to be focused on projects and systems being developed and implemented in more

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Multi-Agent Systems in Developing Countries

developed countries; such as in North America, Asia and Europe. This is most likely because most of the multi-agent systems research and activity is conducted and carried out in developed countries. But recently there has been an increasing amount of literature published about the use of multi-agent simulations in developing countries, most noticeably around simulating the spread of infectious diseases. This is probably a reflection of the increasing awareness that the general public in developed countries has around infectious diseases in developing countries, such as HIV, Tuberculosis and Avian Flu. This awareness is also creating concerns about the potential spread to the developed world, and due to these concerns more effort and research is occurring in modeling epidemics in order to understand how to react to and contradict these outbreaks. The application of Multi-agent Simulations to the area of infectious disease management and response is a promising avenue for public health in terms of modeling these potential epidemics. If we were to take a hypothetical SARS epidemic, a multi-agent simulation would be a great method in modeling the spread of the disease and the effects on the general population. Containment strategies could then be applied against the developed model to see what reaction is the most effective and by what time that reaction would need to be executed in order have an impact on counteracting the outbreak. Agents could be created to simulate people in the community and their (relevant) interactions. The infectious disease could then be modeled into the population and various factors examined such as the incubation time of the virus, the period of time that a person is contagious to others, and the mortality rate once infected. Other agents could be designed around transportation activities. Details about transportation mode and transfer locations could then be factored into the model. This would allow public health investigators to answer questions such as: How many people may have been passengers on planes from an infected area? How many other passengers were on those

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aircraft and might have potentially been infected? What are the destinations of those flights, and should those cities or countries be warned as to the potential threat? Not only could this hypothetical SARS scenario be implemented as a multi-agent simulation, but it could also be adapted into a multi-agent system acting as a real-time global health warning system. With the creation of agents able to connect to real-time information systems, such as air traffic control and flight scheduling data, comes the possibility of a quicker response to incidents and consequently containment of such incidents (outbreaks). In addition to applying MAS to public health issues, there are many other healthcare areas where MAS could be applied in developing countries. Some examples of these include: • • •

•

•

Decision Support applied to direct clinical care. Training using agent-based resources. Telemedicine, having the ability to access information or other medical personal either nationally or internationally. Pharmacy management, including drug interaction alerts and drug treatment compliance monitoring. Inventory and logistic management for Medical Supplies.

However, in order to be successful in implementing these systems we also need to understand the environment in which many developing countries exist. Challenges such as not having enough medical personnel or not having the proper equipment or supplies are common problems in many hospitals and clinics. In addition, the working environment and infrastructure may have challenges such as phone lines that are inoperative and electricity systems that are unreliable, especially during certain times of the year such as the rainy season in sub-Saharan Africa.

Multi-Agent Systems in Developing Countries

For many of these challenges, the advantages of using multi-agent systems are rather clear. MAS have the ability to function in a distributed manner and can be designed to act autonomously, making them ideally suited for applications in environments that are lacking a reliable telecommunications or electrical network. Autonomy allows the agents to be opportunistic, waiting for required resources to become available and then acting immediately. Unlike humans, agents are never too tired to realize that there is an opportunity, or too busy to be able to check if the required resources align. In this chapter, we will further explore the technological and environmental advantages and disadvantages of developing countries, including a look at what issues are encountered in the implementation of not only MASs but any kind of health-related information technology system. We will then examine the special health care issues that developing countries are encountering. These include such things as limited resources; increasing demand; poverty; a poor overall health status compared to developed countries; and the impact of diseases such as HIV/AIDS, TB, Malaria and increasingly Avian Flu. Based on this, we will explore how MASs have been applied to developing countries from a healthcare perspective. A key part of this chapter is the presentation of a multi-agent simulation system within a simulation visualization and analysis framework that looks into the pressing issue of the decreasing availability of nurses and other medical professionals in many developing countries, using Sub-Sahara Africa as the case study. We conclude with some areas for future work that we believe will have a great potential in improving healthcare in developing countries, utilizing the advantages of multi-agent systems.

BACKGROUND The healthcare problems in developing countries are extremely challenging. Issues such as poverty,

limited resources, and a shortage of medical professionals all contribute to a challenging environment in which to address the population’s healthcare needs. Many developing countries also have a high incidence of infectious diseases. Vector borne diseases such as malaria and food- and water-borne diseases such as bacterial diarrhea and hepatitis A put a considerable strain on the healthcare systems and the people’s health status in such countries. These not only result in sickness that may affect a family’s household, but in many cases is a leading cause of death. HIV/AIDS is another concern in developing countries, especially in Sub-Saharan Africa where its impact has been most felt. In 2003, Malawi had a 14.2% HIV prevalence rate while Zambia had a 16.5% HIV prevalence rate (CIA 2008). Due to these factors, Malawi in 2008 had a life expectancy of just over 43 years compared to life expectancies in Canada of 81 years, 78 years in the United States and 82 years in Japan (CIA 2008). Developing countries also face extreme shortages of medical professionals such as physicians and nurses for treating the general population. For example, in 2004 it was reported (WHO 2008) that Malawi had only 266 physicians and 7,264 nursing and midwifery personnel. This worked out to 1 physician and 6 nurses/midwifery personnel per 10,000 people. In comparison, Canada in 2006 (WHO 2008) had 62,307 physicians and 327,000 nursing and midwifery personnel, which works out to 19 physicians and 101 nursing/midwifery personal per 10,000 people. In addition, many developing countries are faced with the constant recruitment (“poaching”) of their medical professionals by developed countries (Hangoro C., et al. 2004). Developing countries also face major infrastructure challenges. These infrastructure challenges can have a significant impact on the application of information technology by making the system unreliable, increasing the costs of implementation and complicating on-going

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support for these systems due to a wide variety of factors including the electrical, telecommunications, and technological environment.

Electrical Infrastructure Electrical systems in developing countries may be unreliable due to a wide range of factors, including a lack of investment and maintenance in the underlying infrastructure and an over subscription of the capacity of available energy. Severe weather during certain times of the year can also affect the reliability of the electricity grid, such as during the rainy season in Sub-Saharan Africa. The limited electrical supply and severe weather can often lead to black outs, brown outs and spikes in the electricity grid. There are ways to address these issues, such as with the use of Uninterrupted Power Supplies (UPS) or diesel powered generators. However, often an organization does not have the financial resources to invest in this equipment. Furthermore, this is an additional expense that developing countries need to incur in their operations compared to developed countries. The mentioned events can also permanently damage the computer equipment, making replacement extremely difficult in developing countries reliant on foreign aid and grants for many of their healthcare and technological supplies. These power outrages also affect the ability of an organization to perform work, as occasional black outs can affect the entire organization’s performance. Finally, the electrical grid may also not reach into specific parts of the country due to a lack of investment in the electrical system on a national level.

Telecommunications Infrastructure The telecommunications infrastructure in developing countries can also be a significant barrier in the implementation of computer systems. The adoption of cellular technology, especially amongst end consumers, is an exciting development as it

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allows the end user to be in touch with others in a very cost effective manner. Even though the use of cell phones has been widely adopted by most people, there can still be an issue around Internet connectivity in these countries. Even where Internet connectivity exists, it may be cost prohibitive to many people and projects, or lack capacity (bandwidth) resulting in slow performance accessing the Internet. This may have an impact on the opportunities to perform many tasks relevant to healthcare, such as telemedicine or even downloading journal articles due to bandwidth constraints. Even what we might consider basic web page activity may be too bandwidth intensive for developing countries, resulting in the inability to access this content. Options such as satellites (VSAT) allow remote communities’ access to the Internet, but cost continues to be a major factor against the wide-spread adoption of satellite technologies.

Technological Environment Finally, the technological environment is another major challenge for developing countries. Computer equipment is often donated or sourced from a wide variety of countries. The results of this have often been healthcare clinics with computer equipment from multiple countries, with varying levels of capabilities and software consistency across the computers in the facility. Even keyboards from multiple countries with different languages may exist. This can be a challenge for implementing new systems, as the end user computer system may be varied and outdated, therefore incapable of running new application(s). A lack of human resources with the required technical knowledge and skill set is yet another factor that may impact the successful implementation and adoption of new information technology solutions.

Multi-Agent Systems in Developing Countries

Application of MAS in Developing Countries The application of healthcare-related multi agent systems in developing countries has been rather limited to date. Few clinical systems have been referenced in the literature. One such system that was represented was the use of a multi agent approach for diagnostic expert systems (Shaalana K., et al. 2004). Shaalana (2004) described a system which consisted of several agents, including diagnostic and expert agents that contained autonomous knowledge-based systems and was accessed via the Internet. The use of agent based systems for creating simulations has been applied in many applications for studying the impact of infectious diseases in developing countries. Several systems have been created looking at a wide range of diseases, including Trypanosomiasis (Muller G., et al. 2004) otherwise known as sleeping sickness, SARS (Gong J., et al. 2006), Avian Flu (Yergens D., et al. 2006a) and HIV (Teweldemedhin E., et al. 2004). The role of multi agent systems in humanitarian or disaster response (Scalem M., et al 2004, Yergens D., et al 2006b) has also been discussed. Scalem (2004) describes a decentralized disaster management system utilizing mobile multi agent systems. Yergens (2006b) discussed the collaboration of multi agent simulation for modeling epidemics with a low-bandwidth satellite surveillance system for application in developing countries. There have also been several applications of multi agent systems applied to other health-related areas such as environmental, international development, and demining activities in developing countries. Several authors have presented work related to the environmental sector by addressing water management (Adamatti DF., et al. 2004, Berger T., et al. 2002, Ducrot R., et al. 2004). Adamatti (2004) developed an application that integrated both an agent based system and a role playing game approach. This system was called “Jogo-

Man”, and was used for helping water management decisions with its stake holders. Berger (2002) also looked at water management and land use through a multi agent framework addressing the complex interdependencies that exist between the two. Ducrot (2004) investigated the use of a multi agent system for addressing water management and the issues faced with urbanization. Due to the close relationship between the reduction of poverty and better health status, we have included international development as a potential area of improving healthcare using multi agent technologies. One application identified was a multi agent simulation used for investigating development policies in less favored areas in Uganda and Chile (Berger T., et al. 2006). An additional area of application of multi agent systems has been in a humanitarian capacity of developing robots for clearing minefields (Santana P.F., et al. 2005, Kopacek P., 2002). Kopacek (2002) presented the concept of autonomous intelligent swarms of robots for clearing minefields. All of these present novel applications of the use of multi agent systems and multi agent simulations in the domain of healthcare in developing countries.

MEDSTAFF: AN EXAMPLE OF THE USE OF MULTI-AGENT SIMULATION IN DECISION MAKING IN DEVELOPING COUNTRIES The idea behind MedStaff was to create an environment in which health care policy decision makers in developing countries could create and analyze simulations about the impact of healthcare staffing in their region. These simulations are intended to help answer questions such as: How many health care facilities need to be involved? What kind of facilities do they have to be? And how many medical professionals of what types need to be working at each facility?

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The staffing component also needed to answer questions such as the effect of aging staff and how retirement would affect a facility and the entire environment. It also needed to address illness amongst staff, especially in countries that have a high prevalence of HIV. In addition to retirement and disease, we also needed to examine what factors affected staff retention in a healthcare facility. This could include other in-country healthcare facilities recruiting away staff, other in-country organizations such as Non-Government Organizations (NGOs) or Government Agencies recruiting personal. Finally, as indentified in the previous section, we also needed to analyze how external poaching of staff is affecting staffing needs within the country. The requirements for the simulation model involved many different players, such as the many healthcare facilities and their function and locations, the various kinds of medical staff and issues such as retirement age, illness which might shorten the duration of the healthcare worker’s employment, and a multitude of external factors such as recruitment from in-country organizations such as NGOs and the increasing recruitment from the international community. To address this we needed to construct a multi-agent simulation that would allow us to factor in all of these different Figure 1. MedStaff multi-agent system architecture

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players and have them act independently and in their own interests, much like in real life.

The MedStaff System Architecture In the creation of MedStaff, we needed to create several different components that would operate in collaboration to provide the full multi-agent simulation environment. This system not only needed to provide the multi-agent simulation, but also have the capability to adapt to new data sources, such as data from other countries or other organizations that may have a large number of healthcare facilities and staff that would be facing the same issues. The MAS environment also needed the ability to expand upon itself to factor in new kinds of healthcare facilities, new staffing professionals and also new players that affect those healthcare facilities and staff both in a positive and in a negative way. Finally, the MedStaff system also needed a visualization and reporting environment for analyzing the data created by a multi-agent simulation run. To accomplish this, the MedStaff multi-agent simulation system architecture was composed of the following components, each of which will be described in detail later in the chapter.

Multi-Agent Systems in Developing Countries

Data Management Component The data management component provides a repository for all the data that is required in defining a scenario for the MedStaff system, and in analyzing a simulation within it. This data comes from outside partners such as the Ministry of Health or some other organization that has the relevant information. Relevant data for defining the model includes details about the healthcare facilities, such as type of facility (hospital or healthcare clinic), location (town, province, latitude, longitude) and types of services provided (Emergency Room, Diagnostic Imaging, etc). The second type of data that is required is related to staffing information and includes data elements such as type of position (Nurse, Nurse Assistant, Birth Attendant, etc), age, sex and other factors related to a healthcare worker. The last function that the data management component provides is the collection of the MedStaff multi-agent simulation results. When a simulation scenario is run, all statistics and certain key interactions are logged in the database. This then allows us to analyze the scenario run later.

Multi-Agent Configuration Component The Multi-agent configuration component is where a user essentially creates all the agents and defines the simulation and its parameters. Agents are created using two methods. The first method is by having the agents automatically generated from the information stored in the database of the data management component. This includes creating all the healthcare facility agents and also creating all the healthcare worker agents. The second method that can be applied is the creation of other kinds of agents. These other kinds of agents include teaching institution agents and poaching agents. The latter two agent types are the ones that will most likely have the greatest effect on the simulation environment.

Multi-Agent Simulation RunTime Component This component is the core of the MedStaff environment, as this is where a simulation scenario is compiled and executed. The run-time environment takes the scenario developed in the multi-agent configuration component in collaboration with the data management component and executes it. Both the results and additional specific logging of the scenario are saved back to the data management component to be used for analysis later.

Visualization and Analysis Component The last component is the visualization component. This component is a collection of tools that can be used for visualizing and analyzing the data contained in the data management component. This component provides basic charting, a geographical information system and advanced statistical capabilities. In addition, this component is used not only to visualize and analyze the results of a MedStaff simulation but also for understanding the source data before it is used in the generation of the agent simulation scenario.

The Data Management Component in Detail The first component of the MedStaff system is the data management component. This component provides a repository for storing all of the data required by the MedStaff multi-agent simulation system. The data management component consists of three functions. The first of these three functions is the management of the data for the creation of the multi-agent system for a scenario. As described later under the Multi-agent Configuration Component section, two types of agents are dynamically created from the database. These two types of agents are healthcare facility agents (FA) and medical staffing agents (MSA). The creation of these types of

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agents is done dynamically based on the relevant information from external sources. The external sources that were utilized in our MedStaff modeling for Zambia and Malawi were provided by the Malawi Ministry of Health and the Churches Health Association of Zambia (CHAZ), and utilized healthcare survey data produced by the Japanese International Community Agency (JICA). The source data varied slightly between Malawi and Zambia. However, the core information that we utilized included information about the healthcare facilities, such as type of facility (hospital or healthcare clinic) and geographical information related to location of the healthcare facility (town, province, latitude, longitude). While types of services for the healthcare facilities were available (Emergency Room, Diagnostic Imaging, etc), these were not utilized in the MedStaff MAS we will report on. The second piece of data that was provided by the source databases was related to staffing information, and included information such as type of position (Nurse, Nurse Assistant, Birth Attendant, etc) and some demographic information such as the sex of the medical professional. The staffing information was linked to the appropriate healthcare facility. In several cases we had to generate statistical information for staffing based upon similar facilities to make up for information that was missing from the source databases. The second function of the data management component is to act in collaboration with the MedStaff MAS. This involved interacting with both the Multi-agent Configuration component and the Multi-agent Runtime component. The Multi-agent configuration component needs to access the information stored in the data management component in the creation and generation of the healthcare facility agents (FA) and the medical staffing agents (MSA). The Multi-agent Runtime component also has to communicate with the data management component which provides the capability of storing the results and logging

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activities from the multi-agent simulation while it is running. The final function of the data management component is to act in collaboration with the visualization environment component and to provide the necessary data for the display and analysis of the MedStaff MAS. Not only does the visualization environment component access the results of the MAS, it also has the ability to display and analyze the source data from the data management component to create a graphical interface to the system.

Simulation Environment and Agent Customization As already stated, there are four main types of agents represented in a MedStaff MAS scenario. The majority of the agents in the MedStaff simulation environment are dynamically created using the information contained in the data management component (i.e. the FAs and the MSAs). The other two types of agents are Teaching Institute Agents (TIA) and Poaching Agents (PA). The core agent of the MedStaff MAS is the medical staff agent. The MSA is essentially the healthcare worker in the system and basically what we are the most interested in. We are concerned about retaining these medical staff members and we are concerned that we have adequate staffing both short term and long term at our healthcare facilities. Each MSA works at a specific healthcare facility and thus is associated with a Facility Agent. Each FA represents a real hospital or healthcare clinic in the scenario and aggregates information such as location and number of staff (MSA) currently working in that facility. There also exists Teaching Institute Agents. These are agents that represent universities, colleges and training schools that add supply of medical staff resources into the MedStaff simulation environment. Since teaching institutes vary by what kind of positions they produce, how many students graduate, when these students graduate

Multi-Agent Systems in Developing Countries

and which geographical location they are located in, each TIA is unique and created as such. The TIAs feed MSAs into the various FAs. The last type of agent is the Poaching Agent. We defined a PA for any action that reduces the supply of MSA in the MedStaff MAS scenario. A PA may take on various forms such as a Non Government Organization (NGO) that is starting a new project in the country and needs medical professionals to staff the project, or an international agency that recruits medical staff for international assignments, thus removing them from the overall supply of MSAs in the simulation scenario. Another type of PA may be illness or disease related, such as an HIV PA that expires MSAs from the MedStaff MAS scenario. The following figure shows the general interactions between the different agent types. Notice that for our application (which is one of several for which MedStaff can be used for) the Poaching Agents are the driving force of the interactions between agents (the other agents essentially do what the PAs want them to do and they provide information for the Visualization Component).

In the following, we will describe the agents and the simulation environment in more detail.

Healthcare Facility Agent Implementation Every healthcare facility that is to be represented in the MedStaff environment is created as its own agent. The FAs are automatically generated from the data that is stored in the data management component and include information such as name and facility type. In addition, there may be various levels of a healthcare facility such as a Level I or Level II of a hospital which would indicate the severity, acuity or services that a specific level of hospital may have. The FA is also assigned information about the ownership of the facility. This is used to indicate if the healthcare facility is government-run, such as within the Ministry of Health; whether it is a private hospital and which organization operates it; or if it is part of a larger organization like a faith-based network, which is very common in developing countries. The type of ownership

Figure 2. Agent interaction

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is essential, as often retention or recruitment strategies are organization-specific and need to be modeled with that level of detail. The FA can also be assigned other data such as population catchment. This attribute simply references the population base that that healthcare facility may serve in its community. This also can be used in the model for identifying a baseline of how many medical professionals (per profession) are needed per 1,000 people in the catchment area. Other information related to the location of the healthcare facility also includes the district/ region that the facility is in and, for visualization purposes, the latitude and longitude of the facility. This allows an FA to be displayed within a Geographical Information System. The following table (Table 1) is a list of the attributes that are assigned to each Facility Agent.

Medical Staff Agent Implementation As already stated, at the core of a MedStaff MAS scenario is the medical staff agent. The MSA basically functions as the representation of a real-life medical staff professional. At the start of the run of a MedStaff scenario, the MSAs are created dynamically based upon the information in the data management component. This component then populates the MedStaff environment with all the medical staff that the system is aware of assigned to the corresponding FAs.

Once the MSA are created there is only one other way to add more MSA to the simulation scenario, which is through Teaching Institute Agents (TIA) to be discussed later. However, there are multiple ways that MSAs may be removed from the system, which is accomplished via Poaching Agents (PA). Any action or situation that may reduce the supply of MSAs in the scenario is modeled by a PA. For example, there are international and national recruitment PAs, and PAs representing retirement events or disease events that remove MSAs from the model. A MSA is assigned demographic information such as age and sex (which are used to determine if they can be the target of a PA). In addition, other attributes can be assigned to the MSA such as HIV Status. HIV status, especially in developing countries in sub-Saharan Africa, is a serious issue and has a tremendous impact on the retention of medical professionals in the healthcare facilities. The HIV status attribute also influences how PAs interact with this specific agent. An example of this could be an International Poaching Agent (Inter-PA) that is responsible for recruiting a specific type of medical professional for employment overseas. However, many organizations (and therefore their Inter-PAs) have as a requirement that an MSA to be recruited must have a negative HIV status. Obviously, the InterPA for recruiting certain MSAs will not interact with HIV positive MSAs.

Table 1. Healthcare facility agent attributes Attribute

Description

Name

The name of the healthcare facility.

Type

The type of healthcare facility it is. Examples could be a Hospital Level I or a Healthcare Clinic.

Distinct

The district or region that the healthcare facility is located within.

Catchment Population

The number of potential patients that this healthcare facility may serve.

Ownership

The organization or institution that the healthcare facility is associated with.

Latitude

The latitude GPS coordinate of the healthcare facility.

Longitude

The longitude GPS coordinate of the healthcare facility.

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An MSA that is removed from the simulation as a result of being poached will naturally inform its FA about this fact. The FA then provides us with the statistics regarding its MASs, and especially with the possibility to identify facilities that do not retain enough MSAs of a certain profession/type. The following is the list of attributes that are assigned to each Medical Staff Agent (MSA) in our current system. Naturally this list can be extended to allow other simulations. (Table 2)

Training Institute Agent Implementation Every training institute to be represented in the MedStaff environment is created as its own TIA. Each TIA is created by hand and not created directly from the data management component, as we did with the Facility Agents or the Medical Staff Agents. The reason for this is that each TIA has its own unique characteristics, such whether it is a university or a college, whether it is a public or a private institution, what kind of medical professions it trains, how long the program takes, how many students it can train per class and how many classes graduate per year. Another factor that we had to build into each unique TIA was the set of details about the internal organization itself. For example, a TIA may have special factors that either make it unique or create some variation in the programs that may vary the number and timing of student graduations. A prime example of this might be a government university that is faced with limited resources and

thus incurs strikes from either the students or the faculty and therefore does not produce graduates on a constant basis. Because of this variation, and the limited number of teaching facilities that are often found in a developing country, we took the approach to develop each TIA on its own and again not dynamically from information stored in a database. Being able to handcraft the behavior of a TIA is one of the strengths of a multi-agent simulation approach, although it makes creating scenarios more work intensive. We naturally used a specific TIA in several scenarios, and we also tried to parameterize the behaviors for each TIA as much as possible to allow them to be easily modified. With regard to interaction with other agents, each TIA performs the task of placing the graduates of its various teaching programs into the various FAs. This is because a specific healthcare facility, represented by the FA, may need a specific type of graduate; or the organizations that run the FA may also run the TIA and therefore recruit from its own TIA. Location may be a factor in the recruitment, as a medical professional trained in one region of the country may prefer to continue working in that same region.

Poaching Agent Implementation We took the approach of defining any kind of action that took a medical professional away from a healthcare facility as a Poaching Agent. Examples of what we consider a PA to be include

Table 2. Healthcare staff agent attributes Attribute

Description

Position

The position of the healthcare worker. Examples could be a Registered Nurse or a Nurse Assistant.

Education Level

The educational level of the healthcare worker.

Sex

The Sex of the worker.

Age

The Age of the worker.

Years

How many years the healthcare worker has been at that Healthcare Facility

HIV Status

The HIV Status of the healthcare worker.

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a Non-Government Organization (NGO) that recruits medical professionals for running various projects within the country. Another example is external organizations or countries that recruit medical professionals away from the country to work internationally. Illness, accident, or disease are also implemented as PAs, as in the case of HIV. HIV has a significant effect on the MSA, just like in real life. The major impact of HIV on the MSA is that the MSA will expire and be terminated from the model. Like in real life, this has a negative impact resulting in less MSAs and a shorter working life. Again, due to the complexity and variation of the types of actions that could affect employment at the various healthcare facilities we took the approach of creating each PA by hand on its own, like we did for the Training Institute Agents, and not dynamically like we did for the Healthcare facility agents or the Medical Staffing Agents. The advantage of creating individual PAs by hand compared to having the system dynamically create these agents from a database was that we can model more complex behavior of the various PAs. This allows decision makers to do better what-if simulations, which is naturally the goal of MedStaff. We developed a generic poaching agent. This generic poaching agent was then used as the foundation for creating specialized poaching agents. The generic agent was extended to address different poaching (i.e. MSA removing) actions that we might see in a developing country. In the

following, we will describe several implementations of poaching agents. (Table 3) The first major type of PA needed in our scenarios was an agent that recruited specific types of medical professionals away from a healthcare facility for international work, which is currently one of the major issues in developing countries. The PA received its own unique name and characteristics and behavior assigned to it. These characteristics include how many and what type of medical professionals were required on a given time basis. In the MedStaff MAS scenarios, we used 1 month as the time slice for the actions to take place. This meant that the agent seeks out specific types of healthcare medical professionals on a monthly time interval; and it can be adjusted for specific healthcare facility criteria such as the type of hospital, the location and also the organization that runs the healthcare facility. We can also introduce these PAs at various times into the environment simulated by MedStaff. For example, if we know that a specific Non-Government Organization will be starting a new reproductive health and family planning project in the country in two years we can then model that poaching behavior as the project is being implemented. To expand on this scenario, we can then create the PA to become active 1 year prior to the implementation of the project. Second, perhaps from previous experience we know how this NGO operates, we can then factor into the PA that it only poaches specific types of medical professionals from specific kinds of healthcare

Table 3. Types of poaching agents Poaching Agent

Description

NGO

A Non Government Organization that may recruit medical professionals away from healthcare facilities. There may be many NGO Poaching Agents in the system.

International Recruitment

A country or agency that recruits MSAs for international assignment out of the country. There may be many International Recruitment PAs in the system.

Illness and Disease

Illness and disease, such as HIV would be modeled as a PA. The MSA is then removed from the system if the HIV PA has selected that MSA based upon statistics.

Retirement

Once a medical professional reaches a certain age, he/she is retired and therefore removed from the system.

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facilities or from specific kinds of organizations that are represented in the FA. In addition, it might only be targeting FAs in a specific geographical location as that is where the project will be concerned. Each new instance of a PA receives its own unique name.. Another type of PA that can have a major impact on the MedStaff simulations is related to illness and disease. As noted before, HIV has had a major impact on not only the general population in certain developing countries, especially sub-Saharan Africa, but also on the medical professional workforce working in those regions. We implemented an HIV PA that, based upon pre-defined statistics, targets specific types of medical staff agents and renders them inoperative (expired) thus removing them from the MedStaff simulation. Of course, this HIV PA can become more sophisticated with additional statistics from external sources (that would be added to the data management component). When the Multi-agent Configuration component is initialized, then the HIV statistics could be applied towards the MSA in the system. However, we have not implemented this to date. Other diseases, illness or even traffic accidents (which are extremely common in developing countries due to less reliable vehicles and poor road infrastructure), could also be factored into the system by the implementation of a new PA.

Multi-Agent Simulation Run Time Component The MedStaff Run Time component is where the actual MAS for the simulation runs and scenarios are initialized and executed. The Run Time component utilizes the multi-agent configuration component and starts to create the environment that MedStaff will operate in. The first part of the execution is the creation of all the agents that can be created utilizing information from the data management component. This includes

the healthcare Facility Agents and the Medical Staffing Agents. Once those agents are created, the custom agents such as the Teaching Institute Agents and the Poaching Agents are created and activated. These agents then interact with both the FAs and MSAs for the duration of the simulation scenario. The MedStaff Run Time environment works on a one month time slice and we initialize the MAS to run for a 10 year period as the default. As the scenario is running, various statistics and events are recorded to the data management component. This then allows us after the MAS simulation is performed to display and analyze the results of that specific scenario using the Visualization Environment Component.

Visualization Environment Component A visualization environment was included to aid in the display and analysis of the MedStaff MAS simulations. This visualization environment includes basic charting and trending, statistics and a geographical information system component. The visualization component was implemented as a web-based display that runs separately from the actual MedStaff MAS simulations. This allows multiple decision makers to look at the data and explore the various scenarios that the MedStaff MAS simulation was applied to. The visualization component utilizes data that is stored in the data management component. This allows the source data that was used to generate MedStaff MAS to be studied and analyzed. In any simulation project, having a good understanding of the source data and (in this case) the current system is essential. The results data generated from the MedStaff MAS simulations can also be viewed and analyzed within the visualization environment. As mentioned before, several components make up the visualization environment. These are described below.

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Basic Charting and Trending Several charting components were implemented in the web-based framework. These include the ability to graph any piece of data in the data management component in a variety of ways including Line, Bar, Pie, Scatter, Radar and Gantt charts. The customized interface also allows multiple charts to be displayed in the same report or webpage and provides the user with the ability to filter the results. This provides the ability to display various aspects of data, such as reporting on only the healthcare facilities and associated medical staff that are in hospitals belonging to the ministry of heath in a certain region of the country. In addition, the ability to view data as a network chart was also added.

Advanced Statistics For advanced statistics, the use of R (www.rproject.org) was included to provide statistical analysis for both the simulation data and also for the data that is currently in the system. R was embedded in the web-based framework and also included the provision to filter the data through Figure 3. Screenshot of the visualization environment

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the interface for the ability to analyze that specific information.

Geographical Information System The last component of the visualization environment is the inclusion of a Geographical Information System style interface. Two approaches were taken here to display geographical data. The first was through an interface with Google Maps. The second method was through using R (www.rproject.org) and the mapproj R package (www.rproject.org) for displaying geographical data.

Experimental Evaluation For our experimental evaluation of the MedStaff MAS simulations we used Malawi as the test country. We chose Malawi because we had a good understanding of the geographical layout of the country and could indentify with the location of the various healthcare facilities and the organizations they belonged to. We also had a pretty good understanding of potential poaching agents. We utilized facility and staffing data provided by the Malawi Ministry of Health. We used this informa-

Multi-Agent Systems in Developing Countries

tion to create the healthcare Facility Agents and the Medical Staffing Agents. We then created several Teaching Institutes Agents and Poaching Agents to evaluate how poaching agents and teaching institute agents are interacting with the rest of the simulation model.

MULTI-AGENT SIMULATION INITIALIZATION AND RUN A MedStaff MAS scenario for Malawi was initialized and executed. We limited the scenario to only focus on “Nurse/Midwifes” MSAs. All other medical staffing professions were also created in the model, but were not targeted by any poaching agents or additionally produced by teaching institutions. 574 FAs were created during the initialization of the MedStaff MAS scenario. There were an additional 8336 Medical Staff Agents generated and assigned to the various FA. To include the HIV status attribute in the MSA we used a HIV prevalence rate of 14.2% from Malawi based upon 2003 data (CIA 2008). The details of the FA and MSA can be seen in the following two tables. In addition, region, geographical location, organization membership and population catchment area were also included in the FA. Five Teaching Institute Agents were also introduced in the environment. For demonstration purposes the TIA were initially set at producing 30 “Nurse/Midwives” in two classes every year. Four Poaching Agents were created and all of them were modeled as Non Government Organizations (PA-NGO) that recruited “Nurse/Midwives” on a system wide (national) scale. In addition, a fifth Poaching Agent (PA) was created to function as an International Donor that had different poaching behavior than the PA-NGOs described previously. An important element of the MedStaff MAS simulation is the ability to add new Teaching Institute Agents (TIA) and Poaching Agents (PA)

and adjust the parameters and alter the behavior of the agent. As seen in the simulation example, the ability to adjust and focus the scenario on only “Nurse/Midwifes” allows great flexibility in implementing more complex multi-agent simulations. To look more closely at some of the results, the following figure presents the simulation results for 23 FAs in one regional district in the southern part of Malawi over a 3 year horizon. Of those 23 FAs, 14 facilities are health clinics. As these results show, most FAs are hit by the simulated poaching; some of them, like the Thondwe Health Centre or the Ngwelelo Health Centre, lose all of their nurses. The standard actions of the one TIA in this region, that becomes visible after month 17.5, are not able to deal with this constant lose of nurses due to the PAs. Decision makers need to come up with different strategies for this TIA, if they want the mentioned FAs to be adequately staffed. Another FA, namely the Makwapaia Health Centre, seems to attract new MSAs, but is not able to keep them. Table 4. Top 5 healthcare facility types Healthcare Facility Type

Count

Health Centre

384

Dispensary

64

Rural Hospital

37

Hospital

25

District Hospital

22

Table 5. Top 5 medical staff positions Medical Staff Position

Males

Females

Total

Health Surveillance Assistants

2849

1417

4266

Nurse/Midwives

50

2404

2454

Clinical Officers

363

15

378

Medical Assistants

342

33

342

Environmental Health Officers

210

22

232

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Figure 4. 36 month simulation results for health clinics

This is naturally only one simulation run and we looked at only a part of it, but it shows how multi-agent simulations in general and MedStaff in particular can be used to provide decision makers with at least some idea of the potential consequences of their decisions concerning staffing. An obvious follow-up simulation should explore if giving priority to the troubled Health Centers when it comes to recruiting can fix their problems or just provide PAs with more opportunity to move MSAs out of the system.

MedStaff Potential Enhancements Possible next steps in the evolution of the MedStaff system would be to strengthen the user interaction in terms of defining the simulation scenario. As we detailed previously in this chapter, the Training Institution Agents and the Poaching Agents are created by hand and not automatically from the database like with the healthcare facility Agents and Medical Staffing Agents. Having the ability for the end user to directly configure TIAs and various PAs would be an added benefit and would help in the deployment of the system to multiple partners or agencies that might not have the required computer skills to directly alter the agents. Another major enhancement that we would like to see would be to have real-time medical staffing

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information from the various healthcare facilities so that the MSA could be updated remotely. At present, we are relying on medical staffing surveys that often have a long lag time, often years, before they are completed and shared. In terms of the multi-agent simulation itself, we would like to focus next on retention strategies in the FAs. This would allow the model to investigate what incentives would potentially keep medical staff from accepting other positions (especially international placements).

FUTURE TRENDS One area that presents a real opportunity for vastly improving healthcare by utilizing multi agent systems is mobile devices, especially the use of cell phones. As cell phones continue to be widely adopted throughout the developing world, most notably in Africa and Asia, the ability to harness them towards medical services and clinical care is very promising (and often not hindered by competing financial demands with other healthcare priorities). As the technology continues to advance in developing countries and the cost of these mobile devices decreases, the possibility of running agent based systems on cheap cellular devices is becoming more of a reality. There exist many medical applications that could benefit from the distributed and autonomous nature of cellular technology. One example is to use the distributed ability of cellular technology for establishing Public Health surveillance networks for monitoring water borne diseases that may appear in the community’s drinking supply. Another example might be for monitoring drug treatment and patient compliance for tuberculosis (TB) and Malaria, in an effort to reduce the virus from gaining resistance by treatment plans that are not always fully followed by the patient. We also envision a greater role in the application of multi-agent systems and simulations in the area of humanitarian response and disaster

Multi-Agent Systems in Developing Countries

management. Like the previous example about cellular phones, the ability to have distributed agents that can act autonomously when required presents itself very well to humanitarian relief and disaster management. The intelligent agents could be used for keeping end users in a constant loop and communicating relevant information to other agents within the system. Furthermore, with the increasing use of RFID for tracking materials, these agents could keep track of logistical items such as food and medical supplies and communicate that information back to a central command station when the opportunity presented itself. The area of privacy and security will also be an increasingly important area of research and implementation, especially in the healthcare domain. This will be just as important in developing countries as it currently is in the developed world. One final point that should be indentified in the area of healthcare, technology and developing countries is around the ability to implement new systems. As already mentioned healthcare organizations in developed countries have many legacy systems and because of this are often very resistant to new information technology approaches. This is often because they have already invested in a previous technology solution and do not have the resources to implement a new technology solution. Developing countries often do not have this issue around having to support legacy systems, and are therefore more nimble to adopt the latest technology. A prime example of this is in the area of cell phone adoption in developing countries. Developing countries have essentially jumped over traditional land lines for telecommunications and have moved forward with cellular based telecommunications. This ability to adopt new technology solutions creates an opportunity for technologies like multi-agent systems to be applied that might have faced difficulty moving forward in a traditional developed healthcare facility.

CONCLUSION Healthcare in developing countries offers both challenges and opportunities for the application of multi-agent systems and general agent technology. The fact that these countries are still developing their healthcare systems allows for the introduction of new technologies without the various obstacles that are presented by legacy systems (and legacy decisions). But in order to focus, health policy decision makers need to get a good grip on the consequences of the decisions that they have to make. This produces even more of a need for good simulation systems than we have in developed countries. Key requirements for such simulation systems are simplicity that allows easy adaptation of the model, the use of “standard” components, and finally the ability to get results quickly without the need for unnecessary committees and large development groups. Multi-agent based simulations fulfill these requirements nearly by default. Our MedStaff simulation system is an example of how multi-agent based simulation allows decision makers in developing countries to explore new possibilities. It also helps them by aiding their decision- making process to minimize the impact of outside influences on their jurisdiction. In addition to responding to the health policy makers questions, MedStaff provides the necessary simulation infrastructure, analysis infrastructure and basic agent implementations for answering their questions, but also gives users the flexibility to enhance existing agents with new behaviors customized for tomorrow’s questions. Finally, due to the absence of healthcare legacy systems in developing countries, systems that solve the unique challenges faced in developing countries will most likely find rapid adoption. This presents a new and untapped environment in which multi agent systems can be applied, and show their true potential.

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ACKNOWLEDGMENT We would like to thank the University of Calgary International Grants committee, the University of Calgary Centre for Health and Policy Studies and the following significant contributors: Dr. Tom Noseworthy, Dr. Simon Mphuka, Mr. Thouse O’Dala, Dr. W.O.O. Sangala, John Ray and Kevin Mackie. We would also like to thank the Malawi Ministry of Health and Churches Association of Zambia (CHAZ) for helping shape the MedStaff project and also for providing the source databases of all the healthcare facilities and staffing information. We would also like to acknowledge the Simkit Simulation Kernel developed by the TeleSim Group, Department of Computer Science at the University of Calgary, Canada.

REFERENCES Adamatti, D. F., Simão Sichman, J., & Ducrot, R. (2004). Using multi-agent systems and roleplaying games to simulate water management in peri-urban catchments. Proc. Sixth International Conference on Social Science Methodology. Amsterdam, The Netherlands. Berger, T., & Ringler, C. (2002). Trade-offs, efficiency gains and technical change – Modeling water management and land use within a multiple-agent framework. Quarterly Journal of International Agriculture, 41(1-2), 119–144.

Ducrot, R., Le Page, C., Bommel, P., & Kuper, M. (2004). Articulating land and water dynamics with urbanization: an attempt to model natural resources management at the urban edge. Computers, Environment and Urban Systems, 28, 85–106. doi:10.1016/S0198-9715(02)00066-2 Gong, J., Zhou, J., Li, W., & Lin, H. (2006). Design and implementation of an intelligent virtual geographic environment for the simulation of SARS transmission. Proceedings of the 2006 ACM international conference on Virtual reality continuum and its applications (pp. 383-386). SESSION: Session S2: VR applications Hongoro, C., & McPake, B. (2004). How to bridge the gap in human resources for health. Lancet, 364(9443), 1451–1456C. doi:10.1016/ S0140-6736(04)17229-2 Kopacek, P. (2002). Demining robots: A tool for international stability. 15th Triennial World Congress IFAC, 2002. (pp. 1–5). Barcelona, Spain. Muller, G., Grébaut, P., & Gouteux, J. P. (2004, January). An agent-based model of sleeping sickness: simulation trials of a forest focus in southern Cameroon. Comptes Rendus Biologies, 327(1), 1–11. doi:10.1016/j.crvi.2003.12.002 Santana, P., Barata, J., Cruz, H., Mestre, A., Lisboa, J., & Flores, L. (2005, September). A Multi-Robot System for Landmine Detection. Emerging Technologies and Factory Automation, 2005. ETFA 2005. 10th IEEE Conference on, 1, 721-728.

Berger, T., Schreinemachers, P., & Woelcke, J. (2006). Multi-agent simulation for development of less-favored areas. Agricultural Systems, 88, 28–43. doi:10.1016/j.agsy.2005.06.002

Scalem, M., Bandyopadhyay, S., & Sircar, A. (2004, October). An approach towards a decentralised Disaster Management Information Network. Lecture Notes on Computer Science, Springer Publications, 3285, AACC2004.

CIA. (2008). CIA World Factbook Washington, D.C.: Central Intelligence Agency, 2008. Retrieved September 8, 2008, from https://www. cia.gov/library/publications/the-world-factbook/

Shaalana, K., ElBadryb, M., & Rafea, A. (2004). A multiagent approach for diagnostic expert systems via the internet. Expert Systems with Applications, 27, 1–10. doi:10.1016/j.eswa.2003.12.018

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Teweldemedhin, E., Marwala, T., & Mueller, C. (2004). Agent based modelling: A case study in HIV epidemic. Proceedings of the 4th International Conference on Hybrid Intelligent Systems (HIS’04) (pp. 154–159). Washington, DC, USA: IEEE Computer Society.

Yergens, D., Hiner, J., Denzinger, J., & Noseworthy, T. (2006a). IDESS - A Multi Agent Based Simulation System for Rapid Development of Infectious Disease Models. [ITSSA]. International Transactions on Systems Science and Applications, 1(1), 51–58.

WHO. (2008). Core Health Indicators the latest data from multiple WHO sources. WHO Statistical Information System (WHOSIS). Retrieved September 8, 2008, from http://www.who.int/ whosis/database/core/core_select_process.cfm? country=cai&indicators=healthpersonnel

Yergens, D., Noseworthy, T., Hamilton, D., & Denzinger, J. (2006b, May). Agent Based Simulation combined with Real-Time Remote Surveillance for Disaster Response Management. 5th International Joint Conference on Autonomous Agents and Multi-Agent Systems, (Workshop on Agent Technology for Disaster Management), Hakodate, Japan.

This work was previously published in Multi-Agent Systems for Healthcare Simulation and Modeling: Applications for System Improvement, edited by Raman Paranjape and Asha Sadanand, pp. 215-233, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 5.3

Primary Care through a Public-Private Partnership:

Health Management and Research Institute Sofi Bergkvist ACCESS Health Initiative, India Hanna Pernefeldt ACCESS Health Initiative, India

ABSTRACT The primary care delivery model developed by the Health Management and Research Institute (HMRI) in India, integrates innovative technical solutions and process-oriented operations for the provision of healthcare services, while supporting the public health system. Through a publicprivate partnership with the state government of Andhra Pradesh, HMRI has a unique base to pilot large scale health interventions. The HMRI Model includes components such as a medical DOI: 10.4018/978-1-60960-561-2.ch503

helpline, rural outreach health services, a disease surveillance program, a blood bank application, and telemedicine projects. Both clinical and nonclinical procedures are strengthened by technology that enables research, tailored and evidence-based interventions, as well as improves efficiency and quality of healthcare delivery. Health management and decision-making is assisted by the organization’s large database of electronic medical records. Challenges to implementation include implications of large government contracts, funding issues, as well as technical constraints and human resources issues. This chapter describes the Model’s various components and its contextual

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Primary Care through a Public-Private Partnership

framework with enabling and constraining factors. HMRI has developed a unique system for preventive and primary care that can serve as a model for low, middle, and high income countries, though external evaluations are critically needed for further assessment of best practices.

INTRODUCTION The paradox of the Indian healthcare system lies in the startling gap between its world-class medical and technical expertise and its abysmal support systems, outreach, and delivery mechanisms. India’s scientific and technical prowess seems inadequate when confronted with disturbing maternal mortality rates and limited healthcare access. Concerned state and central governments, committed public-private partnerships, and technology initiatives are, however, helping to narrow this gap through efficient healthcare delivery systems that focus on comprehensive healthcare services. This chapter takes a closer look at the collaborative initiative between the state government of Andhra Pradesh and the non-profit organization, the Health Management and Research Institute (HMRI). The aim is to further investigate HMRI’s healthcare delivery model (the “HMRI Model” or the “Model”), the technology solutions incorporated therein, its functions, and the possibility of its large scale implementation. This chapter is not intended to present an assessment of costeffectiveness or to evaluate the delivery model, but will provide insights into its various components. It presents HMRI’s innovative solutions in harnessing technology and human resources solutions, as well as discusses possible implications, challenges to implementation, and benefits of this particular primary care delivery model. Since its inception, HMRI has focused on augmenting public health delivery systems with the use of information and communication technologies and supporting the public health system

to improve the access and quality of services to vulnerable sections of the society. The aim of the organization is to expand the reach of out-patient care to a larger population by providing three times the primary care services at ten per cent of the costs of the government provided services. While technology is not the sole solution to the delivery of quality primary care, the HMRI Model serves as an example of how technical solutions can support the management and provision of services.

BACKGROUND Primary Care in India The contextual picture of health care in India is the challenge to provide health services to a population of more than 1.16 billion people, the majority of whom, 71 per cent, live in rural areas. The country has a heavy burden of disease and an evolving epidemiological transition is shifting the burden of disease toward non-communicable diseases. India has one of the lowest healthcare expenditures in the world; in 2006 it was only 3.6 per cent of the GDP (WHOSIS, 2009). At the same time, the proportion of private spending on health in India is one of the highest in the world (Government of India, 2008). This context highlights the need for innovative approaches to the design and implementation of healthcare delivery models in order to meet India’s shifting healthcare needs. To fill the gaps of the public health sector, the private sector has been recognized for innovative approaches that could strengthen the quality and scope of healthcare delivery in the country. The two sectors have different strengths that should not go unnoticed; the government on one hand has an advantage in creating scale through access to funds, while the private sector has provided many efficient healthcare delivery solutions. HMRI is an example of a public-private partnership where the strengths of the private sector are amplified and brought to scale with government funding.

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On the same note, it is recognized that India is establishing new global standards for delivery of low-cost, quality health care through its breakthrough innovations. It has been stated that the private sector in India is driving the evolution of an ecosystem of innovations for world-class healthcare delivery (Prahalad, 2006).

Barriers to Health Care Reliable provision of primary care is not a given in many parts of the world, including India, due to many barriers to delivery. Barriers include, but are not limited to, access, availability, affordability, and quality of care, and are further exacerbated by a dearth of appropriately skilled human resources. Obstacles related to Access include individual geographic and financial factors. In addition, pre-existing inequalities as well as social and educational deprivations are contributing factors in determining accessibility to services. As for Availability of healthcare services, the already limited public healthcare infrastructure suffers from understaffed facilities, inadequate equipment, and insufficient medical supplies. The undersupply of qualified medical staff and the uneven distribution of staff between urban and rural communities are important variables in the availability equation (WHO, 2007). In India, where 71 per cent of the population lives in rural areas, about 80 per cent of the medical doctors practice in urban areas (Schmidt, 2006). Human resources are the essential joining factor between financing, technologies, physical infrastructure and information, which in turn make health care available or not. Affordability is one of the most significant barriers to health care in a country like India. High out-of-pocket spending is a severe problem, especially for the many people living below the poverty line. The vicious cycle of poverty and disease is the reality for many, where illness of one individual can devastate entire families. High costs of private care impose a financial burden that

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further limits accessibility to essential healthcare services. Public facilities are supposed to be free of cost to the beneficiary but studies have shown that corrupt practices result in significant costs to the patients (National Commission on Macroeconomics and Health, 2005a). This dilemma often prevents individuals from accessing preventive care or seeking timely treatment, which may complicate the onset and progress of disease and, in the worst case, render it irreversible, resulting in higher costs to the individual and society in the long run. Affordability also poses a challenge for the private providers because patient payments are unpredictable and this unpredictability hampers business planning and decreases interest from investors (IFC, 2008). Quality of care is a major problem in India where 80 per cent of out-patient visits are handled by private healthcare providers, most of these informal and unregulated practitioners (WHO, 2008). The National Commission on Macroeconomics and Health has stated that poor management is one of the main factors for inadequate care within the public sector. Other factors that adversely affect the functioning of the public health system are absence of performance-based monitoring and conflicting job roles, making accountability problematic (National Commission on Macroeconomics and Health, 2005a). On an organizational level, standardization and transparency are key concepts within this context. In terms of the day-to-day routine, some people argue that using standardized guidelines and protocols may limit and weaken critical personal interactions with the patient. However, clinical and non-clinical protocols for healthcare provision can be valuable resources for improving the quality of care, in terms of diagnosis and treatment. Regardless of the standpoint of public or private sector healthcare delivery, the same barriers need to be addressed to conquer issues of efficiency and quality in the provision of care. Modern health management practices, scale of operation, and state-of-the-art technology are factors that can

Primary Care through a Public-Private Partnership

be used to overcome these barriers. The Health Management and Research Institute is a non-profit organization that is focused on augmenting the public health delivery system by harnessing these components. The organization’s primary care delivery model has a rural reach and it continuously refines its activities by developing technologies and strategies to improve service provision and to address the barriers to service delivery. However, further assessments and evaluations of the HMRI Model need to be done, such as cost-benefit analysis of the interventions.

Technology Solutions and Applications on the Market There are various technology solutions on the market with a mission to improve the efficiency and quality of care. Technology can be used to improve clinical as well as non-clinical processes, human resources management and monitoring. In India, technology has merged with the need for low-cost solutions at a large scale. A broad base of committed doctors, entrepreneurs, and philanthropists has made the country a source for solutions that can improve healthcare systems globally. Still, preventive and primary care are absent to many people and a lack of human resources is endured, especially in rural settings. This chapter will bring light to the multifaceted picture of where technology can play an important role. The example of the HMRI, a primary care delivery model with essential technology support, portrays this very picture.

PRIMARY CARE THROUGH PUBLIC-PRIVATE PARTNERSHIP What differentiates HMRI from other healthcare providers? First of all, HMRI operates at a unique scale with mobile vans for a target population of 40 million people and with health advisory services for everybody with access to a phone. Most

telemedicine initiatives in India provide services at secondary and tertiary levels of care, while many people in rural areas never even reach this level of care due to affordability and geographical barriers. HMRI has created a comprehensive approach with telecommunication-based technology as an integrated part of the entire model. In relation to the barriers to health care, the HMRI Model addresses these barriers through its various delivery mechanisms. Access and availability are addressed through the outreach program by providing medical vans on fixed-day services in rural areas and through a toll-free helpline for medical advice. Affordability is created through the public-private partnership structure, where the government funds the private organization to provide care, free of cost to patients. HMRI is continuously striving towards cost reductions through improved efficiencies and low-cost equipment without compromising on quality. Quality is improved through the use of technology, standardized procedures, and service protocols, as well as through monitoring and evaluation, including a system for provision of patient feedback. The various fault-recording systems put in place by HMRI, provide the foundation for enhancing the quality of the offered services and for developing improved operational solutions.

Health Sector Reform and Partnership Structure Many innovative solutions for low-cost quality care are being developed in India and state governments are actively working to support these. Inefficiencies in public sector healthcare delivery and the problems of affordability have prompted state governments, such as the government of Andhra Pradesh, to focus on a role of stewardship in healthcare delivery and to recognize and collaborate with private providers. In the case of HMRI, its collaboration with the state government is one of the most significant factors contributing to its success in healthcare

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delivery. As a result of this partnership, which includes funding support from the central government through the National Rural Health Mission, HMRI has been able to build a unique model for primary care and rapidly scale up services to cover the entire state of Andhra Pradesh. The aim of this partnership structure is to deliver out-patient care to three times the population currently serviced by the public sector at ten per cent of the costs. While the benefits of this partnership structure are evident, the risks must not be ignored. The partnership is political as the government can reach out to rural populations with health care and political messages. In addition, elections can lead to a shift in power and interventions like the partnership with HMRI may be questioned and terminated. Therefore, the sustainability of publicprivate partnerships must not be taken for granted.

Motives for the Development of the Model In response to the inadequate emergency response system in Andhra Pradesh, Ramalinga Raju of Satyam Computers Services Ltd (“Satyam”), created the Emergency Management and Response Institute (EMRI) to provide emergency response services in the urban areas. EMRI’s response system vastly improved the state’s existing apparatus and soon the state government approached Mr. Raju to develop the same system for the rural areas, with the government funding 95 per cent of the costs of ambulance services in all districts of the state. Other states followed suit and EMRI soon expanded, now reflected in the 1,900 ambulances that are applied across 10 states, as of August 2009. EMRI quickly proved how a private organization with government funding can build a high-quality and efficient model in a short period of time. In the development of the HMRI partnership with the government, the emergency response system managed by the sister organization EMRI had created an essential trust component, upon which the collaboration with HMRI was built.

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The man behind the two healthcare delivery models of EMRI and HMRI, Mr. Raju, drew on his experiences from the IT industry and business insights to revitalize the field of healthcare in terms of operations and bringing businesses to scale (Timmons & Kahn, 2009). As a result of his previous achievements, he had developed a close relationship with the government, which may have enabled the public-private partnerships and the rapid deployment of the two healthcare delivery models. In addition, Mr. Raju’s proven social orientation and commitment to rural development further advanced the development of HMRI. He had previously created the Byrraju Foundation in 2001, which focuses on holistic rural transformation, with health care as a core component. It has been estimated that more than 50 per cent of the population in Andhra Pradesh has more than three kilometers to travel to a public primary health center and that this population is de-linked from the public healthcare system. The Indian government has appointed Accredited Social Health Activist (ASHA) workers to provide basic health services in rural areas. These healthcare workers are women, with limited training, who battle the constraining factors, such as long distances to primary health centers. Even in cases where these centers are within reach, they are often inadequately staffed and the monitoring of service provision insufficient. This inaccessibility is one of the primary problems which the HMRI Model intends to address. Successful identification of the deficiencies in the area of primary care provision, as well as the existing relationship of its founder and sister organization with the government, gave the team at HMRI the confidence to take the next steps and develop strategies to address these issues. The resulting partnership between HMRI and the state, which is 95 per cent funded by the government, has enabled the provision of primary care in Andhra Pradesh since 2007. As an official statement of the partnership all EMRI ambulances and all HMRI mobile medical vans display a large picture of the politician Rajiv

Primary Care through a Public-Private Partnership

Gandhi on their exterior. The purpose as stated is to show the government’s active role in the initiative and convey that the healthcare services are provided for free.

INNOVATIVE CHARACTERISTICS OF HMRI The basic premise of the HMRI Model is the delivery of inclusive primary care, free of charge to everyone regardless of urban or rural location, religion, caste, or socio-economic status. The term primary care, as used here, includes curative (treatment), preventive, promotive, and rehabilitative services, and the HMRI Model tailors its delivery components to include these four cornerstones. Examples of preventive and promotive elements are HMRI’s public health education programs and school health initiatives. The rural outreach services, with mobile vans, are examples of the treatment services, further strengthening preventive aspects and health promotion. The medical helpline, where consultations take place over the phone, is a further example of where creation of health awareness, prevention of illness, and health promotion, is made possible. Rehabilitation services are provided through chronic disease management and continued check-ups as part of the rural outreach services. Part of what makes the HMRI Model unique is the way various technical solutions and applications are integrated with organizational processes to create a fertile ground for efficient healthcare delivery. Although telecommunication is the core component of the HMRI Model, various other technology-driven solutions are also essential. According to the founder, Mr. Raju, the solution is not the technology in itself, but the passion of the people managing the organization (SMS Conference, 2008). HMRI uses a process-oriented approach with well-defined tasks, where technology is a tool for facilitating processes in operations, monitoring, and quality control. However,

technology alone does not solve the daily issues faced by the organization. Technology can be cost intensive and difficult to scale. HMRI does not aim to invent new solutions, but rather to lower costs in order to enable delivery of healthcare services to a larger segment of the population without compromising the quality of care.

The Healthcare Delivery Components Central to HMRI’s integrated solutions are a 24hour call center for medical advice, a fixed-day rural outreach service that uses mobile health vans to bring a team of health workers to rural villages to provide diagnostics, prenatal care, treatment, and over-the-counter medication, as well as a health awareness program in the local schools. Other components of the Model are telemedicine pilots, blood bank applications, an Innovation Lab where technologies for health services are developed, and a disease surveillance program facilitating research, as well as disease and disaster management. HMRI addresses the demandsupply gap in out-patient care, through designing last mile solutions including integrated service delivery mechanisms. Dr. Balaji Utla, one of the founders and the CEO of HMRI, has stated that the Model addresses the problem of geographical distances by bringing health care to people with the support and use of technology (Healthcon’08, 2008). The model has enabled awareness creation for prevention, school screening programs, and maternal health monitoring at a level that a few years ago was implausible. This particular primary care model focuses on community health care and strengthening the links to public institutions and the public health delivery system, as well as empowering the community itself. The organization focuses on inclusiveness, equity, and sustainability.

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Medical Helpline: 24Hour Call Center HMRI has together with the state government implemented a comprehensive “Health Information Help Line” where people can call roundthe-clock free of cost. The population of Andhra Pradesh can access these medical tele-consulting services through the toll-free number “104”. The objective is to assist people with medical advice, particularly those in rural areas, who face difficulties in getting information on health problems and accessing a qualified doctor (Punetha, 2008). HMRI monitors the health status of patients and engages in early detection of disease by using its linkages with local ASHA workers present in the rural areas, as well as by monitoring the calls placed to the call center. In addition, HMRI gathers various kinds of data other than pure health indicators, such as literacy levels, upon request from the state government through the patient interaction at the call center. The services at the call center are provided by qualified professionals, resulting in free medical advice over the phone to people in both rural and urban locations. The 104-workers are currently operating from a single call center in Hyderabad with an employee strength of 1,900 people, including 1,505 paramedics, 200 registration officers, and close to 200 doctors. The workforce is working in four shifts round-the-clock. In addition, more than 500 software engineers were initially involved in the development of the software, and 55 Satyam engineers and 45 HMRI in-house engineers now maintain the software and service. To support the system 50 management personnel are also on staff. With this structure the use of the doctors’ time is maximized, as each doctor can deal with more than 100 patients in an 8-hour shift. Out of all the incoming calls, paramedics handle 80 per cent, while only 20 per cent need to be passed on to the doctors (Hammond, 2009). The operators at the center are generally paramedics, with a professional degree, e.g., Bachelor

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of Nursing with an additional six months training provided by HMRI, who follow protocols that detail what advice to give for each specific problem. The protocols consist of 140 directories, 400 algorithms and 165 disease summaries, and were developed in collaboration with experts familiar with local conditions and diseases. Each protocol is continuously updated based on empirical data collected from the calls. The programming has been managed by Satyam in collaboration with HMRI and external experts.

The Route of a Call Anyone dialing the toll-free number will receive free medical advice from the paramedic team at the HMRI helpline call center based in Hyderabad. The location and history of each caller is displayed and the calls are screened to determine which ones might need immediate assistance. Urgent cases are immediately connected to the 108-number of the EMRI and their ambulance services, or to the police or fire department. When a call is about an acute, but non-life threatening condition, the caller is advised on how to stabilize the situation and provided information on where a nearby clinic or hospital is located for further assistance. Each agent is supported by map listings of nearby public health services and facilities, displayed in relation to the caller’s location and the road system in the region. All other cases are primarily handled by paramedics, or, depending on the type of medical advice required, routed to a doctor, nurse practitioner, psychologist, or to an ayurvedic or homeopathic medical practitioner. Of the received calls, about one fifth of the callers require professional medical advice rather than basic health information. Nearly 16,000 first time callers are registered in the database everyday. In August 2007, the helpline received 4,500 calls. Two years later the call volume had reached over 50,000 per day, without any marketing of the toll-free number. With this number of calls, HMRI is the world’s largest telehealth provider,

Primary Care through a Public-Private Partnership

followed by the NHS Direct telehealth initiative in the United Kingdom. Sixty per cent of the helpline callers are between the ages of 15-25 years. The vast majority of calls, 90 per cent, are placed by men and in total, 19 per cent of the callers are illiterate. In terms of geographical dispersion, 66 per cent of calls originate from rural areas and an astonishing 99 per cent of calls are placed from mobile phones. In addition, call trends show that in a regular week more calls are received on Sundays than any other day, with call volume surging 15 per cent on that day. During holiday seasons, call volumes increase 16 per cent over normal times. On Festival days, call volumes increase by 21 per cent. Of the calls received, 94.5 per cent deal with medical advice, 3.6 per cent seek counseling, 1.8 per cent need information about the nearest healthcare facility and 0.1 per cent are complaints or suggestions for service improvement registered to the complaint line. The compilation of this kind of information brings about possibilities for development of tailored strategies for providing broader coverage and more efficient services. One example to further ease and increase overall access to services is the current incorporation of email and chat functions into the system structure of the helpline.

Mobile Health Services with Rural Reach One of the main barriers to universal healthcare delivery is the lack of healthcare professionals willing to live in rural areas. It is therefore important to design a model for healthcare delivery that minimizes dependence on professionals in rural areas and especially the presence of a doctor. HMRI has addressed this problem through its “Fixed Day Health Service”. Specially designed and equipped medical vans travel to rural areas on a fixed-day basis, each staffed with a team working in collaboration with the local ASHA workers. The objective of this program is to provide monthly access to primary care services to the

target group of approximately 40 million people at 21,981 service points across Andhra Pradesh. These service points are either single or grouped habitations, chosen to provide care to people who are more than three kilometers from the closest primary health center. To overcome geographical barriers, the fleet of mobile vans travel the roads of rural India, thus enabling patients to access consultation services in their villages instead of having to travel far to get to the closest health center, paying for transportation, and also losing their day’s wages. The initiative was commenced in September 2008 in collaboration with the central government under the National Rural Health Mission. It was initially implemented in four underserved districts in Andhra Pradesh with 100 vehicles, each with a capacity to service 300 beneficiaries per day. By August 2009, the “Fixed Day Health Service” had served 5.3 million people with a fleet of 474 operational mobile vans, which makes it the largest provider of mobile primary health service in the world (HMRI, 2009). The strategy is to pay a monthly visit, on a fixed day, to each village with an approximate population of 1,500 people that is more than three km away from the closest healthcare center. If the population of a village is less than 1,000 people, two villages are grouped together to more efficiently use the services. Each van visits two locations per day, one before lunch and one after. The interface between health workers is highly valued when providing rural healthcare services. Each mobile van transports a team of seven staff; including the driver, three Auxiliary Nurse Midwives (ANMs), a lab-technician, a pharmacist, and a data entry operator. In addition, district coordinators/supervisors are appointed at the local level to respond to the local needs. The government appointed ASHA worker, who lives in the village, has the responsibility to inform people of the van’s visit, and she is the local anchor, maintaining the communication with the villages. She further monitors pregnant women

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and chronic disease patients, as well as promotes better health-seeking behavior among people in the community.

Service Provision In short, the primary focus in the rural populations is pregnant women, infants, children, and people with chronic diseases, as well as promotion of school health (Punetha, 2008). These preventive and curative health services include provision of clinical and laboratory diagnostics, identification of disease, monitoring and treatment and referral of high-risk cases. HMRI is further focused on registering newborns in the state and creating electronic records for all patients. By August 2009, 17.9 million diagnostics tests had been conducted through the mobile services, with an average of 30,000 diagnostic tests being done per day. The database of electronic patient records contains 9.9 million records, by August 2009, generated through the outreach services and the helpline. The detailed focus is on the areas of (1) pregnancy care, e.g. prenatal check-ups, testing, and iron and folic acid supplementation; (2) pediatric care, e.g. birth defect diagnosis, immunizations, vitamin supplementation, growth monitoring, nutritional supplementation, and treatment for diarrhea; (3) chronic ailments, e.g. diagnostic testing, focusing on diabetes, hypertension, blindness, and tuberculosis; and (4) registration and vital events recording, e.g. electronic registration of births, deaths, and marriages (Punetha, 2008). HMRI is waiting for government approval to provide expanded services, such as ultrasound scans once a trimester, supplementing its ongoing pregnancy monitoring, and expanding the public health education activities. As for preventive services, one of the auxiliary nurse midwives (ANM) is specifically designated to work with the local schools in rural villages. When the van reaches the village, the designated ANM visits the school to conduct a screening of

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the students and provide education on hygiene and a healthy lifestyle. The mobile service also expands the access to both over-the-counter drugs and prescribed medicines in rural areas. The personnel in the van cannot prescribe medication, but the pharmacists provide over-the-counter drugs and prescribed medicines for those who already hold valid prescriptions.

Components of Technology The technology used by the mobile service includes the same Geographic Information System (GIS) and map listings as the helpline service, where nearby facilities and clinics are displayed along the road system in the region. The control center in Hyderabad can follow the vans as they visit two villages per day. One fixed day of service per month may seem limited and ineffective, but service is supported by the ASHA worker in each village who has been provided with a cell phone by HMRI. This structure enables HMRI to connect with local ASHA workers continuously, and allows patients to contact the helpline through the ASHA worker, for example to request information on the location of the closest HMRI medical van or other healthcare facility. In addition, ASHA workers use their cell phones to provide information on the status of patients in between the monthly visits, through a specially designed template SMS-alert system. This in turn feeds into a separate application called the Integrated Disease Surveillance Program, which monitors disease outbreaks and helps to plan interventions accordingly.

A Day with the Vans The van rolls up to the village location where the health station is to be unfolded, usually at the village school building or a village community center, chosen by the district coordinator. Inside the van, the station is neatly packed in overhead compartments and storage spaces. The seven staff

Primary Care through a Public-Private Partnership

Figure 1. Organizing the registration of patients in the village of Inganpalle, Andhra Pradesh.

members are transported in the van, from village to village, serving two villages per day. The vans are assigned different regions and reach their villages on a fixed-day every month. As they drive up, people start to gather. Tables, chairs, scales, and boxes of supplies and medicines are carried out of the van and organized to create the health station where the paramedics take their positions. Patients gather at the first desk, where they are registered. Using a fingerprint scanner, the paramedics search the existing patient record database where the patients are either identified through existing profiles or are recorded for the first time. Depending on the case, the patients are directed to the appropriate counter set up at the health station. Each counter is designated for one of the following tasks: (1) taking measures such as blood pressure, height, and weight; (2) pre-and post-natal care; (3) chronic diseases; (4) laboratory work; (5) pharmacy; and (6) records. The patient is given a manual record to bring to the counters, which is filled out by HMRI staff, and entered into the system at the end of the patient’s visit to update the patient’s profile. Nutritional supplements, e.g. iron and folic acid for pregnant women, are provided by HMRI staff, while the ASHA workers assist in the distribution of food supplements to both malnourished patients and children.

Figure 2. Registration of a female patient.

Figure 3. At the first counter of the health stations, new electronic patient records are created and already registered patients records are pulled up through the use of fingerprint technology.

Figure 4. Fingerprint technology is used to register a new patient. If already in the system, the patient record and history is pulled up before the consultation begins.

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Figure 5. An ongoing consultation in Inganpalle, Andhra Pradesh, as a part of the mobile health services.

Figure 6. The pharmacist team member in action.

(about US $ 55 million), or seven per cent of the total state health budget in Andhra Pradesh. No robust cost-effectiveness study has been done of the program. HMRI has, through the Fixed Day Health Service, seen more than 5.3 million patients at an annual cost of US $50 million. The annual cost per patient is currently US $9.3, of which 40 per cent is the cost for medicines. This may seem high but it is important to recognize that the mobile vans reach out to a population of 40 million people with preventive programs including school health programs. The cost per person in the target population is hence US $1.25. All the services are offered by HMRI free of cost to the beneficiary. The patients save both time and money, since health services are brought to them, instead of forcing them to travel to the closest primary health center.

An Accredited Social Health Activist Workers’ Voice The Accredited Social Health Activist (ASHA) workers are tied to the HMRI initiative and serve as its local anchors and community links. They work to increase community awareness of health issues and provide information about the services

Throughout the service visit, the local ASHA worker is present and fosters the dialogue with the HMRI staff. Once all the patients have been seen, the team packs up the station and heads off to the next site.

Coverage and Cost of Care The total program costs of the HMRI Model (including call center, mobile vans, and other components) are INR 2.65 billion per annum

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Figure 7. Engaging the government appointed ASHA worker, to complete the HMRI-team for the rural fixed day health services is part of the strategy of the rural outreach component of this primary care model.

Primary Care through a Public-Private Partnership

offered by HMRI and other healthcare providers in the region. These female health workers have the potential to impact health-seeking behavior at the local level. Women often find it easier to turn to a female health worker with their health problems, which favors the HMRI Model’s strong focus on pregnant women. When talking to an ASHA worker in Narayanpalle, Andhra Pradesh, she stated that more services ought to be covered by the HMRI program, such as family planning. She emphasized that ASHA workers should be able to provide injections in her village. This ASHA worker said that she had received training from HMRI and that she goes to the closest public primary health center once per week for a meeting, in addition to following cases referred to this or other healthcare facilities.

Telemedicine Pilot Projects To bridge the rural and urban divide, HMRI has embarked on a project to merge and utilize information technology, telecommunications, and medical electronics, along with the strength of urban specialist and specialized health centers. Telemedicine services are piloted in six primary health centers where a virtual physical examination is done through remote diagnostics, including an ECG. HMRI further utilizes a chat function and a web-camera for its telemedicine consultations, where these technologies complement each other and can substitute for one another if connectivity is insufficient for video. Connectivity is provided via broadband or wireless, but HMRI is also exploring the options of WiMax, which has not yet been launched in India, and the use of satellites in remote areas where connectivity is an issue. The results from the consultations are communicated to the patient and to the call center in the city, where the patient’s electronic health record is recorded in the central repository and made available for future reference and treatment. The telemedicine projects started in January 2008 and as of April 2009, 12,000 teleconsultations

had been provided. The pilot projects yielded positive results and specialty modules of telemedicine are being developed through the Innovation Lab. Telemedicine services were also set to be piloted in one of the medical vans for a period of 30 days and then, if successful, will be rolled out in all mobile health vans. However, current issues with both funding and connectivity have impeded the use of telemedicine services by the vans and the pilot has been put on hold. Current telemedicine pilots, as of August 2009, are experimenting with rural “Point Of Care” solutions, where lab equipment will be available at a stationary point of care accessible to the rural population. Currently, each telemedicine unit in the primary health centers costs about US $10,000 and includes eight medical devices as well as a laptop. HMRI is working on reducing this cost to about US $4,000. The telemedicine units are structured with multipoint-to-one connections for better utilization by the doctors. The efficiencies derived from the use of a multipoint-to-one structure are partly due to the creation of a “waiting list” where patients are placed on hold, and automatically connected when a doctor signs into the network, or when an online doctor is done with a consultation, thus making better use of the doctors’ time.

Bloodline Applications In order to boost the blood supply at Andhra Pradesh’s blood banks, HMRI designed a bloodline application which is currently used by ten blood banks in Hyderabad. “104-Bloodline” is a web-based application that enables blood banks to interlink their operations and assists healthcare providers in locating the appropriate blood supply for a given patient. It is further integrated with the call center, where patients receive information about the blood bank where a supply of their specific blood group is available, including contact information and directions. An objective of the bloodline application is to prevent “professional donors”, who donate blood

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for money and misuse the system. The HMRI application registers donors using their pictures and fingerprints, shifting the control of donations to the blood bank and making it impossible for a donor to donate too many times within a set period of time, or with unsafe blood. Using this system, an ineligible donor will not be able to donate, and blood older than 90 days will not be used. The goal is to have the application implemented in an additional 54 blood banks by 2010. From a technical point of view, the requisite hardware and software are available and expansion can begin as soon as funding is secured.

Electronic Medical Records Patient medical records are created at the call center and through the mobile health services, and the database holds close to ten million electronic medical records. HMRI is poised to hold the world’s largest database of electronic patient records, expected to include 40 million, within the state of Andhra Pradesh, by 2010. Patient data is collected during mobile health visits where patient records including fingerprints are created, as well as through the calls placed to the helpline. Electronic patient records are continuously updated for each call or visit and the historic data is made accessible through this system.

The Innovation Lab: Development of Technology Solutions The organization’s large-scale direct contact with patients, particularly rural patients, and close collaboration with the state government, provides an opportunity to develop a thorough understanding of the healthcare needs in the region. The organization’s Innovation Lab is a unique platform to leverage this knowledge and enable the development of technology that can be easily tested and quickly implemented for high impact. The telemedicine solutions are examples of developments from the Innovation Lab, with the potential

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to provide a high impact on health care. The lab is exploring future innovations relating to rural outreach, such as the idea of a “doc-in-a-box”, a concept to provide all the necessary equipment for basic healthcare services, to expand the reach of services into even more remote areas, unreachable by the medical vans or other vehicles. The concept entails a utility bike that, including all its delivery equipment, would cost US $4,000, though HMRI hopes to bring it down to about US $2,500.

Disease Surveillance System: Tailored Interventions and Alerts The large pool of health related data contained in the electronic medical records, created, and maintained by HMRI, serves as a base for the development of interventions, prevention strategies, health management, and further research. HMRI’s Integrated Disease Surveillance Program (IDSP) analyzes the information collected through the patient records, to spot trends in disease patterns, and issue alerts in case of outbreaks. In addition, the IDSP gathers information from ASHA workers, in the areas serviced by HMRI, who are assigned with the task of collecting data and sending a special template SMS to HMRI whenever they encounter a patient with a serious condition, or if they detect an outbreak of disease. These data are immediately analyzed by HMRI and immediate feedback and support is directed to the ASHA workers if the report exceeds the normal pattern for the specifically reported condition or disease. The appropriate government officials in that region are informed of the situation so that they can prepare for further action. Through this system, the surveillance is interlinked and consistent monitoring brings about valuable insights that can be scaled and used as information guiding decision-making and action. The disease surveillance program can be compared to Lego® blocks, where the services can easily be modified and tailored to the specific needs of any region or country to monitor different

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diseases and health indicators. The information can be shared at multiple levels, maximizing the use for health management, prevention, disaster management, and emergency response. However, a cost-benefit analysis of this program has not yet been conducted.

Public Education In order to provide public health education and create health awareness in the population, HMRI uses various media to reach out to the rural community, i.e., print media, audio, video, and traditional media (shows and plays). The printed material covers seasonal diseases and precautions that should be taken, for example, HMRI has published an educational book about water pollution, waterborne diseases, and appropriate precautions dealing with each issue. The book further educates pregnant women on precautions and how to ensure a safe delivery. Audio health messages, packaged in the format of songs to attract the attention of children and others, are currently being played in various means of transportation, primarily shared autos. Videos are believed to have a large penetration among the rural masses and the HMRI public education team has created short films on various topics, each addressing different health issues, as well as the services offered by HMRI and how people can benefit from these free services. Traditional media is composed of plays based on short stories that contain a preventive health message. This medium has been popular in rural villages and reaches people who do not have access to radio or television, or those who cannot read.

Preventive Measures Preventive health is the primary focus of the HMRI Model, and with its broad outreach it has the potential to serve as a platform for creation of health awareness and preventive health measures. The helpline and the rural outreach services are

channels through which preventive health messages can be delivered to large populations. The patient data captured in HMRI’s database and the information generated from the analysis of the medical records play an important role in the formulation of preventive health measures and facilitates the promotion of timely interventions. For example, if data analysis indicates that water-borne diseases are common in a specific region, HMRI can take an active role in designing preventive efforts. The collected data provides useful information about the best ways to reach people with tailored health messages and proactive health services. HMRI’s overall data collection, management, and analysis feed not only into the improvements of its health services, but also provide inputs for research, evidence-based interventions, and decision-making.

THE ORGANIZATION In order to be successful, HMRI’s model requires the trust of the communities it serves, as well as government funding for its operations. In India, trust is often built through family reputation or from bottom-up approaches where community participation is strong. HMRI has been gaining the trust of its serviced communities through its personnel’s interaction with patients and its collaboration with local ASHA workers. Increased utilization of services will strengthen this even further. HMRI’s association with the government is yet another important component related to building trust.

Management One of the strengths of the HMRI Model is partly due to its strategy to ensure professional management of the organization, to make the best use of the model’s potentials. Mr. Raju assembled the management team, bringing in experts in efficient management and rapid scale-up from the private

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sector, as well as experienced professionals working in the public health sector (Healthcon’08, 2008). HMRI offered competitive salaries for its top managers in order to recruit professionals with strong leadership and values aligned with HMRI’s mission. These salaries could not be provided with government funding and were covered by Mr. Raju (Haseltine, 2008). The termination of his contribution forced HMRI to seek new sources of funding. External funds are now raised through donations at the HMRI website and the organization has initiated fee-based services for additional revenue streams.

Scalability and Replication As services expand, adjusting the system to properly process the increased call volumes to the helpline becomes a major challenge. So far, the technical support provided by Satyam has been viewed as sufficient to surmount this challenge while this support now is uncertain. Currently, the helpline component of the Model has the potential to be scaled across India and internationally. Although HMRI wants to make the program fully operational in Andhra Pradesh before moving to other states, other states have already begun to contact HMRI and inquire about the implementation of the Model. This development mirrors the experience of the sister organization EMRI, with its scale up of the emergency response system. International expansion of the HMRI helpline service is definitely on the horizon, but it is difficult to comment on the speed of the process at this time. The set up of the helpline in different regions and countries will be easier than the initial set up in Andhra Pradesh, due to the considerable amount of knowledge gained from its experiences.

Human Resources Management HMRI has adopted an innovative approach to human resources management that focuses on the

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utilization of paramedics with special training and locally recruited health workers to create a network of skilled personnel for primary care, instead of solely relying on doctors. This strategy increases the sustainability of the model by tapping multiple sources of medical knowledge. Task-shifting, using paramedics in the helpline service and the medical outreach program, and engaging local health workers for the rural outreach program are strong components of the HMRI Model. The healthcare personnel providing medical advice for the helpline service are mainly paramedics and general practitioners, with specialists at hand to address specific cases. An important strength of the helpline is that all of its medical practitioners are in-house and available at all times, enabling the provision of quick, alternate access to scarce medical professionals, such as psychologists. Other strengths of the helpline are its inclusion of alternative and traditional medical professionals, such as biochemists, microbiologists, hematologists, and pathologists, as well as ayurvedic and homeopathic practitioners. The medical outreach teams consist of seven health workers which usually take on additional responsibilities than what they are individually assigned, e.g. the drivers take on the role of an assistant in organizing the patients for registration. This evolving task-shifting of available resources could be used more and should be a part of the training for staff. The ASHA workers are appointed and paid by the government. In addition to their salary, they receive INR 100 (US $2.18) as compensation from HMRI for each occasion that a medical van visits their village. Similarly, the ANMs in the vans are also paid directly by the government, while the remaining team members are covered by HMRI. In addition to these personnel, HMRI has district coordinators, assistant district coordinators, and supervisors in the rural areas.

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Training

MONITORING AND EVALUATION

Pharmacist and lab technicians working in the mobile health unit participate in a training program conducted by a specialized training group at HMRI, where they learn how to increase the speed of their services while on location. The plans are to provide training and continuing education to the rest of the team. The ASHA workers, who most often are illiterate, receive special training regarding their responsibilities and the usage of the application templates installed on the mobile phones that they are given. In addition to its internal training programs, HMRI is to introduce a 1,000 hour certificate course for rural medical practitioners in order to improve the quality of care provided by these unorganized health workers. The course has four components: class room training, practical training in government hospitals, on-the-job training with the 104-Mobile Fixed Day Health Service, and distance learning through the 104-Advice contact center. The course will take one year and successful candidates will be given a certificate from the Andhra Pradesh Paramedics Board. It has been estimated that 100,000 rural medical practitioners are operating in Andhra Pradesh, where presently 80,000 candidates have registered for the course. The government has decided to offer a pilot course before accepting all the registered candidates. The goal of the course is to improve the services provided by these practitioners, through training and certification, and to harmonize service standards with the HMRI model to increase the impact of the services (Punetha, 2008). The Andhra Pradesh Paramedics Board and the Andhra Pradesh state government have recently given HMRI the approval to carry on with this initiative, and HMRI has, through this training component, opened the door for an additional revenue source with the state government paying for the course.

In order to ensure quality control, HMRI has designed strategic mechanisms for data management, monitoring, and evaluation. These mechanisms include patient feedback systems, standardized protocols, and treatment guidelines. The database of patient records is analyzed and assists further development of technologies and improvements of the ongoing operations.

Data Collection and Management HMRI has with help from Satyam developed world-class applications to collect data and analyze health profiles. This type of information assists the overall monitoring of ongoing services and enables evidence-based decisions for the interventions. Patient satisfaction, among callers to the helpline, is documented through interviews as part of routine monitoring. These interviews are conducted with one per cent of the callers, currently resulting in about 500 interviews per day. Customers’ responses are analyzed by using a customer delight index that is evaluated internally by the research and analysis team on a monthly basis. In addition to its internal processes, HMRI is also exploring the use of third party organizations to conduct evaluations. Thus far, 90 per cent of the surveyed callers have expressed satisfaction with the service, 70 per cent have followed the advice given to them, and 75 per cent have recommended the services to others (Punetha, 2008).

Protocols and Standardization As described earlier, the basic operations of the helpline call center are primarily performed by paramedics, who refer severe cases to the appropriate specialists. The paramedics provide guidance normally given by general practitioners and this saves costs and is considered an efficient method for care delivery. It however, raises concerns in

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regards to quality of the services provided by paramedics. To address these concerns, HMRI has developed protocols that set forth the procedures that can be performed, as well as instructions on how procedures are to be carried out by the paramedics. In addition, hundreds of algorithms have been developed to assist the guidance of patients over the phone. The algorithms are accessible through specially designed, user-friendly software, connected to the electronic patient records database. If the caller is an existing patient, his or her medical history is displayed to the paramedic during the call, otherwise the paramedic will create a patient profile for the caller, using the algorithms as the underlying supporting structure for the phone conversation with the patient.

Patient Feedback HMRI’s complaint line mechanism is an innovative approach to routine monitoring and reporting of ongoing activities. This mechanism gathers patient feedback for public and private healthcare service provided in rural and urban areas, and it was developed at the request of the government. Patients are encouraged to share information regarding the service provision. The complaints that are gathered through this mechanism are usually related to the negligence of a healthcare professional or lack of services. Registered complaints are compiled and prioritized by a supervisor at the call center in accordance with the urgency for action, e.g. in cases of repeated complaints or malpractice that requires immediate attention. The information is then forwarded to the appropriate government official through SMS or email and is logged and stored at the center. The caller is given the option to remain anonymous throughout the process and can choose to be notified by the complaint line staff when action has been taken in response to the complaint. This method is expandable as well as responsive to local needs, since it allows

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beneficiaries to communicate complaints about the flaws or lack of services in local healthcare facilities.

Monitoring of the Mobile Health Services Technology for monitoring is also used to ensure the quality of mobile health services. The vans are equipped with devices that track the vehicle in real time, including the General Packet Radio Services (GPRS) tracking system that operates on the Global System for Mobile Communications (GSM) network, and enables supervision of the vans to ensure their proper operation. For example, many ambulatory services are unable to control the uses of their vehicles, with drivers sometimes using the vehicles as busses to make extra money by transporting people into the cities. The vehicle tracking technologies used by HMRI and EMRI address this problem. The monitoring systems are also important for collecting data from the mobile vans and the village health workers. Once the data is uploaded from the vans and reaches the central server at HMRI, it will become accessible to the HMRI management and government officials at any time through a web-based application. These monitoring systems and their effects on the development and refinement of operations, enhance the overall implementation, and the quality of services.

Independent Evaluation HMRI claims to provide cost-effective services. The use of technology has enabled monitoring and operational research to continuously advance the model. Many internal evaluations have been done, but there has yet not been any independent evaluation of HMRI. The number of patient records indicates a large scale of operations, but they are not equal to a measure of impact or outcomes. There is a need for an independent evaluation to assess the effectiveness of HMRI. An indepen-

Primary Care through a Public-Private Partnership

dent evaluation could strengthen HMRI’s efforts in creating a best practice model for preventive and primary care.

CHALLENGES IN IMPLEMENTATION The aim of this chapter is to show that high-quality health care can be delivered in both urban and rural India with the support of technology solutions, as well as to describe and highlight some of the key challenges to the Model. There are many constraining factors and this section on challenges is not exhaustive. The main challenges identified are linked to funding, human resources, governance, and technology. During the preparation of this chapter, Ramalinga Raju, the founder of HMRI, was accused of one of the biggest corporate frauds in Indian history through Satyam Computer Services Ltd (“Satyam”) and related companies. HMRI has financial and human resources related ties to Satyam and Mr. Raju. The scandal has provided an example of challenges that face public-private partnerships and large government contracts. Initially, five per cent of the operational costs of HMRI were funded by Mr. Raju, that covered the senior managements’ salaries and the Innovation Lab through which new technologies were piloted (Haseltine, 2008). This contribution was cancelled, which prohibited HMRI from developing new health interventions, and several pilots had to be put on hold. Since then, this has been a subject for fundraising and the organization has explored alternate revenue sources.

Governance Large transactions in emerging markets are often associated with governance issues; personal relationships and trust are often keys to major contracts with the government. Ramalinga Raju is a well-known business figure in India, who was internationally recognized as not only a success-

ful business man but also an honest person. He was the recipient of numerous awards, such as the Ernst & Young Entrepreneur of the Year Services Award in 1999 and CNBC’s Asia Business Leader – Corporate Citizen of the Year Award in 2002 (BusinessWeek, 2005), which strengthened this reputation. Allegations were made that Mr. Raju illegally siphoned investors’ money through Satyam (Timmons, 2009) and this directly affected HMRI. The association of Mr. Raju with HMRI raised suspicion in regards to the transparency of HMRI’s contracts with the government (The Times of India, 2009). This came to light in the same year as the election, and the future of HMRI was highly uncertain if there had been a change of government. The Congress party was reelected through strong public support, and HMRI’s continuation was secured. The event highlights the importance of vigilance and transparency of public-private partnerships. The sustainability of these partnerships cannot be taken for granted due to its dependency on political support and the accountability and responsibility of the partners on both sides.

Funding HMRI has developed comprehensive yet expensive programs; its contract with the state government is for an estimated US $55 million annually, which constitutes seven per cent of the state’s total health budget. The funding issues caused by the Satyam scandal posed obvious operational challenges for HMRI. The termination of the five per cent contribution from Mr. Raju could be picked up by the government, but this is not the wish of HMRI or the government. The model would then become a public scheme and benefits of the public-private partnership structure, with some independency by the private provider would be lost. This has forced HMRI to be innovative and identify new funding sources. HMRI is now raising funds thorough its website where individuals can donate money.

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They have initiated fee-based services, such as call center services for a health insurance company and a training course for rural medical practitioners. However, the government is the contributor in both these additional revenue sources; the government is paying for the training course and has contracted the health insurance company. It is not only the philanthropic contribution that can be seen as uncertain. Change in government will always be a threat to this kind of public-private partnership. One sustainable solution may be to collect funding from the beneficiaries who have a direct incentive to secure continuation of service provision. The proven high out-of-pocket spending on health care in rural areas indicates a willingness to pay for healthcare services. Now that the rural population has experienced the quality and convenience of HMRI’s services and trusts the organization, a nominal premium per person could be collected to secure ongoing services. It may also be possible to create a community health insurance scheme for primary care provision, including medication. An evaluation of the beneficiaries’ willingness to pay and their previous annual healthcare expenditure is necessary to assess the viability of such a model. This analysis is also relevant for an understanding of the replicability of the program, since a self-sustainable model is more likely to be implemented in other locations. Planned implementations and further developments of applications and services have been suspended. Halted projects include planned expansions of call centers and the Bloodline application, as well as the implementation of utility bikes in the rural outreach program and telemedicine components for the mobile health vans. In addition, the proposed merger of the electronic medical records of EMRI and HRMI into a single database in order to improve healthcare services has also been suspended due to funding issues. It would seem natural to merge these databases given the fact that the organizations have the same founder, technical partner, and primary funding

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source. If funded, the merger will take 12 to 18 months to complete.

Technical and Practical Aspects Lack of adequate infrastructure in the rural areas poses a significant challenge to adequate operation of services. For example, while the Model contemplates complete coverage of all locations beyond the three-kilometer radius of a primary health center by its mobile outreach program, in reality its mobile clinics are unable to reach many rural areas due to a lack of local infrastructure that can support four wheel vehicles. In order to reach the people residing in these remote areas, HMRI developed the doc-in-a-box concept, where necessary lab equipment and video conferencing technology are to be carried by motorbike or donkey back to the most remote areas. This concept is entering the pilot phase and is currently a subject for fundraising. In addition, the applications supporting the rural outreach program could be used to their optimum capacity, if not for the poor connectivity in most rural areas. Connectivity issues interfere with the day-to-day operations of the mobile vans. Uploading patient data into the electronic medical records database on the central HMRI server is forced to wait until the van has reached a parking point in a less remote area where connectivity is available. This creates a risk of data loss or improper transmission, in addition to increasing the work load and hours of the mobile van team. The HMRI Model is still fairly new and sufficient data is not yet available for an in-depth analysis of the technical challenges it faces or how to further address them. But operational challenges can be further investigated and evaluated, directing refinements of the applications. In addition to technical refinements, operational improvements for the mobile vans require an evaluation of the physical features and spatial structure of the vehicles. At the time of the launch of the mobile outreach service, no design modifications had

Primary Care through a Public-Private Partnership

been made for the initial fleet of vans due to time constraints. However, future orders of vans will incorporate changes to address any challenges to functionality. Another practical challenge arises in the context of the medications that are distributed by the mobile health team. HMRI obtains its stock of medications from the government, however, according to HMRI, problems within the government’s supply chain management are major constraining factors to the stock and service provision. Through more transparent and automated supply chain management, related efficiency and costs will potentially improve. There are also “soft” challenges that need to be addressed in programs such as the rural health initiative. Currently, the fixed day health services are intended to serve people suffering from chronic diseases, as well as pregnant women and infants, in an effort to reduce infant and maternal mortality rates. People with minor ailments are expected to contact the call center for advice, so that the mobile team’s time is spent on people who need it the most. However, according to the mobile teams, this segregation is not recognized at all. This challenge can be addressed by educating people about the priorities of the vans or by increasing the frequency of visits to enable coverage of other conditions. Whether these solutions are implemented will depend primarily on the state government, who will need to provide the financial support for expanded services.

Human Resources The literacy level of the ASHA workers poses a unique set of communication challenges, especially with regards to the operations of the mobile phones. To address this issue, a training team continuously travels to different villages and trains these women on how to properly use the templates on their phones. Thus far, the majority of the 500 software engineers involved with HMRI were employed

by Satyam and HMRI only employs a small in-house team for the development of needed applications. Now the primary goal is to devise a strategy that will minimize the dependence on a technical partner. HMRI may expand its team and develop the in-house capacity to make the operations more sustainable.

FUTURE RESEARCH DIRECTIONS HMRI provides a good example of how information technology can address many of the barriers to access to quality health care. The amount of data HMRI is compiling creates a foundation for research to improve interventions and to support a paradigm shift in the field of public health to evidence-based interventions. The benefits of technology are clear, but securing sustainable funding is a prominent challenge. There is still much that needs to be assessed to understand the potential of technology for primary care delivery.

Assessments Needed The government has been criticized for sporadic, disjointed, and tentative collaborations with the private sector; not the result of a well-thought out strategy aimed at achieving national health goals. In the absence of evaluation of these arrangements, it is difficult to assess their utility or impact on Government budgets (National Commission on Macroeconomics and Health, 2005b). HMRI has been established to achieve national goals with a focus on maternal and child health. It is claimed to be cost-effective, but no independent assessment has been done. The HMRI Model is still at an early stage and requires further assessment in order to create a set of relevant best practices. The following are suggested areas for assessment: •

The overall cost-efficiency of the Model, as well as its various components.

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Primary Care through a Public-Private Partnership

•

• •

Taking other services and the burden of disease into account, is this the ideal allocation of public spending? Are desired outcomes achieved in terms of improved health? Are there means for additional revenue sources or areas for cost reduction? ◦⊦ Many pharmaceutical companies have expressed interest in the data collected by HMRI. Can HMRI monetize the data without compromising on ethics, or expand fee-based services? ◦⊦ HMRI reaches out to millions of peoples. Are there opportunities to sell advertisement space at the vans or through the phone during the waiting time for health advice services? ◦⊦ In terms of technology, are technologies available, or possible to develop, at lower cost?

CONCLUSION In short, the Health Management and Research Institute (HMRI) is process-oriented in its approach to primary care delivery. It is health systems-oriented in terms of identifying and addressing gaps for effective healthcare provision. Its services are brought to a large scale through the public-private partnership with the government of Andhra Pradesh, thus providing a unique platform to explore further interventions and the potential of high impact on health outcomes. HMRI’s service delivery is further strengthened by technology that assists research and epidemiological studies, as well as evidence-based interventions and decision-making. Primary care services are provided through the mechanisms of a round-the-clock helpline for medical advice, medical outreach activities sending vans to rural areas, telemedicine consultation projects, and a disease surveillance program, with various innovative components as part of each

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one. In terms of human resources management, the organization focuses on specialization of paramedics and the use of government appointed health workers, such as Auxiliary Nurse Midwives (ANM) and local Accredited Social Health Activist (ASHA) workers. HMRI is likely to hold the world’s largest database of electronic medical records by 2010, providing a unique platform to pilot new technologies in line with its aim to become the world’s largest non-profit healthcare provider. There are some evident advantages of the model and these can serve as inspiration for providers in other locations, where HMRI can help pave the road ahead in the field of primary care delivery. The Model’s strengths include: • •

•

• •

Integrated technology and delivery components. Process orientation guides the services with well-defined tasks and responsibilities of the health workers. High utilization of human resources and task-shifting with support systems for quality care. A primary care model strengthening the public sector healthcare delivery. Access to health data, assisting research for evidence-based interventions and policy recommendations.

Main challenges to the continuation and sustainability of the HMRI Model relate to continued funding and technical support. Reliance on one individual technology provider is problematic. The cost-effectiveness of the program has still not been evaluated by an independent entity. An evaluation of the entire Model and the individual components could support the government in making informed decisions regarding efficient resource allocations. Could this primary care model be made available in other regions and to other populations?

Primary Care through a Public-Private Partnership

•

•

It is scalable and replicable in the sense that the systems are in place, the technologies have been piloted, training programs and manuals have been developed. One call center can cover many remote regions. What is needed to bring the model elsewhere?

•

•

• •

•

Funding is critical since the model requires capital investments. An independent costeffectiveness study of the HMRI model could assist decision-makers in other regions. The operating costs must be covered, for example, by the government or a fee-forservice paid by the beneficiaries. Leadership and management are keys to success. Community involvement is important to increase awareness of the services and their most effective use, as well as to increase the participation and compliance of the beneficiaries. A trusting working relationship with the national and state governments is important for linking with public facilities, medical practitioners, and public village health workers.

HMRI has proven how technology can be used to improve access to quality care through cell phones, electronic patient records, and telemedicine consultations. It enables standardization of care and assists research for evidencebased interventions. Costs are generally the main challenge with technology, and HMRI is actively working towards reducing cost that could benefit healthcare providers globally. The cost-effectiveness of the model is however still to be assessed. This is important in order to give informed recommendations for implementation of similar programs in other locations. The work of HMRI can surely inspire and motivate others,

such as policy makers, healthcare managers, and technology developers, as well as scholars and students, healthcare professionals, and all those interested in health informatics. The work done by HMRI within the field of healthcare delivery can serve as an example for the road ahead.

ACKNOWLEDGMENT We would like to pay gratitude to Dr. Nitin Verma and Venu Madhav Chennupati at the Health Management and Research Institute for their time and engagement in the development of this chapter. We also want to thank Dr. Prashanth NS at Karuna Trust for feedback, as well as Lipika Nanda at Family Health International and Asieh Nariman at the Indian School of Business, for valuable input on our text. Photos were taken by Hanna Pernefeldt and Sofi Bergkvist, ACCESS Health Initiative.

REFERENCES BusinessWeek. (2005). Satyam company profile. BusinessWeek [Online]. Retrieved February 9, 2009, from http://www.businessweek.com/ it100/2005/company/SAY.htm Government of India. (2008). Eleventh Five-Year Plan 2007-2012. Vol. 2, Social Sector. Planning Commission, Government of India. Retrieved August 2008, from http://planningcommission. nic.in/plans/planrel/fiveyr/11th/11vol2.pdf Hammond, A. (2009). Notes from the field: I have seen the future of healthcare. Retrieved February 6, 2009, from http://nextbillion.net/blogs/topic/ health Haseltine, W. A. (2008). Dial M for medical care. The Indian Express [Online]. Retrieved August 22, 2008, from http://www.indianexpress.com/ news/dial-m-for-medical-care/351754/

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Health Management and Research Institute (HMRI). (2009 March). Connect. HMRI’s internal magazine, 1(3). Retrieved August 2009, from http://www.hmri.in/hmri_connect3.pdf

Schmidt, S. (2006). Competition framework: How to improve health for all. Retrieved May 25, 2008, from http://proxied.changemakers.net/ journal/300603/framework.cfm

Healthcon’08. (2008). Public-private partnerships and growth potential in healthcare. Organized by Confederation of Indian Industry (CII), Hyderabad, India, 19 November 2008.

Strategic Management Society (SMS) Conference. (2008). Emerging India: Strategic innovation in a flat world. Strategic Management Society Special Conference. Organized by Indian School of Business, Hyderabad, India, 13 December 2008.

http://www.who.int/nha/country/ind.pdf International Finance Corporation (IFC). (2008). The business of health in Africa: Partnering with the private sector to improve people’s lives. Washington, DC: The World Bank. National Commission on Macroeconomics and Health. (2005a). Financing and delivery of health care services in India. Ministry of Health & Family Welfare, Government of India. National Commission on Macroeconomics and Health. (2005b). National Commission on Macroeconomics and Health. Ministry of Health & Family Welfare, Government of India. Prahalad, C. K. (2006). Building eco systems for breakthrough innovation: Healthcare delivery systems in India. Working draft. Final version to be published. Ross School of Management, The University of Michigan. Punetha, A. (2008, June 2). National rural health mission, 2005-2012. Multi-dimensional workshop at Goa. [PowerPoint Presentation]. IAS, Commissioner of Family Welfare and EO Principal Secretary to Government. Government of Andhra Pradesh. Hyderabad, India.

The Times of India. (2009, January 10). EMRI a scam too? The Times of India [Online]. Retrieved February 9, 2009, from http://timesofindia.indiatimes.com/Cities/Hyderabad/EMRI_a_scam_too/ articleshow/3958274.cms Timmons, H. (2009, January 17). Indian executive is said to have siphoned cash. The New York Times [Online]. Retrieved February 9, 2009, from http://www.nytimes.com/2009/01/18/business/ worldbusiness/18india.html?_r=1 Timmons, H., & Kahn, J. (2009, January 11). As Indian outsourcing changed, Satyam hesitated. The New York Times [Online]. Retrieved February 9, 2009, from http://www.nytimes.com/2009/01/12/ business/worldbusiness/12outsource.html World Health Organization (WHO). (2007). 11 health questions about the 11 SEAR countries. Retrieved August 2009, from http://www.searo. who.int/LinkFiles/Country_Health_System_ Profile_4-India.pdf World Health Organization (WHO). (2008). World health accounts. Retrieved September 2008, from World Health Statistical Information System (WHOSIS). (2009). World health statistics 2009. Retrieved August 2009, from http://www.who.int/ whosis/whostat/EN_WHS09_Table9.pdf

This work was previously published in Human Resources in Healthcare, Health Informatics and Healthcare Systems, edited by Stéfane M. Kabene, pp. 127-153, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 5.4

Strategic Fit in the Healthcare IDS Evelyn H. Thrasher University of Massachusetts Dartmouth, USA Terry A. Byrd Auburn University, USA

INTRODUCTION Interorganizational networks are defined as “clusters of organizations that make decisions jointly and integrate their efforts to produce a product or service” (Alter & Hage, 1993, p.2) and “advanced organizational structures perceived to improve efficiency, flexibility, and innovativeness and described as decoupled units developed because of rapid growth or knowledge and technology” (Schumaker, 2002). The healthcare integrated delivery system (IDS) is a distinct example of an interorganizational network. Defined as networks of healthcare organizations linked for the goals of DOI: 10.4018/978-1-60960-561-2.ch504

clinical integration and an effective patient care continuum (Deluca & Enmark, 2002; Kilbridge, 1998; Young & McCarthy, 1999; Zucherman, Kaluzny, & Ricketts, 1995), IDSs may assume various organizational forms; namely, strategic alliances, contracted networks, or joint ventures, and may be comprised of multiple forms within a single network (Page, 2003). Also of interest is the distinction of the IDS as a lateral network of stakeholders, all directly serving the patient. This study uses the healthcare IDS to test a model of strategic fit and to examine differences in the nature and strength of the strategic fit to performance relationship across two distinct levels of IDS development.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Strategic Fit in the Healthcare IDS

The conceptual model (Figure 1) is tested using data from HIMSS Analytics and the American Hospital Directory for 130 US IDSs. The IDSs are categorized into two levels of development, High Integration Aligned (HIA) and Low Integration Aligned (LIA), based on the alignment of IT integration/sophistication and organizational maturity. The relationship between strategic fit and IDS financial and quality performance is tested with comparisons made between the two groups.

BACKGROUND Strategic Fit Henderson and Venkatraman (1993), in the strategic alignment model, defined strategic fit as a process of adaptation in which organizational changes must be supported by complimentary IT resources and integration. Similarly, Chan and Huff (1993) defined strategic fit as “the fit between business strategy and IS strategy” (p. 345). We adapt these definitions to define strategic fit as the point of equilibrium at which the level of interorganizational network structure and maturity is properly aligned with a complementary level of IT integration and sophistication. Venkatraman (1994) developed the IT-Enabled Business Transformation Framework as an extension to the strategic alignment model, positing that IT is no longer simply an operational support resource, but rather a strategic tool with which to transform the firm’s organizational structure and Figure 1. Conceptual model of strategic fit for the interorganizational network

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processes. He further proposed that IT’s strategic role emerges more readily as firms establish strategic alliances and partnerships, as is the case with interorganizational networks. In advanced stages of organizational development, the need for and benefits from strategic fit should increase as interorganizational networks expand into more complex structures (Venkatraman, 1994). Yet little empirical evidence exists to support these suggestions. Much research around IT value and strategic fit has been conducted at the firm level (Bergeron, Raymond, & Rivard, 2001; Chan, Huff, Barclay, & Copeland, 1997), but research at the interorganizational network level is rare (Straub, Rai, & Klein, 2004). Some researchers have examined the differences in firm performance between organizations at various levels of strategic fit. Zajac, Kraatz, and Bresser (2000) demonstrated significant positive links between strategic fit and ROA in savings and loan organizations that achieved advanced levels of strategic fit. These authors empirically supported the argument that organizations responding in a timely manner to needed strategy changes and achieving fit at this new level of development will realize greater benefits than those that do not. Similarly, Bergeron, et al. (2001) found that small enterprises with a high level of strategic fit realized improved financial performance. These authors examined strategic fit through different lenses—profile deviation, moderation, matching, and other perspectives—to measure the performance impacts of fit. The results suggested that those organizations that pursue a highly strategic organizational and IT strategy tend to outperform organizations that fail to reach these higher levels. Thus, these past studies support the idea of a difference in the strategic fit to performance relationship, depending upon the achieved level of IT integration and organizational structure. More specifically, these studies suggest that performance improvements should be more apparent among highly integrated mature organizations that have achieved strategic fit.

Strategic Fit in the Healthcare IDS

Quality Outcomes Quality outcomes are intangible, quality-related measures of organizational service and performance (Devaraj & Kohli, 2000; Li & Collier, 2000) and are of concern to IDSs due to the need for continual patient care quality improvement (Snyder & Paulson, 2002). Research suggests that patient-centered measures such as mortality rate, patient satisfaction, and average length of hospital stay (ALOS) are appropriate quality performance outcomes for the IDS (Devaraj & Kohli, 2000; Dowling, 1997; Smith & Swinehart, 2001); thus, ALOS is adopted for this study. Initial formation and growth of IDSs led to reduced ALOS. For instance, Kim (2000) examined the impact of IDS formation on ALOS in the 1990s and found that ALOS was shorter for IDSs that had achieved a high degree of functional process integration across network participants. Those IDSs that had shortened ALOS had done so through the streamlining of patient care with expanded services and reductions in the time associated with administrative tasks. To the contrary, more recent studies indicate that ALOS reductions may have slowed or stalled (Page, 2003), suggesting that IDS formation alone is insufficient to ensure long-term quality improvements. Regarding IT’s role in healthcare performance improvements, Li and Collier (2000) investigated the impact of IT on performance through a survey of hospital administrators. The results indicated that IT had a significant positive impact on hospital quality and financial performance through IT’s perceived ability to enhance accuracy, timeliness, and patient care effectiveness. Research suggests that increasing IT resource complexity and sophistication results in increasing pressure to pursue further business transformation for improved coordination and integration. Thus, as IDSs mature, IT’s role seems to evolve from business process support to enabler of business transformation (Schumaker, 2002). Similarly, as IDSs expand, increasing differentiation of services

often results, thereby forcing these networks to re-evaluate and improve both organizational and IT integration to maintain a streamlined patient care continuum (Schumaker, 2002). Researchers suggest that strategic fit has a more significant impact on financial and quality performance among interorganizational networks with high IT integration and organizational maturity than among those with lower levels (Sabherwal & Chan, 2001; Smith & Swinehart, 2001). For instance, Sabherwal and Chan (2001) studied defenders, prospectors, and analyzers, and identified IT strategy profiles appropriate for various levels of organizational structure. These researchers found empirical support for a greater degree of performance improvement among more mature organizations with a focus on innovation and flexibility as opposed to immature organizations with an operational efficiency focus. Extending the firm level findings and theories to the interorganizational network level, we propose the following hypothesis: H1: The relationship between strategic fit and ALOS will be significantly more negative in HIA IDSs than in LIA IDSs.

Financial Outcomes The current study uses operational cost as a measure of IDS financial performance to satisfy two conditions. First, by using cost measures, we can study both for-profit and not-for-profit IDSs, as both are concerned with reducing costs despite differences regarding profit-centered measures. Second, the healthcare industry is facing increasing pressure to reduce costs while continuing to improve patient care quality (Snyder & Paulson, 2002); thus, it is important to examine the impact of strategic fit on cost reduction. The formation of IDSs represents one attempt to control costs through the anticipated streamlining and improvement of the patient care continuum. Yet historical performance of IDSs has not supported this aim.

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Strategic Fit in the Healthcare IDS

Possibly due to the organizational complexity of newly formed healthcare networks, researchers suggest that organizational changes alone are insufficient to produce financial performance improvements for the IDS (Coddington & Moore, 2001). In recent years, IDSs have begun to look closer at the benefits of IT integration in hopes of improving operational cost (Etchen & Boulton, 2000). Prior evidence suggests that IT may have a significant influence on the reduction of operational costs through improved efficiency and effectiveness (Barua, Kriebel, & Mukhopadhyay, 1995; Byrd, Thrasher, Lang, & Davidson, 2006; Coddington & Moore, 2001). For instance, a case study of 11 IDSs looked at steps taken to potentially reduce operational costs. In addition to the formation of the IDS and the streamlining of patient care within the IDS, all 11 stated that automation of clinical and administration processes through IT had resulted in significant operational cost reductions (Coddington & Moore, 2001). Building upon these past results, Barua, et al. (1995) and Byrd, et al. (2006) also demonstrated support for the indirect cost benefits of IT associated with quality improvements. The premise behind these studies was that often the quality benefits of IT may be realized first and should, in turn, lead to financial performance improvements over time. Extending the firm-level evidence to

the interorganizational network, we propose the following hypotheses: H2: The relationship between strategic fit and operational cost will be significantly more negative in HIA IDSs than in LIA IDSs. H3: The relationship between ALOS and operational cost will be significantly more positive in HIA IDSs than in LIA IDSs.

EMPIRICAL EVIDENCE OF IDS STRATEGIC FIT VALUE Using 130 US IDSs, a strategic fit model was tested for the healthcare IDS. Profile matching (Chan et al., 1997) was used to categorize HIA IDSs and LIA IDSs, with 65 IDSs assigned to each group. Variables from the 2004 HIMSS Analytics Database were used to complete the categorization (Table 1). Using standardized z scores and a six-point rating scale, each attribute was rated and averaged together to form an IT integration/sophistication rating and organizational maturity rating. Those scoring an overall rating of 4 or above were classified as HIA IDSs; those with an overall score of 3 or below were LIA IDSs. Table 2 presents descriptive statistics and ratings. The measurement model was a second order model with IT integration/sophistication and or-

Table 1. Variables for IDS classification Construct

HIMSS Analytics Variables for the IDS

IT Integration and Sophistication

Number of enterprise applications in use Number of different types of enterprise applications (e.g., ERP, Clinical Decision Support, Case Mix Analysis) Number of available network nodes Number of personal computers in use Percentage of desktops with Internet access

Organizational Maturity

Number of facilities Number of different types of facilities (e.g., acute care, home healthcare, physician organization, insurer) Age (in years) of the IDS Number of hospital beds staffed Number of full-time employees Service population

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Strategic Fit in the Healthcare IDS

Table 2. Descriptive statistics and IDS rating HIA IDS Rating N=65

Criteria

Mean

SD

LIA IDS Rating N=65 Mean

SD

Enterprise Applications (Mean=20.57, SD=23.26, Median=13)

4.78

1.04

3.20

0.44

Types Enterprise Applications (Mean=6.60, SD=0.70, Median=7)

3.94

0.24

3.40

1.04

Available Network Nodes (Mean=1744.35, SD=2460.84, Median=905)

4.89

0.87

3.28

0.52

PCs in Use (Mean=1272.42, SD=1809.48, Median=600)

4.98

0.93

3.22

0.41

Percentage Personnel with Internet Access (Mean=76.85%, SD=26%, Median=90%)

3.75

0.66

3.35

0.89

Overall IT Integration and Sophistication

4.47

0.38

3.29

0.35

Facilities (Mean=14.92, SD=17.24, Median=10)

4.78

0.91

3.15

0.40

Diversity of Facilities/Services (Mean=3.72, SD=1.02, Median=4)

4.72

0.91

3.72

0.93

IDS Age (Mean=56.79, SD=38.22, Median=52)

2.94

1.04

3.66

1.05

Hospital Bed Capacity (Mean=431.63, SD=490.38, Median=269)

5.28

0.78

3.28

0.48

Full-Time Employees (Mean=2256.95, SD=2903.53, Median=1300)

5.22

0.80

3.26

0.44

Service Population (Mean=1157848.28, SD=9172584.12, Median=270000)

3.72

0.57

3.23

0.42

Overall Organizational Maturity

4.44

0.30

3.38

0.23

Overall Strategic Fit Rating

4.06

0.24

2.97

0.17

*Descriptives in italics

ganizational maturity as first-order factors forming strategic fit as a second-order factor. IT integration/sophistication and organizational maturity were measured reflectively using the factors in Table 1. The dependent variables, ALOS and operational cost, were measured using data from the 2004 American Hospital Directory.

Results PLS was used for model analysis (Byrd et al., 2006; Sambamurthy & Chin, 1994), and T-tests were performed where needed to assess significance of differences. The results and measurement model are presented in Table 3 and Figure 2.

Discussion Hypothesis 1 was supported, suggesting that HIA IDSs should see reduced ALOS, while ALOS may be increased in LIA IDSs. Researchers attribute increased efficiency, effectiveness, and knowledge sharing to high levels of IT integration and effective business processes. Perhaps these benefits contribute to improved ALOS in HIA IDSs. As researchers noted (Kim, 2000; Li & Collier, 2000), strategic fit with higher levels of IT and organizational integration may contribute to improvements in administrative processes and clinical care effectiveness, thereby resulting in shortened ALOS.

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Table 3. Hypotheses test results Path Coefficient Hypothesis

HIA IDSs

LIA IDSs

H1: Strategic Fit to ALOS (Supported)

-0.158**

0.043

H2: Strategic Fit to Operational Cost (Supported)

0.812***

0.905***

H3: ALOS to Operational Cost (Not Supported)

-0.074**

0.019

t-Statistic for Difference 1.63*

*p<.10. **p<.05. ***p<.01.

Figure 2. Strategic fit model

and operational cost was not supported, the time lag effect of IT may also help to explain the lack of support for a positive relationship between ALOS and operational cost. As noted previously, using multiple years of cost data in future research might provide better insight into the effects of IT investment and strategic fit on operational cost, both directly and indirectly, through quality performance improvements (Barua et al., 1995; Byrd et al., 2006).

Hypothesis 2 was supported. Although positive for both groups, the strategic fit to operational cost relationship was significantly weaker for HIA IDSs, suggesting favorable progress is being made. History suggests that often the financial benefits of IT and strategic fit may not be readily apparent for a few years beyond the initial changes and investments. Thus, future research should consider including multiple years of operational cost data to better address the time lag issues of IT investments (Barua et al., 1995; Byrd et al., 2006). Hypothesis 3 was not supported. HIA IDSs demonstrated a significant negative ALOS to cost relationship, while this relationship was insignificant and positive for LIA IDSs. One possible explanation is that as a patient’s hospital stay is shortened, more frequent patient turnovers may result in increased operational costs. Another possibility relates to the proposed indirect IT effect on financial performance through direct quality improvements (Barua et al., 1995; Byrd et al., 2006; Currie & Guah, 2006). Because a negative direct relationship between strategic fit

FUTURE TRENDS

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Unfavorable historical performance has made IDS administrators hesitant regarding additional investments in IT, and some have considered dissolving IDS arrangements (Parker, Charns & Young, 2001). This study has demonstrated that those IDSs that have achieved strategic fit with high levels of IT integration/sophistication and organizational maturity should see improved quality and financial performance. An emphasis on IDS performance research is warranted and needed to further define the impact of strategic fit on a variety of healthcare performance measures. IDS formation and IT integration are relatively young concepts in healthcare; therefore, it is probable that those who have reached the level of HIA have done so only in recent years. Thus, the time lag effect of IT investment suggests that direct effects on operational cost may not yet be fully realized. Further investigation of the proposed model is needed with longitudinal data and in

Strategic Fit in the Healthcare IDS

varying contexts to further refine the model, to examine a broad spectrum of interorganizational arrangements, and to build a body of knowledge around IT’s value for the IDS.

CONCLUSION This study used the healthcare IDS to empirically test a model of strategic fit across two levels of interorganizational network development. The results suggest that the benefits of strategic fit differ across distinct levels of interorganizational development, thus further illuminating the complexities associated with taking IT research to the interorganizational network level of analysis. Even within a single industry, tremendous variation of interorganizational network form, structure, goals, and management increases the difficulty of empirical studies of IT value for these networks. Nevertheless, this level of analysis brings with it the potential for a rich body of knowledge around the issues of complexity, interorganizational structure, variance of scope and strategy, and other similar phenomena. Thus, the current study lays the groundwork for a research agenda centered around the complexities and nuances of the interorganizational network, an area of focus lacking in the IT literature.

REFERENCES Alter, C., & Hage, J. (1993). Organizations working together. Newbury Park, CA: Sage Publications. Barua, A., Kriebel, C. H., & Mukhopadhyay, T. (1995). Information technologies and business value: An analytic and empirical investigation. Information Systems Research, 6(1), 3–23. doi:10.1287/isre.6.1.3

Bergeron, F., Raymond, L., & Rivard, S. (2001). Fit in strategic information technology management research: An empirical comparison of perspectives. Omega, 29, 125–142. doi:10.1016/ S0305-0483(00)00034-7 Byrd, T. A., Thrasher, E. H., Lang, T., & Davidson, N. (2006). A process-oriented perspective of IS success: Examining the impact of IS on operational cost. Omega, 34(5), 448–460. doi:10.1016/j. omega.2005.01.012 Chan, Y. E., & Huff, S. L. (1993). Investigating information systems strategic alignment. In Proceedings of the International Conference on Information Systems, 345–363. Chan, Y. E., Huff, S. L., Barclay, D. W., & Copeland, D. G. (1997). Business strategic orientation, information systems strategic orientation, and strategic alignment. Information Systems Research, 8(2), 125–150. doi:10.1287/isre.8.2.125 Coddington, D. C., & Moore, K. D. (2001). The right strategy and perseverance can make an IDS profitable. Healthcare Financial Management, (December): 35–39. Currie, W. L., & Guah, M. W. (2006). IT-enabled healthcare delivery: The UK National Health Service. Information Systems Management, (Spring): 7–22. doi:10.1201/1078.10580530/45925.23.2.2 0060301/92670.3 Deluca, J. M., & Enmark, R. (2002). The CEO’s guide to healthcare information systems. San Francisco: John Wiley & Sons, Inc. Devaraj, J. M., & Kohli, R. (2000). Information technology payoff in the health-care industry: A longitudinal study. Journal of Management Information Systems, 16(4), 41–67. Dowling, W. L. (1997). Strategic alliances as a structure for integrated delivery systems. In D.A. Conrad (Ed.), Integrated delivery systems: Creation, management, and governance (pp. 45–80). Chicago: Health Administration Press.

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Etchen, L. L., & Boulton, J. L. (2000). Designing the IDS business portfolio. Healthcare Financial Management, (January): 29–33. Henderson, J. C., & Venkatraman, N. (1993). Strategic alignment: Leveraging information technology for transforming organizations. IBM Systems Journal, 28(2), 472–499. Kilbridge, P. M. (1998). The role of information systems in IDS-physician relationships. Healthcare Financial Management, 52(6), 31–34. Kim, Y. K. (2000). An impact of integrated delivery systems on hospital financial and quality performance under managed care [doctoral dissertation]. University of South Carolina. Li, L. X., & Collier, D. A. (2000). The role of technology and quality on hospital financial performance. International Journal of Service Industry Management, 11(3), 202–224. doi:10.1108/09564230010340715 Page, S. (2003). Virtual healthcare organizations and the challenges of improving quality. Health Care Management Review, 28(1), 79–92. Parker, V. A., Charns, M. P., & Young, G. J. (2001). Clinical services lines in integrated delivery systems: An initial framework and exploration. Journal of Healthcare Management, 46(4), 261–275. Sabherwal, R., & Chan, Y. E. (2001). Alignment between business and IS strategies: A study of prospectors, analyzers, and defenders. Information Systems Research, 12(1), 11–33. doi:10.1287/ isre.12.1.11.9714 Sambamurthy, V., & Chin, W. (1994). The effects of group attitudes toward GDSS designs on the decision-making performance of computer-supported groups. Decision Sciences, 25(2), 215–242. doi:10.1111/j.1540-5915.1994.tb01840.x

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Schumaker, A. M. (2002). Interorganizational networks: Using a theoretical model to predict effectiveness of rural healthcare delivery networks. Journal of Health and Human Services Administration, (Winter): 372–406. Smith, A. E., & Swinehart, K. D. (2001). Integrated systems design for customer focused healthcare performance measurement: A strategic service unit approach. International Journal of Health Care Quality Assurance, 14(1), 21–28. doi:10.1108/09526860110366232 Snyder, K. D., & Paulson, P. (2002). Healthcare information systems: Analysis of healthcare software. Hospital Topics: Research and Perspectives on Healthcare, 80(4), 5–12. Straub, D., Rai, A., & Klein, R. (2004). Measuring firm performance at the network level: A nomology of the business impact of digital supply networks. Journal of Management Information Systems, 21(1), 83–114. Venkatraman, N. (1994). IT-enabled business transformation: From automation to business scope redefinition. Sloan Management Review, (Winter): 73–87. Young, D. W., & McCarthy, S. M. (1999). Managing integrated delivery systems: A framework for action. Chicago: Health Administration Press. Zajac, E. J., Kraatz, M. S., & Bresser, R. K. F. (2000). Modeling the dynamics of strategic fit: A normative approach to strategic change. Strategic ManagementJournal,21(4),429–453.doi:10.1002/ (SICI)1097-0266(200004)21:4<429::AIDSMJ81>3.0.CO;2-# Zucherman, H. S., Kaluzny, A. D., & Ricketts, T. C. (1995). Alliances in healthcare: What we know, what we think we know, and what we should know. Health Care Management Review, 20(1), 54–64.

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KEY TERMS AND DEFINITIONS Average Length of Stay (ALOS): Number of days a patient must spend in the hospital, averaged across all lengths of patient stay for the hospital for one year. Healthcare Integrated Delivery System: Network of healthcare organizations linked for the goals of clinical integration and an effective patient care continuum. Interorganizational Network: A “cluster of organizations that makes decisions jointly and integrates their efforts to produce a product or service” (Alter & Hage, 1993, p.2).

Profile Matching: Profile matching assigns a score to both the IT integration/sophistication profile and the organizational maturity profile of the entity. If the two scores are equal or close within a very small predetermined range, the organization is deemed to have achieved strategic fit. Strategic Fit: Point of equilibrium at which the level of interorganizational network structure and maturity is properly aligned with a complementary level of IT integration and sophistication.

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, pp. 1269-1276, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 5.5

Regional and Community Health Information Exchange in the United States Adi V. Gundlapalli University of Utah School of Medicine, USA Jonathan H. Reid University of Utah School of Medicine, USA & Utah Department of Health, USA Jan Root Utah Health Information Network, USA Wu Xu Utah Department of Health, USA

Things do not happen. Things are made to happen”.

“

– John F. Kennedy

ABSTRACT A fundamental premise of continuity in patient care and safety suggests timely sharing of health information among different providers at the DOI: 10.4018/978-1-60960-561-2.ch505

point of care and after the visit. In most healthcare systems, this is achieved through exchange of written medical information, phone calls and conversations. In an ideal world, this exchange of health information between disparate providers, healthcare systems, laboratories, pharmacies and payers would be achieved electronically and seamlessly. The potential benefits of electronic health exchange are improved patient care, increased efficiency of the healthcare system and decreased costs. The reality is that health information is electronically exchanged only to a limited extent

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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within local communities and regions, much less nationally and internationally. One main challenge has been the inability of health information exchange organizations to develop a solid business case. Other challenges have been socio-political in that data ownership and stewardship have not been clearly resolved. Technological improvements over the past 20 years have provided significant advances towards safe and secure information exchange. This chapter provides a general overview of community health information exchange in the United States of America, its history and details of challenges faced by stakeholders. The lessons learned from successes and failures, research and knowledge gaps and future prospects are also discussed. Current and future technologies to facilitate and invigorate health information exchange are highlighted. Two examples of successful regional health information exchanges in the US states of Utah and Indiana are highlighted.

INTRODUCTION Imagine a situation where a patient is sitting in a medical office for a visit. The doctor arrives, greets the patient, logs in to the electronic medical record and accesses the patient’s medical chart on a computer in the room. After verifying the full name and date of birth with the patient, the doctor proceeds to a screen that provides a historical view of the patient’s medical visits. “I see that you live in the neighboring town, and you have been seen by a medical group there for the past 5 years. I also see that you have transferred your care to my practice today, and your doctors have provided a summary of your condition for me to review. You have a history of high blood pressure, diabetes and high cholesterol. Looks like you have had laboratory testing performed in the past 3 months and all your numbers look great! Your recent chest x-ray and echocardiogram show no abnormalities. You are on several medications for your medical conditions and have been

regular in picking up and refilling your medications. I also note that you experienced chest pain while visiting your daughter in another state last year and, fortunately, the hospital admission and testing there revealed no damage to your heart. According to your personal health record that you maintain and have given me access to, you have been feeling well and are quite active. Your local public health department identifies you as an early recipient of the new H1N1 swine flu vaccine. Let’s pick up from here. Please tell me how you are feeling today…” This elaborate introduction signals the start of an excellent medical visit that is both satisfying and time well spent for the patient as well as the doctor. If you look carefully at this visit, the patient’s medical history, detailed medical records, laboratory testing, diagnostic imaging and pharmacy records from several different unaffiliated providers and healthcare systems were available to the doctor instantaneously at the point of care. This held true whether the prior visit was in the neighboring town or another state. The new doctor would not need to repeat any of the testing recently performed nor would he/she have to “reinvent the wheel” in caring for this patient. The personal health record was a bonus feature that provided information on current and ongoing health issues as reported by the patient. The result is increased patient satisfaction, preserved patient safety, improved efficiency of the healthcare system and overall decreased expenditures. All of this was made possible by an efficient and seamless exchange of electronic health information between providers, most likely linked by a master patient index and a record locator service. The exchange was facilitated by cooperative agreements between healthcare systems and providers and the use of robust health information technology (HIT). These represent the quintessential “meaningful use” and “meaningful users” concepts that have been proposed by the US federal government to encourage the adoption of HIT across the highly fragmented health case

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system in the US to improve quality of care using health information exchange (HIE). Does such an ideal health information exchange exist anywhere in the United States? In a few settings and to a limited extent, this is possible within integrated healthcare systems such as the US Department of Defense, US Veteran’s Administration (VA) (Dayhoff and Maloney 1992; Lloyd 1992; Martin 1992; Lau, Banning et al. 2005; Warnekar, Bouhaddou et al. 2007; Bouhaddou, Warnekar et al. 2008), Kaiser Permanente in California (Kaiser Permanente 2009) and Intermountain Healthcare in Utah (Intermountain Healthcare 2009). There are examples of regional HIE among healthcare providers that have achieved some meaningful exchange of information. A state-by-state directory is listed by the eHealth Initiative (eHealth Initiative 2009) that includes successful ventures in Indiana and Pennsylvania. There are also examples of opportunities for HIE with public health departments; these have mainly focused on immunization records and other data relevant to public health emergencies (Public Health Informatics Institute 2005; Shapiro 2007; Hessler, Soper et al. 2009; Hinman and Davidson 2009). For the most part though, this scenario and vision of seamless HIE remains utopian. Communication and exchange of information are central themes of healthcare and form the basis of relationships between patients and their providers (Weiner, Barnet et al. 2005). Furthermore, the various unaffiliated providers involved in healthcare such as clinicians, laboratories, imaging facilities, healthcare systems and payers such as insurance companies have to exchange health information to ensure continuity of care for patients and payments for services rendered. At the present time, for the most part, patientprovider interactions are in person, by phone and/ or by written communication. Electronic means of communication are slowly increasing between patients and their providers (Gagnon, Legare et al. 2009). These include email communications (Gagnon, Legare et al. 2009), patient-centered

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medical homes (American Association of Family Physicians 2009), personally controlled health records (PCHR) (Ball and Gold 2006; Einbinder and Bates 2007; Mandl, Simons et al. 2007; Ozkaynak, Marquard et al. 2007; Montelius, Astrand et al. 2008) and web-based consumer health solutions such as Google Health (Google 2009) and Microsoft HealthVault (Microsoft 2009). Electronic exchange of information is more advanced in certain areas that have government mandates and standards such as coding and billing, where the majority of the exchange is electronic. Other patient-provider and provider-provider exchanges remain largely paper-based. The basic premise of HIE is to improve patient care, contribute to patient safety increase efficiency and decrease overall healthcare expenditures (Kaelber and Bates 2007; Adler-Milstein, McAfee et al. 2008; Adler-Milstein, Bates et al. 2009). In an ideal world, all patient encounters with the healthcare system would be electronic. Data generated from these encounters would be available for exchange between all parties that had legitimate need to see and use these data. The access to these data would be with appropriate security restrictions to protect patient privacy. To elaborate on these themes, the remainder of this chapter will review arguments and use cases to support HIE, the barriers to large scale implementation of HIE, lessons learned from current successful efforts and from failures. There will be a focus on technology and informatics related to HIE. We will also identify current and future areas of research that ultimately should form the evidence base for policy and funding for HIE. In this chapter, we use the term health information exchange (HIE) to broadly refer to electronic exchange of health information between two or more parties.

BACKGROUND Electronic health information exchange traces its roots to the advent of modem technology that

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allowed transmission of information over local telephone lines. Released in 1981, the Hayes SmartModem was the first modem that allowed computer aided dialing and answering of calls. This meant that users no longer had to manually initiate or receive calls. This was achieved automatically by placing the telephone handset onto a modem coupler. As a result, unmanned computers could host services that were accessible anytime. This led to the explosion of the computerized Bulletin Board System (BBS). A BBS is a system that allows users to connect and share information, files, electronic mail or real-time chat with other users. As more users starting using modems to communicate, small community networks started to appear among individual users and organizations. These networks were the forerunners of the modern internet with early innovators being the US Government and academic universities. As the technology adoption increased, the bulletin board systems offered a way for public users to connect to community networks and use the services being offered through the internet. A community network consists of one or more computers providing services to people using computers and terminals to gain access to those services and to each other (Cisler 1994). In 1986 Dr. T.M. Grunder at Case Western Reserve University in Cleveland, Ohio, created a free community computing network known as Free-Net (Sievers 1992). Free-Net was envisioned to be a completely free, open-access community computer system that allowed anyone to call in 24 hours a day and access a wide range of electronic services and features. Free-Net offered users the ability to exchange health information between patients and clinical staff, and even hospital to hospital for the first time. Early HIE was referred to as electronic data interchange. With the ability to share information electronically demonstrated and validated, a new health information sharing experiment started. This was referred to as the Community Health Information Network (CHIN) and can be considered the precursor to current day regional HIE (Noss and Zall

2002). While there were many types, generally, a CHIN could be defined as an inter-organizational system using information technologies and telecommunications to store, transmit and transform clinical and financial information. This information can be shared among cooperative as well as competitive participants, such as payers, hospitals, alternative delivery systems, clinics, physicians and home health agencies (Payton and Brennan 1999). A CHIN comprised three main components: key stakeholders from the health community, a network to transmit data between the CHINs trading partners and a central data repository of clinical data (Rubin 2003). At that time, CHIN supporters were focused on improving care and reducing operating costs. The essential concept behind the CHINs was to “connect the dots” for providers. In other words, the idea was to allow complete patient-level information to be available to authorized clinicians at the point of care. An early example of a CHIN was a system called ComputerLink. This was a networkdelivered, computer-based utility that provided health services to homebound persons. This network represented the first effort to systematically replicate functions provided by formal support services such as peer support, professional advice, education, and counseling for vulnerable populations (Brennan 1995; Payton, Brennan et al. 1995; Brennan 1999). The concept and hope for CHINs was laudable; however, they faced tremendous challenges, and by the year 2000 most CHINs had failed (Appleby 1995; Furukawa 1997; Lassila, Pemble et al. 1997; Noss and Zall 2002; Lorenzi 2003). One main reason was lack of evidence to show benefits such as increased efficiency or effectiveness. Another reason was the perceived loss of control over the data. The ownership issues related to patient data were never resolved between the participating organizations, and this led to a lack of trust. Another reason was financial. There were no sustainable business models, and there was constant disagreement on how a CHIN should

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be financed. The information technology was not advanced enough to create a federated data model, so, out of necessity; a centralized data repository was created. This increased the lack of trust and brought data ownership and stewardship issues to the forefront again. CHINs served to highlight the basic premise and potential benefits of health information exchange. Their failure forced stakeholders and supporters to regroup and rethink data stewardship and business models. The business case for exchanging data electronically was sound and provided the market to exchange claims electronically between payers and providers through the use of proprietary clearinghouses. However, the essential problem of how to create a single “virtual” health record that would be available to appropriate providers remained largely unsolved. In addition, healthcare costs continued to rise far above inflation. The failure of CHINS may have, in part, contributed to the high costs of healthcare in the US. The current healthcare crisis has fueled a renewed sense of urgency in achieving the fundamental CHIN goal. This led to the birth of Regional Health Information Organizations (RHIO) that promoted heath information exchange in the region (McDonald, Overhage et al. 2005) (Brennan, Ferris et al. 2005; Conn 2005; Koval 2005; Orlova, Dunnagan et al. 2005; Rollins 2005; Blair 2006; Raths 2006; Scalise 2006; Stein 2006; Lawrence 2007; Martin 2007; McGowan, Jordan et al. 2007). The business case or funding model proposed was a mix of federal grants, private funding and user fees. The data stewardship was more federated and less restrictive. Several RHIOs have been formed in the past two decades, and many have survived using these funding models. It is important to highlight the failure of one of the most famous RHIOs, the Santa Barbara County Care Data Exchange. Despite good intentions, fairly advanced technology and adequate funding, this RHIO failed after only a few months of data exchange. The main reason cited is the lack of value for the data exchange as

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perceived by the stakeholders (Holmquest 2007; Miller and Miller 2007). This is a significant concern that needs to be addressed for the future of HIE to be secure in terms of financial models, stakeholder participation and perception of benefit (Adler-Milstein, McAfee et al. 2008, Rudin, 2009 #186; Grossman, Kushner et al. 2008; Hurd 2008; Maffei 2008; Nocella, Horowitz et al. 2008; Adler-Milstein, Bates et al. 2009). From RHIOs, the next iteration of the theme is health information exchange (HIE), the term that is in vogue today.

Table 1. Key concepts of Regional and Community Health Information Exchange Regional and Community Health Information Exchange Stakeholders

• Patients • Providers • Healthcare systems • Hospitals • Clinical laboratories • Diagnostic imaging facilities • Insurance companies • Public health agencies • Consumer health groups

Models

• Private • Public • Federated • Centralized • Blended

Challenges and Barriers

• Access to electronic medical systems • Financial support • Legal and regulatory issues involving privacy of patient data • Technology • Trust across institutions • Data stewardship

Lessons Learned and Potential Solutions

• Develop workable business model • Establish trust • Reward use of HIE • Prove usefulness, efficiency and security • Newer technologies to address data stewardship, security and privacy issues

Future Research Directions

• Outcomes research to demonstrate benefits • Research from other fields on sustainable business models

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HEALTH INFORMATION EXCHANGE Stakeholders In general, patients in the US receive their healthcare from disparate medical providers that are not affiliated with each other. Most practice as small businesses with less than 10 physicians or providers. Regardless of their affiliation or financial ties, intuitively it would appear that all parties involved in healthcare are natural stakeholders in supporting HIE. Starting with patients and providers, this would include various healthcare systems, clinical laboratories, imaging facilities and insurance companies. Though the common goal should be to share information electronically to improve the quality of healthcare for their mutual patients, in reality, this is not the case. There are market forces and competing theories among individual providers and healthcare systems who consider patient data to be owned assets that should not or cannot be shared (Grossman, Bodenheimer et al. 2006; Malepati, Kushner et al. 2007). Reasons for not sharing patient data range from competitive interests in provided services to financial constraints as small outpatient clinics rarely have the resources to invest heavily in health information technology. For the larger healthcare systems, a long-held marketing strategy that seeks to tie patients and physicians to a particular system by using exclusive HIE mechanisms has also kept information from being shared. Key stakeholders in HIE vary based on the type of information being exchanged (Table). The most common types of information exchanged are laboratory and diagnostic imaging study results (eHealth Initiative 2009). For this type of HIE, the stakeholders are medical providers, hospitals, clinical laboratories and providers of diagnostic imaging. Hospital and independent pharmacies provide prescription medication data. If information on outpatient visits and emergency department visits are included, the stakeholders include hospital and healthcare systems that own

outpatient practices and emergency departments. These groups of stakeholders also facilitate other functions of HIE such as the ability to provide alerts to medical providers regarding specific topics and electronic prescribing of medications. Health insurers who are the payers for healthcare also have a need to receive clinical information. Insurers need clinical information for utilization management, for prior authorizations and for claim adjudication. Connectivity to electronic medical records and exchange of these records forms the next major functions of HIE. For these functions, the stakeholders are hospitals and practices with their disparate electronic medical record systems. A more recent function of HIE has been to provide and electronically exchange information of relevance to public health such as immunizations, notifiable and reportable infectious diseases and deaths (Shapiro 2007; Hessler, Soper et al. 2009; Hinman and Davidson 2009). For these HIE efforts, public health agencies such as local and state/provincial health departments become key stakeholders along with medical providers, hospitals and clinical laboratories. From a patient perspective, consumer health groups are engaged in safeguarding the interests of the patients amidst all the exchange that is or could take place. These groups are key stakeholders that could advocate for and facilitate HIE. Even though HIE is rarely profitable, additional stakeholders include health information technology vendors that provide infrastructure, software and support services for HIE. Finally, standards organizations that provide expertise on interoperability and messaging standards for effective sharing of health information should be considered important stakeholders.

Models There are several models of HIE currently in the US. A common type is a private HIE that is formed when affiliated organizations exchange informa-

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tion. An example of this (and a likely forerunner of HIE) is the electronic transmission of laboratory results from laboratory information systems to ordering providers. This type of direct exchange of information may be considered domain-specific HIE. Other domains in which private HIE is routinely seen is administrative functions such as insurance verification, pre-authorization of services by the payer and provider’s billing office. A public HIE is formed when a third-party organization, usually a non-profit, brokers the exchange of information between stakeholders. A common version of this is seen in regional health information organizations (RHIOs) where entities facilitate the exchange between medical providers and hospitals or laboratories. They provide the means of exchanging information to increase efficiency and patient care in the system while maintaining privacy and confidentiality. These “honest brokers” typically do not have access to the information and function much like a post office—they deliver the letter without ever looking inside. Logistically and functionally, HIEs can be federated, centralized or blended. The federated model appeals to many stakeholders as each participating organization retains control of its information. The organization then responds to specific queries for information and provides information conforming to strict privacy and confidentiality laws. This usually requires pre-determined levels of access to information and business agreements to exchange legitimate health information. In the centralized model, the coordinating organization collects, collates and stores information in a centralized location. Here, too, the access is carefully controlled when providing information across organizations. This system works well within large integrated or affiliated healthcare providers. When there are no organizational, financial or other strong affiliations, it is unlikely that entities will part with large amounts of information for storage in a centralized location.

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Usually, HIE follows a blended model, whereby data, such as a master patient index or record locator service, are centrally stored and stakeholders are queried for other information in a federated manner.

Challenges and Barriers There are several challenges and barriers to the large scale adoption of HIE in the ↜US. Broadly, these fall into the categories of access to electronic medical systems, financial, socio-political and, to an extent, technological. One major barrier to the adoption of HIE between key stakeholders such as private physicians and hospitals appears to be the surprisingly low penetration of electronic medical records in both the outpatient and hospital setting. A recent survey of ambulatory care practices in the US has revealed that 13% of physicians reported having a basic system in their outpatient offices, and only 4% had a fully functional system (DesRoches, Campbell et al. 2008). A survey of hospitals in the US revealed that only 8% had a basic electronic medical record in at least one unit of the hospital, and an astonishingly low 1.5% of them had a comprehensive electronic records system (Jha, DesRoches et al. 2009). Major arguments against investing in HIE by private stakeholders are financial investment and lack of a solid business model. As noted above, with CHINs, there still exists a perception that HIE fails to improve efficiency or be otherwise useful. The HIE concept also challenges a longheld marketing strategy of tying providers and patients to a particular system (usually a hospital or a laboratory information system) through a private HIE. An additional challenge has been the current healthcare reimbursement system. The United States uses a fee-for-service payment model for healthcare. Essentially, providers are paid only when they perform a service; therefore, more services and procedures equal more income.

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Hence, there is no financial incentive for providing more efficient and effective care, particularly if it involves fewer procedures. Apart from the initial set-up cost of infrastructure, time and personnel, there are operational costs involved in maintaining and supporting HIE. Many entities charge fees for facilitating exchange of information, either a flat fee or a per transaction fee. It is not encouraging that a recent survey of HIE in the US found only 55 active RHIOs. Of these, only 41% reported receiving enough revenue from participating organizations to cover their annual operating costs (Adler-Milstein, Bates et al. 2009). Most of the exchange was for laboratory test results and pharmacy data. Of those who had been surveyed in the prior year (2007), most had made no progress in expanding exchange capabilities. The failure rate over the past year was a very significant 20%. The authors concluded that the scope of RHIOs remains limited and their viability remains uncertain. The business model for HIE remains a challenge from inception to operation to viability (Adler-Milstein, McAfee et al. 2008). Even if financial concerns were overcome, there still remain key concerns about data stewardship and the feeling that patient data are proprietary and not to be shared with other competitor entities in the same market place (Grossman, Bodenheimer et al. 2006; Malepati, Kushner et al. 2007). Other barriers often mentioned are the legal and regulatory issues involved in exchange of patient data. Most countries, including the US, have enacted strict privacy and confidentiality laws governing the exchange of patient health information. Many of these regulations vary by state creating a quagmire of conflicting rules about who may exchange what information. Electronic exchange of patient information between entities without appropriate safeguards, business agreements and patient authorizations is perceived to increase risk, and has dampened the large scale adoption of HIE, particularly across state lines. As noted above, data stewardship and ownership issues contributed to the failure of CHINs.

These concerns were not overcome with existing technology in the 1990s. With advances in technology and the notion of “federated” data, technology is not considered a major barrier to HIE. The availability of high-level security that meets federal standards further bolsters the argument that current technologies will support HIE to the satisfaction of all stakeholders involved. In summary, financial concerns, lack of tangible benefits given the current reimbursement system, competitive pressures and regulatory concerns have limited the large scale adoption of HIE in the US. It appears that the notion of “common good” is not incentive enough to initiate and sustain HIE.

Lessons Learned and Potential Solutions The barriers facing HIE are not easy to overcome but inform the lessons learned. From the failures, we have learned that the business model for HIE needs to be revamped. It may not be possible for all HIEs to have an identical business model; very likely the “successful” model will vary according to the local or state healthcare market landscape. A fee-for-service or subscription model are options that should be considered by all HIE, with adequate safety nets of federal grants and private funding for initial start-up and maintenance. The culture must evolve to allow stakeholders to believe that investing their time and money in return for effective HIE will result in tangible benefits such as improved reimbursement. Widespread HIE will fundamentally shift market forces in ways that are probably not well appreciated at this time. The importance of data ownership and trust issues have been difficult lessons learned from both failures and successes. Here again, trust between competitors and with patients must be built slowly between stakeholders. In reviewing the success of regional HIE, it is evident that stakeholder buy-in is key. The HIE must develop a defensible business plan that offers significant

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return on investment for the various stakeholder groups in the market. In addition, it is crucial that the HIE include the right stakeholders and utilize technology that is widely available. Patience is an important virtue. The impetus to receive and exchange records electronically across disparate healthcare systems will increase as the adoption of electronic medical records and information systems increases in the US. This is a slow and tedious process that will be driven by government and private funding, increased awareness and, ultimately, reimbursement systems that reward or even mandate the use of electronic records and HIE. The recent push for increasing the use and adoption of electronic medical records by the US government, through the American Recovery and Reinvestment Act (ARRA) of 2009 Title XIII – Health Information Technology (HITECH), will provide a much needed boost to this field. Similarly, federal funding to initiate and sustain HIE through regional partnerships will also provide an impetus to HIE financing in general. The perception that HIE is not useful or that it may not improve efficiency can only be solved with continued research into the field. There are examples of limited success of HIE in specific areas (Hayward, Warren et al. 2007; Kuhn, Giuse et al. 2007; Phillips and Welch 2007; Frisse, King et al. 2008; Nocella, Horowitz et al. 2008). However, there are no large scale successes reported in the literature. On the other hand, there are studies that show a high failure rate and fears regarding sustainability among entities engaged in HIE (Adler-Milstein, McAfee et al. 2008; AdlerMilstein, Bates et al. 2009). A consensus needs to be reached among different stakeholders regarding the common goals, benefits and risks, especially for large scale HIE (Kuhn, Giuse et al. 2007). The perception that HIE may fundamentally change market strategies for the large systems— hospitals and clinical laboratories—inevitably increases resistance to the adoption of HIE in markets dominated by large for-profit systems.

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These markets will require significant changes in the reimbursement system to change, and even then the change will be strongly resisted. Barriers such as trust among stakeholders in the HIE, who are usually competitors, will only be overcome on a broad geographic scale when the financial incentives are compelling enough to require (in a financial sense) a change to their current business practices of using patient data as a competitive advantage. A recent push has been to actively involve the patient in HIE. It is the patient’s data that is exchanged, and if patients are convinced of the benefits, they may be advocates for increased adoption of HIE (Diamond and Ricciardi 2006; Tripathi, Delano et al. 2009). However, without adequate education, patients could also kill the HIE effort if they are not convinced that this is a change that directly benefits them. Closely aligned with trust issues are issues associated with privacy and confidentiality of patient information and the law. While these issues have been used as an excuse to not encourage HIE, with greater involvement from stakeholders, including patients, and with greater safeguards, the situation will change in favor of HIE (McGraw, Dempsey et al. 2009). This will involve changes to both state and federal laws in the US. In areas where HIEs have been successful, they have largely been driven by health insurers. It could be argued that the benefits of making the healthcare system more efficient and effective through an HIE largely accrue to health insurers. Private and government insurers in states such as Florida, Michigan, Colorado and Delaware have funded HIEs that have brought significant return on investments. This approach is limited by the structure of the various healthcare markets across the country and is most successful in markets that are dominated by a single insurer. In the majority of the country where there is active competition, it has been a significant challenge to bring competing payers together to support an HIE. To resolve the issues raised above, it is hoped that “disruptive technology” will enable and

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foster HIE. For example, privacy and security issues of access to patient data are solved with greater security and controlled access built in to current HIE systems (Carter, Lemery et al. 2006; Gravely and Whaley 2009; Marshall, Gillespie et al. 2009). The current “paper” system is extremely insecure and has essentially no “audit” functionality. Technology could also enable more cost-effective solutions to exchange of information as has been shown in every industry that has adopted information exchange to date. Adoption of national messaging standards such as HL7 will also translate to improved HIE and interoperability that may in turn lead to increased adoption of HIE (Orlova, Dunnagan et al. 2005; Walker, Pan et al. 2005; Donnelly, Mussi et al. 2006; Zafar and Dixon 2007; Hammond 2008; Raths 2008; Rogoski 2008). Technology may contribute to HIE with regard to software and architecture. Open source technologies may have the benefit of a cost-effective solution for HIE (Open Source Health Records Exchange 2009). Service-oriented architecture (Wright and Sittig 2008) and grid-based systems

have the potential of transforming HIE both regionally and nationally by creating federated databases and offering solutions to data stewardship. An example of grid-based exchange of data that could be emulated for the HIE world is the widely used and accepted cancer Biomedical Informatics Grid, caBIG (Manion, Robbins et al. 2009). This concept may also benefit exchange of information with public health agencies (Staes, Xu et al. 2009).

AN EXAMPLE HEALTH INFORMATION EXCHANGE SET UP AND ARCHITECTURE HIE: Health Information Exchange, HIS: Health Information System, CA: Certificate Authority. HIE between stakeholders can be set up using various systems and architectures. In general, HIE consists of data sources, an intermediary HIE services layer and a portal to access data. (Figure 1) provides a simplified example of a HIE architecture that could be established within a region.

Figure 1. A simplified system architecture for health information exchange between stakeholders

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In this example, a hospital and a clinical laboratory are the data sources that provide authorized access to patient data through secured communication. They each set up a resource server that resides outside the entity firewall so that no breach of internal security occurs. The hospital and laboratory identify the data that they would like to provide to the HIE; this could be negotiated by prior agreement based on stakeholder views and needs. The middle layer is the HIE services layer that provides the basic functionality of identifying patients uniquely, identifying and authorizing users and exchanging data. The most common elements in an HIE are the record locater service (RLS) or master patient index (MPI), data exchange service (DES), consent management service, the data repository, user authentication and auditing server. The RLS is an electronic index of identifying information that allows the system to uniquely identify a patient and locate associated data. This index directs providers to the data source location of the patient health records. The core responsibilities of the RLS are to 1) identify a patient within an HIE or in a remote community and 2) identify the electronic location of a patient’s clinical data. HIE users search for a patient with full or partial demographic information including first name, last name, date of birth, gender, zip code or other parameters. An RLS improves the efficiency of the HIE by allowing it to send the data request to the correct data source. The DES provides HIPAA-compliant clinical data exchange in standard data formats, including HL7 2.x, CCS, CCR, XML, CSV, DICOM, other text files, images, scans, etc.. Security is usually achieved used PKI and Web Services (SOAP, XML-RPC) technologies. The second part of HIE services consists of a HIE portal that includes a web server with auditing and authentication capability. This is the component that directly interacts with the end-users, which, in this example, are patients and physicians. Each user is authenticated to a

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level of access that has been pre-determined by a business agreement with the HIE after discussions with the stakeholders. When a provider wishes to access records on a particular patient, he or she would log on to the system using a secure web portal and be presented with a menu of options. The provider must first attest that he or she has a treatment relationship with the patient prior to accessing records. The provider could then search for records using patient identifiers and, in this case, the RLS would locate records associated uniquely with that patient. The data would then be retrieved on demand from the resource servers of the hospital or laboratory and displayed for the provider. While retaining these basic components and architecture, HIE could incorporate data from pharmacies, outpatient clinics, public health agencies and any other entity with an interest and need for HIE.

UTAH HEALTH INFORMATION NETWORK Utah has long been a national leader in HIE. The Utah Health Information Network (UHIN) was founded in 1993 as a public-private partnership in response to a community problem: how could a state with a (relatively) small population, where the healthcare market was dominated by local group health insurers and local healthcare providers create an efficient system for exchanging health information, specifically claims? Blue Cross Blue Shield of Utah, as the Medicare contractor, had been successfully receiving electronic claims for many years, but most of the other Utah payers were just beginning to implement systems. At the time, Utah was such a small market that it was of no interest to the then-major clearinghouses. Rather than each payer implementing its own proprietary system, which would have resulted in chaos for the Utah provider community, the entire community, payers and providers alike, came together to create a single commonly held

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utility, UHIN, that would benefit the entire community. Thus, all stakeholders were represented and claimed ownership of UHIN. Because UHIN was serving members who were competitors, it was decided that UHIN would not function as a clearinghouse; instead, it would act as a Value Added Network (VAN). That is, it would not “open the envelope” and edit or format data; it would essentially be a post office picking up and delivering electronic mail. UHIN utilizes a single electronic commerce agreement (ECA) that all members must sign. The ECA is available on the UHIN web site (www.uhin.org). The ECA covers HIPAA privacy and security issues and allows the members to avoid signing HIPAA Business Associate Agreements with everyone else on the network. This forced the fledging UHIN into using electronic data interchange standards. In 1993 the Utah Legislature adopted a law that gave the Utah Insurance Department (UID) the authority to adopt standards for administrative exchanges. The UID gave UHIN the responsibility of convening the community to create the administrative standards that UID would subsequently adopt, and UHIN’s long-standing career as a Standards Development Organization for the State of Utah began. Since that time, the Utah healthcare community, through the UHIN Standards process, has created over 80 standards and has adopted available national standards as well. The business case for UHIN is based on the premise that all the parties on the exchange (payers and providers) benefit from exchanging patient claim/billing data. Although both parties must make a significant initial investment to enable this exchange, the return on investment occurs very quickly. UHIN started its operations with the exchange of administrative data. Since its inception, UHIN has operated by collecting membership fees. The basic UHIN formula for determining membership fees is to first determine who receives value for the transaction. In the case of a claim, the UHIN board

decided that payers received 70% of the value and providers received 30% of the value. The basic idea is that the “price” of each claim exchanged through UHIN is divided 70-30: each stakeholder group pays for its share of the value received by exchanging that claim. UHIN payers pay a click fee for claims, and UHIN providers pay an annual membership fee. Currently more than 90% of all insurers’ administrative claims, remittance and eligibility data in Utah are exchanged through the UHIN electronic communication system The business model and pricing strategy for UHIN stakeholders involves a “click charge” for payers to receive and send claims and remittances. Providers using the services pay an annual membership fee. As the number of stakeholders and the volume of transactions increases, the average price per claim and the average cost of each transaction goes down. This savings is passed on directly to the payers and providers in the form of lower charges and annual fees. After 16 years of service to the community, it is clear that UHIN is one of the successful regional HIE entities (Rubin 2003). One of the reasons for the success is that its governing board required that it be operated first and foremost as a business: a non-profit business, but nonetheless a business. It had to bring value to its members (reduce the cost of care). Second, the board decided to move in small increments. UHIN began operations by exchanging healthcare claims. Claims, from a business perspective, are a win-win: payers and providers alike benefit from moving from paper into electronic data interchange. UHIN has spent many years solidifying their position with regard to exchange of claim data before even considering the next step in HIE. And third, the board decided to be inclusive. From its beginning, UHIN’s business strategy was to offer electronic data interchange to any Utah entity that wanted to participate. This helped develop trust among the stakeholders and set the stage for a successful venture. UHIN is currently moving into its next phase by expanding its portfolio beyond administra-

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tive data. Since early 2009, UHIN has initiated pilot testing of exchange of clinical data between partners in two communities of Utah.

INDIANA HEALTH INFORMATION EXCHANGE Indiana Health Information Exchange (IHIE) is another example of a successful statewide venture with advances in clinical information exchange. Its history can be traced back to Indiana University Medical Center’s adoption of the Regenstrief Medical Record System (RMRS) in 1973. The RMRS evolved into a comprehensive clinical data repository used extensively by three hospitals and 30 clinics in Indianapolis (McDonald, Overhage et al. 1999). In 1994, Regenstrief Institute of Medical Informatics expanded the RMRS into the Indiana Network for Patient Care (INPC) with funding from the National Institutes of Health and the National Library of Medicine. The INPC is a citywide electronic medical record system exchanging information among 5 health systems (McDonald, Overhage et al. 2005). The INPC’s success led to the formation of the Indiana Health Information Exchange, Inc. (IHIE) in 2004 as a not-for-profit umbrella organization for the region’s HIE projects. The IHIE provides clinical data to clinicians at the point of care and serves more than half of the population in the state of Indiana. The IHIE’s clinical data include laboratory results, radiology reports and images, pathology reports, dictated reports, admission discharge and transfer (ADT) data, electrocardiograms, vital signs, etc. Since 2004, over 50 million test results and other clinical information have been exchanged among clinicians, hospitals, emergency departments, laboratories, imaging centers, pharmacies, public health, payers, researchers and patients (www.ihie.com). The IHIE technical architecture consists of federated data repositories, centralized Concept Dictionary, Global Patient Index, and Global

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Provider Index. Patient registration records from all participating providers are linked in the Global Patient Index. The data received from a wide variety of sources are converted to standard representations such as HL7 and LOINC. These standardized data are then used to deliver a variety of HIE services for stakeholders (McDonald, Overhage et al. 2005). Health informatics research plays an instrumental role in the formation and development of IHIE. The Director for Medical Informatics at Regenstrief Institute is also IHIE’s President and Chief Executive Officer. The IHIE’s technical architecture and applications were developed by researchers at the Regenstrief Institute. The Institute continuously provides clinical information repository services to IHIE and operates the INPC. IHIE’s Vice President for Marketing described this collaboration as: The functions, services, and technogolgy of IHIE and Regenstrief are intertwined. … Regenstrief does informatics research and “invents” and proves really cool HIE technology; IHIE uses the really cool HIE technology and “sells” HIE services to the healthcare stakeholders in Indiana. (Kansky 2008). Besides HIE application development, Regenstrief researchers have solved several major technical challenges for effectively managing and improving the operational system over the years. For example, studies by Grannis and Overhage compared the linkage quality of deterministic vs. probabilistic linkage algorithm and supported decision-making for operational efficiency (Grannis, Overhage et al. 2002; Grannis, Overhage et al. 2003). Vreeman developed automated tools to reduce manual efforts to keep up with changing source system terms among disparate systems which contribute data for the ongoing health information exchange (Vreeman 2007). To some degree, IHIE becomes a HIE translational research laboratory for the Regenstrief Institute.

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Though research-based innovation is one of IHIE’s strengths, grant-based research projects usually have a limited life span. For example, the INPC has largely been funded by various federal grants and its sustainability was a concern. Having been integrated into IHIE, INPC has finally found a fiscal/business home for its long-term success. The collaboration between Indiana Health Information Exchange and Regenstrief Insitute is a powerful demonstration of mutual values of HIE translational research for stakeholders, academic researchers and health practitioners.

FUTURE RESEARCH DIRECTIONS Many reports of failures and successes of HIE have been reported in the US. (See references and additional reading.) These are mainly descriptive reports of regional HIE with regard to successes and failures, perspectives and surveys of entities engaged in HIE. There are also a limited number of published articles on the technology and standards involved. Several research areas need to be addressed in the field of HIE. A major research need is outcomes research to show the benefits of HIE. These studies are difficult to conduct as not all benefits of HIE are tangible and can be measured using traditional metrics. In some instances, simply the availability of key patient information that affects patient care is a benefit. Tangible benefits such as reduced costs, increased efficiency and positive patient outcomes are difficult to ascertain. Even more difficult is to show a meaningful difference in patient care. In many instances, it is appropriate to postpone these “outcome studies” (McDonald, Overhage et al. 2005; Vest 2009). Outcomes research may have to wait for research on sustainable business models. It may be appropriate to have researchers from other fields such as traditional business economics assist in these evaluations in an inter-disciplinary manner. A similar approach may be necessary to

further research on technologies related to HIE, bringing in professionals from other fields such as standards, computer science, grid, etc.

CONCLUSION HIE is a key component of healthcare. Effective exchange of information between stakeholders is necessary for patient care and for the business of healthcare in the US. Regional and community HIE has evolved from humble beginnings and is still in its infancy with regard to wide-scale adoption. From the community health information networks in the early 1990s to the robust health information networks of today, technology has been a key factor in advancing HIE and offering solutions to barriers such as security, privacy and controlled access to patient data. Stakeholders have learned many valuable lessons from the large number of failures and few successful ventures. The basic premise of HIE is intuitive and should be universally accepted for the “common good.” However, major issues such as financial viability, sustainability, marketing strategies, reimbursement, data stewardship and perception of usefulness of HIE remain. The result is that there are only a limited number of regional and community HIE entities that are providing any meaningful exchange of patient information today. Increased funding from government and private sources, more outcomes research and increased stakeholder buy-in combined with further advances in health information technology are necessary to secure the future of HIE in the US. The increased adoption of electronic medical records and information systems is also a key pre-requisite for furthering HIE. The full potential of HIE is yet to be realized.

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ACKNOWLEDGMENT The authors wish to gratefully acknowledge funding and support from the CDC-funded Rocky Mountain Center for Translational Research in Public Health Informatics; Division of Epidemiology, University of Utah School of Medicine; Utah Health Information Network and Utah Department of Health. We would also like to thank our colleagues in our parent institutions for providing references and description of various HIE efforts in Utah and elsewhere. We also acknowledge technical assistance from Ms. Marianne Madsen of the University of Utah.

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ADDITIONAL READING http://healthit.ahrq.gov/ portal/ server.pt/ gateway/ PTARGS_0_ 3882_ 874953_ 0_0_18/ 09-0071EF.pdf: A US Government-sponsored report on liability coverage for regional health information organizations http://healthit.ahrq.gov/ portal/ server.pt? open=514 &objID= 5554&mode= 2&holder DisplayURL =http://prodportallb. ahrq.gov:7087/ published content/ publish/ communities/ k_o/ knowledge_ library/ key_topics/ health_ briefing_ 01232006093812/ health_ information_ exchange.html: The official website of the US Agency for Healthcare Research and Quality (AHRQ) with information on current projects and reports on HIE

http://www.ahima.org/ hie/ index.asp: The official website of the The American Health Information Management Association (AHIMA) and information on HIE toolkit http://www.cdc.gov/ phin/: The official website of the US Government sponsored Public Health Information Network (PHIN) http://www.ehealth initiative.org/: The official website of non-profit organizations eHealth Initiative and Foundation for eHealth Initiative whose mission is to engage multiple and diverse stakeholders to define and implement actions to address quality, safety and efficiency challenges through the use of interoperable information technology. http://www.ehealth initiative.org/ ehealth- initiative- releases- results- 2009-survey- health- information- exchange.html: A current listing of HIE initiatives in the US http://www.ehealth initiative.org/ ehealth- initiative- releases- results-2009- survey- health- information- exchange.html: The eHealth Initiative 2009 Annual Survey on National Health Information Exchange http://www.himss.org/ asp/ topics_ rhio.asp: A portal for information on regional health information exchange in the US http://www.nhin watch.com/ index.cms: A portal to US health information exchanges with channels for federal and regional initiatives

http://healthit.hhs.gov/ portal/ server.pt? open=512 &objID= 1142&parentname= Community Page & parentid= 2&mode= 2&in_ hi_ userid= 10741 & cached= true: The official website of the US Government initiative National Health Information Network (NHIN)

http://www.uhin.org/: The official website of the Utah Health Information Network

http://health.utah.gov/ phi/publications/ Utah Clinical Info Exchange 2008.pdf: Progress report of clinical health information exchange in the state of Utah, 2008

Health Information Exchange (HIE): The electronic movement of health-related information among organizations within a hospital system, community, region or nation. In its broadest sense,

KEYTERMS AND DEFINITIONS

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this refers to electronic exchange of health information between two or more parties. Community Health Information Network (CHIN): Information network linking providers, insurers, patients and suppliers throughout a community. A CHIN could be defined as an inter-organizational system using information technologies and telecommunications to store, transmit, and transform clinical and financial information. Regional Health Information Organization (RHIO): A nongovernmental, multi-stakeholder organization that enables or oversees the business and legal issues involved in the exchange and use of health information in a secure manner for the purpose of promoting the improvement of health quality, safety and efficiency. Electronic Health Records (EHR): A digital form of an individual’s longitudinal health record. EHR is often used interchangeable with Electronic

Medical Records (EMR). Health care providers with electronic health records can participate in health information exchange (HIE). Master Patient Index (MPI): A database that maintains a unique index for every patient in the network of Health Information Exchange. To assure the uniqueness of linked patient records from diverse sources is a major technical challenge for all HIE. Indiana Health Information Exchange (IHIE): A non-profit HIE organization established in 2004. IHIE is the most successful RHIO for clinical health information exchange in the United States. Utah Health Information Network (UHIN): A non-profit HIE organization established in 1993. UHIN is the most successful RHIO for administrative health information exchange in the United States.

This work was previously published in Biomedical Engineering and Information Systems: Technologies, Tools and Applications, edited by Anupam Shukla and Ritu Tiwari, pp. 198-218, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 5.6

Regional Patient Safety Initiatives:

The Missing Element of Organizational Change James G. Anderson Purdue University, USA

ABSTRACT Data-sharing systems—where healthcare providers jointly implement a common reporting system to promote voluntary reporting, information sharing, and learning—are emerging as an important regional, state-level, and national strategy for improving patient safety. The objective of this chapter is to review the evidence regarding the effectiveness of these data-sharing systems and DOI: 10.4018/978-1-60960-561-2.ch506

to report on the results of an analysis of data from the Pittsburgh Regional Healthcare Initiative (PRHI). PRHI consists of 42 hospitals, purchasers, and insurers in southwestern Pennsylvania that implemented Medmarx, an online medication error reporting systems. Analysis of data from the PRHI hospitals indicated that the number of errors and corrective actions reported initially varied widely with organizational characteristics such as hospital size, JCAHO accreditation score and teaching status. But the subsequent trends in reporting errors and reporting actions were different. Whereas the

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number of reported errors increased significantly, and at similar rates, across the participating hospitals, the number of corrective actions reported per error remained mostly unchanged over the 12-month period. A computer simulation model was developed to explore organizational changes designed to improve patient safety. Four interventions were simulated involving the implementation of computerized physician order entry, decision support systems and a clinical pharmacist on hospital rounds. The results of this study carry implications for the design and assessment of data-sharing systems. Improvements in patient safety require more than voluntary reporting and clinical initiatives. Organizational changes are essential in order to significantly reduce medical errors and adverse events.

PATIENT SAFETY For more than a decade, studies in the United States (Brennan et al., 1991; Gawande et al., 1999; Leape et al., 1991; Thomas et al., 2000) and other countries (Baker et al., 2004; Davis et al., 2002, 2003; Vincent et al., 2001; WHO, 2004; Wilson et al., 1995) have reported that adverse events in health care are a major problem. These studies estimate that anywhere from 3.2% to 16.6% of hospitalized patients in the United States and Australia respectively experience an adverse event while hospitalized. A recent Canadian study of hospital patients estimated a rate of 7.5 adverse events per 100 hospital admissions (Baker et al., 2004). Over 70% 0f these patients experience disability and 14% die as a result of the adverse event. The Institute of Medicine (IOM) report, To Err is Human: Building a Safer Health System (Kohn, Corrigan & Donaldson, 2001), estimated that between 44,000 and 98,000 deaths occur in the United States each year as a result of medical errors. In fact, there is evidence that morbidity and mortality from medical errors increased

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between 1983 and 1998 by 243% (Phillips & Bredder, 2002). A significant number of these errors involve medications. A meta-analysis of 39 prospective studies indicated that adverse drug reactions from medication errors account for a significant proportion of these events in the U.S. (Lazarou, Pomeranz & Corey, 1998). One study of medication errors in 36 hospitals and skilled nursing facilities in Georgia and Colorado found that 19% of the doses were in error; seven percent of the errors could have resulted in adverse drug events (ADEs) (Barker et al., 2002). ADEs also occur among outpatients at an estimated rate of 5.5 per 100 patients. A recent analysis of hospital emergency departments in the United States, estimated that ADEs account for 2.4 out of every 1000 visits (Budnitz et al., 2006). Based on these studies the Institute of Medicine recommended that confidential voluntary reporting systems be adopted in all health care organizations (IOM, 2001). Traditionally efforts to reduce errors have focused on training, rules and sanctions. Also, hospitals have relied on voluntary reporting of errors. Currently only 5-10% of medication errors that result in harm to patients are reported (Cullen et al., 1995). As a result little progress has been made since the IOM report five years ago (Leape & Berwick, 2005).

Data Sharing Systems Studies have indicated that adverse events in health care settings primarily result from deficiencies in system design (Anderson, 2003). A study of adverse drug events in Utah and Colorado estimated that 75% of ADEs were attributable to system failures (Gawande et al., 1999; Thomas et al., 2000). Consequently, there is growing consensus that improvements in patient safety require prevention efforts, prompt reporting of errors, root-cause-analysis to learn from these errors and system changes to prevent the errors from reoccurring.

Regional Patient Safety Initiatives

Currently only 5-10% of medical errors are reported (Cullen et al., 1995). Incident reporting represents a major strategy to address growing concerns about the prevalence of errors in healthcare delivery (Billings, 1998). The Patient Safety and Quality Improvement Act was signed into law in 2005. This act encourages health care providers to report medical errors to patient safety organizations that are being created. Patient safety organizations are authorized to analyze data on medical errors, determine causes of the errors, and to disseminate evidence-based information to providers to improve patient safety. Currently, over 24 states have mandated some form of incident reporting (Comden & Rosenthal, 2002). Also, there has been a steady increase in the number of regional coalitions of providers, payers, and employers working to improve patient safety (Halamka et al., 2005). These efforts are driven by the premise that the identification of unsafe conditions is an essential first step toward analyzing and remedying the root causes of errors. Such reporting systems often occur in the context of an infrastructure for inter-organizational sharing of these data. The emphasis on data-sharing is based on the premise that when organizations share such data about incidents and the lessons learned from them, it will lead to accelerated improvements in patient safety across the board. In other words, data-sharing is expected to result in communitywide learning. Indeed, patient safety centers in states that have created them are charged with facilitating such data-sharing. Not surprisingly, these data-sharing systems vary widely. They differ in the data that is shared (from specific processes/outcomes such as medication errors and/or nosocomial infections to a broad range of incidents); the participants (individual clinicians to entire healthcare organizations); geography (regional, state, and national); technology (paperbased to online); and regulatory expectations about participation (voluntary or mandatory) (Flowers & Riley, 2001; Rosenthal et al., 2004).

Despite such differences, data-sharing systems are typically based on the premise that threats to patient safety arise from the unwillingness/discomfort of healthcare providers to openly discuss errors and their resulting lack of awareness of the magnitude of the problem. The identification and reporting of unsafe conditions is a necessary first step in a systemic approach to revamping patient safety. But technological, psychological, cultural, legal, and organizational challenges pose formidable barriers to the blame-free identification and discussion of unsafe conditions. Whereas individual organizations by themselves may not be able to take on these challenges, participation in a data-sharing coalition provides a shared rationale and the subtle benefits of peer influence. Second, the data from increased reporting facilitates the diagnosis of systemic causes of unsafe conditions and the implementation of systemic solutions. So data-sharing, it is assumed, will accelerate the identification of unsafe conditions, encourage analysis of the underlying causes, and enable continuous process improvement. Although different organizations may benefit differently from participating in a data-sharing system, a strong implicit assumption underlying these systems is that they will benefit the entire community of participating organizations. To date, few studies have examined the anticipated benefits of medical error data-sharing systems. In the following, we report the results of a study of developmental trends in two indicators of the effectiveness of one regional datasharing coalition. The indicators are the reported number of medication errors and the number of corrective actions taken by hospitals as a result of these errors. The objectives of the study were to examine whether hospitals that participated in medication error data-sharing consortium experienced increased reporting over time. The second objective was to determine whether error reporting resulted in organizational actions aimed at reducing future errors. A third objective was to

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explore organizational interventions designed to reduce mediation errors in hospitals.

The Pittsburgh Regional Healthcare Initiative (PRHI) A consortium of providers, purchasers, insurers and other stakeholders in healthcare delivery in southwestern Pennsylvania was formed in 1997 (Siro et al., 2003). Its purpose was to improve patient care by working collaboratively, sharing information about care processes and their links to patient outcomes, and using patient-centered methods and interventions to identify rapidly solve problems to root cause at the point of care. Clinicians, 42 hospitals, four major insurers, several large and small-business healthcare purchasers, corporate and civic leaders, and elected officials make up the consortium. In order to improve clinical practice and patient safety PRHI created a regional infrastructure for common reporting and shared learning. The consortium focuses on two patient safety goals, reducing nosocomial or hospital acquired infections and medication errors. The organizational learning model that underlies the PRHI strategy is based on the science of complex adaptive systems (Plsek, 2001). Healthcare delivery systems are viewed as a collection of individual agents whose actions are not always predictable. At the same time, agents are interconnected so that the actions of one agent can change the organizational context for other agents. Accordingly sustainable system-wide improvements in patient safety require real-time error reporting and decentralized problem solving. PRHI has relied on several strategies to promote improvements. The system chosen for reporting of medication errors was the USP’s Medmarx (Hicks et al., 2004). The system standardizes medication error reporting by using the National Coordinating Council for Medication Error Reporting and Prevention (NCCMERP) error categories. The Medmarx system is anonymous and voluntary.

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Health care providers can report medication errors online using a standardized format. The following information is collected on each reported order: 1. 2. 3. 4. 5. 6. 7. 8.

Inpatient or outpatient setting Type of error Severity Cause of error Location Staff and products involved Contributing factors Corrective actions taken

Data reported by consortium members is analyzed and quarterly reports are provided to participating hospitals. These reports contain facility-specific regional and national data. The quarterly reports provided data on reporting volume reflecting the early strategic emphasis on increasing reporting. The reports also contain data on the corrective actions being reported by each hospital. These reports provided an opportunity to compare the trends in reporting of errors with reporting of corrective actions. It was hypothesized that growth in the reporting of medication errors reported through the data sharing system would predict growth in corrective actions taken by the hospitals in response to the reported errors.

Effectiveness of Data Sharing We set out to examine the effects of data-sharing on the group of participating hospitals. The data analyzed consisted of approximately 17,000 reports of medication errors submitted over a 12-month period by 25 hospitals that are participating in PRHI. There were two outcome variables: the number of medication errors reported by each hospital each quarter and the ratio of corrective actions reported by each hospital to the number of errors reported each quarter. Control variables included the hospital’s teaching status (i.e., teaching versus non-teaching), the hospital size in terms of the number of beds, and the latest

Regional Patient Safety Initiatives

JCAHO accreditation score. A latent growth curve analysis was used to examine longitudinal trends in error reporting and organizational corrective actions (Anderson, Ramanujam, Hensel, & Siro, 2007). This analysis permitted the investigators to determine whether statistically significant changes in error reporting and corrective actions occurred over time; whether these trends varied significantly among the hospitals; and whether hospital characteristics were associated with these trends. Figure 1 shows the distribution of medication errors reported by severity. Fifty one percent of the events had the capacity to cause harm but did not affect the patient. Another 41% of the errors reached the patient but did not cause harm. The remaining medication errors caused patient

harm and in two cases may have resulted in the patient’s death. Figure 2 shows the trends in error reporting and corrective actions over the four quarters. During the first quarter, hospitals reported on average 45 medication errors. The number of errors reported rose steadily and had almost doubled by the fourth quarter. In contrast, the number of corrective actions taken by the hospitals in response to the errors remained fairly constant and, in fact, decreased slightly by the fourth quarter. Furthermore, our analysis indicated that, although there were significant differences between hospitals in error reporting at the baseline, subsequent error reporting increased at similar rates among the hospitals. By contrast, while there were

Figure 1. Percentage of medication errors reported by severity

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Figure 2. Medication errors and organizational changes reported over four quarters

significant differences among hospitals in their base line reporting of corrective actions, the number of corrective actions reported per error remained unchanged during subsequent quarters. The finding that the increase in reporting rates were similar across participating hospitals is consistent with the notion that data-sharing provides opportunities for organizations to observe others’ actions and adjust their behaviors. This is especially likely because in focus groups conducted during this period, informants from eight of these hospitals stated that medication error reports were reviewed by senior managers and that their typical response was “how are we doing with respect to others?” If the response of participating hospitals was to initiate actions to increase the reporting rate in line with the regional trend, it would partly explain how the reporting trends across hospitals moved in tandem. This finding is important because our analysis controlled for differences in baseline reporting, hospital size, teaching status, and accreditation scores. Corrective actions taken by hospitals as a result of medication errors indicate how important patient safety is to the institution. First-order interventions include individual interventions such as: 1. Informing staff who made the error 2. Informing other staff involved in the error 3. Providing education/training

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4. 5. 6. 7.

Informing the patient’s doctor Informing the patient/caregiver Instituting policies/procedures Enhancing the communication process

Second-order interventions include system changes such as: 1. 2. 3. 4. 5. 6.

Computer software modified/implemented Staffing practice/policy modified Environment modified Policy/procedure instituted Formulary changed Policy/procedure changed

First-order interventions are aimed at individuals and are likely to have short-term effects and thus are unlikely to be effective in preventing future errors from occurring. Second-order interventions involve system changes and are much more likely to prevent errors from reoccurring. Figure 3 shows the types of actions taken in response to reported medication errors. Eighty-five percent of the actions taken by the hospitals in response to reported errors involved individuals. Only 15% of the organizational actions involved system changes. A second analysis was based on a computer simulation model constructed to model medication error reporting and organizational changes needed to improve patient safety (Anderson et

Regional Patient Safety Initiatives

Figure 3. Number of organizational actions taken in response to 1,760 reported errors

al., 2006). Several potential organizational interventions were simulated (Anderson et al., 2002; Anderson, 2004). First, baseline conditions were simulated. Intervention 1 involved introducing a basic computerized physician order entry (CPOE) system with minimal decision support for medication prescribing and administration. The second intervention assumed implementation of a CPOE system with decision support. Intervention 3 involved the inclusion of a clinical pharmacist on physician rounds who reviewed all medication orders. The fourth intervention assumed an organizational commitment to undertake root-cause

analyses and system changes to prevent errors from reoccurring. Figure 4 shows the results of the simulation. The model predicts that the introduction of a basic CPOE system will have little effect on the number of medication errors that could result in adverse drug events. Even the addition of decision support to the CPOE system is likely to result in only about a 20% reduction in medication errors. The inclusion of a clinical pharmacist on hospital physician rounds is like to reduce errors by only about 27%. Finally, the model predicts that when a commitment is made to root-cause-analysis of

Figure 4. Estimated average number of medication errors that could have resulted in ADEs by quarter

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errors and system changes to prevent errors from reoccurring medication errors can be reduced by as much as 70% over time.

CONCLUSION The results of this study carry implications for the design and assessment of data-sharing systems. Organizational actions taken in response to errors indicate how aggressive the organization is in responding to errors. Efforts that only affect individual staff and involve voluntary reporting and clinical initiatives are likely to have little effect in reducing errors long term. System-wide organizational changes are essential in order to significantly reduce medical errors and adverse events. In general, there is a mismatch between patient safety goals and hospital actions to reduce the risk of future medication errors. Hospitals increasingly recognize the need to implement error reporting systems. At the same time they fail to implement organizational changes needed to improve patient safety. Actual error reduction will require organizational changes to be carefully institutionalized and integrated into long term plans. Currently only 5-10% of medical errors are reported. There are a number of barriers that must be overcome in order to implement data-sharing systems designed to improve patient safety (Ferris, 2006; Rosenthal & Booth, 2004). First, competition inhibits provider participation. There is a lack of trust of other providers. Also, concerns about information ownership and reliability and privacy of data impede cooperation. Second, there is lack of a business case for patient safety. Healthcare delivery systems are complex. Implementation of information technology such as electronic medical records (EMR), electronic prescribing, clinical decision support, bar coding is expensive. Providers do not perceive a return on their investment in new technology such as electronic medical records and electronic prescribing.

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Third, the culture of medicine presents significant barriers. Medicine is committed to individual professional autonomy. This results in a hierarchical authority structure and diffuses accountability. Furthermore, there is a culture of “blame and shame.” The fear of malpractice litigation inhibits reporting of errors. Fourth, there are technical barriers to implementation of data-sharing. There is a lack of an accepted error reporting system and standards. Also, there is a lack of agreement of what constitutes an error. The difficulty in identifying problems, measuring progress and demonstrating improvement makes many healthcare institutions reluctant to participate in data-sharing coalitions. Moreover, voluntary reporting does not support comparative analysis of institutions on overall safety performance. What is more, the current reimbursement structure militates against improving safety. Some of the steps that need to be taken to overcome these barriers to data-sharing include (Institute for Healthcare Improvement, 2006): 1. Diffusion of safe practices such as those identified by the National Quality Forum (NQF). 2. Training on safety and team work. 3. Implementation of error reporting systems. 4. Establishment of a National Patient Safety Agency similar to the one established in the UK. 5. Changes in reimbursement policies to provide incentives to hospitals and physicians for safe care. 6. Provision of disincentives for unsafe practices and adverse events (e.g., Minnesota’s decision to stop paying hospitals for preventable adverse events). 7. Bringing together the Joint Commission on Accreditation of Healthcare Organizations (JCAHO), National Quality Forum (NQF), American Hospital Association (AHA), American Medical Association (AMA), Leapfrog Group, the Centers of Medicare/

Regional Patient Safety Initiatives

Medicaid Services and major payers to set explicit goals for patient safety to include a 90% reduction in nosocomial infections, a 50% reduction in errors associated with medications, and a 100% elimination of errors on the NQF “never” list. Leape and Berwick (2005) observed “The primary obstacles to achieving these [improved safety] results for the patients who depend on physicians and health care organizations are no longer technical; the obstacles lie in beliefs, intentions, cultures, and choices.”

FUTURE RESEARCH DIRECTIONS Despite a great deal of publicity concerning the 1999 IOM report, To Err is Human, little progress has been made in improving patient safety. Despite the implementation of error reporting systems it is estimated that less than 10% of errors are reported and relatively weak organizational actions are taken to prevent errors from reoccurring. Error reporting systems are important tools that can be used to educate providers and improve systems. However, in most instances, submission of reports fails to make a difference in patient safety. Major research efforts need to be directed at ways to overcome these barriers to improved patient safety. Also research is needed into new ways of translating error reports into meaningful organizational actions. Information technology undoubtedly can help to improve patient safety. For example, electronic medical records, electronic prescribing, bar coding of medications and decisions support systems at the point of care can reduce medical errors. However, few physicians and hospitals have made major investments in information technology such as EMRs. It will be necessary to make a stronger business case for patient safety in order to overcome provider resistance to the required capital expenditures. At the same time, there are reports

of system failures and errors introduced by IT systems. Research is needed into better ways of implementing IT systems in health care that avoid provider resistance and technology induced errors. An important area for research involves the health care reimbursement system. The current reimbursement system does not provide incentives to hospitals and physicians to invest in patient safety. Errors can result in increased revenue under the present system. Furthermore, the economic interests of physicians and hospitals frequently are at odds. New models for reimbursement systems that promote patient safety are needed. Finally, 24 states have passed legislation or regulations related to hospital reporting of adverse events. All but one are mandatory. To make significant improvements in patient safety, research is needed into improving the collection, analysis and feedback of the data collected by these state systems.

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Anderson, J. G., Ramanujam, R., Hensel, D. J., Anderson, M. M., & Siro, C. A. (2006). The need for organizational change in patient safety initiatives. International Journal of Medical Informatics, 75(12), 809–817. doi:10.1016/j. ijmedinf.2006.05.043 Anderson, J. G., Ramanujam, R., Hensel, D. J., & Siro, C. (2007). Reporting trends in a regional medication error data-sharing system (unpublished manuscript). Baker, G. R., Norton, P. G., Flintoft, V., Blais, R., Brown, A., & Cox, J. (2004). The canadian adverse events study: The incidence of adverse events among hospital patients in Canada. Canadian Medical Association Journal, 170(11), 1678–1686. doi:10.1503/cmaj.1040498 Barker, K. N., Flynn, E. A., Pepper, G. A., Bates, D. W., & Mikeal, R. L. (2002). Medication errors observed in 36 health care facilities. Archives of Internal Medicine, 162, 1897–1903. doi:10.1001/ archinte.162.16.1897 Billings, C. E. (1998). Some hopes and concerns regarding medical event-reporting systems: Lessons from the NASA aviation safety reporting system. Archives of Pathology & Laboratory Medicine, 122(3), 214–215. Brennan, T. A., Leape, L. L., & Laird, N. (1991). Incidence of adverse events and negligence in hospitalized patients: Results of the Harvard Practice Study. The New England Journal of Medicine, 324(6), 370–377. Budnitz, D. S., Pollock, D. A., Weidenbach, K. N., Mendelsohn, A. B., Schroeder, T. J., & Annest, J. L. (2006). National surveillance of emergency department visits for outpatient adverse drug events. Journal of the American Medical Association, 296(15), 1858–1866. doi:10.1001/ jama.296.15.1858

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Hicks, R. W., Santell, J. P., Cousins, D. D., & Williams, R. L. (2004). MEDMARX 5th anniversary data report: A chartbook of 2003 findings and trends 1999-2003. Rockville, MD: U.S. Pharmacopeia. Institute for Healthcare Improvement. (2006). Leadership guide to patient safety. Cambridge, MA: Institute for Healthcare Improvement. Institute of Medicine. (200l). Crossing the quality chasm: A new health system for the 21st century. Washington, D.C.: National Academy Press. Kohn, K. T., Corrigan, J. M., & Donaldson, M. S. (Eds.). (1999). To err is human: Building a safer health system. Washington, DC: National Academy Press. Lazarou, J., Pomeranz, B. H., & Corey, P. N. (1998). Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. Journal of the American Medical Association, 279, 1200–1205. doi:10.1001/ jama.279.15.1200 Leape, L. L., & Berwick, D. M. (2005). Five years after ‘to err is human:’ What have we learned. Journal of the American Medical Association, 293, 2384–2390. doi:10.1001/jama.293.19.2384 Leape, L. L., Brennan, T. A., & Laird, N. (1991). The nature of adverse events in hospitalized patients: results of the Harvard Practice Study. The New England Journal of Medicine, 324(6), 377–384. Phillips, D. P., & Bredder, C. C. (2002). Morbidity and mortality from medical errors: An increasingly serious public health problem. Annual Review of Public Health, 23, 135–150. doi:10.1146/annurev. publhealth.23.100201.133505

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ADDITIONAL READING Altman, D. E., Clancy, C., & Blendon, R. J. (2004). Improving patient safety: Five years after the IOM. The New England Journal of Medicine, 351(20), 2041–2042. doi:10.1056/NEJMp048243

Devers, K. J., Pham, H. H., & Liu, G. (2004). What is driving hospital’s patient safety efforts? Health Affairs, 23(2), 103–115. doi:10.1377/ hlthaff.23.2.103

Aspden, P., WolcottJ.A., Bootman, J.L., & Cronenwett (Eds.) (2007). Preventing medication errors. Washington, DC: National Academies Press.

Doing what counts for patient safety: Federal actions to reduce medical errors and their impact. (2000). Rockville, MD: Agency for Health Care Research and Quality Report of the Quality Interagency Task Force (QuIC) to the President.

Bates, D. W., Evans, R. S., Murff, H., Stetson, P. D., Pizziferri, L., & Hripcsak, G. (2003). Detecting adverse events using information technology. Journal of the American Medical Informatics Association, 10(20), 115–128. doi:10.1197/jamia. M1074

Enabling patient safety through Informatics. (2002). Selected works from the 2001 AMIA annual symposium and educational curricula for enhancing safety awareness. Journal of the American Medical informatics Association, 9(6), S1-S132.

Bates, D. W., & Gawanda, A. A. (2003). Improving safety with information technology. The New England Journal of Medicine, 25, 2526–2534. doi:10.1056/NEJMsa020847

Flowers, L. (2002). State responses to the problem of medical errors: An analysis of recent state legislative proposals. Portland, ME: National Academy for State Health Policy Report.

Berwick, D. M. (2003). Errors today and tomorrow. The New England Journal of Medicine, 348(25), 2570–2572. doi:10.1056/NEJMe030044

Greenfield, S., & Kaplan, S. H. (2004). Creating a culture of quality: the remarkable transformation of the Department of Veterans Affairs health care system. Annals of Internal Medicine, 141(4), 316–318.

Bodenheimer, T. (1999). The movement for improved quality in health care. The New England Journal of Medicine, 340(6), 488–492. doi:10.1056/NEJM199902113400621 Classen, D. C., Pestotnik, S. L., Evans, R. S., Lloyd, J. F., & Burke, J. P. (1997). Adverse drug events in hospitalized patients: Excess length of stay, extra costs, and attributable mortality. Journal of the American Medical Association, 277, 301–306. doi:10.1001/jama.277.4.301 Clinton, H. R., & Obama, B. (2006). Making patient safety the centerpiece of medical liability reform. The New England Journal of Medicine, 354(21), 2205–2208. doi:10.1056/NEJMp068100 Comden, S. C., & Rosenthal, J. (2002). Statewide patient safety coalitions: A status report. National Academy for State Health Policy.

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Institute of Medicine. (2001). Crossing the quality chasm. Washington, DC: National Academy Press. Leape, L. L., & Berwick, D. M. (2005). Five years after ‘to err is human:’ What have we Learned? Journal of the American Medical Association, 293, 2384–2390. doi:10.1001/jama.293.19.2384 Leape, L. L., Berwick, D. M., & Bates, D. W. (2002). What practices will most improve safety? Evidence-based medicine meets patient safety. Journal of the American Medical Association, 288(4), 501–508. doi:10.1001/jama.288.4.501 Leatherman, S. (n.d.). (2203). The business case for quality: case studies and an analysis. Health Affairs, 22(2), 17–30. doi:10.1377/hlthaff.22.2.17

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Making Health Care Safer. A Critical Analysis Patient Safety Practices. Evidence Report/ Technology Assessment: Number 43. Rockville, MD: AHRQ Publication No. 01-E058, July 2001. Agency for Healthcare Research and Quality, http://www.ahrq.gov/clinic/ptsafety/ McNeil, B. J. (2001). Hidden barriers to improvement in quality of care. The New England Journal of Medicine, 35(22), 1612–1620. doi:10.1056/ NEJMsa011810 O’Conner, P. J. (2006). Improving medication adherence: challenges for physicians, payers and policy makers. Archives of Internal Medicine, 166, 1802–1804. doi:10.1001/archinte.166.17.1802

Ortiz, E., & Clancy, C. M. (2003). Use of information technology to improve quality of health care in the United States, AHRQ Update. Health Services Research, 38(2), xi–xxii. doi:10.1111/14756773.00127 Romano, P. S. (2005). Improving the quality of hospital care in America. The New England Journal of Medicine, 353(3), 302–304. doi:10.1056/ NEJMe058150 Wachter, R. M. (2004). The end of the beginning: Patient safety five years after “to err is human.”. Health Affairs, W4, 534–545. Wachter, R. M., & Shojania, K. G. (2004). Internal bleeding: The truth behind America’s terrifying epidemic of medical mistakes. New York: Rugged Land Press.

This work was previously published in Human, Social, and Organizational Aspects of Health Information Systems, edited by Andre W. Kushniruk and Elizabeth M. Borycki, pp. 167-179, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 5.7

Use of Handheld Computers in Nursing Education Maureen Farrell University of Ballarat, Australia

ABSTRACT The use of mobile technologies in nursing education is rapidly increasing. Handheld computers are the most frequently used of these technologies as they can provide students with information for point of care clinical reference, such as diagnostics, medical terminology, and drug references. Integrating the management and processing of information into clinical practice is an effective learning approach for students and reflects a changing paradigm in nursing education. Traditionally, nursing programs have the tendency to separate the acquisition of academic knowledge from clinical DOI: 10.4018/978-1-60960-561-2.ch507

practice, and the process of integrating academic information into the decision making processes in the clinical area has been difficult for student nurses. This chapter will provide an overview of the use of handheld computers in nursing and medical education, including a brief synopsis of current use in clinical practice. It will discuss the advantages and disadvantages of their use, barriers to implementation and future directions.

INTRODUCTION Healthcare in the developed world is characterised by a rapidly increasing use of mobile technologies to deliver effective and quality patient care.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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Handheld computers or personal digital assistants (PDAs), have the potential to change how healthcare is taught and delivered in the future as they have the ability to merge and integrate distinct functionalities in one device that is versatile, customizable, and portable (Baumgart, 2005). Research and anecdotal evidence suggest that PDAs can be used in clinical practice to deliver valuable decision-making information at the patient’s bedside. This significant improvement in the delivery of information results in improvements in patient safety and quality of care. PDAs provide instant access to diagnostics, references, and clinical decision support systems, as well as e-prescribing, patient information and dictating notes (Blair, 2006; Colevins, Bond & Clark, 2006; Choia et al., 2004; Fisher, Stewart, Mehta, Wax & Lapkinsky, 2003; George & Davidson, 2005; Lewis, 2001; Ruland, 2002). Educational processes in healthcare have also become increasingly complex. Students require timely access to resources. Organisational constraints in both technology and personnel may further limit students’ access to teaching resources. Having access to information at the bedside in real time with patients has the potential to improve the quality and safety of care by reducing adverse events and improving patient health outcomes; a solution to the problem of constrained resources. The National League for Nursing, the American Association of Colleges of Nursing, and the Institute of Medicine, to name a few, recommend the incorporation of technology into the curriculum of nursing education (George & Davidson, 2005). The incorporation of PDAs into daily practice by medical staff is far more advanced than in nursing, however this is changing and nurses are now adopting this technology in nursing practice and education. To encourage change in practice technology must be harnessed to revolutionise the design, delivery and evaluation of nursing education (Jeffries, 2005). Increasingly there is an educational shift to provide students with more learning opportunities to create innovative

teaching practices and to promote current, accurate information retrieval systems for clinical nurses. As learning and teaching adapts to the demands of the 21st century, both educators and students in the nursing profession are finding that the most effective learning approach integrates the management and processing of information into both their practice area and their personal growth and development. In higher education, there has been a shift from the notion of content to a focus on ‘process based education’ and this is occurring more rapidly in the healthcare arena and in nursing programs in particular. The focus will be increasingly on teaching students how to learn rather than what to learn. There is considerable evidence that suggests PDAs have the potential to improve nursing education and practice through connecting people, unifying the education process and enhancing learning. Patient safety is one of the most important aspects of nursing care and these devices can promote the quality and immediacy of bedside information and improve the accuracy of record keeping (Bates, 2000; Celler, Lovell, & Basilakis, 2003). This chapter will provide a review of PDA usage in nursing and medical education, including the advantages and disadvantages, barriers to implementation, and future perspectives.

PDAS IN CLINICAL PRACTICE PDAs have been adopted by healthcare professionals (predominately physicians) in clinical practice and this adoption has led to a number of different uses including clinical decision support, education, and accessing or collecting data. The literature reveals that the information on usage is usually descriptive rather than evidence based, although some preliminary impact studies are indicating improved patient outcomes with regards to PDA usage. Specialty areas are prominent in the use of PDAs.

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A trauma team used the PDAs to update daily progress notes, access laboratory values and medications. The nurses within the team also used the device to take photographic record of patients’ wounds which were kept on their medical record. (Eastes, 2001; 2004). PDAs were used in the operating theatre by nurses as a tool to assist in updating, maintaining and retrieving surgical preference cards as well as accessing current treatment and medication references (McCord, 2003). Infusion therapy and infection control teams used PDAs for routine daily surveillance, time and information management and communication. The usage resulted in a significant improvement in time management as data could be entered while waiting for other tasks to be completed (Goss & Carrico, 2002). The use of PDAs for clinical decision making was examined by Honeybourne, Sutton, and Ward (2006), who found that most healthcare professionals used the devices to support evidencebased practice and education, but with varying frequency and this depended on the skills of the user. These researchers believed the PDAs could provide a critical mass of information that was relevant, quickly accessible and in a coherent format, delivering clinical information at the point of need with a resulting benefit to patient safety (Honeybourne, Sutton & Ward, 2006). The use of wireless PDAs to reduce medical errors by facilitating access to needed patient information and communication between clinicians (Choi et al., 2004) was also examined however studies were limited. Most of the applications used on PDAs either involved a one-time installation of information unto the device or obtained new information or updates through synchronization. One study by Tassani et al. (2007) reported on the use of PDAs in an orthopaedic wireless ward. The system patient ward note was shown and collected on the PDA but saved directly on the Hospital Information System. Medical images were shown on the device display, but also transferred to a high-resolution monitor and large amounts of data

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were dictated and translated by remote continuous speech recognition. There are some studies in progress examining the use of wireless PDAs by nurses in acute care hospitals to determine the impact on patient care in reducing adverse events or nursing errors (Farrell, McGrath, D’Arcy, & Abaloz, 2007). The adoption of wireless PDAs is also dependent on the organisation or the hospital. The wireless environment has to be set up to accommodate all wireless healthcare technologies - a complex task requiring extensive planning. Hospitals worldwide are now establishing wireless networks as the capabilities have the potential to improve the quality and safety of patient care. In summary, medicine has dominated in the use of PDAs in the clinical area and the key functionalities were clinical decision support, computerised patient records, patient surveyor tools and clinical data repository (Kuziemsky, Laul, & Leung, 2005). The evidence suggests in nursing that adoption of PDAs by nurses is higher in education and advanced practice rather than general nursing (Degroote & Doranski, 2004; McNeil, et al., 2003).

PDAS IN NURSING EDUCATION Although the literature on the use of PDAs in nursing education is limited - most emanating from North America - there has been an increased interest in the use of handheld computers as they have the potential to transform nursing practice and the number of nursing schools or faculties adopting this technology has increased. With the increasingly complex and vast amount of information presented in nursing education, PDAs act as a valuable resource for nursing students, graduate nurses, clinicians and academics. These devices offer a powerful and portable means of managing nursing information and increasing clinical knowledge. PDAs may be used in the classroom for formal instruction and at the bedside in real

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time with the patients. Nurses can check drug orders and interactions and consult evidence based practice nursing guidelines. PDAs are also becoming an important component of patient care and documentation through electronic order entry and patient tracking applications. This section will focus on common areas that emerged from the literature including general attitudes and use, formal teaching, feedback and evaluation, and clinical education. There was limited literature on how PDAs impacted on patient outcomes although a small study did reveal that patients believed PDA use could increase nurses’ efficiency in data retrieval and calculation but were concerned about data accuracy and privacy. They preferred the nurses explained the reasons for PDA use and suggested more PDA functions (eg, entertainment for paediatric patients, wireless paging for clinicians), and valued nursing care over technology use (Lee, 2007). As medical educators are the most advanced in PDA use, comparisons and lessons learnt from their experience will be discussed where relevant.

General Attitudes and Use There is no definitive literature identifying the number of nursing schools or faculty using PDAs in the education of nurses, however as stated previously, some nursing academic institutions (Table 1) are using the technology and others are following suit. Also the importance of PDA use in nursing education was identified in a nursing survey where 60 percent of the respondents reported that PDA use in nursing education was very important. (NursingPDA Listserve Survey, 2004). A PDA use survey undertaken by Courtney, Pack and Porter (2004) showed that 40.3 percent of nurses (50 of 124) in an academic health system reported using a PDA. Types of nursing practice, gender, age, nursing experience level, initial educational preparation level, self described technology adoption level and number of coworkers with PDAS were not significantly related to PDA

Table 1. Examples of Nursing Schools using PDAs • Duke University • Robert Morris University • RMIT University • Curtin University • Washington State • University of Louisville • Columbia University • University of Tennessee • Yale University • The University of Texas at Austin • Vanderbilt University • Unitec New Zealand • University of British Columbia • University of Alberta • Drexell University • University of Virginia

ownership among nurses. Practice in a research, education or advanced practice setting, higher self-rated computer literary, and self-description as an innovator/early adaptor predicted PDA ownership and use. In another study nurses initially resisted using the PDA then came around to using the device on a regular basis (Lee, 2006). Berglund, Nilsson, Revay, Petersson and Nilsson (2007) examined nurses and students demands of function and usability in a PDA and found that they expected access to information about the patients, knowledge resources and functions for their daily work. Where PDAs have been used in nursing education the general attitude of the students has been positive and this often correlated with the level of experience and encouragement by their educators or clinicians to use the device (Farrell & Rose, 2008; Shortis, McGovern, Berry, & Farrell, 2006). In medicine approximately 60 to 70 percent of medical students and residents use PDAs for educational purposes or patient care. Although there are no statistics available for nursing students the number for medical students is high, over 50

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percent and this reflects the value of the device for student learning and delivery of patient care. Satisfaction was generally high and like nursing correlated with the level of handheld computer experience and use (Greenberg, 2004; Kho, Hendersen, Dressler & Kripaline, 2006).

Formal Teaching, Feedback and Evaluation Handheld computers are becoming a valuable tool in the classroom setting for both teacher and the student and this is most evident in medical education (Kho et al., 2006). Nursing educators have been too slow to adopt this technology for classroom teaching, feedback and evaluation mainly due to cost, large numbers of nursing students and computer literacy of the user. Lehman (2003) reported on her own experience of using a handheld computer for keeping nursing and clinical evaluations and found that it solved the problem of having to carry text methods of record keeping and evaluation and provided an improved method of maintaining records. Farrell (2005) conducted a small pilot study that examined the use of handheld computers by clinical teachers for record keeping and clinical evaluation. Application software programs were designed to facilitate the clinical evaluation of the student nurses in the medical surgical nursing clinical area. The results showed that the clinical teachers embraced the use of the PDA for evaluating and recording student data and saw the electronic method as more efficient and effective than text based. There were some functional issues with the synchronization of the programs to the handheld computer. Synchronization, which involves information exchange between the PDA and a desktop computer, provides a method for educators to monitor the daily journaling of their students, for record keeping, clinical evaluation and other assessments (Cimino & Bakken, 2005). A wireless PDA based clinical learning tool for professional reflection was designed and

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evaluated by Garrett and Jackson (2006). Nursing and medical students were able to access clinical expertise and resources remotely, and record their clinical experiences in a variety of media (text, audio and images). The PDA e-portfolio tool was developed to support and enhance clinical learning, promote reflective learning in practice, engage students in the process of knowledge translation, help contextualize and embed clinical knowledge, and to reduce the isolation of students while in supervised clinical practice. The PDA e-portfolio synchronised wirelessly with the user’s personal Web based portfolio from any remote location where a cellular telephone signal or wireless connection could be obtained. The evaluation of the tool by nurse practitioner and medical students was positive, yet there were some limits in use due to inherent interface restrictions of the PDA. In medicine, the educators preload lecture material and display multiple-choice questions as a Webpage while students respond to questions using PDA and wireless Bluetooth cards. This educational modality allows for interactivity and real-time assessment of students’ knowledge. The devices were also used for teaching evaluation and student assessment of special activities in the clinical area. Recording such activities in real time avoids much of the recall bias associated with time period evaluations and allows the educators to track a variety of parameters with relative ease (Kho et al.2006). The descriptive evidence suggests that PDAs are a valuable tool for formal classroom teaching, feedback and evaluation. However there needs to be more rigorous studies undertaken to determine the impact of PDA use on student learning outcomes and patient care, including rates of medication errors. Also there is little evidence from the literature to suggest that the evaluative loop is being closed in practice and that the data is actually being used to provide good feedback or changes in teaching and this appears to be a major obstacle, if the goal is to develop a culture of use by the teachers and the students. Nursing

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educators can learn from their medical colleagues in this area as they are far more advanced and have implemented quite diverse educational modalities.

Clinical Education Many nursing academic institutions use PDAs for the clinical education of undergraduate students. The devices are more often used in clinical settings by nursing students and graduates where they use the PDA as a portable resource providing rapid, point of care information to guide patient care and to enhance self directed learning. Wilson, Dignam and Hay (2002) reported on a PDA and personal computer patient care documentation system that was used by undergraduate students in the clinical setting. This initiative contributed to an informatics -integrated curriculum enabling future graduates to better represent practice-based requirements. Miller et al (2005) reported on the use of PDAs by second year undergraduate nursing students. The devices were preloaded with selected software, including drug reference databases, medical laboratory references, and dictionaries. The PDAs were found to be an effective student learning resource. Students made substantial use of their PDAs and the use was linked to access speed and reliability. Huffstutler, Wyatt, and Wright (2002) explored issues concerning the implementation of handheld technology into their graduate pharmacology course. This study identified challenges associated with changing faculty practice to enable acceptance of PDAs into the program. However, the students primarily perceived the inclusion of handheld technology as a positive feature of the course that enhanced their development of pharmacology knowledge. Greenfield (2007) also focussed on pharmacology knowledge and investigated the use of PDAs to reduce medication error of undergraduate baccalaureate nursing students. The findings showed that the student group using the PDAs were far more accurate in answering drug calculation and drug administration questions

than another group using a textbook. Time could also be saved if medication information was in the palm of the nurse’s hand rather than in a book. Farrell and Rose (2008) examined the use of PDAs in improving the pharmacological knowledge of second year undergraduate nursing students in the medical surgical clinical area. Findings showed a slight improvement in the student nurses’ pharmacological knowledge however students reported positively on their experience of using handheld mobile technology. The technology improved the efficiency of their nursing practice in real-time with their patients, and improved their overall medical surgical learning experience, although no definitive conclusions can be drawn about any improvement in learning. Students also had little difficulty in using the technology. Another study undertaken by Farrell (2007) investigated the use of PDAs for improving student nurses’ medical surgical knowledge in the clinical area. The findings showed that students were frequent users of the PDAs during patient encounters in medical surgical units, particularly in the area of drug administration. These students medical surgical knowledge showed slight improvement and they all believed the PDAs enhanced their pharmacological knowledge, and to a lesser extent their medical surgical knowledge and saw them as a valuable educational tool to be used in the classroom, clinical area, and at home. Scollin, Patrick, Healey-Walsh, Kafel, Mehta, and Callahan (2007) explored student’s attitude toward the use of PDAs - preloaded with medical/ nursing databases - during their clinical placements. They found that there were differences in the students’ perceptions and acceptance of the PDA as a learning tool. White et al. (2005) reported on the use of PDAs by student nurses and found that their learning was enhanced in the clinical environment by the rapid and efficient acquisition of relevant information. Furthermore leadership skills and professional confidence were improved by providing students with reinforcement of core knowledge and

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evidence-based information. In another project PDAs were also provided to student nurses who practiced in medically underserved areas, rural communities and clinics serving migrant/ethnic populations to enhance confidence. The students were able to directly access medical information and databases used in clinical decision making that otherwise would not be available in the rural clinical settings because of a lack of clinical experts and the lack of current technologies (Rice, 2003). Stolworthy (2003) described an initiative where undergraduate and graduate, students were provided with PDAs, in preparation for their first clinical courses. Graduate students used preloaded reference applications in their precepted clinical courses (six months) and some added additional software applications for their specialty practice setting. Others used the PDAs as reference tools to search for drug information and other clinical information. In addition, 29 percent of the advanced practice nurse preceptors supervising the students owned and used PDAs for their practice, which is slightly higher than the national average of 18 percent of nurses using PDAs. This project is still in progress and all faculties have now been provided with the PDAs to facilitate interaction and support between teachers and students. MobileNurse, a protype point of care system using a PDA, was developed by Choi, et al.(2004), comprising four modules: patient information management, medical order check, nursing recording and nursing care plan, which was interfaced with the hospital information system. The clinical trial was performed on simulated patients and proved to be helpful and convenient than other non-mobile care systems. To be accepted into real practice the researchers concluded that the system needed an effortless interface and had the ability to replace all paper based nursing records. In medical education, students and residents use PDAs, like nursing students, as a mobile resource providing rapid, point of care information to direct patient care and to augment self directed learning. Medication reference tools, electronic

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textbooks and clinical computational programs were the most commonly used applications followed by clinical decision support software, practice guidelines, prediction rules, and physician order sets for common diagnoses. Electronic reference users perceived a time savings of about 1 min/encounter compared with traditional references (Kho et al.2007). A clinical management approach to evidence based medicine has been applied to PDA use and current studies have been undertaken to determine whether the device could assist this approach at the point of patient care. Medical students were given PDAs preloaded with clinical decision support software (CDSS) or a variety of commercial applications regularly used by physicians. The findings revealed that perceived usefulness of the PDAs with CDSS depended on supportive faculty attitudes, sound knowledge of evidencebased medicine, and enhanced computer literary skills. There was also greater satisfaction with the CDSS than with the commercial applications and this was associated with increased use in a clinical setting and improved success in search rates (Baumgart, 2005; Johnston et al.2004; Sutton, Stockton, McCord, Gilchrist & Fedyna, 2004). There are numerous clinical decision support systems available for use in clinical practice for all healthcare clinicians, however, there is limited research on how these systems are actually used (Weber, 2007). PDAs uploaded with clinical decision support systems have the potential to facilitate the practice and learning of evidence based medicine and this approach to the clinical education of medical students is an innovation that nursing academics should consider for adoption in nursing programs.

ADVANTAGES OF PDAS The advantages of PDA use for the clinician and the educator are numerous and many of these have already been discussed. PDAs have become

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a valuable learning tool and resource for both nursing and medical students and graduates over the past decade. Users of the handhelds appear comfortable and generally satisfied with them. Nursing programs are using PDAs to disseminate references and other course materials, as well as tracking students’ clinical exposure (see Table 2). The advantages have been summarised in Table 3 and they reflect only the benefits to the student user but also to the clinician and consumer of health care. The advantages of PDAs far outweigh the disadvantages (Table 4) yet there are certain aspects that institutions must be aware of when implementing handheld computers. These include security, patient confidentiality and different system interfaces. Other limitations of PDAs include an inability to handle large graphics programs and complete electronic medical records. Acceptance and consistency of use is another potential challenge when utilising PDAs in nursing education (Fischer et al.2003; White et al.2005) The cost of the devices and initial training are significant disadvantages that may act as barriers to implementation of the devices into new areas. In addition, the relatively small size of handheld computers means they can be easily lost or stolen (Lehman, 2003). Another significant disadvantage of PDAs is the short life expectancy due to rapid technological development (Eastes, 2004). Most devices will be obsolete within a two to three year time frame as new technology is released onto the market. PDAs should not be considered as a replacement for the desktop computer; rather they should complement existing hardware and software (Huffstutler, Wyatt, & Wright, 2002).

Barriers to Implementation The major barriers to PDA adoption in nursing and medical education include small screen size, the slow speed of images being obtained, other connectivity problems, lack of available software

Table 2. PDA Applications frequently used by nursing and medical students Calculators Evidence based clinical guidelines Diagnostic tests Dictionaries Document readers Electronic nursing and medical textbooks Five-minute clinical consults Nursing and medical literature Medication references Physicians’ desk reference Patient-Tracker Prescription writing University resources

Table 3. Advantages of PDAs Portable Integrated platform for formal education, feedback and evaluation between learner/teacher Integrated platform for point of care clinical reference, patient management and data communication Clinical reference programs obtained from Internet and evidence based practice guidelines Patient management programs allow healthcare professionals to access and store clinical information Rapid exchange of clinical laboratory results and “efficient patient handovers” through wireless technology Point-of-care data collection to enhance quality assessment and outcomes in healthcare organisations Patient education and interaction to prevent and manage diseases such as obesity, diabetes, asthma or urticaria. Data collection and processing in research

Table 4. Disadvantages of PDAs Size of screen Data loss due to battery mishap or failure Cost and short life of device Patient confidentiality, security and different system interfaces Inability to deal with large graphic programs and complete electronic medical records Acceptance and consistency of use Small size of device – lost and theft

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or programs for specific areas of healthcare, such as critical care, and patient data security concerns. Other barriers, not related to technical aspects that can cause problems for full adoption of a PDA project is length of time in designing databases and compliance with institutional confidentiality requirements. In regards to users, certain barriers still exist, such as lack of computer experience, a preference for pen and paper, difficulty handling the small device, and concerns about data loss and security. Training and ongoing technical support was identified as a barrier and needs to be increased by nursing education, particularly for clinicians or supervisors in the clinical area, who often lack familiarity with the devices (Farrell & Rose, 2008; Kho et al. 2006). Most nursing programs also lack the full range of technological resources to implement and provide ongoing support for handheld technology use by faculty and students (Koeniger-Donohue, 2008). In clinical practice the nursing culture does not encourage nurses to use information technology, including PDAs, to manage information. To ensure adoption nurses need to be aware of PDAs, see themselves as users, and understand what PDAs can do. With increased PDA use nurses will shape their own application development by demanding that the technology meets nursing needs. Also educating student and graduate nurses in the use of PDAs contributes to changing the culture in the clinical area.

FUTURE DIRECTIONS Lindberg and Humphreys (2005) believe the future of information exchange in healthcare education and delivery will be digital and wireless. PDAs will be lighter, have more memory and users will be able to obtain and share opinions on patients with colleagues and international experts with adhoc multimedia conferencing. Whatever evolves is uncertain. Baumgart (2005) warns that PDAs are not peripheral brains and are a poor substitute for

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ad-hoc clinical knowledge and that “no computer system can ever replace dedicated, experienced clinicians and their empathic interaction with patients and families.” The fact that healthcare professionals can carry an entire shelf of reference textbooks or a handheld computer’s memory card does not automatically mean that clinicians know their contents or can apply their knowledge appropriately in clinical practice. The increasing incidence of this reliance on PDAs is referred to as the residents and medical students’ palmomental reflex and clinical teachers, and nursing educators need to be aware of this (Crelinsten, 2004). Future research also needs to be conducted that examines the impact of PDA use on patient outcomes including the reduction of nursing and medical errors.

CONCLUSION This chapter has provided an overview of PDA use in nursing and medical education. These devices are becoming an important and evolving part of the students’ resources in nursing and medical education and patient care. Incorporating PDAs in nursing and medical education provides valuable access to point of care information that has the potential to positively impact learning and patient care. The challenge to most educators in nursing education is keeping up with the latest knowledge, including drugs, as most textbooks are outdated even before the student purchases it. Consequently, the question that needs to be asked by nursing academics is: “What resources are best for students to purchase that meets their learning needs yet provides the most current relevant information?” Medicine predicts that the handheld computer will become a necessary part of a physician’s equipment like the stethoscope and this trend is likely to extend to nursing and other healthcare professionals. Nursing and medical practice requires instant access to accurate information and because it is possible, through

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clinical information technologies, it is now becoming necessary. The nursing culture does not encourage the use of information technology so the future lies in the education of our student and graduate nurses, as they will influence the adoption of mobile technologies in the clinical area.

ACKNOWLEDGMENT I wish to acknowledge the Nurses Board of Victoria and RMIT University for providing funds that have allowed me to conduct research in the use of PDAs in nursing education. Special thanks go to the students who participated in the studies and also to Hewlett Packard and MIMS Australia for providing the handheld computers and the pharmacological databases for the devices.

Choi, J., Chun, J., Lee, K., Lee, S., Shin, D., & Hyun, S. (2004). MobileNurse: Hand-held information system for point of nursing care. Computer Methods and Programs in Biomedicine, 74(3), 245–254. doi:10.1016/j.cmpb.2003.07.002 Cimino, J. J., & Bakken, S. (2005). Personal digital educators. The New England Journal of Medicine, 352(9), 860–862. doi:10.1056/NEJMp048149 Colevins, H., Bond, D., & Clark, K. (2006). Nurse refresher students get a hand from handhelds. Computers in Libraries, 26(4), 6–9. Courtney, K., Pack, B., & Porter, G. (2004). But will they use it? Nursing utilization of handheld computers. Journal of Medical Informatics, CD, 1560.

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Farrell, M. J. (2007). Use of handheld computers in medical surgical nursing education. Final Report. Nurses Board of Victoria, Retrieved June 10, 2008, from http://www.nbv.org.au/media/67988/m%20 farrell%20final%20report%20-%20major%20 grant%202006.pdf. Farrell, M. J., & Mcgrath, I., D’arcy, M., & Abaloz, E. (2007). Improving patient health outcomes in acute care hospital units using mobile wireless technology and handheld computers. Computers, Informatics, Nursing, 25(5), 308. doi:10.1097/01. NCN.0000289174.77110.ba Farrell, M. J., & Rose, L. (2008). The use of handheld computers in clinical nursing education. The Journal of Nursing Education, 47(1), 13–19. doi:10.3928/01484834-20080101-03 Fischer, S., Stewart, T. E., Mehta, S., Wax, R., & Lapinsky, S. E. (2003). Handheld computing in medicine. Journal of the American Medical Informatics Association, 10(2), 139–149. doi:10.1197/ jamia.M1180 Garrett, B. M., & Jackson, C. (2006). A mobile clinical e-portfolio for nursing and medical students, using wireless personal digital assistants (PDAs). Nurse Education Today, 26(8), 647–654. doi:10.1016/j.nedt.2006.07.020 George, L., & Davidson, L. (2005). PDA use in nursing education:Prepared for today, poised for tomorrow. Online Journal of Nursing Informatics, 9(2). Retrieved June 10, 2008, from http:// eaa-knowledge.com/ojni/ni/9_2/george.htm. Goss, L., & Carrico, R. (2002). Get a grip on patient safety: outcomes in the palm of your hand. Journal of Infusion Nursing, 25(4), 274–279. doi:10.1097/00129804-200207000-00011 Greenberg, R. (2004). Use of the personal digital assistant (PDA) in medical education. Medical Education, 38, 570–571. doi:10.1111/j.13652929.2004.01882.x

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Greenfield, S. (2007). Medication error reduction and the use of PDA technology. The Journal of Nursing Education, 46(3), 127–131. Honeybourne, C., Sutton, S., & Ward, L. (2006). Knowledge in the Palm of your hands: PDAs in the clinical setting. Health Information and Libraries Journal, 23(1), 51–59. doi:10.1111/j.14711842.2006.00621.x Huffstutler, S., Wyatt, T. H., & Wright, C. P. (2002). The use of handheld technology in nursing education. Nurse Educator, 27(6), 271–275. doi:10.1097/00006223-200211000-00008 Jeffries, P. R. (2005). Technology trends in nursing education: Next steps. The Journal of Nursing Education, 44(1), 3–4. Johnston, J. M., Leung, G. M., Tin, K. Y., Ho, L. M., Lam, W., & Fielding, R. (2004). Evaluation of a handheld clinical decision support tools to improve learning of evidence based medicine in medical studies. British Medical Journal, 327, 1090. Kho, A., Henderson, L. E., Dressler, D. D., & Kripalani, S. (2006). Use of handheld computers in medical education. Journal of General Internal Medicine, 21, 531–537. doi:10.1111/j.15251497.2006.00444.x Koeniger-Donohue, R. (2008). Handheld computers in nursing education: PDA pilot project. The Journal of Nursing Education, 47(2), 74–77. doi:10.3928/01484834-20080201-01 Kuziemsky, C. E., Laul, F., & Leung, R. C. (2005). A review on diffusion of personal digital assistants in healthcare. Journal of Medical Systems, 29(4), 335–342. doi:10.1007/s10916-005-5893-y Lee, T. T. (2006). Nurses’ perceptions of adopting a PDA system--application of Lewin’s change theory. Studies in Health Technology and Informatics, 122, 958.

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Lee, T. T. (2007). Patients’ perceptions of nurses’ bedside use of PDAs. Computers, Informatics, Nursing, 25(2), 106–111. doi:10.1097/01. NCN.0000263980.31178.bd Lehman, K. (2003). Clinical nursing instructors’ use of handheld computers for student record keeping and evaluation. The Journal of Nursing Education, 42(1), 41–42. Lewis, A. (2001). Handheld technology: The newest practice tool for nurses? Online Journal of Nursing Informatics. Retrieved February 19, 2007 from http://www.hhdev.psu.edu/nurs/ojni/ handheld_technology.htm Lindberg, D. A. B., & Humphreys, B. L. (2005). 2015 - the future of medical libraries. The New England Journal of Medicine, 352(11), 1067–1070. doi:10.1056/NEJMp048190 McCord, L. (2003). Using a personal digital assistant to streamline the OR workload. Association of Perioperative Registerered Nurses Journal, 78(6), 996–1001. McNeil, B. J., Elfrink, V. L., Bickword, C. J., Peirce, S. T., Beyea, S. C., & Averill, C.(20030. Nursing information technology knowledge, skills and, and preparation of student nurses, nursing faculty and clinicians. A U.S. survey. The Journal of Nursing Education, 42, 341–349. Miller, J., Shaw-Kokot, J. R., Arnold, M. S., Boggin, T., Crowell, K. E., & Allegri, F. (2005). A study of personal digital assistants to enhance undergraduate clinical nursing education. The Journal of Nursing Education, 44(1). Nursing, P. D. A. Listserv, & Survey. (2004). Retrieved 15 November, 2007, from http://www. perseus.com/cgi-bin/survsol40.pl. Rice, M. J. (2003). Bridging the digital divide in medically underserved areas with PDAs [Electronic Version]. Retrieved June 10, 2008, from http://www.pdacortex.com/rural_pdas.htm.

Ruland, C. M. (2002). Handheld technology to improve patient care: Evaluating a support system for preference-based care planning at the bedside. Journal of the American Medical Informatics Association, 9(2), 192–201. doi:10.1197/jamia. M0891 Scollin, P., Healey-Walsh, J., Kafel, K., Mehta, A., & Callahan, J. (2007). Evaluating students’ attitudes to using PDAs in nursing clinicals at two schools. Computers, Informatics, Nursing, 25(4), 228–235. doi:10.1097/01. NCN.0000280593.58561.4e Shortis, M., McGovern, J., Berry, M., & Farrell, M. J. (2006). Two case studies on the influence of mobile computing on student learning behaviour. Proceedings of the 17th Annual Conference of the Australasian Association for Engineering Education: Creativity, Challenge, and Change- Partnerships in Engineering Education, Auckland, New Zealand. Stolworthy, Y. (2003). RNs are mobilizing. Retrieved, June 17, 2004 from www.pdacortex.com/ RNs_are_Mobilizing.htm. Sutton, J., Stockton, L., McCord, G., Gilchrist, V. J., & Fedyna, D. (2004). Hanheld computer use in a family medicine clerkship. Academic Medicine, 79, 1114–1119. doi:10.1097/00001888200411000-00024 Tassani, S., Baruffaldi, F., Testi, D., Cacciari, C., Accarisi, S., & Viceconti, M. (2007). Personal digital assistant in an orthopaedic wireless ward: The HandHealth project. Computer Methods and Programs in Biomedicine, 86(1), 21–29. doi:10.1016/j.cmpb.2006.12.009 Weber, S. (2007). Clinical decision support systems and how critical care clinicians use them. Journal of Healthcare Information Management, 21(2), 41–52.

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White, A., Allen, P., Goodwin, L., Breckinridge, D., Dowell, J., & Garvy, R. (2005). Infusing PDA technology into nursing education. Nurse Educator, 30(4), 150–154. doi:10.1097/00006223200507000-00006 Wilson, S., Dignam, D., & Hay, E. (2002). Addressing the digital divide between nursing practice and education using a personal digital assistant (PDA). Winds of Changing in the Sea of Learning, Proceedings of the 19th Annual Conference of the Australian Society for Computers in Tertiary Education (ASCILITE), Auckland, New Zealand.

KEY TERMS AND DEFINITIONS Adverse Event: An incident in which unintended harm results to a person receiving health care. The outcome to the person may be a temporary or permanent disability or death. The injury or harm is caused by healthcare management rather than the disease process and includes acts of omission (inaction failure to diagnose or treat) and commission (affirmative actions) from treatment and management. Medication errors, infections, and pressure ulcers are the major areas of harm. Bluetooth: A telecommunications industry specification describing how mobile computers can be easily interconnected using a short run wireless connection. Clinical Decision Support Systems: Computer decision support systems are computer software or applications designed to aid health care professionals in making diagnostic and therapeutic decisions in patient care. These systems can simplify access to data needed to make clinical decisions, provide reminders and prompts at the time of a patient encounter, and assist in establishing a diagnosis and in entering appropriate requests. Another advantage is that they can also alert clinicians when new patterns or trends in patient data are recognized. Clinical decision support systems have been shown to be

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highly effective, sustainable tools for changing clinician behavior. Functionalities: Practical applications of a PDA - includes basic functions such as an electronic diary, address book, to-do-list, notepad, calculator, expense tracker and alarm clock. Other software and applications used by health care professionals include clinical decision support systems, computerized patient record, patient surveyor tools, clinical data repository, drug reference databases, medical laboratory references, electronic textbooks and clinical practice guidelines. Mobile Technologies: Mobile technology devices amalgamate hardware, operating systems, networking and software. Personal digital assistants, mobile phones, laptops, notebooks and notepads are all examples of hardware. Applications are the software programs, networks are the infrastructure that supports the transfer of information and the operating system manages the tasks and resources on the mobile device. Personal Digital Assistant (PDAs): A portable computer that is small enough to be held in one’s hand. PDAs are small, lightweight, durable, secure, safe, and have low power requirements that do not interfere with medical equipment. There are two types of PDAs, handheld computers and palm sized computers. Handheld computers tend to be larger and heavier and use a miniature keyboard, usually in combination with touch-screen technology, for data entry. Palm sized computers are smaller and lighter and use stylus/touch-screen technology and handwriting recognition programs for data entry. Point of Care Technology: Allows clinicians to enter and access data at the point of care. Has the potential to reduce nursing and medical errors and data entry effort, as well as eliminating paperwork. Data is also immediately accessible by administrators, nurses, or other professionals. Preload, Upload, Download: These terms are used in computing to refer to data transfer. Preload is when the handheld device or personal computer is loaded with software programs or

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applications. Upload is the transfer of data from a handheld computer or other mobile devices to a personal computer. Download is the transfer of data from a server to a personal computer. Process Based Education: Focuses on what happens when student learning takes place: the how or why change occurs. Learning “how” or improving ability is not like learning “that” or acquiring information, which is known as content or product based education Synchronisation: PDAs have the ability to synchronise to personal computers. This is achieved through synchronisation software provided with the handheld. Data synchronization allows users to access the same information on the PDA as the personal computer. Synchronising also prevents the loss of information stored on the device in case it is stolen, lost or destroyed.

Wireless Technology: Wireless refers to telecommunication technology, in which radio waves, instead of cables or wires, are used to carry a signal to connect communication devices. Wireless technology enables users to physically move while using a device, such as a laptop, PDA, paging device, or phone. The Internet, Intranets or Extranets can all be accessed by the PDAs. Healthcare professionals can access and update electronic medical records (EMR) at patients’ bedsides, match bar-coded patient wristbands and medication packages to physician orders, and use wireless badges for voice communication. They can also use the Internet and the Intranet to access email, research, patient education, daily schedules, memo writing, voice, dictation, photography, drug calculations, and diagnostic tests and results.

This work was previously published in Nursing and Clinical Informatics: Socio-Technical Approaches, edited by Bettina Staudinger, Victoria Höß and Herwig Ostermann, pp. 239-252, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 5.8

Women’s Health Informatics in the Primary Care Setting Gareth Parry Horsmans Place Partnership, UK

ABSTRACT Women’s health in primary care is a large part of the generalist’s practice. Information technology (IT) is now an integral part of the generalist’s office, often more so than in secondary care and therefore this chapter is a key starting point in the book. Initially there is an introduction of the role of IT in primary health and the many areas it may encompass. We then move onto organizing clinical information and the ways that this maybe represented electronically in the “cradle to grave” electronic health record. In addition to recording information, can IT help the primary care doctor? The area of IT in screening, prevention and

alerts is discussed. The role of the computer in the clinician’s office and the impact it has on the consultation is explored. Can computer help clinicians perform better? Areas of discussion include the role of computers in audit and systems using artificial intelligence to improve patient care. IT is increasingly important in scheduling both within the practice and at the local hospital. This can be done by the primary care doctor and in some instances by the patient his or herself. The ideal situation is the primary care doctor having a system which can “talk” to external systems (e.g. local hospital notes, with a secure portal). In some countries such as the United Kingdom, this is becoming a reality, though there are problems which are discussed.

DOI: 10.4018/978-1-60960-561-2.ch508

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Women’s Health Informatics in the Primary Care Setting

INTRODUCTION INFORMATION TECHNOLOGY AND THE GENERALIST The generalist medical function occupies an important role in most models of health service provision. The terminology may vary (Primary Care, Family Medicine General Medical Practice) however there are a number of consistent themes which characterise this role and impact on the information requirements. 1. Generalists have long term, relationships with patients covering the whole spectrum of medical, psychological and social functioning. 2. Successive generalists looking after a patient should inherit the entire existing record and its summary, add to it appropriately and pass it on in a coherent state to the succeeding clinician. 3. Generalists manage many of the day to day issues of patient care in house and refer and accept back patients from episodes of specialist or increasingly multidisciplinary care and need to record these events and their outcomes, both for the coherence of the record and to retain information pertinent to further requests for specialist advice or intervention Hence clinicians, patients and administrators demand much of our medical record systems. Different elements of the record (datasets) are required for different clinical situations, such as the recording of allergies, the past obstetric history, the most recent blood pressure measurement. Each element of the history assumes a different importance at different times. •

Some conditions may be recurrent; recording post natal depression in the past may alter and direct the care offered in a further

•

•

pregnancy and change the index of suspicion for the obstetrician. Some conditions change the subsequent risk profile. Gestational diabetes though resolved postnatally remains a risk factor for Type 2 diabetes in later life. Some elements of the record become highly relevant only in very specific situation – a difficult endotracheal intubation has little relevance to a generalists care but is critical information to an anaesthetist. This information may not transfer in a hospital to hospital situation an argument for patient held records in the form of an accessible alert.

Medical decisions are often dependent on observing trends in a specific test or measurement or recognising a constellation of features of the medical history which point to a recognised pathological process. Recording and presenting data to highlight these trends aids correct management of the clinical situation. The maintenance of accurate, comprehensive and up to date records supports this process Communication with other health care professionals often requires specific datasets of information to be mutually agreed and easily transferred and integrated into the appropriate record. Standardisation of the method of recording, transferring and distribution data is central to the effectiveness of multidisciplinary medical care. Organised proactive care of patient subgroups in the form of screening, vaccination and analysis of combined risk factors requires a recall or reminder system which has to be updated in real time. Missed recalls need to be recorded and acted on while the patient choice to cease or postpone recall need to be respected. Opportunities to remind patients opportunistically allows further explorations of the patient’s choice and the presentation of personalised explanation and encouragement – backed up by specific literature

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explaining why engagement with the service is in the patient’s interest. Increasingly the quality and the outcomes of medical activity need to be fed back to the central authorities for payment, audit, public health (or sometimes disciplinary or medico-legal) reasons. The aggregated records of patients and those specific subgroups of interest have to be organised with this requirement in mind. A further and very topical theme related to the recording of medical information relates to the contrasting pressure to share and combine information about patients for a number of very worthy administrative or policy making aims including the analysis of gaps in service provision and tailoring better quality services. This can be in conflict with an overriding need for confidentiality of the patient/clinician relationship and the records content. There are also tensions between a patient viewing or even holding their records and the need for records to be secure and contemporary. Patients are encouraged to view their records as an aid to involving them in informed choices about their care. Patients can edit or contest them if they are inaccurate and may want part or all of what they consider to be contentious or sensitive deleted or made available in a specific restricted distribution. Experience shows that what a patient may consider sensitive is a highly individual issue and not easily predicable even by the clinician that knows them well. For these reasons the long term health record (sometimes called the ‘cradle to grave’ record) is best placed in with the generalist. Increasingly in part or in full this is and electronic record. Generalists tend to be at the hub of the patient care and hence inevitably the electronic record. Clinicians develop skills and experience in tacit areas of knowledge i.e. the ability to consult with patients in a caring and effective manner. As far as the generalist is concerned it is impossible to keep fully abreast of changes in medical knowledge, advances in pharmacology and specialist

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practice. Computer systems can aid the reference and acquisition of what might be termed ‘just in time knowledge’ for the physician and increasingly by way of algorithms and other techniques advise on therapy for example the specific doses of anticoagulants. Full clinical pathways can be modelled on computer systems; prompts can be set up to alert for opportunities for productive intervention and the agreed form and datasets for multidisciplinary interaction Computerisation of the generalist medical record holds many advantages for the generalist but also involves some threat in terms of the new IT skills required and often a perceived loss of autonomy of behalf of the (perhaps senior) clinician. New computer systems and advances in functionality need to be handled sensitively, by consensus and allied to training. Computers should do what they’re good at and people should do what they’re good at. Modern medical computer systems store and organise large volumes of patient data in an easily accessible and consistent format however this comes at a price of embracing and managing the change from paper records which has significant potential pitfalls.

ORGANISING CLINICAL INFORMATION ON COMPUTER Data, information and knowledge (and arguably wisdom) are on a continuum of increasing complexity and uncertainty. Binary data stored on computer discs as areas stored and organised by the computers operating system and is the province of the computer engineer scientist and computer programmers. Major challenges relate to how the storage of increasing amounts of data can produce clinically useful information and enhance medical knowledge while avoiding information overload. The discipline of health informatics sits between computer science and clinical medicine and attempts to understand the needs and language of

Women’s Health Informatics in the Primary Care Setting

both activities, in particular creating guiding principles by which effective clinical record systems are developed. The nature of medical information is complex, uncertain and ever-changing the task of modelling the convoluted processes of medicine falls to the system developer. Well-designed information systems will easily integrate new knowledge, will support the decision maker and also the choices and information available to the end user of the service the patient. It will in other words enhance the familiar medical process. Interest in structuring the medical record going back over many decades and change related to the introduction of the problem orientated medical record in the late 1950s by Larry Weed. Thinking about records changed from simple maintenance of a list of past events to an orientation around problems. A problem could be anything of relevance to the patient and their care and evolves with time. Generalists who are dealing with patients over time have a specific need to be able to edit the record to reflect the way that the problems change with time. Menorrhagia and pelvic pain was initially recorded as a problem in general practice, after investigation and referral, ultrasound scanning and laparoscopy the problem evolves to a diagnosis of endometriosis in secondary care then treated by a combination of hormonal and surgical intervention. Once treated and asymptomatic endometriosis is regarded a an inactive problem but may become reclassified as active in the future when subsequent complications of pelvic scarring become apparent. The problem begins as a symptom then is overwritten as a diagnosis then a complication but the database maintains the underlying links with the data items that supports the progressive understanding of the evolution of this problem. A modern database system will also have the functionality to recreate the state of the database in the past for audit purposes. This change modelled the real world medical process more closely and offered the possibility

of enhancing ongoing clinical care by using the problem list as a predefined query for medical knowledge databases in real time as an aid to the clinician (Problem /Knowledge Couplers) resulting in Time magazine leading with the headline ‘The computer will see you now’. However implementing problem oriented medical records (POMR) in paper form suffered from significant limitations which hampered its universal adoption. In a Problem Oriented approach each observation may be relevant to more than one problem. This means that in POMR, an observation could be entered more than once. For all but the most avid advocates this was difficult to maintain. The underlying reason for this difficulty is that relationships between data can be more convoluted than is apparent at first glance and change over time. One-to-one relationships between data exist for instance the patients date of birth. This is not going to change with time as long as it is correct, you don’t need the ability to edit it once entered. Some data relationships are more complex, for example patients requiring a cervical smear test on a regular basis both for screening and for follow-up following colposcopy. It is not predictable at the outset how many cervical smears will be needed during the course of a lifetime and each point we need to record data about the smear including the date of the test the cytological result and the name of the smear taker and the analyser. This is an example of a one to many relationship in information terms (One patient can have many smears) and in relational database system terms is represented by two tables, one which relates to the patient and one which relates to the smears linked by a column containing an identifier common to both tables. Other complex and subtle data relationships exist. Consider for instance the patient has two problems. The patient is taking the oral contraceptive pill (Contraceptive Care-Problem1) and is also hypertensive (Hypertension-Problem2). Blood pressure measurements are relevant both

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problems. How does the clinician record one measurement with both problems without recording the same data under two headings? This is an example of a many to many relationship. The blood pressure recording is relevant to more than one problem and the blood pressure needs to be recorded under more than one problem heading. This can be modelled by the system developer by creating a linking intermediate table of data which only contains records blood pressure values and the links to both problems. This can be achieved seamlessly in a modern relational computer database system from the enduser’s point of view – the reading once entered appearing automatically under both problems. Medicine it is a complex subject and many different concepts need to be represented in clinical records. Clinical concepts such as in Snomed CT are in general hierarchical and need to be linked in subtle ways. To illustrate a hysterectomy (Concept: Hysterectomy) is an example of an operation (Concept: Operation). Since all operations can have complications (Concept: Post operative complication) such as infection (Concept: Post operative Infection) then following a logical path a hysterectomy can be coded as complicated by a post operative infection. A clinical nomenclature or coding system models these relationships and is not just a list of concepts. Coding systems also has the advantage to the programmer that the concepts can be allocated a shorthand code which saves storage space and allows for combined concepts to be recorded in coded form as above. For more on coding see chapter 3. A further issue is that different users have different requirements of the coding system if they each create their own system it will lead to multiple poorly compatible systems in healthcare. Hence a method of translating between coding systems has to be developed and maintained. The large overhead in maintaining and translating between coding systems has increased

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interest in producing a single extensive health nomenclature where different users will view and use relevant subsets of the same system. One of the most obvious benefits is to provide medical language translations so that the user enters data in their native language but other users can view the same data in their own tongue by use of their specific language subset and a table of synonyms connecting concepts.

MOVING TO ELECTRONIC INFORMATION SYSTEMS Paper records whether handwritten or typed have major limitations of storage (they are bulky and degrade over time), access (only one copy exists able to be read in one place at one time) and safety (loss through physical damage or simply being misfiled). Radiological and other images such as histology, electrophysiological recordings such as CTGs, sound recordings and video streams can be incorporated by modern database systems filed by a single patient identifier and accessible to appropriate staff (sometimes remotely by access to the Internet). However adoption of computer systems involves significant financial investment and management drive not least in running the legacy paper systems in parallel for a time and converting the historic paper based data to electronic form. Most large scale business now runs electronically and benefits from the resulting efficiencies and the clinical barriers and the assurances required both to clinicians and patients can be addressed. The adoption of electronic records can be thought of as a stepwise process and the key to acceptance include an appropriate user interface for each type of user depending on their role and computer literacy, appropriate training in protected time and environment and an early gain for the users. To illustrate this early primary care computer systems often concentrated on prescribing, which

Women’s Health Informatics in the Primary Care Setting

was often chaotic and poorly recorded on paper. By converting to electronic systems early adopters banished the drudgery of writing prescriptions by hand, the system became safer (introducing allergy checks and better legibility) and was now easily auditable. News of these early gains spread and encouraged take-up and supported development of more sophisticated systems. The developing computer systems then look to solve more clinically contentious issues such as recording sensitive patient data and working with computers during consultations. There are real dangers in aggregating patient data in a searchable patient database format and issues such as confidentiality have become a significant barrier in the UK to take up of the national Summary patient record as well as the formidable technical challenge of a national database of some 60 million records. Modern database systems have sophisticated user definable interfaces, can check data entry between specified parameters can issue warnings and advice triggered automatically and because of their structure allow complex queries involving multiple parameters to be developed. Practice defined options such as drug formularies can be enabled and every aspect of practice clinical activity can be monitored. Do we really have a choice about using Electronic Medical Information Systems in the information age, is it reasonable to expect that patients were able to look at their bank balance on the Internet would not have access to their medical records? In my opinion there is not really a choice about changing or about moving to electronic information systems in the information age however the principles of good practice in developing clinical systems have to be followed. The introduction of information systems fail •

When they do not involve the users fully in development

• • •

They don’t work at an acceptable level of speed and reliability. They are not seen to be an improvement on the paper based system. Their specification and functionality is not determined correctly at the outset

If we are asking users to adapt the process by which they work than that must be to move to a more efficient system. The system doesn’t have to be perfect but it has to be acknowledged to be better than what went before by a measure relevant to the user.

HEALTH INFORMATICS IN ACTION Health informatics is essentially a practical discipline which allows a medical process to progress as effectively as possible, to illustrate this some case studies are presented. The reader is invited to consider what contribution informatics makes at each stage and how practical and effective it would be without computers.

Prevention Case Study Involving Osteoporosis The aim is to detect the subtle, progressive age and gender related process of osteopenia/osteoporosis in order to prevent a negative outcome for the patient in the form of pathological fractures with their associated morbidity and mortality. Initially requires a systematic process of inquiry requiring team input to establish a protocol which will involve defining characteristics of patients who should be screened (By DEXA scan) by reference to literature searching, previous policy, national policy or directives. The implementation group then defines the risk factors to search for on computer and identify all the codes that would be relevant. Can we search free text? It is possible with some systems- it’s likely however to be much slower and less precise.

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Identify other risk factors such as prolonged steroid use by querying the patient database. Identify within the potential screening group already established on treatment or unsuitable for screening in some other respect. Be aware of the need to manage the process at all levels so that secondary care not overwhelmed and can establish what level of take-up is expected, consider doing a pilot study. Make sure that there is sufficient capacity and finance for performing the Dexa scans, for assessing the results, and the pharmaceutical budget to treat patients once identified. Establish a method of contacting these patients about the new facility for screening for osteoporosis and look into providing them with information required to make an informed choice whether to go ahead. Contact patients by their preferred method. Look to raise the awareness in staff to bring the issue up opportunistically. Address confidentiality issues. A number of audits of outcome measures will need to be in place looking at issues such as the uptake of the service, the number of positive results and the progress of those established on treatment. Formal cost/benefit and cost effectiveness studies could be performed.

Case Study: Early Detection of Disease - Cervical Cytology Although many of the organisational issues are in common with osteoporosis cervical screening has been in place for many years and data is now available to suggests it reduces death and morbidity rates for cervical cancer Cervical cytology and associated call/ recall systems did predate use of computers in general practice in the UK. This was a card based system whereby the patients recall status was determined by the position of their card - as they came to the front they were invited for screening, once screened satisfactorily the card was replaced at

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the back of the box. Woe betides anyone who inadvertently tipped the cards out of the box.

Some Other Issues that Relate to Cervical Screening Part of the population to be screened is young and mobile so it is critical that contact data is correct, and that the contact is in a form the patient prefers -software is now available to send smear and vaccination reminders by SMS message.(texting) Further targeting the invitation for screening contact may be appropriate. Does for instance one age group require a tailored invitation letter to encourage compliance? A particularly difficult group is those that have declined smears in the past. It is ironic that this group has a higher rate of cervical cancer Audits are in place to judge the quality of the sample takers as well as the cytology screeners and cytopathologists, new systems of image pattern recognition by may mean that the initial image analysis is done by computer in the future. See Chapter eleven for more information. Screening can occur in several settings - GP surgery, hospital clinics, colposcopy clinics for follow up, family planning clinics and privately. All this has to be coordinated by exchange of information. In the future identifying patients who have had or not had a prior course of HPV vaccination - these patients represent a different risk group from those that haven’t had the vaccination. Hence should they be screened less often if at all? This illustrates the need for vaccine status to follow the patient in the generalist record over an extended period of time.

CAN COMPUTERS ENHANCE THE CONSULTATION? Currently in the UK majority of general practitioners record consultations on a computer and

Women’s Health Informatics in the Primary Care Setting

the written records are not retrieved for each consultation (but are still usually available on site). This is in contrast to the position 10 years ago where so called paperless practices were in a small minority. This change has had a number of drivers not least since 2004 the contractual climate rewards information gathering in a systematic and predefined way in the Quality and Outcomes Framework (QOF) which determines a significant proportion of income. The QOF clinical points score is solely determined by computer audit. Personal experience suggests the historical consultation data and its relevance to the current concentration relates mainly to its age, the most recent entries seems to the most relevant and so when a few years worth of consultation data and the important older historical data is held in the problem list the doctor rapidly sheds the need to refer back to paper to function effectively. More generalist consultations are now structured evidence and observation gathering activities reviewing chronic or recurrent disease which sit well with systematic computer data entry but less so with the patient centred intuitive ‘freestyle’ that many GPs feel produce the best doctor patient interactions. Structured consultations are easier to model from an informatics perspective but all consultations have some information content and opportunistic intervention. There remains hostility from clinicians who dislike ‘button pushing’, or seeming inexpert in front of the patient (by fumbling with a keyboard or mouse). Using a computer positively to enhance the consultation requires new skills on behalf of the clinician and informatics is involved directly in system design, functionality and user friendliness enhancing rather than hindering the medical process. Moving further into the future will computers enable the doctors and patients to take better decisions? We have already noted support required in the repeat prescribing area is fairly clear so that a contemplated current prescription can be checked against allergies and previously recorded

side-effects. Should not the computer suggest which alternative drugs or modalities of treatments are appropriate, and rank them according to established efficacy? Should decision support software and pharmaceutical database suggest in certain cases that the patient may be experiencing a side effect from an existing prescription as the reason for consultation and suggest modifications? Increasingly scheduled care for chronic disease is being organised around predetermined pathways, clinical staff other than doctors are making therapeutic changes and initiating investigations according to agreed protocols. This situation seems primed for the introduction of increasingly sophisticated decision support systems which interact in real time according to the current and historical data entered on the computer. In contrast there are political moves in the UK to remove the GPs hold on first contact care in the community setting and to open walk in centres open for extended hours of the day manned by a number of different team members where at present there can be no real time exchange of data until the Summary care record arrives in 2014 or there are some rapid advances in patient held data or data held on the internet such as healthspace. Only as a recent development and with a number of outstanding unsolved issues have GPs been able to transfer the majority of the electronic record to another GP directly. Clinical Knowledge Summaries are a computerised tool, a development from Prodigy (http://www.prodigy.nhs.uk/home), which allows clinicians to use the computer to assimilate the evidence behind a clinical guideline rapidly and accurately and suggest the rationale for intervention in defined clinical scenarios. Some referrals from primary care are principally around seeking advice. Can we upgrade the GP to be confident in managing conditions to a higher standard by using computer tools which may obviate the need for referral or at least target them in an improved fashion, saving both the patients time and the clinic’s resources?

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Modern aspects of artificial intelligence seek to model the medical process, medicine is an information rich activity and there are difficulties in disseminating and applying the sheer volume of new knowledge that is published. Knowledge management and Knowledge engineering are developing area of activity in many organisations; the reader is referred to the National Library for Health as an example of how different information sources can be brought together to smooth this process

CAN COMPUTERS HELP CLINICIANS PERFORM BETTER? Modern health services need to demonstrate improving quality of service whether these are organisational or clinical matters. The concepts of clinical governance have now taken root in all aspects of medicine. Reviewing procedures and auditing clinical outcomes and the assessment of patient satisfaction has become part of everyday practice. Using paper methods it’s been difficult and time-consuming to audit even a simple aspect of a hospital service or general practice. Computerisation offers the opportunity of a continuous background process of audit taking away the emphasis on laborious data collection and analysis and focusing on changing clinical outcomes. By structuring the input of clinical data in a way to facilitate subsequent audit we immediately realise a number of gains. The process of data entry can be standardised by means of input screens (called forms in database systems). During the process of data entry the system can remind us of missing data, can alert to abnormal or possibly erroneous or rogue data, and can be programmed not to consider the data entry complete until obligatory items have been entered. The clinical system can also remind users about conditions that are due or overdue for review; these commonly take the form of messages or notes on the screen to perform certain actions or to check certain results.

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The business process of a medical practice and medical communication can be facilitated by an electronic document management system. Modern systems scan and electronically file paper documents into the medical database, can identify and code pieces of information with user supervision and can conduct the distribution of the important issues identified for follow up. These can be an efficient and auditable way of communicating between different medical and paramedical personnel based at different sites and/or in different specialities. By creating the correct output of the data (in database terms a report) we can customise a presentation of data in a usable and understandable format whether it by text, graphs or columns of figures. These can be compared with targets and by directing the output to a spreadsheet the remaining numeric targets to be achieved can easily be appreciated. It is possible to drill down further into the data to compare the data to national or international norms not just local targets. By accumulating data automatically in particular in relation to activity we can pose quite tricky questions. Do we have enough appointments? Do we have enough access to pathology tests or imaging and how does this impact on the total waiting time and patient experience? Are there things we like doing or have traditionally done which are ineffective? Beyond this in British general practice the audit is not only being used as a clinical tool but also as a contractual and financial tool rewarding practices which achieve the requisites quality of service and patient satisfaction over a range of activity. Payments relate to an external audit by way of questionnaire evaluating the patient experience and we can see the whole face of medicine changing from the traditional somewhat paternalistic approach where the doctor knows best to one where the system has to become flexible and responsive to the needs, and arguably wants, of its users to flourish.

Women’s Health Informatics in the Primary Care Setting

This driver for change is potent comprising both the pressure of the personal relationship that the practice has with its patients to correct issues where it falls below par combined with the pressure from the quality, contractual and financial pressures that concern the Health Authority. There are some predictable difficulties resulting from this approach in terms of game playing by contractors and health authorities and creative presentation of data commonly known as spin. Taking this one step further the results of these audits and assessments are being made public with the intention to influence the choice of the patients in terms of their choice of general practice or hospital. League tables are considered a blunt instrument in terms of assessing quality however in a health service driven by government objectives and in a situation where information about public bodies is readily available through statute such tables are an inevitable development.

A NATIONAL ELECTRONIC PATIENT RECORD SYSTEM Not only local healthcare systems are undergoing a revolution but systems at a national level also have to adapt and mature. Modern standards of data storage and transfer mean that multiple function specific legacy systems are redundant and in the UK a decision was made to start again from scratch. The model chosen in the UK was a central database with multiple functionality or services - those relevant to doctors include:• • • •

Patient demographic service: To maintain non clinical patient data. Secondary user service: Collect anoymized management information Care records service: A limited subset of exchangeable clinical data. Choose and book: Electronic referrals according to patient choice

• •

GP2GP: Transferring electronic records between GPs ETP: Electronic prescriptions direct to the pharmacy

Coalescing patient information to one database has obvious advantages to information accuracy and cascading changes for example patients address changes only have to be edited once at the point the patient interacts with any part of the health service, however recent experience in data security point up that the systems are highly vulnerable to unthinking or sloppy practices which may reduce public confidence in the system to the point it becomes less effective in meeting its goals. However as with any bespoke IT solution these systems are expensive, prone to delay, difficult to project manage and roll out and vulnerable to continuing changes in specification and in the case of national projects to the political climate of the time. Adopting a new IT infrastructure and national database gives potential clinical and organisational advantages to the generalist and these include:•

• •

•

•

The chance to retrieve at least a minimum dataset of information in the unscheduled care situation. Potential to aggregate information to inform the reorganisation of the service. Opportunities to support different ways of working including working at a distance e.g. telemedicine, to have advice on hand from specialists remotely or for clinicians seeking support from experts. To standardise and audit referral systems into pathways while offering patient choice backed up with up to date patient relevant information concerning their choice. Information for patients and clinicians can be held centrally and be modified or adapted efficiently from a central distribution point.

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•

Becoming a more coherent service from a patient perspective.

ELECTRONIC PATIENT SERVICES It is relevant to consider how patients may benefit from advances in health related IT systems since it is considered that the reintroduction of patient choice allied to better information about the performance of healthcare providers will create pressure to innovate and improve health services. National patient oriented UK services online include •

•

• •

•

•

•

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NHS Direct, a telephone based advice and triage services accessible to patients. Also available online. NHS Choices website that brings together patient information on NHS services and their performance information as well as magazine style articles on healthy living and information on specific diseases and self help. National Library for health has some searchable patient oriented information. Map of Medicine UK mainly contains information for professionals about care pathways but links to patient information on NHS choices for specific diseases. Choose and Book allows a patient armed with a unique booking reference number (UBRN) and password supplied by their GP to book their own outpatient appointment by negotiating directly with secondary care. Healthspace (‘a secure online health organiser’) it is envisaged will allow patients to view their summary care record as well as entering their own health notes online and will act as a patient portal for choose and book. Department of health website tells patients amongst other things how successful the

•

government health reforms have been to date and will be in the future. Electronic Transfer of Prescriptions (ETP) will mean that electronically authorised prescriptions and in particular repeat prescriptions can be collected from a nominated pharmacy without requiring a paper based signed prescription form to be collected from the doctor’s surgery.

GP clinical systems also give access to patient support information designed to be printed during the consultation, many GP practices have their own website to supply information to patients and many practices produce newsletters and annual reports for patients some distributed by e-mail. With the emphasis now moving towards patient information on the web the plight of the information poor group i.e. without internet access is becoming increasingly problematic.

FUTURE RESEARCH DIRECTIONS Clearly the area of health informatics in primary care is a fast moving field and is one of the leading areas for change. Much implementation is taking place across the world with an assumption that it will improve outcome. However, there is a not much research examining the real outcomes of importance: disease progression and illness, in parallel with these changes. Research in this area however is difficult as systems are often introduced across large groups in an ad hoc way. Data will emerge with time but will probably be retrospective based rather than randomised controlled trials. The role of IT in supporting decision making is interesting and many workers are active in this area. Chapter X discussed the use in cardiotocograph interpretation and primary care doctors are already used to pharmaceutical drug alerts. It is likely that more complex systems will emerge in the future.

Women’s Health Informatics in the Primary Care Setting

REFERENCES Bolton, P., Douglas, K., Booth, B., & Miller, G. (1999). A relationship between computerisation and quality in general practice. Australian Family Physician, 28(9), 962–965. Bomba, D. (1998). A comparative study of computerised medical records usage among general practitioners in Australia and Sweden. Paper presented at the Medinfo 9. Department of Health UK. (1998). Information for Health. Retrieved. from. Ho, L., McGhee, S., Hedley, A., & Leong, J. (1999). The application of a computerized problem-oriented medical record system and its impact on patient care. International Journal of Medical Informatics, 55(1), 47–59. doi:10.1016/ S1386-5056(99)00019-2 Llewelyn, H., & Hopkins, A. (1993). Analysing how we reach clinical decisions. London: Royal College of Physicians Noone, J., Warren, J., & Brittain, M. (1998). Information overload: opportunities and challenges for the GP’s desktop. Paper presented at the Medinfo 9.

Roger, F., De Plaen, J., Chatelain, A., Cooche, E., Joos, M., & Haxhe, J. (1978). Problem-oriented medical records according to the Weed model. Medical Informatics, 3(2), 113–129. Tilyard, M., Munro, N., Walker, S., & Dovey, S. (1998). Creating a general practice national minimum data set: Present possibility or future plan? The New Zealand Medical Journal, 111(1072), 317–318, 320. Weed, L. (1971). The problem-oriented record as a basic tool in medical education, patient care and clinical research. Annals of Clinical Research, 3, 131–134. Weed, L. (1975). The problem-oriented record-its organizing principles and its structure. League Exchange, 103, 3–6. Weed, L., & Zimny, N. (1989). The problemoriented system, problem-knowledge coupling, and clinical decision making. Physical Therapy, 69(7), 565–568. Zanstra, P., Rector, A., Ceusters, W., & de Vries Robbe, P. (1998). Coding systems and classifications in healthcare: the link to the record. International Journal of Medical Informatics, 48(1-3), 103–109. doi:10.1016/S1386-5056(97)00115-9

This work was previously published in Medical Informatics in Obstetrics and Gynecology, edited by David Parry and Emma Parry, pp. 53-64, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 5.9

Assistive Technology for Individuals with Disabilities Yukiko Inoue University of Guam, Guam

INTRODUCTION Census 2000 figures indicate that more than 19% of the U.S. population aged five and older are people with disabilities (Goddard, 2004). Technology has the great potential for improving the education and quality of life of individuals with special needs. Blackhurst (2005) identifies six distinct types of technology impacting education: (1) technology of teaching (instructional approaches designed and applied in very precise ways); (2) instructional technology (videotapes and hypermedia); (3) assistive technology (AT) (devices designed to help people with disabilities); (4) medical technology

(devices which provide respiratory assistance through mechanical ventilation); (5) technology productivity tools (computer software and hardware); and (6) information technology (access to knowledge and resources). AT (also called “adaptive technology”) can particularly help balance weak areas of learning with strong areas of learning for students with disabilities (Behrmann & Jerome, 2002). There is a growing recognition that an appropriate upto-date preparation of teachers/tutors and other educational professionals working with students with disabilities has to focus on information and communication technology (ICT), especially on AT (Feyerer, Miesenberger, & Wohlhart, 2002).

DOI: 10.4018/978-1-60960-561-2.ch509

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Assistive Technology for Individuals with Disabilities

Since educational attainment can enhance occupational attainment, individuals with disabilities (mobility impairment, visual impairment, hearing impairment, speech impairment, and learning disabilities) should be encouraged to participate in higher education. AT for students with disabilities increases options for assisting students with a variety of exceptional learning needs, allowing them to accomplish educational goals that they could not accomplish otherwise in the same amount of time or in the same manner (Rapp, 2005).

BACKGROUND AT was practically unknown in 1975, the year of landmark legislation establishing equal educational rights for students with disabilities (and personal technology tools for education were in their infancy at that time); in 1997, the Individuals with Disabilities Education Act (IDEA) amendments required AT consideration in every student’s Individualized Educational Program (IEP) (Dalton, 2002). IDEA is the nation’s special education law, originally enacted in 1975 (Boehner & Castle, 2005): “The Act responded to increased awareness of the need to educate children with disabilities and to judicial decisions requiring states to provide an education for children with disabilities if they provide an education for children without disabilities” (p. 1). The late 1970s and early 1980s saw the introduction and refinement of the micro-computer; the 1980s also witnessed an increased emphasis on AT and the emergence of technology literature and computer software targeted directly at special education; and major technology advances such as the evolution of the Internet occurred during the 1990s (Blackhurst, 2005). The first significant law dedicated to AT was the Technology Related Assistance for Individuals with Disabilities Act (TRAID) of 1988 (Public Law 100-407), which established a definition and

criteria for those in the field of AT (Campbell, 2004): The legislation’s primary accomplishment was to provide grant funding for states to establish AT resource centers…. Although many regard AT (such as computer software) as high tech, this definition is all encompassing. The law also provides for low-tech devices, such as pencil grips, weighted writing implements, and magnifying glasses…. In 1998, the federal government passed the Assistive Technology Act (ATA) (Public Law 105-394), which reaffirmed the government’s commitment to AT. (p. 168) With the implementation of these federal laws, institutions of higher learning are able to utilize state agencies in the development of technology programs based on a universal design model. In 2001, the American Library Association Council approved the AT policy that libraries should work with people with disabilities, agencies, organizations, and vendors to integrate AT into their facilities and services to meet the needs of people with a broad range of disabilities, including learning, mobility, sensory, and developmental disabilities (Goddard, 2004).

ASSISTIVE TECHNOLOGY FOR INDIVIDUALS WITH DISABILITIES IN THE INCLUSIVE EDUCATION SYSTEM Over the past two decades, for instance, the enrollment of students with disabilities and the demands for related services in higher education have greatly increased (Christ & Stodden, 2005). Online programs have worked to make Web sites accessible to deaf and blind users particularly by providing closed-captioned text and textual descriptions of graphics, even though experts have found out that online programs often lack accommodations for students with learning dis-

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abilities such as dyslexia and attention-deficit disorder (Carnevale, 2005).

Current Applications and AT Resources

Inclusion and AT Devices

AT is divided into two categories: (1) any item, piece of equipment or product system, whether acquired commercially-off-the-shelf, modified or customized, that is used to increase, maintain, or improve functional capabilities of individuals with disabilities; and (2) any service that directly assists a university’s teacher education programs to provide future teachers with knowledge of AT and its importance in helping students learn (White, Wepner, & Wetzel, 2003). The typical AT products or devices for individuals with learning needs are outlined in Table 1. Table 2 describes valuable AT Web sites for students with disabilities.

Inclusive education (the practice of keeping special education students in regular classrooms as much as possible and feasible) is part of the regular school system in many European countries, and inclusive teachers should be able to reach the special educational needs of all students (Feyerer, 2002). ICT can facilitate this challenging task, and AT has the enormous potential to improve access to education and employment for disabled individuals. AT also has the potential to ensure that computing is as effective and as comfortable as possible for all learners. AT devices include: books on tape for a student who cannot read; word processors, laptop computers for a student who has a problem with writing; augmentative communication devices for a student who has communication problems; and a large monitor for a visually impaired student. A vast array of application program software is available for instructing students through tutorial, drill-and-practice, and simulation; AT can be combined with instructional programs to develop and improve cognitive, reading, and problem-solving skills (Behrmann & Jerome, 2002). Students with disabilities often need adaptations made for them so that they can be successful in school. AT can give learners the help that they need by providing “low” technology strategies (switches, writing devices, or software applications), and “high” technology strategies (those that use sophisticated devices or software applications for students with mild and severe disabilities that enable them to access information) so that they can perform tasks that they would otherwise be unable to do (Lewis, 1998). Inclusive teachers should be able to reach the special educational needs of all learners, and AT should be part of inclusive teacher training (Feyerer, 2002).

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Challenging Questions, Universal Design, and AT Research The primary goal of AT is the enhancement of capabilities and the removal of barriers to performance. Five Guiding Principles for Assistive Technology (2004) planning are quite useful: (1) AT can be a barrier; (2) AT may be applicable to all disability groups and in all phases of education and rehabilitation; (3) AT is related to function, not disability; (4) assessment and intervention involve a continuous, dynamic process of systematic problem solving; and (5) AT does not eliminate the need for social and academic skills instruction.

Challenging Questions Teachers face challenging questions: Are there simple tools that might be incorporated with the student that would provide enough support so that referral to special education would not be necessary? And would these provisions allow the student to remain in the regular education classroom? That is why the perceived usefulness of AT by teachers and their perceptions of ability positively affect students, and their understanding

Assistive Technology for Individuals with Disabilities

Table 1. AT products or devices for individuals with learning needs Alternative keyboard

It is different from standard keyboards in size, shape, layout, or function. It offers individuals with special needs greater efficiency, control, and comfort.

Captioning

A text transcript of the audio portion of multimedia products, such as video and television, which is synchronized to the visual events taking place on screen.

Digitized speech

Human speech that is recorded onto an integrated circuit chip and which has the ability to be played back.

Electronic pointing devices

It allows the user to control the cursor on the screen using ultrasound, an infrared beam, eye movements, nerve signals, or brains waves.

Joysticks

It may be used as an alternate input device. Joysticks that can be plugged into the computer’s mouse port can control the cursor on the screen.

Keyboard additions

A variety of accessories have been designed to make keyboards more accessible.

Onscreen keyboard

These keyboards are software images of a standard or modified keyboard placed on the computer screen by software.

Optical character recognition (OCR)

OCR software works with a scanner to convert images from a printed page into a standard computer file.

Pointing and typing aids

A pointing or typing aid is typically a wand or stick used to strike keys on the keyboard.

Screen reader

A screen reader is a software program that uses synthesized speech to “speak” graphics and text out loud.

Switches and switch software

Switches offer ways to provide input to a computer when a more direct access method, such as a standard keyboard or mouse, is not possible.

Talking word processors (TWP)

TWPs are writing software programs that provide speech feedback as the student writes, echoing each letter as it is typed and each word as the spacebar is pressed.

Touch screens

This is a device placed on the computer monitor (or built into it) that allows direct selection or activation of the computer by a touch of the screen.

A telecommunication device for the deaf (TDD)

TDD is a device with a keyboard that sends and receives typed messages over a telephone line.

Voice recognition

Voice recognition allows the user to speak to the computer instead of using a keyboard or mouse to input data or control computer functions.

Voice synthesizer

Under control of the screen-reader software, voice-synthesizers can vary the rate, pitch, volume, and language of the information.

Word prediction programs

They enable the user to select a desired word from an on-screen list located in the prediction window.

Source: The Family Center on Assistive Services and Technology (n.d.)

of inclusion, in serving students in inclusive settings; thus various in-service AT training sessions are extremely important. Technology should be used by individual students who are entitled to special education services if it is needed to access the general education curriculum; recently, there has been a strong commitment on the part of audiologists and educators to improve the acoustic environment for all students through the development of national standards that can be used in the construction and remodeling of schools (Marttila, 2004). Any tech-

nology that is necessary to aid a student in meeting IEP goals and objectives qualifies as an AT, and students who are entitled to special educational services access AT through the IEP process. The purpose of the IEP is to design an individualized education program to ensure that students with disabilities have adequate educational planning to accommodate their unique instructional needs and that these needs are met in appropriate learning environments; IDEA requires that each student’s IEP be reviewed at least annually by IEP team members including parents (Copenhaver, 2004).

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Table 2. AT Web sites for students with disabilities Alliance for Technology Access (ATA) (http:// www.ataccess.org/)

This is a national network of 41 technology resource centers which help children/adults with disabilities, parents, teachers, and others to explore computer systems, adaptive devices, and software.

Assistive Technology for People with Mental Retardation (http://thearc.org/faqs/assistqa. html)

This fact sheet describes devices that are used by children/adults with mental retardation and other disabilities to compensate for functional limitations and to enhance learning, independence, mobility, communication, environmental control, and choice.

Center for Electronic Studying (http://ces. uoregon.edu/)

Funded by the U.S. Department of Education, the Center has launched three projects blending portable computer technology with instruction on computer-based study strategies.

Closing The Gap (http://www.closingthegap. com/)

This is an organization that focuses on computer technology for people with special needs through its bi-monthly newspaper, annual international conference, and extensive Web site.

Disability & Technology: A Resource Collection (http://home.nas.net/~galambos/tech.htm)

Most sites will refer to assistive/adaptive devices that are computer-based and/or related to computer access.

DREAMMS for Kids, Inc. (http://www. dreamms.org/)

DREAMMS (Developmental Research for the Effective Advancement of Memory and Motor Skills) is a non-profit parent and professional service agency, which specializes in AT-related research, development, and information dissemination.

EASI – Equal Access to Software and Information – K12 Connection (http://www.rit. edu/~easi/)

The philosophy behind this Information Technology Centre is to ensure that students and professionals with disabilities must have the same access to information and resources as everyone else.

Early Connections – Technology In Early Childhood Education (http://www.netc.org/ earlyconnections/)

Connecting technology with the way young children learn: resources and information for educators and care providers.

LD Resources (http://www.ldresources.com/)

This site contains resources for people with learning disabilities, with a focus on the use of AT to help individuals with learning disabilities become successful.

Literacy Instruction Through Technology (LITT) (http://edweb.sdsu.edu/SPED/ProjectLitt/LITT)

This is a research project focusing on the use of technology to improve the reading skills of students with learning disabilities. Project LITT is located at San Diego State University.

Speaking to Write (http://www.edc.org/ spk2wrt/)

This is a federally-funded project which explores the use of speech recognition technology by secondary students with disabilities.

Tools for Understanding (http://www.ups.edu/ community/tofu/)

This site is for educators who teach mathematics and are interested in integrating common technologies into their daily instruction.

Source: TheFamily Village School (2006)

Universal Design The Americans with Disabilities Act (ADA) of 1990 requires that AT be provided as an accommodation to students with disabilities, and one way to ensure equal access to all students is to utilize a universal design model (Campbell, 2004). Universal design principles and guidelines for AT, which are defined by the World Wide Web Consortium (W3C) that make it possible for people with disabilities to use electronic resources easily make those resources more accessible to a wide variety of devices, such as handhelds (Goddard, 2004). Universal instructional design is “the design 1534

of instructional materials and activities that make the learning goals achievable by individuals with wide differences in their abilities to see, hear, speak, move, read, write, understand English, attend, organize, engage, and remember (Burgstahler, cited in Campbell, 2004, p. 167). Colleges should not restrict the use of AT to those students being serviced by disability service providers (Campbell, 2004): “Often there are individuals who benefit from AT that do not have disabilities or have disabilities and have not registered with service providers. Universal design makes room for users of all abilities” (p. 172).

Assistive Technology for Individuals with Disabilities

AT Research Hetzroni and Shrieber (2004) investigated the use of a word processor for enhancing the academic outcomes of students with writing disabilities in high school. Their research indicated the clear difference between handwritten and computer phases. In paper-and-pencil phases, students produced more spelling mistakes, more reading errors, and lower quality of organization and structure in comparison with the phases in which a computer equipped with a word processor was used (the word processor could be considered a writing tool for those students who have writing difficulties where compensation for disabilities becomes more appropriate). According to Sharpe, Johnson, Izzo, and Murray’s (2005) research, students with disabilities (N = 139) identified the following AT products or devices that they were generally satisfied with: scanner (35%), talking books (20%), portable note taking (17%), text help software (15%), optical character recognition (14%), specialized tape recorders (12%), voice recognition (12%), mouse/switch options (10%), adapted workstation (10%), word prediction software (9%), talking dictionary (8%), screen readers (6%), adapted keyboard (6%), screen magnificationsoftware (5%), real-time captioning (5%), screen magnification-devices (4%), pointer (4%), talking calculators (3%), Braille note takers (3%), assistive listening devices (3%), speaker phones (3%), video captioning (3%), hearing aides (1%), and augmentative communication (1%).

FUTURE TRENDS Currently, AT is used primarily as an equalizer— a compensatory tool—and on occasion applied universally. As institutions of higher learning become more willing to develop a universal design approach to educating, the need to provide separate accommodations for those individuals

with disabilities will diminish (Campbell, 2004). One of the principles with respect to AT that can be applied universally, it is important to develop a technology curriculum that is based on universal design principles (to improve skill areas, such as reading, writing, organization, note-taking, and using the Internet particularly); doing so definitely sets the educational foundation for all learners within the classroom environment (Copenhaver, 2004). Colleges and universities could develop an interdepartmental curricula and students from each target audience could attend and learn how interdependent they are in serving the AT needs of students and adults, and, according to Osborn (2006), this would insure that participating students: (1) gain an awareness of AT services and devices, (2) understand the principles of universal design, and (3) know about federal and state laws that impact rights to AT devices and services. In the near future, knowledge of AT may become a requirement for licensing. Telematic multi-disciplinary AT is essentially ICT or e-learning (video conferencing and the Internet, for example) and offers to many the solution to common obstacles associated with attending educational courses, such as classroom and lecture availability, and lack of adequate transportation. Traditional higher education will increasingly adopt greater components of e-learning. As Turner-Smith and Devlin (2005) maintain, e-learning has enormous potential for use as a component of AT education; AT will be increasingly recognized as an umbrella term for any device or system that allows individuals to perform a task they would otherwise be unable to do, and that increases the ease and safety with which the task can be performed.

CONCLUSION Currently there are over 20,000 items classified as “AT devices,” for all disabilities, all ages, and all

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levels of functioning; AT can help individuals talk, write, move, see, read, and hear for themselves (Center for Innovations in Special Education, 2002). The devices range from low-tech supports (large pencils for writing, and calculators for building math skills) to high-tech supports (specialized software, and voice-output communication devices). Since each student’s technology needs are unique, the support necessary for implementing technology requires a variety of types of AT awareness training for teachers and other educational professionals. Such awareness training could be provided as a staff in-service training under the institution’s comprehensive system of personal development plan under IDEA (Copenhaver, 2004). In the final analysis, a major challenge is to move decisions about technology applications to the point where they reflect a state of the science; such technology applications must be studied continuously in objective ways so that educators can make informed decisions about their AT selection to best meet the needs of individuals with learning disabilities (Blackhurst, 2005).

REFERENCES Behrmann, M., & Jerome, M. K. (2002). Assistive technology for students with mild disabilities. (ERIC Digest #E623) Blackhurst, E. A. (2005). Perspectives on applications of technology in the field of learning disabilities. Learning Disability Quarterly, 28, 175–178. doi:10.2307/1593622 Boehner, J., & Castle, M. (2005). Individuals with disabilities education act (IDEA): Guide to frequently asked questions. Retrieved December 25, 2005, from edworkforce.house.gov/issues/109th/ education/idea/ideafaq.pdf

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Campbell, D. M. (2004). Assistive technology and universal institutional design: A postsecondary perspective. Equity & Excellence in Education, 37, 167–173. doi:10.1080/10665680490454057 Carnevale, D. (2005, August 12). Lawsuit charges online university does not accommodate learningdisabled students. Chronicle of Higher Education, 51(49). Retrieved December 26, 2005, from http:// web26.epnet.com Center for Innovations in Special Education. (2002). Do you know... Special Education Programs, 4(1), 3-6. Washington, DC: Author. Christ, T. W., & Stodden, R. (2005). Advances of developing survey constructs when comparing educational supports offered to students with disabilities in postsecondary education. Journal of Vocational Rehabilitation, 22, 23–31. Copenhaver, J. (2004). Guidelines in special education. Logan, UT: Mountain Plains Regional Resource Center. Dalton, E. M. (2002). Assistive technology in education: A review of policies, standards, and curriculum integration from 1997 through 2000 involving assistive technology and the individuals with disabilities education act. Issues in Teaching and Learning, 1(1). Retrieved January 2005, from http://www.ric.edu/ itl/print Dalton.html Family Center on Assistive Services and Technology. (n.d.). Retrieved February 3, 2006, from http://www.fctd.info/ resources/ glossary. php Family Village School. (2006, January 19). Assistive technology for students with disabilities. Retrieved February 3, 2006, from http://www. family village.wisc. edu/education/ at.html

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Feyerer, E. (2002). Computer and inclusive education introduction to the special thematic session. In K. Miesenberger, J. Klaus, & W. Zagler (Eds.). Computer Helping People with Special Needs: 8th International Conference Proceedings, ICCHP 2002, Linz, Austria (pp. 107-114). HeidelbergBerlin: Springer-Verlag. Feyerer, E., Miesenberger, K., & Wohlhart, D. (2002). ICT and assistive technology in teacher education and training. In K. Miesenberger, J. Klaus, & W. Zagler (Eds.). Computer Helping People with Special Needs: 8th International Conference Proceedings, ICCHP 2002, Linz, Austria (pp. 64-67). Heidelberg-Berlin: Springer-Verlag. Goddard, M. (2004). Access through technology. Library Journal, 129(7), 2–6. Guiding Principles in Assistive Technology. (2004). Retrieved January 2004, from www. orclish.com/ 1_assistive_ tech/ techmanual/ manual4.html Hetzroni, O. E., & Shrieber, B. (2004). Word processing as an assistive technology tool for enhancing academic outcomes of students with writing disabilities in the general classroom. Journal of Learning Disabilities, 37(2), 143–154. doi:10.1177/00222194040370020501 Lewis, R. B. (1998). Assistive technology and learning disabilities: Today’s realities and tomorrow’s promises. Journal of Learning Disabilities, 31(1), 16-26, & 54. Marttila, J. (2004). Listing technologies for individuals and the classroom. Topics in Language Disorders, 24(1), 31–50. Osborn, S. R. (2006). Trends in assistive technology services and technology. Florida alliance assistive services & technology. Retrieved February 3, 2006, from http://faast.org/ atr_trends.cfm

Rapp, W. H. (2005). Using assistive technology with students with exceptional learning needs. Reading & Writing Quarterly, 21, 193–196. doi:10.1080/10573560590915996 Sharpe, M. N., Johnson, D. R., Izzo, M., & Murray, A. (2005). An analysis of instructional accommodations and assistive technologies used by postsecondary graduate with disabilities. Journal of Vocational Rehabilitation, 22, 3–11. Turner-Smith, A., & Devlin, A. (2005). E-learning for assistive technology professionals—A review of the TELEMATE project. Medical Engineering & Physics, 27, 561–570. doi:10.1016/j.medengphy.2004.09.019 White, E. A., Wepner, S. B., & Wetzel, D. C. (2003). THE Journal, 30(7). Retrieved December 26, 2005, from http://web26.epnet.com

KEY TERMS AND DEFINITIONS Adaptive Technology: The use of hardware and software to assist individuals who have difficulty accessing information systems using conventional methods. It often refers to assistive technology. Early Intervention Services: A program of activities and services, including assistive technology, required by the Individuals with Disabilities Education Act for children from birth through age two. Learning Disabilities: Conditions that cause people to understand and process information more slowly than average. These individuals may require information to be presented in multiple formats before they completely understand it. Multisensory Learning: An instructional approach that combines auditory, visual, and tactile elements into a learning task. Tracing sandpaper numbers while saying a number fact aloud would be a multisensory learning activity.

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Prereferral Process: A procedure in which special and regular education teachers develop trial strategies to help a student showing difficulty in learning remain in the regular classroom. Special Education: Specially-designed instruction to meet the unique needs of a student with disabilities including but not limited to instruction conducted in the classroom.

Universal Design: Designing programs, services, tools, and facilities so that they are usable, without additional modification, by the widest range of users possible, taking into account a variety of abilities and disabilities.

This work was previously published in Encyclopedia of Information Technology Curriculum Integration, edited by Lawrence A. Tomei, pp. 56-62, copyright 2008 by Information Science Reference (an imprint of IGI Global).

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Chapter 5.10

Ageing, Chronic Disease, Technology, and Smart Homes: An Australian Perspective Jeffrey Soar University of Southern Queensland, Australia

ABSTRACT This chapter explores ageing, chronic disease, technology and social change. Healthcare has been transformed through medical technology but there is much still to be done to enable seamless exchanges between all carers, which is expected to improve safety, quality and efficiency. There is massive potential for technology to transform the experience of ageing including assisting with the management of chronic disease, coordinated care DOI: 10.4018/978-1-60960-561-2.ch510

and guided self-care for consumers. Innovative technologies are increasingly available to assist in maintaining health and independent living. This includes telecare, telehealth, assistive technologies, robots and smart homes. A challenge is in providing access to and support in the use of technologies where there are clear benefits to consumers, carers, providers and funders of healthcare. The chapter also reports on the Queensland Smart Home Initiative which is one of several organisations internationally that share a mission of assisting people to be supported through these technologies.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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INTRODUCTION An aspect of the grey digital divide is the difference between the support that older people currently receive and the potential that technology has for enabling independent living and better access to care. That there are opportunities for the support that smart homes and assistive technology can provide is evident in the rates of events such as falls, medication difficulties and social isolation. Technology can help to maintain social connections, provide access to information for self-care, enhance access to professional services, and it can also help to ensure people are safe, receiving needed support and participating in activities of daily living. Over the subsequent sections of this chapter we will explore technology and social change, healthcare technology, ageing, chronic illness and disability, coordinated care and self-management, guided self-care, ageing and technology, robots, adoption issues, smart homes and the experiences of the QSHI (Queensland Smart Home Initiative). Technology has the potential to transform ageing, aged care and healthcare just as it has other industries. Technology is highlighted in the healthcare reform agendas of Australian and other governments. It has the potential to overcome the digital divide and provide the elderly with better access to support arrangements, information, care and other services; technology can also reduce travel of patients and carers across often vast distances and sometimes just for routine checks which could be provided more safely and conveniently through telecare and telehealth systems. There is increasing interest and attention given to the unprecedented ageing of populations which has captured the attention of the public and policymakers. The phenomenon of ageing is discussed along with associated issues of increasing chronic illness and disability. Recent strategy around chronic illness self-management is explored along with the needs to ensure the availability of

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quality, relevant and timely sources of consumer health information. The technology itself is developing at a dramatic rate and there are prototype robots that are expected to be our carers of the future. The robot may be a mechanical device or it may take the form of software that interacts through other devices, or it may be a combination of both. Given how pervasive robots are in automobile production and other manufacture it is reasonable to expect they will be available in people’s homes. There are smart homes as demonstrators in many countries and some of these are discussed. An exciting development has been the large-scale deployment of some of this technology into people’s homes in some countries and localities. The QSHI is discussed as one of a number of industry-government research consortia around the world that promotes the adoption of smart home and assistive technology. The QSHI does this through research evaluating the benefits of technology for consumers, families, carers, provider organisations, funders and governments.

TECHNOLOGY AND SOCIAL CHANGE There are many aspects to the grey digital divide that will be explored in this paper. These include a lower level of use of ICT (Information and Communication Technologies) by older people (Madden & Savage, 2000), the unequal availability of ICT across sectors of healthcare and aged care, the need for ICT infrastructure and applications for home and community care, the need for technology to assist with chronic illness and the need to help consumers and carers with access to information. That our lives have been and continue to be transformed by technology is a given. It has liberated people from drudgery and has been the key to social transformation over millennia. Where modern technology is applied, there are many-

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fold increases in productivity, higher quality, greater convenience, lower costs and lower prices. Technology usually results in a range of changes including simplification of work processes and disintermediation as whole steps in the production process are eliminated; it also provides for both greater standardisation and individual customisation. There are endless examples of this including banking, e-commerce. e-procurement and supplychain management, freight and shipping, hotels and airline bookings. Subject to Internet access and payment of fees, students from around the world have access to some of the best universities. Through on-line learning systems lecturers can provide a rich educational experience to ten or more times as many students as they might have in a physical class-room. Learning is shifting from instructor-centered to learner-centered, and is undertaken anywhere, from classrooms to homes and offices. E-Learning, referring to learning via the Internet, provides people with a flexible and personalised way to learn. It offers learning-ondemand opportunities and reduces learning cost (Zhang & Nunamaker, 2004, 204). Students can interact on-line with instructors and other students, review on-line lectures and use interactive learning tools. Many of the world’s knowledge repositories are freely accessible, and in most cases the lowest prices for goods and services are available through the Internet. People on the wrong side of the digital divide and without access to the Internet miss out on the convenience and the ready access to current information; in most cases they will pay more for purchases. This change is not without conflict going back as far as the Industrial Revolution which transformed society from an agricultural base to the beginnings of a modern capitalist society. In more recent years technology has transformed shipping and stevedoring with massive reductions in labour demand and huge increases in productivity (Duras, 2007). The introduction of automation in newspaper production in the mid-1980s saw the

protracted strikes around the world. Newspaper production changed forever with the introduction of electronic processes and the abolition of a range of trades and skills. Transformation of business and commerce through technology is a continual process. This has been accelerated since the advent of information and communication technology (Friedman, 2005). Industries have been transformed or lost and new ones created and many industries are now totally dependent upon new information technologies.

HEALTHCARE TECHNOLOGY Healthcare technology is linked to our better health and longevity. Lives can now be saved that would previously have been lost and people are enabled to lead fulfilling lives through lifecritical technologies such as cardiac pacemakers, renal dialysis and new medications. The healthcare industry is generally not as good at using technology for sharing patient information between provider organisations. In many cases a person’s health history is likely to be spread across the computer systems or filing cabinets of all the providers they have received services from including primary care practitioners, retail pharmacies, hospitals, and diagnostic laboratories. In only very few countries are there centralised repositories of patient information. Healthcare consumers still have little access to or control over their own information (Eysenbach & Jadad, 2001). Few health professionals exchange emails with their consumers or allow consumers to access their computer systems in the way, for example, that airline passengers can access their airline flight bookings, personal profiles and preferences, or travel histories. In many countries there are now strategies aimed at providing electronic personal health records, electronic referrals between care providers, better linking of clinical practice systems with research evidence databases, and decision-

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support tools to assist busy clinicians. Healthcare around the world has shared a common agenda for reform for several decades (Giaimo, 2002). This includes moving from an episodic focus to a more holistic approach; from fragmentation to better coordinated care; moving from end-stage intervention focus to more prevention; from an acute care focus to more public health and primary care, and from provider-driven care to a more consumer focus. Enabling access to hospital patient records may be some time away. The author is involved in a research project in Australia which aims to link professional carers in community settings, particularly General Practice physicians and community aged-care providers, with hospital records for sharing of patient data. Achieving this is not without challenge. The project will be a first in this region in sharing data electronically between primary and secondary providers. Currently, a common practice of hospitals in Australia is to use fax machines to send patient data to community-based providers; however to have this information sent electronically raises issues of processes, security, confidentiality and control. If it is a challenge for professional carers to access their patients’ data held by other providers, then it is likely to be even more of a challenge to enable consumer access. The majority of the investment in health ICT (Information and Communication Technology) in Australia has been in hospital-based applications, and there is still little technology infrastructure for managing patient information between carers outside of hospital settings as well as between carers across hospitals. That may change with the report of the National Health and Hospitals Reform Commission (2009). This is likely to require providers to better facilitate access to patient data by other providers and by patients themselves.

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AGEING The future sustainability of public funding for social and health services is a major policy concern for many counties as it is clear that the need for publicly provided services will increase with the ageing of the populations and the shrinking of the work force (Marin, Leichsenring, Rodrigues & Huber, 2009, 5). The ageing of Australia’s population, already evident in the current age structure, is expected to increase rapidly. This is the result of sustained low levels of fertility combined with increasing life expectancy at birth. The age composition of Australia’s population is projected to change considerably as a result of population ageing. By 2056 there will be a greater proportion of people aged 65 years and a lower proportion of people under the age of 15 years. In 2007 people aged 65 years and over made up 13% of Australia’s population. This proportion is projected to increase to between 23% and 25% in 2056 and to between 25% and 28% in 2101 (ABS, 2009). There are similar demographic changes and predicted increases in the percentages of the elderly in the populations of most countries (Cohen, 2003; Kinsella & Velkoff, 2001). The prevalence of chronic illness and disability usually increases with age. About one-quarter (23%) of all people aged 65 years and over in Australia have a profound or severe core activity limitation, and chronic illnesses such as dementia, hypertension, asthma and diabetes are common conditions (AIHW, 2006a). However, Rice and Fineman (2004) found the prevalence of disability among the elderly in the USA is declining, and expenditures for their care are increasingly concentrated at the end of life rather than during extra years of relatively healthy life. The same relative decline in disability could well occur in Australia, which has a similar standard of living to the USA. Nevertheless, health care costs will undoubtedly increase during the next 30 years, if only as a result of the large numbers of Babyboomers entering late life.

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At any time older people occupy more than half of the hospital beds in Australia and have a longer average length of stay than younger patients (AIHW, 2008a). The media, as in many other countries, carries frequent reports of healthcare systems that are struggling to cope with an ageing population. Whether these are occasional slip-ups in otherwise reasonably well-functioning systems or whether they reflect profoundly dysfunctional systems is debatable. Regardless, health systems are under pressure and ageing is yet to fully impact on these systems. The Australian Government’s Intergenerational Report indicates that much of the pressure in the growth of healthcare costs is due to medications and medical technologies (Australian Government Treasury, 2007). The first of the Babyboomers are now moving into their mid 60s and this is likely to herald the beginning of an upward spiral in demand for healthcare services. In many countries governments have recently renewed their interest in healthcare reform. A notable example of this is several attempts which have been made to introduce reforms to the health system in the USA. In Australia the 2009 National Health and Hospitals Reform Commission report advocated for greater prevention, individual responsibility, e-health and use of technology (NHHRC, 2009). Prior to that report the Australian Health Ministers’ Council (AHMC, 2005) had already developed a national strategic policy approach to chronic disease prevention and care. This approach included the National Chronic Disease Strategy and supporting National Service Improvement Frameworks, which cover the national health priority areas of asthma, cancer, diabetes, heart, stroke and vascular disease, osteoarthritis, rheumatoid arthritis and osteoporosis. The current ageing population and increasing incidence of long term conditions presents the health sector with a major challenge because many older people, who may be living with more than one long term condition, often have both clinical and social problems. Caring for these people consumes a large proportion of health and social

care resources. Better coordination in community settings aided by technology can be expected to improve the effectiveness and safety of care for the frail aged population in particular, and for people generally who need support (Bowes & McColgan, 2005). The spectre of a “tsunami” of ageing Babyboomers is likely to compel governments and other healthcare funders to more fully explore the potential of assistive technology for independent living and home-care.

CHRONIC ILLNESS AND DISABILITY The digital divide will also impact on how chronic illness sufferers and their carers are equipped to best manage their conditions. Associated with increasing life-expectancy is an increase in the incidence of chronic disease. Chronic illnesses such as asthma, dementia and coronary heart disease are the leading causes of disability and mortality (Australian Institute of Health and Welfare, 2008). Chronic diseases disproportionately affect older adults and are associated with disability, diminished quality of life, and increased costs for health care and long-term care. In the USA about 80% of older adults have at least one chronic condition, and 50% have at least two (CDC, 2009). These conditions can cause years of pain, loss of function and lead to depression. More than fifteen million Australians are directly affected by at least one chronic disease (Australian Institute of Health and Welfare, 2006). One third of problems presented in primary care general practice are chronic in nature (ACT GP Taskforce, 2009). Older people commonly present to hospitals with multiple, complex conditions. It may also be the case that some elderly people are admitted to hospital because adequate facilities for their care are not available in the community (Siggins Miller, 2003). Gains in life expectancy have been accompanied by an increase in expected years of life lived with disability, including severe or profound limi-

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tations. People who identify as having a disability account for almost 20 per cent of the Australian population (ABS, 2004). Today on average, people can expect to live with a disability for almost 20 years (AIHW, 2006b, 210). This will rise as the percentage of older people in the population increases resulting in increasing demands for support (AIHW, 2008b). The unprecedented increase in dementia around the world is of particular concern for the quality of life of individuals and their families as well as the associated surge in demands for support and care. More than 35 million people worldwide will have dementia in 2010 (ADI, 2009, 2). This is a 10% increase over previous global dementia prevalence reported in 2005 (Ferri et al, 2005). The ADI report predicts that dementia prevalence will nearly double every 20 years, to 65.7 million in 2030 and 115.4 million in 2050. If chronic conditions are not managed effectively in primary and community care, they can progress to produce acute care episodes with impacts and cost implications. Poorly managed chronic illness can progress to severe episodes resulting in premature death or permanent disability. There has been growing interest over several decades in CDM (Chronic Disease Management) (National Health Priority Action Council, 2006), and more recent attention to patient self-management (Australian Department of Health and Ageing, 2008). The interest is associated with expectations of the likelihood of benefits including better partnerships between patients, their carers and clinicians in the management of their conditions, better guidance for patients to ensure the right information is provided at the right time and place to guide self-management, clinical outcomes including a slowing of the advance of chronic conditions, earlier and more timely interventions with clinical and economic benefits, reduction in unplanned hospital attendances and admissions and reductions in health expenditure on patients with chronic illness (Soar & Wang, 2009). Indications are that successful self-management

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is dependent on the engagement of health care professionals, particularly General Practitioners (Jordan & Osbourne, 2007). Assistive technology can greatly improve the independence and quality of life for people with a disability and/or chronic disease through enhancing self-care. It can provide quality and relevant information at the point of care and help reduce or reverse the progression of disease.

COORDINATED CARE AND SELF-MANAGEMENT A further aspect of the digital divide is the difference in availability of technology support and integrated information management between sectors of care. Hospitals have long been the focus of much of the investment in healthcare ICT. The health sector historically has had an episodic approach and an acute care focus and has been not been well structured to provide coordinated care in community settings. Coordination of care is difficult in countries such as Australia with fragmentation of funding and payment systems, a mix of public and private funding and care provision, and a complexity of reporting lines and accountability (Dwyer & Eager, 2008, 5). Consequently care providers are rarely financially rewarded nor equipped to provide coordinated care. There are increasing pressures on home care services as governments explore strategies to provide more healthcare for the aged and chronic ill in their homes (CSCI, 2006). Home care is generally presumed to be a less costly alternative to institutional care and is usually preferred by patients. Disease-specific associations and programmes such as for asthma, diabetes, stroke and many others provide information and other services for sufferers of chronic disease. CDM programmes are also provided by community health services, Divisions of General Practice and others to better manage conditions and slow the progress of

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disease. There is however no common approach to providing this information and it is often provider rather than consumer-driven. Social marketing is also used by health departments to encourage behaviour change and current media programmes address diabetes, obesity, alcohol abuse, smoking and gastric reflux. Some evidence suggests that patients with effective self-management skills make better use of health care professionals’ time and have enhanced self-care (Jordan and Osborne, 2007). The concept of self-management and its practice is changing (Kralik, Koch, Price and Howard, 2004; Newman, Steed & Mulligan, 2004; NIH, 2000). The availability of health information on the Internet is an indication of the interest people have in independently searching for information to help them understand and cope with their health conditions. A more proactive self-management role is being promoted rather than a health care provider giving instructions and hoping the patient will adhere to them. The UK has introduced the Expert Patients Program that recognises this change (NHS, 2007). The Expert Patients Programme is a lay-led self-management programme that has been specifically developed for people living with long-term conditions. The aim of the programme is to support people to increase their confidence, improve their quality of life and better manage their condition.

GUIDED SELF-CARE A grey digital divide also exists between the elderly who have good access to technology and are skilled in its use, and those people without either the access or skills. In 2009, 44% of Australians over the age of 65 had never used the Internet (ACMA, 2009). By contrast, many Babyboomers routinely use the Internet and as this technology-savvy group moves through retirement the disparity between elderly Internet users and non users should drop sharply. Self-management has been an expectation

for individuals with a chronic disease for much of the past century although the dominant model of care is one where the clinician, particularly the physician, provides advice for patients to follow. Community-based associations for many of the chronic illnesses assist patients in accessing quality information for self-care. There are also CDM programmes provided by community health providers, General Practice and others to better manage conditions and to slow the progress of disease. For some time it has been recognised that people living with a long term illness develop expertise and wisdom about their condition and want to play a part in making decisions about their own health care (Wilson, 1999). There is encouragement or perhaps recognition that care often involves partnerships between individual patients and healthcare professionals and between consumer disease organisations and the healthcare system. The latter can be effective at influencing government policy development and resource allocation. A successful use of consumer health information has been the social marketing used by health departments to encourage behaviour change through media campaigns addressing smoking, obesity, alcohol abuse and gastric reflux. The World Health Organisation’s framework for innovative care of chronic diseases identifies self-management support as part of the building blocks for effective health care organisations (World Health Organisation, 2002). Self-management needs to be guided to be most effective and to guard against patients being mis-informed or influenced by the poor quality information that is also available through the Internet. Successful self-management is related to the engagement of health care professionals (Jordan and Osborne, 2007). Technology offers the means to link consumers, family carers and professional carers, to provide timely access to quality information and to enhance the patient-clinician partnership (Coye, Haselkorn & DeMello, 2009).

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Telecare may involve consumer health information. It can be simply passive involving the use of devices that collect data for professional carers to view or alternatively it may help people to better manage their own conditions through providing information back to consumers. In the latter case consumers will require telecare services to provide them with quality information in addition to feedback from the devices involved. Telehealth links can be between patients and their clinicians or between clinicians such as between a remote or general practice clinician and a specialist for advice. This can provide guidance and quality information to assist consumers and enhance the partnership between the consumer and healthcare professionals. The technology can be of particular value for isolated communities (Weinert, 2008). An essential component for addressing this aspect of the grey digital divide that has not as yet been provided for is in developing a model for the information that patients need and a model for capturing, managing and transmitting quality and timely information. Most self-management support has been for specific conditions although many people, particularly as they age, suffer from multiple co-morbidities. Many of the technology developers have focused on the technology itself and there is an ever more sophisticated range of products can be found through an Internet search for Assistive Technology.

AGEING AND TECHNOLOGY Assistive technology (AT) can be defined as ‘any item, piece of equipment, product or system that is used to increase, maintain or improve the functional capabilities of individuals and independence of people with cognitive, physical or communication difficulties’ (UK Audit Commission 2004). AT aims to increase the ability of a person to remain independent, reduce risk and maintain engagement in meaningful activities, and includes a wide range

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of products from simple, low tech and low cost to very technologically complex. Technology is expected to offer significant potential for equipping societies to respond to pressures of ageing populations. Provided that successful efforts are made to bridge many aspects of the digital divide which were addressed earlier, technology will assist aged people in extending active and independent lives, maintaining productivity and in delivering care in home and community settings. Globally there is an increasing level of activities, strategy development, research projects, and adoptions of telecare, telehealth, smart homes and assistive technologies by consumers and care provider organisations (Soar, 2008). Several of these are discussed in different chapters of this book. The application of these technologies has an array of added benefits including a reduction in the level of incidence of adverse events, providing support and new service interventions for conditions amongst the elderly such as chronic illness, falls, dementia, medication problems, wandering and social isolation (Horner, Soar & Koch, 2009). There is recognition of the potential for technology to enhance the safety and independence of frail older people, enable access to quality care services and to extend their ability to remain in their own homes. Intelligent monitors can keep a continuous watch on older people’s vital signs, activity patterns and their safety and security (Soar & Croll, 2007). Technology can monitor indicators of their state of health, provide alerts to events such as falls, and give early warnings of potential problems. The technology can detect changes in activities and alert a carer. Monitoring devices can be more accurate guides to the health risks such as a heart attack than are the patient’s symptoms, providing advance warnings and reducing unnecessary emergency callouts. Home automation can enhance security, safety and independence at home. This helps to maintain quality of life and decrease the demand for carer support hours. Communication technologies provide important

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benefits for people whose mobility is limited, or who live alone. Richards (2006) was commissioned by the Scottish Executive Development Department to review housing for older people. The review reported that AT projects in Scotland were considered to be helping people to continue living in their own homes, and that users had become accustomed to the technology and appreciated the benefits it afforded them. Some older people had fears about whether the technology might go wrong and that carers and relatives would be bothered in that event. Some people also had concerns about the pace of technological change leading to rapid obsolescence and issues with upgrading. Bowes and McColgan (2003) found that a control group without the technology were more likely to have received increasing hours of home care, been admitted to care homes or hospitals, or visited their GP more frequently than those using the assistive technology care package. The largest known study of the benefits of home telehealth implementation was undertaken amongst clients of the Veterans’ Health Administration in the USA (Darkins et al., 2008). This involved 17,025 home telecare and telehealth installations. The results included a 19% reduction in numbers of hospital admissions and a 25% reduction in numbers of hospital bed days. Clients reported a high level of satisfaction with the telecare/telehealth service with a satisfaction score of 86%. Technology can also assist in directing people to the most appropriate form and place for care delivery, which is not always admission to hospital. The author is involved in a project to evaluate the benefits of integration of patient information in community settings including reductions in hospital admissions. An Internet search using the term “hospital avoidance” reveals a number of pilots and projects with similar aims around most Australian health jurisdictions as well as in other countries.

Technology has the potential to extend older persons’ physical independence and thus enable them to remain living in their own homes for longer. This has the dual benefit of affording individuals a more dignified life while potentially leading to savings of public and private money. In geographically diverse countries like Australia, technology has great potential for bringing services into communities as well as to individuals. There are reports of reductions in health service use associated with the availability of home telecare (Barlow, Singh, Bayer & Curry, 2007). In circumstances when patients do present, the emergency department staff may be more confident of them returning to their technology equipped homes.

ROBOTS Robots have for many years performed much of the work in manufacturing, have made some inroads into medicine and particularly surgery, but have yet to impact home and aged care. There is a report of a robot to assist stroke victims with limb movement (Cox, 2005). Work is underway to build a humanoid robot personal assistant or carer, and developments in this field suggest that a robot assistant in homes may not be far off. The robot may be a mechanical device or it may take the form of software that interacts through other devices, or it may be a combination of both. Some robotic devices are already available to provide support for a range of basic activities, including eating, bathing, dressing, and toileting (Forlizzi, DiSalvo & Gemperle, 2004); and evidence suggests that available robotic pets provide pleasure and interest to people with dementia (Filan & Llewellyn-Jones, 2006). There is work on a humanoid robot assistant and even a robot dog carer. In France, the AlcatelLucent research laboratory enhanced a robot dog to become a personal carer (Alcatel-Lucent, 2008). The dog has a camera, microphone and wireless connection. It can provide reminders and alerts

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and through pattern recognition it will know its owner and can continuously monitor his/her safety and well-being. If, for example, a carer was unable to contact an older person, the dog could be contacted and could search for its owner. The carer could see though the dog’s camera “eyes”. The dog was originally designed as a robot companion for children in hospital and particularly for those in isolation rooms. The robot dog would provide a companion to an isolated child patient as well as a multimedia terminal complete with camera, microphone, loudspeaker, gaming and links the child could use to clinicians, friends and family. The potential for similar use in care of older people is obvious. Given the pace of automation in industries such as vehicle production or newspaper printing it is reasonable to expect that robots will play a role in healthcare in the future, both in home and institutional settings. This is likely to include robots as personal home carers.

ADOPTION ISSUES High levels of assistive technology abandonment are well documented (Wessels, 2003). Approaches to reduce abandonment include processes for the selection of assistive technology that involve the end-user, access to expert advice, making the right choice and social factors. The latter includes whether the device might stigmatise the user and remind them of their disability. If assistive technologies are to meet their intended purposes with older users they must be user friendly (Livingstone, Soar & Wang, 2009). Technology is often developed by young people who may not always be aware of age-related difficulties that afflict many older people, for example fading eye-sight or poor dexterity. If the intended users cannot easily see or operate the devices, they will not persevere with these. At the person-to-person interface older people are not always treated with respect and as partners in

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their own care. Indications are that if older people are involved in decisions such as selection of assistive technologies then there is a higher rate of successful adoption.

SMART HOMES Terms such as ‘intelligent home’, ‘smart home’, ‘digital home’ and ‘connected home’ describe the convergence of a range of technologies and their increased use (Essen & Conrick, 2007). There has also been increasing use of intelligent controls engineered into modern buildings (Horner, Soar and Koch, 2009). Examples include smoke detectors linked to the building fire sprinkler systems and elevators that are remotely monitored. CCTV (Closed Circuit Television) for security is now ubiquitous in commercial and public places. In Queensland, Australia the Department of Health has begun installing intelligent controls with remote monitoring for some of its hospitals. These control, for example, hot water systems that can monitor use and then reduce energy-consumption according to demand. Knowing the periods of peak demand allows advance scheduling of the boilers. These kinds of controls applied to heating, lighting, air-conditioning and other services can learn patterns of use and reduce energy consumption at quiet times and build up in preparation to periods of peak demand.

QUEENSLAND SMART HOME INITIATIVE The interest internationally in the promotion of ambient living and wellness models of health care through the utilisation of Smart Homes particularly for the frail elderly is shared in Australia. In 2006 a consortium of stakeholders, the Queensland Smart Home Initiative (QSHI), was established with the aim of investigating new models of care delivery based on supporting independent liv-

Ageing, Chronic Disease, Technology, and Smart Homes

ing, improving quality of life and enabling new models of home care particularly for the frail elderly, chronically ill, and people with disabilities (see http://www.qshi.org.au/) The QSHI aims to enhance the national and regional capacity for research and development, commercialisation and adoption of assistive technologies. The focus is, through research, to encourage the wide-scale adoption of assistive technologies where there are demonstrable social, clinical, environmental and economic benefits. The QSHI aims to demonstrate those benefits through the quality of its research. The first phase of the project involved building the research consortium based upon a shared high-level vision of the potential ICT and assistive technologies for care in home and community settings. The selection of organisations for invitation to participate in the QSHI was based upon whether an organisation was considered to share a common interest in the field as well as on its capacity to contribute a unique perspective. Selection of organisations was also based upon an assessment of complementary skills and interests, and avoidance of duplication. Organisations that committed to support the initiative included a department of health, the home and community care funder, a major aged and community care provider, a home care technology supplier, a smart home environment supplier, an association of owners and operators of aged and community care facilities, an aged care consumer organisation, a multi-national manufacturer of ICT componentary, a telecommunications company and universities with researcher expertise in this or related fields. The QSHI established its first Smart Home in an aged care campus in Brisbane, Australia in 2007. The objective was to demonstrate working technologies in a live environment. It also aimed to increase awareness, provide an educational resource and gather feedback from stakeholders. The site had around 250 visitors over 12 months and people visiting the Smart Home were invited to share their impressions. Each visit was hosted

and the technology presented and explained (Soar, Livingstone & Wang, 2009). Subsequently a study was undertaken to distil the vision, research needs, priorities and expectations of the participant organisation representatives. The aim was to arrive at a collective vision on the potential of the Smart Home project and the development of prioritised research programs. The methodology involved individual consultations and focus groups with managers and senior executives of each of the participating organisations. Semi-structured interviews covered vision, expectations, desired outputs, desired projects, anticipated benefits and other impacts. This was followed by a facilitated focus group workshop. Thematic analysis of the data assisted with analysis and further interpretation. The results highlighted the different perspectives and expectations of participants (Soar & Croll, 2007). Government agencies were concerned with managing demand for services, improving access, and enhancing independence of consumers. Care provider organisations were particularly interested in technology to assist in workforce issues. These include making aged-care a more desirable place of work, providing staff with efficiency and productivity tools, and improved work-force satisfaction and retention. Some functions that are labour-intensive could be aided or eliminated by technology. These include monitoring or taking vital signs. There was an expectation that technology offered particular potential for consumers in rural and remote locations where access was restricted by the vast distances in Australia. Technology providers were interested in knowing more about the barriers to adoption and in seeing quality research evidence that would inform policy development and resource deployment. Privacy, security and trust in systems were consistent themes. As the consumers included vulnerable groups it was seen as important that systems could be trusted, were aged appropriate and as transparent as possible to the users.

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The QSHI has now completed its first phase involving a smart home demonstrator unit. It has recently commenced a “connected-community for care” project that is supported by the Australian Research Council and industry partners. The project will examine the effect on hospital attendances, admissions and length of stay following better sharing of information in community settings. The QSHI second phase will involve a large-scale roll-out of assistive technologies into the homes of people needing support. These people will be supported by software intelligent agents to assist with reminders, access to information and decision-making. Links to a Call Centre will ensure an operator is always available when needed. The project is informed by the small but increasing number of roll-outs of assistive technologies around the world. Also as part of the QSHI second stage, a modern facsimile demonstrator home has been set up close to the city centre to allow easy drop-in access by the general public, six days a week. It is expected that the demonstrator will result in considerably greater understanding and acceptance of some of the relatively inexpensive, assistive technologies that can effectively increase the independence of frail elderly people.

CONCLUSION This paper has covered a wide range of issues including demographic trends, the increasing prevalence of chronic disease and associated changes in demands for health care and support services. Technology has been reviewed for its capacity to improve support for frail elderly, chronic illness sufferers and people with disabilities. This includes the potential benefits for consumers, carers and the professional workforce. Further research will be required to provide models for adoption and to provide research evidence to support a shift in resourcing more towards prevention and community care supported by innovative technology. Assistive technology will play a major role in promoting

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new health and wellness models; it can accelerate the international healthcare reform agenda. Finally the Queensland Smart Home Initiative was reported on as one of several consortia around the world aiming to contribute to the national and international agendas for quality care and independent living through assistive technologies.

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Soar, J., Livingstone, A., & Wang, S. Y. (2009). A case study of an ambient living and wellness management health care model in Australia. In M. Mokhtari, I. Khalil, J. Bauchet, D. Zhang & C. Nuget (Eds.) Ambient Assistive Health and Wellness Management in the Heart of the City, Refereed Proceedings of the 7th International Conference On Smart Homes and Health Telematics (ICOST2009) 1-3 July, 2009–Tours, France, Springer. Soar, J., & Wang, S. Y. (2009). Information for guided chronic disease self-management in community settings. In Sintchenko, V., & Croll, P. (Eds.), Frontiers of Health Informatics – Redefining Healthcare, HIC 2009 Proceedings. Health Informatics Society of Australia.

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This work was previously published in Intelligent Technologies for Bridging the Grey Digital Divide, edited by Jeffrey Soar, Rick Swindell and Philip Tsang, pp. 15-29, copyright 2011 by Information Science Reference (an imprint of IGI Global).

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Chapter 5.11

Synthetic Speech Perception in Individuals with Intellectual and Communicative Disabilities Rajinder Koul Texas Tech University, Health Sciences Center, USA James Dembowski Texas Tech University, Health Sciences Center USA

ABSTRACT The purpose of this chapter is to review research conducted over the past two decades on the perception of synthetic speech by individuals with intellectual, language, and hearing impairments. Many individuals with little or no functional speech as a result of intellectual, language, physical, or multiple disabilities rely on non-speech communication systems to augment or replace natural speech. These systems include Speech Generating Devices (SGDs) that produce synthetic DOI: 10.4018/978-1-60960-561-2.ch511

speech upon activation. Based on this review, the two main conclusions are evident. The first is that persons with intellectual and/or language impairment demonstrate greater difficulties in processing synthetic speech than their typical matched peers. The second conclusion is that repeated exposure to synthetic speech allows individuals with intellectual and/or language disabilities to identify synthetic speech with increased accuracy and speed. This finding is of clinical significance as it indicates that individuals who use SGDs become more proficient at understanding synthetic speech over a period of time.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Synthetic Speech Perception in Individuals with Intellectual and Communicative Disabilities

INTRODUCTION One of the most significant advances in enhancing the communicative abilities of individuals with severe communication impairment has been the development of Speech Generating Devices (SGDs). The use of SGDs for interpersonal communication by individuals with severe communication impairment has increased substantially over the past two decades (Koul, 2003). This increase in the use of SGDs is primarily a result of technological advances in the area of synthetic speech. Most high-end SGDs use text-to-speech synthesis in which graphic symbols, alphabets, words, and digits are entered from an input device, such as touch screen/keyboard/switch/infrared eye tracking technique, and are converted into a speech waveform using a set of mathematical rules. This chapter has three general aims. The first aim is to review the literature on the perception of synthetic speech by individuals with language, intellectual, and hearing impairments. The second aim is to use that review to understand the effects of degraded acoustic input on the synthetic speech perception by individuals with developmental communicative and intellectual impairments. The final aim is to present the research on effects of synthetic speech output on acquisition of graphic symbols by individuals with developmental disabilities.

PERCEPTION OF SYNTHETIC SPEECH BY PERSONS WITH INTELLECTUAL DISABILITIES Data from the United States, Department of Education (2002) indicate that 18.7% of the children ages 6 through 21 who receive services under the Individuals with Disabilities Education Act have a diagnosed speech and/or language impairment and 9.9% have a diagnosed intellectual impairment. Further, about 3.5% and 1.0% of individuals with intellectual impairment fall in the categories of severe and profound impairment respectively

(Rosenberg & Abbeduto, 1993). Many individuals with severe-to-profound intellectual disabilities and severe communication impairments are potential candidates for SGDs. Thus, it is critical to investigate the factors that influence synthetic speech perception in individuals with intellectual impairment. Unlike non-electronic communication books and boards, SGDs provide speech output (synthetic or digitized) to the individual user and the communication partner (Church & Glennen, 1992). A retrospective study conducted by Mirenda, Wilk, & Carson, (2000) on the use of assistive technology by individuals with autism and intellectual impairment indicated that 63.6% of the students with severe intellectual impairment used SGDs to augment their communication. Although substantial research exists on the perception of synthetic speech systems by typical individuals (e.g., Duffy & Pisoni, 1992; Higginbotham & Baird, 1995; Koul & Allen, 1993; Logan, Greene, & Pisoni, 1989; Mirenda & Beukelman, 1987, 1990), limited data are available about the intelligibility and comprehension of synthetic speech by individuals with intellectual disabilities (Koul & Hester, 2006; Koul & Clapsaddle, 2006; Koul & Hanners, 1997; Willis, Koul, & Paschall, 2000). Further, there are differences in aspects of natural language comprehension and information-processing between individuals with intellectual disabilities and mental-age matched typical peers (e.g., Abbeduto, Furman, & Davies, 1989; Abbdeduto & Nuccio, 1991; Berry, 1972; Kail, 1992; Merrill & Jackson, 1992; Rosenberg, 1982; Taylor, Sternberg, & Richards, 1995). Individuals with intellectual disabilities have receptive language delays that exceed their cognitive delays (Abbeduto et al., 1989) and they demonstrate difficulty understanding linguistic information that requires extensive analysis of the acoustic-phonetic aspects of the speaker’s words (Abbeduto & Rosenberg, 1992). These differences in language and cognitive domains between typical individuals and individuals with intellectual impairments make it difficult to generalize find-

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ings obtained from research in synthetic speech perception with typical individuals to individuals with disabilities. The following sections will focus on perception of synthetic speech by individuals with intellectual disabilities across word, sentence, and discourse tasks.

Single Words Koul and Hester (2006) examined the perception of synthetic speech by individuals with severe intellectual impairment and severe speech and language impairment using a single-word closedformat task. They reported that individuals with severe intellectual and language impairment (Mean percent correct word identification score =80.95) exhibited significantly greater difficulties than mental age matched typical individuals (Mean percent correct word identification score =91.19). In contrast, no significant differences were observed between individuals with mildto-moderate intellectual disabilities and typical individuals on a single word identification task (Koul & Clapsaddle, 2006; Koul & Hanners, 1997). Using an ETI Eloquence synthesizer, Koul & Clapsaddle (2006) reported a mean word identification score of 92% for participants with intellectual disabilities and a mean score of 96% for typical participants. Similar results were obtained by Koul and Hanners (1997). They reported a mean percent single-word accuracy score of 99% and 99% for DECtalkTM male and female voices respectively for typical individuals. Participants with intellectual disabilities in this study obtained a mean percent single-word accuracy score of 98% for each of the DECtalkTM male and female voices. These results show that perception of single words presented using synthetic speech in a closed-set task by persons with mild-to-moderate intellectual impairment is similar to that of mentalage matched typical peers. However, persons with severe intellectual impairment exhibit greater difficulty in understanding single words presented

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in synthetic speech in comparison to matched typical individuals.

Sentences Two studies that have investigated perception of sentences presented in synthetic speech indicate that individuals with mild-to-moderate intellectual disabilities obtain significantly lower accuracy scores than typical individuals on sentence verification and identification tasks (Koul & Hanners, 1997; Koul & Clapsaddle, 2006). Koul and Hanners used a sentence verification task to study perception of synthetic sentences. In this task, participants first had to comprehend a sentence, then make a judgment based on world knowledge as to whether sentence was true (e.g., rain is wet) or false (e.g., bears build houses). Results indicated that sentence verification scores for individuals with intellectual disabilities were significantly lower than those for typical individuals for DECtalkTM male and female voices. Participants with intellectual disabilities obtained mean sentence verification scores of 90% and 85% for DECtalkTM male and female voices respectively. In contrast, typical participants obtained sentence verification scores of 99% and 97% respectively. These results were supported by Koul & Clapsaddle who used a sentence identification task. For this task, the participant heard a series of sentences preceded by a carrier phrase and then pointed to a drawing depicting the sentence. Participants with intellectual disabilities obtained a mean sentence identification score of 80.35 across three trials. Their performance was substantially lower than the performance of typical individuals who obtained a mean score of 89% across three trials. In summary, the current data on perception of three-tofour word sentences presented in synthetic speech indicate that persons with mild-to-moderate intellectual disabilities exhibit greater difficulties in perception of even high-quality synthetic speech (e.g., DECtalkTM, ETI Eloquence synthesizer) than matched typical individuals. However, it is

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important for clinicians and educators to realize that irrespective of the statistically significant differences between persons with intellectual disabilities and typical individuals on sentence perception tasks, the ability of individuals with mild-to-moderate intellectual disabilities to understand 80% to 90% of sentences presented to them in synthetic speech has significant clinical and educational implications. This indicates that people with intellectual impairments who use synthetic speech can understand most, if not all sentences produced by their SGDs.

Discourse Individuals who use SGDs must correctly identify and derive meaning from all of the words in the sentence or message before the next sentence begins (Willis, Koul, & Paschall, 2000). Thus, discourse comprehension for a SGD user involves not only deciphering the acoustic and phonetic properties of the speech signal produced by their devices, but also integrating sentences, connecting conversational turns and deriving meaning using linguistic and world knowledge. To facilitate effective interactions between SGD users and their listeners, it is critical that users comprehend the synthetic speech produced by their SGDs for feedback purposes. In the absence of such feedback, SGD users will not be able to sustain conversations as required for effective and efficient communication (Koul, 2003). Although, substantial data exist on the discourse comprehension of synthetic speech by typical individuals (e.g., Higginbotham, Drazek, Kowarsky, & Scally, 1994; Paris, Gilson, Thomas, & Silver, 1995; Ralston, Pisoni, Lively, Greene, & Mullennix, 1991), little research is available on the discourse comprehension of synthetic speech by individuals with intellectual disabilities (Willis, Koul, & Paschall, 2000). Willis et al. (2000) evaluated the performance of a group of individuals with mild-to-moderate intellectual disabilities on a post-perceptual discourse comprehension task. Three synthetic

voices (DECtalkTM Paul, MacinTalkTM Fred, and RealVoiceTM) were used to present three first grade level passages. The passages were matched for complexity; participants listened to the passages and then responded to multiple choice questions by pointing to pictures on a computer screen. Results revealed superior comprehension scores for DECtalkTM compared to the other two relatively low-quality synthesizers (i.e., RealVoiceTM and MacinTalk FredTM). Additionally, authors reported that, like typical individuals persons with intellectual impairment use information from passages together with world knowledge in selecting an answer. Furthermore, the types of errors made in a task involving comprehension of synthetic speech were similar to those involving comprehension of natural speech. These results indicate that strategies used to comprehend conversations or text by individuals with intellectual and communicative disabilities do not differ across natural and synthetic speech.

Practice Effects A strong body of research indicates that typical listeners become much more adept at correctly recognizing synthetic stimuli as a result of repeated exposure to it (e.g., Greenspan, Nusbaum, & Pisoni, 1988; McNaughton, Fallon, Tod, Weiner, & Neisworth, 1994; Reynolds, Isaacs-Duvall, Sheward, & Rotter, 2000; Rounsefell, Zucker, & Roberts, 1993; Schwab, Nusbaum, & Pisoni, 1985; Venkatagiri, 1994). In contrast, only limited data are available on effects of repeated exposure to synthetic speech in individuals with intellectual and communicative impairments (Koul & Hester, 2006; Koul & Clapsaddle, 2006; Koul & Hanners, 1997, Willis et al., 2000). These data indicate that both individuals with severe intellectual impairment and individuals with mild to moderate intellectual impairment demonstrate significant practice effect as a result of repeated exposure to high quality synthetic speech. Koul and Hester reported that individuals with severe

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intellectual impairment demonstrated significant reduction in their response latencies for single synthetic words as a result of repeated exposure to synthesized speech produced using DECtalkTM synthesizer. These results are supported by Koul & Clapsaddle who reported that individuals with mild-to-moderate intellectual impairment demonstrate significant improvement in both single word and sentence accuracy scores as a result of repeated listening to synthetic speech produced using ETI Eloquence speech synthesizer. Further, the most interesting finding of the above two reviewed studies with individuals with intellectual impairment was the absence of significant effect for stimulus type (i.e., novel vs. repeated) stimuli. The repeated stimuli were presented across all sessions, whereas a different list of novel stimuli was presented in each listening session. It was anticipated that practice effects for repeated stimuli would be higher than those for novel stimuli because individuals with intellectual impairment may require more redundancy than their typical mental age matched peers to comprehend linguistic stimuli (Haring, McCormick, & Haring, 1994). However, the results of these studies indicate that individuals with intellectual impairment are able to generalize the knowledge of acoustic-phonetic properties of synthetic speech to novel synthetic stimuli. The ability of individuals with intellectual and communicative impairments to generalize to novel synthetic stimuli has significant clinical implications, as SGDs are increasingly being used by these individuals for information transfer and interpersonal communication.

Discussion Although, only limited data are available on the perception of synthetic speech by individuals with intellectual disabilities, two conclusions are evident. The first is that repeated exposure to synthetic speech allows individuals with intellectual and communicative disabilities to identify synthetic speech with increased speed and accuracy. The

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second conclusion indicates that as the synthetic speech stimuli or the listening task becomes more complex, the accuracy of responses to such stimuli and/or tasks is reduced and the response latency is increased. The latter conclusion is also true for typical individuals. The difficulty that both typical listeners and listeners with intellectual disabilities experience in processing synthetic speech can be explained through a resource-sharing model of spoken language comprehension (Kintsch & van Dijk, 1978; LaBerge & Samuels, 1974; Moray, 1967). This model proposes that all analyses in the language comprehension system share a limited pool of cognitive resources required for processing information. Thus, according to Koul and Hester (2006) and Duffy and Pisoni (1992), listening to even high-quality synthetic speech may require that a substantial portion of cognitive resources be allocated to deciphering the acoustic-phonetic structure of synthetic speech, leaving fewer resources available for higher level processing such as understanding the word and the semantic content of the message. Persons with intellectual disabilities have reduced cognitive capacity as compared to their typical peers, and their performance seems to rapidly deteriorate as the complexity of the synthetic speech stimuli increases (Haring, McCormick, & Haring, 1994; Hutt & Gibby, 1979; Kail, 1992; Taylor et al., 1995). However, significant practice effects observed in individuals with intellectual disabilities following repeated listening to synthetic speech may be the result of their ability to learn to analyze the acoustic-phonetic structure of synthetic speech in a more efficient manner (Koul & Hester, 2006; Koul & Clapsaddle, 2006). Thus, it can be extrapolated that repeated listening to synthetic speech results in individuals with intellectual disabilities devoting minimal resources to processing the acoustic-phonetic properties of synthetic speech and greater resources to extracting meaning from the synthetic speech signal. The possible shifting of cognitive resources away from processes involved in identifying phonemes, syllables, and

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words to processes that are involved in extracting semantic content of the message may result in both faster and accurate recognition of synthetic speech stimuli.

PERCEPTION OF SYNTHETIC SPEECH BY INDIVIDUALS WITH HEARING IMPAIRMENT It is important to investigate perception of synthetic speech in individuals with hearing impairment because many individuals with developmental and acquired disabilities who may benefit from a SGD also demonstrate hearing loss. Twenty percent of individuals with cerebral palsy present with hearing deficits (Robinson, 1973). Hearing loss may also co-occur with neurological disorders, such as Parkinson’s disease, amyotrophic lateral sclerosis (ALS), and aphasia as a consequence of stroke. Further, the communication partners of SGD users may also have hearing impairment. It is estimated that one in three people older than 60 and half of those older than 85 have hearing loss in the United States (National Institute of Deafness and Other Communication Disorders, 2001). Research addressing perception of synthetic speech in individuals with hearing impairment indicates that hearing loss may not have a detrimental influence on the processing of synthetic speech (Kangas & Allen, 1990; Humes, Nelson, & Pisoni, 1991). Humes et al. (1991) provided evidence that for listeners with hearing impairment, DECtalkTM synthetic speech is as intelligible as natural speech. The authors investigated the performance of three groups of participants: older individuals with hearing impairments; young individuals who listened to natural speech and DECtalkTM speech in the presence of background noise; and young adults who listened to DECtalk TM speech and natural speech in quiet. Their results indicated that there was no difference in performance on a single word task between elderly hearing-impaired individuals and young adults who listened to DECtalkTM

synthetic speech in the presence of background noise (i.e., simulated hearing loss condition). Kangas and Allen (1990) also reported that in hearing impaired individuals, the ability to recognize single synthetic words was not affected by the degraded acoustic-phonetic properties of the synthesized speech. These authors presented a list of words using DECtalkTM to two groups of older adults: a normal hearing group and a group with acquired sensori-neural hearing loss. Results indicated that intelligibility scores for synthetic speech were significantly lower than those for natural speech across groups. Further, intelligibility scores for individuals with hearing impairment were significantly lower than those for normal hearing listeners across synthetic and natural voices. However, there was no significant interaction between hearing ability and voice type.

DISCUSSION Current research indicates that hearing impairment affects processing of synthetic and natural speech in an identical manner.

PERCEPTION OF SYNTHETIC SPEECH BY INDIVIDUALS WITH SPECIFIC LANGUAGE IMPAIRMENT Individuals with specific language impairment (SLI) demonstrate language disorders in the absence of underlying deficits such as intellectual impairment, hearing impairment, motor impairment, emotional disturbance or environmental deprivation (Bishop, 1992). Synthetic speech stimuli have been used to investigate the nature of auditory perceptual deficits in individuals with SLI (Evans, Viele, Kass, & Tang, 2002; Reynolds & Fucci, 1998).Individuals with SLI are not candidates for SGDs and are able to communicate using speech. However, it is critical to understand perception of synthetic speech in people with audi-

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tory perceptual deficits because many individuals who either use or are candidates for SGDs may also demonstrate auditory processing disorders. Previous research suggests that performance of individuals with SLI on tasks that involve processing of synthetic stimuli is significantly lower than matched typical individuals (Evans et al., 2002; Reynolds & Fucci,1998). Reynolds and Fucci observed that response latencies for DECtalkTM synthetic speech were longer than for natural speech for typical children as well as children with SLI. However, there was no significant interaction between group (i.e., typical vs. SLI) and voice (i.e., synthetic speech vs. natural speech). This indicates that synthetic and natural speech may be processed in a similar manner by individuals with SLI. Relatively longer latencies on synthetic speech for both typical children and children with SLI may be due to lack of acoustic phonetic redundancy of synthetic speech. Further, Massey (1988) observed that individuals with language impairment demonstrate greater difficulty understanding synthetic speech than natural speech. However, in contrast to the results obtained by Reynolds and Fucci, no significant differences in accuracy scores between natural and synthetic speech were noted for the typical group. The difference in results between the two above referenced studies may be due to the nature of the experimental task. Reynolds and Fucci used a relatively more sensitive latency task whereas Massey used a less sensitive intelligibility task.

EFFECTS OF SPEECH OUTPUT ON ACQUISTION OF GRAPHIC SYMBOLS Graphic symbols such as photographs and line drawings have often been used as an alternative form of communication for individuals with little or no functional speech. Further, many SGDs provide in-built software programs that produce synthetic speech upon activation of graphic sym-

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bols. Additionally, there are many stand-alone graphic symbol software programs that are compatible with personal computers and allow the user to use those computers as SGDs. One of the variables that has been noted to influence graphic symbol acquisition is iconicity (Brown, 1978). Iconicity can be viewed on a continuum. At one end of the continuum are transparent symbols and at the other end are opaque symbols. Transparent symbols are those that can be easily guessed in the absence of cues such as written words or verbal hints. The opaque symbols are those that bear no relationship to the referents that they represent, and the meaning of opaque symbols cannot be deciphered even when both symbol and referent are presented together. Translucent symbols fall along the middle of the continuum between transparency and opaqueness. The relationship between a translucent symbol and its referent may only be perceived when the symbol and the referent appear together (Bellugi & Klima, 1976; Lloyd & Fuller, 1990). Low translucent symbols like opaque symbols have no to little resemblance to their referents and high translucent symbols like transparent symbols have some resemblance to their referents. A number of studies have been conducted to investigate whether adults with severe to profound intellectual disability learn to associate transparent, translucent, and/or opaque symbols with referents more efficiently with synthetic speech output than without it (Koul & Schlosser, 2004; Romski & Sevcik, 1996; Schlosser, Belfiore, Nigam, Blischak, & Hetzroni, 1995). Schlosser et al. investigated the effects of synthetic speech output on acquisition of opaque symbols in three young adults with severe to profound intellectual disabilities. They observed that the provision of synthetic speech output in association with the selection of a target symbol resulted in a more efficient acquisition of graphic symbols. Also, there were fewer errors in the condition in which synthetic speech output occurred in association with the selection of a graphic symbol in contrast to the condition in which selection of a graphic

Synthetic Speech Perception in Individuals with Intellectual and Communicative Disabilities

symbol did not result in the production of a synthesized word representing that symbol. Koul and Schlosser (2004) examined the effects of synthetic speech output on the learning of symbols high in translucency versus symbols low in translucency. Two adults with little or no functional speech and severe intellectual disabilities served as participants. Both participants learned more low translucent symbols in the synthetic speech output condition. For the non-speech output condition, no consistent across subject differences were obtained. In the speech output condition, the participants’ selection of a target symbol resulted in the production of a verbal equivalent of that symbol in synthetic speech. In the non-speech output condition, the synthetic speech output did not accompany selection of a target symbol. The results of this study appear to support the hypothesis that feedback from speech output may facilitate acquisition of low translucent and opaque graphic symbols in adults with severe intellectual and communicative disabilities. The effects of synthetic speech output on requesting behavior of children with autism was investigated by Schlosser et al., (2007). Participants were trained to request preferred objects using opaque graphic symbols in two conditions. In one condition, the participants heard synthetic speech upon selection of a graphic symbol and in the second condition, the synthetic speech output did not accompany selection of a graphic symbol. The results of this study were mixed. Only two of the five participants requested objects using opaque symbols more effectively with speech output than without it. Two of the remaining three participants did not show any difference in requesting behavior between speech output and non-speech output conditions. One participant did better in the non-speech output condition. The authors indicate that the inconsistency of the results across subjects may have been due to methodological constraints that increased task difficulty beyond a desirable level.

The positive effects of speech output on the acquisition of opaque graphic symbols by individuals with severe intellectual disabilities has also been observed in studies in which synthetic speech output was one of the components of the treatment package (Romski, Sevcik, Robinson, & Bakeman, 1994).

DISCUSSION In summary, research indicates that synthetic speech output has a positive effect on learning of graphic symbols by individuals with severe speech, language, and intellectual disabilities. Further, synthetic speech output allows the individual with little or no functional speech to compensate for the use of a visually based graphic symbol or orthographic system. The speech output by connecting the visual communication system with an auditory modality facilitates communicative interactions by providing communicative partners, a familiar auditory signal to comprehend the intended message (Romski, Sevcik, Cheslock, & Barton, 2006).

DIRECTIONS FOR FUTURE RESEARCH Great technological strides have been made in producing text-to-speech systems in the past twenty years. SGDs are being increasingly used to enhance and facilitate the communicative abilities of individuals with a range of developmental and acquired communication disorders. However, there are very little empirical data on the perception of synthetic speech by individuals with little or no functional speech and on the effects of synthetic speech output on the acquisition of symbols and other communicative behaviors such as requesting, choice making, and exchanging information. It is hoped that this chapter will focus the attention of researchers and clinicians on identifying variables

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that can facilitate clinical and educational applications of synthetic speech.

REFERENCES Abbeduto, L., Furman, L., & Davies, B. (1989). Relation between the receptive language and mental age of persons with mental retardation. American Journal of Mental Retardation, 93, 535–543. Abbeduto, L., & Nuccio, J. B. (1991). Relation between receptive language and cognitive maturity in persons with intellectual disabilities. American Journal of Intellectual Disabilities, 96, 143–149. Abbeduto, L., & Rosenberg, S. (1992). Linguistic communication in persons with mental retardation. In S. Warren & J. Reichle (Eds.), Causes and effects in communication and language intervention (pp. 331-359). Maryland: Paul H. Brookes. Bellugi, U., & Klima, E. S. (1976). Two faces of sign: Iconic and abstract. Annals of the New York Academy of Sciences, 280, 514–538. doi:10.1111/j.1749-6632.1976.tb25514.x Berry, B. P. (1972). Comprehension of possessive and present continuous sentences by nonretarded, mildly retarded, and severely retarded children. American Journal of Mental Deficiency, 76, 540–544. Bishop, D. V. M. (1992). The underlying nature of specific language impairment. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 33, 3–66. doi:10.1111/j.1469-7610.1992. tb00858.x Brown, R. (1978). Why are signed languages easier to learn than spoken languages? (Part Two). Bulletin - American Academy of Arts and Sciences. American Academy of Arts and Sciences, 32, 25–44. doi:10.2307/3823113

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Church, G., & Glennen, S. (1992). The handbook of assistive technology. San Diego: Singular Publishing Co. Duffy, S. A., & Pisoni, D. B. (1992). Comprehension of synthetic speech produced by rule: A review and theoretical interpretation. Language and Speech, 35, 351–389. Evans, J. L., Viele, K., Kass, R. E., & Tang, F. (2002). Grammatical morphology and perception of synthetic and natural speech in children with specific language impairments. Journal of Speech, Language, and Hearing Research: JSLHR, 45, 494–504. doi:10.1044/1092-4388(2002/039) Greenspan, S. L., Nusbaum, H. C., & Pisoni, D. B. (1988). Perceptual learning of synthetic speech produced by rule. Journal of Experimental Psychology. Human Perception and Performance, 14, 421–433. Haring, N. G., McCormick, L., & Haring, T. G. (Eds.). (1994). Exceptional children and youth (6th ed.). New York: Merrill. Higginbotham, D. J., & Baird, E. (1995). Analysis of listeners’ summaries of synthesized speech passages. Augmentative and Alternative Communication, 11, 101–112. doi:10.1080/0743461 9512331277199 Higginbotham, D. J., Drazek, A. L., Kowarsky, K., Scally, C. A., & Segal, E. (1994). Discourse comprehension of synthetic speech delivered at normal and slow presentation rates. Augmentative and Alternative Communication, 10, 191–202. do i:10.1080/07434619412331276900 Humes, L. E., Nelson, K. J., & Pisoni, D. B. (1991). Recognition of synthetic speech by hearing-impaired listeners. Journal of Speech and Hearing Research, 34, 1180–1184. Hutt, M. L., & Gibby, R. G. (1979). The mentally retarded child: Development training and education (4th ed.). Boston: Allyn and Bacon.

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Kail, R. (1992). General slowing of information processing by persons with mental retardation. American Journal of Mental Retardation, 97, 333–341. Kangas, K. A., & Allen, G. D. (1990). Intelligibility of synthetic speech for normal-hearing and hearing-impaired listeners. The Journal of Speech and Hearing Disorders, 55, 751–755. Kintsch, W., & van Dijk, T. A. (1978). Towards a model for text comprehension and production. Psychological Review, 85, 363–394. doi:10.1037/0033-295X.85.5.363 Koul, R. K. (2003). Synthetic speech perception in individuals with and without disabilities. Augmentative and Alternative Communication, 19, 49–58. doi:10.1080/0743461031000073092 Koul, R. K., & Allen, G. D. (1993). Segmental intelligibility and speech interference thresholds of high-quality synthetic speech in the presence of noise. Journal of Speech and Hearing Research, 36, 790–798. Koul, R. K., & Clapsaddle, K. C. (2006). Effects of repeated listening experiences on the perception of synthetic speech by individuals with mild-tomoderate intellectual disabilities. Augmentative and Alternative Communication, 22, 1–11. doi:10.1080/07434610500389116 Koul, R. K., & Hanners, J. (1997). Word identification and sentence verification of two synthetic speech systems by individuals with intellectual disabilities. Augmentative and Alternative Communication, 13, 99–107. doi:10.1080/07434619 712331277898 Koul, R. K., & Hester, K. (2006). Effects of repeated listening experiences on the recognition of synthetic speech by individuals with severe intellectual disabilities. Journal of Speech, Language, and Hearing Research: JSLHR, 49, 1–11.

Koul, R. K., & Schlosser, R. W. (2004). Effects of synthetic speech output in the learning of graphic symbols of varied iconicity. Disability and Rehabilitation, 26, 1278–1285. doi:10.1080 /09638280412331280299 LaBerge, D., & Samuels, S. L. (1974). Toward a theory of automatic information processing in reading. Cognitive Psychology, 6, 293–323. doi:10.1016/0010-0285(74)90015-2 Lloyd, L. L., & Fuller, D. R. (1990). The role of iconicity in augmentative and alternative communication symbol learning. In W. I. Fraser (Ed.), Key issues in mental retardation research (pp. 295-306). London: Routledge. Logan, J. S., Greene, B. G., & Pisoni, D. B. (1989). Segmental intelligibility of synthetic speech produced by rule. The Journal of the Acoustical Society of America, 86, 566–581. doi:10.1121/1.398236 Massey, H. J. (1988). Language-impaired children’s comprehension of synthesized speech. Language, Speech, and Hearing Services in Schools, 19, 401–409. McNaughton, D., Fallon, D., Tod, J., Weiner, F., & Neisworth, J. (1994). Effects of repeated listening experiences on the intelligibility of synthesized speech. Augmentative and Alternative Communication, 10, 161–168. doi:10.1080/074346194 12331276870 Merrill, E. C., & Jackson, T. S. (1992). Sentence processing by adolescents with and without intellectual disabilities. American Journal on Intellectual Disabilities, 97, 342–350. Mirenda, P., & Beukelman, D. R. (1987). A comparison of speech synthesis intelligibility with listeners from three age groups. Augmentative and Alternative Communication, 5, 84–88.

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Mirenda, P., & Beukelman, D. R. (1990). A comparison of intelligibility among natural speech and seven speech synthesizers with listeners from three age groups. Augmentative and Alternative Communication, 6, 61–68. doi:10.1080/074346 19012331275324 Mirenda, P., Wilk, D., & Carson, P. (2000). A retrospective analysis of technology use patterns of students with autism over a five-year period. Journal of Special Education Technology, 15, 5–6. Moray, N. (1967). Where is capacity limited? A survey and a model. Acta Psychologica, 27, 84–92. doi:10.1016/0001-6918(67)90048-0 National Institute of Deafness and Other Communication Disorders. (2001). About hearing. Retrieved October 24, 2001 from http://www. nidcd.nih.gov/health Paris, C. R., Gilson, R. D., Thomas, M. H., & Silver, N. C. (1995). Effect of synthetic voice on intelligibility on speech comprehension. Human Factors, 37, 335–340. doi:10.1518/001872095779064609 Ralston, J. V., Pisoni, D. B., Lively, S. E., Greene, B. G., & Mullennix, J. W. (1991). Comprehension of synthetic speech produced by rule: Word monitoring and sentence-by-sentence listening times. Human Factors, 33, 471–491. Reynolds, M. E., & Fucci, D. (1998). Synthetic speech comprehension: A comparison of children with normal and impaired language skills. Journal of Speech, Language, and Hearing Research: JSLHR, 41, 458–466. Reynolds, M. E., Isaacs-Duvall, C., Sheward, B., & Rotter, M. (2000). Examination of the effects of listening practice on synthesized speech comprehension. Augmentative and Alternative Communication, 16, 250–259. doi:10.1080/074 34610012331279104

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Robinson, R. O. (1973). The frequency of other handicaps in children with cerebral palsy. Developmental Medicine and Child Neurology, 15, 305–312. Romski, M. A., & Sevcik, R. A. (1996). Breaking the speech barrier: Language development through augmented means. Baltimore: Brookes. Romski, M. A., Sevcik, R. A., Cheslock, M., & Barton, A. (2006). The System for Augmenting Language: AAC and emerging language intervention. In R. J. McCauley & M. Fey (Eds.), Treatment of language disorders in children (pp. 123-147). Baltimore: Paul H. Brookes Publishing Co. Romski, M. A., Sevcik, R. A., Robinson, B., & Bakeman, R. (1994). Adult-directed communications of youth with intellectual disabilities using the system for augmenting language. Journal of Speech and Hearing Research, 37, 617–628. Rosenberg, S. (1982). The language of the mentally retarded: Development, processes, and intervention. In S. Rosenberg (Ed.), Handbook of applied psycholinguistics: Major thrusts of research and theory (pp.329-392). Hillsdale, NJ: Erlbaum. Rosenberg, S., & Abbeduto, L. (1993). Language and communication in mental retardation: Development, processes, and intervention. Hillsdale, NJ: Erlbaum. Rounsefell, S., Zucker, S. H., & Roberts, T. G. (1993). Effects of listener training on intelligibility of augmentative and alternative speech in the secondary classroom. Education and Training in Mental Retardation, 12, 296–308. Schlosser, R. W., Belfiore, P. J., Nigam, R., Blischak, D., & Hetzroni, O. (1995). The effects of speech output technology in the learning of graphic symbols. Journal of Applied Behavior Analysis, 28, 537–549. doi:10.1901/jaba.1995.28-537

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Schlosser, R. W., Sigafoos, J., Luiselli, J. K., Angermeier, K., Harasymowyz, U., Schooley, K., & Belfiore, P. J. (2007). Effects of synthetic speech output on requesting and natural speech production in children with autism: A preliminary study. Research in Autism Spectrum Disorders, 1, 139–163. doi:10.1016/j.rasd.2006.10.001 Schwab, E. C., Nusbaum, H. C., & Pisoni, D. B. (1985). Some effects of training on the perception of synthetic speech. Human Factors, 27, 395–408. Taylor, R. L., Sternberg, L., & Richards, S. B. (1995). Exceptional children: Integrating research and teaching (2nd ed.). San Diego: Singular.

U. S. Department of Education. (2002). Implementation of the Individuals with Disabilities Education Act: Twenty-first annual report to congress. Washington, DC: Author. Venkatagiri, H. S. (1994). Effects of sentence length and exposure on the intelligibility of synthesized speech. Augmentative and Alternative Communication, 10, 96–104. doi:10.1080/0743 4619412331276800 Willis, L., Koul, R., & Paschall, D. (2000). Discourse comprehension of synthetic speech by individuals with mental retardation. Education and Training in Mental Retardation and Developmental Disabilities, 35, 106–114.

This work was previously published in Computer Synthesized Speech Technologies: Tools for Aiding Impairment, edited by John Mullennix and Steven Stern, pp. 177-187, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 5.12

Telepractice:

A 21st Century Model of Health Care Delivery Thomas W. Miller University of Connecticut, USA Jennifer A. Wood VA Texas Valley Coastal Bend Health Care System, USA

ABSTRACT Telehealth is viewed as the removal of time and distance barriers in the provision of health care and patient education to underserved populations. Examined is a twenty first century clinical consultation model of healthcare. Offered are specific applications within a broad spectrum of services utilizing telehealth technology. Important technology shifts for administrative paradigms, clinical models, and educational information technology DOI: 10.4018/978-1-60960-561-2.ch512

for healthcare services through telehealth technology are examined. The future of telehealth and its interface with various critical components of society needs to examine the potential benefits over risks in providing healthcare consultations and services through the educational settings available. Addressed is a technology model, which demonstrates the capability of reducing time and distance barriers in the provision of health care and education through telehealth technology. The use of telehealth technology in rural settings is seen as a viable medium for providing needed diagnostic and clinical consultation for underserved and rural.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Telepractice

INTRODUCTION The purpose of this chapter is to examine contemporary applications of telepractice within a rural community setting.. A brief review of current data regarding the impact of telepractice upon access and satisfaction with clinical care is provided. A clinical telepractice consultation model is outlined along with illustrative vignettes. Readers are also provided with suggested clinical practice guidelines and practical considerations for telepractice implementation. Telehealth, or the use of telecommunication technology to provide access to health assessment, diagnosis, intervention, consultation, supervision, education, and information across distance, has become a well recognized vehicle for delivering services and disseminating information to a variety of consumer populations as well as professionals and practitioners (Nickelson, 1998; Miller & Hutchins 2008). Given its ability to transcend many of the economic, cultural, and geographic barriers that often prohibit or restrict the provision of health care, the use of telehealth has reshaped traditional systems of care. Moreover, due to its unique capacity to negate many of the traditional obstacles in service delivery, telehealth is often a desirable option for the provision of health care to rural, confined, underserved or isolated groups (Miller & Holcomb 2007). As a result, a large proportion of telehealth studies have focused on evaluating the effectiveness of telecommunications technology in delivering health services to rural and specialty populations (Wood, 2000). Numerous studies suggest that telehealth can be successfully utilized to improve access to health care services amongst underserved populations and that the quality of care delivered via telehealth is similar to or surpasses that of face-to-face services (Bischoff, Hollist, Smith, & Frank, 2004; Miller, Miller, Kraus, & Sprang, 2003; Norman, 2006) while maintaining a high degree of satisfaction

Since its inception, one of the primary advantages of telepractice has been its ability to improve access to health care services for people living in rural or remote areas where health care professionals are often scarce or absent. In the words of Nickelson (1998), “Telehealth is simply a tool that…makes it easier to practice already established professional skills across distance and to serve individuals and organizations who may not, but for telehealth, have access to such services” (p. 527). This ability to transcend geographic barriers has been the basis for three decades worth of demonstration projects targeted at rural populations. The use of telepractice to improve access to mental health care has since expanded to include other isolated groups, such as inner city families (Yellowlees, 2005; McLaren, Blunden, Lipsedge, & Summerfield, 1996; Straker, Mostyn, & Marshall, 1976), prison inmates (Ax et al, 2007), and homebound elderly (Maheu, Whitten, & Allen, 2001). Overall, these projects suggest that the use of telepractice is an effective means of improving access to both health care services as well as improving the exchange of information between providers (Blackmon, Kaak, & Ranseen, 1997). Efforts to assess the quality of telepractice care compared to traditional face-to-face services indicate that there is little difference in diagnostic and assessment outcomes across the two treatment modes (Ball & Puffett, 1998; Biggins, 2000; Zarate, et al., 1997 and that telehealth applications may serve to enhance the continuity and efficiency of care (Ghosh, McLaren, & Watson, 1997). Research assessing patient and provider satisfaction with telecare services reveals uniformly positive results (Miller, 2006; Morgan, Patrick, & Magaletta, 2008; Wood, 2006). In one of the earliest studies of patient satisfaction with telepractice, Solow, Weiss, & Bergen (1971) reported that patient acceptance was impressively high even among highly paranoid patients. Since that time, further research has indicated that satisfaction with telepractce care remains high even when patients are acutely or chronically psychotic or

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agitated (Kavanagh & Yellowless, 1995). Such data are especially relevant in the public sector where mental health services are often lacking. As a response to service deficits, prison systems (e.g., the Federal Bureau of Prisons, the Texas Department of Criminal Justice) have developed sophisticated telepractice care networks. It is noted that offenders are generally satisfied with mental health services received via telepractice (Leonard, 2004; NIJ, 2002). Of particular importance is that incarcerated mentally ill offenders, historically perceived as resistant to mental health services, reported no significant differences in the working alliance between treatment provider and client, post-session mood or treatment satisfaction when receiving psychiatric services regardless of method of service delivery (i.e., telemedicine vs. face-to-face) (Morgan et al., 2008). Similar findings have been found among parents and children who participate in telepsychiatric consultations (Blackmon, Kaak, & Ranseen, 1997), geriatric clients (Wood, O’Quin, & Eftink, 2004), innercity families (Straker, Mostyn, & Marshall 1976), active-duty military personnel (Jerome, 1999), veterans (Wood, 2006), and adults with mild to moderate mental retardation and their mental health care providers (Wood & Hargrove, 2006).

TELEPRACTICE CARE MODEL Realizing its high level of user acceptability and distinct ability to transcend geographic and social barriers, a visionary telehealth model for public education that focuses on the role of psychology and its interdisciplinary partners can be used to create a national, regional, state and local stratified network of health care professionals and other stakeholders engaged in a nationwide public education campaign (Miller, 2007). The Telehealth Intervention Project (TIP) is a proposed model designed to offer teleconferenced psychological and health related informational and demonstrational sessions focused on a broad spectrum of health

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related topics. Telepractice technology is uniquely suited to reach underserved populations and rural practitioners offering relevant evidence-based psychological information to the community. The telepractice network can be used to educate a range of target populations including clients, service providers, educators and community personnel including children and families, school populations, prison inmates, legislators, public officials, and inpatients and outpatients in various public and private sector programs. As modeled in Figure 1, telepractice affords a sophisticated medium for reaching these targeted populations (consumers and providers) by making psychology a household word. Outlined below are mechanisms (processes) for successfully implementing the Telehealth Intervention Project, a proposed model for implementing a dimension of the public education campaign to reach underserved target groups in need of health and prevention education information nationally.

USE COMMUNICATION STRATEGICALLY TO IMPROVE HEALTHCARE Health care communication encompasses the study and use of communication strategies to inform and influence individual and community decisions that enhance health. It links the domains of communication and health and is increasingly recognized as a necessary element of efforts to improve personal and public health (Kreuter, Strecher, & Glassman, 1999). Health care communication can contribute to all aspects of disease prevention and health promotion and is relevant in a number of contexts, including (1) health professional-patient relations, (2) individuals’ exposure to, search for, and use of health information, (3) individuals’ adherence to clinical recommendations and regimens, (4) the construction of public health messages and campaigns, (5) the dissemination of individual and population health risk information,

Telepractice

Figure 1. A telepractice intervention model for rural health care. Adapted from: Miller, T.W.; Miller, J.M.; Burton, D. (2003). Telehealth: A model for clinical supervision in allied health. The Internet Journal of Allied Health Sciences & Practices, 1(2)

that is, risk communication, (6) images of health in the mass media and the culture at large, (7) the education of consumers about how to gain access to the public health and health care systems, and (8) the development of telehealth applications (Miller, Burton, & Kraus, 2004; Wood, Miller, & Hargrove, 2005; Miller, 2007) Utilizing health care communication can help raise awareness of health risks (including physical and psychological) and solutions, provide the motivation and skills needed to reduce these risks, help consumers find support from other people in similar situations, and affect or reinforce attitudes. Health care communication also can increase demand for appropriate health services and decrease demand for inappropriate health services. It can make available information to assist in making complex choices, such as selecting health plans, care providers, and treatments. At a macro level, health communication can be used to influence the public agenda, advocate for policies and programs, promote positive changes in the socioeconomic and physical environments, improve the delivery of public health and health care services, and encourage social norms that benefit health and quality of life (Monagan, 2010).

Health care communication may also inform health care providers for improved service delivery. The Institute of Medicine (IOM) criticized current health care training and reported that training of health professionals requires a “major overhaul.” Specifically, the IOM stated that health care professionals are not properly trained with regard to changes in patient demographics and health related needs, evolving expectations within health care systems, evolving practice methods and technological advances, or enhanced quality control. Improved health communication among health professionals may alleviate these shortcomings, and telepractice offers a mechanism for improved training that is immediate and accessible. For example, the TIP could be used to offer health professionals an opportunity to discuss physical and psychological issues for targeted populations, interactive education, and face-to-face consultations with specified experts.

PUBLIC EDUCATION VIA THE INTERNET The environment for communicating about psychology and health has changed significantly.

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These changes include dramatic increases in the number of communication channels and the number of health issues vying for public attention as well as consumer demands for more and higher quality physical and psychological health information. Psychology has the potential of communicating through the home, the school, and the work environment through group, organizational, community, and the Internet interactive telehealth options (Stamm, 1999). For instance, health care providers can take advantage of digital technologies, such as CD-ROM, World Wide Web,and listservs to target audiences, tailor messages, and engage people in interactive, ongoing exchanges about health. Brief sessions via web-based interactive video can provide easier access to needed information and reduce health care cost, travel time, and expenses. Research indicates that health communication best supports health promotion when multiple communication channels are used to reach specific audience segments with information that is appropriate and relevant to them. Evidence based decision-making suggests that effective psychological health promotion and communication initiatives adopt an audience-centered perspective, which means that promotion and communication activities reflect audiences’ preferred formats, channels, and contexts. Targeting specific segments of a population and tailoring messages for individual use are two methods to make reaching home based health promotion care through telepractice activities relevant to audiences. Advances in health informatics are changing the delivery of health information and services and are likely to have a growing impact on individual and community health. Advantages include (1) improved access to personalized health information, (2) access to health information, support, and services on demand, (3) enhanced ability to distribute psychologically accurate information quickly, and (4) just-in-time expert decision support and advice.

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INCREASING ACCESS TO HEALTH INFORMATION THROUGH TELEPRACTICE The goals of community based and professional organizations should dovetail with Healthy People 2010 and other similar initiatives worldwide with the focus on using communication strategically to improve health. This can be accomplished through advocacy for: households with Internet access, health literacy, and the quality of Internet health information resources (Zarate, Weinstock, Cukor, Morabito, Leahy, Burns, Baer, 1997); DeLeon, Crimmins, Wolf, 2003; Karlinky, 2004; Hilty, 2004). According to the Computer and Internet Use Supplement to the Current Population Survey, U.S. Department of Commerce, Bureau of the Census (US Department of Commerce, 2002), approximately 26 percent of households had access to the Internet at home. Importantly, the greatest growth in Internet access was for lower income households (approximately 25 percent between 1998 and 2001). Continued growth in household access to the Internet is critical to improve psychological health as technical literacy, or the ability to use electronic technologies and applications, will be essential to gain access to health information. This is particularly important for lower income individuals who may be lacking health insurance or financial means for regular health care. Internet availability in the home is an important indicator of equitable access to health information among targeted populations. Critical to the success of telepractice technology is literacy level of the consumer. The data regarding health and technology literacy indicate that approximately 90 million adults in the United States have inadequate or marginal literacy skills. Psychology can play a role in the implementation of this initiative by increasing the proportion of health communication activities that include research and evaluation. Effective health communication programs are built on sound research and

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evaluation (Casper, 2004; Web Based Education Commission, 2000). Providing counseling about health behaviors could enhance adherence and compliance with healthy behavior (both physical and psychological). Culturally appropriate and linguistically competent community health promotion programs, substance abuse treatment, HIV counseling and testing in prisons, worksite promotion of nutrition education and weight management, public access to information and surveillance data, perception of risk associated with substance abuse and tobacco targeting adolescents and young adults are all potential targets for psychology in the home (Deleon et al., 2003).

DEVELOPING A TELEPRACTICE INTERVENTION PROGRAM Providers or organizational groups who wish to establish a telepractice service delivery system or network should begin by identifying existing programs and/or local and national technological resources. Organizations such as the Department of Veterans Affairs and the Kentucky Department of Criminal Justice are currently using technology to disseminate health care information and direct patient care. Within the VA, employees have access to an intranet, which contains training modules, informational handouts, clinical guidelines, etc. In addition, employees have the ability to participate in local and national informational broadcasts, videoconferences, and conference calls. With regard to patient care, the VA has developed an Internet-based program called My HealtheVet, which serves as a gateway to veteran health benefits and services as well as a tool for enabling veterans to better understand and manage their health. It provides access to trusted health information, links to Federal and VA benefits and resources, a Personal Health Journal, and an online VA prescription refill program. Eventually, veterans will be able to view their appointments,

co-pay balances, and access portions of their VA medical records online (My HealtheVet, 2006). By examining programs such as these, it is possible to identify the technological resources needed, learn from challenges existing programs have overcome, and to manage various types of informational programs. In addition to reviewing established programs for implementation strategies, it is important to identify existing technological networks, which might be more effectively used to disseminate health information to consumers and providers. For example, many states now include videoconferencing or cable networks in public school systems, public libraries, health care facilities, and other community facilities for efficient connection capabilities. Such existing networks could be utilized to broadcast health information programs targeted toward specific populations or providers. For providers who may not have access to large-scale telepractice resources, there are simple strategies for utilizing existing resources to obtain and disseminate health information. For example, health care providers who wish to obtain information on evidence-based practice or innovative techniques may consult reputable websites or join one of the many professional listservs designed to promote dissemination of information. Providers may also refer their clients to reputable websites (e.g., National Heart Association, National Center for PTSD Research) in order to assist them in gathering information or obtaining support. Rural school systems are often faced with providing qualified care to their student population and may not have ready access to needed expertise. Consider the following scenario: A rural school district needs expert consulation to address the needs of a seriously disruptive student. The nearest board certified child psychiatrist is located 500 miles away. To remedy this situation, it was proposed that the needed clinical consultation could be provided to this school system using telepractice technology.

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The clinical consultation team included a child psychologist on site working with a school psychologist, an advanced practice nurse, clinical social worker, special education specialist and the consultation of a child psychiatrist by video link from a university based psychiatry clinic through a cost effective model of telepractice. Through the use of this specialized telehealth link using video telephones, the needed interdisciplinary clinical consultation and service was provided to the rural school system and its staff. The services included diagnostic and treatment planning and classroom observation of the student in question with a secure telepractice video link to the care provider. Illustrated in Figure 1 is the conceptual model that exemplifies a community based telepractice technology system. Telepractice healthcare links in this model exist between health care specialists at the university-based medical center, physicians at the rural community clinic and psychologist, social worker and advanced practice nurse at the rural district. Within this configuration a secure two-way video link exists for interactive staffing of patients and pediatric emergency care. In addition, Internet-based case conferences via video link can provide necessary and essential faceto-face discussions, as well as video images and streaming video of case materials for review. The combined web based and interactive videoconferencing allows for clinicians and school personnel to exchange information that can be to the benefit of both educational and health care planning and consultation. Each of these segments is noted in the model presented in Figure 1. This model provides children with special needs in rural settings the necessary and appropriate consultation to the benefit of client, clinician and families in our society. This collaborative partnership between educators and clinicians may be enhanced and facilitated by the use of telepractice using video telephones designated for confidential patient care and planning. School personnel may incorporate consultation with a local or regional specialist and may use

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Internet video technology through a community clinic. In some cases, the need to have specialized psychiatric consultation services from a medical center-based child psychiatric clinic linked to a community clinic and to a school district may be offered through telehealth technology. In this case, patient health information, patient education materials, clinical records can be shared through electronic medical records sent to consulting clinicians, thus providing them with an opportunity to review information individually and then respond to relevant clinical issues. Clinical staffing of the child involving the referring clinician in the rural setting, the community consulting specialists and the university-based specialist can be beneficial and more effective when time and distance barriers are removed via the use of telehealth technology. The need for telehealth technology is obvious in the face of the obstacles clinicians face in providing a high standard for health care delivery in a time efficient and cost-effective manner (Miller, 1998; Miller, Miller, Kraus, Burton, Sprang & Adams 2003). Emerging systems of managed health care that interface with the educational system have the potential to benefit from the needed services for children as noted in the Young and Iresaon (2003) study. Telehealth is seen as the use of various models of telecommunication, which connect the consumer with the health care provider through live, two-way video transmission, across, distances and which permit diagnosis, treatment, and other health care services. This definition stresses a focus on delivery of services across distances with a sense of concern for ethical provision of services and confidentiality of the health care needs in our society. Telehealth benefits for clinical health care and for educational settings include: increased access to clinical consultative services and health education programs for school and community organizations and populations; improved and expanded psychiatric health care services to underserved areas in rural communities and areas; provision of a feasible and sustainable system of

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clinician-directed consultations using telehealth video conferencing; access to current approved education and training programs for health care providers in settings where access to advanced clinical training may be a barrier due to geographic location; reduction of the isolation for educators and health care providers in rural areas through the development of innovative consultation video conferencing systems that enhance clinical support and consultation regarding the diagnostic, treatment, consultative and educational services available to clinicians and other health care providers in certain geographical regions; and use of telepractice technology in rural communities to decrease the time, inconvenience, expense and risks of travel, as well as other associated problems that distance and isolation cause in obtaining psychological and other health care services.

PRACTICE GUIDELINES FOR TELEPRACTICE HEALTHCARE Community-based telepractice services offer a visionary consultation model for providing standardized and special needs coverage to underserved areas by linking specialists and expert providers with consumers and practitioners in underserved communities. As an alternative way of providing traditional health services, telepractice is considered by some to be a solution to America’s toughest health care challenges: increasing access to clinical consultation service and involving health care professionals while decreasing the costs involved in providing quality care (Office of Rural Health Policy, 1994). In order to insure standardization of care, practice guidelines offer a way of incorporating quality care, consistency in educational material and standard models for information technology. Clinical algorithms and pathways of care are developed and used to provide case management. The goal is to make the client management guideline the accepted professional behavior and a

reward in itself. To the extent that this is successful, five components occur: (a) the guideline is widely used and becomes habitual, (b) multidisciplinary professionals can use it to anticipate care events, (c) clinicians can use it as a shorthand or outline to guide their decisions and their communications to others, (d) the logistics for delivering the guideline components are convenient and reliable, and (e) the guideline defines the measure of performance and incorporates information collected that can be used for its evaluation and improvement. The individualized plans clinicians may use also contribute information for guideline revision. Case management through algorithms presents a systematic perspective. Algorithms try to answer the question, “What is the best way to systematically handle this problematic condition?” Algorithms have a problem solving orientation coupled with functional specific actions or critical pathways to be taken. If desired results are produced, the problem was managed effectively. If the desired results are not produced, adjustments can be made to achieve the desired results. The point to be made is that in our professional roles, it is important to be a creative problem solver who can translate relevant research into functional interventions. The model practice guideline that appears in Figure 2 outlines a situation in which the clinician recognizes that it would be more efficient if telemedicine had a place in the treatment intervention process. Note that a question is asked as to whether a telecare option exists in the patients’ locality. If yes then the clinician begins the review with the client of the differences between tradition and telemedicine delivery of care. Informed consent is provided and compliance with all state and federal laws is to be reviewed. Then the clinician assesses the appropriate needs of the medium dependent upon the clinical nature of the consultation. Appropriately training the patient in the use of telemedicine and the equipment follows this. Risk management options are offered, a practice session is to occur to assure the patient can use the telemedicine option. The use

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Figure 2. Clinical alogorithm for telepratice care

of web based educational programs is provided. Finally there is a review and monitoring process in place so the clinician can monitor its use. Through this configuration, issues related to liability for negligence or liability for abandonment would be considerable reduced and hopefully eliminated.

LIABILITY ISSUES IN THE USE OF TELEPRACTICE TECHNOLOGY Liability is a primary risk factor for service providers utilizing telepractice models and technology. Risk management strategies should educate

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psychologists and consumers about telemedicine equipment. It is important to consider who will provide training, what mechanisms will be used to evaluate the effectiveness of training, and how to document deficits in knowledge following completion of initial training. Another concern with respect to liability involves “liability for abandonment.” Abandonment may occur when the clinician unilaterally terminates the relationship with a client or the relationship was terminated without reasonable notice; and termination occurred when further attention was needed. Practitioners using telepractice technology continuously monitor the patients’ ability to

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participate in telemedicine activities and confirm their understanding of their responsibilities in the use of telepractice equipment. While there are certain advantages, telepractice is subject to many of the same shortcomings associated with face-to-face care as well as several technologyspecific limitations. Most notably, users should be aware of and attend to the following issues prior to establishing a telepractice network. Security, Privacy, and Confidentiality. In an electronically mediated society, concerns regarding security, privacy and confidentiality may take on new meaning with telepractice models. One of the most serious compromises to security and to one’s privacy is the unauthorized access to confidential patient health and educational information. Fortunately, these risks may be reduced by utilizing secure or closed networks, encryption programs, and by adhering to the standards set forth in the Health Insurance Portability and Accountability Act (HIPAA) which provides national standards to reduce health care inefficiencies by encouraging the use of information technology to better secure and protect patient information. In addition, one must consider technological risks that originate from software or computer systems. For example, computer viruses may be designed to destroy data or disrupt computer systems. To avoid these dangers, system managers must continually update virus scan programs, be alert for system glitches, and work to ensure compatibility of all system components (Stamm, 1999; Striefel, 2000). Technology Expences. The cost of acquiring and maintaining the necessary technology including maintenance and update expenses) should be considered. With availability of the Internet, e-mail, listservs, and chat rooms may serve as a cost effective, accessible medium of communication. Equipment needs are minimal; requiring only a computer and Internet access at each location. Fortunately, for organizations interested in incorporating technology-mediated face-to-face contact, several types of videoconferencing applications exist. When considering

these options, providers must take into account both the clinical demands on the system as well as the type of transmission infrastructure required to reliably support a particular application. Options for videoconferencing applications include, but are not limited to, videophones, PC-based desktop systems, and/or high-quality integrated videoconference units. The speed with which advances in technology, prices for the above equipment vary from a few hundred to several thousand dollars. Ironically, the cost of developing and maintaining telehealth systems tends to be the highest in the regions where telepractice would be most beneficial (e.g., rural areas) (Nickelson, 1998). Fortunately, federal programs such as the Universal Service Program for Rural Health Providers may help to defray the operating costs of such systems (Stamm, 1999). Transmission quality. Along with variations in the technical infrastructure and expenses required for various telepractice applications, the quality of data transmission also varies accordingly. When considering options for videoconferencing, providers must take into account both the clinical demands on the system as well as the type of transmission infrastructure required to reliably support a particular application. Videoconferencing systems vary in the type of transmission channel required and thus may operate at different bandwidths. As a measure of a communication channel’s ability to carry information, bandwidth directly influences the quality of video transmission. In other words, if detecting fine motor movements is critical, a system with higher bandwidth would be most suitable. On the other hand, lower bandwidths might be more appropriate when movement is not an important factor or when the cost of the infrastructure needed to support higher bandwidth is prohibitive. Among telepractice networks in the United States, the most common transmission rate is currently 384-786 Kbps (Mahue, Whitten, & Allen, 2001). Consumer Satisfaction. Regardless of the specific type of technology implemented, the

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success of a telecommunications system often hinges on its acceptance among participants. As noted by Young and Irenson (2003), once providers and clients become familiar with telepractice technology, there seems to be an acceptable level of satisfaction with the use of this equipment. Adequate and appropriate training based on the intent of the users of such equipment is essential. Beyond training, it is important to normalize the equipment as part of the environment to desensitize those unfamiliar to telehealth practices and technology. Scope of Practice. The legal and ethical issue of licensure and scope of practice in the use of telepractice may well extend beyond state boundaries or jurisdictions. Licensure requirements, as a limitation to interstate practice, are often cited as a major barrier to the development of services. As with most health care professionals, licensure is on a state-by-state basis, which requires that a practitioner hold a full, unrestricted license in all states he or she practices. For many professionals, acquiring and maintaining multiple licenses is a significant professional and financial burden, which falls particularly hard on rural health care providers who often experience significant travel, lost work time, and other costs in complying with multiple state regulations. In anticipation of legislative changes with respect to the use of telecommunication technology, there have been efforts on the part of the medical and nursing professionals to develop alternative licensure models for their professions. For instance, Texas offers a “special purpose license” for out-of-state physicians who provide telepractice services to Texas residents. Similarly, the Joint Commission on the Accreditation of Healthcare Organizations recently revised its Hospital Medical Staff Standards which now state that practitioners who treat patients via telemedicine are subject to the credentialing processes of the organization that receives the telemedicine service (Joint Commission on the Accreditation of Healthcare Organizations, 2003). Utilizing

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a different approach, the National Council of State Boards of Nursing has developed a mutual license recognition program which allows nurses who hold a valid license in one state to enjoy a “multi-state licensure privilege” to practice in states that are members of the licensure compact (DeLeon, 2003). Legally and ethically the direct and immediate delivery of patient care is always the responsibility of the on-site licensed professional.

APPLICATIONS FOR TELEPRACTICE MODELS IN RURAL EDUCATIONAL SETTINGS Multiple applications hold significant potential for educational settings seeking to meet the health related needs of patients through telepractice. These include: 1. clinical interview and/or evaluation by a clinician specialist; consultations for crisis stabilization in the healthcare and community settings; 2. Brief screening and assessment prior to comprehensive evaluations for various diagnostic entities; 3. Team assessment and treatment planning may include multiple links with physicians, child psychiatrists, school psychologists, occupational therapists, physical therapists, speech-language pathologists, counselors, nurses, teachers, social workers, and other allied health profesionals; 4. Diagnostic testing with the assistance of a technician on site; 5. Short-term health education and counseling interventions; 6. Vocational assessment, placement and counseling services; 7. Evaluations and diagnostics related to second opinions; 8. Short-term case management for children with special needs;

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9. Consultation-liaison services for patients and care givers. 10. Consultation with primary care physicians and/or specialists, clinicians and educators regarding patients with special needs; 11. Continuing medical and health education for administrators, faculty, students, caregivers and others.

CONCLUDING COMMENTS AND LESSONS LEARNED The future of telepractice healthcare delivery models and their interface with various critical components of society needs to examine the potential benefits over risks in providing healthcare consultations and allied services. The Department of Veterans Affairs, the largest healthcare delivery system in the United States sees telepractice healthcare a critical medium for serving its patients in rural and underserved areas of our country (Monagan 2010) Health care provision through telepractice technology must be vigilant to the challenges and cognizant of the added value when realizing that this technology will reach populations who have been traditionally underserved because of their remote location to needed services. Examined herein has been a model, which demonstrates the capability of reducing time and distance barriers in the provision of health care and education through telepractice technology. Best practice guidelines have emerged and provide both ethical and legal parameters for practitioners (Miller and Wader, 2002). Vignettes highlighting the usage of this technology through educational settings have been presented as have the advantages and potential disadvantages of its use. Several suggested applications have been noted, as have the changing paradigms in delivery of healthcare through community-based services. When consultation with a needed health care professional is not readily available, the use of telepractice technology

in rural settings is a viable medium for providing needed diagnostic and clinical consultation for underserved and rural populations in our society.

ACKNOWLEDGMENT The authors wish to acknowledge the support and assistance of Rob Sprang Director of University of Kentucky Telecare,, Lon Hays, MD, Charles Lowe, Ph.D. Robert Kraus MD, Patricia Beckenhaupt RN, MS, MPH, Catherine Russell, Jill Livingston MLS, Lane J. Veltkamp MSW, and Brenda Frommer in the preparation of this manuscript.

REFERENCES Armfield, N., Donovan, T., & Wootton, R. (2005). Real-time video in the neonatal intensive care nursery. Journal of Telemedicine and Telecare, 11, 238–245. Ax, R. K., Fagan, T. J., Magaletta, P. R., Morgan, R. D., Nussbaum, D., & White, T. W. (2007). Innovations in correctional assessment and treatment [Special issue]. Criminal Justice and Behavior, 34, 893–905. doi:10.1177/0093854807301555 Ball, C., & Puffet, A. (1998). The assessment f cognitive functioning in the elderly using videoconferencing. Journal of Telemedicine and Telecare, 4, 36–38. doi:10.1258/1357633981931362 Bischoff, R. J., Hollist, C. S., Smith, C. W., & Frank, P. (2004). Addressing the mental health needs of the rural underserved: Findings from a multiple case study of a behavioral telehealth project. Contemporary Family Therapy, 26. Blackmon, L. A., Kaak, H. O., & Ranseen, J. (1997). Consumer satisfaction with telemedicine child psychiatry consultation in rural Kentucky. Psychiatric Services (Washington, D.C.), 48, 1464–1466.

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Casper, E. S. (2004). A formative evaluation of a rent subsidy program for homeless substance abusers. Journal of Service Research, 31(2), 25–39. Casper, L.M., & King, Rosalind, K.B. (2004). Changing families, shifting economic fortunes, and meeting basic needs [Comment/Reply]. Work Family Challenges for Low-Income Parents and Their Children, 55-79. Casper, V. (2004). Beyond feeders and growers: Changing conceptions of care in the Western Cape. Journal of Child and Adolescent Mental Health, 16(1), 55–59. DeLeon, P. H., Crimmins, D. B., & Wolf, A. W. (2003). Afterword-the 21st century has arrived. Psychotherapy (Chicago, Ill.), 40(1-2), 164–169. doi:10.1037/0033-3204.40.1-2.164 Ghosh, G. J., McLaren, P. M., & Watson, J. P. (1997). Evaluating the alliance in video-link teletherapy. Journal of Telehealth, 3(suppl. 1), 33–35. Kavanagh, S., & Yellowlees, P. (1995). Telemedicine: Clinical applications in mental health. Australian Family Physician, 24, 1242–1246. Kreuter, M. W., Strecher, V. J., & Glassman, B. (1999). One size does not fit all: The case for tailoring print materials. Annals of Behavioral Medicine, 21(4), 276–283. doi:10.1007/BF02895958 Mahue, M., Whitten, P., & Allen, A. (2001). Ehealth, telehealth, and telemedicine: A practical guide to start-up and success. San Francisco: Jossey-Bass. McLaren, P., Blunden, J., Lipsedge, M., & Summerfield, A. (1996). Telepsychiatry in an innercity community psychiatric service. Journal of Telemedicine and Telecare, 2, 57–59. doi:10.1258/1357633961929178 Miller, T. W. (1998). The psychologist with 20/20 vision. Consulting Psychology Journal: Practice and Research, 50(1), 25–35. doi:10.1037/10614087.50.1.25

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Miller, T. W. (2006). Telehealth issues in consulting psychology practice. Consulting Psychology Journal: Practice and Research, 58(2), 82–90. doi:10.1037/1065-9293.58.2.82 Miller, T. W. (2007). (Accepted). Telehealth Issues in Consulting Psychology Practice. Consulting Psychology Journal: Practice and Research. Miller, TW, DeLeon, P., Morgan, R., Penk, W. Magaletta, P. (2006) The Public Sector Psychologist with 2020 vision. Professional Psychology: Research and Practice. 37-2, 531-538. Miller, T. W., & Holcomb, T. F. (2007). Telehealth Applications for Counselors in the Schools. Kentucky Counseling Association Journal, 26(1), 6–13. Miller, T. W., & Hutchins, S. A. (2008). (Accepted). 21st Century Teaching technology: Best Practices and Effectiveness in Teaching Psychology. International Journal of Instructional Media. Miller, T. W., & Miller, J. (1999). Telemedicine: New directions for health care delivery in Kentucky schools. The Journal of the Kentucky Medical Association, 97(4), 163–166. Miller, T. W., Miller, J. M., Burton, D., Sprang, R., & Adams, J. M. (2003). A Telehealth Model for Clinical Supervision In Allied Health. Internet Journal of Allied Health, 2(1), 1–10. Miller, T. W., Miller, J. M., Kraus, O. F., & Sprang, R. (2003). Telehealth a clinical application model for rural consultation. Consulting Psychology Journal, 55(2), 119–127. doi:10.1037/10614087.55.2.119 Miller, T. W., & Wader, B. (2002). Best Practices for Psychologist using 21st Century Technology. Annual Convention Connecticut Psychological Association, November 1-2, 2002. Westbrook, CT.

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Monagan, B. (2010) Telehealth Tools Seen as Key to VA’s Rural Health Care Strategy. Healthcare IT News June 28, 2010. Web Based retrieval at: http://www.ihealthbeat.org/articles/2010/6/24/ telehealth-tools-seen-as-key-to-vas-rural-healthcare-strategy.aspx Morgan, R. D., Patrick, A. R., & Magaletta, P. R. (2008). Does the use of telemental health alter the treatment experience?: Inmates’ perceptions of telemental health vs. face-to-face treatment modalities. Journal of Consulting and Clinical Psychology, 76, 158–162. doi:10.1037/0022006X.76.1.158 My Health. eVet. (n.d.). Retrieved June 2, 2006, from https://www.myhealth.va.gov/mhvPortal/ anonymous.portal?_nfpb=true&_nfto=false&_ pageLabel=mhvHome National Advisory Committee on Rural Health and Human Services. (2004).The 2004 Report to the Secretary: Rural Health and Human Services. Available at: ftp://ftp.hrsa.gov/ruralhealth/ NAC04web.pdf Nickelson, D. (1996). Behavioral telehealth: Emerging practice, research, and policy opportunities. Behavioral Sciences & the Law, 14, 443–457. doi:10.1002/ (SICI)1099-0798(199623)14:4<443::AIDBSL256>3.0.CO;2-G Nickelson, D. W. (1998). Telehealth and the evolving health care system: Strategic opportunities for professional psychology. Professional Psychology, Research and Practice, 29, 527–534. doi:10.1037/0735-7028.29.6.527 Norman, S. (2006). The use of telemedicine in psychiatry. Journal of Psychiatric and Mental Health Nursing, 13, 771–777. doi:10.1111/j.13652850.2006.01033.x

Office of Rural Health Policy, Health Resources and Services Administration. (1994). Reaching: Rural health travel in the telecommunications highway. Rockville, MD: Federal Office of Rural Health Policy. Sammons, M. T., & DeLeon, P. H. (2004). Whither online counseling: Conceptualizing the Challenges and promises of distance mental health service provision. In Kraus, R., Zack, J., & Stricker, G. (Eds.), Online Counseling: A Handbook for Mental Health Professionals. New York: Elsevier Press. Sargent, I. (1999). School based mental health in rural Kansas: A novel use for telepsychiatry. [abstract]. Telemedicine Journal, 5(1), 52. Shaw, P., Goodwin, J., Whitten, P., & Doolittle, G. (1999). Tele-Kid Care: An urban telemedicine service for an underserved pediatric population. [abstract]. Telemedicine Journal, 5(1), 25. Solow, C., Weiss, R., Bergen, B., & Sanborn, C. (1971). 24-hour psychiatric consultation via TV. The American Journal of Psychiatry, 127, 120–123. Stamm, B. H. (1999). Creating virtual community: Telehealth and self-care updated. In Stamm, B. H. (Ed.), Secondary traumatic stress: Self-care issues for clinicians, researchers, and educators (pp. 179–208). Lutherville, MD: Sidran Press. Straker, N., Mostyn, P., & Marshall, C. (1976). The use of two-way TV in bringing mental health services to the inner city. The American Journal of Psychiatry, 133, 1202–1205. Striefel, S. (2000, Summer). Telehealth uses in biofeedback and applied psychophysiology. Biofeedback, 28 (2). Retrieved February 4, 2004, from http://www.aapb.org/public/ Whitten, B., Cook, D. J., Shaw, P., Ermer, D., & Goodwin, J. (1998). Telekid Care: Bringing health care into schools. Telemedicine Journal, 4(4), 335–343.

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Whitten, P. S., & Cook, D. J. (1999). School-based telemedicine: Using technology to bring health care to inner-city children. [abstract]. Journal of Telemedicine and Telecare, 5(Suppl 1), S23–S25. doi:10.1258/1357633991932423

Wood, J. A., O’Quin, J., & Eftink, S. (2004). A preliminary investigation of the use of videophone technology as a training tool for students working with older adults. Journal of Teaching in Social Work, 24, 35–46. doi:10.1300/J067v24n03_03

Wood, J. A. (2000). The effectiveness of videophone technology in bringing mental healthservices to rural residents. Unpublished master’s thesis, Stephen F. Austin State University, Nacogdoches, TX.

Yellowlees, P. (2005). Successfully developing a telemedicine system. Journal of Telemedicine and Telecare, 11, 331–335. doi:10.1258/135763305774472024

Wood, J. A. (2006, April). Patient and provider satisfaction with telemental health services. Poster presented at the VA Psychology Leadership Conference, Dallas, TX. Wood, J. A. & Hargrove, D. S. (2006). Use of videoconferencing to deliver mental health services to adults with mental retardation. Manuscript submitted for publication. Wood, J. A., Miller, T. W., & Hargrove, D. S. (2005). Clinical supervision in rural settings: A telehealth model. Professional Psychology, Research and Practice, 36(2), 173–179. doi:10.1037/0735-7028.36.2.173

Young, T., & Irenson, C. (2003). Effectiveness of School based Telehealth Care in Rural Elementary Schools. Pediatrics, 112(5), 1088–1096. doi:10.1542/peds.112.5.1088 Zarate, C. A., Weinstock, L., Cukor, P., Morabito, C., Leahy, L., Burns, C., & Baer, L. (1997). Applicability of telemedicine for assessing patients with schizophrenia: Acceptance and reliability. The Journal of Clinical Psychiatry, 58, 22–25. doi:10.4088/JCP.v58n0104

This work was previously published in Healthcare Delivery Reform and New Technologies: Organizational Initiatives, edited by Matthew Guah, pp. 226-240, copyright 2011 by Medical Information Science Reference (an imprint of IGI Global).

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Chapter 5.13

The Electronic Health Record to Support Women’s Health Emma Parry The University of Auckland, New Zealand

ABSTRACT The seamless electronic health record is often hailed as the holy grail of health informatics. What is an electronic health record? This question is answered and consideration is given to the advantages and disadvantages of an electronic health record. The place of the electronic health record at the centre of a clinical information system is discussed. In expanding on the advantages several areas are covered including: analysis of data, accessibility and availability, and access control. Middleware technology and its place are discussed. Requirements for implementing a DOI: 10.4018/978-1-60960-561-2.ch513

system and some of the issues that can arise in the field of women’s health are elucidated. Finally, in this exciting and fast moving field, future research is discussed.

INTRODUCTION Electronic health records (EHR) also known as Computerised Medical Records (CMR) and many other variations, have been an active area of research for more than 30 years. Many aspects of women’s health lend themselves to computerised records, but the overarching aim of a computerised, holistic and appropriately accessible electronic record is still to be realised. A definition of the

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

The Electronic Health Record to Support Women’s Health

EHR from PubMed is “Computer-based systems for input, storage, display, retrieval, and printing of information contained in a patient’s medical record.” It is important to note that the existence of an EHR does not exclude the continuing existence of paper-based systems and that the EHR is just a part of a clinical information system. It is also common that information about individuals is held by multiple systems. This chapter will give a brief history of the use of EHR, an introduction to what EHR systems are for, what they do, and how they can assist clinical care, research and administration. A review of the particular role EHR’s hold in women’s health and some speculation on future trends complete the chapter

HISTORY Computers have developed with a speed that has surprised even those working in the industry. The well known “Moore’s law” postulates a doubling of computer performance every year to 18 months (Schaller & Schaller, 1997). The medical profession have traditionally shunned computers in their practice as a just another time-wasting device, however in recent years computers have shown themselves to be useful and time-saving (Horwood & Richards, 1988). One of the most striking features of the development of EHR, has been the widespread adoption of administration, billing and laboratory systems ahead of the clinical record. In 1968 the first attempt to use computers in obstetrics was initiated by Thatcher in Australia (Thatcher, 1968). A pilot system was introduced in Australia at the Royal Hospital for Women, Sydney in 1968. In 1971 a pilot study was set up at St Thomas’s Hospital in London (South & Rhodes, 1971) and in Victoria Australia(Cope, Greenwell, & Mather, 1971) whilst in America, the Duke University Medical Centre also went on line in their obstetric department in 1971. All these systems were based around a mainframe computer,

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which were cumbersome and slow. These were the first ‘microcomputers’ which were within reach of smaller companies in terms of price. In these early systems information was entered after the event (a retrospective system) by trained computer staff. The medical and midwifery staff had no role in data entry. It is well recognised data entered in this fashion is less accurate than data entered by a user (e.g. midwife) prospectively (at the time of the event). As new faster microprocessors were developed, along with improved software and user interfaces, user based systems could be developed (Chard, 1987; Horwood & Richards, 1988; Lilford & Chard, 1981). The advent of the personal computer in the early 1980’s made computers more accessible, smaller and cheaper (Shipton, 1979). The possibility of having computers in clinical areas began to be raised. Interest in computers in Obstetrics increased again. During the 1980’s many hospitals developed systems, although most of these were retrospective in their method of data collection. One of the first hospitals to develop a prospective data collection system was King’s College Hospital in London (Horwood & Richards, 1988). This system, EuroKing, relied on staff using a barcode reader and code book to take a booking history. This group found that with this system, the time to take a full booking history fell from one hour to ten minutes.

Systems in Current Use Obstetrics lends itself to data collection. Each pregnancy is discrete with a final outcome. The options for most variables are limited. Most obstetric patients are well with no confounding illnesses. Over the last twenty years there has been a revolution in patient attitude and with it doctors have been called to account for their actions. This has resulted in all disciplines of medicine being required to keep records of the service they are offering. Audit has become an integral part of our training and clinical practice. Obstetrics as

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a speciality has responded relatively well to this challenge and most units will be able to quote numbers of deliveries, induction, caesarean section and forceps delivery rates. Increasingly through the necessity of data collection, computers are being introduced into obstetrics (Chard, 1987; Horwood & Richards, 1988; Kohlenberg, 1994; Lilford & Chard, 1981; South & Rhodes, 1971).

Figure 1. The electronic health record as a the centre of an information system

WHAT IS AN EHR? An EHR is based around a person. The aim of the EHR is to support the clinical care of the patient, although other functions, such as data collection for secondary purposes or administration and billing may be included. It may form part of a larger clinical or health information system. Such systems are often very large and complex. Traditionally, computer systems are built as “proprietary” entities – that is the developing company owns the intellectual property associated with a system and may be reluctant to allow purchasers or users to modify or distribute it. Currently there is great interest in so-called “open source” software. The open source movement allows users of the software to copy it and modify it without payment. Often many users collaborate on the development and maintenance of open source products – such as the very popular “Apache” web server (McDonald et al., 2003). Generally EHR systems are built around commercial Database management systems (DBMS), although open source solutions(Kantor, Wilson, & Midgley, 2003) which may be based on common standards such as the such as openEHR (openEHR Foundation, 2007) have been proposed. DBMS support the recording and search of structured and semi structured data. Many of the advantages that potentially accrue to the use of EHR depend on the degree to which the features of DBMS are exploited. The EHR records details of clinical findings, actions to be taken, and decisions made, including diagnosis and treatment.

The EHR sits at the centre of a clinical information system (Figure 1), and may communicate with other systems – such as a patient administration system, Laboratory systems, picture archiving and communication (PACS), and other electronic health records, that may exist for other clinical areas or care providers. There are a wide range of approaches to the degree of coding included in an EHR. Although the coded representation of diagnoses and outcomes is often important (see Chapter III), some EHR systems simply record free text, along with some numerical data. Direct coding into the EHR can be difficult and there is a trade off between convenience and speed during the clinical encounter and accuracy of coding. Diagnostic codes cannot be assigned before a diagnosis is made. However, with the increasing use of clinical vocabularies such as SNOMED CT (American College of Pathologists, 2000), coding of observations, symptoms and actions becomes easier. Indeed there is beginning to be some progress towards the goal of automatic coding of free text description using SNOMED(Patrick, Wang, & Budd, 2007). Hybrid systems that track paper records or include scanned images of the paper record as part of a document management system have been used in a number of circumstances. In some cases these are used temporary solutions, but they may

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be useful when the implementation issues, including the wide diversity of requirements of different clinical areas prevent a fully digitised approach.

Disadvantages of Paper Records Paper records have been used in medicine since at least the time of Hippocrates. One of the major aims of EHR has been to replace paper records, as these have a number of disadvantages. Firstly, paper records can be lost or not available when needed (Bates et al., 2001). Some patient administration systems also record the location of the paper records in order to make them more easily available. Some maternity care providers have used patient-held notes to ensure higher availability, and increase patient involvement in the care process(Webster et al., 1996). Photocopies of paper records, or scanned copies of records can be made available, but these require strict procedures for version control – that is ensuring that the most up to date records are being used. Secondly paper records are often difficult to organise. Although a lot of work has been done on problem-based records (Weed, 1968) this often requires duplication of effort by manually extracting a problem list, and keeping it up to date. Generally paper based records are organised in a temporal sequence, but linking relevant items together may be difficult. Thirdly, paper based records may simply be difficult to read or understand – especially when they are being used as a communication medium between clinical staff. It may not be obvious that a particular action has been performed, or where the results of a particular test will be recorded. The same issues arise when using records for research or audit purposes, where proving that a particular action has not been taken involves a huge amount of searching, and particular items of interest may be located in odd locations. However, paper records are unaffected by power cuts, shortage of terminals or hardware or software failure. They are generally more accepted

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for legal purposes and generally do not require expensive conversion to be used in different health systems and are not rendered obsolete by hardware or software upgrades. Even electronic record systems produce vast quantities of paper in practice.

Advantages of the EHR Apart from correcting the deficiencies of the paper records as described above, EHR have particular advantages.

Flexibility of Display and Data Entry The use of a computer system means that there does not need to be a choice between temporally ordered and problem based records – both can be display as required. In addition, data trend lines can be produced for example to show response to therapy (see Figure 2). This also extends to associated reports – for example letters to patients and referral destinations and communication with other systems. The vision of (Weed, 1968) can be realized with records that can change their display instantaneously. Not only can the record go from being a list of events, to a problem-based approach, but also time series of data, or events of particular interest can be identified. Modern interface technology can incorporate options to choose the “skin” that is the set of rules corresponding to appearance of the screen, and allow sorting of data lists and arrangement of windows on the screen according to personal preferences.

Analysis of Data Analysis of data – and use for decision support – is simple and virtually instantaneous. Reminder and recall systems can be integrated into or communicate with e.g., the PRODIGY system (Wilson, Purves, & Smith, 2000). In this case, the EHR triggers warnings or reminders when

The Electronic Health Record to Support Women’s Health

Figure 2. Views of data from EHR

certain criteria are met. This allows the encoding of clinical protocols, and treatment rules within a system. This approach is particularly useful for triggering recall messages eg when an abnormal PAP smear is discovered, or when the patient requires regular health checks for example after starting oral contraceptives. Other uses of data are covered later in the chapter. There is a wide literature concerning the importance and usefulness of electronic systems that can support decision making The NEONATE extension for the HELP system described in (Franco, Farr, King, Clark, & Haug, 1990) used an expert system to produce a problem list automatically.

Accessibility and Availability Accessibility and availability of data is much higher. Compared to paper records, access to data can be controlled if there is private or sensitive information contained in a record. For example, past obstetric or sexual health history may not need to be accessible by all legitimate users of the record. At the same time, data can be simultaneously accessed and updated in different locations, by different users – as may be the case if ultrasound

results are being produced while the patient is at the antenatal clinic.

Access Control Audit of access and change to records is possible in a computerised system. By assigning each user a user ID, individual’s use of the system can be monitored. Role-based access, where users are assigned rights to see and update data based on their function can assist in making such rules flexible but enforceable. For example administrative staff may require access to all patient’s demographics, and clinic booking information, which may take some data from the EHR. However, only the patients own OBGYN, or their locum may be allowed access to sensitive data. In an emergency, most systems allow an override function which allows normally unauthorized access – for example in the case where a woman is admitted to hospital without their own physicians being available. However, a record of this access is maintained which may be helpful when reviewing this activity. Similarly, EHR systems should maintain a change log, so that when important data is changed, the previous values can be recovered if necessary. These approaches are

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vital if the EHR is to retain the status of a legal document held by the paper record. Users of the EHR must be confident that the record includes not only the correct data but that the source of that data is verifiable and traceable. On a different note, access tracking can also be used as part of a reminder system so that clinical staff are prompted to review results, or note outcomes, if there has not been any viewing of the record.

DBMS Technology As most EHR systems are based around DBMS, it should be noted that all these features noted above are common to many electronic record systems, such as those used by banks or law-enforcement agencies. Indeed modern DBMS include many tools to support multiple views of data and analysis tools, access and change logging, security and links to “triggers” – programs that run when particular combinations of parameter values are discovered. Modern DBMS are also designed to store multimedia information, which in clinical terms could include images or ultrasound cineloops. One of the greatest advances in DBMS technology in recent years has been the explosion in XMLbased data transfer and Web interfaces to data. This allows data within databases to be reliably converted to and from transfer formats – such as HL7 CDA (see Chapter III). The use of standard Structured Query Language (SQL) to build and maintain databases also allows the structure and content of databases to be saved and transferred to other varieties of DBMS systems relatively easily. Native XML databases – that is where data is stored within the DBMS in XML format, and queried using XQUERY and other techniques are becoming more popular, although performance issues may restrict their growth. This is not intended to be a tutorial on database design, but one important aspect of SQL-based DBMS is the need to link tables, often by means of an unique identifier, and maintaining these

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across systems is a major task in countries without national health index numbers. The “middleware” approach to the EHR accepts that data will be stored in different systems, but needs to be drawn together for clinical decision making. For example screening systems such as cervical or osteoporosis testing may feed in information to a web or other portal that also includes feeds from lab results, discipline-based systems e.g., nursing and midwifery systems and specialised areas such as operating room or outpatient management. A modern approach to the use of DBMS in system design may use n-tier approach, where the data level is separate from the business rules level – which may include for example editing and audit processes, which is again separate from the presentation layer, which is seen by the user. (Figure 3) In classical database design, the normalisation process attempts to remove multiple storage locations for data to avoid conflicting information being held. Given the often heterogeneous nature of health records, with data being held by different providers in different places, this approach is often not yet practicable.

REQUIREMENTS In order to act as a replacement or adjunct to a paper-based system the EHR must act as a leFigure 3. A middleware approach to the EHR

The Electronic Health Record to Support Women’s Health

gally useful record of the observations, results, diagnosis, plans and actions made as part of the clinical process. As different professionals are often involved in the care of women, each group should be assured that the EHR reflects their needs, and identifies where information came from, and presents it in a usable form. User interface design is a vital aspect of electronic systems. Just as the views of the data held in the record may need personalisation, data entry and avoidance of error requires careful thought. In many cases for example, bench space is at a premium, so mouse-driven systems may be awkward. Recent work using tablet PC’s (Main, Quintela, Araya-Guerra, Holcomb, & Pace, 2004) and personal digital assistants (PDA) (Skov & Th. Hoegh, 2006) has indicated that these systems may be more usable in the clinical environment than traditional desktop machines. Such systems bring their own overheads in the form of the need for secure wireless networks, and means of securing expensive devices and keeping batteries charged. The EHR acts a source of data for many purposes(Kenney & Macfarlane, 1999) and the concept of the 5 (or 6) uses of clinical data has been introduced. Data collected should be available for: • • • • •

Supporting clinical intervention – in direct patient care and public health surveillance. Clinical Governance – including audit and performance management Administration (in all parts of Health) – both for funding, and logistic purposes Strategy and policy development – at all levels Research – including the identification of rare events and support for clinical trials.

A 6th use – patients self-management is becoming increasingly important – patients are getting more involved in the decision-making process and the “Smart patient” (R. L. Sribnick & W. B. Sribnick, 1994) is a partner rather than a consumer.

EXAMPLES IN WOMEN’S HEALTH Supporting women’s health offers a number of opportunities for EHR. Pregnancy care offers the opportunity for time-limited “stand-alone” databases, often known as Perinatal databases. Perinatal systems have a number of differences from common episode-based EHR systems. Collaboration between care groups is common in pregnancy, and such systems need to support this. Uniquely, pregnancy episodes end with more patients involved than in the beginning, so that the offspring’s data may need to be transferred to neonatal and infant information systems (see chapter 9). Perinatal systems often record information about previous gynecological and medical history, and in a integrated system, this can come from primary care or personal health information. Similarly, at the end of the pregnancy, appropriate information needs to be fed back to primary care. In contrast to perinatal systems, screening programs require administrative support in terms of recall and results notification systems, for long-term care. Systems to support “well woman” and screening programs are often use to support recall for PAP smears etc. and ensure that appropriate interventions are performed. Genetic and familial information may also be needed to assist with counseling before conception and uring early pregnancy. The extensive use of imaging in pregnancy will often require integration with an ultrasound reporting and assessment, including appropriate growth charts and risk calculated from Nuchal translucency.

ISSUES Privacy issues change considerably when personal health information is stored electronically. Recent work has shown (Whiddett, Hunter, Engelbrecht, & Handy, 2006) that patients have strong views on who can view their data, and that many are

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unaware of the degree of data sharing that already occurs. Generally EHR systems are covered by data protection legislation, and usually patients would have the right to view information held about them and alter it if incorrect. There is a conflict here between the rights of the health provider, to record events, and the rights of the patient to adjust or delete those records. The chapter on ethics (Chapter II) deals with this in more depth. Because of the personal and sometimes sensitive nature of the data being stored woman’s health informatics has particular issues in this regard. However privacy rules are addressed – whether for example, different information is available to different users, a reliable method of identification of users is required. Currently most EHR systems use individual accounts with “role-based” access. That is a user will log in, be identified as a particular person, and then have rights to view and change data depending on rules relating to their role in relation to a patient. Thus an administration clerk may have unlimited rights to view and change demographic data relating to any patients, but no access to any clinical details, whereas a OBGYN may be able to access any clinical data relating to patient’s identified as being under their care. Complexities arise when people change roles or locums are brought in and there are often fairly complex rules dealing with these cases. What is “sensitive” information can vary between cultures and situations and individuals, and many EHR systems provide a fairly crude method of identifying sensitive information during data entry. Unauthorised access to data, either by using other peoples user identification or password, or by back-door access to the system e.g. by directly accessing the DBMS is a serious disciplinary offence in most organisations. However, unjustified data access that is, access to data which the system allows, but is not necessary for care, may be harder to detect. Some systems record all data access by users and can be audited to discover “suspicious” activity, for example apparently inappropriate access to data concerning famous people or staff

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members or their relatives. This can be followed up, but is time consuming and may be used in a limited number of cases. Security of data is obviously vital to the success of EHR’s. There is extensive guidance on protecting the security of information systems in the international standards such as IS0 17799, and these are particularly applicable to health systems. Security includes not only the prevention of unauthorised access but also this includes reliability and availability of the data on the system. There is therefore a need to backup the EHR data, in a suitable fashion, and ensure that the process for backup and restoration is reliable. Consideration also needs to be given to disaster recovery, and the use of paper records for example if there is a hardware or network failure. Even in normal operation, EHR systems have a role as a contemporaneous record, so that data that is revised, should be noted as such. This often involves the retention of a revision log, as for example, if the estimated date of delivery (EDD) is changed, decisions that were made based on previous EDD may other wise seem incomprehensible. Generally, all data entered into an EHR should be recoverable, just as a paper system record that fact that writing may have been crossed out or marked as incorrect. Although computerised systems are often thought to reduce the time taken to perform tasks, this may not be true in the case of EHR systems, and computerised order-entry systems that are used for ordering laboratory tests etc. A recent systematic review (Poissant, Pereira, Tamblyn, & Kawasumi, 2005) showed that although nurses appeared to take less time in documentation tasks, this was not true of physicians. There is still no certainty in this area – it may be that time spent documenting via EHR’s reduces the need for other means of communication, which is considerable (Coiera & Tombs, 1998), that EHR’s do not fit into workflow patters so well, or that availability of terminals is an issue.

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One of the most concerning gaps in research at the moment is the relative paucity of studies that actually show benefit to patient care by the use of EHR (Mitchell & Sullivan, 2001). This is a relatively difficult area to research, and impossible to blind. Cost benefit in some areas has been shown (Wang et al., 2003), but clinical benefit is more elusive, partly because of the fact that the introduction of EHR is often associated with other changes in practice and, for example the introduction of decision-support tools. The process of implementation of EHR and clinical information systems seems particularly fraught, and failures are relatively common (Littlejohns, Wyatt, & Garvican, 2003). As with all information technology implementations, an understanding of the domain is vital. Comparisons between health systems, although controversial,(Feachem et al., 2002), seem to indicate that, on balance, increased spending on information technology does provide benefit.

FUTURE WORK As with many other aspects of computerisation, the focus of much work appears to have moved to the web. Both Microsoft and Google have announced plans to develop personal health records, stored on the web. Issues arise in terms of security, privacy, accuracy and accessibility in the case of emergency. Who owns the data in the EHR is a matter of some debate, and it may be that women feel that they should choose the custodians of the data. Web-based systems offer the possibility of effectively free storage, integration with webbased health tools, and availability of the data in any context. The rise of pervasive and ubiquitous computing (Weiser, 1993), has raised the expectation that information should be collected and used everywhere, though devices that quietly work in the background of our everyday life (Honey et al., 2007). Mobile phones have become the most

common expression of this stream of development and some systems are already collecting clinical information via sensors attached to the phone, or giving health advice in this way. Electronic health record systems have had a very diverse range of uptake, and there are major projects underway to increase their use in a number of countries, for example the UK, Canada and Australia and in particular organisations such as Kaiser Permanente. Success in these implementations requires more than just technical expertise, clinical staff and patients must all agree on the need to gain the benefits and the appropriate uses of the technology to improve care. Such agreement will require education, debate and acceptance of change.

REFERENCES American College of Pathologists. (2000, 2000). About SNOMED. Retrieved January 11, 2002, from http://www.snomed.org/about_txt.html Bates, D. W., Cohen, M., Leape, L. L., Overhage, J. M., Shabot, M. M., & Sheridan, T. (2001). Reducing the Frequency of Errors in Medicine Using Information Technology. Journal of the American Medical Informatics Association, 8(4), 299–308. Chard, T. (1987). Computerisation of Obstetric Records. In J. Studd (Ed.), Progress in Obstetrics and Gynaecology: Volume Six (1 ed., pp. 3-22). London: Chuchill Livingstone. Coiera, E., & Tombs, V. (1998). Communication behaviours in a hospital setting: an observational study. British Medical Journal, 316(7132), 673–676. Cope, I., Greenwell, J., & Mather, B. S. (1971). An Obstetric Data Project- General Description. The Medical Journal of Australia, 1, 12–14.

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Feachem, R. G. A., Sekhri, N. K., White, K. L., Dixon, J., Berwick, D. M., & Enthoven, A. C. (2002). Getting more for their dollar: A comparison of the NHS with California’s Kaiser Permanente. BMJ (Clinical Research Ed.), 324(7330), 135–143. doi:10.1136/bmj.324.7330.135 Franco, A., Farr, F. L., King, J. D., Clark, J. S., & Haug, P. J. (1990). “NEONATE”--An expert application for the “HELP” system: comparison of the computer’s and the physician’s problem list. Journal of Medical Systems, 14(5), 297–306. doi:10.1007/BF00993936 Honey, M., Øyri, K., Newbold, S., Coenen, A., Park, H., Ensio, A., et al. (2007). Effecting change by the use of emerging technologies in healthcare: A future vision for u-nursing in 2020. Paper presented at the Health Informatics New Zealand (HINZ), 6th Annual Forum., Rotorua. Horwood, A., & Richards, F. (1988). Implementing a Maternity Computer System. Mid Chron Nurs Notes(11), 356-357. Kantor, G. S., Wilson, W. D., & Midgley, A. (2003). Open-source software and the primary care EMR. Journal of the American Medical Informatics Association, 10(6), 616. doi:10.1197/jamia.M1403 Kenney, N., & Macfarlane, A. (1999). Identifying problems with data collection at a local level: survey of NHS maternity units in England. BMJ (Clinical Research Ed.), 319(7210), 619–622. Kohlenberg, C. F. (1994). Computerization of Obstetric Antenatal Histories. Australian and New Zealand Journal of Obstetrics and Gynaecology, 34(5), 520–524. doi:10.1111/j.1479-828X.1994. tb01099.x Lilford, R. J., & Chard, T. (1981). Microcomputers in antenatal care: A feasibility study on the booking interview. B Med J, 283, 533–536. doi:10.1136/ bmj.283.6290.533

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Littlejohns, P., Wyatt, J. C., & Garvican, L. (2003). Evaluating computerised health information systems: Hard lessons still to be learnt. British Medical Journal, 326(7394), 860–863. doi:10.1136/ bmj.326.7394.860 Main, D. S., Quintela, J., Araya-Guerra, R., Holcomb, S., & Pace, W. D. (2004). Exploring Patient Reactions to Pen-Tablet Computers: A Report from CaReNet. Annals of Family Medicine, 2(5), 421–424. doi:10.1370/afm.92 McDonald, C. J., Schadow, G., Barnes, M., Dexter, P., Overhage, J. M., & Mamlin, B. (2003). Open Source software in medical informatics--why, how and what. International Journal of Medical Informatics, 69(2-3), 175–184. doi:10.1016/ S1386-5056(02)00104-1 Mitchell, E., & Sullivan, F. (2001). A descriptive feast but an evaluative famine: systematic review of published articles on primary care computing during 1980-97. BMJ (Clinical Research Ed.), 322(7281), 279–282. doi:10.1136/ bmj.322.7281.279 Moore’s law: past, present and future. Spectrum, IEEE, 34(6), 52-59. openEHR Foundation. (2007). Welcome to openEHR. Retrieved 1st April 2008, from http:// www.openehr.org/home.html Patrick, J., Wang, Y., & Budd, P. (2007). An automated system for conversion of clinical notes into SNOMED clinical terminology. Paper presented at the Conference Name|. Retrieved Access Date|. from URL|. Poissant, L., Pereira, J., Tamblyn, R., & Kawasumi, Y. (2005). The Impact of Electronic Health Records on Time Efficiency of Physicians and Nurses: A Systematic Review. Journal of the American Medical Informatics Association, 12(5), 505–516. doi:10.1197/jamia.M1700

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Schaller, R. R., & Schaller, R. R. (1997). Moore’s law: past, present and future Shipton, H. W. (1979). The microprocessor, a new tool for the biosciences. Annual Review of Biophysics and Bioengineering, 8, 269–286. doi:10.1146/annurev.bb.08.060179.001413

Weed, L. L. (1968). Medical records that guide and teach. New England Journal of Medicine, 278(11+12), 593–600 + 652–597. Weiser, M. (1993). Some computer science issues in ubiquitous computing. Communications of the ACM, 36(7), 75–84. doi:10.1145/159544.159617

Skov, B., & Hoegh, Th. (2006). Supporting information access in a hospital ward by a contextaware mobile electronic patient record. Personal and Ubiquitous Computing, 10(4), 205–214. doi:10.1007/s00779-005-0049-0

Whiddett, R., Hunter, I., Engelbrecht, J., & Handy, J. (2006). Patients’ attitudes towards sharing their health information. International Journal of Medical Informatics, 75(7), 530–541. doi:10.1016/j. ijmedinf.2005.08.009

South, J., & Rhodes, P. (1971). Computer Service for Obstetric Records. B Med J, 4, 32–35. doi:10.1136/bmj.4.5778.32

Wilson, R. G., Purves, I. N., & Smith, D. (2000). Utilisation of computerised clinical guidance in general practice consultations. Studies in Health Technology and Informatics, 77, 229–233.

Sribnick, R. L., & Sribnick, W. B. (1994). Smart Patient, Good Medicine: Working With Your Doctor to Get the Best Medical Care. New York: Walker and Company. Thatcher, R. A. (1968). A package deal for computer processing in Obstetric records. The Medical Journal of Australia, 2, 766–768. Wang, S. J., Middleton, B., Prosser, L. A., Bardon, C. G., Spurr, C. D., & Carchidi, P. J. (2003). A cost-benefit analysis of electronic medical records in primary care. The American Journal of Medicine, 114(5), 397–403. doi:10.1016/S00029343(03)00057-3 Webster, J., Forbes, K., Foster, S., Thomas, I., Griffin, A., & Timms, H. (1996). Sharing antenatal care: Client satisfaction and use of the ‘patientheld record’. Australian and New Zealand Journal of Obstetrics and Gynaecology, 36(1), 11–14. doi:10.1111/j.1479-828X.1996.tb02912.x

ADDITIONAL READING Electronic Health Records: A Practical Guide for Professional and Organizations, 3rd Edition by MBA, RHIA, CHPS, CPHIT, CPEHR, FHIMSS Margret K. Amatayakul Implementing an Electronic Health Record System (Health Informatics) by James M. Walker, Eric J. Bieber, Frank Richards, and Sandra Buckley Electronic Health Records: Understanding and Using Computerized Medical Records by Richard W. Gartee

This work was previously published in Medical Informatics in Obstetrics and Gynecology, edited by David Parry and Emma Parry, pp. 65-76, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 5.14

Managing Paramedic Knowledge for Treatment of Acute Myocardial Infarction Tom Quinn Coventry University, UK R. K. Bali Coventry University, UK Kathrine Dale Worcestershire Royal Hospital, UK Pete Gregory Coventry University, UK

INTRODUCTION There are about 280,000 acute myocardial infarctions (AMI) each year in the United Kingdom (British Heart Foundation (BHF), 2004). Up to half of these events will prove to be fatal, many within the first few hours following symptom onset as a result of disturbances of cardiac rhythm. For those patients who survive the initial period of vulnerability, subsequent mortality and the risk of disabling chronic heart failure can be markDOI: 10.4018/978-1-60960-561-2.ch514

edly reduced by prompt restoration of blood flow through the affected coronary artery. This can be achieved in patients sustaining a STEMI by mechanical means (percutaneous coronary intervention) or pharmacologically by thrombolytic (“clot buster”) therapy (Antman et al., 2004) or where initial thrombolytic treatment fails—“rescue” intervention. Mortality benefit from thrombolysis is time dependent; for patients presenting with STEMI in the first 2 to 3 hours from onset, each minute’s delay equates to 11 days loss of life expectancy (Department of Health (DoH), 2003).

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Managing Paramedic Knowledge for Treatment of Acute Myocardial Infarction

BACKGROUND In England, publication of the National Service Framework (NSF) for coronary heart disease (CHD) has resulted in dramatic improvements in the timeliness of thrombolytic treatment (DoH, 2000), with half of eligible STEMI patients starting treatment within the NSF standard of 60 minutes from the call for help (Birkhead et al., 2004) compared to one in 10 in the years preceding the NSF (Quinn, Allan, Birkhead, Griffiths, Gyde & Murray, 2003). These improvements are largely a result of improved hospital processes (Birkhead et al., 2004), but further increasing the proportion of patients treated within the NSF “call to needle time” is reliant on thrombolysis commencing prior to hospitalization (Quinn et al., 2003), administered mainly by ambulance paramedics. Significant government investment in emergency ambulance services in England has facilitated universal availability of 12 lead electrocardiograph (ECG) equipment, programs to enhance training of paramedics in assessment of patients with suspected STEMI for thrombolysis eligibility (and other aspects of cardiac care), and procurement of the appropriate thrombolytic drugs. The United Kingdom provides a mainly paramedic-led ambulance service in contrast to several other countries where some ambulances are manned by physicians or intensive-care trained nurses (Bouten, Simoons, Hartman, Van Miltenburg, Van der Does & Pool, 1992; Leizorovicz et al., 1997). This has led to concern in some quarters about the feasibility and safety of nonphysicians administering thrombolysis in the ambulance setting. Recent UK studies, however, have confirmed that paramedics can accurately identify patients with suspected AMI requiring specialist coronary care (Quinn, Allan, Thompson, Pawelec & Boyle, 1999) and, in particular, patients presenting with ST segment elevation (the cornerstone of decisionmaking in this context) on the ECG (Whitbread et al., 2002) and eligibility for thrombolysis (Pitt,

2002; Keeling et al., 2003). Moreover, a recent international clinical trial has demonstrated that the efficacy and safety of prehospital thrombolysis was independent of physician presence (Welsh et al., 2005). Around 4,000 patients in England have received “paramedic thrombolysis” since 2003. Nevertheless, the government’s own Review of Early Thrombolysis (DoH, 2003) has acknowledged concerns about paramedics’ retention of knowledge and skill in relation to thrombolytic treatment; in particular, in relation to the low exposure to this aspect of emergency care in lowdensity populations in rural areas. The review acknowledges the need for research to address the question of a “volume-outcome” relationship between paramedics’ exposure to the procedure and patient safety.

LEARNING, SKILL, AND KNOWLEDGE RETENTION It is appropriate to make a distinction between learning (a change in behavior as a result of experience) (Haskell, 2001) and skill retention (persistence of skill proficiency after a period of no practice) (Cree & Macauley, 2003). Although there is a paucity of research specific to the areas of learning and skill retention around paramedics and thrombolytic treatment, the literature on other aspects of paramedic practice may be relevant. For example, paramedics’ skill acquisition, retention, and decay have been assessed in relation to tracheal intubation (Barnes, 2001), neonatal resuscitation (Skidmore & Urquhart, 2001), and laryngeal mask intubation (Coles, Elding & Mercer, 2001). Studies of skill decay in the delivery of cardiopulmonary resuscitation demonstrate poor skill and knowledge retention with a return to pretraining levels within six months (Roach & Medina, 1994). Issues of motivation, competence, confidence, and assessment come in to play (Chamberlain & Hazinski, 2003).

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COMPETENCE While these are essentially psychomotor skills, the safe administration of thrombolytic treatment requires paramedics to demonstrate a range of competencies (Quinn, Butters & Todd, 2002) involving patient assessment, ECG interpretation, identification of contraindications to treatment (the latter largely aimed at reducing risk of intracranial and other haemorrhagic complications), weight estimation (Hall et al., 2004), and explaining the risks and benefits of treatment to patients. The presence of a so-called rural paramedic paradox has been suggested in which patients living further away from hospitals tend to be in low-density, rural populations where an individual paramedic’s exposure to severe emergencies (e.g., STEMI) may be low, but the distance and time to reach a hospital may mean that the need for highly skilled paramedic intervention is highest (Rowley, 2001). Whether this results in poorer patient care is unknown. A volume outcome relationship for STEMI patients has been reported in relationship to physician exposure to the condition (Tu, Austin & Chan, 2001). However, with an estimated 700,000 emergency attendances with chest pain to hospitals in England and Wales per annum (Goodacre et al., 2005), it is likely that paramedics will be performing many more assessments and ECG recordings than they will be administering thrombolysis to or referring for primary intervention; therefore, the key assessment skills will be subject to frequent exposure.

KNOWLEDGE MANAGEMENT A number of studies have put forth the notion of incorporating KM in health care as a way out of the imminent information explosion crisis (Koretz & Lee, 1998; Wyatt, 2001). The average physician spends as much as a quarter of his or her professional activities managing information

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and has to learn an estimated 2 million clinical specifics (Wyatt, 2000), a burden compounded by the doubling of biomedical literature every two decades. The burden on other health professionals, including paramedics, has not been reported. Paramedics, as registered health professionals, also are required to maintain their knowledge base, although currently didactic clinical guidelines are prepared and distributed nationally. As with other generalist health professionals, paramedics’ scope of practice (and thus, underpinning knowledge) is potentially enormous. “Knowledge is the enemy of disease” (Brice & Gray, 2003) and serves ultimately to protect the patient from harm. Data from NHS information sources (Brice & Gray, 2003) indicate the scale of the problem of serious failures in health care. From these sources, we learn that annually: • •

•

•

•

•

400 people die or are seriously injured in adverse events involving medical devices. Nearly 10,000 people are reported to have experienced serious adverse reactions to drugs. Around 1,150 people who have been in recent contact with mental health services commit suicide. Nearly 28,000 written complaints are made about aspects of clinical treatment in hospitals. The NHS pays out around £400 million a year for settlement of clinical negligence claims, and has a potential liability of around £2.4 billion for existing and expected claims. Hospital acquired infections, around 15% of which may be avoidable, are estimated to cost the NHS nearly £1 billion.

As skills and knowledge exist in both individuals and organizations, the essence of knowledge management is how best to identify and capture knowledge and experience in order to disseminate it further throughout the organization. There have

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been several attempts to bring various forms of clinical information to the clinician at the point of care. They include: •

•

•

Developing computer applications that stand alone, although they are often network accessible and are available to the clinician upon his or her request. Incorporating the clinical knowledge directly into clinical information systems used by clinicians while giving care (once there, the computer-based system can automatically prompt the clinician or the clinician can request help). Requesting help from an outside source (Sittig, 2005).

FUTURE TRENDS While many of these approaches relate to physicians in primary and secondary care settings, the use of computerized decision aides in emergency cardiac care has been reported (Grisjeels et al., 1996; Lamfers et al., 2004); although these are not widely used in the ambulance service in England, the development of the National Programme for Information Technology across the National Health Service (NHS) raises new opportunities (Humber, 2004), including wider utilization of on-board computers on emergency ambulances (Bastow, 2003). Paramedics often are required to make rapid life or death decisions that can have far-reaching consequences for patients. As previously stated, these decisions include such competencies as patient assessment, ECG interpretation, identification of contraindications to treatment, weight estimation, and explaining the risks and benefits of treatment to patients. Therefore, paramedics need to embrace appropriately designed, tested, and reliable tools and techniques of knowledge management to facilitate rapid and safe processing of pertinent knowledge.

IT-based clinical knowledge is managed by way of such tools as medical decision support systems, which themselves are a product of clinical practice guidelines issued by various government and medical associations. Clinical practice guidelines are effectively rule-based knowledge that guides the decision makers in a medical setting (Field & Lohr, 1992). These guidelines have been developed over the years to reduce the variations among medical practices with a common goal to provide cost-effective and high-quality health care services. This has to be balanced against the reality of the contemporary environment and associated employee performance and safe practice within this rapidly changing practice environment. There is an acknowledged gap between the importance of assessing what employees can do and not what they know. Del Bueno, Weeks, and Brown-Stewart (1987) report a study carried out among test takers who have difficulty performing a procedure or recognizing warning signs in a patient experiencing difficulty. The use of criterion-based performance measures determines practice competencies in employees as well as identifies where need exists to correct skill or knowledge deficiencies.

TOWARD A CARDIAC CARE KNOWLEDGE MANAGEMENT SYSTEM (KMS) FOR PARAMEDICS Knowledge Management Systems (KMS) consist of appropriate information technologies (IT), policies, processes, and procedures to manage the creation, storing, sharing, and reuse of knowledge. In this context, knowledge is any information that is relevant and actionable and is based on a person’s experience (Davenport, De Long & Beers, 1998). For paramedics, an accessible cardiac care KMS would assist in the reduction of health care costs, which are growing at an unsustainable rate (up 20% since 2001) and placing a major burden on the economy (Warner, 2004). KMSs have the

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potential to reduce medical errors, which cause an estimated 1 million injuries and 98,000 deaths each year (Davenport & Glaser, 2002; Warner, 2004). For the creation of a viable cardiac care KMS, further work is required to assist health care organizations in carrying out activities better, faster, and cheaper (Dwivedi, Bali, James, Naguib & Johnson, 2002). Such research work would include (a) analyzing and forming a suitable KM strategy for the paramedic function; (b) adopting best practice from established KM strategies and implementations (from the nonclinical environments); and (c) capturing and integrating paramedic perspectives on clinical knowledge which few previous studies have managed effectively.

CONCLUSION In a publicly funded health system driven by accountability, defining competence has been described as one of the most challenging and essential components in the accountability paradigm (Redman, Lenburg & Hinton Walker, 1999). With more and more medical interventions such as thrombolysis being delegated to nonphysicians (and in this case, particularly paramedics), there is a need for improved understanding of how knowledge is managed and how best to optimize effective clinical decision-making.

REFERENCES Antman, E. (2004). ACC/AHA guidelines for the management of patients with ST-elevation myocardial infarction: A report of the American College of Cardiology/American Heart Association task force on practice guidelines (committee to revise the 1999 guidelines for the management of patients with acute myocardial infarction). Journal of the American College of Cardiology, 44, E1–E211. doi:10.1016/j.jacc.2004.07.014

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Barnes, T.A. (2001). Tracheal intubation: Training and retention. Respiratory Care Journal, 46(3). Bastow, P. (2003). Electronic patient-report forms in the ambulance service: Ready for national rollout. British Journal of Healthcare Computing Information Management, 20, 44–46. Birkhead, J. (2004). Improving care for patients with acute coronary syndromes: Initial results from the National Audit of Myocardial Infarction Project (MINAP). Heart (British Cardiac Society), 90, 1004–1009. doi:10.1136/hrt.2004.034470 Bouten, M. J. M., Simoons, M. L., Hartman, J. A. M., Van Miltenburg, A. J. M., Van der Does, E., & Pool, J. (1992). Prehospital thrombolysis with alteplase (rt-PA) in acute myocardial infarction. European Heart Journal, 13, 925–931. Brice, A., & Gray, M. (2003). Knowledge is the enemy of disease. An organisation with a memory. Library and Information Update, Department of Health, 2(3), 32–34. British Heart Foundation. (2004). Coronary heart disease statistics database. Retrieved February 21, 2005, from www.heartstats.org. Chamberlain, D. A., & Hazinski, M. F. (2003). Education in resuscitation. Resuscitation, 59, 11–43. doi:10.1016/j.resuscitation.2003.08.011 Coles, C., Elding, C., & Mercer, M. (2001). A laboratory assessment of the learning and retention skills required to use the Combitube and Laryngeal Mask Airway by non-anaesthetists. Critical Care, 5(Suppl), 1 4. Cree, V., & Macauley, C. (2003). Transfer of learning in professional and vocational education. Routledge. Davenport, T. H., De Long, D. W., & Beers, M. C. (1998). Successful knowledge management projects. Sloan Management Review, (Winter): 43–57.

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Davenport, T. H., & Glaser, J. (2002). Just-intime delivery comes to knowledge management. Harvard Business Review, (July): 107–111.

Humber, M. (2004). National programme for information technology. British Medical Journal, 328, 1145–1146. doi:10.1136/bmj.328.7449.1145

Del Bueno, D. J., Weeks, L., & Brown-Stewart, P. (1987). Clinical assessment centers: A costeffective alternative for competency development. Nursing Economics, 5(1), 21–26.

Keeling, P. (2003). Safety and feasibility of prehospital thrombolysis carried out by paramedics. British Medical Journal, 327, 27–28. doi:10.1136/ bmj.327.7405.27

Department of Health. (2000). National service framework for coronary heart disease. London: Department of Health.

Koretz, S., & Lee, G. (1998). Knowledge management and drug development. Journal of Knowledge Management, 2(2), 53–58. doi:10.1108/13673279810249387

Department of Health. (2003). Review of early thrombolysis—Faster and better treatment for heart attack patients. London: Department of Health. Dwivedi, A., Bali, R. K., James, A. E., Naguib, R. N. G., & Johnson, D. (2002). Towards a holistic knowledge management framework for healthcare institutions. In Proceedings of the Second Joint EMBS/BMES Conference (pp. 1894-1895), Houston, TX. Field, M. J., & Lohr, K. B. (1992). Guidelines for clinical practice: From development to use. Washington, DC: National Academy Press. Goodacre, S. (2005). The health care burden of acute chest pain. Heart (British Cardiac Society), 91, 229–230. doi:10.1136/hrt.2003.027599 Grisjeels, E. W. M. (1996). Implementation of a pre-hospital decision rule in general practice. Triage of patients with suspected myocardial infarction. European Heart Journal, 17, 89–95. Hall, W. L. (2004). Errors in weight estimation in the emergency department: Comparing performance by providers and patients. The Journal of Emergency Medicine, 27, 219–224. doi:10.1016/j. jemermed.2004.04.008 Haskell, R. E. (2001). Transfer of learning: Cognition, instruction and reasoning. San Diego: Academic Press.

Lamfers, E. J. (2004). Prehospital versus hospital fibrinolytic therapy using automated versus cardiologist electrocardiographic diagnosis of myocardial infarction: Abortion of myocardial infarction and unjustified fibrinolytic therapy. American Heart Journal, 147, 509–515. doi:10.1016/j. ahj.2003.10.007 Leizorovicz, A. (1997). Pre-hospital and inhospital delays in thrombolytic treatment in patients with suspected acute myocardial infarction. Analysis of data from the EMIP study. European Myocardial Infarction Project. European Heart Journal, 18, 248–253. Pitt, K. (2002). Prehospital selection of patients for thrombolysis by paramedics. Emergency Medicine Journal, 19, 260–263. doi:10.1136/emj.19.3.260 Quinn, T., Allan, T. F., Birkhead, J., Griffiths, R., Gyde, S. N., & Murray, R. G. (2003). Impact of a region-wide approach to improving systems for heart attack care: The West Midlands Thrombolysis Project. European Journal of Cardiovascular Nursing, 2, 131–139. doi:10.1016/ S1474-5151(03)00030-6 Quinn, T., Allan, T. F., Thompson, D. R., Pawelec, J., & Boyle, R. M. (1999). Identification of patients suitable for direct admission to a coronary care unit by ambulance paramedics. Pre-hospital Immediate Care, 3, 126–130.

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Quinn, T., Butters, A., & Todd, I. (2002). Implementing paramedic thrombolysis: An overview. Accident and Emergency Nursing, 10, 189–196. doi:10.1016/S0965-2302(02)00160-1 Redman, R. W., Lenburg, C. B., & Hinton Walker, P. (1999). Competency assessment: Methods for development and implementation in nursing education. Journal of Issues in Nursing. Retrieved September 30, 1999, from www.nursingworld. org/ojin/topic10/tpc10_3.htm. Roach, C. L., & Medina, F. A. (1994). Paramedic comfort level with children in medical and trauma emergencies: Does the PALS course make a difference. The American Journal of Emergency Medicine, 12, 260–262. doi:10.1016/07356757(94)90263-1 Rowley, T. (2001). Solving the paramedic paradox. Rural Health News, 8, 3. Sittig, D. F. (2005). An overview of efforts to bring clinical knowledge to the point of care. In R.K. Bali (Ed.), Clinical knowledge management: Opportunities and challenges. IGP. Skidmore, M. B., & Urquhart, H. (2001). Retention of skills in neonatal resuscitation. Paediatrics and Child Health (Oxford), 6(1), 31–35. Tu, J. V., Austin, P. C., & Chan, B. T. (2001). Relationship between annual volume of patients treated by admitting physician and mortality after acute myocardial infarction. Journal of the American Medical Association, 285, 3116–3122. doi:10.1001/jama.285.24.3116 Warner, M. (2004). Under the knife. Business 2.0, 5(1), 84–89.

Welsh, R. C. (2005). Time to treatment and the impact of a physician on pre-hospital management of acute ST elevation myocardial infarction: Insights from the ASSENT–3 PLUS trial. Heart (British Cardiac Society), 91(11), 1400–1406. doi:10.1136/hrt.2004.054510 Whitbread, M. (2002). Recognition of ST elevation by paramedics. Emergency Medicine Journal, 19, 66–67. doi:10.1136/emj.19.1.66 Wyatt, J. C. (2000). Intranets. Journal of the Royal Society of Medicine, 93(10), 530–534. Wyatt, J. C. (2001). Management of explicit and tacit knowledge. Journal of the Royal Society of Medicine, 94(1), 6–9.

KEY TERMS AND DEFINITIONS Emergency Cardiac Care: Includes resuscitation from cardiac arrest and care of patients with suspected heart attack (acute myocardial infarction) and its complications. Knowledge Management: The collection, organization, analysis, and sharing of information held by workers and groups within an organization. Myocardial Infarction: Another term for a heart attack; occurs when a blood vessel (an artery) to the heart is blocked. Paramedic: Registered health care professional who provides emergency and urgent care and treatment to patients who are either acutely ill or injured; paramedics usually work outside the hospital environment and are able to perform a variety of advanced skills, administer a range of drugs, and carry out certain surgical techniques. Thrombolysis: Use of drugs to break down a blood clot that is obstructing a blood vessel (usually an artery).

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, pp. 838-843, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Section VI

Managerial Impact

This section presents contemporary coverage of the social implications of clinical technologies, more specifically related to the corporate and managerial utilization of information sharing technologies and applications, and how these technologies can be facilitated within organizations. Section 6 is especially helpful as an addition to the organizational and behavioral studies of section 5, with diverse and novel developments in the managerial and human resources areas of clinical technologies. Typically, though the fields of industry and education are not always considered co-dependent, section 6 provides looks into how clinical technologies and the business workplace help each other. The interrelationship of such issues as operationalizing, supervision, and diagnosis management are discussed. In all, the chapters in this section offer specific perspectives on how managerial perspectives and developments in clinical technologies inform each other to create more meaningful user experiences.

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Chapter 6.1

Operationalizing the Science:

Integrating Clinical Informatics into the Daily Operations of the Medical Center Joseph L. Kannry Mount Sinai Medical Center, USA

ABSTRACT Healthcare IT (HIT) has failed to live up to its promise in the United States. HIT solutions and decisions need to be evidence based and standardized. Interventional informatics is ideally positioned to provide evidence based and standardized solutions in the enterprise (aka, the medical center) which includes all or some combination of hospital(s), hospital based-practices, enterprise owned offsite medical practices, faculty practice and a medical school. For purposes of this chapter, DOI: 10.4018/978-1-60960-561-2.ch601

interventional informatics is defined as applied medical or clinical informatics with an emphasis on an active interventional role in the enterprise. A department of interventional informatics, which integrates the science of informatics into daily operations, should become a standard part of any 21st century medical center in the United States. The objectives of this chapter are to: review and summarize the promise and challenge of IT in healthcare; define healthcare IT; review the legacy of IT in healthcare; compare and contrast IT in healthcare with that of other industries; become familiar with evidence based IT: Medical informatics; differentiate medical informatics from

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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IT in healthcare; distinguish medical, clinical, and interventional informatics; justify the need for operational departments of interventional informatics.

INTRODUCTION: THE PROMISE AND CHALLENGE OF INFORMATION TECHNOLOGY IN HEALTHCARE The promise has always been that healthcare information technology (HIT) should be able to deliver rapid, relevant, and accurate information to clinical providers thereby providing greater efficiencies in patient care, facilitating excellence in patient care, and making improvements in patient safety possible (Bates & Gawande, 2003; Chaudhry et al., 2006; Millenson, 1997; Pizzi, 2007). Healthcare is an information intense industry (Stead, 1999) and by its very definition information technology “…specializes in the delivery and the management of information” (IT Definition, 2007). Not surprisingly HIT is frequently cited as the solution to all that ails healthcare (Coye, 2005; Institute of Medicine (U.S.) Committee on Improving the Patient Record, Dick, & Steen, 1991; Institute of Medicine (U.S.) Committee on Improving the Patient Record, Dick, Steen, & Detmer, 1997; Marchibroda & Gerber, 2003). This belies a repeated inability of industry vendors to fully deliver on that promise as noted in a 1997 panel in Healthcare IT. In 1997 a panel of CEOs from Cerner, Eclipsys, HBOC and MedicaLogic noted only 60 percent of implementations of stable clinical products occurred on time and in budget, only 50 percent of available clinical function is used (Kuperman, Leavitt, McCall et al.,1997). There is general agreement that implementation problems stem from inability to integrate projects into existing workflow (Stead, 1999; Stead, Miller, Musen, & Hersh, 2000). This author and Ms. Kristin Myers have similarly noted that its process, people and workflow integration that are the key and not technology (Kannry,

Mukani, & Myers, 2006; “Thinking About… Implementing the EMR,” 2006). At the same time there is general agreement that healthcare in the United States is in crisis whether it be due to the cost of healthcare, the lack of standardization and delivery of best practices, or issues of patient safety. Healthcare is an information intense domain (Kleinke, 2005) and clearly needs the efficiencies that IT can deliver. If information technology should be good at one task that task is managing information. A frequent rejoinder by industry regarding the Internet around the turn of the century was that the Internet was providing information “just in time” which is defined as arriving just as needed (Strategos Inc.). For example, manufactured goods would arrive in the store based on information on sales, stock, and so on and thus reduce holding and storage costs (Wikipedia). In healthcare, where clinical information is a mission critical commodity, this could mean that when a test is ordered, the results of all previous tests of the same time are presented just in time to perhaps avoid re-ordering of the test. However, just in time information and applications never reached the shores of healthcare. Few would disagree that IT in the rest of the world (ROW) seems to achieve efficiencies that HIT cannot. For purposes of this chapter, ROW is broadly defined as IT in any domain except healthcare meaning business, banking, industry, etc. A significant portion of this disparity between ROW IT and HIT can be traced to the beginning and evolution of healthcare IT. The earliest applications of information technology in healthcare were designed for support of financial transactions. In the later 1950s and early 1960s HIT began in earnest in response to a U.S. Government request to provide documentation for reimbursement. In the early 1990s, before the advent of managed care, sending just enough information to meet federal reimbursement requirements was good enough. Clinical information had little or no cost as tests could be re-ordered if lost or done at another

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center. Clinical applications such as computerized order entry, electronic medical records, and clinical repositories were just being developed and deployed with only one famous exception which dates back to the late 1970s TDS/Eclipsys 7000 (Bukunt, Hunter, Perkins et al., 2005). Why is information technology so different in ROW when compared to HIT? There are several reasons: inherent mobility, multiple handoffs, data entry, security, information needs, information intensity and perhaps cost. Mobility is the rule and not the exception in the health care. Physicians, nurses, medical technicians (i.e., the workers) are, frequently mobile and untethered. Caring for patients requires healthcare professionals and employees to go to patients whether it is at the hospital bedside, in the nursing home, in the emergency room, and so forth. These settings of care have no formal fixed offices in which for example, physicians sit at desks all day. When physicians sit down to write a note in the hospital it is at a semi-public space in a healthcare setting. Even in the one setting which is most analogous to a business office, the private practitioner’s office, the physician is only at any one location there for parts of each day. In contrast, ROW mobility is extremely desirable but most ROW employees are not mobile 95 percent of the time in contrast to physicians seeing patients, and so forth. Particularly in academic centers, there are multiple handoffs regarding the same patient as teams of doctors’ care for the patient. For example, patient Sally Smith is seen in the hospital by an attending physician Dr. Able, a resident Dr. Baker, and an intern Dr. Calloway. At night and weekends, each of these three doctors will have physician coverage. These multiple handoffs can and do cause medical errors (Are handoffs too ‘automatic’? QI experts fear errors could rise, 2006; Gandhi, 2005; Greenberg et al., 2007; JCAHO to look closely at patient handoffs, 2006; Petersen, Orav, Teich et al.,1998; Streitenberger, BreenReid, & Harris, 2006) In contrast, in industry there are handoffs but not as frequently and not

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regarding information that is both complex and critical. SignOut is a process in which medical information is transferred each night regarding all patients, and it is difficult to find a comparable process in ROW. Anecdotally this author was once asked by Information Technology staff if they could use the (medical) SignOut System (Kannry & Moore, 1999; Kushniruk, Karson, Moore, & Kannry, 2003) because there no software or analogous process for IT coverage at night and on weekends. Entry of business data is another area of difference. In ROW business data entry usually involves the lowest paid and least skilled to entering business data. In contrast in healthcare IT the personnel entering clinical (i.e., the business of health care is patient (clinical) data) are among the healthcare field’s most highly trained, skilled and paid personnel, the providers. ROW information needs are role based, do not change often, and the characteristics of the data change even less. The chief executive officer (CEO), whose role is to run the company, is not going to need detailed information regarding elevator repair unless his business is elevator repair. The information needs of the CEO will not vary dramatically day-to-day so that one day the CEO needs the schedule of workers in the factory and the next day blueprints of the corporate office in Zurich, and on the next day information on corporate sanitation. Finally a CEO who measures corporate success will not measure profit and loss one day in dollars and cents and another day using shoe size. In contrast, information needs in health care IT vary significantly by role, change often, and the characteristics of the data can vary significantly. For example, one physician may have many roles. A physician can be primarily responsible for the patient and require extensive information or consult on the patient requiring limited information set specific to the question being asked. The information needs change often as patients present with different diseases. What the physician needs

Operationalizing the Science

to know about a patient with asthma and a patient with diarrhea are generally different. Data characteristics vary widely by and within medical specialty and by patient. Medical specialties are subject domains that require and recognize specialized knowledge, diagnostic procedures, examinations, test interpretation, and therapeutic procedures including invasive procedures such as surgery. Examples of medical specialties include general medicine (entire adult patient), cardiology (heart), pulmonary (lung), obstetrics gynecology, general surgery, and so forth. For example, a general internist may deal with 17 different groupings of organ systems whereas a cardiologist may focus on one, the heart. The cardiologist though will want a great deal more information on the heart though than the internist. Within a specialty, a physician may have differing needs because of patients with different diseases. For example, a cardiologist may see one patient with congestive heart failure and need the echocardiogram and may see another patient who just suffered a heart attack and the physician needs to see the results of a cardiac catheterization. Information intensity as defined by this author refers to that volume of information sent and received. The information intensity in ROW is relatively fixed with anecdotal reports of five to 30 data elements required per banking transactions. Contrast this with a portion of the data elements in HIT that may be contained in a routine progress note for a follow-up visit. The physical exam and review of systems which are only a part of the progress note alone may contain up to 34 elements with one to six pieces of data per element. Finally, there are significant differences in cost between ROW IT and HIT. The total federal budget for IT is $66 billion in the 2008 proposed budget (Budget, 2007). In contrast, HIT costs are significantly higher. Ideally, there would be one national health information network (NHIN) in the United States so all relevant clinical information for any patient was available at any site of care (Stead, Kelly, & Kolodner, 2004; Walker et

al., 2005; Yasnoff et al., 2004). In other words, the NHIN would be the realization of THE EMR defined by the Institute of Medicine over a decade ago. This definition says THE EMR is “all electronically stored information about individual’s outpatient lifetime health status and health care” (Institute of Medicine (U.S.). Committee on Improving the Patient Record. et al., 1991; Institute of Medicine (U.S.). Committee on Improving the Patient Record. et al., 1997). The estimated cost of a NHIN varies from $156 billion to $287 billion with maintenance from 16 billion to 48 billion per year (Hillestad et al., 2005; R. Kaushal et al., 2005; Walker et al., 2005). Savings range from $21 to $81 billion per year but depending on the analysis does not kick in until several years into the project (Hillestad et al., 2005; Walker et al., 2005). However, an NHIN begs the question of connecting what to what. It is estimated that two thirds of the cost of the NHIN will be purchasing and implementing EMRs (Kaushal et al., 2005). Separate analyses, which looked at the cost of a nationwide implementation of EMR(s) without a nationwide network (i.e., no NHIN), estimated cost at $100 billion in the United States (Hillestad et al., 2005; Quinn, 2004). Hillestad et al. (2005). One of the $100 billion estimates assumed an existing EMR penetration of 20 percent before implementation which is either higher or lower than existing estimates of EMR penetration (Ash, Gorman, Seshadri, & Hersh, 2004; Bates, Ebell, Gotlieb et al., 2003; Kemper, Uren, & Clark, 2006; Miller & Sim, 2004; Simon et al., 2007) depending on definition of EMR (i.e., inpatient, outpatient or both), setting (i.e., individual office practices or large enterprises), medical specialty (e.g., pediatrics) or state (e.g., Massachusetts). It is beyond the scope of this chapter to determine whether estimated HIT costs are from years of under funded investments, the complexity described above, or combination of both. The author suspects both.

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Despite differences in HIT and ROW described above, arguments continue to be made that that healthcare is as easy to automate as other industries such as finance (Walker, 2003). This needs to change.

MEDICAL INFORMATICS AND HIT: DEFINITIONS, MODELS, AND RELATIONSHIPS One approach to improve the current state of affairs in healthcare information technology would be to employ the vast evidence based storehouse of medical informatics. The field of medical informatics can best be defined an interdisciplinary science of information management in support of patient care education, and research (Greenes & Shortliffe, 1990; Shortliffe, 2001). The medical informatics literature contains 30 plus years of scientific findings from numerous studies (Shortliffe, 2001). How does medical informatics differ from healthcare information technology (HIT)? Unfortunately there is a great deal of confusion over the differences between the two domains with both terms being used mistakenly and interchangeably. Healthcare IT is a division of the business dedicated to deliver a service, while for information technology (IT Definition, 2007) the objective is to develop and maintain solutions for the enterprise. The enterprise for purposes of this paper is the (aka, the medical center) which includes all or some combination of hospital(s), hospital based-practices, enterprise owned offsite medical practices, faculty practice and a medical school. Directions are set by senior management, institutional funding, and some user input. User input is frequently used to reaffirm or support decisions that have already been made. In contrast the goal of informatics is to expand scientific frontiers and disseminate scientific knowledge. Directions are set by the state of research field, research interests, institutional and research fund-

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ing and user experience/input. Frequently, health informaticists are practitioners and users as well. Contrasting solution methodology also highlights the difference. HIT is a service that uses a methodology which centers around a business approach with standardized tools sets and proven solutions preferred. HIT may develop solutions internally when needed. In contrast informatics may play a service role but the informatics methodology is the scientific approach, builds what is needed, prefers cutting edge, and in some places develops solutions. The enterprise need for informatics research and innovation is quite high (Glaser, 2005). Evaluation methodology differs in that HIT focuses on system performance, and overall user satisfaction with usage monitoring and interviews. As a result as projects are completed and users become accustomed to system, complaints become less focal and a form of silent dissatisfaction can develop (Kannry, 2007; Murff & Kannry, 2001). Post implementation there is less incentive to make changes as it requires going out to the users and asking what’s wrong or in a sense looking for trouble. Additionally, there may be little budget especially if these changes require custom development work. In contrast, informatics uses scientific analysis, scientific instruments and looks at endpoints as improvements in healthcare. Informatics has actually lead the way at looking at issues that occur post implementation (Bates et al., 1999; Han et al., 2005; Horsky, Kuperman, & Patel, 2005; Hsieh, Gandhi, Seger et al., 2004; Kannry, 2007; Koppel et al., 2005). For example, Koppel’s study of a CPOE (computerized physician order entry) system post implementation found that the system lead to decreased patient safety. Hsieh’s study of drug allergy alerts in a CPOE system found that one in 20 overrides lead to and adverse drug event and that while the overrides were clinically justifiable, the alerts needed greater specificity to be effective. Murff and Kannry identified significant dissatisfaction among house staff after a CPOE implementation at one site and this dissatisfaction lead to a process

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to optimize the system in which over 200 changes were made (Kannry, 2007; Murff & Kannry, 2001). There are many models of medical informatics and information technology partnerships ranging from an onsite physician champion to CMIOs (chief medical information officers) to divisions of informaticists and informaticist-CIOs (Gardner, Pryor, & Warner, 1999; Halamka, 2006; McDonald et al., 1999; Miller, Waitman, Chen, & Rosenbloom, 2005; Murray et al., 2003; Safran, Sands, & Rind, 1999; Slack & Bleich, 1999; Teich et al., 1999). This section will focus on models involving physicians. The classic physician champion is a physician who serves as a vehicle of communication between IT and the physician community. If a system is implemented, it is the physician champion’s job to champion the system to users and address their concerns. There is one problem, however, and that is the physician serves as advisor. There is no fiscal responsibility, limited authority over personnel and in some cases limited input on and control of strategy and direction. The chief medical information officer (CMIO) CMIO is an executive position in which the physician champion is formally recognized and given responsibility to advice and on all clinical IT matters and at times makes final decisions. In a study of five CMIOs by Leviss et al. (Leviss, Kremsdorf, & Mohaideen, 2006) CMIOs have influence over policy making, served an advocacy role for important HIT initiatives, served as HIT consultant internally to their respective organizations and in some circumstances had limited budgetary authority. This is similar to the author’s own experience and knowledge of similar positions. However, Leviss found the position to be one of a physician executive with informatics knowledge and not that of a “highly trained informaticist with secondary management expertise or support.” In some organizations a new structure is emerging in which the informatics reports to the CMIO who reports to the CIO.

Unfortunately CIOs may not find CMIOs to be a valuable addition to the management team. A study by CHIMES (the College of Physician Health Executives) found that healthcare CIOs were either unsure of the need for a CMIO or were in no rush to hire one. The CHIMES survey found that 34 percent of respondent CIOs had a CMIO, a number essentially unchanged form 2002. However, 24 percent of CIOs stated that a CMIO was necessary but they did not have one and 23 percent of CMIOs stated that CMIOs are not necessary which means 47 percent. Of CIOs either did not have a CMIO nor wanted one. The author has no further information as CHIMES is a closed (members only organization) and did not publish the survey results. These findings are consistent with a survey reported by Leviss in which 23 percent of CIOs identified heavy involvement of physicians in clinical information systems. Neither the Leviss nor the CHIME study identified CMIOs routinely having full budgetary authority over clinical IT, responsibility for setting strategic directions, personnel, authority to make decisions for all of clinical IT. This again is consistent with the authors experience (Leviss et al., 2006). The author would also note that CMIO is the one C (e.g., chief operating officer, chief financial officer, chief quality officer, and so on) that routinely reports to another C such as the CIO or CMO (chief medical officer). Units, divisions and departments of medical informatics are frequently constructed as research entities which must seek their funding from external sources such as the National Institute of Health, National Library of Medicine, AHRQ, and so on (Cimino, 1999; Frisse, 1992; Murray et al., 2003; Talmon & Hasman, 2002). However, research entities whose research focused on applied medical informatics frequently found their innovations diffusing into and even leading clinical IT initiatives (Chessare & Torok, 1993; Gardner et al., 1999; Halamka, Osterland, & Safran, 1999; McDonald et al., 1999; Miller et al., 2005; Safran et al., 1999; Slack & Bleich, 1999). For example,

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research on CPOE lead to the development of institutional CPOE systems at Brigham and Womens, Beth Israel Boston, Intermountain Health System, Vanderbilt, and hospitals affiliated with Indianapolis University School of Medicine. In almost all of these examples, informatics worked closely with IT. In some of these examples, informatics successes lead to informatics either running clinical IT or all of clinical IT. It should also be noted that the development work and lessons learned done at the informatics sites also lead to commercial partnerships with companies which wished to incorporate and build upon the innovation. Clinical decision support systems (CDSS) are computerized generation of patient-specific assessments or recommendations for clinicians (Hunt, Haynes, Hanna, & Smith, 1998; Randolph, Haynes, Wyatt et al., 1999). The CDSS developed at Brigham and Womens as part of their CPOE was licensed/purchased by Eclipsys. The work done at Intermountain Health Systems was licensed/purchased by 3M and transformed into a commercial portable data dictionary. There are clear examples in which recognized informaticists are CIO. These examples include Vanderbilt, Beth Israel-Boston and Columbia (Cimino, 1999; Halamka et al., 2005; Miller et al., 2005). In the vast majority of these circumstances enterprise IT is part of larger informatics entity, informatics is integrated into daily operations, and there is a distinct academic unit if informatics. For purposes of this chapter we will look at a case study from Mount Sinai Medical Center in NY, NY.

CLINICAL INFORMATICS AND MEDICAL INFORMATICS: A BRIEF WORD For purposes of this chapter, the terms medical informatics and clinical informatics are used interchangeably. Clinical informatics is a part of the broader scientific field of medical informatics

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which includes both basic and applied science. Clinical informatics focuses on applied investigation and science (Shortliffe, 2001).

CASE STUDY OF THE RELATIONSHIP BETWEEN CLINICAL INFORMATICS AND HIT At the time of this writing the division of clinical informatics is part of the information technology division and to date informatics personnel have been practicing physicians who have academic appointments. Mount Sinai Medical Center consists of The Mount Sinai Hospital 1,136-bed tertiary care hospital, Mount Sinai Hospital of Western Queens, and the Mount Sinai School of Medicine. The Mount Sinai School of Medicine has one university affiliation, NYU (New York University). The Department of Information Technology serves the entire enterprise and informatics is division of IT. Informaticists were practicing physicians as practice provides credibility satisfaction and reality. Practice also gave the Informaticists the ability to relate problems and solutions to actually patient care experiences and was source of problem identification and problem solving. We will focus on examples of informatics work that partnered with IT but also employed cutting edge research or informatics knowledge, which in some cases lead to diffusion of innovation into operations. The examples will range the gamut from analysis to operational use. An example of analysis would be the determination of the characteristics of the ideal notification device for healthcare. Numerous discussions and some experimentation with IT lead to the following list which actually ruled out the use of Blackberry as a notification device for housestaff. These characteristics are: rugged, cheap, rechargeable (since addressed by blackberry) but detachable power source, role based assignment to individual devices, confirmation of message delivered without reply, failure results in escalation algorithm

Operationalizing the Science

on server, confirmation message, received/read, forwarding/rerouting, easy answer, loud (alarms), and integrates with existing paging system. Over a decade ago this author identified a fundamental problem in the hospital where there was inconsistent identification of the attending of record (i.e., the physician primarily responsible for the patients care during their hospitalization). This observation lead to development of the SignOut System which was essentially a prototype application drafted into a production system (Kannry & Moore, 1999; Kushniruk et al., 2003; Moore & Kannry, 1997). A SignOut System allows housestaff (i.e., physician trainees such as residents and fellows) to generate patient lists and include information relevant to the on-call coverage team. Although two subsequent studies demonstrated that the SignOut had superior accuracy in identifying the attending of record as the information comes from the physicians, an operational decision was made not to use SignOut information to correct the attending of record in other hospital systems (Kannry & Moore, 1999). The SignOut system at Mount Sinai was subsequently redesigned in a partnership between informatics and IT as an enterprise IT system and critical source of clinical information (Kannry, Moore, & Karson, 2003; Kushniruk et al., 2003). An interesting anecdote was at one point someone in IT asked “Do we have to do this?” Two versions of this system are now in operation at two different medical centers that used to share one IT Department: Mount Sinai and NYU. At Mount Sinai there are over 700 users and an estimated 75 percent of all discharged patients pass through the SignOut system. The system is now considered a critical inpatient system at Mount Sinai. The Signout System at Sinai was designed to also facilitate the creation and completion of Discharge Summaries. Informatics, anticipating the problems with continuity of care between inpatient and outpatient care developed interim discharge summaries (Kannry & Moore, 1999; Kannry et al., 2003; Moore & Kannry, 1997) developed interim

discharge summaries. These summaries were designed to fill gaps between time of discharge and summary completion. The interim discharge summaries eventually lead to informatics development of institutionally approved and compliant discharge summaries. These summaries are now sent to the medical records where attendings can edit and electronically sign the summaries. The discharge summary functionality had been anticipated and built four years prior to a critical need. Most of the requirements for a full summary were already completed minus a few fields and the development of an interface to medical records. When the institution was faced with a costly and unwieldy manual program to solve the problem, informatics had a solution in hand and was able to gain the support to operationalize the project. The discharge summary component of the SignOut System was so successful the SignOut System is now known as the SignOut and Discharge Summary System. Without the informatics solution, the institution might still be faced with significant issues regarding the timely completion of discharge summaries by housestaff. However, partnership has its challenges. Informatics frequently faces the dilemma of not controlling budget resources, no to little direct control over and no ability to hire technical IT resources such as programmers, and, an uneven ability to introduce innovation into operations at Mount Sinai. This results in well received projects being aborted or not developed. One such instance is DocFind, an application that was designed to provide attending providers with easy to understand and accurate identification of patient specific housestaff coverage. This application was a prototype production system that was never further developed despite extensive use and very positive and vociferous user feedback. Another example demonstrates informatics contribution from operations to operations. Operations had been fascinated by the use of RFID (Radio Frequency IDentification) tags which involves placing a tag on people and equipment

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that permits location tracking of people and supplies. Informatics knew from the literature that RFID was frequently an expensive technology that required significant infrastructure (i.e., wireless, robust networking) software etc. but with little scientific proof of efficacy in clinical settings. Informatics had observed that patient discharge would be an ideal to test small scale RFID and observes significant differences in time of discharge recorded versus RFID. Operations requested that the experimentation stop prematurely as the case had been made and that project planning begin immediately (Kannry, Emro, Blount, Ebling, & 2007). Finally, application of informatics science into an operational process can be seen in selection and implementation of an EMR. The “Division of Clinical Informatics had been following, analyzing, and reviewing the EMR literature for several years with particular focus on Ambulatory EMRs.” Literature from several informatics research sites in regards to Ambulatory EMRs was cited including: Beth Israel-Boston OMAR (Hsieh, Gandhi et al., 2004; Rind & Safran, 1993; Safran et al., 1999; Sands, Libman, & Safran, 1995; Slack & Bleich, 1999), Brigham and Womens BICS and LMR (Poon et al., 2003; E. G. Poon, Wang, Gandhi, Bates, & Kuperman, 2003), Regenstrief (McDonald et al., 1999), Stanfords Pen and Ivory (Poon & Fagan, 1994; Poon, Fagan, & Shortliffe, 1996) and LDS’ HELP (Gardner et al., 1999) and comprehensive analyses such as those written by Astrid M. van Ginneken (2002) and David Bates (Bates et al., 2003). A subsequent literature review was conducted to ensure being current state of knowledge as well as to answer any additional queries that occurred as the selection process evolved. Over 75 papers were identified that significantly shaped the selection process, resulted in identification of achievable goals, and created appropriate level of expectations breaking a 10 year paralysis on deciding to implement and select an ambulatory EMR (Kannry et al., 2006). One of the findings noted was the close partnership with

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the project manager IT) resulted in the success the combined the science and professional IT portions. Yet, there are challenges in the partnership as formal informatics analysis and research is unfunded mandate on the project.

RECOMMENDATION: OPERATIONAL DEPARTMENT OF CLINICAL INFORMATICS For informatics to be effective and make a substantive contribution to the enterprise, there is a need to think differently about the role clinical informatics in medical center operations. Otherwise informatics methodology and results will never become a routine part of enterprise operations and decision making. The models previously described, while starting to become more commonplace, are unfortunately the exception and not the rule. Informatics needs to become an operational department in the enterprise. One innovative model worth noting is the one at Partners in Boston. Dr. John Glaser, CIO at Partners describes a partnership between informatics and HIT in which .25 percent of the HIT budget (though not an insignificant amount in dollars) is spent on informatics to fund hardware, software, and time of existing personnel (Glaser, 2005). However, he notes and the author concurs that this is also partly the result of the culture at his institution, informatics personnel already in place or associated with the institution, and the long track record of informatics success at his institution including the building of the present and still in use CPOE system. It this author’s contention that clinical informatics needs to be a distinct operational department at each medical center. This department of informatics would have as its cornerstone the scientific knowledge and methodology unique to informatics and apply this science to operations of the medical center. The shape and scope of this department could take many forms depending on

Operationalizing the Science

whether the department was designed to be an analysis and research department, research and development, or in charge of clinical information systems. The department could be responsible for conducting informatics research of value to operations such as the role of new technologies such as RFID, and questions about existing technologies such as effective use of clinical decision support in drug allergy checking. However, the department would need the authority, budget, and personnel to ensure diffusion of innovations and addressing questions as they arose during projects. The department could play a key or leading role in strategic decisions making about HIT as well as selection, implementation, and post implementation processes. The greater the range and integration of informatics the greater the potential benefit to the enterprise. For example, informatics could analyze and assist in evaluation of utilization of clinical decision support. Yet, informatics might not be involved in the re-design of clinical decision support suggested by informatics findings. Such a structure would limit the utility and effectiveness of the science as the hypotheses can never be tested or implemented. Unfortunately an operationally integrated informatics department is the exception and not the rule today. The majority of exceptions can be found in the discussion on models of informatics organization. Contributing to this state of affairs is little understanding from the multiple constituencies in need of informatics guidance whether it is in the form of research, application development, evaluation. As a result, many thoughtful articles have explored the need to define how informatics could be integrated into operation and real world HIT setting (Bakken, 2001; Glaser, 2005; Hersh, 2006; Lorenzi, Gardner, Pryor, & Stead, 1995; Stead, 1999; Stead & Lorenzi, 1999) and provide guidance on exactly what informatics does or why it is necessary at all. Comments from the IT, vendor and physician communities regarding informatics topics or participation range from “Doctors should

do what they do best…doctoring, Doc….you can leave…..this is the IT portion of the presentation, informaticists are what I say they are, nobody here knows what informatics is and it would not get funding, we need to have the vendors socialize with the executives to demonstrate CPOE value, all I need to know about clinical decision support is in a book at home, and there is no informatics research budget for the EMR.” It is this author’s assertion that no constituency: physicians, IT, vendors etc is not in its own way contributing to the confusion over, the poor allocation of, or misuse of informatics in the enterprise. Not so surprisingly penetration rates are low for electronic medical record (EMR) and computerized physician order entry (CPOE) (Ash, Gorman, & Hersh, 1998; Ash, Gorman et al., 2003; Ash et al., 2004; Bates et al., 2003; van Ginneken, 2002). CPOE is defined as the part of HIS (hospital information system) that handles the physician and nursing orders sent to the laboratory, radiology, pharmacy, and other ancillary departments (Bemmel, Musen, & Helder, 1997) and an EMR is defined as all electronically stored information about individual’s outpatient lifetime health status and health care” (Institute of Medicine (U.S.). Committee on Improving the Patient Record. et al., 1991; Institute of Medicine (U.S.). Committee on Improving the Patient Record et al., 1997). It is this author’s suspicion that the biggest barrier to diffusion is not cost but qualified people. There is a shortage of qualified informaticists with the expertise to make the case, demonstrate success, and identify post implementation problems (AMIA, 2006). For example, this author has previously stated that the errors identified by Koppel (Koppel et al., 2005) could have been preventable with a process called optimization (Kannry, 2007). Yet, despite the informaticist shortage, there are proposals to fund nationwide implementation of EMRs and the NHIN for hundreds of billions of dollars with no linkage to increasing the number of informaticists and operational departments of informatics.

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The recommendation for an operational informatics department at each medical center assumes that there is an affiliation with an academic unit or department or the group. It is beyond the scope of this chapter to discuss possible relationships between such groups as this would require an extensive discussion of the organization and function of academically housed informatics groups. However, it should be noted that the cross fertilization of personnel and research between two groups would benefit the academic as well as the operational group. The need for operational departments of clinical informatics only grows with each passing day while at the same time there is a continually worsening shortage of informaticists (AMIA, 2006). It is the author’s belief that government and market forces would lessen this shortage if there was a place for these newly trained personnel to go to; an operational department of informatics.

A BRIEF WORD ABOUT CLINICAL AND INTERVENTIONAL INFORMATICS The author would like to formally introduce the term Interventional informatics which is a term suggested to him by Dr. Charles Safran. While interventional and clinical informatics are essentially one and the same, Interventional informatics emphasizes that informatics is an active part of the enterprise that both informs and intervenes ultimately changing the enterprise for the better.

SUMMARY In summary, healthcare is an information intense field. Yet HIT which should be able to go a long way in addressing information management which is under funded with reports of three to five percent of budget spending on IT compared to industry (ROW) rates of at least 5-10 percent

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(Andriole, 2005; Presidential Advisory Commission on Consumer Protection and Quality in Health Care Industry, 1998). One article notes that any company that funds investment in IT at a four percent or lower rate essentially views IT as a service and cost leader and not a source of innovation and transformation (Andriole, 2005). In a financially strapped environment with insufficient budget there is very little margin for error and repetition of mistakes and in particular the NIH/NDH (not invented here/not done here) syndrome. In this syndrome mistakes are repeated because the organization has never experienced the mistakes firsthand. The solution to avoid mistakes in, leverage resources for, and bring innovation on demand to HIT is to create an operational department of interventional informatics. Such a department should be a standard operational department at each medical center much the way there are departments of medical records, compliance, risk management, quality improvement/assurance, and so forth. Interventional informaticists who work in such a department should be a cadre of EMPOWERED and formally trained experts capable of analysis, development, implementation, and evaluation with an ability to know when to use standardized tools sets architectures. HIT needs to become like the rest of healthcare; that is, scientifically grounded and evidence based. An operational department of intervention informatics with budgetary authority, personnel, and institutional responsibility would be a very important step in bridging the evidence gap between HIT and the rest of health care. Every medical center in the 21st century should have an operational department of interventional informatics.

FUTURE RESEARCH DIRECTIONS Recent extramural funding for studies in clinical informatics has significantly decreased from its heyday of the mid-late 80s and 90s. Yet there is a

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growing need for clinical informatics research particularly regarding clinical information systems. This need is especially acute at the operational level as questions are asked on a daily basis that the informatics literature has yet to fully explore and in some instances answer. For instance it is difficult to find qualitative let alone quantitative studies on a fundamental operational topic such as enterprise master patient index (EMPI) (Arellano & Weber, 1998; Lenson, 1998; Mercer, Widmer, Prada, Grogan, & Tresnan, 1995; Mills, 2006). The EMPI assures that every patient has one medical record number irrespective of the number of registration systems any one medical center or integrated health delivery systems (e.g., multiple hospitals) might have. Studies of EMPI need to quantitate before and after states, impact on workflow, and so forth. Research on EMPI might give insight into the challenges of creating inter-institutional and national patient identifiers which will require the merging of multiple medical record numbers (Arellano & Weber, 1998; Mills, 2006). Many of the lessons and benefits demonstrated by 30 years of informatics studies have yet to be replicated in commercial systems. At the same time there has been an increasing number of studies demonstrating either harm or no benefit from clinical information systems and in particular commercial CPOE and EMR systems (Gesteland, Nebeker, & Gardner, 2006; Han et al., 2005; Horsky et al., 2005; Koppel et al., 2005; Murray et al., 2004; Potts, Barr, Gregory, Wright, & Patel, 2004). Research is needed to further examine which lessons learned can be extrapolated to commercial systems as well as what factors affect replication of benefits. Even informatics developed systems have not been immune to studies demonstrating adverse events caused by CPOE and EMR systems (Gesteland, Nebeker, & prokosch, 2006; Hsieh, Kuperman, Jaggi et al., 2004; Nebeker, Hoffman, Weir et al., 2005). This raises some broader ques-

tions regarding unintended consequences of CPOE and EMR systems. Previous studies demonstrating the benefits of EMR have recently been called into question. Two newly published studies looking at ambulatory EMRs, found that EMRs were neither associated with improvements in care quality, enhancements in patient safety nor reductions in cost of care (Eslami, Abu-Hanna, & de Keizer, 2007; Linder, Ma, Bates, Middleton, & Stafford, 2007; Welch et al., 2007). Further research is clearly needed for both informatics and commercial systems to explore what factors lead to successful implementation and realization of benefits in clinical information systems and particularly those systems with clinical decision support systems (CDSS). Future studies would benefit greatly from the participation of multiple sites in the same study. There is a need to be able to eliminate variables and control for factors by studying the same aspect of the same system whether it be the VA’s Vista System (Brown, Lincoln, Groen, & Kolodner, 2003) or the same commercial system and studies conducted at multiple sites would address that need. Such studies might be able to highlight organizational factors such as culture, leadership etc that affect implementations for the better or worse (Ash, Stavri, & Kuperman, 2003). One such attempt to study one system at multiple sites found differences in user satisfaction with the same version of the same CPOE system in the same specialty at two different sites (Kannry, 2007). The differences in satisfaction were attributable to differences in the functionality used (i.e., at one site physicians directly placed orders while at the other site clerks placed orders for physicians) though the study could not rule out other external factors as well. There is also need to compare implementations and outcomes within an institution to identify intra-institutional factors such as the study that looked at two practices with the same EMR (O’Connell, Cho, Shah et al., 2004). Operationally, informatics is faced with a plethora of questions regarding implementation

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process and decision making as well as training. There are few investigations that focus on training of users and particularly one of the largest segments of users, housestaff (Aaronson, Murphy-Cullen, Chop, & Frey, 2001; Chessare & Torok, 1993; Gamm, Barsukiewicz, Dansky, & Vasey, 1998; Hier, Rothschild, LeMaistre, & Keeler, 2005; Keenan, Nguyen, & Srinivasan, 2006; Retchin & Wenzel, 1999; Swanson et al., 1997). While a great deal of thought has been given and published on lessons learned and requirements for successful implementation and potential factors, there is limited research on the implementation process itself. Further research is needed to create a framework for laying out step to a successful implementation and the relative contributions of those steps to successful outcome. There is little in the way of systematic analysis or studies of ROI (return on investment) on enterprise clinical information system (Frisse, 2006; Grieger, Cohen, & Krusch, 2007; Kaushal et al., 2006; Miller, West, Brown et al., 2005; Piasecki et al., 2005; Wang et al., 2003; Welch et al., 2007; Zaroukian & Sierra, 2006). Studies need to place greater emphasis on revenue enhancement as opposed to cost avoidance. Revenue enhancement looks at real dollars while cost avoidance estimates dollars that would have been lost or spent but may not have been. Two examples are illustrative of the need for further research on the ROI of enterprise clinical information systems. Avoidance of medical errors may save millions but that does not translate to millions actually saved (Kannry, 2007). Prior studies of the ROI for ambulatory EMRs attribute significant savings to reduced chart pulls, eliminating the chart filing (i.e., medical records room), and reducing or eliminating medical records personnel. Chart pulls are defined as pulling the chart for each patient visit and the cost of chart pulls is frequently estimated as the cost of personnel and the amount of time spent on pulling the chart. These cost savings may be true for individual or small to medium size practices but do not seem applicable at the

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enterprise level where such savings are a negligible portion of the millions allocated for the EMR and personnel reduction/elimination does not occur as readily. In the authors’ experience at the enterprise level reducing chart pulls and eliminating the chart filing room do not contribute to significant savings and reducing/eliminating personnel is difficult. If anything the medical records personnel whose previous responsibilities were to file and pull charts need to be re-deployed to scanning the many paper documents still produced. For example, private consultants may send patients to the medical center and these consults need to be scanned into the EMR. For example, research consents may need to be scanned into the chart. The fully paperless office is along ways away from reality. Preliminary analysis by the author of ROI for an Ambulatory EMR at a large academic medical center, found revenue enhancements that neither depends on chart pulls, personnel reductions, or elimination of chart filing rooms. Significant challenges and questions remain regarding the link between clinical information systems and clinical research systems (Boers, van der Linden, & Hasman, 2002; Embi, Jain, Clark, & Harris, 2005; Gerdsen, Mueller, Jablonski, & Prokosch, 2005; Goldstein et al., 2004; Green, White, Barry et al., 2005; Hanzlicek, Zvarova, & Dostal, 2006; Platt, 2007; Powell & Buchan, 2005; Tilghman, Tilghman, & Johnson, 2006). For example, EMRs do not easily, if at all, link to clinical research systems (Sim, Olasov, & Carini, 2003; Sim, Owens, Lavori, & Rennels, 2000; Sim, Wyatt, Musen, & Niland, 1999). More studies need to be done to better identify problems with and solutions for linking commercial clinical information systems and commercial (research) trial management systems. Subject accrual (i.e., the recruitment and enrollment of human subjects) in clinical trials is critical to the success of clinical trials and there are only a handful of studies looking at using EMR for subject accrual (Embi et al., 2005; Embi et al., 2005). Much work still needs to be done

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creating and testing portable models for subject accrual that can be used in any commercial EMR. Translational research is research that “the clinical application of scientific medical research, from the lab to the bedside” (USC/Norris Comphrehensive Cancer Center, 2007). Clinical application of scientific research will require linking discoveries to the bedside through the use of clinical information systems. For example, a new study linking treatment of disease y to gene x would ideally require alerting the physician to the fact that disease y and gene y have a relationship, obtaining a genetic sample from the patient, sending the findings back to the physician and recommending treatment based on the genetic analysis. Translational research would benefit from research on how best to use and link to clinical information systems (Martin-Sanchez, Maojo, & Lopez-Campos, 2002). It is difficult to find peer reviewed studies that specifically examine patient satisfaction with physician use of enterprise EMRs, yet, this is of vital interest to operations (Gadd & Penrod, 2000; Garrison, Bernard, & Rasmussen, 2002; Legler & Oates, 1993; Rouf, Whittle, Lu, & Schwartz, 2007; Solomon & Dechter, 1995). If investigations could demonstrate increased patient satisfaction with physician use of EMRs, institutions with EMRs could potentially differentiate their delivery of patient care in competitive markets. However, investigations are first needed to identify which aspects of physician use of EMRs translate into in increased patient satisfaction. For example, are patients happier with physicians who use EMRs because physicians always have the patient’s chart, now possess comprehensive knowledge of their illnesses and medications, get test results quicker, spend more time on them as opposed to hunting for documentation etc. Finally, there is a need to study the role and impact of clinical informatics itself on operational processes. Specifically, what role does clinical informatics have in the success and failure of clinical information systems? Is there any relationship

between organizational success with HIT and informatics? There are tantalizing hints (O’Connell et al., 2004) though anything conclusive has yet to be proven. The effect of informatics itself has yet to be the focus of investigation and definitive studies have yet to be done.

REFERENCES Aaronson, J. W., Murphy-Cullen, C. L., Chop, W. M., & Frey, R. D. (2001). Electronic medical records: the family practice resident perspective. Family Medicine, 33(2), 128–132. AMIA, A. A. (2006). Building the work force for health information transformation. Andriole, S. (2005). 10 questions you should be able to answer. Retrieved January 20, 2007, from http://itmanagement.earthweb.com/columns/ bizalign/article.php/3574421 Are handoffs too ‘automatic’? QI experts fear errors could rise. (2006). Healthcare Benchmarks Qual Improv, 13(1), 1-4. Arellano, M. G., & Weber, G. I. (1998). Issues in identification and linkage of patient records across an integrated delivery system. Journal of Healthcare Information Management, 12(3), 43–52. Ash, J. S., Gorman, P. N., & Hersh, W. R. (1998). Physician order entry in U.S. hospitals. Proceedings of AMIA Symp, 235–239. Ash, J. S., Gorman, P. N., Lavelle, M., Payne, T. H., Massaro, T. A., & Frantz, G. L. (2003). A cross-site qualitative study of physician order entry. Journal of the American Medical Informatics Association, 10(2), 188–200. doi:10.1197/jamia.M770 Ash, J. S., Gorman, P. N., Seshadri, V., & Hersh, W. R. (2004). Computerized physician order entry in U.S. hospitals: results of a 2002 survey. Journal of the American Medical Informatics Association, 11(2), 95–99. doi:10.1197/jamia.M1427

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Ash, J. S., Stavri, P. Z., & Kuperman, G. J. (2003). A consensus statement on considerations for a successful CPOE implementation. Journal of the American Medical Informatics Association, 10(3), 229–234. doi:10.1197/jamia.M1204

Bukunt, S., Hunter, C., Perkins, S., Russell, D., & Domanico, L. (2005). El Camino Hospital: using health information technology to promote patient safety. Joint Commission Journal on Quality and Patient Safety, 31(10), 561–565.

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Chaudhry, B., Wang, J., Wu, S., Maglione, M., Mojica, W., & Roth, E. (2006). Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care. Annals of Internal Medicine, 144, E-12–E22.

Bates, D. W., Ebell, M., Gotlieb, E., Zapp, J., & Mullins, H. C. (2003). A proposal for electronic medical records in U.S. primary care. Journal of the American Medical Informatics Association, 10(1), 1–10. doi:10.1197/jamia.M1097 Bates, D. W., & Gawande, A. A. (2003). Improving safety with information technology. The New England Journal of Medicine, 348(25), 2526–2534. doi:10.1056/NEJMsa020847 Bates, D. W., Teich, J. M., Lee, J., Seger, D., & Kuperman, G. J., Ma’Luf, N., et al. (1999). The impact of computerized physician order entry on medication error prevention. Journal of the American Medical Informatics Association, 6(4), 313–321. Bemmel, J. H. V., Musen, M. A., & Helder, J. C. (1997). Handbook of medical informatics. AW Houten, Netherlands; Heidelberg, Germany: Bohn Stafleu Van Loghum; Springer Verlag. Boers, G., van der Linden, H., & Hasman, A. (2002). A distributed architecture for medical research. Studies in Health Technology and Informatics, 90, 734–738. Brown, S. H., Lincoln, M. J., Groen, P. J., & Kolodner, R. M. (2003). VistA—U.S. Department of Veterans Affairs national-scale HIS. International Journal of Medical Informatics, 69(2-3), 135–156. doi:10.1016/S1386-5056(02)00131-4

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Eslami, S., Abu-Hanna, A., & de Keizer, N. F. (2007). Evaluation of outpatient computerized physician medication order entry systems: A systematic review. Journal of the American Medical Informatics Association, 14(4), 400–406. doi:10.1197/jamia.M2238 Frisse, M. E. (1992). Medical informatics in academic health science centers. Academic Medicine, 67(4), 238–241. doi:10.1097/00001888199204000-00005 Frisse, M. E. (2006). Comments on Return on Investment (ROI) as it applies to clinical systems. Journal of American Medical Information Association, M2072. Gadd, C. S., & Penrod, L. E. (2000). Dichotomy between physicians’ and patients’ attitudes regarding EMR use during outpatient encounters. Proceedings of AMIA Symp, 275–279. Gamm, L. D., Barsukiewicz, C. K., Dansky, K. H., & Vasey, J. J. (1998). Investigating changes in end-user satisfaction with installation of an electronic medical record in ambulatory care settings. Journal of Healthcare Information Management, 12(4), 53–65. Gandhi, T. K. (2005). Fumbled handoffs: one dropped ball after another. Annals of Internal Medicine, 142(5), 352–358. Gardner, R. M., Pryor, T. A., & Warner, H. R. (1999). The HELP hospital information system: Update 1998. International Journal of Medical Informatics, 54(3), 169–182. doi:10.1016/S13865056(99)00013-1 Garrison, G. M., Bernard, M. E., & Rasmussen, N. H. (2002). 21st-century health care: The effect of computer use by physicians on patient satisfaction at a family medicine clinic. Family Medicine, 34(5), 362–368.

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Grieger, D. L., Cohen, S. H., & Krusch, D. A. (2007). A pilot study to document the return on investment for implementing an ambulatory electronic health record at an academic medical center. Journal of the American College of Surgeons, 205(1), 89–96. doi:10.1016/j.jamcollsurg.2007.02.074 Halamka, J. (2006). Early experiences with eprescribing. Journal of Healthcare Information Management, 20(2), 12–14. Halamka, J., Overhage, J. M., Ricciardi, L., Rishel, W., Shirky, C., & Diamond, C. (2005). Exchanging health information: Local distribution, national coordination. Health Affairs (Project Hope), 24(5), 1170–1179. doi:10.1377/hlthaff.24.5.1170 Halamka, J. D., Osterland, C., & Safran, C. (1999). CareWeb, a web-based medical record for an integrated health care delivery system. International Journal of Medical Informatics, 54(1), 1–8. doi:10.1016/S1386-5056(98)00095-1 Han, Y. Y., Carcillo, J. A., Venkataraman, S. T., Clark, R. S., Watson, R. S., & Nguyen, T. C. (2005). Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics, 116(6), 1506–1512. doi:10.1542/peds.2005-1287 Hanzlicek, P., Zvarova, J., & Dostal, C. (2006). Information technology in clinical research in rheumatology domain. Studies in Health Technology and Informatics, 124, 187–192. Hersh, W. (2006). Who are the informaticians? What we know and should know. Journal of the American Medical Informatics Association, 13(2), 166–170. doi:10.1197/jamia.M1912 Hier, D. B., Rothschild, A., LeMaistre, A., & Keeler, J. (2005). Differing faculty and housestaff acceptance of an electronic health record. International Journal of Medical Informatics, 74(7-8), 657–662. doi:10.1016/j.ijmedinf.2005.03.006

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Linder, J. A., Ma, J., Bates, D. W., Middleton, B., & Stafford, R. S. (2007). Electronic health record use and the quality of ambulatory care in the United States. Archives of Internal Medicine, 167(13), 1400–1405. doi:10.1001/archinte.167.13.1400

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Lorenzi, N. M., Gardner, R. M., Pryor, T. A., & Stead, W. W. (1995). Medical informatics: The key to an organization’s place in the new health care environment. Journal of the American Medical Informatics Association, 2(6), 391–392.

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Marchibroda, J. M., & Gerber, T. (2003). Information infrastructure promises. Better healthcare, lower costs. J Ahima, 74(1), 28-32; quiz 33-24. Martin-Sanchez, F., Maojo, V., & Lopez-Campos, G. (2002). Integrating genomics into health information systems. Methods of Information in Medicine, 41(1), 25–30. McDonald, C. J., Overhage, J. M., Tierney, W. M., Dexter, P. R., Martin, D. K., & Suico, J. G. (1999). The Regenstrief Medical Record System: a quarter century experience. International Journal of Medical Informatics, 54(3), 225–253. doi:10.1016/S1386-5056(99)00009-X Millenson, M. L. (1997). Demanding medical excellence: doctors and accountability in the information age: With a new afterword (Pbk. ed.). Chicago: University of Chicago Press. Miller, R. A., Waitman, L. R., Chen, S., & Rosenbloom, S. T. (2005). The anatomy of decision support during inpatient care provider order entry (CPOE): Empirical observations from a decade of CPOE experience at Vanderbilt. Journal of Biomedical Informatics, 38(6), 469–485. doi:10.1016/j.jbi.2005.08.009 Miller, R. H., & Sim, I. (2004). Physicians’ use of electronic medical records: Barriers and solutions. Health Affairs, 23(2), 116–126. doi:10.1377/ hlthaff.23.2.116

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Murff, H. J., & Kannry, J. (2001). Physician satisfaction with two order entry systems. Journal of the American Medical Informatics Association, 8(5), 499–509. Murray, M. D., Harris, L. E., Overhage, J. M., Zhou, X. H., Eckert, G. J., & Smith, F. E. (2004). Failure of computerized treatment suggestions to improve health outcomes of outpatients with uncomplicated hypertension: Results of a randomized controlled trial. Pharmacotherapy, 24(3), 324–337. doi:10.1592/phco.24.4.324.33173 Murray, M. D., Smith, F. E., Fox, J., Teal, E. Y., Kesterson, J. G., & Stiffler, T. A. (2003). Structure, functions, and activities of a research support informatics section. Journal of the American Medical Informatics Association, 10(4), 389–398. doi:10.1197/jamia.M1252 Nebeker, J. R., Hoffman, J. M., Weir, C. R., Bennett, C. L., & Hurdle, J. F. (2005). High rates of adverse drug events in a highly computerized hospital. Archives of Internal Medicine, 165(10), 1111–1116. doi:10.1001/archinte.165.10.1111 O’Connell, R. T., Cho, C., Shah, N., Brown, K., & Shiffman, R. N. (2004). Take note(s): Differential EHR satisfaction with two implementations under one roof. Journal of the American Medical Informatics Association, 11(1), 43–49. doi:10.1197/ jamia.M1409

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Office of Management and Budget. (2007). Budget of the United States Government Fiscal Year 2008. Retrieved July 27, 2007, 2007, from http://www. whitehouse.gov/omb/budget/ Petersen, L. A., Orav, E. J., Teich, J. M., O‘Neil, A. C., & Brennan, T. A. (1998). Using a computerized sign-out program to improve continuity of inpatient care and prevent adverse events. The Joint Commission Journal on Quality Improvement, 24(2), 77–87. Piasecki, J. K., Calhoun, E., Engelberg, J., Rice, W., Dilts, D., & Belser, D. (2005). Computerized provider order entry in the emergency department: pilot evaluation of a return on investment analysis instrument. AMIA ... Annual Symposium Proceedings / AMIA Symposium. AMIA Symposium, 1081. Pizzi, R. (2007). Healthcare IT a key aspect of physicians’ reform principles. Retrieved Janaury 15, 2007, from http://www.healthcareitnews.com/ story.cms?id=6165 Platt, R. (2007). Speed bumps, potholes, and tollbooths on the road to panacea: making best use of data. Health Affairs (Project Hope), 26(2), w153–w155. doi:10.1377/hlthaff.26.2.w153 Poon, A. D., & Fagan, L. M. (1994). PEN-Ivory: the design and evaluation of a pen-based computer system for structured data entry. Proceedings of the Annual Symposium on Computer Applications in Medical Care, 447–451. Poon, A. D., Fagan, L. M., & Shortliffe, E. H. (1996). The PEN-Ivory project: exploring userinterface design for the selection of items from large controlled vocabularies of medicine. Journal of the American Medical Informatics Association, 3(2), 168–183. Poon, E. G., Wald, J., Bates, D. W., Middleton, B., Kuperman, G. J., & Gandhi, T. K. (2003). Supporting patient care beyond the clinical encounter: three informatics innovations from partners health care. AMIA ... Annual Symposium Proceedings / AMIA Symposium. AMIA Symposium, 1072.

Poon, E. G., Wang, S. J., Gandhi, T. K., Bates, D. W., & Kuperman, G. J. (2003). Design and implementation of a comprehensive outpatient Results Manager. Journal of Biomedical Informatics, 36(1-2), 80–91. doi:10.1016/S15320464(03)00061-3 Potts, A. L., Barr, F. E., Gregory, D. F., Wright, L., & Patel, N. R. (2004). Computerized physician order entry and medication errors in a pediatric critical care unit. Pediatrics, 113(1 Pt 1), 59–63. doi:10.1542/peds.113.1.59 Powell, J., & Buchan, I. (2005). Electronic health records should support clinical research. Journal of Medical Internet Research, 7(1), e4. doi:10.2196/ jmir.7.1.e4 Presidential Advisory Commision on Consumer Protection and Quality in Health Care Industry. (1998). Building the capacity to improve quality. Chapter Fourteen: Investing in Information Systems. Retrieved January 26, 2007, from http:// www.hcqualitycommission.gov/final/chap14. html Quinn, J. (2004). Vendor perspectives: Critical do’s and dont’s. In Spring, A. M. I. A. (Ed.), McClean, Va. Randolph, A. G., Haynes, R. B., Wyatt, J. C., Cook, D. J., & Guyatt, G. H. (1999). Users’ guides to the medical literature: XVIII. How to use an article evaluating the clinical impact of a computer-based clinical decision support system. Journal of the American Medical Association, 282(1), 67–74. doi:10.1001/jama.282.1.67 Retchin, S. M., & Wenzel, R. P. (1999). Electronic medical record systems at academic health centers: advantages and implementation issues. Academic Medicine, 74(5), 493–498. doi:10.1097/00001888-199905000-00013 Rind, D. M., & Safran, C. (1993). Real and imagined barriers to an electronic medical record. Proceedings of the Annual Symposium on Computer Applications in Medical Care, 74–78. 1619

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Rouf, E., Whittle, J., Lu, N., & Schwartz, M. D. (2007). Computers in the exam room: Differences in physician-patient interaction may be due to physician experience. Journal of General Internal Medicine, 22(1), 43–48. doi:10.1007/ s11606-007-0112-9 Safran, C., Sands, D. Z., & Rind, D. M. (1999). Online medical records: a decade of experience. Methods of Information in Medicine, 38(4-5), 308–312. Sands, D. Z., Libman, H., & Safran, C. (1995). Meeting information needs: analysis of clinicians’ use of an HIV database through an electronic medical record. Medinfo, 8(Pt 1), 323–326. Shortliffe, E. H. (2001). Medical informatics: Computer applications in health care and biomedicine (2nd ed.). New York: Springer. Sim, I., Olasov, B., & Carini, S. (2003). The trial bank system: Capturing randomized trials for evidence-based medicine. AMIA ... Annual Symposium Proceedings / AMIA Symposium. AMIA Symposium, 1076. Sim, I., Owens, D. K., Lavori, P. W., & Rennels, G. D. (2000). Electronic trial banks: A complementary method for reporting randomized trials. Medical Decision Making, 20(4), 440–450. doi:10.1177/0272989X0002000408 Sim, I., Wyatt, J., Musen, M., & Niland, J. (1999). Towards an open infrastructure for clinical trial development and interpretation. Paper presented at the 1999 Fall AMIA Symposium, Washington DC. Simon, S. R., Kaushal, R., Cleary, P. D., Jenter, C. A., Volk, L. A., & Orav, E. J. (2007). Physicians and electronic health records: A statewide survey. Archives of Internal Medicine, 167(5), 507–512. doi:10.1001/archinte.167.5.507

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Slack, W. V., & Bleich, H. L. (1999). The CCC system in two teaching hospitals: A progress report. International Journal of Medical Informatics, 54(3), 183–196. doi:10.1016/S13865056(99)00006-4 Solomon, G. L., & Dechter, M. (1995). Are patients pleased with computer use in the examination room? The Journal of Family Practice, 41(3), 241–244. Stead, W. W. (1999). The challenge to health informatics for 1999-2000: Form creative partnerships with industry and chief information officers to enable people to use information to improve health. Journal of the American Medical Informatics Association, 6(1), 88–89. Stead, W. W., Kelly, B. J., & Kolodner, R. M. (2004). Achievable steps toward building a national health information infrastructure in the United States. Journal of American Medical Information Association. Stead, W. W., & Lorenzi, N. M. (1999). Health informatics: Linking investment to value. Journal of the American Medical Informatics Association, 6(5), 341–348. Stead, W. W., Miller, R. A., Musen, M. A., & Hersh, W. R. (2000). Integration and beyond: Linking information from disparate sources and into workflow. Journal of the American Medical Informatics Association, 7(2), 135–145. Strategos Inc. Lean Manufacturing History. Retrieved June 18, 2007, from http://www.strategosinc.com/just_in_time.htm Streitenberger, K., Breen-Reid, K., & Harris, C. (2006). Handoffs in care—can we make them safer? Pediatric Clinics of North America, 53(6), 1185–1195. doi:10.1016/j.pcl.2006.09.010

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Swanson, T., Dostal, J., Eichhorst, B., Jernigan, C., Knox, M., & Roper, K. (1997). Recent implementations of electronic medical records in four family practice residency programs. Academic Medicine, 72(7), 607–612. doi:10.1097/00001888199707000-00013 Talmon, J. L., & Hasman, A. (2002). Medical informatics as a discipline at the beginning of the 21st century. Methods of Information in Medicine, 41(1), 4–7. Teich, J. M., Glaser, J. P., Beckley, R. F., Aranow, M., Bates, D. W., & Kuperman, G. J. (1999). The Brigham integrated computing system (BICS): Advanced clinical systems in an academic hospital environment. International Journal of Medical Informatics, 54(3), 197–208. doi:10.1016/S13865056(99)00007-6 Thinking About. …Implementing the EMR [Electronic (2006). Version]. Digital Office, Volume 1. Retrieved January 15, 2007 from http://www. himss.org/Content/files/digital_office_enews/ digitaloffice_200606.html Tilghman, C., Tilghman, J., & Johnson, R. W. (2006). Integration of technology in a clinical research setting. The ABNF Journal, 17(3), 112–114. USC/Norris Comphrehensive Cancer Center. (2007). USC/Norris > Glossary. Retrieved August 3, 2007, from http://ccnt.hsc.usc.edu/glossary/ van Ginneken, A. M. (2002). The computerized patient record: Balancing effort and benefit. International Journal of Medical Informatics, 65(2), 97–119. doi:10.1016/S1386-5056(02)00007-2 Walker, J. (2003). Clinical-information connectivity nationwide. Healthcare can use the model of success frontiered by banks. Healthcare Informatics, 20(10), 62–64.

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ADDITIONAL READING Abdelhak, M. (2007). Health information: Management of a strategic resource (3rd ed.). St. Louis, MO: Saunders/Elsevier. Bemmel, J. H. V., Musen, M. A., & Helder, J. C. (1997). Handbook of medical informatics. AW Houten, Netherlands, Heidelberg, Germany: Bohn Stafleu Van Loghum; Springer Verlag. Friedman, C. P., & Wyatt, J. (2006). Evaluation methods in medical informatics (2nd ed.). New York: Springer. Glaser, J. (2002). The strategic application of information technology in health care organizations (2nd ed.). San Francisco, CA: Jossey-Bass. Griffith, J. R., & White, K. R. (2006). The wellmanaged healthcare organization (6th ed.). Chicago: Health Administration Press. Kannry, J. (2007). CPOE and patient safety: Panacea or pandora’s box? In Ong, K. (Ed.), Medical informatics: An executive primer. Chicago: HIMSS.

Kannry, J., Mukani, S., & Myers, K. (2006). Using an evidence-based approach for system selection at a large academic medical center: lessons learned in selecting an ambulatory EMR at Mount Sinai Hospital. Journal of Healthcare Information Management, 20(2), 84–99. Murff, H. J., & Kannry, J. (2001). Physician satisfaction with two order entry systems. Journal of the American Medical Informatics Association, 8(5), 499–509. Ong, K. (Ed.). (2007). Medical informatics: An executive primer. Chicago: HIMSS. Shortliffe, E. H., & Cimino, J. J. (2006). Biomedical informatics: Computer applications in health care and biomedicine (3rd ed.). New York, NY: Springer. Wager, K. A., Lee, F. W., & Glaser, J. (2005). Managing health care information systems: A practical approach for health care executives (1st ed.). San Francisco: Jossey-Bass.

This work was previously published in Human, Social, and Organizational Aspects of Health Information Systems, edited by Andre W. Kushniruk and Elizabeth M. Borycki, pp. 219-239, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 6.2

Current Challenges in Empowering Clinicians to Utilise Technology Jean M. Roberts University of Central Lancashire, UK

ABSTRACT This chapter is designed to outline the current situation and challenges to successful deployment of technologies to support clinical activities. It utilises action research and cooperative enquiry within the community of practice. It is grounded in UK experiences but will have international resonance in many key areas. Increasingly members of the public are joining the clinical professions in using health data to maintain and improve health DOI: 10.4018/978-1-60960-561-2.ch602

status of the individual; however this chapter predominantly focuses on catalysts and inhibitors to professional use. The second objective of this chapter is to consider the opportunities presented by emerging technologies, and restrictions to effective deployment such as cultural reluctance, ethical issues and privacy concerns. It is hoped that highlighting the key issues for consideration will reassure clinicians that they are faced with similar informatics challenges to all other in the health domain and that, for all, the benefits of persisting with utilising technology in support of their clinical work are considerable.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Current Challenges in Empowering Clinicians to Utilise Technology

INTRODUCTION The first information technology / computer (IT) systems were introduced in the 1960s into primary (non-hospital) and hospital environments in the UK for clinically related purposes. Whilst initially there was a tendency for interested clinicians to get computer scientists to write programs for their purposes or to use off-the-shelf core computing applications, it was not long before interested parties started to write their own solutions. It was some time before formal courses in IT for health were established, and indeed there are still occurrences of medical and nursing courses without significant explicit informatics content. The term ‘clinical’ as used here, does not distinguish between that relating to doctors, the nursing professions and other healthcare practitioners with direct patient care impact. Citizens are also increasingly enabled by technologies to play a role in their own care and lifestyle management; and thus will wish to access information about themselves personally which was previously used solely by clinicians and those that manage the facilities in which they work. ‘Informatics’ relates to the whole spectrum of information technology, application and operating systems, information handling and data quality; and health informatics (HI) applies these in the health domain. Having such a collective term means that job roles and titles are also varied. The recent publication (Equalitec, 2007) adds contextualisation to Health Informatics per se.

OPERATIONAL INFORMATICS Use of technologies in health is pervasive but not yet ubiquitous (Roberts, 2006). For example administrative (component) solutions which identify which patients are in what bed in a particular ward of a hospital are in use almost everywhere but functionality which facilitates independent living, providing autonomous wireless signal alerts

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which indicate vital signs have become critical and alerts a clinician or induces an emergency action are less prevalent (Lymberis, 2004). Within a hospital, similar monitoring become more critical – whether applied to a patient directly or the life support services they need, such as anaesthesia or pain control. In addition, the results of laboratory tests requested by clinicians as part of their day to day practice can now be uploaded directly to the patient’s individual record, with only abnormal results pertinent to the specific patient being drawn to the attention of attending clinicians. The information available, from increasingly comprehensive systems (NHSCFH, 2008), not only contains the detailed clinical interventions and outcomes within an episode of care in hospital or in the community but contains significant aggregated data about the lifelong clinical history of the patient, wherever that care was delivered. Readily available techno-based sources for patient information may result in risks that clinicians under pressure may rely solely on records and ignore clinical signs presented by evaluation of the patient themselves. The e-Skills UK Sector Skills Council in its workforce survey (e-Skills, 2006) makes a distinction between IT professionals and ‘superusers’, whilst still indicating that overall 220,000 informatics competent individuals will be required in the health domain in the UK within a period of four years – a huge challenge that will not be met by new modular content in academic qualifications or ad hoc short courses alone. Clinicians with informatics competencies will, with notable exceptions, I submit, form part of the super-user community. As a basic medical qualification is typically gained over a five year period (a nursing registration over three) and curricular additions only accepted once every three then much training will have to be gained ‘on the job’. This in itself produces challenges as junior doctors rotate between locations normally twice a year, which may require them to work with different hospital information systems in each

Current Challenges in Empowering Clinicians to Utilise Technology

location; risking limited system use to common core functionality. Vendors of health informatics solutions are contracted to provide some end-user training in the operation of their functionality but rarely have obligations to train all end-users or to inform them about the requirements for information governance, data analysis or data quality per se. Additional ‘training’ is provided pragmatically on a ‘need to know’ basis by local training leads to the whole workforce, clinicians and others. This limited resource is thus prioritized and rationed out, with the implication frequently being that clinicians on rotation frequently have only just learned to ‘drive’ a locally configured system before they have to move on. Primary care overall includes the General Medical Practitioner (GP or family physician) services to which a member of the public can refer themselves, the non-hospital services such as District Nursing, Health Visiting and School Nursing in addition to many non-hospital and domiciliary services. Let us first look at the systems to support General Practice, the GP systems. The first reported system was anecdotally used in 1967 for clinical audit purposes; probably the first of many ‘GP systems’, which Dr Glyn Hayes, President of the British Computer Society Primary Health Specialist Group collectively refers to as ‘a jewel in the crown of UK healthcare computing’. In the upcoming BCS Reflections publication (BCSHIF, 2008) he pragmatically, and legitimately, states that GP computing was the ‘result of substantial uncoordinated and piecemeal developments by a number of individuals and commercial and noncommercial organisations operating in the absence of, or occasionally at odds with national policy’. The market for GP systems has fluctuated over time, through ‘DIY’ systems that served a specific purpose and made only a slight indentation into the marketplace, through commercial vendor solutions that have come and gone through merger or market withdrawal, to the current market leaders, the largest of which has approaching half of the overall market (as of 2008).

Typically general practice has adhered (give or take the vagaries of the national political environment and local demands) to key principles: •

•

• •

•

GPs have independent contractor status and are paid under a contract for what they deliver (which may now include health promotion and prevention activities). Each person in the UK is registered with a general practitioner (and only one) who provides both primary care and referral to the more expensive secondary sector when clinical conditions require further interventions. GP income is based predominantly on the size of their ‘list’ of registered patients. A GP (and their fellow GPs collectively in a joint practice) has 24/7 and 365 days per year responsibility for those individuals on their list. Standards of practice are managed at a ‘localised’ level, the scope of which has varied over time.

In terms of equipping GPs to learn how to use their application systems, a paper (Teasdale, 1997) from key primary care informatics researchers from the University of Nottingham compared an adult learning model for training in information management with previous ways of learning, such as written instructional documentation and ‘learning on the job’. Significant improvement in the effectiveness from the on-site adult learning was observed, including considerable customization of the information systems in their practices, and heightened appreciation of the importance of highquality data, both for patient care and reporting requirements. These types of clinician, used to being their own bosses, prefer to learn at their own pace with tools that are locally accessible when they have some time free, rather that having to be available formally for a pre-prescribed period. These benefits have been realised from subsequent

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e-learning and blended learning solutions applied to other types of informatics in support of health. Reflections from a young research informatician (Hunscher, 2008) about what junior hospital doctors may expect provide a salutary lesson in the pace of technology. Hunscher observes that ‘The hospital resident physicians of 2010 (who entered medical school in 2006) have never known a world in which they were not always in communication with their peers, via IM, SMS or a cell phone; have never known a time when they did not have virtually immediate access to almost any knowledge that can be expressed in words, sounds, and/or pictures and they work more collaboratively than earlier generations, using computers for communication far more than for data processing (not even counting the computers that are their cell phones, Blackberries, iPods, etc.)’. He also suggests that information technology in 2010 will have moved on to ‘putting today’s highend workstation power into tablets and ultra-light laptops’ (which is already beginning to be seen in the English NHS National Programme for IT work stream) and ‘wireless connectivity will be ubiquitous in every urban area in the rich countries and many of the poor ones as well, and will have penetrated rural areas to a large degree as well’. Community services outside the hospitals may be provided from local Health Centres from which the GPs also practice or from Community offices or from a Primary Care Trust location, the management centre for an area of approximately 200,000 people. Clinical staff who also work from these locations include District Nurses and Health Visitors and a range of other clinical professionals who operate both clinics and do domiciliary (house) calls. All these non-hospital professionals previously maintained patient records on those who were or had been in their care. Some records were paper-based and some electronic, with the key point being that the records were not formally linked to hospital systems or frequently the GPs systems either. In the UK, many care interventions are increasingly being moved to non-hospital

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locations and also pathways of care are being delivered with interventions across the sectors. Widely utilized e-systems will facilitate consistent recording and interpretation of patient data. Public Health doctors have both a responsibility for a population’s general health and for health promotion. Epidemiology looks at the projected demands, expressed needs and actual treatment and care uptake. The rich picture of the health of the locale can practically only be evaluated and interpreted with comprehensive information about that area. Electronic systems that can handle data input from professionals in hospitals and the community and from the family physicians / GPs are necessary to be able to describe the features of the area, monitor incidence of clinical conditions and activities and project prevalence of health situations across the ‘geographic area or the work areas of a health organisation’.

Overall Goals for Utilizing Technologies to Support Operational Healthcare Whilst not yet fully realized anywhere in the world, there are some good examples of selective deployment which demonstrate many if not all the key reasons for such implementation. Wherever the clinical professional works, in or out of hospital, with patients or populations, they need technologies to support certain functions to support their day to day activities, typically: • •

•

•

Appropriate information for decision support Access on a ‘just in time’ basis, wherever and whenever, for all authorised and authenticated professionals. Ability to transmit information to other appropriate professionals in a robust, secure and ubiquitous manner. Capability to link data sources where appropriate to their clinical responsibili-

Current Challenges in Empowering Clinicians to Utilise Technology

•

•

•

ties, and confidence in the validity of the merged content. Personal competence (and systems functionality) to interrogate / manipulate all data that they are permitted to access. Availability of necessary information either directly or in conjunction with other sources, in a language which is non-controversial, consistent and common to all professionals’ understanding. Selective extraction of data to allow analysis on both a chronological / longitudinal and selective cohort basis.

Clinicians using technological solutions need to be confident that the above functionality will be available consistently when they need it and will facilitate handling of sensitive patient information in a manner that allows only authenticated authorised and competent professionals to access specific health data for purposes for which they are permitted by the subject of the records and their employing organisation.

Clinical Reluctance to Adopt Technologies If clinicians have not been explicitly trained, in pre-qualification or on-the-job, they may feel that to utilise technology or systems in a less than effective manner may expose them to others realising they are not invincible. A frequent assertion from clinicians is that technology produces a ‘triadic consultation’ (Hayes, 1993) and could be detrimental to positive patient interaction, gathering a rich patient history or details of current symptoms. Others cite objections to use based on perceived restraints on the way they record their interventions with patients. The way information is requested by computer systems may be considered counter-intuitive or that pop-up driven options presented automatically by a system force (or give the option to) clinicians to think less about the nuances of their documentation of the patient

condition and their findings / recommendations for further treatment. Vimarlund and colleagues (1999) indicate that much knowledge may be gained from informal sources which are often undocumented or only available to professionals within eclectic ‘social networks’. This presents risks to consistent comprehensive informatics use. Until a clinician (or any systems user) is competent and confident in the use of their available systems they may feel that too large a proportion of their valuable time is being diverted into operating a system rather than on directly patient-related activities. This transient situation is being addressed by both more informatics exposure within their academic courses (Royal College of Surgeons of Edinburgh, 2008) and structured training programmes embedded in the implementation plans of major solutions deployments. Clinicians, as advocates for their patients, also have real concerns about the privacy and robustness of the security of patient records (and some patients also have similar concerns). This relates to when, for example, under the English NHS National Programme for IT (NHSCFH, 2008) electronic record contents will be held both locally and extracted / distilled to provide a summary care record that (could be) more widely available to those who are not in a direct legitimate care relationship with that individual. Debate and pilot development is ongoing on reducing such concerns. One concern currently in the media headlines relates to concern that in the electronic environment, patient records contents may be accessible by those without clinical training, with a concomitant risk of misinterpretation, exploitation or wrongful input that could jeopardise the patient. One has to ask whether the situation is in practice any more ‘insecure’ now than it was in the paper records era; the administrative secretariat of a clinical team or health organisation acted professionally in the past whilst writing patient letters, answering patient queries and providing support to clinical consultations.

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As the BCSHIF Way Forward document (BCSHIF, 2006) recommends “There are major issues about the sharing of electronic patient data which need to be resolved whatever the shape of future informatics in the NHS. These must not be hijacked by technical issues, and informed patient consent should be paramount”. Whilst these issues are real, they are also recognised and being addressed on an ongoing basis. It must be noted that from a socio-technological perspective, these concerns are shared, to a greater or lesser extent, across all sectors where technology is deployed (Peltu, 2008). Those involved in Health Informatics must therefore look both across the health domain and ‘outside the box’ for potential solutions to their issues.

CURRENT CHALLENGES AND THEIR RESOLUTION This section considers a number of specific challenges and how they may be addressed in order to reduce concerns about technology use and to increase the benefits that will accrue from that deployment. This cannot be an exhaustive treatise, because unpredicted problems may arise, those seen today may be eclipsed or superseded by others tomorrow in a similar way to how concerns from the past have been reduced by long term usage and proof in practice of their positive contribution. There are major issues about the sharing of electronic patient data which need to be resolved whatever the shape of future informatics in the NHS (and beyond). These must not be hijacked by technical issues, and informed patient consent should remain paramount. The work pattern of junior doctors (or aspiring consultants) may require them to move frequently between locations to gain experience in different aspects of their chosen specialty or clinical discipline. In an organisational landscape where different technological solutions are operated in different places, this will require them to change

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the way they are informed by existing patient history and also record information on their findings and interventions. Differences may be seemingly as minor as different display formats on screen, different terminology for invoking certain functions, but there are inherent risks of omission and commission in having to change your mindset of operation. Even if the general context of the tasks a clinician undertakes in patient care remains similar regardless of location, the cognitive distance (Alpay, 2008) before they become ‘expert’ may increase if they have to learn to operate a different technological solution for recording care. The BCS HI Forum paper (BCSHIF, 2006) in its summary recommendations urges that “basic informatics elements [should be established] that are standard across the UK to enable coherent treatment of patients irrespective of their movement across home country borders. Ensure that other facets of the English strategy support this coherence” recognising the mobility of patients in addition to that of staff in training and career development. This risks will only reduce as more standardised common user interfaces are developed (Bainbridge, 2008); mirroring the establishment of standards for cards issued by financial institutions (Banks and Building Societies) that can be used in Cash points around the world regardless of the country of issue. Similar challenges to controlled access arise where one professional holds a number of posts in different organisations and may have to interact with different systems, frequently not allowing remote access to the alternate sites, thereby fragmenting their clinical practice. Until and unless systems robustness is improved and the current concerns from the professions, citizens and the media are allayed, this issue will remain. Electronic patient records are increasingly used by many professions. The records contain input from a number of professions, reflecting both complex patient histories and episodes of care managed by different clinical specialties, and a multi-professional team approach to care. Thus it

Current Challenges in Empowering Clinicians to Utilise Technology

is important for the content to be appropriate for consistent interpretation, by whatever professional accessing it, and for a permitted purpose. Historic communication between professionals of the same discipline has included ‘jargon’ and short forms of description that could be problematical to others outside that cohort. When a patient’s presenting clinical condition is complex, a second opinion, from other disciplines or from experts in other locations through a tele-consultation may be indicated. As much face-to-face patient interaction is with other clinical professions the real need for a common language for expression and documentation of relevant information is evident. Concern about the ‘fitness for purpose’ of electronic patient records was highlighted (Gurbutt, 2007) reflecting on the transition of nursing professions’ documentation from paper-based uni-professional recording to input to a collective electronic patient record. All the above relate to greatly increased inter-working by clinical professionals; and this has given rise to reluctance by some to input to the collaborative record in case their judgement is called into question by other than their direct peers. This will only reduce when cultural and territorial issues, unrelated to the introduction of novel technologies, are addressed.

External Scrutiny In addition to opportunities for informal peer evaluation, there is some suspicion of using electronic records in case an external third party (not involved in the direct care of the record subject or in tactical management of the facility in which that patient is being seen) audits clinical or personal practices, without reference back to the circumstances of the creation of that record. It is usual for the managers of a hospital or the partners in a GP practice to look at ‘high cost’ or clinical priority data to determine if the organisation’s activity is developing as per a national strategic direction, previous local projections or organisational plans.

It is also feasible to compare and contrast profiles, for example - of drug prescribing or operating theatre procedures, against national standards or local target levels. If clinicians either have activities to hide (Department of Health, 2000) or can be held up to scrutiny (Redfern, 2002) evidence of selected recorded input or unauthorised amendment of content may be identifiable by embedded functionality within the computer. Evaluation of the patterns of laboratory testing can indicate potential areas of over-testing which when explored with the clinicians concerned could indicate either defensible (legitimate and appropriate) or defensive medicine (protectionist). Whilst judgments should not be made on activity profiles alone, the new technologies can highlight areas for further investigation. To some clinicians this type of scrutiny could be seen as questioning their competence, and so they make all attempts to record only the minimum amount of data to avoid such investigation. The new generations of systems solution have embedded functionality to record any interaction with electronic records, whether just enquiry or input / additions are made (note: many date fields cannot be deleted, only changes recorded when previous contents are retained for transaction tracking, security backup and disaster recovery). As an enhanced culture of accountability and responsibility emerges and the stigma of a ‘blame culture’ recedes then clinicians will become more tolerant of the principles of the ubiquity of audit and its positive use.

Challenges from the Technology Per Se As the professional interaction increases, the concept of delivery of healthcare as either a sequential process or islands of activity disappears. Inter-working requires systems which have previously stood alone to be interfaced to present collective data to a body of users whose requirements are eclectic. There are a number

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of models to use to develop a solution to such requirements – these may be a novel design to produce an integrated solution to the collective agenda effectively from scratch. This may be led by a procurement and production cycle that keeps the ultimate ‘end-users’ (the clinicians) at a distance until a prototype solution is put up for consultation or is prescriptively applied to the work situation. The major risk to such a tactical plan is that the final product may be totally inappropriate for the task it was planned to address (National Audit Office, 2008). This could be either because of organisational and demand change during the development in isolation or such an isolationist route has alienated the potential ‘end-users’ who then will not own such a solution, on principle. Applying the converse model, that of allencompassing consultation, may result in:

As stated (BCSHIF, 2006) “Until very recently the sponsors of NHS CFH have seen information technology (IT) as a fix for the challenges faced by the NHS. This is a common mistake: IT enables change, is sometimes a catalyst for change, but it is not an end in itself”. As Professor Jim Norton, board member of the Parliamentary Office of Science and Technology, said: “There is no such thing as an IT project, merely business change projects mediated by people and ICT” (Norton, 2006). Research in the domain (BCSHIF, 2006), (Greenhalgh, 2007), (Fulop, 2008) suggest that a middle way is indicated as the most likely to achieve adoption of fit for purpose technologies to support clinical practice, notably:

•

•

•

•

A solution that fails on all counts because there are irreconcilable differences between the demands of various end-user groups, resulting in fragmented acceptance or rejection of the solution. A protracted lengthened design and development phase that risks chronological creep from a changing technological or clinical landscape, missed deadlines and increasing costs that become unsustainable. A never-ending specification re-scoping brought about by increasingly articulate end-users whose demands develop over time in the light of changing externalities.

Figure 1.

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•

•

•

Involvement of representatives of the enduser community from the start of the design, development and delivery phases. Investment in communication of progress at regular points through the process, coupled with effective channels for answering queries and responding to suggestions. Recognition of the introduction of a system to be used by clinicians and other professionals in the health domain as an ITenabled change management process not solely an IT project; a business transformation to improve clinical services. Developments should be seen as local and patient-centred in order to maximise the likelihood of adoption and effective use. (See Figure 1)

Current Challenges in Empowering Clinicians to Utilise Technology

Competency and Capability This section does not delve into health informatics competency requirements for all professionals in the health domain, but uses the preamble of a CHIME, University of London course (Murphy, 2008) to indicate a potential need on entry. Continuing professional development for medical doctors (hospital and GP in the UK) is a mandatory requirement and is financially recognised, so where a clinician did not get such learning during their pre-medical registration period they can consider taking one of many academic courses or commercial offerings in this area to improve their information handling skills in general. The contracts for new health informatics systems to address operational areas of care delivery and support (for example – patient administration, GP practice management, radiology, appointment booking, electronic prescription transfer) will most likely include some measure of application training. However this will be delivered directly to a nominated small group of trainers who will then cascade what they have learned to other people on a need to know basis over time. Thus the actual range of taught competencies will be wide and inconsistent, some having only learnt from their colleagues, in some cases passing on less than efficient good practice or pragmatic ways of working. The UK Council for Health Informatics Professions (UKCHIP) was established formally in October 2004 to promote professionalism in Health Informatics across the UK. Its objective is “to support individuals working in Health Informatics by providing recognition of their qualifications and experience, and a mechanism to demonstrate continuing professional development (CPD) as evidence of their fitness to practice”. Individuals are required to sign up to a professional code of conduct and are assessed against a set of standards at three levels. If their application is successful, the individual is placed on an open register of Health Informatics professionals for a period of twelve

months. Registration may be renewed by continued acceptance of the code of conduct and evidence that planned or alternative CPD has been achieved. All individuals working in Health Informatics in any substantive manner and others aspiring to work in Health Informatics are encouraged to apply for registration, regardless of the nature of their employer. The register (www.ukchip.org) was first published in November 2006. Those in clinical informatics or who feel the informatics component of their work is substantial would meet the criteria, which in due course could assist in career development. However one inhibitor to registration for medical persons is that Health Informatics is not recognised as a core theme for General Medical Council registration and substantive work in this area could jeopardise medical progression and financial awards.

Content as Evidence Apocryphally, it is suggested that if a clinician in general practice was to try and read all the published material that related to their chosen interests they would have to spend three months reading one month’s output. This is patently not a feasible option, so technology can assist in selection of priority relevant materials and their presentation on a ‘push’ (unsolicited) basis or funnelled in (Tetroe, 2007) to the desktop of each clinician individually. There are many objectives in making material available ‘on the web’ whether via an intranet within an organisation, through a specialist closed site or more generally on the Internet per se. Commercial incentives, personal benevolence, academic motivation or for the public good can underpin uploading of information in a manner searchable by all or selected groups of people. Clinicians, as with everyone else, must have the capability to evaluate whether the material they find through (structured) searching really is appropriate to their needs. Content has to be appropriate to the purpose of the search. To access a site with

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photographs that are five years old may not be crucial but a site which purports to contain current laboratory test results must be contemporary and contain the most recent results in order to support effective clinical decision making. Similarly a site which is sponsored / managed / facilitated by a certain product supplier will focus on their own products and not necessarily provide a balanced view of the whole market. Even independent sites which do not source the evidence they rely on, do not state their date of last update / next review and the credentials of the material authors may require clinical judgement of the contents before use. Not all sites give a comprehensive verified evidence base to their claims; and this is one of the risk factors which clinicians will have to bear in mind when explaining to patients why their particular stage of clinical presentation does not warrant the interventions suggested. Further features, which when properly understood by researchers, can empower clinicians to achieve added value from sources found through Internet searches is the use of multiple expressions of clinical terms (Greco-Roman originated words) and the tools of stem searching (simplistically, looking for ‘cardi’ as a portmanteau word to identify anything related to cardiac conditions). However the message from such technological aids is ‘caveat lector’ (reader beware). Evidence output from Internet searches requires considerable clinical evaluation before use.

Pressure from the InternetInformed Citizen Many patients, especially once diagnosed, will resort to generic search engines and extract information about their clinical condition, its treatment and potential outcomes from the Internet. This is borne out by research which shows that the second-most popular topic for Internet searching is ‘health’. There is still some clinical antagonism and lack of clarity with regard to how to deal with patients who bring large bundles of

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printed Internet material to their clinical consultation. Clinicians have a tightly focussed area of specialism and may not be well-informed about the generalities of a specific manifestation of a clinical condition, thus there is a strong likelihood that the patient will be more ‘informed’ than the doctor or nurse, especially if their condition is chronic or long term. The material presented by the patient may not have previously been known about by the clinician, so the clinician/patient relationship may be at risk if the imbalance of knowledge is handled inappropriately. Clinicians who are not comfortable in operating in a partnership with their patient could dismiss the patient’s findings as ‘not appropriate’ rather than explore the similarities and explain the areas where the Internet material does not apply (or may not without additional research) to the individual. There are additional skills that could help clinicians to harness the potential involvement of the ‘expert patient’. (Gray, 2002), (Coulter, 1999).

Ethical and Legislative Challenges Clinical professionals have guidance to adhere to, such as the Hippocratic Oath and Professional Codes of Conduct. These have been in place for considerable time and are not as relevant as current legislation in many areas of explicitly using technologies. Hippocrates states that doctors should focus on ‘interventions that do no harm’, but it is sometimes necessary to ‘be cruel to be kind’, that is it could be necessary to experience short term pain for long term positive outcome. Issues can arise when indirect harm (for example – social stigma, educational selection or damage to personal image) may result from failures of the technology. That issue may explain some of the reluctance of English clinical professionals to sign up themselves or their patients to the Summary Care Records Service (NHSCFH, 2008). Since the emergence of widespread technology to support medical general practice the President

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of the British Computer Society, Dr Glyn Hayes, has repeatedly posed the question “When will it be [challenged as] negligent for a GP not to use computer-based protocols & guidelines [to guide their practice]?”. No legal challenge has yet clarified the answer to that question, but it may just be a matter of time! Using the European Directive (European Parliament, 1995) as an example, there are situations where the legislation and custom and practice may come to be at odds with each other. The Directive in summary states eight principles covering data use in all situations. Data should be: • • • •

• • • •

Obtained fairly and lawfully Kept only for described purposes Not disclosed to anyone else for any other reason Not collected ‘just in case’, the content must be enough for the purpose stated but no more Correct and kept up to date Not kept for any longer time than is necessary Available if the ‘subject’ (you!) wants to check it for correction or deletion Kept securely protected, so no one else can hijack or destroy it

Concerns about the deployment of health technologies are compounded by the fact that increasingly such tools as blood pressure readers, diabetes management tools, vital signs monitors can be purchased directly by members of the public, raising questions over liability, maintainability and the documentation of self-management within a clinical history. Confusion may arise when any of the following clinical situations occur; in life-threatening situations where it is not possible to gain explicit subject consent (they may be unconscious) for using information that is available in their electronic record; in leading edge genetic engineering / genotyping research where unique data that could identify an individual may be

found; in situations where a patient has a mental health condition that for their safety or that of the public should be declared to a third party; when making decisions for and about children or when respecting patient choice in dying with dignity. The systems must support functions to make it clear when such decisions are made, who by and under what authority. This may be necessary for reference if, say, a member of the patient’s family makes a challenge to the clinical decision or questions the outcome of an intervention in such sensitive situations. Both clinical governance and information governance require that not only should the professional do the right and appropriate action, but should be additionally able to prove that they did so. Technological systems that contain comprehensive patient data are able to help prove probity in such cases, and are increasingly, but not yet universally, being harnessed by clinicians for this purpose. Technologies currently only available to clinicians could be acquired, without clinical guidance, by members of the public to manage their own health. For example, signal generators to de-stress themselves when they feel anxious or light-headed. How then do you (or should you) record these alternative therapies that can demonstrably already have an effect on hypertension and related areas? In addition, if such self-management techniques do not have the desired effect but the citizen is reluctant to give them up, will ‘chill-out chips’ become a treatment stopper in the way smoking cigarettes is viewed in cancer cases? (Roberts, 2006). A number of non-hospital systems in the UK and elsewhere already enable patients to access their electronic records (frequently through web access) adding observations from their perspective, checking the interventions and triggers recommended by their physicians to manage their clinical situation (Hannan, 2008). As yet the professional – lay partnership and the added value of the multiple perspectives of clinicians and patient

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input information has not (yet) been thoroughly quantified. However as Doctor Hannan claims, such technology-enabled collaborative working does engender greater trust between clinician and patient.

EMERGING EMPOWERING TECHNOLOGIES Much development work is ongoing addressing novel technologies and their application in the health domain, in order to achieve solutions that will empower clinical practitioners. Currently the developments are in ‘islands’ and there is considerable untapped potential for synergy, once solutions are proven to bring positive benefit to patient care and / or increased effectiveness, efficiency and efficacy to clinical service provision. Minaturisation of technologies makes new less-invasive, therapeutic tools potentially available to clinicians such as ambient technologies, agent-based solutions and embedded chips. For example, remote communication of vital signs from a pregnant but anxious lady could result in reassurance by a clinician looking at transmitted signal traces from a fetal monitors that ‘all is well’ without costly and potentially traumatic ambulance journeys to a major facility. Such telemonitoring is also effective use of scarce clinical time that may otherwise be spent in travelling to an emergency call-out. Emerging technologies can now start to integrate healthcare seamlessly into day to day activities; such as routine monitoring of daily living to capture indications of sporadic cardiac conditions, the facilitation of independent living for the older person through smart homes technologies, and lifestyle management tools that alert the patient if their actions are problematic to their underlying clinical condition. A self-initiated panic alarm button or autonomous wireless signal from an assistive device can alert a clinician to the patient’s condition. Thereafter more technologies from a telephone call or text to a geographic posi-

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tion system instruction to emergency services can be initiated by a clinician after reference to the electronic record history of the patient. (Lymberis, 2004). Within a hospital, the monitoring and alarm initiators become more critical – whether applied to a patient directly or the life support services they need, such as anaesthesia or pain control. In future it is possible that technologies will also put monitors into the body during operations or diagnostic interventions (ARTIST, 2004). Tele-health (compliance monitoring, consultation, clinical assessment care and guidance) applications are beginning to ‘make geography history’. They reduce unnecessary travel for the patient and the clinician and can provide intervention to avoid a drama becoming an escalating clinical crisis. Management of care caseloads remotely through tele-clinics (e-Envoy, 2002) and techno-equipped ambulance units can monitor cancer patients, check progress with burns and wound care, support patients with mental health challenges, or triage accident victims. The concept of interventions initiated by a set of embedded control factors that are not readily visualized could be difficult to accept. Will a clinician readily accept results and indicators from a device bought ‘over the counter’ and calibrated by the patient themselves? This general concern must be addressed before any objection to the use of such technologies becomes widespread.

CONCLUSION The use of technology in support of clinical practice, both in terms of electronic patient records and efficient access to the clinical evidence base, is widespread but not yet ubiquitous. Residual concerns are based on aspects from cultural traditions, prior education and lack of IT competency through to concerns about the robustness and opportunistic third party capabilities presented by emerging technologies. Whilst education and continuing professional development can address

Current Challenges in Empowering Clinicians to Utilise Technology

some of the issues, case studies of positive deployment and benefits realisation use cases will present strong encouragement for clinicians to capitalise on existing and emerging technologies. However, reservations relating to risks of clinicians relying on external sources rather than their training and embedded expertise must be addressed on an ongoing basis. In order to empower clinicians to move forward in their use of technology, a marketing and communications exercise about new developments will need to address clinical reservations like those considered in this chapter. Informatics solutions and technology in the health domain will emerge within a context of escalating citizens’ use of technologies and continued innovation in applied technology in academia, business and social circumstances. Clinicians are right to demand proof that use of technologies will not jeopardise patient safety and professional good practice; and the signs are that the roll-out of developments will meet their requirements in due course.

REFERENCES Alpay, L., Verhoef, J., Te’eni, D., Putter, H., Toussaint, P. J., & Zwetsloot-Schonk, J. H. M. (2008). Can contextualization of information reduce the user’s cognitive distance during man-machine communication? A theory- driven study. Bentham Open (in production). ARTIST IST-2001-34820. (2004). Advanced realtime systems embedded systems design: Roadmaps for research. Brussels: European Commission. Bainbridge, M. (2008). NHS CUI guidelines. Retrieved in March 2008, from www.mscui.org British Computer Society Health Informatics Forum. (2006). The way forward for NHS health informatics. Retrieved in March 2008, from www. bcshif.org

Coulter, A., & Gilbert, D. (1999). Sharing decisions with patients: Is the information good enough? BMJ (Clinical Research Ed.), 318, 318–322. Donaldson, L. (2000). Harold Shipman’s clinical practice 1974-1998. London: Department of Health. Equalitec. (2007). Health informatics: An area of emerging opportunities. London: Equalitec. European Parliament. (1995). Directive 95/46/ EC: The protection of individuals with regard to the processing of personal data and on the free movement of such data. Brussels: CEC. e-Skills Council UK. (2004). IT insights: Trends and UK skills implications. Retrieved in March 2008, from www.eskills.com Fulop, N. (2008). Is your privacy protected? Retrieved in March 2008, from http://www.hospitalmanagement.net/features/feature1311/ Gray, J. A. M. (2002). The resourceful patient. Oxford, UK: eRosetta Press. Greenhalgh, T. (2007). Supplementary evidence submitted by the Department of Health (EPR01A). Retrieved in March 2008, from www.publications.parliament.uk/pa/cm200607/cmselect/ cmhealth/422/422w202.htm Gurbutt, R., & Roberts, J. (2007, March). Is nursing decision making adequately supported in informatics solutions today? Weybridge: BJHC&IM. Hannan, A. (2008). Record access in practice. Retrieved in February 2008, from recordaccess. icmcc.org/?p=1123 Hayes, G., & Barnett, D. (2008). UK health computing recollections and reflections. London: British Computer Society Publishing. Hayes, G. M. (1993). Use of the computer in the consultation. Update, 465–468.

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Hunscher, D. (2008). Future of health IT: Trends and scenarios. Retrieved in March 2008, from hunscher.typepad.com

Redfern, M. (2002). Royal Liverpool children’s inquiry. Department of Health.

Kaner, E., Heaven, B., Rapley, T., Murtagh, M., Graham, R., Thompson, R., & May, C. (2007). Medical communication and technology: A video-based process study of the use of decision aids in primary care consultations. BMC Medical Informatics and Decision Making, 7(2).

Roberts, J. (2006). Pervasive health management and health management utilizing pervasive technologies: Synergy and issues. In Black, McAllister, McCullagh, Nugent, Augusto (Eds.), Pervasive health management: New challenges for health informatics. Special Issue of International Journal of Universal Computer Science, 12(1), 6-14.

Lymberis, A., & de Rossi, D. (Eds.). (2004). Wearable e-health systems for personalised health management. Amsterdam: IOS Press.

Royal College of Surgeons of Edinburgh. Faculty of Health Informatics. Retrieved in March 2008, from www.rcsed.ac.uk/site/469/default.aspx

Murphy, J. (2008). IT skills for medical students. Retrieved in March 2008, from www.chime.ucl. ac.uk

Teasdale, S., & Bainbridge, M. (1997). Improving information management in family practice: Testing an adult learning model. In Proc AMIA Annual Fall Symp (pp. 687-692).

National Audit Office. (2008). The national programme for IT in the NHS: Progress since 2006. London: NAO. NHS in England. (2008). National programme for IT. Leeds: Connecting for Health. Retrieved in March 2008, from www. connectingforhealth.nhs.uk NHS Connecting for Health. Retrieved in March 2008, from www.connectingforhealth.nhs.uk Norton, J. (2006). Institute of directors response of 2nd February 2006 to ‘transformational government command paper (CM 6683). Department of Health. Peltu, M., & Clegg, C. (2008). Using IT to deliver better NHS patient care: How the sociotechnical approach can help NPfIT meet its goals. London: BCS Publishing

Tetroe, J. (2007). Knowledge translation at the Canadian institutes of health research: A primer focus technical briefing no. 18. Canada: National Center for the Dissemination of Disability Research. UK Government. (2002). Policy documents, open source software. Retrieved in March 2008, from www.govtalk.gov.uk/policydocs/consult_subject_document.asp?docnum=780 Vimarlund, V., Timpka, T., & Patel, V. (1999). Information technology and knowledge exchange in healthcare organizations. Jnl America Medical Informatics Association, 632-636

This work was previously published in Handbook of Research on Information Technology Management and Clinical Data Administration in Healthcare, edited by Ashish N. Dwivedi, pp. 507-520, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 6.3

Challenges and Solutions in the Delivery of Clinical Cybersupervision Kenneth L. Miller Youngstown State University, USA Susan M. Miller Kent State University, USA

ABSTRACT Supervision is both a special case of instruction and a critical aspect of professional development. The ongoing development of Web-based infrastructures and communication tools provides opportunities for cybersupervision. Advantages of cybersupervision for counselor training include opportunities to provide location-independent, “live” supervision of counseling sessions in which: (a) evaluative feedback is communicated in “real time” using text or graphical modalities; (b) audio DOI: 10.4018/978-1-60960-561-2.ch603

evaluative feedback is digitally recorded in “real time” for post-session playback; and, (c) weekly, hour-long, supervision sessions are conducted using either synchronous (e.g., multifeatured video conferencing, chat room) or asynchronous (video recording, email) Web-based communication tools. Challenges to quality online supervision include communicating critical supervisor characteristics, developing an effective supervisor/ supervisee online relationship, insuring requisite personal dispositions and computer skills, implementing a theoretical model of supervision, and resolving legal and ethical problems. Authors examine these advantages, challenges, and solu-

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Challenges and Solutions in the Delivery of Clinical Cybersupervision

tions in the context of two online supervision/ instructional models for training counselors (i.e., professional counselors, psychologists, clinical social workers, and psychiatrists) and discuss the generalizability of the cybersupervision model for professional training in a variety of fields that include medicine, law, and education.

CHAPTER OBJECTIVES The reader will be able to: •

• •

•

Identify advantages, challenges, and solutions related to the delivery of online clinical supervision/instruction Apply methods of two supervision theories in the delivery of cybersupervision Understand how synchronous and asynchronous communication tools can be used to facilitate the development of clinical skills through the delivery of online supervision Draw conclusions regarding the value of cybersupervision for clinical and other professional training and development

INTRODUCTION Supervision is a critical aspect of training and development for professionals in a variety of fields. It is a process by which a highly skilled or master practitioner observes and evaluates the performance of a novice with the goal of promoting the development of requisite knowledge and skills for competent practice. One can scarcely imagine a psychotherapist or medical doctor providing services to patients without benefit of supervision during their training programs. Yet, supervision is a costly and time consuming process that frequently strains the financial and human resources of organizations charged with these responsibilities. These costs are incurred

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as supervisors take time from routine duties that generate income for businesses and organizations to provide supervision that does not typically produce income. Supervisors are also frequently required to provide supervision services at remote locations that require compensation for travel time and mileage. Taken together, these challenges create circumstances that may limit both the quantity and quality of supervision. Supervision of clinical work typically occurs during the middle to later stages of graduate education of future mental health professionals. Students will have completed didactic coursework (i.e., theories, clinical methods/techniques, ethical/legal issues) and are ready to apply this knowledge in direct services to clients in a practicum or internship setting. In most cases, clinical programs are affiliated with a counseling clinic so that students’ (i.e., supervisees’) first introduction to counseling occurs in a highly-structured environment. Viewing sessions from an observation room, the course instructor (i.e., supervisor) carefully observes supervisees’ performances and session dynamics, and intervenes when necessary. This initial practicum experience is usually followed in subsequent semesters by field placements or internships in a variety of mental health, hospital, community agency, and/or school settings. Internship supervision is typically provided by an on-site supervisor on a weekly basis and by a university supervisor at less frequent intervals. What should be evident from this brief description of clinical supervision is that it is a special case of instruction. In order to provide clinical supervision, laws in many states mandate that counselors complete specialized training and earn a supervision credential. During this training, counselors are typically taught to provide supervision using two to three major supervision models (with specialized training in alternative models). What they learn, in fact, are different instructional models for providing supervision. Each model corresponds to a particular theoretical perspective and instructional methodology, which

Challenges and Solutions in the Delivery of Clinical Cybersupervision

determine supervision goals, supervisor roles, and learning outcomes.

BACKGROUND The capacity to supervise counseling sessions has changed dramatically as new technologies have provided more sophisticated communication options. Early supervision utilized an observation room in which a supervisor could observe a limited number of counseling sessions through one-way mirrors. The supervisor communicated to the student-counselor by knocking on the door and providing feedback or suggestions. This knock on the door approach is still used, but has generally been replaced by the use of technologies to facilitate communication from the supervisor to the supervisee (i.e., student-counselor). These included installation of telephones and video cameras in counseling rooms and multiple television monitors in an observation room, which increased the number of sessions that can be readily supervised. This configuration permitted supervisors to provide live, evaluative feedback via telephone installed in the counseling room (i.e., telephone call-in), a low-tech approach, or through a mid-level technology approach called bug-in-the-ear. For the latter method, the studentcounselor received feedback from the supervisor through an audio ear-bud. Research findings suggest that supervisory interruptions are not as disruptive as might be expected (Champe & Kleist, 2003). Supervisees have reported that live supervisory feedback is beneficial, especially when the supervisor is perceived as flexible and supportive. Moorhouse and Carr (1999) found a tentative relationship between frequent and complex supervisory feedback using the telephone call-in method and client cooperation. Although research findings reveal that student-counselors approach the supervisory experience with some anxiety, Mauzey, Harris, and Trusty (2000) reported that neither the

phone-in method nor the bug-in-the-ear method increased the anxiety or anger levels during live supervisory sessions. A relatively new, and potentially much less intrusive, supervision approach is called bug-inthe-eye (BITEBITEBITE: Klitzke & Lombardo, 1991). Computers in an observation room are linked to monitors located in counseling rooms. Supervisors send live evaluative text-based or iconographic messages to monitors that are positioned in the counseling rooms so that only the counselor/supervisee can read the screen. Because the supervisee controls when he or she reads the message on the computer screen, bug-in-the-eye technology may facilitate greater integration of supervisory feedback without distractions that can impede the normal flow of the counseling process (Gallant, Thyer, & Bailey, 1991; Klitzke & Lombardo, 1991) and in a manner that is efficient and timely (Liddle, 1991; McCollum & Wetchler, 1995). One of the first attempts of what was to become known as bug-in-the-eye technology involved linking two computers to provide live supervision in a basic counseling skills course (Neukrug, 1991). Student-counselors (i.e., supervisees) participated in counseling role-plays with studentclients. Supervisory feedback provided during these role-plays included general positive statements about the supervisee’s performance (i.e., “good”) as well as comments directed at targeted performance areas. Neukrug reported that the supervisees readily mastered use of the system. “Bug-in-the-eye feedback can be text or iconbased” (see Miller, Miller, & Evans, 2002 for more details). Text-based feedback may include directives such as, “Ask the question again,” or “Explore the client’s emotional response to her divorce.” This feedback may also include positive statements such as, “Good job of confronting client on this issue,” or specific recommendations for language use such as, “Say to the client, ‘You feel angry and betrayed.’” Supervisors may use text-based feedback to focus on immediate con-

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cerns (e.g., “Determine if the client has a plan to commit the murder, an identified victim, and the means to carry it out.”) or on broader issues after observing session dynamics over longer time periods (e.g., “You are responding to the client from a position of powerlessness and fear…BE MORE ASSERTIVE.”). Icon-based feedback is designed as graphical shorthand for communicating complex ideas. Examples of iconic messages include the image of a downward pointing arrow to communicate the need for the supervisee to explore the current issue in greater depth and the image of a judge’s gavel to communicate that the supervisee is communicating in a judgmental manner. In order to insure immediate and accurate interpretation of iconic messages during counseling sessions, prior training is required. A complex system of bug-in-the eye supervision was installed by the first author when he served as director of the University Counseling Center/ Community Counseling Clinic at Youngstown State University. Wall-mounted computer monitors were installed in six counseling rooms in the clinic and each monitor was connected to a laptop computer in an observation room (see Miller, Miller, & Evans, 2002 for details). Supervisees in a first counseling practicum were exposed to three modes of supervisory feedback (i.e., telephonecall-in, bug-in-the-ear, and bug-in-the-eye) as part of a qualitative study. At the end of the semester, supervisees participated in structured interviews to assess perceptions of their supervisory experience. Feedback from the first students to use the BITE technology was positive: they reported a preference for bug-in-the-eye technology over bug-in-the-ear supervision, were comfortable using the technology, and thought that the use of BITE increased their confidence as they worked with clients. These findings are consistent with those of Scherl and Haley (2000) who reported that their trainees preferred BITE technology over knock-on-the-door and telephone call-in approaches.

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Although live supervision has many advantages, the most popular supervisory method has remained the post-supervisory review (Champe & Kleist, 2003). For this approach, counseling sessions are either audiotaped or videotaped and the supervisor later reviews the session with the supervisee. However, newer technologies now provide the opportunity to combine some of the advantages of live supervision with the review method. While viewing a live session through an observation window or via a computer monitor, analog or digital recordings of the supervisor’s feedback can be overlaid on the video recording of the session or can be saved simultaneously in a sound file. This method has been called the audio track overlay protocol (ATOP) (Evans, Miller, & Miller, 2005). With little modification, this system can also be used to provide supervision at a distance. This abbreviated history of technology used in clinical supervision reveals that the enhanced communications options afforded by new technologies have been adapted for use in the supervision process. Supervisors have used these new technologies to modify the methods and timing of supervision. What has remained common to all of these approaches is the assumption that the supervisor and supervisee communicate in the same location. Only recently has the option of providing supervision that is location-independent been a viable option. This phenomenon may be do to the fact that some counselor educators view the computer, and more recently the Internet, as more appropriate for the dissemination of didactic or content information, rather than a method for teaching skills based and practicum/internship courses (Wantz, et al., 2003). It is worthwhile to discuss the context in which online supervision has evolved. Beginning in the early 1990s, the delivery of online instruction promised increased university enrollments and decreased faculty costs. University administrators responded by encouraging faculty to develop online courses. In 2003,

Challenges and Solutions in the Delivery of Clinical Cybersupervision

Wantz, et al. conducted a comprehensive survey of counseling programs to determine the nature and extent to which online learning opportunities were part of the curriculum. The final sample consisted of faculty from 127 counseling programs (out of 416 who received the survey). Less than half of the programs (42%, n = 53) included some type of online distance learning: 23 programs offered blended courses (i.e., online plus a face-to-face component); 11 programs offered courses that were entirely online; and 12 programs offered both blended and full online courses. Respondents were also asked whether they conducted “supervision and consultation with practicum and internship counselor trainees via distance learning” (p. 336). Thirty-eight percent reported that they did. What is not clear from the reported findings is the number of programs in which the online element was used for supervision rather than for general consultation, the number of programs that used live or post-sessions formats for online supervision, and the types of Web-based communications and other technologies that were used. The number of faculty who did not offer online supervision courses should be understood in light of their expressed concerns regarding the suitability of the Internet to deliver courses designed to teach clinical skills. The delivery of supervision via the Internet is a relatively new phenomenon and is known by a variety of names that include online supervision, e-supervision, or cybersupervision. The latter term became popular following the introduction of the term, cybercounseling. Coursol (2004) defined cybersupervision as the use of “…Internet videoconferencing to enhance the supervision of student counselors” (p. 83), although she also discusses the use of e-mail for on-going contact. The National Board of Certified Counselors (NBCC) uses the term WebCounseling, which is defined as “the practice of professional counseling and information delivery that occurs when client(s) and counselor are in separate or remote locations and utilize electronic means to com-

municate over the Internet” (Bloom, 1997, p. 1). Descriptions of cybercounseling (i.e., the direct delivery of counseling services to a client using the Internet) are readily available due to the popularity and availability of online counseling (Chester & Glass, 2006). Since cybersupervision is a much newer phenomenon, there are fewer descriptions available. The previously mentioned article by Miller et al. (2002) described a bug-in-the-eye technology system that can be adapted for online supervision. Coursol (2004) identified minimum hardware and software requirements for an online BITE system. Brown (2000) reported on the use of the Internet by faculty in three programs (counselor education, school psychology, and school social work) at the University of North Carolina at Chapel Hill to communicate with students at off-campus field sites for purposes of staffing and treatment planning. Myrick and Sabella (1995) described what they called e-mail supervision that was offered in the school counseling program at the University of Florida. Students were placed at elementary, middle, and high schools for practicum and internship experiences. They participated in individual and group supervision and were supervised by both an on-site counselor and a faculty member. Supervisees communicated with their supervisor via e-mail to describe a case or to ask for assistance. When appropriate, a case was sent via e-mail to the supervision group, which consisted of other practicum/internship students. It is worth it to note the fact that a case was sent to members of the International Counselor Network and the Counselor Education and Supervision Network, thereby substantially enlarging the volume and diversity of feedback to the supervisee. Authors reported positive supervisee reactions to the use of written versus spoken words. Emails could be saved and reviewed. Because of the need to use precise language, authors indicated that the written word seemed to have a “powerful effect” (p. 6). Reported limitations of this model included omission of information due to the time required

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for typing versus speaking and the absence of nonverbal cues in written feedback. As revealed by information presented in this section, computer technologies have generated effective, efficient, and cost-effective modalities to provide clinical supervision. When these technologies are used in conjunction with the distributed communications capabilities of the World Wide Web, unique options and advantages are created for the delivery of online supervision. The following scenario is presented to illustrate the advantages of online supervision: Imagine that you are a counselor supervisor on a journey by train to present a paper at a regional conference. Three hours into your trip, your laptop computer alarm sounds to remind you that, in 15 minutes, two of your supervisees (Stacey and Lawrence) will be conducting counseling sessions at the university’s counseling clinic that is now 200 miles from your present location. Before you left campus, you assured both supervisees that you would provide “live” supervision of their counseling sessions. You boot your wireless laptop and login to the password protected network and digital cameras in predetermined counseling rooms at the clinic. Displayed at the perimeter of the laptop monitor are thumbnail images of the two counseling rooms at the clinic. As Stacey’s counseling session begins, you click on the thumbnail image of her counseling room, which fills the center of your laptop computer’s screen. For the next several minutes, you observe Stacey’s counseling session in real time. With the aid of headphones, you hear counselorclient dialogue with digital clarity. After observing the session for 10 minutes, you determine that the Stacey needs to confront the client more directly on her failure to comply with her treatment plan to reduce her depressive symptoms. You open a password-protected, Internet-based, instant messaging program and prepare such feedback. After composing your feedback, you depress a series

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of keystrokes to send it instantly to a flat-screen monitor mounted in the counseling room so that only Stacey can view your feedback. From your laptop, you observe the message flash onto her monitor. You note by her head nod that Stacey has received the feedback and immediately depress a key combination to retrieve the message screen back to your computer in preparation for additional feedback. As you continue to observe the session, you open a secure, Web-based audio recording file on the university server that is simultaneously video recording the counseling session. This audio recording file enables you to attach a voice recording to the session in real time (i.e., synchronized to the video recording). Using the microphone on your laptop computer, you record several strategies for dealing with client resistance and instruct Stacey to read two journal articles on this topic in preparation for your regularly-scheduled, weekly, one-hour supervision session. On the alternate thumbnail image, you observe that Lawrence’s counseling session has begun. You click on this image, which fills the center of your computer screen while the thumbnail image of Stacey’s counseling session remains visible at the perimeter of your monitor. After observing the second session for five minutes, you determine that Lawrence is flirting with the client as she makes inappropriate sexual overtures. Using the same Internet-based, instant messaging program, you send an immediate directive to Lawrence to stop flirting with the client and, for emphasis, include a graphic of a large red stop sign in this message. You then retrieve the message screen in preparation for additional feedback. As you continue to observe Lawrence’s session, you open the same secure, Web-based audio recording file on the university server that is simultaneously video recording the counseling session. This audio recording file enables you to attach a voice recording to the session in real time (i.e., synchronized to the video recording). Using the microphone on your laptop computer, you remind Lawrence of both legal and ethical prohibitions against such

Challenges and Solutions in the Delivery of Clinical Cybersupervision

behavior with clients and require him to write a 10-page paper on this topic for submission via e-mail attachment in two days. After insuring that Lawrence has responded appropriately to this directive, you click on the thumbnail image of the Stacey’s counseling session and continue to provide live supervision. You use this procedure to alternate between sessions, insuring that both counselors are receiving high quality, live supervision with immediate evaluative feedback. At the end of each counseling session, you send text messages to Stacey and Lawrence informing them of the need to review both the digital video and audio files of their counseling sessions now stored on the university server in preparation for regular “live,” weekly, one-hour supervision sessions with them. Using a multi-featured video conferencing program, you complete the live supervision sessions at regularly scheduled times.

CHALLENGES AND SOLUTIONS There are a number of issues involved in providing effective cybersupervision. We have conceptualized these as challenges for which solutions can be generated. As noted at the beginning of the chapter, we will examine five challenges in the provision of cybersupervision: (a) communicating critical supervisor characteristics; (b) developing an effective supervisor-supervisee online relationship; (c) insuring requisite personal dispositions and computer skills; (d) implementing a theoretical model of supervision; and, (e) resolving legal and ethical problems. This is not an exhaustive list of challenges, although these are some of the most compelling.

Communicating Critical Supervisor Characteristics Supervisees are more likely to implement supervisory feedback if they believe that the supervisor possesses expertness, trustworthiness, and attrac-

tiveness (Dorn, 1984). Rickards (1984) attempted to isolate the relationship between attributions made by supervisees and verbal interactions during face-to-face supervision. He found that negative feedback to supervisees and negative comments made by supervisees were negatively associated with supervisees’ positive attribute ratings of supervisors. A positive relationship was found between supervisee statements that “ask for information, opinions, or suggestions,” and attributions of supervisor attractiveness. This and similar studies reveal the need to study processes by which supervisees make attributions about their supervisors in online environments. Research findings on computer-mediated communication reveal that individuals communicate differently online than in face-to-face situations. Strengths of cybersupervision include the use of Web-based communication tools that permit supervisor-supervisee interactions that are independent of location and time. Asynchronous communication modes such as e-mail permit time for both parties to reflect on written communications and make thoughtful responses (Harper, 1999). However, the absence of nonverbal cues in e-mail communications may limit a supervisee’s ability to form accurate attributions regarding a supervisor. Without access to a supervisor’s facial cues, gestures, and tone of voice, a supervisee can easily exaggerate and distort negative supervisory feedback that can lead to inaccurate supervisor attributions. Similarly, a supervisor may be unable to detect a supervisee’s expression of anxiety or frustration (Kanz, 2001) or notice the degree to which a supervisee is resistant to suggestions or directives (Mallen, Vogel, & Rochlen, 2005). This may lead the supervisor to respond inappropriately or inaccurately to the supervisee and may cause the supervisee to make misattributions regarding the supervisor’s expertness and trustworthiness. One solution for problems generated by the absence of visual communication cues is the use of video conferencing. Other strategies are discussed in the following section.

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Developing an Effective SupervisorSupervisee Relationship Although attributions of expertness, trustworthiness, and attractiveness are necessary, they are not sufficient for the establishment of a successful supervisor/supervisee relationship. For supervision to be effective, the supervisor and supervisee must establish a working alliance. Mallen, Vogel, and Rochlen (2005) suggested that the development of such an alliance may take longer in online environments. Due to the absence of research on cybersupervision, literature on cybercounseling provides some insight into the development of effective counselor-client online relationships. In text-based environments it is difficult to detect a client’s emotional state, to demonstrate empathy, or use non-verbal encouragers (Harper, 1999; Mallen, Vogel, & Rochlen, 2005). In their review of cybercounseling, Mallen, Vogel, Rochlen, and Day (2005) reported mixed findings from three studies on the development of successful counselor-client relationships in face-to-face versus online environments: Cohen and Kerr (1998) and Cook and Doyle (2002) found no differences in the development of a therapeutic relationship between face-to-face and computer-mediated counseling, but Hufford, Glueckauf, and Webb (1999) found that teenage clients reported better relationships in face-to-face sessions. Day and Schneider (2000) reported no differences in the relationship between counselors and clients in audiotaped, videotaped, and faceto-face sessions as perceived by an external set of raters. Interviews with participating counselors revealed mixed feelings about distance technology: some counselors felt hindered in development of the therapeutic relationship but other counselors preferred the computer-mediated format. Mallen and Vogel (in Mallen, Vogel, & Rochlen, 2005) found that student-counselors who interacted with a client using synchronous chat formed gender-stereotyped impressions of their clients. Unknown to the student-counselor,

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the client was a research confederate and the reported gender of the client was randomly manipulated. The importance of this finding is that in the absence of sufficient information during counseling interactions, individuals may engage in stereotypical thinking. This dynamic may also occur during cybersupervision in the absence of a video component or face-to-face interactions between the supervisor and supervisee. One technique used to compensate for the absence of nonverbal cues is emotional bracketing, a concept developed by Murphy and Mitchell as part of an e-mail-based counseling practice they called therap-e-mail (Collie, Mitchell, & Murphy, 2000). Along with the content of the communication, when appropriate, the counselor or client inserts a description of their feelings by the use of brackets. Murphy and Mitchell also developed a set of strategies they call presence techniques. One such technique, descriptive immediacy, involves the counselor describing his or her non-verbal behavioral reactions. The goal of this descriptive narrative is to bring the counselor and client into the same virtual space, thus deepening the online therapeutic relationship. Although these techniques were developed to overcome the absence of nonverbal cues in text-based online counseling, the strong parallels between cybercounseling and cybersupervision suggest that these same strategies would be useful for developing working alliances between a supervisor and supervisee. It is important to note the similarity between Murphy and Mitchell’s emphasis on the role that “presence” plays in forming counselor-client relationships, and the general problem of establishing social presence during online instruction discussed in the chapter by Snelbecker, Miller, and Zheng (2008). The cybersupervisor’s responsibilities are to teach clinical skills; assess supervisee knowledge, attitudes, and performance; communicate feedback; and challenge irrational thinking. Broadly defined, these are the responsibilities of any teacher. Online “presence” techniques used to promote a therapeutic relationship between coun-

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selors and clients and working alliances between supervisors and supervisees can be generalized to the development of a teaching-learning relationship between teachers and students.

Insuring Requisite Personality Dispositions and Computer Skills Implicit in the previously discussed challenges is the assumption that supervisors and supervisees possess the necessary dispositions and computer skills to be fluent in an online environment. Although research using the Myers-Briggs Type Indicators (MBTI) has revealed the personality types of effective counselors in face-to-face settings, authors have noted that MBTI indicators are unknown for effective online counselors (e.g., Harris-Bowlsbey, 2000). Research suggests that warmth, genuineness, and empathy are essential characteristics of successful counselors and by extension, successful supervisors (Parrott, 2003). It is not yet known how these dispositional characteristics affect the choice to engage in cybersupervision. It may be that as more counseling professionals use technology to provide clinical services, perceptions of cybercounseling will change, thereby attracting students who possess dispositions necessary to work effectively in an online environment. What is unknown is the degree to which these individuals will be successful counselors. Given the unknowns about dispositional characteristics and use of the Internet for counseling and supervision, counselor educators and program administrators should stay alert to interaction effects between students’ dispositions and success in cybersupervision. A related challenge is insuring that supervisors and supervisees are computer literate and competent in the use of online technologies. Participants should be adept at using hardware (e.g., video cam), software telecommunication and utility programs (e.g., chat, email), and be familiar with limitations of online communication. The profession has taken steps to resolve this chal-

lenge. The Association for Counselor Education and Supervision (ACES, 1999) has established Technical Competencies for Counselor Education Students: Recommended Guidelines for Program Development. These guidelines should result in the development of new technology intensive courses in counselor training programs, more technology-based assignments in existing courses, and competencies designed to insure that trainees possess abilities to communicate effectively using written text (Alleman, 2002). Counseling students can and should take responsibility for developing these competencies by enrolling in technical courses and online workshops and by obtaining information on cybercounseling (Mallen, Vogel, & Rochlen, 2005).

Implementing a Theoretical Model of Supervision A fourth challenge is for supervisors to adapt and implement a theoretical model of supervision using a Web-based approach. This is a significant challenge in light of the fact that considerable knowledge and skill are required to execute a theoretical approach even in face-to-face supervision. This problem is exacerbated by the complexity of the supervision process that typically requires supervisors to: teach clinical skills; assess supervisee knowledge, attitudes, performance, and responses; effectively communicate evaluative feedback; challenge irrational thinking and clinical limitations; and, offer a supportive learning environment. Each supervision model delineates the extent to which these and other tasks must be accomplished. Three primary categories of supervision models are used in counselor training and include developmental, psychotherapy-based, and integrative models. Other categories include supervision-specific models, transgenerational models, purpose-systematic models, and postmodern models of family supervision (Haynes, Corey, & Moulton, 2003). Each category includes

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specific models that posit unique supervisory roles, responsibilities, and methods for the delivery of effective supervision. For purposes of this chapter, we will discuss two specific supervision models: (a) the cognitive-behavioral model from the psychotherapy-based category; and, (b) the integrated developmental model (Stoltenberg, McNeill, & Delworth, 1998) from the developmental category. These models were selected because of their popularity and common use in clinical supervision and because they represent divergent approaches to the supervision process with diverse solutions for Web-based applications.

The Cognitive-Behavioral Model Cognitive-behavioral therapy (CBT) focuses on the influence of thoughts/beliefs on both emotions and behaviors (Haynes et al., 2003). A primary goal of cognitive-behavioral supervision is to teach specific techniques and to correct supervisees’ inaccurate beliefs about the use of CBT in their work with clients. Supervision sessions are typically highly structured and are designed to educate the supervisee. A fundamental purpose of cognitive-behavioral supervision is help the supervisee understand how her or his beliefs regarding counseling ability influence her or his performance in counseling. In this process, supervisees translate what they have learned in supervision to their work with clients (Haynes et al., 2003). Liese and Beck (1997) specified nine steps that provide the structure for a cognitive-behavioral supervision session. These steps are designed to parallel those that occur in cognitive-behavioral therapy, specify the supervisor’s roles, and provide useful information for adapting this model for Web-based supervision. Haynes et al., (2003) delineate the nine steps: 1. Check-in: The supervisor asks the supervisee how she or he is doing.

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2. Agenda setting: The supervisor encourages careful preparation for the supervision session by asking the supervisee to provide a focus. 3. Bridge from the previous supervision session: The supervisor asks the supervisee to focus on learning from the previous session. 4. Inquiry about previously supervised therapy cases: The supervisor asks the supervisee to review successes and problems with cases previously discussed. 5. Review of homework since the previous supervision session: This may include reviews of assigned readings or progress in using specific techniques with clients. 6. Prioritization and discussion of agenda items: The supervisor focuses on a review of the supervisee’s recorded therapy sessions using role plays to teach new skills. 7. Assignment of new homework: Based on information gleaned in the supervision session, the supervisor assigns homework designed to help the supervisee develop knowledge and skills in cognitive-behavioral therapy. 8. Supervisor’s capsule summaries: The supervisor reflects on important issues to provide a focus for supervision. 9. Elicit feedback from the supervisee: The supervisor solicits feedback throughout the session with the goal of helping the supervisee recognize gains in knowledge and skills. This structure clearly identifies the primary role of the cognitive-behavioral supervisor as a teacher. This role is consistent with the identification of supervision as a special case of instruction. Features of and communication tools available for use on the World Wide Web make is possible to adapt the instructional aspects of the cognitive-behavioral supervision model for the provision of cybersupervision. Because live video conferencing most closely approximates face-to-face interaction, we

Challenges and Solutions in the Delivery of Clinical Cybersupervision

recommend it as the primary tool for delivery of cybersupervision. Using the nine-step process and assuming the use of a multi-featured, live video conferencing program, these features and sample tools are described following. 1. Check-in: Because it is critical for the supervisor to assess the supervisee’s appearance, affect, and psychological readiness to engage in supervision, this step is best accomplished using a synchronous communication tool such a live video conferencing that permits access to both verbal and non-verbal behaviors in real time. 2. Agenda setting: The supervisor requests that the supervisee prepare for the live video conference by emailing a proposed session agenda prior to the supervision session. 3. Bridge from the previous supervision session: Prior to the cybersupervision session, the supervisor assigns the supervisee to review digital video recordings of two counseling sessions completed by the supervisee during the previous week, prepare a two-page text document in which she discusses knowledge and skills gained during these sessions, and to upload this document into the live video conference for comment and discussion. 4. Inquiry about previously supervised therapy cases: Consistent with the request made in Step 3, the supervisor assigns the supervisee to create and save digital video files that reflect three examples of her most effective work and three examples of her least effective work during counseling sessions completed by the supervisee over the past two months. During the cybersupervision session, these digital files are uploaded and jointly reviewed for discussion and evaluative feedback. 5. Review of homework since the previous supervision session: During the previous cybersupervision session, the supervisor as-

6.

7.

8.

9.

signed the supervisee to create a 15-minute digital video in which she demonstrates a new counseling technique. During the current cybersupervision session, this digital video is uploaded and both supervisor and supervisee evaluate her performance. Prioritization and discussion of agenda items: During the cybersupervision session, the supervisor uploads and reviews two, 15-minute digital video segments of counseling sessions completed by the supervisee during the previous week. Both the supervisor and supervisee are able to view the uploaded video using a multifeatured video conferencing program. The supervisor evaluates the supervisee’s clinical performance by focusing on opportunities to teach new skills. Assignment of new homework: During the cybersupervision session, the supervisor uses the capabilities of the multi-featured video conferencing program to access a jointly viewed Website in which a targeted counseling skill in the use of cognitive-behavioral therapy is demonstrated on digital video. Upon completion of the video review, the supervisor assigns the supervisee to create a 10-slide Microsoft PowerPoint presentation that highlights critical aspects of this cognitive-behavioral approach for uploading and joint review at the next cybersupervision session. Supervisor’s capsule summaries: The supervisor digitally records the cybersupervision session during which she reflects on important issues for the supervisee to consider in the coming week. The supervisor asks the supervisee to review the digitally recorded cybersupervision session and to prepare brief responses to each summary for discussion at the next session. Elicit feedback from the supervisee: The supervisor solicits feedback throughout the cybersupervision session and asks her to

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share additional feedback via email before the next session. The process and online communication tools do not reflect a comprehensive approach to cybersupervision. They are cited as examples of how online supervision can be provided effectively and efficiently from a cognitive-behavioral theoretical perspective.

The Integrated Developmental Model The integrated developmental model (IDM) (Stoltenberg et al., 1998) posits that supervisees pass through a sequence of three developmental stages that reflect varying levels of professional competence and which suggest corresponding supervisory roles and responsibilities. At Level 1, the supervisor provides structure and support to encourage the supervisee’s autonomy and self-awareness. As the supervisee develops more autonomy (Level 2), the supervisor shares more responsibility with the supervisee, creates appropriate clinical and personal challenges, and promotes a focus on process issues. At Level 3, the supervisor provides collegial supervision by encouraging the supervisee to structure supervision sessions and by reinforcing professionalism and competence. Stoltenberg et al., (1998) identified eight clinical practice domains that serve as the basis for assessing the supervisees’ developmental levels: 1. 2. 3. 4. 5. 6. 7. 8.

Intervention skills competencies Assessment techniques Interpersonal assessment Client conceptualization Individual differences Theoretical orientation Treatment plans and goals Professional ethics (Haynes et al., 2003)

Through the lens of the IDM, developmental processes are ongoing, but not necessarily linear.

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Supervisees may demonstrate a high level clinical competence in the provision of family counseling, but possess limited skill in working with individuals. Additionally, newly learned skills may decay if not practiced regularly. Consequently, the roles and responsibilities of supervisors and supervisees will vary from session to session based upon an assessment of the supervisee’s developmental level in a particular clinical practice domain. The goal of supervision using an IDM approach is to help supervisees develop clinical competence through performance assessments in the eight domains of clinical practice identified above. This approach requires the IDM supervisor to assume primary roles as an assessor (to determine supervisees’ developmental levels) and developmental specialist (to promote professional development in a manner consistent with current developmental level). Although theoretically distinct from the cognitive-behavioral model, the roles adopted by the IDM supervisor are consistent with the identification of supervision as a special case of instruction. Instructors are responsible for assessing and promoting the novice’s development of learners. Communication tools available for use on the World Wide Web make is possible to adapt the IDM for the provision of cybersupervision. As noted above, live video conferencing most closely approximates face-to-face interaction. Therefore, we recommend it as the primary tool for delivery of cybersupervision. Assuming a Level 1 stage of development (i.e., supervisor provides structure, direction, and support) and use of a multifeatured, live video conferencing program, we will describe sample communication tools and techniques used in IDM cybersupervision for assessing and promoting development in eight clinical practice domains. 1. Intervention skills competencies: The supervisor uploads two digital videos of counseling sessions completed by the supervisee during the previous week. He additionally

Challenges and Solutions in the Delivery of Clinical Cybersupervision

2.

3.

4.

5.

uploads a “Clinical Skills Evaluation Form,” that can be viewed and completed online by both supervisor and supervisee. Following a 15-minute review of each digital video, the supervisor asks the supervisee to evaluate his performance on each clinical skill and provides evaluative feedback on each self- rating. Assessment techniques: The supervisor assigns the supervisee to conduct an intake assessment interview that includes a mental status examination. During the following cybersupervision session, the supervisor uploads the digital interview file, jointly reviews it with the supervisee, and comprehensively evaluates the supervisee’s performance in conducting these assessments. Interpersonal assessment: The supervisor assigns the supervisee to complete and save two personality tests available on the World Wide Web. Before the cybersupervision session, the supervisor accesses and scores the completed tests. During the session, the supervisor uploads the digital files that contain the scored tests and provides interpretations to the supervisee as they relate to the congruence of his personality profile with profiles of professional counselors. Client conceptualization: The supervisor assigns the supervisee to write an eight-page, comprehensive case conceptualization paper for a client the supervisee has seen for four months and to send it by e-mail prior to the next cybersupervision session. The supervisor evaluates the case conceptualization paper and uploads it during the next session, at which time they jointly review the paper and his feedback. Individual differences: The supervisor assigns the supervisee to complete readings and visit three Websites that provide detailed information on Hispanic, Asian, and Eastern European cultures. He then requires the supervisee to create three digital videos in

which he demonstrates culturally-sensitive practices in counseling three classmates who represent each cultural group. At the following session, the supervisor uploads and evaluates the supervisee’s performance as they jointly review each video. 6. Theoretical orientation: The supervisor assigns the supervisee to create a Microsoft PowerPoint presentation in which he contrasts and compares basic tenets and counseling techniques associated with psychodynamic, cognitive-behavioral, and Gestalt counseling theories. At the next cybersupervision session, the supervisor uploads the PowerPoint file, asks the supervisee to present the content, and provides evaluative feedback. 7. Treatment plans and goals: During the cybersupervision session, the supervisor uploads digitally saved treatment plans created by the supervisee over the preceding six months. As each plan is jointly reviewed in chronological order, the supervisor provides evaluative feedback on the appropriateness and comprehensiveness of identified goals and behavioral objectives as they relate to diagnoses. 8. Professional ethics: The supervisor directs the supervisee to observe a one-hour digital video of a counseling session conducted by the supervisee with a client, who was later reported to be a “former girlfriend.” He assigns the supervisee to review the codes of ethics of three national counseling organizations and to specify standards that he violated in each ethical code in a ten-page paper to be e-mailed before the next counseling session. During the cybersupervision session, the supervisor uploads both the digital video and assigned paper, which are jointly reviewed and discussed.

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Resolving Legal and Ethical Problems A range of potential ethical and legal problems face online counselors and online supervisors. A unique aspect of clinical supervision is that supervisors are legally and ethically responsible for the actions of supervisees. This vicarious liability of supervisors poses special risks in the delivery of cybersupervision. The limitations of providing cybersupervision at remote sites become obvious when a client reveals plans to harm self or others. As previously noted, the absence of sufficient information and visual cues in an online format may lead the cybersupervisor to misinterpret or underestimate the client’s potential for harm. In order to address this problem, the cybersupervisor should develop a protocol for online interventions in emergent situations that involve the use of explicit questions and procedures. Privileged communication, confidentiality, and privacy are at the core of the counseling process. Although technological safeguards, such as firewalls, password protection, and encryption, increase the likelihood that online counseling communications will be confidential, breaches of these security measures are less than uncommon. Consequently, online clients and supervisees must be informed that although appropriate precautions may have been implemented to protect online communications, supervisors cannot guarantee confidentiality. The ability to save emails, other narrative communications, and audio or video files is useful from a clinical perspective. However, these same storage capabilities create opportunities for breaches of confidentially through unauthorized access and legal processes. Jurisdiction is a problem frequently mentioned with regard to online counseling. State laws vary, but the trend is to assume that jurisdiction resides in the client’s home state (reported in a 2006 interview with John Bloom (ACA Ethics Revision Task Force member) conducted by David Kaplan (ACA

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Chief Professional Officer). This issue influences the delivery of cybersupervision as supervisees complete practicum/internship experiences in states where the supervisor is not licensed. The counseling profession is guided by a code of ethics. Practitioners must adhere to the standards of conduct prescribed in these codes or face sanctions by the profession. Because cybersupervision is a relatively new practice, there are no current technology codes to cover its practice. This challenge will be addressed as cybersupervision processes and techniques are explicated in the professional literature. This information will be used to create standards designed to protect the confidentiality and appropriate use of online supervision communications and to insure the competent delivery of cybersupervision The American Counseling Association Code of Ethics was revised in 2005 to include a substantial section on technology (see Bloom interview previously mentioned). The code addresses some of the challenges we noted in this subsection: the need for encrypted communication to maintain client privacy and the use of passwords to protect client confidentiality. The first professional organization to provide substantial guidance regarding online counseling was the National Board of Certified Counselors (NBCC). In 1997 NBCC published standards for WebCounseling (Bloom, 1997), which were later revised (NBCC, 2005). An affiliated organization, the Center for Credentialing and Education established the Distance Counselor Credential (DCC) for counselors who successfully complete a two-day training course on distance counseling. Detailed information regarding technology-related ethical codes are available on the organization’s Website and in the literature (e.g., Layne, & Hohenshil, 2005; Mallen et al., 2005; Shaw & Shaw, 2006).

Challenges and Solutions in the Delivery of Clinical Cybersupervision

CONCLUSION

FUTURE TRENDS

Supervision is a specialized form of instruction. Cybersupervision is a specialized form of online instruction. As technological infrastructures continue to emerge that support its use, clinical supervisors will be required to demonstrate proficiency in the use of online supervision models. The authors have attempted to address this need by describing how cybersupervision can be delivered using divergent theoretical perspectives and practices. They have further identified challenges and solutions in the delivery of cybersupervision. The primary advantages of clinical cybersupervision are opportunities to: (a) provide immediate, corrective, supervisory feedback during live counseling sessions from remote locations; (b) conduct regular, weekly, one-hour supervision sessions using a multifeatured video conference program from remote locations; (c) use a host of synchronous and asynchronous Web-based communication tools to facilitate the supervision/ instructional process; (d) save time, money, and human resources, thereby enhancing the efficiency of supervision. Challenges to the use of cybersupervision include: (a) difficulties in communicating supervisor characteristics that are necessary for the development of an effective supervisor/supervisee working relationship; (b) insuring that supervisees possess requisite dispositions and computer skills; (c) implementing a theoretical model of supervision in an Web-based format; and, (d) resolving technological, legal, and ethical problems unique to the online supervision format. Although these challenges may temporarily slow its expansion, the advantages of cybersupervision insure its implementation not only in clinical training programs, but also in the majority of university-based professional preparation programs throughout the world in the twenty-first century.

Despite the challenges posed by cybersupervision, the advantages of this instructional medium virtually assure its growth. The ongoing development of Web-based infrastructures and communication tools, ease of remote access to clinical practice situations, and savings in time, money, and human resources will contribute to its popularity and widespread use. However, like all innovative practices, the development of cybersupervision will pose unique dilemmas and problems. In the section below, we delineate the dynamics of these advantages and limitations in a discussion of future trends in cybersupervision.

Trend 1: The Future Will Witness Ongoing Development of Internet Infrastructures and Communication Tools That Support Cybersupervision Since its inception, efforts to enhance communication structures and capabilities of the Internet have been unceasing. Spurred by needs of the military, business, education, and a variety of other entities, Web-based communications have evolved from simple text-based messages, to the transmission of graphics, video, multifeatured video conferencing, and a host of other options. Because Internet-based communications are vital to infrastructures that support modern societies, the tools that enhance such communications will continually be refined. All of these developments will support and sustain the use of cybersupervision.

Trend 2: Cybersupervision Will Become the Norm for Counselor (and all Professional) Training The twenty-first century will witness sustained growth in Internet-based counselor training and supervision as political leaders, policy makers, and administrators at Institutions of Higher Education

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(IHEs) realize the cost effectiveness of these options. Larger numbers of students will be enrolled in fewer university programs that will be taught by fewer faculty at lower costs. The implementation of cyber teaching and cybersupervision will be heralded by politicians as necessary steps to insure equal access to a university education and to reduce tax burdens. University administrators will support the expansion of these initiatives as dwindling tax revenues create pressures for efficiency and fiscal accountability. In response to these developments, professional organizations have developed technology standards as well as standards for online counseling and supervision. The result is that counseling program curricula will be revised to insure that students possess computer skills necessary to communicate in multiple electronic formats with instructors, understand legal and ethical issues associated with confidential online communications, complete on-line assignments and tests, and respond to cybersupervision feedback.

Trend 3: Ease of Access to the Internet will Create more Opportunities for Unauthorized Access to Confidential Communications The first two trends will generate needs for additional legal and ethical mandates to protect Internet-based, confidential communications. Because confidentiality is a cornerstone of clinical service, legislative bodies and professional organizations will draft increasingly restrictive laws and ethical standards regarding the use of instruction that employs confidential communications (e.g., Internet-based supervision). Computer chip manufacturers and software engineers will be required to design systems that defeat attempts at such access and subsequent breaches of confidentiality. Growth in the use of Web-based counseling programs may be limited by the ability to insure

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confidential communications, through technological, legal, or legal interventions. Firewalls, password-protection, and encryption technologies are used to protect sensitive or confidential information transmitted via the Internet. However, these safeguards can be, and have been, compromised. As the volume of confidential communications swells in a new era of online instruction and cybersupervision, technology-based measures that guarantee secure communications must be developed. Legislatures in a few states have created statutes to control the transmission of confidential, Webbased, counseling communications. However, because cybercounseling and cybersupervision are in their infancy, legislators often lack sufficient information to create effective laws. Several professional counseling organizations (i.e., American Counseling Association, American Psychological Association, Association for Counselor Education and Supervision, National Board for Certified Counselors) have revised current or developed new ethical standards designed to protect the confidentiality of Web-based counseling communications. Fewer standards have been established to protect confidential communications in the provision of cybersupervision.

Trend 4: Cybersupervision will Become the Instructional Medium of Choice in a Wide Variety of Professional and Nonprofessional Applications. Although this chapter has focused on advantages and challenges of cybersupervision in the clinical training of counseling professionals, the benefits of this instructional medium insure that it will be used across multiple disciplines. Supervisors in the fields of medicine, law, education, and a host of others will use the remote access and instantaneous feedback features of cybersupervision to monitor trainees’ work in the delivery of services to patients, clients, and students. For example, medical

Challenges and Solutions in the Delivery of Clinical Cybersupervision

supervisors will work from the convenience of their offices to observe and provide live, immediate corrective feedback to interns and residents as they conduct medical examinations, diagnose patient conditions, and prescribe medications in hospitals across the street and across the country. Supervisors of law interns will remotely access mock trials being conducted at affiliated universities or justice centers, provide instantaneous performance feedback that affords opportunities to correct mistakes, and offer additional commentary on performance via use of ATOP technology discussed previously. Teacher supervisors will use cybersupervision to observe and provide in vivo performance feedback to student teachers working in public schools from the instructional environment of their university classrooms. In this way, both supervisors and students enrolled in teacher education programs will benefit from the use of this instructional approach. Supervisors and trainers in nonprofessional settings, such as construction and industry, will also use cybersupervision to provide immediate information and feedback without the expense and inconvenience of travel to worksites. For example, as civil engineers in New York City redesign a complex bracket for refitting a bridge girder in Seattle, Washington, the procedures for installing the new bracket will be provided via cybersupervision. A digital recording of the installation procedure will be played for site supervisors and ironworkers at the worksite and opportunities for live questions and answers will be provided via live videoconference. Live video of the new installation will be viewed by engineers in New York and immediate procedural feedback will be provided.

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Neukrug, E. S. (1991). Computer-assisted live supervision in counselor skills training. Counselor Education and Supervision, 31, 132–138.

Mallen, M. J., Vogel, D. L., Rochlen, A. B., & Day, S. X. (2005). Online counseling: Reviewing the literature from a counseling psychology framework. The Counseling Psychologist, 33, 819–871. doi:10.1177/0011000005278624 Mauzey, E., Harris, M. B. C., & Trusty, J. (2000). Comparing the effects of live supervision interventions on novice trainee anxiety and anger. The Clinical Supervisor, 19, 109–120. doi:10.1300/ J001v19n02_06 McCollum, E. E., & Wetchler, J. L. (1995). In defense of case consultation: Maybe “dead” supervision isn’t all that dead after all. Journal of Marital and Family Therapy, 21, 155–166. doi:10.1111/j.1752-0606.1995.tb00150.x Miller, K. L., Miller, S. M., & Evans, W. J. (2002). Computer-assisted live supervision in college counseling centers. Journal of College Counseling, 5(2), 187–192. Moorhouse, A., & Carr, A. (1999). The correlates of phone-in frequency, duration and the number of suggestions made in live supervision. [from Academic Search Premier database.]. Journal of Family Therapy, 21, 241–249. Retrieved November 30, 2006. doi:10.1111/1467-6427.00128 Myrick, R. D., & Sabella, R. A. (1995). Cyberspace: A new place for counseling. Elementary School Guidance & Counseling, 30, 35–44.

Parrott, L., III. (2003). Counseling and psychotherapy. Pacific Grove, CA: Brooks/Cole. Rickards, L. D. (1984). Verbal interaction and supervisor perception in counseling supervision. Journal of Counseling Psychology, 31, 262–265. doi:10.1037/0022-0167.31.2.262 Scherl, C. R., & Haley, J. (2000). Computer monitor supervision: A clinical note. The American Journal of Family Therapy, 28, 275–282. doi:10.1080/01926180050081702 Shaw, H. E., & Shaw, S. F. (2006). Critical ethical issues in online counseling: Assessing current practices with an ethical intent checklist. Journal of Counseling and Development, 84, 41–53. Snelbecker, G. E., Miller, S. M., & Zheng, R. (2008). Function relevance and online design. In R. Zheng & P. Ferris (Eds.), Understanding online instructional modeling: Theories and practice. Hershey, PA: Information Science Reference. Stoltenberg, D. D., McNeill, B., & Delworth, U. (1998). IDM supervision: An integrated developmental model for supervising counselors and therapists. San Francisco: Jossey-Bass. Wantz, R. A., Tromski, D. M., Mortsolf, C. J., Yoxtheimer, G., Brill, S., & Cole, A. (2003). Incorporating distance learning into counselor education program: A research study. In J. W. Bloom & G. R. Walz (Eds.), Cybercounseling and cyberlearning: An encore. (ERIC Document Reproduction Service No. ED 481146).

This work was previously published in Understanding Online Instructional Modeling: Theories and Practices, edited by Robert Zheng and Sharmila Pixy Ferris, pp. 223-241, copyright 2008 by Information Science Reference (an imprint of IGI Global).

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Chapter 6.4

Technology in the Supervision of Mental Health Professionals: Ethical, Interpersonal, and Epistemological Implications James “Tres” Stefurak University of South Alabama, USA Daniel W. Surry University of South Alabama, USA Richard L. Hayes University of South Alabama, USA

ABSTRACT As communication technology is increasingly applied to the training and supervision of mental health professionals, a more robust analysis of how such approaches fundamentally change the relationship between supervisor and supervisee and how these approaches both enhance and limit the outcomes of supervision is sorely needed. In this chapter clinical supervision is defined and discussed and the various technology platforms DOI: 10.4018/978-1-60960-561-2.ch604

that have been used to conduct supervision at-adistance are reviewed along with the supervision outcomes observed in the research literature with each method. The potential for technology to reduce geographic and financial barriers to the provision of quality supervision is discussed. However, the chapter also outlines the potential negative impacts technology might have to the supervisory relationship, the ethical dilemmas posed by technology-mediated supervision, and the ways in which technology-mediated supervision may place limits upon the elements of supervision that rely upon a constructivist epistemology.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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INTRODUCTION Since the advent of telephone technology and later television technology health care providers have sought ways to utilize communication technology to extend the reach of health services and; this approach has come to be known as telemedicine (Strehle & Shabde, 2006). The majority of the research and design in telemedicine has occurred in the last 20-30 years as computer, internet and audio/visual technology have matured and become increasingly available and affordable. Among the forces propelling the interest in telemedicine have been the rising costs of health care and the desire by providers to reach underserved populations (Craig & Patterson, 2005). Similar forces related to shortages of specific health care professional groups and the increased demand for health care services for traditionally underserved populations have promoted the application of technology to the training and supervision of health care providers as well. Such interest in applying technology to enhance both service delivery and training of professionals has certainly been present across the range of medical and allied health professions, and this includes the mental health professions in which the present authors are most interested. Within the mental health professions a variety of technologies including telephones, email, text messaging, internet chat, web-based groups, virtual reality environments, and videoconferencing have been used to deliver a variety of mental health services including consultation, assessment and diagnosis, and medication evaluations (Hilty et al., 2002), as well as counseling/ psychotherapy (Caspar, 2004; Oravec, 2000). This work has been conducted across the range of mental health professions including applied psychology (Caspar, 2004; Kanz, 2001), social work (Park-Oliver & Demiris, 2006; Parrott & Madoc-Jones, 2008), counselor education (Vaccaro & Lambie, 2007; Myrick & Sabella), and psychiatry (Hilty, et al., 2006).

In addition to aiding the delivery of mental health services to clients, technology, particularly internet-based technology and video conferencing, has also been applied to the task of training and supervising mental health professionals (hereafter MHPs). Scholars across the mental health professions have explored and put forth models of training and supervision that utilize email (Clingerman & Bernard, 2004), web-based supervision groups (Gainor & Constantine, 2002; Yeh et al., 2008), web/computer-based training systems (Berger, 2004; Weingardt, 2004) videoconferencing (Wood, Miller & Hargrove, 2005), and virtual reality technology (Beutler & Harwood, 2004) to enhance the acquisition of knowledge and skills involved in the competent delivery of mental health services such as assessment, diagnosis and psychotherapy. Despite these existing efforts, other authors have noted that the potential of technology-enhanced clinical supervision and training of mental health professionals has yet to be fully tapped (Berger, 2004). It is the area of technology-enhanced clinical supervision of MHPs to which the present chapter applies its focus.

THE MENTAL HEALTH PROFESSIONS Before proceeding to a discussion of the nature of supervision, a brief discussion of the nature of the mental health service delivery systems and the mental health professions is warranted to comprehend how technology has been adopted differentially to some degree across such professions. First, what are the mental health professions? Answering that question is not as simple as it might seem. Mental health is a field characterized by professions that have different philosophical traditions with sometimes only subtle differences in the scope of clinical practice that separates them. A potential list of these professions is as follows in no particular order:

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Professional Counseling (School, Mental Health, Rehabilitation, etc.) Psychology (Clinical, Counseling, School, and Combined-Integrated) Clinical Social Work Marriage and Family Therapy Psychiatry Psychiatric Nursing

training, this chapter focuses on a common set of supervisory goals across these professions that generally surround the acquisition of skills in the areas of clinical interviewing and assessment as well as counseling/psychotherapy.

There are also a variety of specialties within these professions that target a particular age group, e.g. Child-Clinical Psychology, School Counseling; or a particularly type of problem/disorder, e.g. addictions, behavioral medicine, or a particularly modality of intervention, e.g. art therapy, family therapy, prevention work, etc. Each of these professions and specialties will potentially have somewhat different sets of concerns and priorities in regards to utilizing technology to supervise junior members of the profession. For example Counselor Education as a field has displayed interest in utilizing technology to deliver both didactic classroom instruction as well as clinical supervision. Applied Psychology has focused particular attention on the use of technology to disseminate scientifically-based or evidence based practices to supervisees or to MHPs already in practice and participating in continuing education. Psychiatry has focused the most upon the use of technology for both supervision as well as the assessment of clinical competencies through the use of video conferencing and virtual patients. Social Workers and Marriage and Family Therapists appear to have experienced a more gradual degree of interest and scholarship on this topic and models of technology-enhanced supervision and training are still emerging in these fields. Finally, nursing scholars have explored the application of technology to nurse education in general, though little focus has been applied to the specific nature of technology-enhanced supervision and training of psychiatric nurses as a specialty group. Despite the somewhat unique focus applied by each specialty to technology-enhanced supervision and

In order to delineate the vagaries of enhancing clinical supervision with technology, a proper understanding of precisely what MHPs mean by “supervision” is in order. The definition of supervision in these professions is unique and differs significantly from what the terms conveys in other disciplines. Bernard and Goodyear (2009) offer the following definition of clinical supervision in their widely utilized text: Supervision is an intervention provided by a more senior member of a profession to a more junior member or members of that same profession. This relationship

• • • • •

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DEFINING CLINICAL SUPERVISION

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Is evaluative and hierarchical Extends over time, and Has the simultaneous purposes of enhancing the professional functioning of the more junior person(s); monitoring the quality of professional services offered to the clients that she, he, or they see; and serving as a gatekeeper for those who are to enter the particular profession.

Falender and Shafranske (2004) add the priority of training supervisees in science-based practice, facilitating the supervisee’s ability to self-assess and self-correct and encourage supervisee selfefficacy. From these two examples of a definition of supervision we can see that supervision is more than merely a vehicle to promote acquisition of knowledge. Knowledge is but one prerequisite to a MHP becoming competent in any given area of practice. Falender and Shafranske (2004) describe

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“competence” as embodying knowledge, communication skills, technical skills, clinical reasoning and judgment, emotional awareness and appropriate use of emotions, ethical values, and capacity for regular reflection and self-assessment. In this sense becoming competent is a dynamic process in mental health work that exceeds merely learning objective knowledge. We will later describe how the inculcation of “competence” in supervision is a process that hinges upon an interpersonal alliance between supervisor and supervisee. Supervision is typically seen as the central and most important mechanism in the education of MHPs (Falender & Shafranske, 2004). Indeed clinical supervision has been referred to as the “signature pedagogy” in the mental health professions (Bernard & Goodyear, 2009). Supervision involves a relationship between a MHP-in-training and a supervisor and typically is provided to the trainee when she or he has reached the point in their degree program where they are required to complete a clinical practicum in which they are delivering services such as assessment and psychotherapy on their own. The supervisor observes and evaluates the trainee’s clinical services delivered to clients against professional standards to determine the degree to which the supervisee can practice competently and ethically (Falender & Shafranske, 2004). Just as is the case in the delivery of mental health services to clients the relationship between the supervisor and supervisee has typically been viewed as paramount to successful outcomes in supervision. In fact, when supervisees are asked to identify where important events occurred during the supervision process, they most often cite events related to the supervisory relationship (Ladany, et al, 2005). Though didactic/teaching tasks are often embedded within the practice of supervision, and supervision and teaching have some similar goals; clinical supervision is distinct from the practice of teaching and didactic training in the mental health professions. Both teaching and supervision share a focus upon evaluating the student.

However, teaching within MHP training programs typically involves and explicit pre-set curriculum delivered to a group of students (Bernard & Goodyear, 2009). Programs in higher education that train clinicians of all sorts including MHPs have historically separated didactic instruction from clinical supervision, with the former occurring early in the training program within coursework and the latter occurring when the student ultimately enters a clinical service environment. Though this paradigm may be changing as problem-based learning approaches are increasingly adopted in fields such as clinical psychology (Stedmon et al., 2006). While continued training in specific pieces of knowledge continues during the student’s clinical placement through supervision, clinical supervision is distinct in that it involves a more intimate and more individualized approach to teaching, guidance, evaluation and feedback; and the material evaluated in supervision is the supervisees actual clinical work with clients rather than academic product as is produced in classroom settings. It is in supervision’s increased individualization of learning and the more intimate relationship that is formed between supervisor and supervisee that a distinction between supervision and teaching can most clearly be discerned (Bernard & Goodyear, 2009). However, as will be explored the distinction may also go beyond this to include differing epistemologies across the task of teaching vs. supervision, a separate distinction which may have important implications for how or when technology should enhance or wholly deliver supervision. Clinical supervision is typically delivered through a combination of individual and group face-to-face meetings. Supervision may also be provided by both clinical faculty as well as clinicians working at an external site. Supervision meetings that occur at the clinical site may be scheduled simultaneously with a client contact so that the supervisor could observe the supervisee delivering therapeutic services live. Often “bugin-the-ear” technology is utilized in which the

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supervisor can communicate with the therapist during the session through an ear bud speaker while watching the supervisee and client either through a one-way mirror or video link. More typically the supervision session consists of either a review of a taped example of the supervisees clinical work and verbal discussion of the work and does not necessarily physically or temporally coincide with the actual service delivery. Usually supervisees would also meet as a group with a supervisor during training concurrent with attending individual supervision. Inherent in this traditional face-to-face approach is the idea that the interpersonal processes that occur in both individual and group supervision are crucial in helping supervisees learn about self, learn to apply knowledge they have gained, and grow in their interpersonal skills necessary to deliver mental health services. However, the traditional supervisory activity described above are bounded by a specific time at which the supervision appointment occurs and by a specific space where the appointment must occur so that all parties can be present together. Technology can remove obstacles associated with time and space, and carries with it unique benefits and deficits relative to the traditional face-to-face model of supervision.

OVERVIEW OF ISSUES IN TECHNOLOGY & SUPERVISION The role of technology in higher education, in general, and the training of mental health professionals in particular, have received increasing attention since the advent of the internet and other advanced technologies that facilitate human communication and interaction in a manner that removes the obstacles of time and space. Technology has been observed to have three broad uses within clinical supervision: (a) to facilitate the provision of work samples for the supervisor to review, evaluate and provide feedback to the supervisee, (b) to allow the provision of supervision at-a-distance,

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and (c) to influence the process of supervision in deliberate ways (Bernard & Goodyear, 2009). Additionally there are types of technology that we have selected to highlight; these include: (a) electronic text-based communication such as email, (b) web-based groups, and (c) video-conferencing and other audio/visual media.

ELECTRONIC TEXT COMMUNICATION & SUPERVISION Early versions of electronic communication technology such as telephone, fax, email, and chat allowed for delivery of aspects of supervision that decreased the impact of distance and allowed for extensions to how a supervisor and supervisee communicate as well as how the supervisee and their peers communicate (Harvey & Carlson, 2003). In addition to reducing the impact of geography and timing, text-based electronic communication such as email and threaded discussions, because they are an asynchronous form of communication the contents of which remain preserved over time, allow for more reflection and thought by the supervisor and supervisee because the text material of email communication is available for repeated review and the asynchronous nature of email allows for more time spent in reflecting upon the information being shared. Also, email and other electronic text communication may foster a larger amount of disclosure on the part of supervisees and therefore promote engagement and intimacy in the supervisory relationship (Clingerman & Bernard, 2004). Such methods build upon older approaches such as journaling and reaction papers that had a similar goal in mind, but without the efficiencies of electronic communication. Email has been observed to have specific effects upon supervisees reflection and thinking during clinical practicum experiences in counseling graduate training. The two research efforts reviewed here both asked supervisees to regularly send emails to their supervisor in the

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time between face-to-face individual and group supervision meetings (Graf & Stebnicki, 2002; Clingerman & Bernard, 2004). First, the topics of such emails tend to fall into specific categories: (a) conceptualization and intervention with clients, (b) site supervisor and site issues, and (c) personalization/self-analysis. Clingerman and Bernard (2004) found that the number of emails sent each week declined across the semester, but messages related to personalization issues, involving topics such as self-awareness, self-efficacy and selfreflection, a were consistently the most frequent category. Graf and Stebnicki (2002) limited emails to one per week and observed that emails about personalization/self-reflection issues began with concerns about the supervisee’s confidence and self-efficacy, and progressing to an increased sense of responsibility and frustration with a perceived lack of impact, and finally ending in a more balanced realistic perspective on self-as-helper. The two studies differed in that Clingerman and Bernard (2004) observed less emphasis on emails regarding the site supervisor and site than did Graf and Stebnicki (2002). Obviously more research is desperately needed here and these two studies suffer from a lack of a control group and lack of data controlling for the impact of the face-to-face supervision that participants in both studies continued to attend while participating in the email portion of supervision.

WEB-BASED GROUPS These early electronic text-based communication technologies now sit alongside a host of collaborative technological tools mostly centered on web-based interactive platforms, which further enhance the range of communication options. These newer techniques include the use of threaded discussions, wikis, podcasts, blogs, social networking sites all collectively referred to as Web 2.0 applications (Conn, Roberts, & Powell, 2009). Such applications have allowed for not just com-

munication between supervisor and supervisee ata-distance, but collaboration between supervisors and between supervisees in regards to acquiring clinical skills and learning new and better ways to best serve clients. Moreover, interactive web technologies have allowed supervisors and supervisees to engage in co-construction of knowledge regarding the needs of a given client as well as facilitating the acquisition of objective knowledge about treatment approaches and specific mental health problems/disorders. We shall discuss this in more detail later. Now emerging are internet technologies that have been collectively referred to as Web 3.0 or the “Semantic Web” (Ohler, 2010). These webbased endeavors will further allow larger groups of mental health professionals beyond the parameters of MHP graduate training programs or the supervisory relationship to impose meaning structures on bodies of knowledge in the field. Advanced computer software will potentially be able to comprehend the ways in which large groups of MHPs have labeled and organized concepts in a given domain of practice and be able to create detailed and tailored information regarding questions that arise in the process of learning to be a MHP. As Ohler (2010) suggests the semantic web will not link users to “pages” or discrete locations on the internet or within an internal network, but to “reports” of information tailored to the user’s specifications and informed by the contributions of a broad audience of users’ perceptions of said information. One intriguing approach is peer-based online supervision and support groups that seek to address supervisee’s needs for support, identity development and skill development. Given that supervisees have generally been shown to benefit more from peer feedback than supervisor feedback, such approaches may represent a significant ability to augment and extend what is initiated in face-to-face group supervision. However, such approaches face obstacles from the degree of student acceptance of receiving web-based supervision.

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Gainor and Constantine (2002) investigated the effects of web-based versus in-person peer group supervision to foster multicultural counseling skills. Results indicated significantly lower levels of supervisee satisfaction with the webbased format. Furthermore, supervisees assigned to in-person peer group supervision achieved significantly higher scores on a skill measure of multicultural case conceptualization than did those assigned to the web-based supervision. The authors point out that supervisees conceptualization skills did increase significantly in both supervision formats, only more so in the in-person format. They speculate that one major hindrance in learning such skills in group supervision over the web is the lack of non-verbal cues and information, combined with the typical reluctance of supervisees to self-disclose in a group format, in general, as well as their reticence to self-disclose or discuss their reactions to multicultural issues, in particular. This is in contrast to the view that supervisee to supervisor emails appear to foster more disclosure. They concluded that a significant degree of interpersonal intimacy is required for supervision of any type to be effective, let alone supervision focused on multicultural competence, and that training programs should value in-person over web-based group or a combination of the two supervision rather than an exclusively webbased approach. In a related study, Yeh and colleagues (2008) investigated the process and outcome of an online peer support group for counseling students who were completing clinical field experiences. They found that topics most frequently covered in this group were similar to those covered in face-to-face supervision groups. The authors interpreted their results to suggest that group process occurs in an online peer support group in a manner similar to face-to-face peer, or supervisor-lead group supervision. Interestingly, these researchers also found that students reported that they were more comfortable discussing some topics in the online peer group than in face-to-face supervision. An

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online group supervision format, therefore, may offer some additional benefits not found in a face-to-face group supervision format. According to Yeh et al. these advantages include: (a) ready availability of feedback and guidance from other members over a relatively long time interval; (b) the ability to get feedback and support for immediate concerns: and, (c) extended time for discussion of student concerns that is not possible in a time-limited face-to-face group.

VIDEO RECORDING & VIDEO CONFERENCING Audio and visual communication has also seen advances from telephone and voice-only communication with the advent of affordable personal computers and broadband internet connectivity. First, Voice Over Internet Protocol technology services have allowed low cost voice communication over long distances among multiple parties. Most recently audio and visual communication have been linked together in the form of video conferencing technology which has offered a means to deliver individual and group supervision sessions at-a-distance as well as conduct live video observation of a supervisee’s clinical work without the full loss of non-verbal/visual data that is inherent in voice only and text-based supervisor mediums. This advancement may represent the strongest strategy to most fully overcome the aforementioned disadvantaged of technologybased supervision and enhance the ability of the supervisor to evaluate supervisee competence. A primary role of the mental health supervisor is to function as a gatekeeper within their respective profession (Vaccaro & Lambie, 2007). This necessitates adopting evaluation practices that afford the supervisor an opportunity to see valid samples of the clinician’s skills in action. Technology has also long been used to produce work samples that can be viewed and evaluated by the supervisor. This includes technologies to aid live

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supervision such as the aforementioned “bug in the ear” technology, as well technology that allows recording and dissemination of assessment and therapy sessions for review by supervisors and peers. An alternative to the “bug in the ear” approaches to live supervision includes “bug in the eye”. Involves placing a computer monitor in the session room behind the client and allowing the supervisor to type text message to the monitor which can only be seen by the clinician (Bernard & Goodyear, 2009; Miller, Miller & Evans, 2002). This also includes the recording of sessions, which presently is almost always done utilizing digital media recordings. In fact, recordings of therapy sessions has been combined with the “bug in the eye” approach to produce tapes that have the “bug in the eye” text submitted by the supervisor inserted as subtitles during the recording of a session (Rosenberg, 2006). If live supervision and recording technology is combined with streaming media over the internet and video conferencing technology the dimension of distance from the supervisor can also be conquered in regards to collecting and disseminating work samples. Lastly, these technological enhancements fundamentally shape the process of supervision. It is likely obvious that supervision takes a different tone when a supervisee knows that a live or recorded sample of their work will be observed by their supervisor, or through the internet observed by a bevy of expert supervisors. This factor may promote increased accountability by all parties, increased professional commitment by the supervisee, and perhaps even increased cohesion and trust in the supervisory relationship. With the integration of audio and video twoway communication at-a-distance video-conferencing may be the most promising medium for the technologically-based provision of individual and group supervision sessions given its potential ability to allow full verbal and non-verbal communication between multiple parties in real-time. However, VC technology has only recently advanced to the point of being a cost-effective and

efficient means of facilitating clinical supervision at-a-distance, and much remains to be discovered in regards to the vagaries of supervision in this format. The VC format has been found to have unique advantages, relative to face-to-face supervision; these include: a. Ability to present and process both auditory and visual information during interaction, and thereby maintain some of the spontaneity and “realness” present in face-to-face communication (Olson, Russell, & White, 2001) b. Decreased stress related to traveling to supervision (Marrow, Hollyoake, Hamer, & Kenrick, 2002) c. Ability to pair supervisor and supervisees working in similar specialties, but who reside in different geographical areas (Marrow, Hollyoake, Hamer, & Kenrick, 2002) d. Ability of participants to use VC technology with little preparation and training (Garrett, & Dudt, 1998) e. Fostering clearer and more structured cmmunications between supervisors and supervisees (Gammon et al., 1998) f. Support real-time interaction between multiple individuals that includes visual and auditory information and with less time lapse between responses by participants However, VC supervision has also been found to be associated with various drawbacks including: a. Difficulty accessing and utilizing equipment, particularly when ongoing technical support is lacking (Marrow, Hollyoake, Hamer, & Kenrick, 2002), b. The tendency for participants to withhold emotionally difficult material for future face-to-face supervision (Gammon et al., 1998), c. Reduced tolerance for silence by supervisees in the supervision session and a consequent

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increase in exaggerated assertive behaviors in attempts to ensure they are heard (Gammon et al., 1998), d. Concern that the supervisor’s ability to maintain a working alliance with supervisees maybe diminished when using VC technology. Wood, Miller,and Hargrove (2005) have offered a specific model of supervision that includes video-conferencing group supervision as a component. Their model of supervision is intended to offer supervision to mental health professionals working in remote areas and is based on practices established in previous telehealth consultation and supervision models. These authors outline a combination of face-to-face and electronic communication between supervisors and supervisees. They stress the importance in phase one of the model of training all parties in the technology being utilized in order to increase comfort levels and smooth the transition to conducting some supervision over electronic media. Their model includes the use of case studies that are provided to supervisees through a web page, followed by discussion between supervisor and supervisees via email and chat rooms. The third phase of this supervision model involves the provision of group supervision via live video-conferencing. The fourth and final phase involves provision of traditional face-to-face individual supervision. The authors note that telesupervision in general offers the same cost effectiveness found in group supervision, and gives supervisees in remote settings an opportunity they might otherwise not be offered. However, the role of remote technologyassisted supervision is still as an adjunct to some degree of face-to-face supervision in their model. Gammon et al. (1998) conducted a qualitative study of supervisors and supervisees reactions to VC-based psychotherapy supervision. The study involved 8 participants (2 supervisors and 6 trainees). Interestingly they found that VC supervision had positive effects on supervision

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such as more structure and more preparation on the part of involved parties for supervision. This was at the cost of less perceived spontaneity and sensed emotional presence of the other. Overall, the participants viewed VC supervision as a potentially useful adjunct to face-to-face supervision, but not as a replacement. Video conferencing has an enormous amount of potential, particularly if it is integrated with other technologies and used as an adjunct to some degree of face-to-face contact.

COMPUTER-MEDIATED VIRTUAL REALITY Most recently the use of standardized virtual patients has provided a more standardized and reliable method of assessing clinician competency (Srinivasan et al., 2006). This topic is covered more thoroughly in a separate chapter of this text, but deserves a brief mention here. One downside of work samples with actual clients is the lack of standardization of the client responses and behaviors towards the clinician. The creation of “intelligent” virtual clients/patients has allowed supervisors to examine a clinician’s responses to a standardized stimuli, which facilitates comparison between supervisees and provides the supervisor with a higher degree of control over the clinical stimuli with which the learner is presented.

COMPUTER/WEB-BASED TRAINING Lastly, though it is also beyond the scope of this chapter; web-based learning systems have the potential to revolutionize the “training” of MHPs in a variety of models and approaches to clinical service. As mentioned the training or teaching aspect of MHP education is somewhat distinct from the process of clinical supervision. Regardless a authors such as Cucciare, Weingardt and Villafranca (2008) have described how using multiple learning systems including web-based

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learning systems can enhance the acquisition of evidence based psychotherapy knowledge and skills. Additionally Weingardt (2004) has argued for a closer integration of the fields of mental health and instructional design to aid in the creation of effective training systems that would allow for a long sought improvement of the large scale transfer of research-based knowledge into practice settings. A final example in this area would be the use of virtual reality technology to train MHPs in psychotherapy and assessment skills (Beutler & Harwood, 2004). What follows is a more detailed review of selected technology-based approaches to clinical supervision in the following areas: email, web-based groups, and video-conferencing.

BENEFITS AND RATIONALES FOR TECHNOLOGY-BASED CLINICAL SUPERVISION While interest appears to be steadily increasing in regards to utilizing technology to deliver supervisory services to MHP trainees, little attention has been paid to how such approaches may fundamentally change how MHP students and supervisors relate and interact with one another. Additionally, technology-based supervision is likely to change a supervisee’s relationships with their peers, their clients and with the organizations with which they interact and by which they are employed. To be certain these changes are likely a mixture of disadvantages relative to face-to-face supervision as well as unique advantages relative to traditional supervision paradigms. To this end the chapter will review research and theoretical work in regards to how technology might enhance the practice of supervision while not detracting from the professional skill development of supervisees. We will also attempt to propose what fruitful next steps might be in the application and research of emerging technologies in supervision. In the field of counselor education Layne and Hohenshil (2005) have argued that the use of tech-

nology to serve clients as well as train counselors is here to stay, and that members of the field must adapt to these new technologies. The question is now not whether clinical supervisors will adopt features of technology-enhanced supervision, but how. Much of this necessity is likely driven by falling budgets in higher education as well as increasing demand for mental health services and consequently demand for more mental health providers. Technology-based training and supervision of MHPs may require less of the supervisor’s time and travel and, in theory, allows faculty to deliver more supervision services to larger number of students. In addition to the acknowledged necessity of utilizing technology in supervision, scholars have noted important functional benefits of integrating technology into clinical training, and weighing those benefits against known costs. Benefits of technology-enhanced clinical supervision that potentially are present in regards to educational institutions, supervisors, supervisees, and to the consumers in the mental health system at large. Consumers may benefit by technology’s ability to allow for training of mental health professionals working in rural and remote mental health services with populations that are historically underserved and have poor access to care (Olson, Russell, & White, 2001). Supervisees also may experience reduced costs associated with travel to face-toface supervision (Olson, Russell & White, 2001). Consumers may also benefit if technology allows for increased dissemination and implementation of evidence based approaches which are likely to work better than the status quo approaches to treatment in the field (Weingardt, 2004). Benefits to the learner include accessing more specialized and “master” supervisors who may be geographically remote (Janoff & Schoenholtz, 1999), and having the content of supervision communications available for a longer period of time, which might enhance reflection and synthesis of knowledge (Hara, Bonk, & Angeli, 2000). This ability to overcome geography is perhaps the most often

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cited rationale and benefit of technology-based supervision. An emerging consensus is present in mental health that technology-assisted training and supervision has its own unique advantages and disadvantages from face-to-face supervision and can serve, at least, as a useful adjunct to traditional face-to-face supervision (Olson, Russell, & White, 2001; Wood, Miller, & Hargrove, 2005).

LEGAL AND ETHICAL CONCERNS Perhaps chief argument or concerns regarding technology based supervision is the same concern most often cited regarding the technology-based mental health services—the potential loss of client confidentiality. By using email, threaded discussions, chat or videoconferencing supervisors and supervisees run the risk of having client data shared in supervision become compromised. In addition to statutes that give privileged communication status to information shared with mental health professionals and ethical code requirements that all client information is kept confidential; the Health Insurance Portability and Accountability Act now requires significant safeguards be implemented when communicating about clients across electronic formats, in particular the use of password protected computing stations, steps to ensure the identity of recipients of electronic communication and the use of encryption technology to render the electronic data transmitted impenetrable to those seeking to compromise such data. In general the internet cannot be viewed as a secure or private medium for communication. However, Kanz argued in 2001 that encryption technology had advanced greatly, and at present many computer networks and online communication software systems do allow for a greatly increased assurance of privacy. Secure encryption has become a primary means of protecting client data and has also become more available at low costs across a wide range of software from email to web-based social groups and video con-

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ferencing software. Even if the communication is encrypted there are still requirements that the identity of the information recipient’s identity be verifiable. Other steps typically taken to keep supervisory data confidential include the use of regularly changing passwords to access webbased supervision resources and deidentifying any client data transmitted electronically (Janoff & Schoenholtz, 1999). Supervisors should seek consultation on such matters from both other mental health professionals as well as technology experts and make every effort to both institute practices that will protect client confidentiality as well as provide appropriate informed consent to supervisee’s clients as to the supervisory process, specifically how their private data is being disseminated electronically, the risks posed by such dissemination and the steps that are being taken to protect such data (Olsen, Russell, & White, 2001). Another ethical concern is how crisis events are adequately addressed through the use of technology to provide supervision at-a-distance. This concern would typically necessitate that a clinician-in-training have some level of available face-to-face supervisory oversight if working at a site physically remote from the supervisor of record for their clinical work, and have the ability to access modes of communication such as a cell phone that are faster than is found on web-based modes of communication (Janoff & Schoenholtz, 1999). This concern is typically allayed by having immediately available on-site supervision in addition to remote electronic supervision, i.e. multiple layers of supervision. Legal arguments and cautions against technology-based supervision have been offered in regards to how supervision delivered remotely via technology impacts the supervisor’s degree of liability for the services delivered to clients by the supervisee. The primary question here is what is the nature of the supervisor-supervisee relationship when it is constituted over a virtual technological medium? Scholars have argued when health services or supervision are delivered

Technology in the Supervision of Mental Health Professionals

through a remote technology-based medium the provider or supervisor may be less liable than if they were delivered face-to-face. This is because the technological medium creates a perception of a less intimate and less clear set of obligations between physician and patient or supervisor and supervisee (Granade & Sanders, 1996). Related to this concern is a simpler question of whether supervisor liability insurance policies clearly and adequately cover supervision delivered over a virtual electronic medium (Panos et al., 2002). Lastly, if supervisors deliver supervision services across state lines they may be practicing in their respective MHP without a license in that a license typically is only valid for the state the supervisor resides in and sought licensure (Bernard & Goodyear, 2009). Another concern regarding technology-based supervision is in regards to the competence with which supervisors and supervisees are able to utilize tools such as email, web-based groups, and video-conferencing. This concern links to the ability to supervise and practice ethically as well (Vaccaro & Lambie, 2007). Barak (1999) noted that supervisor and supervisees who do not understand the capacities and limitations of technology may unwittingly compromise client data, e.g. sending emails to the wrong recipient or posting data on a web-group that is not deidentified or rely on technology to meet clinical requirements for which the technology is inappropriate, e.g. sending an email to a supervisor to address a crisis. These lapses would not necessarily be due to poor ethical comprehension, but a lack of adeptness with the technology itself.

serve as both a “tutoring system” to facilitate the acquisition of objective knowledge, as well as a “interactive learning environment” that facilitates the construction of knowledge. While supervision does involve, in part, “teaching” the supervisee pieces of “objective” knowledge about psychological disorders, treatment techniques, ethical precepts etc.; the endeavor also inherently leads to co-construction of knowledge. This second aspect of supervision occurs in the context of the supervisory relationship, in which the supervisee and supervisor work together to produce a case conceptualization, diagnosis, treatment plan and then proceed in the interpersonal task of helping the client recover and make changes. This set of tasks involves a blending of objectivist/positivist epistemology with constructivist epistemology as the pathway during supervision through which the clinician translates nomothetic concepts gleaned from such sources as scientific research, expert opinion, and best practices based on professional consensus with idiographic data gleaned from both the supervisory relationship as well as the clinician-client relationship. This idiographic data includes the particular values, skills, and limitations of all parties involved. Thus supervision aided by technology such as web-based textual communication, video-conferencing or telephone communication must be able to allow for sufficient exchange of a range of discrete information as well as interpersonal information between supervisor and supervisee so that the more nuanced co-construction of clinical knowledge in a given case can occur.

EPISTEMOLOGIES IN CLINICAL SUPERVISION

IMPACT OF TECHNOLOGY ON INTERPERSONAL RELATIONSHIPS IN CLINICAL SUPERVISION

Within supervision are goals for supervisee learning that fall within both positivist and constructivist epistemologies. As Berger (2004) notes technology-based learning systems can

At the heart of supervision as well as all clinical interventions are the supervisor-supervisee relationship and the clinician-client relationship respectively. Mental health is a field that generally

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values the interpersonal dimensions of service delivery. In fact outcome research suggests that the therapeutic relationship accounts for a moderate but consistent amount of the variance in treatment outcomes (Norcross & Lambert, 2006). The supervisory relationship is frequently viewed as crucial in the practice of clinical supervision as it is in the practice of assessment and psychotherapy (Bernard & Goodyear, 2004). Concerns exist regarding the impact of technology-based communication upon the supervisory relationship primarily due to the potential lack of sufficient face-to-face contact (Barak, 1999). This may be reduced with the use of video conferencing approaches, but until such technologies are ubiquitous, text-based electronic communications are likely to be widely relied upon when web-based supervision is undertaken. The fear is that primarily text-based electronic communication will reduce the full range of verbal and non-verbal information shared in supervisor-supervisee interactions and inhibit the conveyance of more complex and subtle meanings when discussing clinical work (Vaccaro & Lambie, 2007). Ultimately this could impede a clinician’s professional development and ultimately place an upper limit on the quality of the services they deliver to clients. In a sense one of the dominant epistemologies within supervision practice is a view that the relationship yields insights, ways of knowing and “truths” that are an outgrowth of the relational frame provided by faceto-face interaction with a supervisee over time. While web-based supervision allows those interested in the clinical training of mental health professionals to transcend many of the limitations of distance, such approaches may also fundamentally alter the interpersonal factors present between supervisee and supervisor. Interestingly research has shown that these interpersonal changes do not appear necessarily deleterious. For example Janoff and Schoenholtz (1999) examined a combined face-to-face and computer-mediated group supervision format and found that group members with a high degree of status and power were less likely

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to steer the content of the dialogue in computermediated group supervision. Men and women were equally likely to initiate discussion points in a computer-mediated group, perhaps because the inability to see one another may reduce the pressure to conform to gender roles triggered by visual cues. Group supervisees in such a format are more likely to generate a diversity of perspectives when the pressure to conform or avoid negative social consequences is attenuated. The downside to this lack of non-verbal information is that participant’s may be more easily offended due to the ambiguity of text-only computer-mediated communication. Similarly, Conn, Roberts and Powell (2009) examined a hybrid model of counseling supervision with school counselors and found no differences in the perception of the quality of a face-to-face supervision paradigm vs. a mixed face-to-face and web-based paradigm. Though, there was a significant trend for self-reported comfort with technology to correlate with reported satisfaction with the hybrid model. Such research is a first step in assessing whether web-based distance supervision has a necessarily deteriorating impact upon the supervisory relationship and/or the relationship between supervisees. Despite such preliminary finding of certain unique advantages of web-based supervisory environments a common thread in the literature is to assert that web-based supervision is a useful adjunct, but not replacement for face-to-face supervision (Janoff & Schoenholtz-Read, 1999). The basis for such a sentiment lies in the limitations technology-based supervisory mediums place upon communication and interpersonal relationships. In general, online relationships are viewed as circumscribed forms of actual face-toface relationships (Kanz, 2001). Miller and Gergen (1998) argued that virtual, online communities decrease the level of interpersonal accountability among participants in a group. Participants receive little immediate feedback, and are unlikely to get useful feedback about the impact they have upon other members in groups that exist only in a virtual

Technology in the Supervision of Mental Health Professionals

space. Gammon et al. (1998) has further argued that the loss of non-verbal cues in many forms of electronic communication leads to a task-oriented, depersonalized approach that lacks spontaneity or authenticity. Also, without other confirming evidence, the level of authenticity inherent in a participant’s responses is essentially unknowable to members of the group. Thus, participants have little ability to gauge whether a response is congruent with the respondent’s inner state and values (i.e. how genuine is the responder). As a consequence, comments by participants may have a diminished or unintended impact upon other group members, and thereby limiting how cohesive the group can become. Given the challenges of fostering a full and textured relationship between a supervisor and supervisee in text-based electronic communication; the task of facilitating such a relationship with and between members of a training group is significantly complicated. Many of the limitations noted above may be at least partially overcome by video conferencing (VC) based supervision. However, VC technology is the youngest communication technology and still more expensive than other technologies. Supervisees may find that technical limitations of computing equipment make VC supervision less then desirable (Walter, Rosenquist & Bawtinhimer, 2004). Also, as Gammon et al.’s (1998) early study of VC-based psychotherapy supervision revealed supervisees typically still view VC as an adjunct, not a replacement, for face-to-face supervision. That said, a clear case can be made that when face-to-face supervision, particularly with supervisees working in isolated or remote areas or when the services of a geographically distant expert supervisor is needed, would not otherwise be possible; VC-based supervision would provide significant “add-on” benefits and replicate face-to-face group supervision reasonably well. An example of this is present in research by Panos (1010) who delivered VC supervision to social work field students working in remote international areas. In such a scenario where face-

to-face supervision with a known supervisor is not possible VC technology provides an opportunity to deliver supervision and guidance that would otherwise not be possible.

CONCLUSION & FUTURE DIRECTIONS The application of technology, particularly webbased groups and videoconferencing, to clinical supervision of mental health services has offered intriguing possibilities for improved training of supervisees in remote/rural areas and early research suggests that such an approach has advantages above and beyond its cost effectiveness. The textbased medium of web groups appears to provide a forum where concerns that would likely not normally be voiced are put forth and different interpersonal processes of an egalitarian nature may be easier to facilitate than in face-to-face supervision. The primary concerns that linger in regards to technology-based supervision are outlined below. Those who are in the position of designing MHP training programs, and who are providing supervision must content with these questions if they take up the gauntlet of leveraging technology to enhance and extend traditional face-to-face supervision. The first lingering question is whether virtual supervision replace face-to-face supervision? The immediate sentiment appears to be a resounding no in mental health given the increasing interest in hybrid forms of supervision that mix face-to-face with technology-enhanced. Such an approach leverages the strengths of both and minimized the weaknesses of both. One of the primary concerns within this question is the threat to the supervisory relationship or alliance posed by virtual supervision. Also, a basic guiding idea here is that technology-based supervision is used when a clear unique benefit is provided by doing so, i.e. supervision via internet, video conferencing is needed because it offers an opportunity that without utilizing such methods supervision would be fundamentally compromised

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if not impossible. Where face-to-face supervision is possible, it should probably constitute at least part of the supervision practice. Second, how does a supervisor best protect the confidentiality of client data when conducting technology-based supervision? Encryption technology appears to have the potentially to most solidly answer this question, though issues of existing and not yet foreseen federal laws, and client subjective reactions to virtual supervision may present continued obstacles. Third, will the proliferation of virtual supervision ultimately add to the overall supervisory workload of faculty and site-based supervisors and thereby reduce the quality of clinical supervision particularly by the deleterious effects this might have to the supervisory relationship. Here the ethical priorities and professional identities of clinical supervisors may come in conflict with the legitimate requirement for higher education administrators to look for and exploit cost saving measures in an era of falling budgets in colleges and universities. More than ethical concerns there is the fear that such technology fueled proliferation of supervision will add to the threats to the unique nature of the supervisory relationship by reducing the degree of trust, intimacy and disclosure in supervisory contexts. The end result could be not just a change in how supervision is conducted to a more technically-oriented objectivist endeavor, but also a rippling effect in which the identity of mental health professionals, the culture of the mental health professions and the relationships between provider and client are all similarly changed.

Berger, T. (2004). Computer-based technological applications in psychotherapy training. Journal of Clinical Psychology, 60(3), 301–315. doi:10.1002/jclp.10265

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Bernard, R., & Goodyear, J. (2009). Fundamentals of Clinical Supervision (4th ed.). Boston: Allyn & Bacon. Beutler, L. E., & Harwood, T. M. (2004). Virtual reality in psychotherapy training. Journal of Clinical Psychology, 60(3), 317–330. doi:10.1002/ jclp.10266 Caspar, F. (2004). Technological developments and applications in clinical psychology and psychotherapy: Introduction. Journal of Clinical Psychology, 60(3), 221–238. doi:10.1002/jclp.10260 Clingerman, T. L., & Bernard, J. M. (2004). An investigation of the use of e-mail as a supplemental modality for clinical supervision. Counselor Education and Supervision, 44, 82–96. Craig, J., & Patterson, V. (2005). Introduction to the practice of telemedicine. Journal of Telemedicine and Telecare, 11, 3–9. doi:10.1258/1357633053430494 Cucciare, M. A., Weingardt, K. R., & Villafranca, S. (2008). Using blended learning to implement evidence-based psychotherapies. Clinical Psychology: Science and Practice, 15(4), 299–307. doi:10.1111/j.1468-2850.2008.00141.x Falender, C. A., & Shafranske, E. P. (2004). Clinical Supervision: A Competency-Based Approach. Washington, D.C.: American Psychological Association. doi:10.1037/10806-000

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Ladany, N., Friedlander, M. L., & Nelson, M. L. (2005). Critical Events in Psychotherapy Supervision: An Interpersonal Approach. Washington, DC: American Psychological Association. doi:10.1037/10958-000

Garrett, J. L., & Dudt, K. (1998). Using videoconferencing to supervise student-teachers. Technology and Teacher Education Annual, 1084-1088.

Lambie, G. W., Sias, S. M. (2009). An integrative psychological development model of supervision for professional school counselors-in-training. Journal of Counseling & Development, Summer, (87), 349-356.

Graf, N. M., & Stebnicki, M. A. (2002). Using E-mail for clinical supervision in practicum; A qualitative analysis. Journal of Rehabilitation, 68(3), 41–49. Granade, P. F., & Sanders, J. H. (1996). Implementing telemedicine nationwide: Analyzing the legal issues. Defense Counsel Journal, 63, 67–73. Harvey, V. S., & Carlson, J. F. (2003). Ethical and professional issues with computer-related technology. School Psychology Review, 32, 92–104. Hilty, D. M., Alverson, D. C., Alpert, J. E., Lowell, T., Sagduyu, K., & Boland, R. J. (2006). Virtual reality, telemedicine, web and data processing innovations in medical and psychiatric education and clinical care. Academic Psychiatry, 30(6), 528–533. doi:10.1176/appi.ap.30.6.528 Hilty, D. M., Luo, J. S., Morache, C., Marcelo, D. A., & Nesbitt, T. S. (2002). Telepsychiatry: An overview for psychiatrists. CNS Drugs, 16(8), 527–548. doi:10.2165/00023210-20021608000003 Janoff, D. S., & Schoenholtz-Read, J. (1999). Group supervision meets technology: A model for computer-mediated group training at a distance. International Journal of Group Psychotherapy, 49(2), 255–272. Kanz, J. E. (2001). Clinical-supervision.com: Issues in the provision of online supervision. Professional Psychology, Research and Practice, 32(4), 415–420. doi:10.1037/0735-7028.32.4.415

Layne, C. M., & Hohenshil, T. H. (2005). High tech counseling: Revisited. Journal of Counseling and Development, 83(2), 222–226. Marrow, C. E., Hollyoake, K., Hamer, D., & Kenrick, C. (2002). Clinical supervision using video-conferencing technology: A reflective account. Journal of Nursing Management, 10, 275–282. doi:10.1046/j.1365-2834.2002.00313.x Miller, K. L., Miller, S. M., & Evans, W. J. (2002). Computer-assisted live supervision in college counseling centers. Journal of College Counseling, 5, 187–192. Myrick, R. D., & Sabella, R. A. (1995). Cyberspace: New place for counselor supervision. Elementary School Guidance & Counseling, 30(1), 35–45. Norcross, J. C., & Lambert, M. J. (2006). The Therapy Relationship. In Norcross, J. C., Beutler, L. E., & Levant, R. F. (Eds.), Evidence-Based Practices in Mental Health: Debate and Dialogue on the Fundamental Questions. Washington, DC: American Psychological Association. doi:10.1037/11265-000 Ohler, J. (2010). The power and peril of web 3.0: It’s more than just semantics. Learning and Leading with Technology, (May): 14–17.

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Olsen, M. M., Russell, C. S., & White, M. B. (2001). Technological implications for clinical supervision and practice. The Clinical Supervisor, 20(2), 201–215. doi:10.1300/J001v20n02_15 Panos, P. T., Panos, A., Cox, S. E., Roby, J. L., & Matheson, K. W. (2002). Ethical issues concerning the use of videoconferencing to supervise international social work field practicum students. Journal of Social Work Education, 38, 421–430. Parker-Oliver, D., & Demiris, G. (2006). Social work informatics: A new specialty. Social Work, 51(2), 127–134. Parrott, L., & Madoc-Jones, I. (2008). Reclaiming information and communication technologies for empowering social work practice. Journal of Social Work, 8(2), 181–197. doi:10.1177/1468017307084739 Rosenberg, J. I. (2006). Real-time training: Transfer of knowledge through computermediated, real-time feedback. Professional Psychology, Research and Practice, 37, 539–546. doi:10.1037/0735-7028.37.5.539 Srinivasan, M., Hwang, J., West, D., & Yellowless, P. M. (2006). Assessment of clinical skills using stimulator technologies. Educational Assessment, Interventions, and Outcomes, 30(6), 505–515. Stedmon, J., Wood, J., Curle, C., & Haslam, C. (2006). Development of PBL in the training of clinical psychologists. Psychology Learning & Teaching, 5(1), 52–60. Strehle, E. M., & Shabde, N. (2006). One hundred years of telemedicine: Does this new technology have a place in paediatrics? Archives of Disease in Childhood, 91, 956–959. doi:10.1136/ adc.2006.099622 Trepal, H., Haberstroh, S., Duffey, T., & Evans, M. (2007). Considerations and strategies for teaching online counseling skills: establishing relationships in cyberspace. Counselor Education and Supervision, 46, 266–279.

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Trolley, B., & Silliker, A. (2005). The Use of WebCT in the supervision of counseling interns. Journal of Technology in Counseling, 4 (1). Retrieved from http://jtc.colstate.edu/Vol4_1/ Trolley/Trolley.htm on 4/15/10 Vaccaro, N., & Lambie, G. W. (2007). Computerbased counselor-in-training supervision: Ethical and practical implications for counselor educators and supervisors. Counselor Education and Supervision, 47, 46–57. Walter, D. A., Rosenquist, P. B., & Bawtinhimer, G. (2004). Distance learning technologies in the training of psychiatry residents: A critical assessment. Academic Psychiatry, 28(1), 60–65. doi:10.1176/appi.ap.28.1.60 Weingardt, K. R. (2004). The role of instructional design and technology in the disseminatin of empirically supported, manual-based therapies. Psychology: Science and Practice, 11(3), 313–331. doi:10.1093/clipsy/bph087 Wood, J. A. V., Miller, T. W., & Hargrove, D. S. (2005). Clinical supervision in rural settings: A telehealth model. Professional Psychology, Research and Practice, 36(2), 173–179. doi:10.1037/0735-7028.36.2.173 Yeh, C. J., Change, T., Chiang, L., Drost, C. M., Spelliscy, D., Carter, R. T., & Chang, Y. (2008). Development, content, process and outcome of an online peer supervision group for counselor trainees. Computers in Human Behavior, 24, 2889–2903. doi:10.1016/j.chb.2008.04.010

KEY TERMS AND DEFINITIONS Telemedicine: The practice of delivering direct or ancillary health care services and sharing health care information through electronic communication, particularly interactive audio and visual electronic communication.

Technology in the Supervision of Mental Health Professionals

Clinical Supervision: Supervision is an intervention provided by a more senior member of a profession to a more junior member or members of that same profession. This relationship: (a) is evaluative and hierarchical, (b) extends over time, and, (c) has the simultaneous purposes of enhancing the professional functioning of the more junior person(s); monitoring the quality of professional services offered to the clients that she, he, or they see; and serving as a gatekeeper for those who are to enter the particular profession. Mental Health Professional: A cluster of professions including psychiatry, psychiatric nursing, professional counseling, clinical social work, clinical/counseling/ school/combined-integrated psychology and marriage/family therapy, all of which focus on delivering assessment, diagnosis, and intervention services to individuals living with mental, emotional and behavioral problems. Video-Conferencing Supervision: A set of technologies that allow real-time interaction through cameras linking two locations with both audio and visual information. Video conferencing can be used to facilitate the delivery of health and mental health services as well as clinical supervision and consultation with mental health professionals in training at-a-distance. Live Supervision: The practice of the supervisor observing a supervisee’s clinical work sample in real-time either through a two way mirror or

through a video link. Live supervision is often enhanced with “bug in the ear” and “bug in the eye” technologies which allow for the supervisor to communicate with the supervisee as they are interacting with the client either through, respectively, auditory means using a speaker in the supervisee’s ear or through visual means through displaying text messages on a screen that sits behind the client. Email Supervision: The practice of conducting the interactions between supervisor and supervisee via email messages. Typically this method is not used to deliver all of the supervisory services, rather only as a means to further supervisory communication in the interim period between face-to-face individual or group supervision meeting. Web-Based Group Supervision: The practice of delivering group and peer supervision through web-based platforms that allow for asynchronous text-based communication via threaded discussions and email as well as synchronous text communication via chat and instant messaging. This approach also allows for the uploading of work samples such as case reports, treatment plans, videos of client sessions, etc. Such methods allow a group of supervisors and supervisees to discuss and provide feedback without the obstacle of time or space.

This work was previously published in Technology Integration in Higher Education: Social and Organizational Aspects, edited by Daniel W. Surry, Robert M. Gray Jr. and James R. Stefurak, pp. 114-131, copyright 2011 by Information Science Reference (an imprint of IGI Global).

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Chapter 6.5

Optimization of Medical Supervision, Management, and Reimbursement of Contemporary Homecare B. Spyropoulos Technological Education Institute of Athens, Greece M. Botsivaly Technological Education Institute of Athens, Greece A. Tzavaras Technological Education Institute of Athens, Greece K. Koutsourakis Technological Education Institute of Athens, Greece

INTRODUCTION The concepts of health, sickness, and illness are subject to the specific sociocultural conditions under which they are considered, and, on the basis of which medical care is provided (Spyropoulos & Papagounos, 1995). The concepts and the methods involved in diagnosis and treatment are subject DOI: 10.4018/978-1-60960-561-2.ch605

to the prevalent at the time theoretical model of disease. Hospitals, as a social institution, emerged as a response to particular needs and corresponded to the specific level of the understanding of health and disease. About 2,500 years ago, the temples of Asklepeios, the god of medicine, were probably the first well organized houses of refuge for the sick and training schools for physicians. Hospitals also existed in India under Buddhist auspices as early as the 3rd century BCE. The number of

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Optimization of Medical Supervision, Management, and Reimbursement of Contemporary Homecare

hospitals grew in the first centuries of the Christian era. In the 4th century AD, hospitals were founded in Caesarea and in Rome. Throughout the Byzantine time, the Middle Ages, the Arab and Ottoman dominance periods, the Renaissance, and even later, hospitals were almost entirely run by religious Christian or Islamic groups. During all these centuries, home care remains the main and usually the unique mode of treatment for the majority of the people world-wide. Only during the 18th century, hospitals ceased to be purely philanthropic institutions and they started to assume the character of a social institution where a systematic and theory-infused approach to disease prevailed. From the middle of the 19th century on, the number of hospitals, particularly in Europe and in the USA, increased, principally because of the discovery of anesthesia, aseptic surgical techniques, and, by the end of the century, the introduction of the x-rays. The demand for hospital services expanded further with the spread of prosperity, and with the introduction of various forms of hospitalization insurance, especially in England and in Germany, where the first obligatory and generalized social insurance system is introduced. It is the first time in the human history that home care ceased to be the main way of providing healthcare, and hospital treatment became gradually an important social right. The modern hospital emerged gradually and successively, during a very long historical development, from a religious philanthropy institution to the contemporary managed care Establishment. The civil structure, the social demands, and the individual performance were always and are still reflected, on the hospital, throughout the centuries. Therefore, the 21st century hospital will provide a radically different professional activity environment, and a quite different professional-patient interaction modus; it will increasingly encourage telemedicine (De Leo, 2002) supported home care because of the increase of mean life expectancy, and the hospital care cost avalanche (Wipf & Langner, 2006; Woolhandler, Campbell, & Him-

melstein, 2003). Its mission will be completed by a network of various associated Institutions, providing care rather closer to home care, than to that of the traditional hospital-care (Brazil, Bolton, Ulrichsen, & Knott, 1998). Adapting medical and managerial decisionmaking (Spyropoulos, 2006a) in the modern home care environment is a cardinal prerequisite, in order to ensure, first, an economically sustainable development of the aging population healthcare (Scarcelli, 2001); second, the rehabilitation services required for impaired persons; and finally, the psychosomatic support necessary in the developed countries, during the next decades. Thus, a strategic question emerges that is how home care will be medically supervised and financially reimbursed. The present study attempts to describe the present situation and the contemporary technological trends in home care; more specific, it is focused on a system developed by our team that intends first, to enable the optimal documentation of the provided home care, and second, to facilitate the acquisition of all relevant financial data, leading to a fair remuneration of the services offered.

BACKGROUND Contemporary home care is evolving on the foundation of a variety of applications in healthcare support services, and we argue that a qualitatively new “mobile” home care is presently emerging out of the combined employment of, first, the modern wireless mobile telephony networks and equipment, second, the contemporary digital entertainment electronics, and third, the commercially available computer hardware and software. This new mobile home care allows us for to be optimistic about the reduction of patients’ unnecessary hospitalization in the near future, as well as, the dramatic reduction of the home care costs. It is essential to summarize the most important emerging innovative aspects of modern home care

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before describing in detail the developed therefore Management System. Adapting medical decision-making and treatment in the home care environment begins with the effort to carry out periodically preventive examinations, as a kind of ambulant and emergency home care. The miniaturization of equipment, and the falling prices trend that could be developed by the opening of Biomedical Technology to the immense population of potential consumers, if combined with a “smart” home environment, then it may result in periodical, individually adapted, preventive medical examinations, and, consequently, a decreasing number of patients in hospitals and other costly traditional ambulatory services. Diagnosis and Treatment of an emergency patient at home is inevitable, in order to reduce morbidity, following acute pathological reasons or an accident, and it is paramount that the patient is first evaluated and stabilized on site. In such cases, there are two complementary ways to intervene: First, the continuous monitoring of high-risk groups suffering from chronic cardiovascular, respiratory, and other diseases; and second, equipping and training the “first responder” with appropriate hardware and standardized guidelines (Spyropoulos, 2006b). These two main options allow for an improvement of the efficiency of ambulant emergency services, especially if supported by telematics. Concerning home-based in vitro diagnostics, “Dry Clinical Chemistry” offers today a variety of products, covering the whole range of important parameters, like metabolites, enzymes, electrolytes, and so on. Although diabetes every-day strip-based monitoring (Lewis, 2001) is a routine for the last two decades, a little has been done to establish a “home- based” in vitro diagnostics laboratory that would be very useful, especially in facilities offering housing and some kind of medical care for elderly people (Gill, 2002). In vitro testing, combined with biosignals monitoring at home, provide for a safe and pleasant living environment for sensible populations. Concerning

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home imaging, although several mobile systems have been developed, most of them, with the exception of ultrasonic equipment, will remain for the near future the “status symbols” of the hospitals. However, external patient-imaging, together with high-quality biological sound processing and transmission capabilities, are already available. Although surgery will remain more or less the “monopoly” of the hospital, the postoperative hospital stay will be further dramatically shortened and replaced by home care in a familiar and pleasant room, instead of the impersonal, sterile ward. This becomes feasible, first, because of the recently developed minimally invasive surgical techniques, and second, as a result of decentralizing postoperative care to general practitioners and nurses, and finally, due to the emerging option of, the acquisition at home of vital-signs, postoperative wound images, and so on, and their transmission to the specialized surgical center, if necessary. Although “home-ICU” sounds like a joke, there are always circumstances that may lead to interim intensive care settings, for instance on an uneasily accessible site after an accident. Virtually, within a telematics supported home care environment, the patient’s monitoring, until safe transportation becomes possible, is not a major problem. Even Cardio Pneumonary Resuscitation (CPR) and defibrillation are feasible options. However, the access to specialized human and material resources will keep the ICU, as a hospital dedicated department, for the years to come. Furthermore, in cases like Chronic Obstructive Pulmonary Disease (COPD), Chronic Asthma, and other such diseases that cause air-flow limitation, leading to inability to carry out daily activities, an appropriate home care environment may contribute, first, to home-treatment patient-customized programs, aiming to improve the subject’s performance and, thereby, the quality of life; second, to the evaluation of preceding pharmacological or surgical treatment; and third, to the definition of optimal selection criteria for home-based assisted ventilation (Simonds, 2003).

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Several entertainment and medical electronics features already developed or under development, can easily be adapted to serve persons with limited mobility, poststroke patients, patients suffering from certain neurological and/or mental disorders. Such features include, among others, first, Web camera-supported presentations on interactive high-definition monitors, creating the feeling of being together, when physically separated from relatives and friends; second, every-day life equipment remotely controlled, third, electrical stimulators, developed by pacemaker manufacturers that improve poststroke incontinence control, reduce tremor on Parkinson’s disease patients, and so on. Finally, interactive Internet and TV could contribute, first, to the reduction of the enormous rehabilitation costs, by providing online instructions to home-based posttraumatic or poststroke rehabilitation exercises; second, to the restriction of the agoraphobic symptoms, and of the selfrespect-less behavior of depressive patients; and finally to the continuous home-treatment and/or to the special education of patients, especially young ones. However, the presently major difficulty is not the hardware and telecommunication infrastructure, but the development of appropriate content and user interfaces, serving reliably the aims mentioned.

MEDICAL SUPERVISION, MANAGEMENT, AND REIMBURSEMENT OF CONTEMPORARY HOME CARE The developed system attempts to relate home care to the Diagnosis Related Groups (DRGs) based Reimbursement (Botsivaly, 2006) by introducing an innovative adapted Continuity of Care Record (CCR). The proposed solution consists of a typical CCR-system, equipped with an extension that, first, acquires and processes an individually designed structured set of data to be employed during the post-discharge home care

period, and second, allows for the estimation of the homecare cost vs. the DRGs code issued at discharge from the hospital, supporting, thus, a more accurate DRGs-related home care remuneration. Medical records are indispensable in home care, and are also used in a variety of ways, serving a multiplicity of purposes (Papagounos & Spyropoulos, 1999). A major problem of the contemporary health services, including home care, is the scarcity of resources. Patients’ records provide information concerning the expenses incurred and the resources allocated for the treatment of the individual patient within the healthcare system. Home care resources consumption must also be monitored, and we argue that the most efficient and time saving way is through the employment of an appropriately adapted Continuity of Care Record (CCR). (Figure 1) The Continuity of Care Record (CCR) is a standard for exchanging basic patient data between two care providers, in order to enable the second one to access relevant patient information. It is intended to foster and improve continuity of patient care, to reduce medical errors, and to assure at least a minimum level of health information transportability when a patient is referred, transferred, or otherwise goes to another provider. The standard was proposed and developed by the E31 Committee on Healthcare Informatics of ASTM, an American National Standards Institute (ANSI) standard development organization (ASTM E2369-05, 2005). The CCR is being developed to cover the need to organize and make transportable a set of basic patient information consisting of the most relevant and recently facts about a patient’s condition. These include, first, patient and provider information; second, insurance information; third, patient’s health status such as, allergies, medications, vital signs, diagnoses, recent procedures; fourth, recent care provided, as well as recommendations for future care (that is, a care plan); and finally, the reason for referral or transfer. The structure of the CCR perfectly fits to the needs of

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Figure 1. Details of system’s flow chart

homecare, both, the administrative and the medical ones, because it is designed to be technology- and vendor-neutral for maximum applicability. It is being developed on the extensible markup language (XML) platform in order to offer multiple options for its presentation, modification, and transmission. Through XML, the CCR can be prepared, transmitted, and viewed in a browser, in an Health Level Seven (HL7) Clinical Document Architecture (CDA) compliant document, in a secure e-mail, or in any XML-enabled word processing document. This makes it possible for recipients to access and view the information in the manner that they prefer (electronic or paper), to extract the data as required, and even to store them on a portable storage device for use as a personal health record. Because the CCR will be a simple XML-document, different Electronic Health Record (EHR) systems will be able to, both import and export all relevant data to and from the CCR document, and enable automated transmission with minimal workflow disruption for individual caregivers. Thus, the CCR will increase interoperability between different EHR systems, with minimal or no custom programming. Finally, in countries with minimal EHR

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and healthcare networks infrastructure, the CCR is the only advisable interim operational mode. We have designed and developed a first version of a CCR-extension for comprehensive payer-specific information. The developed model consists of various related databases, and allows for every user, first, to select one of the available DRG-codes; second, to create a typical CCR that contains the appropriate demographic and administrative data, as well as, the relevant clinical information; third, to set up for each DRG code a custom-made typical profile of home care activities; and fourth, to attach them either a nominal fee, proposed by the system, which is based upon realistic, empirically collected data, or to independently price them. The developed system employs the Australian Refined DRGs (AR-DRGs). Starting point is the loading of a number of well established medical classification systems, like the International Classification of Diseases Version 9 (ICD9), the Australian Refined DRGs (AR-DRGs) and the Nursing Interventions Taxonomy of the Clinical Care Classification (CCC) databases (Saba, 2002). The AR-DRGs database figures out about 660 diagnosis-related groups, classified within 25 Major Diagnosis Categories (MDC), 30 Specialties, and three major groups

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(medical, surgical, and other). From this root databases, every user is able to individually assign an appropriate set of care activities to specific diagnosis codes that are coded according to ARDRGs. These activity sets consist of diagnostic, monitoring and treatment activities that can be actually performed in a home environment, together with an appropriate nursing—activity treatment plan. These profiles of care activities are custom-made and every user (i.e., every physician responsible for discharging a patient from the hospital) is actually able to set up his own profiles. During the formation of these profiles the user can attach to each activity a set of nominal fees. This later is estimated by another already developed software tool (Botsivaly, 2003), and allows for a rational approximation of the effective mean cost for several elementary medical activities, over different medical specialties. Thus, the developed system ignites, when relevant, the corresponding revision of an implicitly associated latent financial record that allows for an approximation of the individual case-cost. Once the modified database for specific homecare activities has been created, the program constructs a custom-made Structured Query

Language (SQL) file, appropriate for Medical and administrative use. (Figure 2) The user can interactively modify any description on this database, and it is possible to add any information for specific instances, in order to adapt the home care profile to emerging new needs. This interactively modified database is further used to create each time a typical CCR that contains all the necessary patient information. The appropriate patient and administrative data, as well as, the relevant medical information, are acquired, either by using an already installed EHR system or even manually, if no EHR is available. Then, the individual CCR is completed, by selecting the needed home care activities, which are automatically inserted in the CCR-section of Care-Plan. Simultaneously the CCR-system latently combines each of them to the predefined “cost.” (Figure 3) However, the system, apart from producing, electronically or in paper-format, the CCR, it also produces a number of additional forms, including advisory and informational notes for the patient himself, or for his relatives, and diagrams of physiologic measurements, such as glucose, blood pressure, and so on that the patient should monitor. The system also provides for forms that will

Figure 2. Homecare activities selection for a specific patient

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Figure 3. XML to HTML transformation of the produced CCR

be filled by the nursing personnel during the carevisits, in order to document their activities. The filled forms, both the ones regarding the nursing activities and interventions, and the ones regarding the monitoring of physiological parameters, are returned to the responsible physician who evaluates them and, depending on his evaluation, can modify the care-plan of the specific patient, in any suitable way. The structure and data of the produced CCR are complying with the ASTM E2369-05 Specification for Continuity of Care Record, while XML is used for the representation of the data. The XML representation is made according to the W3C-XML schema proposed by

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ASTM. The CCR that is produced by the system, is currently automatically transformed to HTML format, using the Extensive Stylesheet Language (XSL), in order to be viewable and printable.

FUTURE TRENDS The previously described employment of some kind of records, as the main instrument for the management of home care, assigns them an additional dimension, which is related to the nature and function of technology. The development of patients’ records exhibits the state-of-the-art

Optimization of Medical Supervision, Management, and Reimbursement of Contemporary Homecare

Biomedical Technology, the dominant—at the time—medical knowledge and practice, and it reflects the existing value system of a given society. The assimilation of new technologies is a complex phenomenon, which is affected by the wider sociocultural, epistemic, and axiological parameters at work, within the specific social context in question. In other words, the Technologies employed presently in the construction of medical records, were developed to satisfy concrete social needs. The availability of these technologies permitted new questions to be asked and new needs to arise, and this procedure will determine the future trends of the structure, the content, and the technological substratum of the medical records of the future.

CONCLUSION The employment of this method enables the formation of a CCR, that is intrinsically, however latent, related to the costs caused and the expected reimbursement. Thus, the updating of the patients’ relevant medical data, ignites, when relevant, the corresponding revision of an implicitly associated financial record, that allows for, both, first, a good approximation of the individual “case” cost, and second, the follow-up of cost evolution of a hospitalbased or freestanding home care group. Thus, the developed system introduces a nondisturbing method for the home care personnel, to combine the collection of the necessary CCR information, and the creation of the associated documentation, with a latent simultaneous acquisition of important financial data. The correlation of these data to the DRG-coded principal diagnosis, allows for the estimation of the home care cost vs. the DRGs code issued at discharge from the hospital. In view of the fact that there is a clearly higher discharge to home care incidence, for specific DRG-coded principal diagnoses, the developed system is now being tested with an EHR system that has been developed by our team under vir-

tual home care conditions, and is being fed with financial data related to home care activities for some of the most frequent cases. The implementation indicates, so far, that the system, whether interfaced to an EHR or not, is stable enough for practical use, and it actually provides a simple, effective, and expandable tool for the formation of both a CCR and a home care plan, offering at the same time a good approximation of the individual case cost and a flexible HTML-format for data representation. The system allows for the improved approximation of the home care cost vs. the DRGs code issued at discharge from the Hospital supporting, thus, a new more accurate DRGs-related home care remuneration modus. Finally, the system constitutes an effective educational tool, supporting personnel training in operational cost management.

REFERENCES ASTM E2369-05. (2005). Standard specification for continuity of care record. Retrieved February 5, 2008, from http://www.astm.org Botsivaly, M. (2006). Software development for the estimation of the mean DRGs related treatment cost. Studies in Health Technology and Informatics, 124, 307–312. Brazil, K., Bolton, C., Ulrichsen, D., & Knott, C. (1998). Substituting homecare for hospitalization: The role of a quick response service for the elderly. Journal of Community Health, 23(1), 29–43. doi:10.1023/A:1018770820728 De Leo, G., et al. (2002). WEB-WAP based telecare. JAMIA Symposium Supplement 2002, 200–205. Gill, T. (2002). A program to prevent functional decline in physically frail elderly persons who live at home. The New England Journal of Medicine, 347(14), 1068–1074. doi:10.1056/ NEJMoa020423

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Lewis, C. (2001). Home diagnostic tests: The ultimate house call? U.S. Food and Drug Administration Consumer Magazine, 35(6). Retrieved February 5, 2008, from http://www.fda.gov/ fdac/601_toc.html Papagounos, G., & Spyropoulos, B. (1999). The multifarious function of medical records: Ethical issues. Methods of Information in Medicine, 38(4), 317–320. Saba, V. K. (2002). Nursing classifications: Home healthcare classification system (HHCC): An overview. Online Journal of Issues in Nursing. Retrieved February 5, 2008, from http://nursingworld.org/ojin/tpc7/tpc7_7.htm Scarcelli, A. J. (2001). Cost-effective approaches to corporate compliance for hospice and home health providers. Home Health Care Management & Practice, 13(4), 176–183. doi:10.1177/108482230101300302 Simonds, A. K. (2003). Home ventilation. The European Respiratory Journal, 22(47), 38–46. doi:10.1183/09031936.03.00029803 Spyropoulos, B. (2006a). Application tools supporting decision-making in the modern hospital. International Journal of Scientific Research, 16, 129–133. Spyropoulos, B. (2006b). Development of a system supporting patient supervision and treatment in contemporary homecare: Status report. Studies in Health Technology and Informatics, 124, 91–96. Spyropoulos, B., & Papagounos, G. (1995). A theoretical approach to artificial intelligence systems in medicine. Artificial Intelligence in Medicine, 7(5), 455–465. doi:10.1016/0933-3657(95)00015-X Wipf, K., & Langner, B. (2006). Policy approaches to chronic disease management. Home Health Care Management & Practice, 18(6), 452–462. doi:10.1177/1084822306290346

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Woolhandler, S., Campbell, T., & Himmelstein, U. D. (2003). Costs of healthcare administration in the united states and canada. The New England Journal of Medicine, 349(8), 768–775. doi:10.1056/NEJMsa022033

KEY TERMS AND DEFINITIONS American Society for Testing and Materials International (ASTM): An international voluntary standards organization established 1898 in the United States by Charles Benjamin Dudley. ASTM develops technical standards for materials, products, systems, and services, and maintains presently more than 12,000 standards that have been incorporated into or are referred to by many federal regulations. Clinical Care Classification (CCC): A categorization system that consists of interrelated terminologies—the CCC of Nursing Diagnoses and Outcomes and the CCC of Nursing Interventions and Actions—with both classified by 21 Care Components, that classify, and track care based on the six steps of the Nursing Process Standards of Care recommended (1998) by the American Nurses Association (ANA): assessment, diagnosis, outcome identification, planning, implementation and evaluation. Clinical Document Architecture (CDA): An XML-based markup standard intended to specify the encoding, structure, and semantics of clinical documents for exchange. The CDA tries to ensure that the content will be human-readable and hence is required to content narrative text, yet still contain structure, and most importantly, allows for the use of codes to represent concepts. Continuity of Care Record (CCR): A health Record standard (ASTM E2369-05) specification that constitutes a patient health summary and contains the most relevant and timely core health information to be sent, usually in electronic form, from one care giver to another. It contains various

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sections such as patient demographics, insurance information, diagnosis and problem list, medications, allergies and care plan, representing a “snapshot” of a patient’s health data that can be useful or possibly lifesaving, if available at the time of clinical encounter. The CCR standard is expressed in the standard data interchange language known as XML and can potentially be created, read and interpreted by any EHR and EMR software applications. A CCR can also be exported in other formats, such as pdf, doc, and so on. Diagnosis Related Groups (DRGs): A system that classifies hospital cases into one of approximately 500–600 groups, which relate types of patients treated, to the hospital resources they consumed. It was first developed by Robert Barclay Fetter and John Devereaux Thompson at Yale University, in the slate 1970s. The DRGs are assigned by a “grouper” program based on ICD diagnoses, procedures, age, sex, and the presence of complications or comorbidities. Electronic Health Record (EHR): A personal medical record in digital format, typically accessed on a computer or over a network. An EHR almost includes information relating to the current and historical health, medical conditions, and medical tests of its subject. In addition, EHRs may contain data about medical referrals, medical treatments, medications and their application, demographic information, and other nonclinical administrative information. Extensible Markup Language (XML): A general-purpose markup language designed to be reasonably human-legible, and therefore, abruptness was not considered essential in its structure. Its primary purpose is to facilitate the sharing of data across different information systems, particularly connected through the Internet, and to allow for diverse software to understand information formatted in this language. Health Level Seven Inc. (HL7): HL7 is a notfor-profit organization, established 1987, involved

in the development of international healthcare standards, accredited by the American National Standards Institute (ANSI). HL7 is currently the selected standard for the interfacing of clinical data for most hospital information systems worldwide. Homecare: Healthcare provided in the patient’s home by healthcare professionals or by family and friends. Homecare aims to enable people to remain at home rather than use institutional-based nursing care. Care workers visit patients in their own home to help them with daily tasks and supervision of treatment. Homecare is generally paid for by private health insurance, by public payers, or by the family’s or patients’ own resources. International Statistical Classification of Diseases and Related Health Problems (ICD): The ICD is published by the World Health Organization (WHO), and provides codes to classify diseases and a wide variety of signs, symptoms, abnormal findings, complaints, social circumstances and external causes of injury or disease. Every health condition can be assigned to a unique category and given a code, up to six characters long. Such categories can include a set of similar diseases. The ICD is used world-wide for morbidity and mortality statistics, reimbursement systems and automated decision support in medicine. This system is designed to promote international comparability in the collection, processing, classification, and presentation of these statistics. The ICD is the core classification of the WHO; it is revised periodically, and is currently in its tenth edition (ICD-10). Medical Classification: The method of transforming descriptions of medical diagnoses and procedures into universal Medical code numbers. These diagnosis and procedure codifications are used by health insurance companies for reimbursement purposes, they support statistical analysis of diseases and therapeutic actions, they are employed in knowledge-bases and medical decision support, and in a variety of other uses.

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, pp. 1032-1040, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global). 1683

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Chapter 6.6

Systems Engineering and Health Informatics:

Context, Content, and Implementation Kalyan Sunder Pasupathy University of Missouri, USA

ABSTRACT Healthcare organizations are struggling to provide safe and high quality care while reducing costs. Abundant data on various aspects of the care delivery process (both clinical and non-clinical) are collected and stored in large databases in different parts of the organization. Informatics, as an area of study with roots in computer science and information science, has grown and evolved to enable collection, storage, retrieval, and analysis of data, and reporting of useful information. Health

informatics (HI) ranges from bioinformatics to public health informatics depending on the level of focus and applications. At the same time, systems engineering (SE), as an interdisciplinary field of engineering, has grown to encompass the design, analysis, and management of complex health systems to improve their quality and performance. HI and SE are complementary in their approach to identification of problems, methodology, and solution procedure for improvement. This combination brings forth implications for industry and education to address pressing issues of today’s health care delivery.

DOI: 10.4018/978-1-60960-561-2.ch606

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Systems Engineering and Health Informatics

INTRODUCTION With advances in clinical sciences and medical technology, people are living longer and there is a greater demand for healthcare services. The Institute of Medicine’s report To Err is Human (Kohn et al., 2000) describes serious concerns in health care owing to undesirable outcomes related to safety and quality issues. These issues have been attributed to breakdowns in processes embedded in the service delivery structure (Institute of Medicine, 2001). Healthcare delivery systems have two major goals – doing things right and doing the right things. These are known as efficiency and effectiveness, respectively, in systems theory. With rising costs, efficiency is an important goal for all systems, including health care and this can be ensured through proper allocation of resources. Effectiveness and obtaining the desired outcomes, on the other hand, is all the more important in health care, considering the dire consequences of errors and process breakdowns that may lead to harm and even death. Yet, healthcare organizations are struggling to provide safe and high quality care, while reducing costs. Healthcare expenditure has been on the rise, for example, the United States spent nearly two trillion dollars in 2005. This amount accounted for 16% of its GDP, a proportion which is higher than any other country in the world (England, 2007; OECD, 2009). Hence, healthcare providers, insurance companies, and health care policymakers are striving to find more cost-effective methods to deliver health care. Providers are increasingly looking at methods that would help them reduce costs without compromising on the quality of care. The Institute of Medicine (IOM) identified a four-level, patient-centered conceptual model as the unifying framework and guiding principle for redesigning and improving the healthcare system, to achieve better performance goals (Reid et al., 2005, p. 20). This IOM report proposes using information technology and systems-engineering

tools to provide safe and high quality care that is efficient, effective, and patient-centered. In today’s healthcare organization, data is collected every day and stored in large databases. Intuitively, when data are abundant and no other sources of expert knowledge exist, one could expect that knowledge could be gathered from the data that are available. Informatics is the science of collecting, storing, retrieving, analyzing, and reporting information acquired from health data (Coiera, 2003; Protti, 1995). Given the abundant information on clinical, financial, human resources, care delivery, quality, patient satisfaction and outcomes, data mining tools are needed for decision-making. These tools extract hidden information from large databases so that organizations are able to identify important patterns, predict future behaviors, and make proactive, knowledge-driven decisions (Medina-Borja & Pasupathy, 2007). Such decisions (both clinical and managerial) will not be effective, unless they are based on true representations of processes that transcend any of the individual areas above, within a health system (e.g. clinical, financial). If no one within the organization has a holistic understanding of the system, finding an expert who will clarify the relationships or finding enough organizational documentation to point in the right direction is a challenging task. To be able to have a holistic representation of the processes within a hospital, an understanding of the broader systems, including the clinical system, hospital organizational system, etc. is necessary. Further, how can the relationships “mined” and the resulting patterns be validated against the real-world behavior of health systems? Several systems engineering tools classified under system-design, system-analysis, and system-control tools can come to the rescue. These are proposed by the Institute of Medicine report in 2005 under the umbrella of systems engineering (Reid et al., 2005). The goal of this chapter is to accomplish the following,

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• • •

Provide an introduction to health informatics Establish the need for systems engineering in the health system Discuss the impact of the integration of health informatics and systems engineering for healthcare organizations.

This chapter is organized into three major sections - context, content, and implementation - followed by consideration of future directions before providing concluding remarks. The context section describes data usage for decision making, introduces health informatics, and describes the need for models. The content section describes systems engineering concepts and tools. And finally, the implementation section describes the use of health informatics and systems engineering for healthcare improvement.

CONTEXT Decision Making Decision making is the process of implementing an action within a system to solve a problem or improve the system. The goal is to accomplish some change. Decision making involves understanding of the system under consideration, understanding facts and/or data from the existing system, and knowledge of the science to determine the change needed (Dinkelbach, 1990). The action can range from a minor intervention to a total redesign of the system. A patient opting to undergo a certain procedure, a physician ordering a specific medication for a patient, the pharmacy manager adding a technician to a shift, administrators adding a new care delivery process, or an insurance firm determining the amount of payment for a certain treatment are a few examples of decisions made. They are similar because they all go through the same process of fact gathering, analysis of existing facts, understanding the domain (e.g. care

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delivery team, pharmacy), and then implementing the action. They are different because they range in terms of the specific context, the level of aggregation within the healthcare sector (see Figure 1), the time horizon, effort (or money) involved in both conducting the analysis and implementing the change. The patient’s decision is at the individual (or patient) level. The physician’s and the pharmacist’s decisions are at the care team level. The administrator’s decision is at the organizational (or hospital) level. Based on the level at which the decision is made, it can be task-oriented, operational, tactical, or strategic in nature. Typically, strategic decisions are at the highest level ranging over long time periods (say, three years or more), involve organizational leaders, transcend various departments, and require maximum effort (time and money). Tactical decisions are one step lower for medium term decisions and consume lesser effort. Operational decisions are further down, followed by task-oriented decisions. And finally, the insurance decision is at the environment (or outside the hospital) level. The people who make the decisions are physicians, nurses, pharmacists, administrators such as the Chief Executive Officer, Chief Operating Officer, Chief Financial Officer, Chief Information Officer, operational managers, insurance analysts, community health experts, etc. The person who makes the decision, the decision maker is posed with certain needs and preferences and at least two options. Considering these, the decision maker makes a choice to satisfy his/her needs. All of the needs, the preferences, and the options available constitute a decision problem. To arrive at a decision, certain analyses need to be done. For this purpose, the real-life decision problem should be represented in a mathematical framework called a decision model (Dinkelbach, 1990; Glaser, 2002).

Data for Decision Making Data are vital pieces of evidence in the decision making process and data are collected at all levels.

Systems Engineering and Health Informatics

Figure 1. Four levels of the healthcare system (Source: Modified from Reid et al. (2005, p. 20))

Data help in better understanding the system being studied. Large quantities of data are collected day after day, at various levels within the hospital – daily staff logs, prescription orders, medical records, patient satisfaction, human resources, billing and financial data – and are stored in various systems. The quantity of data stored by organizations doubles every three years (Lyman & Varian, 2003). For example, prescriptions ordered by the physician are captured in a computerized order entry system; pharmacy orders are stored in an electronic pharmacy system, medication administration to the patient in electronic medical record systems, and others (Lehmann & Shortliffe, 2003). Non-clinical data is captured and stored in other systems, such as for human resources, financial electronic systems for expenses, reimbursement, etc. There is tremendous enthusiasm in recent years to automate medical records and create a complete health information infrastructure (Shortliffe, 2005). How can such information be used to make effective decisions? To understand this better, let us consider the case of a nursing unit manager, and her system – the nursing unit domain. Nursing unit managers supervise a team of nurses and are key decision

makers, and often receive reports on their units from multiple sources. They make decisions primarily at the tactical and operational levels focusing on a time horizon ranging from a few weeks to a few years. To improve operations and thereby improve safety, quality, and value, managers need to be pointed in the right direction, and the information should guide them and provide action items to make interventions and changes. Because of the existence of data sets in separate departments in a de-compartmentalized manner, often reports end up being aggregations or summaries of granular data. This fails to provide the necessary linkage to process items where actual interventions can be made. For instance, patients provide a wealth of data describing their experience during the stay at the hospital through satisfaction questionnaires. These data are rarely coupled with, and analyzed against, processes within the delivery structure that have an impact on perceptions of the patient. Certain data items are highly sensitive. Databases such as prescription orders and medical records may be stored in departments exclusively for security reasons. There are strict legislative regulations such as those of the Health Insurance Portability and Accountability Act (HIPAA) set forth by the Centers for Medicare and Medicaid Services (CMS) available at http://www.cms.hhs.gov/HIPAAGenInfo/ and institutional guidelines for storage, accessibility, and dissemination of such data for the purpose of security. But this does not necessarily preclude analyzing data and providing useful information, while still ensuring confidentiality of individuals and key identifiable data. The analysis of such data can range from a simple statistical analysis to building relationships and identifying hidden patterns. Such advanced tools of “mining” data are referred to as data mining and a related concept of knowledge discovery in databases (KDD) (Fayyad, Piatetsky-Shapiro, & Smyth, 1996). Data mining is performed using several types of models and algorithms such as neural networks and genetic programming. Data

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mining includes two major tasks – knowledge discovery and prediction. The first step is used to gather knowledge out of the data set, and the second step is used to determine what the future might look like, and make predictions based on the results. For example, data mining can be used to discover patterns and identify opportunities for improvement in quality of care and patient satisfaction (Hebbar, Pasupathy & Williamson, 2008). Hospitals send out satisfaction questionnaires to patients to gather their perceptions on the care that was provided. These survey data give the hospital an evaluation of the quality as perceived by the patient, along with their level of satisfaction that is required for reporting purposes by the Centers for Medicare and Medicaid Services. However, more important than the rating is the analysis of the data, where relationships can be built between the process-related data and quality measures, impact on outcomes such as referrals can be understood, and decisions for interventions can be made (Pasupathy & Triantis, 2007a). Even though perceived quality and satisfaction are the result, such as in a rating, mining of the data can give areas within the process where change can be effected.

CONTENT Data mining is one component of a broader field of study called informatics. Informatics is the science of collecting, storing, retrieving, and analyzing data, and presenting the information to make decisions. The term informatika was originally coined by Mikhailov, Chernyl, and Gilyarevskii (1966). Informatics is different from information technology and information management. According to the Information Technology Association of America, information technology deals with the design, development, and maintenance of hardware including computers and networks to provide the underlying infrastructure to support the processing of information. Information

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management deals with the day-to-day operations necessary to process information (Wigand, Picot, & Reichwald, 1997). Informatics is a much broader area of study. Informatics is not restricted to just the computational aspects, but also concerns itself with the study of cognitive and social/ organizational aspects of information analysis and use. This includes enabling an individual’s interaction with information technology, as well as interaction with other individuals who are part of teams and work groups. The data stores are mined, analyzed, patterns discovered, and decisions are made for change to improve quality and performance (Friedman et al., 2004; Hersh 2002; Hersh 2006). Health informatics narrows down the study of information science to applications within the healthcare sector (Hersh, 2008a; 2008b). Specifically, health informatics can range from bioinformatics to public health informatics depending on the level of focus and applications (Coiera, 2003; Friedman et al., 2004). The major sub-specialties in health informatics along with the level of focus and some application areas of study are highlighted in Table 1. To better understand informatics, it is important to understand the data-information-knowledge hierarchy which was originally proposed by Blum (1986) and wisdom was added by Nelson and Joos (1989). Information occurs along the learning path from data to wisdom (see Figure 2). As we move from data to wisdom, both complexity and learning increase, with data being at the lower end in Figure 2. Data are comprised of numbers and symbols without the awareness of any structure. It is the lowest level in the hierarchy and carries no meaning. Information is one step higher and has some meaning attributed to it because of organization, and processing of data by building relationships between data items. Knowledge is still higher and emerges as patterns are discovered in the information that is available. Knowledge is useful in decision making. Further learning provides understanding of systems that leads to people forming mental models or para-

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Table 1. Sub-specialties in health informatics Sub-specialty in Health Informatics

Level of Focus

Application Area

Bioinformatics

Micro-level

Molecular biology, protein sequence analysis, gene expression, etc.

Imaging informatics

Tissue or organ level

Radiology, diagnostic imaging, scanning, cancer treatment, etc.

Consumer informatics

Individual level (patient)

Methods for analyzing patient’s need for information and making it available.

Medical/Clinical informatics

Care team level (physicians, nurses, pharmacists, therapists, patient’s family, etc.)

Patient’s diagnosis, treatment, care delivery, teamwork, etc.

Organizational/Health management informatics

Organizational level (departments, operating units, suppliers, insurance firms, etc.)

Organization’s capacity and capability, performance, operations, process integration, adaptive systems, etc.

Public health informatics

Community level

Public health policy, behavioral risk analysis, emergency response, etc.

digms. Paradigms are frameworks and mindsets that people form based on what they have seen, heard, and felt. It is the sum total of all their life’s experiences. This forms reference points in their minds that they use for comparison purposes when they come across situations. These can also be conceived of as filters that are formed, through which the individual views and analyzes the world.

The whole process of learning is to broaden this mindset. The final step in the hierarchy is principle generation which is formed from paradigms. Principles cannot be proven right or wrong, they just exist. These principles give rise to wisdom. Wisdom is the highest level in the hierarchy and to achieve this level, one has to go through the other levels from bottom-up.

Figure 2. The data to wisdom hierarchy

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Let us look at a simple example in public health (similar examples can be found in other areas). Analyzing the impact of demographic variables on say, the incidence of cancer in a community is important for designing interventions. A composition of 75% African Americans, and 33% cancer incidence rate separately are examples of data. If we were to build a relationship between these two data items to say that African Americans beyond a certain age have a 33% incidence rate of cancer becomes a piece of information. When the analyst is able to discover a pattern, and say that African Americans have a higher incidence rate than the general population with a rate of 25%, then that is knowledge. Several such patterns help develop an understanding of how various demographic variables affect cancer incidence rate, and hence a paradigm of the system can be formed. And finally, these paradigms help to form principles of what kinds of interventions (ways to educate and change behaviors, increase screening, along with others) can be made to improve diagnosis and treatment, and transfer concepts from one application area to another. To be able to make sensible decisions with systems, one ought to have knowledge, as well as a thorough understanding, and move farther from data, and closer to wisdom on the hierarchy. Thus, even though the complexity increases, learning also increases along the way. Often, the decisions need to be evaluated for their impact in the real world. Further, paradigms may inhibit the individual from seeing the big picture. At this juncture, a model can be helpful to compare one’s paradigms with the real world to change and/or better understand the real world system. Hence, models are necessary. A model is an abstraction of the real world, where a replica is created that can be manipulated for scenario analysis, without having to deal with the consequences in the real world. Models can be statistical, optimizationbased, or simulation-based. Models represent a given system in the form of any combination of variables, equations, relationships, etc. Models

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replicate the real world and enable the analyst to study ‘what-if’ scenarios and predict future behavior of the system (Neelamkavil, 1987). Such models have to be validated to ensure that they succinctly and sufficiently capture the real world system. Models should be large and detailed enough to capture necessary complexities in the real world, so that they are useful for understanding and decision making. However, no one model that is developed can be comprehensive enough to capture every aspect of the real world, because the model itself, is being built, and is restricted by assumptions and paradigms (Silvert, 2001). Even if such a comprehensive model can be built, we are restricted by available computing power to be able to simulate and/or analyze such a model. Computing power is growing by the day and more powerful computers are able to handle ever larger models. More discussions on this topic are presented in the implementation section. The modeling process is shown in Figure 3. The process involves building a model based on the real world system. This is done based on the modeler’s understanding of the real world. The modeler gathers information from the real world and processes it forming the paradigm. Based on this paradigm, the modeler then “acts” to build the model. Once the model is built, the information gathered from running the model and additional information from the real world help change the paradigm. This feedback helps the modeler to go back and make changes to (act on) the model. This is repeated until the model sufficiently represents the real world. Then the model is run to analyze various scenarios and based on the results, decision is made to act in the real world.

Systems Engineering and Health Care So far we have discussed the context of health informatics and informatics used for decision making. The modeling described in the latter part of the previous section is a component of an

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Figure 3. The modeling process

area of study called systems engineering. Systems engineering is an interdisciplinary field of engineering concerned with the design, analysis, and management of complex systems (Blanchard & Fabrycky, 2005). Traditional engineering disciplines such as civil, mechanical, electrical, and so forth, are all content-intensive and focus on natural laws, fundamental principles, and specific processes (Kurstedt, 2000). Systems engineering is a different discipline based on the systems process(es) and approaches involved in solving a problem based on “engineering” (transforming or (re)designing) any system. Systems engineering is being referred to with various names, some based on the tools used, and others based on the potential impact on outcomes. Most common references are listed in Table 2 with a prefix followed by a suffix to describe the area of focus and interest.

Table 2. Alternative terms for systems engineering Prefix Process Operations Quality Management Performance Health Systems Healthcare Systems Business Process Operational Performance

Suffix

Management Engineering Improvement Reengineering

Some of the other terms include Management Systems Engineering, and the well-known Industrial Engineering. When the systems engineering focus is on health care, then it is called health systems engineering. Health (or Healthcare) Systems Engineering (HSE) refers to a focus area related to industrial engineering that is applied within a healthcare setting. It is a harmonious and robust blend of the systems approach and the engineering process as applied to the healthcare system. It is a new field of engineering that takes a comprehensive approach to health systems, and is increasingly becoming popular in the healthcare industry as a method for improving quality and safety of care, while bringing down costs. It refers to the process of using the disciplines, practices, and methods of engineering analysis and design, to systematically diagnose and correct problems in processes. HSE professionals, unlike clinical professionals, act or operate behind the scenes. They rarely have contact, if any at all, with a patient. They may not directly impact the outcomes in health care as physicians and nurses do, but work on improving the embedded processes in the delivery of care (Arveson, 1998). Health systems engineering involves identifying the problem areas, prioritizing changes, getting feedback on decisions, and using the data to improve processes. It uses practices that are accepted in other engineering fields, such as measurements,

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testing, feedback, control loops, work breakdown structures, and risk mitigation, and applies them appropriately to a healthcare organization. Each health systems engineering project includes five basic steps, (1) problem identification; (2) data collection; (3) analysis; (4) solution; and (5) design and/or intervention. Along with these steps, it is important that health systems engineers foster an environment of continuous learning and improvement in the organization. As can be seen with the steps, there is an overlap between informatics and systems engineering. The modeling approaches can be used in a wide range of systems in terms of aggregation, from the micro level through community level, as shown in Table 1 (Friedman et al., 2004). Health management engineering is a subset of health systems engineering that focuses on the management aspects within the health system. In recent times, health systems engineering has also been referred to as health management engineering (Belson, 2007). Health systems engineering involves using a set of tools to design, analyze, and control health systems and evaluate performance for improvement. Several such tools have been identified by the Institute of Medicine and the National Academy of Engineering report (Reid et al., 2005, pp. 28, 35, 46). Each of these tools requires additional skills to be able to accomplish the systems engineering process. The various tools are listed in Table 3 along with more specific skills necessary to be able to use the respective tools. These tools could be used in isolation or in conjunction with one another. For instance, productivity measuring and monitoring can be used in conjunction with the balanced scorecard concept to analyze a system (Medina-Borja, Pasupathy, & Triantis, 2007). Here, the balanced scorecard gives a broad framework, including various perspectives (customer, financial, internal business, and innovation, learning and growth) from a systems standpoint. Productivity monitoring can be done using tools such as data envelopment analysis that is based on systems theory and pro-

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duction economics. Another example is the combination of system dynamics modeling with productivity monitoring (Pasupathy & Triantis, 2007b). Simulation is another tool used by management engineers to improve an existing process. Computer-based modeling and simulation are used to practice tasks in life-like circumstances using digital models, with feedback from observers, peers, and video cameras (Noor, 2007). Using simulation, management engineers see whether a particular decision can be implemented by an organization and predict its consequences, given the organization’s structure, resources, and processes. Various alternatives could be experimented with, to see which one would improve efficiency and capacity management of the organization. To avoid duplication of services, management engineers apply dynamic modeling of healthcare delivery and analyze the system as a supply chain (Noor, 2007). They use artificial intelligence-based decision support and risk management systems to help health care organizations make clinical decisions under uncertainties.

IMPLEMENTATION Several of the tools listed in Table 3 are data and computation-intensive. This means that they require significant investments in computing resources. Availability of computing resources has been on the increase. Several healthcare organizations are investing in large information technology systems to house clinical, business operations, and financial data (Curry & Knowles, 2005; Feld & Stoddard, 2004; McDermott, 1999; Pare, 2002). Firms such as 3M, Cerner Corporation, Eclipsys, General Electric Systems, MediTech, are pioneers in designing and developing large-scale systems with multiple modules for specific operations in pharmacy, radiology, surgery, etc. Organizations should also be in a position to have the capability to extract data from multiple sources relating

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Table 3. Systems engineering tools

System Design

System Analysis

System Control

Other Performance and Quality Improvement

Specific Tool

Skills Required

Concurrent engineering (Winner et al., 1988) and quality function deployment (Sullivan 1986, 1988; Hauser & Clausing, 1988; Chaplin et al., 1999)

Structured and detailed analysis of stakeholder requirements, Stakeholder perceptions and benchmarking against competitors, Translation of stakeholder wants into care delivery system concept, Identification of detailed processes within delivery system, Deployment of service delivery

Human-factor engineering, cognitivesystems engineering, computersupported cooperative work and resilience engineering (Cook et al., 1989; Klein & Isaacson, 2003; Klein & Meininger, 2004; Patterson et al., 2004)

Activity analysis, Ethnographic analysis, Focus groups, Meta analysis, Persona, Subjects in tandem, User as analyst, Questionnaire, Think aloud, Prototyping, Wizard of Oz, Usability lab, Observations, Interviews, Failure analysis, Failure-mode effects analysis, Fault tree analysis, Root-cause analysis, Creation of interdisciplinary team, Data collection, Data analysis to determine how and why an event occurred, Identification of clinical and administrative processes to be redesigned

Modeling and Simulation (Queuing methods, Discrete-event simulation) (Murray & Berwick, 2003; Brandeau 2004; Schaefer et al., 2004)

Flowchart or Process map, Data collection, Fitting arrival and service distributions, Model in simulation software, Quantitative analysis for staffing/capacity planning

Enterprise-Management Tools Supply-chain management (Feistritzer & Keck, 2000; Thomas et al., 2000) Game theory (Tsay & Nahmias, 1998) Systems-dynamics models (Sterman, 2000; Lattimer et al., 2004; Taylor & Dangerfield, 2005) Productivity measuring and monitoring (Ness et al., 2003; O’Neill & Dexter, 2004; Ozcan et al., 2004)

Demand forecasting, Inventory tracking, Inventory modeling, Enterprise Resource Planning, Network optimization, Transportation strategy, Procurement and Vendor Relationship Management Microeconomics, Mathematical modeling Systemic thinking, Cause-and-effect formulation, Data collection, Stock-and-flow modeling, Model in simulation software, Analysis for policy making Process mapping, Input-output-outcome relationships, Production economics

Financial Engineering and Risk Analysis Tools Stochastic analysis and Value-at-risk (Jorion 1997) Optimization tools for decision making Distributed decision making and agency theory

Risk prediction and quantification, Credit assessments, Pricing of services, Valuation of combined risks for engaging in multiple markets Stochastic programming, Revenue management and pricing Agent-based modeling

Knowledge Discovery in Databases (Data mining, Predictive modeling and Neural networks) (Eich et al., 1997; Mitchell 1997, McCarthy, 1997; Friman et al., 2002; Ray et al., 2004; Wei et al., 2004)

Data warehousing, Probability and statistics, Data mining

Statistical process control

Control charts

Scheduling (Hershey et al., 1981; O’Keefe, 1985; Mullinax & Lawley, 2002; Green 2004)

Demand forecasting, Setting service standards, Optimization

The Baldrige National Quality Program (NIST 2009)

Organizational profile, Strategic planning, Process management, Measurement, analysis, knowledge management

Six Sigma Method (Harry 1988; Chassin 1998; Batalden et al., 2003; Godfrey et al., 2003)

Process Map, Cause and Effects matrix, Failure Mode and Effects Analysis

Toyota Production System/ Lean Enterprise (Monden 1983; Spear & Bowen 1999)

Waste definition, Kaizen, Value stream mapping, Spaghetti diagramming

Balanced Scorecard (Kaplan & Norton 1992, 1996a, 1996b)

Various perspectives, Cause-effect relationships, Leading and lagging indicator identification

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to care delivery processes and conduct analyses. Computer technology is required for advanced modeling and analysis (Pasupathy et al., 2008). Organizations can develop such capabilities inhouse and implement data mining and systems modeling (Medina-Borja, Pasupathy, & Triantis, 2007; Pasupathy & Medina-Borja, 2008; Pasupathy, Medina-Borja, & Triantis, 2008). Based on the qualifications of systems engineers and health informaticians (Friedman et al., 2004), there is a lot of overlap in their capabilities and competencies, and their responsibilities in the workplace. These professionals who perform such work may be required to serve as in-house consultants. Systems engineers and health informaticians are required to perform projects involving process improvement, staffing analysis, scheduling, etc. Others may provide internal consultation, facilitation, and analytic services throughout a medical center using principles of continuous improvement, systems analysis, and change management. Activities may include productivity monitoring, systems improvement, operational analysis, work process design, information system development and implementation, facility planning, and other process improvement efforts. These professionals can work at various levels within a healthcare setting ranging from entry level through intermediate positions to leadership positions. As an individual at the entry level, those with such training may work as Health Management Engineer, Decision Analyst, Decision Support Analyst, Quality/Performance Improvement Analyst, Project Management Analyst, or Financial Analyst. These typically require less than two years of experience. At the intermediate level, they may work as managers or supervisors overseeing analysts and/or engineers and require two to five years of experience. Those with seven or more years of experience can be recruited for leadership positions such as Director of Management Engineering or Quality Improvement, Vice President of Operations, Chief Operating Officer, Chief Information Officer, or Chief Financial Officer.

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Systems EngineeringHealth Informatics within the Organizational Structure Systems engineering-health informatics cannot be successful without a suitable organizational setting where the culture promotes learning and growth and is conducive for employees to voice their thoughts and concerns. For such professionals to be successful in a hospital/health facility, it is critical that health systems engineers have easy access to important information and high-level executives. According to the Healthcare Information and Management Systems Society (HIMSS), factors that such positions require for effectiveness in the organization include full visibility throughout all functions and levels; authorization to directly observe or work with any/all levels of the organization; access or response to executive-level decision makers; direct reporting lines to executive management and their support; high-level recognition of process improvement activities to drive and facilitate interdisciplinary teams; Chief Executive Officer alignment for support of the organization’s vision; Chief Operating Officer alignment for support of operational optimization; and Chief Information Officer alignment to meet the challenges involved in collecting, utilizing, and maintaining information. As Crane (2007) points out, factors that are imperative for systems engineering-health informatics to succeed in a hospital environment include, (1) backing of the hospital’s top-level management, and (2) cooperation from physicians, clinical staff, and non-clinical staff. Strong backing of the management is necessary for health systems engineers to coordinate activities from one department to another, solve problems, and be effective on a system wide basis. Cooperation of clinical and non-clinical staff is necessary for any intervention to be successfully implemented. While dealing with healthcare data and especially those involving patient information, Institutional/Ethics Review Board and Health

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Insurance Portability and Accountability Act (HIPAA) aspects need to be considered. According to HIMSS, around 20% of healthcare organizations have not implemented the necessary technology and processes to comply with HIPAA, and remain non-compliant due to issues ranging from regulation misinterpretation to lack of employee competence with technology (Fogarty 2006). Johnson and Warkentin (2008) demonstrate the importance of factors both at the organizational and the individual professional levels for privacy and information security. Specifically, the authors show the importance of organizational support and self-efficacy for compliance. Integration of systems engineering with health informatics and successful implementation of improvement interventions cannot happen without strong involvement of the executive level in change management, as shown in Figure 4. This requires a deep and renewed understanding of the healthcare sector, expertise in organizational design, organizational change, and intervention methodology (Buchan 2004; Hempel 2004). As Melville, Kraemer, and Gurbaxani (2004) point out, although it is possible to apply information systems/technology for improved organizational performance with minimal changes, successful

implementation can only happen with significant changes in organizational policies, rules, structure, workplace practices, and culture. In Figure 4, a need is identified by the President/Chief Executive Officer in conjunction with the Chief Medical Officer/Chief Nurse where clinical effectiveness is evaluated, and errors, for instance, are reported. This triggers a plan to improve quality by the Chief Operating Officer. The Chief Human Resources Officer is involved in developing mechanisms to enable behavior change and the Chief Information Officer is responsible for providing information systems support for process change. These mechanisms and support systems are provided to the clinicians and others involved in the delivery of care. The Vice Presidents of the various regions are responsible for sustaining the improvements obtained. This shows an example of a quality improvement initiative involving all senior leaders. The illustration is the application of theory of change to a specific clinical example and can be modified to other quality improvement initiatives. Also, the need does not necessarily arise from the CEOlevel and can start at any position within the organization.

Figure 4. Integration of systems engineering-health informatics within the organizational setting (an example) (Source: Adapted from Deming, 1986 and Langley et al., 1996)

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At the University of Missouri Hospital, systems engineering-health informatics capability and involvement to perform process and quality improvement is coordinated through the Office of Clinical Effectiveness (OCE). The hospital is a 307-bed hospital with 527 active medical staff admitting more than 20,000 patients per year. The Office is led by a director and staffed with physicians, nurses, and health services researchers. OCE promotes and facilitates high-quality clinical operations and works on initiatives throughout the health system. The office is positioned within the executive team in the organization with direct reporting lines to the Chief Executive Officer and access to strategic planners and key decisionmakers. Table 4 shows the working relationships maintained by the Office of Clinical Effectiveness (OCE) with key hospital executive offices. The office and the hospital take advantage of the close working relationship with the university to leverage expertise in systems engineeringhealth informatics through two associations. The first association involves OCE’s close working relationship with the Center for Healthcare Systems Engineering to gain access to engineering experts at the university. OCE charters several process improvement projects (e.g., seven in 2007-2008) for industrial engineering students directed by faculty experts in the Industrial & Manufacturing Systems Engineering Department. These projects vary from the design of improvements in managing insulin orders to analysis of patient transport systems and redesign of hospital-

ity coordinator roles. The other association is with the Department of Health Management & Informatics and other departments within the Schools of Medicine, Nursing, and Health Professions through the Center for Health Care Quality. The hospital also has ties with the University of Missouri Informatics Institute (MUII). The hospital recruits expertise in health systems engineering, for analyzing and improving processes within inpatient pharmacy to reduce medication errors and improve capacity utilization (Silver, Zhang and Pasupathy, 2010; Zhang and Pasupathy, 2009). Some of the other projects include analyzing human-computer interaction and building better decision support systems (Gong et al., 2008; Gong 2009). OCE works closely with other offices and departments to lead and direct quality improvement initiatives. According to the University of Missouri Health Care System’s 2007 Annual Report, in December 2006, an interdisciplinary team was organized to develop detection procedures for impending cardiac arrest or other medical emergencies before hospital patients experienced deterioration that led to a drop of cardiac and respiratory arrest rates by about 33 per cent. Another initiative also reported in the annual report involved combining skills of five physician services, nurses, case management, and physical therapy to develop innovative care models and protocols for hip fracture care to reduce process variability. This reduced the mortality rate, while

Table 4. Working relationships of OCE with key hospital executive officers Executive Office led by

Working relationship

Chief Executive Officer (CEO)

Alignment with vision and mission of the organization

Chief Operating Officer (COO)

Interventions and improvement in operations

Chief Information Officer (CIO)

Collection, storage, retrieval and analysis of clinical process data

Chief Medical Officer (CMO)

Culture change in clinical services and performance improvement of physician practices

Chief Financial Officer (CFO)

Access to budget to fund process improvement projects, programs and initiatives

Chief Human Resources Officer (CHRO)

Ability to involve and facilitate interdisciplinary teams

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also reducing the length of stay by more than 30% and the cost of care by about 40%. Data mining has been used to analyze large groups of data sets on patients, to predict relationships between variables. Mitchell (1997) used data mining to identify groups of symptoms that exhibited high risk of women requiring emergency C-sections. Merck-Medco Managed Care used data mining to discover patterns and identify drugs and treatments that are less expensive without compromising the effectiveness of the treatment (McCarthy, 1997). Medicare databases have been mined to discover risk of cardiac arrest when commonly prescribed antibiotics are used in combination with other drugs that have drug interactions with the antibiotics (Ray et al., 2004).

Systems EngineersHealth Informaticians – Education and Training Health Informaticians (HI) are professionals that develop and deploy systems and methods based on information and communication technologies within the healthcare sector (Covvey et al., 2001; 2002). They require a knowledge base in the health system, computer science, and information systems. Together, systems engineering and health

informatics are complementary. The need for systems engineering and health informatics capability also has implications for curriculum design in programs at both the baccalaureate and graduate degree levels. Health systems engineering tools should be introduced early in the curriculum and students should be required to work on problems and case studies to gain hands-on exercises and experience. Systems engineering enables the integration of informatics with modeling and analysis using the systems methodologies and enable decision making. Students need to use the wealth of data to identify meaningful information for decision-making purposes (Molina & MedinaBorja, 2006; Sterman, 2002; Sweeney & Sterman, 2000). Competency in systems engineering-health informatics will provide the required orientation and enable graduates to perform and succeed in today’s complex healthcare system. Based on the needs in the healthcare sector, a National Science Foundation-funded curriculum design project was conducted and evaluated at the University of Missouri (Columbia, MO). Table 5 describes a curriculum that was designed at the undergraduate level and offered as a specialization in industrial engineering in conjunction with Health Management and Informatics (Wu et al., 2007).

Table 5. Content of an undergraduate curriculum emphasizing systems engineering-health informatics Content

Title of course

Knowledge and/or Skills Developed

Systems concepts

Systems theory

Systems theory, systems perspective, systems structure, systems processes, systems communication, systems hierarchy and control, systems operation.

Health care context

Introduction to healthcare systems, structure and operations

Basic understanding of the structure and functions of the US healthcare system. Emphasis is placed on the various components within the system.

Systems engineering

Methodologies and tools of systems engineering and health informatics

Systems engineering methodologies, tools and techniques; use of data; informatics

Healthcare systems

Healthcare systems analysis and design

Application of analytical methodologies, quantitative tools and techniques including optimization, simulation, data mining, etc. to healthcare

Application of learning

Capstone project

Real-life problems, teamwork, problem situation, holistic and systems-orientation, use of tools and techniques.

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FUTURE RESEARCH DIRECTIONS There has been a major influx of capital by healthcare organizations and funding by government agencies to acquire and research information technology infrastructure. While this direction is necessary for healthcare delivery systems to move forward, it is not sufficient to deliver efficient and effective care. Information technology should be more than just a tool that automates existing processes. It should enable organizational capability and elevate the processes for streamlining and improvement, in present day complex adaptive systems – delivery systems that can adapt to variations and day-to-day complexities and evolve on a continuous basis. Organizational learning will be embedded in the system and not in individual experts or consultants. Systems engineering concepts can make the link in terms of enabling the organization by the engineering approach. The National Science Foundation and the National Institutes of Health in the United States are in the forefront with specific programs targeted towards funding research along these lines. The National Science Foundation has a Service Enterprise Systems program that funds research in the optimization of healthcare delivery systems. The National Institutes of Health, through the Office of Behavioral and Social Sciences Research, funds projects that use systems science, including system dynamics modeling, agent-based modeling, and network modeling, for health care problems. These research endeavors will pave the way for an increased focus on systems engineering-health informatics. Through the American Recovery and Reinvestment Act of 2009, the National Institutes of Health has designated at least 200 million USD for a new initiative called the Challenge Grants in Health and Science Research. A major component has been designated toward informatics-related research for processing healthcare data in various realms such as clinical, public health, biological, genetic, etc. Some examples under the cyber-infrastructure

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research potential include building technologies to support data coordination and computational thinking; analytic approaches to obtain large scale observational data and actionable information from electronic health records; post-marketing surveillance; and decision support for complex clinical decisions. Health systems informatics is emerging as an area of study that brings together the strength of health informatics and systems engineering. It combines the power of informatics to build patterns and systems engineering to model, validate, simulate, and analyze the processes within the healthcare system, be it at the gene or protein level or at the public health or community level, and anywhere in-between.

CONCLUSION Systems engineering or health informatics cannot sufficiently improve quality of healthcare delivery in isolation. Together the power that they can bring to care delivery and knowledge management can go far to transform the existing organization. As Reid et al. (2005) point out, the lack of adequate use of systems engineering and informatics in the healthcare sector is a challenge and an opportunity for improvement. Organizations should focus on information technology innovation that goes beyond supporting day-to-day procedural operations with a short-term focus, to enabling and elevating the organization as a whole onto a path of continuous learning and growth. Such a transformation cannot be done individually within functional areas, be it operations, or clinical or information technology. Systems engineeringhealth informatics should acquire an in-depth understanding of the clinical and managerial aspects of the organization. Systems engineering-health informatics should also be involved in, for instance, clinical and inter-professional efforts to foster and grow collaboration and teamwork. Such capability can

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enable health professionals to breakdown process barriers and organizational silos to see the broader picture of the process, the entire breadth of the service encounter between the patient and various clinicians. This starts to create organizational learning and accumulates knowledge within the organization (Khatri, 2006a; Hansen & Thompson, 2002; Pearlson, 2001). Knowledge thus acquired cannot be easily captured and stored, to be transferred over time. In the past, experts experienced the organization in different capacities, and gathered knowledge over time. We are living in changing times, when there is greater specialization and functional separation, and knowledge does not exist in one person or place within the organization. Data can be stored easily in the form of databases, and information in the form of reports. But, it is hard to capture and store knowledge, since knowledge tends to exist in a tacit form. Here is where systems engineering and informatics can step up to the challenge. Traditional information technology systems end up just supporting existing operations and automating manual data collection. Information technology projects and implementation initiatives should be better coordinated and well aligned to go above and beyond strategic initiatives on an organizational scorecard. Systems should be developed to not only collect and store information, but also to place a greater emphasis on analysis and synthesis. These systems should challenge existing organizational mindsets and paradigms. Systems engineering modeling tools and techniques, such as system dynamics and agent-based modeling, can be used to model the organization’s underlying structure. These models demonstrate the behavior of the organization as a system. The fundamental principle is that the structure of the system predicts behavior. Thus, systems engineering and informatics together, exhibit tremendous opportunity for extracting information from the wealth of data that are stored within health facilities and the health system. This information thus generated, can be used to test if

the existing paradigms modeled hold, or inform the organization for a paradigm shift, thus creating a knowledge-intensive learning organization.

REFERENCES Ackoff, R. L. (1989). From data to wisdom. Journal of Applied Statistics, 16, 3–9. Arveson, P. (1998). The emerging field of management engineering. Retrieved March 31, 2008, from http://www.balancedscorecard.org/ManagementEngineering/tabid/134/Default.aspx Batalden, P. B., Nelson, E. C., Edwards, W. H., Godfrey, M. M., & Mohr, J. J. (2003). Microsystems in health care, Part 9. Developing small clinical units to attain peak performance. Joint Commission Journal on Quality and Safety, 29(11), 575–585. Belson, D. (2007). Improving efficiency, management engineering comes to the safety net. Retrieved March 30, 2008, from http://www.chcf.org/documents/hospitals/ManagementEngineering.pdf Blanchard, B. S., & Fabrycky, W. J. (2005). Systems engineering and analysis. Upper Saddle River, NJ: Prentice Hall. Blum, B. J. (1986). Clinical Information Systems. New York: Springer-Verlag. Brandeau, M. L. (2004). Allocating resources to control infectious diseases. In Brandeau, M. L., Sainfort, F., & Pierskalla, W. P. (Eds.), Operations research and health care, A handbook of methods and applications (pp. 443–464). Boston: Kluwer Academic Publishers. Buchan, J. (2004). What difference does (“good”) HRM make? Human Resources for Health, 2, 1–7. doi:10.1186/1478-4491-2-6

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This work was previously published in Healthcare and the Effect of Technology: Developments, Challenges and Advancements, edited by Stéfane M. Kabene, pp. 123-144, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 6.7

Critical Factors for the Creation of Learning Healthcare Organizations Nilmini Wickramasinghe RMIT University, Melbourne, Australia

ABSTRACT The need to transform the U.S. healthcare system became clear during the aftermath of Hurricane Katrina. Katrina was not an unexpected disaster nor was it an exceptionally large event. And yet in the wake of Katrina the loss of life was tragic and emergency health care following the storm was severely hampered by the lack of paper health records that had been washed away or ruined. What is required is a transformation of the current health system to an intelligent health DOI: 10.4018/978-1-60960-561-2.ch607

system that maximizes technology and utilizes valuable knowledge assets. To effect such a change healthcare organizations must become learning organizations. The objective of this chapter is to provide a link between principles of organizational learning and knowledge management in order to build the learning healthcare organization. The major thrust of this approach is that the human side of the organization must lead knowledge management technology and not the other way around. The chapter distinguishes between organizational learning as a structure laying the foundation for learning, and the learning organization as a process for maintaining and perpetuating continuous

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improvement in the organization supported by incorporating a process-centric view of knowledge management (KM) realized through the establishment of a KM infrastructure. Moreover, it emphasizes that since health care is a knowledge intensive industry knowledge management is an integral component in building the learning healthcare organization.

INTRODUCTION In his key note address at the 2005 Medical Innovation Summit at the Cleveland Clinic, Newt Gingrich noted that it is imperative to transform healthcare to meet 21st century standards. He emphasized how Hurricane Katrina illustrated most vividly the problems with the current paperbased bureaucratic health system and sent a clear message that an intelligent healthcare system that is value-driven, knowledge-intense, and electronically based is required. The question then becomes how do we go about realizing such a system? To understand this we must first understand the key drivers of demand for health care and for changes in health services delivery. These include, but are not limited to: demographic challenges, i.e., an ageing population that develops more complex health problems; technology challenges, i.e., trying to incorporate the latest technology advances to keep the population healthier; and financial challenges, i.e., trying to curb escalating healthcare delivery costs. In response to these challenges the health system is focusing on enhancing access, quality, and value through the incorporation of technology solutions. However, just simply incorporating technology is insufficient to effect the needed transformation of an intelligent, flexible, and appropriate health system; rather simultaneous and parallel layers of health transformation at the individual and institutional levels must also be considered. In the current management environment, knowledge is recognized as the driver of pro-

ductivity and economic growth, leading to a new focus on the role of data, information, technology, and continuous improvement in the enhancement of economic performance. A key raw material for all organizations in the knowledge economy is data and this resource can be further refined into information and ultimately knowledge, the source of all sustainable competitive advantage (Davenport & Grover, 2001; Von Lubitz and Wickramasinghe, 2006; Wickramasinghe, 2003, Wickramasinghe, 2005, Wickramasinghe & Lichtenstein, 2005). Generally, organizations have been slow to maximize the potential of this raw asset, while healthcare organizations have been particularly deficient. In the case of healthcare organizations, these data assets are generated during care processes and are used in part to develop new treatment models and more efficient administrative processes among providers, insurers, payers, and patients (Wickramasinghe & Schaffer, 2005). Given the significant volumes of heterogeneous data that are generated during care and the considerable impact that these data can have on treatment outcomes, this current state of incomplete utilization of knowledge assets is unacceptable. Hence a useful starting place for transforming the current healthcare system into an intelligent healthcare system is to transform healthcare organizations into learning healthcare organizations that actively manage and thereby maximize their knowledge resources. Creating such a learning healthcare organization requires the integration of organizational learning techniques and a process centric perspective to knowledge management.

BACKGROUND: KNOWLEDGE MANAGEMENT AND ORGANIZATIONAL LEARNING A sound knowledge management (KM) infrastructure is a critical consideration for organizations in any industry as they try to wrestle with current challenges to increase efficiency and efficacy of

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their core business processes, while simultaneously incorporating continuous innovation (Alavi & Leidner, 2001; Markus, 2001; Sharma, Gupta, & Wickramasinghe, 2005). However, the connection between incorporating KM tools, techniques, and strategies, and thereby building a strong KM infrastructure, and simultaneously evolving into a learning organization is not well discussed in the literature and even more poorly achieved in practice (Alavi & Leidner, 2001; Huber, 1991; Markus, 2001; Sharma et al., 2005; von Lubitz & Wickramasinghe, 2006). By recognizing the direct connection between organizational learning and knowledge management it is not only possible to develop a successful KM initiative, but more importantly make the transition to a learning organization. Conversely, by ignoring this integral connection it is highly probable that the KM initiative will not realize its anticipated goals nor be truly successful (Sharma et al., 2005; von Lubitz & Wickramasinghe, 2006). Figure 1 presents an integrative framework that serves to underscore the connection between KM, organization learning, and a learning organization. These will be discussed in turn so that each component is understood in isolation. What

is particularly important to note from this figure is the distinction between organizational learning and the learning organization. The first constitutes the culture and environment which is greatly affected and shaped by the actions and interactions of teams and individuals with each other and with various tools and technology that reside within the organization. A learning organization in contrast, refers to the processes that make the organization a continuous learning one that adapts and builds on its extant knowledge base over time. Further, it is useful to note that KM both impacts and is impacted by organizational learning. Finally, the feedback loop ensures that the environment and culture of organizational learning is updated and modified over time so it will continue to lead and support the learning organization.

LEARNING ORGANIZATIONS Learning is defined as acquiring new knowledge and enhancing existing knowledge (Wickramasinghe & von Lubitz, 2006). A learning organization is an organization that has an enhanced capacity to learn, adapt, and change (Levine, 2001). Learning

Figure 1. The fundamental elements for building a learning organization

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takes place at two levels - individual and organizational. While organizations do not have brains, they do have cognitive systems and memories (Popper & Lipshitz, 2000) made up chiefly from teams of knowledge workers. As individuals develop their personalities, personal habits, and beliefs over time, organizations develop their views and ideologies (Dodge, 1991). Learning at both individual and organizational levels involves the transformation of data (un-interpreted information) into knowledge (interpreted and contextualized information). Individual learning and organizational learning are similar except that organizational learning involves an additional phase, dissemination, i.e., the transmission of information and knowledge among different persons and organizational units (Popper & Lipshitz, 2000). This transformation or dissemination and use/re-use is a critical aspect of KM and thus KM impacts the process of organizational learning while it is simultaneously impacted by organizational learning (Wickramasinghe & von Lubitz, 2006), as depicted in Figure 1. A learning organization consists of knowledge workers who are continuously enhancing their capacity to learn in the corporate culture. It is where learning processes are analyzed, monitored, developed, and aligned with the organization’s goals (Kapp, 1999). Most companies underestimate the importance of intangible assets such as knowledge, creativity, ideas, and relationships. Yet not only do these assets account for more value in the current knowledge economy than the tangibles, but they are also a competitive necessity for the achievement of an intelligent enterprise. In 1990, Peter M. Senge introduced the concept of the “learning organization” to the business world in his landmark book The Fifth Discipline: The Art and Practice of the Learning Organization. A learning organization is a complex interrelationship of systems composed of people, technology, practices, and tools designed so that new information is embraced (Simon, 1999). Learning organizations are organizations

that embed institutionalized learning mechanisms into a learning culture (Popper & Lipshitz, 2000). Integral to learning organizations is the ability to maximise their human capital (people power both individual and team based) and structural capital (databases, patents, intellectual property, and related items). In the years to come, the organization that becomes a learning organization will hold an advantage over its competitors because of the ability to learn faster. For health care, without the development of such learning organizations it will not be possible to make the needed transformations to an intelligent healthcare system. Learning organizations are generally described as those that continuously acquire, process, and disseminate knowledge about markets, products, technologies, and business processes (Nevis, DiBella, & Gould, 1997). This knowledge is often based on experience, experimentation, and information provided by customers, suppliers, competitors, and other sources (Ellinger, Watkins, & Bostrom, 1999; Senge, 1990, 1994). As we move into the 21st century, the need for rapid access to relevant knowledge has never been greater. The business world is becoming increasingly competitive, and the demand for innovative products and services is very high. Organizations are becoming more knowledge-intensive in order to learn from past experiences and from others to reshape themselves and to change in order to survive and prosper. Organizations need to utilize knowledge across processes and functions, to become knowledge-driven organizations or learning organizations (Nevis et al., 1997).

ORGANIZATIONAL LEARNING VERSUS A LEARNING ORGANIZATION Organizational Learning and Learning Organization are related terms where the former refers to the process while the later is concerned with

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the structure. It is useful to define organizational learning as the ability of an organization to gain insight and understanding from experience through experimentation, observation, analysis, and a willingness to examine both successes and failures (Brown & Duiguid, 1991). Central to organizational learning is the incorporation of knowledge management.

KEY BENEFITS OF LEARNING Despite all of the advances in technology to help make work easier and more effective (e.g., information technology), it is the people within the organization that make a real difference. Hence it is important that organizational learning efforts are centered first and foremost on enhancing the effectiveness of their knowledge workers. If any organization wants to be successful, it must depend on its workforce who possesses the skills (Kapp, 1999). Many benefits can be gained by shifting to a learning organization – one that is focused on continuous training or learning among their employees. A well-trained workforce will improve performance throughout the organization that will then lead to an improvement in its competitive position. Also, training helps to support morale within the organization. All of these things create a positive atmosphere that is present throughout the organization, both internally and externally, making the organization attractive to prospective employees (Dodge, 1991; Kapp, 1999).

TYPES OF LEARNING The learning organization is an organization that facilitates the learning of all its members and continuously transforms itself. Learning is the key competency required by any organization that wants to survive and thrive in the new knowledge economy. Market champions keep

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learning how to do things better, and keep spreading that knowledge throughout their organization. Learning provides the catalyst and the intellectual resource to create a sustainable competitive advantage (Croasdell, 2001). There are two key types of learning: (1) incremental: learning that is characterized by simple, routine problem solving and that requires no fundamental change to your thinking or system, and (2) radical: breakthrough learning that directly challenges the prevailing mental model on which the system is built. Learning can be further classified as adaptive and generative learning (Wickramasinghe & von Lubitz, 2006). While the chapter focuses on these two types of learning it is important to note that learning is not necessarily a binary construct and as well as individual and organizational learning there is also group learning. It may even be useful to think about learning as a continuum across these areas with incremental learning at one end and radical learning at the other (Crossan, Lane & White, 1999); however this is beyond the scope of this chapter.

ADAPTIVE LEARNING AND GENERATIVE LEARNING The current view of organizations is based on adaptive learning, which is about coping. Senge (1990) notes that increasing adaptiveness is only the first stage; companies need to focus on generative learning or “double-loop learning” (Argyris, 1982). Generative learning emphasizes continuous experimentation and feedback in an ongoing examination of the very way organizations go about defining and solving problems. In Senge’s view (1990), generative learning is about creating - it requires “systemic thinking,” “shared vision,” “personal mastery,” “team learning,” and “creative tension”. In contrast, adaptive learning or single-loop learning focuses on solving problems in the present without examining the appropriateness of cur-

Critical Factors for the Creation of Learning Healthcare Organizations

rent learning behaviors. Adaptive organizations focus on incremental improvements, often based upon the past track record of success. Essentially, they don’t question the fundamental assumptions underlying the existing ways of doing work. The essential difference is between being adaptive and having adaptability. A learning organization distinguishes itself, in particular, by a culture that encourages generative or double-loop learning in addition to possessing receptivity to adaptive or incremental improvement (Argyris, 1982). Generative, or double loop, learning is thinking that challenges the dominant logic or assumptions that guide decision making within an organization from the lowest level of operation to the CEO’s office. Therefore, it is incremental and transformational. Generative learning contrasts with adaptive learning, adaptive learning seeks improvements within the mental constraints of the prevailing assumptions about how an organization currently does or should conduct business (Morecroft & Sterman, 1994). Generative learning, unlike adaptive learning, requires new ways of looking at the world. Generative learning requires seeing the systems that control events. When we fail to grasp the systemic source of problems, we are left to “push on” symptoms rather than eliminate underlying causes. Without systemic thinking, the best we can ever do is adaptive learning. The secret of the learning organization is to find the leadership, institutional arrangements, and cultural elements that result in generative learning as a continuous process.

IMPORTANCE OF KNOWLEDGE MANAGEMENT (KM) FOR LEARNING ORGANIZATIONS In this century of creativity and ideas, the most valuable resources available to any organization are human skills, expertise, and relationships. Knowledge management (KM) is about capital-

izing on these precious assets (Duffy, 2001). Intangible assets include human capital (tacit knowledge and competencies), structural capital (intellectual property, methods and policies), social capital (relationships), and organizational capital (customer relationships and agreements). Modern organizations are transformed by a shift from physical to mental work. The management literature suggests that successful business organizations are those that are flexible and are particularly effective in introducing and adapting to change, i.e., Senge’s learning organizations. Learning organizations are capable of more than simply reacting to change. The effective learning organization must be able to generate and embrace change and quickly adapt to unanticipated change. They are organizations involved throughout in processes of continuous improvement and regeneration of knowledge. They are organizations that are continuously involved in information acquisition, critical review of basic assumptions, focused dialogue, and intra organizational information and idea sharing and creating repository of knowledge (Sharma et al., 2005). To remain competitive, organizations must efficiently and effectively create, locate, capture, and share their organization’s knowledge and expertise. KM infrastructure would be an appropriate technological infrastructure to support a learning organization framework. KM infrastructure (tools and technologies) provides the platform upon which learning can be built. KM infrastructure includes repositories for unstructured data (i.e., document and content management); structured data (i.e., data warehousing, generation, and management); and groupware that supports the collaboration needed for knowledge sharing. It also includes tools such as e-mail or other forms of interpersonal communication required for the efficient, time-, and location-independent exchange of information (Davenport, Jaevenpaa & Beers, 1996; Davenport & Prusak, 1998; Duffy, 2001).

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THE KNOWLEDGE ECONOMY AND KNOWLEDGE MANAGEMENT In general, economists are agreed that the world has experienced three distinct ages – the Agrarian Age, the Industrial Age, and now the Information Age (Persaud, 2001; Woodall, 2000). The hallmark of the Information Age is the rapid adoption and diffusion of ICT (information and communication technologies) that has had a dramatic effect on the way business is conducted, as well as on people’s life styles. An important consequence of globalization and rapid technological change has been the generation of vast amounts of raw data and information, and the concomitant growth of the capabilities to process them into pertinent information and knowledge applicable to the solutions of business problems. Knowledge has become a major organizational tool in gaining and sustaining competitive advantage. Traditionally, economists have emphasized land and the associated natural resources, labor, and capital as the essential primary ingredients for the economic enterprise. However, in the Information Age, knowledge is considered as important as the three original prerequisites. Hence, the new term “knowledge economy” has emerged and managing knowledge has become one of the primary skills that organizations need to acquire in order to survive and prosper.

KNOWLEDGE There are many plausible definitions of knowledge. For the purposes of this chapter the definition of knowledge given by Davenport and Prusak (1998, p.5) will be used because it is not only broad and thus serves to capture the breadth of the construct, but also, and perhaps more importantly for this discussion, it serves to underscore that especially in an organizational context, knowledge is not a simple homogeneous construct but a compound construct.

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Knowledge is a fluid mix of framed experiences, values, contextual information, and expert insights that provides a framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of knowers. In organizations, it is often embedded not only in documents or repositories but also in organizational routines, processes, practices and norms Davenport and Prusak (ibid).

Types of Knowledge In trying to understand the knowledge construct it is necessary first to recognize the binary nature of knowledge; namely its objective and subjective components. Knowledge can exist as an object, in essentially two forms; explicit or factual knowledge and tacit knowledge or “know how” (Haynes, 1999, 2000; Polyani, 1958, 1966; Wickramasinghe & Mills, 2001). It is well established that while both types of knowledge are important, tacit knowledge is more difficult to identify and thus manage (Nonaka, 1994; Nonaka &Nishiguchi, 2001). Of equal importance, though perhaps less well defined, knowledge also has a subjective component and can be viewed as an ongoing phenomenon, being shaped by social practices of communities (Boland & Tenkasi, 1995). The objective elements of knowledge can be thought of as primarily having an impact on process, while the subjective elements typically impact innovation. Both effective and efficient processes as well as the functions of supporting and fostering innovation are key concerns of knowledge management. Thus, we have an interesting duality in knowledge management that some have called a contradiction (Schultz, 1998) and others describe as the loose-tight nature of knowledge management (Malhotra, 2000). The loose-tight nature of knowledge management comes into being because of the need to recognize and draw upon some distinct philosophical perspectives; namely, the Lockean / Leibnitzian stream and the Hegelian /Kantian stream. Models

Critical Factors for the Creation of Learning Healthcare Organizations

of convergence and compliance representing the tight side are grounded in a Lockean / Leibnitzian tradition (Malhotra, 2000). These models are essential to provide the information processing aspects of knowledge management, most notably by enabling efficiencies of scale and scope and thus supporting the objective view of knowledge management. In contrast, the loose side provides agility and flexibility in the tradition of a Hegelian / Kantian perspective. Such models recognize the importance of divergence of meaning which is essential to support the “sense-making”, subjective view of knowledge management.

underlying dualities and taking a singular focus such as only a Lockean inquiring system as the lens to view knowledge creation.

The Knowledge Spiral

Process-Oriented Knowledge Generation

Knowledge is not static; rather it changes and evolves during the life of an organization. What is more, it is possible to change the form of knowledge; i.e., turn tacit knowledge into explicit and explicit knowledge into tacit, or to turn the subjective form of knowledge into the objective form of knowledge (Wickramasinghe & Mills, 2001). This process of changing the form of knowledge is known as the knowledge spiral (Nonaka, 1994; Nonaka & Nishiguchi, 2001). Integral to this changing of knowledge through the knowledge spiral is that new knowledge is created (ibid) and this can bring many benefits to organizations. In the case of transferring tacit knowledge to explicit knowledge for example, an organization is able to capture the expertise of particular individuals; hence, this adds not only to the organizational memory, but also enables single loop and double loop organizational learning to take place (Huber, 1984). Implicit in this is the underlying dualities and a dynamic equilibrium or “right mix” between dualities that is determined by context. In the process of creating “valid” knowledge, innovating organizations are in fact enacting these transformations and thus “experiencing” these dualities. Moreover, it is unconscious and to a great extent much of the knowledge that potentially could be acquired is never captured by ignoring the

KNOWLEDGE CREATION The processes of creating and capturing knowledge, irrespective of the specific philosophical orientation (i.e. Lockean/Leibnitzian versus Hegelian/Kantian), has been approached from two major perspectives; namely a people-oriented perspective and a technology-oriented perspective.

The connection between the process of organizational learning and knowledge management is best seen when focusing on one of the key aspects of KM; namely the generation and acquisition of knowledge. Within knowledge management, the two predominant approaches to knowledge generation are people-centric and technology-centric (Wickramasinghe, 2005). A people oriented perspective draws from the work of Nonaka as well as Blacker and Spender (Newell, et al, 2002; Nonaka, 1994; Nonaka & Nishiguchi, 2001; Schultze & Leidner, 2002). Essential to this perspective of knowledge creation is that knowledge is created by people and that new knowledge, or increasing of the extant knowledge base, occurs as a result of human cognitive activities and the effecting of specific knowledge transformations (ibid). A technology driven perspective to knowledge creation is centred around the computerized technique of data mining and the many mathematical and statistical methods available to transform data into information and then meaningful knowledge (Adriaans & Zantinge, 1996; Award & Ghaziri 2004; Becerra-Fernandez &Sabherwal 2001; Bendoly, 2003; Cabena, Hadjinian, Stadler, Verhees & Zanasi, 1998; Choi & Lee 2003; Chung &Gray,

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1999; Fayyad, Piatetsky-Shapiro & Smyth, 1996; Holsapple & Joshi 2002). In contrast to both of these approaches, a process-centric approach to knowledge creation not only combines the essentials of both the people-centric and technology-centric perspectives but also emphasises the dynamic and on going nature of the process. Process centred knowledge generation is grounded in the pioneering work of Boyd and his OODA Loop (Observe, Orient, Determine, Act), a conceptual framework that maps out the critical process required to support rapid decision making and extraction of critical and germane knowledge (Boyd, 1987, 2002; von Lubitz &Wickramasinghe, 2006) as is depicted in Figure 2. Multispectral data and information are collected by all organizations on a daily basis. By utilizing tools and techniques such as data mining it is possible to transform these raw data assets into germane knowledge. This also requires a clear delineation of the specific context. Nongermane information and knowledge should be stored in data and/or knowledge bases for use on

a subsequent occasion. The germane knowledge should then be distilled and used to facilitate superior decision making (i.e., the Action stage). As learning in the organization is more prevalent cognitive shortcuts can be taken but initially it is imperative that critical and thorough analysis is conducted.

CREATING LEARNING HEALTHCARE ORGANIZATIONS As depicted in Figure 1, integral to creating a learning organization is the need to foster KM technologies and enable knowledge workers. The process-centric perspective of knowledge management serves to underscore theoretically how important it is to foster organizational learning through the incorporation of KM tools and technologies, as well as fostering human expertise so that superior decision making will ensue. What remains to be discussed is how healthcare organizations should effect such a transformation. Critical to any successful KM initiative is

Figure 2. Process perspective of knowledge management

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Table 1. Elements in the KMI Model (adapted from Wickramasinghe, 2006). Element of the Knowledge Management Infrastructure

Description The key to competitive advantage and improving customer satisfaction lies in the ability of organizations to form learning alliances; these being strategic partnerships based on a business environment that encourages mutual (and reflective) learning between partners (Holt, Love & Li, 2000). Organizations can utilize their strategy framework to identify partners, and collaborators for enhancing their value chain. For healthcare organizations this means developing means so that cross functional teams can easily communicate and collaborate and interactions between various actors in the web of health care can be enhanced.

Infrastructure for Collaboration

Organizational memory is concerned with the storing and subsequent accessing and replenishing of an organization’s “know-how” that is recorded in documents or in its people (Maier & Lehner, 2000). However, a key component of knowledge management not addressed in the construct of organizational memory is the subjective aspect (Wickramasinghe, 2003). Organizational memory keeps a record of knowledge resources and locations. Recorded information, whether in human-readable or electronic form or in the memories of staff, is an important embodiment of an organization’s knowledge and intellectual capital. Thus, strong organizational memory systems ensure the access of information or knowledge throughout the company to everyone at any time (Croasdell, 2001). For healthcare organizations this is particularly significant given the trend towards evidence-based medicine.

Organizational Memory

This deals with the participation and willingness of people. Today, organizations have to attract and motivate the best people; reward, recognize, train, educate, and improve them (Ellinger et al., 1999), so that the highly skilled and more independent workers can exploit technologies to create knowledge in learning organizations (Thorne & Smith, 2000). The human asset infrastructure then, helps to identify and utilize the special skills of people who can create greater business value if they and their inherent skills and experiences are managed to make explicit use of their knowledge. For healthcare organizations this requires maximizing and supporting the capabilities and decision making skills of knowledge workers both at the individual level and in cross-functional teams so that superior healthcare outcomes can result.

Human Asset Infrastructure

This element is concerned with the dissemination of knowledge and information. Unless there is a strong communication infrastructure in place, people are not able to communicate effectively and thus are unable to effectively transfer knowledge. An appropriate communications infrastructure includes, but is not limited to, the Internet and intranets for creating the knowledge transfer network, as well as discussion rooms, bulletin boards for meetings and for displaying information. For healthcare organizations this means ensuring that the latest treatment protocols, techniques, and medications are made known to those who are making treatment decisions, i.e., the healthcare providers.

Knowledge Transfer Network

In an intelligent enterprise, various information systems are integrated with knowledge-gathering and analyzing tools for data analysis and dynamic end-user querying of a variety of enterprise data sources (Hammond, 2001). Business intelligence infrastructures have customers, suppliers and other partners embedded into a single integrated system. Customers will view their own purchasing habits, and suppliers will see the demand pattern that may help them to offer volume discounts, etc. This information can help all customers, suppliers, and enterprises to analyze data and provide them with the competitive advantage. The intelligence of a company is not only available to internal users but can even be leveraged by selling it to others such as consumers who may be interested in it. For healthcare organizations this means incorporating various data mining techniques so that the volumes of heterogeneous data that reside in healthcare processes and within various databases can be appropriately analyzed so that superior access, quality, and value may ensue.

Business Intelligence Infrastructure

the development of the KM infrastructure. The key components of the KM infrastructure are summarized and presented in Table 1. To illustrate the points presented in Table 1 the following case vignette is presented. In 2007 it was decided to address inefficiencies of patient flow and treatment in the urology department at

Northwestern Memorial Hospital in Chicago. This was addressed through the design, development, and implementation of an electronic patient previsit system with multi-language facilities, most especially English-Spanish / Spanish-English real-time translation. During the design and development stages of this project all components

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of the KMI model were addressed in turn: (1) infrastructure for collaboration – this was to some extent already in place with the medical team, but it was extended and enhanced to support collaboration among the medical professionals, laboratory, radiology, IT specialists, billing and patients; (2) organizational memory – various aspects of treatment protocols were stored and analyzed to ensure that lessons learnt could be developed to continually build on the extant knowledge base; (3) human asset infrastructure – as a teaching hospital with a commitment to research and keeping up-to-date with technology initiatives and the delivery of superior health care to its patients, all actors were very committed to effecting a successful change; (4) knowledge transfer network – since the project knowledge is being transferred effectively and efficiently within the urology department and the next steps are to expand this throughout the hospital and health system; (5) business intelligence infrastructure – at the start of this project no intelligence tools were in place. Now tools are being embraced to help and support preventative medicine, for example, by identifying high risk patients for prostate cancer. The process is complex but the team is committed to moving forward. Overall, the system is a first for the U.S. and the project won an award from the Illinois Institute of Commerce. It is still early and results should be reviewed in about two years when the system has been operational for a significant period of time. To date physicians and patients appear to be pleased, and the clinical results have been positive.

DISCUSSION If a learning healthcare organization had been in place at the time of Katrina the loss of life, injury, and devastation would have been minimized, as well as the level of chaos and panic. Germane knowledge regarding how to treat people would

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have been available or easily extracted electronically from relevant information and data available to healthcare professionals. Emergency service and first responders would have been better able to advise people what to do effectively and efficiently. If we compare the debacle associated with Katrina and the efforts made after hurricane Rita, even without the establishment of a formal learning healthcare organization, we can see evidence of ad hoc learning at the individual, group, and organizational levels that directly contributed to a faster and more efficient handling of the crisis.

FUTURE RESEARCH DIRECTIONS Healthcare delivery, especially in the U.S., requires significant redesign. We are seeing more and more investment in various types of ICT including PHRs (personal health record systems), and EHRs (electronic health record systems). However such investments in technology will only exacerbate existing problems of inefficiencies and increasing costs if a solid KM infrastructure that facilitates the development of a learning healthcare organization and leverages the expertise of knowledge workers does not occur concomitantly. It is imperative not only for more research in this area, but also for the results of such research to be disseminated throughout the web of healthcare players.

CONCLUSION To date the information age has been leaving health care behind. Health care can no longer allow this situation to continue and it is imperative that an intelligent healthcare system is developed that leverages the tools, technologies, and strategies of the knowledge economy. To effect such a transformation requires changes at both the individual and, more importantly, at the institutional level. Changes at the institutional level must be substantive. What is required is the creation of Learning

Critical Factors for the Creation of Learning Healthcare Organizations

Healthcare Organizations that are flexible, focus on the effective and efficient management of their knowledge assets, foster organizational learning strategies so that superior access, quality, and value; i.e. the value proposition for any health system, can in fact be realized. As eluded to in this chapter, to create learning healthcare organizations requires the establishment of a robust knowledge management infrastructure that supports human and technical organizational assets, the incorporation of a process-centric perspective of knowledge creation, and the fostering of organizational learning strategies. This is summarized in Table 1 and the successful health care example of Northwestern Memorial Hospital. It is important to stress that success is only possible if people, most especially key actors such as healthcare professionals, are motivated to change and adapt practice patterns, and the underlying infrastructure supports and facilitates the change (as happened in the instance described in the urology department at Northwestern Memorial Hospital). Of paramount importance is strong leadership and organizational commitment to change. Without such a commitment, healthcare organizations can never become learning healthcare organizations and the required change to the existing and out-dated healthcare system will be incremental at best, analogous to rearranging the deck chairs on the Titanic.

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Von Lubitz, D., & Wickramasinghe, N. (2006). Creating germane knowledge in dynamic environments. [IJIL]. International Journal of Innovation and Learning, 3(3), 326–347. doi:10.1504/ IJIL.2006.009226 Wickramasinghe, N. (2003). Do we practise what we preach: Are knowledge management systems in practice truly reflective of knowledge management systems in theory? Business Process Management Journal, 9(3), 295–316. doi:10.1108/14637150310477902 Wickramasinghe, N. (2005). Knowledge creation: A meta-framework. International Journal of Innovation and Learning, 3(5), 558–573. Wickramasinghe, N. (2006). Fostering knowledge assets in healthcare with the KMI model. [IJMED]. International Journal of Management and Enterprise Development, 4(1), 52–65. doi:10.1504/ IJMED.2007.011455

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This work was previously published in Human Resources in Healthcare, Health Informatics and Healthcare Systems, edited by Stéfane M. Kabene, pp. 47-61, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 6.8

Supporting KnowledgeBased Decision Making in the Medical Context: The GLARE Approach

Luca Anselma Università di Torino, Torino, Italy Alessio Bottrighi Università del Piemonte Orientale , Alessandria, Italy Gianpaolo Molino AOU San Giovanni Battista, Torino, Italy

ABSTRACT Knowledge-based clinical decision making is one of the most challenging activities of physicians. Clinical Practice Guidelines are commonly recognized as a useful tool to help physicians in such activities by encoding the indications provided by evidence-based medicine. Computer-based approaches can provide useful facilities to put guidelines into practice and to support physicians in decision-making. Specifically, GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent prototypical DOI: 10.4018/978-1-60960-561-2.ch608

Stefania Montani Università del Piemonte Orientale, Alessandria, Italy Paolo Terenziani Università del Piemonte Orientale, Alessandria, Italy Mauro Torchio AOU San Giovanni Battista, Torino, Italy

tool providing advanced Artificial Intelligence techniques to support medical decision making, including what-if analysis, temporal reasoning, and decision theory analysis. The paper describes such facilities considering a real-world running example and focusing on the treatment of therapeutic decisions.

INTRODUCTION Nowadays, the rationalization of the healthcare systems is a task of fundamental importance in order to grant both the quality and the standardization of healthcare services, and the minimization

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Supporting Knowledge-Based Decision Making in the Medical Context

of costs. Clinical Practice Guidelines (CPGs) are one of the major tools that have been introduced to achieve such a challenging task. CPGs are, in the definition of the USA Institute of Medicine, “systematically developed statements to assist practitioner and patient decisions about appropriate health care in specific clinical circumstances” (Institute of Medicine, 2001, p. 151). Thousands of CPGs have been devised in the last years. For instance, the Guideline International Network (http://www.g-i-n.net) groups 77 organizations of 4 continents, and provides a library of more than 5000 CPGs. CPGs are commonly recognized as a tool to encode and support the practical adoption of evidence-based medicine (EBM). They can be used by managers as a means to optimize the organization of clinical processes. However, the main purpose of CPGs is to support physicians in their everyday knowledge-based decision making when treating patients. The adoption of computerized approaches to acquire, represent, execute and reason with CPGs can further increase the advantages of CPGs, providing crucial advantages to: 1. Patients, granting them that they will receive the best quality medical treatments (since CPGs are actually a way of putting EBM into practice); 2. Physicians, providing them with a standard reference which they may consult, with a way of certifying the quality of their activity (e.g., for insurance or legal purposes), as well as with advanced support to their decision-making activity; 3. Hospitals and health-care centers, providing them with tools to grant the quality and the standardization of their services, as well as with a means to evaluate quality, and to optimize costs and resources. As a matter of fact, in the last two decades, several computer-based approaches to CPGs have been developed (see the “Related Work” section).

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GLARE (GuideLine Acquisition, Representation and Execution) (Terenziani, Molino & Torchio, 2001; Terenziani et al., 2008) is one of these systems, which we sketch in the next Section. The unique feature of GLARE, distinguishing it from the other approaches in the Medical Informatics literature, is its focus on the development of advanced techniques (mostly based on Artificial Intelligence methodologies) in order to support physicians in knowledge-based decision making. As a matter of fact, GLARE (as well as most computer-based approaches to CPGs (de Clercq, Kaiser, & Hasman, 2008) provides a formalism to encode the “logic” underlying diagnostic and therapeutic decisions. However, often physicians have to decide under uncertainty, in the sense that the status of the specific patient may be partially unknown, and the effect of therapies on him\ her is partially unpredictable. Thus, additional knowledge may be helpful in order to take the most appropriate decisions. While diagnostic decisions are taken by the physician at hand, therapeutic decisions may also involve different forms of analysis. For instance, when choosing among clinically “equivalent” (with respect to the patient at hand) therapies, the “long term” effects of the therapeutic choice (e.g., what path of actions should be performed next, what are their costs, durations and expected utilities) may be helpful to discriminate. Such a knowledge is implicitly present in the CPGs, in sense that it can be automatically inferred on the basis of the local pieces of information scattered in the CPGs. Computer-based approaches can provide a crucial support to physician decision making by “gathering” and “elaborating” these relevant pieces of information, and by presenting the results to physicians. In this paper, we describe the facilities provided by GLARE to support physicians facing therapeutic decisions, on the basis of a real-world running example. The paper is organized as follows. First, we briefly sketch GLARE. The running example is introduced in section “Case Study”. Then, we

Supporting Knowledge-Based Decision Making in the Medical Context

present GLARE’s what-if facility, which is used to identify the different paths of actions in a CPG stemming from a decision (possibly focusing on some of them only), and to evaluate their costs and durations. We then discuss GLARE’s decision theory facility, which provides the utilities of such paths. The “Related Work” section briefly considers other computer-based CPG systems in the literature, and the concluding section addresses our conclusions and future work directions.

GLARE: A SKETCH GLARE (Terenziani, Molino & Torchio, 2001; Terenziani et al., 2008) is a prototype of a computerized domain-independent system to acquire and represent CPGs, and to apply them to specific patients. It has been successfully tested considering different CPGs, ranging from ischemic stroke to reflux esophagitis, from bladder cancer to asthma. The core of GLARE (see the box on the left of Figure 1) is based on a modular architecture. CG-KRM (Clinical Guidelines Knowledge Representation Manager) manages the internal representation of CGPs, and stores the acquired CGP in a dedicated Clinical Guidelines Database (CG-DB). The Clinical Guidelines Acquisition Manager (CG-AM) provides expert-physicians with a user-friendly graphical interface to acquire

a CPG. It may interact with four databases: the Pharmacological DB, storing a structured list of drugs and their costs; the Resources DB, listing the resources that are available in a given hospital; the ICD DB, containing an international coding system of diseases; the Clinical DB, providing a “standard” terminology to be used when building a new CPG, and storing the descriptions and the set of possible values of clinical findings. The execution module (CG-EM) executes an acquired CPG for a specific patient, considering the patient’s data (retrieved from the Patient DB). CG-EM stores the execution status in another DB (CG Instances) and interacts with the user-physician via a graphical interface (CG-IM). GLARE has an open architecture: new modules and functionalities can be added. In the latest years, in GLARE new modules and\or methodologies has been added in order to cope with automatic resource-based contextualization (ADAPT module), temporal reasoning (TR), decision making support (DECIDE_HELP), and model-based verification (VERIFY). Although not all the prototypical software modules have been implemented yet, we envision the modular architecture shown in the right part of Figure 1, in which all such modules will be loosely coupled with the core of GLARE. GLARE has been developed at the University. Currently, it is only a prototype, although it is

Figure 1. Architecture of GLARE. Rectangles represent computation modules, and ovals represent data/ knowledge bases. The left box contains the “core” of GLARE, while additional modules in the right of the figure depict advanced AI support for several tasks.

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Supporting Knowledge-Based Decision Making in the Medical Context

ready to be produced as an engineered software tool and to be commercialized. As a consequence, GLARE has not been adopted yet in the clinical practice. However, its decision-making facilities have been usefully exploited in e-learning environments, as a tool to train students in medicine to select appropriate knowledge-based decisions. Currently, GLARE is adopted in the RoPHS (Report on the Piedmont Health System) project, to train practitioners in hemergency health-care.

CASE STUDY In this section we illustrate our running example consisting in (part of) the CPG for the treatment of hepatic encephalopathy (see Figure 2) (Al Sibae & McGuire, 2009). Hepatic encephalopathy (also known as portosystemic encephalopathy) is the occurrence of confusion, altered level of consciousness and coma as a result of liver failure. It may ultimately lead to death. It is caused by accumulation in the bloodstream of toxic substances that are normally removed by the liver. Hepatic encephalopathy is reversible with treatment. This relies on suppressing the production of the toxic substances in the intestine. This is most commonly done with lactulose or with non-absorbable anti-

biotics. The part of the CPG in Figure 2 describes the recommended treatments for patients with hepatic encephalopathy who have been evaluated to be in Grade 3 according to West Haven criteria (Ferenci et al., 2002) (i.e., the patient is characterized by cross disorientation, confusion, by somnolence or by semistupor, but s/he is responsive to verbal stimuli, and if the patient gets worse, s/he risks the coma (Grade 4)). Note that in this case the therapeutic decision described by action 3 in Figure 2 is very important. A correct interpretation of Figure 2 requires a brief introduction of the CPG representation primitives. At a sufficiently abstract level, most CPG representation formalisms share the following assumptions (however, for the terminology used here, we refer in particular to (Fox, Johns, Rahmanzadeh, & Thomson, 1998; Terenziani, Molino & Torchio, 2001)). First, a CPG can be represented as a graph, where nodes are the actions to be executed, and arcs are the control relations linking them. It is possible to distinguish between atomic and composite actions (plans), which can be defined in terms of their atomic components via the has-part relation. Three different types of atomic actions can then be identified:

Figure 2. Part of the CPG for the treatment of hepatic encephalopathy

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Supporting Knowledge-Based Decision Making in the Medical Context

(1) Work actions (blue circles), i.e., actions that describe a procedure which must be executed; (2) Query actions (green parallelograms), i.e., requests of information from the outside world; (3) Decision actions (yellow diamonds), used to model the selection between different alternatives. Decision actions can be further subdivided in diagnostic decisions, used to make explicit the identification of the disease the patient is suffering from, and therapeutic decisions, used to represent the choice of a path in the CPG, containing the implementation of a particular therapeutic process. Each node has an internal description. For example, Table 2 in the following shows the description of the therapeutic decision 3. Control relations establish which actions can be executed next, and in what order. For example, actions could be executed in sequence, or in parallel. The arc labels define the minimum and maximum delay between the linked actions (e.g., action 2 follows action 3 with a delay between 10 minutes and 30 minutes). If an arc has no label (e.g., between actions 5 and 8), the second action can be immediately executed after the completion of the first one. The double lines (e.g., between actions 20 and 21) represent the fact that such actions must be executed in parallel (temporal

constraints on the time of execution of parallel actions can be specified). Furthermore, every action has a duration that can be precise or expressed by a minimum and a maximum value. For convention, the duration of a decision is considered to be 0. Some work actions can be cyclic, i.e., they have to be repeated several times (possibly a number of times not known a priori, until a certain exit condition becomes true). For example action 2 is a cyclic action and has duration of 5 days and consists in three administrations each day (see Table 1 for the temporal information of the example CPG actions)1. Every action has a monetary cost (see Table 1): the work action price is the unitary activity price (e.g., the drug price), the query action price is the examination one, and the decision action cost is considered to be 0. For the sake of clarity and space, in Figure 2 the therapeutic treatment path starting with rifaximin administration (action 6) has not been shown, since it is very similar to one starting with lactulose administration (action 2). The part of CPG in Figure 2 starts with a query action (action 1), where the physician requires blood examination (i.e., s-AST, s-Bilirubin Total, s-ALT, s-Sodium, s-Potassium, I.N.R., sALP, s-Ammonium (venous)). Such action is followed by a therapeutic decision. The patient may be treated by three different drugs: lactulose

Table 1. Costs and durations of actions in the example CPG in Figure 2 Action

Action number

Unitary cost

Total duration

Cyclic information

Diagnostic Decision

8, 9, 36-43

0

0

-

Therapeutic Decision

3, 14-17

0

0

-

Query action

5, 7, 28-35

0

10 minutes

-

Query action

1, 10-13, 44-59

24.30 €

1-2 hours

-

lactulose

2, 18, 20, 24, 26

0.25 €

5 days

three times a day

lactulose

22

0.25 €

10 minute

-

rifaximin

6, 19, 21, 25, 27

1.46 €

5 days

three times a day

flumazenil

4, 23

38.10 €

5 hours

once an hour

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Supporting Knowledge-Based Decision Making in the Medical Context

Table 2. Qualitative parameters in action 3 Action

Cost

Side effects

Compliance

Duration

Effectiveness

+

+

+++

+++

+

rifaximin (action 6)

++

++

++

+++

++++

flumazenil (action 4)

+++

+

+

+

++

lactulose (action 2)

(action 2), rifaximin (action 6), and flumazenil (action 4). The first alternative stemming from the therapeutic decision 3 starts with a lactulose administration (action 2), and continues with a query action (action 5), in which medical history and physical examination data are required. Then a diagnostic decision (action 8) follows, which analyses whether the patient status has been improved or worsened by the lactulose therapy. If the patient disease gets better, the physician evaluates whether the lactulose treatment has some side effects (i.e., s/he requires data - action 10 - and decides the following therapies - action 14). With no side effects, the therapy consists of another lactulose administration; otherwise there will be a rifaximin administration. Both work actions are followed by an evaluation of their effectiveness, i.e., a new request for historical data (actions 28 and 29) and a new diagnostic decision (actions 36 and 37). Then a blood examination will be required (actions 44-47) and a new therapeutic decision will be taken (not reported in Figure 2). If, after the first lactulose administration, the patient disease gets worse, the physician should evaluate for a different therapeutic treatment (i.e., s/he requires data - action 11 - and decides the following therapy - action 15). There are two different treatments: one that combines lactulose with rifaximin (actions 20 and 21), and one that combines lactulose with flumazenil (actions 22 and 23). Then, as in the path previously described, an evaluation of treatment effectiveness will be performed and it will be followed by actions that are needed to decide about a new therapeutic treatment.

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The other alternative reported in Figure 2 starts with a flumazenil administration (action 4), followed by request (action 7) of medical history and physical patient data and then by a diagnostic decision (action 9). Observe that from a structural view point, the paths after decision action 9 are very similar to the ones presented above, since the first path (starting with action 12) applies when the patient gets better and the second one (starting with action 13) when the patient gets worse. In the case that the patient disease gets better, the physician should also evaluate a new therapeutic treatment, since the flumazenil treatment can not continue. The physician can decide between lactulose administration and rifaximin administration. After the implementation of the treatment, there will be a request for historical and physical patient data followed by a diagnostic decision in order to evaluate the treatment effectiveness. Then blood examination will be required in order to enable the physician to decide about the new therapy. As a running example we consider the application of the CPG to a specific patient and illustrate how our methodology can support the physician in her/his decision process. The specific patient is in Grade 3 of West Haven Criteria, with liver cirrhosis and renal failure. Moreover, such a patient is allergic to rifaximin. The physician has executed action 1: the patient’s blood examination results are s-AST 36 UI/L, s-Bilirubin Total 2,1 mg/dL, s-ALT 28 UI/L s-Sodium 130 mmol/L, s-Potassium 3,5 mmol/L, I.N.R. 1,69, s-ALP 147 UI/L, s-Ammonium (venous) 453/ug/dL. Basically, the three different therapies stemming from therapeutic decision 3 are clinically equivalent, but they have different costs, dura-

Supporting Knowledge-Based Decision Making in the Medical Context

tions and side effects. In GLARE, as well as in several other approaches in the literature (Ten Teije, Miksch, & Lucas, 2008), therapeutic decision criteria are described through a qualitative evaluation of the alternative therapies, according to a pre-defined set of parameters (costs, side effects, compliance, duration and effectiveness). For instance, Table 2 represents the fact that the therapy based on flumazenil is more expensive, has the same side effects, has a lower compliance and duration, and is more effective than lactulose. It is worth stressing that, in CPGs, qualitative information associated with (therapeutic) decision is somehow “local”, in the sense that it only regards the specific therapies being compared, regardless of the rest of the actions to be performed later on the patient (thus, e.g., after action 2, actions 5, 10, 18 etc. might be performed, but they are not considered in the description of the first alternative in Table 2). In case such “local” pieces of information are not sufficient to help physicians to discriminate, GLARE supports extended facilities to “grasp” and “elaborate” further knowledge from the rest of the CPGs. In the following such facilities are presented.

THE WHAT-IF FACILITY The “what-if” analysis provides the physician with additional information, by taking into account not only the next action, but also the rest of the CPG, including future actions and their consequences. This is done in two stages: by eliciting the possible therapeutic paths stemming from the action at hand and by evaluating their costs and durations. Path Elicitation. GLARE provides a facility to navigate the CPG in order to make explicit the possible therapeutic paths applicable when executing a CPG. During navigation, the tool allows the physician to prune the actions/paths that are not eligible on the basis of specific patient-related contextual

information. Such a path selection functionality has been conceived as the first step of the interaction with the user. Technically speaking, the mechanism works as follows: through a user-friendly graphical interface, the physician is asked to indicate the starting node (normally the decision at hand) of the paths to be compared and (optionally) the ending nodes (otherwise all possible paths exiting the starting node will be taken into consideration, until the end of the CPG). For every decision action within each path, s/he is allowed to focus on a subset of alternatives. Moreover, the selection process is recursively applied to composite actions. This step can emphasize to the physician the possible consequences of her/his decisions, i.e., what are the subsequent actions to be performed depending on her/his choices. Moreover, all the paths pruned by this procedure will be ignored by the subsequent steps of the reasoning process. For instance, since the patient in the running example is allergic to rifaximin, the therapeutic alternatives that include the administration of rifaximin are not eligible. In this case, the physician selects to prune all the rifaximin administrations. Thus, s/he prunes actions 6, 19, 20 and 21, 25, and 27, and the system prunes all the paths which include the pruned actions and reports as output the feasible alternative paths (see the “Path” column in Table 3) corresponding to the possible different treatments. It is worth noting that the physician will be presented with a visual depiction of the paths. For instance, Fig. 3 reports a screenshot of GLARE graphical interface, showing how the physician can restrict the analysis to the paths stemming from LACTULOSE and FLUMAZENIL. As observed, in our example the physician has pruned the path starting at action 6 (rifaximin therapy), due to allergy problems, thus focusing only to the other two therapeutic alternatives. Moreover, s/he could benefit from further infor-

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Supporting Knowledge-Based Decision Making in the Medical Context

Table 3. Output of the “what-if” facility Path 3, 2, 5, 8, 10, 14, 18, 28, 36, 44

Cost 56.10 €

Duration (min-max) 10 days 6 hours 50 min - 10 days 13 hours 50 min

3, 2, 5, 8, 10, 14, 18, 28, 36, 45

56.10 €

10 days 6 hours 50 min - 10 days 13 hours 50 min

3, 2, 5, 8, 11, 15, 22, 23, 31, 39, 50

243.10 €

5 days 11 hours 50 min - 5 days 18 hours 50 min

3, 2, 5, 8, 11, 15, 22, 23, 31, 39, 51

243.10 €

5 days 11 hours 50 min - 5 days 18 hours 50 min

3, 4, 7, 9, 12, 16, 24, 32, 40, 52

242.85 €

5 days 11 hours 50 min - 5 days 18 hours 50 min

3, 4, 7, 9, 12, 16, 24, 32, 40, 53

242.85 €

5 days 11 hours 50 min - 5 days 18 hours 50 min

3, 4, 7, 9, 13, 17, 26, 34, 42, 56

242.85 €

5 days 11 hours 50 min - 5 days 18 hours 50 min

3, 4, 7, 9, 13, 17, 26, 34, 42, 57

242.85 €

5 days 11 hours 50 min - 5 days 18 hours 50 min

Figure 3. GLARE graphical interface, supporting path selection

mation about costs and durations of the therapeutic paths. Costs and durations of the alternative therapeutic paths. GLARE allows the physician to compare the feasible alternative paths along their global costs and their global durations. The computation of the costs consists in the sum of the costs of the actions in each path (the output is shown in column “cost” in Table 3). It is worth noting that the first two paths – deriving from the administration of lactulose (action 2) and an improvement of the patient (branch starting from action 10) – are cheaper (since flumazenil – which is an expensive drug – is not administered), while the other paths are more expensive

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(since all of them contemplate the administration of flumazenil). The computation of the duration of the paths can be more complex. Indeed, the CPGs can include non-trivial temporal information such as temporal indeterminacy, constraints about duration, delays between actions, and periodic repetitions of actions. A temporal framework has been devised to represent temporal constraints in CPGs and reason (i.e., perform inferences in the form of constraint propagation) with them (Anselma et al., 2006). A temporal reasoning framework has been designed in order to support tractable, correct and complete temporal reasoning. A description of the algorithms and the proofs of their correctness, completeness and computational complexity are

Supporting Knowledge-Based Decision Making in the Medical Context

outside the goals of this paper and can be found in (Anselma et al., 2006). In particular, the temporal reasoning approach provides for the following features, important in CPGs: (i) Qualitative and quantitative constraints, as well as repeated/periodic events representation; all types of constraints may be imprecise and/or partially defined (e.g., in Fig. 2 the delay between actions 4 and 7 is imprecise – i.e., between 30 minutes and 1 hour, the delay between actions 7 and 9 is qualitative – i.e., action 1 is before action 3, and actions 20 and action 2 are repeated – three times a day); (ii) Structured representation of complex events (in terms of part-of relations), to deal with plans; (iii) Consistency checking of the temporal constraints in the CPG with the (possibly imprecise) execution times of the CPG actions on specific patients. Our approach provides the following temporalreasoning facilities: during the acquisition of a CPG, to be assured that temporal information in the CPG is consistent; during the execution of a CPG, to check whether the temporal constraints in the CPG have been respected by the actions executed on specific patients; the what-if query facility, to assume some hypothetical temporal constraint and ask hypothetical queries (e.g., “If I perform action 2 at 12:30, when will I have to perform actions 5, 8, …?”). Although it includes indeterminacy and repetitions, the specific CPG in Figure 2 does not involve complex temporal reasoning, so that it is possible to compute minimum and maximum durations of the paths by summing up the minimum and maximum durations of the actions and their delays. However, this is not the general case, as discussed in (Anselma et al., 2006; Terenziani, German & Shahar, 2008), so that the complex

forms of temporal reasoning provided by GLARE are usually needed. The results of temporal reasoning are reported in the “duration” column of Table 3. Overall, Table 3 reports the information grasped by the “what-if” analysis facility. We can see that the choice of lactulose administration (action 2) can originate two kinds of path: one (lines 1 and 2 in Table 3) which corresponds to a less expensive but longer therapy (with cost 56.10 € and duration between 10 days 6 hours 50 minutes and 10 days 13 hours 50 minutes), and another one (lines 3 and 4 in Table 3) which corresponds to a more expensive but shorter therapy (with cost 243.10 € and duration between 5 days 11 hours 50 minutes and 5 days 18 hours 50 minutes). GLARE can show paths and their related pieces of information through a user-friendly graphical interface, as depicted in the screenshot in Figure 4. Besides costs and durations of the alternative paths, also the utilities may provide physicians with crucial information to discriminate. GLARE implements the decision theory facility in order to accomplish such a task.

THE DECISION THEORY FACILITY GLARE has a further decision support facility which relies on decision theory concepts. Decision theory (Russel & Norvig, 2003) is concerned with decision support under uncertainty. In particular, it allows identifying the optimal policy, i.e., the (sequence of) action(s) changing the states of the system in such a manner that a given criterion is optimized. In order to compare the different action outcomes, one commonly assigns a utility to each of the reached states. Since there is uncertainty in what the states will be, the optimal policy maximizes the expected utility. The utility of every other non-optimal policy (i.e., sequence of actions) can also be calculated. The adoption of decision theory has required a preliminary knowledge representation work

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Supporting Knowledge-Based Decision Making in the Medical Context

Figure 4. Graphical representation of the first path in Table 3, and of the related costs and durations

(Montani & Terenziani, 2006), aimed at mapping the CPG primitives to decision theory concepts themselves. In particular, the decision theory concepts of states and state transitions have been defined in terms of CPG primitives. In order to clarify how the mapping has been realized, we can observe that, in a well-formed CPG, a decision action is always preceded by a query action (see Figure 2), that is adopted to collect all the patient’s parameters necessary (and sufficient) for taking the decision itself2. In particular, the authors decide to concentrate on non-trivial therapeutic decisions, which imply therapy selections among clinically equivalent alternatives - see action 3 in Figure 2. By means of the query action, each decision is therefore based on an (explicit or implicit) data collection completed at decision time, which does not depend on the previous history of the patient (i.e., on previous data collections and on previous decisions found along the CPG path). The authors can thus say that the CPG describes a discrete-time

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first-order Markov model, because each time a query action is implemented, the patient’s state is completely reassessed, and an (explicit or implicit) state assessment through a query action is always found before a decision action. This observation justifies the application of decision theory to CPGs, and in particular allows us to represent a CPG as a Markov Decision Process (MDP) - which has been recognized as a basic representation framework for dynamic decision making under uncertainty. Note that Markov models have been widely used in the last decades in medical decision making and represent nowadays a well-understood instrument to cope with time-dependent medical decision problems (Sonnenberg & Beck, 1993; Marshall, Guo, & Jones, 1995; Provan, 1994). Note that the resulting MDP is also completely observable, since in a CPG a decision can be taken only if all the required parameters have been collected: if some needed data are missing, the query action will wait for them and the decision will be delayed.

Supporting Knowledge-Based Decision Making in the Medical Context

Given these premises, it is reasonable to define the concept of state as the set of patient’s parameters that are measured for making decisions and for assessing therapy outcomes. As already observed, the extraction of patients’ parameters is obtained through a query action in the CPG domain. Each parameter is thus a state variable. The query action is the means for assessing the patient’s state at time t, where t is also the time instant at which the decision has to be made. State transitions are changes in the patient’s state variables, due to the effect of an action; the goal of decision theory is to decide what action to take to maximize utility. Understanding which CPG primitives can produce state transitions requires a detailed analysis of the primitives themselves. In particular: •

•

•

Query actions just take a snapshot of the state and are not able to produce any change in it; Decision actions are used to make explicit which path will be followed, in a range of alternatives; Work actions represent operative steps in the CPG, such as providing a drug.

Therefore, all (and only) work actions will potentially have an effect on the patient’s state variables. However, since the state can be observed only by means of query actions, the state transition produced by a single work action may be not observable. Thus the CPG process can be modeled as a discrete-time one, where time discrimination is performed by the query actions preceding decisions; the state transition between time t (the time of the first therapeutic decision) and time t+1 (the time of the following one) is due to all the work actions between the two time instants. Note that the times t and t+1 are qualitative values used in the Markov model to describe state evolution (while the quantitative evaluation of durations and delays is performed by the what-if analysis - see the “What-if Facility” section). State transitions

are thus produced by all the work actions between two consecutive decisions. The probability that a therapy produces a given effect on a patient provides the probability of a certain state transition, given that a specific set of actions have been implemented after the therapeutic decision. Table 4 reports the state transition probabilities in our running example. For instance, consider the execution of action 2 (lactulose administration): the patient state is initially observed in “Grade 3” (as evaluated in the query action 1) and can change into “Grade 3 improved” state with probability of 52% (line 1 in Table 4), or into “Grade 3 worsened” state with probability of 48% (line 2 in Table 4). Notice that in the CPG in Figure 2 the state of the patient is used by the physician in the diagnostic decision 8 in order to choose between the path starting at action 10 (which is chosen in the case of “Grade 3 improved”) and the path starting at action 11 (which is chosen in the case of “Grade 3 worsened”). The utility of reaching a state can be evaluated in terms of life expectancy, corrected by Quality Adjusted Life Years (QALYs) (Gold, Siegel, Russell., Weinstein., 1996). Table 5 reports the state utilities in our example. Observe that the utility-range value is an integer between 0 and 100, where 100 represent the best utility. We can derive the utility of a state and the probability of state transitions from the medical literature (in our example from Huang, Esrailian, & Spiegel, 2007; Gyr et al., 1996). Given the concept mapping described above, an algorithm for producing the Markov Model corresponding to a CPG has been defined. Our algorithm operates for every elicitated path (see the “What-if Facility” section), and in correspondence to a therapeutic decision (or to an exit point of the CPG) it generates a new state. It then collects all the work actions between two consecutive states and builds a macro-action; the macro-action determines the transition between the two states with a given probability. Probability values are

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Supporting Knowledge-Based Decision Making in the Medical Context

Table 4. Probabilities of transition Action

From

To

Probability

lactulose (2)

Grade 3

Grade 3 improved

52%

lactulose (2)

Grade 3

Grade 3 worsened

48%

flumazenil (4)

Grade 3

Grade 3 improved

60%

flumazenil (4)

Grade 3

Grade 3 worsened

40%

lactulose (18)

Grade 3 improved

Grade 3 improved

52%

lactulose (18)

Grade 3 improved

Grade 3 worsened

48%

lactulose (22) + flumazenil (23)

Grade 3 improved

Grade 3 improved

60%

Lactulose (22) + flumazenil (23)

Grade 3 worsened

Grade 3 worsened

40%

lactulose (24)

Grade 3 improved

Grade 3 improved

52%

lactulose (24)

Grade 3 improved

Grade 3 worsened

48%

lactulose (26)

Grade 3 worsened

Grade 3 improved

55%

lactulose (26)

Grade 3 worsened

Grade 3 critical

45%

Table 5. State utilities State

Utility

Grade 3

60

Grade 3 improved

80

Grade 3 worsened

55

Grade 3 critical

50

extracted from the medical literature - when possible; otherwise, in the current implementation, they can be obtained from interviews with expert physicians. In our example, state transition probabilities were extracted from the literature, and are reported in Table 4. Figure 5 describes the Markov model in our example, showing all possible state transitions. Once the Markov model has been produced, our decision theory facility allows to: (1) identify the optimal policy (i.e., the path in the CPG that maximizes utility), and (2) calculate the expected utility along a path, or the probability of reaching a specific state, having fixed a path of the CPG. Such functionalities are illustrated below. Optimal policy evaluation. On the Markov model, classical algorithms for evaluat-

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Figure 5. The Markov model for the CPG in Figure 2. The figure shows the possible state transitions when the state at time t is Grade 3 and the first therapy is implemented. Observe that the transition from “Grade 3 worsened” to “Grade 3 Critical” is possible only in case that the implemented therapy is flumazenil administration.3

Supporting Knowledge-Based Decision Making in the Medical Context

ing the optimal policy can be relied on. In particular, in the context of CPGs, it is not infrequent that the number of states, though finite, is not known a priori. This situation may be induced by the presence of iterations to be repeated several times, until a certain exit condition becomes true. The number of repetitions could therefore vary among different executions of the same CPG. To cope with these issues, in GLARE an algorithms for calculating the optimal policy on an infinite time horizon (as a matter of fact, “infinite” can be used with the meaning of “unknown a priori”) has been relied, and namely on value iteration (Tijms, 1986). However when dealing with a finite time horizon (i.e., when the number of states is known), a simpler dynamic programming algorithm can be applied (Bellman, 1957). For instance, our running example is a case of finite time horizon (three states). The output in the example is a flumazenil administration followed by a lactulose one. Path utility evaluation. Calculating the expected utility of a path selected by the user and the probability of reaching a specific state along it corresponds in the decision theory context to applying a specific policy, i.e., to making

the hypothesis of knowing what is the decision to be taken at any decision node. In case of a finite time horizon, since the policy is known, it is possible to calculate the utility by applying the dynamic programming algorithm, avoiding to maximize the expected value of the cumulative utility function with respect to the different actions. As an example, Table 6 reports the utility of end states and the probabilities of reaching the various states, for a set of specific CPG paths (one path per line). For instance, the path lactulose-lactulose (i.e., a lactulose administration - performed in action 2 - followed by a lactulose administration - performed in action 18) allows reaching two different end states: “Grade 3 improved”, which has utility 80 and probability 27.04%, and “Grade 3 worsened”, which has utility 60 and probability 24.96%. Integration of costs and utilities. If the user is interested in calculating a cost/utility ratio, s/he can rely on the what-if facility for gathering monetary costs and/or durations and on the decision theory facility for calculating the utility of a path; finally GLARE provides the calculation of the resulting ratio.

Table 6. The expected utility along different paths in the CGP and the probability of reaching such states Policy

End state

lactulose (2) - lactulose (18)

Grade 3 improved

Utility 80

Probability 27.04%

lactulose (2) - lactulose (18)

Grade 3 worsened

60

24.96%

lactulose (2) - lactulose (22) + flumazenil (23)

Grade 3 improved

80

28.80%

lactulose (2) - lactulose (22) + flumazenil (23)

Grade 3 worsened

60

19.20%

flumazenil (4) - lactulose (24)

Grade 3 improved

80

31.12%

flumazenil (4) - lactulose (24)

Grade 3 worsened

55

28.80%

flumazenil (4) - lactulose (26)

Grade 3 improved

80

22.00%

flumazenil (4) - lactulose (26)

Grade 3 critical

50

18.00%

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Supporting Knowledge-Based Decision Making in the Medical Context

RELATED WORKS In the last two decades, several computer-based approaches to CPGs have been developed, such as Asbru (Shahar et al., 1998), EON (Musen et al., 1996), GEM (Shiffman et al., 2000), GLIF (Peleg et al., 2000), GUIDE (Quaglini et al., 2000), PROforma (Fox et al., 1998) (see also the collection in Gordon & Christensen, 1995). A comparative analysis of these systems is outside the goals of this paper (a comparison of several approaches can be found in Peleg et al., 2003). The recent book by Ten Teije (Ten Teije et al., 2008) represents a “consensus” of a large part of the “computer-oriented” CPG community. It presents an assessment of the “state of the art”, as well as a collection of several recent approaches (including GLARE). In particular, Chapter 2 of the book (de Clercq et al., 2008) compares different formalisms coping with computer-interpretable CPGs, considering also GLARE’s formalism. As regards the representation formalism, GLARE differs from most of the other approaches for the expressiveness of the language used in order to cope with the temporal constraints between actions (Anselma et al., 2006). An expressive temporal formalism is also supported by Asbru (Shahar, Mirksch, & Johnson, 1998), in which temporal reasoning facilities similar to GLARE’s one are also provided (Duftschmid, Miksch, & Gall, 2002). However, differently from GLARE’s approach, the correctness and completeness of temporal reasoning in Duftschmid et al. (2002) has not been proven, and the complexity of reasoning has not been studied. On the other hand, to the best of our knowledge, no other approach in the literature provide an analogous of GLARE’s “What-if” facility, including the support for Decision Theory described in this paper.

CONCLUSION AND DISCUSSION In this paper, we presented the decision-making facilities provided by GLARE, a prototype of a

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computer-based system coping with CPGs, to support physicians with the appropriate knowledge when taking therapeutic decisions on a specific patient. In this paper for the first time we provide a comprehensive picture - taking advantage of a real-world running example - of GLARE’s “what-if” (Terenziani et al., 2002), temporal reasoning (Anselma et al., 2006), and decision theory (Montani & Terenziani, 2006) facilities, which have been separately described elsewhere. To the best of our knowledge, no other CPG computer-based approach provides such a wide range of facilities. Specifically, we have shown that, besides a qualitative “local” evaluation of the alternatives, GLARE provides physicians with a way of looking at the paths of actions stemming from a decision, and of evaluating costs and durations of each path. Additionally, the possible outcomes of each path can be further analyzed, calculating their utilities and the probability of reaching such outcomes. Two issues need to be discussed here, to further clarify the “philosophy” underlying GLARE. First, the definition of a facility that, weighting the different outcomes, provides a “best scored” choice is technically feasible. However, it has not been deliberately provided in GLARE. As a matter of fact, we do not believe that, in many cases, there is an “absolute” best choice. For instance, local institutions may give priority to different criteria. Additionally, even in a context in which, e.g., an hospital uses costs as the main criterion of choice, other criteria may be occasionally preferred (e.g., duration or utility, in the case of critical patients). For many reasons, including the fact that no software system can actually capture all the explicit and implicit knowledge used by physicians when taking a decision, no physician is willing to follow the suggestion of a “black box” software system, which just provides him\her with the “best scored” solution to his\her problems. GLARE is intended to support physicians in their decision-making activities, not to substitute them. We strongly believe that the best way to achieve such a goal is the one that is offered in GLARE, providing (on demand!) physicians with as much “relevant” knowledge as possible, and leaving

Supporting Knowledge-Based Decision Making in the Medical Context

them free to use it according to their needs, which are often context- and patient-dependent. Second, it is worth noticing that GLARE does not “invent” any new knowledge: it automatically collects the knowledge already present in the CPG (and additional knowledge about utilities, state transition probabilities, etc.) and “elaborates” it according to the physician’s needs. For clarity reasons and space constraints, the running example we have used in this paper is necessarily quite simple, so that readers may have the impression that the same computations, although quite boring and time consuming, might be easily performed by humans. Unfortunately, this is not the general case. For instance, in order to cope with most of the types of temporal constraints present in the CPGs, GLARE’s temporal reasoner performs (using an extension of Floyd-Warshall’s algorithm (Anselma et al., 2006)) a number of operations which is cubic with respect to the number of actions in the CPG (and such a complexity becomes quite impractical for humans, even with a relatively small number of actions). To conclude, it is worth remembering that, while CPGs support, by definition, a “patient-centered” view of medical activities, workflows are usually adopted by healthcare institutions in order to model the “process-centered” view of medical processes. The integration of workflows and CPGs modeling healthcare processes is a hot topic of research in Medical Informatics, and is likely to provide a major advance in the IT support to healthcare (Lenz & Reicher, 2007). However, as discussed in (Terenziani, 2010), due to the different views they embed, the integration of healthcare CPGs and workflows is a challenging problem. We aim to address it in our future work.

REFERENCES

Anselma, L., Terenziani, P., Montani, S., & Bottrighi, A. (2006). Towards a comprehensive treatment of repetitions, periodicity and temporal constraints in clinical guidelines. Artificial Intelligence in Medicine, 38(2), 171–195. doi:10.1016/j. artmed.2006.03.007 Bellman, R. (1957). Dynamic programming. Princeton, NJ: Princeton University Press. de Clercq, P., Kaiser, K., & Hasman, A. (2008). Computer-Interpretable Guideline Formalisms. In Ten Teije, A., Miksch, S., & Lucas, P. (Eds.), Computer-based medical guidelines and protocols: A primer and current trends (pp. 22–44). Amsterdam, The Netherlands: IOS Press. Duftschmid, G., Miksch, S., & Gall, W. (2002). Verification of temporal scheduling constraints in clinical practice guidelines. Artificial Intelligence in Medicine, 25(2), 93–121. doi:10.1016/S09333657(02)00011-8 Ferenci, P., Lockwood, A., Mullen, K., Tarter, R., Weissenborn, K., & Blei, A. T. (2002). Hepatic encephalopathy- definition, nomenclature, diagnosis, and quantification. Hepatology (Baltimore, Md.), 35, 716–721. doi:10.1053/jhep.2002.31250 Fox, J., Johns, N., Rahmanzadeh, A., & Thomson, R. (1998). Disseminating medical knowledge: the PROforma approach. Artificial Intelligence in Medicine, 14(1-2), 157–182. doi:10.1016/ S0933-3657(98)00021-9 Gold, M. R., Siegel, J. E., Russell, L. B., & Weinstein, M. C. (1996). Cost-Effectiveness in Health and Medicine. New York: Oxford University Press. Gordon, C., & Christensen, J. P. (Eds.). (1995). Health Telematics for Clinical Guidelines and Protocols. Amsterdam, The Netherlands: IOS Press.

Al Sibae, M. R., & McGuire, B. M. (2009). Current trends in the treatment of hepatic encephalopathy. Therapeutics and Clinical Risk Management, 5, 617–626.

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Gyr, K., Meier, R., Häussler, J., Boulétreau, P., Fleig, W. E., & Gatta, A. (1996). Evaluation of the efficacy and safety of flumazenil in the treatment of portal systemic encephalopathy: a double blind, randomised, placebo controlled multicentre study. Gut, 39, 319–324. doi:10.1136/gut.39.2.319

Peleg, M., Tu, S. W., Bury, J., Ciccarese, P., Fox, J., & Greenes, R. A. (2003). Comparing computer-interpretable Guideline models: a case-study approach. Journal of the American Medical Informatics Association, 10(1), 52–68. doi:10.1197/jamia.M1135

Huang, E., Esrailian, E., & Spiegel, B. M. R. (2007). The cost-effectiveness and budget impact of competing therapies in hepatic encephalopathy – a decision analysis. Alimentary Pharmacology & Therapeutics, 26(8), 1147–1161. doi:10.1111/ j.1365-2036.2007.03464.x

Provan, G. M. (1994). Tradeoffs in knowledgebased construction of probabilistic models. IEEE Transactions on Systems, Man, and Cybernetics, 24, 1580–1592. doi:10.1109/21.328909

Institute of Medicine. (2001). Committee on Quality Health Care in America. Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academy Press. Lenz, R., & Reicher, M. (2007). IT Support for healthcare processes – premises, challenges, perspectives. Data & Knowledge Engineering, 61(1), 39–58. doi:10.1016/j.datak.2006.04.007 Marshall, G., Guo, W., & Jones, R. H. (1995). MARKOV: A computer program for multi-state Markov models with covariables. Computer Methods and Programs in Biomedicine, 47, 147–157.. doi:10.1016/0169-2607(95)01641-6 Montani, S., & Terenziani, P. (2006). Exploiting decision theory concepts within clinical guideline systems: towards a general approach. International Journal of Intelligent Systems, 21(6), 585–599. doi:10.1002/int.20149 Musen, M. A., Tu, S. W., Das, A. K., & Shahar, Y. (1996). EON: a component-based approach to automation of protocol-directed therapy. Journal of the American Medical Informatics Association, 3(6), 367–388. Peleg, M., Boxawala, A. A., Ogunyemi, O., Zeng, Q., Tu, S., & Lacson, R. (2000). GLIF3: The evolution of a Guideline Representation Format. In. Proceedings of AMIA, 2000, 645–649.

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Quaglini, S., Stefanelli, M., Cavallini, A., Miceli, G., Fassino, C., & Mossa, C. (2000). Guidelinebased careflow systems. Artificial Intelligence in Medicine, 20(1), 5–22.. doi:10.1016/S09333657(00)00050-6 Russel, S., & Norvig, P. (2003). Artificial Intelligence: a Modern Approach (2nd ed.). London: Prentice Hall. Shahar, Y., Miksch, S., & Johnson, P. (1998). The Asgaard Project: a Task-Specific Framework for the Application and Critiquing of Time-Oriented Clinical Guidelines. Artificial Intelligence in Medicine, 14, 29–51. doi:10.1016/S09333657(98)00015-3 Shiffman, R. N., Karras, B. T., Agrawal, A., Chen, R., Menco, L., & Nath, S. (2000). GEM: a proposal for a more comprehensive guideline document model using XML. Journal of the American Medical Informatics Association, 7(5), 488–498. Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13, 332–338. doi:10.1177/0272989X9301300409 Ten Teije, A., Miksch, S., & Lucas, P. (Eds.). (2008). Computer-based medical guidelines and protocols: A primer and current trends. Amsterdam, The Netherlands: IOS Press.

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Terenziani, P. (2010). A Hybrid Multi-Layered Approach to the Integration of Workflow and Clinical Guideline Approaches. In proceedings of the third ProHealth workshop, Business Process Management Conference, Ulm, Germany, (LNBIP 43, pp. 539-544).

ENDNOTES 1



2



3



Terenziani, P., German, E., & Shahar, Y. (2008). The Temporal Aspects of Clinical Guidelines. In Ten Teije, A., Miksch, S., & Lucas, P. (Eds.), Computer-based medical guidelines and protocols: A primer and current trends (pp. 81–100). Amsterdam, the Netherlands: IOS Press. Terenziani, P., Molino, G., & Torchio, M. (2001). A modular approach for representing and executing clinical guidelines. Artificial Intelligence in Medicine, 23(3), 249–276. doi:10.1016/S09333657(01)00087-2 Terenziani, P., Montani, S., Bottrighi, A., Molino, G., & Torchio, M. (2008). Applying Artificial Intelligence to Clinical Guidelines: the GLARE Approach. In Ten Teije, A., Miksch, S., & Lucas, P. (Eds.), Computer-based medical guidelines and protocols: A primer and current trends (pp. 273– 282). Amsterdam, the Netherlands: IOS Press. Terenziani, P., Montani, S., Bottrighi, A., Torchio, M., & Molino, G. (2002). Supporting physicians in taking decisions in clinical guidelines: the GLARE “what-if” facility. Journal of the American Medical Informatics Association Symposium, (Supplement), 772–776. Tijms, H. C. (1986). Stochastic modelling and analysis: a computational approach. New York: Wiley and Sons.

In GLARE, information about cyclic action is stored as an internal property of nodes. This holds both for therapeutic and diagnostic decisions. However a diagnostic decision only allows one to classify the disease the patient is suffering from. The physician cannot “freely” indicate one alternative, but s/ he simply has to formulate a diagnostic hypothesis on the basis of the patient’s data. On the other hand, therapeutic decisions embed a real “choice” for which the physician is responsible; in this paper, we will thus focus on therapeutic decisions, which we want to support. Note that, for those medical fields in which the medical literature does not provide these numbers, it is reasonable to expect this information to be available in the near future, as a desirable side-effect of the adoption of software systems. As a matter of fact, the increasing exploitation of Hospital Information Systems and of computerized CPG management tools will enable the collection of large amounts of clinical practice data. For instance, in the GLARE approach, the whole history of the execution of a CPG on a specific patient is recorded in the patient clinical record. Thus, if GLARE were extensively adopted, the traces of the clinical treatments of patients would be available, making thus feasible the task of deriving statistical data.

This work was previously published in International Journal of Knowledge-Based Organizations (IJKBO), Volume 1, Issue 2, edited by John Wang, pp. 42-60, copyright 2011 by IGI Publishing (an imprint of IGI Global).

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Chapter 6.9

Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials Tillal Eldabi Brunel University, UK Robert D. Macredie Brunel University, UK Ray J. Paul Brunel University, UK

ABSTRACT This chapter reports on the use of simulation in supporting decision-making about what data to collect in a randomized clinical trial (RCT). We show how simulation also allows the identification of critical variables in the RCT by measuring their effects on the simulation model’s “behavior.” Healthcare systems pose many of the challenges, including difficulty in understanding the system being studied, uncertainty over which data to DOI: 10.4018/978-1-60960-561-2.ch609

collect, and problems of communication between problem owners. In this chapter we show how simulation also allows the identification of critical variables in the RCT by measuring their effects on the simulation model’s “behavior.” The experience of developing the simulation model leads us to suggest simple but extremely valuable lessons. The first relates to the inclusion of stakeholders in the modeling process and the accessibility of the resulting models. The ownership and confidence felt by stakeholders in our case is, we feel, extremely important and may provide an example to others developing models.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

INTRODUCTION: CHALLENGES IN MODELING HEALTHCARE DECISION MAKING In recent years the healthcare sector in the UK and around the world has witnessed a wave of organizational reforms and changes, which mirror trends in the business sector. As a result of such changes there has been an increase in the level of constraints against health services provision. This has meant that health managers and professionals are increasingly faced with difficult decisions (Delesie, 1998), trying to balance the competing pressures of meeting ever-increasing needs and demands of the population whilst keeping the cost of treatment under strict control (Lagergren, 1998). In this environment, professionals are constantly faced with situations where they have to make immediate decisions, which, in many cases, can affect the quality of life of patients. To support decision-making, we often seek to develop models of the decision space, which will allow us to gain insight into the likely outcomes of different decisions that we may take. There are several reasons, however, that make this modeling difficult in the healthcare domain. Most contemporary healthcare systems are very complicated in terms of their structure, operation, and the diversity of people involved. Complexity often stems from the problems in healthcare lacking well-defined corners (Delesie 1998), with many interdependent entities competing for limited resources. This complexity can make it very difficult to predict the behavior of a healthcare system, since prediction is based on the application of collected historical data. Where a healthcare system is complex, it can be both difficult to determine the factors on which data collection should focus and challenging to define effective and appropriate data collection mechanisms. As a result, modelers tend to resort to assumptions in order to define the basic features of the problem, particularly when using mathematical models to represent the system. Another problem with healthcare systems is that

they are multifaceted with different interdependent components. For example, in making decisions about patient treatment, the clinician has to look at the patient’s profile alongside the cost of different treatments and the availability of resources to support such treatments. The involvement of different professionals in the healthcare system— such as clinicians and health managers—also makes modeling difficult (Delesie, 1998). These different stakeholders can have different views about the problem, expect different outcomes, and may make different decisions based on any model developed. This usually leaves the modeler with a major problem in integrating (and sometimes resolving) the different stakeholder perspectives involved in the problem. The potential importance of modeling in supporting decision-making and the complexity of the situations that arise in healthcare suggest that studies of modeling approaches are worthwhile. The main purpose of modeling is to present an abstract picture of the real system and to examine the system’s responses to different levels of inputs (Pidd, 1996). Many types of modeling are already used in healthcare problems. Lagergren (1998), for example, reports on a range of applications of modeling: in epidemiology for predicting future incidence, prevalence and mortality for broad sets of chronic diseases or for different specific diseases; for the evaluation of intervention strategies or disease control programs; in healthcare systems design, where the main concern is designing healthcare systems and estimating future resource needs as a training instrument for health managers; in healthcare systems operation, where the main objective of modeling is to improve performance by offering techniques for analyzing how existing resources could be used more efficiently; and in medical decision-making, where models are developed as support for analysis and decision-making in medical practice. Given our emphasis on complexity and unpredictability, it is important to identify and study a case where these factors are prominent. A suitable

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Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

area is randomized clinical trials (RCTs), which usually involve patients with a range of clinical needs, complex treatment pathways and states, and different medical professionals. Data collection also tends to be constrained by restrictions on time and cost, with an RCT based on random sampling of observations from different cohorts of patients. Determining the variables on which data is to be collected is extremely difficult, since it is likely that only a limited amount is known about the healthcare system that is to be studied in the trial. This will, however, be guided by the broad purpose of the RCT, such as the focus on cost-effectiveness of different treatments. As well as defining a suitable application area, our interest lies in exploring the usefulness of a specific modeling approach: discrete event simulation. This chapter examines the suitability of the modeling approach in identifying the critical variables ahead of conducting the RCT, providing an insight into the role of simulation in countering the complexity and unpredictability issues and supporting medical decision-making. The chapter also shows how simulation can help the stakeholders—health economists in this particular example, since we are interested in this RCT from cost-effectiveness point of view—understand the healthcare system by allowing them increased access to the model. This includes allowing users to flexibly change the model’s structure and study the implications of such changes. The next section discusses our main field of study in healthcare—randomized clinical trials (RCTs)—and the use of modeling techniques alongside them. This will highlight limitations of these modeling techniques with respect to healthcare decision-making. We will then go on to look at the use of discrete event simulation in healthcare decision-making and the ways in which it might overcome such limitations. To illustrate our position, we will then present an RCT case study, starting with the users’ requirements and moving into the development and appraisal of a simulation model to support decision-making. We

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conclude by considering the lessons of transferable value learned from this study and the limitations that should be considered.

RANDOMIZED CLINICAL TRIALS (RCT) AND MODELING RCTs represent one of the most complex classes of systems in healthcare. RCTs are used to examine new treatments based on randomization of treatment pathways or patterns of care for a sample of patients. The evaluation of treatments is usually based on the quantity and quality of life for each cohort in terms of cure, side effects, and relapses. Target data is collected throughout the course of trial for subsequent analysis to evaluate the efficacy of introducing a new treatment from clinical and economic perspectives. A number of modeling techniques have been used for designing and analyzing RCTs, most commonly Markov modeling (Hillner et al., 1993; Sonnenberg & Beck, 1993). However, Markov modeling has characteristic restrictions: model cycles are limited to fixed time intervals, which means that patients can only change their health state at the end of each period in time; transition probabilities are time independent and are not influenced by previous health states experienced by the patient; Markov models only represent aggregate levels of the system without providing a view about individual cases or rare events; and Markov models require specialism, which means that they are usually inaccessible to healthcare professionals. To support decision-making in RCTs more effectively, an alternative modeling approach that does not display these characteristics would be helpful. Such an approach should: offer the flexibility of dealing with systems at individual as well as aggregate levels; allow patients to experience events at any point of time after the previous event; support the recording and retention of the patient’s history throughout the course of the trial

Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

so that it can be used to influence the pathways through the model; and allow the recording of other information about a patient, such as cost and quality of life effects associated with the events undergone. In summary, a modeling technique is required to follow patients individually as well as in aggregate through the model and decide their next states based on their history and current state.

SIMULATION MODELING IN RCTs We see discrete event simulation as a candidate for use in modeling RCTs. Simulation as a modeling technique has been widely used as an aid for decision-making in many health areas (Pidd, 1996; Klein et al., 1993). It has been used in diverse healthcare application areas, from predicting the increase or decrease of certain illnesses then making decisions about their corresponding treatments (Davies & Flowers, 1995; Davies & Roderick, 1998), through understanding the appointment systems in outpatients clinics in order to make decisions about scheduling strategies (Paul, 1995), to supporting strategic planning given the limited resources available (Pitt, 1997). A growing use of simulation in healthcare is in supporting economic evaluation (Halpern et al., 1994)—a key issue in RCTs. Where simulation is employed, a common objective is to use the models to provide some answers about the problem in hand. This reflects a tendency to see simulation as a way of deriving future outcomes and determining the effect of different model configurations on system behaviors, thus subsequently enabling decision-makers to appraise the implications of their decisions. However, we see simulation as an approach that is not just for deriving answers. We are also looking at simulation modeling as a process to support problem understanding. To this end, we can identify two central capabilities of simulation modeling. Firstly, simulation provides a systematic debating vehicle between the different stakeholders who will contribute to decision-making in the

healthcare system. Secondly, simulation offers the flexibility to accommodate as many changes as possible in the system model, either in aggregate or individually, to enhance the understanding of the system. In this chapter we aim to demonstrate the suitability of simulation modeling as a decisionmaking aid for designing RCTs by identifying the key variables. The use of modeling in economic evaluation alongside clinical trials is not new (Schulman et al., 1991; Buxton et al., 1997), but its use to identify key variables for further data collection has thus far been under-recognized. The main purpose here is to understand the structure of the trial and identify the main variables for data collection. To achieve this we make use of the two central capabilities of simulation modeling identified above: the model may be built in a way that makes it easier to understand the system thus enhancing the level of debate between the different stakeholders; and the model is designed to provide flexibility for the users, allowing them to vary the simulation configurations and input factors in order to identify the main variables in the RCT.

THE RCT CASE STUDY The particular RCT we modeled concerns the economic analysis of adjuvant breast cancer (ABC) treatment. The problem owners (or users of the model) are health economists who are interested in the economic assessment of the treatments, and clinicians who are expert in the way that the treatments are followed and the different possible side effects and relapses that may occur. From the perspective of our modeling, the health economists were our primary stakeholders, since they were our “customer” (as this was an advocacy modeling exercise). The trial structure may be thought of as representative of a typical healthcare system in terms of complexity and the large number of variables that are included. The remainder of

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Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

this section provides a detailed description of the ABC RCT as background to the later discussion of the development of the simulation model in the fifth section. This background includes relevant clinical details of the RCT and of the basics of economic evaluation for the RCT, and provides justification for the use of a modeling technique to support decision-making for the RCT’s design. The ABC trial is a national collaborative RCT undertaken in the UK. The main objective of adjuvant therapy for early breast cancer is to prolong survival while maintaining a high quality of life. The principal aim of this trial is to assess the value of combining alternative forms of adjuvant therapy for early breast cancer. More formally, the trial aims to assess the value of adding cytotoxic chemotherapy and/or ovarian suppression to prolonged adjuvant Tamoxifen in order to treat pre/perimenopausal women with early breast cancer, and cytotoxic chemotherapy to prolonged adjuvant Tamoxifen in order to treat postmenopausal women with early breast cancer. The possible treatment alternatives are listed in Table 1. At the beginning of the trial path, patients are routed based on their menopausal state. Patients who are pre/perimenopausal may be treated with Tamoxifen, Tamoxifen with chemotherapy, Tamoxifen with ovarian suppression, or Tamoxifen with both ovarian suppression and chemotherapy. Patients who are postmenopausal may only be treated with Tamoxifen or Tamoxifen with chemotherapy. After the menopausal classificaTable 1. The randomization options for the ABC RCT

Pre/perimenopausal women

Postmenopausal women

Treatment plan

Randomization

Tamoxifen+OS

CT

Tamoxifen+CT

OS

Tamoxifen

CT and OS

Tamoxifen

CT

Key: OS=ovarian suppression; CT=chemotherapy

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tion, clinicians specify treatment paths that are randomly selected. All random alternatives are based on a simple rule: for each level or treatment a patient is randomized between having a treatment (adjuvant) or not having that particular treatment (control). Then patients are sent to the next level. A patient who is prescribed Tamoxifen and ovarian suppression may be randomized for receiving chemotherapy or not. A patient who is prescribed Tamoxifen and chemotherapy may be randomized for receiving ovarian suppression or not. A patient who is prescribed Tamoxifen may be randomized for receiving chemotherapy and/ or randomized to have ovarian suppression. Generally, each individual is allocated to the randomization option most suitable for her. Based on a patient’s condition, clinicians may decide to allocate, or not allocate, a patient to specific treatment in any way and then randomize for the rest of treatment. It must be noted that all pairs of alternatives have equal probabilities (i.e., probabilities of 50-50 for each arm in a pair). The ABC RCT aims to include four thousand pre/perimenopausal and two thousand postmenopausal women. The clinical endpoints of the trial are overall and relapse-free survival for five years. Health economists have the task of evaluating the economic implications of the ABC RCT to determine the cost effectiveness of the various adjuvant treatment combinations by comparing the additional resource use with the survival gains and quality of life effects. Comparisons for pre/perimenopausal women are based on those randomized to ovarian suppression with those randomized not to have ovarian suppression, regardless of whether they have had chemotherapy or not. Also, an aim is to compare all those who are randomized to chemotherapy with those who are randomized not to receive chemotherapy, regardless of whether they have had ovarian suppression or not. For postmenopausal women, the comparison will be between those randomized to receive chemotherapy and those randomized not to receive chemotherapy.

Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

Table 2. Comparison for the clinical endpoints Pre/perimenopausal women 1

Randomized to +OS, regardless of CT

Randomized to -OS, regardless of CT

2

Randomized to +CT, regardless of OS

Randomized to -CT, regardless of OS

3

Tamoxifen+CT(given) +OS(randomized)

Tamoxifen+CT(given) -OS(randomized)

4

Tamoxifen –CT(not given) +OS(randomized)

Tamoxifen-CT(not given) -OS(randomized)

5

Tamoxifen+OS(given) +CT(randomized)

Tamoxifen+OS (given) -CT(randomized)

6

Tamoxifen-OS(not given) +CT(randomized)

Tamoxifen-OS (not given) -CT(randomized)

Postmenopausal women 7

Tamoxifen+CT(randomized)

TamoxifenCT(randomized)

Key: OS=ovarian suppression; CT=chemotherapy

Comparison of the clinical endpoints is presented in Table 2.

Economic Evaluation Alongside RCT Assessment of cost-effectiveness of the different treatments in the RCT is undertaken by an economic evaluation technique called costeffectiveness analysis (CEA). This technique is actually concerned with the systematic comparison of alternative treatment options in terms of their cost and effectiveness (Drummond et al., 1997). CEA is the most commonly used form of economic evaluation and it is particularly used as an aid for decision-making. For detailed accounts of the role and recommendations for conducting CEA in healthcare the reader is referred to Russell et al. (1996) and Weinstein et al. (1996). The results of the economic evaluation for the ABC trial are to be expressed in terms of the difference in costs, the difference in life years and difference in quality adjusted life years (QALYs) for the comparison groups. A QALY, as defined by health economists involved in the trial, is a duration of one year where the patient is in perfect

health. Where appropriate the cost per additional life year gained and additional cost per QALY gained are to be estimated.

The Role of Modeling in the ABC RCT Given the complex nature of the ABC RCT and the fact that it is time limited, those responsible for the trial felt that data collection for the economic evaluation should be kept to a minimum. It was suggested that data collection should be focused on the key variables or important factors that have direct effects on the objectives of the study. A pragmatic approach to data collection, without employing some form of modeling to identify key variables, was thought to be in danger of not identifying relatively infrequent but large resource-consuming activities. Hence the first phase of the evaluation was to conduct a modeling exercise using existing data and “expert opinion” prior to any primary data collection, in order to determine the key parameters influencing the cost-effectiveness of adjuvant therapies for early breast cancer. Further data collection could then be concentrated on those key parameters, thus ensuring that data collection during the trial was minimized whilst retaining the stakeholders’ confidence in the data being collected.

THE MODELING PROCESS Our basic objective was to produce a simulation model that enabled the health economists to understand the ABC RCT and establish the major variables that might affect the behavior of the cost-effectiveness of the adjuvant therapy. In this case modeling aims to provide facilities for flexible input alteration, which enables stakeholders to “play” with the model in order to establish the pattern of the different outputs. Another use of the model is as a medium of communication between health economists and clinicians. This meant that

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Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

the model had to be built in a way that made it easily understandable by both parties. As we noted earlier, the complicated nature of the trial with so many randomization options, treatment options, side effects, and relapses made discrete event simulation an appropriate modeling technique (Paul & Balmer, 1993; Davies & Davies, 1994). The approach provides a mechanism for participants to run a range of “what-if” scenarios, thus allowing the effect of many different assumptions—such as ways in which treatment is delivered, the age of the patients, and the effect of varying the values assigned to the costs and utilities—to be tested.

Key Issues in Building the Simulation Model To conform to the above objectives the model is split into two distinct parts: the simulation engine and the user interface. The role of the simulation engine is to represent the model’s structure and behavior in terms of treatment duration, patient routing, and clock setting. The interface is used as the input/output facility for the health economists (our customer). There are many aspects to the interface. Firstly, the look and feel of the interface to the model are based on the users’ requirements—this helps ensure that they are able to “play” with the model. That is, the interface ensures that the model is accessible to them, overcoming the problems that non-experts often find in using and making sense of simulation packages. In this case, the user interface was seen as a medium through which they could communicate with the model. The interface also had to be built to accommodate all the input factors and output responses that may be important in the ABC RCT. The interface had to offer the users the flexibility to modify the values associated with variables in the model. Since an aim of the model was to identify the important variables prior to data collection, there were no specific values or standard probability distributions for the model’s variables at

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the start of the modeling exercise. We can think of the model parameters as being divided into two main classes: parameters with deterministic (fixed) values such as treatment costs; or those with probabilities such as different types of menopausal symptoms. Dealing with the first type of variables was straightforward; all we had to do was identify the parameters and provide variables in the model to hold their corresponding values. To model the stochastic parameters, we assigned a number of levels to represent each variable and their probabilities based on expert opinions gained through discussion with clinicians and health economists. The probabilities were based on the basic probability axioms (Hines & Montgomery, 1990). That is, each probability value lies between zero and one, with the sum of the values being one. An example in this study is that each patient has a probability associated with falling within one of four age groups (rather than giving a mean and standard deviation for age), where these probabilities are classed as follows: Pr(Age < 40) = A1, Pr(40 <= Age < 50) = A2, Pr(50 <= Age < 60) = A3, Pr(Age >= 60) = A4. Given that A1 + A2 + A3 + A4 = 1.00 Note: the interface ensures that all probability values are nonnegative and that they sum to one. Even though this may seem a simplistic structure for modeling probabilistic behavior, we think it is quite flexible and it is not restricted to standard probability distributions. This method is appropriate for cases where there is no known input data or behavior, and allows users to model input data in any form or shape. The reader is reminded here, that in asking “what-if” questions, users will be able to examine the system’s behaviors not just by changing the values of the defining parameters, but also by changing the entire distribution of the input variables, in an easy yet powerful way. A large sample is required to give the precise estimate of these probabilities in real life. However, the simulation model is used to explore the effect

Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

on the results/outputs of the model when these probabilities are distributed in a certain manner. As such, the probability values are presented to users in an accessible manner so that they can be changed to explore their effect on the model. All probability distributions in the model are presented in this way. The events modeled were the administration of the adjuvant therapies (chemotherapy and ovarian suppression) in addition to Tamoxifen, which is common in all treatments. After the administration of the adjuvant therapy the patient could remain in remission until death or relapse. Relapse was modeled as local/regional recurrence, non-bone metastases and bone metastases. Non-bone and bone metastases are followed by death. The division between non-bone and bone metastases was based on prognosis and intensity of resource use (Hurley et al., 1992; Goldhirsch et al., 1988). Local regional recurrence may be operable and followed by a second remission, or may become an uncontrolled local/regional recurrence, uncontrolled non-bone or uncontrolled bone metastases, which are followed by death. Following a second remission, after operable local/regional recurrence, the patient could be in second remission

until death or develop non-bone or bone metastases. The events and their consequent pathways were drawn up using specialist help from clinical oncologists involved in the ABC RCT, and are shown in Figure 1 and Figure 2. In both figures, each node represents a separate event which starts and ends at discrete points in time. As patients move through the model’s pathways, they are assigned attributes. The attributes of patient age, menopausal side effects and toxicity side effects associated with chemotherapy are assigned by sampling from non-specified probability distributions. Utility values (associated with administering the adjuvant therapies, menopausal and/or toxicity side effects, remissions and relapses) and the costs (associated with administering the adjuvant therapies, remissions, relapses, and with the treatments of toxicity side effects and menopausal side effects) were also modeled as attributes. The age at which a patient is diagnosed as having breast cancer is important as it influences the adjuvant therapy that she will receive, the probability of therapy-induced menopausal symptoms and the maximum number of future years of life.

Figure 1. A Simul8 visual representation of the event pathways which make up the Treatment Phase of the model

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Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

Figure 2. A Simul8 visual representation of the event pathways in the Recurrence Phase of the model

The menopausal side effects were modeled as “none,” “mild,” “moderate,” or “severe.” The modeling of menopausal symptoms as a side effect of adjuvant therapy was complicated by the fact that use of Tamoxifen alone can also induce early menopause (Canney & Hatton, 1994). Menopausal effects associated with Tamoxifen alone were thus modeled so that the net effects due to the adjuvant ovarian suppression and/or chemotherapy could be estimated. The model assumed that all women naturally have a permanent cessation of menses once they reached the age of 50. However, before the age of 50 induced menopause resulting from the use of luteinising hormone-releasing hormone (LHRH) agonists is temporary. A proportion of women following the use of chemotherapy or Tamoxifen may also experience temporary menopausal effects. The toxicity side effects were modeled as “none,” “mild,” “moderate,” or “non-fatal major,” each assignment depending on the corresponding complications. Chemotherapy itself is based on sets of six-month treatments, each comprised of six monthly treatment cycles. The model allowed for the number of chemotherapy cycles completed to be affected by the toxicity side effects. (This is shown on the sample input screen presented as Figure 3).

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Cost attributes were assigned to administering the ovarian suppression and each cycle of chemotherapy. A monthly cost was assigned to the mild, moderate and non-fatal major toxicity effects and to the mild, moderate and severe menopausal symptoms. A fixed cost was assigned to local/regional recurrence, regardless of its duration, and monthly costs to the treatment of the non-bone metastases, bone metastases and uncontrolled local/regional recurrence, uncontrolled non-bone and uncontrolled bone metastases. ABCSim allows patients to carry individual attributes throughout the different states of the model. Each patient is assigned a suitable set from the attributes (detailed in the above discussion) after they are sampled from the corresponding distribution. Routing to any of the post-treatment states (shown in Figure 2) is based on the combination of attributes a patient has collected from the treatment states (shown in Figure 1). For example, a patient arriving at the first remission (“Remis” in Figure 2) from the first arm of treatment (n.o.C.T.1.1 + O.S.1.1 in Figure 1) would have a different set of branching probabilities for any of the different recurrences and second remission than those for a patient arriving from the second arm of treatment (C.T.1 + O.S.1.2 in

Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

Figure 3. Branching probabilities for patients arriving from different arms of treatments

Figure 1). A patient’s stay in either first or second remission is also influenced by the combination of attributes obtained from the treatment phase. Figure 3 shows an example of how branching probabilities for the different recurrences can be altered for the different treatment arms. Three points are worth noting here: (i) the duration of a specific treatment is pre-specified and the same for all patients; (ii) the duration of remission is dependent on a patient’s treatment history; and (iii) moving between events in the model does not take any time (i.e., the paths between events are purely directional and have zero duration). It is also worth noting that although there are 14 treatment arms in Figure 1 and 14 comparison arms in Table 2, this is purely coincidental, highlighting the important point that patients from a single treatment arm may belong to more than one comparison arm and vice versa. A patient’s treatment history is assigned to her through values for associated attributes, so her information may be accumulated within corresponding comparison groups as she leaves the system. Information about costs, the time a patient spends in the model, and quality adjusted life years (QALYs) are collected

from individual patients at the “DEATH” state to be aggregated for economic analysis after the completion of a simulation run. It must be noted that all information is accumulated for each patient throughout her life in the model. It is assumed in the ABC trial that all resources are available to all patients, which means that patient flows through the trial are independent of any external entities. This makes values collected from individual patients independent of each other and identically distributed (i.e., all values are of equal probability in terms of selection through sampling), meaning that classical statistical methods based on normal distribution theory can be applied to the resulting data.

The Computer Model Simul8 is one of the most widely used discrete event simulation packages. It is perceived as an appropriate simulation tool from both the industrial and the academic community. Although there are various modeling packages such as ARENA, Witness and ProModel, most of these are not as easy to use as Simul8 (Oakshott, 1997). It might

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Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

be argued that simulation technology has changed so rapidly in the past three years that most packages are intrinsically similar. Simul8 is generally user-friendly and usually no additional skills are needed for programming in order to construct new models. Literature suggests that Simul8 has the ability to animate objects and visually present interaction of objects with each other (Hauge & Paige, 2004). As an additional aid, Simul8’s visual logic editor (VLE) allows the ability to organise a system’s data in a variety of information stores by way of referencing. Furthermore, these visual logic functions allow the modeler to write simple statements that add dynamic controls to the simulation. Such a performance allows one to record distribution measures of the system under different scenarios. Based on its support features it can be considered as one of the most flexible simulation packages because special plug-ins can be added in order for specific modules to be integrated within the package. One of the main advantages of this Simul8 is the ability to represent large scale and complex systems with less time in contrast with models that have been developed with other simulation packages.

Two pitfalls are identified in the simulation input modeling. These are the substitution of a distribution by its mean and the application of the wrong distribution. Simul8 can combine some features in order to eliminate the possibilities of existence of these two pitfalls. More precisely Simul8, with the range of appropriate distributions that offer to end-users, can eradicate the application of wrong theoretical distribution. Detailed description of Simul8 objects can be found in Table 3. For more detailed description of how to develop models using Simul8 please consult Hauge and Paige (2004). Another tool which was used for this model was Visual Basic to build an interface (which we called ABCSim) between Simul8 and the users of the model (the health economists). ABCSim provides the facilities for inputting data, displaying the model’s outputs, exporting outputs to spreadsheets for further analysis, and saving the different model configurations for comparison and analysis. Figure 1 and Figure 2 depict the Simul8 model while Figure 3, Figure 4 and Figure 5 represent some sample screens from ABCSim.

Table 3. Description of Simul8 objects Simul8 Objects

Activities

Work Entry Point

This object was used to identify patients arriving into the system. Timing plays a large part in this object, therefore in order to specify the arrival timings of the patients an inter-arrival time was identified within the Work Entry Point Properties Dialogue. Note: Inter - arrival time specifies the amount of time between arrivals and is the inverse of the arrival volume. Due to the random nature of arrivals, a named distribution was used.

Work Centre

This is an active object and has the ability to perform the work involved in a task. It can pull in work from queues or accept work from other work centres. In addition certain aspects of an item can be changed and on completing processing a dynamic decision on what to do with the item can be done. Note: Since a work centre is an active object, there is a need to add a resource to go along with the task.

Resources

Resources cannot function unless they are attached to a work centre. For example, resources represent a staff person, specialised piece of equipment and so many others. The purpose of this function is to constrain work centres from operation. Note: Without the availability of a resource work centres may not work.

Storage Bin

These passive objects are used as queues or storage areas but in the context of this study there are used to represent waiting areas where patients or items are waiting to be worked on.

Work Exit Point

These passive objects correspond to work items heading out of the system model. In the context of this study, this is a marking point for the patients to leave the system.

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Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

Figure 4. A sample input screen from the ABCSim interface program

Figure 5. A sample output screen output (Average Cost/Patient) from the ABCSim interface program

Inputs to the Model Input variables include information on treatment duration, costs and utility values; the input variables were identified by expert opinion and the interface to the model allows the user to change their values as required. Probability distributions were required for the permanent and temporarily induced menopause, menopausal and toxicity effects, relapses and second remission. Probability distributions were also required for duration of remissions, the duration of menopausal and toxicity symptoms, and relapses (as the structure in

Figure 2 suggests; see also Figure 3 for how this is presented in the system). The probability distributions are defined by the user, with the system providing a flexible interface to allow them to be defined and changed as required. Information was also required on the “discount rate,” which is another of the system’s variables, the value of which is user defined and can be altered as required.

Outputs from the Model The model’s results are, for each of the comparison options, the average cost, average life years, and

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Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

average QALYs. For each comparison option, the model estimates the average difference in cost, life years and QALYs, undiscounted and discounted. Confidence intervals for these averages are also estimated using normal statistical techniques. Where appropriate the additional cost per life year gained and additional cost per QALY gained, undiscounted and discounted, are presented.

Verification of the Model The purpose of verification is to make sure that the model is running as it is supposed to be or as is expected (Paul & Balmer, 1993). In this study, the model was verified collectively and individually in terms of the variables. Collective verification was performed by running the model with values for which there are logical expected results, which can easily be estimated without the computerized model. The results produced by the model are then compared to those that are expected. For the individual verification, the technique followed was to set a deterministic value to the variable of interest while resetting other unrelated variables to zero. For example, to verify the average cost for certain patients they were given constant cost values. If the average value was equal to the particular cost value then that cost variable was verified, otherwise the model was revised before repeating the verification.

Validation of the Model The purpose of validation is to make sure that the developed model is the right model given the objectives of modeling (Paul & Balmer, 1993; Balci, 1997). Given the purpose of this particular modeling exercise and the fact that the modeled system is a non-existent sampling process, the validation process concentrated on the structure and behavior of the model rather than on producing estimated values. For example, it was not feasible to estimate the exact probabilities for which type of recurrence would occur after remission. The

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validation of the structure is based on making sure that the pathways are the correct representation of the trial. Owing to the wide-ranging discussions and consultations that took place with the stakeholders during the formative stages of the project, the structural validity was considered high. As discussed earlier the model is built based on expert opinion, hence the validation process is mostly based on expert opinions. Information was supplemented by expert opinion to identify crude estimates of the overall expected effectiveness and costs of the different treatment options. This was used to validate the model’s behavior. Given the large number of interactions in the model based on the different factors that may affect one patient at a particular health state, it was necessary to validate these interactions. For example, a patient may experience some side effects from previous treatment while undergoing a new treatment with similar side effects, such as menopausal symptoms. It was important in cases like this to identify the effect of such interactions and their corresponding entities. It must be noted that interactions in this model are not interactions between different classes of entities competing for different resources. Rather, they are interactions between different attributes of the same entity in general simulation terms. More specifically, these interactions are between the patients’ health states and effects from previous or current treatments. Results of such interactions affect the patients’ quality of life. Life expectancy is directly affected by the combination of treatment that a patient receives, however the side effects of the treatments affect only the quality of life, rather than life expectancy. This judgement is based on expert opinion elicited during the development of the model. Treatment side effects do not have direct influence on the quantity of life, however life expectancy is affected by the treatment combination. Table 4 gives a detailed account of the different elements in the interactions integrated in the model based on the validation process. The first section of the table gives the different possible

Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

Table 4. Elements of the interactions formulated in the model effects Toxicity

Reflects side effects of CT and may affect the course of the treatment. Four levels of toxicity are possible (none, mild, moderate, non-fatal). User defined distribution specifies which level of toxicity a patient will have as a result of chemotherapy treatment. Treatment duration is then determined on basis of severity of toxicity. Post-treatment phase is also affected if effect is existing.

Menopausal Symptoms

Reflects menopausal side effects of CT, OS, and Tamoxifen. Four levels of symptoms are possible (none, mild, moderate, severe). User defined distribution specifies which level of severity a patient will have as a result of each treatment. Posttreatment phase is also affected if effect is existing. Note: “none” effect is only possible for CT and Tamoxifen Revised (P. 3 editor)

results Utilities

Quality of life utility range is between 0 and 1. Assigned to a patient at each event based on the health state and the effects of toxicity and menopausal symptoms.

Recurrences

Recurrences of cancer possibly occur after adjuvant treatment. Different recurrences exist. The model provides input facilities for each recurrence in terms of duration and recurrence probabilities based on the current health state of the patient.

treatment effects experienced by the patients. Effects may occur individually or in combination, and there are 16 possible occurrences (derived from the possible combinations of the levels of toxicity and menopausal symptoms). Health states can be seen in Figure 1 and Figure 2. The “treatment phase” in Figure 1 is considered as a single health state; each of the seven nodes in Figure 2 are considered as separate health states. This gives eight health states in total. Having 16 possible effects and eight possible health states gives 128 possible interactions. The second section of Table 4 gives the expected results arising from such interactions in terms of QALYs. QALYs are calculated by multiplying the utility value of a health state by the duration of that health state (adjusted to years mainly by dividing by 12, as time units in the model are in months). These are summed to give overall QALYs for each patient as she goes through the different health states. The quantity of life— the duration of the post-treatment phase—arising from the probabilities of remission duration and types of recurrences (discussed in the fifth section), is directly affected by the treatment received. Once the structural and behavioral validation had been established, it was possible to measure the effects and trends of the different variables in

order to identify the major factors. For example, it is possible to vary the duration of disease-free periods based on chemotherapy to see the subsequent trend of survival. Validation of some of the estimates in the model had to be deferred, so that the users could alter the estimates as necessary after more data have been collected from the ongoing ABC RCT. This type of validation will depend on the early streams of data generated from the trial. The purpose of such validation is to predict the effects of the different variables on the model’s results more accurately.

Use of the Model As stated earlier in the chapter, the central purpose of the model was to identify the main variables of the ABC RCT and to act as a communication vehicle between the stakeholders. Accordingly, the model has mainly been used for pilot experimentation without real data, developing an understanding of the interdependencies in the model. Health economists have full control of the model as they are our customer in this exercise. That is, they are able to change all of the variables in the model based on their experimentation plan to establish the significant variables. Health economists are also able to explain the results of

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their economic evaluation to clinicians using the simulation model. Health economists follow two steps for identifying the important variables defined in terms of sensitivity to the model’s outputs using health economic methods. The first step is to examine whether such variables are important or whether the results are highly sensitive to them. Input sensitivity is measured by the percentage of change in cost-effectiveness ratio (CER), arising from the use of different sets of input variables, compared with initial results arising from initial input values. Initial input values are suggested by experts involved in the trial. Undertaking traditional sensitivity analysis [see, for example, Box & Draper (1987), Kleijnen (1987), or Myer & Montgomery (1995)) would provide a spurious level of accuracy given that the inputs to the model are derived from expert opinion rather than validated input data. Rather, the approach taken in this study was to undertake crude sensitivity analysis by “flexing” the input data and appraising its impact on the model’s output. Therefore, the stance is taken that if the change of the CER for a comparison pair (see Table 2) is agreed to be significant based on expert opinion, the variable is seen as significantly important with regard to that particular pair. Usually health economists and clinicians agree on a certain percentage as being significant based on the situation to hand. In our case, the model acts as a communication medium between health economists and clinicians in deciding what would be regarded as a significant change in percentage. This reflects one of the main objectives of this exercise: to facilitate this type of communication between stakeholders. The second step is to examine the impact of changing the initial value of a variable on the order of the different comparison pairs based on their corresponding CER. This is done by ranking the effects of these variables on the different comparison pairs. The previous step measures the significance of the model’s sensitivity to the

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specific variable(s) within the same pair, whilst the second step measures the significance of the model’s sensitivity to the specific variable(s) between the different pairs of comparison. An example is given as part of the following section to demonstrate how this process is conducted.

RESULTS ARISING FROM THE MODEL An example is given here to demonstrate how the model can be used to identify important variables and explore the impact of their change on the overall results. It should be noted that the aim of this example is to demonstrate relevant aspects of modeling of ABCSim and the data used in this example does not, therefore, necessarily reflect the real data used by health economists for analysis. Rather, the example shows how the model is developed and facilitated to give users full advantage of exploring different situations about the ABC RCT. We are, after all, interested in this chapter in the value of the process of modeling that we adopted, rather than in the specific model developed In this example we will show the results of altering the values of three variables; the cost of ovarian suppression (OS), the cost of a chemotherapy (CT) cycle, and the side effects of having CT. Results from OS cost will be analyzed in depth to give a realistic picture of how these results are analyzed by the users. As mentioned earlier, such changes are often conducted in an arbitrary manner and there is no standard methodology for such changes. This example shows the impact of changing the three variables which are detailed in Table 5. The initial values, like most values in the model so far, are suggested by expert opinion. “Change 1” represents the first change carried out on the variables, and represents an increase on initial costs for OS and CT of 50%. “Change 2” represents a 100% increase. For CT side effects,

Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

Table 5. An example of altering values of the model’s variables Base value

Change 1

Change 2

OS Cost

£1968

£2952 (50% more)

£3936 (100% more)

CT Cost

£269/cycle

£403.5 (50% more)

£538 (50% more)

CT Side Effects (none, mild, moderate, nonfatal)

0, 69, 30, 1 (probability distribution)

0, 40, 30, 30

0, 20, 20, 60

both changes are concerned with the probability distribution of the different types of effects. For the purpose of this example we populated the model with 2,000 patients arriving in the span of around 13 months, which is one possibility based on the advice of health economists and clinicians involved in the trial. Tables 5, 6, and 7 show results for the above example. In each of the tables, model 1 shows results of runs with the initial values, model 2 shows results for “Change 1” and model 3 shows results for “Change 2.” Results are given in terms of CERs. CERs are calculated as follows: R=

CostA − CostC

EffectA − EffectC



Table 6. Cost-effectiveness results of altering OS costs Pair 1*

Model1

Model2

Model3

1907.11

3381.34

4778.11

while ‾CostA is the average cost of the adjuvant treatment, ‾CostC is the average cost for the control treatment, and ‾EffectA and ‾EffectC are average life years gained for adjuvant and control treatments respectively.1 Since we have a ratio of two averages then the resulting value is a biased estimator for the CER, for a large sample size (Cochran, 1977). However, the existing formula for deriving the standard error (SE) of a ratio (Bratley et al., 1987; Cochran, 1977) is not traceable for CERs. In this case the sum of the differences is not obtainable, as the values are sampled individually rather than as pairs. Also, the numbers of observations from both sets are not necessarily equal (since, for example, patients may die during their time in the model). For this reason we adopted a formula which evaluates the SE of a CER given by O’Brien et al. (1994), and used in healthcare. (see Box 1) For brevity subscript (CC) denotes the cost of treating control patients and (EC) is the life years gained by control patients. The same applies for adjuvant patients. C and A refer to control patients and adjuvant patients respectively. While m is the average value, s is the standard error, r is the correlation between costs and life years gained, n is the number of observations, all for their corresponding subscripts. It can be seen from the Tables 5, 6 and 7 that the effects of changing the variables are actually more considerable on some comparison pairs than Table 7. Cost-effectiveness results of altering CT costs Model1

Model2

Model3

1907.11

1040.41

3126.33

Pair 2

1362.02

2744.88

1205.02

Pair 1

Pair 3

2100.26

5331.62

9123.81

Pair 2

1362.02

3330.07

5829.9

Pair 4

1287.75

2410.69

3553.13

Pair 3

2100.26

922.04

52991.54

1287.75

1043.6

1479.28

Pair 5

1993.48

2145.78

1382.45

Pair 4

Pair 6

1034.26

1290.88

1010.85

Pair 5

1993.48

2333.73

6997.37

Pair 7

1596.68

1234.19

1297.41

Pair 6

1034.26

5727.42

3987.69

Pair 7

1596.68

1411.46

2200.26

* see Table 2 for identifying comparison pairs

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Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

Box 1. €€€€€€€€€€Consider

R= then

CostA − CostC

EffectA − EffectC 2 CA

2 CC

σ +σ nC var (R ) ≈ n A 2 (µEA − µEC ) (2)

(1)

  2  2    ρ σ ρ σ σ σ EA EC A C CA CC  σ EA + σ EC    +    2 n n n n A C A C  − 2 (µ − µ )   + (µCA − µCC )  CC CA 4 3   (µ EA − µ EC )    µ EA − µ EC ) (          

Table 8. Cost-effectiveness results of altering CT side effects distribution Model1

Model2

Model3

Pair 1

1907.11

2304.07

2581.5

Pair 2

1362.02

2856.9

12982.95

Pair 3

2100.26

2700.44

8703.19

Pair 4

1287.75

1622.74

1226.87

Pair 5

1993.48

2934.4

10385.59

Pair 6

1034.26

2159.65

4692.44

Pair 7

1596.68

1551.47

1963.01

others. Naturally, in arms where, for example, OS is not prescribed or randomized then the change in CER is irrelevant. A change in such arms is mostly based on sampling fluctuations. Figure 6 shows that, in terms of changes to OS cost, pair 1 is less sensitive to the 50% increase than to the 100% increase. Yet pair 3 is moderately sensitive for 50% and strongly sensitive for the 100% increase. We also find in Figure 6 that pair 2 is positively sensitive for 50% increase with a minute negative sensitivity when OS costs are increased by 100%. This is due the fact that both comparators may or may not have women who had OS, and whether the cost will increase or decrease will depend on the number of women who had OS at that particular run. For pair 4,

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results are similar to those of pair 1, with pair 4 being more sensitive to the 100% increase. For pair 5 we find that there is no change in results as both groups include women who had OS, which makes the proportion of change similar in both groups. The same principle applies for pairs 6 and 7, as there were no OS patients. The process of variable selection, as mentioned earlier, is based on the percentage of increase or decrease of a factor and its corresponding percentage of change in the CER. This analysis is actually conducted by the health economists. Based on the above example, health economists may decide, in the case where the cost of OS is 100% more than the initial value, that OS data is only important for pair 3. Yet the decision-making process may take other factors into consideration. The purpose of the model is mainly to help the health economists to make better decisions, rather than making the decision for them. This example shows the flexibility of the ABCSim model to accommodate all types of possible change for experimentation, by being able to alter input values (aggregately or individually) and examine their impacts on the model’s output. Facilities for such changes are advantageous in this exercise because they are tailor-made with regard to user requirements. Data presented in this example does not represent real data yet we are confident that

Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

Figure 6. Cost-effectiveness results: percentages of change based on model 1 and 2

this model will enable health economists to gain more insight into their problem and experiment with as many situations as they wish to develop their understanding of the problem space. As mentioned earlier, in addition to so many input variables, there are also output variables other than the CER, such as cost/utility ratios. Exploring the effects of variable changes on outputs enables the users to examine other angles of the problem. As well as being useful in carrying out detailed experimentation for variable identification, as shown in the previous example, we see the model as a key resource for stimulating and facilitating the development of understanding of the problems associated with adjuvant treatments of breast cancer. These understandings have been facilitated by the model, which has offered a configurable and accessible way for the different stakeholders to develop and negotiate their problem understandings. Involvement of the stakeholders throughout the development of the model also provided them with a sense of ownership and confidence in the model. This is central to the subsequent ABC RCT, which will only be informed by the modeling experience if those responsible for the RCT are prepared to act on the lessons that they learn from the model’s use.

CONCLUSION: LESSONS OF TRANSFERABLE VALUE Although the focus of this study was solely on clinical trials, the experience of developing the simulation model leads us to suggest simple but extremely valuable lessons, which we feel will be of transferable value across complex and unpredictable healthcare problems. The first relates to the inclusion of stakeholders in the modeling process and the accessibility of the resulting models. The ownership and confidence felt by stakeholders in our case is, we feel, extremely important and may provide an example to others developing models. This raises an associated area of limitation, though. The chapter clearly reports an example of advocacy modeling, where one takes on board the view of a particular stakeholder for whom one is working. For this particular example, health economists happened to come to us with the problem, asking that we helped them “identify key factors in the ABC trial for economic evaluation.” This naturally restricts the domain to their views of the real system, though these clearly change over time with the modeling. The interim and final models may well have been different if the problem owners or the modeling requirements had been different (i.e., if clinicians have been the primary or sole stakeholders). Even though there

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was a considerable contribution from clinicians with respect to the validity of the model, the final outcomes have mainly been driven by the health economists. As such, we consider this specific exercise as aimed at healthcare managers rather than healthcare professionals, and the model’s usefulness to different stakeholder groups should be seen in this light. This highlights the importance of being clear about the purpose of the model and its relation to the stakeholder(s) for whom it has been developed. The second lesson relates to the perception of models in complex domains such as healthcare. We feel that it is important that they are seen as vehicles for more closely understanding the system being modeled rather than techniques for finding solutions to those problems. Again, we would suggest that stakeholders have to have their expectations managed and see the real value of modeling in developing problem understanding and communication between stakeholders with differing perspectives. If this is not handled well, the stakeholders may well be unhappy when the model either does not provide a workable solution or provides a solution that is unacceptable to stakeholder groups. The closeness of stakeholders to develop negotiated models is important. A final lesson, which is often underplayed in modeling, is the value of using the model to look at the sensitivity of areas of the model. Since models are only ever approximations of the real system, there are times when we look to simplify the model. Varying the values of parameters in the model and examining the impact on the model’s outputs helps us gain a better appreciation of areas on which we should focus our modeling efforts. Given that many healthcare applications are extremely complex and that modeling is both difficult and time-consuming, exploring the sensitivity of the model as it is developed may help us target our efforts more wisely. In our case, the modeling was further complicated by a lack of data on which to draw in developing and assessing the structure and opera-

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tion of the model. The approach that was taken in this study to identify key variables was, we would argue, efficient and may form a basis for a methodology to model other systems where input data for modeling is unavailable. The power of such a methodology would lie in its ability to enhance stakeholder understanding of their problem space without the need to undertake large data collection exercises.

REFERENCES Balci, O. (1997). Principles of simulation model validation, verification, and testing. Transaction of the Computer Simulation Society International, 14(1), 3–12. Bratley, P., Fox, B. L., & Scharge, L. E. (1987). A guide to simulation (2nd ed.). New York: Springer Verlag. Buxton, M. J., Drummond, M. F., van Hout, B. A., Prince, R. L., Sheldon, T. A., Szucs, T., & Vray, M. (1997). Modeling in economic evaluation: An unavoidable fact of life. Health Economics, 6(3), 217–227. doi:10.1002/(SICI)10991050(199705)6:3<217::AID-HEC267>3.0.CO;2W Canney, P. A., & Hatton, M. Q. F. (1994). The prevalence of menopausal symptoms in patients treated for breast cancer. Clinical Oncology, 6(5), 297–299. doi:10.1016/S0936-6555(05)80270-5 Cochran, W. G. (1977). Sampling techniques (3rd ed.). New York: John Wiley & Sons. Davies, R., & Davies, H. T. O. (1994). Modeling patient flows and resource provision in health systems. Omega. International Journal of Management Science, 22(2), 123–131. Davies, R., & Flowers, J. (1995). The growing need for renal service. OR Insight, 8(2), 6–11. doi:10.1057/ori.1995.7

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Davies, R., & Roderick, P. (1998). Planning resources for renal services throughout UK using simulation. European Journal of Operational Research, 105(2), 285–295. doi:10.1016/S03772217(97)00235-X Delesie, L. (1998). Bridging the gap between clinicians and health managers. European Journal of Operational Research, 105(2), 248–256. doi:10.1016/S0377-2217(97)00232-4 Drummond, M. F., O’Brien, B., Stoddart, G. L., & Torrance, G. W. (1997). Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press. Goldhirsch, A., Gelber, R. D., & Castiglione, M. (1988). Relapse of breast cancer after adjuvant treatment in premenopausal and perimenopausal women: Patterns and prognosis. Journal of Clinical Oncology, 6(1), 89–97. Halpern, M. T., Brown, R. E., Revicki, D. A., & Togias, A. G. (1994). An example of using computer simulation to predict pharmaceutical costs and outcomes. In J.S. Tew, S. Manivannan, D.A. Sadowski, & A.F. Siela (Ed.), Proceedings of the 1994 Winter Simulation Conference (pp. 850-855). Lake Buena Vista: ACM. Hauge, J. W., & Paige, K. N. (2004). Learning Simul8: The complete guide (2nd ed.). Hillner, B. E., Smith, T. J., & Desch, C. E. (1993). Assessing the cost-effectiveness of adjuvant therapies in early breast cancer using a decision analysis model. Breast Cancer Research and Treatment, 25(2), 97–105. doi:10.1007/BF00662134 Hines, W. W., & Montgomery, D. C. (1990). Probability and statistics in engineering and management science (3rd ed.). Singapore: John Wiley & Sons. Hurley, S. F., Huggins, R. M., Snyder, R. D., & Bishop, J. F. (1992). The cost of breast cancer recurrences. British Journal of Cancer, 65(3), 449–455.

Klein, R. W., Dittus, R. S., Roberts, S. D., & Wilson, J. R. (1993). Simulation modeling and health care decision making. Medical Decision Making, 13(4), 347–354. doi:10.1177/0272989X9301300411 Lagergren, M. (1998). What is the role and contribution of models to management and research in the health services? A view from Europe. European Journal of Operational Research, 105(2), 257–266. doi:10.1016/S0377-2217(97)00233-6 O’Brien, B. J., Drummond, M. F., Labelle, R. J., & Willan, A. (1994). In search of power and significance: Issues in the design and analysis of stochastic cost-effectiveness studies in health care. Medical Care, 32(2), 150–163. doi:10.1097/00005650-199402000-00006 Oakshott, L. (1997). Business modelling and simulation. London: Pitman Publishing. Paul, R. J. (1995). Outpatient clinic: The CLINSIM simulation package. OR Insight, 8(2), 24–27. doi:10.1057/ori.1995.11 Paul, R. J., & Balmer, D. W. (1993). Simulation modeling. Lund: Chartwell-Bratt. Pidd, M. (1996). Tools for thinking: Modeling in management science. Chichester: John Wiley & Sons. Pitt, M. (1997). A generalised simulation system to support strategic resource planning in healthcare. In S. Andradottir, K.J. Healy, D.H. Withers, & B.L. Nelson (Ed.), Proceedings of the 1997 Winter Simulation Conference (pp. 1155-1162). New York: ACM. Russell, L. B., Gold, M. R., Siegel, J. E., Daniels, N., & Weinstein, M. C. (1996). The role of cost-effectiveness analysis in health and medicine. Journal of the American Medical Association, 276(14), 1172–1177. doi:10.1001/ jama.276.14.1172

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Schulman, K. A., Lynn, L. A., Glick, H. A., & Eisenberg, J. M. (1991). Cost effectiveness of low-dose zidovudine therapy for asymptomatic patients with Human Immunodeficiency Virus (HIV) infection. Annals of Internal Medicine, 114(9), 798–802. Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making. Medical Decision Making, 13(4), 322–338. doi:10.1177/0272989X9301300409 Treasury, H. M. (1997). Appraisal and evaluation in central government. Treasury Guidance. London: The Stationary Office.

Weinstein, M. C., Siegel, J. E., Gold, M. R., Kamlet, M. S., & Russell, L. B. (1996). Recommendations of the panel on cost-effectiveness in health and medicine. Journal of the American Medical Association, 276(15), 1253–1258. doi:10.1001/ jama.276.15.1253

ENDNOTE 1



The model also provides results for discounted QALYs, which means that users can also use discounted QALYs for measuring cost-effectiveness should they prefer to do so.

This work was previously published in Simulation and Modeling: Current Technologies and Applications, edited by Asim El Sheikh and Abid Thyab Al Ajeeli, pp. 219-243, copyright 2008 by IGI Publishing (an imprint of IGI Global).

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Chapter 6.10

How Can Human Technology Improve the Scheduling of Unplanned Surgical Cases? Janna Anneke Fitzgerald University of Western Sydney, Australia Martin Lum Department of Human Services, Australia Ann Dadich University of Western Sydney, Australia

INTRODUCTION Human technology in health care includes managerial knowledge required to marshal a health care workforce, operate hospitals and equipment, obtain and administer funds, and, increasingly, identify and establish markets. In this article, the authors focus on human technology and improvement of decision-making processes in the context of operating theatre scheduling of unplanned surgical cases. DOI: 10.4018/978-1-60960-561-2.ch610

Unplanned surgery refers to unscheduled and unexpected surgical procedures in distinction to planned, elective surgery. The management of unplanned surgery is a strategic function in hospitals with potential clinical, administrative, economical, social, and political implications. Making health care management decisions is complex due to the multidisciplinary and the multifocussed nature of decision-making processes. The complexity of multidisciplinary and multifocussed decisionmaking is further exacerbated by perceived professional identity differences.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

How Can Human Technology Improve the Scheduling of Unplanned Surgical Cases?

This article presents findings from interviews with doctors and nurses about the scheduling of unplanned surgical cases. The interviews focused on current decision-making determinants, the acceptability of using a model to guide decisionmaking, and enablers and barriers to implementing the model. The key finding was the limited practicality of a model to guide the scheduling of unplanned surgery. While it could guide decisions around clinical determinants, logistical determinants, and ideal timeframes, it would have difficulty reshaping inter- and intra-professional dynamics.

LITERATURE REVIEW The scheduling of unplanned surgery typically involves negotiating (and renegotiating) established surgery lists, whereby patients requiring nonelective surgery are attended to before those requiring elective surgery (Gabel, Kulli, Lee, Spratt, & Ward, 1999). However, delayed patient access to unplanned surgery can inflate economic costs (Jestin, Nilsson, Heurgren, Påhlman, Glimelius, & Gunnarsson, 2005; Pollicino, Haywood, & Hall, 2002). Poor patient health necessitates more health care services, including prolonged hospital stays, ongoing access to health care professionals, and medication. The tension between health care cost containment and the high cost of surgical operations is a powerful incentive for health care organisations to improve the management of the surgical suite (Gabel et al., 1999). Thus, an investigation of current scheduling practices of unplanned surgical cases is well-justified. Empirical research on the scheduling of unplanned surgery is limited. This is somewhat reflected in the ad hoc practices found in some operating rooms when organising surgical queues (Fitzgerald, Lum, & Kippist, 2004). In Canada for instance, it was found that the waiting times for elective surgery were not only determined by the number of patients on the waiting list, or by how

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urgently they required treatment, but also by the management of the waiting list (Western Canada Waiting List Project, 2001). To ensure consistent practice, criteria were developed to guide decisionmaking processes. However, clinicians who managed the waiting lists were somewhat reluctant to change their management practices, preferring to adhere to less-standardised, conventional methods (Martin & Singer, 2003). In the United Kingdom, surgical lists are typically compiled in an unplanned manner, and the negotiations and modifications that follow are also extemporised (Hadley & Forster, 1993). Even when theatre lists are established, they are seldom observed, often because of the need to accommodate patients who require unplanned surgery (Ferrera, Colucciello, Marx, Verdile, & Gibbs, 2001). This gives rise to extended surgery delays. Given such inconsistencies, the National Health Strategy (NHS) Executive circulated a national directive to guide good practice (Churchill, 1994). The directive emphasised the significance of operating room services, the prominence of patient care, the effective management and supervision of operating room use, staff morale, and communication, efficient transportation for patients, and dependable activity and cost information. A standardised approach to manage operating room lists is also lacking in New South Wales (NSW), Australia (NSW Health, 2002a). This is not to suggest that NSW public hospitals lack direction for scheduling unplanned cases. Many have adopted priority codes to guide these decisions (NSW Health, 2002b). These help health care professionals to classify patients from highest to lowest priority based clinical need and/or timeframes. However, research in NSW suggests that, in addition to clinical determinants, other factors influence the scheduling of unplanned surgery (Fitzgerald et al., 2004; King, Kerridge, & Cansdell, 2004); notably, logistics and inter- and intraprofessional dynamics between staff involved in the process. Current policies for scheduling

How Can Human Technology Improve the Scheduling of Unplanned Surgical Cases?

surgery, particularly unplanned surgery, fail to acknowledge these, providing little direction for accepted practice. A review of literature on surgical decisionmaking practices suggests that the normative model of decision-making is decision theory (Gordon, Paul, Lyles, & Fountain, 1998; Magerlein & Martin, 1978; Parmigiani, 2002; Wright, Kooperberg, Bonar, & Bashein, 1996). Its ability to comprehensively consider information from diverse sources, especially in situations of great uncertainty, makes it particularly valuable. Decision theory in operating theatres manifests through several models, including queuing theory (NSW Health, 1998) and the Poisson distribution model (Kim & Horowitz, 2002). Queuing theory commonly operates on a first-come-first-served basis, whereby priority is determined by chronology. However, in the case of unplanned surgery, where patient health outcomes are at-risk, this is illogical; and in the absence of explicit queuing rules, the theory causes confusion (Fitzgerald et al., 2004). In contrast, the Poisson distribution model operates with greater autonomy and is able to represent occurrences of a particular event—like unplanned surgery—over time or space. Using the Poisson distribution model for unplanned surgery would allow for the random arrival of patients, for assuming independence from other patient arrivals, and it supposes independence from the state of the hospital system (Clemen & Reilly, 2001). However, the model does not account for the multifactorial nature of clinical determinants and their relationship with time. Nor does it guide decisions in an environment where logistics and professional dynamics influence efficiency. Therefore, research into decision-making practices is needed to explore the tensions affecting the applicability of different queuing models.

Clinical Determinants and Time Most operating rooms classify unplanned surgery according their relative urgency (Gabel et al.,

1999). Although formalised classification systems are often lacking (NSW Health, 2002a), hospitals typically use a triage system to consider clinical determinants when scheduling both planned and unplanned surgery. Clinical decision-making practices therefore involve elements of rational choice theory (Johnson, 2000). It is deductive, based on evidenced-based practices that maximise patient health, and is therefore judicious. However, there is no consistent system to allocate priority between unplanned cases (King et al., 2004). Furthermore, there is little quality auditing of the way clinical priorities are assigned. It is therefore possible that other factors determine the scheduling of unplanned surgery; critics of decision theory allude to this (Kahneman & Tversky, 1979; Redelmeier, Rozin, & Kahneman, 1993). The unpredicted and often urgent nature of unplanned surgery suggests that time is an important factor when scheduling such surgery. Indeed some triage guidelines are entirely time-based, whereby certain clinical conditions must be done within a certain timeframe to prevent further loss of quality of life; there are thus serious time pressures within the operating room (Gabel et al., 1999) that may have direct consequence for clinical decision-making.

Logistical Determinants Logistical determinants can further complicate the scheduling of unplanned surgery. A report concerning two public hospitals in Sydney, Australia, recognises the difficulties caused by limited resources and the ineffective management of operating theatres (King et al., 2004). For instance, it explains that while middle management personnel perform the executive management of the operating theatres, they are often absent or involved with other duties, leaving direct decision-making responsibilities to other, variably qualified staff. The report also mentions the physical fragmentation of operating theatre space; the management of large emergency and semi-urgent caseloads; and

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the inadequacy of systems to ensure that trauma patients are scheduled and prepared efficiently. There are many more logistical determinants, including the availability of expert and support staff, specialist instruments, and theatre time and space. Therefore, despite the presenting clinical needs of the surgical patient, other factors can delay timely surgical intervention (Lebowitz, 2003). These in turn, have considerable economic implications (Bogner, 1997).

economic restraints, such as the staff-overtime budget. Together, these circumstances can create a climate of tension (Gabel et al., 1999). For those managers who use finesse and compromise to pursue a goal, confrontational behaviour from colleagues who demand perfectionism maybe difficult to manage. They may find themselves struggling to communicate appropriately, which, in turn, stifles the facilitation of effective management within the operating theatre.

Professional Identity

Summary

Ineffective management is also partly consequent to surgeon desire to retain professional discretion (Buchanan & Wilson, 1996). Sociological literature has long recognised the domination of the medical profession (Freidson, 1970; Illich, 1977; Olesen, 2001). It is a profession characterised by authority, autonomy, and sovereignty (Bogner, 1997). However, these attributes are not created and maintained by the medical profession in isolation; they are also influenced by the perceptions others have of the medical profession (Fitzgerald, 2002). These perceptions help to create strongly-defined professional boundaries that can divide different professional paradigms. Some of these boundary constructs include power, status, scientific educational background, clarity of task, and reward. The professional boundary between doctors and nurses is crystallised on each of these constructs. Additionally, communication difficulties within and between professions are exacerbated by the lack of clear power structure between surgeons, anaesthetists, and nurse-managers within the operating theatre. This is particularly the case between surgeons and anaesthetists (Bogner, 1997; King et al., 2004). Most operating room logistics are managed by the operating theatre manager, who is typically a nurse. This person is frequently the decision-maker who has a managerial perspective, incorporating the availability of beds and human resources with

A number of factors affect the scheduling of unplanned surgery. These include clinical determinants and time, logistical determinants and the dynamics between professionals. Because the complex nature of scheduling unplanned surgery can increase workplace frustration (Creswell, 2003; Stake, 1995), it is important to identify a framework that will facilitate effective decisionmaking practices in this area (Myers, 1997; Wilson, 2005). However, the implementation of this framework may pose difficulties. To examine this possibility, this article presents findings from interviews with doctors and nurses, all of whom were presented with a decisionmaking model for scheduling unplanned surgical cases. The model was informed by previous research, which identified the clinical determinants, logistical determinants and ideal timeframes for scheduling unplanned surgical cases, according to doctors and nurses (Fitzgerald, Lum, & Dadich, 2006). Because the value of the model was largely unknown, it was crucial that its practicability be examined by those at the coalface.

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METHOD To recruit interviewees, the research team attended a statewide workshop at which 71 NSW operating rooms were represented. Operating room managers were invited to nominate their hospital as a

How Can Human Technology Improve the Scheduling of Unplanned Surgical Cases?

possible research site. From the nominations, the researchers selected four metropolitan and four rural hospitals, each from different Area Health Services to limit geographical bias. Approval to conduct the research was gained from the university and Area Health Service ethics committees. Hospital site coordinators were appointed for each participating hospital to assist with data collection. Hospital Site Coordinators were asked to recruit decision-makers in operating rooms, including surgeons, anaesthetists, nurse-managers, floor-managers, and administrators. Coordinators were then asked to invite interviewees to voluntarily contribute to the study. A semistructured interview schedule was devised (and piloted) to explore the following themes: • • • •

•

•

Current practices in the scheduling of unplanned surgery; The influence of clinical and time determinants; The influence of logistical determinants; The role of inter- and intra-professional dynamics when scheduling unplanned surgery; Methods to improve decision-making around the scheduling of unplanned surgery; and Thoughts about the proposed model (Fitzgerald et al., 2006).

Each interview was audio-taped and transcribed verbatim. QSR N-Vivo® software was used to aid detailed analysis of the data, facilitating the interpretation process (Creswell, 1998). This facilitated the emergence of core themes. To ensure consistency within each theme, codebooks were developed that included detailed descriptors of each theme, inclusion and exclusion criteria as well as exemplars from the research material.

RESULTS Interviewees included surgeons (n=17), anaesthetists (n=9), and nurse- and nonclinical managers (n=12). There was much diversity in managerial qualifications—two (7%) doctors had undertaken internal management courses, while nine managers (83%) held qualifications ranging from certification to master degrees in management. Corresponding with the literature review and the preceding discussion of clinical determinants and time, logistical determinants, and professional identity, the following section presents research findings that verify the influence of these factors on the scheduling of unplanned surgery. However, unlike existing literature, the findings suggest that the speed and efficiency of decision-making processes are mostly influenced by inter- and intra-professional dynamics.

Clinical Determinants and Time Presented with the proposed model, the interviewees were asked to consider a taxonomy in which three clinical states were labelled urgency 1, urgency 2, and urgency 3. Collectively, they agreed that these categories represented conditions of extreme urgency, intermediate urgency, and no urgency. Within each category, there was recognition that specific physiological states were associated with different timeframes for surgical treatment: The things that matter most are the urgency one and no one need a protocol to work out what’s an urgency one… I have never seen an argument about that, everyone’s in total agreement about its uniformity (surgeon). It was evident that for categories other than extreme urgency, intuition was used to order the cases and times of surgery:

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we got to feel it…. how things are likely to flow and give them our best guess (surgeon). Although the three categories were statistically distinct, variations in acceptable responsiveness produces overlap and some ambiguity between the categories. Interviewees agreed that the overlap and ambiguity contributed to a “grey-zone”: when you got two cases that are very close, they probably would all come within more than one of these type of the categories (clinical care coordinator). Although there was clear distinction between extreme urgency, intermediate urgency, and no urgency, there was ambiguity around the boundaries that separated each entity. Thus, five categories of clinical urgency might have the scope to accommodate individual case variation and overlap. A five-category model may help clinicians accept the need for dynamic decision-making. It also makes more explicit the clinical reasoning process for more transparent inputs, and provides common ground for reaching inter- and intra-professional agreement.

Logistical Determinants Several logistical determinants influence the scheduling of unplanned surgery within NSW public hospitals, including the availability of space, theatre time, and staff, particularly surgeons. Because these are highlighted in relevant literature (Bogner, 1997; King et al., 2004; Lebowitz, 2003), focus will be given to findings seldom noted in the literature.

Inter- and Intra-Professional Dynamics Most interviewees suggested that inter- and intraprofessional dynamics influence the scheduling of unplanned surgery. These suggestions were often

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couched in observations about communication between personnel, which was sometimes cantankerous. This in turn, created clear distinctions between professions that are typically involved in the decision-making process—surgeons, anaesthetists and nurses. Furthermore, the comments suggest a strong hierarchy that sees surgeons having most decision-making power, followed by anaesthetists and nurses: Some [surgeons] get very impatient and make a lot of noise… and we have used the anaesthetic department sometimes to adjudicate (nurse). the consultant on-call has a power to veto [the decision to continue with planned surgery] and if you aren’t happy with that decision, you call the consultant anaesthetist and see whether they’ll… open an extra operating theatre… I guess… it comes down to… how hard the surgeon is prepared to push for the continuation of planned surgery, verses how hard the anaesthetic people are prepared to push for an extra theatre (anaesthetic registrar). it’s basically a situation where the nursing staff are really not making any decisions about patients; it’s purely related to the medical team (surgeon). Explicating these professional divisions, some interviewees noted the personal agendas that personnel bring to the decision-making process. Medical terms that lack an irrefutable definition are used to further individual interests: surgeons tell fibs about what might be an emergency to suit themselves. That makes decisions difficult because they don’t care if they categorise something a little bit differently, because they might have something to do elsewhere. They actually come out and admit that, but then they stick to their guns, that, “Yes this is an emergency” (nurse).

How Can Human Technology Improve the Scheduling of Unplanned Surgical Cases?

The hierarchical divisions between the three professions suggest concord within the professions. However, some interviewees recognised times of limited consensus among members of the same profession when attempting to schedule unplanned surgery. I’ve seen conflict occur… There might be 100 surgeons of high-standing and ask them to assess the patient and you will get 100 different opinions about the diagnosis, let alone the urgency of treatment! (surgeon) Occasionally, intra-professional disharmony was due to the different roles assumed by individual professionals. Some surgeons, anaesthetists, and nurses have accepted managerial roles within the hospital. With added responsibilities, they often bring an organisational perspective to the decision-making process. In the scheduling of unplanned surgery, they adopt a system-approach, ensuring the efficient use of existing resources: We get caught up by nursing intensive care patients in recovery because there was no thought given to the aftercare the patient might require (manager). Thus, a multifaceted decision-making process becomes more complex by managerial responsibilities that can (or should) constrain decisions. The dual-identity of clinician-cum-managers can also generate intra-professional dissension. Non-managerial personnel are seen to “lack insight”; they are believed to have a limited understanding of the way decision-making processes affect the hospital as an organisation: It is all very well to keep on providing services at all hours, at all costs. But we can’t continue to do this in that way (manager). Complex inter- and intra-professional dynamics suggest that models to guide decision-making

practices can only be successful when there is opportunity to enhance these interactions.

DISCUSSION This article demonstrates that while clinical state, time, and logistics influence the scheduling of unplanned surgical cases, equally important are inter- and intra-professional dynamics. Divergent views about the role of different professions depict an individualistic view of a context that requires collaborative decision-making. Surgeons in particular portray clear jurisdictions for clinical decision-making when scheduling unplanned surgery. Nevertheless, there was some consensus between the different professions. Those interviewed recognised value in a model that guided the scheduling of unplanned surgery. The example presented to them resonated well, reflecting internal processes that are used intuitively (Fitzgerald et al., 2006). While some doctors and nurses warned against prescriptive and mechanistic protocols, surgeons, anaesthetists, and nurse-managers generally accepted a tool that provides common dialogue and facilitates effective decision-making. The proposed model is thus a framework to bridge gaps in professional positioning. It allows dialogue to be redirected from individualistic drivers to greater collaboration. Reflecting existing literature, the interviewees highlighted key determinants that influenced the scheduling of unplanned surgery; namely, clinical determinants and time (Gabel et al., 1999; Johnson, 2000), logistical determinants (Bogner, 1997; King et al., 2004; Lebowitz, 2003), and inter- and intra-professional dynamics (Buchanan & Wilson, 1996; Fitzgerald, 2002). With regard to clinical determinants, the interviewees made clear distinctions between cases that are of extreme urgency, intermediate urgency, and no urgency. However, there was little agreement about where the boundaries lie that separate each

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category. Making decisions around the parameters of these three urgency classification may be assisted by a risk assessment model that incorporates reflection about likelihood and consequence of adverse events when delaying unplanned surgery. Logistical determinants were also regarded as significant. The interviewees highlighted the availability of space, theatre time, and staff, particularly surgeons. The intensity and volume of discussion on workplace interactions suggests that inter- and intra-professional dynamics impact more on the scheduling of unplanned surgery than current literature suggests. Dynamics between and within professions involved in this process can hinder the effective prioritisation of cases. Factors that thwart communication include the hierarchy that divides surgeons, anaesthetists, and nurses, personal agendas, difference of medical opinion within a profession, and the hybridity of roles assumed by clinician-cum-managers. However, a number of methodological limitations must be considered. The cross-sectional nature of this study indicates that the interviewees provide a snapshot of the decision-making practices around the scheduling of unplanned cases within NSW public hospitals. Furthermore, qualitative research is limited by time, context, and the nature of individual perspectives (both the interviewee’s and the researcher’s interpretation). The use of convenience sampling to recruit interviewees may have also biased the findings.

FUTURE RESEARCH Despite these limitations, the findings are consistent with those found in the existing body of relevant literature. However, most of this literature focuses on concrete triage determinants and little is published on inter- and intra-professional dynamics. Therefore, these findings pave the way for future research, particularly on the changing nature of professional jurisdictions in a climate

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where inter- and intra-professional decisionmaking is expected, as well as the organisational effects of hybrid roles.

CONCLUSION In conclusion, this article confirms the dynamic and complex nature of decision-making around the prioritisation of unplanned surgery. It also suggests variability in the way unplanned cases are scheduled. In light of this, efforts to develop standardised practices through policy development may be somewhat futile, for they fail to appreciate the contextual differences of each hospital setting. Nonetheless, a decision-making tool, such as was presented to the interviewees, can improve decision making practices by acting as a catalyst for dialogue between and within professions when scheduling unplanned surgery.

REFERENCES Bharadwaj, A., Sen, A., & Vinze, A. (1999). Scheduling cardiac procedures: A knowledge-based approach. IEEE Transactions on Engineering Management, 46(3), 322–334. doi:10.1109/17.775284 Bogner, M. S. (1997). Naturalistic decision making in health care. In C. E. Zsambok & G. A. Klein (Eds.), Naturalistic decision making (pp. 61-69). Mahwah, NJ: Lawrence Erlbaum Associates. Buchanan, D., & Wilson, B. (1996). Occasional paper 34: Re-engineering operating theatres: The perspective assessed. Leicester: Leicester Business School. Churchill, K. (1994). Good practice in operating theatre management. Leeds: NHS Executive. Clemen, R. T., & Reilly, T. (2001). Making hard decisions with decision tools. Pacific Grove, CA: Duxbury.

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Creswell, J. W. (1998). Qualitative inquiry and research design: Choosing among five traditions. Thousand Oaks, CA: Sage Publications.

Health, N. S. W. (1998). Booked patient and waiting time management: Operating guidelines. Sydney, Australia: NSW Health.

Creswell, J. W. (2003). Research design: Qualitative, quantitative, and mixed methods approaches (2nd ed.). Thousand Oaks, CA: Sage Publications.

Health, N. S. W. (2002a). NSW Health operating theatre management project report. Sydney, Australia: NSW Health.

Ferrera, P. C., Colucciello, S. A., Marx, J. A., Verdile, V. P., & Gibbs, M. A. (2001). Trauma management: An emergency medicine approach. St Louis, MO: Mosby Inc.

NSW Health. (2002b). NSW Health operating theatre management project report: Supplementary report on information management survey. Sydney, Australia: NSW Health.

Fitzgerald, J., Lum, M., & Dadich, A. (2006). Scheduling unplanned surgery: A tool for improving dialogue about queue position on emergency theatre lists. Australian Health Review, 30(2), 219–231.

Illich, I. (1977). Disabling professions. In I. Illich, I. K. Zola, J. McKnight, J. Caplan & H. Shaiken (Eds.), Disabling professions (pp. 11-39). London, England: Marion Boyars Publishers.

Fitzgerald, J. A. (2002). Doctors and nurses working together: A mixed method study into the construction and changing of professional identities. Unpublished Doctorate, University of Western Sydney, Sydney, NSW. Fitzgerald, J. A., Lum, M., & Kippist, L. (2004, September 25-27). Operating theatre bottlenecks: How are decisions about emergency theatre schedules made? Paper presented at the 5th International CINet conference, Sydney, NSW. Freidson, E. (1970). Professional dominance: The social structure of medical care. Hawthorne, NY: Aldine Publishing Company. Gabel, R. A., Kulli, J. C., Lee, B. S., Spratt, D. G., & Ward, D. S. (1999). Operating room management. Woburn, MA: Butterworth-Heinemann. Gordon, T., Paul, S., Lyles, A., & Fountain, J. (1998). Surgical unit time utilisation review: Resource utilisation and management implications. Journal of Medical Systems, 12, 169–179. doi:10.1007/BF00996639

Jestin, P., Nilsson, J., Heurgren, M., Påhlman, L., Glimelius, B., & Gunnarsson, U. (2005). Emergency surgery for colonic cancer in a defined population. British Journal of Surgery, 92, 94–100. doi:10.1002/bjs.4780 Johnson, A. G. (2000). The Blackwell dictionary of sociology: A user’s guide to sociological language. Oxford, UK: Blackwell Publishers. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 262–291. doi:10.2307/1914185 Kim, S.-C., & Horowitz, I. (2002). Scheduling hospital services: The efficacy of elective-surgery quotas. Omega, 30(5), 335–346. doi:10.1016/ S0305-0483(02)00050-6 King, D., Kerridge, R., & Cansdell, D. (2004). Ryde/Royal North Shore Hospital review. Sydney, NSW: MA International. Lebowitz, P. (2003). Why can’t my procedures start on time? Association of Operating Room Nurses Journal, 77(3), 594–597.

Hadley, R., & Forster, D. (1993). Doctors as managers: Experiences in the front-line of the NHS. Harlow: Longman.

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Lum, M., & Fitzgerald, A. (2007). Dialogues for negotiating priorities in unplanned emergency surgical queues. In R. Iedema (Ed.), Discourses of hospital communication: Tracing complexities in contemporary hospital organizations. Basingstoke: Palgrave-Macmillan. Magerlein, J. M., & Martin, J. B. (1978). Surgical demand scheduling: A review. Health Services Research, 13, 418–433. Martin, D., & Singer, P. (2003). Canada. In C. Ham & G. Robert (Eds.), Reasonable rationing: International experience of priority setting in health care (pp. 42-63). Berkshire, England: Open University Press. Myers, M. D. (1997). Qualitative research in information systems. MIS Quarterly, 21, 241–242. doi:10.2307/249422 Olesen, H. S. (2001). Professional identity as learning processes in life histories. Journal of Workplace Learning, 13(7/8), 290–297. doi:10.1108/13665620110411076 Parmigiani, G. (2002). Modeling in medical decision making: A Bayesian approach. West Sussex, England: John Wiley & Sons. Pollicino, C., Haywood, P., & Hall, J. (2002). Economic evaluation of the proposed surgical scheme at Auburn Hospital (No. 19). Sydney, Australia: CHERE (Centre for Health Economics Research and Evaluation). Redelmeier, D. A., Rozin, P., & Kahneman, D. (1993). Understanding patients’ decisions: Cognitive and emotional perspectives. Journal of the American Medical Association, 270, 72–76. doi:10.1001/jama.270.1.72 Stake, R. E. (1995). The art of case study research. Thousand Oaks, CA: Sage Publications. Western Canada Waiting List Project. (2001). From chaos to order: Making sense of waiting lists in Canada: Final report. Edmonton: WCWL Project, University of Alberta. 1768

Wilson, T. (2005). Structure in research methods. Retrieved February 5, 2008, from http://informationr.net/tdw/publ/ppt/ResMethods/sld001.htm Wright, I. H., Kooperberg, C., Bonar, B. A., & Bashein, G. (1996). Statistical modeling to predict elective surgery time: Comparison with a computer scheduling system and surgeon provided estimates. Anaesthesiology, 85, 1235–1245. doi:10.1097/00000542-199612000-00003

KEY TERMS AND DEFINITIONS Clinical Determinants: Physical or physiological patient characteristics that influence the priority assigned to a surgical case, and in turn, influence patient health and economic outcomes. These include (but are not limited to) degree of risk to life, limb or senses, type of wound or injury, degree of haemodynamic stability, extent of pain, patient age, and diagnostic procedure required. Emergency Surgery: Emergency surgery is required by a patient whose poor health requires urgent attention; this is most evident when there is risk to life, limb, or organ. An emergency is commonly understood to be an unforeseen combination of circumstances that demands immediate action, or an urgent need for assistance or relief. In the medical setting, it implies an injury or illness that poses an immediate threat to a person’s health or life. In the clinical context, however, determining the immediacy and urgency of interventions becomes the ground for discourse between health care providers within the hospital setting. Each stakeholder offers different understandings of the term, emergency (Lum & Fitzgerald, 2007). Human Technology: The body of information, skills, and experience developed for the production and use of goods and services. Logistical Determinants: Practical or operational factors that influence the priority assigned to a surgical case, and in turn, influence patient health and economic outcomes. These include (but are not limited to) the availability of beds,

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hospital equipment, specialist instruments, surgery schedules, theatre time and space, as well as expert and support staff. Non-Elective Surgery: See emergency surgery. Professional Identity: An ongoing concern of the professional involving the practice of his or her work, social interactions with colleagues and patients, and his or her place within the professional institution and the professional discourse. It is thus socially bestowed, socially sustained, and socially transformed.

Scheduling: “A complex activity where human schedulers tend to make use of a wide range of knowledge, heuristics, and intuition” (Bharadwaj, Sen, & Vinze, 1999, p. 322). Unplanned Surgery: Any surgical procedure that is not scheduled and thus, unexpected. While typically referred to as emergency surgery, the term unplanned surgery is preferred because of the variable understandings associated with the term emergency surgery.

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, pp. 686-694, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 6.11

TACMIS:

A Total Access Care and Medical Information System M. Cassim Ritsumeikan Asia Pacific University, Japan

ABSTRACT TACMIS is an inclusive solution to the management of health care and medical information and its design is based on a detailed process analysis of patient journeys and the pathways of clinical care of stroke patients as they progress from acute care, through rehabilitation to discharge and independent living, often with a residual disability. The findings are the work of a team based in the Discovery Research Laboratory at Ritsumeikan University in Japan. The clinical analysis was DOI: 10.4018/978-1-60960-561-2.ch611

conducted at King’s College Hospital in London and in several care institutions for the disabled and the aged in Japan.

INTRODUCTION Background, Aims and Focus How can disabled and aged populations gain access to and benefit from information and communications technologies (ICT) through the development of inclusive design systems? This was the fundamental question asked when the

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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program began in May 2000. It was initiated as a cross-national collaborative research and development program of the Centre for Global Education and Research (CGER) at Ritsumeikan University, and is currently being executed at the Discovery Research Laboratory (DRL) established within CGER to incubate projects that link ICT with human, social and environmental needs (Cassim, M., 2004). TACMIS is a project that aims to create exemplars for this form of interlinking in the field of health care. This chapter will focus on the inclusive design aspects of TACMIS. The TACMIS system is a composite of three integrated subsystems: •

•

•

HIMS: A Hospital Information Management System, which largely deals with the acute care phase and rehabilitation in a secondary care situation; SEAHCSS: A Socio-Economic and Health Care Support System, which extends the findings of HIMS into primary care situations and into the aggregate realm of epidemiology and health care policy; and PEECSS: A Patient Empowerment and Environmental Control Support System, which extends care into the home environment and supports independent living.

The development work carried out thus far focuses on HIMS and PEECSS, with SEAHCSS seen as likely to evolve as a natural extension through dialogue with stakeholders involved in health care policy formulation. The chapter describes the access technologies used for integrated and inclusive solutions to health informatics issues in general and for dealing with stroke disability in particular. The findings indicate that such solutions will enhance the quality of electronic patient and health records, enabling them to contribute directly to improvements in a patient’s individual care. They will also support a more enjoyable level of independent living for stroke victims with a

residual disability, who are seen as a microcosm of the wider disabled and aged populations.

TACMIS System Design and Key Questions TACMIS commenced with an analysis of health informatics needs in several care and medical institutions in Japan and of national trends in several selected countries, including the United Kingdom. Based on this, the core technologies to be used in working towards prototype development were clarified, selected after discussions at several rounds of Technology Seeds Seminars, held in Japan and the USA. Next, the scope of the project was defined when it was decided to work with stroke patients and their residual disabilities of neurovascular origin, and a case study was designed. This has been conducted as a collaborative exercise between DRL/CGER at Ritsumeikan University, GKT Medical School at King’s College London and the Acute Stroke Unit at King’s College Hospital. The output of this exercise, the conceptual systems design of an integrated and inclusive health informatics system for the TACMIS prototype, is described below. As noted above TACMIS is tripartite in composition (Figure 1), comprising of: (1) HIMS: A Hospital Information Management System, which largely deals with the acute care phase and rehabilitation in a secondary care situation; (2) SEAHCSS: A Socio-Economic and Health Care Support System, which extends the findings of HIMS into primary care situations and into the aggregate realm of epidemiology and health care policy; and (3) PEECSS: A Patient Empowerment and Environmental Control Support System, which extends care into the home environment and supports independent living. The three components are integrated into a holistic health informatics record, with the patient/care receiver seen as the integrating element.

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Figure 1. The TACMIS design concept (Source: Cassim, 2004)

In this chapter integration is seen as the process of bringing together critical elements of the system, the information it contains and the stakeholders involved. The purpose of integration is to enhance the performance of the system (in terms of speed, quality, reliability, etc) and to work towards enabling those participants (stakeholders) in the system who are disadvantaged to contribute more effectively. Inclusion, in the case of TACMIS, is seen as the process which makes it possible for the target of health care (the stroke patient) to participate to the fullest extent possible in the care process, in daily living activities (DLA) and in contributing socially and culturally at the highest conceivable level. Access technologies are the means by which this possibility is afforded to the target of care either directly or indirectly. The latter includes technologies that facilitate system integration. Work conducted thus far on TACMIS indicates that information integration is the key to arriving at successful inclusive solutions. This includes the integration of the following three categories: 1. Disability needs, lifestyle preferences and health condition information, including links to a referral case record of clinical narratives;

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2. Environmental constraints information, covering the physical, sensory and cognitive environments; and 3. Capability assessment information, including a referral case record of individual coping strategies. Understanding the nature of this information and analysis of the modules of information relevant to the design task being addressed would be the starting point for designers involved in TACMIS. The design tasks generated by TACMIS would include systems design, interface design, product design, service design, policy design and the design of a successful business model.

ACCESS TECHNOLOGIES: THE TAP-TAS CORE CONCEPT AND OTHER SELECTED TECHNOLOGIES Keeping abreast of technological developments is a daunting but necessary condition for TACMIS’ design team members. Both the exponential increase in computing power (Moore’s Law) and advances in telematic transmission technology make developments in ICT a continual and rapidly

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moving target. Thus, it was felt that a binding underlying concept was required to prevent the project team from becoming too enamoured by devices, gimmicks and gadgets, many of which might soon become obsolete. The answer came in the form of the integrated system design based on a detailed analysis of the processes involved in stroke patient care, indicated in Figure 2. This became the guide for directing the different work components into a single holistic entity. Under this framework, for selecting access technologies, Neil Scott’s concept of a Total Access System (TAS) and its accompanying core device, the Total Access Port (TAP), was inspirational (Scott, 2003a& b; Figure 2). The beauty of Scott’s idea was to separate the accessor, which was often very expensive when it had to be customized for disability, from the target system, which was often very cheap and mass-produced as in the case of PC-type computer systems, through a bridging device, a port which he called the Total Access Port (TAP). He described the integrated entity, comprising of accessor, port and target system, as the Total Access System (TAS). TAP has been refined and made more intelligent since its debut at the Archimedes Project of the Center for the Study of Language and Information (CSLI) in Stanford University in the early 1990s. Scott now runs the Archimedes Project from the University of Hawaii. The patient ‘J.B’. can be seen in Figure 3, using an early wired-version of TAP at CSLI. He is paralyzed from the neck downward and can move only his head on his own volition. He is seen using an expensive laser

head-tracker, his customized accessor, with runof-the-mill personal computers via the little box in front of the screen, the TAP. Figure 4 shows a later version of the now wireless TAP channeling voice commands to operate household appliances. A pre-prototype test bed 2003-version of TAP (i-TAP) developed by Scott (2003a) for DRL in Ritsumeikan University was the inspiration for the PEECSS component of TACMIS. The i-TAP concept design is indicated in Figure 5. The underlying concept of TAP-TAS, however, prevails throughout the thought processes that led to the tri-partite TACMIS model, particularly in the design of accessors to meet: 1. The independent living needs of the care receiver in PEECSS; 2. The variety of clinical needs of the professionals providing the care in HIMS, and (3) the diverse epidemiological and policy information needs of the larger range of stakeholders in SEAHCSS. The advantage of TAP-TAS is that it is a robust system that can respond to change and incorporate new technologies.

THE BROAD CANVAS OF HEALTH INFORMATICS ISSUES: INTEGRATION AS THE KEY TO INCLUSION AND EMPOWERMENT An analysis of information needs in several longterm care institutions in Japan and of trends in

Figure 2. Conceptual diagram of TAP-TAS in the early 1990s (Source: Scott, 2003a)

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Figure 3. JB operates PCs with laser head-tracker via TAP (Source: Scott, 2003b)

the use of information and communication technologies (ICT) in care and medical institutions in Japan and the UK led to the identification of the key issues which TACMIS would have to address. They deal with: (1) Health information system design and integration; (2) Easing the burden of information input, retrieval and analysis; (3) Patient empowerment; and (4) Privacy control. They are an invaluable brief for members of the design team and are elaborated upon below:

Figure 4. Home appliances controlled by voice commands (Source: Scott, 2003a)

1. Health Information System Design and Integration: In designing an integrated care and medical information system, as a patient journeys through the health care system, a number of discrete subsystems and several legacy systems have to be taken into account. Integrating this information transforms it into a useful evidence base for capturing epide-

Figure 5. Operating principles and functional components of I-TAP (Source: Scott, 2003a)

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miological trends and for charting health care policy. However, the accumulated body of tacit knowledge and judgment born of experience, or the rich narrative base (Figure 6) of medical practice, best represented in the patient-clinician interaction, can get left out of the evidence base. It is important that this semantic aspect, extremely important for clinical interpretation and inference must be included in designing TACMIS, especially when critical decisions that hover over life and death have to be made. The systems design aspects of TACMIS and the system integrating devices/software protocols and the data mining/management software developed will have to address this challenge. 2. Easing the Burden of Input, Retrieval and Analysis: This aspect has emerged as important following an analysis of the operational needs of medical and care institutions. Against a background of increasing ICT investment by the health care sector, even in developing countries, we find that such investments are under-utilized. Health care professionals constantly have to adapt to rapidly evolving hardware and software environments. Furthermore, in most ICT systems used, access devices commonly used for data input and retrieval, and the software used for analysis, are not user-friendly to health care professionals and their patients. Easing the burden of engaging with ICT systems is of paramount importance. The accessor design aspects of the research exercise, working in concert with system integrating devices/protocols, will address this challenge; 3. Patient Empowerment: The isolation and dependency of the disabled patient or of the aged and infirm are a serious concern. High quality long term residential care at affordable prices is increasingly difficult to provide. Independent living, in familiar environments with social interaction for as

long as possible, appears to be the alternative. The same accessors that facilitate patient engagement with the data management system can be used to enhance patient interaction with the surrounding physical and social environment. Patient control of electrical appliances in the home, interactive engagement with navigation aids and information regarding the surrounding environment can assist in enhancing the quality of life for the patient/care receiver, function as an emergency help line and also act as a conduit for the continual transmission of clinical information relating to vital functions. This last is especially important when the patient leaves the closed confines of the hospital or residential care. Knowing patient/ care receiver needs in respect to hindering environments, in order to be able to respond to them, is also crucial. The empowering of patients with information about their health and enabling them to make choices is another important challenge; 4. Privacy Control: Given the confidentiality of much of the personal information that has to be managed within the system, a privacy control policy has to be articulated at the outset. The information flowing through the system can be likened to several strands that originate from the different stakeholders involved (Figure 8). These strands intertwine from time to time and it is necessary to have a clear indication of who the custodian of each strand of this information is, and who should be the overriding authority when these strands intertwine. What happens when the target of care is incapable of sound judgment? Whose decisions prevail when the balance between life and death is at stake? Also, to optimize the use of these information strands in clinical decisions, their channeling along the care pathways is likely to be automated. Thus, the system will not be error-free and nodes of human judgment, possibly where

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Figure 6. Capturing the narrative base of medicine (Source: Cassim, 2004)

the information strands intertwine, will have to be built into the health informatics system. Needless to say, the patient/care receiver’s rights in regard to exercising control over this information and the conditions under which it can be overridden need to be defined.

FOCUS ON STROKE DISABILITY: AN ANALYSIS OF PATIENT JOURNEYS AND THE PATHWAYS OF CARE Stroke was chosen as the focus of analysis for developing the TACMIS prototype because it is a widely prevalent medical condition of sufficient epidemiological impact. Also, stroke care involves information dealing with a range of conditions, from conditions prior to the onset of stroke, at the onset of stroke, in emergency and acute care, in rehabilitation and chronic care, in hospice care and preparation for death where treatment is unsuccessful, or discharge into community care or independent living, usually with a chronic

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disability, when the patient survives. This patient journey, seen in juxtaposition with the clinical care pathways and other stakeholder involvement in the process of care, was mapped with an analysis of timelines, conditions, treatment/engagement and outcomes. This is indicated conceptually in Figure 7. The vascular origins of stroke indicate a strong correlation with lifestyle related factors, including stress, diet, alcohol consumption, physical exercise regimes, etc. Thus information relating to patient history prior to the onset of stroke is of great value in the treatment of stroke in the acute, chronic and community care phases of treatment. Considering that stroke leads to neurological impairment, the principal types of disability that have to be addressed are related to: • • • • • •

Paralysis; Sensory disturbances, including pain; Using/understanding language; Swallowing difficulties; Thinking and memory; and Emotional disturbances.

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Figure 7. Stroke patient journeys and the stakeholders involved (Source: Cassim, 2005)

Designing a prototype based on an analysis of the residual disability of stroke patients and of stroke care could be quite easily extended to deal with conditions of both disability and ageing, the usual targets of inclusive design. The role of the clinicians dealing with these disabilities is indicated in Figure 8. The analysis of stroke patient journeys at King’s College Hospital (KCH) has indicated two broad domains of patient care which are important after the patient leaves the secondary care institution (KCH) and goes into intermediary care (for rehabilitation, largely), community care or independent living:

1. Preventing the recurrence of stroke. This involves ensuring that the patient follows the medication and treatment regimen in his/her care package as well as the monitoring of key vital functions and lifestyle factors. In the event of recurrence, this information is most valuable to the clinicians in emergency and acute care, as well as those in rehabilitation and chronic care. However, it is precisely this information that falls in the crevice between secondary and primary care and is either not collected or often not shared if collected. Information and communication technologies, particularly ubiquitous devices, microengineering electro-mechanical systems

Figure 8. Stroke disability and the clinicians involved (Source: Cassim, 2005)

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(MEMS) incorporating RF transmitters can come to the rescue in this regard, provided the issue of privacy control mentioned earlier can be dealt with; 2. Supporting independent living. This involves helping overcome the chronic residual disability that invariably accompanies a discharged stroke patient. Using the TAPTAS concept, separating the customized user accessor from the mass-market target system, a series of disability aids can be designed. They may range from mobility aids to smart beds, smart rooms and smart homes. Intelligent sensors can monitor the patient condition discreetly and warn the appropriate care/medical service facility when matters take a turn for the worse. An analysis of the environmental barriers around the patient’s “home” environment and an understanding of the coping strategies employed by patients are important inputs for designing these aids. Aids to the enjoyment of life should be the aim of the inclusive design exercise. A whole raft of inclusive design products could emerge from this. Based on the above needs, the Patient Empowerment and Environmental Control Support System (PEECSS) component of TACMIS is seen as a framework for designing products/services which are intended to facilitate self-care, support independent living and empower the patient through control over his/her surrounding environment, thereby enriching the patient’s quality of life. With the spread of ubiquitous telematic devices, lifestyle information monitoring of populations at risk (or even of the population at large) might well become a common phenomenon, although with caveats for ensuring privacy and confidentiality. As indicated earlier in Figure 1, the three domains considered for product/service design under PEECSS are:

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1. Medication and Therapy; 2. Vital Functions and Lifestyle Factors; and 3. Environmental Constraints and Control. The same three domains will be considered for collating information into a Personal Prescription and Preferences Record (PPPR), which is to be ultimately integrated with the Electronic Patient Record (EPR) at the Hospital Information Management System (HIMS) level and the Electronic Health Care Record (ECHR) at the Socio-Economic and Health Care Support System (SEAHCSS) level of TACMIS. The integrated whole may be termed a Holistic Health Informatics Record (HHIR). The ideal situation would be where inclusive products/services designed for PEECSS also function as information accessors, contributing to the information base of the PPPR. Although the Discovery Research Laboratory was an invaluable focal point in the innovation and developmental stages, and in creating social awareness through its Technology Seeds Seminars, it is important to establish a framework for partnership among industry, academia, government and civil society within which TACMIS can operate in the long term as it moves towards institutionalization and commercialization. Being cross-national in the composition of its team of contributors, the system must also be able to deal with the intellectual property rights (IPR) of the contributing parties. A scheme for this management of technology (MOT) exercise for TACMIS, which could be used for other DAITS exemplars as well, is shown in Figure 9.

CONCLUSION: CONTRIBUTIONS TO INCLUSIVE DESIGN Observations from the stroke patient study at KCH and efforts to implement the TACMIS conceptual design indicated in Figure 1 provide us with a basis for summing up the work done thus far from

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Figure 9. Partnerships for commercializing TACMIS outputs (Source: Cassim, 2005)

an inclusive design perspective. The empirical findings of TACMIS in this regard can be briefly described as follows: 1. Stroke needs are a microcosm of needs which have to be addressed in the wider context of the disabled and ageing population (refer Figures 7 and 8); 2. TACMIS sees information integration as a prerequisite for social inclusion and personal empowerment and places the patient at the central axis of this process (refer Figure 1); 3. TACMIS aims at creating electronic records at the patient level (PEECSS-PPPR), the institutional level (HIMS-EPR) and the societal level (SEAHCSS-EHCR), linked to a series of referral records (which could include legacy systems) that can be integrated into a holistic health informatics record (TACMIS-HHIR, refer Figure 1); 4. The customized accessors used in PEECSS, HIMS and SEAHCSS are easy to use tools which reduce the burden of input and enhance the quality of information gathered, but can also be seen as powerful aids to inclusion and empowerment (refer Figures 2, 34, 4 and 5); 5. The TAP-TAS concept enables an inclusive solution at affordable cost by freeing the expensive customized accessor from the target system, which could include computers, household appliances, sensor-based moni-

toring systems and even, perhaps, domestic robots (refer Figures 2, 3, 4 and 5); 6. The neural network based data-mining techniques currently being experimented with to mine information in the narrative base of medicine (NBM, Figure 6) could be used to analyze a variety of descriptive case information and chaotic or nonlinear phenomena, thereby providing a valuable input for inclusive product and service designers; 7. The creative use of a neutral university-based focal point, such as Ritsumeikan’s DRL, which can attract cross-national collaboration in the early stages of the developmental work, when social awareness building is the key, was an important factor; 8. As the project (TACMIS) enters the stages of institutionalization and commercialization of the outputs, a longer term system of public, private and academic partnership is necessary (Figure 9). Paraphrasing D’Souza, 2004, if knowledge is seen to accumulate as a consequence of an action (or event), then universal design (or inclusive design) may be seen as a knowledge accumulation (and transfer) process which is a consequence of design action. While TACMIS can provide a considerable range of empirical evidence which could shape the design brief for inclusive product/ service development, it has no overt mechanism for mobilizing the design community. Also, in its cur-

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rent form, TACMIS has no explicit mechanism for recording the design action and its consequences, although this can be easily remedied. As a referral data base appended to TACMIS, perhaps at the SEAHCSS level, it could become a powerful tool for the advocacy of inclusive design solutions and for setting guidelines, specifications and performance standards drawn from exemplary practice.

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D’Souza, N. (2004). Is Universal Design a Critical Theory? In Keates, S., & Clarkson, J. (Eds.), Designing a More Inclusive World. London: Springer.

Story, M. F. (2001). Principles of Universal Design. New York: McGraw-Hill.

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Turner-Stokes, L. (2003). The Development of Clinical Governance in the UK: Its Implications for Rehabilitation Medicine. Clinical Medicine, 3(2), 135–141.

Wolfe, C., Rudd, T., & Beech, R. (1996). Stroke Services and Research: An Overview With Recommendations for Future Research. London: The Stroke Association.

Williams, D. D. R., & Garner, J. (2002). The Case Against “The Evidence”; A Different Perspective on Evidence-Based Medicine. The British Journal of Psychiatry, 180, 8–12. doi:10.1192/bjp.180.1.8 This work was previously published in Biomedical Knowledge Management: Infrastructures and Processes for E-Health Systems, edited by Wayne Pease, Malcolm Cooper and Raj Gururajan, pp. 315-326, copyright 2010 by Medical Information Science Publishing (an imprint of IGI Global).

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A Distributed Approach of a Clinical Decision Support System Based on Cooperation Daniel Ruiz-Fernández University of Alicante, Spain Antonio Soriano-Payá University of Alicante, Spain

ABSTRACT The incorporation of computer engineering into medicine has meant significant improvements in the diagnosis-related tasks. This chapter presents an architecture for diagnosis support based on the collaboration among different diagnosis-support artificial entities and the physicians themselves; the authors try to imitate the clinical meetings in hospitals in which the members of a medical team share their opinions in order to analyze complicated diagnoses. A system that combines DOI: 10.4018/978-1-60960-561-2.ch612

availability, cooperation and harmonization of all contributions in a diagnosis process will bring more confidence in healthcare for the physicians. They have tested the architecture proposed in two different diagnosis, melanoma, and urological dysfunctions.

INTRODUCTION Medicine has been one of the most important disciplines in society since mingled with magic and religion in the Egyptian era. The importance that medicine represents in society makes it one of

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

A Distributed Approach of a Clinical Decision Support System Based on Cooperation

the major destinations of technological advances: from elements that provide proofs of diagnosis such as medical image acquisition systems, for example, radiographies, echographies, CAT, PET images, etc. (Rangayyan, 2004); till technical support applied to treatments, for example, electro-stimulation in rehabilitation or prosthesis (Vitenzon, Mironov, & Petrushanskaya, 2005) or telecommunications applied to medicine (Moore, 1999; Wootton, Craig, & Patterson, 2006). Although technology is used to apply certain treatments or to make diagnosis tests, it is still not considered as a real aid to the main medical task: the diagnosis decision process. On the other hand, medical diagnosis is defined as “the discovery and identification of diseases from the examination of symptoms” (Collins, 2003). This definition involves two steps in any act of medical diagnosis. Firstly, the “research” task in which the specialist tries to determine the symptoms of a patient by using his medical record and diagnostic tests. Secondly, a task of analysis of these symptoms and the decision, based on the medical knowledge, of which illness is associated to the symptoms with the greatest probability. An important detail is noting that medical diagnosis is essentially a decision-making process based on the lesser or greater probability of a patient’s symptoms of being related to specific information. Medicine has evolved since the days of Esculapio, when the physician was a wise expert on all the medical knowledge, problems and treatments; research and discoveries have broadened the field of medical knowledge, making necessary the creation of specialities: neurology, traumatology, rheumatology, urology or gerontology (one of the last specialities incorporated). Moreover, most of these specialities are divided into two groups: adult and paediatric specialities (Weisz, 2005). Have you ever wondered how many known diseases are presently now? We might have a slight idea of the number of known diseases by checking the International Classification of Diseases proposed by the World Health Organization in its

last revision (ICD-10) (WHO, 2005): the group of infectious and parasitic diseases is divided into 21 subgroups (and each subgroup includes dozens of disease families), the group of tumours is divided into 19 subgroups, the group of nervous system diseases has 11 subgroups, the group of circulatory dysfunctions is subdivided in 10 subgroups, etc. Along with this enormous amount of diseases, we find the corresponding symptoms: physicians must not only know the name and treatment for diseases, they must also be to identify their diagnostic signs and distinguish them from others corresponding to similar diseases. The evolution of medicine has also led to the gradual change in diagnosis techniques (Adler, 2004; Porter, 2006). In the early days of medicine, diagnosis was based exclusively on clinical data, that is to say, on the symptoms and the physical examination of the patient. With medical advances and the application of technology, new diagnosis tests and laboratory analysis were incorporated. The discovery of new diseases and their grouping into families and specialities has facilitated the development of differential diagnosis, which consists of determining the different illnesses that could affect a patient, after a comparative study of the symptoms and injuries suffered. The large number of diseases and organic dysfunctions coupled with the growing number of diagnostic signs (that increase thanks to new diagnostic tests) are paradoxically hindering the process of diagnosis. Computer engineering has techniques for the treatment of knowledge that may be useful for the processes of medical diagnosis (Burstein & Holsapple, 2008; Greenes, 2007). Most of these techniques are based on artificial intelligence and have been drawn from biology to be applied to computer science as neural networks or genetic algorithms (Haas & Burnham, 2008; Morbiducci, Tura, & Grigioni, 2005; Rakus-Anderson, 2007). These techniques can classify patients into groups according to whether or not they have certain diagnostic signs. There are many examples of researching applica-

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tions of these techniques to diagnosis support: in (Roberts, 2000) a system based on Bayesian networks is proposed to assist the diagnosis of breast cancer; (Georgopoulos & Malandraki, 2005) shows a soft computing system to help in the differential diagnosis of dysarthrias and apraxia of speech which is able to distinguish among six types of disarthria and apraxia; systems of clinical decision support are also applied with diagnostic tests based on images, for example in diuresis renography (Taylor, Manatunga, & Garcia, 2007) or in radiographs for diagnosing lung diseases (Katsuragawa & Doi, 2007). A good example of the importance of the clinical decision support systems is the OpenClinical project. OpenClinical is an international non-profit organization created and maintained as a public service with support from Cancer Research UK. One of the objectives of OpenClinical is to promote decision support and other knowledge management technologies in patient care and clinical research. This organization, through its web site, presents an interesting group of resources related with support systems. There are several clinical decision support systems that have gone beyond the field of research and are being continually applied in health centres (Coiera, 2003; Greenes, 2007). Examples of these systems are GIDEON®, ERA and Isabel. GIDEON® (Berger, 2001) is a system designed to diagnose infectious diseases, based on symptoms, signs, laboratory testing and dermatological profiles. It is made up of four basic modules: diagnosis, which enables the user to introduce all the signs and symptoms and provides a ranked list of differential diagnosis; the epidemiological module lets the user retrieve epidemiological parameters or access a list of the world wide distribution of any disease; the therapy module provides detailed information about choices in drug therapy; the microbiology module provides full laboratory characteristics for almost 900 organisms. The Early Referrals Application (ERA) (Coiera, 2003) is a system to support physicians

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in identifying those patients with suspected cancer that should be referred to a specialist in a short time period. ERA is intended to be used within the consultation so the workflow has been designed to be as simple as possible: during a typical user session just four web pages, clear and concise, will be encountered. ERA was developed by the Advanced Computation Laboratory of Cancer Research UK at London in collaboration with Infermed Ltd. Isabel (Ramnarayan et al., 2006; Ramnarayan et al., 2003) is a web system created by physicians to offer diagnosis decision support at the point of care. Isabel covers all ages and specialities as Internal Medicine, Surgery, Gynaecology, Paediatrics, Geriatrics, Oncology, etc. Isabel gives the physician an instant list of diagnoses for a given set of clinical features (symptoms, signs, results of tests, etc.). Isabel consists of a proprietary database of medical content and a tutored taxonomy of over 11000 diagnoses and 4000 drugs and heuristics. Medicine is a context where the massive quantity of knowledge makes it perfect to use distributed knowledge; in fact, as it has been previously explained, clinical knowledge is divided into medical specialities: oncology, cardiology, urology, etc. In computer science, distributed knowledge is implemented in different ways and levels. On a basic level, distributed databases are used (Luo, Jiang, & Zhuang, 2008; Waraporn, 2007); information can be stored in different databases according to a location criterion (store data near the location where it is going to be used) or a homogeneity criterion (store information about the same topic in the same location). One important advantage of distributed databases is that each instance (of the database) can be managed by a different group of experts, so there is an implicit sharing of knowledge (apart from sharing data). Distributed data mining techniques can be used to extract knowledge implicit in a distributed database (Cannataro, Congiusta, Pugliese, Talia, & Trunfio, 2004; Han & Kamber, 2005).

A Distributed Approach of a Clinical Decision Support System Based on Cooperation

In a higher level of distributed of knowledge we have the agents paradigm (Lin, 2007; Pop, Negru, & Sandru, 2006). In this paradigm, the knowledge is shared in an explicit way between different entities called agents. An agent analyzes external information and makes actions (according to its internal logic) to achieve an objective. There are different types of agents but all of them are based in three main components: perception, decision and execution. With the perception, the agent obtains information from the environment; in the process of decision, the agent uses its “knowledge” (represented internally for example with an artificial intelligence method: neural networks, rules, fuzzy logic, etc.) to decide what action to take in order to achieve an objective; finally, with the execution, the agent tries to influence the environment to achieve the objective. An important characteristic of the agents is their ability to interact with other agents. Thanks to this interaction, agents can exchange information or share knowledge. This characteristic of the agents paradigm is the basis of the idea used in the cooperative approach presented in this chapter. We can find an example of cooperation between agents in business processes in which software agents must negotiate in order to reach an objective, for example, the best price. In our scheme, several entities with different knowledge about the same topic, collaborate to get a better diagnosis. In the following sections, we are going to introduce the concept of clinical decision support systems, detailing their features, advantages and disadvantages. Then, we will introduce the natural evolution of these systems into cooperative systems, propose their design and explain its performance in a scenario. Next, we will show some examples and results of the application of cooperative systems to diagnosis support. Finally, we will present different future lines regarding this subject.

CLINICAL DECISION SUPPORT SYSTEMS A decision support system (DSS) can be defined as a multi-model, interactive system used by a decision maker to perform an exploration (Berner, 2007; Pomerol, 1997). The main objective of a DSS is to provide an aid to make unstructured decisions thanks to its capacity to extract and manage information. It is important to note that, many times, the information provided by a DSS becomes another variable of the group of variables involved in the problem; therefore, the experts should determine the value of the decision automatically provided by the DSS. Decision support systems are used in a wide range of fields such as economics (Modarres & Beheshtian-Ardekani, 2005), industry (Delen & Pratt, 2005), medicine (Vihinen & Samarghitean, 2008), etc. DSS can be classified according to different criteria such as the mechanism used to represent knowledge; this is the case of rule-based DSS, DSS based on decision trees, etc. (Burstein & Holsapple, 2008). Another criterion used to classify DSS is their operation autonomy: solicited advice, when the system helps the user when requested; unsolicited advice, when the system provides diagnosis information without any request from the user. In the latter group, there are, for example, intelligent alarm systems that analyse constants and warn about a possible diagnosis that requires an emergency treatment. When we use a DSS to support the medical diagnosis task, we have what is known as Clinical Decision Support System (CDSS), that could be defined as “active knowledge systems which use two or more items of patient data to generate case-specific advice” (van Bemmel & Musen, 1997). In this definition, we have the three main elements of a DSS: the knowledge of medical diagnosis, the patient data or the information to be analysed, and the results provided by the DSS as a recommendation. A result coming from a DSS applied to diagnosis will usually indicate the

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probability that the symptoms will correspond to a particular illness. The operation of a traditional CDSS is as follows: a user inputs data associated to any illness; the system analyses the information and provides one or more results along with their corresponding probabilities of success. Difficulties found in traditional medical diagnosis can be divided into two main groups: sources of error in the examination of the symptoms and problems in forming hypotheses. The first group covers the problems stemming from confused information provided by the patient, low reliability of this information or deliberate concealment of the true cause of consultation (due to cultural reasons or personal embarrassment). The second group includes those situations associated with direct problems of the diagnosis task: patients that present incomplete signs or common illnesses with unusual manifestations, low frequency of diseases (rare diseases), mimicking diseases, and, in general, the difficulty the specialist has in associating the symptoms to a specific disease. Although a CDSS can be useful for both groups, in the first one, its support capacity is more limited, as problems are found in the input data. Additionally, the same difficulties found by the specialist are going to be found by the CDSS. In the second group, the problems are related to the enormous amount of symptoms and diseases (impossible to remember for a specialist) coupled with human diversity (the same illness can be manifested in different ways in different patients). These problems are directly associated with the ability to analyze and compute, a question on which a computer system can be particularly helpful. Two parameters are used to validate a diagnosis system in medicine, sensitivity and specificity (van Bemmel & Musen, 1997). Sensitivity is the probability of classifying a diseased individual correctly (as diseased); that is to say, a high sensitivity of a CDSS implies a low number of false negatives. On the other hand, specificity is the probability of classifying a healthy individual as healthy and is directly related to false positives.

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In general, it is a priority that a CDSS has a high sensitivity and, therefore, a low number of false negatives (diseased individuals classified incorrectly as healthy). A CDSS may be helpful to a medical specialist in several aspects: •

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To support a clinical decision. Sometimes, if the symptoms are not well defined, the physician may have doubts about several diagnoses. The CDSS may help increase the physician’s confidence in a particular diagnosis. To propose alternatives. The large number of existing illnesses can cause a physician overlooks diagnostic tests for less common diseases. A CDSS is able to propose an alternative diagnostic that could be easily refuted or accepted by means of another diagnostic test or symptom the physician had not initially thought about. To question a clinical decision. A CDSS can provide a more objective diagnosis support than a medical specialist. This can cause that sometimes the CDSS issues, as a first option, a diagnosis result relegated by the physician due to subjective reasons (for example, an emotional feeling).

Advantages and Disadvantages The use of computer technology in the medical field, particularly in diagnosis, involves both advantages and disadvantages. Next we will describe some of the advantages related to the use of a CDSS: •

Permanence. A computer system does not age or lose power over the course of time. It may need maintenance and updating as well as specialists must constantly update their knowledge, but once it stores a specific knowledge, this information lasts the

A Distributed Approach of a Clinical Decision Support System Based on Cooperation

•

•

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duration of its working life (without lost of reliability). Duplication. Initially, the cost of developing a CDSS may be high, but once it is implemented, duplication is simple and inexpensive. Reliability. The reliability of a computer system is independent of external conditions such as fatigue, personal affinity or pressure. Ubiquity. A computer system can be accessed via communication networks from anywhere; computers can work in environments that are hostile or dangerous for a human being. Availability. Access can be permanently available, achieving an availability of 24 hours a day, 7 days a week.

The limitations of an expert system that provides diagnosis support are directly associated to the limitations of any computer system when it is responsible for tasks that may require human skills. Some of these limitations are: •

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Lack of common sense or limitation of knowledge. Without the proper knowledge, a male could be diagnosed 80 weeks pregnant by a computer system. Inability to maintain an informal conversation in natural language. There are subtle differences when a patient is expressing his symptoms that could be essential for the diagnosis. Flexibility. When issuing a diagnosis, a human specialist can be flexible because he is aware of his limitations; this flexibility is not possible with a computer system. Need for structured knowledge. Any knowledge that is incorporated in a computer system requires a task of structuring. Lack of feelings. Its operation is governed by strict rules that have nothing to do with

•

human sensitivity, often necessary in the relationship between patient and doctor. Ethical problems. This limitation especially arises when computer systems are applied in health sciences. As computer systems cannot accept responsibility for their own decisions, their operations should be supervised by a medical specialist at all times.

Classical Design The typical architecture of a decision support system consists of three main elements: the user interface, the reasoning module and the knowledge database on which the reasoning is based. This architecture is schematically shown in Figure 1. The user interface has evolved over the course of time, from the input of data and questions via console until the most modern graphical systems that give access ubiquity properties thanks to interconnection networks. Additionally, interfaces have been adapted to the main users, the physicians, who are not technical experts. Figure 2 shows a screenshot of a user interface for clinical decision support system in urology that has been adapted to the clinical methodology used by the urologists. The knowledge database is the part of the system that contains the basic information for the diagnosis issuing. This information may include Figure 1. DSS architecture

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Figure 2. Interface of a DSS for urologists

both structured knowledge and data used to extract information or to train the reasoning module. Finally, the reasoning module implements an algorithm that analyses the input data and provides a result that basically consists of a classification: in the case of medical diagnosis, certain symptoms are classified as belonging to a certain disease with a degree of certainty. The main problem of the classical design for a CDSS is the limitation to tackle problems inside the domain of the CDSS but not well represented in the knowledge database. Another problem is that the only way to improve the functioning of the CDSS is to increase the knowledge database and to improve the reasoning module. This increases the complexity of the CDSS and, therefore, increases the possibility of failure; moreover, maintenance tasks become more difficult. Cooperation between simple CDSS can manage both problems, the lack of a complete knowledge of the domain and the way to improve the reliability easily.

COOPERATIVE CLINICAL DECISION SUPPORT SYSTEM In the previous section, we have explained what a clinical diagnosis support system consists of and

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how it can be a tool to help medical specialists. The evolution of the CDSS causes them to meet the same limitations that the physicians have: the large amount of medical knowledge makes a CDSS specialise in just one kind of disease. The approach presented in this point is the design of a cooperative clinical decision support system (CCDSS). The cornerstone of this architecture is the improvement of the system reliability thanks to the participation of several entities, with diagnosis functions, which work together to provide a single diagnosis. Such cooperation can be oriented towards various directions; different entities can be specialised in different illnesses or organ systems affected by the same illness and their collaboration facilitates the diagnosis. Additionally, the use of several diagnosis entities operating in parallel increases the global system availability. Cooperation in diagnostic tasks, thanks to the participation of several physicians, is a common working manner in many medical teams. When faced with cases of difficult diagnosis, several physicians work together to issue different diagnosis ideas. They all together discuss ideas, propose diagnostic tests and, finally, reach a consensus. The architecture of a CCDSS wants to automate this working manner.

A Distributed Approach of a Clinical Decision Support System Based on Cooperation

The architecture of a CCDSS is primarily meant to satisfy the reliability requirements. Maintaining a high availability of the system is also very important. To meet these criteria, we define a distributed architecture based on the paradigm of intelligent agents. The components of the architecture are: the user interface, the diagnosis entities, the system core and the communications subsystem (Figure 3). The user interface will be the means through which healthcare professionals will be able to request the CCDSS opinion. It will also be in charge of presenting the consensus information that must be evaluated by the specialist that requested it. The diagnosis entities may be physicians, and they will be human entities, or decision-making support software, and they will be then software entities. The software entities will receive the symptoms and diagnostic tests and, next, they will issue a set of possible diagnoses along with their corresponding probabilities of certainty. The system core will be responsible for receiving diagnosis requests and distributing them to the appropriate diagnosis entities. It must keep the system security, preventing intrusions of false entities that will destabilize the system. In addition, it will also be responsible for collecting the

results from the different entities and providing a consensus. The communications subsystem consists of both the communications network and the protocol to be used to transmit information. The structure of the communications network must preserve the security of the communications and the system integrity.

User Interface This is a key module of the system, as it will influence the degree of user acceptance. The interface must be friendly and adapted not only to the user but also to the device from which is being operated. Authentication will be the first contact with the system, as it is necessary to maintain the appropriate security levels and to provide each user with the most suitable interface. After the authentication process (Figure 4), the system core will be responsible for adapting the data according to the device from which the system is being accessed; this involves the design of different environments to facilitate the access from the most common devices used to access a data network: PC, Tablet PC and PDA. These three kinds of devices include the main differences between working environments. For the PC environment, there are

Figure 3. Distribution of the components in the CCDSS

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Figure 4. System access process scheme

no graphical limitations and the size of objects can be small, as the commonly used screens allow it. For the Table PC environment, the touch screen function implies the adaptation of the graphical interface to this working manner; in addition, the size of the screens is often smaller than that in the PC environment. Finally, the PDA environment involves working with a device with graphical limitations, touch screens and a very small size. Once the device environment has been selected, the system will need to format the information according to the role and user permissions as a last step. The system, as a part of the security mechanisms, will distinguish three levels of accessibility according to a set of roles and individualized permissions. There will be six roles: •

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Healthcare reviewer. This will be an accredited professional in charge of reviewing the ethics of the system, both the operation of the artificial entities and the activities of consulting physicians and medical officers. It will be responsible for supervising the consensus operations. Medical officer. This is the role for those professionals who are working together in a diagnosis, giving their opinion on certain symptoms. Consulting physician. Users that use the system to get a possible diagnosis will be authenticated under this role.

•

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Practice. If users use the system under the practice role, only artificial entities will participate and the results of clinical cases will not be used to update the system. Student. This role allows the system to serve as a teacher. The operation will be the opposite: the system will provide some symptoms and the user will issue the diagnosis. Thus, the CCDS becomes a system that could help with the task of teaching future physicians, thanks to the enormous amount of clinical cases stored in the database. Administrator. This will be the engineer responsible for the proper operation of the technical side of the system. The activities involved will be coordinated with those corresponding to the healthcare reviewer in charge of the medical supervision.

If a consulting physician accesses the system, the data input will depend on the speciality related to the issue he wants to consult. This does not imply that the information will also be analysed by entities with different specialities if deemed necessary. Moreover, the consulting physician is more familiarised with a certain kind of information related to his speciality and this is why this feature will be taken into account. After introducing the symptoms and diagnostic tests, a diagnosis will be required from the system, indicating a time limit for issuing this diagnosis.

A Distributed Approach of a Clinical Decision Support System Based on Cooperation

This time will allow the adjustment of the accuracy level of some artificial entities as well as indicate the possibility of participation or not of human diagnosis entities (when available during that time limit). Once time has expired, the system will provide a list of possible diagnoses along with their corresponding probabilities of confidence. Furthermore, the listing will show which entities have supported each diagnosis, indicating whether they are artificial entities or physicians and their confidence level within the system.

Diagnostic Entities The second component of a CCDSS is a set of diagnostic entities, each one understood as an element that, after receiving information about the symptoms of a patient, issues a possible diagnosis with a certain degree of certainty. These entities may be either human or artificial. Diagnostic human entities are consulting physicians who want to participate voluntarily in diagnosis activities within the system. Artificial entities are software that can propose a classification and, therefore, a possible diagnosis, thanks to statistical or artificial intelligence techniques. Diagnostic entities are distributed and get the symptoms and report their results to the system core through the communications subsystem. There is a quality value associated to each diagnostic entity. This quality value will be used by the system core to make a decision on the final diagnosis. We can distinguish between human and artificial entities to assign these quality values. Since it is difficult to automatically evaluate the quality of a diagnosis from a physician participating in the system, the healthcare reviewer will be the person who will assign the quality value of a particular doctor when a physician is registered in the system. This value may be altered at any time by the healthcare reviewer. In the case of diagnostic artificial entities, the quality value can be automatically calculated. In order to do this, artificial entities must pass

an initial testing phase through which the probability of diagnosis success is calculated as well as sensitivity and specificity. Taking into account these three variables, a value of certainty for the entities can be calculated, for example, calculating the average. Moreover, this value can be updated with the performance of the artificial entity so that the diagnosis successes improve its value of certainty and the failures decrease this value. This process of constant updating gives the system an added value, because as new artificial entities are joined to the system (with improved artificial classification techniques), old entities go obsolete automatically and their diagnoses lose importance with regard to the final consensus. For the proper operation of a CCDSS, it is very important that all artificial entities follow the same method to calculate their value of certainty, because if this value is obtained by different means the consensus process will lose reliability. Differences between human and artificial entities should not affect the consensus negatively thanks to the control by the healthcare reviewer; additionally, the final consensus will include the influences of both kinds of entity. However, different CCDSS can use different methods to evaluate the value of certainty because, if we wanted to combine the results of several CCDSS, we would take the final consensus results and not the particular ones corresponding to the entities working together in the consensus. As explained, medical officers are identified with the diagnostic entities. Every medical officer, when accessing the system by the authentication, will find a set of diagnosis possibilities related to his speciality and will be able to choose which ones he wants to use to issue a diagnosis. It is important to say that, with regard to the consulting physician (who is requesting a diagnosis for certain symptoms), the detail of diagnosis will not include any identification of the medical officers who have participated in the diagnosis. If he needs any explanation about the diagnosis, he should ask the healthcare reviewer. Despite

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this, due to legal and ethical reasons, detailed information of every diagnosis from a medical officer will be stored and only accessible to the healthcare reviewer. Depending on the CCDSS, the medical officer may act in an altruistic way or receive remunerations for his contributions. Figure 5 shows the design of the diagnostic artificial entities. Each artificial entity consists of a diagnostic module, a local storage and a module of perception or interaction with the environment. The diagnostic module has the algorithm to classify the information received (diagnostic data) and decides whether this information corresponds to a healthy individual or to a diseased person. The local storage will be in charge of storing the information relevant to the diagnosis process; furthermore, it will store the data related to the entity itself such us the entity designer, the last update date, the achieved percentages of classification, sensitivity and specificity, etc. The module of interaction with the environment is responsible for receiving the data, adapting them to the analysis to be done by the diagnostic module and, afterwards, formatting the results in a form suitable for transmission; besides, this module will also update the diagnosis algorithm and provide information about the entity itself when required by the system core of the CCDSS. This structure is similar to that corresponding to software agents (Ferber, 1999; Lin, 2007), Figure 5. Diagnostic artificial entity

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which are able to perceive changes in the environment, to consider what actions to take and to try to influence the environment to achieve an objective. In the case of a medical agent, the perceptions correspond to the symptoms; the actions would be the diagnoses (that will give rise to treatments), and the main objective would be to give an accurate diagnosis in order to cure the patient.

System Core The system core is a key element in the CCDSS management. This module is responsible for distributing the diagnosis requests to the entities, for reaching a consensus of the different diagnoses and for controlling accesses and information transmitted through the system. The structure of the system core has three different parts: the consensus module, the security control and the request manager. The consensus module is, after receiving the diagnoses from the different entities and taking its values of certainty as a reference, elaborates a consensus listing possible diagnoses and their corresponding probabilities. This is one of the most complex tasks of the CCDSS, as there are numerous factors involved and always surrounded by degrees of uncertainty. The consensus algorithm must be able to solve extreme situations; for example, a case in which several artificial entities with a high degree of certainty agree on a diagnosis and the opinion from a medical officer differs completely. Moreover, depending on the organization that manages the CCDSS, there can be other factors that may influence the decision such us the economic cost of the treatment or whether a diagnosis has a favourable prognosis; thus, faced with two diagnoses, being the second more likely to has a better prognosis, the decision algorithm could consider this factor and change the diagnoses order, keeping an optimistic approach. There are a lot of decision algorithms that can be

A Distributed Approach of a Clinical Decision Support System Based on Cooperation

used and a lot of studies done about the decision problems, especially in economics (White, 2006). Another aspect that must be controlled by the consensus module is whether the request is related to several specialities. In this case, maybe the decision from all entities has the same value or maybe there is a major speciality and secondary specialities; in this case, the diagnosis from the entities belonging to the major speciality will have more value that those coming from the remaining entities. Security control is mainly performed in two ways: user authentication and entities control. User authentication involves not only the access control and the selection of the appropriate role but also a complete monitoring of their actions within the system. It is important to prevent a malicious user that has taken a medical officer role from modifying a diagnosis, causing a malfunction of the CCDSS. On the other hand, the system core also manages the artificial entities, which must be registered in a database containing information about the entity, the engineer responsible for the development, the speciality, the initial degree of certainty, etc. All this information will be used by the system to control to which artificial entities it is possible to request a diagnosis. The healthcare reviewer is not only in charge of distributing the profiles among the physicians with access permissions, he is also in charge, along with the administrator, of allowing an artificial entity to join the system (and of deciding the deleting). The request manager is responsible for distributing the diagnosis requests and for collecting the results before transmitting them to the decision module. In order to make the distribution, the database with the register data of the entities (both artificial and human) is taken into account, and the symptoms input by the consulting physician are sent to those entities that correspond to the speciality or specialities related to the request (which have been input by the consulting physician as well).

Along with the transmission of information necessary for the diagnosis, it should be included the time limit for the entities to issue the diagnosis. Once the time has expired, the manager will submit all diagnoses received with their corresponding probabilities, stating the entity from which they come in order to consider the relative importance of each diagnosis in the consensus task.

Communications Subsystem and Protocols The communications subsystem is the basis for the operation of a CCDSS. The information flows between different components through secure channels and standardised protocols. Artificial entities work like web services and they use Hyper Text Transfer Protocol (HTTP) and Simple Object Access Protocol (SOAP) to inter-component communication. In order to ensure a secure channel, HTTP is secured by using the Secure Socket Layer (SSL) protocol, which makes communication secure on the transmission level. Moreover, better security levels can be achieved by encrypting the information on the implementation level. The artificial entities secure the messages they send and receive with the XML-Encryption specification of the World Wide Web Consortium (W3C). They also sign and validate the messages with the XML-signature specification, also of the W3C. The structure of the messages transmitted within the system should be based on a standard. For this project, we intend to use the HL7 standard, so that the XML messages are consistent with the Reference Information Model (RIM) of the HL7 version 3 specification (Hinchley, 2005).The data considered to be managed are only diagnostic data, as the aim of the posed system is the diagnosis process. In accordance with the RIM specifications, data are modelled as observations, both the data coming from the medical examination (joined to the diagnostic tests) and the diagnosis.

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SCENARIOS The first proposed scenario is located in the surgery of a family doctor from a city; we assume that there are no limitations with regard to the resources for making diagnostic tests. The physician, after checking the medical record of the patient and examining him, concludes that the patient suffers from an intestinal problem. In order to determine the diagnosis, the physician will order urine and blood tests and ask the patient to come back with the results after three days. During this period, the physician may use the system to consult experts on the digestive system, sending the patient symptoms and waiting for a result within three days, before the patient comes back. The results provided by the system can confirm the physician’s diagnosis, but also can help him look for alternative diagnoses when checking the laboratory results that the patient will bring on his next visit. In this case, the use of the system has avoided the referral of the patient to a specialist on the digestive system, if the laboratory tests did not confirm the hypothesis of the family doctor, and he had not thought about any alternatives. In this scenario, the system has helped reduce waiting lists for specialists. Another scenario could consist of a patient living in a rural environment with a widely disseminated population, away from the city and from the most sophisticated healthcare resources. Faced with the case of a patient with an acute backache, the physician should decide whether to order more tests and, therefore, refer the patient to a medical centre (perhaps, many kilometres away), where the resources necessary for the tests are available. One of the ways of interacting with the system in this case could be to determine which diagnostic tests to do, thus reducing the number of trips the patient should make to the hospital. The physician would check the patient’s symptoms with the system while he prescribes an analgesic treatment to see if the pain goes away. When the patient comes back to the medical centre, the physician

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will already have a listing of possible diagnoses provided by the entities connected to the system and will be able to order diagnostic tests at the hospital, which confirm any diagnosis. In this case, the system has helped the physician select the diagnostic tests needed to obtain a differential diagnosis; at the same time, inconveniences for the patient due to the several trips to the hospital, far away from his home, has been avoided. Finally, we propose a different scenario that does not involve problems related neither to specialised knowledge (as in the first case) nor to resources (as in the second case). A patient goes to the hospital with strange symptoms that do not correspond to any illness. In this case, a team of physicians takes responsibility for the patient and, usually, begins to study the case in order to get a differential diagnosis. In this scenario, the system could be regarded as one more expert opinion (with the value that the team leader wants to give it), taking into account that this opinion is a consensus of many diagnoses; therefore, it is as if the team of physicians at the hospital had at its disposal another team of external physicians, also taking part in the same differential diagnosis. With these three scenarios, we want to show the reader several possible uses of a CCDSS, in situations with some kind of restriction as well as in cases in which the systems becomes one more opinion to be considered depending on the evaluation of a medical director. Although we have provided only three scenarios, there are many more cases in which the system could be useful; for example, in a prison or war the location characteristics make it difficult for a complete medical team to work together, so a CCDSS would be a viable option. Although the architecture proposed is focused on the diagnosis of human diseases, it could be used in any decision-making support in which a consensus among different opinions is useful. If we stay in the healthcare field, we have the same situation in veterinary medicine. A CCDSS could be also used to propose economical questions

A Distributed Approach of a Clinical Decision Support System Based on Cooperation

(maybe related to decisions on stock market values) or auto mechanics.

EXPERIMENTATION In the research group of bioinspired engineering and healthcare computing, we have developed a prototype of this architecture. The main objectives for this first development were, on the one hand, to confirm that more accurate diagnoses can be obtained thanks to a consensus among the diagnoses provided by different CDSS. On the other hand, we wanted to study the overall performance of the system with the incorporation of medical opinions and to know what the physicians’ acceptance level was. In order to study whether the consensus among several diagnostic artificial entities improved the individual diagnosis provided by each entity, we selected the diagnosis of melanoma as an experimental test. Two classification algorithms were implemented: a Bayesian classifier and a multilayer perceptron (Greenes, 2007). To increase the number of diagnostic artificial entities, multiple instances of the multilayer perceptron were created by modifying the number of hidden neurons and the training set. Finally, in addition to the entity based on the Bayesian classifier, we also obtained four entities based on the multilayer perceptron (with 3, 4, 5, 6, 7 and 8 hidden neurons), which

Table 1. Performance of the multilayer perceptron entities Hidden neurons 3

Classification Rate (%)

Specificity (%)

Sensibility (%)

70.59

22.54

46.08

4

74.22

55.02

52.94

5

81.00

70.89

55.00

6

84.37

92.00

70.58

7

90.62

93.20

77.35

8

87.50

91.15

76.47

had acceptable classification rates. Table 1 shows the performance of the multilayer perceptron with regard to the number of neurons of the hidden layer. Instead of working with a list of symptoms, the prototype was adapted to work with vector features taken from a pre-processing of the melanoma images. Regarding the consensus module, we used a simple voting system based on the classification rate of the entities: the importance of one entity was equivalent to its classification rate. Thus, if an entity with a classification rate of 85% diagnosed a skin injury as healthy and two more entities with rates of 80% and 82% diagnosed the injury as melanoma, the final consensus would be melanoma. This consensus mechanism can involve the following problem: two entities with very low classification rates (45% and 47%) can induce a wrong diagnosis within the system, over the diagnosis provided by an entity with a classification rate of 90% (45+47>90). In order to solve this problem, we have selected, from all possible artificial entities, only those with classification rates over 75%. Figure 6 shows a comparison of the classification rate between the Bayesian algorithm, the best multilayer perceptron entity (7 neurons in the hidden layer) and the CCDSS (obtaining a consensus from all the artificial entities). The classification rate of the CCDSS (91.32%) is very similar to the one obtained with the multilayer perceptron entity. Regarding the specificity and the sensibility, both measures are higher in the CCDSS: 93.58% and 78.04%, respectively. The Bayesian entity has a classification rate of 81.25%, a sensibility of 93.15% and a specificity of 76.70%. In the other experiment, we developed artificial entities to diagnose different urological disorders based on urodynamic tests (Ruiz Fernández, Garcia Chamizo, Maciá Pérez, & Soriano Payá, 2005). In this experiment, we used a urology expert and several family doctors interested in participating as consulting physicians. The experimentation outcome is shown in Figure 7. In particular, we used two different multilayer per-

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Figure 6. Comparison between CCDSS and other DSS diagnosing melanoma

very specific fields in which they do not have deep knowledge.

CONCLUSION

ceptron (MLP1 and MLP2, in which we modify the hidden layer and the training data set) and a Kohonen Self-Organising Map (SOM) to classify three types of urological dysfunctions: obstruction, hyperreflexia and effort incontinence. As it is possible to observe in the graph, the classification rate obtained with the consensus is higher than the individual rates for each entity for all the dysfunctions studied. In the consensus we used the same voting system as in the other experiment, so the weight of one entity in the final consensus was equivalent to its classification rate (obtained in a previous test stage). The most important conclusion of this second experiment was the physicians’ opinions with regard to the system; they found the system very useful in supporting those diagnoses involving

Figure 7. Classification rates for different artificial entities and CCDSS in urological dysfunctions

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Throughout this chapter, we have tried to introduce the reader to the relevance that the decision-making support systems may have in the healthcare field, particularly in medical diagnosis. Moreover, given the characteristics of the medical environment, we have proposed a new kind of system that involves the cooperation between different entities as the core of the diagnosis support process. Thanks to cooperation among diagnostic entities, physicians and artificial entities, we want to achieve higher precision in the final decision, as well as provide wider system availability. The proposed architecture is based on the paradigm of intelligent agents and distributed computing. The diagnostic entities are distributed to the system and act as web services when asked by a control element (system core); consequently, a lot of diagnosis processes can be performed in parallel. These processes are carried out by physicians and software programs. Finally, the system performs a consensus process among the overall results in order to give the user a set of possible diagnoses with their corresponding probabilities of occurrence. The distribution of knowledge is a powerful tool in a clinical decision support system. By adapting the system to a distributed architecture, any number of future sources of knowledge could be integrated into the network, generating an expanding knowledge-base. In addition, different knowledge sources specialized in the same problem can provide different points of view, a key factor in decision support. The experimentation carried out by our research group has confirmed that cooperation between different diagnostic entities can determine a diagnosis from a set of symptoms, improving the individual performance of each entity. We

A Distributed Approach of a Clinical Decision Support System Based on Cooperation

have presented two examples, one related to the melanoma diagnosis and another based in the diagnosis of urological dysfunctions. In both cases, diagnosis results obtained with the cooperative approach are better than individual results. It is important to say that at no time did we propose that a CCDSS replace a qualified professional at the diagnosis activity; the CCDSS operation cannot be understood without the supervision of physicians that check the proper operation of the artificial entities and evaluate the quality of the consensus diagnoses provided by the system.

FUTURE TRENDS One problem with implementing a CDSS is the acquisition of the knowledge related with the diagnosis. It is necessary to contact a group of experts and organize their knowledge, before including the diagnosis rules or heuristics in the system; all these actions take a lot of work and time. In the future, the knowledge should be extracted by more automatic means which take less time. Journals and conference proceedings are an easy alternative way to collect useful information about diagnosis; on the other hand, in hospital information systems there are huge databases with data related to diseases and their diagnosis evaluation. Furthermore, the use of data from journals or electronic health records in hospitals allows for an automatic and constant update of the knowledge rules of the clinical decision support systems. An important future goal is the interaction between different decision support systems. These systems should evolve in the same way as medicine. Each CDSS should be an artificial expert in one speciality or even in just one group of diseases. This implies not only incorporation of information about the diagnosis and therapy of diseases but also including information about others organic systems that can be affected by a disease (in order to interact with the CDSS expert in these organic

systems). The consensus between the different artificial entities will be more difficult because each entity will be an expert in just one topic so each entity will have a relative importance in the final diagnosis. It will be necessary to improve the consensus algorithms. As a summary we can suggest three specific objectives for the future. First, it will be necessary to involve a higher number of healthcare professionals in order to validate the system when collaboration of entities expert on different specialities is required. Another objective will be to improve the diagnostic capability of the artificial entities, particularly during the self-learning process, based on their performance within the CCDSS and the feedback which is available after the physician approves the consensus. Finally, it will be find necessary to do an in depth study of the consensus algorithms used, in order to ensure more precise results and to ensure that they can take into account additional variables like the morbidity of the diagnosed illness.

REFERENCES Adler, R. E. (2004). Medical Firsts: From Hippocrates to the Human Genome (1st ed.): Wiley. Berger, S. A. (2001). GIDEON: A Computer Program for Diagnosis, Simulation, and Informatics in the Fields of Geographic Medicine and Emerging Diseases. Paper presented at the 2000 Emerging Infectious Diseases Conference. Berner, E. S. (Ed.). (2007). Clinical Decision Support Systems. Theory and Practice (2nd ed.): Springer. Burstein, F., & Holsapple, C. W. (Eds.). (2008). Handbook on Decision Support Systems 1: Basic Themes. Springer.

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Cannataro, M., Congiusta, A., Pugliese, A., Talia, D., & Trunfio, P. (2004). Distributed data mining on grids: services, tools, and applications. IEEE Transactions on Systems, Man, and Cybernetics. Part B, 34(6), 2451–2465. Coiera, E. (2003). Guide to Health Informatics (2nd ed.). London: Hodder Arnold. Collins (Ed.) (2003) Collins English Dictionary. Collins. Delen, D., & Pratt, D. B. (2006). An integrated and intelligent DSS for manufacturing systems. Expert Systems with Applications, 30(2), 325–336. doi:10.1016/j.eswa.2005.07.017 Ferber, J. (1999). Multi-Agent Systems. An Introduction to Distributed Artificial Intelligence: Addison-Wesley. Georgopoulos, V. C., & Malandraki, G. A. (2005). A Fuzzy Cognitive Map Hierarchical Model for Differential Diagnosis of Dysarthrias and Apraxia of Speech. Paper presented at the 27th Annual International Conference of the Engineering in Medicine and Biology Society. Greenes, R. A. (Ed.). (2007). Clinical Decision Support. The Road Ahead: Elsevier. Haas, O. C. L., & Burnham, K. J. (Eds.). (2008). Intelligent and Adaptive Systems in Medicine (1st ed.): Taylor & Francis. Han, J., & Kamber, M. (2005). Data Mining: Concepts and Techniques (2nd ed.): Morgan Kaufmann. Hinchley, A. (Ed.). (2005). Understanding Version 3 - A Primer on the HL7 Version 3 Communication Standard (3rd ed.). Katsuragawa, S., & Doi, K. (2007). Computer-aided diagnosis in chest radiography. Computerized Medical Imaging and Graphics, 31(4-5), 212–223. doi:10.1016/j.compmedimag.2007.02.003

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Lin, H. (Ed.). (2007). Architectural Design of Multi-Agent Systems: Technologies and Techniques. IGI Global. Luo, Y., Jiang, L., & Zhuang, T. (2008). A GridBased Model for Integration of Distributed Medical Databases. Journal of Digital Imaging. Modarres, M., & Beheshtian-Ardekani, M. (2005). Enterprise support system architecture: integrating DSS, EIS, and simulation technologies. International Journal of Technology Management, 31(1/2), 116–128. doi:10.1504/ IJTM.2005.006626 Moore, M. (1999). The evolution of telemedicine. Future Generation Computer Systems, 15(2), 245–254. doi:10.1016/S0167-739X(98)00067-3 Morbiducci, U., Tura, A., & Grigioni, M. (2005). Genetic algorithms for parameter estimation in mathematical modeling of glucose metabolism. Computers in Biology and Medicine, 35(10), 862– 874. doi:10.1016/j.compbiomed.2004.07.005 Pomerol, J.-C. (1997). Artificial Intelligence and human decision making. European Journal of Operational Research, 99(1), 3–25. doi:10.1016/ S0377-2217(96)00378-5 Pop, D., Negru, V., & Sandru, C. (2006). MultiAgent Architecture for Knowledge Discovery. Paper presented at the Eighth International Symposium on Symbolic and Numeric Algorthms for Scientific Computing, Timisoara. Porter, R. (Ed.). (2006). The Cambridge History of Medicine (1st ed.): Cambridge University Press. Rakus-Anderson, E. (2007). Fuzzy and Rough Techniques in Medical Diagnosis and Medication (1st ed.): Springer.

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Ramnarayan, P., Roberts, G. C., Coren, M., Nanduri, V., Tomlinson, A., & Taylor, P. M. (2006). Assessment of the potential impact of a reminder system on the reduction of diagnostic errors: a quasi-experimental study. BMC Medical Informatics and Decision Making, 6(22). Ramnarayan, P., Tomlinson, A., Rao, A., Coren, M., Winrow, A., & Britto, J. (2003). ISABEL: a web-based differential diagnostic aid for paediatrics: results from an initial performance evaluation. Archives of Disease in Childhood, 88(5), 408–413. doi:10.1136/adc.88.5.408 Rangayyan, R. M. (2004). Biomedical Image Analysis (1st ed.): CRC. Roberts, L. M. (2000). MammoNet: a bayesian network diagnosing breast cancer. Machine Perception and Artificial Intelligence, 39, 101–148. Ruiz Fernández, D., Garcia Chamizo, J. M., Maciá Pérez, F., & Soriano Payá, A. (2005). Modelling of dysfunctions in the neuronal control of the lower urinary tract. Lecture Notes in Computer Science, 3561, 203–212. Taylor, A., Manatunga, A., & Garcia, E. V. (2008). Decision Support Systems in Diuresis Renography. Seminars in Nuclear Medicine, 38(1), 67–81. doi:10.1053/j.semnuclmed.2007.09.006

van Bemmel, J. H., & Musen, M. A. (Eds.). (1997). Handbook of Medical Informatics (1st ed.). Houten, the Netherlands: Springer-Verlag. Vihinen, M., & Samarghitean, C. (2008). Medical Expert Systems. Current Bioinformatics, 3(1), 56–65. doi:10.2174/157489308783329869 Vitenzon, A. S., Mironov, E. M., & Petrushanskaya, K. A. (2005). Functional Electrostimulation of Muscles as a Method for Restoring Motor Functions. Neuroscience and Behavioral Physiology, 35(7), 709–714. doi:10.1007/s11055-005-0114-1 Waraporn, N. (2007). Confidence levels for medical diagnosis on distributed medical knowledge nodes. Paper presented at the International Conference on Computer Engineering and Applications, Gold Coast, Queensland, Australia. Weisz, G. (2005). Divide and Conquer: A Comparative History of Medical Specialization: Oxford University Press (USA). White, D. (2006). Decision Theory: Aldine Transaction. WHO (Ed.). (2005). The International Statistical Classification of Diseases and Related Problems (2nd ed.). Geneva: World Health Organization. Wootton, R., Craig, J., & Patterson, V. (Eds.). (2006). Introduction to Telemedicine (2nd ed.): Rittenhouse Book Distributors.

This work was previously published in Mobile Health Solutions for Biomedical Applications, edited by Phillip Olla and Joseph Tan, pp. 92-110, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 6.13

Applying Personal Health Informatics to Create Effective Patient-Centered E-Health E. Vance Wilson The University of Toledo, USA

ABSTRACT E-health use is increasing worldwide, but no current e-health paradigm fulfills the complete range of users’ needs for Web-enabled healthcare services. Moreover, a number of obstacles exist that could make it difficult for e-health to meet users’ expectations, especially in the case where the users are patients. These dilemmas cloud the future of e-health, as promoters of e-commerce, personal DOI: 10.4018/978-1-60960-561-2.ch613

health records, and consumer health informatics paradigms vie to create e-health applications while being hampered by the implicit constraints of each perspective. This chapter presents an alternative approach for designing and developing e-health titled personal health informatics (PHI). PHI was developed to overcome the limitations of preceding paradigms while incorporating their best features. The chapter goes on to describe how PHI can be applied to create effective patient-centered e-health for delivery by healthcare organizations to their own patients.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Applying Personal Health Informatics to Create Effective Patient-Centered E-Health

INTRODUCTION E-health is broadly defined as “health services and information delivered or enhanced through the Internet” (Eysenbach, 2001). Overall use of e-health continues to expand worldwide. Harris Interactive reports the number of Americans who have searched for health information online has increased to 117 million, and 85% of these individuals searched within the month prior to being surveyed (Krane, 2005). Outside the U.S. and Europe, e-health use has grown more slowly (e.g., see Holliday & Tam, 2004). But even in these areas further expansion seems likely as the World Health Organization and similar groups ramp up efforts to increase availability of e-health in developing nations (Kwankam, 2004; WHO, 2005). Although some aspects of successful e-health are well-established, such as the need to provide encyclopedic health content, other aspects are less obvious. For example: •

•

•

Which services should be deployed online and how should users interface with these services? If communication is offered, what is the best way to coordinate this to balance needs of the public with those of healthcare representatives, for example, physicians and clinic staff? How should personal health records (PHR) be incorporated into e-health, who “owns” the data in these records, and what (if any) data should PHR share with records of the healthcare provider, insurer, and payer, such as employer or government agency?

These are no idle questions to the health informatics and IT practitioners who must design and deploy e-health applications. Given the large number of healthcare providers who currently are investing in e-health as an important part of organizational strategy (Lazarus, 2001; Martin, Yen & Tan, 2002), learning how to create suc-

cessful e-health applications is a key topic for both research and practice. In developing effective approaches for designers and developers of e-health, I propose that it will be helpful to view e-health, as broadly defined above, from a user-centered perspective that can incorporate best practices of preceding e-health paradigms without being limited by their implicit constraints. This chapter presents the foundational concepts underlying this approach and then describes how the approach can provide guidance in the specific context of e-health applications that healthcare providers develop to serve their own patients.

PARADIGMS OF E-HEALTH E-health is a broad domain that describes numerous aspects of the convergence of healthcare and Internet technology (Oh, Rizo, Enkin & Jadad, 2005). A frequently-cited definition by Eysenbach highlights e-health’s interdisciplinary underpinnings. E-health is an emerging field in the intersection of medical informatics, public health and business, referring to health services and information delivered or enhanced through the Internet and related technologies. In a broader sense, the term characterizes not only a technical development, but also a state-of-mind, a way of thinking, an attitude, and a commitment for networked, global thinking, to improve healthcare locally, regionally, and worldwide by using information and communication technology (Eysenbach, 2001). Indeed, “state-of-mind” has been much more critical to e-health than is the case for most other major Internet applications, such as online banking. Historically, three major paradigms have played key roles in developing e-health to its current state. Although each paradigm has been important in promoting specific feature sets and in raising overall awareness of e-health, I will argue that all have essential constraints that make

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Applying Personal Health Informatics to Create Effective Patient-Centered E-Health

it impossible to fulfill the complete range of users’ needs for online health services.

E-COMMERCE PARADIGM Prominent early developers of e-health services operated within an e-commerce paradigm, in which vendors expected to profit from users paying directly for products and services acquired through the site or from advertisers paying for exposure to users. Typically, vendors were not affiliated with healthcare providers, thus they are essentially constrained from providing services that link individuals with their own physician, clinic, or pharmacy. Although numerous vendors developed e-health within the e-commerce paradigm, few survived the ensuing shakeout due to failure to provide value to customers or adequately control costs, lack of effective revenue models, or simple inability to ensure sustainable competitive advantage (Itagaki, Berlin & Schatz, 2002; Rovenpor, 2003). Among the prominent e-health ventures representing the e-commerce paradigm, such as DrKoop.com, MediConsult.com, and PlanetRx. com, only a handful remain. The best-known of these is WebMD, which provides an exceptionally wide range of health services but continues to struggle toward profitability.

PERSONAL HEALTH RECORD PARADIGM A second approach to e-health that was based initially on profit motives is the personal health record (PHR) paradigm. The PHR is defined as an electronic, universally available, lifelong resource of health information needed by individuals to make health decisions. Individuals own and manage the information in the PHR, which comes from healthcare providers and the individual. The PHR is maintained in a secure and private

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environment, with the individual determining rights of access. The PHR is separate from and does not replace the legal record of any provider (AHIMA, 2005). The idea of computerized storage of personal health data is not new. A number of vendors introduced PHR products during the 1990s both as standalone software and online services. To date, demand for commercial PHR products has been low—only 2% of individuals who maintain health records use purchased PHR software (Taylor, 2004)—and none of these products has gained financial success (Holt, 2005). However, several factors may prompt increased use of PHRs in the near term. First, the U.S. public gained increased awareness of the value of portable health records as a result of the massive human displacement and damage to healthcare facilities sustained from hurricanes during 2005. This awareness is likely to affect public sentiment and governmental policies for some time to come (Kloss, 2005). Second, PHR software is continuing to improve. Current offerings have become easier to use and are more fully capable of integrating health resources beyond the individual’s immediate data, such as linking to relevant medical journals and FDA drug information (Campbell, 2005). Finally, researchers are beginning to study how individuals go about maintaining health records in non-computerized settings, and this research is clarifying the opportunities and constraints that PHR developers face. For example, two recent studies in this area assess the importance of privacy and security in health records (Taylor, 2004) and shed light on the diverse strategies that individuals prefer to employ in storing health records (Moen & Brennan, 2005). Even if PHRs gain wider acceptance, however, this paradigm is essentially constrained by its data-centric focus. Less data-centric services, for example, relating to communication and peer-group support, are accorded little attention within the PHR paradigm.

Applying Personal Health Informatics to Create Effective Patient-Centered E-Health

CONSUMER HEALTH INFORMATICS PARADIGM Although healthcare providers did not participate strongly in early stages of e-health development (Lazarus, 2001), these organizations recently have been instrumental in developing e-health within a consumer health informatics (CHI) paradigm. CHI is “a branch of medical informatics that analyzes consumers’ needs for information; studies and implements methods for making information accessible to consumers; and models and integrates consumers’ preferences into medical information systems” (Eysenbach, 2000). Where e-commerce vendors hoped to achieve competitive advantage, healthcare provider organizations appear to be driven more by concerns of achieving competitive equity, both with competing providers as well as commercial e-health sites. Because CHI is produced by healthcare provider organizations, e-health produced within this paradigm has been oriented toward augmenting customer service, increasing healthcare delivery quality, and containing costs rather than selling products, services, or advertising access. Although the CHI paradigm is designed to address needs of the individual user, it inherently takes an organizational perspective that imposes a passive view of users as consumers of information. This represents an essential constraint, as provision of services in which the user is an active information provider has not typically been a priority for e-health applications created within the CHI paradigm.

PERSONAL HEALTH INFORMATICS Each of the three historical paradigms described above identifies an area of need in the overall population of e-health users. However, none addresses the total range of needs that users identify. In the case of patients, for example, these include communicating with physicians and clinic staff, arranging services (e.g., scheduling appointments

and renewing prescriptions), checking bills and making payments, viewing lab results, accessing records of procedures, tests, and immunizations, receiving online alerts, monitoring chronic conditions, and interacting with online support communities (e.g., patient chat groups), in addition to accessing encyclopedic health information (Harris, 2000; Taylor & Leitman, 2002; Fox, 2005). Patients also want the ability to control their personal data, to ensure that record-keeping aspects of e-health are interoperable and portable, and to be assured of privacy and security (Taylor, 2004; Holt, 2005). In order to capture the complete set of knowledge and skills necessary to create e-health applications that meet users’ needs, I propose it is essential to consider a new paradigm that can transcend limitations while incorporating best practices of the three paradigms discussed previously. Because of its user-centered focus, in which e-health is viewed from the perspective of an individual in the role of information provider as well as information consumer, this paradigm is titled personal health informatics (PHI). PHI is defined as the knowledge, skills, practices, and research perspectives necessary to develop e-health that is effective, efficient, and user-centered (Wilson, 2006a). A conceptual model of PHI is presented in Figure 1. Four structural components define the content of PHI within three focal areas. Web e-service infrastructure is the hardware, software, and networking capabilities that support all e-health functions. Because e-health is primarily service-oriented, the informatics focus in PHI centers on infrastructure that is specialized for e-service presentation and delivery. Personal health management and usercentered development comprise the personal focus that PHI presents to individual users. User-centered development methods provide tools for eliciting user needs, designing solutions, and evaluating the utility of these solutions in meeting user needs. Personal health management addresses individual practices as well as psychological, social, and cultural aspects of the management, storage, and retrieval of personal health information. Personal

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Figure 1. Conceptual model of key PHI structural components

health management and the health informatics domain combine in the healthcare focus of PHI. Content within the health informatics domain addresses the skills, knowledge, and surrounding use of IT within the subject health area(s). For practitioners to enter the health informatics and IT workforce with sufficient preparation to develop e-health that can fully support individual users’ needs, training is needed in each of the structural areas of PHI that are presented in Figure 1. None of these areas is beyond the grasp of college students or working health-IT practitioners. However, the combination of content areas represents a set of specializations that are rarely combined in current academic programs. Thus, for the time being, PHI training will necessarily have to be “pieced together” to some extent from existing textbooks, research reports, and industry cases. In the following sections, I address content and training issues relating to each structural component of PHI.

WEB E-SERVICE INFRASTRUCTURE E-health is essentially a form of e-service, defined as a service provided over electronic networks such as the Internet (Rust & Lemon, 2001). Thus, texts

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that focus on development and administration of Web e-service infrastructure will be applicable to PHI. Texts that address the general topic of e-commerce are typically less applicable, as large portions of these dwell on factors that are not central to e-health, such as general business models, marketing and consumer behavior issues, and methods for increasing online sales. Web infrastructure changes rapidly, so texts in this area must not be allowed to become stale. New texts should be chosen based upon inclusion of emerging technologies, such as mobile devices, speech recognition, and natural language parsers, as well as effective coverage of more mature technologies, such as XHTML, database connectivity, and network communications..

USER-CENTERED DEVELOPMENT User-centered development refers to a software development process that incorporates the user viewpoint, assesses user needs, and validates that user needs are met. There are two complementary approaches to this process that will be valuable to students of PHI. The first of these is the design guide approach. Design guides emphasize generic or context-specific design principles, such as the following example for use of color on Web sites. In the physical world, color has embedded meaning, usually culturally-based. In the American culture, at a simplistic level, red means stop and green means go, red means hot and blue means cold, black means death and white means birth, and green represents money and greed. These embedded meanings of color differ from culture to culture; for instance, the color white is symbolic not of birth, but of a funeral in Japan or India (Lazar, 2001). Design guides are valuable in establishing general guidelines in conventional use of design features and in broadening students’ appreciation of ways that others’ perspectives may differ from their own.

Applying Personal Health Informatics to Create Effective Patient-Centered E-Health

The second approach emphasizes the design process by establishing an algorithmic approach or “how-to guidance” for developing functionality and user interface features. For example, the SALVO method (Wilson & Connolly, 2000) guides readers through five development stages. The first three of these are planning stages in which designers specify user needs, adopt a technology-specific design guide, and leverage appropriate matches between technology and user needs to enhance user abilities and support user disabilities. Once planning stages are initially completed, developers perform visualization techniques including mock-ups to elicit user feedback early in the development cycle. Visualization is followed by an observation stage in which designers observe and evaluate attempts by users to perform representative tasks using the mock-up or completed software. SALVO takes an iterative approach to development, and it is accepted throughout the development cycle that prior stages and sequences of stages can be repeated as necessary. The design process approach provides PHI students with a methodology and specific tools for customizing e-health to meet the needs of a specific user audience, which may differ substantially, for example, between a macular degeneration support group and the general public.

PERSONAL HEALTH MANAGEMENT Personal health management is an emerging area of research, and new findings will likely emerge that add appreciably to current knowledge in this field. Although it may be too early to look for texts that focus on personal health management, good empirical studies and cases are available that would be highly applicable to PHI. One exemplar is a study by Moen and Brennan (2005) that investigates strategies for health information management (HIM) that are applied by individuals in their own homes. Their research identifies four HIM strategies:

•

•

• •

Just-in-time: Information is kept with the person at most times in anticipation of immediate need Just-at-hand: Information is stored in familiar, highly accessible locations, often serving as a reminder Just-in-case: Information is stored to be accessible in case of need, but out of view Just-because: Information is retained in interim storage pending a decision to file or discard it

This study presents several important implications for developers of HIM software that would be highly relevant for PHI students to consider and discuss. These include effects of trade-offs between information accessibility and visibility on the success of individuals’ efforts to coordinate HIM, implications of the high degree of reliance individuals place on paper-based records, and design decisions that would be necessary to align electronic records to support the differentiated storage strategies that individuals are found to prefer.

HEALTH INFORMATICS DOMAIN The health informatics domain encompasses use of IT to support the delivery of healthcare services (including communication, coordination, logistics, and other business processes) as well as applicable practices and procedures that surround use of IT in the healthcare setting. It is important to recognize that, while the health informatics domain is a key component of PHI, it is not the central element of e-health from the user’s perspective. In order to preserve a user-centered focus, it is essential that distinctive components of PHI should not be replaced by health informatics courses that focus on IT from a perspective that is internal to the organization (i.e., managerial or technical). Where PHI is taught within an existing health informatics program, a range of courses will

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already be in place to effectively cover topics in the domain. Where PHI is taught in technology programs, topics in the health informatics domain may be covered effectively by traditional health informatics or medical informatics texts.

and further educational opportunities in the field of health informatics and IT.

SUGGESTED MODES OF TRAINING ORGANIZATION

Patients comprise a large and growing constituency of e-health users (Krane, 2005), and surveys indicate there is very high interest among patients in increased access to provider e-health for a variety of specific interaction needs, for example, physician- patient communication (Taylor & Leitman, 2002). Leaders in the medical community are coming to recognize that patients expect to be empowered in making healthcare decisions (Institute of Medicine, 2001), and that the expectation of personal control is especially strong for e-health applications (Markle Foundation, 2004; Lafky, Tulu & Horan, 2006). However, healthcare provider organizations have only recently begun to provide their patients with online access to healthcare services, and numerous factors can obstruct development of effective e-health applications within this context. Examples of these factors include:

PHI training does not necessarily require development of new academic programs. Specialized subject areas can be presented to health-IT audiences within professional seminars and continuing education programs and to student audiences by augmenting existing health informatics or technology programs, such as information systems, computer science, or information science (Wilson, 2006b). As a specialization in a health informatics program which already incorporates domain coverage, in-depth PHI training can be achieved by a curriculum that offers one course-equivalent each in user-centered development and personal health management, and up to two courses in Web e-service infrastructure. If two courses are offered, the first should focus on basic development methods including XHTML, XML, and simple database connectivity and the second on administration issues and advanced development methods including Web services, mobile applications, and emerging technologies. As an augmentation to a technology program which already incorporates Web eservice infrastructure coverage, substantial depth of PHI training can be achieved by offering a specialization with one course-equivalent in each of user-centered development, personal health management, and health informatics. A final alternative for college students is to offer a basic introduction to each of these subjects within a single course-equivalent. Although not ideal for career preparation, this alternative could be an effective way to direct technology-area undergraduates toward careers

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THE NEED FOR PATIENTCENTERED E-HEALTH

•

• • • •

Financial disincentives for participation by outside parties (i.e., participants in e-health other than the patient, including physicians and provider administration) Reduced work quality resulting from participation by outside parties Reluctance of outside parties to relinquish control to patients Restrictive interpretation of privacy and security regulations Discomfort of patients and other key parties with computing environments

A complete discussion of obstructions and means for overcoming them is beyond the scope of this chapter (for such a discussion, see Tan, Cheng, & Rogers, 2002). However, I argue that

Applying Personal Health Informatics to Create Effective Patient-Centered E-Health

patient-centered e-health can help to meet patients’ expectations and thereby avoid or mitigate obstructions to availability and use of e-health.

GUIDING PRINCIPLES FOR CREATING PATIENTCENTERED E-HEALTH In order to meet patients’ expectations, it is essential for developers to focus on several guiding principles that are distinct from alternative approaches. Specifically, these are: 1. Focus on desired interactions in which the patient is an active participant 2. Incorporate only those services that meet the expressed needs of patients or that are validated against patient needs 3. Be understandable to patients 4. Provide easy access for patients to completely manage and control functionality 5. Provide support for interaction with outside parties (e.g., physicians and pharmacy) and with other healthcare information systems (e.g., hospital billing) These principles correspond to a large extent with user-centered development principles that are known to be important to success of Web applications across numerous contexts outside the domain of healthcare (Lazar, 2001). Principle 1 focuses on patient involvement, thereby distinguishing patient-centered e-health from other applications, such as telemedicine, where the patient is primarily an object of the interaction rather than an active participant. Principle 2 addresses patient interest and specifically cautions against relying on untested assumptions about patients as a basis for e-health design. By emphasizing patients’ involvement and interest, the first two principles help to ensure that patients will have inherent motivation to use related ehealth applications. They also guide the process of

eliciting patients’ interaction needs and mapping these to e-health services. The remaining principles center on accessibility and source of control. Principle 3 proposes that e-health information and communication should be understandable to patients. Some researchers argue that it is patients’ own health literacy that must increase in order for e-health to succeed (Norman & Skinner, 2006). However, this type of idealistic mindset ignores, first, that patients’ need for healthcare services is not dependent upon their literacy level and, second, that patients can benefit greatly if e-health designers make the effort to incorporate simple explanations and illustrations where these are practical. Many individuals who are only marginally literate have proved to be highly capable of interacting with online applications, such as banking, when they are provided with effective interface support. Further, requiring patients to be highly literate in order to use e-health is no more defensible logically than requiring high literacy in order to schedule medical examinations or other healthcare services that the provider may offer. From the patient’s perspective, e-health is simply an extension of the providers’ other services, thus it is reasonable for patients to expect e-health to be generally understandable and for the provider to offer mechanisms by which better explanations can be obtained if these are needed. Principle 4 presents a clear statement that ultimate control of patient-centered ehealth must flow to the patient. Increasing patients’ involvement in e-health has been promoted recently as part of the U.S. plan for “Delivering consumer-centric and information-rich healthcare” (Thompson & Brailler, 2004). However, achieving patient control faces two key obstacles. First, medical institutions are only slowly moving away from a firmly-embedded authoritarian or “paternalistic” model of physician-patient relationships (Emanuel & Emanuel, 1992) in which physicians expect to control virtually all aspects of their interaction with patients (Eysenbach & Jadad, 2001). Embedded cultural practices can take significant time

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to change, even in the face of substantial social pressures. Therefore, it is foreseeable in the near term that many physicians and other outside parties will resist allowing patients to exercise complete control over e-health or other aspects of healthcare. Second, healthcare provider organizations are reluctant to open up healthcare information systems (HIS) to access by patients. Reluctance is based on several factors, none of which is necessarily unreasonable. Providers have the responsibility to maintain privacy and security of patient and provider data, which could be compromised by increasing accessibility of the HIS. Significant labor expenditures are likely to be necessary in order to interconnect HIS with e-health, and it may be difficult for providers to identify ways that these expenses can be offset by increased income. Furthermore, access to HIS may be structurally blocked by disparate storage and communication formats among various proprietary systems. Lack of access to the provider’s HIS or to specific parts, such as medical records and test results, leaves patients with relatively little to control, as the resulting e-health application will not offer direct interaction with key functions, such as billing and appointment scheduling. Thus, it is important to develop strategies that can economically enhance access to HIS without compromising security or that can establish alternatives to direct access, for example, by applying a data warehouse model where only a copy of the original data is accessible to patients. It also is key to promote among physicians and clinical staff the understanding that patients have the right to play a central role in their own healthcare decisions. Principle 5 emphasizes that interaction with outside parties is essential for creating effective patient-centered e-health. Technical approaches can be important to achieve interoperability, as demonstrated by Wilson and Lankton (2003) in their proposal of a guided-mail system as an alternative to e-mail for patient-physician communication. However, social issues should not be overlooked in achieving interoperability, as these

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may be even more important than technical aspects in motivating outside parties to participate. For example, patients embrace the idea of having access to their own medical records, yet physicians are skeptical that patients would benefit from access and worry that their workload would increase if records became available (Ross, Todd, Moore, Beaty, Wittevrongel & Lin, 2005). By addressing these five principles, developers can work to create e-health that is centered on patients’ needs and meaningful in its capabilities. It may be anticipated that other design principles will also apply, for example, requirements arising from privacy and security regulations. However, care should be taken to ensure that these do not unnecessarily obstruct any of the five principles of patient-centered e-health that are enumerated above.

PERSONAL HEALTH INFORMATICS AND PATIENT-CENTERED E-HEALTH Through a joint focus on patients (personal focus), healthcare, and informatics, the PHI approach provides valuable tools for satisfying the guiding principles of patient-centered e-health. •

•

Background knowledge that includes usercentered development methods and personal health management (PHI personal focus) is key in understanding how to elicit patients’ perceptions regarding which interactions and e-health services they desire (Principles 1 and 2) and assess comprehensibility and usability of e-health application prototypes (Principles 3 and 4). Training in Web e-service infrastructure (PHI informatics focus) provides technical skills necessary to develop accessible controls for patients to use in managing e-health functions (Principle 4) and to enable connections to disparate healthcare IS (Principle 5).

Applying Personal Health Informatics to Create Effective Patient-Centered E-Health

•

Knowledge of the health informatics domain (PHI healthcare focus) enables effective interfacing with specialized healthcare IS, which frequently implements specialized protocols and nomenclatures (Principle 5).

Patient surveys consistently show a groundswell of desire for e-health applications that meet patients’ specific needs, including needs for electronic support for managing personal health information (Taylor, 2004), patient-physician communication (Taylor & Leitman, 2002), and high-quality online health information (Krane, 2005). The PHI approach can effectively integrate the skills, knowledge, and patient-centered perspective that are necessary to meet these growing patient demands.

CONCLUSION Although not always profitable for commercial vendors, e-health has been very beneficial to users, who already conduct many types of transactions and information searches on the Internet and strongly desire to be able to access healthcare services and information in the same manner. Patients comprise a large and growing constituency of e-health users, and I have argued in this chapter that the interests of this population would be served best by creating e-health from the patient’s perspec tive. The guiding principles outlined above take important steps toward the goal of patient-centered e-health. Yet this goal faces numerous obstacles, including fears of financial disincentives and reduced work quality for outside parties who are called upon to participate. In order to be effective, patient-centered e-health initiatives will require champions who will be aided by establishment of training programs, identification of best practices, and development of a shared identity. I propose that PHI offers a practical approach to undertake these actions.

REFERENCES AHIMA. (2005). The role of the personal health record in the EHR. Journal of American Health Information Management Association, 76(7), 64A–64D. Campbell, R. (2005). Getting to the good information: PHRs and consumer health informatics. Journal of American Health Information Management Association, 76(10), 46–49. Emanuel, E., & Emanuel, L. (1992). Four models of the physician-patient relationship. Journal of the American Medical Association, 267(16), 2221–2226. doi:10.1001/jama.267.16.2221 Eysenbach, G. (2000). Consumer health informatics. British Medical Journal, 320(7251), 1713–1716. doi:10.1136/bmj.320.7251.1713 Eysenbach, G. (2001). What is e-health? Journal of Medical Internet Research, 3(2), e20. doi:10.2196/ jmir.3.2.e20 Eysenbach, G., & Jadad, A. (2001). Evidencebased patient choice and consumer health informatics in the Internet age. Journal of Medical Internet Research, 3(2), e19. doi:10.2196/ jmir.3.2.e19 Fox, S. (2005). Health information online. Pew Internet & American Life Project. Retrieved December 13, 2005 from www.pewinternet.org Harris. (2000). Healthcare satisfaction study. Harris Interactive/ARiA Marketing Final Report. Retrieved December 13, 2005 from http://www. harrisinteractive. com/news/downloads/HarrisAriaHCSatRpt.PDF Holt, M. (2005). An archaeology of the commercial PHR movement. Presentation to QTC-CGU Symposium. The many faces of person-centric electronic health systems: A national symposium to examine use-cases for healthy, chronically ill and disabled user communities. Claremont, CA.

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Institute of Medicine. (2001). Crossing the quality chasm. Washington, D.C.: Institute of Medicine, National Academies Press. Itagaki, M., Berlin, R., & Schatz, B. (2002). The rise and fall of e-health: Lessons from the first generation of Internet healthcare. Medscape General Medicine, 4(2). Retrieved December 13, 2005 from http://www.medscape.com/viewarticle/ 431144_Print Kloss, L. (2005). Greater urgency, sharper focus for PHRs. Journal of American Health Information Management Association, 76(10), 27. Krane, D. (2005). Number of “cyberchondriacs” —U.S. adults who go online for health information—increases to estimated 117 million. Healthcare News, 8(5). Retrieved December 13, 2005 from http://www.harrisinteractive.com/ news/ newsletters_healthcare.asp Kwankam, S. (2004). What e-health can offer? Bulletin of the World Health Organization, 82(10), 800–802. Lafky, D., Tulu, B., & Horan, T. (2006). A userdriven approach to personal health records. Communications of the Association for Information Systems, 17, 1028–1041. Lazar, J. (2001). User-centered Web development. Sudbury, MA: Jones and Bartlett. Lazarus, I. (2001). Separating myth from reality in e-health initiatives. Managed Healthcare Executive, June, 33-36. Markle Foundation. (2004). Achieving electronic connectivity in healthcare. Markle Foundation. Martin, S., Yen, D., & Tan, J. (2002). E-health: Impacts of Internet technologies on various healthcare and services sectors. International Journal of Healthcare Technology and Management, 4(1-2), 71–86. doi:10.1504/IJHTM.2002.001130

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Moen, A., & Brennan, P. (2005). Health@Home: The work of health information management in the household (HIMH): Implications for consumer health informatics (CHI) innovations. Journal of the American Medical Informatics Association, 12(6), 648–656. doi:10.1197/jamia.M1758 Norman, C., & Skinner, H. E-Health literacy: Essential skills for consumer health in a networked world. Journal of Medical Internet Research, 8(2), article e9. Oh, H., Rizo, C., Enkin, M., & Jadad, A. (2005). What is e-health?: A systematic review of published definitions. Journal of Medical Internet Research, 7(1), article e1. Rovenpor, J. (2003). Explaining the e-commerce shakeout. E-Service Journal, 3(1), 53–76. Rust, R., & Lemon, K. (2001). E-service and the consumer. International Journal of E-Commerce, 5(3), 85–102. Tan, J., Cheng, W., & Rogers, W. (2002). From telemedicine to e-health: Uncovering new frontiers of biomedical research, clinical applications & public health services delivery. Journal of Computer Information Systems, 42(5), 7–18. Taylor, H. (2004). Two in five adults keep personal or family health records and almost everybody thinks this is a good idea: Electronic health records likely to grow rapidly. Healthcare News, 4(10). Retrieved December 13, 2005 from http://www. harrisinteractive.com/news/newsletters_healthcare.asp Taylor, H., & Leitman, R. (2002). Patient/physician online communication: Many patients want it, would pay for it, and it would influence their choice of doctors and health plans. Healthcare News, 2(8). Retrieved December 13, 2005 from http://www.harrisinteractive.com/news/newsletters_healthcare.asp

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Thompson, T., & Brailler, D. (2004). The decade of health information technology: Delivering consumer-centric and information-rich healthcare. Washington, D.C.: Department of Health and Human Services. WHO. (2005). E-health: Report by the Secretariat. World Health Organization, 58th World Health Assembly. Wilson, E. (2006a). The case for e-health in the information systems curriculum. Issues in Information Systems, 7(1), 299–304. Wilson, E. (2006b). Building better e-health through a personal health informatics pedagogy. International Journal of Healthcare Information Systems and Informatics, 1(3), 69–76.

Wilson, E., & Connolly, J. (2000). SALVO: A basic method for user interface development. Journal of Informatics Education & Research, 1(2), 29–39. Wilson, E., & Lankton, N. (2003). Strategic implications of asynchronous healthcare communication. International Journal of Healthcare Technology and Management, 5(3-5), 213–231. doi:10.1504/IJHTM.2003.004166 Wilson, E., & Lankton, N. (2004). Modeling patients’ acceptance of provider-delivered e-health. Journal of the American Medical Informatics Association, 11(4), 241–248. doi:10.1197/jamia. M1475

This work was previously published in Healthcare Information Systems and Informatics: Research and Practices, edited by Joseph Tan, pp. 344-359, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 6.14

Diagnostic Cost Reduction Using Artificial Neural Networks: The Case of Pulmonary Embolism Steven Walczak University of Colorado at Denver, USA Bradley B. Brimhall University of New Mexico, USA Jerry B. Lefkowitz Weill Cornell College of Medicine, USA

ABSTRACT Patients face a multitude of diseases, trauma, and related medical problems that are difficult and costly to diagnose with respect to direct costs, including pulmonary embolism (PE). Advanced decision-making tools such as artificial neural networks (ANNs) improve diagnostic capabilities for these problematic medical conditions. The research in this chapter develops a backpropaga-

tion trained ANN diagnostic model to predict the occurrence of PE. Laboratory database values for 292 patients who were determined to be at risk for a PE, with 15% suffering a confirmed PE, are collected and used to evaluate various ANN models’ performance. Results indicate that using ANN diagnostic models enables the leveraging of knowledge gained from standard clinical laboratory tests, significantly improving both overall positive predictive and negative predictive performance.

DOI: 10.4018/978-1-60960-561-2.ch614

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Diagnostic Cost Reduction Using Artificial Neural Networks

INTRODUCTION Medical and surgical patients today face a variety of conditions that are both difficult and costly to diagnose and treat. With ever skyrocketing medical costs (Benko, 2004), the use of information technology is seen as a much-needed means to help control and potentially reduce medical direct costs (Intille, 2004). Deep vein thrombosis (DVT) and pulmonary embolism (PE) are medical conditions that are particularly difficult to diagnose in the acute setting (Mountain, 2003). Frequent usage of costly clinical laboratory tests to screen patients for further treatment is commonplace. All too commonly hospitals provide treatment to patients without PE as a preventative measure (Mountain, 2003). Furthermore, patient mortality, morbidity, and both direct and indirect costs for delayed diagnosis of these conditions may also be substantial. Recent studies show that 40 to 80 percent of patients that die from a PE are undiagnosed as having a potential PE (Mesquita et al., 1999; Morpurgo et al., 1998). DVT may occur as the result of patient genetic and environmental factors or as a side effect of lower extremity immobility (e.g., following surgery). When a blood clot in the veins of a lower extremity breaks away, it may travel to the lungs and lodge in the pulmonary arterial circulation causing PE. If the clot is large enough it may wedge itself into the large pulmonary arteries leading to an acute medical emergency with a significant mortality rate. Approximately 2 million people annually experience DVT, with approximately 600,000 developing a PE and approximately 10% of these PE episodes result in mortality (Mesquita et al. 1999; Labarere et al., 2004). Documented occurrence of DVT in postoperative surgical populations ranges from 10% (Hardwick & Colwell, 2004) to 28% (Blattler et al., 2004). Direct costs associated with DVT and PE come from the expensive diagnostic and even more expensive treatment protocols. It may be possible to lower these direct costs, especially when ad-

ditional testing or treatment may be ruled out due to available knowledge. Artificial neural network (ANN) systems enable the economic examination (Walczak, 2001) and nonlinear combination of various readily available clinical laboratory tests. Laboratory tests typically performed on surgical patients, for example, blood chemistry, form the foundation for analysis and diagnostic model development. One such test is the D-dimer assay that measures patient plasma for the concentration of one molecular product released from blood clots. When blood vessels are injured or when the movement of blood is too slow through veins of lower extremities, blood may begin to clot by initiating a series of steps in which fibrin molecules are crossed linked by thrombin to form a structure that entraps platelets and other coagulation molecules—a blood clot. As healing begins to occur, plasmin begins to break down the clot and releases, among other things, D-dimer molecules. D dimers are actually small fragments of cross-linked fibrin and provide the basis for assessing blood clotting activity. Patients with DVT and PE frequently have elevated levels of D-dimer in their plasma. Consequently, many hospitals now employ the D-dimer assay as a first test in the diagnostic pathway for these conditions. Usage of the results of a D-dimer assay effectively reduces the direct costs of DVT by reducing the requirement for downstream testing and treatment, specifically Doppler ultrasound tests (Wells, 2003). The use of nonparametric models enables the analysis of laboratory tests without regard to the population distribution, which may be a problematic factor when combining more than one laboratory test for the diagnostic prediction. Additionally, ANNs provide nonlinear modeling capabilities that may be beneficial in combining pathology tests. The research reported in this chapter will examine the efficacy of using ANN models to predict the likelihood of a PE in surgical patient populations. A corollary research question is whether less invasive and less costly diagnostic

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methods are both available and reliable in predicting PE. The benefit of the reported research is twofold. First, the reported research examines new combinations of laboratory tests to determine if a combined model may be more reliable than currently used single variable medical models. Second, the research evaluates the viability of utilizing an ANN model to predict PE (and potentially DVT). The ANN provides improved positive predictive performance of the combined laboratory test model over the more traditional step-wise logistic regression models that are currently employed in medical modeling (León, 1994; Tran et al., 1994; Walczak & Scharf, 2000). The cost effectiveness of utilizing the described neural network pathology tool is determined by examining the direct costs to patients, where direct costs represent the costs of diagnostic work ups and the costs of any goods, services, and other resources utilized in any subsequent intervention (Wildner, 2003; Patwardhan et al., 2005). Implementing the described ANN-based PE predictive model is capable of reducing patient evaluation direct costs by well over $1,200 per patient suspected of having a potentially life-threatening PE, as well as reducing medical risks associated with some of the more advanced diagnostic methods. The organization of the remainder of this chapter follows. The next section gives background on some of the issues associated with diagnosing and treating PE and DVT as well as examining the utilization of ANN systems in medicine and issues surrounding the design of ANN systems. The methodology section describes the patient population and development of the neural network models for predicting PE occurrence. The next section performs an analysis of the ANN system’s performance and consequent savings in direct costs. The last section concludes with a summary of the research findings and directions for future research.

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BACKGROUND Diagnostic Difficulties of PE Various tests exist for attempting to detect the presence of a DVT or PE and are commonly administered to patients viewed as being at risk based on medical history as well as patient signs and symptoms. For example, leg pain or swelling may indicate a patient with DVT; however, such symptoms are far from specific. Venography (x-ray with injected intravenous radioactive contrast dye) of the legs, Doppler ultrasound scans, plethysmography (measuring differential blood pressure between arms and legs), and the D-dimer assay blood test are some of the tests that may be employed in the evaluation of DVT (see http://www.nlm.nih.gov/medlineplus/ency/ article/000156.htm).Venography is the “gold standard” for diagnosing DVT, which is commonly treated with anticoagulant therapy such as intravenous heparin, requiring hospitalization, or injected low molecular weight heparin combined with oral warfarin medication, which may be continued for six months. Patients with a sudden onset of chest pain or tachycardia or rapid shallow breathing may have a PE, but these are also general signs of a wide variety of medical conditions (Mesquita et al., 1999; Mobley et al., 2005). PE’s are often first evaluated using ventilation perfusion scanning (costing $300-$500 and involving the injection of radioactive-tagged material) and chest x-rays that may be followed by pulmonary angiography (see http://www.nlm.nih.gov/medlineplus/ency/ article/000132.htm). If the diagnosis of PE is made, the patient requires emergency treatment, typically thrombolytic therapy to dissolve the clot, and hospitalization. While the pulmonary angiogram is considered the “gold standard” for determining PE presence (Evanders et al., 2003; Mountain, 2003), it has a direct cost between $800 and $1,200 and involves injecting contrast through a catheter that has been

Diagnostic Cost Reduction Using Artificial Neural Networks

threaded into major coronary blood vessels, with associated morbidity and mortality risks. If use of the D-dimer assay can better determine the likelihood or absence of a PE, then a significant reduction in risks to the patient and costs ensues. The D-dimer assay has a direct cost of less than $7. Another factor that encourages the use of the D-dimer and other blood tests is the timeliness of the test, which average approximately 30 minutes total time including acquisition of the sample, transportation, analysis, and delivery of results. Improved detection of PE and early management will decrease the mortality and morbidity associated with PE (Stein et al., 2004b). Currently, the D-dimer assay has strong negative predictive reliability that is excluding DVT or PE patients who are exceedingly unlikely to benefit from further invasive testing (e.g., angiography) or very few false negatives. Unfortunately, the test does not appear to have reliable positive predictability (Stein et al., 2004a), leaving the diagnostic role of the D-dimer uncertain. Maximizing both the positive and negative predictive values of laboratory tests should be a goal of diagnostic tests (Kara & Güven, 2007). A potential cause of the lack of positive predictability is that due to the potential life-threatening outcome of PE, the screening threshold for the D-dimer result is set very low (e.g., 400 or 450) to avoid false negative tests. As a result, many patients who do not have a PE must undergo a pulmonary angiogram or other expensive testing procedure to determine if a PE exists. Additionally, no standard for the Ddimer result threshold has been established allowing for a wide-range of cutoff values between hospitals (Stein et al., 2004a) and subsequent variation, though usually high, of patient population percentages undergoing unnecessary treatment.

Neural Networks in Medicine The issues related to the use of D-dimer assay results: lack of standardized cutoff value, lack

of positive predictive capability, and other influencing factors, pose a modeling problem. ANNs provide strong modeling capabilities when population dynamics of the independent variables are unknown or nonlinear (Smith, 1993; Walczak & Cerpa, 2002), as would happen when using multiple laboratory test results to perform a diagnosis. Before examining the factors that affect ANN development (in the next section), a brief review of ANN applications in medical domains is provided in this section. Neural networks or artificial neural networks applied to solve or provide decision support for a variety of medical domain problems started two decades ago (Leese, 1986). Many physicians are reluctant to utilize artificial intelligence technologies including ANN, especially neural networks that attempt to perform diagnosis or treatment in other than a decision support role (Kleinmuntz, 1992; Hassoun et al., 1994; Baxt, 1995; Baxt & Skora, 1996; Hu et al., 1999; Walczak, 2003). The reluctance of physicians to adopt neural networks has led to the usage of neural networks as primarily image processing and laboratory test analysis tools. Figure 1 provides a brief overview of historic and more recent neural network applications in medicine. A full review of all neural network applications in medicine is beyond the scope of this chapter1, and as such Figure 1 only provides a representative sample of research over the past decade. Although representative and not comprehensive, Figure 1 indicates that ANN applications in medical domains tend to be used in either imaging or laboratory settings (Walczak & Scharf, 2000), with diagnostic neural networks occurring rarely. Of interest from Figure 1 for the research presented in this chapter are the three PE-related ANN applications. Each of these ANN research applications (Fisher et al., 1996; Evander et al., 2003; Serpen et al., 2003) examines the improvement in identification or validation of PE from chest x-ray images or pulmonary angiogram data. Thus, each of these existing methods still requires the usage of and incurrence of the direct costs

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Figure 1. Types of artificial neural network applications in medicine

associated with these more costly diagnostic methods. While ANNs previously performed image analysis to confirm a diagnosis of PE, the use of ANNs to predict a PE from standard low direct cost blood tests is a novel application of this nonparametric modeling paradigm. The next section examines factors impacting the development of ANN models, with an emphasis on the medical domain application of predicting PE.

Neural Network Design Issues Patient treatment information must necessarily be of high quality (Silverman, 1998), since the consequences of prediction error can lead to increased morbidity and even mortality. ANNs have been shown to outperform various statistical modeling methods in medical domains with respect to the accuracy of results (White, 1992; Buchman et al., 1994; León, 1994; Dybowski & Gant, 1995; Lapuerta et al., 1995; Stair & Howell, 1995; Baxt & Skora, 1996; Walczak & Scharf, 2000; Walczak, 2005)ANN design can be problematic and should

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account for the following influencing design factors (Walczak & Cerpa, 1999; Walczak & Cerpa, 2002): training algorithm selection, input variable selection, and hidden layer architecture. Various ANN training algorithms exist, however Alpsan et al. (1995) have claimed that the backpropagation training algorithm produces classification and prediction models that generalize (on out of sample data) as well as or better than most other ANN training algorithms, at least for their specific medical domain problem. Backpropagation trained ANN in particular have been proven to be robust models of arbitrary complex equations (Hornik et al., 1989; White, 1990; Hornik, 1991). Backpropagation trained ANNs are commonly used to provide solutions to business and engineering problems and also are the most common ANN type for medical domain applications which facilitates cross research comparison of methodologies (Cherkassky & Lari-Najafi, 1992; Dorffner & Porenta, 1994; Montague & Morris, 1994; Alpsan et al., 1995; Baxt, 1995; Dybowski & Gant, 1995; Rodvold et al., 2001).

Diagnostic Cost Reduction Using Artificial Neural Networks

A reason for selecting another training algorithm besides backpropagation, such as the radial basis function, is to overcome problems with very noisy input data in the training sets (Barnard & Wessels, 1992; Carpenter et al., 1995). Since the proposed ANN prediction model utilizes the results of laboratory tests, many already controlled by computers and robotics, the resultant error rate in the input data is minimal and should not require a training algorithm that may not generalize well in order to alleviate error-prone data problems. Generally considered the best and most robust training method for ANN classification and prediction models, backpropagation works well when low-noise input data exists (Walczak & Cerpa, 2002). The backpropagation algorithm has been widely discussed in literature and readers should see Hertz et al. (1991), Fu (1994), Jain et al. (1996), and Rodvold et al. (2001) for further details on the function of the backpropagation ANN training methodology. A critical step in the design of any ANN model and especially in medical applications is the selection of high quality predictive input (independent) variables that are not highly correlated with each other (Smith, 1993; Pakath & Zaveri, 1995; Tahai et al., 1998; Walczak & Cerpa, 1999). Domain expert physicians identify all available data that is related to the PE diagnosis problem. The final influencing factor from above in developing ANN models is the determination of the hidden node architecture (Smith, 1993; Walczak & Cerpa, 1999). The quantity of hidden nodes has a direct effect on generalization performance (Hung et al., 1996), with both too few and too many hidden nodes decreasing performance. The number of hidden layers also affects the smoothness and closeness of fit of the solution surface (Barnard & Wessles, 1992). A common heuristic method to determine the optimal number of hidden nodes is to start with a small quantity of hidden nodes (commonly ¼ to ½ the quantity of input nodes) and increment the hidden node quantity by one until generalization performance begins

to deteriorate. This method will safeguard against under-fitting and over-fitting the model (Walczak & Cerpa, 1999). The research reported below will evaluate the efficacy of utilizing an ANN methodology for developing a diagnostic model of PE that utilizes tests with a lower direct cost.

METHODOLOGY: AN ANN MODEL FOR DIAGNOSING PE As stated above, the evaluation mechanisms for determining if a patient has a PE and requires treatment currently have high direct costs (e.g., ventilation perfusion scan costing from $300 to $500 and pulmonary angiogram costing from $800 to $1,200). In addition to other demographic and medical risk factors, the purpose of this research is to determine if less costly evaluation mechanisms for PE are available, especially using the D-dimer assay results which has a direct cost of under $7 and is commonly used to negatively screen out patients for PE (Stein et al., 2004a).

Research Population Description The patient population in this research is 292 surgical patients admitted to a large Midwest teaching hospital during a 9½ month period. All patients during the period of the study diagnosed by physicians as being `at risk’ for either DVT or PE are included in the study with no exclusions. Database records maintained by the hospital clinical laboratory as well as clinical information provided from radiology and chart records provide the data used in the ANN input vector (independent variables). Patient records used in this study only come from patients clinically identified as being ‘at risk’ for a DVT or possible PE by attending physicians based on signs (e.g., swelling in legs or irregular breathing) and symptoms (e.g., report of pain in legs for DVT or chest for PE). Just over 15% of this patient population, 44 out of 292, suffered

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a confirmed PE, which is consistent with other findings on the rate of DVT occurrence in surgical populations and consequent occurrence of PE (Blattler et al., 2004; Hardwick & Colwell, 2004). A pulmonary angiogram confirmed or negated the presence of PE. The ‘at risk’ population of surgical patients for DVT and a possible PE has some basic statistical measures that are displayed in Figure 2. From Figure 2, it can be seen that the D-dimer results alone are not significantly different between patients with PE and patients without PE, although the PE patients have a much larger average D-dimer result and also a wider distribution. High D-dimer values may result from a variety of causes in addition to DVT or PE, which further confuses the application of the D-dimer for diagnosing PE.

ANN Design of a PE Diagnosis Model The focus of this study is on the use of the D-dimer assay results in possible combination with other values to improve the overall positive predictive performance to reduce false positives, which lead to unnecessary PE evaluation direct costs. As discussed in a previous section, the small error associated with laboratory tests and because of the reported robustness of the backpropagation training algorithm implied selection of backpropagation as the training algorithm for implementing the ANN-based PE diagnosis model. Radial basis function (RBF) neural networks and step-wise logistic regression (LR) models were also implemented simultaneously to produce multiple models for comparison and to evaluate if Figure 2: Typical D-dimer surgical population values

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a backpropagation trained neural network would produce the optimal model. Using the model selection perspective (Swanson & White, 1995), comparison of each of the independent model types yielded the backpropagation ANN model as the optimal model. The backpropagation models outperformed, with regard to overall accuracy (the sum of true positives and true negatives divided by all cases) both of the other model types (p < .01 against the RBF-trained ANN and p < 0.10 for the LR models). Domain-expert physicians identified blood test results with minimal direct costs that would be available for suspected DVT and PE patients prior to the performance of an angiogram. Since the research question of trying to determine if one or more of these test results could improve the PE diagnosis accuracy, a correlation matrix of all blood chemistry and other blood test results is performed to determine those variables that appear to be correlated with a positive diagnosis of PE. The variables include: D-dimer, reactive glucose (Glu-R), blood urea nitrogen, serum creatinine, serum sodium, serum potassium, serum chloride, serum carbon dioxide level, serum calcium, white blood cell count, red blood cell count, hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, red cell distribution width, and platelet count. Other values including: mean platelet volume, absolute neutrophil count (and %), absolute lymphocyte count (and %), absolute monocyte count (and %), absolute eosinophil count (and %), and absolute basophile count (and %) were considered for use

Diagnostic Cost Reduction Using Artificial Neural Networks

but eliminated due to missing data for many of the patients (i.e., the tests had not been ordered by their attending physician). The predictive correlation of different blood test results measured by a Pearson’s correlation matrix indicated that the D-dimer had the highest correlation with a validated PE for those test results included in the matrix and that randomized glucose (Glu-R) and CO2 also both had significant (p < 0.05) predictive correlations with the PE dependent variable. The age and sex of the patient are also included as independent variables to determine if these may be predictive when combined with blood test results2. Different backpropagation trained ANN models use various combinations of these five variables: age, sex, D-dimer, Glu-R, and CO2 as the input vector of independent variables, producing 23 different sets of input variables and corresponding diagnosis models. ANN models for each of the 23 different input vectors each start with a single hidden processing node. Both one and two hidden layer architectures are evaluated since the complexity of the diagnosis solution surface is unknown (Fu, 1994). When a second hidden layer is implemented it is also started with a single hidden processing node. The quantity of processing nodes in each layer is incremented independently of the other layer and by a quantity of one. Re-initialization of each new, larger, model produces random interconnection weights before training occurs. Hidden node expansion stops once the generalization performance begins to deteriorate and the model with the best generalization performance is kept.

Training of each ANN model uses one sample of data and then validation uses a separate hold out sample. Due to the relatively small size of the population data, we use an 8-fold cross validation technique to increase the quantity of hold out samples analyzed in the results to the full population of 292. The majority of the ANNs evaluated performed similarly to just using the D-dimer result alone, meaning they had very good specificity (identifying positive PE cases) but very poor sensitivity (identifying negative PE cases). The finding that the sex-independent variable did not influence the performance of the various neural supports the findings of Stein et al. (2003b) who found that sex was not a factor in the diagnosis of PE. The best performing ANN model utilized both the D-Dimer and GLU-R result for each patient. The name of the ANN PE diagnosis model is PEDimeNet, since it utilizes the D-dimer laboratory test in combination with another test to maximize positive PE predictive performance. The hidden node architecture for this backpropagation trained ANN is 2 input nodes representing the D-dimer and Glu-R results, 4 hidden nodes, and 1 output node, as shown in Figure 3. This ANN is the one discussed in the next section.

RESULTS AND DISCUSSION Backpropagation trained ANNs provide a real number output between -1 and 1 (though some ANN shell tools allow this range to be extended)

Figure 3. PEDimeNet ANN architecture

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and not a true or false result for predicting the occurrence of a PE in a patient. These real number output values require interpretation to determine the optimal cutoff point for partitioning the solution space into the two desired groups: probable PE and unlikely PE. A ROC (Receiver Operating Characteristic) curve analysis is commonly performed by those involved in medical diagnostics to determine the optimal threshold cutoff value for maximizing positive predictive performance on the generalization sample (Kamierczak, 1999; Obuschowski et al, 2004) and this technique is used with the ANN training sample output values to determine the optimal real number threshold value for performing the classifications. A similar and independent ROC curve analysis for the RBF and LR models reported in the previous section determines each model’s cutoff value. The area under the ROC curve for the training data was 0.79. Once the ROC threshold value is determined, it is then applied universally to all out-of-sample output values produced by the ANN to determine the ANN’s PE diagnosis for the corresponding patient. Figure 4 displays the prediction results of the resulting ANN model. Similar to increasing the D-dimer cutoff value to include more of the patient population in the group that undergoes further evaluation via an angiogram or other PE verification method, a tradeoff exists between the positive predictive accuracy and the negative predictive accuracy using the ANN model. Lowering the PE cutoff point causes the positive predictive accuracy to increase and creates a corresponding decrease in the negative predictive accuracy will Figure 4. ANN diagnostic prediction results for PE

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be seen. Due to the possible outcome of mortality for PE patients, a positive predictive performance of 90% sensitivity is desired as specified by the domain-expert physicians (where the TP rate in Figure 4 is the sensitivity and the TN rate is the specificity). The overall diagnosis accuracy ([TP+TN]/Total) performance of the PEDimeNet ANN model is 62.33%, with almost 91% of all PE patients identified and also nearly 57% of patients that do not have a PE and therefore do not require any additional testing for PE with subsequent reduction in direct costs. Recall that traditional usage of the D-dimer, with a relatively low threshold values, produces a false negative (FN) rate very close to 0%. As a result, a negative PE test result usually means that the patient will truly not have a PE. The false positive rate for the standard D-dimer test is very high, which leads to the increased medical costs through unnecessary costly procedures for follow up evaluation (e.g., pulmonary angiogram). For the test population used in the research reported in this chapter, a simulation of adjusting the D-dimer cutoff value to produce a false negative rate identical to the proposed ANN model, produced a false positive rate of 91.53% (227 patients that require unnecessary evaluation tests), meaning that only 21 of 248 patients did not have further costly PE evaluation procedures recommended. Whereas the PEDimeNet ANN was able to save additional laboratory testing for 141 patients, as opposed to just 21 when using the traditional Ddimer only diagnosis. Therefore, an ANN model of PE that utilizes both D-dimer and Glu-R test results provides a high-quality positive predictor.

Diagnostic Cost Reduction Using Artificial Neural Networks

The ANN PE prediction model exceeds using a non-ANN D-dimer-based evaluation mechanism with respective to true negatives (patients without PE) by well over 600%, resulting in significant cost savings for the patients. Direct cost savings would be approximately $96,000 to $144,000 for the 9½ month period of the research study, which translates to a $121,000 to $182,000 direct cost annualized savings. A possible limitation of this study is that although the ANN was evaluated using data from a large urban hospital with a demographically diverse population, regional differences in pathological values for PE patients may exist (Stein et al., 2004b). As such, the methodology described would require each hospital to independently train their own ANN model and to establish their own ROC cutoff threshold value for interpreting the generalization (out-of-sample) diagnosis values of the ANN in order to establish confidence in the ANN’s values fro specific regions of the world. Utilizing the proposed ANN system would require only very minor workflow changes in the emergency department and clinical laboratory of the studied hospital and would integrate similarly at most hospitals that follow similar diagnostic methodologies. Medical user acceptance of new decision support technology is highly dependent

on two factors: first that the decision support technology not be perceived as attempting to make diagnosis (Baxt, 1995; Baxt & Skora, 1996; Walczak & Scharf, 2000) and second that the new information technologies not disrupt normal workflows (Walczak, 2003; Kirkley & Stein, 2004). Physicians receive laboratory results at most hospitals either electronically or on paper after encoding and storing the results in a pathology database. The PEDimeNet ANN-based decision support system would architecturally be positioned in the pathology departments IT framework as displayed in Figure 5. This integration of the PEDimeNet ANN diagnostic tool would then appear seamless to physicians, integrating with their existing workflow by simply adding the analysis to the pathology report already received by the physician. The PEDimeNet PE diagnosis decision support tool is composed of the PEDimeNET ANN, an interface that allows the entry of D-dimer and Glu-R laboratory test results (which may be handled electronically through an EDI interface or performed manually at a keyboard), a backend program that contains the ROC curve analysisbased decision cutoff value (which may also be read from a database to allow for movement in the cutoff value over time perhaps due to new

Figure 5. PEDimeNet relative to other pathology IT systems

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pathology equipment sensitivity) and produces the go/no go diagnostic recommendation for probable PE in a patient. Future enhancements would include a database or agent-oriented Web browser to recommend additional tests if necessary and treatment protocols. The following scenario presents an illustration of the functioning of the proposed PEDimeNet system within a hospital setting. A physician would request a diag- nostic workup including a patient blood draw. The clinical laboratory receives the patient’s blood sample. Laboratory tests are completed using the existing laboratory equipment. The results are delivered to the physician either electronically (with EDI requiring a small hardware modification to interface with the existing pathology instrumentation) or on paper (after entering the results into the laboratory database with a simultaneous transmission to the PEDimeNet). A software interface to the database or other peripheral devices (in C# or Java) encodes the received value into the format required by the ANN. The PEDimeNet diagnostic decision support system would then interpret the ANN’s results based on a cutoff value established by applying the ROC curve analysis determined threshold. The backend analysis of the ANN output provides a report of the interpreted ANN prediction in either electronic or printed format for the attending physician, who would then determine if any further evaluation was required. Other than the step in the lab necessary to transmit the results to the PEDimeNet decision support system, the automated process is similar to standard decision-making processes for evaluating the need to screen for a PE. From the physician’s perspective, there is no change in workflow from the traditional receipt of the original non-interpreted D-dimer assay results.

CONCLUSION The research presented in this chapter demonstrates the efficacy of utilizing ANNs for predict-

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ing the occurrence of PE in patients presenting to the emergency department and those at risk of PE following surgery. The reported results were able to match the very low false negative rate of a more traditional test that relied on the D-dimer alone. Although the usage of a D-dimer screening test is questioned in cases of PE (Stein et al., 2004a), it has proved reliable for evaluating the presence of DVT (Wells et al., 2003), a leading cause of PE. Furthermore, over a 600% increase in true negative predictions with a corresponding decrease in associated medical risks to patients and decreased medical costs of hundreds of thousands of dollars per hospital per year demonstrates the benefits of using a nonlinear and nonparametric modeling technique such as the backpropagation trained ANN. Another benefit derived from this research is the identification of the superior positive predictive performance for PE when using the combination of the D-dimer result value in combination with the Glu-R result value. The D-dimer, individually, was unable to reliably distinguish between positive PE cases and negative ones, creating a high corresponding false positive assessment rate so that only a very small number of actual PEs would be misdiagnosed. The utilization of an ANN as the statistical analysis component of the PEDimeNet ANN decision support system enabled the rapid combination of input values in nonlinear ways without regard to population distribution characteristics. A potential drawback of using ANNs as a model determination mechanism to evaluate the contribution of various independent variables is the need to determine the ideal architecture. While the current research utilized a brute force incremental process to analyze both single and two hidden layer networks, new neural network shell tools are able to automate some or all of the ANN node architecture optimization process. While the research reported in this chapter focused on the prediction of PE, the relationship between PE and DVT and previous research has indicated a strong positive correlation between

Diagnostic Cost Reduction Using Artificial Neural Networks

the D-dimer assay results and accurate DVT diagnosis (Wells et al., 2003), which indicates that a similar ANN model would be able to accurately predict DVT. This would permit the operation of two side-by-side (or possibly one) ANNs in the PEDimeNet decision support system to evaluate both PE and DVT simultaneously and is a topic of future research. In addition to the specific research benefits just described above, generalizing the research method and results provides implications for both researchers and practitioners. Future research efforts will benefit by realizing that nonparametric modeling techniques in general and neural networks specifically are a viable method for analyzing complex and potentially ill-defined (unknown population and error distributions) problems. Researchers should embrace the most advanced modeling techniques available, especially since neural networks have repeatedly demonstrated increased performance over more traditional parametric statistical methods. The data set for the presented ANN PE diagnosis application is relatively error-free. Researchers should note that designing optimal neural network models has many implementation factors and careful attention in selecting input variables, selecting the training algorithm, and determining the hidden layer and hidden node architecture will help to produce the optimal ANN model (Smith, 1993; Walczak & Cerpa, 1999; 2002). An implication for practice is to challenge existing protocols and heuristics. The addition of a low direct-cost variable to the PE diagnosis model significantly increases the accuracy and reduces direct costs. Other medical diagnostic and assessment problems may derive benefit from examining new sets of variables. The ANN modeling method provides an inexpensive and reliable way to rapidly asses the impact of adding new variables or removing existing decision variables (Walczak, 2008). The research reported in this chapter demonstrates the impact of ANN diagnosis models through the reduction of direct

costs. Future research is needed to realize the overall benefit gained from ANN diagnosis models by examining the impact on indirect costs, such as reduced patient healing times and overall quality of care received.

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Papadopoulos, A., Fotiadis, D., & Likas, A. (2005). Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines. Artificial Intelligence in Medicine, 34(2), 141–150. doi:10.1016/j. artmed.2004.10.001

Scott, J., Aziz, K., Yasuda, T., & Gewirtz, H. (2004). Integration of clinical and imaging data to predict the presence of coronary artery disease with the use of neural networks. Coronary Artery Disease, 15(7), 427–434. doi:10.1097/00019501200411000-00010

Patwardhan, M., Matchar, D., Samsa, G., McCrory, D., Williams, R., & Li, T. (2005). Cost of multiple sclerosis by level of disability: A review of literature. Multiple Sclerosis, 11(2), 232–239. doi:10.1191/1352458505ms1137oa

Serpen, G., Iyer, R., Elsamaloty, H., & Parsai, E. (2003). Automated lung outline reconstruction in ventilation-perfusion scans using principal component analysis techniques. Computers in Biology and Medicine, 33(2), 119–142. doi:10.1016/ S0010-4825(02)00063-X

Pereira, A. (2004). Performance of time-series methods in forecasting the demand for red blood cell transfusion. Transfusion, 44(5), 739–746. doi:10.1111/j.1537-2995.2004.03363.x Petrovsky, N., & Brusic, V. (2004). Virtual models of the HLA class I antigen processing pathway. Methods (San Diego, Calif.), 34(4), 429–435. doi:10.1016/j.ymeth.2004.06.005 Polak, S., Skowron, A., Mendyk, A., & Brandys, J. (2004). Artificial neural network in pharmacoeconomics. Studies in Health Technology and Informatics, 105, 241–249. Rodvold, D., McLeod, D., Brandt, J., Snow, P., & Murphy, G. (2001). Introduction to artificial neural networks for physicians: Taking the lid off the black box. The Prostate, 46(1), 39–44. doi:10.1002/1097-0045(200101)46:1<39::AIDPROS1006>3.0.CO;2-M

Silverman, B. (1998). The role of Web agents in medical knowledge management. M.D. Computing, 15(4), 221–231. Smith, M. (1993). Neural networks for statistical modeling. New York, NY: Van Nostrand Reinhold. Stair, T., & Howell, J. (1995). How long will it take? How much will it cost?: Multiple regression and neural network programs at ED triage. The American Journal of Emergency Medicine, 13, 118–119. doi:10.1016/0735-6757(95)90283-X Stein, P., Hull, R., Patel, K., Olson, R., Ghali, W., Alshab, A., & Meyers, F. (2003a). Venous thromboembolic disease: Comparison of the diagnostic process in blacks and whites. Archives of Internal Medicine, 163(15), 1843–1848. doi:10.1001/ archinte.163.15.1843

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Stein, P., Hull, R., Patel, K., Olson, R., Ghali, W., Alshab, A., & Meyers, F. (2003b). Venous thromboembolic disease: Comparison of the diagnostic process in men and women. Archives of Internal Medicine, 163(14), 1689–1694. doi:10.1001/ archinte.163.14.1689 Stein, P., Hull, R., Patel, K., Olson, R., Ghali, W., & Brant, R. (2004a). D-dimer for the exclusion of acute venous thrombosis and pulmonary embolism. Annals of Internal Medicine, 140(8), 589–602. Stein, P., Kayali, F., & Olson, R. (2004b). Estimated case fatality rate of pulmonary embolism, 1979 to 1998. The American Journal of Cardiology, 93(9), 1197–1199. doi:10.1016/j. amjcard.2004.01.058 Suzuki, K., Horiba, I., Sugie, N., & Nanki, M. (2004). Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector. IEEE Transactions on Medical Imaging, 23(3), 330–339. doi:10.1109/TMI.2004.824238 Swanson, N., & White, H. (1995). A model-selection approach to assessing the information in the term structure using linear models and artificial neural networks. Journal of Business & Economic Statistics, 13(3), 265–275. doi:10.2307/1392186 Tahai, A., Walczak, S., & Rigsby, J. (1998). Improving artificial neural network performance through input variable selection. In: P. Siegel, K. Omer, A. deKorvin, & A. Zebda, (Eds.), Applications of fuzzy sets and the theory of evidence to accounting II (pp. 277-292). Stamford, CT: JAI Press. Tran, D., VanOnselen, E., Wensink, A., & Cuesta, M. (1994). Factors related to multiple organ system failure and mortality in a surgical intensive care unit. Nephrology, Dialysis, Transplantation, 9(4Supp), 172–178.

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Übeyli, E. (2005). A mixture of experts network structure for breast cancer diagnosis. Journal of Medical Systems, 29(5), 569–579. doi:10.1007/ s10916-005-6112-6 Walczak, S. (2001). Neural networks as a tool for developing and validating business heuristics. Expert Systems with Applications, 21(1), 31–36. doi:10.1016/S0957-4174(01)00024-0 Walczak, S. (2003). A multi-agent architecture for developing medical information retrieval agents. Journal of Medical Systems, 27(5), 479–498. doi:10.1023/A:1025668124244 Walczak, S. (2005). Artificial neural network medical decision support tool: Predicting transfusion requirements of ER patients. IEEE Transactions on Information Technology in Biomedicine, 9(3), 468–474. doi:10.1109/TITB.2005.847510 Walczak, S. (2008). Evaluating medical decisionmaking heuristics and other business heuristics with neural networks. In: G. Phillips-Wren & L. Jain (Eds.), Intelligent decision making: An AI-based approach (chap. 10). New York, NY: Springer. Walczak, S., & Cerpa, N. (1999). Heuristic principles for the design of artificial neural networks. Information and Software Technology, 41(2), 109–119. doi:10.1016/S0950-5849(98)00116-5 Walczak, S., & Cerpa, N. (2002). Artificial neural networks. In: R. Meyers (Ed.), Encyclopedia of physical science and technology, (3rd ed., vol. 1, pp. 631-645). San Diego, CA: Academic Press. Walczak, S., & Nowack, W. (2001). An artificial neural network approach to diagnosing epilepsy using lateralized bursts of theta EEGs. Journal of Medical Systems, 25(1), 9–20. doi:10.1023/A:1005680114755

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Walczak, S., & Scharf, J. (2000). Reducing surgical patient costs through use of an artificial neural network to predict transfusion requirements. Decision Support Systems, 30(2), 125–138. doi:10.1016/ S0167-9236(00)00093-2

Zini, G. (2005). Artificial intelligence in hematology. Hematology, 10(5), 393–400. doi:10.1080/1 0245330410001727055

Wells, P., Anderson, D., Rodger, M., Forgie, M., Kearon, C., & Dreyer, J. (2003). Evaluation of D-dimer in the diagnosis of suspected deepvein thrombosis. The New England Journal of Medicine, 349(13), 1227–1235. doi:10.1056/ NEJMoa023153

ENDNOTES

White, H. (1990). Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings. Neural Networks, 3, 535–549. doi:10.1016/0893-6080(90)90004-5 White, H. (1992). Consequences and detection of misspecified nonlinear regression models. In: H. White (Ed.), Artificial neural networks: Approximations and learning theory (pp. 224-258), Oxford, UK: Blackwell. Wildner, M. (2003). Health economic issues of screening programmes. European Journal of Pediatrics, 162(Suppl.), S5–S7. doi:10.1007/ s00431-003-1341-5



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A database inquiry on EBSCO using the terms “neural network” AND (medicine OR medical) produced 997 articles. A similar query on PubMed, the NIH article database, produced 6,120 articles. The occurrence of PE between blacks and whites has been found to be comparable (Stein et al., 2003a) and as such is not included in the variables considered for the ANN input vector. Additionally, this variable is subjective being either self-reported by patients or reported as an observation by the admitting nurse in the studied hospital and as such may have a higher than acceptable error rate for use in a backpropagation trained ANN

Yeong, E.-K., Hsiao, T.-C., Chiang, H., & Lin, C.W. (2005). Prediction of burn healing time using artificial neural networks and reflectance spectrometer. Burns, 31(4), 415–420. doi:10.1016/j. burns.2004.12.003

This work was previously published in Healthcare Information Systems and Informatics: Research and Practices, edited by Joseph Tan, pp. 108-130, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Section VII

Critical Issues

Section 7 details some of the most crucial developments in the critical issues surrounding clinical technologies. Importantly, this refers to critical thinking or critical theory surrounding the topic, rather than vital affairs or new trends that may be found in section 8. Instead, the section discusses some of the latest developments in ethics, law, and social implications in clinical technology development. Within the chapters, the reader is presented with an in-depth analysis of the most current and relevant issues within this growing field of study.

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Chapter 7.1

eHealth and Ethics:

Theory, Teaching, and Practice Diane Whitehouse The Castlegate Consultancy, Malton, UK Penny Duquenoy Middlesex University, London, UK

ABSTRACT The use of information and communication technologies (ICT) is increasing rapidly in many spheres of contemporary life in Europe. The ethical use of ICT in all areas of its application is of growing importance. This is especially evident in the field of healthcare. The regional, national, and Europe-wide electronic aspects of health services and systems are related fundamentally to these two developments. This chapter explores the relevance of ethics to eHealth generally. It outlines DOI: 10.4018/978-1-60960-561-2.ch701

two main contrasting ideas that have influenced ethical thought: Kantian ethics and consequentialism. It investigates the ways in which teaching and practice for ICT professionals and trainees can be enhanced and extended to increase the awareness of ethical issues in eHealth. It takes as examples two technological applications that are in increasing use in the eHealth field: electronic health records and radio frequency identification devices. The chapter ends with a brief discussion and conclusions about how this ethical awareness can be expanded beyond ICT professionals to other stakeholder groups, and to other eHealth technologies or applications.

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

eHealth and Ethics

INTRODUCTION eHealth has variously been referred to as medical informatics or medical information systems, clinical informatics or clinical information systems, health informatics or health information systems, or information and communication technologies (ICT) for health (Duquenoy et al., 2008a). A number of definitions have been outlined in both the academic literature and in policy-related documentation (COM(2004) 356 final; COM(2007) 860 final; Eng (2001); Eysenbach (2001); Oh et al. (2005); Pagliari et al. (2005)). In this paper, we have selected from the text of the eHealth action plan (COM(2004)356, p4) one of the more pragmatic definitions: [eHealth] describes the application of information and communications technologies across the whole range of functions that affect the health sector. This paper is written in the context of health as it affects people’s daily lives, enhances the overall well-being of Europe’s citizens, and influences the continent’s social and economic status. We focus on health supported by electronic means. The paper describes what eHealth is, what ethics is, and how the two relate to each other particularly within a teaching or training context. It is directed chiefly towards raising ethical awareness about eHealth applications for ICT professionals and for trainee or prospective professionals. Choosing a comprehensive definition of eHealth enables us to explore ICT applications in their variety and richness. Here, however, we concentrate our analytical efforts on radio frequency identification (RFID) devices and electronic health records. The paper is completed by asking whether, and in what way, this ethical awareness can be extended to the design, implementation and use of other types of eHealth-related applications, and included in the education and training of other

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stakeholders. While the proposals outlined here limit themselves to the European scene, they can certainly be extended to a wider, international, perspective. The paper is particularly intended as a complement to the longstanding work of Professor Gunilla Bradley who focused her ideas so keenly on the importance of human needs in relation to ICT, and has always had a profoundly holistic approach to ICT (cf. Bradley, 2009).

EHEALTH IN EUROPE: GENERAL OVERVIEW eHealth has been under development in Europe for two decades and, elsewhere, for over four. In the European Union, the early foundations of eHealth were laid in the late 1980s. Pilot studies were co-financed as early as the second stage of the European Union. From an initial funding of €20 million in 1988, investment in this domain of research and development later expanded tenfold during its Sixth Framework Programme (2002 to 2006). The Commission is now co-financing the Seventh Framework Programme that runs from 2007 to 2013. The amount of financing provided by the Commission dedicated to eHealth in this latest Framework Programme over this timeperiod is expected to be well over €200 million. Large amounts of co-financing are now being invested in the deployment of eHealth. The research and development commitment of the Commission has been paralleled by work on the practical aspects of eHealth in the Competitiveness and Innovation Framework Programme (CIP) Information and Communication Technologies (ICT) Policy Support Programme (PSP) (more frequently known simply by its abbreviation as the CIP ICT PSP). This scheme supports the practical advance and integration of ICT use in various public sector domains among the Member States. In eHealth, the ministries of health, eHealth competence centres and industry partners in 12

eHealth and Ethics

Member States are focusing on electronic health data (health records/medication records or “patient summaries”), and ePrescribing. The first of these areas is one on which we concentrate in this paper. eHealth also became an area for strong policy development with the formulation of a plan for policy convergence (COM(2004) 356 final). This plan is soon due to be completed and – presumably – also for some form of renewal or update. Although progress has been steady over the plan’s seven-year lifetime, many of its accomplishments started to come to fruition – and have even been reinforced by further policy documentation – over its last three years of operation. In particular, 2008, 2009 and 2010 have been key years for eHealth in Europe. For example, in the specific contexts of patient mobility, crossborder health services, and eHealth interoperability, a Proposal for a Directive – which has now received even greater support – and a Recommendation were adopted (COM(2008)414 final; COM(2008)3282 final). In 2008, a policy document was also published on telemedicine (COM(2008)489 final).

EHEALTH IN EUROPE: A GROWING COMMITMENT TO DEPLOYMENT Europe’s Member States have committed themselves to collaborate on eHealth1. They are currently working together intensively to form a high-level governance framework that will begin to operate formally by the end of 2010. The aim of this governance will be to ensure that health ministries and their high-level leaders and decision-makers cooperate on the key aspects of an eHealth strategy for Europe, working together on eHealth implementation and on monitoring their collective progress2. These initiatives are highly likely to incorporate the involvement of a wide range of stakeholders. There is much current emphasis on the deployment and application of eHealth. eHealth

is commonly perceived by policy-makers and decision-makers as a key enabler of good healthcare and as a means of reinforcing the European Union’s common values and goals for its health systems. Two-thirds of Europe’s Member States believe that their health policy priorities can be supported by eHealth (European Commission, 2007). Every European Member State possesses its own eHealth road map or action plan, and all the States are now building their own initiatives to implement eHealth services, systems, and applications. There are many commonalities among the 27 States. However, there is still considerable disparity among them with regard to their stages of innovation and how they are putting eHealth into practice. This 2007 overview (Ibid) shows that the deployment of the main, common eHealth services in European countries has been motivated by such concerns as the quality of care and access to care for patients/citizens, which both have ethical aspects. A further review is taking place in 2009-20103. The majority of Member States are now introducing, building, and using three technical domains: infrastructure, electronic health records or cards, and interoperability (Ibid, p13-15). The link between Europe’s cited policy directions in eHealth and the actual commitment of the Member States to these particular fields is currently becoming clearer. The co-financing initiatives of the CIP ICT PSP have increased the extent of collaboration among European Member States. Since 2008, two large-scale undertakings have been established: the first, a European-wide pilot project known as epSOS4 which focuses on eHealth interoperability; the second, a thematic network which supports comprehensive stakeholder engagement in eHealth called CALLIOPE5. Several other similar initiatives also illustrate complementary Europe-wide action. An example here is STORK6, which focuses on information security and the identification and authentication of users in such settings as eHealth.

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eHealth and Ethics

European and international organisations are showing a renewed interest in eHealth as a market or business. Many elements of the relevant industries are endeavouring to work together on a number of eHealth-related initiatives: one example is the Continua Health Alliance7. In late 2007, the European Commission also launched a platform known as the Lead Market Initiative. This initiative emphasises the notion of the public sector as a driver of technological innovation and potential industrial growth – eHealth is one of the six domains to which attention is directed (COM(2007)860 final). The health sector has always been a field strongly bound up with ethical questions. Here, we offer a brief overview of some key issues that relate to ethics generally before we apply them in more detail to eHealth.

ETHICS Ethics is a branch of moral philosophy. It has several schools of thought and action. The consideration of ethics and ethical theory in relation to human behaviour is known as normative ethics. It contrasts with more abstract discussions on morality (i.e. meta-ethics). In the context of this paper, we are interested in normative ethics – the practical application of ethics. Ethical theories are useful points of departure to enable people to make appropriate choices and to act according to those choices. They provide people with a form of toolkit that can enable them, at any moment in time and in any specific context, to understand the particular moral position taken and the reasoning which underpins a specific moral choice. Each moral choice is complemented by its own criteria and constraints. In recent years, different ethical theories have been used to assess the ethical implications of ICT. Two of the most common theories used are Kantian ethics and utilitarian ethics.

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First, Kantian ethics argues that it is human will that motivates moral action. However, the will can only motivate itself from a rational foundation (Kant, 1981). Rationality implies autonomy (i.e. self-determination); rational argument dictates that all human beings must be equal. Two propositions are the result of this approach. They are, to treat humanity always as an end in itself and never as a means to an end; and, then, in the words of Kant, “Act only on that maxim which you can at the same time will to be a universal law” (Ibid, p421). Second, utilitarian ethics is located in the field of ‘consequentialist’ ethics, and is sometimes referred to as consequentialism. Here, the principles of moral actions are considered to be based on their consequences. The principle of utility (the ‘utility principle’) is that right actions bring the greatest happiness to the greatest number of people. This ‘greatest happiness’ is determined as being either that which is of the highest value or which does the least harm. Kantian and utilitarian ethics have led to the taking of two distinct positions. In the first, there is a consideration of human autonomy and respect for others. In the second, a basis is provided for addressing, deciding on, and assessing a specific course of action that is focused on the greatest benefit.

eHEALTH AND ETHICS High-level, ethical, principles can be brought to bear on specific areas of application. They have been applied to the practice of medicine, the field of health and, more recently, the combined fields of eHealth (i.e., medicine or health and ICT). The more applied the field, the more specific, focused, and contingent are the particular ethical questions. The technologies that are involved in a particular context add yet another layer of complexity. Codes of ethics often articulate the ethical principles that underpin any given context or setting. These codes provide the ethical founda-

eHealth and Ethics

tion for many organisations, and particularly for professional bodies. The complexity of modern society and communities of work means that the ethics of specific occupations (e.g. their codes of ethics, behaviour, or practice) need to be given careful consideration when two or more such professional or occupational groups collaborate. Challenges might arise: are the professions/occupations similar, do they share the same values, are there any areas of potential conflict among the two (or more) fields, and are there any particular gaps in their thinking/acting? The ICT industry encompasses a range of disciplines that include electronic engineering, computer science, and information management. The ethical principles of these professions usually fall into different groupings. However, in general, their codes of practice state that they each protect the public interest, uphold the standards of the specific profession, promote knowledge transfer, and require a commitment to personal integrity. Of direct relevance in the case of this paper are the rules of conduct for Health Informatics Professionals which were drawn up in the United Kingdom under the auspices of the Health Informatics Committee of the British Computer Society. These rules of conduct recognise the role played by ICT in the field of medicine (Kluge, 2003). Several fundamental ethical principles were laid down in this document. They are the: Principle of Autonomy; Principle of Equality and Justice; Principle of Beneficence; Principle of Non-Malfeasance; and the Principle of Impossibility (the last of these principles relates to the assumption that it must be possible to meet the rights and duties that are expressed by the preceding four). These principles are transposed into concrete and practical uses that are aligned with the responsibilities of Health Informatics Professionals. Here, the ICT professional has “a duty to ensure that appropriate measures are in place that may reasonably be expected to safeguard: The security

of electronic records; The integrity of electronic records; The material quality of electronic records; The usability of electronic records; The accessibility of electronic records.” (Ibid, p14). These five characteristics of electronic health records are regarded by Health Informatics Professionals as important to the provision of healthcare. Each characteristic describes an aspect of the data store that could be compromised by technical mediation. In simple terms, these are the possible crisis-points of technically-mediated patient information. The effective presentation of patient information can be construed as providing “the correct information at the right time, to the right people”; it is the basis for a strong ethical foundation to eHealth (Duquenoy et al, 2008b). This is not an easy task given the increasingly complex organisational and technical interactions implied by eHealth.

TEACHING ETHICS TO ICT PROFESSIONALS In this section, we highlight the challenges of teaching ethics in the kind of discipline, such as ICT, where ethics has traditionally been an unfamiliar topic. We advocate the benefits of using examples, cases, and analogies. Whereas we concentrate here on teaching ethics to ICT professionals, using eHealth as our specific example, we later expand our discussions to a more holistic approach of interacting with other stakeholders in the eHealth domain. In some professions, such as medicine and law, the consideration of ethics forms an inherent part of the particular profession’s (and professionals’) job. Such an approach has a long history and is well understood; ethics is a traditional component of any training offered to the student, and is likely to be treated in each of the various topics taught to the trainee professional.

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eHealth and Ethics

This is not, however, the case for students of information systems or informatics. In this field of interest, unfortunately, the ethical implications of technologies were not recognised for a long time. Even today, many ICT practitioners find it hard to associate or reconcile the topic of ethics with their training about technologies. Teaching ethics to technology students is made difficult in several ways. First, ethics tends to offer an abstract, highly conceptual, and even – at times – ambiguous approach to solving problems. Second, technology students tend to be practically-minded, hands-on problem solvers. Third, technology is usually developed with a focus on a specific task that is isolated from a wider context of use. As a result, using the electronic database of patient records as in our example, it can be difficult for technology students to see how building such a database has some form of ethical importance. Ethics must therefore be made relevant to technology students. They need to be shown the direct connection between technology applications and human beings and the impacts – whether beneficial or detrimental – on people’s everyday lives. How best to do this? Ethics is about promoting benefits and reducing harm. While this is a relatively simple message, it can actually be more difficult to assess what might constitute a benefit and what might constitute a harm. These are fundamental elements of ethical discussion, and it is here that reference to different ethical theories can help. Technology students do not necessarily need to be persuaded by the principles of a particular theory (although many students will lean towards one theory rather than another in their personal preferences). They do, however, need to use the principles as a foundation for discussion in order to draw out the key elements of the subject under debate. These two elements – the connection between technology and people, and the consideration of ethics – can also be brought together quite simply. First, an introduction to a set of ethical principles is needed. Alongside this, one simple approach can

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be developed by selecting well-chosen case studies (whether hypothetical or real) or by focusing on concrete examples – what can be called eHealth in practice. A much wider range of technologies with pertinence to eHealth could of course be explored. Here, we consider two technological applications in further detail: patient electronic health records and RFID devices. We cover in particular the issues of justice and equity. There has generally been a trend to examine justice and equity in the domains of eInclusion and eAccessibility8 rather than in the eHealth field, however, this is becoming increasingly a pressing field of examination.

ELECTRONIC HEALTH RECORDS AND RADIO FREQUENCY IDENTIFICATION DEVICES For the sake of simplicity, here we look at only two specific forms of technology that have a relevance to the healthcare field. These two applications were explored in detail at the International Federation for Information Processing summer school on “The Future of Identity in the Information Society – Challenges for Privacy and Security” held in the Czech Republic in 20089. The theoretical and empirical background, especially to the field of RFID, was examined in an earlier paper (Whitehouse and Duquenoy, 2009). We demonstrate how the application of ethics to specific technologies can be handled in a very practical way. The creation of illustrative templates or charts can encourage the consideration of particular ethical issues. At stake are questions that relate to the impacts of the technology on human beings and on information storage and transmission. We place our discussion of ethics in the framework of the four issues that were raised earlier in relation to the work of Kluge (2003): non-malfeasance, beneficence, autonomy, and justice/equity. However, rather than covering all four of the potential ethical issues at stake, we

eHealth and Ethics

choose to focus on the specific matter of justice/ equity. Our preference for the choice of this field relates to the growing importance in Europe of users’ rights in relation to technology use10 and, in parallel, the rise in awareness of patients’ engagement and involvement in their own healthcare11. Here, we discuss the usefulness of a specific ethical theory or an ethical approach (cf. Kluge, 2003), and how these underlying principles can provide a tool to tease out, identify, and assess ethical issues.

Electronic Health Records Patients’ health records may be held in a computer database either locally or centrally. Locally-held records may be stored at a doctor’s premises or in a group practice shared among several general practitioners. Centrally-held records may be stored in a national database at either an institutional or a governmental level. Generally, locally-held data are less likely to be compromised by security issues than are centrally-held data which are transmitted over a network. Patient data may also be held in a more distributed manner and/or collated from a range of sources. They could eventually be held by the patient him or herself. It is these options which may be increasingly widely explored, especially given the importance of cloud computing and the possibility of shared services (including health, social, and care services – whether public or private or a mix of the two). In its widest possible context of use, the development of an electronic health-related database could cause many complex situations to arise that have a bearing on ethics. For example, the sharing of patient data among or between different healthcare professionals, departments, and other information systems could affect the confidentiality of a patient’s data or it could compromise the integrity and timeliness of the treatment of the data. There are at least two concerns that relate to the records application itself, and to the informa-

tion that it holds. First, a lack of understanding of the particular technologies involved could place certain patients at a disadvantage. Second, the digital format of records could allow for the categorisation of patients according to fields such as age, gender, incidence of ill health (e.g., cancer), the geographic location of their home, or type of lifestyle (such as a person’s addiction to cigarettes, leisure drugs, or alcohol). The technical and organisational capabilities for sorting these various data fields could impact on equality and equity generally. Such information can of course be used in ways that reinforce justice and equity positively e.g., by increasing the understanding of the level of good public health in the community, region, or the country generally or by ensuring equal health treatment whatever the person’s socio-economic background. On the other hand, the information could be used negatively to reduce the amount of treatment available to the particular individual and the ways in which such treatment is distributed. A well-recognised approach to justice/equity would indicate that all patients should be able to have electronic records, so that no person (patient/ citizen) is discriminated against12. The implication is that all patients should be treated equally. The same rationale and basis for information collection would be used, and non-discriminatory judgements would be made in relation to the basis of the information that is collected. All instances of data collection and transmission should be equal and fair for all patients. All patients should be reassured of the material quality of the record and that the record’s content can be interpreted appropriately. If devices or records are designed to be used by patients (for example, for people who may have to read data on-screen or who may have to record and change data for themselves), then devices should be designed with a view to ensuring that all patients should be capable of using them easily. If patients are to have access to records, then all patients should have the same kinds of opportunities to access their own records.

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eHealth and Ethics

Radio Frequency Identification Devices RFID is based on small devices that can store data (information) which can be communicated to a receiver for some designated purpose. The device is attached to a host (artefact, human, animal). The ethically interesting – and provocative – characteristics of RFID devices are that they are small (possibly even un-seen) forms of ICT; they can be attached in some way to someone or some object (such as clothing, a piece of equipment, or a badge). They can collect, store, and transmit information using a range of radio frequencies. To examine the ethical issues that might arise from RFID, we need to consider the fundamentals of the technology and its relationship to the ‘ethical entity’ which in the case of this paper is a human being. In terms of the location of the RFID device, it can matter substantially whether the device is worn externally on a wristband, or is embedded on an internal artefact such as a hip replacement, or is implanted in human tissue. If the device is attached externally, could it cause the patient embarrassment or could it cause them to be discriminated against? If an internal device is deemed to be less discriminating (as well as being beneficial in other important ways), do all comparable patient-cases have the opportunity to benefit from similar technologies? Is there some form of potential interference that could be triggered by a device and cause undue harm to others? Are all patients who carry/use such devices assessed individually in terms of potential harmful effects, i.e., is there an equal interest to assess the needs of each patient? In terms of location, issues arise with regard to complementary or parallel use of other devices and technologies: operationally, is the device likely to interfere with other nearby devices or vice versa? Can any radio waves from the device itself, or from one or a combination of nearby devices, affect the patient? Relating the use of RFID devices to the ethical principles of non-malfeasance, beneficence,

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autonomy and justice (equity) can help to facilitate ethical decision-making (cf. Kluge, 2003). Thus, very generally, a range of questions would need to be asked when considering the envisioned use of the RFID device for a health-related purpose. Specifically, is anyone harmed (such as the patient, or others)? Does the technology promote well-being (does it protect the patient from harm or keep the patient safe)? Is the patient’s autonomy respected (i.e. is there respect for the patient’s degree of personal choice and independent decision-making)? Does the use of the device promote justice and equity (or, conversely, does it permit discrimination and inequality)? Approaching equity/justice in relation to RFID devices might particularly emphasise such issues as: the rationale and basis for use; non-discrimination with regard to the security of the data transmitted; fairness and equality of data collection and transmission; assurances with regard to the data’s eventual interpretation; the ease of usability of the devices and equipment; and the access and availability of the devices to all patients on an equitable basis. These kinds of concerns are indeed very similar to those which are also raised in this paper in relation to electronic health records.

RAISING ETHICS AWARENESS IN OTHER AREAS This paper has focused on raising an awareness of ethics with ICT professionals and trainees. It has used eHealth as a specific example, and has drawn attention to two applications in this domain: electronic health records and RFID devices. Given the increasing spread of the use of ICT generally, and specifically within the field of eHealth, there are a number of other elements that need to be considered when raising an awareness of ethics. First, not all ICT professionals enter their profession through the route of higher education

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– although clearly that entry-point has become more and more the norm. Other levels of education and other professional domains are important. Second, the debate and discussion strategy used in teaching to bring out the ethical dimensions of technology is equally applicable to other groups, but may need to be conducted in a different context. Forums, or ‘spaces for discussion’ (Berleur et al. 2000; Berleur et al. 2009) enable participants within a particular domain to explore and gain understanding of different ethical perspectives, and to relate discussions to personal experiences and situations. Third, formal training programmes are not necessarily going to be either appropriate or helpful with regard to the relevant education of patients, their carers or their families, and they are unlikely to be a feasible option for these groups. What can be achieved, however, would be a general raising of ethical awareness on the part of those who are outside a conventional institutional setting (e.g., a university; a training centre; a hospital; or a clinic), and yet who are nevertheless direct stakeholders in eHealth. Fourth, in recent times, technologies have begun to provide opportunities to participate in discussions remotely – for example, discussion spaces started from blogs, or facilitated by social networking sites (Whitehouse, 2009). Some of the benefits of face-to-face interaction may be lost using these means, but gains can be made by virtue of the fact that physical presence is not required. This opportunity could be especially relevant to people who experience constraints, in health terms, in terms of physical presence, geographic mobility, or employment conditions. Fifth, raising ethical awareness is especially important where technologies are used at-adistance (e.g. telemedicine or telecare), and where the ethical implications may not be so easily or immediately perceived. For example, a diabetes monitoring aid may send information electronically (e.g. via wireless connections) to a remote healthcare practitioner. Would either of the users

(patient or healthcare professional) understand the potential for compromising data integrity using this means (Duquenoy 2009)?

DISCUSSION eHealth by its very definition involves the use of ICT in a field which is likely to involve not only a far wider range of professional personnel than simply ICT professionals but also many other people with a wide range of educational and professional competences. In the eHealth domain, a wide range of different professions and occupations may need to work together alongside ICT professionals, e.g. administrators, clinicians, insurers, nurses, pharmacists, and researchers. These may also interact with other groups of individuals, e.g. patients, carers, and family members. The ethical aspects of technology are as valid for healthcare professionals as they are for their ICT counterparts. Healthcare professionals would probably not need to be convinced of the role of ethics within their own professional domain, but they should also know how the introduction of technology could impact on their own ethical practices. Ethical training is therefore increasingly a multi-lane highway rather than simply being a two-way street. The needs involve: an understanding of ethics on the part of the technologists; an understanding of technology on the part of the healthcare (and other) practitioners; and an understanding of both ethics and technology on the part of people who may be neither ICT nor eHealth specialists, but may simply be concerned about their own health, the health of those close to them, or the health of those for whom they care or with whom they work. Key challenges in this context are likely to be faced by people who have either so far resisted deliberately an introduction to or use of the tech-

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nologies or whose age and circumstances have caused them to avoid their use. The complexity of ethical choices does not necessarily have to be simplified. However, it needs to be more easily understood using tools, techniques, and approaches that can aid decisionmaking. Understanding ethics in a specialist field is in itself challenging, however, understanding it in its more everyday context may be even more provocative. Analytical frameworks are needed to facilitate this, as well as means to apply more theoretical ethical thinking to actual practice. We anticipate that the framework which we outline in this paper may facilitate a growing clarity of thinking with regard to the possible ethical options at stake in any given practical situation and the various rationales that underpin them.

CONCLUSION In a larger context, there is a growing convergence among the various domains, services, technologies and applications that are under development and use in modern society. This trend is as relevant to eHealth as it is to other social and organisational domains. Indeed, it may be even more so, given the considerable challenges – social, organisational, demographic, and economic – to Europe’s healthcare. The range of technologies that can be used contemporarily is already impressive. This list is likely to be increased still more comprehensively in the future: this ever-widening range of possibilities is explored, for example, in work by the Institute for Prospective Technology Studies (IPTS) assisted by empirica13 and projects such as The Senior Project14. The IPTS study explains that eHealth is helping to move healthcare from a more institutionally-based model to a patientcentred one (increasingly, there is also a portrayal citizen-driven healthcare). The technologies to be used are being operated close to the patient, whether e.g., on the skin of or even within a pa-

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tient, or are located in a patient’s more intimate, domestic sphere e.g. in her or his home. As these trends develop, an understanding of technologies and their ethical concerns will become increasingly relevant to patients themselves, their carers, and families. There may indeed be trade-offs or compromises that need to be made among these various challenges. One potential polarity may be the ethics of computing use and the economics of technology use. On the other hand, these dilemmas may also share common ground. Today’s focus on the sustainability of society provides a larger context in which such issues can be resolved and it may even encourage their resolution. We can look forward to a greater in-depth investigation of ethical issues and ICT. Important examinations of applied ethics are already being undertaken in relation to a wide range of technological applications, whether contemporary or future-oriented. These occur in current European co-financed projects such as ETICA and EGAIS.15 More specifically, in relation to eHealth, various ethical dilemmas are being explored in European projects such as ETHICAL.16

ACKNOWLEDGMENT We wish to acknowledge that this paper builds on earlier work relating to eHealth and ethics (Whitehouse and Duquenoy, 2009) although this text enhances and expands considerably that first set of ideas.

REFERENCES Berleur, J., Burmeister, O., Duquenoy, P., Gotterbarn, D., Goujon, P., Kaipainen, K., et al. (Eds.). (2008). Ethics of Computing Committees. Suggestions for Functions, Form, and Structure: IFIP-SIG9.2.2. IFIP Framework for Ethics of Computing (pp. 1-35), Laxenburg, AU: IFIP Press.

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Berleur, J., Duquenoy, P., d’Udekem-Gevers, M., Ewbank de Wespin, T., Jones, M., & Whitehouse, D. (2000). Self-regulation instruments – Classification – A preliminary inventory. HCC5, Geneva 1998; SIG9.2.2 January 2000; SIG9.2.2. June 2000; IFIP – WCC-SEC 2000. Bradley, G. (2009). The Convergence Theory and the Good ICT Society - Trends and Visions. In Schlick, C. M. (Ed.), Industrial Engineering and Ergonomics (pp. 43–55). Aachen, DE: Springer Berlin Heidelberg. doi:10.1007/978-3642-01293-8_4 COM 3282 final (2008, July 2). Commission recommendation of 2nd July 2008 on cross-border interoperability of electronic health record systems. COM 356 final (2004, April 30). e-Health – making healthcare better for European citizens: An action plan for a European e-Health area. Luxembourg: European Commission COM 414 final (2008, July 2). Proposal for a Directive on the application of patients’ rights in cross-border healthcare. COM 489 final (2008, November 2). Telemedicine for the benefit of patients, healthcare systems and society. COM 860 final (2007, December 21). A lead market initiative for Europe. Duquenoy, P. (2009). Taking a holistic approach to exchanging health information over a global network. In Mordini, E., & Permanand, G. (Eds.), Ethics and health in the Global Village: Bioethics, globalization and human rights (pp. 287–304). Rome: CIC Edizioni Internazionali.

Duquenoy, P., George, C., & Solomonides, A. (2008b). Considering Something ELSE: Ethical, Legal and Socio-Economic Factors in Medical Imaging and Medical Informatics In H. Muller, G. Xiaohong, L. Shuqian (Eds.), Special Issue of the International Conference MIMI2007 on ‘Medical Imaging and Medical Informatics’ (pp.227-237), Beijing, China. Computer Methods and Programs in Biomedicine, 92 (2008), 2(3). Ireland, Elsevier. Duquenoy, P., George, G., & Kimppa, K. (Eds.). (2008a). Ethical, Legal, and Social Issues in Medical Informatics. IGI Global: Medical Information Science Reference. Eng, T. R. (2001). The eHealth Landscape: A Terrain Mapping of Emerging Information and Communication Technologies in Health and Health Care. The Robert Wood Johnson Foundation. European Commission. (2007). eHealth priorities and strategies in European countries. Luxembourg: Office for Official Publications of the European Communities. Eysenbach, G. (2001). What is e-health? Journal of Medical Internet Research, 3(2), e20. doi:10.2196/ jmir.3.2.e20 Kant, I. (1981). Grounding for the Metaphysics of Morals (Ellington, J. W., Trans.). Indianapolis: Hackett Publishing Company. Kluge, E.-H. (2003). A Handbook of Ethics for Health Informatics Professionals. London: Health Informatics Committee, British Computer Society. Oh, H., Rizo, C., Enkin, M., & Jadad, A. (2005). What is eHealth? (3): A Systematic Review of Published Definitions. Journal of Medical Internet Research, 7(1), e1. doi:10.2196/jmir.7.1.e1 Pagliari, C., Sloan, D., Gregor, P., Sullivan, F., Detmer, D., & Kahan, JP. (2005). What is eHealth? (4): A Scoping Exercise to Map the Field. Journal of Medical Internet Research, 7(1), e.

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Whitehouse, D. (2009) Book review. The Medicalization of Cyberspace. In Journal of Information, Communication and Ethics. February 2009. 7 (2/3), 211-213. Whitehouse, D., & Duquenoy, P. (2009). Applied Ethics and eHealth: Principles, Identity, and RFID. In V. Matyáš et al. (Eds.) IFIP AICT 298 (pp. 4355). Laxenburg, AU: IFIP Press.

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See the conference declarations of at least three high-level (Ministerial) conferences in 2007 and, 2008, and 2009: http://ec.europa. eu/health-eu/news/ehealth/ehealth2007_ en.htm ; and http://www.ehealth2008. si/ ; and http://www.epractice.eu/en/library/281916 Accessed 5 January 2010 See the Council Conclusions of 1 December 2009: http://eur-lex.europa.eu/LexUriServ/ LexUriServ.do?uri=OJ:C:2009:302:0012:0 014:EN:PDF Accessed 6 January 2010 “eHealth strategies” http://www.empirica. com/themen/telemedizin/projekte_en.php Accessed 10 January 2010 http://www.epsos.eu/ Accessed 6 January 2010. http://www.calliope-network.eu/ Accessed 6 January 2010. https://www.eid-stork.eu/index. php?option=com_frontpage&Itemid=1/ Accessed 6 January 2010. http://www.continuaalliance.org/ Accessed 5 January 2010.

Examples of relevant studies include ICT and ageing http://www.ict-ageing.eu/ Accessed 10 January 2010. http://www.buslab.org/SummerSchool2008/ schedule.html/ Accessed 7 January 2010. One example is the possible future focii of a European Commission Directorate-General on justice, fundamental rights, and citizenship. A second is the increasing perception that the European Union’s post-i2010 and EU2020 initiatives may concentrate, at least in part, on user rights in relation to the Internet. A two-year project called Value+ (which ended in February 2010) developed a policy statement on patients’ meaningful involvement in health-related projects, and produced a toolkit and handbook for patients’ associations as well as project leaders. See http://www.eu-patient.eu/Initatives-Policy/ Projects/ValuePlus/ Accessed 20 July 2010. On the other hand, there may be individuals who for various reasons prefer not to have such an electronic health record held on their health data. http://www.empirica.com/themen/telemedizin/projekte_en.php/ Accessed 10 January 2010. http://www.seniorproject.eu/ Accessed 11 January 2010 ETICAhttp://moriarty.tech.dmu.ac.uk:8080/ pebble/default/2009/05/28/1243516800000. html/ and EGAIS http://www.egais-project. eu/?q=node/3/ Accessed 10 January 2010. http://www.ethical-fp7.eu/ Accessed 10 January 2010.

This work was previously published in Information and Communication Technologies, Society and Human Beings: Theory and Framework (Festschrift in honor of Gunilla Bradley), edited by Darek Haftor and Anita Mirijamdotter, pp. 454-465, copyright 2011 by Information Science Reference (an imprint of IGI Global).

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Chapter 7.2

Standards and Guidelines Development in the American Telemedicine Association Elizabeth A. Krupinski University of Arizona, USA Nina Antoniotti Marshfield Clinic Telehealth Network, USA Anne Burdick University of Miami Miller School of Medicine, USA

ABSTRACT The American Telemedicine Association (ATA) was established in 1993 to promote access to medical care for consumers and health professionals via telecommunications technology. The ATA Standards and Guidelines Committee has been charged by the Board of Directors with identifying, overseeing, and assisting work groups to develop individual standards and guidelines for specific technical, clinical and administrative areas. This chapter will review the mission of the DOI: 10.4018/978-1-60960-561-2.ch702

ATA Standards and Guidelines Committee, the process by which standards and guidelines documents are produced, and report on its progress to date in providing the telehealth community with standards and guidelines for the practicing medicine at a distance.

INTRODUCTION The American Telemedicine Association was established in 1993 as a non-profit organization to bring together a variety of groups from medicine, academia, technology and telecommunications

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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companies, e-health, m-health, medical societies, government and others to overcome barriers to the advancement of telemedicine. The Association works to achieve this goal through a variety of mission-related activities. One of its main activities is to educate government and the public about telemedicine in order to validate its role as an essential component in the delivery of modern medical care. The ATA also serves as a resource for information and services related to telemedicine. Its 2009 Annual Meeting in Las Vegas, NV had an attendance of over 2700 people – a 12% increase over the previous year, attesting to its role as a leader in telemedical information and resources. The ATA website (www.americantelemed.org) is a leading source for telemedicine news. In its effort to foster networks and collaboration, the ATA sponsors 14 Special Interest and Discussion Groups and has two Regional Chapters (Latin America & Caribbean and Pacific Islands). Each of the groups meets regularly and discusses critical issues related to their interest focus (e.g., Business & Finance, Teledermatology). The ATA also promotes research and education by sponsoring the Annual Meeting with its associated scientific program and exhibition showcase, and periodically forming task forces to develop vision papers on a specific telemedicine topic (Krupinski et al., 2006). Finally, the ATA is creating the basis for assuring uniform quality in the delivery of remote healthcare services, particularly via the Standards and Guidelines Committee efforts.

BACKGROUND There have been efforts towards establishing standards and guidelines for telemedicine practice prior to the formal establishment of the ATA Standards and Guidelines Committee. For example, radiology has a number of technical and practice guidelines in place for digital image acquisition, storage, transfer and display via Picture Archiving and Communications Systems (PACS) and tele-

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radiology (Seibert, et al., 2004; Van Moore, et al., 2005; Siegel, et al., 2006; Williams et al., 2007; Krupinski et al., 2007); and the Society of American Gastrointestinal and Endoscopic Surgeons has guidelines for the Surgical Practice of Telemedicine (SAGES, 2004). The first set of guidelines from the ATA actually pre-dated the formation of the Standards and Guidelines Committee, and was created for Telepathology in 1999 (ATA, 1999). In 2004 Richard Bakalar, MD and the ATA Ocular Telehealth Special Interest Group developed and published the first truly formal set of ATA practice guidelines. The guidelines specifically addressed diabetic retinopathy telehealth clinical and administrative issues and provided guidelines for designing and implementing a diabetic retinopathy ocular telehealth care program (ATA, 2004). This document established the general framework for future efforts in terms of addressing technical, administrative and clinical aspects associated with a particular clinical specialty using telemedicine as a means to deliver patient care. A very important precedence was established in the creation of these guidelines. Early in the development process the decision was made to contact the National Institute of Standards and Technology (NIST) for help and guidance regarding the accepted means by which standards and guidelines are produced and approved. Since then, NIST and the ATA have collaborated extensively in the guideline development process. The formal effort to create a standing committee and establish a process for developing standards and guidelines within the ATA was initiated by Hon Pak, MD when he was Vice President of the Association in 2005-2006. As a practicing tele-dermatologist, he recognized the need for quality and consistency in the practice of teledermatology, so the first practice guidelines developed by the Standards and Guidelines Committee were the Practice Guidelines for Teledermatology (Krupinski, et al., 2007). This document followed the general format established in the

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diabetic retinopathy guidelines and addressed technical, administrative and clinical aspects of teledermatology. Under guidance from NIST, the teledermatology guidelines were the first to incorporate the idea that practice recommendations could have different levels of importance and that the guidelines are required whenever feasible and practical as determined by the referring clinician practicing under local conditions. Thus, there are Guidelines that “shall” be implemented in order to meet a basic minimum level of operation in order to provide quality telehealth care. At the next level are Recommendations for Best Practices that “should” be adopted for optimal practice if possible. Finally, there are optional or permissible actions indicated by “may/attempt to” that can be adopted to further optimize the teleconsults process. The Teledermatology guidelines were also the first to incorporate a preamble that broadly stated the intent of the document and the manner in which it should be used. A number of specific points were included that continue to be incorporated into current and future guideline documents. Three of these points are worth mentioning. The first is that compliance with the guidelines does not guarantee accurate diagnosis or successful outcomes. The goal of the guidelines is to assist the clinical practitioner in pursuing a sound course of action to provide effective and safe medical care founded on current information, available resources, and patient needs. The second point follows from the first and states that the primary care practitioner is responsible for all decisions regarding the appropriateness of a specific procedure or course of action. They must consider all presenting circumstances, and if they choose to use an approach that differs from the guideline it does not imply that the approach varied from the standard of care. Reasonable judgment based on local circumstances and the assessment of what is feasible and practical should be used at all times. Finally, the preamble notes that the guidelines are

not designed nor meant to be unyielding requirements of practice and are not meant to serve as or be used to establish a legal standard of care.

CORE STANDARDS Telemedicine is a very broad field, encompassing nearly every clinical sub-specialty being practiced today. Thus it is impractical, and likely not necessary, to develop standards and guidelines for every possible clinical specialty or telemedicine application. Recognizing this, the ATA Standards and Guidelines Committee decided early on to focus initially on a core set of principles that could guide telemedicine practice in general. The first part of this effort was to create a Telemedicine/ Telehealth Glossary of Terms (ATA, 2006). The list is composed of terms and definitions that are commonly used in telemedicine/telehealth. It was assembled for the purpose of encouraging consistency in employing these terms in ATA related documents and resource materials, including standards and guidelines. The list is not all-inclusive and is designed to be augmented as specific groups develop practice guidelines for particular clinical applications. The Core Standards for Telemedicine Operations document was completed in 2008 (ATA, 2008). This document covers broad policies and procedures that should be used by institutions providing remote medical services, interactive patient encounters, and any other electronic communications between patients and practitioners for the purposes of health care delivery. In order to define more precisely who the document was addressing, it noted that the standards apply to individual practitioners, group practices, health care systems, and other providers of health related services where there are telehealth interactions between patients and service providers for the purposes of health care delivery. As with the earlier ATA standards documents, this one provides

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guidance with respect to administrative, clinical and technical aspects of telemedicine practice. The Administrative Standards section is divided into three parts, one directed at Organizations one at Health Professionals, and the third covers Telemedicine Ethics. The Organization and Health Professionals sections address those providing services via telehealth and covers primarily issues of responsibility and standard operating procedures. Briefly it notes that organizations and providers shall follow the standard operating policies and procedures of the governing institution especially with respect to: human resource management; privacy and confidentiality; federal, state and other credentialing and regulatory agency requirements; fiscal management; ownership of patient records; documentation; patient rights and responsibilities; network security; telehealth equipment use; and research protocols. Quality improvement and assessment procedures are required to be place as are all other aspects of patient care that require adherence to federal, state and local regulatory bodies (i.e., patient consent, protection of health information, patient rights and responsibilities, integration of telemedicine into a practice, collaborative agreements/contracts). Health professionals in particular shall be fully licensed and registered, aware of credentialing requirements, aware of their locus of accountability, cognizant of when a provider-patient telehealth relationship has been established, and shall have the necessary education and training to insure safe provision of health services. Clearly, none of these administrative guidelines differ from the standard operating procedures that organizations and health professionals follow without involving telemedicine. They are meant in a sense to reiterate and emphasize that the practice of telemedicine needs to be carried out with the same high standards of patient care and safety that are normally followed. Of particular importance in this document is the section on telemedicine practice ethics. It notes that although telemedicine is not a practice in and of itself, practicing at a distance does create a

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unique relationship with the patient. This requires attention to and adherence to professional ethical principles that may be new to most healthcare practitioners. Specifically, the ethics section states that an organization or healthcare professional shall (1) incorporate organizational values and ethics statements into the administrative policies and procedures for telemedicine; (2) be aware of medical and other professional discipline codes of ethics when using telemedicine; (3) inform the patient of their rights and responsibilities when receiving care at a distance (through telemedicine) including to influence decisions made about, for, or with patients who receive care via telemedicine. These Administrative Standards are applicable to all telemedicine encounters and thus are referred to in any specialty practice guidelines developed. The Clinical Standards section of the Core document is rather brief, as clinical practice guidelines will be different to some degree for every clinical specialty and are thus more fully dealt with in the specialty guidelines. In the Core document, only two points are noted. One is that the organization and health professional shall be satisfied that health professionals providing care via telehealth are aware of their professional discipline standards and that those standards shall be upheld in telemedicine. The second point is that health professionals shall be guided by existing professional discipline and national practice guidelines when practicing telehealth and if modifications exist specifically for telehealth they shall be followed. Finally, the Technical Standards deal with broad equipment and technology issues. Again, certain specialties will utilize equipment unique to their specialty and thus more specific technical standards are provided in each specialty set of practice guidelines. For example, the Diabetic Retinopathy guidelines provide technical guidelines for non-mydriatic devices and image acquisition, while the teledermatology guidelines provide technical specifications for store-forward digital cameras used to acquire images of the

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skin. In the Core document, technical standards include such aspects as insuring the equipment used is available and properly functioning; that the technology meets all relevant laws, regulations and codes for safety; that infection control policies and procedures are in place; patient information is safe and complies with local and federal legislation and rules regarding privacy and confidentiality; that systems have appropriate redundancies in place especially for network connectivity; that published technical standards are met for safety and efficacy of devices used with patients; and that equipment is properly maintained.

THE DEVELOPMENT PROCESS The ATA has clearly evolved over the years in terms of the way it approaches the development of standards and guidelines for telemedicine. In recent years, the Committee has established a more formal process for guideline development. One of the main reasons for this has been the increased interest and need of the ATA Special Interest Groups (SIGs) in creating practice guidelines. Although many members of the SIGs are members of specialty societies, in general telemedicine seems to be a relatively low priority focus area for these societies and thus practice guideline development for telemedicine is rarely an activity they initiate. Thus, the ATA has become the go to organization for standards and guidelines development. In order to deal with the increased demand and the number of groups seeking to develop guidelines documents, the Committee developed a standard operating procedure. In part, the goal is to ensure success and be in compliance with federal anti-trust regulations. Therefore, it was deemed critical that the process for implementing guidelines development be clear and open. At the core of process is the Standards and Guidelines Committee, which is appointed by the President of ATA in consultation with the ATA Executive Committee. The Committee has a Chair and Vice-

Chair and a committee of interested members who all serve on a voluntary basis. The Committee meets by phone once a month and at the Annual Meeting. This Committee is charged with provided overall guidance in the following. First it identifies Working Groups to be charged with developing individual standards and guidelines and then recommends the relative priority for each specific area to the Board of Directors for approval. The Working Groups typically emerge from the SIGs. SIGs that are interested in initiating development of standards and guidelines prepare a petition to the Standards Committee that includes suggestions for who will lead the effort, the names of people who will participate on the working group, and the purpose, objectives, and area(s) or general scope of the effort. The area or scope of effort to be addressed is determined by consensus among the members of the SIG in consultation with the ATA Staff and the Committee. In general, we have found that it is possible to work on an average of three sets of guidelines per year, so priority areas are chosen based on interest by ATA members, the relative level of current clinical activity compared to other areas in telemedicine, the availability of sufficient member support, the interest and willingness of allied organizations to contribute to the effort, and the availability of any established and recognized gold standards within the specialty area. Once a Working Group is established, the ATA determines when the official commencement of activities will begin and what timeframe will be followed. Members and chairs of each Working Group are appointed by the Chair of Standards Committee with the approval by the full Committee. In recruiting members, it is encouraged highly that they achieve a balanced representation from clinical, industry, government and other potentially affected parties, both from within and outside the ATA membership. In particular, the involvement of NIST representatives is encouraged. Once the Working Group is established, a notice is circulated publicly noting that the ATA

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is forming a Working Group and information about its purpose, process, timelines and proposed products is described. If anyone not officially recruited to be a member chooses to be involved, they can attend future meetings, but only officially appointed members have a voice or a vote. Prior to any formal meetings, all participants are required to fully disclose any potential conflicts and this information is shared with the Working Group and is made public. After the Working Group is established, there is a Suggested Work Plan encompassing 10 steps towards the development of a final document. A timeline is provided to help keep the momentum of the initial enthusiasm going. The first step is a Preparation Workshop in which the Document Scope and Definition are established (1 day kickoff meeting). During this meeting, the Working Group is expected to review standards and guidelines policy and guidance documents and the Telemedicine/Telehealth Glossary of Terms. They should then develop consensus on scope and definitions; identify and catalogue any existing clinical benchmark references or “as is” gold standards; identify stakeholder and committee membership; and develop one or more appropriate use cases. The second step is a Stakeholder Forum/ Component Workshop. Depending on the Working Group needs this may be a single or multiple Forum/Workshop. This should take place within 300 days of the process kickoff and is designed to collect information and knowledge based on the agenda established in the Preparation Workshop. The assigning of authors should also take place at this meeting, and it is recommended to split the writing responsibilities into three sections – administrative, technical and clinical. Once the author teams have been created, they have 30 days to develop drafts of their respective document sections. Following this stage, the drafts are made available for 30 days for an on-line comment period open only to internal stakeholders. At the end of this period, the document sections must be incorporated into a single document that

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takes into account the stakeholder comments as deemed appropriate. Various working groups carry this step out in different ways, but typically the Chair and Vice-Chair of the Working Group or a designated Editorial Board create the final document version (45 days). The final draft document is then posted on the ATA web site for an on-line comment period of 90 days. This comment period is for external stakeholders and the general public. At the end of this period, the Working Group Chair and Vice-chair or Editorial Board reviews the comments and decides which, if any, should be incorporated into the document. At this point it is considered final and the Working Group submits it to the ATA Chair of the Standards and Guidelines Committee who then presents it to the ATA Board of Directors for final approval within 30 days. Endorsement by external societies can also be sought and is highly recommended. As noted, the Working Groups are encouraged to have representatives from external societies on board during the development process, with one goal being to facilitate the adoption by these societies. The final document is posted on the ATA web site and is available for purchase by interested parties. The Working Group is also encouraged to publish the document in a relevant peer-reviewed journal. At this point the Working Group should also consider development of an Education and Implementation Strategy, and possibly a Certification Program. For example, the Teledermatology SIG has already published their standards document (Krupinski et al., 2007) and based on feedback from the dermatology community has initiated a two-pronged education effort. The original document was very comprehensive and included a detailed set of guidelines. Although thorough and complete, it is a rather dense document that is daunting to many healthcare providers. In response, the Teledermatology SIG is creating a modified version of the guidelines directed primarily at the healthcare provider that gives a clear description of the minimum set of clinically relevant recommended guidelines needed

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to practice safe and effective teledermatology. The second education effort is creating a “Teledermatology 101” on-line course with two levels of difficulty. The first level will serve more as an introduction to teledermatology and again provide a clear description of the minimum set of clinically relevant recommended guidelines needed to practice safe and effective teledermatology. The second level course will more closely parallel the original practice guidelines document, reviewing the administrative, technical and clinical guidelines in more detail with specific reference to the actual guideline language and recommendations (shall, should and may).

ISSUES, CONTROVERSIES, PROBLEMS As already noted previously, one of the issues the ATA Standards and Guidelines Committee has faced in the past couple of years has been the increased demand for practice guidelines for specific areas within telemedicine. This is not a problem per se since the development of practice guidelines is precisely what the Committee has been charged with doing. Additionally, such guidelines are needed in the telemedicine community to help educate practitioners about the technology, procedures, benefits and limitations of practicing telemedicine. Having practice guidelines in place that are approved by the ATA Board of Directors and endorsed or approved by other professional also provides a source of data and information that can be used to develop public policy, lobby to the government for reducing limitations on the practice of telemedicine, and lobby to payors for increased telemedicine reimbursement. A related issue is how to develop practice guidelines for clinical areas that in themselves are quite diverse and in the past typically have not had clear and/or published practice guidelines. For example, rehabilitation covers a broad spectrum of services from physical therapy, to

occupational therapy, speech therapy, audiology and other related services. The challenge being faced is whether it is more appropriate to develop broad tele-practice guidelines that cover an entire specialty but at a more superficial level perhaps, or do we develop focused sets of practice guidelines for each sub-specialty? Unfortunately there is no one-size-fits-all solution and the ATA Committee currently decides this on a case by case basis. One final issue is whether the ATA should or even can enforce the practice guidelines it develops. As described previously, the goal of the guidelines is to assist the clinical practitioner in pursuing a sound course of action to provide effective and safe medical care founded on current information, available resources, and patient needs. The guidelines specifically note that compliance does not guarantee accurate diagnosis or successful outcomes, and that the primary care practitioner is responsible for all decisions regarding the appropriateness of a specific procedure or course of action, using reasonable judgment based on local circumstances and the assessment of what is feasible and practical. The guidelines are not designed or meant to be unyielding requirements of practice and are not meant to serve as or be used to establish a legal standard of care. Currently the ATA does not have any certification or enforcement policies or procedures in place. The practice guidelines are developed, approved and disseminated as already described. It then becomes the responsibility of the individual practitioner to decide how and whether to implement them. The ATA is not a credentialing or certifying body and to become one would require the Association to set up a separate incorporated body specifically designed to take on this responsibility and function. At this point in time, the ATA has not seen the need for this level of regulation regarding the practice standards developed to date, and thus has not initiated any efforts to devise enforcement strategies.

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SOLUTIONS AND RECOMMENDATIONS In order to deal with the increased demand for developing telemedicine standards and guidelines, one of the first things the Committee did was to standardize the process as detailed above. In order to initiate a request to start the development process, the SIG leading the effort is required to complete an official request form to be submitted to the Committee. This form requires the SIG to designate a project chair and committee, a description of the project and its deliverables, the scope of the project, and an estimated timeline. In order to complete this document and prepare for the initial Preparation Workshop, it is expected that the SIG has already reviewed the literature and the field in general to determine existing guidelines are available and whether they are appropriate for telemedicine. In other words, the need for a new set of guidelines must be established. If there are existing, appropriate guidelines available, the SIG may choose to work on getting them endorsed by the ATA Board of Directors “as is”, or they may choose to get permission from the body that created them to adapt them to the particular telemedicine application under consideration. In general, the ATA Standards and Guidelines Committee encourages incorporation of existing standards and guidelines into telemedicine practice guidelines whenever appropriate. For example, the Core Standards for Telemedicine Operations document was amended in 2009 to include a new section on electronic health information security. As such guidelines already exist in a number of forms, the amendment was not a detailed description of all of the possible measures one should take to keep electronic health information secure. Instead, it noted that such standards do exist and that practitioners of telemedicine should be aware of them and incorporate them into their practice whenever feasible and appropriate. In the Standards and Guidelines section of the ATA web site, there is a list with links to existing guidelines and

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position statements developed by other organizations, as well as a list and links to various technical standards (e.g., International Telecommunications Union (ITU) and the Digital Imaging and Communications in Medicine (DICOM) standards) that are typically used in the telemedicine.

FUTURE DIRECTIONS The ATA currently has four completed practice guidelines – Core Standards, Telepathology, Diabetic Retinopathy, and Teledermatology. During 2009, at least two new sets of guidelines will be completed - Evidence Based Practice for Telemental Health and Telepresenting. Two new groups are initiating the development process – Telerehabilitation and Home Telehealth and Remote Monitoring. The Committee also recognizes that any set of guidelines created is dynamic rather than static. Technology changes, clinical practices change, and new diagnostic methods and tools are developed. Practice guidelines developed even a couple of years ago can quickly become outdated and need to be revised to reflect the current stateof-the-art. Therefore, the Ocular Telehealth SIG is currently updating the Diabetic Retinopathy document and expanding it to include additional ocular conditions amenable to telemedicine. The Teledermatology SIG, as noted previously, is working on developing a more concise and easy to read version of its original practice guideline document. In the future, the Committee intends to develop approximately three practice guidelines per year as well as update and revise older guidelines as appropriate.

CONCLUSION The development of guidelines and standards for telemedicine is an important and valuable process to help insure effective and safe delivery of quality healthcare. The ATA has made the de-

Standards and Guidelines Development in the American Telemedicine Association

velopment of standards and guidelines a priority within the Association and over the past five years has significantly streamlined and formalized the development process. The practice guidelines developed so far have been well received by the telemedicine community and are being adopted in numerous practices as attested to by the feedback we have received. It is interesting to note that the practice guidelines that are available are appearing in the research literature. For instance, Rudnisky et al., (2007) carried out a study to validate a teleophthalmology system and incorporated the Diabetic Retinopathy Practice Guidelines into their study both in terms of acquisition and display of the images, and in terms of grading them into diagnostic categories. Studies that utilize the published guidelines not only help bring them into grater public awareness, but they also provide the evidence needed to validate the existing guidelines and guide the revision of future versions. As telemedicine continues to grow and be adopted by more healthcare practitioners as a regular part of their clinical practice, the ATA practice guidelines will be adopted as well. As dynamic documents, they will continue to evolve to reflect the changes in the way that telemedicine is practiced, and the ATA will continue to draw on the expertise and experience of representatives from medicine, industry, government and other potentially affected parties, both from within and outside the ATA membership.

REFERENCES American Telemedicine Association. (1999). Guidelines for Telepathology. Retrieved May 13, 2009, from http://www.americantelemed.org/i4a/ ams/amsstore/category.cfm?category_id=2 American Telemedicine Association. (2004). Telehealth practice recommendations for diabetic retinopathy. Telemedicine and e-Health, 10(4), 469-482.

American Telemedicine Association. (2006). Telemedicine/Telehealth Terminology. Retrieved May 13, 2009, from http://www.americantelemed. org/files/public/standards/glossaryofterms.pdf American Telemedicine Association. (2008). Core Standards for Telemedicine Operations. Retrieved May 13, 2009, from http://www.americantelemed. org/i4a/pages/index.cfm?pageID=3311 Krupinski, E., Burdick, A., & Pak H. (2007). American Telemedicine Association’s Practice Guidelines for Teledermatology. Telemedicine and e-Health, 14(3), 289-302. Krupinski, E., Dimmick, S., & Grigsby, J. (2006). Research recommendations for the American Telemedicine Association. Telemedicine and eHealth, 12(5), 579-589. Krupinski, E. A., Williams, M. B., & Andriole, K. (2007). Digital radiography image quality: image processing and display. Journal of the American College of Radiology, 4(6), 389–400. doi:10.1016/j.jacr.2007.02.001 Rudnisky, C. J., Tennant, M. T. S., & Weis, E. (2007). Web-based grading of compressed stereoscopic digital photography versus standard film photography for the diagnosis of diabetic retinopathy. Ophthalmology, 114(9), 1748–1754. doi:10.1016/j.ophtha.2006.12.010 SAGES (Society of American Gastrointestinal and Endoscopic Surgeons). (2004). Guidelines for the Surgical Practice of Telemedicine. Retrieved May 13, 2009, from http://www.sages.org/sagespublication.php?doc=21 Seibert, J. A., Kent, J. S., & Geiss, R. A. (2004). Practice guideline for electronic medical information privacy and security. Retrieved May 13, 2009, from http://www.acr.org/SecondaryMainMenuCategories/quality_safety/guidelines/med_phys/ electronic_medical_info.aspx

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Siegel, E., Krupinski, E., & Samei, E. (2006). Digital mammography image quality: image display. Journal of the American College of Radiology, 3(8), 615–627. doi:10.1016/j.jacr.2006.03.007 Van Moore, A., Allen, B., & Campbell, S. C. (2005). Report of the ACR task force on international teleradiology. Journal of the American College of Radiology, 2(2), 121–125. doi:10.1016/j. jacr.2004.08.003

Williams, M. B., Krupinski, E. A., & Strauss, K. J. (2007). Digital radiography image quality: image acquisition. Journal of the American College of Radiology, 4(6), 371–388. doi:10.1016/j. jacr.2007.02.002

This work was previously published in E-Health Systems Quality and Reliability: Models and Standards, edited by Anastasius Moumtzoglou and Anastasia Kastania, pp. 244-252, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 7.3

The Regulation of Genetic Testing and the Protection of Genetic and Medical Information in Singapore Terry Kaan1 National University of Singapore, Singapore

ABSTRACT In the decades since its independence in 1965, the transformation of Singapore’s economy and its transition to a relatively developed economy has also in like manner transformed its health care system, and of the demands made of it. The emergence and availability of new medical technologies has put into sharp focus many novel legal, ethical as well as social issues. This DOI: 10.4018/978-1-60960-561-2.ch703

chapter looks at how Singapore has attempted to respond to issues thrown up by genetic testing and screening technologies. A particular focus of this chapter will be the tension between privacy concerns, and the imperatives of access for biomedical research, given that biomedical research has been championed by the Singapore government as one of the future leading sectors of the economy of Singapore. This chapter also examines Singapore’s approach to the question of “genetic exceptionalism:” Does genetic information possess special qualities or attributes that remove it

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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from the realm of ordinary personal information, and which thereby demands special treatment and protection? In this context, the impact of the doctrine of genetic exceptionalism on industry (in this case the insurance industry) is examined.

THE BACKGROUND On November 25, 2005, the Singapore Bioethics Advisory Committee released a Report entitled “Genetic Testing and Genetic Research” (The Singapore Bioethics Advisory Committee, 2005).2 In the Report (the “Genetic Testing Report”), the BAC laid down ethical guidelines for an array of genetic testing procedures and for genetic research. A year and a half later, the BAC released a further Report entitled “Personal Information in Biomedical Research” (The Bioethics Advisory Committee, 2007) (“the Personal Information Report”) which inter alia took up and expanded on some of the themes first examined in the Genetic Testing Report. Both the Genetic Testing Report and the Personal Information Report was subsequently accepted in full by the Singapore Government, and (through the mechanism which will be explained below) effectively assumed the status of binding professional guidance for the medical profession as regards genetic testing and screening procedures, and on matters on the use of genetic testing and genetic information used in research. Some contextual background may be useful at this point for a proper appreciation of the Genetic Testing Report and the Personal Information Report, and on their impact and implications3. Towards the end of 2000, the Singapore Cabinet decided to establish a national-level agency to establish guidelines, and to make recommendations for supporting legislation and regulatory structures for the development of biomedical research in Singapore. To this end, the Singapore Bioethics Advisory Committee was appointed by the Cabinet. Ap-

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plying an approach commonly employed by the government in Singapore, its constitution was and continues to be that of a committee of experts4 (the majority of which were drawn from the medical, legal and biomedical research sectors) without any separate official or legal identity of its own, reporting and making its recommendations to a committee of ministers charged with steering the Government’s plans for the development of the biomedical industry and research sector in Singapore5. One of the impetus for the establishment of the BAC was no doubt the launch by the Singapore Government of the Singapore Biomedical Sciences Initiative (BMS Initiative) in June 2000; the vision was for Singapore to be the “Biopolis of Asia,” with the BMS Initiative targeted at the development of the biomedical industry and research sector “as the fourth pillar of Singapore’s industry cluster, alongside electronics, chemicals and engineering.”6 That the BMS Initiative has had a profound impact on the reshaping of the present and future of the Singapore economy is not in dispute. For 2007, the biomedical sector’s contribution to the manufacturing sector amounted to S$24 billion (US$17 billion as at January 2010 exchange rates), and accounted for 6% of Singapore’s GDP (Agency for Science, Technology and Research, 2008). Currently, by far the great bulk of the contribution of the biomedical sector comes from pharmaceutical manufacturing, as opposed to biomedical research (including biomedical research services). The Singapore Government, however, recognizes that research is a fundamental support pillar of the biomedical sector, and to this end has poured considerable resources and funds into developing the biomedical research services sector. The most visible result of this push towards the development of biomedical research in Singapore has been the construction of the Biopolis complex at the new new one-north [sic] development situated in close proximity and designed as a complement to the two existing Science Parks, the Fusionopolis

The Regulation of Genetic Testing and the Protection of Genetic and Medical Information in Singapore

complex, the National University of Singapore and the National University Hospital. From a regulatory and legal point of view, however, the breathtaking pace of these economic and physical infrastructural developments has not been matched by developments in the formal law and the regulatory structure. Perhaps surprisingly, Singapore did not until the establishment of the BAC in 2000 have a national-level body aimed specifically at the development and coordination of the ethical governance of biomedical research. Until the establishment of the BAC, that responsibility was in part addressed by the National Medical Ethics Committee (the NMEC). The NMEC was established in 1994 by the Ministry of Health to identify and advise the Ministry on, among other things, “…the prevailing ethical issues relating to public health, medical practice and research in Singapore [and] … on the potential ethical issues which may occur in Singapore based on the trends in other developed countries” (National Medical Ethics Committee, Singapore, 1998). Although most of the work of the NMEC dealt with matters of medical practice, it did have occasion to advocate law reform in areas that had considerable repercussions in the social and public spheres.7 The main focus of the NMEC, however, was on ethical issues arising in the context of medical practice in Singapore, and not issues concerning biomedical research. In the context of the practice biomedical research in Singapore, directions issued by the Ministry of Health on the advice of the NMEC to clinics, hospitals and medical practitioners made for the effective but indirect regulation of biomedical research in Singapore– but only because most biomedical research was carried out in hospitals (which are subject to the regulatory jurisdiction of the Ministry of Health),8 or involve the recruitment of subjects or access to information held by such hospitals, or by medical practitioners (who are also subject to the regulatory jurisdiction of the Ministry of Health).9 But this mechanism of indirect regulation by the Ministry

of Health through its regulatory levers on the hospitals and the medical profession left open the question of how future biomedical research activities that did not involve hospitals or medical practitioners were to be regulated–n obvious development if Singapore were to develop itself as a biomedical research and development hub. From a different perspective, there was also the difficulty that the interests of a body charged with the development of professional ethics within the context of medical practice did not necessarily coincide entirely with that of a body tasked with the development of guidelines for the ethical governance of research. Fundamentally, the relationship of physician and patient is not the same as that of the relationship of research and subject. Put in its bluntest form, there is a fundamental conflict involved in asking a patient to participate as a human subject in a research trial involving physical interference with the patient or with his treatment regime: except in very few cases, 10 there is arguably always some degree of risk of harm11 involved in biomedical research trials involving human subjects. The establishment of the BAC at the end of 2000 resulted in a neater separation of functions between the two bodies. The NMEC was freed to concentrate on matters falling within the sphere of the physician-patient relationship and professional practice; and the BAC on the development of a professional and regulatory framework for the ethical governance of biomedical research in Singapore. Nonetheless, some of the basic framework for the ethical governance of biomedical research in Singapore had been put in place by the NMEC a few years before the establishment of the BAC. In September 1997, the NMEC had issued the first set of national guidelines for the ethical governance of biomedical research, entitled Ethical Guidelines on Research Involving Human Subjects.12 As the NMEC explained, the objectives of the guidelines were specifically “…to ensure that the rights and the welfare of research subjects were

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protected. They were prepared to assist the ethics committees of the various hospitals in assessing the ethical acceptability of research proposals. …The Guidelines were accepted by the Ministry and sent to all the hospital ethics committees and the National Medical Research Council for their reference” (NMEC, 1998, pages 8-9). With the implementation of this Ministry of Health directive, the basic framework for the ethical governance of biomedical research was established. Each hospital now had an ethics committee or institutional review board (IRB) distinct from the Medical Board (which dealt with issues of professional and operational policy within the hospital) or the hospital ethics committee (which dealt with ethical issues arising from medical practice, such as whether new medical devices or forms of surgery or therapy should be permitted) which concentrated entirely on the ethical review of proposed research protocols13. Before any biomedical research of any kind could be carried in a hospital, or by researchers employed by or affiliated to the hospital, or carried on or involving patients of the hospital or their medical records or on the tissue collections held by the hospital, a research protocol proposal had to be drafted for review by the hospital research ethics committee or its IRB. The sole exception were clinical trials (pharmaceutical trials) involving the testing of drugs for their safety, efficacy, appropriate dosage and other effects: these were regulated on a separate track by the much older statutory regime laid down under the Medicines (Clinical Trials) Regulations 1978, 14 which applied a set of procedures designed to conform to international norms as represented by the ICHGCP Guidelines.15 Several challenges of particular urgency confronted the BAC from the moment of its establishment. The first was that biomedical research other than clinical trials had far overtaken the traditional clinical trials in both volume and importance. As observed by the BAC, by 2002, “hospital ethics committees of the five main restructured hospitals

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reviewed nearly three times as many applications for such research as they did for pharmaceutical trials” (The Singapore Bioethics Advisory Committee, 2004).16 The second challenge was that, unlike for clinical trials, there was no statutory scheme in place for the regulation of biomedical research other than clinical trials. The third challenge was related to the second. Unlike for clinical trials (for which international consensus had been practically achieved in the form of the ICH-GCP documents), there were not any convenient international consensus on the body of rules to be applied to biomedical research apart from clinical trials. This situation was not surprising, given the bewildering range and diversity of research falling within the definition of biomedical research. Internationally, the situation as far as harmonization of national ethical codes was not much better: indeed, it should be remembered that it was not until 1993 did the CIOMS and the WHO bring to publication the first edition of the CIOMS International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS, 2002). Prior to the CIOMS document, there were essentially only the World Medical Association’s Declaration Helsinki (World Medical Association, 2008) and the Nuremberg Code (1949) to go on.

THE BAC REPORTS To date, the BAC has issued a total of six reports.17 These examined and made recommendations for legislation and laid down practice guidelines for research involving human cloning and human embryonic stem cell research (“the Cloning and Stem Cell Report”) (Bioethics Advisory Committee, Singapore, 2002a); on human tissue research (“the Human Tissue Report”) (BAC, 2002b); on research involving human subjects, and on the kind of institutional infrastructure required for the ethical governance of research within hospitals and other research institutions (“the Human Research

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Report”) (BAC, 2004); on genetic testing and research (“the Genetic Testing Report”) (BAC, 2005); on the use of medical records and personal information in biomedical research (“the Personal Information Report”) (BAC, 2007); and finally on the use of human eggs donated for biomedical research (BAC, 2008). Of these, the discussion in this chapter will concentrate on the guidelines laid down by the BAC in the Genetic Testing Report and the Personal Information Report. One of the first tasks of the BAC was in effect to define its own terms of reference: it was charged with formulating and recommending policy to the Government in relation to biomedical research, but what was biomedical research? At the very least, it had to be clearly distinguished from clinical trials, which fell outside of the ambit of the BAC’s terms of reference, if only because there was already in existence a clear statutory regime for the control of clinical trials. But unlike as with the ICH-GCP for clinical trials, there were no universally accepted international body of standards which could be readily and directly adopted. This new kind of biomedical research was of a much more amorphous and heterogeneous nature than clinical trials. Ultimately it came to be defined both in Singapore and elsewhere in negative terms: effectively, it was any biomedical research not covered by current rules governing clinical trials. So it was thus defined by Singapore’s national Bioethics Advisory Committee as: …human biomedical research that involves an interaction (whether direct or otherwise) with a human subject or human biological material, and therefore exclude any human biomedical research in relation to: (a) Genetically modified organisms, (b) Animals and their treatment; and (c) Economic, sociological and other studies in the disciplines of the humanities and social sciences … (BAC, 2004, paragraph 3.2). Inherent in this definition is the careful separation and teasing out of biomedical research as a

distinct activity from the broader category medical therapy. The BAC recognized the importance of this distinction, and adopted an earlier guideline of the NMEC which distinguished between the two in the following terms: Human research can be broadly defined as studies which generate data about human subjects which go beyond what is needed for the individual’s wellbeing. The primary purpose of research activity is the generation of new information or the testing of a hypothesis. The fact that some benefit may result from the activity does not alter its status as “research.”18 This careful distinction captures well some of the inherent tensions and potential conflicts that may occur when a biomedical researcher is a physician, and his subjects or potential subjects are the patients that he is treating as a physician. This tension is well demonstrated by the controversy over the changing texts of the World Medical Association’s Declaration of Helsinki in its recent reiterations; there is increasing recognition that there are inherent conflicts of interests between that of a physician acting as a physician in relation to his or her patients (the physician-patient relationship), and that of the physician-researcher acting as a researcher-investigator in relation to his or her research subjects, who may be also the physician-researcher’s patients (the researchersubject relationship).19 This is the inherent and inescapable tension that occurs whenever a physician takes on two hats simultaneously, namely that of the physician to his or her patient, and that as a researcher with the same patient as his or her research subject. This duality of function of most biomedical researchers currently carrying out research in Singapore is a recurrent theme that emerges in all the BAC Reports, and is an important key to the understanding and interpretation of the Reports.

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THE GENETIC TESTING REPORT AND THE PERSONAL INFORMATION REPORT On November 25, 2005, the BAC released the fourth of its Reports, the Genetic Testing Report (Bioethics Advisory Committee, Singapore, 2005). Unlike any of the other BAC Reports, the Genetic Testing Report deals with ethical issues that straddled the boundaries between research and therapy. Indeed, when viewed as a whole, it might be argued that the guidelines and recommendations set out in the Genetic Testing Report deals overwhelming with therapy and medical practice than it does with biomedical research.20 The Report deals with ethical considerations arising from a diverse assemblage of procedures which had genetic testing as their common ground. In the Report, the BAC set out to consider the ethical implications and the social impact of genetic testing and screening technologies--defining the difference between a genetic test and otherwise ordinary medical diagnostic procedure that might yield genetic information directly or indirectly as well, laying out general principles in relation to genetic testing and screening for heritable conditions, considering the use of genetic testing technologies in assisted reproductive technologies, and deliberating whether genetic tests ought be allowed to be supplied directly to the public (for example, mail-order genetic tests) that cut out a medical intermediary.

Definitions and Genetic Exceptionalism The unspoken link between all the diversity of themes in the Report was the question of genetic exceptionalism, i.e., the controversy over whether or not genetic information is so qualitatively different from ordinary medical information that it deserves or requires special protection and treatment (Green and Botkin, 2003). The answer to this question would determine whether or not

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the Genetic Testing Report itself was required. If genetic exceptionalism as a doctrine was rejected, then it would follow that genetic testing procedure and genetic information would fall to be bound by the same rules as any other kind of clinical procedure or medical information, and it would not be necessary to have a report dealing with special rules for genetic testing and genetic information. To this first question, there was a corollary: If it were to be accepted that special protection and treatment ought to be accorded to genetic information, how was genetic information to be defined? The BAC sought to tackle this question first if only because it was necessary to define the very scope of the Report. If the question of genetic exceptionalism was to be tackled, then it was necessary first to define what might have to be given special status apart from ordinary medical information. But this second question is not as straightforward as it may appear at first glance. For example, should genetic information relate only to heritable mutations or germ-line mutations? If the answer is yes, then from this perspective, a test designed to confirm non-heritable somatic mutations giving rise to conditions such as Down Syndrome (trisomy 21) would not result in genetic information, even though the cause of the condition is manifestly genetic in the sense that it is an error of chromosomal replication. On the other hand, if genetic information is to be defined in terms of information about heritable mutations of the human genome, the definition may become too vague or unwieldy because it would capture too much. For example, it is not necessary to carry out any kind of medical or clinical test (let alone a genetic test of any description) to elicit information about heritable conditions. For example, taking a family history is one of the most basic procedures known to physicians, and that family histories (if sufficiently complete and accurate) can provide powerful predictive information about the genetic inheritance of patients.

The Regulation of Genetic Testing and the Protection of Genetic and Medical Information in Singapore

The difficult line between these two extremes was drawn by the BAC in the following terms. First, genetic testing: Genetic testing is the analysis of human DNA, RNA, genes and/or chromosomes, or the analysis of human proteins or certain metabolites, with the primary purpose of detecting a heritable genotype, mutation, phenotype or karyotype (BAC, 2005, paragraph 2.1). Then the BAC went on to restrict the definition and scope of genetic information by reference to its definition of genetic testing: Genetic information broadly refers to any information about the genetic makeup of an individual. It can be derived from genetic testing as defined in paragraph 2.1 in either clinical or research settings or from any other sources, including details of an individual’s family history of genetic diseases. This report is concerned with information about heritable conditions obtained by genetic tests whether it is for clinical purposes or for research (BAC, 2005, paragraph 3.1).” [emphasis added] The difficulty is of course that many kinds of routine clinical investigations will throw up medical information which clearly has a genetic dimension. For example, a purely clinical diagnosis confirmed through pathological examination of tumor samples exhibiting the same kind of breast cancer in several closely-related women from the same family clearly has genetic implications, and would be properly regarded as qualifying as genetic information. But this kind of information would be excluded by the BAC definition. On the other hand, even simple clinical tests such as blood typing may throw up uncomfortable genetic probabilities (or improbabilities, as the case may be) such as the paternity of a child. In such cases, it is not clear such information which clearly pertain to the genetic inheritance (or non-

inheritance or non-relatedness) of one individual to another would fall within the BAC definition. The BAC then went on to consider the question of genetic exceptionalism. This doctrine is strongly opposed by some biomedical researchers (and the life insurance industry, as discussed below), who argue strenuously against it, both on point of principle and on grounds of pragmatism. On the point of principle, the arguments against genetic exceptionalism are essentially that there is no logical or substantive basis to distinguish genetic information derived from genetic tests from any other kind of medical information pertaining to the individual; that many kinds of genetic information are patently obvious to any casual observer and cannot be hidden (e.g., eye or skin color, or any identifiably distinct ethnic or population-based characteristic) and therefore cannot and should not attract special privacy protection; that genetic information of predictive value has been collected for many years in the form of family histories without opposition for legitimate and fair purposes such as assessing the fair actuarial basis for the premium to be paid on an insurance policy; and finally that medical information (and other kinds of personal information) are already strongly protected by law in most developed countries. On the grounds of pragmatism, there are fears that rules even more stringent that those applied to ordinary medical information will stifle participation by otherwise willing volunteers in biomedical research trials, and discourage biomedical researchers from engaging in muchneeded research because of the high compliance administrative overheads imposed by laws based on genetic exceptionalism. The BAC took the path of moderation, rejecting the strict legal exceptionalism institutionalized by legislation in some countries, notably the United States and Germany (The Genetic Information Non-Discrimination Act, 2008; HumanGenetic Examination Act of Germany, 2009). It preferred instead the more nuanced approach in the United

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Kingdom and in Australia, with their emphasis on flexible informal or indirect regulation rather than direct regulatory intervention in the form of substantive legislation (BAC, 2005, pages 20-22). The BAC agreed with the observations of the UK Human Genetics Commission in holding that there were undeniably some qualities which set genetic information apart from ordinary medical information. In particular, the BAC noted that much of the sensitivity attaching to genetic information related to the potential or perceived21 predictive power; that such predictive power is related not only to a given individual’s future health, but also potentially to the individual’s relations, children and other descendants; and that such information could be gathered from very small samples such as hair samples or cheek swabs. But the BAC concluded, in the very first guideline that it laid down in the Genetic Testing Report, that: Genetic information derived from clinical genetic testing should be regarded as medical information and the usual standards in medical ethics apply in its derivation, management and use (BAC, 2005, page 22). At casual glance, this would appear to be a very decisive nail in the coffin of genetic exceptionalism. But this first recommendation needs to be read in context. What the BAC was in effect saying was that, at minimum, all kinds of genetic information covered by the Report attracted the same kind of legal and ethical protection and obligations attaching to medical information. In other words, genetic information was to be protected as a species of medical information, if nothing else. It is in the recommendations following this seemingly surprising first statement that the intent of the BAC becomes clear: If genetic exceptionalism were to be completely rejected out of hand, then there would be little need for the recommendations which followed. In effect, the rest of the Genetic Testing Report deals with the

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special kind of consideration and protection which the BAC felt that genetic information merited over and above ordinary medical information. One of the reasons advanced by the BAC for not adopting the strict formal exceptionalism as enshrined in the legal systems of some countries was its view that genetic information in itself did not give rise to any special quality that entitled it to special consideration or protection (again, over and above that already accorded to ordinary medical information) as a category. Instead, the BAC agreed with the Australian Law Reform Commission and the Australian National Health and Medical Research Council in noting that a “commensurate level of protection may be required where there is a likelihood of special threat to privacy or discrimination,” a position echoed by the statements from the Council of Europe, and the Bioethics Committee of Japan (BAC, 2005, paragraph 3.5). The idea, then, was that instead of applying a blanket regime with a protective threshold high enough to cover all sorts of genetic information, the level of special protection (if any was required) should be commensurate with the vulnerability of that information to abuse, and with the seriousness of the consequences of such abuse. In the BAC’s words, the stringency of the special protection should be “proportional”: this was a theme that it would eventually expand upon in the later Personal Information Report. We will take this up later on in this chapter, but first let us consider the application of the earlier issue of genetic exceptionalism in the context of an application in real life.

GENETIC EXCEPTIONALISM AND LIFE INSURANCE It has long been accepted practice in Singapore, as elsewhere, for life insurers to require prospective customers to give their consent to the insurance companies contacting their medical providers

The Regulation of Genetic Testing and the Protection of Genetic and Medical Information in Singapore

or physicians directly for information about the prospective customers’ state of health, and/or require their prospective customers to undergo a medical examination and medical tests carried out by physicians appointed by the insurance companies. In this way, insurers obtain medical information about their prospective customers. Should insurance companies by extension have the right to whatever genetic information (in the BAC sense of information derived from genetic tests, and not from family histories) that an individual may already possess, and should insurance companies have the right to require genetic testing of prospective customers? One difficult point in this context is of course that from a national health policy perspective, genetic testing and screening is of benefit to both individuals and the state in at least some situations. For instance, screening for thalassemia in populations such as Singapore’s with a relatively high incidence of such inherited conditions (so that informed decisions can be made as to whether or not to have a child), or for genes strongly linked to certain forms of breast cancer if there is a strong trend towards breast cancer in the women in a particular family (so that those confirmed to be carrying such genes can take the precaution of having themselves examined and tested more frequently). So there is a state interest in promoting such tests, both to prevent tragedy at the individual level, and the burden on public health care costs at the state level. The problem is that the genetic information in the second situation would be the kind of information which life insurance companies in Singapore regard as vital information about the health of their prospective customers which their prospective customers ought to disclose. The life insurance companies, however, concede that they should not want to mandate genetic tests for any prospective customer as a condition of the acceptance of a proposal; the line is simply drawn between genetic information which the customer may already have, as opposed to genetic information which

the customer does not at that point in time have, and the life insurance industry in Singapore urged the BAC not to bar access to genetic information already available to the prospective customer at the time of the proposal. 22 The conflict put the government and individuals on one side (although, as will be explained, their interests do not necessarily coincide), and the insurance industry on the other. Understandably, the insurance industry would prefer to regard genetic information (to be fair to the industry, they have made clear that they are interested only in genetic information of proven, and not speculative, predictive value) as any other kind of medical information on which they may make an actuarial assessment of the risks involved in underwriting a policy. There is a strong argument in their favor of not allowing individuals to conceal relevant and reliable information about the nature of the risk to individuals–this may in at least some circumstances amount to fraud on the insurance companies involved. There is also the argument that the insurance industry already require the disclosure (without any consumer opposition) information of a clearly genetic nature–for example, an individual’s family history, particularly in relation to conditions such as cancers, heart disease, high blood pressure and diabetes. The argument here of course is that genetic information is already being asked for, and given, and that genetic information obtained from a genetic test is not substantially different from that derived from a family history because both kinds of genetic information are merely predictive of possibilities or probabilities in actuarial terms, and do not represent genetic certainties.23 From a public policy point of view, however, the more precise the information that insurance companies can extract from their customers, the better position insurance companies are in to cherry-pick their customers, with the effect of making insurance prohibitively expensive for those considered by the insurance industry to be at a particular risk because of their genes. Or, in

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the worst case, to be denied insurance altogether24. This runs against the principle of the pooling of risks, which governments and consumers arguably see to be main reason for having insurance in the first place. From the consumers’ perspective, there is also the added unfairness and danger inherent in the asymmetry of information; a given consumer will not be told of why his or her proposal has been rejected, or why the premiums asked for appear to be unduly loaded, or of the actuarial or statistical bases justifying such a loading. A consumer would have no means of knowing whether an insurance company may be assigning a speculative value (or a value unsupported by empirical evidence or actuarial experience) to a disclosed genetic trait. From the public health care perspective too, giving the insurance industry free rein to demand genetic information derived from tests would have a severe chilling effect on voluntary preventive or prophylactic genetic testing by individuals at risk, on population screening programs, and on participation by volunteers in biomedical research trials. Why find out whether you may be at risk for X, Y or Z condition if such information might result in your being refused insurance, or charged a premium? Many would also point out that it would be counter-productive for the insurance industry to demand such information because it would simply result in those people who have private suspicions that they might be genetically at risk would simply defer testing themselves until after they had taken out the necessary insurance policy. There is also the question of whether genetic test results form part of the disclosable family history of a potential customer: If X applies for an insurance policy, and has not undergone any kind of genetic testing, does X have to disclose that he or she is aware that siblings Y and Z have undergone genetic testing for a strongly familial and deadly kind of cancer, and that the results of those tests were positive? Access by insurers to genetic information has been seen as an invasion of privacy by consum-

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ers in many developed jurisdiction. In the United States, for example, one response (if a somewhat belated and flawed one) was passage of the Genetic Information Nondiscrimination Act of 200825 by the US Congress, which prohibits insurers from requiring prospective clients to undergo genetic tests or from using genetic information to discriminate among policyholders.26 On the issue of whether any one should be compelled to undergo genetic testing as a condition of acceptance by an insurance company, the BAC came to the conclusion that “no one should be compelled to undergo genetic testing in order to obtain insurance coverage” (BAC, 2007, paragraph 6.11). This was perhaps the easier of the two decisions to make, since the life insurance industry in Singapore was not in any event asserting such a right. But what of the right that they were asserting, i.e., the right to genetic test results that an individual might already have? On this second point, the BAC chose27 to defer the decision by adopting the position taken by the UK Government by recommending a moratorium on the use of genetic information by insurance companies until November 2011. Under the terms of the Concordat and Moratorium agreed between the UK Government and the Association of British Insurers (HM Government and the Association of British Insurers, 2005), prospective clients may not be asked to submit to testing as a condition for acceptance of an insurance proposal, nor may insurance companies ask for disclosure of genetic test results unless the policy is for an amount in excess of £500,000 for life insurance, or £300,000 for critical illness insurance28, and then only for predictive genetic tests for conditions approved by the Genetic and Insurance Committee (GAIC).29, 30 Critically, the Concordat and Moratorium makes clear that genetic information derived from participation in a research program (as opposed genetic information derived from a clinical genetic test) is not something that insurance companies may ask for under any circumstances; a prospective

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customer is not to “be asked to disclose any predictive or diagnostic genetic test results acquired as part of clinical research” (HM Government, 2005, Clause 16(iii)). This provision is important from a public health perspective, and perhaps also from the perspective of a country eager to promote and develop its biomedical research sector, if ordinary individuals are to be encouraged to participate in biomedical research programs. The absence of such a provision might occasion a pause for thought for many people who may otherwise be willing to participate in biomedical research programs, because of the risk of the research results exposing a genetic condition which insurers may deem to be relevant to actuarial risks. Secondarily, there is also the concern that genetic testing carried out in the context of a research program may not be to standards or protocols appropriate to a clinical genetic test (which may, for example, require further or more sensitive confirmatory tests to eliminate false positives or negatives before a definitive diagnosis is offered). The Concordat and Moratorium also makes clear that so-called genetic “relational information” (the best example being family history: information not about oneself, but about one’s family members, and often information that is not obtained first hand but through other persons: e.g. information that the informant’s grandfather X died of colon cancer at the age of 35 before the informant was even born) should not be asked for by insurance companies (HM Government, 2005, Clause 16(ii)). The limitation by the GIAC of “relevant” genetic conditions to Huntington’s disease also points to a different consideration. Apart from a small number of conditions such as Huntington’s Disease which is a monogenic condition of full penetrance, the effects of the vast majority of known or suspected genetic risks are not wellknown; we know that certain genes may predispose a person carrying that gene for a heritable disease, but in most cases, there is not the certainty as is

tragically the case in Huntington’s Disease (BAC, 2005, paragraph 3.6). A good analogy might be that we know that it is well-established that heavy smoking and allowing oneself to get morbidly obese will increase one’s chances of dying earlier of cancer or from heart disease, but there is no guarantee of an early demise through such factors even for the determinedly suicidal. At least in the case of heavy smoking and obesity, life insurance companies have assembled sufficiently large databases for actuarial tables, but there is much less information available for actuaries to work on when it comes to the effect of genes. There is a further level of uncertainty; even if a life insurance company has in place an actuarial estimate of the risks of some common genetic conditions, these actuarial assessments are not available to the public, nor are the basis of the actuarial calculations. These points further buttress the current argument that any actuarial weight given by life insurance companies to genetic conditions, except for monogenic conditions of full penetrance, fails the test of transparency, and is intrinsically unfair to the consumer. Ultimately, however, it should be noted that in both Singapore and the United Kingdom, unlike the United States with its Genetic Information and Non-Discrimination Act, the recommendations in place merely postpone the substantive decision that has eventually to be made. The final recommendation by the BAC in this regard merely recommends a moratorium without specifying any particular time limit, to “allow time for both the insurance industry and the government to look into the substantive issues” (BAC, 2007, paragraph 6.14). For now, at least, given the representations of the life insurance industry to the BAC, it does not appear that the industry will be pressing the issue of access to genetic information. But with genetic tests (particularly those for “genetic markers”) becoming more and more commonplace and forming part of ordinary clinical investigations, there may be pressure in the future for a clear and substantive resolution of this issue.

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PROPORTIONALITY

GENETIC TESTING PROCEDURES

In its Personal Information Report, the BAC enlarged on the concept of proportionality that it first hinted at in its Genetic Testing Report. It urged researchers and IRBs to “be mindful of possible public sensitivity towards certain kinds of research” in the taking of consent for participation in biomedical research trials, or to the use of personal (and thereby presumably also genetic) information for the same, noting that the UK Nuffield Council on Bioethics had identified “certain types of genetic research that may be of public concern, such as those relating to personality, behavioral characteristics, sexual orientation or intelligence” (BAC, 2007, paragraph 5.11). Here then was a recommendation that stricter measures (than for “ordinary” medical information) for the protection of privacy and for the taking of consent for the use of the information ought be applied depending on the perceived sensitivity of the information involved. The BAC was careful to make a distinction between “actual and perceived risk of harm to the individual concerned (BAC, 2007, paragraph 5.13);” there was a recognition that some information might be regarded as being of a very sensitive nature for reasons other than purely scientific or medical ones.31 It was not the scientific probability of something happening or a condition eventuating that determined whether or not information was sensitive. Instead, respect for the individual demanded that information be treated as sensitive if society generally, or a subset of it, should regard it as being so. There is inevitably the question of how information which is regarded as being sensitive only by the person to whom the information pertains. In such a case, the general principle of proportionality when applied would appear to require the information to be accorded the level of sensitive sought for it by the person to whom the information pertains, although there are obviously practical and logical limits.32

In this section we will consider the guidelines laid down by the BAC for specific genetic testing procedures. In these recommendations, we may perhaps observe more clearly the interplay of the doctrines of genetic exceptionalism and of proportionality. Essentially, the question being repeatedly asked in all of the cases boils down to a risk/benefit analysis. But unlike in biomedical research involving human subjects, or in clinical trials, where “risk” is taken in terms of the risk of physical to the subject, “risk” in the context of pure medical information or genetic information generally deals with emotional or social harm to the individual concerned resulting from a breach of his right to privacy and confidentiality of such information. Most of the BAC’s recommendations relating to genetic testing procedures are uncontroversial and are in agreement with those in most developed countries. So the BAC is at pains to insist that all genetic testing should be completely voluntary, and that it should be “conducted in a manner that is respectful of the welfare, safety, religious and cultural traditions and perspectives of individuals.”33 Not only did the BAC take the view that no testing should be done without the appropriate consent, it felt strongly enough to recommend that “the non-consensual or deceitful taking of human tissues for the purpose of genetic testing should be prohibited (BAC, 2005, page24);” and that the government should consider legislation similar to that in England criminalizing such conduct.34

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Genetic Testing: Children and Minors But as in many areas of medical law, the main difficulties lie not in the main body but at the interstices and cracks of the law. It is easy enough to say that full and informed consent must be obtained before a genetic test is to be carried out, but what if the person to be tested is an incompetent adult (for example, unconscious, or insane), or a

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child or a minor? In none of these cases would the person concerned have the necessary legal capacity to give full and informed consent. A particularly difficult situation is the situation where parents seek to have their minor children tested for genetic conditions which the parents may know or fear that they themselves have, and which they fear may have been inherited by their children. It would certainly be useful in some situations, and directly beneficial to the children concerned, for the parents to have clear information about whether or not a child has a genetic inheritance that will give rise to a high probability or certainty that the child will develop a certain hereditary genetic condition, especially if there are available medical interventions and treatment which can be started while the child is still a child or minor to the improvement of the child’s health, quality of life or life expectancy. But there is also the situation where nothing can be done for the child even if an inherited disease is diagnosed upon genetic testing. Or it may be an adult-onset genetic condition which will affect the child or minor only later in adult life. The BAC’s response was that it did not support “the broad use of genetic testing on children and adolescents” as a general rule, and that genetic testing “should generally be deferred until the child is mature or when required to make reproductive decisions.” This was on the basis of respect for the right of a person not to know about his or her genetic makeup: Once told, the person loses that right to remain in deliberate blissful ignorance of any dreadful genetic inheritance. For many, the prospect of the likelihood of an early death through an incurable genetic condition is information that they would rather not have. The exception which the BAC allowed was the situation where genetic testing “for genetic conditions where preventive intervention or treatment is available and beneficial in childhood.” In this situation, where genetic testing would be directly and immediately of benefit to the child or

minor, the BAC held that genetic testing should be permitted, and indeed encouraged. To this general principle that genetic testing ought not be carried out in children or minors where no effective intervention was available, the BAC however conceded a loophole: Genetic testing in such situations where testing might not be normally recommended on the principles outlined above might, under certain circumstances, be permitted if “compelling interests of other family members or public health interests exists, and the physician should be able to decide, together with the parents, whether or not determine the carrier status of the child” (BAC, 2005,paragraphs 4.8 – 4.14). Whether this get-out clause will eventually prove to be the exception swallowing the rule remains to be seen, but the need for such a provision may be more understandable when seen in the context of very successful populational genetic screening programs for hereditary conditions common in the local population such as that for thalassemia which have greatly reduce the incidence of the inheritance of such diseases.35 One possible situation in which this loophole may be used might be by parents seeking to test their children for what effectively amounts to their own interests, rather than the children. For example, if both parents are carriers of X recessive gene, there is a one-in-four chance that a child born to them will have full-blown X genetic double recessive condition. Assume that such a child born with such a condition is doomed to an early death in childhood, and there is no treatment that can help the child. Would the parents be entitled to have their child tested, with a view to helping them plan for another child (which they would otherwise not have had) to replace the doomed one? On a different but related point, the BAC is clear that no one should be given genetic about himself or herself against his or her will: There is a “right not to know” (BAC, 2005, paragraphs 4.24 - 4.26). But what the BAC does not make clear is whether a person has the right not to know

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about genetic results that may have a bearing on his or her own genetic inheritance. For example, if siblings W, X and Y have had themselves tested (with positive results) for a gene strongly linked to the development of a specific kind of cancer in later life, do they even have the right to tell their sibling Z of their results, let alone urge Z to have himself tested so that he can take prophylactic measures?

Genetic Selection: Testing the Unborn The discussion so far has dealt with genetic testing of children and adults. But what of the unborn? One of the issues which the BAC had to grapple with in its Genetic Testing Report was whether a number of new developments in assisted reproductive technologies (ART) ought to be allowed. All of them involved some form or other of genetic testing, not just of the fetus, but of the unimplanted embryo. The new ART technologies, collectively labeled “Preimplantation Genetic Testing” (PGT) by the BAC, encompassed specific procedures such as Preimplantation Genetic Diagnosis (PGD), Preimplantation Genetic Screening (PGS), and Preimplantation Tissue Typing (PTT). All these technologies involve the same central procedure. Early embryos are created in vitro outside the womb through standard IVF procedures using sperm and eggs outside the womb. But unlike in standard IVF procedures, genetic tests are carried out on the early embryos before implantation, with a view to choosing which embryos should be implanted. In PGD, the objective is to select an embryo that is free of a deadly genetic flaw that of which one or both of the contributing parents are known carriers. In PGS, the objective is similar, but the screening is carried out for parents who have no known specific genetic condition but who have had difficulties having a child through natural means. In PTT (the “savior sibling” genetic selection procedure), the objective is to select an embryo that is immunogenetically

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compatible with that of an existing sick sibling, with a view to bringing about a child who can be a source of lifesaving cord blood or bone marrow to the sick sibling.36 Fertility is a matter of consuming public interest and national concern in Singapore. One of the unwelcome consequences of economic prosperity in recent decades has been a precipitous decline in the total fertility rate, which fell from 4.66 per female at independence in 1965 to just 1.28 in 2008–far below replacement rate for the population (Singapore Department of Statistics, 2009). The law on abortion in Singapore certainly has had an impact. The Termination of Pregnancy Act (Chapter 324, enacted December 27, 1974), enacted at a time in Singapore’s history when it was struggling to control a burgeoning population, permits abortion on demand up till 24 weeks of pregnancy. It has been reported that in 2006, the number of abortions exceeded 12,000 (Perry, 2008) in a year for which only 38,317 live births was reported (Singapore Department of Statistics, 2009, page. 29). Against this background, any help for parents desperately seeking to have a child was to be encouraged. But PGT went beyond the bounds of ordinary ART procedures; the process of selection for implantation through genetic testing raised uncomfortable issues of whether it was ethically acceptable for parents to select for desired characteristics in this way. Following a review of the position in other countries, the BAC came to the cautious conclusion that PGD and PGS ought to be allowed, but should be strictly “limited to preventing to preventing serious genetic conditions,” for example, if there was a high risk of a child being born with a serious or eventually lethal genetic condition because of the parents’ carrier status (BAC, 2005, paragraphs 4.27-4.43). It further held that the “use of preimplantation genetic testing for the selection of desired traits or gender for nonmedical reasons should not be allowed” (BAC, 2005, paragraphs 4.44-4.46) and selection for “social” considerations such as gender was out

The Regulation of Genetic Testing and the Protection of Genetic and Medical Information in Singapore

of the question. 37 A much tougher ethical nut to crack was the question of PTT: Should genetic testing for the selection of a “savior sibling” be permitted? The BAC gave a cautious yes, but on the condition of strict licensing on a case-by-case basis. (BAC, 2005, paragraphs 4.47-4.50). One question which might be asked, following on the principles explored in the discussion on the genetic testing of children and minors, is whether the fact of such selection by genetic testing, together with the relevant genetic information about their parents’ genetic heritage as well as their own, ought be disclosed to children born of such PGT procedures. In the case of children born as a result of PGD or PGS procedures, there is arguably no objection to children being informed of what they do not have, as opposed to what they might have. For example, if both parents are carriers for thalassemia, and have a child selected through PGD to be free of any gene for thalassemia, there seems to be no ethical difficulty in letting the child know, because the disclosure is ultimately about a gene of concern which the parents have, and which the child does not. The situation is a bit more clouded in the situation where the parents settled for a child (also selected through PGD) who retains one “bad” gene from one of the parents (and hence carrier status, but who in consequence will not suffer fullblown thalassemia major, and will therefore be of normal health). If the child is told in this case, he or she will be effectively given information about his or her own genetic makeup, which the BAC has ruled that the child has a right not to know. Here lies the difficulty in the PTT and the child or minor genetic screening cases: If a deleterious gene is uncovered during the course of the screening, at what stage of the child’s life should he or she be told? If he or she has a right not to know, then how is the subject to be broached at all? Merely asking someone whether he wishes to have some information about his genetic inheritance (particularly if that offer comes from a parent) alerts that person to the fact that potentially important

information is available which could affect his or her health, or his or her progeny. If the genetic information relates to an adultonset disease which only manifests itself in later life, then it seems clear according to the BAC guidelines that the child or minor be informed upon the age of majority because such information would be “required to avert serious harm” (BAC, 2005, Recommendation 7). Again, it is the silent carrier status situation that presents the difficulty. Information about the silent carrier status of the child will have no impact on the future health of the child himself or herself. But when the child grows to maturity and has children of his or her own (“the grandchildren”); his or her carrier status may have a direct (and devastating) impact on the health and survival of the grandchildren. Here again, it seems that the avoidance of “serious harm” recommendation would probably apply, but again on the proviso that the information is only offered when the child attains the age of majority.

THE SINGAPORE REGULATORY LANDSCAPE One obvious question which might be asked is: What kind of regulatory or legal force does the pronouncements of the BAC have in Singapore? The BAC is not a government department, and has no legislative constitution or even a distinct legal identity of its own, beyond that of an advisory committee to the Singapore government. Technically, it reports to and advises only the Singapore government that appointed it. Nor has, apart from a few notable exceptions, 38 the main body of the BAC recommendations and guidelines been enacted into law despite the acceptance of the Reports by the Singapore government. Despite this, however, the BAC guidelines and recommendations have from the practical and professional perspective the force of mandatory practice rules backed by powerful sanctions through a remarkable indirect regulatory

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mechanism. The Ministry of Health (or through its delegate, the Director of Medical Services) has regulatory jurisdiction and direction over all hospitals and clinics 39 and all registered physicians 40 in Singapore. By a directive issued on January 18, 2006, by the Director of Medical Services addressed to “all registered medical practitioners,” it was announced that the “Singapore Medical Council would, in evaluating appropriateness of a doctor’s actions in research involving human subjects, rely on the BAC’s recommendations as the standard for ethical conduct.” 41 Under the Medical Registration Act, the Singapore Medical Council is the statutory body charged with professional discipline of registered physicians in Singapore.42 Under this indirect regulatory scheme, a registered physician who breaches any of the guidelines laid down by the BAC would be liable to disciplinary action by the Singapore Medical Council. The catch, of course, is that the Singapore Medical Council’s jurisdiction extends only to registered physicians. Although most if not all biomedical research in Singapore is currently carried out by registered physicians, or in hospitals subject to the regulatory oversight of the Ministry of Health under the Private Hospitals and Medical Clinics Act, or involve the recruitment of patients from such hospitals or information from such hospitals or their patients, there will eventually come a time when biomedical research without such links will emerge in Singapore. Without direct legislation governing biomedical research (as currently exists for clinical trials), it would be possible (theoretically, at least) for a biomedical researcher who is not a registered physician to carry out biomedical research outside of the regulatory jurisdiction of the Ministry of Health, provided that the research did not involve any collaboration with a registered physician or with any hospital or clinic subject to the jurisdiction of the Ministry of Health. In practice, such deliberate attempts to evade the jurisdiction of the Ministry of Health in order

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to carry out research which might not be approved under the BAC guidelines is highly unlikely, if only for the fact that such researchers might face considerable difficulties in obtaining the allimportant peer and industry acceptance of the results of their research. Indeed, the concern of researchers in precisely such a situation is likely to be the very opposite, that they should not be left in a legal vacuum, and have the comfort of clear law backing their efforts. In 2003, the Ministry of Health did float a draft Regulation of Biomedical Research Act 2003, 43 but this was subsequently shelved. No new draft has yet been announced.44 Coming as it did after only the second of the BAC’s report (and particularly before the BAC’s Human Subject Research Report released November 23, 2004), the draft Act was perhaps premature. In its report following public feedback on the draft Act, the Ministry of Health stated that it had “decided to adopt a step-by-step approach to regulating biomedical research activities” (Ministry of Health, Singapore, 2010). But with a considerable body of recommendations and practice guidelines now in place as a result of a total of six Reports, and with the maturity of biomedical research in Singapore, it may be time again for the Singapore government to look into the matter once more and consider afresh a more direct approach to the regulation of biomedical research in Singapore. Just as was done for to facilitate the development of clinical trials so many decades ago, it is now perhaps time to do the same for biomedical research through the introduction of direct and universal regulation, complete with rules on legislative basis.

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REFERENCES Association of British Insurers. (2005). Insurers and government reach agreement on genetic testing and insurance (March 14, 2005). Retrieved from http://www.abi.org.uk/Media/ Releases/2005/03/Insurers_and_Government_ reach_agreement_on_genetic_testing_and_insurance.aspx. Accessed January 9, 2010. Bioethics Advisory Committee. Singapore (BAC). (2002a). Ethical, Legal and Social Issues in Human Stem Cell Research, Reproductive and Therapeutic Cloning: A Report from the Bioethics Advisory Committee, Singapore (June 21, 2002). Retrieved from http://www.bioethics-singapore.org/. Bioethics Advisory Committee. Singapore (BAC). (2002b). Human Tissue Research: A Report by the Bioethics Advisory Committee, Singapore (November 12, 2002). Retrieved from http://www. bioethics-singapore.org/. Bioethics Advisory Committee. Singapore (BAC). (2004). Research Involving Human Subjects – Guidelines for IRBs: A Report by the Bioethics Advisory Committee, Singapore (November 23, 2004). Retrieved from http://www.bioethicssingapore.org/. Bioethics Advisory Committee. Singapore (BAC). (2005). Genetic Testing and Genetic Research: A Report by the Bioethics Advisory Committee, Singapore (November 25, 2005). Retrieved from http://www.bioethics-singapore.org/. Bioethics Advisory Committee. Singapore (BAC). (2007). Personal Information in Biomedical Research: A Report by the Bioethics Advisory Committee, Singapore (May 7, 2007). Retrieved from http://www.bioethics-singapore.org/. Bioethics Advisory Committee. Singapore (BAC). (2008). Donation of Human Eggs for Research: A Report by the Bioethics Advisory Committee, Singapore (November 3, 2008). Retrieved from http://www.bioethics-singapore.org/.

Epps. (2006). Singapore’s multi-billion gamble. The Journal of Experimental Medicine, 203, 1139-1142. Genetic Information Non-Discrimination Act of 2008, Public Law 110-233, 12 Stat. 881, May 21, 2008. Green, M., & Botkin, J. (2003). “Genetic exceptionalism” in medicine: clarifying the differences between genetic and nongenetic tests. Annals of Internal Medicine, 138, 571–575. HM Government and the Association of British Insurers. (2005). Concordat and Moratorium on Genetics and Insurance (March 2005). Retrieved from http://www.abi.org.uk/Information/ Codes_and_Guidance_Notes/528.pdf. Human and Fluss. (2001). The World Medical Association’s Declaration of Helsinki: historical and contemporary perspectives (World Medical Association: 24 July 2001). Retrieved from http:// www.wma.net/en/20activities/10ethics/10helsin ki/draft_historical_contemporary_perspectives. pdf. Accessed 20 November 2009. Human and Fluss. (2003). Dismantling the Helsinki Declaration. Editorial Canadian Medical Association Journal, Nov. 11, 169.10. Human Genetic Examination Act of Germany. (Gesetz über genetische Untersuchungen bei Menschen (Gendiagnostikgesetz - GenDG). Enactment No. 374/09 of the German Federal Parliament (Bundestag), April 24, 2009, original German text and a parallel English translation available from EuroGentest at http://www.eurogentest.org/ uploads/1247230263295/GenDG_German_English.pdf, and accessed January 10, 2010. Janson-Smith, D. (2002). The genetics and insurance committee, July 19, 2002. Retrieved from http://genome.wellcome.ac.uk/ doc_WTD021010.html. Accessed January 10, 2010.

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Kaan, T. (2009). Walking the tightrope: the regulation of biomedical research in Singapore. Paper presented at the 2009 ILST Conference on Innovation, Competition and Regulation, Taiwan on December 3-4, 2009, organized by the Institute of Law for Science and Technology, National Tsing Hua University, Hsinchu, Taiwan.

The Council for International Organizations of Medical Sciences (CIOMS) in collaboration with the World Health Organization. (WHO). (2002). International Ethical Guidelines for Biomedical Research Involving Human Subjects. Current edition, Geneva. Retrieved from http://www. cioms.ch.

Ministry of Health Singapore. (2003) Public consultation on the draft regulation of Biomedical Research Bill - summary of feedback received 10 November 2003 - 30 November 2003. Retrieved from http://www.moh.gov.sg/mohcorp/econsultationpast.aspx?ecid=81. Accessed January 10, 2010.

The International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use. (1996). Guideline for Good Clinical Practice E6 (R1), 10 June 1996.

Normile. (2007). An Asian tiger’s bold experiment. Science, 316(5821), 38-41. Perry, M. (2008). Channel NewsAsia report, “Sexually active women advised to use the Pill to reduce abortion rate”, posted 21 May 2008 1950 hrs, from http://www.channelnewsasia.com/ stories/singaporelocalnews/view/349189/1/.html, re-accessed 10 January 2010. (1801-1803). Reynolds. (2000). Declaration of Helsinki revisited. Journal of the National Cancer Institute, 92(22). Singapore Department of Statistics. (2009). Yearbook of Statistics Singapore 2009. Retrieved from http://www.singstat.gov.sg/pubn/reference.html. Accessed 10 January 2010. The Agency for Science, Technology and Research, with the Ministry of Health. (2008). Joint Media Release: “13th BMS IAC meeting announces key achievements in Translational & Clinical Research efforts in Singapore,” 17 October 2008. Retrieved from https://www.nmrc.gov.sg/corp/uploadedFiles/NMRC/News,_Views_And_Events/ News/IAC%20Media%20Release.pdf. Accessed 20 November 2009.

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The National Medical Ethics Committee (NMEC). Singapore. (1998). National Medical Ethics Committee: A Review of Activities, 1994-1997. Ministry of Health Singapore, 18 March 1998. Retrieved from http://www.moh.gov.sg/mohcorp/ publicationsreports.aspx?id=2406. The Nuremberg Code: Directives for Human Experimentation. (1949). Trials of War Criminals before the Nuremberg Military Tribunals under Control Council Law No. 10, Vol. 2. Washington, D.C.: U.S. Government Printing Office. Retrieved from http://ohsr.od.nih.gov/guidelines/ nuremberg.html. The World Medical Association. (2008). The Declaration of Helsinki (Ethical Principles for Medical Research Involving Human Subjects). Current edition by the 59th General Assembly of the WMA, Seoul, 22 October 2008. Retrieved from http://www.wma.net. Williams. 2008. The Declaration of Helsinki and public health. Bulletin of the World Health Organization, 86(8). Yeoh, K. C. (2008). Singapore’s biomedical sciences landscape. Journal of Commercial Biotechnology, 14(2), 141–148. doi:10.1057/palgrave. jcb.3050083

The Regulation of Genetic Testing and the Protection of Genetic and Medical Information in Singapore

ENDNOTES 1.

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3.

4.





Terry Sheung-Hung Kaan is Associate Professor at the Faculty of Law, National University of Singapore, where he teaches Tort and Biomedical Law & Ethics. Correspondence address: Faculty of Law, NUS, Eu Tong Sen Building, 469G Bukit Timah Road, Singapore 259776. Email: lawterry@ nus.edu.sg. The views expressed in this paper are the author’s own personal opinions, and do not reflect the views of the NUS or that of any committees or bodies of which he may be or have been a member, or with which he is or may have been affiliated. . The Singapore Bioethics Advisory Committee, Genetic Testing and Genetic Research (November 25, 2005) (BAC, 2005). The full text of the Report together with all its appendices is available from the website of the BAC at http://www.bioethics-singapore. org/. Some parts of this paper, and in particular the summary of the economic background, the history of establishment of the BAC and its relationship with the National Medical Ethics Committee, and the general overview of the regulatory framework for ethical governance are adapted from and first presented in an oral presentation based on an unpublished draft (Kaan, 2009). The author was a member of the BAC from January 2001 until December 2006. During most of this time, he was also the Chair of the BAC’s Human Genetics Sub-Committee (HGS), which spearheaded initial work on 3 of the first 4 Reports eventually issued by the BAC: the BAC reports on Human Tissue Research (November 12, 2002), Research Involving Human Subjects: Guidelines for IRBs (November 23, 2004), the Genetic Testing Report, and Personal Information in Biomedical Research (May 7, 2007). The author wishes to make clear that all the

5.



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8.



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views presented in this paper are entirely the author’s own personal opinion, and do not reflect that of the BAC or any other committee or body with which he is currently or may have a member of or have been affiliated to or associated, including the BAC and the National Medical Ethics Committee (NMEC). Initially, the BAC reported to the Government’s Life Sciences Ministerial Committee. Currently, it reports to the Steering Committee on Life Sciences, led by Dr Tony Tan, a former Deputy Prime Minister, and currently Chairman of the National Research Foundation. See Yeoh, 2008, p. 142. (Mr. Yeoh is the Executive Director of the Economic Development Board’s Biomedical Sciences Cluster). For other perspectives of Singapore’s ambitions in the biomedical industry and research sector, and the BMS Initiative, see also Epps, 2006 and Normile, 2007. As for example, its reports on living wills and on the principles governing human organ transplantation. See NMEC, 1998. Under the Private Hospitals and Medical Clinics Act (Chapter 248). Under the Medical Registration Act (Chapter 174). For example, Phase 1 clinical trials involving experimental cancer drugs which double as a experimental therapy of last resort for cancer patients who have exhausted every other proven therapeutic option, or biomedical research involving the use of wholly and irreversible anonymized and aggregated information. Which may be physical harm in direct effects, or the loss of opportunity for a better therapeutic option; or social or emotional harm in the case of biomedical medical research involving personal information if that information is made public or made available to an unauthorized party.

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12.



13.



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The full text of these guidelines are set out in Annex IV/D (NMEC, 1998). In recent years, further kinds of ethics committees have been added to the mix–hospitals which carry out human organ transplants are now required to have transplant ethics committees under the Human Organ Transplant Act (Chapter 131A). S54/1978, promulgated as subsidiary legislation by the Minister for Health on 27 March 1978 in exercise of his powers under the parent statute, the Medicines Act (Chapter 176). The International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (1996). This document forms the basis of the Singapore Guideline for Good Clinical Practice referred to in the Medicines (Clinical Trials) Regulations. BAC, 2004. Full text and appendices accessible at http://www.bioethics-singapore.org/. As at the end of 2009. NMEC, 1998, paragraph 2.2.1, adopted by the BAC at paragraph 3.21 of the BAC’s Human Research Report (2004). See, for example, Reynolds, 2000; Williams, 2008; Human and Fluss, 2001; and Human and Fluss, 2003. A chapter on genetic testing in the context of biomedical research proper is relegated to a brief 3½ pages (pages 50-53) right at the end of the Report. Obviously, most of the principles articulated for genetic testing and screening in the context of the physician-patient relationship, especially those in relation to consent and privacy, can be applied directly to genetic testing in the research context. Not the same thing, as uninformed lay people may have completely unfounded assumptions about the actual predictive power of genetic tests and their results.

22.



23.



24.



25.



26.



27.

28.



See the written responses received by the BAC from the Life Insurance Association, Aviva Ltd and the Chief Actuary, Great Eastern Life Assurance Co Ltd, Annex F, The Personal Information Report, at F-68, F-157 and F-158, as well as the Position Paper entitled “Genetics and Life Insurance: A Position Paper by the Life Insurance Association, Singapore” at page A-4-1 of the Personal Information Report. Although representations had already been received on this point from the life insurance industry following the call for feedback and representation on the Consultation Paper issued by the BAC on April 5, 2005 which eventually formed the basis of the Genetic Testing Report on November 25, 2005, the BAC deferred their recommendations on this thorny issue to their subsequent Personal Information Report issued on May 7, 2007. Except in the uncommon situation of a fullpenetrance monogenic genetic condition such as Huntington’s Disease, as discussed below. It should be borne in mind that a rejection of an underwriting proposal by any one insurance company has a significant impact on the insurability of a given individual, as insurance companies commonly require potential customers to disclose rejections by other insurance companies. Public Law 110-233, 12 Stat. 881, May 21, 2008. Sec. 101 (b). The BAC made its recommendations in 2007 before the passage of the Genetic Information Non-Discrimination Act in May 2008. The Concordat and Moratorium, Clauses 16 and 20 (HM Government, 2005). The Concordat states that these policy value thresholds would exclude 97% of all policies underwritten in the UK in 2004.

The Regulation of Genetic Testing and the Protection of Genetic and Medical Information in Singapore

29.



30.



31.

32.

33.







Currently, only Huntington’s Disease has been approved (Association of British Insurers, 2005). “The GAIC was set up by the [UK] government in 1999 in response to growing public concern about how the industry wanted to use genetic test results, and the potential for discrimination through genetic screening. …The concern is whether we know enough about the relationship between genes and health to say whether insurers are justified in using the results of such tests. …[and the GAIC] is an independent review body set up to evaluate the use of genetic tests by the insurance industry” (Janson-Smith, 2002). An example might be the case of an individual, without any scientific basis whatsoever, that being of X blood type may genetically doom him to social incompatibility with persons of Y or Z blood type, thus diminishing in his eyes (and others with like beliefs) his chances for matrimony, if information about his blood type were to be leaked to the public. For example, if a person should irrationally insist on “keeping secret” patently and publicly observable characteristics such as the color of his eyes, or his apparent ethnicity. For this part generally, see BAC, 2005, pp. 23-41.

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Section 45 of the UK Human Tissue Act 2004. No proposal for such a provision has to date been put before the Singapore Parliament. For accounts of these programs in Singapore, see the Commissioned Position Papers in Annex C of BAC, 2005. The descriptions of these procedures are adapted from the review of these procedures in BAC, 2005, pp. 30 – 37. Gender imbalance is not an issue in Singapore: as at June 2008, there were approximately 1,839,700 female to 1,803,000 male Singapore Residents (Singapore Department of Statistics, 2009, p. 26). Notably the Human Cloning and Other Prohibited Practices Act (Chapter 131B), which implemented some of the recommendations in the BAC’s Human Cloning and Stem Cell Report; and the National Registry of Diseases Act 2007 (Chapter 201B) in relation to the BAC’s Personal Information Report. Through the Private Hospitals and Medical Clinics Act (Chapter 248). Through the Medical Registration Act (Chapter 174). Ministry of Health Directive 1A/2006. . Parts II and VII, Medical Registration Act. Available from http://app.reach.gov.sg/Data/ adm05/c6/p984/draft_BMR_bill.pdf, last accessed January 10, 2010. As at January 10, 2010.

This work was previously published in Genomics and Bioethics: Interdisciplinary Perspectives, Technologies and Advancements, edited by Soraj Hongladarom, pp. 199-219, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 7.4

A Bio-Psycho-Social Review of Usability Methods and their Applications in Healthcare Morgan Price University of Victoria, Canada & University of British Columbia, Canada

ABSTRACT The purpose of this chapter is to provide the reader with an overview of several models and theories from the general HCI literature, highlighting models at three levels of focus: biomechanical interactions, individual-cognitive interactions, and social interactions. This chapter will also explore how these models were or could be applied to the design and evaluation of clinical information systems, such as electronic medical records and hospital information systems. Finally, it will DOI: 10.4018/978-1-60960-561-2.ch704

conclude with how an understanding at each level compliments the other two in order to create a more complete understanding of the interactions of information systems in healthcare.

INTRODUCTION The field of human computer interaction (HCI) is a fast growing field of research in computer science. It is interested in understanding how we use devices and how the usability of those devices can be improved. HCI sits between several disciplines including computer science, psychology, cognitive

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

A Bio-Psycho-Social Review of Usability Methods and their Applications in Healthcare

science, sociology, and anthropology. Although a young field, it offers a wealth of understanding into the use of systems. It provides a relatively rich collection of quantitative and qualitative models and methods that have been applied to the design and evaluation of information systems (IS) in many domains (Carroll, 2003). Despite the advances in HCI to guide design and evaluate systems, their published impact in health information systems has been limited in scope—systems are still designed, evaluated, and selected without often formally considering issues of usability, cognitive load and fit. There is a need to better understand where models from HCI can assist in the design and adoption of clinical information systems in health care. In medicine, a biomedical, reductionist view has been the prevailing perspective through the 20th century. It has led to a great number of advances in medical science: from Pasteur’s experiments in microbiology that led to the popularization of the germ theory at the end of the 19th century (Ewald, 2004) through to the mapping of the human genome at the beginning of the twenty-first (Venter, Adams, Myers et al., 2001), the science of medicine has seen an explosion in information, in diagnostic options, and in the treatment of diseases. During this time, prominence and understanding of illness, the patient’s experience of the disease, decreased. In 1977, George Engel proposed a new conceptual model for illness: The bio-psycho-social model. His approach expanded the biomedical model to include both the psychological and the social impacts of a disease. It was meant to aid in better understanding and management of a patient rather than simply treating a disease (Engel, 1977). In this model, Engel stresses that the reductionist biomedical model, while it is powerful and has moved our understanding of disease forward, is not sufficient to describe the impact of illness to a patient and their surroundings. Indeed, an illness typically has a biologic component, but it also has a psychological impact on the patient as well as a social impact on those around the patient.

In this chapter, we will review applications of HCI in health information systems development and evaluation and will propose a model that aligns with Engel’s bio-psycho-social model.

Clinical Example Throughout this chapter, the reader will be brought back to aspects of a common clinical example: electronic prescribing (e-prescribing). Recent reports on healthcare in North America describe high error rates (Baker et al., 2004; Kohn, 2000). Recommendations to improve the processes of care delivery are often focused on the increased use of information technology, information systems and, specifically, electronic medical records (EMR) (Romanow, 2002) with the expectation that these systems will improve care and reduce errors (Bates et al., 2001; Wilcox & Whitham, 2003). Despite promise, however, the adoption of clinical information systems has been slow and problematic. There are many reasons for this and strategies to align systems to support the adoption of electronic tools to support delivery of better care (Middleton, Hammond, Brennan, & Cooper, 2005). One reason for failure of adoptions of clinical information systems, and the focus on this chapter, is the usability of systems (Walsh, 2004). Computerized provider order entry, and more specifically e-prescribing is complex and involves interactions between the computer system and the user (Horsky, Kaufman, & Patel, 2003) and between members of the care team, making e-prescribing a good example for this chapter.

Clinical Example: Prescriptions These examples will use the example of writing a prescription for Ramipril. Ramipril is a commonly prescribed, but expensive, blood pressure medication. It will be used to highlight some of the strengths of methods described in this chapter. Different steps in prescribing will be used to highlight aspects of models presented as appropriate.

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BIOMECHANICAL MODELS OF INTERACTION Early work in human computer interaction stemmed from human machine interaction and focused on physical interaction between humans and computers. Nearly all computer interaction is through various forms of physical movement and control of input devices, such as keyboards, mice, and so on, so it makes sense to start here. Biomechanical models are useful as they can help to predict time taken to perform functions in the system. These estimated durations can be based on empirically or theoretically derived values of a user’s ability to perform types of actions, combined with calculated number of keystrokes, the measured size and position of buttons, degrees of freedom for input devices, and delays in computation of a response. Models, such as Fitts’ law, have had a significant impact on the design of systems that we use today. They have been used to predict potential positive and negative costs to changes in design of systems.

Fitts’ Law In 1954, decades before the personal computer and the mouse driven graphical user interface became common, Paul Fitts applied basic information theory concepts to measuring and predicting abilities of the human motor and sensory systems (Fitts, 1954). From some very simple experiments, such as reciprocal tapping (Figure 1), Fitts discovered that the ability to process information was essentially constant and that one could predict performance. Through his experiments he was able to describe that smaller objects that were further away took longer to accurately touch than closer or larger objects. More importantly, he could mathematically predict how size and distance impacted speed and accuracy. From these simple experiments, Fitts and others extended the research. Fitts’ model, which later became know as “Fitts’ law,” has had a signifi-

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cant impact on the development of machines and computer systems and on HCI. It is considered to be one of the most complete and used models that describe movement (Ware, 2003). The reprint of his article in the Journal of Experimental Psychology (Fitts, 1992) has been cited over 1217 times by 2007 and the model has been adapted and extended to suit many applications including the design of graphical user interfaces on computers. Fitts’ Law has had resurgence in recent years under the umbrella of ubiquitous computing as novel interfaces on mobile devices (phones and personal digital assistants) are being designed and developed (MacKenzie, 2002; Silfverberg, MacKenzie, & Korhonen, 2000). As powerful as Fitts Law has been in humanmachine interaction (including HCI) it is limited to the level of movement. It does not take into account other sources of variability of how to perform a specific task, learning, other cognitive processes, or other higher-level confounders. Despite these gaps, Fitts’ Law is an important tool and provides us with some understanding into how to design systems to improve key aspects of the human computer interaction (MacKenzie, 1992).

Clinical Example: Fitts’ Law Writing a prescription is a common task in general practice. Fitts’ law can predict that having a small “Rx” button near the top left corner of the screen on a menu bar may take more time for the user to accurately press the button than a larger button that is available near where the physician is already writing her clinical note.

Keystroke Level Model (GOMS) The keystroke level model (KLM) is a simple model that can predict duration of tasks based on predefined duration of physical activities, such as mouse pointing, clicking, etc. KLM was derived from the GOMS model (GOMS stands

A Bio-Psycho-Social Review of Usability Methods and their Applications in Healthcare

Figure 1. Fitts’ reciprocal tapping apparatus

for the four key elements of the framework: goals, operators, methods, and selection rules), which is one of the first cognitive models in modern HCI work. As KLM is simplified to focus primarily on the biomechanical and temporal aspects of task prediction it is best described here instead of with GOMS below. The KLM was developed as a simple, quantitative, predictive model that could be used to calculate the duration of known tasks. It is a relatively low level model that can be used to compare task durations for different

processes in a system during the design of a system or between systems. The original KLM (Card, Moran, & Newell, 1980) uses a series of six primitive subtasks to estimate total time for activities. The primitives are: K (keystroke/button press), P (point to target), H (homing hands to different device, e.g., mouse to keyboard), D (draw line), M (mentally prepare for action), and R (system response). Table 1 provides experimentally derived time estimates for each primitive. As these are predefined estimates,

Table 1. Keystroke-level model Operator

Description

Time (Range)

K

Keystroke or button press

0.08-1.2s

P

Point with mouse or other device

0.8-1.5s

H

Homing hands (from one device to another)

0.40s

D

Drawing lines (nD = number of straight line segments and lD= total length in cm)

0.9nD +0.16lD

M R(t)

Mental preparation for executing physical action

1.35s

Response of the system (provided by the developers as t in seconds)

t

Note: The six original subtasks for the keystroke-level model (KLM). The times described were based on the review of 1280 HCI scenarios in the original experiments. (Based on Card et al., 1980).

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evaluators and designers can use KLM without using the system. The authors of the KLM acknowledge that the simplification of HCI to only these six primitives and the focus on interaction times does not take into account a variety of aspects, including: errors, learning, functionality, recall (e.g., remembering the name of a medication), concentration, fatigue, and acceptability. Despite these limitations, the KLM is a useful, inexpensive tool to estimate performance times of tasks and has been adapted by others to evaluate a variety of systems. It is particularly well suited when comparing data entry methods for routine actions.

duration. A timesaving of 30 seconds means an additional 5 percent of a typical 10-minute visit is spent interacting with the patient and not the computer, which is quite significant.

Clinical Example: KLM

How Do These Physical Models Inform Health Informatics?

Deciding to treat someone’s high blood pressure with Ramipril is a multi-step process. Broken down in a KLM model, these steps are illustrated in Table 2, along with the predicted duration for each step. This particular example sequence has a total predicted time of 6.7 seconds. The predicting would continue until the act of prescribing (selecting dose, frequency, duration, etc) was complete and a total estimated time could be calculated. Other design options could be reviewed and times compared to see which option has the shortest

Ergonomics Ergonomics, or human factors, considers the physical aspects of components, the type of components that are needed, and how the user’s body is placed to reduce injury and strain. As medicine is increasingly computerized, physical factors are important to consider. However, these models will not be discussed in this chapter in any detail.

Human factors research and the application of Fitts’ law to user interface design have an important role to play in the comparison of user interfaces. Ergonomics research in medicine is important as the application of technology into the clinical domain has a physical presence. The proper design and planning need to occur for the placement and physical design of systems to minimize user injury, but also reduce errors due

Table 2. KLM study: Selecting Ramipril KLM Step

Time (s)

Mentally Decide on need for Medication

1.35

Home to Mouse

1

Point Mouse

1

Press Button

0.2

Decide on Ramipril

1.35

Home to Keyboard

1

Type (“Ram”)

0.6 (3 x0.2)

Press Enter to Select Ramipril

0.2

TOTAL TIME:

6.7 seconds

Note: This table illustrates an example KLM evaluation of the predicted time to decide to treat a patient’s high blood pressure with the medication Ramipril.

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to poor access and other performance problems (Stone & McCloy, 2004). Biomechanical theories, such as Fitts’law, were important in the development of novel computer input devices, such as the mouse. They are still valuable today, particularly in the development in novel tools for procedures in healthcare and in developing smaller form factor tools for pervasive computing. For example, the improvement of endoscopic devices has an impact on “efficiency, safety and comfort” (Berguer, 1998). Development of new telepresence endoscopic surgical tools have benefited from the application of human factors research (Hill, Green, Jensen, Gorfu, & Shah, 1994; Hills & Jensen, 1998). personal digital assistants (PDAs) and other mobile computing devices are making their way into healthcare quite rapidly. With their smaller interfaces and data input methods, the application of Fitts’ law may well be important in improving rapid entry of medical data into these systems, something that is currently quite challenging. The KLM model can also help designers refine small interfaces to reduce data entry time, a problem facing the use of PDAs, particularly in healthcare. A recent study showed that nursing ordering times were reduced (P < 0.0001) through a change in a user interface from text based to a GUI based interface, by changing some of the physical interaction aspects (Staggers & Kobus, 2000). The key limitation of these models is that they miss some key challenges in design that can impact decision-making and the context of care delivery. To address these issues we will first turn to individually focused models of HCI that look at cognitive usability and then to group focused HCI.

INDIVIDUALLY FOCUSED COGNITIVE MODELS The application of the theories of cognitive psychology was seen as a significant step in the development of HCI as both a theoretical and

applied discipline. In the 1970s-1980s the human information processor was the dominant model in cognitive psychology and its application moved the study HCI from the hands to the head of the individual users. This was a significant advance as it gave a framework for discussing, evaluating and predicting user actions based on internal goals and decision-making rules. Several key theories and methods were developed during this evolutionary phase of HCI. Most notably, the GOMS family of models, but also methods of cognitive task analysis, and cognitive walk through. As a compliment to these models, heuristic evaluation will also be discussed.

GOMS Models The basis for the GOMS family of models began with some of the work of Xerox PARC in the 1970s. Key researchers began to explore the application of then modern psychological research to human computer interaction. From that work Card, Moran, and Newell published The Psychology of Human-Computer Interaction (Card, Newell, & Moran, 1983). In their book, the authors describe both the theoretical basis and the application of the human as information processor theory to HCI. Their primary computer application was text editing. They developed an engineering model that considers the user’s goals, the system’s operators, the methods (well defined sub-tasks) that the user is familiar with and how a user selects from the methods (GOMS). GOMS was developed from the pervasive cognitive psychology paradigms of that time, the human as information processor. In this model, humans have three very high level processors: perceptual (which record stimuli through various senses), cognitive (which compare stimuli with internal working memories, long term memories and principles), and motor (which is triggered to respond). The model becomes more detailed and provides an established framework to explain observed phenomenon and to predict behavior.

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From this theoretical background, the authors developed a more streamlined engineering model that was applicable in HCI. There are several reasons why one might chose to apply GOMS tools to evaluate systems (B. John, 1995). It can be used as a predictive tool to estimate: skilled-performance times, learning times, and error rates due to memory overload. From these, estimates can be made for training and the impact of errors can be assessed. Both of which can be factored into overall cost of systems deployment before development has occurred. Findings from a GOMS study can drive design changes as well direct the development of specific teaching materials where redesign is not possible or practical. The biomechanical models discussed previously cannot assess these aspects of a system. Through the 1980s, the original GOMS model has been enhanced and extended. There are several major GOMS adaptations (John & Kieras, 1996) as well as many individualized applications. These different adaptations each address some of the shortcomings of the GOMS model including complexity of applying the method, ability to handle flexible workflows, and predicting tasks that can occur in parallel to other tasks. One of the significant challenges of applying GOMS is the effort and time required to complete an analysis. It is often greater than what is available. This is particularly true for complex systems, such as clinical information systems (hospital systems, electronic records, etc.). Because of the detail level, a GOMS analysis can sometimes miss higher-level usability issues of information systems.

Clinical Example: Defining the “GOMS” for E-Prescribing Ramipril •

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Goals: The goal is to “write Ramipril prescription” with sub-goals that include “select medication” “select dose” “select route,” “select frequency,” “select duration,” and “print prescription.”

•

•

•

Operators: Operators of the system include button presses, keystrokes, and selecting an item from a list. Methods: Methods in this system include “prescribe new medication,” “refill existing medication,” and “prescribing from a favorites list.” Selection rules: A user’s selection rules might be to refill a medication if it exists and there is no change, use a favorite if it exists, prescribe new medication if no other option.

Cognitive Task Analysis Task analysis is focused on understanding user’s work activities (i.e. tasks) in a systematic manner and with sufficient detail to aid the design of functionality of systems. Task analysis can provide structure to the development of predictive requirements for systems. The process assists in the development of an understanding of what a user actually does in a manner that can be critically reviewed (Kieras, 1997). Task analysis can use a variety of methods from questionnaires, to interviews, to think aloud protocols that are video taped during user walkthroughs. Proponents of task analysis stress the need to consider tasks broadly as it is often not the design of the computer user interface that this the challenge, but the understanding of the tasks themselves and which to computerize (Goransson, Lind, Pettersson et al., 1986). Cognitive task analysis (CTA) is a subset from the more general category of task analysis, which aims to yield information about the underlying complexity of the observed activities at the level of mental activity, knowledge, and processes required for a given task (Schraagen, Chipman, & Shalin, 2000). It is focused on internal tasks (e.g., thinking) instead of the external tasks (e.g., pressing buttons) that the lower level biomechanical models are focused on. CTA breaks down high-level tasks into hierarchical sets of detailed sub-tasks such

A Bio-Psycho-Social Review of Usability Methods and their Applications in Healthcare

that complex decision making processes can be mapped out prior to the evaluation (Zachary, Ryder, & Hicinbothom, 1998). Unlike GOMS, where the focus is on the end state goals, task analysis is more focused on the process. This provides some flexibility as some user tasks may not have an explicit goal. It allows analysis to occur about the “journey” along a task. In general, there are three main approaches to CTA (Roth, Patterson, & Mumaw, 2001). First, a domain can be analyzed to reveal tasks that are then examined in detail for cognitive load. This is useful if the goals and processes are well described. Information can be gathered through interviews with subject experts to understand the detailed nature of tasks (Militello, 1998). Second, typical users can be studied in real or simulated work tasks in order to discover the activities they perform and strategies that they employ. This is useful for domains that are less well understood. It is also helpful when working with users with tacit expert knowledge that may not be able to articulate their own processes, such as can be the case with expert clinicians. The final approach is through computer modeling, which requires explicit descriptions of cognitive steps, but allows for simulated changes to be made easily, once the simulation is created.

Clinical Example: Cognitive Task Analysis Based on user interviews and observations, the main steps are: 1. Determine if a patient requires the medication. 2. Decide if an existing medication that can be refilled or if it is a new prescription. 3. If it is a refill: a. Click the refill action b. Confirm the prescription c. Set the duration and repeats for the refill.

4. If it is not a refill: a. Pick the medication from the master formulary b. Decide on the dose, frequency, route, duration and repeats 5. Print and save the prescription. The designer would then perform each step of the tasks that have been defined, this time with the system, assessing potential failure points and identifying correct actions from the user’s perspective.

Inspection Methods Inspection methods are procedures followed by a designer or evaluator to assess function of the user interface (Neilson & Mack, 1994). Walkthroughs and heuristic evaluation are examples of inspection methods. It is important to note that inspection methods can be theory based, as we see in cognitive walkthrough, or informally structured, as in heuristic evaluation. Inspections and walkthroughs of software can be used early on in the design with paper prototypes or later after systems are completed. Experts select defined tasks of the system and the context they will be completed as well as any assumptions about the user. Then the task is stepped through, predicting how users would act.

Cognitive Walkthroughs Cognitive walkthroughs are a structured version of a walkthrough from a cognitive view that designers can use to evaluate the system design (Polson, Lewis, Rieman, & Wharton, 1992). The cognitive walkthrough steps include: determining representative and important tasks to walk through; listing in a checklist the steps required to complete each task; identifying typical users and determining their goals and their expertise; assessing each user’s goals for “correctness” against the application and determining if there

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are potential problems. Potential problems occur if there are difficulties identifying the actions associated with the goal, or if there are other difficulties performing the actions based on the user’s knowledge and ability. Evaluation of each goal continues in an iterative fashion until all identified goals are completed (Blackmon, Polson, Kitajima, & Lewis, 2002). Although cognitive walkthrough was not a direct descendant of GOMS, it shares roots in cognitive science and shares many similar characteristics with the GOMS model. Both are focused on the individual and both are applications of a goal-oriented model. Both GOMS and cognitive walkthroughs occur at a fine-grained level of analysis compared to other models discussed later in the paper. Walkthroughs are, typically, quicker to perform than a GOMS analysis. The walkthrough model does not deal well with the selection of representative tasks, which limits its usefulness with highly complex programs where it would only be possible to walk through a small set of tasks and task variants (Wharton, Bradford, Jeffries, & Franzke, 1992). In the example fragment, checking to see if a patient needs a prescription renewal for Altace is the illustration of only one sub-goal, which is to check if the patient is taking that medication. Two users are selected for the task: a physician who is trained in pharmacology but is not familiar with computers and an MOA (medical office assistant) who is computer literate but has a limited training in names of medications. Assume that the electronic record is open to a patient’s summary screen that includes a window with current medications listed.

Clinical Example: Cognitive Walkthrough SUBGOAL: Review patient’s medication profile (Physician). Action 1: View list of current medications.

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Potential Problem: If window is too small, the details of each prescription might not be completely shown. Action 2: Identify medication on list Potential Problem: User may not realize that medication profile is larger than list on screen and requires scrolling. Resolution: Train user. SUBGOAL: Review patient’s medication profile. (MOA) Action 1: View list of current medications. Potential Problem: None noted, the user would scroll the list if needed. Action 2: Identify medication on list Potential Problem: The MOA may not be aware that Ramipril is equivalent to Altace. Resolution: Display both brand and generic names in summary.

Heuristic Evaluation Heuristic evaluation is another method in use in HCI. Heuristic evaluation, as described by Nielson and others, is a useful collection of “rules of thumb” (see Table 3 for an example). Although not a theoretical construct, designers have adopted Heuristic evaluation to varying degrees, when developing computer systems (Nielson, 1994). It is typically easier to adopt than many of the formal methods described. In heuristic evaluation, the evaluator moves through the software, often through a planned process covering all screens or by tasks, and looks to see how well each screen adheres to the “rules,” such as consistency of button placement, speaking the users language and providing feedback to the user. The evaluator documents and ranks any errors (e.g., cosmetic (1), minor (2), major (3), and catastrophic (4)) observed during their review for each screen and provide recommendations on how to improve the system.

A Bio-Psycho-Social Review of Usability Methods and their Applications in Healthcare

Table 3. Nielson’s usability principles Principle

Description

Simple and natural dialogue

Dialogue should be a terse as possible as redundant information detracts from the relevant information. Natural flow of information should be achieved.

Speak the users’ language

System oriented terms should be avoided; instead terms that users are familiar with should be used.

Minimize the users’ memory load

The system should not require the user to remember information as they move from screen to screen. Instructions should be embedded into the design.

Consistency

Instructions should be consistent. Buttons that perform the same action should not be named or placed differently from screen to screen.

Feedback

The system should provide feedback to the user about activities within a reasonable time.

Clearly marked exits

Users need to have easily marked back up options if they have entered an area by mistake.

Shortcuts

Accelerators should be available for expert users to speed up common tasks.

Good error messages

Error messages should explain the errors in an understandable manner and suggest a solution.

Prevent errors

Good design prevents errors before they can occur.

Help and documentation

Good help is tailored, focused, searchable, and provides concrete instructions for the user’s tasks.

Note: These principles are used as rules of thumb in heuristic evaluation (Based on Nielson, 1994)

Observational Methods To complement inspection methods, usability practitioners may also use observational methods to assess the usability of information systems. Methods, such as the think aloud protocol analysis, allow the investigator to capture an externalization of the users thoughts as they are observed performing actions in the system (Ericsson & Simon, 1993). Think aloud is often used today in usability testing in the laboratory setting. Usability testing refers to the analysis of the process of use of the system by representative subjects performing representative tasks, for example: a physician entering a medication into the system. These interactions are captured on video/ audio and mapped to the recording of the user’s actions, allows the study to capture a more rich collection of data than retrospective interviews or surveys could. New problems are elucidated using this technique fairly quickly. Think aloud protocols are often used with scenarios chosen by the designer to find cognitive friction in the system. Users’ challenges may well be quickly found through thinking aloud that are not easily predicted by models or by designers, nor would

the user be aware of them when responding in post-evaluation interviews and questionnaires.

How do These Models Inform Health Informatics The study of medical cognitive science, outside of HCI and informatics, has a long history. Medical decision-making, learning, and development of expertise, have all been influenced by cognitive science (Patel, Arocha, & Kaufman, 2001). High error rates in medicine are seen, in part, to be a cognitive problem (Graber, Franklin, & Gordon, 2005; Zhang, Patel, & Johnson, 2002). Not surprisingly, there is a call to apply cognitive science to health informatics (Patel & Kaufman, 1998a). The psycho-cognitive models have been applied to the evaluation of clinical information systems more so than the biomechanical or social models. There has been a growing body of work that focuses on the cognitive methodology of usability evaluation in healthcare (Kushniruk & Patel, 2004). This section will review some example applications of the above model in healthcare evaluation. In one case study, QGOMS (a variant of GOMS) was applied to CT (computed tomog-

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raphy) software design (Beard, Smith, & Denelsbeck, 1996). QGOMS effectively compared various potential CT viewing setups, comparing number of monitors and resolution of monitors to traditional setup costs. They were able to, with reasonable accuracy, predict the impact of using different designs to the radiologist’s review of CT films and selected a design that provided both cost and time savings. Video and audio taped user walkthroughs and patient encounters show that the presence of an EMR effects the way physicians collect information and organize that information, that is the EMR affects the cognitive process of medical decision-making (Patel, Kushniruk, Yang, & Yale, 2000). Cognitive task analysis has been used to study decision making in anesthesia, with a focus on high risk, non-routine events (Weinger & Slagle, 2002). With the increasing focus on safety in healthcare, several studies have looked at how EMR systems reduce or promote potential errors in practice. Heuristic evaluation has been used to evaluate patient safety with medical devices (Zhang, Johnson, Patel, Paige, & Kubose, 2003) as well as telemedicine systems. Likely, there are more informal heuristic evaluations in medical software development than are reported in the literature. One group has actively discussed the role of cognitive theories in health informatics. In 1998, the work of Patel and Kaufman (Patel & Kaufman, 1998b) and Patel and Kushniruk (Patel & Kushniruk, 1998) formally set the stage for this discussion. There has been follow up with their primer of cognition in medicine with Arocha (Patel, Arocha, & Kaufman, 2001) and more recent discussions on cognitive methods in evaluation (Kushniruk & Patel, 2004). One of the benefits of using clinical information systems is the sharing of information between providers. To address the usability of systems used by more than one person, health information

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science can turn to models that incorporate social interaction into HCI.

SOCIALLY AWARE HCI MODELS Historically, HCI has focused on the interaction of a single user with the computer system. Today, however, much of the computer work involves interacting with other people through or with the computer. Inserting a computer into a clinical encounter (reviewing charts, entering orders, printing handouts, etc) places it into the providerpatient relationship, therefore, understanding how it can impact the dynamic of that relationship is important. Socially aware HCI models can help us improve the computer’s role in those interactions. Groupware has become increasingly common as computers become more ubiquitous and more connected. Still, the definition of groupware is elusive (Baecker & Baecker, 1992). For the purposes of this chapter, the definition for groupware will be taken (Ellis, Gibbs, & Rein, 1991) as: “computer-based systems that support groups of people engaged in a common task (or goal) and that provide an interface to a shared environment.” This definition is useful for the discussion of clinical information systems. It includes use of the computer system, a focus on common goals (e.g., the delivery of care) and discusses a shared environment (that, for example, contains clinical data). What it excludes is also useful: it does not restrict the group size or how the group may interact, as both of these can vary significantly in the delivery of care. Limiting to small groups or to real time groupware would be unrealistic as care delivery can include larger groups of people that span both distances (e.g., telehealth) and time (e.g., throughout a patient’s life). In this section, two socially aware HCI models will be examined. First, computer supported cooperative work (CSCW) will be reviewed. It is more of an umbrella term covering several models. One of those models, articulation work and common

A Bio-Psycho-Social Review of Usability Methods and their Applications in Healthcare

information spaces, will be explored as an example from the collection of CSCW models as it might have particular application in healthcare. Second, distributed cognition is reviewed a model that takes the cognitive framework discussed in the previous section and moves it into a shared space. These theories have developed from the common need to better understand the use of systems in context, but have developed relatively independently of each other (Kaptelinin et al., 2003).

Computer Supported Cooperative Work (CSCW) Grief and Cashman coined the term computer supported cooperative work (CSCW) in 1984, as part of a workshop on how computers could better support the work of people working together. It was an important step in more formally understanding the requirements for group work. It was started by technologists in order to learn from a wide array of disciplines including social psychology, sociology, anthropology, economics and education (Grudin, 1994). With this diverse background, rich discussions have been had on what “computer supported” and “collaborative work” mean to different researchers. Schmidt and Bannon reviewed CSCW as a field of study and published in the first issues of “CSCW: An international Journal” (Schmidt & Bannon, 1992). This work grew from previous papers by both authors (Bannon & Schmidt, 1991), and focused on the definition of the terms in the acronym to be the fulcrum for defining a model to explain the problem space. The term “computer support” acknowledges the focus of CSCW on the design of computer tools to support work. “cooperative work” is defined in a neutral sense rather than a positive, non-competitive way. Their definition includes “mutual dependency in work” between members in a group and does include both positive and negative aspects of mutual dependency including division of labor and competing individual goals. It explicitly ex-

cludes the interdependence from simply sharing resources (e.g., CPU cycles, storage), and rather focuses on the more complex issues of members of the group relying on the quality and timeliness of each other’s work to achieve a common goal, the work at hand. In general, CSCW has been described as having two interrelated goals: to improve a traditional group interaction and to allow a distributed group to function as well as a traditional group (Kraut, 2003). The study of CSCW is more often focused on the human-human interaction and reflecting on how the computer intermingles with that process. Accordingly, CSCW has drawn more on sociology than psychology as we have seen individually focused models. CSCW applications can be categorized by describing the interactions between users in two axes: time and space. Geographically, users can be co-located or they can be distributed. Similarly, users may interact in a synchronous manner or asynchronously (Rodden, 1991). This is a simple and effective way of conceptually categorizing CSCW applications. The resulting two-by-two table (see Table 4) is populated with our ongoing e-prescribing clinical example. The methods adopted by CSCW researchers are grounded in the context of the groups and organizations being studied. With the CSCW influences of sociology and anthropology, ethnography has a prominent position in gathering information. As a design focused discipline, much attention has been given to understanding requirements for systems from those ethnographic studies. Experimental design can also be used to test CSCW aspects of software. Usability labs have been expanded to include the ability to test groups of users. In the development of computer systems in the past thirty years, it is hard to deny the need to better understand the support that computers can provider to collaborative work. What has been in debate is the usefulness of a specific theoretical model to support CSCW. In this chapter, we will

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Table 4. CSCW application categorization TIME Synchronous SPACE

Asynchronous

Co-located

Reviewing current medication list with a patient in the exam room, showing drug side effects.

Documenting a prescription in the patient record to be referred to by the nurse the next week.

Distributed

Videoconference discussion with a specialist in picking the most effective medication.

Secure transfer of a prescription to a pharmacist, to be processed the next day.

Note: Two-by-two table showing one categorization of CSCW applications through the comparison of users geographical and chronological cooperation. The table has been populated with examples, or representative artifacts.

look at articulation work and common information spaces as one example.

literature to describe the additional work needed to work with others in the shared information space.

Articulation Work and Common Information Spaces

Distributed Cognition

Bannon and Schmidt have looked at the “articulation work” that is required to dynamically manage the mutual dependencies within a group of people working together. In order to support the articulation work, they propose two key areas for understanding during the design approach: (a) the support and management of dynamic and changing workflows for the group and (b) the development and understanding of “common information spaces” where groups are able share what is known. (Bannon & Bødker, 1997) further describe common information spaces as the combination of physical or external elements—the information, events, objects that are mutually accessible—and the work required to interpret these elements by each human actor involved in the overall process. Common information spaces may be physically and temporally created, such as in a patient examination room or an operating room, or they may be more abstract and span both distance and time, such as a longitudinal patient record that is accessed by people in different locations over a person’s life. It is through these information spaces that articulation work occurs. The concept of articulation work was developed from empirical observations and through the review of CSCW

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Distributed cognition offers a different approach to socially aware HCI evaluation through the application of cognitive science models to groups instead of individuals. It takes the same premise of the information processor as used in GOMS and other cognitive models discussed previously; however, it describes a larger functional unit than the individual. In distributed cognition (DCog), the functional unit is the collection of actors and artifacts that is required to complete a specific goal or task. The model describes the flow of information and actions on that data between actors and between artifacts in the system in order to complete the goal. Distributed cognition takes the stance that individual cognition, no matter how detailed, cannot account for the interactions between individuals and the environment. Therefore, DCog adapts the cognitive model to groups (Hutchins, 1995b). This is different from other socially oriented models in HCI, which have tended to leave cognitive science to the domain of individual HCI and instead adapt sociology and anthropology models to CSCW and groupware (Rogers & Ellis, 1994). Unlike GOMS, distributed cognition maintains that the context of cognition is key. There are many social aspects of work environments that contribute to the achievement of goals and these

A Bio-Psycho-Social Review of Usability Methods and their Applications in Healthcare

must be examined in situ. The concepts of inputs, processes, representations and outputs of cognition are still present, but they are assumed by the whole system. This includes artifacts within the system, which can receive input, perform processing, and generate output. In distributed cognition, it is the interactions between elements have a greater impact than the cognition that each piece does independently. The detailed micro-evaluation of

GOMS is overshadowed by the external processes that occur between elements in the system in context (Perry, 2003). Comparison of Figures 2 and 3 highlight the similarities and differences between individual cognition and distributed cognition models. Key to the distributed cognition model is understanding that objects in the system can perform cognitive acts and can affect the subse-

Figure 2. Human information processor model

Figure 3. Distributed cognition model

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quent cognitive activities in the system. For example, a clinical information system may be able to store physical measurements and lab results for a patient. It can then display a composite graph showing average blood sugars over time as they relate to changes in patient weight. This processing changes the representation of the data to something more immediately accessible to the users. It changes the cognitive processing required of the clinician and patient who then use this information in the next step of the episode of diabetic care. The artifacts are, in essence, accomplishing cognitive work, permitting the human actors to leverage that work and focus on other aspects of work. With the focus on the larger system, the methods used to develop an understanding of distributed cognition within a system or domain are also largely ethnographic (Hollan, Hutchins, & Kirsh, 2000). This has been effective in developing detailed descriptions of how a cognitive process works in complex group environments such as ship navigation (Hutchins, 1995a) and in commercial airline cockpits (Hutchins & Klausen, 1996). Through the inclusion of experimental design with ethnography, a more complete model has been developed that iterates through a design cycle: first, an ethnography study discovers cognitive gaps and areas for improvement. Experimental testing of a new design is performed and, if successful, is incorporated into workplace studies that are reevaluated through further ethnographic study. DCog extends cognitive science, with its deep roots in medical education and key place in usability engineering in healthcare, to the integrated dynamic of group work, which is central to the delivery of care.

How Do These Social Models Inform Health Informatics? “The patient-physician relationship is central to the role of the family physician.” (Canada, 2006)

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The group, in healthcare, is traditionally a confidential dyad made up of the patient and their provider. However, the evolving landscape of healthcare is increasingly team focused with multiple providers supporting patient care in multiple locations over time. These teams are dynamic, with roles and responsibilities changing based on uniqueness of individuals and over time (Patel, Cytryn, Shortliffe, & Safran, 2000). Application of new artifacts into these environments can easily disrupt (positively or negatively) the evolved processes and the intricate interactions between providers, patients and the physical environment. It is argued that the fit of systems needs to be considered, observed, and explained in the application of clinical information systems in order to enhance the likelihood of successful adoption (Berg, 1999; Sjöberg & Timpka, 1998). The CSCW literature has tools that can help highlight the needs and challenges that clinical groups face. As the processes of medicine are collaborative and variable, CSCW methods may have a considerable amount to offer to our understanding of the requirements for clinical information systems (Pratt, Reddy, McDonald et al., 2004). Application of CSCW models have aided in the understanding of the complexity of tightly coordinated clinical areas such as medical wards (Bossen, 2002). As care becomes more coordinated and more distributed, particularly in the delivery of outpatient care where face-toface meetings are not always possible between care providers (for example the patient’s GP and the home care nurse), CSCW may well help to shape and improve integrated care delivery in both primary care and community care. There is an increasing awareness that the lens that CSCW provides can help deal with the challenges of design of distributed electronic records. There is a lack of evaluation of the application of groupware tools in healthcare (Househ & Lau, 2005). Application of CSCW has been applied to small group learning in primary care (Timpka & Marmolin, 1995; Timpka et al., 1995) as well as other group

A Bio-Psycho-Social Review of Usability Methods and their Applications in Healthcare

activities, such as telehealth (Ganguly & Ray, 2000; Kaplan & Fitzpatrick, 1997; Weerakkody & Ray). These can have an important part to play in the coordination of care and the learning from CSCW in other domains can aid in the effective application of these tools. Bannon and Schmidt’s model of CSCW with articulation work and common information spaces may have particular interest in healthcare. The approach fits with the processes we see in health care. A strong focus on common information spaces, that is a patient’s longitudinal record, being the primary repository in the coordination of work and sharing of information in healthcare. Also, with articulation work being variable and dynamic fits with what we see in the complex processes in healthcare. Distributed cognition, as a model to apply in health care, has aided in the design of clinical information systems and their deployment into the environments of healthcare (Xiao, 2005; Zhang et al., 2004), context-sensitive clinical coding (Bång, Eriksson, Lindqvist, & Timpka), and it can enhance the understanding of how physical artifacts are used by clinicians to support memory and collaboration as we transition the paper records to electronic systems (Bång & Timpka, 2003). Distributed cognition’s view that external artifacts are actively involved in the cognitive process has an appeal when considering that electronic records have the ability to dramatically change how different users access patient data and medical knowledge to support the delivery of evidence-based care. Understanding that electronic records can be much more active in supporting both provider and patient decision making may help improve quality of decision making and reduce errors.

DISCUSSION: INTEGRATING HCI THEORIES IN HEALTH INFORMATICS Engel’s bio-psycho-social model has found particular resonance in management of complex chronic illness and provision of longitudinal care delivered in primary care and family medicine. It has been incorporated into the working descriptions of both. Considering the impact at each level is important in understanding the impact that an illness can have. In parallel, the study of HCI has included three levels of analysis: the biomechanical, the psychological/cognitive, and the social. Considering the interactions at all three levels is important in understanding potential impacts of the adoption of clinical information systems in the delivery of care and can help design more effective systems. Biomechanical HCI, such as Fitts’ Law and KLM can help us to design more efficient user interfaces, minimizing time for data entry and improving navigation. Human factors and ergonomics can help us understand physical requirements for the use of information systems and tools for procedures. Cognitive models can help us design systems that support a user to find what they are looking for, make the systems easier to learn, and support better decision making. Understanding what users need and do not need to make decisions is important in healthcare where so much of health is decision making—diagnosing, selecting treatments, and so forth. Decision support, in particular, could see benefits from understanding the cognitive needs and limits of users. This may help reduce problems such as “alert fatigue” and help decrease the errors in healthcare. Social models can aid in the understanding of broader interactions of health care teams, focusing more how to support human-human interactions, which is central to the delivery of care. Each level of HCI offers something to the design and evaluation of clinical information systems. However, as with

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Engel’s model, it is the integration at all three levels where significant benefit might be seen.

Application of Complimentary Bio-Psycho-Social HCI Methods Before concluding this chapter, let us revisit the clinical example of e-prescribing and observe how the use of tools in all three levels can help compare two systems. We will be selecting between two hypothetical e-prescribing systems: a best of breed, e-prescribing application that does not interface with your existing clinical information system (Alone-Rx) and one that can be tightly integrated into the existing system (Integrate-Rx). We have the opportunity to observe both in real practices as well as walk through the system with the designers. For our bio-psycho-social evaluation, we do not have a lot of time, so will use two inspection methods and one, short observational study. First, a KLM (keystroke level model) walkthrough of selecting a patient for a prescription; then a cognitive walk through of selecting an appropriate medication; and concluded with a brief observational study of prescription renewals that is reviewed through a distributed cognition lens.

First, selecting a patient. Alone-Rx appears to have a streamlined process for finding a patient— the search box is available on every screen. The predicted time for finding a patient in Alone-Rx is 4.7 seconds, the details are shown in Table 5. In contrast, searching for a patient in Integrate-Rx does not appear to be as streamlined and requires moving to the scheduling screen and then using a menu to get the search for a patient window. This appears to be much more cumbersome and would take a predicted 12.7 seconds just to find the patient (not shown). However, in routine work, IntegrateRx does not require searching for the patient for each prescription written. As it is integrated to the scheduler, the office’s patient scheduler displays the patients that are being seen that day. This has a positive impact on the time it takes for a provider to find a typical patient, reducing the predicted time to 3.7 seconds (Table 6). Here we can see that there is a time improvement for Integrate-Rx. Admittedly a small difference, but measurable. Further KLM studies may show more time savings for one product or the other depending on the design. Next, choosing an appropriate medication. A key part of prescribing is selecting a medication that is safe for the patient. Adverse drug events are

Table 5. KLM study for fictional “Alone-Rx” to select a patient Step

Time (s)

Place hand on mouse

0.4

Point to search box

1.0

Click mouse

0.1

Place hands on keyboard

0.4

Mentally determine spelling of patient’s name

0.5

Type “s m i t h” <enter>

0.6 (0.1x 6 keys)

Home hands on mouse

0.4

Point to correct Smith

1.0

Click mouse to open prescription screen

0.1

TOTAL TIME:

4.5 seconds

Note: Use of the KLM model to predict the time it takes a typical user to reach for a patient using the fictional stand alone e-prescribing tool “Alone-Rx.”

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Table 6. KLM study for fictional “Integrate-Rx” Step

Time (s)

Place hand on mouse

0.4

Point to schedule button

1.0

Click mouse

0.1

Mentally determine which patient

0.5

Point to schedule button

1.0

Click mouse

0.1

TOTAL TIME:

3.1 seconds

Note: Use of the KLM model to predict the time it takes a typical user to reach for a patient using the fictional e-prescribing tool “IntegrateRx,” which is part of a complete clinical system and can leverage the existing schedule list of patients.

common and can be serious. Many are preventable. Performing a cognitive walkthrough we can see how they can help avoid drug interactions. We have already developed the key goals and actions with a physician working group. One of the sub goals is to determine if a medication would be contraindicated for the patient. Let us use that sub goal with its five key actions to illustrate the differences between the two provider order entry

systems, see Table 7. Here we see that IntegrateRx can potentially support the decision making process more effectively as it can draw on more of the patient’s medical history from the central database that a standalone prescription writing tool (Alone-Rx) cannot; however, Integrate-Rx’s implementation of alerts is not as clear as AloneRx, requiring the user to remember interaction severity.

Table 7. Cognitive walkthrough example Sub Goal: Determine if there are any significant drug interactions

Alone-Rx

Integrate-Rx

Action 1: Determine if the patient is allergic to the medication.

Provides alerts when selecting medications that the patient is allergic to. Displays allergies on the patient screen during prescribing.

Provides alerts when selecting medications that the patient is allergic to. Displays allergies on the patient screen during prescribing.

Action 2: Determine if there are any interactions between the selected medication and the medication that the patient is currently taking.

Provides detailed drug: drug interaction alerts and ranks alerts by potential severity, making it easy to see which need to be focused on and avoided and which are less likely to cause serious problems.

Contains drug: drug interaction checking. This is displayed in pair wise listing in alphabetical order, not ranked by severity. Both pairs are displayed (A interacts with B as well as B interacting with A) increasing the burden on the user to review all duplicates.

Action 3: Determine if the patient has a diagnosis that interacts with the medication.

Does not provide drug: disease checking as diagnoses are not available to Alone-Rx. Must be remembered or looked up in the drug monograph.

Provides drug: disease checking integrated with the other interaction checking.

Action 4: Determine if the patient may have any abnormal lab values that may be exacerbated by the use of this medication.

Does not provide drug: lab checking as labs are not available to Alone-Rx.

Does not provide drug: lab checking, although labs are integrated into the system. NOTE: Users might assume that the system checks.

Action 5: Decide if any interactions are significant for this patient

Alone-Rx provides an easy way for the user to see severity—ranking and color coding. Details can be gathered from reading the alerts.

Integrate-Rx does not provide an easy way to support assessing severity of an interaction. The user either needs to remember the serious reactions or review the summaries for each interaction, which requires clicking each element on the list.

Note: A sample cognitive walkthrough comparison of the two fictional e-prescribing applications “Alone-Rx” and “Integrate-Rx.”

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Finally, completing our three-pronged illustrative, fictional evaluation, we look at the social interactions of e-prescribing. For this fragment we look at prescription renewals. Approval of a prescription renewal is a coordinated effort between patient, pharmacy, office staff, and physician. To understand the processes, we can place an observer in the office and watch the interactions. During a typical day, a physician’s office will have many prescription renewal requests come in by phone. A prescription renewal occurs often enough that several can be observed in a mini-ethnographic study lasting only one morning. In an office using Alone-Rx, the observed workflow proceeds along these lines: the medical office assistant (MOA) receives a phone call from the pharmacy requesting a renewal. The MOA confirms the patient is part of the practice and writes a note to the MD with the renewal request information (date, patient name, medication, dose, etc.) in the existing clinical information system. When the MD is free, she looks through her inbox and, seeing the renewal, searches for the patient in Alone-Rx, sees that the prescription is appropriate, and documents the renewal in Alone Rx. Exiting Alone-Rx and re-entering the EMR, she then responds to the note to the MOA, who then phones the pharmacy back with verbal approval. If the renewal request is wrong (not appropriate, not from this office, wrong patient, etc.) the office performs the same steps until the MD reviews the medication list in Alone-Rx. In the office using Integrate-Rx, the overall process is similar; however, we observe some of the steps are streamlined. For example, as the MOA speaks to the pharmacist, she searches for the patient and confirms the medication exists in the system. The MOA is able to handle erroneous requests at the first point of contact, not several hours later. While on the phone, the office assistant creates a message to the physician and links directly to the details of the prescription. When the physician checks the electronic inbox, she sees a message and opens up the renewal

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request, which is already populated, reducing errors, and so forth. As Integrate-Rx is part of the clinical record, the physician notes that the patient is overdue for a blood pressure check and, after approving the renewal, adds a note back to request that the patient come in for a visit. This is all documented in the chart as part of the workflow. We see that the integrated system is able to help perform information processing at several key points where Alone-Rx cannot. It supports decision making of appropriateness of renewal requests at the first point of contact in the office with the MOA, reducing the number of erroneous requests interrupting the doctor (and then having to be re-handled by the MOA). It also reviews the patient’s care record during a renewal request and can determine when the office might need to call in a patient for follow up. Admittedly, this example is brief, is not a detailed methodological description, and has chosen two systems in order to highlight differences for discussion. One can quickly see that using tools at each level provides a richer picture of the applications strengths and weaknesses than any one approach alone.

FUTURE RESEARCH DIRECTIONS Clinical information systems are, in some ways, analogous to complex chronic illnesses. CIS implementations are long-term (i.e., chronic) pursuits that affect the physical mechanics of daily life, change how we think about certain activities, and impact our social interactions. By considering HCI models through the bio-psycho-social model of care, this chapter has explored representative work from the broad domains of published HCI literature and has reflected on how aspects of each level can inform where systems succeed and fail. With recent calls for use of mix-methods and methodological plurism for healthcare evaluation (Kaplan, 2001), it is very appropriate that HCI in health informatics make known what it can pro-

A Bio-Psycho-Social Review of Usability Methods and their Applications in Healthcare

vide for systems design and evaluation, from the biomechanical, the cognitive, and the social levels. Considering the breadth of discussion of theories in the literature of HCI and CSCW, of which we have only scratched the surface of here, there is a relative dearth of discussion of HCI theory in medicine. While case studies include reference to some of these theories, discussions of formal adoption and adaptation of CSCW and HCI methods into the design and evaluation of healthcare systems have been relatively rare in health informatics literature. Some have begun the discussion around application of cognitive usability and others have encouraged the adoption of CSCW perspectives in healthcare. Formal discussions of composite HCI frameworks in health informatics are difficult to find. This discussion is an important as part of the further establishment of the discipline within health informatics. In general, broad frameworks are needed to evaluate the impact of clinical information systems; frameworks that address issues at many levels, including the system, the individual, the group and organizational levels (Delpierre et al., 2004; Kukafka, Johnson, Linfante, & Allegrante, 2003) HCI can offer methods at each of these levels. Which methods are selected from the three levels may vary depending on need, resources, and so forth. Several models have particular application to healthcare, such as cognitive walk through, common information spaces and articulation work, distributed cognition, as examples. Many have overlap with other theories and methods that you have read about in other chapters of this text. Applying models of HCI into health information science introduces our domain to a rich collection of tools that support the design, development, and evaluation of better clinical information systems that fit the context of healthcare.

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John, B. (1995). Why GOMS? Interaction, 2(4), 80–89. doi:10.1145/225362.225374 John, B. E., & Kieras, D. E. (1996). The GOMS family of user interface analysis techniques: Comparison and contrast. ACM Transactions on Computer-Human Interaction, 3(4), 320–351. doi:10.1145/235833.236054 Kaplan, B. (2001). Evaluating informatics applications-some alternative approaches: Theory, social interactionism, and call for methodological pluralism. International Journal of Medical Informatics, 64(1), 39–56. doi:10.1016/S13865056(01)00184-8 Kaplan, S. M., & Fitzpatrick, G. (1997). Designing support for remote intensive-care telehealth using the locales framework. Proceedings of the conference on Designing interactive systems: processes, practices, methods, and techniques, (pp. 173-184). Kaptelinin, V., Nardi, B., Bødker, S., Carroll, J., Hollan, J., Hutchins, E., et al. (2003). Postcognitivist HCI: second-wave theories. Conference on Human Factors in Computing Systems (pp. 692-693). Kieras, D. E. (1997). Task analysis and the design of functionality. The Computer Science and Engineering Handbook. Boca Raton, CRC Inc (pp. 1401-1423). Kohn, L. T. (2000). To err is human. National Acad. Press. Kraut, R. E. (2003). Applying social psychological theory to the problems of group work. In J.M. Carroll (Ed.), HCI models, theories and frameworks: Toward a multidisciplinary science (pp. 325-356).

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Kukafka, R., Johnson, S. B., Linfante, A., & Allegrante, J. P. (2003). Grounding a new information technology implementation framework in behavioral science: A systematic analysis of the literature on IT use. Journal of Biomedical Informatics, 36(3), 218–227. doi:10.1016/j. jbi.2003.09.002 Kushniruk, A. W., & Patel, V. L. (2004). Cognitive and usability engineering methods for the evaluation of clinical information systems. Journal of Biomedical Informatics, 37(1), 56–76. doi:10.1016/j.jbi.2004.01.003 MacKenzie, I. S. (1992). Fitts’ law as a research and design tool in human-computer interaction. Human-Computer Interaction, 7(1), 91–139. doi:10.1207/s15327051hci0701_3 MacKenzie, I. S. (2002). Introduction to this special issue on text entry for mobile computing. Human-Computer Interaction, 17(2), 141–145. doi:10.1207/S15327051HCI172&3_1 Middleton, B., Hammond, W. E., Brennan, P. F., & Cooper, G. F. (2005). Accelerating US EHR adoption: How to get there from here. Recommendations based on the 2004 ACMI Retreat. Am Med Inform Assoc. Militello, L. G. (1998). Applied cognitive task analysis (ACTA): A practitioner’s toolkit for understanding cognitive task demands. Ergonomics, 41(11), 1618–1641. doi:10.1080/001401398186108 Neilson, J., & Mack, R. (1994). Usability inspection methods. NY: John Wiley & Son. Usability Sciences Corporation (1994) Windows, 3. Nielson, J. (1994). Usability engineering. Morgan Kaufmann. Patel, V. L., Arocha, J. F., & Kaufman, D. R. (2001). A primer on aspects of cognition for medical informatics. Am Med Inform Assoc.

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Patel, V. L., Cytryn, K. N., Shortliffe, E. H., & Safran, C. (2000). The collaborative health care team: The role of individual and group expertise. Teaching and Learning in Medicine, 12(3), 117–132. doi:10.1207/S15328015TLM1203_2 Patel, V. L., & Kaufman, D. R. (1998a). Medical informatics and the science of cognition. Journal of the American Medical Informatics Association, 5, 493–502. Patel, V. L., & Kaufman, D. R. (1998b). Science and practice. Journal of the American Medical Informatics Association, 5, 489–492. Patel, V. L., & Kushniruk, A. W. (1998). Interface design for health care environments: the role of cognitive science. Proc AMIA Symp, 2937. Patel, V. L., Kushniruk, A. W., Yang, S., & Yale, J. F. (2000). Impact of a computer-based patient record system on data collection, knowledge organization, and reasoning. Journal of the American Medical Informatics Association, 7, 569–585. Perry, M. (2003). Distributed cognition. HCI models, theories and frameworks: toward a multidisciplinary science (pp. 193-223). San Francisco: Elsevier Science Polson, P. G., Lewis, C., Rieman, J., & Wharton, C. (1992). Cognitive walkthroughs: A method for theory-based evaluation of user interfaces. International Journal of Man-Machine Studies, 36(5), 741–773. doi:10.1016/0020-7373(92)90039-N Pratt, W., Reddy, M. C., McDonald, D. W., TarczyHornoch, P., & Gennari, J. H. (2004). Incorporating ideas from computer-supported cooperative work. Journal of Biomedical Informatics, 37(2), 128–137. doi:10.1016/j.jbi.2004.04.001 Rodden, T. (1991). A survey of CSCW systems. Interacting with Computers, 3(3), 319–353. doi:10.1016/0953-5438(91)90020-3

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Schmidt, K., & Bannon, L. (1992). Taking CSCW seriously. [CSCW]. Computer Supported Cooperative Work, 1(1), 7–40. doi:10.1007/BF00752449

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Silfverberg, M., MacKenzie, I. S., & Korhonen, P. (2000). Predicting text entry speed on mobile phones. Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 9-16). Sjöberg, C., & Timpka, T. (1998). Participatory design of information systems in health care. Journal of the American Medical Informatics Association, 5, 177–183. Staggers, N., & Kobus, D. (2000). Comparing response time, errors, and satisfaction between text-based and graphical user interfaces during nursing order tasks. Journal of the American Medical Informatics Association, 7(2), 164. Stone, R., & McCloy, R. (2004). Ergonomics in medicine and surgery. Br Med Assoc.

Weerakkody, G., & Ray, P. (2003). CSCWbased system development methodology for health-care information systems. Telemedicine Journal and e-Health, 9(3), 273–282. doi:10.1089/153056203322502669 Weinger, M. B., & Slagle, J. (2002). Human factors research in anesthesia patient safety techniques to elucidate factors affecting clinical task performance and decision making. Journal of the American Medical Informatics Association, 9(90061), S58–S63. doi:10.1197/jamia.M1229 Wharton, C., Bradford, J., Jeffries, R., & Franzke, M. (1992). Applying cognitive walkthroughs to more complex user interfaces: experiences, issues, and recommendations. Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 381-388).

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Wilcox, R. A., & Whitham, E. M. (2003). Personal Viewpoint Reduction of medical error at the point-of-care using electronic clinical information delivery. Internal Medicine Journal, 33(11), 537–540. doi:10.1046/j.1445-5994.2003.00465.x Xiao, Y. (2005). Artifacts and collaborative work in healthcare: Methodological, theoretical, and technological implications of the tangible. Journal of Biomedical Informatics, 38(1), 26–33. doi:10.1016/j.jbi.2004.11.004 Zachary, W., Ryder, J. M., & Hicinbothom, J. H. (1998). Cognitive task analysis and modeling of decision making in complex environments. Making decisions under stress (pp. 315-344) Washington, DC: APA. Zhang, J., Johnson, T. R., Patel, V. L., Paige, D. L., & Kubose, T. (2003). Using usability heuristics to evaluate patient safety of medical devices. Journal of Biomedical Informatics, 36(1/2), 23–30. doi:10.1016/S1532-0464(03)00060-1 Zhang, J., Patel, V. L., & Johnson, T. R. (2002). Medical error: Is the solution medical or cognitive? Journal of the American Medical Informatics Association, 9(90061), 75. doi:10.1197/jamia. M1232 Zhang, T., Aranzamendez, G., Rinkus, S., Gong, Y., Rukab, J., & Johnson-Throop, K. A. (2004). An information flow analysis of a distributed information system for space medical support. Medinfo, 2004, 992–998.

ADDITIONAL READINGS For An Excellent General Overview of HCI, See: Carroll, J. (2003). HCI models, theories, and frameworks: Toward a multidisciplinary science: Morgan Kaufmann.

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Biomechanical: Fitts, P. M. (1992). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology. General, 121(3), 262–269. doi:10.1037/00963445.121.3.262 Individual Psycho- Cognitive: Card, S. K., Newell, A., & Moran, T. P. (1983). The psychology of human-computer interaction. Lawrence Erlbaum Associates. Horsky, J., Kaufman, D. R., & Patel, V. L. (2003). The cognitive complexity of a provider order entry interface. AMIA... Annual Symposium Proceedings / AMIA Symposium. AMIA Symposium, 2003, 294–298. Kushniruk, A. W., & Patel, V. L. (2004). Cognitive and usability engineering methods for the evaluation of clinical information systems. Journal of Biomedical Informatics, 37(1), 56–76. doi:10.1016/j.jbi.2004.01.003 Patel, V. L., Arocha, J. F., & Kaufman, D. R. (2001). A primer on aspects of cognition for medical informatics. Am Med Inform Assoc. Zhang, J., Johnson, T. R., Patel, V. L., Paige, D. L., & Kubose, T. (2003). Using usability heuristics to evaluate patient safety of medical devices. Journal of Biomedical Informatics, 36(1/2), 23–30. doi:10.1016/S1532-0464(03)00060-1 Social: Bannon, L. J., & Schmidt, K. (1991). CSCW: Four characters in search of a context. Berg, M. (1999). Patient care information systems and health care work: A sociotechnical approach. International Journal of Medical Informatics, 55(2), 87–101. doi:10.1016/S13865056(99)00011-8

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Grudin, J. (1994). CSCW: History and focus. IEEE Computer, 27(5), 19–26. Hollan, J., Hutchins, E., & Kirsh, D. (2000). Distributed cognition: Toward a new foundation for human-computer interaction research. [TOCHI]. ACM Transactions on Computer-Human Interaction, 7(2), 174–196. doi:10.1145/353485.353487

General: Kaplan, B. (2001). Evaluating informatics applications-some alternative approaches: Theory, social interactionism, and call for methodological pluralism. International Journal of Medical Informatics, 64(1), 39–56. doi:10.1016/S13865056(01)00184-8

Hutchins, E. (1995a). Cognition in the wild. The MIT Press.

This work was previously published in Human, Social, and Organizational Aspects of Health Information Systems, edited by Andre W. Kushniruk and Elizabeth M. Borycki, pp. 23-48, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global)

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Chapter 7.5

The Gap between What is Knowable and What We Do in Clinical Practice Maartje H.J. Swennen University Medical Centre Utrecht, The Netherlands

ABSTRACT Evidence-based Medicine (EBM) is a tool that aims to bring science and medicine together by enabling doctors to integrate the latest best evidence with their clinical expertise and the individual patient’s wishes and needs. However, EBM has both strong supporters and antagonists and is confronted with many barriers that impede uptake of the best evidence by doctors. To date, it remains poorly understood why doctors, do, and do not, incorporate high quality evidence into DOI: 10.4018/978-1-60960-561-2.ch705

their routine practice. I will take you down the road of how I became more and more intrigued by all the challenges that EBM faces. In addition, I will explain to you how I hope to contribute to closing the gap between what is knowable using EBM methods and what we do.

INTRODUCTION This chapter takes you through the rationale behind and my own reflections on why I became involved in research on the implementation of Evidence-based Medicine (EBM). For this, I have

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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partly rewritten a joint paper on EBM into a first person narrative. The original paper is accepted for publication by the Journal of Evaluation in Clinical Practice (Swennen, Van der Heijden, Blijham, & Kalkman, in press). This chapter adds a more comprehensive description of what EBM aims at and why. In addition, it includes my thoughts and feelings at each step of my learning process. I would not have been able to initiate or continue my research without the support of Dr. Geert van der Heijden, Professor Geert Blijham, Professor Yolanda van der Graaf and Professor Cor Kalkman; all having the University Medical Centre Utrecht (The Netherlands) as their (primary) affiliation. This means that in this chapter ‘I’ often will reflect ‘we’. First, I will provide you with some information about my background. I graduated from Medical School at the Utrecht University (The Netherlands) in 2002 without ever having heard of EBM. (Nowadays, the curriculum of my Medical School comprises EBM). After Medical School I decided not to start my residency in Internal Medicine, because I felt I had to protect myself against the emotional burden of me wanting to become an oncologist. For me, being an oncologist would be all about providing the best care to patients, and pay even more attention to the ones that could not be cured. Hence, after much deliberation I chose to work for the Executive Board of the University Medical Centre Utrecht and became a non-practicing doctor. This allowed me to work on the crossroads of management and doctors, and inspired me to try to bring management and doctors together by solving misunderstandings that impeded collaboration. The Executive Board offered the opportunity to do an MSc on Healthcare Management, and so it happened that late 2003 I first heard about EBM during this MSc. I could not believe that until then I had been completely unfamiliar with EBM. During all my medical lectures and internships I never questioned to which extent the knowledge and skills I was taught were (or were not) evidence-based. I fully trusted

the teachers, i.e. experienced doctors, to be right, just like my fellow students did. In other words, I was blind to whether the advice received from the teachers was authoritative (evidence-based) or merely authoritarian (opinion-based). So, for me, this was quite an eye-opener. Hence, I decided to do my Master Thesis on the implementation of EBM. This Master Thesis resulted in the joint publication and gradually evolved in my PhDfellowship and an academic training in Clinical Epidemiology. My research and this chapter are about my aspiration to help doctors getting the evidence into practice. So, I am a supporter of EBM, in that it concerns both Evidence and clinical Expertise to be crucial: The evidence only becomes valuable when clinical expertise guides its proper use in clinical practice. Therefore, I think the name EBM and some reporting (in science and teaching) are rather unfortunate, because of their focus on the evidence part. It is completely understandable that this still causes many medical students and doctors to conclude that EBM is decontextualized from real life clinical practice; or is all about certainty; or that clinical expertise is considered less important. I believe that all doctors want the best for their patients, and therefore deserve further help to become better able to actually do so. In time I hope my PhD fellowship will render helpful solutions that will enable and inspire doctors to further improve patient care.

What Does Evidence-Based Medicine Imply For Medical Decision Making? Doctors are expected to complement their clinical expertise with findings of research and to take into account the needs and wishes of individual patients. EBM aims to integrate the current best research evidence in a conscientious, explicit, and judicious manner with clinicians’ expertise and patients’ unique values and circumstances

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(see also following list) (Guyatt & Rennie, 2002; Straus, Richardson, Glasziou, & Haynes, 2005). Explanation of key elements of the chosen definition for EBM: • •

•

•

Best research evidence is considered to be valid and clinically relevant research. Clinical expertise refers to the ability to use clinical skills and past experience to rapidly identify each patient’s unique health state and diagnosis, their individual risks and benefits of potential interventions, and their personal circumstances and expectations. Patient values comprise the unique preferences, concerns and expectations each patient brings to a clinical encounter and which must be integrated into clinical decisions if they are to serve the patient. Patient circumstances refer to their individual clinical state and the clinical setting.

How to even begin trying to reach such a complex aim? For starters, the full-blown practice of EBM is transformed into a continuous cycle of five steps, which are described in detail by Straus et al (2005). In the following list you will only find a short summary of these five steps. Separating evidence from judgment should be considered core academic skills (G. van der Heijden, December 11, 2009). Description of the five EBM steps: 1. asking answerable clinical questions, 2. finding current best evidence, 3. critical appraisal of the evidence for its validity and clinical relevance, 4. integrating the critical appraisal with doctors’ clinical expertise and patients’ values and circumstances, and 5. evaluating the four steps for their effectiveness and efficiency.

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I agree with Straus et al (2005) that EBM should hold into account the frequency in which conditions are encountered in clinical practice. For conditions doctors encounter every day, doctors need to be completely up-to-date and very sure about what they are doing. Hence, for frequent conditions doctors need to apply at least the first four steps above. For conditions doctors encounter less often, doctors are allowed to conserve their time by seeking out critical appraisals already performed by others (i.e. skipping step 3). For the problems doctors are likely to encounter very infrequently, they mostly ‘blindly’ seek, accept, and replicate the recommendations they receive from authorities in the relevant branch of medicine. In addition, as evidence is continuously generated, Straus et al also rightly state that EBM implies continuous, self-directed lifelong learning. By not regarding knowledge with humility and by denying uncertainty and curiosity, doctors risk becoming dangerously out of date and immune of self-improvement and advances in medicine.

Unwarranted Variation of Clinical Practice I consider EBM to be an important tool for medical decision making, because increased use should result in more appropriate healthcare for both individuals and populations. Despite more than 20 years of worldwide efforts to progress EBM, the gap between available and applied evidence remains substantial (Asch et al, 2006; Fisher, Bynum, & Skinner, 2009; Fisher et al, 2003a, 2003b; Grol, 2001; Mangione-Smith et al, 2007; McGlynn et al, 2003; Schuster, McGlynn, & Brook, 1998; Sirovich, Gallagher, Wennberg, & Fisher, 2008). This gap is visible through unwarranted variation in clinical practice: When similar people, that is, patients with the same medically relevant characteristics, are not treated in a similar manner, then the question is raised if this variation is justified. It was shown that interventions that were

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likely to be beneficial were not being incorporated into clinical practice and also interventions which were of dubious value or even known to be harmful persisted. Evidence of these variations in medical practice suggests the possibility of inappropriate servicing, wasting of resources and even actual harm to patients. An example of underuse of effective care comes from the US Dartmouth Atlas Project: in many hospital referral regions in 2001, less than 50% of Medicare enrollees with diabetes had eye examinations every two years; in the ‘best’ regions about 75% of enrollees had them. In addition, depending on the region, from 10% to 70% of Medicare enrollees with diabetes did receive annual blood screening tests of their blood sugar and lipid levels, both of which are important predictors of serious adverse clinical outcomes (e.g. blindness and limb amputation). So, similar patients (i.e. diabetics) did not receive similar basic care. Of course, there can be good reasons for doctors to purposively decide not to apply the evidence to particular patients (e.g. co-morbidity, side-effects of medication), but this does not fully explain the wide variation observed in clinical practice (Dartmouth Atlas Project, 2007). As a consequence, appropriateness of care is questioned (Brook, 2009; Burge et al, 2006), and the benefits of healthcare are likely to be suboptimal. Whilst EBM is not necessarily about lower costs, individual and societal costs are arguably higher than would otherwise be the case if high quality evidence was routinely incorporated into routine care (Health Economics Research Group RAND Europe, 2008). By now, you might think: How could this be? Isn’t it obvious that doctors should make evidence-based decisions? To the public and the patients, this is a natural expectation. To doctors, it is a very complex aspiration. In fact, the advance of EBM was a response to several factors which shook the confidently held view that modern scientific medicine was rational,

empirical and founded on a solid research base (Muir Gray, 2001). The affected confidence in the quality of care increased demands for openness and objectivity of medical decisions. There was also a growing awareness of the limitations of many of the traditional sources of advice and guidance in which doctors placed their trust (e.g. personal clinical experience, expert opinion, tradition and pathophysiology).

The Gap between What Is Knowable and What We Do I wondered why it is so difficult to implement EBM. Is it because doctors think EBM to be a tool that adds to the complexity of clinical practice, instead of reducing it? And is this because the EBM movement did not fully acknowledge the complexity of routine clinical practice, medical decision making and change? Many barriers to practicing EBM have been described (e.g. Bryar et al, 2003; Cabana et al, 1999; DeLisa, Jain, Kirshblum, & Christodoulou, 1999; Grol, 1997; Grol & Grimshaw, 2003; Haynes, Devereaux, & Guyatt, 2002; McAlister, Graham, Karr, & Laupacis, 1999; McNeill, 2001; Melnyk, 2002; Newman, Papadopoulos, & Sigsworth, 1998; Retsas, 2000; Sackett, Rosenberg, Gray, Haynes, & Richardson, 1996; Straus & McAlister, 2000). Yet, I still did not understand their underlying mechanisms and their interrelations. For example, “lack of time” is the most often reported barrier in the literature. It however may also be a manifestation of other underlying barriers, such as inefficient workflow, lack of necessary facilities, reluctance to change one’s trusted routines, or fear of reprisal from skeptical colleagues. Second, although attempts have been made to classify the barriers, a comprehensive approach to their classification is lacking. A current and often used taxonomy (Bryar et al; Grol & Grimshaw) comprises five classes: (1) the individual professional, (2) the team, (3) the organization or

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environment, (4) the evidence itself, and (5) the patient. A taxonomy needs to be unambiguous and allow for consistent classification, but barriers have been inconsistently classified. For instance, Bryar et al considered “lack of time” to be the most important individual barrier, while Retsas identified “lack of time” as the most important organizational barrier. Third, the current taxonomy of five classes does not reflect a chronological sequence which complicates prioritizing barriers. This impedes the identification of the sequence of conditions needed to improve the practice of EBM. Fourth, the current taxonomy is too limited to classify barriers relating to personal barriers and interpersonal barriers. Personal barriers include attitude and motivation (i.e. willingness), as well as knowledge and skills (i.e. ability). Interpersonal barriers relate to work setting such as culture, collaboration and facilitation (i.e. circumstances). Most of these personal and interpersonal barriers have a time dependent nature and are probably related to career stage.

The Potential Impact of Differences among Doctors Just like each patient is unique, each doctor is unique too. In routine clinical practice doctors categorize patients into groups with similar medical characteristics. This creates order and structure in their medical decision making. I wondered if I could translate this principle to my research: Could I categorize doctors into groups, each with a specific set of personal and interpersonal barriers as to how they view and use EBM? To my knowledge there are no prior studies that have compared different groups of doctors to explore the impact of differences in career stage and work setting on barriers to practicing EBM. In a questionnaire study among Canadian internists, McAlister et al (1999) found that incorporating EBM into practice cannot be predicted by demographic or practice-related factors. A questionnaire study among residents and staff within the Depart-

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ment of Physical Medicine and Rehabilitation in New Jersey by DeLisa et al (1999) revealed that residents reported better information technology skills while staff reported better skills for practicing EBM. At the same time residents showed more interest in training in EBM. In addition, two qualitative studies showed that residents perceived specific barriers for applying EBM, such as lack of mentorship and fear of reprisal from skeptical faculty. The first, by Bhandari et al (2003), used focus groups and individual interviews among Canadian surgical residents, and the second, by Green and Ruff (2005), performed focus groups among Yale residents in a university-based primary care internal medicine program. These studies proffered the hypothesis that differences among doctors in career stage and work setting could influence their barriers perceived. Therefore, I studied whether differences in career stage and work setting have an effect on the ability and willingness to practice EBM and on the barriers as perceived by anesthesiologists to do so. In addition, I explored a comprehensive theoretical framework for the unambiguous and consistent classification of barriers.

METHOD “Not everything that counts, can be counted”.↜(Albert Einstein, 1879-1955)

I wanted to reveal if, how and why doctors of diverse career stages and work settings differed in how they perceived and used EBM in their routine clinical practice. For this, I would also need to gain understanding of the doctors’ views that are situated below the surface of the overt barriers to practicing EBM.

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Table 1. Characteristics of involved hospitals and study participants (n = 12) University hospital (n=7)

General hospital (n=5)

Number of employees Employment arrangement of doctors

10,000 Salaried academics

2,700 Autonomous practice on self-employed basis

Beds Complexity of surgery

1,042 High (tertiary centre)

559 Moderate (secondary centre)

Study Participants (n = 12) Residents (n = 4) Consultants (n = 4) Seniors (n = 4)

2 out of 40; 1 second-year (female) and 1 last-year (female) 2 out of 33 (one female, one male) 3 out of 3 (male)

2 out of 2; 1 first-year (male) and 1 last-year (female) 2 out of 7 (male) 1 out of 1 (male)

Qualitative Study Design I decided to interview doctors of diverse career stages and work settings. In The Netherlands this means that the Medical Research Involving Human Subjects Act (WMO) does not apply, as was confirmed by the Medical Ethics Review Committee (METC) of the university hospital. So, I was allowed to start with the research. I used purposive sampling of anesthesiologists of diverse career stages in two different departments of Anesthesiology in the Netherlands. I collected data in individual semi-structured interviews. The interview transcripts were analyzed using a grounded theory approach, i.e. inductive theory development based on analysis of qualitative data (Creswell, 2003).

Study Setting I recruited participants from two departments of Anesthesiology to which I had easy access. One was in a university hospital and one in a general hospital (Table 1). Prior document study confirmed the expected differences in work setting. The university hospital had a hierarchical structure, where the divisions each comprised several departments. A management team of medical and non-medical professionals was integrally responsible for patient care, education, research and the financial budget of a division. In contrast, the general hospital was

organized in discipline-specific, self-employed groups of medical consultants. The non-medical professional managers had separate facilitative and coordinating tasks. At the time of study, about twenty times as many residents (40 versus 2) and over three times as many anesthesiologists (43 versus 13) were working in the department of Anesthesiology in the university hospital compared with the general hospital. This university department had a longstanding, i.e. 45 years, resident training program, while the anesthesiologists in the general hospital only had begun involved in training of residents a few months prior to the study. In the Netherlands it takes five years specialization training to qualify as anesthesiologist.

Study Participants I defined three career stages: (a) residents, (b) consultant anesthesiologists and (c) senior anesthesiologists. Residents were in their five years of specialization training. I defined consultants as having at least ten years of clinical experience after qualifying as anesthesiologist. Seniors were consultants with significant additional leadership tasks, such as presiding over the division or department, the research program, or the resident training program. Apart from being a study participant the chair of each department was asked to assist in the purposive sampling from their departments (Table 1). I randomly selected study participants from

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the shortlist of residents, consultants and seniors provided by the chair of each department. None of the department members invited declined to participate. Participants did not receive a fee for their study participation.

Interview Procedures I performed all interviews. Being a medically qualified senior policy advisor without clinical responsibilities, I understood the concepts of medicine, practicing EBM and implementation. In addition, I had to acknowledge the fact that although I was in favor of EBM, I was not allowed to let my personal opinions influence the participants in any way. Based on a literature review, I identified barriers to practice EBM prior to the interviews. I searched PubMed up to May 2004 using the following search filter: (“evidencebased medicine” OR “evidence-based practice” OR “clinical practice guidelines”) AND (“barriers” OR “implementation”). I used the resulting overview of the identified barriers to outline the interview schedule. Design of semi-structured interviews with open question approach: • • • •

• •

Introduction to purpose of interview Definition of EBM and evidence (check of frame of reference1-2) Perceived gap between existing and applied evidence Perceived barriers to practice EBM (list of barriers from literature review as aid for interviews17-29) Prioritizing the barriers perceived as most important Possible solutions suggested for their most important barriers

I followed the same procedure for all interviews: They were performed face-to-face in a protected setting. At the start of the interview, I explained the purpose and approach to manage-

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ment and analysis of interview data to the participants and explicitly guaranteed confidentiality of the interviews. I encouraged each participant to speak freely about his or her personal views on EBM and perceived barriers for practicing EBM. With permission of the participants I tape-recorded and verbatim transcribed the interviews, each lasting for at least one hour. The interview schedule comprised open questions (i.e. no response codes) to enable respondents to give their opinion in full. In addition, I used the interview schedule flexibly at appropriate opportunities to allow me to probe for responses, clarify any ambiguities, and to enable respondents to raise other relevant issues not covered by the interview schedule. At the start of each interview, I checked the frame of reference by asking each participant to define EBM and evidence; I matched their definition with mine in case of deviation. Then, I examined the perceived gap between evidence and practice to obtain a first clue on the doctors’ perceived level of urgency for practicing EBM. I used the interview schedule most flexibly during the questioning of the barriers perceived for practicing EBM to reflect the participants’ various perceptions and their underlying mechanisms. Then, I asked participants which barriers they considered most important to encourage the participants’ contemplation on the interview so far and to find clues why some barriers were considered more important than others. Questioning possible solutions for each participant’s most important barriers was used as a positive roundup of the interview. Data saturation was achieved after the completion of twelve interviews, meaning that performing more interviews in the two settings would probably not render new insights. Hence the final sample comprised the twelve participants originally included.

Qualitative Data Analysis The twelve interviews comprised over 800 minutes of recorded time (245 pages of transcript).

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How was I going to analyze this huge amount of qualitative data? At that point I had no prior experience with qualitative data analysis and I had no software available to structure and facilitate the analysis. So, at first I felt quite lost on how to go from here. What proved to be very helpful was that I performed semi-structured interviews. This allowed me to organize the data according to the topic list and the detailed list of barriers. I started by coding the interview quotes. Then, I tabulated and ranked the quotes according to the interview schedule (Textbox 3) and the list of barriers. Additions to this interview schedule were made based on new information derived from the interview data. I identified data patterns within and between each interview transcript using the conventional taxonomy of barriers to practice EBM: (1) the individual professional, (2) the team, (3) the organization or environment, (4) the evidence itself, and (5) the patient (Bryar et al, 2003; Grol & Grimshaw, 2003). The tables were combined by career stages and departments to facilitate the comparison within and between career stages and departments. Through attempts to classify the resulting coded interview data, an alternative taxonomy for the barriers to practice EBM unfolded: a sequential order of the barriers. In addition to the group discussions two papers inspired the process of reclassification of the barriers. First, the Sicily Statement provided guidance based on the formal logic of empirical (bio)medical research (Dawes et al, 2005).33 Second, a paper by Glasziou and Haynes placed the five EBM steps (Textbox 2) into a broader context. They described seven stages that reflect the paths from research to improved health outcomes. In particular, they focused on describing the ‘leaky evidence pipeline’ showing that much knowledge is lost before it ever reaches the patient (Glasziou & Haynes, 2005). My proposed taxonomy describes sequentially the ten steps taken by an individual clinician before he or she is able to utilize evidence in practice. The model considers this process to be

a joint responsibility: The doctor will not be able to successfully meet these ten steps without full support of colleagues and management. I used this alternative ten-conditions-model to report my findings (see Figure 1 in the Results section). I documented the strategies for coding and interpretation of data in Microsoft Word to prevent selective perception and biased interpretation. My notes were subsequently independently verified by two senior researcher supervisors. Difficulties with interpretation and coding and a random selection were reviewed by the supervisors. The final coding was based on full agreement after discussion of discrepancies.

Sherlock Holmes and the Mystery of the Research Data As you just read the Methods section, you could have concluded that the data analysis was rather straightforward. This can only be concluded in hindsight, when all the pieces of the puzzle have fallen into place. In real life, you have to be aware that the data analysis in qualitative research often is an incremental process that brings you back and forth between the data and your interpretation of these data. Coding, tabulating and ranking the interview quotes is just the beginning. The true research concerns continuous reflection and explicit definition of surpassing themes, their interrelations and their implications for EBM. My Master Thesis was completed in 2004, but it was not until 2009 that my interpretation of the data fell into place with accumulated knowledge from diverse sources. It took so long partly because it was not until the end of 2008 that I resumed the research fulltime, and partly also because sometimes it just takes time before true inspiration hits you: it is like being a very persistent Sherlock Holmes.

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Figure 1. Ten-conditions-model for barriers for EBM

RESULTS “There is always a better strategy than the one you have; you just haven’t thought of it yet”.↜(Pitman, 2003)

At some point, I presumed the analysis of data was completed, so I decided to write a research paper to get my results published. However, as it turned out the analysis was not completed: The proposed taxonomy evolved from seven steps in my Master Thesis (finished in 2004) to ten steps (Swennen et al, in press). This implies that every time I adjusted the proposed taxonomy I had to reanalyze my data. Another challenge was if and how to use quotes. I really wanted to facilitate the reader to identify with the interviewees. I also felt I needed quotes to underpin my findings. Using quotes is not an obvious choice, as

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this significantly makes your word count go up which in turn challenges the standards of journals on maximum word counts. I even tried to write two papers to cut down the number of words: One paper on the finding that differences among doctors created different barriers for EBM (using themes), the other on the proposed taxonomy. As it turned out, this was not a good option either, as I was not satisfied with how I had to present the barriers in the first paper. So, only gradually I found a way to combine both findings into one paper. And, of course, each time I tried to report my findings differently, I had to repeat parts of the data analysis. So Sherlock Holmes kept up the good work.

Let the Doctors Speak In the next part of the chapter I would like to provide a platform for the doctors that I interviewed.

The Gap between What is Knowable and What We Do in Clinical Practice

All were very willing to provide their honest opinions. I would like you to be open-minded: try to look through the doctors’ eyes to gain understanding in their way of thinking and working. Participants acknowledged a gap between available and applied evidence, and confirmed most barriers to implementation of EBM. These barriers were classified according to the proposed taxonomy (Figure 1). •

Condition 1: First orientation: Availability of and access to evidence. For EBM to be possible, evidence must exist (or can be generated). In addition, available evidence has to be accessible to doctors to create the opportunity for use in practice.

1A. Limited evidence available. Evidence in Anesthesiology does exist, although good quality evidence based on large randomized-controlled trials is scarce. For most of the recommendations in the guidelines of the Netherlands Society of Anesthesiology the strength of the evidence can be considered low. For example, a senior stated: “I think the amount of solid evidence in anesthesiology is still very limited, compared to other medical disciplines.” 1B. Limited hospital access to evidence. All participants had access to the evidence available, although in both hospitals access to internet facilities was not optimal; few computers were available and there was a significant distance between the operating theatres and an Internetenabled computer. •

Condition 2: Awareness of and positive attitude towards EBM. Doctors’ use of EBM is unlikely if the concept of EBM is unknown, is misunderstood and/or condemned.

2A. EBM: What is it? All participants declared knowing the concept of EBM, but this contrasted with the definitions given: One senior

used Sackett’s definition. Most participants defined EBM one-sided as “practicing medicine based on scientific evidence”, not mentioning its integration with clinical expertise and patients’ values. One resident described EBM as if it meant ‘experience based’: “Doing things purely based on experience.” I explained ‘my’ definition of EBM after participants had given theirs.1 Non-medical management (i.e. managers not being doctors) was considered to have no or little familiarity with EBM, especially in the general hospital: “I think the [non-medical] management does not know much about EBM. The managers in this [general] hospital are not doctors and that implies many shortcomings where it concerns EBM and related subjects” (consultant). 2B. EBM attitude: Influences from stakeholders. Participants had an overall positive association about the concept of EBM itself. However, consultants were suspicious about the potential abuse of EBM by ‘others’ which could reduce professional autonomy. In particular, they expressed a lack of confidence in non-medical management: “As long as the evidence does not cost money, management supports it heartily. But as soon as it turns out to cost money, they start to tone down the solidness of the evidence, if you know what I mean.” They also did not trust lawyers, because they “don’t know medicine; they only know letters on paper, the protocols and guidelines. So, if you decide to deviate from the protocol and something goes wrong, you immediately have a problem.” In contrast, residents and seniors stated that guidelines and protocols leave sufficient space for well-founded deviation and were less worried about litigation. A resident said “I think that you are free to deviate from a protocol, as long as you have good reasons to do so.” A senior pointed out: “From my experience as an expert witness, a lot has changed in the past ten to fifteen years. At that time many testimonies were based on expert opinion, which made it easy for lawyers to pick the expert that was in their corner. EBM has made the testimonies

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more objective and well-founded, leading to more consistent statements. As a result, EBM should reduce fear of lawsuits as it can counteract subjective medical testimonies.” Their opinion about health insurance companies was ambivalent: On the one hand they were considered a means for getting new (expensive) effective care into clinical practice and to rightfully claim good quality for each euro spent: “We don’t all go to work in a Rolls Royce, because that is the best car.” On the other hand, they feared that health insurance companies would interpret guidelines as ‘the way to do things’, forcing doctors to help patients in a way they can’t always support. A consultant philosophized that “perhaps my suspicions have more to do with change than that there really is a problem. Two generations ago there were no guidelines at all… Some doctors miss that sense of freedom of having one’s own practice and being one’s own boss… But times have changed, in ways that are not always pleasant, yet understandable.” •

Condition 3: Positive attitude towards change. A prerequisite for EBM is lifelong learning through continuous critical introspection of weighing one’s own ideas and practice against ever-advancing scientific insights. This means that clinicians must be willing and able to change their ideas and practice during their whole career.

3A. Change is good, no change is better. Participants considered resistance to change to be a significant barrier: It takes guts and energy to unchain practice-proven habits because of intuitive and/or technical areas of tension. Residents mainly associated resistance to change with ‘older age’: “I am very positive about something I learnt here. … But the staff in [x] is, of course, not going to listen to me. … They are very conservative. I think for many older doctors it’s all a matter of habit, so they will not teach new things to residents either.” Consultants attributed resistance to change essentially to ‘personality traits’: “When

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doctors have passed the age of 60 they won’t change anymore, but beneath this age it’s all a matter of character.” One senior stated that both a doctor’s career phase and personality influenced one’s ability to change: “Residents don’t have critical introspection, but you have to see them in the career phase they are in: They are focused on acquiring knowledge and skills and placing these into a broader, practical context. Among staff you can distinguish two groups; people who apply critical introspection, considering it to be part of being a good doctor, and people who just don’t. This has all to do with someone’s personality.” All participants acknowledged the gap between evidence and practice, thereby implicitly acknowledging the need for change. However, residents and consultants showed little sense of urgency to change as they were satisfied with their own present practice. Seniors being involved in change management learned that in the end “change hurts, is always difficult. … People tend to continue trusted routines and use all kinds of excuses for doing so. As I get older, I increasingly realize that there are just a few people who are really willing to change.” 3B. Use of latest technology of (as yet) unproven effectiveness. In contrast, some participants acknowledged a desire to work with latest technology and medicines, even though there might be no evidence for improved patient outcomes. Participants attributed this desire to convenience (“Sometimes they think the new drug is easier to work with” – senior), to the ambition of doctors (“When there are new means, your ambition obliges you to use them” – consultant), and to marketing strategies of the pharmaceutical industry (“Everybody wants the newest stuff. The industry uses very strong marketing, including improper tactics like gifts and nights out. … For example, there was very strong evidence to use [drug x]; it appeared to be effective and relatively cheap, but anesthesiologists lost all interest as soon as a highly promoted new drug was launched. It still intrigues me.” – senior)

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•

Condition 4: The evaluation of evidence. Doctors must be able to answer any clinical question they might have with the best evidence available. This requires competencies in asking answerable questions, searching for evidence, critically appraising the evidence found and translating this evidence to individual patients.

4A. Scientific evaluation of evidence: Knowledge and skills of EBM. All participants first learned about EBM after graduation, even the residents who graduated in the late 1990s or early in the new millennium, and this was true for graduates of different medical schools. Seniors considered themselves capable of practicing EBM without overestimating their qualifications. For example, “I am no novice [being a research professor and having attended EBM-courses]. I would say my critical appraisal skills are above average …, but I do not belong to the top. … I am not as good as professor X [who is a research professor and a clinical epidemiologist].” In contrast, whereas some residents and consultants considered themselves having insufficient knowledge and skills of EBM, others seemed to overestimate their capabilities. For instance, the resident who originally defined EBM as ‘experience-based medicine’stated: “Finding, selecting and critically appraising the literature has become rather easy. … I never got any supervision in learning EBM, but received plenty of feedback after presentations.” A consultant assumed that he was capable to critically appraise evidence after reading a small manual: “Recently our national Journal of Medicine published a manual of how to appraise scientific literature. I tore it out and pinned it up, so now I know how it works.” 4B. Context evaluation of evidence: Trustworthiness of evidence. The participants distinguished validity of evidence from its trustworthiness. The latter was considered more important. For example, their Netherlands Society of Anesthesiology enjoyed great reputation and

support among the participants because of the perceived trustworthiness and democratic structure of their Society: “I think that anesthesiologists and residents find the Netherlands Society of Anesthesiology very useful. … All anesthesiologists have influence on new guidelines. You share your arguments and in the end policies are finalized with a majority of votes.” (– resident) This caused them to trust their Society guidelines, despite the low strength of evidence to support most recommendations. Doubts about trustworthiness which were voiced included suspicion of a potential financial conflict of interest with the pharmaceutical industry that reduced the scientific integrity of guideline authors (“Some guidelines originate from certain groups … that consist of certain people that are influenced by certain industries … in a way that you think, ‘this cannot be good’.” – consultant) or of researchers (“One of the things I worry about is publication bias. That mainly articles with positive results are published and that most drug research is performed by financially strong pharmaceutical companies that are developing new drugs. There are many old, cheap drugs around that merit study, but when I want to perform research with these drugs I am unable to secure funding.” – senior) •

Condition 5: The integration of appraised evidence with clinical expertise. Clinicians should build well-founded opinions about the evaluated evidence and relate the new data to their own clinical expertise.

5A. Is EBM on the priority list?: Doctors with different competing interests and demands. Residents and consultants showed little sense of urgency to integrate evidence with their clinical expertise. They considered themselves and their colleagues primarily ‘doers’, meaning they relied more on experience than on evidence, when engaged in clinical practice: “In anesthesiology everything is urgent. You have to go on and you immediately see the effects of your actions. …

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So if you are not a ‘doer’, you have a bad life.” Some participants contrasted anesthesiology (‘doers’) with internal medicine (‘thinkers’). A resident stated: “I am capable to make fast decisions. Besides, if I am only studying I get bored very easily. … It is terrible to sift through every detail like internists do.” A senior related this ‘doer’ mode to EBM-use of anesthesiologists and surgeons: “EBM still has a long way to go in my profession. … Anesthesiologists have an immense inclination to consider our profession to be very practical, just like surgeons do. Days are filled with doing things.” A senior who co-organized local postgraduate EBM training for residents complained: “The attendance is just too low.” Residents acknowledged this: “Our attendance varies. We are of course located on all departments, also during shifts. … So you can’t always attend. … Our enthusiasm varies. Well, everybody is interested, but not everybody feels like ‘I need to know it now’.” Residents felt overwhelmed by all the new knowledge and skills they had to acquire. A coping strategy for the young anesthesiologist was ‘tell me how today and why tomorrow’: “It is not that I analyze literature to check if what I am doing is actually the right thing to do. It is more that staff teaches me on the job (‘see one, do one’) and then I use it in the way I was taught.” In addition, residents worked with many bosses (i.e. staff), who often had their own preferred ways of doing things. The hierarchical dependence of residents on their bosses put pressure on adapting their work procedures to the boss concerned: “At the start of your residency it is a great challenge to match the right needle with each boss that supervises you at that moment.” In conclusion, for residents the question ‘what boss does what and how?’ was dominant over ‘what boss does best?’ Likewise, staff was taught on the job when they were residents in the same way as they now trained their own residents (‘see one, do one, teach one’). 5B. Impact of additional leadership tasks on ‘doer’-mentality of seniors. Interestingly,

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seniors stated that their additional leadership tasks had transformed them from a ‘doer’ (jumping into action) into a combined ‘doer-thinker’ (reasoned action, sometimes termed meta-cognition). For example; “I always was an absolute doer, but in my role as manager I learnt that immediate actions are not always the most effective way. Although I am still a doer, I also became a thinker.” New responsibilities triggered the need for critical reflection on their own and on their colleagues’ clinical performances: “In the old days I didn’t realize that my EBM knowledge and skills were insufficient. … Although my EBM capabilities improved, I try to gather the EBM experts able and critical enough to provide input for decisions that I have doubts about.” All seniors stated they weighed (new) evidence against their clinical practice, and encouraged residents and consultants to do the same (as was confirmed by them, for example a resident stated: “Senior X really is a leading figure in research and his enthusiasm for research reflects on our ward.”) •

Condition 6: Medical decision to apply or not to apply evidence. The consequent medical decision process to adopt or not adopt evidence in practice involves the clinicians and the multidisciplinary team in which he or she works (effectiveness, patient safety) and patients (patient values) (Institute of Medicine, 2001).

6A. Justification of diversity in clinical practice: Confidence Based Medicine. Residents in the general hospital noticed little similarity and consensus among anesthesiologists in their clinical routines. According to a resident, the consultants in the general hospital were unaware of this variation: “In the general hospital the anesthesiologists all practice differently. If they work in such various ways, you would expect them to have good reasons to do so … but they have not. … They do not even know that their practices are that different. … In the university hospital the practices are more

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similar … and when their practice appears to deviate from what I have seen so far, they almost apologize for it and start explaining why their practice is probably different than that of their colleagues.” Residents expressed ambiguous feelings about ‘similarity versus variety’ of clinical practice. On the one hand they welcomed the chance to encounter a wide range of practices: “I now have the chance to learn different practices in a safe manner and in the end you will derive your own practice. That is why I think it to be of great value that many different bosses each work in varying ways.” On the other hand they felt uncomfortable when having to satisfy the specific preferences of each consultant. Seniors strived for medical consensus, but struggled with “the inclination of in fact all medical disciplines to cover up the lack of consensus.” Consultants admitted to justify continuing one’s own practice by maintaining that ‘many roads lead to Rome’, for example: “Your gut feeling gets stronger as your clinical expertise builds up. Then, if a guideline’s advice contradicts with your conviction, it’s like working against your own nature. … And I have to admit, as I get older, my intuition proves to be right more often.” 6B. Impact of hierarchy among doctors on team-based medical decision making. The absence of a formal hierarchical structure among doctors in the general hospital set hurdles for team-based medical decision-making, according to the local senior; “In a partnership of selfemployed consultants everybody is considered equal. Each partnership has a chairman, but this person is not able to decide and say ‘tomorrow we all are going to work like X.’ I think that a hierarchical structure is an advantage for doctors working in a university hospital.” Indeed in the academic hospital the hierarchical structure seemed to facilitate medical decision making, but the larger number of anesthesiologists working there created greater challenges to get everybody to agree with and adhere to protocols. The head of the academic department put a lot of effort in

involving staff in protocol development to create broad support: “Mandate is given to a few staff members who consider the specific clinical area of interest as their focus. These staff members then submit a concept protocol to be discussed in the staff meeting [where all anesthesiologists have to attend], and most of the times colleagues accept it unquestioningly and will adhere to it. This is going much better now than five years ago. … it is also clear to residents to what protocols they have to adhere.” •

Condition 7: Evaluation of prior managerial conditions for the implementation of evidence. Implementation of evidence involves health economic issues, and also timeliness and accessibility (Institute of Medicine, 2001). Clinicians should analyze these elements in conjunction with management and payers to determine local use of new technologies.

7A. Management as part of a doctors’ team: medical versus non-medical management. All participants considered doctors to be the most important party involved in dealing with issues concerning the implementation of evidence. When participants were asked to mention other involved parties, neither residents nor consultants mentioned ‘management’. This could be because of their interpretations of ‘medical management’ (i.e. seniors) and ‘non-medical management’ (i.e. managers not being doctors). Interviews showed that the seniors were considered as one of the medical ‘tribe’ and their status of ‘primus inter pares’ was acknowledged. For example, the first-year resident already stated that “the medical profession considers the doctors that participate in the management more as ‘theirs’ than as ‘the management’.” In contrast, as pointed out earlier, the opinions of particularly consultants about nonmedical management were unfavorable: “Especially our financial manager has a thankless task, as many doctors consider her as the ‘evil mind’

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who unjustly blocks changes that doctors believe to be important” (– senior). As a consequence, the impact of management on residents and consultants was stronger when managers had a medical background. On the other hand, the absence of a structural collaboration between medical and managerial experts in the general hospital had a negative impact on integrating medical and managerial forces to solve implementation issues. 7B. Managerial evaluation of implementation of evidence: Knowledge and skills. Only seniors seemed able to evaluate the prior managerial conditions that are relevant for implementing evidence (as will be described in section 7C). Residents had neither knowledge nor skills regarding implementation processes and felt ignorant about all its aspects, as reflected by the following examples; “I have the feeling that implementation more befalls me than that I am actually involved or have influence on the process. It all happens at staff level” and “I think money is important, but the big picture is beyond me.” In addition, seniors also doubted the implementation knowledge and skills of the consultants; “Many anesthesiologists do not have a clear view of all the organizational consequences that come with implementing evidence.” When asked which managerial issues they believed to impede the implementation of evidence at this stage, residents mainly mentioned inadequate communication. Other participants mentioned factors like lack of assistance by management, lack of time (pressure to produce), lack of personnel and finances, lack of direct control on actual implementation behavior, lack of hierarchy among doctors (only in the general hospital), inadequate logistics and computer-associated technology, and the lack of incentives. Except for ‘inadequate communication’, all these factors were seen as relying on ‘others’ to do a better or different job and not on the doctors’ own efforts. 7C. Impact of additional leadership tasks on seniors’ability to handle implementation issues. The leadership tasks broadened the seniors’ view

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on clinical practice as being part of a hospital where both medical and managerial issues require attention. For example, they showed understanding for the role of financial management concerning the economic choices made in the implementation of EBM; “Doctors don’t realize that the financial manager has to take into account more factors than just what doctors want.” Still, seniors felt that their managerial tasks were sometimes hard to reconcile with their professional loyalty: Especially in the academic hospital, where seniors had formal obligations to both their management team (including managers who were not doctors) and their colleagues, extra pressure was put on the seniors’ ability to weigh and reconcile conflicting claims: “Sometimes you feel like you are in splits, but you have to make both ends meet in the management team. […] Sometimes this means you have to explain to doctors that, for the time being, we will not be able to implement something they want.” •

Condition 8: Decision to implement evidence or not. The summary of the evidence comprises all quality aspects: effectiveness, patient safety, patient values, efficiency, timeliness and accessibility (Institute of Medicine, 2001). Not only the multidisciplinary team but also other stakeholders, notably the public and patients who are the potential recipients of the health care technology, should be engaged. This enables a comprehensive approach for decision making to determine the ‘go’ or ‘no go’ signal for implementation of the evidence involved.

As pointed out earlier, results suggested that the organizational structure of the academic hospital offered better opportunities for this multidisciplinary approach. My study did not comprise the implementation stage itself (step 9) nor its integration (step 10).

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•

•

Condition 9: Initiation: involved parties give the evidence a trial. A shared ‘go’ for implementation includes a formal agreement to support the process with all efforts necessary and a clear division of responsibilities. At this stage there will be persuasion rather than enforced cooperation of parties involved. Additionally, discussion will have anticipated both the medical and managerial implications of the implementation process. Condition 10: Integration; making the evidence part of routine clinical practice. Through continuous evaluation and implementation of evidence, the final result is durable improvement of quality of care to individual patients.

DISCUSSION In this study the doctors’ career stage had an impact on their perceptions and use of EBM methods (principles and tactics): residents, consultants and seniors met different barriers (Table 2). Seniors provided support for the use of EBM methods in routine clinical practice.

Doing vs. Thinking Exploration of underlying mechanisms for the barriers revealed that the ‘doer’ mode contrasted with the ‘thinker’ mode needed for applying the EBM methods. How should I interpret this finding that emerged from the data? For example, did this finding specifically apply to anesthesiologists (and surgeons), or did it fit into a broader context? I could not find anything in the medical or EBM literature. So, I started exploring literature in

Table 2. Linking career stages to doctors’ barriers for EBM Conditions 1 – 10

Residents

Consultants

Seniors

Condition 1: Availability of and access to evidence

No differences found between career stages

Condition 2: Doctors’ EBM attitude

Positive

Positive; but fear of autonomy loss through abuse of EBM by ‘others’

Positive

Condition 3: Doctors’ attitude to change

Barrier related to age; Little willingness to change oneself

Barrier related to character; Little willingness to change oneself

Barrier related to age and character; High willingness to change oneself

Condition 4: Doctors’ EBM capabilities

Limited

Limited

Capable

Condition 5: Doctors’ weighing of evidence and expertise

Doer Evidence < Expertise How > Why Hierarchical dependence

Doer Evidence < Expertise

Doer – Thinker Evidence = Expertise

Condition 6: Doctors’ decision to (not) apply evidence

See one, do one; Ambivalence towards the need for consensus

Confidence Based Medicine; No need for consensus

Evidence Based Medicine; Strive for consensus

Condition 7: Doctors’ acknowledgment of management; and doctors’ managerial capabilities

High impact medical management; Low impact non-medical management; Incapable

High impact medical management; Low impact non-medical management; Incapable

High impact medical management; High impact non-medical management; Capable

Conditions 8 – 10

Our study did not focus on these steps

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psychology and behavioral economics to find an answer. It was then that scientific work from Simon (1947, 1982) caught my attention. He stated that this ‘doer’ mode is the dominant human approach to decision making in daily life. People can know neither all possible choices nor the consequences of known choices because their rational capabilities are bounded. Applying this to doctors, it is impossible to read and remember every possible fact relevant to the diagnosis and management of patients in their medical specialty, and even if it were possible knowing more does not necessarily improve decision making – because of bounded rationality. In general, people satisfice, i.e. they are often content with the trusted plausible judgment that quickly comes to their mind. In my experience research further improves through discussing your thoughts with other people. Together with Geert van der Heijden, my co-supervisor, I attended an international conference in October 2009: the 6th Evidence-based Health Care International Conference in Sicily. It was here that two people (Neal Maskrey and Michael Power) draw my attention to Kahneman’s work (2002). Maskrey, Hutchinson, & Underhill (2009) translated Kahneman’s work to the world of medicine. At the conference Maskrey chaired a theme group on ‘How to make decisions better’ that I attended. This inspired me as we immediately understood what the other was talking about and we shared the opinion that Simon’s and Kahneman’s work offered important clues for improving medical decision making. So, back home, I studied Kahneman’s and related work (Gigerenzer, 2008; Maskrey et al) that partly elaborated on Simon’s work (1947, 1982): In behavioral economics the ‘doer’ mode is called “system 1 processing”, where decisions and judgments are based on perception and intuitive thinking. System 1 enables humans to make fast and automatic decisions based on pattern recognition. Some of the most highly skilled cognitive activities are intuitive, for example playing chess at the master level. Translating this to clinical practice, we all can relate to the

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experienced doctors who are respected for their clinical expertise. But system 1 also has its problems. Using short cuts, heuristics and mindlines is comfortable and helpful in clinical decision making, but cognitive biases are lurking, in particular in the absence of reflective thinking. What if the heuristic or mindline a clinician has constructed is faulty or has become obsolete? This is where the ‘thinker’ mode or “system 2 processing” comes in, which is based on reasoning. EBM relates to this system 2 approach – it implies detailed analysis of all the evidence and the available options. In contrast to the system 1 approach that is fast and relatively effortless, system 2 requires effort and is purposeful. “He who learns from others, but does not think for himself, is lost!↜He who thinks for himself, but does not learn from others, is in great danger!”↜(Confucius, 551-479 BC) What does this system 1 and 2 learning imply for routine clinical practice? In my opinion, it is okay that most doctors rely on their clinical expertise (system 1), conditioned they regularly check for cognitive biases (system 2): Are my clinical routines (still) correct? I believe the challenge in medical decision making is to reflect on heuristics and mindlines when appropriate, and to be willing and able to change one’s decision making accordingly. This offers protection for clinicians and patients against cognitively biased decisions. In other words we need to find out when and how doctors could best calibrate their system 1 processing with system 2 processing. This is actually what the EBM community means by continuous, self-directed lifelong learning to reduce the doctors’ risk of becoming dangerously out of date and immune of self-improvement and advances in medicine (Straus et al, 2005). For this doctors would need to learn to practice reflective thinking during their decisions on both diagnosis and patient management.

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People make mistakes, with and without EBM. If EBM includes reflective thinking and lifelong learning, it also is about learning from mistakes. I believe in the importance of combining the practical learning (clinical expertise) with the theoretical learning (EBM). For instance, I know that the anesthesiology department in the university hospital has regular ‘complication meetings’ in which all anesthesiologists and residents have to participate. In these meetings doctors reflect on their clinical practice by discussing complications or mistakes and the lessons learned. In doing so, they underpin their reflections with (lack of) evidence and discuss how to deal with the remaining uncertainties. I realize that EBM often still is a standalone activity, but these complication meetings show the integration of expertise and evidence is possible. Still, at times, in a medical emergency or clinical setting such as an operating theatre where many decisions may need to be made sequentially in a short time frame, rapid system 1 decisions may be superior to slower system 2 decisions even though they have less risk of cognitive bias. Residents, that eventually showed to have very little prior knowledge on EBM, were trained on the job in the ‘doer’ mode by consultants by passing on their ‘confidence based’ clinical practice routines. In my opinion, this teaching on the job is entirely valid educationally providing there is in-built, objective and recorded assessment of competence, but there ought to be an EBM approach to, say, the selection of medicines used by anesthesiologists. This could also be objectively assessed as part of the specialization training program. Active involvement of seniors in applying EBM methods could be crucial in the guidance of both residents and consultants. Through additional leadership tasks seniors seemed to have gone beyond the ‘doer’ mode. By addressing their ‘thinker’ mode these tasks taught seniors to consider both medical and managerial issues of a hospital and thereby put clinical practice into a broader policy perspective. Therefore seniors

could be strong role models in applying EBM methods because of their impact on colleagues through their hierarchical and/or ‘primus inter pares’ position.

The Impact of Differences in Setting Application of EBM methods is expected to have an impact on decision making in patient management by teams. Differences in setting and the organizational context of the general hospital complicated the use of EBM methods. The absence of a formal hierarchy among doctors introduced hurdles for medical decision making by teams. In addition, limited collaboration between doctors and non-medical management posed medical and managerial hurdles for the use of EBM methods; it reduced the opportunities to implement EBM methods.

Doing the Right Things for the Right People: The Impact of Differences among Doctors Differences in doctors’ characteristics have an impact on decisions in patient care. For instance, the Dartmouth Atlas Project found that the large differences in U.S. regional spending are almost entirely explained by unwarranted variation in doctors’ behavior leading to differences in volume of health care services provided to similar patients (Dartmouth Atlas Project, 2007; Fisher et al, 2003a, 2003b, 2009; Sirovich et al, 2008). The above and my findings would also imply that the US and Dutch licensing requirements for continuing medical education do not sufficiently make doctors change their own trusted routines into new evidence- based routines. The results of this study indicate we need a better understanding of how differences among doctors in attitude and behavior result in differential use of EBM and hence different clinical decisions. The data show it may be possible to categorize doctors according to a specific set of barriers for

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EBM, which eventually have an impact on decisions taken. This subgrouping seems to apply to the relationship between career stage, work setting and medical discipline.

Doing Things Right: Prioritizing Barriers An important finding was the ten-conditionsmodel, which evolved from sequencing the reported barriers. This approach helps to determine in which order the barriers have to be dealt with to reach the goal of implementation of high quality evidence into clinical practice, continuous improvement of clinical performance and hence of the quality of individual patient care. The ten-conditions-model could help in classifying doctors among disciplines to their career stage and work setting. This may support structuring the EBM implementation process by identifying priorities to overcome their specific barriers for implementation of EBM methods.

REFLECTIONS ON THE STUDY DESIGN In my in-depth qualitative study using a small purposive sample, my aim was to lower the threshold to speak freely and not influence the responses by using private settings and guaranteeing confidentiality. The interviews allowed me to use the literature review in the exploration of opinions and experiences of participants with using EBM methods. During the semi-structured interviews, I avoided a pre-set order of topics. Instead, I used a topic check list derived from the literature for checking the coverage of all topics in interview. Group interviews, as an alternative approach, would have enabled me to include more participants, to arrange more than one meeting per group and to study interactions within and between doctors. At this stage, however, I preferred to secure ‘free speech’ of individual participants to gather

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rich data about the barriers perceived and the underlying mechanisms involved. The small sample size and qualitative design does neither allow me to conclude that these are the only relationships seen in subgroups of doctors, nor to generalize my findings to anesthesiology in general, to other medical disciplines or to other settings. But my findings justify further exploration of the extent and impact of differences among doctors, career stage and work settings in anesthesiology and other medical specialties on the use of EBM methods. My findings offer a new dimension to the conventional approach of promoting the use of EBM in clinical practice: Triage of doctors to their specific barriers could facilitate doing the right things with the right people. Prioritizing the barriers could help doing things right more often. “To the individual who devotes his/her life to science, nothing can give more happiness than when the results immediately find practical application. There are not two sciences. There are science and the application of science, and these two are linked as the fruit is to the tree.”↜Louis Pasteur (1871) I feel that I am now working on the crossroads of science and medicine, and I hope my research will contribute to a closer collaboration between these two worlds. Both science and medicine are crucial for good quality medical decision making. Medical research is only valuable when used by doctors, and doctors will not have to re-invent the wheel if they have access to scientific knowledge. The next phase of the research program is to substantiate my findings and translate these into a generalisable framework. I would like to invite you to elaborate on or to argue against my findings and arguments for triage of doctors and prioritizing the barriers.

The Gap between What is Knowable and What We Do in Clinical Practice

ACKNOWLEDGMENT First, I would like to thank the following colleagues: Dr. Geert van der Heijden, being my co-supervisor, for his continuing support throughout my research; Professor Geert Blijham, former President of the University Medical Centre Utrecht, for offering me the opportunity of a PhD-fellowship and for allowing me much freedom concerning the what and how of my PhD-work; Professor Yolanda van der Graaf and Professor Cor Kalkman for their support, being my supervisors. I am also much obliged to Neal Maskrey, Director of Evidence-based Therapeutics, National Prescribing Centre, Liverpool, UK for his inspiring and valuable feedback on the research and on the original paper. Finally, I would like to thank Professor Rakesh Biswas for the opportunity to publish this adapted paper as a chapter in his book.

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Bryar, R. M., Closs, S. J., Baum, G., Cooke, J., Griffiths, J., & Hostick, T. (2003). Yorkshire BARRIERS project. International Journal of Nursing Studies, 40(1), 73–84. doi:10.1016/ S0020-7489(02)00039-1 Burge, P., Devlin, N., Appleby, J., Gallo, F., Nason, E., & Ling, T. (2006). Understanding Patients’ Choices at the Point of Referral. Santa Monica: RAND Corporation. Cabana, M. D., Rand, C. S., Powe, N. R., Wu, A. W., Wilson, M. H., Abboud, P. A. C., & Rubin, H. R. (1999). Why don’t physicians follow clinical practice guidelines? A framework for improvement. Journal of the American Medical Association, 282(15), 1458–1465. doi:10.1001/ jama.282.15.1458 Creswell, J. W. (2003). Research design. Qualitative, quantitative and mixed methods approaches. Thousand Oaks, CA: Sage publications. Dartmouth Atlas Project. (2007). Effective care. A Dartmouth Atlas Project Topic Brief. Retrieved from http://www.dartmouthatlas.org/topics/effective_care.pdf Dawes, M., Summerskill, W., Glasziou, P., Cartabellotta, A., Martin, J., & Hopayian, K. (2005). Sicily statement on evidence-based practice. BMC Medical Education, 5, 1. doi:10.1186/14726920-5-1 DeLisa, J. A., Jain, S. S., Kirshblum, S., & Christodoulou, C. (1999). Evidence-based medicine in physiatry: the experience of one department’s faculty and trainees. American Journal of Physical Medicine & Rehabilitation, 78(3), 228–232. doi:10.1097/00002060-199905000-00008 Fisher, E. S., Bynum, J. P., & Skinner, J. S. (2009). Slowing the growth of health care costs – Lessons from regional variation. The New England Journal of Medicine, 360(9), 849–852. doi:10.1056/ NEJMp0809794

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Fisher, E. S., Wennberg, D. E., Stukel, T. A., Gottlieb, D. J., Lucas, F. L., & Pinder, E. L. (2003a). The implications of regional variations in Medicare spending. Part 1: the content, quality, and accessibility of care. Annals of Internal Medicine, 138(4), 273–287. Fisher, E. S., Wennberg, D. E., Stukel, T. A., Gottlieb, D. J., Lucas, F. L., & Pinder, E. L. (2003b). The implications of regional variations in Medicare spending. Part 2: health outcomes and satisfaction with care. Annals of Internal Medicine, 138(4), 288–298. Gigerenzer, G. (2008). Gut feelings: short cuts to better decision making. London: Penguin. Glasziou, P., & Haynes, B. (2005). The paths from research to improved health outcomes. ACP Journal Club, 142, A8–A10. Green, M. L., & Ruff, T. R. (2005). Why do residents fail to answer their clinical questions? A qualitative study of barriers to practicing evidencebased medicine. Academic Medicine, 80(2), 176– 182. doi:10.1097/00001888-200502000-00016 Grol, R. (1997). Personal paper. Beliefs and evidence in changing clinical practice. British Medical Journal, 315(7150), 418–421. Grol, R. (2001). Successes and failures in the implementation of evidence based guidelines for clinical practice. Medical Care, 39(8Suppl 2), 1146–1154. doi:10.1097/00005650-20010800200003 Grol, R., & Grimshaw, J. (2003). From best evidence to best practice: effective implementation of change in patients’ care. Lancet, 362(9391), 1225–1230. doi:10.1016/S0140-6736(03)145461 Guyatt, G., & Rennie, D. (Eds.). (2002). Users’ guides to the medical literature. A manual for evidence-based clinical practice. Chicago, IL: AMA Press.

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Haynes, R. B., Devereaux, P. J., & Guyatt, G. H. (2002). Physicians’ and patients’ choices in evidence based practice. Evidence does not make decisions, people do. British Medical Journal, 321(7350), 1350. doi:10.1136/bmj.324.7350.1350 Health Economics Research Group, Office of Health Economics, RAND Europe. (2008). Medical Research: What’s it worth? Estimating the economic benefits from medical research in the UK. London: UK Evaluation Forum. Institute of Medicine. (2001). Crossing the Quality Chasm: A New Health System for the Twenty-first Century. Washington: National Academy Press. Kahneman, D. (2002). Maps of Bounded Rationality: a perspective on intuitive judgment and choice. Stockholm: Noble Prize Lecture. Mangione-Smith, R., DeCristofaro, A. H., Setodji, C. M., Keesey, J., Klein, D. J., Adams, M. A., & McGlynn, E. A. (2007). The quality of ambulatory care delivered to children in the United States. The New England Journal of Medicine, 357(15), 1515–1523. doi:10.1056/NEJMsa064637 Maskrey, N., Hutchinson, A., & Underhill, J. (2009). Getting a better grip on research: the comfort of opinion. InnovAiT, 2(11), 679–686. doi:10.1093/innovait/inp085 McAlister, F. A., Graham, I., Karr, G. W., & Laupacis, A. (1999). Evidence-based medicine and the practicing clinician. Journal of General Internal Medicine, 14(4), 236–242. doi:10.1046/j.15251497.1999.00323.x McGlynn, E. A., Asch, S. M., Adams, J., Keesey, J., Hicks, J., DeCristofaro, A., & Kerr, E. A. (2003). The quality of care delivered to adults in the United States. The New England Journal of Medicine, 348(26), 2635–2645. doi:10.1056/ NEJMsa022615

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McNeill, B. J. (2001). Shattuck Lecture -- Hidden barriers to improvement in the quality of care. The New England Journal of Medicine, 345(22), 1612–1620. doi:10.1056/NEJMsa011810

Schuster, M. A., McGlynn, E. A., & Brook, R. H. (1998). How good is the quality of health care in the United States? The Milbank Quarterly, 76(4), 517–563. doi:10.1111/1468-0009.00105

Melnyk, B. M. (2002). Strategies for overcoming barriers in implementing evidence based practice. Paediatric Nursing, 28(2), 159–161.

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Muir Gray, J. (2001). Evidence-based medicine for professionals. In Edwards, A., & Elwyn, G. (Eds.), Evidence-based patient choice; inevitable or impossible?Oxford: Oxford University Press.

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Newman, M., Papadopoulos, I., & Sigsworth, J. (1998). Barriers to evidence-based practice. Intensive & Critical Care Nursing, 14(5), 231–238. doi:10.1016/S0964-3397(98)80634-4 Pasteur, L. (1871). Revue Scientifique. Pitman, B. (2003). Leading for Value. Harvard Business Review, (Apr): 41–46. Retsas, A. S. (2000). Barriers to using research evidence in nursing practice. Journal of Advanced Nursing, 31(3), 599–606. doi:10.1046/j.13652648.2000.01315.x Sackett, D. L., Rosenberg, W. M., Gray, J. A., Haynes, R. B., & Richardson, W. S. (1996). Evidence based medicine: what it is and what it isn’t. British Medical Journal, 312(7023), 71–72.

Sirovich, B., Gallagher, P. M., Wennberg, D. E., & Fisher, E. S. (2008). Discretionary decision making by primary care physicians and the cost of U.S. health care. Health Affairs, 27(3), 813–823. .doi:10.1377/hlthaff.27.3.813 Straus, S. E., & McAlister, F. A. (2000). Evidencebased medicine: a commentary on common criticisms. Canadian Medical Association Journal, 163(7), 837–841. Straus, S. E., Richardson, W. S., Glasziou, P., & Haynes, R. B. (2005). Evidence-based medicine: how to practice and teach EBM. Edinburgh: Churchill Livingstone. Swennen, M. H. J., van der Heijden, G. J. M. G., Blijham, G. H., & Kalkman, C. J. (in press). Career stage and work setting create different barriers for Evidence-based Medicine. Journal of Evaluation in Clinical Practice.

This work was previously published in User-Driven Healthcare and Narrative Medicine: Utilizing Collaborative Social Networks and Technologies, edited by Rakesh Biswas and Carmel Mary Martin, pp. 335-356, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 7.6

Trust and Clinical Information Systems Rania Shibl University of the Sunshine Coast, Australia Kay Fielden UNITEC New Zealand, New Zealand Andy Bissett Sheffield Hallam University, UK Den Pain Massey University, New Zealand

ABSTRACT Our study of the use of clinical decision support systems by general practitioners in New Zealand reveals the pervasive nature of the issue of trust. “Trust” was a term that spontaneously arose in interviews with end users, technical support personnel, and system suppliers. Technical definitions of reliability are discussed in our chapter, but the very human dimension of trust seems at least as significant, and we examine what is bound up in this concept. The various parties adopted different means of handling the trust question, and we explain these. Some paradoxical aspects emerge in the context of modern information systems, both with the question of trust and with the provision of DOI: 10.4018/978-1-60960-561-2.ch706

technical or organisational solutions in response to the existence of trust. We conclude by considering what lessons may be drawn, both in terms of the nature of trust and what this might mean in the context of information systems.

INTRODUCTION The use of information technology (IT) in healthcare highlights some issues in the social domain surrounding such technology that might otherwise go unremarked. This chapter discusses the nature of trust and the meanings that this concept might have in the context of computer systems. “Trust” in relation to IT is usually taken to convey an aspect of dependability or security (McDermid, 1991), but it might be that “trust” is both a richer and a

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more useful concept in relation to some computer systems than in the sense of the narrower, more technical definitions such as “dependability” or “reliability” that software and hardware engineering tend to employ. Trust as a human phenomenon in relation to IT is starting to be discussed, and the concept’s use (Raab, 1998) and misuse (de Laat, 2004) have been noted. For the field of computer ethics the term has the advantage that an implicit ethical dimension is captured. We found, when investigating clinical decision support systems (CDSS) in New Zealand, that the term was used unprompted by several different stakeholders when discussing their relationships with other stakeholders and with the technology itself. It seems that trust is an ever-present factor in joint human activity, however technically based that enterprise may initially appear to be. After a brief discussion of the nature of trust we describe the health sector environment in New Zealand, define clinical decision support systems, introduce the case study, go on to a discussion of our findings, and finally present some conclusions about the meaning of trust in the context of IT.

THE NATURE OF TRUST If, as Bottery (2000) says, “Trust is the cement of human relationships” (p. 71), then we may expect it to feature in the worlds of business and technology as much as in individual relationships. Fukuyama (1996) advances an extensive argument relating the flourishing of business and macroeconomic success to the societal prevalence of stable, predictable, trustworthy dealings possible between individual and organisational actors. Echoing this, Bottery remarks how the absence of trust tends to result in, amongst other things: “detailed accountability, exhaustive legal agreements, and extensive litigation … it can mean vastly increased transaction costs, which can have important implications for the efficient use of time and money” (p. 72). O’Neill (2002) takes

up these themes of accountability and litigation to develop a broadly Kantian perspective on trust. She argues that, whilst suspicion and mistrust appear to be increasing in the developed world, the world of the “audit society” (Power, 1997), nonetheless “We constantly place trust in others, in members of professions and in institutions” (O’Neill, 2002, p. 11). Often we have no ultimate guarantee, and at some point, we have to trust in a chain of information and the judgement that it informs: “Guarantees are useless unless they lead to a trusted source, and a regress of guarantees is no better for being longer unless it ends in a trusted source” (p. 6). O’Neill goes on to note the paradoxical circumstance that the proliferation of sources in the “information society” not only does not make trust redundant; it makes it if anything more problematical. Who should we trust in this world of instantaneous and burgeoning communication? How can we make informed judgements in a world of “information overload” and, sometimes, deliberate misinformation? O’Neill (2002) offers that trust is needed “because we have to be able to rely on others acting as they say that they will, and because we need others to accept that we will act as we say we will” (p. 4). Borrowing the concept from monetary instruments such as banknotes or coins, some writers have called this ability to accept something (or someone) at face value as “fiduciary,” and this fiduciary aspect is no less relevant to the duties of the IT professional, as Gotterbarn (1996) has tellingly argued. Noting that as professions gain in maturity so the nature of their ethical codes change, he writes of maturer professions: “The function of the code is not to protect the profession, but to establish the standards of trust required in a fiduciary relationship ... The fiduciary model is an adequate model of professionalism for computer practitioners” (p. 11). Nearly all definitions of trust share the condition that one party (the truster) must willingly place himself or herself in a position of vulnerability to or risk from another party (the trustee) (Gallivan, 2001; Gotterbarn, 1996).

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Karahannas and Jones (1999) note that trust is “closely related to risk, since without vulnerability… there is no need for trust” (p. 347). This brings us to our case study, wherein many risks and vulnerabilities are possible.

driving the funding of research in the health sector in New Zealand.

THE NEW ZEALAND HEALTH SECTOR

Within the medical field, there are large amounts of data and information, and the quantities are continuously growing. A general practitioner needs to be kept up to date with this ever-increasing knowledge. Wyatt (1991) estimated that the doubling time of medical knowledge is about 19 years. Using this calculation, during a doctor’s professional lifetime medical knowledge will have increased four-fold. Computer technology can aid with this abundance of knowledge. Information technology used in the area of clinical medicine is known as clinical information systems. Clinical information systems can be divided into four categories (Simpson & Gordon, 1998). These four categories are based on core activities within healthcare: (1) clinical management; (2) clinical administration; (3) clinical services; and (4) general management. Each category has several activities; each of which can be assisted with an information system. Table 1 shows the list of activities for each category and an example of an associated clinical information system (CIS). The clinical management category is centred around the management of the patient. Clinical information systems in this area often focus on aiding the clinician with the decision making process, hence the use of clinical decision support systems (CDSS). The main essence of a clinical decision support system is that it should provide the clinician with knowledge, which is patient related and relevant to the current dilemma (Farman, Honeyman, & Kinirons, 2003). Clinical decision support systems are used for a number of purposes. Nevertheless, their use is based on their ability to enhance/ improve decisions (Delaney, Fitzmaurice, Riaz,

In the period 1991-2006 New Zealand’s health sector has undergone repeated restructuring (Gauld, 2006). The health sector is highly politicised and complex with a history of poorly implemented national ICT systems. In the various restructuring exercises (Gauld, 2006; Wills, 2006) the move, from a public health point of view, has been from a market-oriented approach to a collaborative health service environment upon which managed health IT systems have been a key requirement. This situation of flux, political involvement, and layered complexity has meant that, at the primary health care provider level, primary practitioner trust in ICT has been compromised. On one hand, primary practitioners know they can benefit in a number of ways from ICT supported decision support systems, and on the other hand, the many structural changes have fostered generalised distrust within the health sector. It is known also that primary practitioners are late adopters of technology (Gauld, 2006) and that voluntary adoption is required for such systems as clinical decision support. Over the last fifteen years, late adoption, together with a public health sector in a continuous state of flux, technologically, has provided a barrier to trust for primary health care decision support systems. Over this period funded research in health IT systems has been directed at managed systems for public health, not in private practice clinical support. It is as if the political agendas operating during this period have fuelled themselves on one restructure after another, with the political stakeholders rather than the primary care practitioners

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CLINICAL DECISION SUPPORT SYSTEMS

Trust and Clinical Information Systems

Table 1. Categories and core activities of clinical information systems Category

Core Activities

CIS example

Clinical management

Assessment of patient observation and investigation and decision formulation in terms of patient care and management.

Decision support system to aid in clinicians decision making process of diagnosis.

Clinical administration

Scheduling, arranging investigations and appointments, clinical correspondence.

Patient management system for creating appointments and billing.

Clinical services

Support services such as laboratory results, imaging facilities, ward, and theatre management.

General management

Financial, HR.

& Hobbs, 1999; Shortliffe, Perreault, Fagan, & Wiederhold, 1990). The role of a CDSS is to improve the clinician’s decision without replacing human judgement (Bemmel, 1997). Clinical decision support systems use artificial intelligence techniques for analysing knowledge and solving problems with that knowledge (Thornett, 2001). They can range from simple systems based on a simple algorithm or complex systems, which draw upon patient data and a database of clinical knowledge and guidelines (Wyatt & Spiegelhalter, 1990). Most clinical decision support systems have been developed for specific medical domains such as diabetes or cardiovascular medicine (Ridderichoff & van Herk, 1997). These medical domains have specific expert knowledge, captured by numerous organisations and codified into knowledge bases. These knowledge bases are the basis of clinical decision support systems and consist of structured and unstructured medical knowledge (Akinyokun & Adeniji, 1991). Typically in a GP’s surgery a CDSS might be used to check the amount of paediatric dose (as opposed to adult dose) when making a prescription, or to check on harmful interactions between two or more prescriptions.

THE CASE STUDY This research is a small-scale case study, which examined four groups of people in the health-

Management Information system for payroll, HR.

care area. In New Zealand the majority of GPs are organised into groups called Independent Practitioner Associations (IPAs). IPAs are now part of “Primary Health Organisations,” which provide the first line of health care to a particular geographical area. In addition, IPAs also provide a range of support services including information technology support—and therefore CDSS operation—for their GP members. The groups were interviewed in depth regarding the use, development, and implementation of a CDSS. The following were the participants of the study: • • • •

Three doctors who would use decision support systems in their work; Two practice managers (IPAs); One knowledge company who wrote their own software; One knowledge company whose data was incorporated into an IS by other organizations (software developers).

It has often been argued that small-scale studies are unlikely to provide general conclusions from a small sample (Bell, 1987; Tellis, 1997). However, it has also been strongly argued (Bassey, 1981; Hamel et al., 1993; Yin, 2003) that the merit of a case study, whatever the size, is the extent to which details are adequate and suitable for someone in a similar situation. Bassey (1981) also states “the relatability of a case is more important than its

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generalisability” (p. 85). Many related cases in different reports by various researchers can later be used to form an overall idea about a situation (Bassey, 1999). Thus, small cases still have a significant role to play in research. The interviews were approximately an hour long and were based on open-ended questions. The interviews were not tape-recorded. The original intention of the discussions was to find partners, GPs, IPAs, knowledge companies, and software developers for an in-depth study into the design and use of CDSS in practice. During the interviews, the four parties all indirectly raised the notion of trust with regard to the use and development of clinical decision support systems. The questions in Table 2 were some of those which related to the issue of trust and DSS infrastructure. The theme of trust arose at different system levels within the investigation and at both sides of the CDSS in question, from the user wanting to place trust in the decisions provided by the system to the database owners wanting enquiries based on their data to be trustworthy. Trust in a CDSS at the highest level could be the medical practitioner’s trust in the CDSS

and in turn the patient’s trust of the practitioner and CDSS working together successfully. From an information systems perspective, however, it is clear that there are a number of other systems involved, each of which has to work effectively and be trusted by other parties for this top level of trust to be on a sure footing. In particular, consider a CDSS where the data is maintained by one company (often a publishing company) and is presented through application software developed by another, yet all of the associated hardware and software are managed by general practitioner clinics themselves. This is the usual case in New Zealand. Naturally there are other systems involved, such as the distribution of data updates from the original publishers through to the GP’s desktop. Thus a chain or network of trust exists. This concept of needing a network of trust has been recognised in the literature (see Muir (1994), for example).

Table 2. Questions used in the interviews which elicited answers related to trust issues Doctors Are there any knowledge companies, which you have issues with? What are these issues? Are there any software developers, which you have issues with? What are these issues?

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Knowledge companies How long have you been in the industry? What made you go electronic? How do you ensure that your publishing is accurate? How do you choose software developers to develop your publishing? Are there concerns with the software development process? How do you identify errors in the software development process? How do you ensure that minimal errors occur in the software development process? How do you ensure doctors use the correct version of your publications?

Knowledge companies (who develop their own software) How long have you been in the industry? Was your company always into software development? What made you decide to develop your own software? What benefits and limitations do you think developing your own software has?

Practice managers How many GPs are in your IPA? What degree of authority do the GPs have? What role does the IPA have towards the GPs? In terms of IT, what role does the IPA have? Who decides which software is to be installed, do the doctors have a say?

Trust and Clinical Information Systems

Trust and Computer Systems: The Technical View Computer users are more complacent when a system is thought to perform correctly, and if an error occurs, the user’s trust in the system will decrease (Muir, 1994). Vries, Midden, and Bouwhuis (2003) found that users who experienced low error rates in a computerised system used the system more than did users who experienced a high error rate. A similar effect was found from a manual system: people displayed a tendency to abstain from use of a manual system with high error rates. However, Vries et al. (2003) found that with the computerised system, the error rates were lower than with the manual system, suggesting that users are more critical of errors occurring with computerised systems and that errors greatly impact the users’ trust of the IS. Several factors underlying trust in automation have been identified, including predictability, reliability, and dependability. Rempel, Holmes, and Zanna (1985) concluded that trust would progress in three stages over time, from predictability to dependability to faith. Muir and Moray (1996) extended these factors and developed a trust model that contains six components: predictability, dependability, faith, competence, responsibility, and reliability. Muir (1987) states that trust is a critical factor in the design of computer systems as trust can impact the use or non-use of computers, and later findings by Vries et al. (2003) confirm this tendency. Yet it is interesting to note here that trust essentially depends upon the human perceptions of the computer system. Alongside the technical dimension of a computer system and intermingling with it lies the very human question of trust. Whilst the studies above discuss the issue of trust in direct usage of a decision support system, there is a lack of research into trust and the various sub-systems or components that provide the infrastructure of a decision support system used in practice. Each of these components can have an impact on the

development, support, and use of the decision support system, and from our research we have seen a number of examples where trust has been a significant factor in the relationship between the various parties involved with different aspects of CDSS infrastructure.

Trust and the Chain of Support Take the situation, previously outlined, for the infrastructure of a CDDS used in practice. The development of a clinical decision support system involves a number of components. The data used in the clinical decision support system is often created by a knowledge company, the data produced depending on the expertise of the company. This data is then used by the software developer to create a system; in some cases the developer is part of the knowledge company. Once the system is created it is checked by the knowledge company, and then distributed either directly to an independent GP or to a group of GPs through an IPA. It can be seen that there are a number of situations where trust needs to exist between the providers of the components. These are discussed next.

Chain of Support: Knowledge Company and the Software Developer In order for the developer to use the data to create the system, the knowledge company needs to give the developer the right (often in the form of a license) to use the data, which in its simplest form are tables. It is important for the knowledge company that the developer creates the system correctly, as an error in the development often results in the user not trusting the system as a whole, including the database. With one knowledge company with which we spoke, they were concerned that the representation of their data was not as they expected once the system was developed. This led them initially to employ someone as a systems

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integrator to ensure that software developers were fully aware of how the data should be handled. Some of the initial problems reported were simple errors in queries, such as only reporting the first match of a drug interaction, rather than showing the full list of interactions held in the data. The second phase of the knowledge company wishing to keep responsibility for its own data has resulted in them changing the format of the data in order to achieve the representation they want. It was reported to us that the software developers also appreciated this situation as they were reluctant to take the responsibility for someone else’s data (knowledge base). In another instance, a knowledge company did not trust any software developer to develop a system with their data, which resulted in them developing their own systems. The main concern with this knowledge company is that other software developers may not produce the end result they are wanting and that it is easier to rely on your own company to get things right. Thus, the issue of trust has caused the knowledge company to act in different ways and take certain actions to ensure that the reliability of their data is maintained.

Chain of Support: Knowledge Company and the GP With the data produced by the knowledge company, there are always additions or changes that need to be distributed to the end user in order for an accurate and up-to-date decision to be made. It is a concern for the knowledge company that GPs may not regularly install their data upgrades. Although this is a major concern for the knowledge company they are unable to enforce any measure to ensure regular updates. The knowledge company do, however, include a statement that they do not guarantee the data if it is not updated. The same issue applies between the software developer and the GP. Again, the development of the CDSS software will often require updates and upgrades. Like the knowledge company, the

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concern for the software developer is that the end user will not install the updates. There is currently no method or measure to ensure the installation of upgrades, either from the data or the system. Thus both the software developer and the knowledge company must place a degree of trust on the end user, as this aspect is out of their control. It is often the IPAs, acting as a kind of intermediary, that take on the role of ensuring that the systems actually in use are the latest versions of software and data. Here the IPAs are providing responsibility on behalf of the other sub-systems in this DSS infrastructure, and as such can be seen as key players in having the systems used in practice be seen as trustworthy by GPs and patients.

Chain of Support: IPA and Software Developer The IPA often has the responsibility to choose a preferred software development company. When choosing a preferred company a number of factors are important, and reliability is one of them. This is often expressed in terms of it being essential for the IPA to choose a company that they can trust. Providing updates and upgrades to the system is also important for the IPA. However, the IPA is not concerned that the software developer will not provide timely updates, since it is in the software developer’s best interest to provide the updates. On the other hand, the IPA is often concerned that the GPs would not install their updates in a timely manner. However, because the IPA is in a situation of some authority, they often resort to insisting that the updates are installed.

Responses to the Lack of Trust It can be seen that there are a number of situations where trust needs to exist between the providers of the components. In this section, we identify the types of strategies adopted to deal with the problems of a lack of trust in certain areas. This

Trust and Clinical Information Systems

classification gives us the headings of: creating a new role or job; providing a technical solution; providing an organizational solution; relying on contractual responsibility; and using an agent to manage issues of trust.

Response 1: Creating a New Role or Job This was the solution adopted by one knowledge company we spoke to who were concerned that the representation of their data was not as expected once the CDSS was developed. The knowledge company created the role of “systems integrator” to ensure that software developers using their data were fully aware of how the data should be correctly handled. This reduced the possibility of errors in the software’s logic giving the appearance of a problem with the quality of information.

Response 2: Providing a Technical Solution This was an additional strategy used in the above scenario. Here some of the query logic was brought back into the knowledge base, thus reducing the responsibility of the software developers when presenting queries in the CDSS. In this situation, we were informed that it was appreciated by the software developers who were uneasy at taking responsibility for information quality in this substantial database, the content of which was out of their domain of expertise. Both of these measures have lead to the knowledge company widening its base of technical skills, which is similar to the next strategy observed.

Response 3: Providing an Organizational Solution Another knowledge company we talked to felt they could not trust any software developer to develop a CDSS with their data, so they developed their own software.

Response 4: Relying on Contractual Responsibility In our area of study, healthcare CDSS, a significant aspect is the currency or up-to-date-ness of the data. There are significant trust issues around whether GPs are using and or getting the latest data upgrades. So another element in this complex modern IS is the distribution system for regular data upgrades. The knowledge companies employ the strategy of declining responsibility if the latest data and system upgrades are not used by the end-user, the GP in this case. This of course is quite a negative strategy and places a difficult burden in terms of expertise on the GP. A more positive and possibly more successful strategy is illustrated by our final heading.

Response 5: Using an Agent to Manage Issues of Trust In our research, the IPA who acted as practice managers for the IT infrastructure of their GPs took on this role of managing issues of trust. It is often the IPAs who act as a kind of intermediary, and take on the role of ensuring that the systems actually in use are the latest versions of software and data. Here the IPAs are providing responsibility on behalf of the other sub-systems in this CDSS infrastructure and as such can be seen as key players in having the systems used in practice be seen as trustworthy by GPs and patients. The IPA often has the responsibility to choose a preferred software development company. When choosing a preferred company a number of factors are important, and reliability is one of them. This is often expressed in terms of it being essential for the IPA to choose a company that they can trust. Providing updates and upgrades to the system is also important for the IPA. However, the IPA is not concerned that the software developer will not provide timely updates, since it is in the software developer’s best interest to provide the updates. On the other hand, the IPA is often concerned that

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the GPs will not install their updates in a timely manner. However, because the IPA is in a situation of some authority, it often resorts to insisting that the updates be installed.

DISCUSSION The factors involved in the trust models mentioned earlier include predictability, dependability, faith, competence, responsibility, and reliability. In our findings, we have come across issues that relate to some of these factors. We found evidence of faulty logic, concern over timeliness, and uncertainty. The trust between the knowledge company and the software developer has several underlying factors, namely competence, responsibility, and reliability. In order for the knowledge company to proceed with the software developer, they must believe that the software developer has some degree of competence, i.e. that they have the expertise to produce such a system. Due to the fact that one knowledge company had issues with trust factors, it was unable to trust any other software developer to develop their systems, and thus it decided to begin software development within its own company. The factors that were involved in the decision were those of reliability and competence. This knowledge company was convinced, perhaps from previous experiences, that having control over software development provided it with greater reliability and competence. In another situation, a knowledge company had issues with dependability and competence of the software developers. However, instead of doing the development themselves, other action was taken. Due to experiences with errors in the display of the data, the knowledge company decided that the software developers were not competent to display the data as was required, causing the knowledge company not to depend on this being fixed in the future. Their course of action was to change the format of the data so as

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not to give the software developers a chance to err in the future. The current situation is that the knowledge companies and software developers are able to predict that the GP will install the updates, but as yet there is no mechanism where the trust reaches the level of faith. It is evident, therefore, that the IPA have a continuing role to play to allow the knowledge company and the software developer to trust that their updates will get through to the end user. With the IPA, there is little concern from their point of view that the knowledge company and the software developer are not reliable in terms of providing the updates, since there is a direct benefit to them in providing timely updates. In some of the interactions outlined above, the knowledge company and the software developers have little option but to trust the other parties. However, where they can take control and responsibility for themselves, it is clear they are keen to do so.

Trust and “Technical” Responses O’Neill (2002) presents a strong argument that, where “technical” measures are taken due to a reluctance to place trust, our underlying ethical values should not be forgotten. Such technical responses often include, along with the strategies that we found and summarised in the previous section: service level agreements, audits, quality indicator metrics, benchmarks and so forth. These may all play a helpful role where there is a lack of trust. There is considerable evidence that, within a quality-promoting framework such as a quality management system, such accountability measures as a minimum set a “floor” under quality of service, and if used judiciously are helpful in improving the quality of software based systems (Bissett & Siddiqi, 1995, 1996). They may also, as O’Neill argues, produce further malign effects such as increasing suspicion, and can often introduce “perverse incentives” (O’Neill, 2002, p.

Trust and Clinical Information Systems

viii). An egregious example of the latter appeared in GPs’ surgeries in the UK during the summer of 2005. In response to a government target that patients should wait no longer than three days for an appointment with their GP, around a third of GP surgeries refused to make appointments more than three days in advance, an absurd and self-defeating scenario that provoked considerable derision. A reliance on accountability mechanisms on their own is not enough. This touches upon a debate that has become especially sharp in the public sectors such as healthcare (Exworthy & Halford, 1999). O’Neill argues that professionals should not lose sight of their fundamental values in their efforts to comply with audit systems. We are reminded once again of Gotterbarn’s (1996) advocacy of a “new professionalism” for those working in ICT. Three paradoxical aspects of trust in the “information society” have emerged here. Firstly, as noted in our discussion of the nature of trust, the “information society” of the twenty-first century has by no means eliminated the issue of trust, and secondly, the proliferation of information has made placing trust, if anything, more problematical. The third paradox is that technical measures to address a lack of trust implicitly put trust back on the agenda; without strong guiding ethical values, such measures alone cannot guarantee the successful employment of information systems. The guarantee that we would really like is that wise, experienced, knowledgeable professionals will conscientiously perform their duties.

CONCLUSION The study of CDSS used by GPs in New Zealand shows that the question of trust is never far from the supply, operation, and maintenance of such systems. Responses to a lack of trust were various in nature; adaptive “technical,” organisational, or procedural measures were found. Typically these were creating a new role or job; providing

a technical solution; providing an organizational solution; relying on contractual responsibility; and using an agent to manage issues of trust. Such strategies can be valuable in the situation studied, where successful use of a CDSS has an immediate impact upon human health. But ultimately the trust issue is difficult to escape; one would expect this human and ethical question to be pre-eminent in other situations where complex information systems have a key role.

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This work was previously published in Ethical, Legal and Social Issues in Medical Informatics, edited by Penny Duquenoy, Carlisle George and Kai Kimppa, pp. 48-64, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 7.7

Data Security in Electronic Health Records Stefane M. Kabene Ecole des Hautes Etudes en Sante Publique (EHESP), France Raymond W. Leduc University of Western Ontario, Canada Candace J. Gibson University of Western Ontario, Canada

ABSTRACT Traditionally, patient information has been recorded on paper and stored in file folders at healthcare facilities and within physicians’ offices. The implementation of electronic health records (EHRs), the lifetime record of an individual’s health and health services delivered, allows for information to be stored on computers and offers the opportunity to store considerably more data, in much less space, with new efficiencies and value added as information is easier to access, legible, timely, non-redundant and readily available. However, there are many issues to consider with the implementation of a fully shared EHR. The protection of the information contained in the record is of the utmost importance as individuals stand to become quite vulnerable if that DOI: 10.4018/978-1-60960-561-2.ch707

personal health information is compromised or accessed by unauthorized users. Therefore, one of the goals of this chapter is to uncover ways in which personal health information is being protected in EHR systems. The second objective, a broader one, examines what regulations, legislation and policies are in place that remove some of the uncertainty and risk and make the use of shared information safe and secure. Many of the techniques and technologies used so far are adopted from the corporate world, where data security has been an issue for some time. Current legislation in the United States and Canada at both the federal and state/provincial levels has addressed the general principles of data security and privacy but are still lacking in specifics with regard to cross-jurisdictional sharing of health information and the implementation and use of EHRs. Many of the researchers and studies on the

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subject find this to be one of the most important areas of concern moving forward. The opportunities for EHR implementation and use are exciting as they have the strong potential to improve both individual health care and population health, but without proper regulation and policies in place it is possible that the risks may outweigh the benefits.

INTRODUCTION It is undeniable that the rapid pace of technological advancement has had an impact on every facet of modern society. Old ways of doing things are often transformed overnight and society is left to catch up. It is also our responsibility to take a step back occasionally and analyze the direction our new technology is taking us, and decide if it is leading us down the path initially intended. One of the most important areas where technology is being applied is in the healthcare industry where advancement and new developments alter people’s lives on a daily basis and can quite literally mean the difference between life and death. Yet as the medical field races ahead with nanotechnology, genome mapping, telerobotic surgery and countless other projects (Anvari, 2007; Godman, 2008), it is imperative that we take a closer look as to where we might end up, and more importantly, what we must be wary of not leaving behind. The subject of this paper is electronic health records (EHRs) that allow patient health information from multiple sources to be stored electronically as opposed to the traditional pencil and paper method. More specifically, we will examine the issue of privacy rights and take a closer look at what technology is being employed to ensure the security of health data. The objective of this paper is to look at ways in which EHRs are currently being used in the healthcare industry. It will focus on the key issues involved in discussions of privacy rights, what different methods are currently employed to ensure data security, and what needs to be

fixed, or kept in mind, with regard to its future potential. EHRs provide an interesting facet of modern health care as the potential benefits are significant (reduction of medical errors, reduction of test duplication and cost savings, accessibility of the record anywhere, anytime, disease surveillance, resource planning and management), but in many ways the technology is proceeding faster than the policies and regulations required to ensure its safe and secure use.

CONCEPTUAL FRAMEWORK With the ability to store large amounts of data from multiple sources in a small space and relatively inexpensively, EHRs are becoming a desired goal in the healthcare industry. The electronic health record, the longitudinal record of an individual’s encounters with the health system and various health providers, is the goal of the ultimate multiuser, multi-facility, multi-purpose record. The EHR is envisioned as connecting institutional or facility-based electronic paper records (EPR) and the physician-provider electronic medical record (EMR) to provide a comprehensive lifetime record of care. The EHR will include information from different healthcare providers and in different formats, for example, text, voice, and digital images. This information, including demographic and clinical data, diagnostic results, alerts, reminders, and evidence-based decision-making support, should be accessible only to authorized users. A truly integrated electronic health record facilitates data linkage and data sharing among a number of different users in geographically different locations. The EHR supports care by multiple providers, and the use of health data for secondary purposes such as research, planning, management and administrative decision making. It provides value well beyond the capabilities of the current paper record. It also presents challenges in sharing information across jurisdictional boundaries and the maintenance of privacy and

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confidentiality (Crook, Gibson, Adam, Levesque, O’Reilly-Brunelle & Hwee, 2009; Dick, Steen & Detmer, 1997). Potentially, patient information including everything from family medical history, laboratory results, medication information, and even a genomic map of the individual could be stored for access to anyone who might have an interest in the information and is authorized to access it. Patients would be able to look up lab results from home, or have prescriptions refilled and delivered (Wiljer et al, 2008). A Health Canada review (EXOCOM Group, 2001) of privacy technology stressed the benefits for healthcare professionals as faster access to health records, facilitation of better care, reduction of costs, accommodation of future developments and support of clinical and health services research. However, this new technology is not without its risks. With the enhanced ability to store and share large volumes of information in cyberspace comes the increased threat of that data being compromised. Kaelber, Jha, Johnston, Middleton and Bates (2008) found that “91% of people [in the US] are very concerned about the privacy and security of personal health information.” This suggests that many patients are cautious about providing information to professionals who use EHRs. An estimated 1.2 million Canadians have withheld personal information from a health care provider because of concerns over how it might be used or with whom the information would be shared (EKOS, 2007). There is some evidence to illustrate that the risks associated with EHRs are not based purely on speculation. Meingast, Roosta and Sastry (2006) identifies risks such as “access rights to data, how and when data is stored, security of data transfer, data analysis rights, and the governing policies.” These are only some of the issues that recur in the literature on EHRs. It is important to recognize that security issues are in reference to both internal and external threats. There are no easy answers to these issues and they have been the topic of

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much debate. There is also evidence to show that in the short time of limited EHR implementation these risks have resulted in legitimate losses or breaches of security (Office of the Information and Privacy Commissioner, 2002). In October 2001, physicians in the ChathamKent Primary Care Network agreed to participate in the first information technology transition pilot project (Office of the Information and Privacy Commissioner, 2002). The purpose was to test different forms of information technology and to identify weaknesses or vulnerabilities before the technology was used province-wide. Patient information was recorded on computer touch screens, then sent to a docking station in the physician’s office and transmitted across the Internet to a central server to be stored. The then Smart Systems for Health agency (SSH) (superseded by eHealth Ontario), provided the security for collection, storage, transmission and exchange of information. In December of the same year that the project went “live” a reporter for the national newspaper the Globe and Mail informed the Information and Privacy Commissioner (IPC) of Ontario that he had received internal documents stored by SSH from an outside source. Further, three separate companies had received access to patient information that was supposed to be confidential. Clearly there was initial reason to be concerned with the ability of institutions to protect patients’ confidential information. Dr. Paul D. Clayton of Columbia Presbyterian Medical Center in New York and chair of the National Research Council’s Committee on Healthcare Privacy and Security (Burke and Weill, 2000) has stated that “sharing of information within the health care industry is largely unregulated and represents a significant concern to privacy advocates and patients alike because it often occurs without a patient’s consent or knowledge.” It is clear that patients are concerned about having the information contained in their EHRs compromised. The information that might be

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contained in an EHR could easily be used for a number of unethical or even criminal reasons. The Federal Trade Commission estimated that 10 million Americans reported being victims of some form of identity theft in 2003 (Diamond, Goldstein, Lansky & Verhulst, 2008). Identity theft can lead to enormous financial losses, not to mention the hardship and violation that is common to most victims. Yet, this is only one of the potential challenges. Others include government misuse of the information, data quality issues, potentially harmful medical and social consequences, and commercial misuse of data (Diamond et al., 2008). Advancements in this field must be approached carefully. Deapen (2006) summed it up by stating that “achieving a proper balance between measures to protect privacy and the ability to guard and improve public health requires careful consideration and development of appropriate policies, regulations and use of technology.” Before we examine what measures are being taken to achieve this balance, it is necessary to have a closer look at what privacy really means. Privacy is defined as the “the right of individuals to be left alone and to be protected against physical or psychological invasion or misuse of their property. It includes… the right to maintain control over certain personal information” (Deapen, 2006). Both the UN Declaration of Human Rights and the International Covenant on Civil and Political Rights recognize the right to privacy (Diamond et al., 2008). It would not be unreasonable to consider a breach of privacy to be a violation of one’s basic human rights. Data security, on the other hand, is the process of assessing and countering threats and risks to information, and implementing the technical and administrative safeguards necessary to protect personal information. Technical safeguards include mechanisms for data back-up (hardware and software); anti-virus protection; intruder alerts; secure networks and the means of providing safe and secure remote and wireless access and transmission of data; the layout and location

of workstations; authentication (that may include level of access determined by the user’s role); and the use of audit trails that can provide transaction logs and track inappropriate use (Rindfleisch, 1997). Administratively, organizations and facilities should have clearly defined policies and procedures for confidentiality and privacy that set out and define access rights (for example by professional status or by the relation between the user and patient, by type of data, and/or intended action) that are consistent with current legislation or policies of professional organizations. The lack of proper data security and clearly articulated privacy policies can lead to‘privacyprotective’ behavior where patients engage in measures to shield themselves from potential harmful uses of their personal health information (CHCF, 2005; Deapen, 2006). This behavior ranges from disguising one’s name to remain anonymous, to disclosing untrue, or refraining from disclosure of information due to their fear of its use within EHR systems. Roughly 50 million Americans, engage in privacy-protective behavior to shield themselves from misuse of their information (CHCF, 2005). These behaviors included asking a physician not to report a health problem or to record a less serious (or less embarrassing) diagnosis, avoiding their regular physician for certain health conditions, avoiding certain diagnostic tests due to anxiety that others might find out about the results, or paying out of pocket for procedures to avoid submitting a claim (CHCF, 2005; Deapen, 2006). Negative consequences from this type of behaviour can lead to increased risk of missed diagnosis or poor quality care. It is therefore extremely important that however EHRs are implemented, privacy be of the utmost importance, and more importantly, that patients recognize this initiative. This also poses important questions for human resources management (HRM) as many companies have considered providing employees with access to their EHRs. HR managers must decide who will have access to the data, who will have the ability to alter the information, how data will

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be protected from unwanted view or use, and how to get employees to trust in the system enough in order to use it properly. Purdy (2000) found that “61% of Americans felt that too many people have access to their medical records.” This is troublesome not only when it leads to privacy-protective behavior, but also when it leads to underutilization of the EHR program and distrust of the company. Thus, in order for EHRs to be successful in the future it is imperative that not only protective measures take place effectively, but also patients/ individuals must be informed and made aware of such protection and who they can speak to about privacy concerns. Two other issues should be considered before moving on. The first is in regards to who owns the data contained in the EHR, and second, whether there are sufficient laws and regulations in place that allow institutions the means to provide the needed security. At first glance, it seems rational to assume that the patient owns the information and would allow him/her the opportunity to decide what should be done with the information, who should have access, and grant them the power to alter it. On the other hand, much of the information is the product of medical tests and examinations stored by medical institutions for their reference and other future purposes, such as research, i.e. the medical record is ‘created’ by the health provider(s). Physicians are not required to seek permission to make notes in a patient’s paper file or to simply look it over. Why should electronic records be any different? This calls for several considerations, for if it is deemed that the individual owns the information, then consent must be sought for each and every activity involving one’s record. Yet, as Burke and Weill (2000) point out, the majority of computer frauds and security breaches are committed internally by authorized employees, therefore restricting access to the patient alone and requiring written consent for its use may be the most prudent option. The second relates to external powers and their effect on assisting or hindering health institutions in providing consis-

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tent health data security. There need to be strong national regulations or policies as data often passes beyond individual state or provincial or regional boundaries. Without regulation as a guideline for EHR implementation it will be difficult to ensure security across different institutions and will be almost impossible to integrate information from multiple sources properly.

METHODOLOGY A selected review of the current peer-reviewed literature on issues of implementation, success and failure of EHRs was conducted. Given the fact that the fully integrated EHR technology is still in its preliminary stages, many of the implementations, revolve around feedback from pilot studies such as the Chatham-Kent project. Articles chosen for analysis were those that conducted a review of such projects, along with related privacy issues. Given the nature of the topic, the majority of the information is of a qualitative manner. Information on the techniques employed for data security examined literature from both pilot projects, what methods were used and literature on the devices/ techniques used for data protection in general. Given that the nature of data security is very similar to that of businesses that seek to ensure security of their corporate and personnel information, it is assumed that many of the same methods could be used in the healthcare industry. This provided insight into some of the latest and most robust technologies for information protection. Current legislation and regulations in North America that deal with personal health information and the EHR specifically (the latter relatively sparse) were also reviewed. Articles and white papers that provided an account of the current efforts to produce policy and regulations for the future were also reviewed.

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RESULTS The results are separated into two categories. The first illustrates a number of the methods that are currently used to implement and maintain the security of EHRs. According to Kaelber et al. (2008) “many patients already see physicians with the proper program for EHRs.” The simplest electronic record systems generally include basic personal computers and other electronic devices such as those used in the Chatham-Kent project (OIPC, 2002). Despite its suspected and reported failures, the IPC did a thorough investigation of the project and discovered five stringent measures that proved to be necessities for EHR implementation. These five measures were: the use of secure servers, physical security such as security guards and video surveillance, secure network services such as firewalls and encryption technology, encrypted email between people in the group, security and user authentication, and ‘privacy-enhancing technologies’ or PETs. The security authentication included measures such as the use of key fobs to carry passwords, i.e. small key chain sized displays that show a different sequence of numbers every 60 seconds that complemented other measures such as user names and role-based access. The privacy-enhancing technologies were mainly focused around public key infrastructures that will be discussed later. These techniques are important in maintaining the security of information and/or restricting access to only approved personnel. Trust Management Systems (TMS) are another security measure that is employed in the use of EHRs. These systems watch over the general collection of information and monitor potentially dangerous operations by analyzing who is requesting the operation, what the local security policy is, what the credentials of the requester are, among other factors. They also provide a standard language for writing policies and credentials that control what is allowed and what is not. Along with the common language for describing how

operations are to be handled, TM systems have four other beneficial characteristics, including a mechanism for identifying principles of the system, a language for application policies, another for specifying credentials and a compliance checker. They are gate keepers of the medical information in the sense that they take into account a number of factors in determining which operations are legitimate and which are not. It can be an effective way to ensure the privacy that is so vital to successful implementation of EHRs. Other technological methods of privacy control also warrant recognition. The first is “smart cards” which contain stored values that are transmitted when the card is inserted into a reader, or more recently, when the card is within a certain range of a receiver. The card stores information about the holder including the amount of access and privileges he is offered with respect to accessing the confidential information. Biometrics is another method of identification that incorporates unique features of the individual. These include features such as retinal scans, fingerprints and/ or voiceprints. This is a more difficult method to duplicate. Smart cards are open to theft or duplication, whereas physical features are much harder to fake or replicate. Finally, public key infrastructure sums up the most potentially beneficial technological privacy control measures. Public key infrastructure involves encryption based around a mathematical formula that takes information and scrambles it or changes it into an unintelligible form. In order to convert it back to its original form an individual must have access to a decryption key. There are generally two keys involved in encryption. The “public key” is available to everyone and is used to scramble the information that is stored. In order to access the information in its proper form a person must have a “private key” which would only be given to authorized personnel. This method is most useful with regards to information transfers, specifically between two or more locations or persons.

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While all these methods are effective tools for guarding against unauthorized access they fail to solve the issue discussed previously, the fact that a large percentage of fraud cases or security breaches result from authorized individuals. However, it is also important to recognize that as the (EXOCOM Group, 2001) stated, “there is not one simple solution, but only a combination of techniques will provide optimal privacy, and consistency within the EHRs.” The other area to be examined regards the laws and regulations that govern the EHR landscape. In the Canadian context, the Supreme Court ruling of McInerney versus MacDonald, a case involving a request by a patient for access to her complete record, states that while not an absolute right, patients have a right to access their personal health information in all but a few circumstances and the information contained in the health record belongs to the patient (Frelick & Jovellano, 2009, p. 319). However, as Wiljer et al. (2008) point out, this ruling came before the widespread use of EHRs and the courts have yet to clarify many of the issues that are raised by sharing of information in the electronic record. Harris, Levy and Teschke (2008) point out that while Alberta, Saskatchewan, Manitoba and Ontario have separate legislation that governs health information, many other provinces do not and there is currently no separate federal law regarding health information. Humayun et al. (2008) conducted a review of studies performed in Canada and discovered that quite a few family physicians do not fully understand their obligations towards patient privacy and confidentiality. In an attempt to meet this need, the Canadian Committee for Patient Accessible Electronic Health Records was formed. It is a group of researchers, clinicians, and information specialists who work to promote patient access to, and involvement with, EHRs. They have taken on a two part project to scan the country for hospital readiness for implementation of EHRs, and to assemble a workshop of stakeholders in the field of EHR (Wiljer et al., 2008). While attempts are

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being made to create regulation and standards for implementation and use of EHRs, a specific federal regulation has not yet been established. The US situation is very similar to that of Canada’s regarding regulations for EHRs. The Health Insurance Portability and Accountability Act (HIPAA) enacted in 1996 outlines the procedures to be followed by doctors, hospitals, and other health care providers to ensure that all medical records, medical billing and patient accounts comply with certain consistent standards for documentation, handling and privacy (Meingast et al., 2006). Each state also has its own laws for disclosure of information. Much of the literature tends to agree that the HIPAA does not contain strong enough regulations to solve many of the current issues. It is also widely believed that the problem is multiplied by having each state governed by its own laws. This makes it difficult to apply regulation to information that is shared beyond state lines. Rosenbaum, MacTaggart, and Borzi (2006) examined Medicaid’s budget (the federal program that provides medical insurance coverage to low income and low resource families) and showed it has historically spent little on information technology, yet it has always contained provisions to ensure the security of collected data. As a system that is built on being interoperable, by combining medical records with education records, income information, child welfare system information, among others, it has been debated as to whether Medicaid’s regulations for privacy should mirror those of the HIPAA. The US is making strides with the Certification Commission for Healthcare Information Technology. Founded in 2004, the commission was created to rigorously test software developed by companies to establish whether or not their systems can support and perform required functions, exchange information with other systems, and maintain data confidentiality and security (Glaser, Henley, Downing & Brinner, 2008). The American Health Information Community has also done its part by creating the Public Health

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Workgroup (PHC) which works to inform policy makers on developments in EHR standards. In March 2007, the PHC group identified four priority areas moving forward, one of them being privacy and security. So while ground is being made and there are those who are working to see that regulations to EHR use are established, we have still not reached a fully integrated and longitudinal electronic record in either country

DISCUSSION From these privacy and security reviews, a number of conclusions become apparent. The ability to record, store and use an increasing amount of personal information has continued to grow with the boom of the information age. As long as this ability has existed, so too has the necessity to ensure protection of sensitive personal health information from unauthorized use and prying eyes. While IT implementation is still fairly new in the healthcare industry, it has been rapidly progressing in the business world for some time. Much can be learned from the world of business, and as EHR system providers continue to improve their programs they are starting to borrow some of the ideas used in the business world for information protection. Public key interfaces, secure servers, authentication techniques, and trust management systems have been used with success in the corporate world. However, one clear message is that the issue of in-house fraud has yet to be addressed. As noted before, the majority of computer fraud cases or security breaches come from authorized users, not outside hackers. Much of the privacy protection techniques involve ensuring that the only people who need to have access to the information are the only ones who will truly see it. Yet, if these people in some cases are part of the problem what is really being solved? This will be a challenge for HR managers and privacy officers going forward, as the solution to this problem will likely depend on the ability to properly screen and

train employees, particularly those who are to have access to the stored information. This could include integrity testing, employee monitoring, confidentiality agreements, and certainly the development of corporate cultures in both hospitals and software companies that value the ethical and moral imperative of privacy protection. Kaelber et al. (2008) provide another interesting implication of the EHR environment. They acknowledge the fact that in many ways the techniques being used for data security create a safer environment for the information. Yet they also point out the fact that by storing the information on a giant network (the Internet) the information is now everywhere as opposed to the paper based forms which would be kept in folders, locked away in cabinets. Some of the main advantages of the EHR system can also be seen as weaknesses as it is possible for people anywhere in the world to hack in and access the information stored on servers. Also, by making attempts to integrate the information so that similar or linked information is grouped together for easier retrieval it is easier to get a greater amount of information. With a paper record an unauthorized party would potentially have to break into numerous filing cabinets and search through many stacks of paper to obtain the complete record; with an integrated EHR server a hacker could access information from any number of sources making misuse of the information much more attractive as the information would be more valuable. Certainly if the data could be safely secured, the benefits of an integrated record (for public health surveillance, accessibility of the record in an emergency, etc) would likely outweigh the risks, but currently there is a strong debate that much of the data cannot be secured, and until security can be ensured it would be wise to consider, and attempt to minimize the risks. Perhaps the most recurring concern when one studies EHRs is the issue of inadequate regulation. The absence of federal regulation or international treaties that govern the broad use of EHRs is holding the advancement of the technology use in the

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health care industry back. There appears to be a general consensus amongst researchers who have examined this issue that one of the most important areas moving forward is the need for regulation that will address cross-jurisdictional use of and sharing of health data. The fact that it has taken this long is an indication of the relative difficulty that is faced in coming to a conclusion on the issue. Not only must the regulations govern what information is to be collected, by whom, who owns it, who may access and edit the information, and a limitless other number of issues, it is also likely that there will be exceptions to the rule. Deapen (2006) suggests that there needs to be exceptions that address times when the need to access personal health information for government purposes may supersede any personal privacy rights, such as in times of epidemic outbreaks and disease control. The variations between state and provincial legislation illustrates the need for consensus and harmonization of existing laws and work to be done to generate effective federal legislation that covers all jurisdictions. Perhaps the delayed process to generate such regulations for EHRs can be viewed as a positive sign. There may be a general consensus after all; the importance of the dilemma is well understood and those charged with creating the regulations want to be sure to do it right. There are few things more sensitive than an individual’s personal health information and when integrated with a great deal of other personal information, there are few things more valuable. The information age has offered a wealth of new opportunities, yet it is important not just to take them at face value; as society changes and evolves old norms, customs and legislation becomes outdated. New technology is often produced and introduced faster than we are able to govern its use. So perhaps instead of viewing the lack of regulation as a deterrent to advancement it should be viewed as an opportunity to take great care in a matter that can have a dramatic effect on people’s lives, beyond

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the simple gains that are often thought of as being the main outcome of EHRs. Another important point to consider is that with attempts to discover ways to spread EHRs across the country, the new technology is being forced on many in the healthcare industry who may not champion the idea. Wiljer et al. (2008) discuss the need for a strong cultural shift in many healthcare institutions that must accompany the adoption and increased integration of advanced technologies. Many have become so used to the ease of use of having patient information stored at hand in a manilla folder and may not be comfortable entering it into computers and having it stored on a server off the hospital or facility site. As a result, it is just as important to convince the provider as well as the patient of the importance of securing health data, as it is to actually do so. Further, healthcare professionals will have to adapt to the idea of ‘custodianship’ of personal health information rather than ‘ownership’ (Wiljer et al. 2008). The fostering of such a cultural shift may take time and patience, but will be instrumental in the ultimate success of the EHR. This will have an impact on the field of human resources management as it may be HR managers within health organizations that are faced with orchestrating this cultural shift and supervising the transition to optimum electronic record use.

RECOMMENDATIONS Given that there is still limited deployment of EHRs much of the literature on the subject focuses on recommendations for the future, and not one, but a number of published recommendations, needs to be considered. Deapen (2006) suggests five steps in implementing any EHR software. First, he suggests the development of technical standards for privacy protection. He then suggests a focused dialogue and documentation of best practices, use and release of information. A centralized repository could be developed and instances of privacy

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breaches as well as instances of interference or hampering of research or public health surveillance collected. This would allow for the collection of information that would aid in further debates about the adaption of regulations to further the efficient use of information. Finally, he suggests creating a board of experts from among several different stakeholders to whom policy makers would send suggestions for proposed regulations and the board would offer advice. These steps would not only allow for a clear set of regulations agreed upon by all relevant stakeholders, but also allow for the continuing collection of information for the purpose of review and to see if new changes would need to be made. Diamond et al. (2008) offers a slightly different perspective for the future that should also be considered to increase EHRs’ future success. They are strong advocates of having patients involved in each step of the collection and use of their EHRs. They offer nine principles for the successful implementation of an integrated information network. The first is openness; this ensures that information is readily available to patients and what is contained in the record is easy to access, they are aware of how it is being used and their informed consent is sought before anything is done with the information. The second step is purpose specification; data will never be collected without the knowledge of the patient and it will be used for the original purpose for which it was gathered. Collection and use limitation are the third and fourth principle. Individual participation and control should be used where patients are seen as key participants in the process, data integrity and quality principles should be developed, along with security safeguards and controls. The final two principles are accountability and appropriate remedies for infringements. They suggest that a Chief Privacy Officer (CPO) position should be created wherever EHRs are used whose task it is to oversee the continued security of the information and to see that best practices are followed. Employee training is also an important part of

accountability. Finally remedies need to be established for those who are victims of privacy breaches. Many of these suggestions have been incorporated into both federal and provincial privacy legislation and within Canada reflect the CSA’s model code for the protection of personal information (Q830) that sets out ten principles (of accountability; identifying purposes for data collection; consent for collection, use and disclosure of information; limiting collection; limiting use, disclosure and retention; accuracy; safeguards; openness; individual access; and challenging compliance) that balance the privacy rights of individuals and the information requirements of private organizations (http://www.csa.ca/cm/ privacy-code). Both Canada’s Personal Information Protection and Electronic Documents Act (PIPEDA) and the US Health Insurance Portability and Accountability Act have used these principles as the basis of their respective laws. Yasnoff (2003, p. 202) offered further suggestions, some relating to potential regulation of the collection of information. They suggest that any regulation regarding the collection of information should be based on the principles of relevance, integrity, written purpose, need-to-know access, capacity for correction and open consent. The use of confidentiality agreements and authentication procedures is proposed to ensure that information is restricted to those who truly need to see and/or use it. These agreements would be signed upon initial hiring and each year thereafter. These agreements would also specify clear disciplinary action if the confidentiality agreement is breached. They also suggest that intrusion detection software be used that looks for unusual access patterns and the implementation of statistical techniques to examine usage patterns. For instance, “most work systems have a peak use in mid-morning and mid-afternoon, and a rare surge in activity in the middle of the night might illustrate potential misuse” (Yasnoff, 2003). These suggestions may also assist in the detection of fraud or breaches generated by internal authorized personnel. These

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could be important considerations in moving forward as internal misuse is still a major concern in the field of data protection. The current landscape with each province or state creating its own privacy laws is not sufficient to cover the widespread potential of EHR in which information may be shared across multiple jurisdictions each with its own set of regulations and legislation. One of the main benefits of EHRs is the centralization of large amounts of data, but this advantage can only be realized if all steps, from collection to final use, are conducted consistently, nationwide. Although a long term initiative and goal is a global EHR system, the preliminary process of implementation can only be controlled locally or domestically. Although this paper focuses on the implementation of EHRs, and specifically the effect on data security and privacy, it is important to note that much of the delay in implementation is due to inconsistencies at the local level. Differences in data collection, data standards and interoperability, even at the lowest level - from hospital to hospital within a given city - are causes for much of the delay. Therefore, although the call for federal legislation is necessary for addressing privacy issues, it can also be beneficial for policies that relate to consistent record keeping and data standards. Within Canada health delivery and hence health policy, to a certain extent, is a provincial mandate, but with regards to the long term effectiveness of EHRs it is important to instigate a nationwide policy to ensure consistency in both privacy issues, and record keeping and data standards, to speed the process of implementation. There is still debate over what minimum information should be collected, how it should be collected, who owns the information, who has the right to use/edit the information, how it should be secured and stored, within a nation-wide electronic health record. These issues need to be resolved and federal legislation must apply to all uses of the EHR that contains information from different systems,

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different organizations, different providers, and in different formats. All of the above suggestions will also have an impact on and help improve the field of HRM. Those in the field of human resources must develop new standards for employees with regards to who is given access to important information, who will be given control to perform different operations with the information, and what types of information will be recorded. Ensuring the security of information will also become an important task for HR managers as they will likely be dealing with information that is personal to each employee/patient and their consent and input will need to be sought before the information can be used. HR managers will also need to find creative ways to use this information to the advantage of their company or institution (e.g. planning fitness programs, provision of personal health records). There is great potential for this new technology, but due to the delicate nature of its content, challenges are on the horizon for the field of human resources.

CONCLUSION In conclusion, there are a number of developments in the field of electronic health records, yet there is still much to be accomplished. There are many different ways and methods in which developers and implementers of EHRs are approaching the issues of security of personal health information. These methods include the use of: • • • •

Secure servers Physical security measures Security authentication Privacy enhancing technologies, including encryption, trust management systems, smart cards, biometrics, and public key infrastructure.

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These methods are all important in ensuring that stored personal health data is not subject to potential misuse. A combination of methods, not a single method is necessary, as pointed out in our privacy technology review - “the most promising technology combination for EHRs appears to be a combination of trust management and public key infrastructure, possibly enhanced with smart cards and biometrics” (EXOCOM Group, 2001). Trust management is the best known technological tool for controlling internal threats, while the latter three address outside threats. Most of the available methods of control are directed at outside threats. The assumption is made that human resources management will be the best suited tool for ensuring confidentiality, through training, disciplinary policies, and other initiatives directed at internal users and minimizing internal threats to security. The other facet of EHR that was examined was the area of regulation and legislation enacted to govern the use of the technology. In both Canada and the US it was found that there is federal legislation to govern private information (through PIPEDA and HIPAA) but that it is often subject to, or superseded by, individual state or provincial law. This will be inadequate for the future as much of the information may travel beyond state or provincial lines, creating uncertainty with respect to the relevant legislation. While both countries have formed a number of committees with the focus of establishing principles of best practice, they have yet to do so and the lack of regulation has hindered the development of EHR systems. So while progress is being made and the opportunities associated with EHRs are still very real, there is much work yet to be done before the full potential is to be realized. It is through a combination of recommendations and privacy principles, the use of privacy enhancing technologies, progressive legislative action, and human resources management that EHRs can be expected to be successful in the future. There is no single solution, and it will take a collaborative effort of all those involved, at all

levels, to ensure progress in the secure implementation of the EHR, with minimal drawbacks and restrictions. Once these initiatives are realized, the full benefits of electronic health record systems can be realized.

REFERENCES Anvari, M. (2007). Impact of information technology on human resources in healthcare. Healthcare Quarterly, 10, 84-88. Retrieved November 9, 2008 from Burke, L., & Weill, B. (2000). Information Technology for the Health Professionals. Upper Saddle River, NJ: Prentice-Hall Inc. California HealthCare Foundation. (November 2005). National Consumer Health Privacy Survey 2005. CHCF Org. Available at: http://www.chcf. org/topics/view.cfm?itemID=115694 Crook, G., Gibson, C. J., Adam, C., Levesque, L., O’Reilly-Brunelle, B., & Hwee, J. (2009). Chapter 7: Towards an Electronic Health Record. In CHIMA (Ed.), Fundamentals of Health Information Management. Ottawa: CHA Press. Deapen, D. (2006). Cancer surveillance and information: balancing public health with privacy and confidentiality concerns (United States). Cancer Causes & Control, 17, 663–637. doi:10.1007/ s10552-006-0002-1 Diamond, C., Goldstein, M., Lansky, D., & Verhulst, S. (2008). An architecture for privacy in a networked health information environment. Cambridge Quarterly of Healthcare Ethics, 17, 429–440. doi:10.1017/S0963180108080559 Dick, R. S., Steen, E. B., & Detmer, D. E. (1997). The Computer-Based Patient Record: An Essential Technology for Health Care. Washington, DC: National Academy Press.

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EKOS Research Associates. (2007, August). (Submitted to). Electronic Health Information and Privacy Survey [Canada Health Infoway, Health Canada, and the Office of the Privacy Commissioner of Canada, Ottawa.]. What Canadians Think. EXOCOM Group. Business Strategy and Consulting IT Division for Office of Health and the Information Highway, Health Canada. (2001). Privacy Technology Review. Retrieved November 14, 2008 from http://www.hc-sc.gc.ca/hcs-sss/ pubs/ehealth-esante/2001-priv-tech/index-eng. php#anne Frelick, K. M., & Jovellano, J. (2009). Chapter 13: Legal Aspects of Health Information Management. In CHIMA (Ed.), Fundamentals of Health Information Management. Ottawa: CHA Press. Glaser, J., Henley, D. E., Downing, G., & Brinner, K. M. (2008). Advancing personalized health care through health information technology: An update from the American Health Information Community’s Personalized Health Care Workgroup. Journal of the American Medical Informatics Association, 15(4), 391–396. doi:10.1197/jamia. M2718 Godman, M. (2008). But is it unique to nanotechnology? Reframing nanoethics. Science and Engineering Ethics, 14, 391–403. doi:10.1007/ s11948-008-9052-y Harris, M. A., Levy, A. R., & Teschke, K. E. (2008). Personal privacy and public health: Potential impacts of privacy legislation on health research in Canada. Canadian Journal of Public Health, 99(4), 293–296. Humayun, A., Fatima, N., Naqqash, S., Hussain, S., Rasheed, A., Imtiaz, H., & Imam, S. Z. (2008). Patients’ perception of actual practice of informed consent, privacy and confidentiality in general medical outpatient departments of two tertiary care hospitals of Lahore. BMC Medical Ethics, 9(14), 1–8.

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Kaelber, D. C., Jha, A. K., Johnston, D., Middleton, B., & Bates, D. W. (2008). A research agenda for personal health records (PHRs). Journal of the American Medical Informatics Association, 15, 729–736. doi:10.1197/jamia.M2547 Meingast, M., Roosta, T., & Sastry, S. (2006). Security and privacy issues with health care information technology. In Proceedings of the 28th IEEE EMBS Annual International Conference, New York, August 30- September 3, (pp. 5453-5458). Office of the Information and Privacy Commissioner. (2002). Privacy Review: Chatham- Kent IT Transition Pilot Project, (pp. 1-32). Toronto: Ontario. Available at http://www.ipc.on.ca/images/Resources/up-042202.pdf Pedersen, C. A., & Gumper, K. F. (2008). ASHP National Survey on Informatics: Assessment of the adoption and use of pharmacy informatics in U.S. hospitals-2007. American Journal of Health-System Pharmacy, 65(23), 2244–2264. doi:10.2146/ajhp080488 Purdy, J. (2000, July). An Intimate Invasion [Electronic Version]. USA Weekend Magazine. Retrieved November 3, 2008 from http://www.usaweekend. com/00_issues/000702/000702privacy.html Rindfleisch, T. C. (1997). Privacy, information technology, and health care. Communications of the ACM, 40(8), 92–100. doi:10.1145/257874.257896 Rosenbaum, S., MacTaggart, P., & Borzi, P. C. (2006). Medicaid and health information: Current and emerging legal issues. Health Care Financing Review, 28(2), 21–30. Wiljer, D., Urowitz, S., Apatu, E., DeLenardo, C., Eysenbach, G., & Harth, T. (2008). Patient accessible electronic health records: Exploring recommendations for successful implementation strategies. Journal of Medical Internet Research, 10(4), e34. doi:10.2196/jmir.1061

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Yasnoff, W. A. (2003). Privacy, confidentiality and security of public health information. In Public Health Informatics and Information Systems. New York: Springer. doi:10.1007/0-387-22745-8_10

This work was previously published in Grid Technologies for E-Health: Applications for Telemedicine Services and Delivery, edited by Ekaterina Kldiashvili, pp. 182-194, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 7.8

Legal Issues in Health Information and Electronic Health Records Nola M. Ries University of Alberta, Canada & University of Victoria, Canada

ABSTRACT This chapter discusses key legal issues raised by the contemporary trend to managing and sharing patient information via electronic health records (EHR). Concepts of privacy, confidentiality, consent, and security are defined and considered in the context of EHR initiatives in Canada, the United Kingdom, and Australia. This chapter explores whether patients have the right to withhold consent to the collection and sharing of their personal information via EHRs. It discusses optin and opt-out models for participation in EHRs and concludes that presumed consent for EHR participation will ensure more rapid and complete implementation, but at the cost of some personal choice for patients. The reduction in patient control

over personal information ought to be augmented with strong security protections to minimize risks of unauthorized access to EHRs and fulfill legal and ethical obligations to safeguard patient information.

INTRODUCTION Healthcare providers have long observed an ethical imperative to respect privacy of patient information. For physicians, this ethical duty originates in the Hippocratic oath, which states: Whatsoever things I see or hear concerning the life of man, in any attendance on the sick or even apart therefrom, which ought not to be noised abroad, I will keep secret thereon, counting such

DOI: 10.4018/978-1-60960-561-2.ch708

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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things to be as sacred secrets (quoted inRozovsky & Inions, 2002, p. 86). In the past, individuals often had a longstanding relationship with a very small number of care providers and health records were maintained in paper files and seldom shared with other health practitioners, or even the patient. Contemporary healthcare is much more complex. Highly mobile individuals seek healthcare in different geographical locations and, with the growth in collaborative, multidisciplinary care, patients are treated not only by family physicians, but by medical specialists and complementary and alternative care providers. Care is delivered in a wide range of settings: practitioners’ offices, walk-in clinics, acute care hospitals, long-term care facilities, and home care situations. To provide appropriate services, patient information must be shared among a wider range of care providers working in different locations. Additionally, many patients take a more active approach to their healthcare and seek access to their records. Health information technology—including electronic health records (EHR)—can facilitate sharing of information in all these ways to the benefit of both patients and professionals. As a longitudinal record of an individual’s healthcare history, EHRs may include summaries of physician visits and care provided in hospital or other facilities, medical test results, x-ray images, prescription drug histories, immunization history, and known allergies. One commentator asserts that EHRs “will transform the purpose of the medical record from a record of information generated by health professionals primarily for their own reference into a shared resource produced and used by all concerned with the process of care” (Cross, 2006b, p. 656). However, the ease with which information can be handled electronically compels special attention to matters of privacy, confidentiality and security. Advances in modern healthcare heighten this responsibility. Novel diagnostic and testing procedures reveal highly

sensitive information about patients (e.g., genetic predisposition to a serious disease) and a growing range of pharmaceuticals and procedures are used to treat conditions about which the patient may feel ashamed or embarrassed (e.g., sexual/ reproductive health, mental health). Patients are likely to have special concern about safeguarding information that would reveal a stigmatizing medical condition. EHRs attract particular concern about unauthorized access and disclosure of personal information contained in the records. Although electronic records have the potential to be more secure than paper records with implementation of sophisticated technical safeguards, they also have potential to reveal vast detail about an individual’s health history. Unauthorized access may occur intentionally or accidentally by persons internal or external to an organization. A major New York City hospital reportedly “thwarted 1,500 unauthorized attempts by its own employees to look at patient records of a famous local athlete” (Freudenheim & Pear, 2006, p. 1). Hackers may also infiltrate EHR systems for nefarious purposes, such as identity theft. Canada’s federal privacy commissioner articulates these concerns: Until relatively recently, privacy was protected pretty much by default. As long as information about us was in paper records, and scattered over a whole lot of locations, someone would have to go to a lot of trouble to compile a detailed dossier on any individual. But now the move to electronic record-keeping is eating away at the barriers of time, distance, and cost that once guarded our privacy. (Privacy Commissioner of Canada, 2001) This chapter discusses key legal issues raised by the contemporary trend to managing and sharing patient information via EHRs. Concepts of privacy, confidentiality, consent, and security are defined and considered in the context of EHR initiatives in several jurisdictions, including Canada, the United Kingdom and Australia. As this chapter

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concentrates on legal issues in health information and EHRs, the following questions are addressed: Do patients have a right to withhold consent to have information collected and/or shared via EHRs? Do laws impose special security requirements for EHRs? Do legal rules hinder or impede the implementation of EHRs and the ability to realize benefits attributed to EHRs? These benefits and challenges are summed up in the following statement: “Most people agree that patient centred care requires comprehensive information to be available wherever and whenever care is provided. There is less agreement, however, on how patients should consent to use of electronic health records and how the data can be kept secure” (Watson & Halamka, 2006, p. 39).

ELECTRONIC HEALTH RECORD INITIATIVES Many countries are investing significant resources in EHR initiatives. In Canada, Canada Health Infoway was launched in 2001 as a not-for-profit organization comprised of deputy ministers of health from the 14 federal, provincial and territorial jurisdictions in the country. Infoway receives funds from the national government, which it then invests in EHR projects, and aims to support interoperable networks across Canada. Its funding agreement mandates that it address personal health information protection in accordance with relevant laws and privacy principles. Each Canadian jurisdiction has its own personal information protection laws, which can pose challenges in ensuring EHR initiatives comply with multiple legal regimes (University of Alberta, 2005). Beginning in 2002, the UK Health Department allocated £18billion (US$32 billion) for National Health Service (NHS) information technology (Chantler, Clarke, & Granger, 2005). The NHS aims to implement the National Programme for Information Technology to “connect over 30,000 GPs in England to almost 300 hospitals and give

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patients access to their personal health and care information, transforming the way the NHS works” (National Health Service, 2005a). This initiative includes several elements: the care records service, the national EHR initiative; electronic prescription transmission; the secondary uses service, which will de-identify patient data to make it available for research; and GP2GP, an initiative to support transfer of patient electronic records from one general practice to another when a patient registers with a new practice. The U.K. Programme “is attempting to create the most comprehensive electronic health records infrastructure of any healthcare system, in which multimedia records compiled at the point of care are made available to authorized users in primary, secondary, tertiary, and community care” (Cross, 2006a, p. 599). The care records service EHR will eventually encompass a comprehensive healthcare history for each patient. A national database, referred to as the “spine” of the system, will contain basic patient information such as name, birth date, allergies, adverse drug reactions, and NHS number. Local EHRs, linkable to the national spine, will contain more detailed information, including medication records, test results, and disease history. A National Electronic Health Records Taskforce for Australia was established in 1999 to bring “a coordinated approach to electronic health record systems and to avoid the potential for duplication and incompatible systems” (National Electronic Health Records Taskforce, 2000, p. 6). Similar to Canada Health Infoway, the Australian national, state and territorial governments created a not-for-profit organization, the National E-Health Transition Authority (NEHTA), to develop e-health standards and infrastructure requirements for the national EHR initiative. A nationally interoperable shared EHR will allow healthcare providers to use a standardized format to create and store a summary each time a patient receives healthcare, including information such as test results, diagnosis, care plan, medication, and referrals to other care providers.

Legal Issues in Health Information and Electronic Health Records

PRIVACY, CONFIDENTIALITY, CONSENT, AND SECURITY The concepts of privacy, confidentiality, consent and security are all fundamental to handling of personal health information and EHRs raise important questions about how to operationalize these requirements in the development of systems for collecting and sharing patient information across various healthcare settings. The concept of privacy refers to an individual’s right to control access to and use of their personal information and the concept of confidentiality refers to another individual’s duty to safeguard information disclosed to them (Marshall & von Tigerstrom, 2002). To obtain healthcare services, patients voluntarily relinquish a degree of privacy by sharing personal details with care providers. However, patients generally do so with the expectation that the healthcare practitioner will keep the information confidential. The concept of consent is also important in the healthcare context. It is a fundamental legal and ethical rule that healthcare providers must obtain informed consent from patients before administering treatment (subject to limited exceptions in, for example, emergency and health situations). Similarly, patients have certain rights to consent in regard to collection, use and disclosure of their personal information. Privacy and consent are interrelated concepts since privacy interests in one’s personal health information are respected if one has an opportunity to exercise some control over it by consenting to, or withholding consent for, various uses or disclosures. Consent may be given explicitly in writing or orally, or it may be implicit. For instance, if a patient visits her family physician and blood work is required, the doctor will collect and send a blood sample to a lab for analysis. The lab will then send the test results back to the medical office. This sharing of the patient’s information is generally done on the basis of implied consent. In seeking care, the patient implicitly agrees to the

sharing of her personal medical details between her physician’s office and the testing lab. An example of explicit consent occurs when a patient signs a written form agreeing that researchers may review and extract identifiable information from his healthcare records. In this circumstance, the patient would make a consent decision after receiving sufficient information about how her information will be used and protected and those who will have access to it. The concept of security is important as security measures are a means by which healthcare providers fulfill their duties of confidentiality. Security measures include technical, physical and administrative measures that guard against unauthorized access to information. In regard to electronic data, these may include password protections, encryption mechanisms and audit trails to monitor access. Security measures for paper records include storage in locked cabinets and shredding documents prior to disposal. As discussed later, privacy laws typically impose a legal obligation on organizations to adopt reasonable security measures to safeguard records over which they have control.

LEGAL PROTECTIONS FOR HEALTH INFORMATION Various legal instruments provide privacy protections for health information, including legislative enactments and professional codes of conduct. Privacy laws are often enforced by independent agencies or commissioners with authority to investigate alleged violations, attempt to resolve complaints through mediation, recommend or order changes to policies or practices to prevent future breaches, and, in some circumstances, impose fines or terms of imprisonment. Healthcare providers may be sanctioned by their professional regulatory bodies for failure to comply with codes of conduct.

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The past 15 years have witnessed remarkable growth in the number of laws regulating privacy in public, private and health sectors. The enactment of these laws indicates that legislators are taking privacy seriously, but a sensitive balance must be struck between protecting personal privacy and delivering healthcare. Indeed, stringent legal rules can hinder implementation of EHR initiatives; at the same time, appropriate legal frameworks may bolster patient and professional support for and trust in EHRs. One commentator observes: EHRs, which facilitate sharing of information by a wide network of people, potentially conflict with privacy principles unless patients control how the record is shared and appropriate security measures are in place. A coherent legal framework to appropriately protect the privacy and confidentiality of personal health records is therefore an essential first step for successful EHRs. (Cornwall, 2003, p. 18) In countries with multiple levels of government—national, state/provincial and local levels— several layers of legislation may apply to personal information contained in EHRs and add complexity for healthcare organizations and providers in understanding rules for lawful collection, use and sharing of patient information. The situation in Canada and Australia demonstrates the complexity of compliance with statutes enacted by various levels of government. Beginning in the 1990s, many Canadian provinces began enacting privacy protection and information access laws applicable in the public sector. These laws generally applied to government health departments and publicly funded hospitals, but did not regulate private healthcare practices. By the late 1990s, several provinces had developed legislation to regulate health information directly and some adopted specific rules governing EHRs (Ries & Moysa, 2005). In 2001, the federal government adopted a personal information protection law to apply to commercial

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activities and this legislation was extended to the health sector in 2004. Several provinces enacted their own private sector privacy laws, which allowed them to be exempt from regulation under the federal law. This patchwork of legal regimes has generated criticism, including concern that it may impede EHR implementation. For example, a 2002 Senate report on the Canadian healthcare system observed: Currently, there is significant variation in privacy laws and data access policies across the country that poses a challenge for EHR systems that are dependent on inter-sectoral and inter-jurisdictional flows of personal health information. Differences in rules on how the scope of purpose is defined, the form of consent required, the conditions for substitute decision-making, the criteria for nonconsensual access to personal health information, periods for retention of data and requirements for destruction, to name but a few, must be seriously addressed in order to enable the development of EHR systems. (Canada, Standing Senate Committee on Social Affairs, Science and Technology, 2002, section 10.4) Like Canada, Australia has both federal and state privacy laws that apply to various healthcare entities (National Health and Medical Research Council, 2004). Federal bodies such as the national health department have been regulated under federal privacy legislation since 1988. This statute was extended to the private health sector in 2001, encompassing physicians, pharmacists and federal private hospitals. State-run organizations are governed by local legislation or, in the absence of legislation, codes of practice. The National E-Health Transition Authority has noted the challenges of navigating this legislative intricacy: Privacy is regulated by a complex amalgam of Commonwealth, State and Territory privacy legislation combined with administrative instructions. Requirements relating to the collection or handling

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of personal information can also be found in other non-privacy legislation … The picture is further complicated by the increased use of outsourcing by Australian governments, and the need to ensure privacy protection follows the outsourced data, as well as the development of independent privacy schemes, particularly in the area of health privacy at the state/territory level. (National E-Health Transition Authority, 2006, p. 10) Alongside the enactment of laws regulating health information, some health professional organizations have adopted codes of conduct that stipulate guiding principles relevant to privacy, consent, confidentiality and security. The Canadian Medical Association Health Information Privacy Code (1998) elaborates on the significance of the right to privacy in the professional-patient relationship:

to infer consent” (Principle 5.5) and further, “implied consent does not deprive the patient of the right to refuse consent ...” (Principle 5.8). If patient information will be included in an EHR for the purpose of providing longitudinal care, then, according to the privacy code, the physician must obtain the patient’s consent to include information on the EHR. Even if patient consent were implied, a right to revoke consent remains. Obtaining specific consent to opt-in to an EHR may be impractical to implement and, as discussed below, some privacy legislation has been revised to eliminate this requirement.

REGULATING COLLECTION, USE AND DISCLOSURE OF INFORMATION IN ELECTRONIC HEALTH RECORDS

The right of privacy is fundamental in a free and democratic society. It includes a patient’s right to determine with whom he or she will share information and to know of and exercise control over use, disclosure and access concerning any information collected about him or her. The right of privacy and consent are essential to the trust and integrity of the patient-physician relationship. Nonconsensual collection, use, access or disclosure violates the patient’s right of privacy.

In developing EHR systems, various strategies may be adopted to respect privacy rights of individuals and ensure healthcare providers can meet their confidentiality obligations. These include:

If codes of conduct impose strict rules about patient control over health information, care providers may have difficulty complying with these rules during EHR implementation. Indeed, professional codes of conduct may create more onerous requirements than privacy laws. For example, the Canadian Health Information Privacy Code states that physicians can infer that patients consent to use and disclosure of health information for their therapeutic benefit. However, the code elaborates that “consent to collection, use, disclosure and access for longitudinal primary purposes must be express unless the provider has good reason

•

• •

• •

Giving patients a choice to opt in or opt out of EHR systems Giving patients a choice to request that specified information be “masked” within the EHR system with limited access to certain healthcare providers Restrict levels of access to EHRs on a “need to know” basis Regular monitoring and auditing of access to EHRs Implement state-of-the-art security measures

While it is uncontroversial that rigorous administrative and security measures should be implemented to protect EHRs from unnecessary and unauthorized access, tampering and disclosure, the issue of patient consent to participation in EHR initiatives has generated much debate and some

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differences in approaches among jurisdictions (Cornwall 2003; Ries, 2006; Ries & Moysa, 2005). Many privacy protection principles and professional codes of conduct emphasize the importance of obtaining patient consent for collection, use and sharing of identifiable health information. Should this principle apply to require individual patient consent before a care provider records information in an EHR, or shares that information with others involved in the patient’s care? Under an opt-in model, healthcare providers would need to obtain explicit, informed consent from patients before their health information would be put onto an EHR system. With an opt-out approach, patient information would be included in the EHR unless the individual specifically instructs the care provider not to collect or share the information electronically. In the United Kingdom, development and implementation of the Care Records Service EHR has been dogged by privacy concerns; critics argue “the scale and proposed content of the electronic health record threatens medical privacy and, potentially, other human rights” (Cross, 2006a, p. 599). The British Medical Association (BMA) has also criticized the government for lack of sufficient consultation with the U.K. medical profession (Powell, 2004). Much confusion has erupted over whether patient participation in the EHR will be voluntary or compulsory and, if compulsory, whether patients may still exercise some control over personal information included in the EHR, such as “lock boxes” on sensitive details a patient may not want generally accessible to anyone who opens their EHR. To remedy this confusion, the National Health Service issued a “care record guarantee” that describes patient rights and health provider obligations in regard to privacy and confidentiality of healthcare records. This guarantee assures patients that the EHR will hold their personal health details “securely, making them available to the right people where and when they are needed for your healthcare, while maintaining your confi-

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dentiality” (National Health Service, 2005b). The guarantee is premised on compulsory participation in the Care Records Service, so patients do not have the option of withholding consent for collection of their information electronically. However, the guarantee allows patients the right to limit sharing of their information: “Usually you can choose to limit how we share the information in your electronic care records which identifies you. In helping you decide, we will discuss with you how this may affect our ability to provide you with care or treatment, and any alternatives available to you.” This guarantee suggests that “UK doctors will be expected to spend time in every consultation discussing with patients what information about them is shared across NHS computers” (Cross, 2005, p. 1226). Canadian and Australian experiences discussed below suggest that it may be very time-consuming and costly to expect physicians to have detailed conversations with patients about consent for sharing information electronically. Despite the potential burdens on physicians, the British Medical Association supports an optin model for patient participation in the national EHR scheme: It is the BMA view that patients should be asked for consent explicitly before any clinical information is shared onto a central system. Doctors feel that some patients may be unhappy about having their personal data uploaded onto a central system and a more gradual approach will allow patients to fully consider what information is contained in their records and whether they wish this information to be shared. Their view is that uploading clinical data without explicitly asking the patient could jeopardise the trust and relationship between doctors and patients, as well as violating a patient’s right to confidentiality. (British Medical Association, 2006) In Australia, a privacy framework is being developed for the national EHR initiative. The ini-

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tial Business Architecture Plan stated that patient participation would be voluntary and individual consent would be required to authorize collection, use and disclosure of personal information via the system (Ries, 2006, p. 703). At each clinical interaction, patients would have a choice of having information recorded in the EHR and who would be authorized to access that information in the future. However, experience from pilot EHR trials in the Australian state of Tasmania demonstrated that care providers often did not obtain informed consent from patients before adding information to the system and such a requirement would be administratively burdensome (McSherry, 2004). The consent model Australia will adopt remains undecided and a December 2006 privacy consultation document states: Consent in the health context has proved to be one of the most intractable policy and legal issues faced by Australian e-health initiatives. Numerous debates about the respective merits of ‘opt in’ v. ‘opt out’; confusion about the plethora of privacy laws in operation in Australia; and the risk of failing to meet all relevant compliance requirements (particularly meeting the test of ‘informed consent’) have deeply affected the debate to date. (National E-Health Transition Authority, 2006, p. 23) In Canada, legislators in some jurisdictions have amended privacy legislation to eliminate requirements to obtain individual patient consent before information is disclosed through an EHR. For example, when the province of Alberta enacted the Health Information Act in 2001, it contained rules that required a healthcare provider to explain a number of details to a patient to obtain permission to share health information electronically, including: the reason for disclosing the information; an explanation of why the information is needed; the risks and benefits of granting or refusing consent; and the right of a patient to withdraw consent at any time. As with

the experience in Tasmania, implementation of these conditions proved difficult in practice. In a pilot project for a pharmaceutical information network, “doctors were taking more than 30 minutes to explain the system, driven by concerns about professional liability” (Cornwall, 2003, p. 22). The Alberta Information and Privacy Commissioner (2003) noted that “getting consent … was going to be difficult and costly” and conceded that the drawbacks of obtaining consent outweighed the benefit. The government revised the legislation in 2003 to remove the consent requirement. However, some provincial health information protection laws may give patients the right to request limits on disclosure of their personal information. For example, legislation in the provinces of Ontario (Personal Health Information Protection Act, 2004, s. 22(2)) and Manitoba (Personal Health Information Act, 1997, s. 38(1)) permit a patient to refuse consent for disclosure of information to other healthcare providers. Under Ontario’s law, if a healthcare provider does not have consent to release all patient information to another professional that may be relevant to the patient’s care, this limitation on disclosure must be communicated to the other professional (s. 20(3)). Canadian health information laws impose a legal duty to implement appropriate security safeguards to protect personal information from unauthorized handling. Information that is stored and transferred electronically is viewed as having greater risk of security breach, so additional security measures are generally required for electronic systems. For instance, Manitoba regulations require that security procedures for electronic health systems ensure regular monitoring and auditing of user activity (Personal Health Information Regulation, s. 4). The record of user activity must specify whose information was accessed, by whom, when, and if information from the electronic record was subsequently disclosed.

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LEARNING FROM EXPERIENCE As experiences from Canada, Australia and the United Kingdom show, legislators, policy-makers and EHR system designers must confront legal issues related to patient privacy and confidentiality of personal health information. Debate about patient control over information in EHRs is heightened as “[c]omputerized databases of personally identifiable information may be accessed, changed, viewed, copied, used, disclosed, or deleted more easily and by more people (authorized and unauthorized) than paper-based records” (Hodge, Gostin, & Jacobson, 1999, p. 1467). To sustain public trust in the healthcare system, patients must have confidence that their information will be secure if it is stored and shared through electronic networks. Patients do not have uniform views about whether EHR systems are based on opt-in or opt-out models; patients with sensitive medical histories may want to make a specific choice about opting in, but others may want governments to avoid further delay with EHR development. Indeed, a patient representative on the U.K. Care Record Development Board argues: So does the advantage of opting in for a minority of patients with sensitive histories stack up against the disadvantage imposed on the majority if a flawed implementation process results in delays and frustration for patients and hard pressed practitioners? Certainly, an effective public information campaign is vital to fulfil the ethical and legal requirements for informed consent. But surely patients have a right to expect the NHS to positively harness technology, to reform the way it uses information, and hence to improve services and the quality of patient care? It would be unfortunate if an over-riding emphasis on the NHS’s role as guardian of patient confidentiality resulted in an unworkable operating model for the implementation of the care record. (Wilkinson, 2006, p. 43)

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Nonetheless, legal and ethical rules require attention to consent rights and confidentiality obligations. To mitigate problems of overlapping or conflicting privacy laws that may impede development of EHRs, cooperative efforts to develop best practices are useful. A national body in Canada has developed a health information privacy framework to recommend harmonized principles for collection, use and disclosure of personal health information across sectors. This harmonization initiative aims to “facilitate healthcare renewal, including the development of electronic health record systems….” (Pan-Canadian Health Information Privacy and Confidentiality Framework, 2005). Similarly, to address gaps in Australia’s personal information protection practices, a National Health Privacy Working Group has promulgated a draft National Health Privacy Code, with three key goals: (1) provide consistent rules across jurisdictions and between public and private sectors; (2) address the impact of new technologies; and (3) safeguard privacy of personal health information (National Health Privacy Working Group, 2003). How this privacy code will apply to EHRs is a matter that remains unresolved. Jurisdictions like Alberta and Tasmania that piloted EHR systems on the basis of an opt-in consent model have found this to be unworkable and it is foreseeable that other initiatives will face similar problems that it is time-consuming and complex for healthcare providers to explain the system, its features, risks and benefits to patients. If governments adopt an opt-out model, patients will be assumed to consent to having at least basic personal and medical information included in an EHR, but patients may refuse consent for collection and disclosure of specified information that is of particular sensitivity. If patients cannot opt-out entirely from an EHR system, then governments “must convincingly show that technical, organisational, and legal safeguards will be implemented in its information technology programme. These safeguards must include strict and transparent

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rules of access to health records, mechanisms of complaint, and open understandable information about the programme and its implications” (Norheim, 2006, p. 3).

CONCLUSION Electronic health records promise important benefits for patients, healthcare providers, and those who plan and manage complex, modern health systems. Yet, EHRs raise special privacy concerns and an appropriate balance must be sought between personal information protection and systems that are reasonably comprehensive and not unduly cumbersome. Privacy principles and ethical standards often emphasize patient consent as fundamental to respect for autonomy and choice. However, if all patients must consent before their personal information is compiled onto an EHR and made available to others involved in their care and treatment, it is foreseeable that EHR implementation will be slow and costly. An opt-out model for EHR participation will ensure more rapid and complete inclusion of patient information, but at the cost of some degree of personal choice for patients. Governmental bodies responsible for developing EHR systems ought to engage in consultation with privacy commissioners, health practitioner groups, and the public on issues of privacy and confidentiality. A transparent approach to these contentious matters will help ensure that EHRs are implemented in ways that respect values and interests of all who have a stake in these major health information technology investments.

FUTURE RESEARCH DIRECTIONS Additional research in two areas would assist in the development of appropriate privacy protections and consent models for EHR systems: (1) patient/citizen expectations and attitudes; and (2)

healthcare professional duties and behaviours. EHRs are adopted within social and professional contexts where patients and healthcare providers each have their own sets of expectations and concerns in regard to personal health information. As this chapter discussed, some health professional codes of conduct may impose more stringent rules for handling of personal information than privacy laws. An important research question here, then, is how can professionals adopt and participate in EHR systems while meeting their fundamental ethical obligations to respect patient privacy? How prevalent and serious are conflicts between professional codes and privacy laws? Do healthcare professionals perceive there are conflicts and are they between the proverbial rock and a hard place, caught between professional ethics that dictate one behaviour (e.g., obtaining patient consent before including information on an EHR) and system-wide policies that dictate another (e.g., presumed consent for inclusion of information on EHRs)? How can competing principles be reconciled? In addition to analyzing interrelationships between professional codes of conduct and privacy laws, further research is needed regarding health professionals’ practices and behaviours in the context of EHR implementation. How do professionals manage issues related to privacy and patient consent? As discussed in this chapter, some experiences suggest it is very time-consuming for care providers to explain EHR systems to patients. Will this experience hold true in other contexts of EHR adoption? How will healthcare professionals explain patients’ rights to them, such as a right to limit disclosure of personal information via an EHR? Will care providers offer the same explanation to all patients or will they only discuss these types of privacy protections with patients who have conditions perceived as stigmatizing? In other words, will a form of medical paternalism influence how professionals communicate with patients about EHRs?

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Further, what are patient views about electronic health records? Is it making a mountain out of a molehill to constantly emphasize the sensitivity of personal health information? Citizen often guard other personal information very closely, such as details about income, investments and financial history, yet the banking industry is years ahead of the healthcare industry in many countries in the adoption of information technology. What can the healthcare sector learn from other sectors about adoption of information technologies in a manner that protects personal information and maintains client trust in organizations? For patients who express concerns about security of EHRs, what measures would make them more comfortable with the technology? What are patient preferences in regard to rights to opt in or opt out of EHR initiatives or to have information protected by lock boxes with the system? Do patients want electronic access to their records? What are their perceptions, preferences and expectations about EHRs? Further investigation of these types of questions are critically important in informing further development and refinement of legal protections for personal health information in the context of EHR systems.

REFERENCES Alberta Information and Privacy Commissioner. (2003). Commissioner’s response to repeal of section 59 and introduction of section 60(2) of the Health Information Act. Press release retrieved December 20, 2006, from http://www.oipc.ab.ca/ ims/client/upload/Repeal_of_s.59.pdf British Medical Association. (2006). BMA statement on Connecting for Health. Retrieved January 25, 2007, from http://www.bma.org.uk

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Canada, Standing Senate Committee on Social Affairs, Science and Technology. (2002). The Health of Canadians—The Federal Role, vol. 1-6. Ottawa: Standing Senate Committee on Social Affairs, Science and Technology. Canadian Medical Association. (1998). Health Information Privacy Code. Retrieved December 1, 2006 from http://www.cma.ca/index.cfm/ ci_id/3216/la_id/1.htm Chantler, C., Clarke, T., & Granger, R. (2006). Information technology in the English national health service. Journal of the American Medical Association, 296(18), 2255–2258. doi:10.1001/ jama.296.18.2255 Cornwall, A. (2003). Connecting health: A review of electronic health record projects in Australia, Europe and Canada. Public Interest Advocacy Centre. Retrieved December 1, 2006, from http://www.piac.asn.au/publications/pubs/ churchill_20030121.html Cross, M. (2005). UK patients can refuse to let their data be shared across networks. British Medical Journal, 330, 1226. doi:10.1136/ bmj.330.7502.1226-b Cross, M. (2006a). Will connecting for health deliver its promises? British Medical Journal, 332(7541), 599–601. doi:10.1136/bmj.332.7541.599 Cross, M. (2006b). Keeping the NHS electronic spine on track. British Medical Journal, 332(7542), 656–658. doi:10.1136/bmj.332.7542.656 Freudenheim, M., & Pear, R. (2006). Health hazard: Computers spilling your history. New York Times, December 3, Section 3. Hodge, J. G., Gostin, L. O., & Jacobson, P. D. (1999). Legal issues concerning electronic health information: Privacy, quality, and liability. Journal of the American Medical Association, 282(15), 1466. doi:10.1001/jama.282.15.1466

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Marshall, M., & von Tigerstrom, B. (2002). Health information. In J. Downie, T. Caulfield & C. Flood (Eds.), Canadian health law and policy (2nd ed.). Markham, Ont: Butterworths. McSherry, B. (2004). Ethical issues in healthConnect’s shared electronic record system. Journal of Law and Medicine, 12, 60. National, E. -Health Transition Authority. (2006). Privacy blueprint—Unique healthcare identifiers, Version 1.0. Retrieved February 1, 2007, from http://www.nehta.gov.au/ National Electronic Health Records Taskforce. (2000). Issues paper: A national approach to electronic health records for australia. Retrieved December 1, 2006, from www.gpcg.org/publications/docs/Ehrissue.doc National Health and Medical Research Council. (2004). The regulation of health information privacy in Australia. Retrieved January 15, 2007, from http://www.nhmrc.gov.au/publications/_files/nh53.pdf National Health Privacy Working Group of the Australian Health Ministers’ Advisory Council. (2003). Proposed National Health Privacy Code. Retrieved December 1, 2006, from www.health. gov.au/pubs/nhpcode.htm National Health Service. (2005a). National Programme for IT in the NHS. Retrieved December 1, 2006 from http://www.connectingforhealth. nhs.uk/ National Health Service. (2005b). The Care Record Guarantee: Our Guarantee for NHS Care Records in England. Retrieved January 12, 2007, from http://www.connectingforhealth.nhs.uk/ crdb/docs/crs_guarantee.pdf Norheim, O. F. (2006). Soft paternalism and the ethics of shared electronic patient records. British Medical Journal, 333, 2–3. doi:10.1136/ bmj.38890.391632.68

Pan-Canadian Health Information Privacy and Confidentiality Framework. (2005). Retrieved January 3, 2007, from http://www.hc-sc.gc.ca/ hcs-sss/pubs/ehealth-esante/2005-pancanad-priv/ index_e.html Personal Health Information Act. (1997).Continuing Consolidation of the Statutes of Manitoba, chapter P33.5. Personal Health Information Protection Act. (2004). Statutes of Ontario, chapter 3. Personal Health Information Regulations, Manitoba Regulation 245/97, updated to Manitoba Regulation 142/2005. Powell, J. (2004). Speech from the Chairman of the IT Committee. British Medical Association. Retrieved December 1, 2006, from http://www. bma.org.uk/ap.nsf/Content/ARM04chIT?OpenD ocument&Highlight=2,john,powell Privacy Commissioner of Canada. (2001). Annual Report to Parliament, 2000-2001. Retrieved December 15, 2006, from http://www.privcom. gc.ca/information/ar/02_04_09_e.asp Ries, N. M. (2006). Patient privacy in a wired (and wireless) world: Approaches to consent in the context of electronic health records. Alberta Law Review, 43(3), 681–712. Ries, N. M., & Moysa, G. (2005). Legal protection of electronic health records: Issues of consent and security. Health Law Review, 14(1), 18–25. Rozovsky, L. E., & Inions, N. J. (2002). Canadian Health Information (3rd ed.). Markham, Ont: Butterworths Canada Ltd. University of Alberta Health Law Institute and University of Victoria School of Health Information Science. (2005). Electronic health records and the Personal Information Protection and Electronic Documents Act. Retrieved December 12, 2006, from http://www.law.ualberta.ca/centres/hli/pdfs/ElectronicHealth.pdf

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Watson, N., & Halamka, J. (2006). Patients should have to opt out of national electronic care records. British Medical Journal, 333, 39–42. doi:10.1136/ bmj.333.7557.39 Wilkinson, J. (2006). Commentary: What’s all the fuss about? British Medical Journal, 333, 42–43. doi:10.1136/bmj.333.7557.42

ADDITIONAL READING Advisory Committee on Information and Emerging Technologies. (2005). Pan-Canadian Health Information Privacy Framework. Ottawa, ON: Health and Information Highway Division, Health Canada. Available at: http://www.hc-sc.gc.ca/ hcs-sss/pubs/ehealth-esante/2005-pancanad-priv/ index_e.html Bassinder, J., Bali, R. K., & Naguib, R. (2006). Knowledge management and electronic care records: Incorporating social, legal and ethical issues. Studies in Health Technology and Informatics, 121, 221–227. Blumenthal, D., & Glaser, A. (2007). Information technology comes to medicine. The New England Journal of Medicine, 356(24), 2527–2534. doi:10.1056/NEJMhpr066212 Buckovich, S. A., Rippen, H. E., & Rozen, M. J. (1999). Driving toward guiding principles: A goal for privacy, confidentiality, and security of health information. Journal of the American Medical Informatics Association, 6, 122–133. Canada Health Infoway. (2007). White paper on information governance of the interoperable electronic health record. Montreal, QC: Canada Health Infoway. Centre for Health Informatics and Multiprofessional Education. (1993). The good european health record: Ethical and legal requirements. Available at: http://www.chime.ucl.ac.uk/workareas/ehrs/GEHR/EUCEN/del8.pdf 1960

Cornwall, A. (2003). Connecting health: A review of electronic health record projects in Australia, Europe and Canada. Public Interest Advocacy Centre. Available at: http://www.piac.asn.au/ publications/pubs/churchill_20030121.html Fairweather, N. B., & Rogerson, S. (2001). A moral approach to electronic patient records. Medical Informatics and the Internet in Medicine, 26(3), 219–234. doi:10.1080/14639230110076412 Goldberg, I. V. (2000). Electronic medical records and patient privacy. The Health Care Manager, 18(3), 63–69. Gritzalis, S. (2004). Enhancing privacy and data protection in electronic medical environments. Journal of Medical Systems, 28(6), 535–547. doi:10.1023/B:JOMS.0000044956.55209.75 Hillestad, R., Bigelow, J., & Bower, A. (2005). Can electronic medical record systems transform healthcare? Potential health benefits, savings, and costs. Health Affairs, 24, 1103–1117. doi:10.1377/ hlthaff.24.5.1103 Kirshen, A. J., & Ho, C. (1999). Ethical considerations in sharing personal information on computer data sets. Canadian Family Physician Medecin de Famille Canadien, 45, 2563–2565, 2575–2577. Kluge, E. H. (2000). Professional codes for electronic HC record protection: Ethical, legal, economic and structural issues. International Journal of Medical Informatics, 60(2), 85–96. doi:10.1016/S1386-5056(00)00107-6 Kluge, E. H. (2003). Security and privacy of EHR systems—Ethical, social and legal requirements. Studies in Health Technology and Informatics, 96, 121–127. Kluge, E. H. (2004). Informed consent and the security of the electronic health record (EHR): Some policy considerations. International Journal of Medical Informatics, 73(3), 229–234. doi:10.1016/j.ijmedinf.2003.11.005

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Lo, B. (2006). Professionalism in the age of computerised medical records. Singapore Medical Journal, 47(12), 1018–1022.

Terry, N. P. (2004). Electronic health records: International, structural and legal perspectives. Journal of Law and Medicine, 12(1), 26–39.

Nordberg, R. (2006). EHR in the perspective of security, integrity and ethics. Studies in Health Technology and Informatics, 121, 291–298.

University of Alberta Health Law Institute and University of Victoria School of Health Information Science. (2005). Electronic health records and the Personal Information Protection and Electronic Documents Act. Available at http:// www.law.ualberta.ca/centres/hli/pdfs/ElectronicHealth.pdf

Petrisor, A. I., & Close, J. M. (2002). Electronic health care records in europe: Confidentiality issues from an American perspective. Studies in Health Technology and Informatics, 87, 44–46. Ries, N. M. (2006). Patient privacy in a wired (and wireless) world: Approaches to consent in the context of electronic health records. Alberta Law Review, 43(3), 681–712. Roscam Abbing, H. D. (2000). Medical confidentiality and electronic patient files. Medicine and Law, 19(1), 107–112. Sharpe, V. A. (2005). Privacy and security for electronic health records. The Hastings Center Report, 35(6), 49. doi:10.2307/3528564

Wilson, P. (1999). The electronic health record—a new challenge for privacy and confidentiality in medicine? Biomedical Ethics (Tubingen, Germany), 4(2), 48–55. Win, K. T., & Fulcher, J. A. (2007). Consent mechanisms for electronic health record systems: a simple yet unresolved issue. Journal of Medical Systems, 31(2), 91–96. doi:10.1007/s10916-0069030-3

This work was previously published in Human, Social, and Organizational Aspects of Health Information Systems, edited by Andre W. Kushniruk and Elizabeth M. Borycki, pp. 260-273, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 7.9

Integrating Telehealth into the Organization’s Work System Joachim Jean-Jules Université de Sherbrooke, Canada Alain O. Villeneuve Université de Sherbrooke, Canada

ABSTRACT With increased use of telehealth to provide healthcare services, bringing telehealth technology out of experimental settings into real life settings, it is imperative to gain a deeper understanding of mechanisms underlying the assimilation of teleheath systems. Yet, there is little understanding of how information systems are assimilated by organizations, more work is then warranted to understand how telehealth can be integrated into administrative and clinical practices and to identify factors that may impinge onto telehealth integration. Borrowing from institutional, structural and organizational learning theories, the authors develop a multilevel model for understanding assimilation of telehealth systems. Their study addresses limitations of past work and will be

helpful for guiding research and managerial actions while integrating telehealth in the workplace.

INTRODUCTION Telehealth has emerged as a key strategy for providing healthcare services to underserved or difficult-to-serve populations and low-cost specialty services to areas where full-time staffing would be uneconomical. It is expected to provide many other benefits such as shortening the timeframe for decision-making related to diagnosis and treatment, cutting emergency transfer costs, reducing expenses for patient travel from remote regions to healthcare service points, reducing delays in providing healthcare, promoting continuous healthcare, and attracting and retaining clinicians in remote regions.

DOI: 10.4018/978-1-60960-561-2.ch709

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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However, the experience with telehealth has not always been positive, for many reasons: lack of acceptance by physicians, poor-quality technology (e.g., low resolution video data), and premature funding termination (Bashur, Sanders and Shannon, 1997). Although some of these limitations have been resolved recently, additional problems have emerged such as legal issues pertaining to professional liability and cross-province licensing; safety guidelines and standards regarding interconnectivity and interoperability; and the privacy, security and confidentiality of individually identifiable health information (DHSS, 2001). Despite these barriers, governments keep funding telehealth projects and programs. Motivated by the collective performance of such programs in terms of clinical value and technical feasibility, governments are trying to integrate telehealth into the mainstream clinical care system. This concern, which seems a priori to be related to management, is also technological in nature, given the central role of information technologies in telehealth projects. Incorporating telehealth into the health-care system means inserting telehealth systems into clinical and administrative routines and integrating them into the technological and information architecture. As a result, integration calls for adjustments not only to healthcare organizations’ administrative and clinical routines (Saga and Zmud, 1994) but also to their work systems and technological configurations (Chatterjee and Segars, 2001; Keen and McDonald, 2000; Cooper and Zmud, 1990; Kwon, 1987). Indeed, to be truly valuable, telehealth systems not only need be accepted but also must be smoothly assimilated (routinized and infused) into existing routines, as well as into clinical and administrative functions (Zmud and Apple, 1992; The Lewin Group, 2000). Many experts recognize that the most effective telehealth programs are those that are most seamlessly integrated into current clinical and business practice and that can operate on their own in the absence

of outside funding (Akerman, 2000). Their success should be measured by the extent to which they are no longer stand-alone applications (Grigsby, Schlenker, Kaehny, Shaughnessy and Sandberg, 1995). Thus, the assimilation of telehealth systems may not be as smooth as we would wish, as The Lewin Group Report states: “Unlike most new technologies that diffuse smoothly into health care delivery, implementing telemedicine systems and teleconsultation in particular, often presents departures from standard means of health care delivery, administration and financing” (2000, p.21). Therefore, understanding the mechanisms whereby telehealth systems are assimilated and the factors that influence this process is a matter of vital importance in both theory and practice. Little is known, however, about the process of telehealth systems assimilation and the enabling and impeding factors since most studies to date have focused on user acceptance and adoption; little has been said on what happens after the initial adoption decision has been taken. In addition to being understudied in the literature on information systems, this phenomenon suffers from a lack of theorization. Consequently, the aim of this chapter is twofold. First, it is intended to enrich our understanding of the process of technology assimilation by making both its dimensions and the underlying mechanisms more explicit. As well, it uses this understanding as a basis for identifying factors that could potentially influence assimilation. Thus, this chapter attempts to add to our knowledge of assimilation of large-scale information systems in healthcare settings. Telehealth constitutes a new field for experimentation involving information technologies. It also provides a new context of study, given the specificities of the healthcare milieu in terms of organization, culture and professional practices. Due to their highly complex nature, healthcare organizations allow us to extend, propose and test theories that go beyond our current understanding of informa-

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tion technology assimilation. Borrowing from institutional, structuration and social cognition theories, this work proposes a multilevel model of the determinants relevant for the assimilation of telehealth systems in healthcare organizations. The chapter begins with a consideration of the nature of telehealth systems and the issues related to their deployment. It then establishes a reference framework and principles guiding the modeling of the phenomenon and undertakes the multilevel development of the conceptual model as such. The chapter concludes by considering the theoretical and practical contributions of the study and specifies future orientations.

THEORIZING STRATEGY As indicated above, this study proposes to develop a model of the assimilation of telehealth systems. Such an undertaking can only have value if the model is anchored in theory and if the theories applied make it possible to take account of the issues associated with the assimilation of these systems and analyze them appropriately. This entails at least two things. First of all, particular attention should be paid to the conceptualization of telehealth systems in order to clarify their characteristics. As well, it is necessary to take into consideration, on one hand, these systems’ cultural and computational aspects and, on the other, the effect of the social, historical and institutional contexts, and the way in which the systems are understood and used (Orlikowski and Iacono, 2001). By articulating the nature and role of these systems within their organizational and institutional contexts (Latour, 1987), we were able to identify the issues related to their assimilation and the theories likely to inform these issues, and thus ultimately to formulate, in a robust and logical way, a network of factors likely to influence assimilation.

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UNDERSTANDING TELEHEALTH SYSTEMS Traditional Conceptualization of Telehealth Systems Given the centrality of information technology in telehealth, many studies in the field of information systems (IS) have investigated telehealth systems. A close examination of these studies revealed three salient streams, namely (1) user acceptance/ adoption of telehealth systems (Mitchell, Mitchell and Disney, 1996; Hu and Chau, 1999; Cohn and Goodenough, 2002); (2) the characteristics of these systems (McKee, Evans and Owens, 1996); and (3) the effectiveness of telehealth systems compared to conventional face-to-face delivery in different medical specialties (Picolo, Smolle, Argenziano, Wolf, Braun et al. 2000; Nordal, Moseng, Kvammen and Lochen, 2001; Bishop, O’Reilly, Daddox and Hutchinson, 2002). In the IS literature on telehealth, the Technology Acceptance Model (TAM) is the most widely used model. The TAM is intended to be a theoretical model of the determinants of information technology acceptance that can explain the behavior of a fairly wide range of applications and user populations. It is based on the Theory of Reasoned Action (TRA) of which it is in fact a specialized version in that TAM ignores the subjective norm. Based on this model, the effective use of a system is determined by the intent to use, which in turn depends on attitude. Attitude is influenced by two intermediate variables: perceived usefulness, which denotes the degree to which individuals believe that using a given system could enhance their job performance at work; and ease of use, which denotes individuals’ beliefs regarding the effort required to use a given system. Venkatesh and Davis (2000) extended this model to include new constructs related to social influence. Their extension arises out of the recognition that the original version of the TAM might be inappropriate to account for the acceptance of systems

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according to whether their use is voluntary or obligatory. This extension links up with the position of Thompson (1998), who found that the addition of appropriate measures related to social factors and motivation could enhance the TAM’s predictive power. Echoing this point of view, Hu, Chau and Sheng (2000) studied the adoption of telehealth. They claimed that, to improve the TAM’s explanatory power, they needed to add the following two constructs: the presence of a champion and technological support. Along the same lines, Croteau and Vieru (2002) combined the constructs of the TAM with those of technology diffusion theory to develop a model that they used to study factors that might influence the intentions of two groups of physicians to adopt telehealth technology. Similarly, Succi and Walter (1999) extended the TAM by incorporating a new construct related to professional status in order to study physicians’ acceptance of telehealth. The models and theories of IS performance presented above have contributed considerably to enriching our understanding of the dynamics related to the adoption, use, impact and, finally, success of information systems and technologies in organizations. Nevertheless, according to certain researchers, they are quite restrictive in view of certain hypotheses that they make and could be inappropriate for analyzing some classes of IS, the development of which requires complex technical and social choices (Iacono and Kling, 1988). In fact, the models and theories of IS success that we described in the previous sections are, in Kling and Scacchi’s (1982) terms, discreteentity models in the sense that they view and analyze IS as discrete entities; the social context in which the technology is developed and used and the history of the participating organizations are ignored. In discrete-entity models, the main impacts of a new technology are interpreted as a transposition of its technical attributes (quality of the system, quality of information, etc.) and its social attributes (better decisions). Thus, the system is examined in isolation from the operating

activities and work organization that it is intended to automate or enable (Kling and Scacchi, 1982), leading to the idea that these models are universally applicable. The popularity of these models is partly due to their simplicity, which makes it easy to grasp their reasoning. An analysis conducted with these models needs to focus on only the technical and economic characteristics of the new technology (Kling and Scacchi, 1982). The technical development of systems according to these models is based on two logics: substitution and incrementation (Kling and Scacchi, 1982). In the first case, to improve a system’s processing capacity, it is sufficient to substitute another that has greater capacity; in the second case, an existing system may be expanded. From the perspective of these models, the causes of system failure are also easily identifiable discrete entities, such as lack of support from senior management, lack of interest by users, insufficient system capacity, etc. Discrete-entity models define an ideal. As such, they bring together several conceptions of information systems and technologies, namely the vision of technology as a tool, the vision of technology as a proxy and finally the informationcentered vision of technology (Orlikowski and Iacono, 2001). The tool point of view is the most widespread conception of what technology is and means (Orlikowski and Iacono, 2001). According to this concept, an IS constitutes an artifact that is supposed to achieve the aim for which it was designed. Thus, the IS nature and functioning are perceived as technical phenomena; that is, they are defined, separable from other phenomena, fixed and subject to human control. As a tool, an IS may constitute a substitute for human work, a lever to increase productivity, a means of processing data and information, or finally, an element of social relations in the sense that we recognize that the introduction of new IS is likely to modify work processes, which can lead to a change in communication methods and means. Thinking of IS as tools has several implications. First of all, they

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constitute a resource whose use is independent of practices, the organization of work and the context in which the system is developed and used (Kling and Scacchi, 1982), since a well-designed tool should clearly indicate what it is intended to be used for. As well, the metaphor of the tool suggests that users have control and that they must be very careful to choose the most appropriate tools for the task or activity to be performed (Nardi and O’Day, 1999). In other words, there is presumably an explicit process of alignment between the task and the tool, and consequently the impact of an IS on individual performance due to the match between the task and the selected tool (task-technology fit) is the result of a decision process. The proxy point of view assumes that the critical aspects of IS can be captured by indirect reflexive measures, which are often quantitative in nature, such as individual perceptions, the diffusion rate, incurred expenses, etc. (Orlikowski and Iacono, 2001). This conception incorporates the perception-centered perspective, with theories and models such as the TRA, the TAM, the theory of interpersonal behavior, etc. It includes the view of innovation centered on diffusion and the view that IS are an element of an organization’s capital. The information-centered view of technology is quite similar to the tool view. IS are viewed as technical artifacts, completely ignoring their social and organizational aspects. In this conception, capacities for information representation, storage, extraction and transmission are the only IS characteristics worthy of interest (Orlikowski and Iacono, 2001). Discrete-entity models have the advantage of simplicity of analysis; they are able to explain the development and use of IS with which only a few actors within an organization interact. However, they have not succeeded in explaining the problems that organizations face with the design and implementation of IS and are of little use in explaining IS impact on individuals and organizations.

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The Very Nature of Telehealth Systems Telehealth systems are IS that use a bundle of technologies designed to remotely deliver healthcare services (disease management, home healthcare, long-term care, emergency medicine) and other health-related social services like tele-education. They are therefore extended systems that connect two or more organizations and several categories of actors from each one. Consideration of the social context is essential to ensure the successful deployment of such systems. An understanding of social relations, the division of labor, cultural factors and the history of technologies in these organizations also appears to be essential. In the case of information systems like those used in telehealth, there are numerous decisions and the technologies are too vast and complex to be grasped by any one person’s cognitive capacity. As well, the decisions to acquire and deploy such systems are not generally at the discretion of a single member of the organization (Eveland and Tornatzky, 1990, p. 124). When the deployment of an information system requires complex organizational arrangements instead of individual decisions, as is the case with telehealth, the deployment is often the result of numerous decisions dictated by economic and social considerations that extend beyond simple managerial logic. In addition to the organizational context, actors and history of the organization, an analysis of information systems and technologies should take account of the nature of the technologies underlying these systems. Telehealth systems are made up of a variety of technologies depending on the specialty in question, and whether the project focuses on telemonitoring, telemedicine or teleeducation. There are therefore multiple telehealth applications deployed on the basis of varied technologies such as videoconferencing, medical imaging equipment such as Picture Archiving and Communications Systems (PACS), content

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entry devices, storage and extraction equipment (sounds, images, plans and alphanumeric data), etc. The distinctive characteristics of these diverse applications mean that they all require the implementation of infrastructure such as Quebec’s health communication network (the RTSS), which permits data transmission, clinical data entry, the creation of multimedia databases, and communication among the various partner organizations. As such, telehealth systems include at least two of the three classes of information technology that can be found in healthcare organizations (Grémy and Bonnin, 1995). In class 1 technologies, the computer performs numerical or logical calculations without interacting with the user and without causing the user to lose any autonomy (Grémy and Bonnin, 1995). The partnership between human and machine can be subdivided into three successive steps: the user defines how the machine must function, the machine processes the instructions and provides the user with the results and new data, on the basis of which the user makes decisions (Grémy and Bonnin, 1995). Class 2 technologies are designed to support clinical activities, reasoning and evaluation, and medical education. This class of technologies presupposes a high level of interaction with professionals since it represents an intrusion in their area of activity. Consequently, individuals may experience a certain loss of autonomy in terms of their control over time and in terms of the representation of the world that the system imposes on them. Reactions can range from fascination for some users to frustration for others (Grémy and Bonnin, 1995). Class 3 technologies operate at a more collective level. They are linked to systems used in large institutions such as hospitals, where the challenge is to manage large volumes of information coming from varied sources and destined for different entities, departments or people. These systems

have no separate existence; they are an integral part of the bureaucratic organization (Grémy and Bonnin, 1995). Telehealth systems can combine technologies from all three classes, as needed. They are therefore composed of an assembly of heterogeneous equipment made up of intrinsically complex and independent components (Paré and Sicotte, 2004). For this reason, it appears more appropriate to use web models to attempt to grasp these IS and capture the complex social consequences associated with their deployment. Unlike discrete-entity models, web models allow one to make explicit the connections between a technological system and its political and social contexts (Kling and Scacchi, 1982). The web concept makes it clear that, even though the artifact is a central element of a technological system, it is just one element of an ensemble that also includes the components needed to apply the technical artifact to a given socioeconomic activity (Kling and Dutton, 1982; Illich, 1973). These components include commitment, additional resources such as training, qualified personnel, organizational arrangements, the compensation policy and system, in short everything that is necessary to foster the effective management and use of the system (Kling and Scacchi, 1982). One can then conceptualize IS as evolving systems embedded in a dynamic and complex social context (Orlikowski and Iacono, 2001) and thus examine how different social influences contribute to modeling the deployment of an IS and how different groups of users take ownership of it. Approaching telehealth IS with a web model also allows one to conceptualize them as structures in the sense that IS incorporate a set of rules and resources (Giddens, 2005/1987). These rules and resources are introduced by the designer during the design phase and are appropriated by users during their interactions with the system.

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TELEHEALTH SYSTEM DEPLOYMENT: RELATED CONCERNS Traditionally, computerization projects in the healthcare environment are analyzed at the organizational level, and concerns generally relate to the individual preferences of the professionals who influence the phenomenon in question. Nevertheless, given the organizational arrangements that the implementation of telehealth projects requires, the analysis of telehealth IS deployment needs to focus on several levels of analysis, including the healthcare system, for two reasons. First of all, the integration of telehealth into the healthcare system has the mission of mitigating the inadequacies of the conventional healthcare system, which it expands and complements. This conceptualization of the usefulness and potential of IS did not come out of nowhere and is not maintained by its own internal logic; rather, it is developed and supported by certain allegiances emerging from the social and economic maneuvers of society that it appeals to (Klecun-Dabrowska and Cornford, 2002). Secondly, the implementation of telehealth projects, and consequently of the IS that support them, requires complex institutional arrangements that involve different organizations and different units within a single organization. This complexity is increased by the fact that each of these organizations possesses specific clinical and organizational routines (Paré and Sicotte, 2004). For example, the telemedicine component of the National Infrastructure Initiative in Taipei brings together hospital centers that provide third-line care as well as university hospitals and regional hospitals providing second- and third-line care. Thus, in addition to extending the provision of care beyond geographical and temporal barriers, this program vertically integrates first-, secondand third-line care (Hu, Wei and Cheng, 2002). For all these reasons, the conventional analysis of individual factors must be taken to a higher

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level so that institutional influences can also be considered. Telehealth projects in fact constitute a new way of providing healthcare services. Because of the institutional arrangements that these projects involve, these IS are likely to substantially change habits, rules and practices in the participating organizations, and thus in turn to influence the institutional environment into which they are inserted. For example, when interacting with a patient in a virtual context, a physician must learn new ways of feeling and seeing, which represents a real challenge from the point of view of learning (Hu et al., 2002). Conditions that may potentially favor or disfavor the deployment of telehealth systems are some of the issues raised by the encounter of the healthcare system as an institution and telehealth projects as the expression of a new process of institutionalization. It is important to recognize that the healthcare system has sufficient weight to foster or constrain the implementation of telehealth projects and that the importance attributed to telehealth projects results from negotiations of meaning among social groups with divergent material interests. Moreover, telehealth systems are not neutral. They are able to influence the institutional environment as much as they are influenced by it. It should also be remembered that institutional weight does not nullify the role of the preferences and the power of social groups and individuals in isolation.

MODEL DEVELOPMENT Theoretical Logic and Principles The development of the model in Figure 1 is based on three premises. The first considers that an organization is a social entity characterized by systems of activities and a permeable border (Daft, 1995) that includes individuals who interact within collectives such as dyads, groups, teams, etc. (Klein,

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Dansereau and Hall, 1994). As a result, the organization is essentially multilevel in nature, and so are organizational phenomena (Rousseau, 1985). In fact, these phenomena possess the properties of dynamic systems, namely critical antecedents and processes and results that affect several levels of analysis within the organization (Chan, 1998). Although it is more complex and more difficult to apply, the decision to adopt a multilevel perspective has some specific advantages (Barley, 1990). The second premise relates to the phenomenon of assimilation. Routinization and infusion are two concepts borrowed from Cooper and Zmud’s (1990) view of the IS implementation process, which comprises initiation, adoption, adaptation, acceptance, routinization and infusion. This model is inspired by Lewin’s (1947) planned change model. Adaptation corresponds to the unfreezing phase, while acceptance, routinization and infusion correspond to the refreezing phase (Saga and Zmud, 1994). However, it is important to point out that researchers in this field soon moved away from the planned change model and began to view the IS implementation process as a dynamic process of mutual adaptation between the organization and the system (Leonard-Barton, 1988). Moving still further in this direction, Orlikowski (1993) and Orlikowski and Gash (1994) proposed the situated change model wherein change is significant but flexible and subtle. This transformation is primarily anchored in organizational routines, and secondarily in actors’ practices. More specifically, it applies when the users of a technology must handle the exceptions, opportunities, contingencies and unexpected consequences related to their tasks (Barley, 1990). To sum up, researchers in this area recognize that technological innovations trigger changes in organizational routines (Barley, 1986; Tyre and Orlikowski, 1994), which have been found to be strongly associated with cognitive and personal changes (Barley, 1986; Orlikowski, 1993). Based on these insights, we consider that the assimilation of IS innovations is essentially a multilevel phenomenon that

originates in individual and collective cognition (social cognition), before manifesting itself at the organizational level. The third premise considers that an organization is an open system. As such, the members of the organization also belong to other social systems. Ultimately, the conceptual model will be a multilevel model in the sense that it articulates the phenomenon we are interested in – assimilation – at several levels of analysis at the same time as it describes the relationships among dependent and independent variables located at different levels of analysis. Such an undertaking requires some guidelines. To guide the multilevel modeling, we were mainly inspired by the work of Rousseau (1985), Klein et al. (1994), Chan (1998), Morgesson and Hofmann (1999) and Kozlowski and Klein (2000). Three major concerns became evident. The first was the necessity of properly laying the theoretical foundations for the model; the second concerns the necessity of adequately clarifying the levels of analysis to avoid flaws in reasoning; and the third refers to the necessity of specifying the sources of variability. These concerns are expressed in the following guidelines. A theorization exercise must start by designating and defining the phenomenon of interest and the theoretical constructs used to conceptualize it, because they determine the levels and the connection processes that the theory or model must address (Kozlowski and Klein, 2000). It is also indispensable to specify the hierarchical levels of interest that are relevant in the development of the theory or model. According to Kozlowski and Klein (2000), the process of specification must be sure to distinguish between formal and informal units. The importance of this distinction resides in the fact that problems can arise if one analyzes a phenomenon within certain formal units, when it is underpinned by informal organizational processes. Consequently, the levels and units of analysis must be coherent with the phenomenon of interest (Kozlowski and Klein, 2000). Rousseau (1985) recommends dis-

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Figure 1.

tinguishing between the reference, measurement and analysis levels. The reference level constitutes the focal level in the sense that it is at this level that generalizations are made. The measurement level corresponds to the unit directly associated with the data. The analysis level refers to the level at which statistical analyses are carried out. Serious validity problems can arise if the different levels are not appropriately identified and are involuntarily mixed up during the formulation of hypotheses, collection and analysis of data, and generalization of results (Rousseau, 1985). Rousseau recommends (1) explicitly defining the appropriate levels at which one can generalize; (2) specifying the functional relationships of constructs at the different levels; (3) considering the potential distortions associated with measuring the constructs at one level and then analyzing them at another; (4) defining the analysis levels coherently with the focal or reference level; and (5) paying particular attention to the determination of the appropriate level for the dependent variable. Given that multilevel models essentially aim to describe the phenomena that take place at one

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level and are generalizable to several levels, it is imperative to specify the formation of collective constructs by clarifying their structures and functions (Morgesson and Hofmann, 1999); this amounts to clarifying how the phenomena are interrelated at different levels (Kozlowski and Klein, 2000). Collective constructs refer to the abstractions used to conceptualize collective phenomena resulting from the collective action of individuals, groups, departments, organizations, institutions, etc. (Morgesson and Hofmann, 1999). Within these collectives, individuals and subgroups meet and interact within space-time. This interaction constitutes an event that is followed by other events, triggering a joint behavioral cycle or pattern defined as collective action. This structure allows collective phenomena to emerge from interactions at the micro level; these phenomena in turn have the capacity to influence subsequent interactions. In this sense, collective constructs are essentially dual. They result from the interactions among their components and become the context in which later interactions take place. Consequently, Morgesson and Hofmann (1999)

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recommend (1) clearly identifying these cycles of events that structure the collective phenomenon; (2) specifying the processes involved in the emergence of collective constructs while being sure to note which ones are associated with critical events; and (3) considering the context in which these collective constructs emerge, while paying particular attention to the influence it could have on their emergence and structure. Collective processes can develop via contextual top-down processes or emergent bottom-up processes Kozlowski and Klein (2000). Top-down processes refer to the influence factors at one level have on lower levels. Conversely, bottom-up processes concern entities located at one level that emerges at higher levels. In the case of emergence, it is necessary to clarify whether composition or compilation is involved. Composition presupposes isomorphism and describes phenomena that are similar when they emerge at different levels (Kozlowski and Klein, 2000). These phenomena or constructs share the same content but are qualitatively different from one level to another (Chan, 1998). There are five types of composition model: additive, direct consensus, referent-shift consensus, dispersion and process (Chan, 1998, p. 235). Consequently, relations among the variables are supposed to be functionally similar at the different levels (Rousseau, 1985). Compilation describes phenomena that concern a common domain but are different when they emerge at different levels. In this case, by adopting a certain configuration, lower-level characteristics combine to form a higher-level property that is functionally equivalent to its constituent elements. In other words, the constructs occupy the same function at different levels but are not structurally identical (Morgesson and Hofmann, 1999; Kozlowski and Klein, 2000). It also means that is mandatory for the theoretician or modeler to specify the construct’s original and current levels (Kozlowski and Klein, 2000), because the construct level determines the measurement and analysis procedures to be used. Once the emergence process has been described

and the nature or structure of the phenomena at different levels have been identified, Kozlowski and Klein (2000) recommend clarifying how the constructs and the phenomenon of interest are related at these different levels. The functional analysis of collective constructs may constitute an excellent means of linking constructs at different levels (Morgesson and Hofmann, 1999). Although the constructs’ structures tend to differ from one level to the next, their functions usually do not. Functional analysis therefore appears to be a mechanism for integrating the constructs into a nomological network. Indeed, a type of process whereby a lower-level construct emerges to form a higher-level construct corresponds to each kind of functional relations among constructs (Chan, 1998). Finally, the sources of variability must be specified (Klein et al., 1994). By applying these principles to the case that concerns us, we were able to develop the conceptual model in Figure 1, which shows the different forms of assimilation at the individual, group and organizational levels and the factors related to these three levels that may influence the process. In the following sections, we describe the construction of the model and discuss its various elements.

Assimilation The concept of assimilation is defined in different ways in the literature, as Table 1 shows. Inspired by these definitions, and especially the last one, we define organizational assimilation as a phenomenon that occurs after a technological innovation is implemented; it has two dimensions: routinization (or institutionalization) and infusion. Routinization expresses the idea that an information system is inserted into the organization’s practices in such a way that, over time, it ceases to be perceived as a novelty and starts to be taken for granted (Saga and Zmud, 1994; Ritti and Silver, 1986; Zucker, 1977). The term infusion is used when the system becomes so deeply embedded in the organizational routines that it configures

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Table 1. Definitions of assimilation Meyer and Goes, (1988)

An organizational process that (1) is set in motion when individual organization members first hear of an innovation’s development, (2) can lead to the acquisition of the innovation and (3) sometimes to fruition in the innovation’s full acceptance, utilization, and institutionalization (p.897).

Fichman and Kemerer, (1997)

Assimilation is defined as the process spanning from an organization’s first awareness of an innovation to, potentially, acquisition and widespread deployment (p.1346)… and is best conceptualized as process of organizational learning wherein individuals and the organization as a whole acquire the knowledge and the skills necessary to effectively apply the technology (Attewell, 1992) (p.1345).

Purvis, Sambamurthy and Zmud, (2001) Chatterjee, Grewal and Sambamurthy, (2002)

Assimilation is defined as the extent to which the use of a technology diffuses accross organization work processes and becomes routinized in the activities associated with those processes (Cooper et Zmud, 1990; Tornatzky et Klein, 1982).

the workplace architecture by contributing to linking different organizational elements such as roles, formal procedures and emergent routines (Cooper and Zmud, 1990; Kwon, 1987). Thus, routinization and infusion refer to the two organizational manifestations of assimilation. The focal or reference level of our study – that is, the level at which generalizations will be made – is the organization. The study, then, is limited to the organizational field. Since we are studying a multilevel phenomenon, its manifestations at the individual and group levels are also presented. By doing this, we plan to show the structure and function of the collective constructs of assimilation at each level, and reveal the processes of composition or compilation whereby the lowerlevel constructs contribute to organizational assimilation, defined in terms of routinization and infusion. Individual assimilation or interpretation. At the individual level, we conceive of assimilation as resulting from an individual’s engagement in a process of making sense when he or she is faced with the potential use of a technological innovation. In the course of this interpretative effort, individuals are engaged at three levels – emotional, behavioral and cognitive (Drazin, Glynn and Kazanjian, 1999; Kahn, 1990) – since interpretation requires individuals to explain by words or actions their understanding, no matter how embryonic, of

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the object, both to themselves and to other people (Crossan, Lane and White, 1999). In the case that concerns us, the need for interpretation – for making sense of telehealth systems – is the result of two conjoined events, namely the nature of these systems and the fact that telehealth constitutes a new way of providing health services. Telehealth systems are complex information systems, not only because of the institutional arrangements necessitated by their deployment but also because of their constituent technologies. For example, the wound care tele-assistance project set up by the Réseau universitaire intégré de santé (RUIS) de Sherbrooke (Sherbrooke integrated university health network) is deploying 73 teleassistance technologies in 65 points of service. The deployment is expected to take place in eight phases, each of which has a planned preparation activity that includes at least the organization of new clinical services, the development of a deployment strategy for these new virtual clinics, etc. (RUIS de Sherbrooke, 2007). In fact, telehealth systems combine diverse technologies such as medical imaging, videoconferencing systems, etc., whose deployment requires frequent interventions to improve and adapt them, which means that the development and implementation of these systems becomes an ongoing process (Weick, 1990). Because of the combination of technologies, the presence of such systems in the healthcare

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environment can cause unaccustomed problems of interpretation and sense-making for both managers and healthcare professionals (Weick, 1990). Since these technological innovations are exogenous to the organizational context, their introduction is likely to create a certain mismatch between the existing systems of meaning, legitimation and domination and the new requirements for day-today activities in the organizational context (Barley, 1986). These new technologies therefore affect the ability of members of the organizational context to reason about the structures making up telehealth systems; because technologies in general and new information technologies in particular lend themselves to interpretive flexibility (Weick, 1990) in the sense that they allow different possible and plausible interpretations by various social groups and may therefore be misunderstood, uncertain and complex (Pinch and Bijker, 1987; Weick, 1990; Orlikowski and Gash, 1994). New technologies mean many things because they are simultaneously the source of stochastic events, continuous events, and abstract events. Complex systems composed of these three classes of events make both limited sense because so little is visible and so much is transient, and they make many different kinds of sense because the dense interactions that occur within them can be modeled in so many different ways. Because new technologies are equivocal, they require ongoing structuring and sensemaking if they are to be managed. (Weick, 1990, p. 2) In addition, the meaning attributed to the technology may depend on a given actor’s ability to access the system’s schemas and artifacts, that is, the ability to reinterpret the schemas of the set of artifacts other than by means of the schemas already incorporated into these artifacts (Chae, 2002). This interpretive flexibility is based on the fact that any information system comprises both information technology artifacts and schemas (Or-

likowski and Iacono, 2001; Chae, 2002). Artifacts include tangible resources such as equipment and applications (Kling, 1987), intangible resources such as networking capacities and programming languages (Chae, 2002) and structural elements incorporated in the technologies (De Sanctis and Poole, 1994). Schemas refer to generalizable procedures that emerge from the context or from pre-existing institutions (Chae, 2002). In the context of implementation and use, they can refer to the installed base of the social organization of computerization (Kling and Iacono, 1989). Unlike resources, which are objective, schemas are essentially virtual: Schemas can be inferred from an array of extant institutional arrangements and cognitive imageries which include an organization’s (or group’s, intra-organization’s) structure and history; value systems, norms, organizational routines and informal procedures, individual and collective memory culture, and existing bodies or rules, laws and regulations. They are the source for enactment (Weick, 1990) of technology (Chae, 2002, p. 52). The ambiguity introduced by telehealth systems is even stronger because in some cases the systems involved are extremely large: not only are numerous resources deployed on a large scale but many different entities are involved. For example, the wound care tele-assistance project includes 13 health and social services centers in three geographic regions of Quebec and anticipates mobilizing 90 resource nurses. Consequently, there are numerous and complex schemas, which, moreover, are embedded in multiple structures. All of the above arguments highlight both the ambiguous nature of telehealth systems for actors and the need for these actors to make sense of the technologies underlying these systems so that they can use them and integrate them into their practices. Interpretation, the process of making sense, is essential to assimilation, since the way in which individuals interact with a technology

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depends on their interpretation of it (Orlikowski and Gash, 1994). Based on their interpretations, actors develop a certain number of hypotheses and expectations that form their understanding of what the technology is and what it can be used for (Orlikowski and Gash, 1994). Thus, the process of interpretation or sense-making is ultimately applied to grasp the reasoning, the philosophy underlying the development of the system, or in other words, its spirit (De Sanctis and Poole, 1994). During the course of interpretation, individuals develop a kind of cognitive map (Porac, Thomas and Baden-Fuller, 1989), schema or frame in which language plays a central role by allowing the individual to start naming and explaining what had formerly been a matter of sensation or feeling (Crossan et al., 1999). This new meaning of technology concerns not only the nature of the information technology but also its applications and the consequences associated with their use in a given context (Orlikowski et Gash, 1994). It conditions the actors’ actions: “… in this sense-making process, they develop particular assumptions, expectations, and knowledge of the technology, which serve to shape subsequent actions toward it” (Orlikowski and Gash, 1994, p. 175). Thus, sense-making is the principal generator of individual action with regard to innovation (Drazin et al., 1999). Group assimilation. At the group level, we think of assimilation as an integration process during the course of which shared understandings are formed and mutual adjustments are made between individual interpretations. Social interaction in the form of dialogue and joint actions is indispensable to this integration (Morgesson and Hofmann, 1999; Crossan et al., 1999; Lauriol, 1998), which continues the process of interpretation by giving it the status of a social activity that contributes to clarifying images and eventually creating shared understandings and meanings. By allowing the group to arrive at a common discourse and decide on a principle for action (Lauriol, 1998), integration helps to reduce

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the ambiguity surrounding innovation (Daft and Weick, 1987). Our conceptualization is similar to that of Hall and Loucks (1977) regarding individuals’ levels of technology use over time. In the view of these authors, individuals first collect information about the technology so that they can make sense of it and prepare to use it. Later, they tend to move up to an increased level of use of the technology. To do this, they refine their understanding of both the technology and its various possible applications by interacting with it more, discussing their experiences with other people and, finally, coordinating their activities with those of other users. This last step is crucial in understanding technology assimilation since it constitutes the essence or engine of organization learning, of which routinization and infusion are the outcome. In fact, when users of a system must carry out interdependent activities using it, they need to confront their understandings of the system’s nature and what it is meant to do. A shared understanding is indispensable to the coherence of collective action and may differ from one social group to another. The social cognition theory of knowledge creation teaches us that every social group, like every individual, is likely to develop common understandings – cultural and cognitive frames (Scott, 2001), also called technological frames (Orlikowski and Gash, 1994) – which are specific to itself. These frames develop due to the close relationships between group members resulting from the coordination of their activities (Schein, 1985; Strauss, 1978) and also due to the group’s influence on its members through its specific system of meaning and norms (Porac et al., 1989; Gregory, 1983; Van Maanen and Schein, 1979). In short, the integration of individual interpretations into shared understandings of what telehealth systems are and what they should be used for establishes a consensus on the meaning of these systems within the reference group. Organizational assimilation. At the organizational level, we claim that assimilation has two aspects: first a process of institutionalization or

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routinization, then a process of infusion. Routinization ensures that actions are specified, tasks are defined and the organizational mechanisms necessary for their achievement are set up (Crossan et al., 1999). Shared understandings lead to collective actions. Over time, the repetition and persistence of these collective actions define patterns of interaction and communication, which routinization tends to formalize. By coordinating their actions with those of others, users or groups of users construct new cognitive coordinations, memorize them and repeat them, transposing them to new situations until their use is perceived as normal, and using them more frequently, which means inserting them into the organization’s routines. As well, coordination requires one to take into consideration the organizing principles for interaction, such as norms and rules, as well as the organizing principles for individuals, such as scripts and previous representations. Taking these principles into consideration contributes to institutionalizing (or routinizing) the use of the system, by reproducing or enacting structures, since these organizing principles may conflict with the group’s sociocognitive orientations. When conflict occurs, it generates new cognitive configurations and thus is likely to lead to a renewed social representation of the system as a function of the social marking that characterizes it (Lauriol, 1998). Thus, as the organization advances in its understanding of the technology and its possibilities, it is likely to modify its work architecture, following a cumulative learning curve: Each successive configuration builds on the functionality of the previous one, which parallels an incremental learning process as the technology’s users gain experience and knowledge about the technology and technology-facilitated work tasks. (Saga and Zmud, 1994, p. 80) According to these authors, this in-depth understanding and the change in work architecture that it induces cause users to (1) use more and more of

the system’s functionalities to execute a larger set of tasks; (2) use the system in a more integrated way to build links between sets of activities; (3) use the technology to perform activities that were not identifiable or feasible before the system was introduced. These three ways of using the system help to infuse it in the organization (Cooper and Zmud, 1987). To sum up, we consider that assimilation occurs first by means of an individual interpretation process in which individuals succeed in making sense of the new technology, then developing new categories and new schemas and scripts that shape their representation of the system. Then, through a process of integration, the individuals interact and integrate their understandings in order to coordinate their activities. By doing this, they develop shared understandings or frames specific to their group. Over time, these frames are incorporated into routines, then infused into practices and beliefs until they endure within the organization even after the individuals who originated them have left.

The Emergence of Collective Assimilation Constructs From the individual to the group: In the previous section, assimilation was viewed as a phenomenon that can be seen at different hierarchical levels of the organization: individual, group and organizational, with the individual as the level of origin. We can deduce at least two things from this: first, at these three levels, assimilation is defined as qualitatively distinct constructs; second, assimilation is manifested as an emergent phenomenon. In this regard, it is important to specify the nature of this emergence from the individual to the group and then on to the organization. Individuals interpret technological innovation within the framework of the organizational context. Individual cognitive frames, namely the expectations and hypotheses they develop regarding the technological innovation, are marked by social interactions with

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their colleagues who are also engaged in the interpretation effort. It is legitimate to consider that, through this interaction, individual scripts, schemas and semantic labels are pooled to develop shared understandings and a principle for action. In fact, when individuals face ambiguous situations, they start to seek the interpretations of other people who are experiencing the same thing (Swanson and Ramiller, 1997; Volkema, Farquhar and Bergmann, 1996). Through these interactions, schemas and individual categorizations are diffused throughout the reference group (Poole and DeSanctis., 1990). Interaction is undoubtedly the vehicle of this pooling, but common frames result primarily from the frequency of these interactions, which make up a cycle of events (Morgesson and Hoffman, 1999). A high level of interdependence among group members’ activities increases the presence of such cycles and consequently the emergence of group assimilation. The collective construct may result from the composition or compilation of individual interpretations (Kozlowski and Klein, 2000). We opt for the first possibility, since assimilation at the individual and group levels involves cognitive processes that result in the generation of reference and categorization frames, etc. In other words, the constructs of individual and group assimilation are similar in function but different in structure. We also hypothesize that individuals act homogeneously within the group when they pool their individual reference frames (Klein et al., 1994), since it is difficult to distinguish between individual and group contributions in the creation of common frames (Drazin et al., 1999). Likewise, the group reflects the steps of individual assimilation in its approach, namely the development of images, categories, a language, expectations and hypotheses, etc. In short, group assimilation and individual assimilation are functionally isomorphic, in that both constructs have the same meaning and share the same content and the same nomological network (Kozlowski and Klein, 2000). This leads to the following hypothesis:

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Proposition 1: Group assimilation results from intragroup consensus around individuals’ assimilation. From the group to the organization: Routinization and infusion, the two manifestations of assimilation, are two essentially organizational phenomena and have no individual counterparts. Consequently, assimilation at the organizational level cannot occur on the basis of group assimilation. It is true that routinization results from the repetition of behavior patterns as a result of group consensus. Nevertheless, when one moves from the group level to that of the organization, the assimilation process becomes less fluid and incremental and more punctuated and disconnected (Crossan et al., 1999). Indeed, organizational assimilation usually entails modifications being made to existing systems and processes (Keen and McDonald, 2000; Chatterjee and Segars, 2001). Such modifications raise issues that extend beyond the field of social cognition. In particular, one might mention the inertia characterizing existing institutions, which results in the more punctuated nature of organizational assimilation mechanisms compared to the greater fluidity of the phenomenon at the individual and group levels (Crossan et al., 1999). Moreover, through routinization, the modified structures, systems and procedures provide a new context for interaction such that the representations of groups, and still more so of individuals, have less weight because they are embedded in the organization (Crossan et al., 1999). Moreover, there are theoretical reasons for believing that the emergence of organizational assimilation, which involves hundreds of people, may be substantially different from assimilation at the group level, which may involve no more than five or six people. In a large organization, individuals only interact regularly with a subset of other employees, whereas they will end up interacting with most, if not all, of the other members in a group (Dawson, Gonzalez-Roma, Davis and

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West, 2008). Thus, organizational assimilation is probably a slower and more risky process and therefore is more sensitive to contextual factors. In fact, each social group within the organization (physicians, nurses, administrators, technicians) may well develop its own technological frames regarding telehealth systems for reasons as diverse as their specialty, occupation, ideology, etc. (Orlikowski and Gash, 1994; Weick, 1995). We can therefore imagine that, at the organizational level, assimilation results from a process of negotiation among the different frames specific to each group involved, with their divergent belief systems and interests (Drazin et al., 1999). In our view, there are two possibilities. First, the different groups’ technological frames, that is to say, their expectations and hypotheses regarding the role of the technological innovation in business processes and its nature and use, may be irreconcilable. In that case, the implementation of the technological innovation will become a source of conflict (Orlikowski and Gash, 1994). On the other hand, when the different groups’ frames are congruent (Orlikowski and Gash, 1994), an implicit and reasonable agreement may result concerning the nature, use and consequences of use of the technological innovation (Finney and Mitroff, 1986). Cognitive frames of any kind guide how individuals or groups understand and act toward an organizational phenomenon. Frames give meaning to events, which then determine courses of action (Goffman, 1974). Thus, the frames specific to each group determine how that group inserts the use of the new technology into its practices. When the different frames are mutually coherent, in the sense that they share a certain number of categories and contents (Orlikowski and Gash, 1994), one can imagine that these specific practices may combine to configure the insertion of the technological innovation into the organizational routines of which it will become an integral part. It is also possible to imagine that, as this use is institutionalized, it will become more widespread and more integrated and modifications may be made to the technological

architecture to take into consideration the way in which various organizational elements such as roles, formal procedures and emergent routines will henceforth be connected (Cooper and Zmud, 1990; Kwon, 1987). On the basis of the above discussion, we consider that organizational assimilation emerges from the compilation of the various frames specific to each social group. In other words, assimilation at the organizational level results not from the convergence of the technological frames of the various groups involved but instead from a combination of these frames in a particular configuration. Consequently, at the group and organization levels, the two constructs are qualitatively different even though they are functionally equivalent (Kozlowski and Klein, 2000). While group-level assimilation results in a consensus concerning the role and use of the technological innovation, organizational assimilation is reflected in the insertion of the innovation into organizational routines and subsequent changes to the administrative and technological infrastructure (Zmud and Apple, 1992). Thus, both constructs concern the same domain, but they are manifested in different ways at the two levels of analysis. This development leads us to formulate the following hypothesis: Proposition 2: Organizational assimilation results from a configuration of each group’s specific assimilation.

Antecedents of Assimilation In the previous section, we established the relationship between the constructs of assimilation at different levels. In addition to the question of the structure of assimilation, which concerns the manifestations of the phenomenon at different levels, we also need to consider how the phenomenon functions and, in particular, to identify its antecedents at the different levels of analysis.

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Individual Factors Psychological Ownership for the Organization and Felt Responsibility Psychological ownership defines a state in which individuals feel that an object belongs to them, in whole or in part (Beggan, 1992; Pierce, Rubenfeld and Morgan, 1991). This feeling may develop for either tangible or intangible objects (Isaacs, 1993; Pierce, Kostova and Dirks, 2001). Empirical studies have shown that individuals may experience feelings of possession toward their work (Beaglehole, 1932), their organization (Dirks, Cummings and Pierce, 1996), the practices of the organization (Kostova, 1998), and particular problems facing their organization (Pratt and Dutton, 2000). As well, psychological ownership of an organization denotes a psychological phenomenon whereby an employee develops feelings of ownership toward a target (Van Dyne and Pierce, 2004). This target may be any material or social object associated with the organization, such as particular problems or the technologies that contribute to solving them. Thus, psychological ownership is an attitude that includes both affective and cognitive components (Pierce et al., 2001). Three main mechanisms participate in the emergence of psychological ownership: control of the target or object for which ownership is felt; intimate knowledge of this target; and finally, self-investment in the target (Pierce et al., 2001). Organizations give their members many opportunities to control, to varying degrees, different factors that then constitute potential targets of psychological ownership (Pierce et al., 2001; Hackman and Oldham, 1980). The greater the degree of autonomy inherent in a job, the greater the level of control required and, as well, the greater the probability that employees will develop a feeling of ownership toward certain targets of the organization (Pierce et al., 2001). In healthcare organizations in particular, professionals enjoy a very high degree of autonomy in their areas of expertise because of the complex tasks they have

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to perform. The low level of bureaucratic formalization and the decentralized decision-making power that characterize healthcare organizations are factors that favor high autonomy for healthcare professionals and, consequently, the likelihood that they will develop feelings of ownership for their work or for specific organizational problems related to it (Pierce et al., 2001). Organizations also give their members opportunities to become more familiar with certain targets of psychological ownership. This intimate knowledge is fostered by the information given to employees concerning the organization’s objectives and problems and, above all, by seniority in the organization. In turn, intimate knowledge favors the development of a feeling of ownership toward the organization or some of its targets (Pierce et al., 2001). The collegial nature of healthcare organizations means that professionals are constantly being informed and consulted about their institution’s problems and objectives and often about those of other establishments in the same administrative region. In addition, these organizations are characterized by low employee turnover, given the structure of the sector. All of these factors contribute to the fact that healthcare professionals know their organizations very well and thus increase the probability that they will develop feelings of ownership toward some of its targets. Finally, organizations give their members opportunities to invest themselves in different aspects of organizational life. This investment may relate to the individual’s ideas, time, or physical and psychological energy (Pierce et al., 2001). The greater the investment, the greater the psychological ownership of the target (Pierce et al., 2001). The degree of investment depends on the characteristics of the activity. Non-routine activities, which require more discretion, mean that the people who perform them invest more of their ideas, their specific knowledge and their personal style (Pierce et al., 2001). These activities therefore favor the development of psychological

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ownership of the result of this self-investment. Now, one of the most important characteristics of healthcare institutions resides in the uniqueness and novelty of the problems they are called upon to solve. This is reflected in non-routine activities whose duration, content and characteristics are unpredictable. Indeed, executing these activities requires healthcare professionals to acquire information and knowledge through the social processes of discussion, reading and education, then to apply this knowledge to solving their patients’ problems. It is therefore legitimate to think that such a working environment promotes the development of psychological ownership of targets such as certain organizational challenges, protocols or practices, etc. When individuals who are members of an organization feel that they own it, they tend to believe they have certain rights such as being kept informed and influencing the organization’s orientations, but they also tend to feel more accountable to it than individuals who do not share this feeling (Pierce et al., 1991; Kubzansky and Druskat, 1993; Rodgers and Freudlich, 1998). And it has been shown that the felt responsibility leads individuals to adopt behaviors that go beyond the requirements of their formal roles (Pearce and Gregersen, 1991). The above discussion helps us to understand that healthcare organizations combine conditions that favor the development of psychological ownership of certain targets by their members. One of these targets could be telehealth, given the fervor it has stirred up because of expectations that it will solve certain limitations hampering the current healthcare system. We also believe that the felt responsibility that results from psychological ownership is one of the factors that explains why individuals in these organizations engage themselves in three ways – emotionally, behaviorally and cognitively (Drazin et al., 1999; Kahn, 1990) – in the effort to understand what telehealth systems are and what they should be used for. We therefore hypothesize that there will

be a positive relationship between psychological ownership and individual assimilation, but that this relationship will be mediated by the feeling of responsibility. Proposition 3a: Psychological ownership of the organization should have a positive impact on the individual assimilation of telehealth systems. Proposition 3b: Felt responsibility will mediate the influence of psychological ownership of the organization on the individual assimilation of telehealth systems.

Group Factors Interdependence One of the fundamental characteristics of organizations is the need for coordination. In addition to administrative prescriptions, coordination is the result of structural factors such as interdependence (Van De Ven, Delbecq and Koenig, 1976). Interdependence expresses the extent to which the behavior of one member of a group affects that of the other members. In this research, the term group has a collective sense and designates any combination of individuals or work units that are interdependent and whose work is purposeful (Morgesson and Hoffman, 1999). The object in respect of which the group members perceive themselves to be interdependent determines the nature and degree of this interdependence. These objects may include the differentiation of roles, the distribution of competencies and resources, the operating processes and technologies supporting them, and how performance is compensated (Wageman, 1995, p. 146). Notwithstanding these different sources, a distinction must be established between task interdependence and goal interdependence (Mitchell and Silver, 1990). Definitions of task interdependence (TI) are quite varied (Pearce et Gregersen, 1991). In some authors’ view, TI designates to what extent the members of a group depend on one another to

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achieve their respective tasks due to the structural relations among group members (Van De Ven et al., 1976; Van der Vegt, Emans and van de Vliert, 1998; Thompson 1967). From this structural perspective, the patterns of interaction among group members vary depending on whether their interdependence is pooled, sequential or reciprocal (Thompson, 1967) or team interdependence (Van De Ven et al., 1976). Team interdependence is associated with the most complex form of interaction. Other researchers have defined TI as the degree of interaction among members due to the nature of the task (Shea and Guzzo, 1987; Campion, Medsker and Higgs, 1993). Ultimately, we consider that TI expresses the extent to which group members must exchange resources (information, advice and expertise) and coordinate their efforts to perform their tasks (Mitchell and Silver, 1990; Wageman, 1995). Goal interdependence (GO) expresses the extent to which members perceive their respective goals as being related to those of the group and acknowledge one another as contributors to a common project (Deutsch, 1973, 1949). The fact that healthcare organizations are considered to be professional bureaucracies (Mintzberg, 1981), in which professionals are given a great deal of autonomy, does not preclude the need for coordination, and therefore the interdependence. On the contrary, the structural changes that have occurred in these organizations in recent years tend to reinforce this need. In Quebec, for example, structural problems related to the accessibility, coordination and continuity of healthcare and services have led to the emergence of several solutions, including the integration of services through the creation of integrated health and social services networks. The goal, of course, is the complementary use of resources and expertise, with the aim of facilitating the ongoing provision of healthcare and services. Despite these changes, two modes of intervention appear to coexist in healthcare institutions: multidisciplinarity and

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interdisciplinarity, with the emphasis upon the former. Multidisciplinarity refers to the juxtaposition of professionals who are working with the same user (D’Amour, Ferrada-Videla, San Martin Rodriguez and Beaulieu, 2005; Payette, 2001). Each aspect of the user’s problem is handled by different professionals, who keep one another informed about the actions to be taken and the progression of results. In this way, they identify specific contributions, set rules for coordination and share information (Viens, 2006). Interdisciplinarity, on the other hand, requires the integration of knowledge, expertise and specific contributions from each discipline in the process of solving complex problems (Payette, 2001). It presupposes a common goal and integrated practices. Thus, while multidisciplinarity is most appropriate for less complex needs (D’Amour, 2006), interdisciplinarity has proven to be more appropriate for complex cases (Payette, 2001). By necessitating convergent care, such cases create a situation of interdependence and partnership among stakeholders (Viens, 2006). Despite their relative autonomy, then, stakeholders in healthcare organizations are not totally independent from one another. The nature of their interdependence varies based on the complexity of the care services they provide. In all cases, if this interdependence is to be operative, stakeholders must share common frames regarding the nature of the care, practices and technologies they need to mobilize. This is especially true in the case of telehealth, where information technologies play a pivotal role. On this basis, it is reasonable to make the following propositions: Proposition 4a: Task interdependence should have a positive impact on consensus building within different groups regarding what telehealth systems are and what they should be used for.

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Proposition 4b: Goal interdependence should have a positive impact on consensus building within different groups regarding what telehealth systems are and what they should be used for.

Organizational Factors At the organizational level, in view of how assimilation is manifested, it may be influenced by specific organizational capacities and by the interaction between IS and the organization. In the context of this study, the organizational capacities that appear most relevant to us are those associated with the technological and sociocognitive environments. The former refers primarily to the technological infrastructure’s IT capacities and the latter to the organizational climate. Regarding the interaction between the system and the organization, we essentially refer to compatibility between the technological innovation and the institutional system, on one hand, and the organization’s existing technological systems, on the other. IT Capacities As indicated above, organizational assimilation implies that changes must potentially be made to the technological architecture to take account of how various organizational elements such as roles, formal procedures and emergent routines will henceforth be connected (Cooper and Zmud, 1990; Kwon, 1987). To support the emergence and implementation of such changes, the organization must possess IT capacities that cover both technological and organizational dimensions (Bharadwaj, Sambamurthy and Zmud, 1999). In particular, the organization needs the capacity to maintain a close, ongoing partnership between the heads of business and IT processes. It also needs the capacity to mutually adjust operating and technological processes to maintain their efficiency and effectiveness and exploit the capacities of emerging IT (Bharadwaj et al., 1999). In order to possess these capacities, the organization must

have a sufficiently flexible, integrated IT infrastructure; this is critically important (Broadbent and Weill, 1997), since it makes it possible to ensure continuous compatibility and interoperability between telehealth systems and the systems already in place in the organization (Kayworth, Chatterjee and Sambamurthy, 2001). Moreover, the wide variety of hardware, operating systems and development tools makes it more and more crucial to maintain an IT infrastructure that is sufficiently coherent to avoid fragmentation and lack of integration of the various systems. However, to ensure this cohesion between telehealth systems and the other information systems, networks and applications that are critical to the organization’s mission, the technological infrastructure must have the necessary architecture. In fact, this integrated IT infrastructure must constitute a platform on which the organization’s shared IT capacities are articulated (Weill, Subramani and Broadbent, 2002). By IT infrastructure, we mean a shared organizational resource comprising physical elements such as technological artifacts and intellectual elements such as knowledge and know-how (Broadbent, Weill, Brien and Neo, 1996), all of which are kept in step by standards (Kayworth et al., 2001). The physical elements determine the infrastructure’s IT capacities, namely its ability to combine the organization’s resources in order to support their effectiveness (Amit and Shoemaker, 1993). As for the intellectual elements, they materialize in the relational and integration architectures required to exploit the IT capacities. Relational architecture is required to configure the operating modalities for IT capacities, while the function of integration architecture is to coordinate IT capacities in relation to the business capacities they are there to support (Sambamurthy and Zmud, 2000). The sophistication of the IT infrastructure has been found to have a significant influence on the assimilation of information technologies. Ultimately, an organization that has the necessary capacities to maintain a close interrelationship

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between IT professionals and users of the IT and continually adjust the interface of its operating and technological processes, and whose technological infrastructure is flexible enough not only to allow these adjustments but to permit the exploitation of emerging technologies, provides favorable conditions for the routinization and infusion of a technological innovation. This leads to the following proposition: Proposition 5: The presence of a flexible infrastructure that can integrate existing and emerging operating and technological processes should have a positive impact on the organizational assimilation of telehealth systems. Organizational Climate For the same reasons mentioned in the previous section, we believe that the work environment, and in particular the organizational climate, may constrain or promote the organizational assimilation of telehealth systems. Organizational climate is defined as the individual perceptions concerning the most salient characteristics of the organizational context (Schneider, 1990). It therefore corresponds, unlike psychological climate, to patterns of meaning shared by the individual members of the organization with regard to certain characteristics of the organizational context (Tracey, Tannenbaum and Kavanagh, 1995). It results from the interaction among objective elements that are observable in the organizational context and the perceptual processes of individual members of the organization (Schneider, 1983). It is therefore theoretically possible to influence perception of the climate by appropriately configuring the elements and processes of the organizational context (Kozlowski and Hults, 1987). As well, organizational climate is useful for understanding the normative response trends of members of the organization (Kozlowski and Hults, 1987). An overall conceptualization of organizational climate could prove somewhat irrelevant for studying a specific phenomenon

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(Kozlowski and Hults, 1987). Rather, the concept of organizational climate should be considered as a broad, multidimensional perceptual domain for which construct definition depends on the variable of interest (Schneider, 1985). Moreover, an organizational environment may have several climates depending on whether individuals attach specific meanings to separate sets of organizational events or factors (Schneider and Reichers, 1983). From this point of view, a work environment may be characterized by a climate of service, a climate of workplace safety or a climate of self-fulfillment (Mikkelsen and Gronhaug, 1999). Bearing this in mind, we shall consider only the dimensions of climate that appear to us to be most relevant to the routinization (or institutionalization) and infusion of telehealth systems. The assimilation of telehealth systems derives its meaning from a long-term vision that views telehealth as a new way of organizing healthcare services that complements and extends existing systems. This perspective highlights at least two points: first, information and communication technologies are constantly changing. Consequently, one must plan for continual updates of both the telehealth systems as such and the other information systems in order to benefit from technological advances to improve the quality of patient care and cope with emerging needs. Secondly, this means that medical, administrative and IT staff members must constantly upgrade their skills since the institutionalization of a technological innovation entails that staff competencies must be developed and updated (Kozlowski and Hults, 1987). Furthermore, the structure of healthcare organizations is such that individuals may belong to one or more groups dedicated to specific activities, but they have to work with members of other groups to provide patient care (Dawson et al., 2008). When this reality is reinforced by well-thought-out integration strategies, as was the case with Quebec’s health and social services networks, it can look like a climate of integration that fosters professional interdisciplinarity and cohesion among case

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managers, teams and departments, which then become institutionalized (Kozlowski and Hults, 1987). In our view, such a climate is favorable both to the routinization of telehealth systems and to their use in more extended and integrated ways, which leads to infusion (Zmud and Apple, 1992). It is therefore reasonable to think that if healthcare organizations implement strategies that reinforce learning, continuous upgrading and integration of their staff members’ competencies, they could induce normative responses by their members that promote continuous upgrading of their skill level. An organizational climate that unites these three dimensions (learning, upgrading and integration of skills) could prove favorable to the assimilation of telehealth systems. This leads to the following proposition: Proposition 6: An organizational climate that focuses on upgrading skills should have a positive impact on the organizational assimilation of telehealth systems. Compatibility Telehealth systems are not deployed in a vacuum, but in organizational contexts with well-established social structures such as practices, professional culture, technologies and other sociotechnical elements (Gosain, 2004). It is essential to consider the impact of these structural elements on the assimilation of telehealth IS, because studies have shown that they can either constitute barriers to IS implementation or facilitate it by providing the necessary infrastructure or strengthening the organization’s absorptive capacity (Kling and Iacono, 1989; Chae and Poole, 2005). Similarly, because of the organizational arrangements they require, telehealth IS have the capacity to structure the behaviors of the organizations involved. Thus, the deployment of a new system, especially a complex system like the ones supporting telehealth, not only triggers a process of mutual structuring among the host organizations and the system but may also raise the possibility of a mismatch (in-

stitutional misalignment) between the institutional regime of the organizations involved and the institutional logics conveyed by the technology (Gosain, 2004). It is therefore possible that conflicting structural components may come into contact. To explain this, one must remember two things: first, the healthcare community is characterized by a strong disciplinary tradition characterized by well-established values, norms and cultural and cognitive schemas that shape and guide the behaviors of members of the medical profession. This tradition develops and is maintained thanks to the activities and vigilance of a number of institutional agents such as the educational system, supervisory agencies, professional associations, etc. The elements of this tradition are instantiated in rules, work procedures, protocols and codes of ethics, and the technologies used in the sector tend to stabilize professional practice. Indeed, disciplines as bodies of knowledge preserve concepts, practices, and values that are employed in action (Pickering, 1995) Moreover, information systems are “complex social objects” (Kling and Scacchi, 1982) laden with intentionality (Chae and Poole, 2005). Because of the artifacts that compose them, IS form a unique combination of three kinds of agency: material, human and disciplinary (Chae and Poole, 2005; Pickering, 1995). Agency denotes the capacity of a person or an object to produce a particular result. Human agency refers primarily to individuals’ reflexivity and ability to adjust their actions with the aim of achieving a defined goal, which may be a plan or intention (Chae and Poole, 2005). Material agency is physical or biological in nature. It is revealed in the actions of the specific powerful forces of certain generative mechanisms (Harre and Madden, 1975). Finally, disciplinary agency corresponds to the shaping and orientation of human action by cultural and conceptual systems (Pickering, 1995). In fact, in the development phase of a system, it goes through a “registration process” during which the dominant interests and the developers’ responses to institutional pressures and their vision of the

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world are reflected in the system’s functioning (Latour, 1992). In this sense, the people who develop telehealth systems have the opportunity to incorporate rules, functionalities and resources into them that are capable of structuring the users’ interaction with the system. The above discussion helps us to understand that the encounter between telehealth systems and the organization raises the problem of compatibility between the systems and the organization’s operating infrastructure, on one hand, and the organization’s technological infrastructure in the sense of software, hardware and IT management procedures, on the other. In the first case, we refer to operational compatibility, whereas the second corresponds to technological compatibility (Jones and Beatty, 1998). We therefore believe that if telehealth systems are compatible with the existing technological infrastructure and operating procedures, this will facilitate the assimilation of these systems into the organizations involved in the telehealth project: Proposition 7: Telehealth systems’ compatibility with the organization’s operating infrastructure will have a positive impact on their assimilation. Proposition 8: Telehealth systems’ compatibility with the organization’s existing technological infrastructure will have a positive impact on their assimilation. Organizing Vision In the case of innovations like telehealth systems, institutional processes play a role from the outset of the diffusion of such systems and help to reduce the ambiguity surrounding them by proposing an organizing vision (Swanson and Ramiller, 1997). Since telehealth systems are essentially interorganizational in their application, their origin and rationale must be sought at the level of the organizational field (DiMaggio and Powell, 1983), which is made up of the various entities

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comprising the healthcare system. The organizing vision (OV) is created and developed at this level: …an interorganizational community, comprised of a heterogeneous network of parties with a variety of material interest in an IS innovation, collectively creates and employs an organizing vision of the innovation that is central to decisions and actions affecting its development and diffusion. (Swanson and Ramiller, 1997, p. 459) This makes it clear that, when the actors involved in telehealth projects must make sense of the system, they are not acting in a vacuum but also making use of the representations of other actors to develop their own, since the organization of which they are members is not isolated: Instead it [organization] belongs to a complex community of organizations, the many members of which actively contemplate the new technology and ponder to varying degrees publicly what it means and where it is going. Thus much of what the sensemakers in the prospective adopter do is search and probe and engage the interpretations of others. (Swanson and Ramiller, 1997, pp. 459-460) In other words, an essential part of actors’ effort to interpret telehealth systems consists of inquiring about and evaluating the interpretations conveyed at the level of the organizational field. Indeed, telehealth projects can be very different from one another, which means that the systems use quite different technologies that have often not stabilized yet and are sometimes still in the prototyping phase (Klecun-Dabrowska and Cornford, 2002). The technologies used and users’ understanding of them are incomplete and unstable (Rosenberg, 1994). In short, the components of these systems are not always well articulated and their implications may not be well understood (Swanson and Ramiller, 1997). In this context, the OV acts to formulate the spirit of the system in the sense of the philosophy underlying the

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artifact and the motives that led to its development (Chae, 2002). The OV is a meaning-making structure that actors make use of to understand the nature of telehealth systems and their roles in the social, technical and economic context (KlecunDabrowska and Cornford, 2002). In this way, the telehealth OV may remove, or at least reduce, the ambiguity characterizing telehealth systems and their possible applications: An organizing vision arises to encode and provide the necessary interpretations and to give institutional coherence to initiatives that might otherwise be viewed as of limited relevance, even organizationally idiosyncratic [p. 460] ... that provide important cognitive structures that shape thought relative to innovation involving innovation technologies. (Swanson and Ramiller, 1997, pp. 460 and 471) By formulating expectations, hypotheses and knowledge regarding the key aspects of telehealth systems, the OV contributes to ensuring the congruence of the different groups of actors’ technological frames (as discussed above) and may also align the institutional logics incorporated in the configuration of these systems with the organization’s institutional regime (values, practices, norms, culture and technologies). When this happens, organizations experience less conflict in the implementation and use of new systems (Orlikowski and Gash, 1994). Thus, we believe that the following proposition applies: Proposition 9a: A preeminent organizing vision should have a positive impact on the assimilation of telehealth systems. The OV also provides a legitimation structure, which complements the meaning-making structure by considering in its discourse the aspects that justify the innovation. The discourse on the system’s legitimacy uses technical and functional arguments, as well as political, organizational

and business arguments (Klecun-Dabrowska and Cornford, 2002). This dimension of the OV endeavors to communicate not only the expected benefits of the innovation, but also its spirit, that is, the underlying philosophies and the reasons motivating its development (Chae, 2002). For example, by presenting telehealth as a solution to the health problems experienced by people in remote regions and by underserved groups, and to the problem of recruiting and keeping physicians in these regions, etc., the OV not only clarifies the benefits of telehealth but also ties in with society’s concern for equity. By doing this, it emphasizes the importance of telehealth systems and strengthens the social norms and values that encourage and value their use (Orlikowski and Gash, 1994), and ensures that users take ownership of them. In turn, legitimacy favors the mobilization of the resources needed to move telehealth from the status of project to the status of current service, which involves changing practices and operating infrastructure so the telehealth system can be integrated. All of this leads us to think that an OV that formulates the rationale for telehealth systems in terms of existing values and social norms in the healthcare sector is likely to have a positive impact on the assimilation of telehealth systems by promoting the mobilization of the necessary resources. On this basis, we make the following proposition: Proposition 9b: An OV that formulates the rationale for telehealth systems in terms of existing values and social norms in the healthcare sector should have a positive impact on the assimilation of telehealth systems.

CONCLUSION This chapter has undertaken a multilevel modeling approach that clarifies the process of assimilation. Our theory development involved introspection concerning the technological artifact to ensure that this concept and the social and institutional

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context are at the core of the model. Moreover, the model is anchored in theory, and the theories mobilized here have enabled us to take into account the issues associated with information system assimilation and to analyze them appropriately. This chapter developed a theoretical perspective that enables us to gain a better understanding of the assimilation of information systems, based on their nature and the problems associated with their development. In this way, this chapter contributes to research in this area by emphasizing the necessity of anchoring the theorization process in the characteristics of the technological artifact and of the social and institutional context in which these systems are applied. Moreover, earlier work on the organizational assimilation of information technologies (Zmud and Apple, 1992; Saga and Zmud, 1994; Fichman and Kemerer, 1997/1999; Meyer and Goes, 1998; Purvis et al., 2001; Gallivan, 2001; Chatterjee et al., 2002) has certainly enriched our knowledge of this phenomenon. Nevertheless, the fact that these studies considered assimilation as an exclusively organizational phenomenon has undoubtedly overshadoweded more micro or meso explanations that could enrich our understanding still more. In particular, these studies are silent about the structure and functions of the assimilation constructs. Unlike those prior studies, our model proposes a detailed account of the assimilation process by clarifying both the structure (Kozlowski and Klein, 2000) and the functional relationships of the phenomenon (Morgesson and Hofmann, 1999). Put simply, the model accounts for how individual, group and organization characteristics interact to structure assimilation. Briefly, the adoption of a multilevel perspective resulted in a more comprehensive understanding of how the assimilation process unfolds across levels in organizations. Our study also has implications for practice. In particular, it points to the importance of examining assimilation within the continuum of IS phenomena surrounding the deployment of telehealth systems. As such, it helps understand why

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managerial actions intended to facilitate the assimilation process should be employed as early as the adoption phase. For instance, issues related to the systems’ compatibility with the organization’s work infrastructure should be managed during the development phase. Briefly, even though routinization and infusion are post-implementation behaviors, factors that are likely to influence them should be taken into consideration before systems are acquired. Moreover, by making explicit the functional relationships at the individual, group and organizational levels, this work highlights the range of managerial interventions required to secure the IS assimilation and consequently the telehealth systems’ effectiveness. In addition, beyond the policy level, the model provides a better understanding of the locus of authority for each specific managerial intervention. In so doing, it will help to enhance the effectiveness of managerial actions and smooth the IS governance aspects of telehealth systems.

FUTURE WORK The next step in our agenda is to test the model in an empirical, real-life setting. In addition to testing the model, this will allow us to identify additional contributing factors so we can better understand the assimilation of telehealth systems into the workplace.

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ADDITIONAL READING Armstrong, C. P., & Sambamurthy, V. (1999). Information technology assimilation in firms: The influence of senior leadership and IT infrastructure. Information Systems Research, 10(4), 304–327. doi:10.1287/isre.10.4.304 Bolloju, N., & Turban, E. (2007). Organizational assimilation of Web services technology: A research framework. Journal of Organizational Computing and Electronic Commerce, 17(1), 29–52. Harnett, B. (2008). Creating telehealth networks from existing infrastructures. Studies in Health Technology and Informatics, 131, 55–65. Jennet, P., Yeo, M., Pauls, M., & Graham, J. (2003). Organizational readiness for telemedicine: implications for success and failure. Journal of Telemedicine and Telecare, 9(suppl. 2), 27–30. doi:10.1258/135763303322596183 Jennett, P., Bates, J., Healy, T., Ho, K., Kazanjian, A., & Woollard, R. (2003). A readiness model for telehealth is it possible to pre-determine how prepared communities are to implement telehealth? Studies in Health Technology and Informatics, 97, 51–55.

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Teo, H. H., Wang, X., Wei, K. K., Sia, C. L., & Lee, M. K. O. (2005). Organizational learning capacity and attitude toward complex technological innovations: An empirical study. Journal of the American Society for Information Science and Technology, 57(2), 264–279. doi:10.1002/ asi.20275 Weaver, L., & Spence, D. (2000). Application of business case analysis in planning a provincewide telehealth network in Alberta. Journal of Telemedicine and Telecare, 6(suppl 1), 87–89. doi:10.1258/1357633001934267 Weinstein, R. S., Lopez, A. M., Krupinski, E. A., Beinar, S. J., Holcomb, M., McNeely, R. A., et al. (2008). Integrating telemedicine and telehealth: Putting it all together. In R. Latifi (ed), Current principles and practices of telemedicine and eHealth, 23-38. Amsterdam:IOS Press.

KEY TERMS AND DEFINITIONS Telehealth: Healthcare services using information and communications technology such as teleeducation, teleconsultation and teletraining, etc. Routinization: the incorporation of an IS into the organization’s work system in such a way that over time the IS ceases to be perceived as a novelty and starts to be taken for granted. Infusion: an information system’s embededness into the organization’s procedures and work Architectures. Top-down processes: address the influence of macro levels such as organizational or group characteristics on micro levels such as individuals. Bottom-up processes: describe phenomena that have their theoretical origin at a lower level but have emergent properties at higher levels. Composition process: describes phenomena that are essentially the same as they emerge across levels. Compilation process: describes phenomena that comprise a common domain but are distinctively different as they across levels.

This work was previously published in E-Health, Assistive Technologies and Applications for Assisted Living: Challenges and Solutions, edited by Carsten Röcker and Martina Ziefle, pp. 161-194, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 7.10

Social Cognitive Ontology and User Driven Healthcare Rakesh Biswas Manipal University, Malaysia

Edwin Wen Huo Lee Intel Innovation Center, Malaysia

Carmel M. Martin Northern Ontario School of Medicine, Ottawa, Canada

Shashikiran Umakanth Manipal University, Malaysia

Joachim Sturmberg Monash University, Australia

A. S. Kasthuri AFMC, India

Kamalika Mukherji Hertfordshire Partnership NHS Foundation Trust, UK

ABSTRACT The chapter starts from the premise that illness and healthcare are predominantly social phenomena that shape the perspectives of key stakeholders of healthcare. It introduces readers to the concepts associated around the term ontology with particular reference to philosophical, social and computer ontology and teases out the relations between them. It proposes a synthesis of these concepts with the term ‘social cognitive ontological constructs’ (SCOCs). The chapter proceeds to explore the role of SCOCs in the generation DOI: 10.4018/978-1-60960-561-2.ch710

of human emotions that are postulated to have to do more with cognition (knowledge) than affect (feelings). The authors propose a way forward to address emotional needs of patients and healthcare givers through informational feedback that is based on a conceptual framework incorporating SCOCs of key stakeholders. This would come about through recognizing the clinical encounter for what it is: a shared learning experience. The chapter proceeds to identify problems with the traditional development of top down medical knowledge and the need to break out of the well meaning but restrictive sub specialty approach. It uses the term de specialization to describe the process of breaking out of the traditional top

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Social Cognitive Ontology and User Driven Healthcare

down mold which may be achieved by collaborative learning not only across various medical specialties but also directly from the patient and her “other” caregivers. Finally it discusses current efforts in the medical landscape at bringing about this silent revolution in the form of a Web-based user driven healthcare. It also supplies a few details of the attempts made by the authors in a recent project trying to create electronic health records in a user driven manner beginning with the patient’s version of their perceived illness with data added on as the patient traverses his/her way through various levels of care beginning from the community to the tertiary care hospital. The data contained within these records may then be effectively and anonymously shared between different patients and health professionals who key in their own experiential information and find matching individual experiential information through text tagging in a Web 2.0 platform.

INTRODUCTION The human body has a limited range of physiological responses to deal with disease and injury. However, humans have developed social responses to dealing with disease and injury; on the individual level they express their experience as illness (being or feeling unwell), and socially an ill person is allowed to adopt and be accepted in a ‘sickness role’(Skoyles 2005, Sturmberg 2007). Healthcare emerged in hunter and gatherer societies who started to look after their sick and weak group members as this proved to enhance the survival chances of the whole group. Over time the role of caring for a sick or weak group member was ascribed to one person – the birth of the first medical professional – the shaman or medicine man (Sturmberg 2007). Disease and injury are biological phenomena; illness, sickness and healthcare are social phenomena (Skoyles 2005, Sturmberg 2007). Disease and illness transcend the individual in

a socially interconnected world, hence disease and illness and the resulting responses can only be fully appreciated by patients, doctors, policy makers and researchers if viewed as the result of interdependent interactions in broad networks. Networks function as complex adaptive systems. In brief, complex adaptive systems consist of a large number of components that interact in non-linear ways through many feedback loops. Such systems are open, they interact with their environment. The characteristic of a system is determined by the patterns of its interactions which cannot be reduced to the behavior of its specific components – systems are ‘emergent’ (Cilliers 1998).

THE DIFFERENT ‘NATURES’ OF HEALTHCARE Throughout the ages medicine has always been a collaborative problem solving effort between an individual patient and health professional. However, with time and globalization there have been major changes. The ‘localized expert’, and thus opinion driven, physician approach to clinical decision-making (as a first step to medical problem solving) has emerged to a ‘global expert’, collaborative evidence based approach that uses aggregated information as the basis to individual patient care (Biswas 2007 b). Much of this aggregated information is now available on the Internet allowing patients and health professionals to learn about diseases and their current treatment approaches. (Murray 2003, Larner 2006, Tan 2006, Giustini 2006). Increasingly this mode of information sharing drives the individual consultation, our understanding of acceptable practice and health service planning. The impact of this information sharing ability can be viewed as ‘User driven healthcare’. We previously defined user driven healthcare as a process leading to improved healthcare achieved with concerted collaborative learning between multiple

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Social Cognitive Ontology and User Driven Healthcare

users and stakeholders, primarily patients, health professionals and other actors in the care giving collaborative network across a Web interface.” (Biswas 2007a). Currently the consumer driven healthcare approach is most prevalent. Consumer driven healthcare is essentially a strategy for users (consumers) to decide how they may pay for their own healthcare through multiple stakeholders like employers who provide the money and insurance companies who receive the premiums (Tan 2005). Patient-centered healthcare, in contrast, is an approach to patient care based on the following ideas: It “(a) explores the patients’ main reason for the visit, concerns, and need for information; (b) seeks an integrated understanding of the patients’ world - that is, their whole person, emotional needs, and life issues; (c) finds common ground on what the problem is and mutually agrees on management; (d) enhances prevention and health promotion; and (e) enhances the continuing relationship between the patient and the doctor.” And finally there is the concept of the ‘expert patient’, one who is confident and has the skills to improve quality of life in partnership with health professionals. In many ways this view reflects an understanding of health and illness as ‘personal’ and healing as ‘sense-making exercise’ of one’s experiences (reflected in the somato-psycho-socio-semiotic notion of health) (Sturmberg 2007). However research has shown that exploring the patient’s understandings about their illness and its management are often ignored having negative consequences in terms of concordance with medications (Horne and Weinman 1999) or appreciating patients’ capacity to deal with their multiple conditions (Shaw and Baker 2004, Martin 2003). Patients’ expertise is valuable because by understanding the patient’s views and situation, the doctor is better equipped to identify a solution that will lead to a successful outcome, however defined. Health understood as a personal construct described as a balance within a somato-psycho-

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socio-semiotic framework will necessitate to extend the ‘scientific’ biomedical worldview with that of the ‘naïve’ patient’s and ‘common sense’ practitioner’s conceptions of this patient’s illness (McWhinney 1996). In this chapter we argue that the approach of User Driven Healthcare – seen as a collaborative learning approach among patients, families, health professional caregivers and other actors across a Web interface – might well achieve a common understanding arising from multiple perspectives.

The Spectrum of Ontology Here we allude to a fundamental philosophical question: “What exists?” or put differently “How do we conceive reality and the nature of being?” The traditional goal of ontological inquiry, in particular, is to divide the world “at its joints,” to discover those fundamental categories or kinds that define the objects of the world (IDEF5 methods report 1994). What ontology has in common in both computer science and philosophy is the representation of entities, ideas, and events, along with their properties and relations and the rules that govern them, according to a system of categories (“Ontology-computer science”, 2007). An often quoted definition of ontology in computer science is that it is an explicit specification of a conceptualization restricted to different domains (Gruber 1993). This has been developed further such that for an information system, ontology is a representation of some pre-existing domain of reality which: 1. Reflects the properties of the objects within its domain in such a way that there obtains a systematic correlation between reality and the representation itself 2. Is intelligible to a domain expert 3. Is formalized in a way that allows it to support automatic information processing (Ontology works inc. 2007).

Social Cognitive Ontology and User Driven Healthcare

For example if one has to write an ontology of driving a car it would involve representation and retrieval of data specifying certain concepts related to controlling the wheel, clutch, brake as well as concepts of objects such as road on which the car needs to be driven and characters such as trees, other cars and people that need to be avoided. Needless to say the ontology program doesn’t actually drive the car (which is more at a spinal level and more to do with robotics) but ontology merely serves to guide with appropriate information delivered in a need based manner (more at a cortical level to use an anatomical analogy). In philosophy the ontologic domain is existence itself and for an individual sustaining that existence his/her personal ontology is his/her personal conceptualization (although rarely explicitly specified) of the roadmap he would need to drive himself through that existence and the objects s/ he would meet on that road that s/he would need to know how to deal with. Social ontology comes close to this as much of an individual human’s existence is social. Health as a personal experience is an individual phenomenon – patients understand and experience their health in the context of their social networks. These networks simultaneously are made up by people and events; structurally they are interconnected and functionally interrelated – and as such interdependent (Bar-Yam 1997). Over time individual patients therefore build their unique conceptual models of their health and illness in the context of their experiences and everyday social interactions. Equally health professionals develop their main constructs of health and disease from the collective interactions with their colleagues in medical institutions. Frequently these constructs are devoid of the ‘white noise’ generated by the personal realities of patients, caregivers and other professionals. These personal individual views form the basis for the socio-cultural construct of health, illness and disease, and the culture of the healthcare system – these understandings are learnt, internalized

and enacted. As Berger and Luckmann (1966) point out persons and groups interacting together in a social system, form over time, concepts or mental representations of each other’s actions, and these concepts eventually become habituated into reciprocal roles played by the actors in relation to each other. According to Akgu¨n et al. (Akgu¨n et al 2003), the socio-cognitive view of learning can be conceptualized as a higher-order construct (process), construed of cognitive processes and social constructs which are (1) distributed through the social network, (2) unfold overtime, (3) involve people in diverse functions and mind-sets, and (4) are embedded in routines and institutional structures by means of social culture.

Social Cognitive Ontological Constructs and Generation of Emotion Most humans utilize their individually acquired SCOCs as a result of their learning behavior in relating to the world around them. Our individual social cognitive ontological constructs can make us attract, repel, laugh, cry and go through all the possible human emotions particularly when they overlap acutely with cognitive social ontological constructs of other humans that although may have certain similar attributes, objects and relations in one plane also tend to have very different attributes, objects and relations on another. Commonly emotions of grief or relief are also generated when an individual acutely disconnects or reconnects with whom s/he has been sharing cognitive social ontological constructs. Also in the same individual when there is an acute shift in attributes contained within his/her own social cognitive ontological constructs, it may lead to a sudden outburst of humor, grief depending on the social context. A sudden coming together of two humans of opposite sex or containing SCOCs with different physical attributes generate sexual passion or infatuation depending on which physical

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attributes share focus, although in such instances the interaction may be tacit as well as cognitive. All this may have been expressed in a simpler manner by just stating that human emotions are generated in different states of being (philosophically ontology maybe simply expressed as a theory of being). However the above detailing was necessary as it will help us to appreciate how it maybe possible to create human emotions in artificially intelligent computer programs by simply figuring out a way to run two different computer programs containing two different social cognitive ontological constructs (written in code) in a manner that creates an acute friction or overlap between the two, such that the rules contained within them are fractured. This may more likely simulate humanto-human natural social interactions although at present Computer human interaction (CHI) programs currently interpret human emotions mainly by recognizing human emotional states that an individual expresses through its facial musculature or other expressive gestures including physiological variables (Frijda 1986, Juan 2005, Nkambou 2006, Neiji 2007). This may at best be classified as imitation rather than artificial creation of human emotion. Such imitative emotion generation is usually seen in the human system in its infancy although tacit sharing of emotions without cognitive interactions is also a known pleasurable adult activity as outlined previously. Intuition by definition is a cognitive short cut that cannot be delineated, least in terms of logic. It however doesn’t rule out the fact that logical pathways may exist within them. Human to human interaction depends on handling of emotions intuitively such that supposed emotional areas in human brain have been termed the affective domain conveniently separated from its supposed cognitive domain that uses logical thinking. However emotions may just be a product of logically sequenced attributes in an individual cognitive ontology generated due to an acute overlap of divergent ontologies resulting in fractures in rules contained within individual ontologies (at

2000

least as far as human-to-human social interaction is concerned). In effect then our affect may be nothing but cognitive states arising out of acute changes in our social cognitive ontological constructs (SCOCs). Just try thinking of it when you follow/look up any joke etc that makes you laugh (in which case our own SCOCs are tolerant to the challenge in the form of rule breaks the acutely overlapping other divergent SCOC poses) or even angry (wherein our individual SCOCs do not tolerate the rule breakages in the divergent SCOCs). Affect is a product of social cognition and affection is a behavior of social animals (ranging from humans, dogs, tigers and apes) even shared between different species (for example humans and dogs). An individual without affect (as in a variety of schizophrenia) may appear all alone, in this wide world. There may be divergent SCOCs in the same human brain and from time to time such humans may generate emotions like joy, sadness etc, states that may be classified as psychotic when observed happening in humans in isolation, without appropriate social stimuli. All this discussion was necessary to understand that ontology in information science may help to discover ways to logically deal with emotions. Negative emotions may perhaps be curbed by inserting the informational strands that are missing in an individual social cognitive ontological construct acutely compromised by a sudden realization of having run over or banged onto another individual social cognitive ontological construct with different informational attributes. Only when informational attributes in clashing SCOCs are balanced, emotional information needs may be fulfilled. Also emotional events may signal acute conformational changes in our individual SCOCs essential to learning. The presence of affect is strongest during childhood and gradually diminishes with age which possibly correlates with and is directly proportional to learning activity.

Social Cognitive Ontology and User Driven Healthcare

The Emotional Need to Know To understand one’s health is based on having the right information and knowledge. Information and knowledge requirements exceed the biomedical domain and include social and emotional components. The latter “information need” however is particularly poorly explored in the consultation despite it well being the most important need, and the biggest stumbling block when a technical solution needs to be implemented (Smith 1996). The need to know is emotionally driven, however, in the clinical encounter the role of the emotional need to know is often missed. Emotions are not merely feelings, they are cognitive, and reveal important information about values and beliefs, some of which may well be false or unreasonable, and thus need clarification. Understanding and clarifying the emotions encountered in the consultation is equally important for patients and health professionals (Robichaud 2003).

Uncertainty in Clinical Practice Many clinical situations occur in the realm of uncertainty (Petros 2003), and both individual patients and health professionals try to seek clarity through more information. However much of the available information is limited and/or difficult to apply to the individual patient (Feinstein and Horwitz 1997). A gap between the paucity of what is proved to be effective for selected groups of patients versus the infinitely complex clinical decisions required

for individual patients has been recently recognized and termed the inferential gap (Stewart et al 2007). The breadth of the inferential gap varies according to available knowledge, its relevance to clinical decisions, access to the knowledge (that is, what the physician actually knows at the time of a clinical decision), the variable ways in which knowledge is interpreted and translated into a decision, the patient’s needs and preferences, and a host of other factors. Clinicians are required to fill in where their knowledge (or knowledge itself) falls short (Stewart et al 2007). An alternative to the currently dominant empirical evidence based approach in medicine has been proposed that has generated considerable discussion (Tonelli 2006). This alternative centers on the case at hand and recognizes the value of considering multiple dimensions, topics and ontologies (SCOCs) in the clinical encounter to aid medical reasoning. The five dimensions/topics identified by Tonelli (and its representative ontologies) for sense making of a clinical encounter to optimize medical decision-making are in Table 1. In summary, medical practice is confronted with patients seeking explanations for their suffering in a complex environment consistent in some parts of well proven biomedical but otherwise largely limited knowledge, and multiple understandings of health, illness and disease arising from diverse individual and societal realities. Healing becomes a collaborative effort driven by ‘the need to know’ – ‘to know’ being understood in the personal sense (Polanyi 1958) of ‘I know’.

Table 1. The five dimensions/topics identified by Tonelli (and its representative ontologies) for sense making of a clinical encounter to optimize medical decision-making are: • Empirical evidence: derived from clinical research. (biomedical ontology) • Experiential evidence: derived from personal clinical experience or the clinical experience of others. (individual ontology) • Patho-physiologic rationale: based on underlying theories of physiology, disease and healing. (biomedical ontology) • Patient values and preferences: derived from personal interaction with individual patients. (Individual ontology) • System features: including resource availability, societal and professional values, legal and cultural concerns. (administrative-bureaucratic and cultural ontologies)

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THE CLINICAL ENCOUNTER AS A SHARED LEARNING EXPERIENCE It can be argued that in a collaborative learning environment there are no teachers and learners – or in the health context doctors and patients – but rather people with different levels of knowledge and experience (Biswas 2007a). Such a view overcomes the more traditional structures of learning characterized by a top-down approach where learners are simply expected to learn and memorize the structure of their chosen field and then apply it to patient care. However medical encounters require more than simply recalling memorized medical facts, and at any rate these facts are increasingly superseded by an ever faster growing volume of information.

The Traditional Approach to the Growth of Health Information The response to the growth of health information has been an ant like division of labor where healthcare workers specialize in certain areas so that they can focus on a smaller volume/area of accumulated information and then offer their ‘perceived greater knowledge’ as experts in a limited field. Interestingly there is not much historical evidence to suggest that this approach to information management is doing wonders to present day healthcare (Loeffler 2000). On the contrary today’s patient satisfaction with healthcare seems to be at an all time low (Kenagy 2002). This is especially true for patients with multiple chronic diseases who find it exceedingly difficult to find a health professional able to appropriately manage their particular set of individual problems. The box offers some of the common narratives around the issues associated with the information explosion. What we need is a large group of health professionals who are willing to learn more and more about more and more (instead of less and less), i.e.

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strengthening primary care and the ‘all knowing’ (though not all powerful) general/family physician. After all primary care physicians are the pillar of the whole healing process. Today the family physician increasingly will share his information and knowledge with a well-informed patient.

The Cycle of Habitualization and Institutionalization of Learning and Its Subsequent Disruption “All human activity is subject to habitualization. Any action that is repeated frequently becomes cast into a pattern, which can then be reproduced with an economy of effort and which, ipso facto, is apprehended by its performer as that pattern. Habitualization further implies that the action in question may be performed again in the future in the same manner and with the same economical effort. Habitualization provides the direction and the specialization of activity that is lacking in man’s biological equipment, thus relieving the accumulation of tensions that result from undirected drives. Institutionalization occurs whenever there is a reciprocal typification of habitualized actions by types of actors. Put differently, any such typification is an institution. The institution posits that actors of type X will perform actions of type X. Institutions further imply historicity and control. Reciprocal typifications of actions are built up in the course of a shared history. They cannot be created instantaneously. Institutions also, by the very fact of their existence, control human conduct by setting up predefined patterns of conduct, which channel it in one direction as against the many other directions that would theoretically be possible.” (Quoted from Berger & Luckman 1966).

Social Cognitive Ontology and User Driven Healthcare

Table 2. “Half of what you are taught as medical students will in ten years have been shown to be wrong. And the trouble is, none of your teachers know which half” Sydney Burwell, Dean, Harvard Medical School - 1956 “Knowing more and more about less and less until one has known everything about nothing” “While the quality of the medical care my mother received was extraordinary, I saw firsthand how challenged the healthcare system was in supporting caregivers and communicating between different medical organizations. The system didn’t fail completely, but struggled with these phases: €€€€€• What was wrong: it took her doctors nine months to correctly identify an illness, which had classic symptoms €€€€€• Who should treat her: there was no easy way to figure out who were the best local physicians and caregivers, which ones were covered by her €€€€€• Insurance, and how we could get them to agree to treat her €€€€€• Once she was treated, she had a chronic illness, and needed ongoing care and coordinated nursing and monitoring, particularly once her illness recurred. (Bosworth 2006)

At a social level, disruptive social forces and innovation challenge established institutional patterns from time to time (Christensen 1997).

De-Specialization as a Disruptive Innovation: Preparing Physicians for People-Centered Healthcare The context of health professional education in the traditional tertiary hospital setting habituates and institutionalizes the role and the worldview of health professionals. Many health professionals around the world are now exposed to community based experiences providing a greater insight into the realities of people centered healthcare, or haven chosen to implement different approaches based on the needs of their community. (detailed discussions about this can be found at The Network: Towards Unity for Health and WHO Western Pacific) The following example describes one potential outcome of the de-specialization approach for a small rural Indian community: … Physicians here are awe-inspiring. Every one here is in the process of ‘de-specializing’. That does not mean that they are losing their skills as specialists. It means, they are learning the other specialties. ENT surgeon here is managing medicine OPD patients for 5 yrs and knows more about approach and management of general OPD issues than I do. A pediatric surgeon has become

a general surgeon and has learnt anesthesiology and practices it when no volunteer anesthetist is available. A pediatrician is learning C-sections. Needless to say, they are all able obstetricians. They all (7 physicians) started without any preconceived notions. Came to a rural setting and started learning how to become rural doctors. Many of my myths were shattered here in the first week itself...”Priyank Jain, Resident Medicine, Wisconsin, Milwaukee on a visit to a rural health set up run by a few committed physicians in Ganiari, Madhya Pradesh, India (personal communication) Another example is the ECHO (Extension for Community Healthcare Outcomes) model that gives physicians who specialize in the treatment of complex and chronic conditions access to ITtechnology, enabling them to share knowledge about best practice to co-manage patients with primary caregivers in rural communities. The key component of the ECHO model is a disruptive innovation called a Knowledge Network. In a one-to-many knowledge network, the expertise of a single specialist shared with several primary healthcare providers, each of whom sees numerous patients. The flow of information in a Knowledge Network is NOT unidirectional; the specialist and community-based primary care providers gain invaluable feedback and case-based 2003

Social Cognitive Ontology and User Driven Healthcare

experience through weekly consultations (Arora 2007). Other workers have also created knowledge networks in recent times in rural India utilizing principles of collaborative learning among primary care workers (Ganapathy 2007). These models sustain themselves through the continued input from all stakeholders, including that of patients (along with those of their relatives) as well as a variety of other health professionals who collaborate to meet the needs of the single patient. This approach transcends the centralized and paternalistic model of healthcare in favor of a truly collaborative one.

Collaborative Learning In a collaborative learning environment two categories of learners meet. One is the patients, relatives and other primary care givers who have no formal training. The other is physicians, other health professionals and secondary care givers who have received formal training and are the source of care when self-care proves to be insufficient for restoring one’s health. Obviously patients and health professionals gain new information independently, and the collaborative learning effort takes part in the consultation. The learning experience however will transgress the consulting room due to the subsequent exchanges with colleagues, peers and friends. The medical record on the one level represents a record of the learning in this consultation, yet we would suggest that learning could be progressed over time through a persistent shared electronic health record.

IMPLEMENTING USER DRIVEN HEALTHCARE Answering multidimensional information needs (AMIN) through persistent clinical encounters

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(Biswas 2008a-text, tables and figures have been reused with permission)

The Technology A Web-based solution to integrate healthcare Elearning needs could lie in a simple forum model already in use at present in various Web sites using what is loosely termed as Web 2.0 technology. In Web sites using this technology user-generated tags allow the site to evolve, enabling individual users to conduct more precise searches, make previously unacknowledged associations between facts, and explore a diverse undercurrent of themes to synthesize learning. It has been recently named Health 2.0 with reference to healthcare and has been described to be all about Patient Empowered Healthcare whereby patients have the information they need to be able to make rational healthcare decisions (transparency of information) based on value. As more information becomes available as a result of increased transparency, there will be a wave of innovation at all points along the full cycle of care (Figure1), which includes phases where healthcare service providers Educate, Prevent, Diagnose, Prepare, Intervene, Recover, Monitor, and Manage the various disease states (Shreeve 2007). The present version of Pub-Med, a popular evidence based resource (http://www.ncbi.nlm. nih.gov/sites/entrez) utilizing the Web 2.0 technology displays related publications as soon as a user clicks on to read an article abstract after entering his/her search terms. However this evidence-based resource is chiefly concerned with empirical collective experimental data and even the case reports available on the site do not offer the richness of the individual lived experience. These apart from the fact that most of the articles full text have restricted access.

Social Cognitive Ontology and User Driven Healthcare

Figure 1.

Introducing and Sustaining an Experiential Network in Routine Clinical Practice The Shared Learning Space All humans have the capacity and likelihood of performing both roles of caregiver and care seeker (patient) in their lifetimes. Each and every individual is an author of his own destiny (as well as his own Web log) that reflects his experiential life processes and decisions that can shape his future. User driven healthcare is an attempt to help make those decisions. It is a proposal to document valuable individual experiences of patients, physicians and medical students in a practicably feasible grassroots manner that has till date regularly gone undocumented and has been lost to the medical literature that may have actually benefited from it. Regular experiential informational input by any individual in the role of caregiver or care seeker may be posted on to an individual user’s password protected Web account that would function as an E-portfolio if s/he were posting as a caregiver and

one to his/her private personal health record if s/he is posting as a patient. The individual user could do this through email (or other mechanisms utilizing a unified communication engine: Figure2). Once individual users key in their unique experiential information that could also reflect pathophysiologic rationale, patient values and preferences, system features including resource availability, societal and professional values they would be presented with related individual experiences mashed up with empirical data immediately at the click of a mouse. Later it would be up to the individual to derive meaning from these multiple dimensions of information. To date this experiential information may exist in difficult to search Web logs but most parts of our day-to-day innumerable clinical encounters remain undocumented. As a result the present day user gets far from optimal satisfaction with the information on the net. Even if one could collate a larger variety of experiential information on the net, it would still be confined to PC literate individuals and to bridge the digital divide there have been recent attempts to develop a unified communication engine for data entry into the Web repository (Biswas 2008btext, tables and figures have been reused with permission). An individual at his leisure or even while waiting in queue to meet his/her physician may SMS (short messaging service) their thoughts and queries about their disease onto the forum that could be responded to by anyone on the Web. Users could choose to use phone, mobile phone, PC/laptop or an embedded system to work with the Web portal (presently in pilot). (Figure2) From a community perspective to integrate all these individual experiential data a personal health record for each individual patient in the community needs to be generated again in a user driven manner beginning with the patient’s version of their perceived illness with data added on by other actors in the care giving collaborative healthcare network as the patient traverses his/ her way through various levels of care beginning from the community to the tertiary care hospital.

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Figure 2.

The data contained within these records may then be effectively and anonymously shared between different patients and health professionals who key in their own experiential information and find matching individual experiential information through text tagging in a Web 2.0 platform.

A Dynamic PHR (Personal Health Record) to Answer Multidimensional Information Needs in a Persistent Clinical Encounter (Biswas 2008b) Analyzing the Personal Health Record (PHR) The PHR needs to begin with the patient as the vital source of information. Even in present day face to face clinical encounters with the history being recorded by the physician no clinical data entry would have been possible without the patient narrating his/her illness details. In the sample PHR the individual patient who is the owner of the PHR, is shown to have over a time frame entered her data in her own narrative (Table 3 middle column). Other than the patient’s contribution the data is further processed at multiple levels by different

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care givers (whoever the patient allows access to his/her data). The physician care giver is by training adept at structuring these narrative data into a compressed front page summary that can be quickly shared between physicians and consequently this comes into the opening page of the PHR as in the sample (Table 3 left column) Most caregivers accessing this PHR would have a brief structured overview of the patient’s problem to begin with and they could quickly follow it up with a glance at the patient’s minimally structured thought narrative to gain a wider perspective by simply clicking on the links (generated through text tagging) on this structured page to the minimally structured narrative of the same patient that would be in a separate page or of other patients with related narratives again in separate pages (Table 3 right column). Relating to different individual patient narratives could generate learning not only for the individual who maintains and browses through his own PHR but also for the other patients who share tagged key words related to their own illnesses in their own PHRs. In the above example of a dynamic PHR we can further add links to the notes of various other actors in the care giving collaborative network

Social Cognitive Ontology and User Driven Healthcare

Table 3. A sample PHR (personal health record) First Page: Structured PHR-individual patient data (centralized SCOCs) with links to further EBM or narrative data

Linked pages: Semi structured-individual patient SCOCs)

Linked pages: Semi structuredOther patient data (other person SCOCs)

Name: Anonymous IC Number: x123 Contact: 123 Diagnosis with duration: Diabetes 20 years, Hypertension 3 years, Hyperlipidemia 3 years (Click on: link to patient’s own semi structured data) Present diabetes status: Last HbA1C 1month back was 7% (click on: link to relevant evidence based information on HbA1c) Mean BP (measured 1-week back 110/70— a mean of 10 readings in a day (once in a week) measured with an electronic device (click on: link to relevant evidence based information on Hypertension in diabetes, role of home BP monitoring etc) Complications: Treatment related: Recurrent hypoglycemias…last episode 6 months back (click on: link to patient’s own semi structured data with relevance to her episodes of hypoglycemias) Target organ related: Microvascular: Kidney (glomerular vessels): Annual test of urine measurement of the albumin-to-creatinine ratio in a random spot collection (preferred method)- normal values (Click on: link to relevant evidence based information on renal Impairment in diabetes) Annual test of Serum creatinine for the estimation of glomerular filtration rate (GFR)-normal (Click on: link to relevant semi structured narratives of other patients with diabetes and chronic renal failure)

Individual patient’s self- profile (Behavioral diabetes institute 2006) Profile at the time of diagnosis I was a relatively happy 24-year-old working and eating whatever I wanted, whenever I wanted. My favorite breakfast was sugary cereal, but other than that, I didn’t eat a lot of sweets. I typically ate 2 to 3 meals per day, always at different times. (Link to other patient’s semi structured data on dietary changes) Here is some of what I remember of my first 6 months of having diabetes. Other people’s comments: My friend D who worked for the local Blue Cross: “Hey good news, Blue Cross now covers kidney transplants for diabetics.” (I didn’t even know kidney problems were something to worry about.) My friend J - “Be careful of your feet. My uncle just had his leg amputated.” On testing blood sugar Initially, I found it interesting. But keeping track of results soon became a pain. Plus, my life did not fit the schedule the little logbooks had preprinted. Who eats dinner at the same time every night? What about weekends? On taking insulin They showed me how to take injections using an orange as a example. I can’t imagine anything less similar to one’s leg or stomach than an orange. I don’t know if they still do this. (Link to other patient’s semi structured data on taking insulin) On my first insulin reaction I was in the hospital, and the nurse’s aide didn’t know what was happening. Neither did I, when I ended up starting to pass out, she decided to call in the reinforcements. Over the course of 20 years, I have been taken to the Emergency about 8 times unconscious due to insulin reactions. About 4 of those times were when I was pregnant. The following chronicles my first 20 years with Diabetes.

(http://www.diabetes-stories.co.uk/ research-transcript.asp) Introductory: (Greenberg R 2007) In many ways diabetes keeps me far healthier than I would be otherwise. It has improved my diet and weight over the years and some would say my daily walking reflects a woman possessed. Also, getting married for the first time at 48, my motivation to hang around for the long haul – and in good shape – surged…more…(link) http://www.diabetesstories.com/) Dietary changes: My diet has changed a lot.╯ The biggest change came to me a few years ago.╯ My next door neighbour‘s son-in-law had a job where he ended up with a lot of spare fruit and vegetables on a Sunday, and she used to give me this enormous bag and I used to have to try and use it all in the week.╯ And I‘ve really got into fresh fruit and vegetables now, and I really feel a lot better for it…more… (link) http://www.diabetes-stories.co.uk/ research-transcript.asp) On taking insulin I think the thought of it was more daunting than the actual injection. The injection, as I‘ve told many people, it doesn‘t... there‘s nothing to fear or hurt or anything. It just... well, there is no pain at all, you know. It‘s in a pen, and you switch up the counts - the amount that you‘re going to have, the units you‘re going to have - and then just inject it in, and it‘s so simple, it‘s... I mean, I can‘t see now, so I just... I am lucky that I can hear still and that I can feel the clicks - you can hear the clicks going round on the pen - so each click it counts as a unit. So, you know how many units you‘re taking, and that‘s it, you know, it‘s so simple…more (link)

like physicians, nurses, pharmacists and medical administrators all of which would reflect the patient’s journey as a persistent clinical encounter through the various processes of healthcare and suffering within an interconnected social framework (Figure 3).

Future Directions: Evolution of Medical Knowledge in Developing Community Ontology Self-organization is the property of well functioning complex adaptive systems that allows 2007

Social Cognitive Ontology and User Driven Healthcare

Figure 3. Developing a community medical ontology toward a persistent clinical encounter

the natural relationships among individuals and groups to shape the nature of an evolving knowledge base (Martin 2007 b). The organizational complexity of an individual’s interactions with his environment defines the level of his functionality. The more the connections an individual is able to develop and more the complexity of his network the more it may reflect his/her vitality (Biswas 2003). This is even witnessed at a micro level inside the human body where there is a demonstrable withering of neuronal connections and complexity with senescense and a resultant loss of neuronal functionality reflected in overall loss of functionality of aging (Lipsitz 1992). Quite a few studies demonstrate the relationship between intimacy and health, and how disease survivors who report positive family relationships or access to support groups consistently live longer than those without them. The challenge for us ahead is coupling our traditional focus on monitoring efficiencies with providing deeper human connections to promote sustainable behavior change (Darsee 2007).

2008

A few physician led companies have already made considerable headway in this direction. SERMO is a Web based US company that uses a forum model to develop experiential knowledge networks among US physicians while ‘Patient opinion’ is a UK based setup that aims to access the collective wisdom of patients using Web 2.0 tools where patients can share their stories and rate the care they receive down to ward and department level. This patient opinion aspect of user driven healthcare is about conversations, democracy and improving services by making the individual voice more audible. Curbside.MD is another US based company that goes one step beyond Pub-Med in data mining and presentation by utilizing Semantic fingerprinting to let users key in natural language queries in their search engine, which returns a variety of useful empirical evidence arranged as, all research, systematic reviews, guidelines, review articles, images etc. It also has begun fingerprinting of individual health profiles and matching similar profiles.

Social Cognitive Ontology and User Driven Healthcare

At the present moment user driven healthcare is heavily dependent on human to human interactions augmented by computers but gradually the role of human moderators in this scheme could be taken over by artificial agents that may be able to emote adequately with social cognitive ontological constructs to match their human counterparts. Promising use case studies from W3C computer human interaction researchers are a pointer to this end (W3C Emotion Incubator Group 2007).

CONCLUSION User driven healthcare is improved healthcare achieved with concerted, collaborative learning between multiple users and stakeholders, primarily patients, health professionals and other actors in the care giving collaborative network across a Web interface. At an individual level, every human forms his or her own personal cognitive conceptual and operational models as a part of her/his social cognitive interactions labeled social cognitive ontological constructs (SCOCs). Other than this, individuals also form similar conceptual constructs from collective social learning handed down from a collective to individual level in a top down manner. Present learning strategies in healthcare are mostly dependant on top down structured content and non-structured bottom up patient physician experiences are paid less importance. This chapter discusses the need to merge dominant centralized health professional expert generated medical ontology with decentralized, naïve patient user generated common sense medical ontology in a manner that generates minimum conflict and negative emotions. This may be attained by sharing individual human experiences in healthcare through matching persistent clinical encounters stored in Electronic health records (EHRs). These individual healthcare seeking and care giving experiences may reflect different levels of granularity or leaves budding with time in a larger tree of medical ontology. Regular

recording of day to day individual patient physician non-linearly structured experiential data and its meaningful sharing through text tagging by a variety of individuals may be a valuable adjunct to structured average patient data that presently exists in our information bases to promote patient physician E-learning in healthcare. User driven healthcare applying multidimensional approaches of persistent clinical encounters and wisdom of crowds (see glossary) has the potential to be transformational in challenging the complex, high cost, institutional approach that typifies healthcare delivery systems today. Relaxing central control makes local health workers feel more engaged in their projects (Kmietowicz 2007). While dominant players are focused on preserving business models of expensive care and technology arsenals, user driven innovations promise cheaper and simpler access to virtual clinical encounters thus meeting learning needs of the vast majority of patients who may otherwise suffer simply due to lack of information.

REFERENCES W3C Emotion Incubator Group, (2007) W3C Incubator Group Report 10 July 2007, Internet document retrieved on 7th November, 2007 from http://www.w3.org/2005/Incubator/emotion/ XGR-emotion-20070710 Akgu¨n, A. E., Lynn, G. S., & Byrne, J. (2003). Organizational learning: A socio-cognitive framework. Human Relations, 56(7), 839–868. doi:10.1177/00187267030567004 Arora, S., Geppert, C. M., & Kalishman, S. (2007). Academic health center management of chronic diseases through knowledge networks: Project ECHO. Academic Medicine, 82(2), 154–160. doi:10.1097/ACM.0b013e31802d8f68 Bar-Yam, Y. (1997). Dynamics of Complex Systems. Addison-Wesley.

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Horne, R., & Weinman, J. (1999). Patients’ beliefs about prescribed medicines and their role in adherence to treatment in chronic illness. Journal of Psychosomatic Research, 47, 555–567. doi:10.1016/S0022-3999(99)00057-4 IDEF5 method report (1994). Retrieved oct 7th 2007 from http://www.idef.com/pdf/Idef5.pdf Kenagy, J. W., & Christensen, C. M. (2002). Disruptive Innovation – New Diagnosis and Treatment for the Systemic Maladies of Healthcare, Business briefing: global healthcare Larner, A. J. (2006). Searching the internet for medical information: frequency over time and by age and gender in an outpatient population in the UK. Journal of Telemedicine and Telecare, 12, 186–188. doi:10.1258/135763306777488816 Loefler, I. J. (2000). Are generalists still needed in a specialized world? The renaissance of general surgery. [Review]. BMJ (Clinical Research Ed.), 320(7232), 436–440. doi:10.1136/ bmj.320.7232.436 Martin, C. (2003). Chronic illness care in General Practice – the practitioner-patient relationship. In C. Walker, C. Peterson, N. Milman, & C. Martin (Eds.), Chronic Disease: New Perspectives and New Directions. Melbourne: Tertiary Press. Martinez-Miranda, J., & Arantza, A. (2005). Emotions in human and artificial intelligence. In Journal Computers in Human Behaviour, 21, 323–341. McWhinney, I. R. (1996). The Importance of being Different. William Pickles Lecture 1996. The British Journal of General Practice, 46(7), 433–436. Murray, E., Lo, B., Pollack, L., Donelan, K., Catania, J., & White, M. (2003). The impact of health information on the internet on the physician-patient relationship: patient perceptions. Archives of Internal Medicine, 163, 1727–1734. doi:10.1001/archinte.163.14.1727

Neji, M., & Ben Ammar, M. (2007). Agent-based Collaborative Affective e-Learning Framework. The Electronic Journal of e Learning, 5(2), 123–134. Nkambou, R. (2006). Towards Affective Intelligent Tutoring System. Workshop on Motivational and Affective Issues in ITS. 8th International Conference on ITS 2006, (pp 5-12). Ontology-computer science. (2007, Aug 12) In Wikipedia. Retrieved oct 7th 2007 from http:// en.wikipedia.org/wiki/Ontology_%28computer_ science%29 Ontology works, inc. What is Ontology? (2008). http://www.ontologyworks.com/what_is_ontology.php Petros, P. (2003). Non-linearity in clinical practice. Journal of Evaluation in Clinical Practice, 9(2), 171–178. doi:10.1046/j.1365-2753.2003.00344.x Piaget, J. (1977). The development of thought: Equilibrium of cognitive structures. New York: Viking. Polanyi, M. (1958). Personal Knowledge. Towards a Post-Critical Philosophy. London: Routledge. Robichaud, A. L. (2003). Healing and Feeling: The Clinical Ontology of Emotion. Bioethics, 17(1), 59–68. doi:10.1111/1467-8519.00321 Shaw, J., & Baker, M. (2004). Expert patient”-dream or nightmare?’. BMJ (Clinical Research Ed.), 328(7442), 723–724. doi:10.1136/ bmj.328.7442.723 Skoyles, J. R. (2007). Here’s what I believe but cannot prove. “The Edge”Retrieved oct 7th 2007 from http://www.edge.org/q2005/q05_6. html#skoyles Smith, B., Kusnierczyk, W., Schober, D., & Ceusters, W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain, Proceedings of KR-MED 2006

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Smith, R. (1996). What clinical information do doctors’ need? BMJ (Clinical Research Ed.), 313, 1062–1068. Stewart, M. (2001). Towards a global definition of patient centred care. BMJ (Clinical Research Ed.), 322(7284), 444–445. doi:10.1136/ bmj.322.7284.444 Stewart, W. F., Shah, N. R., Selna, M. J., Paulus, R. A., & Walker, J. M. (2007). Bridging the Inferential Gap: The Electronic Health Record and Clinical Evidence. Health Affairs, 26(2), w181–w191. doi:10.1377/hlthaff.26.2.w181 Sturmberg, J. P. The Foundations of Primary Care. Daring to be Different. Oxford San Francisco: Radcliffe Medical Press 2007

Tan, J. (Ed.). (2005). E-healthcare information systems, Jossey-Bass: Wiley Imprint Tang, H., & Ng, J. H. K. (2006). Googling for a diagnosis—use of Google as a diagnostic aid: internet based study. BMJ (Clinical Research Ed.), 333, 1143–1145. doi:10.1136/bmj.39003.640567.AE The Social Construction of Reality. (2007, July 26) In Wikipedia, Retrieved Oct 16th 2007, from http://en.wikipedia.org/wiki/The_Social_Construction_of_Reality Uschold M,(2005)An ontology research pipeline. Applied Ontology 1 (2005) 13–16 13. IOS Press

This work was previously published in Handbook of Research on Social Software and Developing Community Ontologies, edited by Stylianos Hatzipanagos and Steven Warburton, pp. 67-85, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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Chapter 7.11

A Treatise on Rural Public Health Nursing Wanda Sneed Tarleton State University, USA

ABSTRACT Nursing informaticists can be leaders in promoting prevention of illness and diseases in the 21st century. Developing an infrastructure for application of preventive and predicative models in healthcare delivery is paramount. This chapter stresses the need for rural regions to develop paradigmatic models for incorporating all aspect of the human ecology domain. While movement in public health nursing is contingent on improvement in public health interconnectivity, nurse informaticists need to develop a classification system for public health nursing, develop databases for evidence–based practice, and incorporate the rural culture in their work. Incorporation of genomics in daily nursing practice will soon be a reality. As consumer-driven healthcare becomes the reality, the platform for healthcare delivery will change. A change to care DOI: 10.4018/978-1-60960-561-2.ch711

delivery in a variety of community sites with electronic information exchanges and personal health records will require robust work by informaticists. Remote monitoring devices in clients’ homes are another arena which will require a new set of skills for nursing interventionists.

A TREATISE ON RURAL PUBLIC HEALTH NURSING INFORMATICS Rural public health nursing needs a classification system, an evidence-based practice database, and development of a model for nursing care delivery in rural environments. Nursing informatics is the tool to accomplish these needs. Nursing informatics is the retrieval of data and information to support nursing clinical practice and research and for the equipping of information management systems. In the discipline of nursing, cognitive systems have grown more rapidly than practice. Hence nursing

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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informatics has grown in depth rather than breadth. A number of reasons support this phenomenon. The history of the nursing profession clearly shows an almost continuous evaluation and re-evaluation of the discipline. Socio-cultural factors have been the most recent mover of nursing science (Institute of Medicine [Committee on Quality of Health Care], 2001; Kimball & O’Neil, 2003). Health information technology has transformed the healthcare arena. As a result of information available to the public, a demand for greater accuracy and transparency in health care has thrust nursing to the forefront of evidence-based care and a focused research orientation. In addition, a desire within the discipline for scientific support for practice has evolved into a primary focus on evidence-based practice. Nurse informaticists are needed to integrate scientific research into public health, public health nursing and population care. Nursing informaticists are needed to develop a classification system for public health nursing, community care and rural healthcare. The objective of this chapter is to promote public health nursing and community health nursing’s role in the new care delivery patterns, with predictive and preventative care models for populations. This entry will broaden the range of information available for informaticists, as their role expands in the new healthcare arena. Articulation with nursing informatics and the ‘quality chasm’ crossings in US healthcare will assist the informaticists with search and retrieval activities. Nursing has a short history of evidence-based practice, unlike medicine which has garnered support from many sources to make evidence-based practice information readily available. Efforts are occurring to utilize research evidence as well as practice acumen to support evidence-based care in nursing (Berg, Fleischer & Behrens, 2005). Research support for public health/community health nursing comes from multiple sources, including biomedical, pharmacological, toxicological, human genomic, and public health sources.

2014

Human Ecology Domain Public health sources cover the ‘gray literature’ of human ecology, i.e., environmental practices, economics, agriculture, nutrition, extreme use of antibiotics, veterinary medicine, and infectious disease profiles. Likewise public health nursing examines the ‘gray literature’ for evidence-based data. Examples of sources include: The Journal of Urban Health; Smithsonian; Scientific American; Journal of Nutraceuticals; Social Justice; National Center for Complimentary & Alternative Medicine; Human Ecology: An Interdisciplinary Journal. These sources provide evidence from experts in their field, historical data, and research data. The skill of informaticists in searching and retrieving evidence from such diverse sources is needed to formalize a database and web-site dedicated to public health nursing and community health nursing. The disciplines of nursing and public health have a shared history of health care and illness prevention in the community. Public health nursing needs an information infrastructure, delineation of a language and classification in the Unified Medical Language System (UMLS). This is a momentous task, given the sparsely developed public health informatics sector. Prime concerns in the discipline of public health are the sociocultural and socio-economic constraints related to the application of primary care and primary prevention activities. The economic concerns have been placed on the national agenda by the American Medical Informatics Association (AMIA), 2007 task force, Healthcare terminologies and classifications: An action agenda for the United States. (2006). A previous publication by the Institute of Medicine (IOM), The Future of the Public’s Health in the Twenty- first Century (2003) portrayed the problem as a societal responsibility, not just a governmental concern. Public health nurses play a major role in providing care to the populations and aggregates defined as the

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‘public’ and consequently share in the need for building of a core infrastructure in public health.

Public Health In 2001 the American Medical Informatics Spring Congress on “Developing a National Agenda for Public Health Informatics” discussed the idea of ‘delivery of just in time information’ (Cimino, 2001) in public health. Current technology barriers and solutions were examined by the presenters and participants. Most agreed on the absence or inadequacy of an electronic infrastructure to provide the needed surveillance. Isolated pockets of technology were identified, but the economic constraints on small, regional public health departments were viewed as paramount. Collaboration to extend nursing and medical care into the community was seen as the focus of current public health efforts. Modest efforts to promote connectivity, introduce primary information web sites and increase interdisciplinary activities have appeared on the horizon (Public Health Informatics Institute, 2006b; Institute of Medicine [Committee on the Future of Rural Health Care], 2005; Tilson & Berkowitz, 2006 In 2006 the Public Health Informatics Institute was designated by the Robert Woods Johnson Foundation as the National Clearing House for grant funding in public health. Two significant and innovative fundings were developed and distributed for public health endeavors in the 21st century. They are titled “Common Ground: Transforming Public Health Information Systems” and a report on collaboration, “Taking Care of Business” (Public Health Informatics Institute, 2006a; Public Health Informatics Institute, 2006b) The grant and conference report are major steps forward in developing and implementing a core for public health informatics. Another major event occurred in late 2007, when the American Medical Informatics Association (AMIA) partnered with the Center for Disease Control (CDC) to launch a 5-year cooperative agreement to advance the

public health workforce. AMIA is a world leader in biomedical and health informatics. This organization will be presenting one of the first summits on translational bioinformatics in early 2008 (AMIA, 2007). AMIA will also host the 11th International Congress on Nursing Informatics [N12012] in Canada on June 23-27, 2012 (American Medical Informatics Association, 2007 The development of evidenced-based nursing practice databases and web sites is crucial in the migration to client-centered care. Predictive and preventative healthcare models will become a nurse informaticists issue. In addition, development of paradigmatic models for incorporating all aspects of the human ecology domain area needed. Toxic environmental exposures, unsafe water, infectious disease and nutritional problems are in need of planning for prevention and treatment. The political forces and financial interests shaping healthcare delivery should become primary knowledge, thereby promoting these issues.

RURAL PUBLIC HEALTH NURSING Public health nursing, nursing informatics and public health are exquisitely interconnected. However, the role of nurses in public health is not always accorded due consideration. Public health nurses have a greater independent role in population health, than just clinical outreach or epidemiological surveillance. Public health nurses provide assurance care, education of populations at-risk for disease, health promotion and lifestyle change information, and case-management of aggregates with chronic disease needs. They also actively participate in control of infectious disease and conduct research. Public health nurses advocate privately and in the political arena for the rights of underprivileged disenfranchised groups. The community-focused care by nurses includes public health functions, plus the managing and delivery of home care to individuals, families and aggregates. Community health nurses perform

2015

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community assessments, develop policies for care delivery, and function as school health nurses. Nurse informaticists have a role to play in rural public health nursing, plus a compelling array of topics to research, develop and implement. These efforts must occur simultaneously, in order to meet the challenges of the future. Public health nurses’ need to articulate the changing healthcare delivery patterns, as a joint endeavor with public health, government and private institutes. The new delivery patterns focus on predictive and preventative measures, and tools for population health. Also considered in these delivery patterns are urgent reforms needed in preparedness for terrorism attacks. Population health ultimately translates into individual care delivery. Personal health information with construction of personal health records (PHR’s) is a major focus for electronic data retrieval. This is a radical change from the previous climate of physician protected disease information. The previous, and largely current, medical monopoly of episodic, acute, disease oriented care delivery will give-way to greater accuracy and transparency in healthcare, with a client-oriented care atmosphere. Public health nurses currently assess populations at greatest risk and target them for health promotion activities, i.e., healthy diets, exercise, saying ‘No’ to tobacco use. They function as environmental and occupational spokespersons, protecting the health and minimizing risks. Examples in rural areas include monitoring of field spraying with herbicides and organophosphates and monitoring the pollutions effects of dairy and hog farm runoff. The groundwater in rural areas is frequently contaminated by farm pollution, generally in the form of nitrates (Electronic Research Service/ United States Department of Agriculture [ERS/ USDA]. 2007). Prevention of illness, such as immunizations for infectious diseases is another primary example of services provided by public health nurses.

2016

Models The preventative model for care delivery has wide-origins. The World Health Organization (WHO)’s report of 1981 comes from years of data collection and information related to health issues in all countries. In 1988 the Institute of Medicine’s report, The Future of Public Health, set in motion the ideas for reforming the U.S. healthcare system. Nurse informaticists can be leaders in promoting prevention of illness and diseases in the 21st century. They are well prepared in communications skills and interdisciplinary activities. The principle preventative model in the U.S. is the public health practice model of primary, secondary and tertiary prevention. This model is comprehensive, basic and based on assessment of known risks and existing hazards. The focus is firstly on education for health promotion, then screening and treatment and follow-thru with management to prevent relapse and complications. Adjunctive to this preventative model are various models and systems of care delivery developed by private centers, academic centers and recently the third-party payers group. These models tend to address managing disease processes to prevent complications. Examples include the chronic care model and the disease management model. The chronic disease model integrates community resources, health system organizations and patient self-management supported by information technology (Bu, Pan, Johnston, Walker, Adler-Milstein, Kendrick, et.al, 2007). The disease management model uses a multidisciplinary team approach to management of a chronic disease, with emphasis on patient self-management. A prime component of this model is information technology support. Disease management is an enhanced version of the medical model, with more evidence-based guidelines and patient involvement. Some variants of population or group care delivery are not classifiable as models, but involve significant changes in the delivery focus. In the United States several recent innovations

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demonstrate the changing face of care delivery. The skills of advanced nurse practitioners are used in a reformed environment. One example is a process called Care in the Express Lane (Waton, 2007, April) which describes the needs of individuals and how they utilize walk-in client services at mini clinics. This system radically reduces the costs, improves the benefits for common complaints and demonstrates the changing face of care delivery. ‘On-site health centers’ are another system which provides corporate wellness strategies and illness prevention strategies for large employers and their employees. Examples include the 10,000 employees at Credit Suisse and the 17,000 client visits in 2006 at the Sprint Nextel health center (Basler, 2007). Other walk-in systems include health service centers in grocery stores, pharmacies, and other high traffic areas of a community. These examples address the need for primary care and secondary care (screening) delivered specifically with clients as the focus. The processes of providing preventative care are the rational solution to comprehensive quality care, the reduction of suffering and disability and the choice of newly empowered, electronic-savvy generations. The design, implementation and evaluation of another new care delivery pattern, e.g., predictive care provision based on genomic profiles will be revolutionary. The Human Genome Project, completed in 2001 formed the basis for identification and control of chronic diseases, gene transmitted diseases, child and adolescent defects and disabilities, infectious diseases and more. Progresses in endeavors for human genome predictions have not been rapidly advanced. Several reasons exist for slow progress. One reason is the lack of trained bioinformatists to analyze and interpret the data which is continuously made available. Another reason pertains to a lack of enthusiasm among care-providers and third-party payers to change an embedded system of acute care. Thirdly, the bioethics of identifying a condition for which no cure or treatment exists remains resolved. Fourthly,

the economics of individual genome analysis has hampered advancement. The medical-insurance complex has apparently been waiting for the $1000.00 gene test. However, a body of information for predictive modeling has emerged. This information is readily available on the Internet. The Center for Disease Control and Prevention provides multiple education programs, seminars, Web Sites, and databases on the subject of genomic data, public health, population health and predictions for the future of healthcare (National Office of Public Health Genomics [CDC], 2007). A focus on population needs, based on consideration of the basic facets of human exposure, can reveal possible patterns of disease (Khoury & Mensah, 2005) and suggest early intervention. Predictive modeling is not a new concept. Predictive modeling as a group of analytical methods has been used by healthcare plans for decades. In the past, the primary use of predictive modeling has been to (1) reduce financial risk for payors and (2) to identify high risk patients with high cost diseases. The latest vendor push is integrated systems which look at healthcare claims data but also look at data gathered for proactive intervenability. This data collection may begin with an enrollees’ self-perception of their health, then, a delineation of behavior and lifestyle and a record of compliance via a personal health record. This is followed with assignment of a healthcare proctor who functions on a one-to-one basis with the enrollee to advise and educate. These models are helpful but not enough, and do not address the science and technology available for patientcentered, patient-directed care based on individual genetic profiles. Care management with predictive models needs to utilize intelligent communications with enrollees/consumers to positively impact their behavior. Effective data gathering yields information which ‘puts a face on the consumer’. More than logistics and a few medical facts are needed. It will be important to know if screening tests

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were initiated by providers or individuals, what are the age-gender-responsibilities of workers, are there financial difficulties and where is the information about community resources that can assist with payments for healthcare. Consumer/ client driven healthcare comes with empowerment and self-management and predictive modeling. Ethically we must provide the consumer with sufficient information to manage their care, as well as decide as a society how to provide for those who make imprudent decisions (Robbins & Brill, 2005). However the out-reach for evidencebased, predictive genomic information will be the revolutionary step to healthcare transformation in the 21st century.

HEALTHCARE DELIVERY PATTERNS Healthcare savvy, computer oriented consumers are seeking support and guidance for planning their health and future life. They already have access to large amounts of data from the Internet regarding the ‘quality chasm’ issues identified by the Institute of Medicine (IOM) (Corrigan, J, Kohn, L, & Donaldson, M., 2000; IOM, 2001). These ‘quality chasm’ issues or gaps in healthcare delivery are caused by a fragmented and poorly organized system that does not utilize its resources efficiently. The needed correction is possible with a new health care system that has the following six attributes: (1) safe care, (2) effective care, (3) patient-centered care, (4) timely care, (5) efficient care and (6) equitable care (IOM, 2001). Appropriate use of technology can improve the safety aspect; evidence-based knowledge and practice will provide effectiveness, and an actual focus on patient-centered care will provide timely proactive interventions. A reduction in redundancy, collaborative care and the use of informatics at the point of care will provide the basis for efficient and equitable care.

2018

Patient-centered, consumer/client driven care requires greater transparency and accountability. No longer is it possible for unquestioning compliance to orders to continue as the norm in healthcare. Quality assurance evaluations of care, healthcare providers, systems and institutions are available on the Internet. Rating systems regarding hospital performance rates, statistics on mortality rates and quality profiles of individual practitioners are readily available. Ratings of insurance plan coverage and responsiveness are posted by governments and private institutions. Healthcare providers, especially those involved in population health have a responsibility to educate individuals, aggregates and countries in quality measures. A scenario for predictive modeling uses genomic medicine, patient health perception and new care delivery formats. Scenario: The case involves a 25 year old white Anglo woman who is knowledgeable regarding her positive family history of breast cancer. She seeks a physicians’ help for predictive testing. Genetic tests exclude BRCA1 and BRCA2 genetic mutations. Further testing demonstrates a spike in metabolic residue from excess hydrogen radical formation, which indicates a beginning pathway to malignancy. Other conventional assessments exclude any breast mass, cysts or abnormalities. She is referred to the advanced nurse practitioner (ANP) for counseling and education. The ANP obtains a detailed history of lifetime environmental exposures, dietary habits, work environments, types of recreational activities, i.e., exposure types in hobbies, and medication usage. The ANP discusses the known exposures to risk factors, specifically xenoestrogens. These include childhood and adolescent exposure to organophosphate chemicals on her parents’ farm, swimming in lakes with runoff from the now-defunct chemical-fertilizer plant near her home. The patient is advised to continue yearly physical examinations and genomic assessments. She is provided information on including antioxidants in her diet, e.g., crucifer-

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ous vegetables, plus use of alternative biological antioxidants. These antioxidants can be obtained in nutraceutical dosage forms and/or functional foods containing nordihydroguaiaretic acid. The ANP enrolls the client in the healthcare payor’s information registry and forwards all existing records to the account. This includes genetic testing results, counseling information and family history genetic algorithm. With client agreement, she also establishes a timeline for notices and reminders of appoints needed. In this scenario the ANP functions as a case manager and clinical specialist with extensive knowledge of genomic testing and interventions for lifestyle behaviors. The role of nurse informaticists in the new care delivery models needs to be developed beyond the standard data and information storage and management techniques. The ‘delivery of just in time information’ (Cimino, 2001) should be the goal and the standard. Public health nurses with advanced degrees have the attributes and knowledge to use predictive modeling of genomic data to provide care for populations. The informaticists’ has to analyze, model and present the necessary knowledge to enable behavioral interventions for groups of individuals and professionals. The individual may be self-managing their health and desire some assistance. Nursing professionals’ need assurance an adequate fund of scientific, evidence-based information exists. This information must be readily available and deliverable to both urban and rural areas. An example of the kinds of genomic information needed by nursing professionals is the text ‘Nutrigenomics and Beyond: Informing the Future’ by Institute of Medicine (2007). This text provides further evidence of nutrient-gene and gene-environment interactions. The impact of human nutritional needs, healthy and unhealthy dietary habits, maintenance of a safe food supply and the economics of food production invades most sectors of life. The data and information being developed in this sphere of health and illness may be the most important event of the 21st century.

EVIDENCE-BASED PRACTICE Evidence-based practice resources are available from a number of entities. These include governmental agencies, universities, academic centers, libraries, veterans system and private vendor organizations. Most of the documents are available via the World Wide Web and in print. The Agency for Healthcare Research and Quality (AHRQ) is one of the oldest and most widely known. AHRQ produces evidence reports and clinical practice guidelines. AHRQ has funded thirteen evidenced-based practice centers, based in technology centers, universities and academic center (AHRQ, 2007). Other governmental sites include the Center for Disease Control and Prevention (CDC) site for public health professionals(CDC, 2007), the Veterans Evidence-Based Research Dissemination Implementation Center (VERDICT, 2007). Private entities have learning resources and websites on the topic of evidence-based practice (Netting the Evidence, 2007). The four databases on evidence-based medicine in the Cochrane Library, produced by the world-wide web virtual Cochrane Collection is available to libraries and via CD-ROM. Recently several universities in Texas have established evidence-based practice centers specifically for nursing. The Academic Center for Evidence-Based Practice (ACE) at the University of San Antonio School of Nursing was established in 2000. The Texas Tech University Health Sciences School of Nursing and the Medical Center Hospital in Odessa, Texas are establishing an evidence-based practice center in 2007. These are encouraging developments that need to make massive moves forward to provide the needed resources for nursing. A critical need exists in nursing informatics for an updated, current practice, public health/ community health terminology and classification (taxonomy). The mission of this chapter is to promote nursings’ role in preventative and predictive care delivery systems which are primarily public health nursing and community

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health. The means to accomplish this is vis-à-vis classification research in public health nursing and evidence based care research. The American Nurses Association (ANA) has endorsed the inclusion of two classification systems related to home care, e.g., The Omaha Community Health Problems and Interventions Classification System (Omaha System) (Martin & Scheet, 1992) and the Clinical Care Classification (CCC), (Saba, 2007) (revised from Home Health Classification System (HHCC) of Nursing Diagnoses & Interventions, Saba, 1991). Both of these systems are outdated and do not reflect the current practice of public health/community health care. They are two of the eleven classification systems recognized by ANA. The Unified Medical Language System (UMLS) also includes the Omaha System and the CCC (formerly HHCC) system. Public health nursing and community health care require sophisticated cognitive knowledge and skills in socio-economic structures, diverse cultures and communication and these requirements are not included in the two classification systems currently fronted. These classification systems are not suited for urban or rural community health. A most glaring omission is the lack of attention to socio-economic and cultural variables for rural populations and poverty areas of a community. Increasingly the evidence demonstrates the pervasiveness of an early, sustained impoverished social status on poverty encased, disenfranchised individuals and their perception of their health (Marmot, 2000; Sapolsky, 2005). The attempt to limit nursing services to bedside-type functions, in both of these systems is outdated and outmoded. The International Classification for Nursing Practice (ICNP®) Version 1 (2005) has the greatest potential for becoming a nursing language system. However this document has succumbed to the designation of “Nursing Diagnoses”. Nursing diagnosis is too limiting and contradictory for public and community health care. Use of the terms ‘Nursing Statement’ or ‘Nursing Emphasis’ would be more inclusive of public health and community

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activities. Public health nursing is focused on population-based care. And the language is about primary prevention of illness and disease. Primary prevention is about wellness, health promotion and healthy lifestyles. Health and wellness do not require a ‘diagnosis’ or ‘problem’ to define their status. Therefore, the North American Nursing Diagnosis Association International (NANDA I) nursing diagnosis approach is not applicable. A new taxonomy is needed, to address wellness terminology and the education needed to sustain a healthy lifestyle. The controversy over NANDA I relates to its focus on physiological conditions and acute care, and exclusion of ‘wellness’ states, prevention, and client preference. It is possible to contend that wellness education topics may start with identification of the major causes (i.e., problems) of chronic disease, but this is albeit to making the foot-fit-the shoe.

IMPACT ON RURAL PUBLIC HEALTH NURSING The impact of new care delivery patterns with predictive and preventative models, i.e., distance monitoring, patient/client controlled information registries and nursing case management are some of the future roles involving nursing informaticists. As the technological infrastructure continues to develop in the rural areas and care delivery expands, research is needed to design and develop solutions for nursing care delivery in sparsely populated areas with multiple socio-cultural differences. Rural care provision is complex and problematic (Williams, A, & Cutchin, M, 2002). Rurality is best defined by locality. Rurality is rife with socio-cultural differences, distance and access problems and the need for collaboration between disciplines (Institute of Medicine [IOM], 2005). A set of research questions for the rural nursing informaticists include defining the role of technology in distance monitoring from rural locations

A Treatise on Rural Public Health Nursing

to urban institutions and from clients homes to home health agencies. Distance monitoring includes both telemedicine and home physical assessment monitoring. Building an information and communication infrastructure in rural America begins with attention to the culture, education and financial assistance needed at the basic level of care delivery. Improving healthcare in rural areas begins with understanding and incorporating the strengths of these environments. Loyalty to family and healthcare providers, concern for neighbors, hardiness and a drive to survive are socio-culture patterns more common in rural environments. Small hospitals, community clinics, physicians’ offices, home health agencies and public health departments are the sources of healthcare in rural areas. Emergency care and transport to these facilities often depends on emergency medical services personnel who may be volunteers, hospital employees or lowwage minimally trained individuals. Communication devices are usually personal equipment, subject to interference from weather, low range capability and lack of consistent transmission towers or satellites. Travel distances and remote home locations with poor road access add to the problem. Small hospitals treat common, chronic conditions, normal labor and delivery situations and minor emergencies. Patients needing specialist care are stabilized and transferred to tertiary care facilities. Regional public health departments are the major source of preventative education, well-child care and infectious disease surveillance and immunization. The Institute of Medicine’s quality chasm series directed to improving rural healthcare illustrates some significant differences in rural versus urban populations. Some of these differences in rural areas are (1) a higher rate of smoking in adolescents and adults; (2) higher rate of self-reported obesity in women; (3) less active; and (4) more threats of death from motor vehicle injuries, falls, poisoning, and suffocation (IOM, 2005). These gaps in health care need to be addressed by a system-wide approach.

Searches for advances in healthcare monitoring techniques are retrieved primarily from commercial and biotechnology web sites. A variety of ‘services’, programs and equipment are available. Some vendors focus on home health services, with home monitoring devices, such as ‘Health Buddy’ (McKesson, 2008) which transmit patient data to field offices. These field offices may be home health agencies or other service type agencies. These types of remote monitoring techniques are low-technology with wireless technology or plugins to power outlets and phones and do not require a computer or Internet access. They are sold to be used with or without home care nurses. The use of these devices has ballooned into an extensive system of remote monitoring. At the high-technology end is the package of telehealth advisor solutions. These include disease management programs, healthcare monitoring, educational programs on prevention and communication technology provisions for care-givers, friends, and family to pharmists, chaplains and others. These health management plans provide tiers of vendor products to be used, and require late-model computers, cameras, monitoring devices and a sophisticated user. These invasive-type telehealth systems are focused on the electronically versed individual, i.e., some of the ‘baby boomers’. They also may include personal health records and healthcare financial management systems. Questions that arise from remote, home monitoring are akin to the questions asked post-humus about expansive and expensive, telemedicine projects. Many are focused on rural participants and tout universal healthcare edicts for most diseases and illness, or focus on a select few chronic disease. No consideration has been given to rural family values, cultural differences, socio-economic variables, and lack of an information infrastructure for delivery of the system. No consideration is given to the individual, client, patient, consumer, user, and recipient of ‘healthcare services’. The major thrust of reform proposed by the Institute

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of Medicine is to make the healthcare delivery system patient-focused! Therefore, nurse informatists’ must be concerned with remote monitoring which does not require intervention by the professional best prepared to assess, monitor and evaluate homecare, i.e., the home-health nurse and the public health nurse. Where is the evidence-base of research which supports remote monitoring? Should comparative logic look at telemedicine projects for evidence? Should questions of acceptance of remote monitoring by rural cultures be addressed? Other questions needing answers from informatists’ include the following: 1. Is a set of data, i.e., weight, pulse, respiration, temperature and possibly, blood pressure, peak flow measurement, and blood glucose monitoring, collected via a remote device compromised by the abilities of the data collector, the quality assurance monitoring of the device itself, and/or the dynamics of the household? 2. How much invasive technology promotes the health and welfare of the client? Is the client in charge? Are other persons assuming or demanding control for nefarious reasons? 3. Privacy issues become problematic: Do friends and neighbors need to know personnel information about an elder client? Does the client acquiesce from fear of abandonment or lack of care? 4. Is treatment delayed or unnecessary due to inappropriate referral? 5. Will third-party payors pay for all aspects of a disease management system, which may be duplicative and ill-conceived for rural patients? Remote monitoring devices, including video, audio and physical assessment monitoring are valuable tools for the armamentarium for the rural public health/community health nurse. However, the cautions need to be in addressed

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prior to investment in technology which may be shelved and/or provide false assurance. These are fundamental areas of work requiring the skills of the nurse informatists. The development of a rural health information infrastructure is a needed component of a reformed healthcare system in the United States. A surprising 75 per cent of the U.S. is rural; 80 per cent of some states are rural, e.g., Texas (ERS/USDA Research emphasis, 2007). Therefore a major force to improve and reform healthcare must traverse these rural miles to attain a connectivity desired for seamless healthcare delivery. In other words a rural-urban continuum must be formed (Ricketts, Savitz, Gesler, & Osborne, 1994). Private investors, grants and governmental funds are needed to finance the technology infrastructure for electronic connectivity (IOM, 2005). Rural residents seeking healthcare face a number of unique obstacles. Some obstacles are personal challenges, while many are system wide, political obstacles. When the rural resident is also a member of an ethnolinguistic culture other than English-speaking, White and Anglo the obstacles begin to expand. For Hispanics, the personal obstacles lessen with acculturation and improved legality of status (Escarce & Kapur, 2006). The political obstacles remain as challenges for healthcare providers and healthcare systems in all rural areas. The non-English speaking rural resident is vulnerable to a lack of healthcare, plus Spanish speaking healthcare workers are minimal to absence in small rural hospitals. The Anglo rural resident is also vulnerable, but most commonly enters the healthcare system due to an acute illness or crisis, and is confronted with multiple bewildering choices. Some of the answers come with electronic connectivity.

PERSONAL HEALTH RECORDS Rural areas are unique in composition, culture and use of the healthcare system. Loyalty to lo-

A Treatise on Rural Public Health Nursing

cal healthcare providers is a component which abrogates some changes, such as ‘brokered care’ insurance management and change in community services. However, healthcare services are seldom self-sufficient in a rural place. Operating ties exist between urban tertiary care systems and rural practitioners. The use of personal health records would be a major improvement in continuity of care and patient-focused care. Personal health records are electronic repositories of a person’s health history and health status. A reduction in over-medication, duplicative medications and repeat testing could be minimized. Patient involvement in their changing health needs would be available for scrutiny and timely engagement in new practices, such as changes in insulin dosage based on glucose monitoring, diet and activity. The Robert Wood Johnson Foundation, a private philanthropic organization, established Project Health Design “to promote the development of interoperable personal health care record systems that will provide a range of flexible tools that support an individuals’ needs and preferences.” (Robert Wood Johnson Foundation, 2007, pg. 1). This project is directed by Patricia F. Brennan, PhD, R.N. The development of personal health records will give back control of information to the clients, allow correction of inaccuracies, and should be developed with client privacy as a major concern. Another major movement for personal health records is in a major private industry development which will significantly impact the healthcare system in the USA. Dossia, an independent, secure Wed-based system of personal health care records will empower individuals to gather their own medical data from multiple sources and to create and utilize their own personal, private and portable electronic health record (Dossia, 2007). Major U.S. employers are partnering with Dossia. These include Applied Materials, BP America, Inc., Cardinal Health, Intel Corporation, Pitney Bowes, AT&T, and Wal Mart (Dossia, 2007).

This is one of several movements to empower the clients and consumers. Patients want to know about their health status which means more transparency, less physiciandominated language, timelier test results directly to the individual and a portable health history record. Personal health records will be in place before electronic health records (EHRs) are available as a significance source of data. Leading technology experts point to recent closures and slow-downs in construction of Regional Health Information Organizations (RHIO), which are the prime vehicle for EMRs. The closure of the leading RHIO at Santa Barbara, California in 2006 portended an urgent look at health data sharing between physicians, hospitals, clinics, pharmacy and insurers (McGee, 2007). Reasons for folding of the project were ending of financial support and a lack of physician support to continue the projects. Predictions are for another decade or two before EHRs are ubiquitous.

FUTURE TRENDS The changing face of healthcare delivery will evolve into a new dimension, with a germane interest in the pendants of socio-cultural and socioeconomic structures. The practice of medicine and nursing will become individualized, personalized and genomic driven. The question of how to introduce genomic information to individuals, care providers, third-party payors and employers will be a major hurdle in re-defining the privacy laws. The care delivery platforms will change to electronic exchanges between patients, care providers and service providers. Nurse informaticists will become widely recognized as the knowledge workers with clinical expertise. Population-based care will continue but on a more select scale with smaller aggregates as the intended clients. Genetic variations and genomic complexes in humans, animals and plants will become the basis for intervenability. Diets, medi-

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cations and behaviors tailored to the individuals present and future requirements will be the norm. A significant caution exists for global effectiveness of healthcare delivery. Will we evolve into a society with two tiers of healthcare? One level would be the electronically enabled middle and upper class with demands and capabilities to direct their health future, with the poor and vulnerable populations left to partake of health services as available.

CONCLUSION Population-based research is available to interested and in-touch healthcare providers. We need more operational population-based research. The population-based care method focuses on broad categories of morbidity as the indicator of health resource use, rather than a few chronic diseases (Mullen, 2007). All players in the healthcare arena will continue to be involved, but hopefully with a more rational policy-making role.

REFERENCES Agency for Healthcare Research and Quality. (2007). Retrieved May 25, 2008, from http:// www.ahrq.gov/clinic/epcix.htm American Medical Informatics Association. (2007). Retrieved October 26, 2007, from http:// www.amia.org/ American Nurses Association (ANA) Recognized terminologies and data elements set (2006, May 11). ANA nursing practice information infrastructure. Retrieved May, 25, 2008, from http://www. nursingworld.org/npii/terminologies.htm Basler, B. (2007, April). Care in the express lane. AARP (American association of retired persons publications) [New York: American Association of Retired Persons Publications.]. Bulletin, 48(4), 12–14. 2024

Berg, A., Fleischer, S., & Behrens, J. (2005). Development of two search strategies for literature in MEDLINE-PubMed: Nursing diagnoses in the context of evidence-based nursing. International Journal of Nursing Terminologies and Classifications, 16(2), 26–32. doi:10.1111/j.1744618X.2005.00006.x Bu, D., Pan, E., Johnston, D., Walker, J., AdlerMilstein, J., Kendrick, D., et al. (2007). The value of information technology-Enabled diabetes management. Charlestown, MA: Healthcare Information and Management System Society. Cimino, J. (2001). Delivery of just in time information. American Medical Informatics Association, 2001 Spring Congress Final Program, Developing a National Agenda for Public Health Informatics. (p.13). Bethesda, Maryland: American Medical Informatics Association. Corrigan, J., Kohn, L., & Donaldson, M. (Eds.). (2000). To err is human: Building a safer health system. National Academies Press: Washington, D.C. Dossia gains momentum toward providing employees with personal, private, portable and secure health records (2007, October 3). [Press release] Retrieved May 25, 2008, from http://www.dossia. org/home/Dossia_Gains_Momentum_Sept_07.pdf Electronic Research Service/ United States Department of Agriculture (ERS/USDA) Research Emphasis. (2007). An enhanced quality of life for rural Americans. Retrieved October 21, 2007, from http//www.ers.usda.gov/emphases/rural/ Escarce, J., & Kapur, K. (2006). Access to and quality of healthcare. In M. Tienda & F. Mitchell (Eds.), Hispanics & the American Future (pp. 410446). Washington, D.C.: National Academies Press. Evidence-based Practice Centers Overview. (2007, July). Agency for Healthcare Research and Quality, Rockville, MD. Retrieved August 19, 2007 from, http://www.ahrq.gov/clinic/epc/

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Healthcare Terminologies and Classification. An action agenda for the United States (2006). American Medical Informatics Association and American Health Information Management Association. Retrieved May, 2008 from, http://www. amia.org/inside/initatives/docs/terminologiesandclassifications.pdf Institute of Medicine. (2003). The future of the public’s health in the 21st Century. Washington, D.C.: National Academies Press. Institute of Medicine, Committee on the Future of Rural Healthcare. (2005). Quality through collaboration: The future of rural health. Washington, D.C.: National Academies Press. Institute of Medicine, Committee on Quality of Health Care in America. (2001). Crossing the quality chasm: A new health system for the 21st Century. Washington, D.C.: National Academies Press. Institute of Medicine (IOM). (2007). Nutrigenomics and beyond: Informing the future. Washington, DC: The National Academies Press. International Council of Nurses. (2005). International Classification for Nursing Practice, Version 1. Geneva, Switzerland: Author. Khoury, M., & Mensah, G. (2005, April). Genomics and the prevention and control of common chronic diseases: Emerging priorities for public health action. Preventing Chronic Disease: Public Health Research, Practice and Policy, 2(2)1- 11. Retrieved June 8, 2005, from, http://www.cdc. gov/pcd/ issues/2005/apr/05_0011.htm Kimball, B., & O’Neil, E. (2003). Healthcare’s human crisis: The American nursing shortage. Robert Woods Johnson Foundation. Retrieved May 25, 2008, from, http://www.rwjf.org/files/ publications/other/NursingReport.pdf Marmot, M., & Wilkinson, R. (Eds.). (1999). Social determinants of health. Oxford OX2: Oxford University Press.

Martin, K., & Scheet, N. (1992). The Omaha System: Applications for community health nursing and The Omaha System: A pocket guide for community health nursing. Philadelphia: Saunders. McGee, M. (2007, May 28). Urgent care. Information Week, 140(1), 40–50. McKesson Corporate. Healthcare Providers (2008). Retrieved May 25, 2008, from http://www.mckesson.com/en_us/McKesson. com/For+Healthcare+Providers/Home+Care/ Telehealth+Solutions/McKesson+Telehealth+A dvisor+Components/Health+Buddy+Appliance. html Mullen, P. (2007, June). A conservation with Jonathan Weiner, DrPH: Mixing populationbased care with market controls. Managed Care, 16(6), 1-7. Retrieved August 8, 2007, from http:// www.managedcaremag.comarchives/0706/0706. qna_weiner.html National Office of Public Health Genomics [CDC] (2007). Retrieved August 10, 2007, from http:// www.cdc.gov/genomics/activities.htm Netting the Evidence (2007). Retrieved August 19, 2007, from http://www.shef.ac.uk/=scharr/ ir/netting/ North America Nursing Diagnosis AssociationInternational (NANDA-I) (2007-2008).Nursing diagnosis: Definitions and classifications. Philadelphia: NANDA. Public Health Informatics Institute. (2006). Taking care of business: A collaboration to define local health department business processes. Decatur: GA: Public Health Institute. Public Health Informatics Institute. (2006). Common ground: Transforming public health information systems (2006, August 1). Retrieved September 10, 2007, from http://commongroundprogram.org/

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Ricketts, S., Gesler, W., & Osborne, D. (Eds.). (1994). Geographic methods for health services research. A focus on the rural-urban continuum. Maryland: United Press of America, Inc. Robbins, D., & Brill, J. (2005, May-June). Blending ethics and empowerment with consumer-driven healthcare. Patient Safety & Quality Healthcare. Retrieved August 4, 2007, from http://www.psqh. com/mayjun05/ethics.html Robert Wood Johnson Foundation. (2007). Improving the health & healthcare of all Americans Project. Retreived from http://www.rwjf.org/ programareas//resources/product.jsp?id=21131 &pid=1140&gsa=pa1140 Saba, V. (2006). Clinical care classification (CCC) system manual. A guide to nursing documentation. New York: Springer Publishing. Saba. V. (1991). Home health care classification project. Washington, D.C.: Georgetown University (NTIS Pub. #PB92-177013/AS). Sapolsky, R. (2005). Sick of poverty. [NewYork: Scientific American, Inc.]. Scientific American, 293(6), 92–99. Tilson, H., & Berkowitz, B. (2006, July/August). The public health enterprise: Examining our twenty-first century policy challenges. Health Affairs, 25(4), 900-910. Retrieved August 10, 2007, from http://content.healthaffairs.org/cgi/ content.full/25/4/900 United States Department of Health and Human Services (USDHHS). (2007, June 20). Medicare preventative services: A healthier US starts here. Retrieved May 25, 2008, from http://www.medicare.gov/Health/Overview.asp Veterans Evidence-Based Research Dissemination Implementation Center (VERDICT). (2007). Retrieved August 19, 2007, from http://verdict. uthscsa.edu/verdict/default.htm

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Waton, K. (2007, June 11). Corporate Wellness. Advance for nurses, 5(13), 31-32. King of Prussia, PA: Merion Publications, Inc. Williams, A., & Cutchin, M. (2002). The rural context of health care provision. Journal of Interprofessional Care, 16(2), 107–115. doi:10.1080/13561820220124120 Work, Stress and Health: Findings from the Whitehall 11 study. (2004). Retrieved September 1, 2007, from http://www.workstress.net/downloads/whitehall_11_study.pdf

KEY TERMS AND DEFINITIONS Chronic Disease Genomics: Is the process of applying family history, gene variations and human genomic information to preventing chronic disease and promoting health in individuals, families and communities (Khoury, M. & Mensah. G., 2005). Nutrigenomics: “Nutritional genomics or nutrigenomics is the application of high-throughput genomic tools in nutritional research. Applied wisely, it will promote an increased understanding of how nutrition influences metabolic pathways and how this regulation is disturbed in the early phase of diet-related disease and to what extent individual genotypes contribute to such disease.” (Nature Review Genetics, 2003). Predictive Modeling: A generally accepted definition is a term for statistical methodologies and support technologies using historical data to predict future behavior; a risk management model use in healthcare to lower cost, predict home health care needs, and assign case management strategies; predictive modeling in this document refers to genomic analysis of biological predictabilities that may be susceptible to clinical interventions. Primary Prevention: Public health efforts to prevent disease and injury by education on healthy lifestyles and safe life practices.

A Treatise on Rural Public Health Nursing

Public Health Nursing: A unique field combining public health practice and nursing practice with the primary focus on population groups; the core functions are assessment, policy development and assurance. Rurality: Rurality is the sum total of factors and experiences lived and archived by individuals living outside population-dense areas. Vulnerable Populations: No consensus is available on who are vulnerable populations; in a medical sense ‘vulnerable populations’ are more

susceptible to disease; vulnerable populations as subgroups frequently include the following: racial or ethnic minorities, uninsured, children, the elderly, the poor, the chronically ill, the physically disabled or handicapped, the terminally ill, the mentally ill, persons with acquired immunodeficiency disease, alcohol or substance abusers, homeless individuals, residents in rural area, individuals who do not speak English, the poorly educated or illiterate, incarcerated individuals.

This work was previously published in Nursing and Clinical Informatics: Socio-Technical Approaches, edited by Bettina Staudinger, Victoria Höß and Herwig Ostermann, pp. 1-15, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Section VIII

Emerging Trends

The final section explores the latest trends and developments, and suggests future research potential within the field of clinical technologies while exploring uncharted areas of study for the advancement of the discipline. The section advances through medical imaging techniques, diagnostics, virtual reality, and more new technologies by means of describing some of the latest trends in clinical research and development. These and several other emerging trends and suggestions for future research can be found within the final section of this exhaustive multi-volume set.

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Chapter 8.1

New Technologies in Hospital Information Systems Dimitra Petroudi National and Kapodistrian University of Athens, Greece Nikolaos Giannakakis National and Kapodistrian University of Athens, Greece

INTRODUCTION A hospital information system (HIS), variously also called clinical information system (CIS), is a comprehensive, integrated information system designed to manage the administrative, financial, and clinical aspects of a hospital. This encompasses paper-based information processing as well as data processing machines. As an area of medical informatics, the aim of an HIS is to achieve the best possible support of patient care and administration by electronic data processing. It can be composed of one or few software components with specialty specific extensions, as well as of a large variety of subsystems in medical specialties (e.g., laboratory information system, radiology information system). CISs are sometimes separated from HISs in that the former concentrate on patient and cliniDOI: 10.4018/978-1-60960-561-2.ch801

cal state-related data (electronic patient record), whereas the latter keeps track of administrative issues. The distinction is not always clear, and there is contradictory evidence against a consistent use of both terms.

Types of HIS 1. Central or exocentric: The difference is supported in whether the information is kept in central computer or is distributed in other computers in all the hospital. 2. Oriented or not to the patient: Even if both of this two types deal with the data of patient, the orientation of HIS can influence the processes and the general “character of” HIS. 3. With terminals or workstations: They are two appliances that resemble and usually are not separated. Terminals are electronic appliances that allow the users to communicate with the computer. Generally,

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New Technologies in Hospital Information Systems

they are connected with mini-computers or mainframes that can find themselves far or near. If they are alone, they have few possibilities, and generally they are not capable to make anything if they are not connected with a functional computer. Workstations are computers drawn for professional use from an individual each time. They are completely functional computers, and they can be connected with other workstations, mainframes, or mini-computers.

An HIS Can Be Placed: 1. Next to the bed of patient: Its placement next to the patient’s bed is essential for the monitor and control appliances. For the recording of situation of patient, nevertheless, there is no advantage. In a study, the results were the recording of data was not improved when the system was found next to the patient, since the bigger part of recording was done outside the room, or in the rooms of other patients. Nevertheless, its placement in this point improved the use of automated drawings of care, the calculation of situation of patient, and the pricing for the care of services. 2. In the corridor near the patient’s bed: Its placement in the corridor is continuously increasing. It allows the nurses to record, very shortly afterwards, their removal from the patient, without the detachment of attention from the presence of patient and the potential requirement of attention. However, there is danger for the safety, since someone can receive information about the situation of a patient simply looking at, indiscreetly, the hour of recording. 3. In a staff’s room: Its placement in regions, where the staff is only allowed, has the advantage of bigger safety. However, it is uncomfortable and time consuming, since the staff should walk enough each time it needs information.

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4. Other possibilities: Electronic clipboards. The unique disadvantage is found in that the users perhaps forget where they left it.

Expected profits from the hospital information systems 1. Reduction or repression of registrations 2. Reduction of office duties for the medical and nursing staff 3. Easier access to the medical data 4. Reduction of duration of staying in the hospital 5. Minimisation the insufficient medical recipes 6. Minimisation of errors in the recording of results 7. Redeployment, reorientation or reduction of staff 8. Improvement of quality of registrations 9. Improvement of quality of care 10. Better communication 11. Reduction of hospital cost 12. Increase of satisfaction of nurses 13. Growth of common hospital database 14. Improvement of perception of patients on their care 15. Improvement of general appearance of hospital

HARDWARE TECHNOLOGIES The patients entrust the organisms of healthcare in order to offer them the higher level of care with the smaller probability of error. The existing technologies help standards of health to be strengthened, but the hardware solutions will save an important cost, also measured in money and in time. Solutions, such as electronic forms, can exclude the problems that come up in a handwritten system. These technologies give the doctors/nurses, and the other clinical, a lot of time in order to focus in what they know better.

New Technologies in Hospital Information Systems

Bar Coding

Browser-Based Technology

The bar coding is a low risk technology (cost, application) that makes it a practical choice for a organisation. Also, in order to be used effectively, it does not need intensive education. The positive results of bar code in the safety of patient are widely acceptable in the healthcare system. The initiative of FDA (Food and Drug Administration) for the safety of the patient was completed in February 2004, and it will ask for the hospitals to use bar codes in the hospitals the next 3 years. The technology uses bar code for the patients, the medicines, the blood, and the vaccines. A tool collects all the codes, and is immediately connected with the medical file of patient. Following the system with the bar codes, the FDA calculated that this will involve economy of 3-9 billion dollars. The solution offers:

Three factors should appear for using the browserbased technologies. Firstly, the technology should contribute to the cost decrease. Secondly, the technology should have competitive advantage. Finally, the technology should have an impact in the improvement of patient. An installation with this technology needs only a Web browser, such as Internet Explorer, Netscape, Mozilla, for running the software. The solution offers:

• • • • • •

Effectiveness Safety Reduction of cost Reciprocity Correctness Precision

Touch Screen Technology This technology allows a nurse to easily export conclusions for the patients using a friendly environment (touch screen). The nurse simply touches upon the screen that she/he wishes. The button in the screen shows the room number at the same time as the name of patient. In this easy two-step access, the nurse shows the category from which she/he asks information. This solution offers: • • • •

Easy to use Precision in the import of data Efficient administration Easy completion of data.

•

•

•

•

Portability - the browser-based software offers an easy access to the information with point and click Easy access each hour (day-night), from everywhere (office, home, everywhere Internet exists) Less hour of employment - It is decreased because the system functions permanently all day Friendly to the user - does not need learning new applications only point and click.

Document Imaging The solution offers: •

•

•

•

Focus to the customer service - the important data can be easily filed. The solution allows the hospital to vindicate the medical data Decreased of printing - All the information is stored in a filed system that is fast approachable Easy Web access - the doctors, as long as the other users can have always access and from everywhere via Web Not other lost files - the e-files give the possibility to the hospital to compose an electronic recording of all given data and afterwards, place the disk in a database

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New Technologies in Hospital Information Systems

•

•

Completed copy of HIS - the medical directives can immediately be placed in a filed e-file with easy access and reuse Better functional space - Better management of existing spaces in the hospital and utilisation of these for more effective ways.

Mobile Carts They make the worker feel comfortable with the utilisation of technology. The professionals can be hesitant in the use of the technology if the programs are not easily used. The unwillingness can be obvious in each employee, which can decrease his/her productivity or increase the overtime and create an unpleasant working environment. With a portable workstation, the aim is for the workers to feel comfortable. The solution offers: • •

Faculty of transport Flexibility.

Pen Tablets This technology helps hospitals to decrease the functional costs and the handwritten work, as well as to improve the healthcare. In this technology, there is a pen that is used as the mouse. Pen tablets allow the professionals of health to store information fast and easily, allow them to focus on the patient and spend less time in tedious debits. The professionals, with this technology, can collect the vital points, statistical data, and other information near to patient’s bed. The solution offers: • • •

Decreased handwritten work It improves the provided healthcare It increases the productivity.

E-Forms E-forms allow the health professionals to organise their registrations without the enormous volume

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of paper that is usually in the bookshelves of an office. The solution offers: •

•

•

•

•

Bigger flow - e-forms give the opportunity to the hospital for research, decreasing the repeated exit for handwritten work. Correctness (precision) - the solution increases the safety of patient by decreasing the errors that exist in a handwritten system. It improves the flow of work - E-forms offer to the hospital processes in order to improve the effectiveness. It releases the personnel from tedious obligations. Seamless integration - the solution offers completion to the hospital without extra programs and maintenance. It increases the professionalism - Improving the appearance and the drawing of medical forms can improve the picture of hospital for the public.

Wireless Technology The medical community continues searching ways in order to maintain the precision, as well as to improve the functional efficiency. For example, the doctors can have access and renew the medical file using computers that are found next to the patient’s bed. The portable computers, the PDA, the Pen Tablets and the mobile telephones were proved a new powerful tool for the improvement of efficiency and effectiveness of services that are provided in the hospital environment. The information with regard to the patient, his/her care, and the programmed examinations is immediately available to the professionals of health in any place in the hospital. More important is the fact that with the import of data straight in the network of hospital, are limited the double written data and errors are decreased at the copy of data. The solution offers:

New Technologies in Hospital Information Systems

• • • •

Mobility Flexibility Speed Safety

BENEFITS For the patients: • •

It builds an electronic medical file It improves the quality of care For the hospital doctors:

• •

It helps the decision-making via the easier access in the information It is capable to program and record the clinical activities For the governors:

• • •

It provides the comprehensive daily lists of activity It simplifies the planning of energies for the care of patient It improves the communication with the colleagues For the management:

• • •

It helps with the required reports at the meetings It improves the efficiency via the automation It helps with the improvement of management of staff

does not become more expensive, 3) the money spent by the government is not increased, 4) there have not been determined the models for the storage and exchange of data, and 5) it demands the education of the staff.

URL REFERENCES http://courses.wccnet.edu/computer/mod/m30c. htm http://en.wikipedia.org/wiki/Hospital_information_system http://www.business2005.gr/ec_pageitem. asp?id=6466 http://www.cdacindia.com/html/his/sushrut.asp http://www.datamed.gr/products/index.asp?Cat_ ID=108&pageID=113 http://www.datamed.gr/products/index.asp?Cat_ ID=109&pageID=119 http://www.datamed.gr/products/index.asp?Cat_ ID=110&pageID=114 http://www.e-m3i.com/content/HospitalInformationSystem.asp http://www.healthcare.siemens.com/soarian/int/ index_flash.html http://www.hmstn.com/solutions/?content_ id=320 http://www.medicomsoft.com http://www.presspoint.gr http://www.pressreleases.gr http://www.stockell.com

CONCLUSION The future of HIS depends on many factors, like: 1) the hardware becomes cheaper, 2) the software

http://www.tsystem.com/ED-Information-System/nurse-charting.asp

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New Technologies in Hospital Information Systems

KEY TERMS AND DEFINITIONS Barcode: (also bar code): A machine-readable representation of information in a visual format on a surface. Hardware: The general term that is used to describe physical artifacts of a technology. Laboratory Information System: (LIS): A class of software that handles receiving, processing, and storing information generated by medical laboratory processes.

Radiology Information System: (RIS): Used by radiology departments to store, manipulate, and distribute patient radiological data and imagery. Software Component: A system element offering a predefined service and able to communicate with other components. Touch Screens: Display overlays that have the ability to display and receive information on the same screen (Touch Panels).

This work was previously published in Encyclopedia of Information Science and Technology, Second Edition, edited by Mehdi Khosrow-Pour, pp. 2817-2820, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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Chapter 8.2

IT-Based Virtual Medical Centres and Structures Bettina Staudinger University for Health Sciences, Medical Informatics and Technology, Austria Herwig Ostermann University for Health Sciences, Medical Informatics and Technology, Austria Roland Staudinger University for Health Sciences, Medical Informatics and Technology, Austria

INTRODUCTION Today, medical infrastructures are subject to organisational change the world over. The reasons for this are manifold. On the one hand, it can be observed through scientific innovation and gaining of knowledge that a more in-depth specialisation is taking place, which means that medical healthcare providers are able to offer in-depth knowledge in narrowing fields. On the other hand, through increased process orientation of treatment pathways for patients, the necessity for superior organisational principles has been established. This effects that organisational cooperation models have to be found, which integrate singular specialised institutions into an organisational whole, which

then employ integrative processes, information, and quality requirements. Such organisational structures can be set up in real structures by the spatial accumulation of scattered service units, so that centralised medical centres establish. Alternatively, organisational and technical integration may substitute the physical integration. The individual medical service units stay dislocated, but appear as a virtual medical centre or as specialised medical networks having a clearly defined profile. The management of the information technology in a virtual medical service centre is subject to different requirements than the IT-management of a hospital resembling more a closed shop. Building a virtual centre calls for performance of an open shop principle, because the entire treatment chain cannot be mapped within one single

DOI: 10.4018/978-1-60960-561-2.ch802

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

IT-Based Virtual Medical Centres and Structures

institution, but requires integrated cooperation in order to manage a patient’s clinical pathway. Not only the spatial displacement, but also the unavoidable higher process orientation within a virtual cooperation deserves particular consideration. Additionally, the information management is challenged by the fact that the provision of relevant information in standardised form is an indispensable element of a virtual centre. In this context, the question about potential structural assembling, and organisational principles and elements of virtual medical service centres has to be answered in order to conclude on the basic requirements of data management and the appropriate solution approaches. This shall be presented partly using the example of the virtual oncological medical centre in Tyrol.

BACKGROUND The question about the role of medical informatics in modified organisational structures has been frequently posed in the context of medical service supply (Power, 1999). In particular, an integrative IT-policy has considerably increased in importance through the standardisation of patient pathways, introduction of evidence-based medicine, standards, guidelines, and directives, and therefore, the inevitable necessary cooperation of healthcare providers. Especially in the earlier stages, exemplary models were introduced which were supposed to make the changed requirements more manageable (Ölvingson, Hallberg, Timpka, & Lindqvist, 2002). This, however, does not only affect the cooperation between healthcare providers, but also the technical-based integration of patient data, as well as the extensive bonding of other persons and units involved with the treatment chain. Particularly opened by the evolution of telemedicine, concepts are currently being developed in this area (Bradley, Williams, Brownsell, & Levi, 2002), which integrate technical possibilities such

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as virtual reality into medical treatment processes (Burdea, 2003). The first and pivotal approaches of information technology in medical networks primarily concentrate on the field of electronic patient record. Correlating to this, scientifically evaluated projects and models introduced criteria which have to be considered by the IT-management in medical networks (Van den Haak, Mludek, Wolff, Bützlebruck, Oetzel, & Zierhut, 2002). It can therefore be adhered to, that through this structural development of the various sectors of the health system which are geared towards integrated care, medical informatics becomes more complex, too. Increased dialogue structures, principles of the standardisation of terminology, data exchange, and the comparability of data, and the resulting necessary transparency of medical healthcare providers have become subject matters of discussion and research (Coddington, 1997; Francis & Hart, 1998). Beside the altered basic conditions, it can be observed that the aspired construct of “patient oriented health networks” actually take shape by the affiliation of medical disciplines towards patient oriented service centres, and hence in practice, forces a complex IT-management (Montreuil & Garon, 2005). Linked to this is not only the question of the requirements and the practical implementation of such systems (Day & Norris, 2006), but also the investigation about barriers and restraints of a successful realisation of new concepts (Cashen, Dykes, & Gerber, 2004). At least the differing intentions and interests of single system partners (e.g., administration) also play an important role (Hassan, 2005), as do the requirements of health politics (Kaushal, Bates, Poon, Ashish, & Blumenthal, 2005). Dealing with the subject of virtual medical centres, science particularly has to focus on standardising the processes in the networks, and also on the quality of data (Hain, 2002; Stoop & Berg, 2003).

IT-Based Virtual Medical Centres and Structures

IT-BASED VIRTUAL MEDICAL CENTRES AND STRUCTURES

Figure 1. Model of a virtual medical competence centre

The Model of the Virtual Medical Centre Based on the newer findings about organisational concepts and structures in medicine, Tyrolean (Austria) health politics initiated a project in 2000, whereby it was attempted to integrate oncological healthcare providers in the province of Tyrol into a virtual network. The network was supposed to be implemented on the basis of uniform medical standards coupled in clearly defined treatment paths. The participation in the virtual medical centre was voluntary, but all participants had to comply with the commonly derived directives. Simultaneously, the basic organisational principles of the virtual oncology centre were developed in order to be able to work out the requirements for the subsystems (e.g., the data management). (see Figure 1) The basic principle of the virtual medical centre in Tyrol can be described through structural tripartism into strategical, tactical, and operative level. On a processual level, this principle can be best compared with a cybernetic model, whereby the learning ability of the system is centrally controlled and ensured by the interaction of all system participants. A medical board is operating on the strategic central level, its main task being that of serving as a steering board for the entire medical centre. Its particular function is to validate directives and processes, as well as to adjust system parameters in the area of quality assurance. Complimentary, those carriers who additionally to medical competency have the key competencies for steering the entire system are part of the board (e.g., clinic management executives). As contract research and pursuit of research projects are related on the board level, an “Open Shop” solution for communication instruments has to be provided (Ohly, 1995). For a consequence,

communication and interaction structures have to be connected beyond the virtual medical centre. Pipelines to supraregionally and internationally active healthcare and research centres are provided, as well as structural ties to industrial partners involved with research. As a tactical element, the central service structure particularly serves data and patient management, and has to include all documentation systems which are of superior importance. Irrespective of this, a regionally spanning consensus still has to be reached about documentation criteria—of patient files for example (Leiner, Gaus & Haux, 1995). This appears feasible if well-described and tested models are employed (Yamasaki & Satomura, 2001). Furthermore, own research projects as well as institutional research is steered and coordinated on the central service level. Treatment structures are only to be maintained up to a certain

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degree as gaps in the treatment chain within the framework of decentralised efficiency structures have appeared, and cannot be qualitatively and quantitatively closed by decentralised healthcare providers. The decentralised medical service units concern patient-related healthcare providers who have been certified by the medical board on the basis of the derived standards and quality directives, as well as the resolved treatment chain. The healthcare providers are therefore responsible for screening right through to aftercare in the areas of standard patient medicare and the provision of research related medicare (Ruggiero, Giacomini, Sacile, Sardanelli, Nicolo, & Lombardo, 1995). These decentralised service structures are represented by health facilities or sectors of health facilities according to which fields were certified in the cooperation agreement. The participation of the healthcare providers as part of a virtual medical centre is on a voluntary basis and is decided by mutual rights and obligations, or rather, by agreed behaviour modalities of the system participants amongst themselves. The area of mutual obligations primarily refers to the willingness to accept and employ standards and directives—which were defined on the part of the board in terms of “good clinical practice” and were subject to international evaluation—as well as the willingness to adapt clinical, therapeutic, and other processes and procedures according to these directives and guidelines (Medical Committee, 1999). This means that certification by the board has to take place when a participant enters the entire system, whereby the objectives of structural networking and system compatibility in particular have to apply, as well as the aspects of training standards and quality assurance. In addition, through certification, it has to be ensured that the processes and procedures, as well as documentation and treatment plans, are adapted to the ongoing and continually changing basic conditions and directives, and therefore correspond to the processual element of medical

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development. This means that certification cannot be one singular act which is coupled to participant entry into the system. It is rather a periodically reoccurring procedure which evaluates the content and quality of the changed processes. As the healthcare providers on this operative level face each other in an interactive and quality assured network, material and formal conformity and transparency of processes and service profiles are ensured (Göbel & Pfeiffer, 2001), and patients can be led through the entire treatment process without having to question the structure quality of a single network participant (Schoop & Wastell, 2001). Out of this basis, the mutual rights of the participants are able to be derived. These mutual rights consist in particular of gaining shortest possible and interpreted access to new medical findings. In conjunction with standardised training programmes and a superior information management, this leads to system participants gaining both medical and forensic confidence. Additionally, competencies and functions which have to be purchased from third parties (e.g., laboratory reports) by single system partners may be integrated simply standardised into their own operational structuring respectively into their processes. A major advantage on this level is the possibility to gain an overall (anonymous) data overview, and to have valid and broad data material available in order to put research cooperation or internal research projects onto a basis, and to find the current status of research prepared in its final form (Schnabel, 1996).

Cooperation Directions Within the Network Not all descriptions of a particular structure or process have the same degree of validity. It is therefore important to make the distinction that the degree of commitment of a directive is clearly higher than the degree of commitment of a guideline which, as the name suggests, should provide guidance in a particular section of the process

IT-Based Virtual Medical Centres and Structures

but with less commitment than is the case with a directive. Both directives and guidelines move within defined medical standards, whereby this term is not without controversy in connection with the history of standards in literature. In particular, newer works point to the problem that within defined standards (e.g., in the branch of clinical pharmacology), industrial interests can be found (Tenery, 2000; Wazana, 2000). This question is of importance, particularly for the medical director, as he is primarily responsible for introducing standards, guidelines, and directives, according to Austrian hospital law (Staudinger & Mair, 2001). Important for describing a virtual medical centre is the semantic discrepancy of the terms “virtuality” and “centre.” These prima-facie of alternative concepts can only then be combined if the centralisation term is not seen in a spatial sense, but rather in its substantial core, and the spatial dimension is reduced to a roughly fenced territory. This network approach—aggregating decentralized healthcare providers to a nonmaterial entirety on an operational level, where, in a spatial sense, central strategic, and tactical elements are effective—corresponds to the cybernetic model approach, as long as self-learning elements from interactive processes are integrated. Within such centres, the subsidiary principle applies to the healthcare providers. Simply put, each of the respective healthcare providers, who just is qualitatively and quantitatively in the position to render a particular service, actually renders this service. It can be assumed that an optimal degree of decentralisation can be achieved with a system-immanent service distribution (Sacile, 1995), but rather in context with a qualitative service hierarchy than a structural one.

Structural and Functional Requirements for Data Management Against the given background, it seems necessary to develop or engage communication tools which

ensure process and directive orientation, as well as the interaction ability of healthcare providers in an entire system (Geißler & Rump, 1999). Linking elements of process-orientated resource planning and optimization items of networked structures additionally arises necessary from the fact that self-learning systems with a high degree of networking and interaction are deeply exposed to a considerable pressure, regarding qualitative and quantitative outcome, as well as cost-effectiveness. Apriori, it can be assumed that data management is carried out on central parameterizable systems. A medical clinical information system (CIS) serves as a basic platform (Steffens, 1995) in which all communication steps necessary for patient care can be carried out. Various types of documents, such as patient-related messages, incoming and outgoing reports for diagnostic results, or other confidential data are transported. An entire hospital information system (HIS) is eked by additional functions which cover all areas not directly consisting of medical documentation, but serve as a basis in all branches of care delivery. Namely order management, patient management, and administration processes serve as examples here (Kaloczy, 2001). A key element of the hospital information system is the compilation of diagnoses and service delivery (usually from standard catalogues such as ICD-9 and ICD-10). As a rule, the catalogues aim at several objectives of patient documentation. Beside the feasibility of diagnoses and medical treatment also, relevant balancing aspects are included (Pernice, Doare, & Rienhoff, 1995). These IT tools have to be implemented according to a specified profile that is aligned with strategic parameters, in order to be able to ensure the corresponding planning and optimisation basis of the entire system, and subsequently, to prevent undesired maximisation activities of individual system participants. This integrative point of view of communication technology in health systems, taking virtual medical centres into particular consideration,

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impedes an over centralisation of data and process management or process and resource planning, rather it advances regional integration and consistent regional structuring of health facilities. Thereby, both individual tracts of the system, as well as the entire system have to remain able to communicate and interact with other regional health structures. This is of particular importance where the patient joins different parallel health systems, as patients might have need for health treatment beside the oncological treatment. The fact that communication systems in context with virtual medical centres quasi take on the function of a transmitter is also of particular importance. Consequently, standardised communication within the predefined processes shall be able take place at any appearance. A transfer of data and results, as well as planning processes (Bachert & Classen, 1995) from one system participant to another, as well as within the whole system on a dialogue-orientated basis is therefore assured. Data redundancies and subsequent error vulnerability in data structures may be minimised (Gustafson, Hawkins, Boberg, Pingree, Serlin, & Graziano, 2001). The reorganisation of a clinical information system and regional interaction system is chained with additional considerable cost and workload (Becker & Arenson, 1995), which can only then be justified when it opposites a benefit. This striven benefit in turn has to legitimate that a change in a running and functioning system leads to irritations within the operational structuring. In particular, each facility which shows a partial monopoly in oncological care is especially affected by this paradigm shift. On evaluating the new information system according to strategic objectives, the focus may not only be on technological solution, but rather the superior aim of organisational development which is compatible with strategic bases and development potential (Haux, 1995) must be drawn into account. The self-conception and right to exist of single medical service structures are defined—along

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side other important factors—by the catchment area and prominence within the treatment chain. The catchment area neither has fixed size nor is it legally determined—leaving the basic political contract for primary healthcare out of equation. For healthcare providers in association with a virtual oncological medical centre, this means that their external communication structures have to be clearly defined to patients and remitters and have to assume a activity standard which ensures stronger quantitative and qualitative integration into the area of competence. Viewing the inner structure of the communication system operation and communication structures not only have to satisfy the current clinical requirements, but also must contain a cost and service orientated component (Kyriacou, Davatzikos, Zinreich, & Bryan, 2001). The integration of the remitter (e.g., general practitioner, practicing specialist, primary care centres) into the clinical information system aims at a change from concerned people into partners. The question whether an organization member is part or not of a legal unit is of superior relevance, if integrative structures are set up. Rather the degree of service and information quality, which is achieved by the system change, comes to the foreground and may be an essential competitive advantage beside the clinical performance and efficiency. This assumes however, that the quality of communication and information within the hospitals or between the hospitals and the decentralized healthcare facilities achieves a level that enables to implement patient-orientated operating procedures. Whilst commercial systems, particularly in the hospital branch, show a high and extensive functional state of development, medical systems are of a minor evolution standard (Clayton, 2001), particularly in context with the above field of problems. Before a specification profile for a communication structure can be realised, it is appropriate to analyse and briefly describe the status of the structural development of healthcare regions,

IT-Based Virtual Medical Centres and Structures

Figure 2. IT conceptions using Tyrol as an example

whereby it has to be pointed out that these structure conceptions may only be used as an example, as new IT conceptions have to primarily support highest structure flexibility. (see Figure 2)

The Tyrolean Concept of an Oncological Network What particularly shall be highlighted with this example, is that both regional and supraregional remittent structures are, as regards function, directly tied into the diagnostic, therapeutic, and subordinated elements. Therefore, the design of the treatment chain forces the initial contact with the patient as a starting point, even if the initial contact occurs during a public screening event. On the level of interdisciplinary diagnostics, an integrative conception of diagnostic services (called “medical setup”) is established on the basis of diagnostic standards (Todd & Stamper, 1994). Particularly through the additional employment of patient and resource planning instruments, as well as optimised time and process management, brings essential quality and cost advantages. All the more as consistent data structures are accessed within the entire system (Mahr, 1995). As the main result, the interdisciplinary diagnostic responds with further treatment and therapy levels (Berner,

Webster, Shugerman, Jackson, Algina, & Baker, 1995), and compiles an optimal (primarily for patients but also for institutions) patient scheduling cooperatively with remitters and clinical facilities. The intensity of employment of the single facility (stationary or ambulatory) within the entire IT-conception, referring planning, communication, and optimizing tools, too, has to represent the targeted importance within the treatment chain respectively within the research chain.

FUTURE TRENDS On the whole, it is to be assumed that the question of the interaction and integration of different medical healthcare providers will increase in importance within areas of medical care, as well as within expert chains of treatment. As a consequence, the development of process and information standards has to be developed by the individual specialist associations. Thereby, the scope of intervention potential of single healthcare facilities has to be taken into increasing consideration in order not to restrain the efficiency of a medical facility during a certain step of the treatment chain caused by the organisational and technical paradigm change. This involves integrative and highly

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standardised IT tools which show a high degree of parameterisation. Beside the impact of more transparent treatment chains, cost efficiency and optimized exploitation of existing health structures, the emerging consistent data will enforce research over the entire treatment chain, as this occurs already (e.g., in nursing with the nursing minimum data set). Minimum data sets for other medical and healthcare providing fields will inevitably arise. Continuing this approach, the question about the interaction and communication ability of different medical networks with each other arises (i.e., the question about the communication of two or more systems on the meta-level, as well as the operational level respective to integrated patient pathways). In this area, the particular challenge of communication ability is fulfilled, which in turn causes increased research activity in connection with the development of internationally useable terminologies and with the definition of superior standards. Finally, it is to be assumed that development towards medical networks, aligned processes and real, as well as virtual, medical competence centres will continue to strengthen. In this context, the evolution of organizations’ profiles will be of considerable importance, as well as the question of information transfer, knowledge transfer, and quality assurance.

CONCLUSION All operative healthcare providers in the healthcare branch, be it hospitals, therapy or diagnostic centres, general practitioners or rehabilitation facilities, are subject to considerable cost restrictions while medically expanding both qualitatively and quantitatively. In the last few years, a higher degree of specialisation of these health facilities has been assessed—considering the entire chain of treat-

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ment—which has led to considerable problems in interaction between the health institutions. Against this background, it appears necessary to create regional cooperations for distinct medical topics. Considering the entire regional treatment chain, the defined care mandates, the areas of competency as well as patient data management, the constitution of a data and communication platform between these institutions on one hand, and shaping this platform dialogue and interaction-capable on the other, challenges organization executives. In addition, it also seems necessary to integrate optimisation tools, guidelines, and standards into defined interactive and comprehensive processes, which enable methodical and action-orientated resource management supervised by quality standards. We have detected that patient-orientated communications and operating structures are of strategic importance. The principle question about system functionality and effectiveness of the entire organisation and the organisational structures associated with it is at least of equal importance. In practically all major hospitals, structural organisation occurs according to the historical development of medicine. Even at first glance, it can be recognised that this practice lacks an organisational theoretical basis if the criterion of strategic efficiency should apply (Zulehner, 1999). The reason for this lies in the fact that the abstract levels, according to which the hospitals were traditionally structured, are of a different nature, and are not necessarily compatible with each other. On the one hand, this must lead to double and multiple structures, and on the other, to gaps in the clinical portfolio. In this way, the hospitals are conceptually organised according to an institution or group of institutions, range of services, age of the patients, certain illnesses or diseases, methods and forms of therapy, as well as technical criteria. The medical organisational development of the past decades has of course authority, too, gained from the different speeds at which innovation progresses for the individual

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lines of expertise. There, it is ensured that single medical sectors that are able to be well combined can develop in the same or similar way, and do not have to experience constraints from other sectors. In the middle and long term, these present organisational structure—if it is not supplemented and reworked in essential areas—will noticeably restrict clinical and economical efficiency. Because of this, it is necessary that new organisational and service structures be defined along side current and continually developing clinical structures for the purpose of a virtual structure or virtual medial centre. This is particularly characterised by combined standardised processes, a continuous chain of treatment, clear terminology, and uniform quality requirements.

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Göbel, G., & Pfeiffer, K.-P. (2001). Qualitätssicherung von patientenorientierten Gesundheitsinformationsnetzen im Internet. In O. Meggeneder, & H. Noack (Eds.), Gesundheitswissenschaften (16) Vernetzte Gesundheit – Chancen und Risken des Internet für die Gesundheit (pp. 193–202). Linz: OOEGKK.

Kyriacou, S. K., Davatzikos, C., Zinreich, S. J., & Bryan, R. N. (2001). Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model. In R. Haux, C. Kulikowski (Eds.), Yearbook of medical informatics, digital libraries and medicine 01 (pp. 429–441). Stuttgart: Schattauer

Gustafson, D. H., Hawkins, R., Boberg, E., Pingree, S., Serlin, R. E., Graziano, F., et al. (2001). Impact of a patient-centered, computer-based health information/support system. In R. Haux, C. Kulikowski (Eds.), Yearbook of medical informatics, digital libraries and medicine 01 (pp. 224–232). Stuttgart: Schattauer.

Leiner, F., Gaus, W., & Haux, R. (1995). Medizinische dokumentation. Stuttgart: Schattauer.

Hain, T. (2002). Improving the quality of health information: The contribution of C-H-i-Q. Health Expectations, 5, 270–273. doi:10.1046/j.13696513.2002.00189.x Hassan, D. (2005). Measuring performance in the healthcare field: A multiple stakeholders’ perspective. Total Quality Management, 16, 945–953. Haux, R. (1995). Stand und weitere Entwicklung von Krankenhausinformationssystemen: Die Empfehlungen der Deutschen Forschungsgemeinschaft. In W. Buchholz, & R. Haux (Eds.), Informationsverarbeitung in den Universitätsklinika Baden-Württembergs (pp. 20–30). Heidelberg. Kaloczy, C. (2001). Kommunikation (Krankenhausinformationssystem). In C. Kaloczy, S. Kindermann, & R. Staudinger (Eds.) Krankenhausorganisation und –finanzierung (pp. 121–136). Hall in Tirol: Berenkamp. Kaushal, R., Bates, D. W., Poon, E. G., Ashish, K. J., & Blumenthal, D. (2005). Functional gaps in attaining a national health information network: What will it take to get there in five years? Health Affairs, 24, 1281–1289. doi:10.1377/ hlthaff.24.5.1281

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Mahr, B. (1995). Principles of open distributed processing in medicine: The Berlin approach. In E. Fleck (Ed.), Open Systems in Medicine (pp. 6–23). Amsterdam: IOS Press. Medical Committee – Royal Netherlands Academy of Arts an Sciences. (1999). Discipline report on (bio)medical and health sciences research in the Netherlands 1998. Amsterdam. Montreuil, B., & Garon, R. (2005). Toward patient centric and distributed healthcare delivery networks. Clinical and Investigative Medicine. Medecine Clinique et Experimentale, 28, 351–352. Ohly, M.-J. (1995). Computer-supported cooperative work. In E. Fleck (Ed.), Open Systems in Medicine (pp. 78–87). Amsterdam: IOS Press. Ölvingson, C., Hallberg, N., Timpka, T., & Lindqvist, K. (2002). Requirement engineering for inter-organizational health information systems with functions for spatial analyses: Modelling a WHO safe community applying use case maps. Methods of Information in Medicine, 41, 299–304. Pernice, A., Doare, H., & Rienhoff, O. (1995). Healthcare card systems: Eurocards concerted action, results and recommendations. Amsterdam: IOS Press. Power, M. (1999). An approach to policy analysis and development of medical informatics. Methods of Information in Medicine, 38, 260–264.

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Ruggiero, C., Giacomini, M., Sacile, R., Sardanelli, F., Nicolo, G., & Lombardo, C. (1995). An environment for computer aided early diagnosis of breast cancer. In International Federation for Medical and Biological Engineering (Eds.), Proceedings of the Health Telematics `95 – “Health in the Information Society” (pp. 185–190). Naples. Sacile, R. (1995). A new approach in home care for oncologic elderly. In International Federation for Medical and Biological Engineering (Eds.), Proceedings of the Health Telematics `95 – “Health in the Information Society” (pp. 23–26). Naples. Schnabel, M. (1996). Expertensysteme in der medizin. Stuttgart: Fischer. Schoop, M., & Wastell, D. G. (2001). Effective multidisciplinary communication in healthcare: Cooperative documentation systems. In R. Haux, & C. Kulikowski (Eds.), Yearbook of medical informatics, digital libraries and medicine 01 (pp. 379–387). Stuttgart: Schattauer. Staudinger, R., & Mair, C. (2001). Rechtstheoretische und rechtstatsächliche Studie zur Frage der Validität krankenanstaltenrechtlicher Bestimmungen am Beispiel der Universitätsklinik Innsbruck. Innsbruck. Steffens, J. (1995). Communication infrastructure. In E. Fleck (Ed.), Open Systems in Medicine (pp. 104–110). Amsterdam: IOS Press. Stoop, A. P., & Berg, M. (2003). Integrating quantitative and qualitative methods in patient care information system evaluation: Guidance for the organizational decision maker. Methods of Information in Medicine, 42, 458–462. Tenery, R.-M. (2000). Interactions between physicians and the healthcare technology industry. Journal of the American Medical Association, 283, 391–393. doi:10.1001/jama.283.3.391

Todd, B.-S., & Stamper, R. (1994). Limits to diagnostic accuracy. In J.-H. Van Bemmel, & A.-T. McCray (Eds.), Yearbook of medical informatics: Advanced communications in healthcare 94 (pp. 255–270). Stuttgart: Schattauer. Van den Haak, M., Mludek, V., Wolff, A. C., Bützlebruck, H., Oetzel, D., & Zierhut, D. (2002). Networking in shared care: First steps towards a shared electronic patient record for cancer patients. Methods of Information in Medicine, 41, 419–425. Wazana, A. (2000). Physicians and the pharmaceutical industry: Is a gift ever just a gift. Journal of the American Medical Association, 283, 373–380. doi:10.1001/jama.283.3.373 Yamazaki, S., & Satomura, Y. (2001). Standard method for describing an electronic patient record template: Application of XML to share domain knowledge. In R. Haux, & C. Kulikowski (Eds.), Yearbook of medical informatics, digital libraries and medicine 01 (pp. 325–330). Stuttgart: Schattauer. Zulehner, C. (1999). Digitalisierte pflegedokumentation: Aber wie? http://www.PR-Internet. com, Pädagogik, Management und Informatik in der Pflege, 3, 56–66.

KEY TERMS AND DEFINITIONS Directive: Operation and action recommendation with lower commitment than SOPs. Directives describe process paths which should be normally followed during the implementation of the process. Deviations in justified circumstances are allowed. Effectiveness of Personnel Management: Based on a theoretical maximal quantitative output of an organisation unit in the area of personnel management in connection with costs and efficiency criteria, the effectiveness of personal management is calculated from the difference between the theoretical output and the actual

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output. It is measured by the different measurement criteria (process quality, data quality, process stability, cost—performance relation, frequency of errors, and so on). Guideline: Operation and action orientation with lower commitment that directives. Guidelines provide an operation framework in which the process should move. Deviations are not usually sanctioned. Human Resource Information System (HRIS): Specifically developed software for Human Resource Management (HRM) (e.g., software packets which document, support, and make analysable the digitisable components of HRM). The Human Resource Administration System (HRAS) is an administration-specific subarea of HRIS.

Process Integration: The process integration specifies the degree of the theoretical and actual commitment of processes within structures. Process Types: Definitions of certain process types. They are distinguishable from another by characteristics in the area of the degree of standardisation and the degree of integration. Standard Operation Procedures (SOPs): Operation and action instructions. They are there to make sure of the uniform, predictable, and integrated dealing of replaceable process individuals within the framework of working structures. Virtual Centre: Network of organisational commitments in order to reach cooperation without frontiers between different service units even if they are members of different legal organisations.

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, pp. 803-81 copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 8.3

Emerging Technologies for Aging in Place Shirley Ann Becker Florida Institute of Technology, USA Frank Webbe Florida Institute of Technology, USA

INTRODUCTION Extraordinary societal changes in the United States have taken place from both human and technological perspectives. Life expectancy continues to increase, due to improvements in both medical care and technology. Older adults, 65 years plus, comprise about 12.4% of the United States population with about one in every eight Americans being in this age group (U.S. Administration on Aging, 2002). By 2030, the percentage will have increased to 20% of the total population representing twice the number as in 2000. Those 85 years and older represent the fastest growing group (He, Sengupta, Velkoff, & DeBarros, 2005). Older adults in the United States not only are living longer, but many are staying longer in their homes before entering institutionalized care. Compared to past generations, an unprecedented DOI: 10.4018/978-1-60960-561-2.ch803

number of adults sixty years and older are aging in place. In 1985, there were approximately 5.5 million functionally disabled elderly living in their communities and an additional 1.3 million living in nursing homes. Each of these figures is projected to double to 10.1 million and 2.5 million; respectively, by 2020 (Manton, 1989). This is significant from both individual and national perspectives in terms of the burden of skyrocketing healthcare costs. The U.S. Bureau of the Census (Fields & Casper, 2001) has reported that more than 55% of older adults live at home with their spouse. The number of adults living without a spouse increases with age, with about 50% of women aged 75 years and older living alone at home. Many older adults prefer to age in place rather than in an assisted living facility (Riche & MacKay, 2005), citing independence and social interaction as being critical to their well-being (Hirsch, Forlizzi, Hyder,

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Emerging Technologies for Aging in Place

Table 1. Types of assistive technologies with emerging trends Type

Assistive Technology

Adaptive Switches

Devices that are used to adjust home systems, including air conditioners, computers, answering machines, and power wheelchairs among others. Emerging technologies include “smart homes” with sensing systems that manage the overall home environment.

Communication Aids

Technology that promotes social interaction, telecare, and other types of communication to promote functional independence. Emerging technologies include the use of Personal Digital Assistants (PDAs), personal computers, and tablets to connect an older adult to an external support system of family, friends, or healthcare personnel.

Computer Accessibility Devices

Software or hardware that supports computer and Web accessibility. This includes screen reader devices, specially designed keyboards, and others. Emerging technologies include user-interface designs of computer, handheld, and mobile devices that promote universal access.

Home Modifications

Construction or modifications that allow an older adult to overcome a physical barrier that might impede living at home. Emerging technologies are illustrated by sensor devices in the floors to detect falls.

Independent Living Aids

Technology that allows an older adult to function independently in the home. Grab bars in bathrooms and hallways are more traditional illustrations of this type of technology. Emerging technologies include medication management (e.g., talking medicine cabinet), and electronic prompts (e.g., close refrigerator), among others.

Mobility Aids

Technology that supports an older adult being mobile within and external to the home environment. Emerging technologies include Global Positioning Systems (GPS) built into mobile devices as a means of identifying a person’s location outside the home.

Goetz, Kurtz, & Stroback, 2000). Complicating the choice to age in place are health and other personal limitations that necessitate continuous care. About seven million older adults over 65 years are estimated to have mobility or self-care limitations (U.S. Administration on Aging, 2002). Similar to other nations, the United States faces a critical challenge in dealing with an aging population that has unprecedented life expectancies. Emerging technologies offer the hope of allowing older adults to remain in their homes longer by empowering individuals to manage daily activities while dealing with chronic health conditions and age-related diseases. These technologies increasingly target a home environment whereby on a regular basis an individual can obtain assistance in performing daily living activities, stay connected to family and friends, manage medication, and be monitored for health-related changes. As important as these assistive technologies are for individuals and families, their potential for positively impacting the United States economy by changing the model of healthcare delivery is equally huge.

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BACKGROUND Recent advances in “assistive technologies” offer older adults the ability to function independently and to age with dignity in their homes and communities. The U.S. Administration on Aging (2005) formally defines an assistive technology as being any service or tool that helps the elderly or disabled perform activities they have always done, but must now do differently. Assistive technology includes communication equipment, computer access, tools for independent living, education, and mobility aids, among others. Table 1 provides a summary of current and emerging assistive technologies. The availability of assistive technologies for an older adult often determines whether he or she is able to live independently or must move to an institutionalized environment. The National Council on Disability (1993) found that 80% of older adults who used assistive technology were able to reduce their dependence on others. Assistive technologies not only support the aging adult, but also the family and friend caregivers. Most often, technologies that increase the independence

Emerging Technologies for Aging in Place

of an older adult will decrease the time required for caregiving assistance (Mann, 2001). Assistive technology and home modifications have also been found to provide caregivers immediate relief from burden, reduce their stress, and help them provide care more easily and safely (Gitlin, Corcoran, Winter, Boyce, & Hauck, 2001). This is significant from an aging in place perspective, given that many adults with chronic conditions and age related diseases are cared for by an aging spouse may also suffer from a chronic medical condition. Many of the emerging technologies are being developed to be used in both home and community environments. They provide an opportunity to improve independence, and safety thus promoting aging in place initiatives (Tran, 2004). Some research efforts are building on existing technologies in order to take advantage of communication and information infrastructures. The Internet along with online information sources, for example, offer a means for older adults to engage in lifelong learning and healthcare management thus promoting quality of life while aging in place. Recent advances in wireless and wired communication provide the technology for home monitoring systems, social interaction, and the potential for virtual support systems linking an older adult to geographically dispersed family and friends. Emerging areas of technology include artificial intelligence and robotics with the opportunity of providing intelligent feedback to the elderly living at home.

EMERGING TECHNOLOGIES Two types of technologies are being developed currently that promote aging in place depending on the category of impairment associated with the individual. (Refer to the definitions in Appendix A of this article for a description of the three levels of impairment). Becker and Webbe’s (2006b) Buddy Coordinated Healthcare System,

and Scott and Gabrielli’s (2005) Ho’alauna (“Good Neighbor”) Tablet projects focus on older adults who have mild or moderate levels of impairment. The underlying concept is to utilize technology to promote independent functioning in both home and community environments. These projects are considered “noninvasive” in that the individual has control over data gathering and dissemination using the proposed technologies (Becker & Webbe, 2006a). Other research projects, such as the Digital Family Portrait (Mynatt, Rowan, Craighill, & Jacobs, 2001) and the CareNet Display (Consolvo, Roessler, & Shelton, 2004; Consolvo & Towle, 2005) would support more severe levels of impairment through a home monitoring environment that utilizes sensors to gather information about daily living activities. Such detection provides the means to keep members of a support network (e.g., family, friends, and healthcare personnel) informed of the older adult’s daily activities. These types of “invasive” technologies do not provide the older adult full control over data gathering and dissemination activities. These are automated as part of the home monitoring system that is put in place. (Table 2 further describes the aforementioned noninvasive and invasive technology research projects as illustrative of emerging technologies.) These are only several of a significant number of research projects currently being conducted in university and laboratory settings. Many related projects have been or are being developed to promote aging in place. Academia and industry partners have answered the call to innovate technologies to address the looming healthcare crisis, due to the aging baby boomer population in the United States. One of the biggest obstacles, however, is that many emerging technologies are not being translated into products and services that are made available to older adult users. Eric Dishman of Intel Corporation (Beck, 2004) told the Senate Special Committee on Aging, “Our biggest problem nationally is an imagination problem, not a technology problem,” in terms of govern-

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Table 2. Assistive technologies illustrating the use of information and communication technologies (ICT) to promote aging in place Project

Description

Buddy Coordinated Healthcare System

Handheld technology is used by the aging adult caregiver to assist in caregiving activities and monitor his or her own well-being and that of the older adult for whom care is provided. A PocketPC is used to gather information about daily living activities, emotions of the caregiver, behaviors displayed by the person receiving care, medication management, and other support systems. The PocketPC has the capability to transparently and unobtrusively link to the Internet thus sharing information gathered on the device via a family and friend web site. Family and friends use the Web site to foster sharing of the responsibilities associated with caregiving. Family and friends may otherwise not be actively involved due to geographic distance, work, children, and other commitments (Becker & Webbe, 2006b).

Digital Family Portrait

This project is part of Georgia Tech’s Aware Home Research Initiative (http://www.awarehome.gatech.edu/). It provides a virtual connection between geographically distant family members to an older adult living at home. The technology, when fully developed, utilizes sensors to gather data about the older adult’s health, relationships, and activities. This information would be shared electronically via the ambient display placed in a distant relative’s home. The ambient display, presented as a digital picture frame, provides user access to detailed information. The information presented to the user is similar to information typically observed by a neighbor or another member living in the older adult’s household (Mynatt et al., 2001).

Ho’alauna Tablet

The Ho’alauna Tablet project utilizes ICT to assist with nutrition, healthcare, financial management, and leisure activities, among others (Scott & Gabrielli, 2004). It is proposed that a TabletPC along with specialized software supported by Internet access assist older adults living at home to interact socially with family and friends, search the web, control home technology, receive emergency information and assistance, track health-related information, and participate in community events, social activities, and educational programs.

CareNet Display

This project uses the concepts of ambient display and home sensors to build a support network of family and friends (Consolvo et al., 2004). The ambient display, an interactive digital picture frame, provides information to a support network regarding the status of meals, medications, outings, activities, mood, falls, and schedules. The ambient display allows the user to view an older adult’s daily activities and interactively access data through touch screen capability. Unlike the Digital Family Portrait, it allows for the sharing of sensitive data regarding medication and activities performed. The older adult would control shared access to personal information. The CareNet Display allows for data sharing of other members in the support network.

ment sponsoring research on assistive technologies for older adults with corporate investment opportunities. We have identified four underlying issues that are directly related to the crisis associated with technology transfer from the academic research setting to those who could benefit from these technologies. These issues have the potential to pose barriers to continued research on emerging technologies, thus impeding the efforts to promote aging in place. As such, these issues must be addressed to facilitate the development of assistive technologies. The issues and directions of development are described briefly: 1. Usability in a real world setting: Researchers need access to older adult volunteers to identify potential usability

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barriers that would impede technology transfer. Although the new technologies can be prototyped with young volunteer users, the special demands of the older end-user require in-vivo testing and iterative hardware and software modifications before introduction into a home environment. 2. Patient safety: Research that introduces new assistive technologies into the home environment must complement the human caregivers and take into account an increase in safety concerns. For example, existing caregivers may assume that the new technology and its keepers “looks out” for them and their family member so that normal precautions are reduced, including contact with their medical professionals. It is incumbent upon the researchers to train the caregivers that

Emerging Technologies for Aging in Place

the new technology is truly assistive to them and under their control. 3. Interoperability: Currently, many projects are developed in isolation of one another, even though they may ultimately coexist within common venues. There must be a substantially greater national push for interoperability of technologies, even at the university level during the developmental phases. The field of assistive technologies is too fragile at the moment to host a “CDMA vs. GSM” type of competition. 4. Social perceptions: Corporations who shy away from the aging marketplace must be convinced that the potential is significant. The government has to be innovative in providing research funding not only to innovate but to transfer the technology to the commercial sector.

CONCLUSION The United States and other countries are facing a significant challenge in dealing with the exploding healthcare costs associated with an aging population. There are already 34 million senior citizens in the United States with an impending explosion as the baby boomers begin to enter the retirement phase of their lives. There has to be an increased effort to provide both invasive and noninvasive assistive technologies to manage proactively the health and well-being of the aging population. This includes a significant emphasis on aging in place in order to reduce the costs associated with assisted living facilities and hospitals when older adults no longer can remain in their homes and communities. Emerging technologies for aging in place provide additional benefits to a younger population with disabilities and chronic diseases. For many middle aged adults with chronic health conditions, assistive technologies provide the means to monitor healthcare proactively. For diseases

such as diabetes, hypertension, arthritis, and others, assistive technologies offer the hope of better living from both longevity and quality of life perspectives. It would be important to address the issues of usability, safety, interoperability, and social perceptions as an integral part of assistive technology research. Researchers, nonprofit organizations, industry, and the government are initiating collaboration in order to address these issues. Although more needs to be done to promote this collective effort to innovate technologies for aging in place, two recent initiatives serve as prototypes of the productive coalitions can be formed. The American Association of Homes and Services for the Aging (http://www.AAHSA.org) and Intel Corporation, for example, initiated the launching of the Center for Aging Services Technologies (CAST) with the objective of advancing technologies for older adults. The Alzheimer’s Association along with industry partners has developed the Everyday Technologies for Ahzheimer’s Care (ETAC) consortium to bring university researchers, nonprofit organizations, government, and industry together to advance assistive technology development while addressing the underlying issues identified in this article. These models both show the feasibility of academic/industrial/agency coalitions, and also through their already considerable outcomes show what products can ensue.

REFERENCES Beck, E. (2004, April 27). Seniors need robots and new technology to help at home. [Electronic version]. SpaceDaily. Becker, S. A., & Webbe, F. (2006b). Use of handheld technology by older adult caregivers as part of a virtual support network. In Proceedings of the First International Conference on Pervasive Computing Technologies for Healthcare 2006. Innsbruck, Austria.

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Becker, S. A., & Webbe, F. M. (2006a). Designing for older adult users of handheld technology. In Proceedings of the 28th IEEE Engineering in Medicine and Biology Society Annual International Conference (pp. 3297–3300). Consolvo, S., Roessler, P., & Shelton, B. E. (2004). The CareNet display: Lessons learned from an in home evaluation of an ambient display. In Proceedings of the 6th International Conference on Ubiquitous Computing: UbiComp’04 (pp. 1–17). Nottingham, England. Consolvo, S., & Towle, J. (2005). Evaluating an ambient display for the home. In Proceedings of the Computer Human Interaction CHI’05 Conference. Portland, OR. Fields, J., & Casper, L. M. (2001). America’s families and living arrangement population characteristics. P20-537. U.S. Bureau of the Census, U.S. Department of Commerce, Washington, D.C. Gitlin, L., Corcoran, M., Winter, L., Boyce, A., & Hauck, W. (2001). A randomized controlled trial of a home environmental intervention: Effect on efficacy and upset in caregivers and on a daily function of persons with dementia. The Gerontologist, 41(1), 4–14. He, W., Sengupta, M., Velkoff, V. A., & DeBarros, K. A. (2005). 65+ in the United States: 2005. Special report issued by the U.S. Department of Health and Human Services and the U.S. Department of Commerce, Washington, DC. Hirsch, T., Forlizzi, J., Hyder, E., Goetz, J., Kurtz, C., & Stroback, J. (2000). The ELDer project: Social, emotional, and environmental factors in the design of eldercare technologies. In Proceedings of CM Conference on Universal Usability (pp. 72–79). Arlington, VA. Kohn, L. T., Corrigan, J. M., & Donaldson, M. S. (2000). To err is human: Building a safer health system. A report of the Committee on Quality of Health Care in America, Institute of Medicine. Washington, DC: National Academy Press. 2052

Mann, W. C. (2001). The potential of technology to ease the care provider’s burden. Generations (San Francisco, Calif.), 25(1), 44–49. Manton, K. G. (1989). Epidemiological, demographic and social correlates of disability among the elderly. The Milbank Quarterly, 67(2), 13–58. doi:10.2307/3350235 Mynatt, E. D., Rowan, J., Craighill, S., & Jacobs, S. (2001). Digital family portraits: Supporting peace of mind for extended family members. In Proceedings of Computer Human Interaction CHI’01 Conference (pp. 333–340). Seattle, WA. National Council on Disability. (1993, March). Study on the financing of assistive technology devices and services for individuals with disabilities: A report to the President and the Congress of the United States. Retrieved February 14, 2008, from NCD Newsroom http://www.ncd.gov/newsroom/ publications/1993/assistive.htm. Riche, Y., & Mackay, W. (2005). Peercare: Challenging the monitoring approach to care for the elderly. In Proceedings of the British Human Computer Interaction 2005 Workshop on HCI and the Older Population. Edinburgh, United Kingdom. Scott, N., & Gabrielli, S. (2004). Overview of the Ho’alauna tablet. Archimedes Project, University of Hawaii. Retrieved February 14, 2008, from http://archimedes.hawaii.edu/Downloads/ HOALAUNA2.pdf. Tran, B. Q. (2004). Technologies to facilitate health and independent living in elderly populations. In D.C. Burdick, & S. Kwon (Eds.), Gerotechnology Research and Practice in Technology and Aging (pp. 161–173), New York, NY: Springer. U.S. Administration on Aging. (2002). A profile of older Americans. Administration on Aging, U.S. Department of Health and Human Services. Retrieved February 14, 2008, from http://www. aoa.gov/prof/Statistics/profile/2002profile.pdf

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U.S. Administration on Aging. (2005). What is assistive technology fact sheet. U.S. Department of Health and Human Services. Retrieved February 14, 2008, from http://www.eldercare. gov/eldercare/Public/resources /fact_sheets/assistive_tech_pf.asp

KEY TERMS AND DEFINITIONS Aging in Place: Older adults remain in their homes and communities through the support of technology and assisted living services. Assistive Technology: A device or system supporting an individual in performing a task that otherwise could not be done, or increases the ease and safety with which it is done. Impairment Categories: The U.S. Department of Health and Human Services (http://www. hhs.gov) provides three categories of impairments with varying assistive technology needs. The Mildly Impaired category (e.g., mild arthritis) benefits from assistive technologies that enhance independent living. This may include special devices such as grab bars and modified cooking utensils. The second category, Moderately Impaired, include those who are functionally impaired in several daily living activities. For example, a person with arthritis may also have diabetes with circulation problems. Assistive technology supplements other types of support

systems (e.g., home caregiver) in order to promote independent living. The third category, Severely Impaired, probably will benefit the most from emerging technologies. These individuals have multiple impairments that impact daily living. Healthcare Technology: This term is typically used to encompass all technologies that are used in the healthcare field inclusive of both medical personnel and patients. Patient Safety: Institute of Medicine defines patient safety as “freedom from accidental injury;” conversely, error constitutes “the failure of a planned action to be completed as intended or the use of a wrong plan to achieve an aim.” (Kohn, Corrigan, & Donaldson, 2000). Proactive Computing: Applications that are designed to anticipate an individual’s needs and to take action to meet the needs on their behalf (taken from http://www.Intel.com’s Web site on Health Research and Innovation). Smart Home Technology: Devices used in a home environment to perform complex tasks that otherwise would be performed by the individual. They facilitate independent living by providing automated support of daily activities, security, building performance, and telecommunications. Technology Assistance Act: Congress enacted the Technology Related Assistance Act of 1988 (P.L.100-407) to expand the availability of assistive technology services and devices to people with disabilities.

This work was previously published in Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, pp. 513-518, copyright 2008 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 8.4

The Development and Implementation of Patient Safety Information System (PSIS) Jeongeun Kim Seoul National University, Korea

ABSTRACT This chapter presents the overview of the current status and developmental stages of the PSIS technology and consensus around the patient safety issues as they emerge, grow, and mature globally. The first section gives the general description of the patient safety reporting system (PSRS), and then provides the brief summary of 23 patient safety information classifications and terminologies to date. In the next section, the development of the international classification of patient safety (ICPS) is overviewed, which evolved from the local to an international level by the joint initiatives of WHO. The essential elements of the PSIS and the clinical decision support system (CDSS) functionalities are explained to make the future goals of PSIS clearer. The patient safety indicator (PSI) is explained in a separate section, which

provides the opportunity to assess the incidence of adverse events and in-hospital complications using administrative data found in the typical discharge record. The ultimate goals of PSIS and PSI are to improve the quality of healthcare and ensure patient safety.

INTRODUCTION “To Err Is Human” report (Institute of Medicine, November 1999), brought to public’s attention the issue of patient safety, and alarmed the U.S. healthcare industry (Kohn, Corrigan, & Donaldson, 2000). It suggested that medical accidents caused between 44,000 and 98,000 deaths annually in American hospitals, great majority of which were preventable. Subsequent studies from a number of other countries demonstrate that patient safety is clearly a problem on an

DOI: 10.4018/978-1-60960-561-2.ch804

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

The Development and Implementation of Patient Safety Information System (PSIS)

international scale. (Baker et al., 2004; Vincent, Neale, & Woloshynowych, 2001). The causes of these adverse events are often complex, as they can be attributed to combination of multiple compounded errors. For example, the hospital system includes testing, diagnosis, treatment, caring of the patients, control of commodities and apparatus management. This complexity often makes it difficult or impossible to determine the real causes of an individual accident (Dew et al., 2004). The “To Err Is Human” report emphasizes that most medical accidents are the result of system failures, and that the renovation of healthcare delivery system it crucial in order for it to operate safely. An ideal system must not only reduce the likelihood of errors, but also be sensitive to the occurrences. Worldwide concerns about safety in patient care have stressed the need to coordinate the monitoring, reporting, and understanding of adverse events and “near misses.” Clearly, better information on the number, types, severity, causes and consequences of adverse events is required to develop the strategies, which will reduce the risk of medical incidents and ameliorate the devastating effects of medical errors.

PATIENT SAFETY REPORTING SYSTEM There are many ways to improve patient safety using information technology (Bates & Gawande, 2003). One way of improving safety is improving detection and reporting systems for error and adverse event (IOM, 2003). In small studies, computerized reporting systems have been associated with an increased rate of spontaneous reporting (Dixon, Wielgosz, & Pires, 2002). Computerized reporting streamlines subsequent evaluation by making it is easier to perform analyses and categorize reports in different ways. One university hospital treating more than 25,000 patients annually reported a feasibility study of a computerized voluntary based medical error reporting system in the ambulatory

setting (Plews-Ogan et al., 2004). The findings showed that the voluntary based medical error reporting system resulted in a 20-fold increased reporting rate, and physicians reported many of these errors. Also the study suggested that new medical error reporting systems should combine reporting with analytic functions to facilitate analysis. A study by Furakawa et al from Japan found that a computerized medical error reporting system was effective and acceptable to providers, and facilitated analysis (Furukawa, Bunko, Tsuchiya, & Miyamoto, 2003). In another study, a web-based reporting system was developed and implemented for medical workers of 54 hospitals who were working in neonatal intensive care units (Suresh et al., 2004). This system was both voluntary and anonymous. Evaluation of the feasibility and utility of this approach revealed that it was well received, and effective for identifying a wide variety of medical errors. In addition, the approach facilitated cooperative, multidisciplinary studies. In developing a medical error reporting system, the key factors to consider are objectives of the system, challenges associated with such objectives, classification system, reporting process, and how the errors will be analyzed (Beasley, Escoto, & Karsh, 2004). In addition, systems should ideally be non-punitive, and voluntary to the greatest extent possible and with certain exceptions. In America, the medical community is currently struggling toward implementing medical error reporting and prevention systems. Reporting systems are required in hospitals by the Joint Commission on Accreditation of Healthcare Organization (JCAHO), which in particular mandates that sentinel events be reported. Sentinel events are particularly serious adverse events. Beginning in 1995, nationwide sentinel event cases have been collected by JCAHO, although a number of other reporting systems predated this one. In the sentinel event database, the annual number of reports continues to increase with time, but most believe that the reported cases are only tip of an iceberg. The sentinel event structure is useful for

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the most serious adverse events; for errors and near-misses. Other structures are used with the intent to use these reports as “free lessons.” For example, the Veterans Hospitals adopted what has been a highly successful medical error reporting system without punitive action in 1997. In November of 1999, the Department of Veterans Affairs in America established the “National Center for Patient Safety (NCPS).” The number of near miss or close calls increased 900 times in comparison with the previous year in the NCPS, but this is believed to likely be associated with a much higher level of patient safety. In June 2002 the American Hospital Association distributed NCPS data from the VA to all hospitals in America as an example. Essentially all hospitals now have in place error reporting systems, and the errors and adverse events reported can be analyzed to develop and prioritize among patient safety interventions. Even though medical error reporting is done systematically as an obligation in medical facilities, one has to remember that the mandatory reporting of medical errors and adverse events is only a beginning, and should not replace spontaneous or voluntary reporting. Generally obligatory or mandatory reporting is essential for serious issues such as wrong-site surgery, or fatal adverse drug events, but fortunately such serious adverse events represent only a small proportion of the safety-related issues that occur. For errors, nearmisses, and less serious adverse events, voluntary anonymous medical error reporting systems with no punitive action are likely superior to obligatory medical error reporting systems. The reason that the voluntary anonymous reporting system is better than that of the obligatory is that one can report the error with detailed fashion without any omission because no punitive action is attached to the reporting, and much higher rates of reports are obtained (Wears et al., 2000). In implementing and operating patient safety programs in medical facilities, voluntary reporting systems represent an essential component. But achieving higher levels of safety requires much

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more than just good reporting systems. Additionally it will require fundamental redesign of many of the processes in the healthcare system, which will in turn require attention to building a strong safety culture. Hospital authorities should put patient safety as a top priority if safety is to be substantially improved.

PAST AND PRESENT OF PATIENT SAFETY INFORMATION SYSTEM (PSIS) “Patient Safety: Achieving a New Standard of care” (IOM, 2003) recommended that standardization and better management of information on patient safety—including near misses and adverse events—are needed to inform the development of strategies that reduce the risk of preventable medical incidents. However, patient safety incident reporting systems differ in design and therefore in their ability to define, count, and track adverse events. Among reporting systems, there are often disparate data fields, conflicting patient safety terminologies, classifications, characteristics, and uses that make standardization difficult. In addition, each source of data on near misses and adverse events usually requires different methods for interpreting and deconstructing these events (Runciman et al., 2000). Finally, misused terminology in the research literature, conference papers and presentations, and media contributes to widespread misunderstandings about the issues of patient safety. The proliferation of reporting systems has created a pressing need for organization and standardization of PSIS and terminology. Unfortunately, much of the work has fallen short in meeting identified needs for epidemiological data (Weingart, Wilson, Gibberd, & Harrison, 2000). Given the current state of the art, it is extremely difficult to achieve broad-based and timely improvements in patient safety, since there is no standard determination as to which events

The Development and Implementation of Patient Safety Information System (PSIS)

to capture and report (Hofer, Kerr, & Hayward, 2000). Additionally, the lack of a common patient safety terminology is a critical obstacle to sharing and aggregating data to support patient safety. Several methods have been developed to define and classify medical errors, adverse events, near misses, and other patient safety concepts and terms (Elder & Dovey, 2002; Runciman, Helps, Sexton, & Malpass, 1998). However, these methods have tended to be, with notable exceptions, narrowly and predominantly focused on specific areas of healthcare—medication errors (Betz & Levy, 1985; Dunn & Wolfe, 1997), transfusion reactions (Kaplan, Battles, Van der Schaaf, Shea, & Mercer, 1998), primary care (Makeham, Dovey, County, & Kidd, 2002), and nursing care (Benner et al., 2002; Woods & Doan-Johnson, 2002). Here are the cases of taxonomy, classification and PSIS to dates describing the purposes and characteristics of those systems (in alphabetical order): 1. Applied Strategies for Improving Patient Safety (ASIPS) Taxonomy (W. Pace, 2004, October): The Victoroff/ASIPS Taxonomy was developed in collaboration with the Colorado Physicians Insurers Association of America to identify, study and ameliorate medical errors in primary care. The taxonomy is designed to highlight the complexity of patient safety issues in primary care and to facilitate learning. It has a multi-axial, hierarchical and relational structure and is capable of mapping to several currently existing classification systems through the Victoroff taxonomy (W. D. Pace et al., 2003). Taxonomic categories include the participants involved and their contribution to the event, the course of the event, the outcome, and observations by the originator or discoverer of the event (WHO 2003; WHO 2005). 2. The Australian Incident Monitoring System (AIMS), the Generic Occurrence

Classification (GOC) and the Generic Reference Model (GRM) (Chang, Schyve, Croteau, O’Leary, & Loeb, 2005; Runciman, 2002, 2004; Spigelman & Swan, 2005; WHO, 2003): The Australian Patient Safety Foundation (APSF) originally developed the AIMS in 1987 to monitor adverse events in anesthesia. In 1993 (AIMS-Generic) and again in 2000 (AIMS-2), the APSF broadened the scope of AIMS to serve as a methodology for reporting, analyzing, and monitoring all patient safety events. The expanded monitoring system is specifically designed to: 1) be used across the entire spectrum of a national healthcare system by staff, patients and relatives, 2) be sufficiently flexible to meet the requirements of specialty based units and generic reporting, 3) allow web based reporting and rapid feedback and analysis, and 4) be suitable for both local and national data collection. The first classification scheme designed to support AIMS was the GOC which was developed in 1994, however, proved to be too complex to use at the health unit level. As a result, the APSF developed the GRM. The GRM maintains the basic characteristics of the GOC, but utilizes the Healthcare Incident Types Classification (HIT Classification™) to extract salient information about an event through a series of user-friendly, cascading, natural language questions. 3. Classification of Medical Errors and Preventable Adverse Events in Primary Care (Elder & Dovey, 2002): This classification was designed to assist family physicians and other primary care researchers in understanding how process errors and preventable adverse events happen during the practice of primary care. This classification categorizes adverse event and near miss data from primary care practice. It defines the three main categories of preventable adverse events related by primary care physicians:

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diagnosis, treatment, and preventive errors, which are presented in a hierarchical order. 4. The Eindhoven Classification Model for System Failure (ECM) and The Prevention and Recovery Information System for Monitoring and Analysis – Medical (PRISMA) (Battles, Kaplan, Van der Schaaf, & Shea, 1998; Boxwala et al., 2004; Van der Schaaf, 2005; WHO, 2003): The ECM was originally designed as an event reporting system for the chemical industry. In 1997, ECM was expanded to be applicable in healthcare. PRISMA, developed by the Faculty of Technology Management at Eindhoven University of Technology, is both a patient safety surveillance system and a process by which to perform root cause analyses. The system has three components – a causal tree, the Eindhoven Classification Model, and a classification/action matrix. The framework consists of modules – system inputs (detection, selection, and description), input processing (classification, computation, and interpretation and implementation), and output evaluation. Errors are classified by: 1) latent errors/technical factors, such as equipment, design, construction, materials, or external factors, 2) organizational factors such as transfer of knowledge issues, protocols, procedures, management priorities, or cultural issues, 3) active errors, including knowledge-based, rule-based, and skillbased errors, and 4) other factors such as patient related factors or unclassified issues. The classification/action matrix aggregates the data categorized by ECM to produce a “PRISMA profile, a graphical representation of the root causes of all incidents or of a certain type of incident,” which can then be used to develop preventive strategies. 5. Edinburgh Incident Classification (EIC) (Busse & Wright, 2000; Wright, Mackenzie, Buchan, Cairns, & Price, 1991): The EIC was developed to analyze and classify anes-

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thetic mishap data collected from an incident reporting system in an Edinburgh Adult Intensive Care Unit. This reporting scheme has been used to collect data about “critical incidents” attributable to human error and equipment related failures. The original EIC comprises 23 categories of contributing factors to critical incidents, and is grouped according to: 1) performance shaping factors (PSF), which affect task performance and produce erroneous behavior that, in turn, might lead to an incident, and 2) domain, environment, and task-specific factors. 6a. Generic Occurrence Classification (GOC) for Incidents and Accidents in the Healthcare System (Runciman et al., 1998): The GOC was developed to code salient features and contributing factors of things that go wrong in patient care, including incidents from near misses to sentinel events. The GOC employs various types of “natural categories” that may be linked together using “natural mapping.” A natural category is a descriptor that is brief, easily and commonly understood, which captures the essence of an adverse event, and is not limited to any class or property. The GOC allows incidents and accidents to be analyzed at the level of detail required to understand and learn about their causes and to develop preventive strategies to reduce their occurrence. A classification process (known as The Generic Reference Model) de-constructs the relevant information about an incident. This process is designed to place the extracted information in context and record the underlying causes, including both system-based and human factors. 6b. Generic Occurrence Classification (GOC) for Adverse Drug Events (Malpass, Helps, Sexton, Maiale, & Runciman, 1999): This “sub-branch” of the GOC is designed to specifically classify or code medication incidents in a way that allows the frequen-

The Development and Implementation of Patient Safety Information System (PSIS)

cies, causes and contributing factors to be analyzed. The GOC has 827 branches of code specific to medication problems and further separated unto sub-branches to the depth of over 12 levels. The detailed medication problems sub-branch enables medication incidents to be coded in depth, and is structured to allow cross-mapping to ICD-10. 7. International Classification of Diseases, Tenth Version (ICD-10) (WHO, 2004): First developed by the International Statistical Institute as the International List of Causes of Death in 1893, the ICD is now maintained by the WHO and provides a standardized classification for causes of both morbidity and mortality. It promotes international comparability of patient safety information by serving as a common reference point for reporting and statistical use. The classification categories are clearly defined into 22 primary chapters describing the basic parameters of health. The structure is hierarchical and scalable. 8. International Classification of Primary Care (ICPC) (IOM, 2003; Okkes, Oskam, & Lamberts, 2005): ICPC, developed by the World Organizations of National Colleges, Academies and Academic Associations of General Practitioners/Family Physicians, allows simultaneous classification of three elements related to an encounter in primary care: the process of care, the reason for the encounter, and the health problem diagnosed. ICPC uses ICPC-2 as an ordering principle (based on the high prevalence of common diagnoses in family practice) and ICD-10 as a nomenclature (based on the wide range of ‘known’ diagnoses). The ICPC bi-axial organizational structure categorizes encounters into 26 chapters and seven components. 9. International Taxonomy for Errors in General Practice (Makeham et al., 2002): This taxonomy was designed to classify the

types of errors that occur in general practice and could be applied in multiple countries with similar primary healthcare standards. This taxonomy was initially based on a preliminary taxonomy designed in the United States (c.f. Taxonomy of Medical Errors in Family Practice) but was further developed and refined in a pilot study to capture a broader range of error types reported from Australia, Canada, the Netherlands, New Zealand and the United Kingdom. The taxonomy has a five-level classification system comprising of 171 error types. At the highest level, errors are classified according to two primary groups: 1) process errors; and 2) knowledge and skills errors. 10. The International Taxonomy of Medical Errors in Primary Care (“The Linnaeus Corporation,” 2002): The International Taxonomy of Medical Errors in Primary Care was developed to code and classify the medical errors reported by family physicians and general practitioners in research undertaken by the American Academy of Family Physicians. Primary classifications were derived from qualitative analysis of comments made by these family and general practitioners. The categories are type of error, action taken, consequences, severity of harm, contributing factors, and prevention strategies. 11. Logical Observation Identifiers Names and Codes (LOINC) (IOM, 2003; UMLS®, 2005): LOINC, developed by the Regenstrief Institute, provides a set of universal names and numeric identifier codes for laboratory and clinical observations and measurements. The laboratory section classifies data according to biomedical categories (i.e., chemistry, microbiology, toxicology); the clinical section classifies data based on clinical findings and results (i.e., vital signs, ultrasound, and imaging). Each data element contains a formal 6-part name, a unique name for tests

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identifying code with check digit, synonyms, and other useful information. 12. Medical Dictionary for Drug Regulatory Affairs (MedDRA®) (Boxwala et al., 2004; IOM, 2003; MedDRA® 2005; Wilson & Sheikh, 2002): The International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use sponsored the development of MedDRA®. MedDRA® offers a comprehensive vocabulary and coding system for safety-related events and adverse drug reactions. Terms are classified into three high level categories: 1) equivalence – grouping of synonymous terms, 2) hierarchical – vertical links between super-ordinate and sub-ordinate descriptors, and 3) associative – hierarchical links between terms which are neither equivalent nor hierarchically related but related by sign, symptom, disease, and diagnosis. 13. Medical Event Reporting System – Total HealthSystem (MERS-TH) and the MERS-TH Taxonomy (MERS-TM, 2005; MERS–TH, 2005): MERS-TH is designed to capture both near misses and adverse events in healthcare. The reporting system is web-based and allows for standardized reporting across inpatient and ambulatory healthcare systems. Both the Eindhoven Classification Model and the MERS-TH taxonomy are embedded within MERSTH. The Eindhoven Classification Model is used to categorize causal relationships. The MERS-TH taxonomy is used to code adverse events, and their corresponding discovery and antecedent events, through a four-tiered system which classifies data by service description, event type, event description and contributing factors. Contributing factors include logistical or process oriented factors, institutional or managerial issues, communication and documentation issues, environmental factors, staff related factors,

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delay in or premature provision of care, and patient issues. It also has the ability to capture narrative data. 14. Medical-Event Reporting System for Transfusion Medicine (MERS-TM) (Battles et al., 1998; Kaplan et al., 1998): MERS-TM is an event reporting system developed for transfusion services and blood centers to collect, classify, and analyze events that could potentially compromise transfusion safety. The MERS-TM classification process assigns standardized codes to describe the event and root causes. Events are classified by using preset event codes and 20 possible causal codes. Causal codes are subdivided into latent failures (organizational and technical), active failures (human-related errors), and patient-related factors. Near miss events - events for which a recovery step (planned or unplanned) allows for interruption and correction of the error - are included in the classification. Near miss data present opportunities to learn from mistakes and improve the overall safety of the transfusion service. 15. National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP) Taxonomy of Medication Errors (Dunn & Wolfe, 1997): The NCC MERP Taxonomy of Medication Error provides a standard language and structure of medication-error related data that address these events, and can be used to record, track, analyze, and benchmark medication error data in a standardized format for U.S. hospitals, and has been adopted by the U.S. Pharmacopeia (USP) and U.S. Food and Drug Administration (FDA), provide a nationally projected measure of errors grouped according to categories established by the NCC MERP. The NCC MERP Taxonomy of Medication Errors is organized into eight major categories: 1) patient information, 2) medication error event, 3) patient outcome,

The Development and Implementation of Patient Safety Information System (PSIS)

4) product information, 5) personnel involved, 6) type of medication error, 7) causes, and 8) contributing factors. In Category 1, patient demographic information is requested. Category 2 is concerned with the specifics of the medication error event. The description of the event is a free text entry field. Category 3 requests information about the patient’s outcome. Information about the product that was actually or potentially given is classified in Category 4. The personnel involved in the error are classified under Category 5. In this category, information about who made the initial error is identified as well as who perpetuated the error. In Category 6, the type of medication error is categorized by such occurrences as dose omission, improper dose, and wrong route of administration, wrong rate, and monitoring. Category 7 identifies communication, name confusion, labeling, and human factors that contributed to the error. Category 8 focuses on the contributing factors to errors. 16. The National Reporting and Learning System (NRLS) and the NRLS Classification: In 2004, the United Kingdom’s National Patient Safety Agency (NPSA) implemented the NRLS. NRLS is the primary mechanism to collect information on patient safety incidents, including near misses that occur within National Health Services-funded healthcare systems. It is designed to operate in acute care and general hospitals and pharmacies, as well as with mental health, disability, community nursing, general practice, ambulance, dental and optometry services. The information is analyzed to identify trends, provide feedback, develop healthcare priorities and to promote learning. A customized classification system, the NRLS Classification, is embedded within the NRLS. Patient safety data is categorized into six nodes: 1) incident details (i.e., type, location, specialty area, and outcome), 2)

patient details (i.e., demographic and pertinent clinical information), 3) medication, 4) medical devices, 5) staff details, and 6) contributing factors. Contributing factors include those related to the patient, the task, team or social dynamics, the environment, communication, education and training, equipment and resources, medication, and/ or organizational structure. 17. Patient Safety Event Taxonomy-Version 1.0 (PSET™-v1.0) (Chang et al., 2005; WHO, 2003): The PSET™-v1.0, developed by the Joint Commission, was endorsed by the United States National Quality Forum, through its consensus development process, as the US national standard in August 2005. It is theoretically based upon Reason’s model of error causation and Rasmussen’s theories on human-system interactions (Rasmussen, 1986). It has 5 root nodes (primary classifications) that are segmented into corresponding priority patient safety concepts - 15 secondary classifications - that are keys to understanding what variables or interaction of variables affect patient safety. These classifications are branched into 140 coded categories with the flexibility to include unlimited free text below the coded categories. Its variable-level classification framework and the shared language of the taxonomy can be adapted to match the patient safety needs of any international healthcare setting through individual modules. Impact: The taxonomy identifies the severity or degree of physical and psychological harm, ranging from the least harm to the most harm, resulting from adverse events. It also categorizes other potential consequences due to error and systems failure that are related to legal, social, and economic issues. Type: There are three priority areas: 1) Communication failures that exist between patient and provider, patient’s proxy and practitioner, practitioner and non-medical staff, and among

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practitioners, 2) Patient management issues that involve improper delegation, failure in tracking or follow-up, wrong referral or consultation, and wrong use of resources, and 3) Clinical performance issues. Domain: The taxonomy is not limited to a specific healthcare setting or type of adverse events. It is intended to be broadly applicable to any incident resulting from patient care. Cause: The causal framework of the taxonomy addresses the entire spectrum of latent and active failures. Categorization of causes and contributing factors reflects the multilevel nature of incident causation. Prevention & Mitigation: Patient safety data obtained from reporting systems and other pertinent data sources can be de-constructed using the analytical framework of the taxonomy, to identify trends, make comparisons, and discern whether there are patterns that indicate recurring problems. These, in turn, can inform the development of preventive and corrective strategies. 18. Systemized Nomenclature of Human and Veterinary Medicine Clinical Terms (SNOMED CT) (Chang et al., 2005; IOM, 2003; SNOMED International, 2005): Developed by the College of American Pathologists in conjunction with the United Kingdom’s National Health Services, SNOMED CT is a concept-oriented reference terminology. The classification consists of a concept table containing codes with “unique meanings and formal logic based definitions;” a description table comprised of “synonyms for flexibility in expressing clinical concepts;” a relationship table with “semantic relationships to enable robust reliability and consistency of data retrieval;” and a list of attributes which are useful, understandable, and reproducible. SNOMED CT is capable of being cross mapped to other medical classifications, such as ICD and LOINC. It is hierarchically organized

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and can be modified to incorporate new information. 19. Taxonomy for Error Reporting, Root Cause Analysis and Analysis of Practice Responsibility (TERCAP) (Benner et al., 2002; Woods & Doan-Johnson, 2002): The TERCAP Instrument - The TERCAP Error Audit Tool- has been used as a proactive reporting system to promote improvement both at the individual level, and at the levels of educational and healthcare delivery and regulatory organizations. TERCAP is an error taxonomy and survey instrument that classifies nursing errors, their causes, patient outcomes, and disciplinary actions. The medication errors discovered and categories of patient harm are captured in TERCAP using the NCC MERP Taxonomy of Medication Errors. It covers nursing errors representing a broad range of possible errors and contributive or causative factors in eight categories: 1) lack of attentiveness, 2) lack of agency/fiduciary concern, 3) inappropriate judgment, 4) medication errors, 5) lack of intervention on the patient’s behalf, 6) lack of prevention, 7) missed or mistaken physician/healthcare provider’s orders, and 8) documentation errors. Also included in the TERCAP Instrument are the following categories of blunt-end factors (system issues) that contribute to the nurse’s practice breakdown: environmental factors; communication factors; employee safety/ support factors; leadership/management factors; backup and support factors; and other factors. TERCAP was designed to identify errors in such a way that the multiple causes of the error could be deduced from the way the error was defined. Events with similar patient outcomes but different causes for the error could be tied to a category to capture causes that could be tied to remediation or prevention.

The Development and Implementation of Patient Safety Information System (PSIS)

20. Taxonomy of Medical Errors in Family Practice (Wilson & Sheikh, 2002): This preliminary taxonomy was designed to organize error reports made by family physicians in both inpatient and outpatient settings and in the transition between care settings. The taxonomy comprises of two primary categories which distinguish between errors attributable to aspects of care delivery systems (process errors) and errors that could only be averted by improving providers’ clinical skills and/ or knowledge or diverting clinical tasks to clinically trained providers (knowledge and skills errors). The “process errors” category contains 34 items in five broad subcategories: office administration; investigations; treatments; communication; payment; and the “knowledge and skills errors” category has three subcategories: execution of a clinical task; misdiagnosis; wrong treatment decision. 21. Unified Medical Language System (UMLS®) (IOM, 2003; UMLS®, 2005): UMLS® was developed and is maintained by the National Library of Medicine. It is a metathesaurus, comprised of existing classifications including SNOMED CT, LOINC, and ICD, that links similar concepts by organizing the various source classification data elements by concept or meaning. Its scope is determined by the combined scope of its source vocabularies. UMLS® preserves the organizational structures, components and relational connections of the source vocabularies while providing a consistent categorization of all concepts represented. 22. The Uppsala Monitoring Centre (UMC), the WHO Data Dictionary (WHO-DD) and the Terminology for Coding Adverse Reactions (WHO-ART) (LOINC, 2005; UMC, 2004): In 1968, the WHO established the International Drug Monitoring Program to collect data on adverse drug reactions and to issue public warnings when

warranted. Now referred to as the Uppsala Monitoring Center, the program continues to collect and monitor drug reaction data and to publish international alerts through “pharmacovigilance (drug safety surveillance).” Reports produced by the UMC are based upon data generated from three classifications – WHO-DD, WHO-ART and MedDRA®. Each of these classifications has a scalable hierarchical structure, is able to incorporate new knowledge and is able to aggregate data at various levels. The WHODD categorizes drug product information at four levels: 1) the Anatomical-TherapeuticChemical (ATC) level denoting the main indication for which a medicinal product is used, 2) the generic level describing the ingredient or combination of ingredients, 3) the pharmaceutical product level indicating the combination of ingredients, form and strength, and 4) the medicinal product level referring to the named product marketed and sold in a particular country. Herbal products are also classified using a unique classification based on the ATC, which links to internally accepted botanical names and synonyms. The UMC simultaneously uses WHO-ART, the adverse drug reaction coding system developed specifically for the UMC, and MedDRA®. WHO-ART consists of two group levels – preferred terms and lower level terms – and is a subset of MedDRA®. Narrative descriptions of adverse reactions are also collected. This information is linked to lexicon tables which contain predefined, allowed values, expressed as formatted text or codes. Data captured by a lexicon table can be standardized and translated into various languages. 23. The VA/NASA Patient Safety Reporting System (PSRS) and the Patient Safety Information System (PSIS) (Heget, Bagian, Lee, & Gosbee, 2002; IOM, 2003; MedDRA®, 2005): The United States

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Department of Veterans Administration (VA) and the National Aeronautics and Space Administration (NASA) jointly developed the PSRS in 2000. The reporting system is fashioned after NASA’s successful Aviation Safety Reporting System constructed for the United States Federal Aviation Administration. PSRS is designed to identify broad system vulnerabilities within the VA healthcare system. PSRS uses the PSIS to classify adverse events and near misses by: 1) the description of the event, 2) the type of event, 3) the location of the event, 4) environmental factors, 5) the actions taken, and 6) other or unclassified factors. Free text data is also captured. PSRS’ organizational structure is scalable.

FUTURE OF PSIS Patient Safety Taxonomy: International Classification of Patient Safety (ICPS) The proliferation of reporting systems has created a pressing need for organization of PSIS and terminology. As one of the main objectives of the PSIS is to share information, the development of the systems should be in the international domain. This section briefly explains the WHO’s global activities of developing the standardized terminology and classification schema for the advancement of future PSIS. The World Alliance for Patient Safety’s ICPS drafting group recognized that while many patient safety classifications exist, no one classification was fit for use on the global level. Therefore, the drafting group made the critical decision to draw upon the work of the Joint Commission’s Patient Safety Event Taxonomy (PSET). The evolution of the ICPS processed as follows (WHO, 2007a):

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•

•

•

•

•

2003: WHO began work on developing an international patient safety event classification. 2004: World Alliance for Patient Safety identified development of the classification as a key priority in its 2005 Forward Programme. 2005: World Alliance for Patient Safety assembled a panel consisting of experts in the fields of patient safety, classification theory and development, health informatics, consumer advocacy, law and medicine (“the drafting group”) to define the mission and purpose and to devise a strategic plan to accomplish this goal. The drafting group identified and defined 621 key concepts and developed a conceptual framework and hierarchical structure to organize these concepts in a meaningful manner conducive to the promotion of learning. 2006: The drafting group implemented a two round web-based Delphi survey to solicit global input to determine the acceptability, applicability and relevance of the classification. Aggregated results from 250 international stakeholders indicated a general consensus that the international classification is a meaningful and useful tool for translating disparate information into a format conducive to learning and improving patient safety 2007-2008: The drafting group is in the midst of field testing the ICPS in each of the WHO regions.

The aim of the ICPS is to “define, harmonize and group patient safety concepts into an internationally agreed classification in a way that is conducive to learning and improving patient safety across systems.” It is intended to enable the translation of data and information into a common standardized language and to allow for analysis, which will result in awareness, accountability and

The Development and Implementation of Patient Safety Information System (PSIS)

knowledge building (WHO, 2007d). It is concept driven, and seeks to integrate easily to existing systems with relatively low resource expenditure, capture both adverse events and near misses, be inclusive of a wide range of stakeholders, be sensitive to cultural and linguistic issues, and be adaptable yet consistent across the continuum of healthcare. The ICPS is not a reporting system; it is a logically oriented hierarchical framework of concepts designed to translate patient safety incident data collected from a range of sources into a standardized classification (WHO, 2007c). “The Conceptual Framework for the International Classification for Patient Safety Version 1.0 for Use in Field Testing 2007 – 2008” can be downloaded for the detail of the ICPS structure and function (WHO, 2007b).

Essential Elements of PSIS This section will describe and provide the core functional elements that will construct the PSIS based on the essential elements of the Electronic Health Record (EHR). The IOM Committee formulated five criteria to identify core EHR system functionalities as follows (IOM, 2003): • • • • •

Improve patient safety Support the delivery of effective patient care Facilitate management of chronic conditions Improve efficiency Feasibility of implementation

• •

Administrative processes Reporting & population health management

The IOM committee hopes its work will be useful in developing functional statements for an EHR system; to government programs and private clients in their efforts to encourage and assist healthcare providers in deploying EHR systems; to providers and vendors as they strive to acquire and build software products that form part of the foundation for a comprehensive health information infrastructure; and to patients as they seek to participate more fully in decisions regarding their own care. These functionalities should be applied and have effects mentioned above as the PSIS is another name of the EHR. According to the research team of JCAHO’ PSET, the desirable attributes of a patient safety event taxonomy, which are the basic elements of PSIS, are (Chang et al., 2005): • •

•

• •

Based on unambiguous and generally agreed terminologies and classifications. Useful for analyzing the processes and outcomes that underlie an event, including its root causes and contributing factors. Facilitates consistent collection and analysis of near miss and adverse event data across the continuum of healthcare delivery settings. Facilitates expedient data exchange and dissemination of patient safety information. Useful for identifying priority areas for remedial attention and opportunities to improve patient safety.

Based on the criteria, the core functionalities of the EHR were identified (IOM, 2003):

CDSS for Patient Safety

• • • • • •

Recent efforts made it possible to reduce the problem of patient safety by adopting technologies such as computerized physician order entry (CPOE), computerized decision support systems (CDSS), computerized monitoring, and evidencebased practice by retrieving information on the

Health information and data Results management Order entry/management Decision support Electronic communication and connectivity Patient support

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Internet. The Leapfrog Group, a consortium of companies that belong to the Business Roundtable, has endorsed CPOE in hospitals as 1 of 3 changes that would most improve patient safety in the United States (Milstein, Galvin, Delbanco, Salber, & Buck, 2000). CPOE and CDSS are promising interventions that target the ordering stage of medications, where most medication errors and preventable Adverse Drug Events (ADEs) occur. CPOE systems with no decision support will almost certainly decrease error rates less than systems with sophisticated decision support (Kaushal, Shojania, & Bates, 2003). Analysis of 70 randomized controlled trials have found 15 decision support system features whose importance has been repeatedly suggested in the literature. Those 15 features in 4 categories are (Kawamoto, Houlihan, Balas, & Lobach, 2005): •

•

•

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General system features ◦⊦ Integration with charting or order entry system to support workflow integration ◦⊦ Use of a computer to generate the decision support Clinician-system interaction features ◦⊦ Automatic provision of decision support as part of clinician workflow ◦⊦ No need for additional clinician data entry ◦⊦ Request documentation of the reason for not following CDSS recommendations ◦⊦ Provision of decision support at time and location of decision making ◦⊦ Recommendations executed by noting agreement Communication content features ◦⊦ Provision of a recommendation, not just an assessment ◦⊦ Promotion of action rather than inaction

◦⊦

•

Justification of decision support via provision of reasoning ◦⊦ Justification of decision support via provision of research evidence Auxiliary features ◦⊦ Local user involvement in development process ◦⊦ Provision of decision support results to patients as well as providers ◦⊦ CDSS accompanied by periodic performance feedback ◦⊦ CDSS accompanied by conventional education

The authors identified four features among 15 strongly associated with a decision support system’s ability to improve clinical practice and patient safety by the statistical analysis: 1) decision support provided automatically as part of clinician workflow, 2) decision support delivered at the time and location of decision making, 3) actionable recommendations provided, and 4) computer based decision support. Furthermore, direct experimental justification was found for providing periodic performance feedback, sharing recommendations with patients, and requesting documentation of reasons for not following recommendations (Kawamoto et al., 2005). The PSIS should adopt the above mentioned features as the core and basic functionalities to achieve the goals of its existence.

PATIENT SAFETY INDICATOR (PSI) This section will overview the Agency for Healthcare Research and Quality (AHRQ)’s outstanding work of developing and adopting the PSI to measure the size of the patient safety problem and make it visible by providing the scientific and practical methods of the analyzing data on medical errors. AHRQ Quality Indicators (QIs) measure healthcare quality by using readily available

The Development and Implementation of Patient Safety Information System (PSIS)

hospital inpatient administrative data. The PSIs are tools to help health system leaders identify potential adverse events occurring during hospitalization. The PSIs are sets of indicators providing information on potential in-hospital complications and adverse events following surgeries, procedures, and childbirth. The PSIs were developed after a comprehensive literature review, analysis of ICD-9-CM codes, review by a clinician panel, implementation of risk adjustment, and empirical analyses. The PSIs form the third of a set of AHRQ QIs which expanded the original Healthcare Cost and Utilization Project (HCUP) QIs. The Prevention Quality Indicators, the first set of AHRQ QIs, were released in November 2001. The second set, the Inpatient Quality Indicators, were released in May 2002. The PSIs were released in March 2003. In February 2006, the fourth QI module, the Pediatric Quality Indicators, was added. The PSIs have the following characteristics (AHRQ, February 2006): •

•

•

•

•

Can be used to help hospitals identify potential adverse events that might need further study. Provide the opportunity to assess the incidence of adverse events and in-hospital complications using administrative data found in the typical discharge record. Include 20 indicators for complications occurring in-hospital that may represent patient safety events. Seven indicators also have area level analogs designed to detect patient safety events on a regional level. Are free and publicly available without charge to hospitals and other users as SAS® and SPSS® programs with software documentation and a user guide that provides a synopsis of the evidence taken from the “Measures of Patient Safety Based on Hospital Administrative Data.”

The PSIs include the following 27 measures: 1. Hospital-level Patient Safety Indicators (20 Indicators): ◦⊦ Complications of anesthesia (PSI 1) ◦⊦ Death in low mortality DRGs (PSI 2) ◦⊦ Decubitus ulcer (PSI 3) ◦⊦ Failure to rescue (PSI 4) ◦⊦ Foreign body left in during procedure (PSI 5) ◦⊦ Iatrogenic pneumothorax (PSI 6) ◦⊦ Selected infections due to medical care (PSI 7) ◦⊦ Postoperative hip fracture (PSI 8) ◦⊦ Postoperative hemorrhage or hematoma (PSI 9) ◦⊦ Postoperative physiologic and metabolic derangements (PSI 10) ◦⊦ Postoperative respiratory failure (PSI 11) ◦⊦ Postoperative pulmonary embolism or deep vein thrombosis (PSI 12) ◦⊦ Postoperative sepsis (PSI 13) ◦⊦ Postoperative wound dehiscence in abdominopelvic surgical patients (PSI 14) ◦⊦ Accidental puncture and laceration (PSI 15) ◦⊦ Transfusion reaction (PSI 16) ◦⊦ Birth trauma -- injury to neonate (PSI 17) ◦⊦ Obstetric trauma -- vaginal delivery with instrument (PSI 18) ◦⊦ Obstetric trauma -- vaginal delivery without instrument (PSI 19) ◦⊦ Obstetric trauma -- cesarean delivery (PSI 20) 2. Area-level Patient Safety Indicators (7 Indicators): ◦⊦ Foreign body left in during procedure (PSI 21) ◦⊦ Iatrogenic pneumothorax (PSI 22) ◦⊦ Selected infections due to medical care (PSI 23)

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◦⊦

◦⊦ ◦⊦ ◦⊦

Postoperative wound dehiscence in abdominopelvic surgical patients (PSI 24) Accidental puncture and laceration (PSI 25) Transfusion reaction (PSI 26) Postoperative hemorrhage or hematoma (PSI 27)

The PSIs could be the tool for assessing the patient safety problems of hospitals, and should be considered to be integrated into the PSIS as a component of retrieving the basic data and analyzing & planning to develop the strategies of solving the problems.

CONCLUSION The PSIS should be the main tool to measure, monitor, analyze, and adopt strategies for patient safety, and having a robust system in place is essential for developing a strong patient safety climate. As patient safety becomes an increasingly important issue in the healthcare industry worldwide and the reporting of adverse events and medical errors becomes a more critical part of quality management, there will be closer scrutiny on classifications and terminology schemes and pressure to adopt standards. Monitoring of events between different adverse events systems and the sharing of best practices within and across countries will create the need for systems with interoperability and commonality. A thorough analysis of the existing Patient Safety Information Systems, including results of past classification developments, is needed to gain a full understanding of the technical issues related to building a PSIS or integrating existing ones. National and International efforts and initiatives are mandatory to make patient safety the prime goal of healthcare arena for the new century of optimum human health.

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Boxwala, A. A., Dierks, M., Keenan, M., Jackson, S., Hanscom, R., & Bates, D. W. (2004). Organization and representation of patient safety data: Current status and issues around generalizability and scalability. Journal of the American Medical Informatics Association, 11(6), 468–478. doi:10.1197/jamia.M1317 Busse, D. K., & Wright, D. J. (2000). Classification and analysis of incidents in complex medical environments. Topics in Health Information Management, 20(4), 1–11. Chang, A., Schyve, P. M., Croteau, R. J., O’Leary, D. S., & Loeb, J. M. (2005). The JCAHO patient safety event taxonomy: A standardized terminology and classification schema for near misses and adverse events. International Journal for Quality in Health Care, 17(2), 95–105. doi:10.1093/ intqhc/mzi021 Dew, M. A., Myaskovsky, L., DiMartini, A. F., Switzer, G. E., Schulberg, H. C., & Kormos, R. L. (2004). Onset, timing, and risk for depression and anxiety in family caregivers to heart transplant recipients. Psychological Medicine, 34(6), 1065–1082. doi:10.1017/S0033291703001387 Dixon, J. F., Wielgosz, C., & Pires, M. L. (2002). Description and outcomes of a custom Web-based patient occurrence reporting system developed for Baylor University Medical Center and other system entities. Proc (Bayl Univ Med Cent), 15(2), 199-202; discussion 208-111. Dunn, E. B., & Wolfe, J. J. (1997). Medication error classification and avoidance. Hospital Pharmacy, 32, 860–865. Elder, N. C., & Dovey, S. M. (2002). Classification of medical errors and preventable adverse events in primary care: a synthesis of the literature. The Journal of Family Practice, 51(11), 927–932.

Furukawa, H., Bunko, H., Tsuchiya, F., & Miyamoto, K. (2003). Voluntary medication error reporting program in a Japanese national university hospital. The Annals of Pharmacotherapy, 37(11), 1716–1722. doi:10.1345/aph.1C330 Heget, J. R., Bagian, J. P., Lee, C. Z., & Gosbee, J. W. (2002). John M. Eisenberg patient safety awards. System innovation: Veterans Health Administration National Center for Patient Safety. The Joint Commission Journal on Quality Improvement, 28(12), 660–665. Hofer, T. P., Kerr, E. A., & Hayward, R. A. (2000). What is an error? Effective Clinical Practice, 3(6), 261–269. IOM. (2003). Key capabilities of an electronic health record system. Washington, D.C.: The National Academies Press. Kaplan, H. S., Battles, J. B., Van der Schaaf, T. W., Shea, C. E., & Mercer, S. Q. (1998). Identification and classification of the causes of events in transfusion medicine. Transfusion, 38(11-12), 1071–1081. doi:10.1046/j.15372995.1998.38111299056319.x Kaushal, R., Shojania, K. G., & Bates, D. W. (2003). Effects of computerized physician order entry and clinical decision support systems on medication safety. Archives of Internal Medicine, 163, 1409–1416. doi:10.1001/archinte.163.12.1409 Kawamoto, K., Houlihan, C. A., Balas, E. A., & Lobach, D. F. (2005). Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success. BMJ (Clinical Research Ed.), 330(7494), 765. doi:10.1136/bmj.38398.500764.8F Kohn, L. T., Corrigan, J. M., & Donaldson, M. S. (2000). To err is human: Building a safer health system. Washington, D.C.: Institute of Medicine National Academy Press.

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LOINC. (2005). Retrieved on August 18, 2005, from www.regenstrief.org/loinc/ Makeham, M. A., Dovey, S. M., County, M., & Kidd, M. R. (2002). An international taxonomy for errors in general practice: a pilot study. The Medical Journal of Australia, 177(2), 68–72. Malpass, A., Helps, S. C., Sexton, E. J., Maiale, D., & Runciman, W. B. (1999). A classification for adverse drug events. Journal of Quality in Clinical Practice, 19(1), 23–26. doi:10.1046/j.14401762.1999.00286.x MedDRA®. (2005). Retrieved on August 22, 2005, from www.meddramsso.com MERS–TH. (2005). Retrieved on August 18, 2005, from www.ahrq.gov MERS-TM. (2005). Retrieved on August 16, 2005, from www.mers-tm.net/about.html Milstein, A., Galvin, R. S., Delbanco, S. F., Salber, P., & Buck, C. R. (2000). Improving the safety of healthcare: The leapfrog initiative. Effective Clinical Practice, 3, 313–316. Okkes, I. M., Oskam, S. K., & Lamberts, H. (2005). ICPC in the Amsterdam transition project. Amsterdam: University of Amsterdam Academic Medical Center, Department of Family Medicine. Pace, W. (2004, October). Perspectives on creating a taxonomy for medical errors and dimensions of patient safety: A taxonomy. Paper presented at the Institute of Medicine Patient Safety Data Standards, Fifth Meeting, Washington, D.C. Pace, W. D., Staton, E. W., Higgins, G. S., Main, D. S., West, D. R., & Harris, D. M. (2003). Database design to ensure anonymous study of medical errors: A report from the ASIPS collaborative. Journal of the American Medical Informatics Association, 10(6), 531–540. doi:10.1197/jamia. M1339

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Plews-Ogan, M. L., Nadkarni, M. M., Forren, S., Leon, D., White, D., & Marineau, D. (2004). Patient safety in the ambulatory setting. A clinician-based approach. Journal of General Internal Medicine, 19(7), 719–725. doi:10.1111/j.15251497.2004.30386.x Rasmussen, J. (1986). Information processing and human-machine interaction: An approach to cognitive engineering. New York: North-Holland. Runciman, W. B. (2002). Lessons from the Australian Patient Safety Foundation: Setting up a national patient safety surveillance system--is this the right model? Quality & Safety in Health Care, 11(3), 246–251. doi:10.1136/qhc.11.3.246 Runciman, W. B. (2004). History of the APSF taxonomy. Paper presented at the International Taxonomy Conference, Orlando, FL. Runciman, W. B., Helps, S. C., Sexton, E. J., & Malpass, A. (1998). A classification for incidents and accidents in the healthcare system. Journal of Quality in Clinical Practice, 18(3), 199–211. Runciman, W. B., Webb, R. K., Helps, S. C., Thomas, E. J., Sexton, E. J., & Studdert, D. M. (2000). A comparison of iatrogenic injury studies in Australia and the USA, II: Reviewer behaviour and quality of care. International Journal for Quality in Health Care, 12(5), 379–388. doi:10.1093/ intqhc/12.5.379 SNOMED International. (2005). Retrieved on August 18, 2005, from www.snomed.org Spigelman, A. D., & Swan, J. (2005). Review of the Australian incident monitoring system. ANZ Journal of Surgery, 75(8), 657–661. doi:10.1111/ j.1445-2197.2005.03482.x Suresh, G., Horbar, J. D., Plsek, P., Gray, J., Edwards, W. H., & Shiono, P. H. (2004). Voluntary anonymous reporting of medical errors for neonatal intensive care. Pediatrics, 113(6), 1609–1618. doi:10.1542/peds.113.6.1609

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The Linnaeus Corporation. (2002). International taxonomy of medical errors in primary careversion 2. Washington, D.C.: The Robert Graham Center.

WHO. (2007a). Evolution of the ICPS-2003 to present, World Alliance for Patient Safety. Retrieved on January 9, 2008, from http://www. who-icps.org/

UMC. (2004). Retrieved on August 31, 2005, from www.who-umc.org

WHO. (2007b). International classification for patient safety (v.1.0) for use in field testing in 2007-2008 (ICPS). Retrieved on February 9, 2008, from http://www.who.int/patientsafety/taxonomy/ icps_form/en/index.html

UMLS®. (2005). Retrieved on August 18, 2005, from www.nlm.nih.gov/research/umls Van der Schaaf, T. (2005). PRISMA-Medical: A brief description. Eindhoven, The Netherlands: Eindhoven University of Technology. Vincent, C., Neale, G., & Woloshynowych, M. (2001). Adverse events in British hospitals: Preliminary retrospective record review. BMJ (Clinical Research Ed.), 322(7285), 517–519. doi:10.1136/bmj.322.7285.517 Wears, R. L., Janiak, B., Moorhead, J. C., Kellermann, A. L., Yeh, C. S., & Rice, M. M. (2000). Human error in medicine: Promise and pitfalls, part 1. Annals of Emergency Medicine, 36(1), 58–60. Weingart, S. N., Wilson, R. M., Gibberd, R. W., & Harrison, B. (2000). Epidemiology of medical error. BMJ (Clinical Research Ed.), 320(7237), 774–777. doi:10.1136/bmj.320.7237.774 WHO. (2003). Reduction of adverse events through common understanding and common reporting tools: Towards an international patient safety taxonomy. A review of the literature on existing classification schemes for adverse events and near misses. Report WHO HQ/03/116334. Geneva: World Health Organization. WHO. (2004). Family of international classifications. Geneva, Switzerland. WHO. (2005). Project to Develop the International Patient Safety Event Taxonomy: Updated Review of the Literature 2003-2005. Final Report.

WHO. (2007c). Report of the WHO World Alliance for Patient Safety drafting group. Retrieved on January 9, 2008, from http://www.who.int/patientsafety/information_centre/reports/WHO%20 Report%20of%20Drafting%20Group%20Geneva%206-7%20Dec%2007.pdf WHO. (2007d). Report of the WHO World Alliance for Patient Safety meeting with technical experts from the Southeast Asian and Western Pacific regions of the WHO. Retrieved on January 9, 2008, from http://www.who.int/patientsafety/ Report%20of%20Mtg-SEARO%20and%20 WPRO%20technical%20experts%2026%20 Nov%2007-Tokyo.pdf Wilson, T., & Sheikh, A. (2002). Enhancing public safety in primary care. BMJ (Clinical Research Ed.), 324(7337), 584–587. doi:10.1136/ bmj.324.7337.584 Woods, A., & Doan-Johnson, S. (2002). Executive summary: Toward a taxonomy of nursing practice errors. Nursing Management, 33(10), 45–48. doi:10.1097/00006247-200210000-00020 Wright, D., Mackenzie, S. J., Buchan, I., Cairns, C. S., & Price, L. E. (1991). Critical incidents in the intensive therapy unit. Lancet, 338(8768), 676–678. doi:10.1016/0140-6736(91)91243-N

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KEY TERMS AND DEFINITIONS Adverse Event: An incident which results in harm to a patient. Classification: An arrangement of concepts into classes and their subdivisions to express the semantic relationships between them. Event: Something that happens to or involves a patient. Error: Failure to carry out a planned action as intended or application of an incorrect plan. Harm: Impairment of structure or function of the body and/or any deleterious effect arising there from. Hazard: A circumstance, agent or action that can lead to or increase risk. International Classification of Patient Safety (ICPS): A logically oriented hierarchical framework of concepts designed to translate patient safety incident data collected from a range of sources into a standardized classification developed by the World Alliance of Patient Safety, WHO. Near Miss: An incident that did not cause harm. Events for which a recovery step (planned or unplanned) allows for interruption and correction of the error. Patient Safety: Freedom, for a patient, from unnecessary harm or potential harm associated with healthcare. Patient Safety Incident: An event or circumstance which could have resulted, or did result, in unnecessary harm to a patient. Patient Safety Indicator (PSI): A set of indicators providing information on potential

in-hospital complications and adverse events following surgeries, procedures, and childbirth using administrative data found in the typical discharge record. Patient Safety Information System (PSIS): A system that provides an understanding of the magnitude of specific patient safety issues, and baseline and trend data to support institutional and national patient safety improvement initiatives. The main features include incident reporting and patient safety data management and utilization to assess, analyze, diagnose, plan, intervene, evaluate and prevent the patient safety problems. Patient Safety Reporting System (PSRS): A system that collect, analyze, communicate, and report the adverse events as well as near misses. This should be a voluntary, confidential, and nonpunitive reporting system, and the staffs are invited to voluntarily report any events or concerns they have, which involve patient safety. Patient Safety Event Taxonomy (PSET): A taxonomy that identifies the severity or degree of physical and psychological harm, ranging from the least harm to the most harm, resulting from adverse events. It also categorizes other potential consequences due to error and systems failure, and was developed by the Joint Commission. Root Cause Analysis (RCA): A systematic iterative process whereby the factors which contribute to an incident are identified by reconstructing the sequence of events and repeatedly asking “why?” until the underlying root causes have been elucidated. Safety: Freedom from hazard.

This work was previously published in Handbook of Research on Information Technology Management and Clinical Data Administration in Healthcare, edited by Ashish N. Dwivedi, pp. 414-432, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 8.5

The Use of Virtual Reality in Clinical Psychology Research: Focusing on Approach and Avoidance Behaviors

Patrice Renaud University of Quebec in Outaouais / Institut Philippe-Pinel de Montréal, Canada Sylvain Chartier University of Ottawa, Canada Paul Fedoroff University of Ottawa, Canada

Joanne L. Rouleau University of Montreal, Canada Jean Proulx University of Montreal, Canada Stéphane Bouchard University of Quebec in Outaouais, Canada

John Bradford University of Ottawa, Canada

ABSTRACT This chapter presents research that is laying a foundation for new simulation applications that promise learning-oriented treatments for mental health conditions. After presenting background on their technologies and measurement techniques, the authors describe experimental applications of this approach. Analysis of negative and positive responses to virtual reality (VR) stimuli, as well

as their complex composites, can lead to a better understanding of patient responses, including fundamental perceptual and cognitive causal relationships. Measuring patients’ dynamic parameters in VR simulations can possibly lead to new treatment approaches for psychopathologies The biological and behavioral feedback obtained by virtual mediation, based on parameters of the perceptivo-motor dynamics such those described in this chapter, represents a promising avenue for future investigation.

DOI: 10.4018/978-1-60960-561-2.ch805

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

The Use of Virtual Reality in Clinical Psychology Research

INTRODUCTION This chapter presents research that, while outside the domains of most SAGE research presented in this volume, is laying a foundation for new simulation applications that promise learning-oriented treatments for mental health conditions. Using virtual reality (VR) immersive technologies with tracking of ocular and physical movements, this work makes possible more in-depth recording, analysis, and understanding of patient responses, eventually leading to more successful simulationbased treatments. After presenting background on our technologies and measurement techniques, we describe experimental applications of this approach.

BACKGROUND AND TECHNOLOGY Capturing Perceptual-Motor Dynamics in the Virtual Reality’s Loop of Data Since the first prototypes proposed by Morton Heilig, Myron Krueger and especially Ivan Sutherland in the 1950s and 1960s, the essentials of understanding technological assembly required by VR have hardly changed (Ellis, 1995; Rheingold, 1991; Stanney & Zyda, 2002). Starting from to the simulator machine, we can arbitrarily identify VR’s technical assembly according to both the inputs transmitted to the computer through reactions recorded from the human operator, and the outputs transmitted to the human operator’ different sensory channels. Inputs are produced via a series of sensors and transducers that transform behavioral and physiological variables into physical ones, which are in turn stored in the computer’s register. Motor displacements in particular are recorded by a tracking system (generally magnetic, using infrared and/or ultrasound) that isolates the coordinates specific to where the sensors are found on the hu-

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man operator’s body (Ellis, 1995; Foxlin, 2002). The operator is localized within a defined sensory space and his movements are registered across a series of orientational and positional changes. As the operator moves and orients himself in a simulated area, he can perform hand, head, eye, or full body movements. Physiological measures that characterize the state of the human operator in virtual immersion can also be transmitted to the computer (Palsson & Pope, 2002; Wiederhold, Jang, Kim, & Wiederhold, 2002; Wiederhold & Rizzo, 2005). In most cases, the inputs’ main function is to vary the parameters that control the state of the virtual environment (VE), i.e., the multimedia arrangement of stimuli oriented towards the human subject.1 Furthermore, they can help analyze the behavioral dynamics that contribute to interactions with simulated objects in virtual reality for experimental or clinical purposes (Foxlin, 2002; Renaud, Bouchard, & Proulx, 2002a; Renaud et al., 2002b; Renaud, Singer, & Proulx, 2001). After the fashion of chronophotography— a technique developed by Étienne-Jules Marey (1830-1904) during the 19th century and described in Paul Virilio’s The Aesthetics of Disappearance (1980) —virtual reality’s technical assembly enables a systematic analysis of motor sequences. This assembly allows for an unusual incursion into the motor activities that support kinematic variations of the subjective viewpoint in virtual immersion, i.e., an analysis of the human subject’s first-person experience while interacting with the simulated content. In VR, the field of vision that is developed by the subject varies simultaneously in terms of displacement and orientation following the movements that are recorded directly or indirectly using a head-mounted display or stereoscopic glasses. Variations in Cartesian coordinates (x, y and z) and in Eulerian coordinates (yaw, pitch and roll) modify in a coherent way the subject’s visual experience. Registering these coordinates allows us to establish an index concerning the spatial relationship between this viewpoint and the

The Use of Virtual Reality in Clinical Psychology Research

geometric properties of virtual objects. As a result, different contexts of approach and avoidance motor actions can be measured in subjects moving from one place to another in virtual immersion (Renaud et al., 2001, 2002a, 2002b). Accounting for the oculomotor activity observed in immersion (which will be discussed later) enables us to complete this analysis by accurately determining the portion of the virtual environment (VE) to which the subject pays his overt visual attention (Duchowski, Medlin, & Cournia, 2002; Renaud et al., 2002a; Renaud, Decarie, Gourd, Paquin, & Bouchard,, 2003; Wilder, Hung, Tremaine, & Kaur, 2002). As for the outputs, the computer produces a number of stimulations for different sensory segments that make up the human subject’s sensorium. This particular event occurs following an analysis of the inputs, which are transmitted by the transduction of the human operator’s voluntary and involuntary behaviors. Sound, touch, olfactory and proprioceptive stimuli may join visual stimuli to reinforce the effect of realism in virtual immersion. The human-computer interface that is unique to the assembly used in VR favors a continuous and coherent perceptual-motor loop in the human subject (Biocca, 1995; Ellis, 1995). Through a feedback mechanism, the outputs become a source of information for the human operator regarding his spatial position in the virtual environment. These outputs then drive the motor behaviors to adjust the virtual environment’s resulting state. Through this perceptual-motor relay, immersive VR becomes possible and the illusion effect (i.e. the feeling of presence) occurs (ISPR, 2000; Renaud 2006a; Renaud et al., 2002a, 2002b, 2006b; Slater, Steed, & McCarthy, 1998; Witmer & Singer, 1998).

Immersion and Presence Slater and Wilbur (1997) define immersion as the measure in which a computer system can offer

illusions of reality that are: inclusive (eliminating the inputs outside of the virtual environment); demanding (mobilizing sensory modalities); panoramic (covering the visual field); and vivid (offering a good resolution of the image). Immersive potential is measured by “the feeling of presence”, a theoretical concept characterized principally as a psychological state or subjective perception that causes an individual to surrender to the illusion created by an immersive technical assembly. This illusion consists in forgetting both the exterior environment and immersive technology in favor of the virtual environment (ISPR, 2000; Witmer & Singer, 1998). The feeling of presence is considered a product of many factors, mainly the level of immersion, the subject’s level of attention and the degree of interaction (Renaud et al., 2007; Schubert, Friedmann, & Regenbrecht, 2001; Slater, Steel, & McCarthy, 1998). Acting as a kind of perception, presence must have perceptual-motor determinants that tie the subjective perspective to a limited set of possible viewpoints. These perceptual processes that create the illusion of presence are most likely mediated by oculomotor behaviors, since they form the main entry to visual perception (Renaud et al., 2006b, 2007).

The Scientific Advantages of VR in Psychology A literature review performed by Riva (2005) deals with the use of VR in psychology and identifies 996 published scientific articles accessed in an April, 2005 PSYCINFO quick search. The author notes that more than one third of these articles (371) were written over the last three years, suggesting a strong growth, as well as an increasing interest for VR research, which began around 1992.2 According to Riva (2005), most controlled scientific studies have clinical samples of more than 10 subjects and show efficiency in VR systems resulting from the integration of cognitive-behavioral or strictly cognitive approaches. These treatment

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methods are known in psychology for their adherence to principles of the scientific method.3 In fact, VR favors the scientific method in clinical research for multiple reasons; one being that virtual reality noticeably improves the external validity (also called ecological validity) of the results that it generates. When compared to oversimplified stimuli found in certain laboratory studies, those that come from VR are much closer to exterior reality, and thus may lead to a better generalization of the results. Contrary to what usually occurs in scientific research, this gain in external validity is not achieved to the detriment of a rigorous control of the experimental variables. Even though stimuli presentation in a virtual environment may be closer to exterior reality, it is rigorously and faithfully the same experimental condition that occurs in each trial. As a result, valid causative inferences based on the variables involved are favored, more specifically the effects of simulated conditions on behavior and the subjective reactions of human subjects (Brewer, 2000). Virtual reality may even strengthen the internal validity of collected laboratory measures by providing recording and quantitative control of the first-person visual content experienced by a human subject in virtual immersion (Duchowski, Medlin, & Cournia, 2002; Renaud et al., 2002b, 2003; Wilder et al., 2002). By analyzing in more detail the contingencies that unite subjective experience in virtual immersion with the human subject’s responses (i.e., responses obtained by means of a questionnaire, as well as behavioral and physiological responses), less ambiguous causative links can be established between psychological subjectivity and its quantifiable manifestations. The possibility of examining subjective experience and its attentional content via VR’s mediation may significantly improve the value of other measures that are obtained simultaneously. In general, when coupled with VR, the use of psychophysiological measurement techniques such as electrocardiography and electroencephalography greatly increases

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validity (Bullinger et al., 2001; Mager, Bullinger, Mueller-Spahn, Kuntze, & Stoermer, 2001).

Clinical Advantages of VR for Treatment From a clinical viewpoint, the use of VR in mental health leads to a number of therapeutic benefits: 1. simulating treatment contexts that are not easily accessible or are practically impossible to reproduce in reality (e.g., simulating an airplane take-off, locomotion in highaltitude places or potential victims for a sexual aggressor); 2. the possibility of repeating on demand a given context in virtual immersion. This controlled repetition allows clinicians to better target a debilitating symptom and to accurately treat it in a patient; 3. the implementation of a clinical treatment protocol, which is both automated and controlled, ensuring a better adherence to its various procedures. This benefit becomes extremely useful when struggling with dimensions of non-compliance or even malingering in some patients; 4. the recording and storage of immersive sessions, facilitating records management and clinical follow-up and helping to bridge the gap between clinical practice and scientific research in psychology; 5. increased self-motivation in patients using VR treatments compared with the use of more standard methods (Garcia-Palacios, Hoffman, & See, 2001; Rothbaum, Hodges, Smith, Lee, & Price, 2000).

The Assessment of Avoidance and Approach Behaviors in Virtual Immersion Any clinical process unique to mental health requires a diagnostic evaluation and an appropriate

The Use of Virtual Reality in Clinical Psychology Research

treatment that can correct a given pathological state. The evaluation process is generally applied before and after treatment in order to verify the efficacy of the therapeutic procedure. Research studies that are attempting to use VR for diagnostic evaluation purposes are less numerous than those focusing on treatment, but studies on attention deficit and hyperactivity disorder (Rizzo et al., 1999; Wann, Rushton, Smyth, & Jones, 1997), anxiety disorders (Renaud et al., 2002a; Wiederhold & Wiederhold, 2004), autistic disorders (Trepagnier, Sebrechts, & Peterson, 2002), addictive behaviors (Baumann & Sayette, 2006) and deviant sexual preferences (Renaud, 2004; Renaud et al., 2005; Renaud, Rouleau, Granger, Barsetti, & Bouchard, 2002c) have been conducted so far. The following two studies show the relevance of assessing perceptual-motor dynamics in phobic avoidance as well as in deviant sexual attraction from the use of virtual stimuli.

STUDY 1: ASSESSING PHOBIC AVOIDANCE IN VIRTUAL IMMERSION Arachnophobia is a specific animal-type phobia that is classified among the most common phobias today. According to a study conducted in England, 32 percent of women and 18 percent of men showed anxiety or great fear in the presence of a spider (Davey, 1994). Although it can appear insignificant at first, this fear gives rise to major debilitating effects in people who are affected by its pathological form. It is characterized by extreme and irrational fear resulting from the presence or anticipation of a spider. The fear is also accompanied by an active avoidance of this animal (APA, 1994). To expand our knowledge of the mechanisms at work in treating phobic disorders with VRE, we have developed a diagnostic evaluation procedure regarding motor behaviors that are necessary for movement and for the orientation of overt visual

attention in arachnophobic patients. In our study, arachnophobic patients were placed in immersion and were exposed to phobogenic stimuli in order to better understand the dynamics of behavioral avoidance and the information processing associated with it (Renaud et al., 2002a). The diagnostic evaluation procedure regarding phobic avoidance behavior is a computerized behavioral avoidance test (BAT) that can capture the nature of a patient’s subjective experience by witnessing events through his visual perspective. We present here preliminary results on these developments.

Methods and Apparatus Subjects We tested a small sample of five women between 24 and 49 years of age (mean = 35.6, sd = 11.5) who were all diagnosed with arachnophobia according to the Diagnostic and Statistical Manual of Mental Disorders criteria (APA, 1994). These subjects entered treatment for their condition after the experimental trial.

Immersive Virtual Reality A CAVE-type immersive vault (Cruz-Neira, Sandin, DeFanti, Kenyon, & Hart, 1992) at the University of Quebec in Outaouais was used in this study. This immersive system consists of a cluster of four computers that generate the VE and one computer that records ocular measures. One of the four computer clusters acts as the master, while the other three are slaves connected to a projector displayed on one of the walls of the immersive vault. All computers communicate through a network using a CISCO 100 Mbps switch. The master computer gathers the inputs provided by a human subject (keyboard, mouse, motion sensors, ocular measures) and distributes them to the slaves so that all cluster machines can calculate the changes made in the VE and also generate a report.

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The graphic cards are interconnected, allowing them to be frame locked. The positional and orientational coordinates are provided by an IS-900 motion tracker from InterSense Inc. The Virtools 3.5 middleware is responsible for creating the appropriate environment and ensuring communication between the computer clusters. Finally, OpenGL 2.0 plays a role in the rasterization process to benefit from active stereoscopy.

Figure 1. Subject wearing stereoscopic glasses coupled with an oculomotor tracking device 1) IS-900TM motion tracker from InterSense 2) active Nuvision 60GXTM stereoscopic glasses 3) oculomotor tracking system (ASL model H6TM) 4) a virtual spider in wired frame 5) a virtual measurement point (VMP) 6) a gaze radial angular deviation (GRAD) from the VMP.

Immersive Video-Oculography Our method performs gaze analysis by way of virtual measurement points (VMPs) placed over virtual objects. The gaze radial angular deviation (GRAD) from VMPs is obtained by combining the six degrees of freedom (DOF) resulting from head movements and the two DOF (x and y coordinates) resulting from the eye-tracking system (Duchowski et al., 2002; Renaud, Chartier, & Albert, 2008; Renaud et al., 2002b). While variations in the six DOF developed by head movements define momentary changes in the global scene experienced in the immersive vault, the two DOF generated by the eye-tracking device allow line-of-sight computation relative to VMPs. The more this measure approaches zero, the closer the gaze dwells in the immediate vicinity of the selected VMP. Moreover, VMPs are locked onto and therefore move jointly with virtual objects, making it possible to examine the visual pursuit of dynamic virtual objects. Therefore, this method allows us to measure the visual response from GRAD patterns relative to VMPs (see Figures 1 and 2). Average GRAD and GRAD standard deviation were taken as dependent variables in the present study.

Computerized Behavioral Avoidance Test (BAT) Applied in Virtual Immersion Avoidance behavior is measured by calculating the distance separating the patient from the VMP

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placed on virtual objects that will be approached by the patient (Renaud et al., 2002a). The coordinates obtained at a frequency of 60 Hz through the motion tracker are fed into a trigonometric function that calculates the distance between the patient and the virtual object. From this calculation, we can get an accurate picture of the temporal evolution associated with phobic avoidance (see Figure 3). The average distance from virtual objects was taken as dependent variable in the present experiment.

Experimental Task and Protocol The subjects were standing at the centre of an immersive vault and were asked to move as much as possible towards a virtual tarantula (condition 1; Figure 4a) or a virtual sphere acting as a neutral stimulus (condition 2; Figure 4b). The following instructions were given to the participants: “Try not to lose sight of the spider (or sphere) while moving as much as possible towards it. You can also move backwards if fear overcomes you, and then proceed forward shortly after by approaching the spider (or sphere) as close as possible until

The Use of Virtual Reality in Clinical Psychology Research

Figure 2. A five-second sample of GRAD fluctuations from a virtual spider (pink) and a virtual neutral object (blue) for one representative subject. The closer the data approach zero, the closer the gaze is to the centre of the moving target.

Figure 3. A three-minute sample showing distance fluctuations from the spider (dark line) and the neutral object (light line) for one representative subject. This person was getting closer to the target in the presence of the neutral object than when she was exposed to a phobogenic stimulus (dark line).

the end of the session. Although this exercise will last three minutes, do not preoccupy yourself with the time. We will notify you when the session is over.” The exercise was held in a virtual room that simulated a kitchen, with a counter on which the spider or sphere was moving. The targets (spider or sphere) in both experimental conditions shared exactly the same kinematic properties, moving according to variable speeds and trajectories that were similar to those that a real spider would trace.

Results Raw data are displayed in Table 1. Repeated analyses of variance were done to compare the subjects’ responses to the neutral target (sphere) and to the phobogenic target (spider). Consequently, we observed that the subjects were on average further away from the phobogenic stimulus (F(1,4) = 8.344, p<0.05). This result fits our usual observations during BATs involving phobic patients.

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Figure 4. (a) The virtual spider and (b) the neutral target; crosshairs depict the immersed subject’s momentary point of regard in the VE.

Table 1. Case summaries

Subjects

Average GRAD with neutral target (deg)

Average GRAD with phobogenic target (deg)

GRAD SD with neutral target

GRAD SD with phobogenic target

Average distance from neutral target (m)

Average distance from phobogenic target (m)

1

8.79

8.93

6.11

4.87

.66

.77

2

11.69

4.37

6.69

2.62

1.30

2.24

3

9.12

5.35

7.96

2.87

1.84

2.67

4

9.74

3.44

12.22

2.29

2.42

2.78

5

9.25

6.49

6.61

4.64

1.06

1.24

Mean

9.72

5.72

7.92

3.46

1.45

1.94

As seen in Figure 3, however, the computerized BAT applied in immersion provides more accurate information on the temporal evolution of avoidance in phobic patients, which depends on where they are spatially located with respect to the phobogenic object. Next, the subjects were shown to have their attention more precisely centered on the phobogenic target when compared to the neutral one, (F(1,4) = 9.134, p<0.05). As measured by the GRAD, the phobic subjects seemed to be more easily attentive to the stimulus associated with their fears. Finally, the patients’ lability of visual pursuit behavior, measured by the GRAD’s standard deviation, was significantly lower when

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tracking the phobogenic stimulus than it was when tracking the neutral one (F(1,4) = 8.475, p<0.05). The greatest mobilization of attention towards the phobogenic stimulus is therefore characterized by a tighter control of the motor processes that maintain critical information processing. (Table 1)

Discussion Although this first study contained preliminary data from a very small clinical sample, it was able to demonstrate VR’s potential in the detailed analysis of motor behavior when coupled with analytical instruments of attentional activity. The VR sys-

The Use of Virtual Reality in Clinical Psychology Research

tem allows us to measure the patients’ attentional engagement in immersion, both qualitatively and quantitatively. In the qualitative sense, researchers or clinicians can witness events through the subject’s visual first-person perspective in immersion. They can also better understand how patients occupy the VE and manage their visual attention in relation to critical simulated zones. As for the quantitative aspect, statistical data that are similar to those obtained in the previous section may accurately illustrate the perceptual-motor parameters reflecting the phobic patients’ attentional organization, while also monitoring their progress during VRE. Taking advantage of this important clinical advantage, diagnostic evaluation is no longer performed exclusively before and after treatment, but over the course of the entire VRE. As a result, the patients’ clinical evolution may be described more accurately, allowing us to more easily show the individual differences that lie in the expression of a given pathology, particularly patterns of motor avoidance and attentional bias. Adjusting the application of clinical protocols to the subjects’ idiosyncrasies may therefore be greatly facilitated.

STUDY 2: ASSESSING PERCEPTUAL-MOTOR NONLINEAR DYNAMICS IN VIRTUAL IMMERSION AS A DIAGNOSTIC INDEX OF SEXUAL ATTRACTION IN PEDOPHILES Aside from the clinical interview, the use of questionnaires and the examination of collateral records, which are essential yet clearly incomplete sources of information, there exist to this day primarily two methods meant to be objective that are used in both research into and the clinical assessment of sex offenders: penile plethysmography (PPG) and approaches based on viewing time (VT) (Laws & Gress, 2004). However, these methods

present certain methodological shortcomings in terms of reliability and validity. Since its introduction by Freund (1963), the measurement of penile tumescence by means of a plethysmograph (a device to measure variations in the blood volume of a sexual organ) has been the target of much criticism on both ethical and methodological grounds (Kalmus & Beech, 2005; Laws, 2003; Laws & Gress, 2004; Laws & Marshall, 2003; Marshall & Fernandez, 2003). In particular, it has been attacked for demonstrating weak test-retest reliability and questionable discriminating validity with respect to distinguishing sexual deviants from non-deviants and to correctly differentiating the categories of sexual deviants among themselves (Kalmus & Beech, 2005; Looman & Marshall, 2001; Marshall & Fernandez, 2000; McConaghy, 1999; Simon & Schouten, 1991). However, a large part of the method’s test-retest reliability and discriminating validity problems have arisen from PPG’s proneness to strategies used by sex offenders to voluntarily control their penile response in order to fake their sexual arousal response and thus present a nondeviant preference profile (Quinsey & Chaplin, 1988; Seto & Barbaree, 1996). This is where the heaviest criticism has been leveled. Indeed, the use of mental distraction strategies is widespread. In this regard, it has been reported that up to 80% of subjects who were asked to voluntarily control their erectile response succeed in doing so (Farkas, Sine, & Evans, 1979; Howes, 1998; Kalmus & Beech, 2005). They generally manage to lower their scores through the use of aversive or anxiogenic (anxiety-inducing) thoughts and images, that is, by diverting their attention from the sexual stimuli that they are exposed to. These distraction strategies are reputed to be difficult or impossible to detect, and attempts to control this factor have yielded mixed results (Golde, Strassberg, & Turner, 2000; Proulx, Cote, & Achille, 1993; Quinsey & Chaplin, 1988). VT-based methods of addressing pedophilia have grown out of the work of Rosenzweig (1942)

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and Zamansky (1956), who demonstrated a positive correlation between VT and sexual interest. On the strength of these findings, teams led by Abel (Abel Assessment for Sexual Interest) (Abel, Jordan, Hand, Holland, & Phipps, 2001), Glasgow (Affinity) (Glasgow, Osbourne, & Croxen, 2003), and Laws (Psychological Assessment Corporation) (Laws & Gress, 2004) developed sexual interest assessment protocols based on VT on photographs of real models (Abel et al., 2001; Glasgow et al., 2003) or on static images of synthetic characters generated by modifying photographs of real models (Laws & Gress, 2004). As pointed out by Fischer and Smith (1999; Smith & Fischer, 1999) regarding the Abel Assessment for Sexual Interest, the use of VT to assess sexual interest presents major problems in terms of test-retest reliability and external validity. Moreover, as is the case with PPG, VT-based measures can have their internal validity affected by the use of result-faking strategies (Fischer & Smith, 1999; Kalmus & Beech, 2005). Although it is not divulged at the time of testing, it is easy enough to identify VT as the dependent variable and, once its significance becomes widely known, subjects have little difficulty “cheating.” Furthermore, both PPG and VT-based methods make it possible to evaluate only a small portion of the sexual interest and preference response. This comprises at least three components: aesthetic interest, sexual attraction, and sexual arousal (Kalmus & Beech, 2005; Laws & Gress, 2004; Singer, 1984). PPG serves to evaluate the response process’s physiological dimension of arousal, whereas VT-based methods discern the aesthetic interest response only in part (Fischer & Smith, 1999). What’s more, the aesthetic interest response itself can be broken down into a series of perceptual and cognitive processes whose opaqueness transforms into important assets in the assessment of sexual interest and preference, as we will see later. Finally, as Laws and Gress (2004) pointed out, the victimization of children is another major shortcoming of assessment pro-

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cedures that use pictures of real models to arouse either physiologic sexual responses or deviant interest as indexed by PPG or VT.

Virtual Reality and Attention Control Technologies as an Alternative The use of fully synthetic characters obviously precludes the above mentioned ethical problems. If those characters could be presented in virtual immersion, coupled with attention control technologies such as eye-trackers, the other methodological shortcomings explained previously could likely also be circumvented. Moreover, data generated by tracking gaze behavior could possibly also be diagnostically indicative in themselves (Renaud, 2004; Renaud et al., 2002c, 2005, 2006b). The following experimental study is based on these theoretical and methodological rationales. It aims first at demonstrating that it is technically feasible to take the patients’ point of view into account while presenting them with clinically relevant virtual stimuli. It also aims at bearing out that gaze behavior, and especially gaze behavior dynamics, is a potential source of diagnostic information about deviant sexual preferences.

Methods and Apparatus Subjects Eight male pedophile patients and eight male nondeviant control subjects were recruited for the present study. Patients were attending treatment at the Forensic program of the Royal Ottawa Hospital. Control subjects were recruited via newspapers. Subjects of both groups were matched according to their age and socioeconomic status.

Immersive Virtual Reality and Video-Oculography The CAVE system and video-oculography technique described above were also used in this study.

The Use of Virtual Reality in Clinical Psychology Research

Our method performs gaze analysis by way of virtual measurement points (VMPs) placed over virtual objects or over features of virtual objects (ex.: sexual organs of the virtual characters; see Figure 5). A gaze radial angular deviation (GRAD) from VMPs is obtained by combining the six degrees of freedom (DOF) resulting from head movements and the two DOF (x and y coordinates) resulting from eye movements tracked by the eye-tracking system (Duchowski et al, 2002; Renaud, 2006a; Renaud et al., 2008; see Figure 5). While variations in the six DOF developed by head movements define momentary changes in the global scene experienced in the immersive system, the two DOF generated by the eye-tracking device allow line-of-sight computation relative to VMPs. The closer this measure approaches zero, the closer the gaze dwells in the immediate vicinity of the selected VMP. In the present study, one VMP is used, the latter being installed at the virtual characters’ genitalia level.

Virtual Sexual Stimuli The sexual stimuli that we use are 3D virtual characters presented in virtual immersion. They depict realistic human male and female characters (see Figure 6). Two stimuli are used in the present study, one depicting a six-year-old girl, and the other one a neutral stimulus, i.e., a textureless virtual character. Both stimuli are animated and Figure 5. Example (Component 4) of a virtual character in wire mesh. (See Figure 1 for component descriptions)

each one is presented in virtual immersion for a 120-second duration. Subjects are seated in front of the virtual character that is presented in life-size. In this study, the three-wall immersive system was used to simulate a room into which the virtual characters were standing in front of the seated subjects. These characters were animated to mimic a neutral attitude; these animations were developed using a motion capture system and the movements of actors wearing data suits. Life-size 3D virtual characters were chosen to increase realism and thus get a better ecological validity.

Sexual Plethysmography Sexual (penile) plethysmography measures variations in sexual organs’ blood volume; it is used particularly to assess sexual arousal. Penile plethysmography requires the wearing of a thin mercury-in-rubber-strain-gauge around the shaft of the penis. This gauge is simply a small rubber tube filled with mercury forming a ring. During an erectile response, the gauge stretches and changes in the mercury column produces variations in electric conductibility, expressed as a voltage gradient. The sexual response is recorded while subjects are immersed with one of the virtual characters. The maximum erectile value recorded in a trial is taken as the response. Figure 6. Left: A censored image of the animated 3D virtual character used as a sexual stimulus depicting a 6-yr old female. Right: A neutral textureless virtual character.

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Figure 7. Typical GRAD signal obtained from a 120-second recording made at 60 hz with one control subject, in one experimental condition; the closer the GRAD value gets to zero, the closer the gaze dwells in the vicinity of the associated VMP.

Procedure

Data Analyses

consistent with a non-fractal object: lines have a dimension of 1, while a plane has a dimension of 2, etc. Calculation of D2 is obtained from the Grassberger-Procaccia algorithm (Grassberger & Procaccia, 1983; Grassberger, Schrieber, & Schaffrath, 1991). The idea is to take a given embedded time series xn and for a given point xi count the number of other points xj that are within a distance of ε. Then repeat this process for all the points and for various distances of ε. This correlation sum is expressed as:

Correlation Dimension (D2)

C (ε) =

Subjects were briefed and given a five-minute training period during which they were immersed in a realistic apartment furnished with various pieces of furniture. They were then simply asked to pay attention to the 3D animations they were about to be immersed with for two 120-second periods.

In order to grasp the dynamic side of the visual scanning response, we analyzed the nonlinear properties of the GRAD signals for each subject, in each condition. To do so, we relied upon a well known fractal index, i.e., the correlation dimension (D2). D2 is a measure of the complexity of the geometric structure of an attractor in the phase space of a dynamic process (Sprott, 2003). The invariant topological properties of the attractor – those that do not vary under the continuous and differential transformations of the system’s coordinates – may be extracted by calculating D2 (Renaud et al., 2008). This measurement is

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N N 2 ∑ ∑ Θ ε − xi − x j N (N − 1) i =1 j =i +1

(

)

(1) where N is the number of points in the time series, ||x|| is the Euclidian norm and Θ(x) is the Heaviside function expressed by  0 for x < 0 Θ(x ) =  1 for x ≥ 0  Consequently, the double sum expressed in Equation 1 counts only the pairs (xi, xj) whose distance is smaller than ε. The sum C(ε) is expected to scale like a power law for small ε and large N.

The Use of Virtual Reality in Clinical Psychology Research

Therefore, a plot of log C(ε) versus log ε should give an approximate straight line. D2 = lim lim

ε → 0 N →∞

d log C (ε) d log ε

(2)

The correlation sum in various embeddings can serve as a measure of determinism in a time series. For pure random noise, the correlation sum satisfies C(m, ε) = C(1, ε)m. In other words, if the data are random, they will fill any given embedding dimension. On the other hand, determinism will be shown by asymptotical convergence of D2 as the embeddings increase. All D2s obtained from the GRAD time series recorded in the present study show this asymptotic stabilization.

Surrogate Data Tests To make sure that D2 exponents are significantly distinct from exponents coming from correlated noise processes, a surrogate data method introduced by Theiler, Eubank, Longtin, Galdrikian, and Farmer (1992) was used to statistically differentiate each computed D2 from a sample of D2s coming from quasi-random processes (McSharry, 2005). For each GRAD time series, we generated twenty surrogate time series by doing a Fourier transform of the original data (the phase of each Fourier component was set to a random value between 0 and 2π) while preserving their power spectrum and correlation function (Sprott, 2003). These surrogate data correspond to a quasi-random trajectory of exploratory visual behavior (GRAD). Using a one-sample T test, we were able to establish that D2s calculated from the original data differ significantly from the mean of the 20 D2 correlation dimensions based on surrogate data (AVG=5.67, SD=0.21; p<.0001). This means that the gaze behavior recorded in virtual immersion using GRAD data is a highly structured and well-organized perceptual-motor process that appears to be significantly distinct

from a quasi-random phenomenon. It also implies that the gaze behavior dynamics are probably identifiable by a fractal signature present at multiple scales of the visual scanning behavior (Renaud et al., 2008).

Statistical Analyses A series of eight ANCOVAs with groups (pedophile vs. non-deviant subjects) as an independent variable were conducted. The erectile response, the average GRAD response, the GRAD standard deviation and the D2 fractal indices obtained with the neutral and the six-year-old female virtual characters were submitted to analyses as dependent variables. GRAD acceleration was used as a covariate.

Results See Table 2 for means and standard deviations. Pedophile and non-deviant subjects did not differ on their sexual responses toward neither the control (F(1,13) = 0.698, N.S.) nor the female child character (F(1,13)= 4.12, N.S.), even though it was quite close with the latter (p=0.064). Pedophile subjects were clearly more inclined towards having a greater sexual response when looking at the child female character, with an average penile gauge stretching of 2.9 mm compared to 0.5 mm for the non-deviant subjects. Subjects did not differ significantly on their average GRAD response, either with the control stimulus (F(1,13)= 0.006, N.S.) or with the sexual one (F(1,13)= 0.103, N.S.). The same was obtained with the GRAD standard deviation (F(1,13)= 3.28, N.S.); F(1,13)= 2.50, N.S.)). However, subjects did differ significantly on their gaze behavior dynamics as expressed by the D2 fractal index. They did so only when facing the sexual virtual stimulus (F(1,13)= 5.1, p < .05), not with the neutral one (F(1,13)= 1.71, N.S.). These last results mean that pedophile and non-deviant subjects appear to display distinct

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Table 2. Means and standard deviations Groups Penile response neutral

non deviants

Statistics Mean Std. deviation

pedophile Penile response 6-yr-old female

non deviants

1.1484

Std. deviation

.67992

Mean

.56609

pedophile

Mean

2.8523

non deviants

Mean Std. deviation

non deviants

Mean

10.3379 4.20964

non deviants

Mean Mean Std. deviation Mean Std. deviation

4.1402 1.47011 5.3579 1.78830 3.1494 1.47214

non deviants

Mean

1.8081

Std. deviation

.30819

Mean

1.5764

Std. deviation

.19322

Mean

1.8831

Std. deviation

.36155

Mean

1.6309

Std. deviation

.26549

non deviants pedophile

perceptual-motor dynamics when facing a simulated encounter with a 3D naked female child. The D2 average value of the pedophile subjects is of a lesser complexity, which means that their gaze behavior dynamic is most probably more readily and steadily attracted to the virtual character de-

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2.07773

Mean

pedophile D2 6-yr-old female

9.1237

pedophile

Std. deviation D2 neutral

9.0917

Mean Mean

non deviants

5.10516

Std. deviation

Std. deviation

GRAD SD 6-yr-old female

8.9235

2.28015

pedophile

pedophile

3.81430

Std. deviation

Std. deviation GRAD SD neutral

.5120

Std. deviation

pedophile GRAD 6-yr-old female

1.70547

Mean

Std. deviation GRAD neutral

1.2073

3.5130 1.80997

picting a 6-yr old female child (i.e. to where the VMP was tagged on the virtual objects). Contrary to the average GRAD response, the calculation D2 allows us to extend our understanding of the perceptual-motor invariance extraction dynamics by more clearly qualifying the processes

The Use of Virtual Reality in Clinical Psychology Research

at work as they unfold over time. The latter information appears to be richer and more able to grasp the impinging influences of psychological factors such as sexual preferences can be.

Discussion First, the method put forward in this second study is about controlling the attentional content of the assessee through the minute examination of his observational behavior. Knowing if the assessee’s eyes are open or not is the very first requisite that our method accomplishes in order to ensure a valid forensic assessment of sexual preferences based upon visual stimuli. Pinpointing the gaze location relative to the layout of virtual sexual stimuli is a second crucial aspect of the method that can significantly increase the internal validity of the sexual preference assessment procedure based on penile plethysmography. Knowing how and when the assessee scans specific parts of the sexual stimuli clearly opens up new vistas on perceptual and motor processes involved in the control of sexual response. Finally, this method is also about the possibility of developing an original index of sexual preferences whose basis would be the inherently dynamic properties of the oculomotor behavior as it probes sexual virtual stimuli. The latter possibility is probably the most interesting avenue, since it taps into aspects of the deviant behavior that are not as obvious and overt, and therefore less subject to voluntary control and faking strategies. Further studies are obviously required to clearly disentangle which factors contribute specifically to these distinguishing organized complexities. In particular, the special role of the inhibiting processes used to alter erectile responses has to be experimentally controlled and linked to dynamic patterns.

CONCLUSION More and more, VR is considered an important therapeutic addition because of its scientific and clinical advantages. This technology will, of course, not replace the therapist’s human input into the treatment process, but it can definitely allow patients that benefit from it to expand and develop a range of important experiences that can lead them to a more adaptive lifestyle. Consideration of the perceptual-motor measures associated with the immersive experience is certainly a tool to advance this purpose. Analysis of negative and positive responses to stimuli, as well as their complex composites, can lead to a better understanding of the manifestations of psychopathologies. By clarifying and deepening our understanding of patient responses, it becomes possible to better understand fundamental perceptual and cognitive causal relationships while revealing the dynamic character of the psychopathology. Finally, measuring patients’ dynamic parameters in VR simulations can possibly lead to new treatment approaches for psychopathologies. The biological and behavioral feedback obtained by virtual mediation, based on parameters of the perceptivo-motor dynamics, such those described in this chapter, represents a promising avenue for future investigation (Renaud et al., 2006c).

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Allanson, J., & Mariani, J. (1999). Mind over virtual matter: Using virtual environments for neurofeedback training. In Proceedings, IEEE Virtual Reality 1999 (pp. 270-273). New York: IEEE. doi 10.1109/VR.1999.756961. American Psychiatric Association (APA). (1994). Diagnostic and statistical manual of mental disorders (4th edition). Washington, DC: APA. Baumann, S. B., & Sayette, S. A. (2006). Smoking cues in a virtual world provoke craving in cigarette smokers. Psychology of Addictive Behaviors, 20(4), 484–489. doi:10.1037/0893-164X.20.4.484 Biocca, F. (1995). Communication in the age of virtual reality. Mahwah, NJ: Lawrence Erlbaum Associates Inc. Brewer, M. (2000). Research design and issues of validity. In H. Reis & C. Judd (Eds.), Handbook of research methods in social and personality psychology. Cambridge, UK: Cambridge University Press. Bullinger, A. H., Mueller-Spahn, F., Mager, R., Stoermer, R., Kuntze, M. F., & Wurst, F. (2001). Virtual reality in future diagnoses and treatment. The World Journal of Biological Psychiatry, (Suppl. 1), 194S. Cruz-Neira, C., Sandin, D. J., DeFanti, T. A., Kenyon, R., & Hart, J. C. (1992). The CAVE, Audio Visual Experience Automatic Virtual Environment. Communications of the ACM, 35(6), 64–72. doi:10.1145/129888.129892 Davey, G. C. L. (1994). Self-reported fears to common indigenous animals in an adult U.K. population: The role of disgust sensitivity. The British Journal of Psychology, 85(4), 541–554. Duchowski, A., Medlin, E., & Cournia, N. (2002). 3-D eye movement analysis. Behavior Research Methods, Instruments, & Computers, 34(4), 573–591.

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Ellis, S. R. (1995). Virtual environments and environmental instruments. In K. Carr & R. England (Eds.), Simulated and virtual realities: Elements of perception (pp. 11-51). London: Taylor & Francis. Farkas, G. M., Sine, L. F., & Evans, I. M. (1979). The effects of distraction, performance demand, stimulus explicitness and personality on objective and subjective measures of male sexual arousal. Behaviour Research and Therapy, 17, 25–32. doi:10.1016/0005-7967(79)90047-0 Fischer, L., & Smith, G. M. (1999). Statistical adequacy of The Abel Assessment for interest in paraphilias. Sexual Abuse, 11(1), 195–205. doi:10.1177/107906329901100303 Foxlin, E. (2002). Motion tracking requirements and technologies. In K. Stanney (Ed.) Handbook of virtual environments: Design, implementation, and applications (pp.163-210). Mahwah, NJ: Lawrence Erlbaum Associates Inc. Freund, K. (1963). A laboratory method for diagnosing predominance of homo- and heteroerotic interest in the male. Behaviour Research and Therapy, 1, 85–93. doi:10.1016/00057967(63)90012-3 Garcia-Palacios, A., Hoffman, H. G., & See, S. K. (2001). Redefining therapeutic success with virtual reality exposure therapy. Cyberpsychology & Behavior, 4(3), 341–348. doi:10.1089/109493101300210231 Glasgow, D. V., Osbourne, A., & Croxen, J. (2003). An assessment tool for investigating paedophile sexual interest using viewing time: An application of single case research methodology. British Journal of Learning Disabilities, 31(2), 96–102. doi:10.1046/j.1468-3156.2003.00180.x Golde, J. A., Strassberg, D. S., & Turner, C. M. (2000). Psychophysiologic assessment of erectile response and its suppression as a function of stimulus media and previous experience with plethysmography. Journal of Sex Research, 37(1), 53–59.

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Grassberger, P., & Procaccia, I. (1983). Characterization of strange attractors. Physical Review Letters, 50(5), 346–349. doi:10.1103/PhysRevLett.50.346 Grassberger, P., Schreiber, T., & Schaffrath, C. (1991). Nonlinear time sequence analysis. International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, 1(3), 521–547. doi:10.1142/S0218127491000403 Howes, R. J. (1998). Plethysmographic assessment of incarcerated nonsexual offenders: A comparison with rapists. Sexual Abuse: Journal of Research and Treatment, 10(3), 183–194. International Society for Presence Resarch (ISPR). (2000). The Concept of Presence: Explication Statement. Available at http://www.temple.edu/ ispr/frame_explicat.htm Kalmus, E., & Beech, A. R. (2005). Forensic assessment of sexual interest: A review. Aggression and Violent Behavior, 10(2), 193–217. doi:10.1016/j.avb.2003.12.002 Laws, D. R. (2003). Penile plethysmography. Will we ever get it right? In T. Ward, D. R.Laws, & S. M. Hudson (Eds.), Sexual deviance: Issues and controversies (pp. 82-102). Thousand Oaks, CA: Sage Publications. Laws, D. R., & Gress, C. L. Z. (2004). Seeing things differently: The viewing time alternative to penile plethysmography. Legal and Criminological Psychology, 9(2), 183–196. doi:10.1348/1355325041719338 Laws, D. R., & Marshall, W. L. (2003). A brief history of behavioral and cognitive-behavioral approaches to sexual offenders, Part 1, Early developments. Sexual Abuse, 15(2), 75–92. doi:10.1177/107906320301500201

Looman, J., & Marshall, W. L. (2001). Phallometric assessments designed to detect arousal to children: The responses of rapists and child molesters. Sexual Abuse, 13(1), 3–13. doi:10.1177/107906320101300102 Mager, R., Bullinger, A. H., Mueller-Spahn, F., Kuntze, M. F., & Stoermer, R. (2001). Realtime monitoring of brain activity in patients with specific phobia during exposure therapy, employing a stereoscopic virtual environment. Cyberpsychology & Behavior, 4(4), 465–469. doi:10.1089/109493101750527024 Marshall, W. L., & Fernandez, Y. M. (2000). Phallometric testing with sexual offenders: Limits to its value. Clinical Psychology Review, 20(7), 807–822. doi:10.1016/S0272-7358(99)00013-6 Marshall, W. L., & Fernandez, Y. M. (2003). Sexual preferences: Are they useful in the assessment and treatment of sexual offenders? Aggression and Violent Behavior, 8(2), 131–143. doi:10.1016/ S1359-1789(01)00056-8 McConaghy, N. (1999). Unresolved issues in scientific sexology. Archives of Sexual Behavior, 28(4), 285–318. doi:10.1023/A:1018744628161 McSharry, P. E. (2005). The danger of wishing for chaos. Nonlinear Dynamics Psychology and Life Sciences, 9(4), 375–397. Norcross, J. C., Hedges, M., & Prochaska, J. O. (2002). The face of 2010: A Delphi poll on the future of psychotherapy. Professional Psychology, Research and Practice, 33(3), 316–322. doi:10.1037/0735-7028.33.3.316 North, M. M., North, S. M., & Coble, J. R. (2002). Virtual reality therapy: An effective treatment for psychological disorders. In K. M. Stanney (Ed.), Handbook of virtual environments: Design, implementation, and applications (pp. 1065-1078). Mahwah, NJ: Lawrence Erlbaum Associates Inc.

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Palsson, O. S., & Pope, A. T. (2002). Morphing beyond recognition: The future of biofeedback technologies. Biofeedback, 30(1), 14–18. Proulx, J., Cote, G., & Achille, P. A. (1993). Prevention of voluntary control of penile response in homosexual pedophiles during phallometric testing. Journal of Sex Research, 30(2), 140–147. Quinsey, V. L., & Chaplin, T. C. (1988). Preventing faking in phallometric assessments of sexual preference. Annals of the New York Academy of Sciences, 528, 49–58. doi:10.1111/j.1749-6632.1988. tb50849.x Renaud, P. (2004, October). Moving assessment of sexual interest into the 21st century: the potential of new information technology. Invited presentation at the 23rd Annual Research and Treatment Conference (ATSA), Albuquerque, NM. Renaud, P. (2006a). Method for providing data to be used by a therapist for analyzing a patient behavior in a virtual environment. US patent 7128577. Renaud, P., Bouchard, S., & Proulx, R. (2002a). Behavioral avoidance dynamics in the presence of a virtual spider. IEEE (Institute of Electrical and Electronics Engineers). Transactions in Information Technology and Biomedicine, 6, 235–243. doi:10.1109/TITB.2002.802381 Renaud, P., Chartier, S., & Albert, G. (2008, accepted). Embodied and embedded: The dynamics of extracting perceptual visual invariants. In S. Guastello, M. Koopman, & D. Pincus (Eds.), Chaos and complexity: Recent advances and future directions in the theory of nonlinear dynamical systems psychology. Cambridge, UK: Cambridge University Press. Renaud, P., Chartier, S., Albert, G., Cournoyer, L.-G., Proulx, J., & Bouchard, S. (2006b). Sexual presence as determined by fractal oculomotor dynamics. Annual Review of Cybertherapy and Telemedicine, 4, 87–94.

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Renaud, P., Singer, G., & Proulx, R. (2001). Headtracking fractal dynamics in visually pursuing virtual objects. In W. Sulis, & I. Trofimova (Eds), Nonlinear dynamics in life and social sciences (pp. 333-346). Amsterdam: IOS Press NATO Science Series. Rheingold, H. (1991). Virtual reality: The revolutionary technology of computer-generated artificial worlds - and how it promises and threatens to transform business and society. New York: Summit Books. Riva, G. (2005). Virtual reality in psychotherapy [review]. Cyberpsychology & Behavior, 8(3), 220–230. doi:10.1089/cpb.2005.8.220 Rizzo, A., Buckwalter, J. G., Neumann, U., Chua, C., Van Rooyen, A., & Larson, P. (1999). Virtual environments for targeting cognitive processes: An overview of projects at the University of Southern California Integrated Media Systems. Cyberpsychology & Behavior, 2(2), 89–100. doi:10.1089/cpb.1999.2.89 Rosenzweig, S. (1942). The photoscope as an objective device for evaluating sexual interest. Psychosomatic Medicine, 4, 150–158. Rothbaum, B. O., Hodges, L. F., Smith, S., Lee, J. H., & Price, L. (2000). A controlled study of virtual reality exposure therapy for the fear of flying. Journal of Consulting and Clinical Psychology, 68(6), 1020–1026. doi:10.1037/0022006X.68.6.1020 Schubert, T., Friedmann, F., & Regenbrecht, H. (2001). The experience of presence: Factor analytic insights. Presence (Cambridge, Mass.), 10(3), 266–281. doi:10.1162/105474601300343603 Seto, M. C., & Barbaree, H. E. (1996). Sexual aggression as antisocial behavior: A developmental model. In D. Stoff, J. Brieling, & J. D. Maser (Eds.), Handbook of antisocial behavior (pp. 524-533). New York: Wiley.

Simon, W. T., & Schouten, P. G. (1991). Plethysmography in the assessment and treatment of sexual deviance: An overview. Archives of Sexual Behavior, 20, 75–91. doi:10.1007/BF01543009 Singer, B. (1984). Conceptualizing sexual arousal and attraction. Journal of Sex Research, 20(3), 230–240. Slater, M., Steed, A., & McCarthy, J. (1998). The influence of body movement on subjective presence in virtual environments. Human Factors, 40(3), 469–477. doi:10.1518/001872098779591368 Slater, M., & Wilbur, S. (1997). A framework for immersive virtual environments (FIVE): Speculations on the role of presence in virtual environments. Presence (Cambridge, Mass.), 6(6), 603–616. Smith, G. M., & Fischer, L. (1999). Assessment of juvenile sex offenders: Reliability and validity of The Abel Assessment for interest in paraphilias. Sexual Abuse, 11(3), 207–216. doi:10.1177/107906329901100304 Sprott, J. C. (2003). Chaos and time-series analysis. Oxford, UK: Oxford University Press. Stanney, K. M., & Zyda, M. (2002). Virtual environments in the 21st century. In K.M. Stanney (Ed.), Handbook of virtual environments: Design, implementation, and applications (pp. 1-14). Mahwah, NJ: Lawrence Erlbaum Associates Inc. Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., & Farmer, J. D. (1992). Testing for nonlinearity in time series: The method of surrogate data. Physica D. Nonlinear Phenomena, 58(1-4), 77–94. doi:10.1016/0167-2789(92)90102-S Trepagnier, S., Sebrechts, M. M., & Peterson, R. (2002). Atypical face gaze in autism. Cyberpsychology & Behavior, 5(3), 213–217. doi:10.1089/109493102760147204 Virilio, P. (1980). Esthétique de la disparition. Paris: Balland.

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Wann, J. P., Rushton, S. K., Smyth, M., & Jones, D. (1997). Virtual environments for the rehabilitation of disorders of attention and movement. Studies in Health Technology and Informatics, 44, 157–164.

International Society for Presence Resarch (ISPR). (2000). The concept of presence: Explication statement. Available at http://www.temple.edu/ ispr/frame_explicat.htm.

Wiederhold, B. K., Jang, D. P., Kim, S. I., & Wiederhold, M. D. (2002). Physiological monitoring as an objective tool in Virtual Reality therapy. Cyberpsychology & Behavior, 5(1), 77–82. doi:10.1089/109493102753685908

North, M. M., North, S. M., & Coble, J. R. (2002). Virtual reality therapy: An effective treatment for psychological disorders. In K. M. Stanney (Ed.), Handbook of virtual environments: Design, implementation, and applications (pp. 1065-1078). Mahwah, NJ: Lawrence Erlbaum Associates Inc.

Wiederhold, B. K., & Rizzo, A. (2005). Virtual Reality and applied psychophysiology. Applied Psychophysiology and Biofeedback, 30(3), 183–187. doi:10.1007/s10484-005-6375-1 Wiederhold, B. K., & Wiederhold, M. D. (2004). Virtual Reality therapy for anxiety disorders. Washington D.C: American Psychological Association Press. Wilder, J., Hung, G., Tremaine, M., & Kaur, M. (2002). Eye tracking in virtual environments. In K. M. Stanney (Ed.), Handbook of virtual environments: Design, implementation, and applications (pp. 211-222). Mahwah, NJ: Lawrence Erlbaum Associates Inc. Witmer, B. G., & Singer, M. J. (1998). Measuring presence in virtual environments: A Presence Questionnaire. Presence (Cambridge, Mass.), 7(3), 225–240. doi:10.1162/105474698565686 Zamansky, H. S. (1956). A technique for measuring homosexual tendencies. Journal of Personality, 24(4), 436–448. doi:10.1111/j.1467-6494.1956. tb01280.x

ADDITIONAL READING Guastello, S., Koopman, M., & Pincus, D. (2008). Chaos and complexity: Recent advances and future directions in the theory of nonlinear dynamical systems psychology. Cambridge, UK: Cambridge University Press.

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KEY TERMS AND DEFINITIONS Approach Behavior: Tropism toward a source, usually expressed in the presence of an appetitive source. Arachnophobia: A psychiatric disorder characterized by a pathological fear of spider. Avoidance Behavior: Tropism away from a source, usually expressed in the presence of an aversive source. Feeling of Presence: A psychological state or subjective perception while using technology such as virtual reality, in which an individual does not maintain full awareness of the role of the technology but perceives partly or fully as though the technology-filtered experience is “real.” Immersive Video-Oculography: Eye-tracking performed in virtual immersion. Nonlinear Dynamics: The study of systems governed by equations in which a small change in one variable can induce large systematic fluctuations. Nonlinear dynamics may lead to chaotic behaviors and generate fractal patterns. Paraphilia: A psychiatric disorder characterized by deviant sexual behaviours. Virtual Reality: An artificially simulated reality that may induce a feeling of presence, generally a 3D environment generated by computer.

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ENDNOTES 1



Recent studies aiming to use electroencephalographic signals for biofeedback purposes in VR have been reported. This biofeedback, mediated by the virtual environment’s intermediate, is also called “neurofeedback” (Allanson & Mariani, 1999). We have developed the first biofeedback prototype of male and female sexual responses mediated by VR (Renaud et al., 2006c).

2



3



VR in clinical psychology dates back to 1992. It was introduced by the Human-Computer Interaction Group at Clark University (North, North, & Coble, 2002). Based on studies conducted by Norcross, Hedges, and Prochaska (2002), these approaches are among some of the most promising ones.

This work was previously published in Educational Gameplay and Simulation Environments: Case Studies and Lessons Learned, edited by David Kaufman and Louise Sauvé, pp. 231-251, copyright 2010 by Information Science Reference (an imprint of IGI Global).

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Chapter 8.6

Novel Data Interface for Evaluating Cardiovascular Outcomes in Women Amparo C. Villablanca University of California, Davis, USA Hassan Baxi University of California, Davis, USA Kent Anderson University of California, Davis, USA

ABSTRACT This chapter discusses critical success factors in the design, implementation, and utility of a new construct and interface for data transfer with broad applicability to clinical data set management. In the context of a data coordinating center for evaluating cardiovascular outcomes in high-risk women, we detail and provide a framework for bridging the gap between extensible markup language (XML) and XML schema definition file (XSD) in order to provide greater accessibility using visual basic for applications (VBA) and Excel. Applications and lessons learned are discussed in light of current challenges to healthcare information technology management and clinical data administration. The authors hope that this approach, as well as the logic utilized and implementation examples, will provide a user-friendly model for data management

and relational database design that is replicable, flexible, understandable, and has broad utility to research professionals in healthcare.

INTRODUCTION The transformation of clinical and translational science requires a visionary approach to management and sharing of information. This can best be accomplished by providing investigators with ubiquitous access to tools and processes that enhance the quality, availability, security, collection, and analysis of data. The informatics needs identified today for clinical and translational research will evolve and be rapidly augmented by new demands coupled to new capabilities. Therefore, the supporting information technology infrastructure needs to be flexible to respond to new challenges as they arise, and scalable to

DOI: 10.4018/978-1-60960-561-2.ch806

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Novel Data Interface for Evaluating Cardiovascular Outcomes in Women

accommodate increases in demand, type, and complexity of data. Researchers and practitioners conducting cutting edge investigation in healthcare information technology management and clinical data administration experience challenges in data and information management that are not confined to technology. Many of the obstacles pertain to ambiguous definition of terms, new concepts, processes, acronyms, and the plethora of research areas, issues, and trends that currently face information technology management and clinical data administration in the healthcare setting. Specifically, there is a need to better understand approaches, frameworks, and techniques for healthcare information technology management in order to develop and implement appropriate solutions that may have applicability to others, and to share lessons learned. The demand currently is for integrated systems of healthcare information. However, there is also a need for technologies and constructs that focus on the implementation, adaptation, development, and application of existing tools for clinical investigators, and their research teams, to facilitate the performance of small and medium sized research studies, specifically related to the collection, management, and security of human subjects study data. There are a limited number of resources which detail models, frameworks, or case studies on how to facilitate creating integrated healthcare solutions for small and medium sized clinical investigations. In this chapter we will discuss one such approach, and techniques for healthcare information technology management solutions, utilizing as a model an innovative custom Excel construct system for data exchange established for a clinical Data Coordinating Center (DCC) at the University of California, Davis. The University of California, Davis (UC Davis), Women’s Cardiovascular Medicine Program, is the Data Coordinating Center for a national joint database involving a total of 6 study investigation sites nationwide. The sites include four

academic health centers, one clinical practice center, and one mobile community-based healthcare delivery program. The study was funded by the U.S. Department of Health and Human Services (DHHS)-Office on Women’s Health (OWH) as part of an award entitled Improving, Enhancing and Evaluating Outcomes of Comprehensive Heart Care in High Risk Women. The purpose of the study was to evaluate cardiovascular disease prevention, knowledge, risk awareness and clinical outcomes in women at high risk for cardiovascular disease due to race, ethnicity, rurality, and/or cardiac risk factor profile, and to compare outcomes at 6 and 12 months of patient follow-up. The intervention involved patient education and applying and utilizing Evidence-Based Guideline for Heart Disease Prevention in Women published by the American Heart Association at the time the study was initiated (Mosca, 2004). Each participating site collected patient demographic, survey, and clinical data. A total of over 1,300 women participated in the study. The joint analysis database developed for the study consisted of a collection of de-identified patient-specific data from human subjects enrolled at each of the 6 participating study investigation sites during a period spanning from January, 2006 to September, 2007. The goal of the Data Coordinating Center (DCC) established and described below, was to manage the construct, design and implementation of the database, manage and coordinate data entry and upload to the database, create a joint database, attend to data validation, storage, integrity and security, secure access for statistical analysts and other project staff, and provide data backup, recovery and maintenance. The joint database was subsequently utilized for joint analysis of the project’s outcomes as follows: 8 required clinical/survey/demographic outcomes and/or sub outcomes, and 5 optional outcomes and/or sub outcomes. Outcomes defined parameters in the following areas:

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•

•

•

•

Knowledge and awareness (e.g., increase the proportion of women who are aware of the early warning symptoms and signs of a heart attack) Clinical (e.g., increase the proportion of women with diabetes who receive formal diabetes education) Cardiac intervention (e.g., increase the proportion of eligible women with heart attacks who receive percutaneous intervention (PCI) within 90 minutes of symptom onset) Laboratory (e.g., decrease the proportion of women with low high-density lipoprotein (HDL)-cholesterol)

In addition, some outcomes were relational and combinatorial (e.g., increase the proportion of women with coronary heart disease who have their low-density lipoprotein (LDL)-cholesterol level treated to a goal of < 100 mg/dl). Each study site submitted a total of 64 data variables to the joint database at each of the three study time points (baseline and at 6 and 12 months of clinical follow-up). The DCC project maintained high standards of subject confidentiality, data security and data accuracy with respect to all privileged patient information, while ensuring compliance with U.S. Health Insurance Portability and Accountability Act (HIPAA) pertaining to data repositories (U.S. Dpt.of Health and Human Services, 2004a), and other Federal regulations. In addition, data was gathered and archived using procedures that complied with each participating organization’s Institutional Review Board approvals, and maintained the confidentiality of the human subjects as per HIPAA guidelines pertaining to clinical research (U.S. Dpt. of Health and Human Services, 2004, & U.S. Dpt. of Health and Human Services, 2004b).

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Chapter Objectives We will utilize our experience in the development of a novel Excel interface construct for the Data Coordinating Center at the University of California, Davis as a model to illustrate the overall objectives for this chapter as follows: •

•

•

•

•

Offer an analysis of important issues, challenges, and trends in Healthcare Information Technology Management and Clinical Data Administration. Provide a programmatic approach and solution to common data transport and exchange dilemmas utilizing tools designed to overcome technological hurdles in information technology management. Provide a framework for bridging the gap between Extensible Markup Language (XML) and XML Schema Definition file (XSD) in order to provide greater accessibility using Visual Basic for Applications (VBA) and Excel. Provide an in-depth description of key terms and concepts related to the management, database design, implementation, data entry, validation, storage, data security, data backup and maintenance for a clinical multi-site database tracking cardiovascular outcomes in high-risk women. Discuss applications and lessons learned from our experience developing a Data Coordinating Center for evaluating cardiovascular disease prevention outcomes in high-risk women.

Current Challenges to Healthcare Information Technology Management and Clinical Data Administration Data Exchange. XML (http://www.w3.org/XML/ XML Schema Definition (XSD) is critical in information modeling data exchange. Data exchange in health care is fraught with complexities that do

Novel Data Interface for Evaluating Cardiovascular Outcomes in Women

not necessarily exist in other fields of study. While financial data is routinely exchanged between multiple institutions, individuals, and competing entities at a very high rate of security, the same is not true of health care information. There are many reasons as to why free exchange of healthcare research data has not become a reality including: • • • •

Restrictions imposed by HIPAA Ethical restrictions for releasing patient health information Lack of an agreed-upon semantic definition of the “healthcare” domain No “business incentive” to drive open exchange – return on investment is simply not there

In the case of pharmaceutical research, while the research results are submitted to the Food and Drug Administration (FDA) as part of the drug approval process, the data collected is largely proprietary. Releasing the raw data from a clinical trial for others to analyze is considered an intellectual property breach, and rarely occurs, despite the efforts of Clinical Data Interchange Standards Consortium (CDISC) to standardize the format by which clinical trial data could be captured and exchanged. CDISC is an open, multidisciplinary, non-profit organization that has established worldwide industry standards to support the electronic acquisition, exchange, submission and archiving of clinical trials data and metadata for medical and biopharmaceutical product development. The mission of CDISC is to develop and support global, platform-independent data standards that enable information system interoperability (Miller, P.) to improve medical research and related areas of healthcare. – CDISC website Mission statement (http://www.cdisc.org/about/index.html) But perhaps most importantly, is the impenetrability of healthcare data exchange tools available

to those in need of exchanging the data. Key issues posing barriers to health care data exchange pertain to accessibility, understandability, ease of use, and technical expertise needs. In addition, there are often real budgetary constraints. For instance, a significant portion of clinical research within academic health centers is conducted with budgets of limited scope, such that the affordability of information technology capabilities may be limited. The expertise required to determine whether CDISC, Health Level Seven (HL7) (http:// www.hl7.org/), Logical Observation Identifiers Names and Codes (LOINC®), or a combination of several data exchange standards should be employed for a given research study does not generally exist in such situations. In addition, the expertise, funding, and incentives required for developing a data capture mechanism that adheres to the aforementioned standards for data capture, storage, and exchange is therefore rarely included in studies with modest research budgets and of limited research scope. For the Women’s Cardiovascular Outcomes Study conducted at UC Davis, a common data schema had not been pre defined prior to the start of the study. As a result, the aggregation of data points on individual patients from multiple study investigation sites needed to be addressed. As in any project with evolving and not pre-determined data aggregation needs, flexibility in the data management process needed to be maintained. We recognized a certain amount of data clean up would be required to properly compare data from one site location to another. Additionally, the collection methodologies used to gather this data were to some degree specific to each institution, and therefore were not standard across all sites. This presented us with both challenges and an opportunity to develop a custom construct for data exchange. In order to avoid potential misinterpretation errors by performing the data clean up at UC Davis, the data validation portion of the data management was performed by each individual site. The data managers at each site were neces-

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sarily closest to their own data and were far better qualified to make the qualitative assessments of their respective datasets needed to transform the study data into a common database schema.

incremental step forward using existing tools, with the end goal of abstracting data modeling and permitting and optimizing data exchange.

Extensible Markup Language (XML)

DEVELOPMENT AND CONSTRUCT OF A NOVEL INFORMATION TECHNOLOGY MANAGEMENT SYSTEMS TAILORED TO INSTITUTIONAL CAPABILITIES

Additional challenges relate to the use of XML. A common platform for transferring health information between institutions under the circumstances detailed above is the Extensible Markup Language (XML) standard (World Wide Web Consortium (W3C) 1998 & World Wide Web Consortium), optionally extending the health care model focused Health Level Seven (HL7) data interchange standards for health data exchange (http://office. microsoft.com/en-us/excel/default.aspx, & http:// www.w3.org/XML/ XML Schema Definition (XSD)). The XML/HL7 data exchange mechanism accounts for a common database schema to be distributed across multiple investigation sites as an XML Schema Definition (XSD) file. The XSD was created to enforce data management rules that restrict the type, and therefore, quality of data prior to submission to a central source. The National Institutes of Health (NIH) employs this method for defining the format of annual progress reports submitted for the Clinical and Translational Science Centers (CTSCs), as well as other large grant programs. The problem with using XML and files for a small to medium size clinical study the scope of the Women’s Cardiovascular Health Outcomes study is that the technical expertise required to implement an XSD schema, populate it properly, and finally to generate a well-formed XML file and transfer back to the Data Coordinating Center simply did not exist uniformly within the study teams of each participating investigation site. We therefore opted for a more attainable goal that employs much the same concepts as the XSD/XML data exchange design, but with certain important adjustments for improving accessibility to nontechnical staff. This new construct represents an

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One of the major problems in the translation of basic research findings into clinical application is lack of tools and robust processes for implementation of single or multi-site small and medium size clinical and/or translational research studies at academic medical centers and their partners. The data management requirements and challenges of clinical and translational research are complex and unique (Holm, 1999; Lusignan, 2005; & Pipino, 2002). Clinical investigators frequently must separately interact with multiple institutional boards and committees prior to the implementation of their research studies. In many cases they must also reformat and/or restructure their written materials to conform to separate requirements of each committee, draft data sharing agreements, and develop data transfer methodologies. The past 20 years have shown significant advancement in technology, yet many applications of those advancements have yet to be efficiently executed in a clinical research setting. In the matter of clinical research data collection, the complexities of data standardization, validation, security, and transfer are often left up to administrative or clinical staff that may be ill-equipped to properly address these deceptively complex tasks. These tasks are reasonably manageable when scope is constrained to a single site and study. When multiple investigation sites are involved, and shared data analysis is required, data management is best handled by professionals with the technical expertise to mitigate the known and sometimes unforeseen hazards of managing sensitive research

Novel Data Interface for Evaluating Cardiovascular Outcomes in Women

data. To complicate matters further, there is not yet a commonly accessible standard for managing such an effort. There seems to be an accessibility gap between the “proper” method to collect and transfer clinical research data, and the methods most commonly used. While significant resources do exist for some types of data transfer or sharing (Jordan, 2006; Musen, 1992; & Wen-Shan, 2007), the ability to integrate the various sources of clinical, laboratory, demographic survey, and/or genetic data is a significant problem for many individuals or small research teams. Collaboration with investigators at other sites can also problematic for investigators who are not part of a large network or for studies that are small or investigator initiated. We herein present a model that attains a balance between the requirements of the stakeholders in small to medium sized clinical studies, and the accessibility limitations that exist in healthcare data management today. In so doing, we developed and identified a novel approach for data file upload and for implementation of solutions using a novel Excel construct. The Excel construct interface we developed offers a unique opportunity for a collaboration of database architects, informaticists, computer scientists, clinician researchers, clinical research coordinators, and health policy advisors to develop, adapt, adopt, or implement flexible solutions that could have a significant impact on the ability of clinical and translational researchers to contribute knowledge to advance treatments. Adoption of standards, best practices, technology, and a commitment to interoperability (i.e., the ability of two or more systems or components to exchange information and to use the information that has been exchanged) (Miller, P. & Wen-Shan, 2007) in focused areas could have a major impact in helping to catapult information technology to be responsive to the current needs of clinical research scientists and their teams.

Logic and Significance of the Excel/VBA Approach Utilized The approach we utilized to collect semi-structured information from multiple participating institutions in the Women’s Cardiovascular Outcomes Study was intended to be as simple as possible for the end users. The goal of detailing our approach in this chapter is to provide a framework for a novel informatics tools to clinical investigators and clinical research teams at and across institutions to enhance the collection and management of data in small and medium sized single and multi-site studies, while ensuring the accuracy, quality, confidentiality, and security of the data. Facilitating sharing of the tools and the research data (as appropriate) are additional overall goals. To this end, the goals of the DCC project were specified, as follows: • •

•

To aggregate similar, but non-identical datasets form several institutions. To minimize or eliminate the amount of subjective assessment on the part of our coordinating center in combining data sets. To lower the threshold of technical knowledge required at each institution to complete the task.

Under many circumstances the transfer of such health information could be done in simple Excel spread sheets, with a small relational database such as Microsoft Access (http://office.microsoft. com/en-us/access/default.asp) or with the more portable comma-delimited text file format. Excel is a spreadsheet application written and distributed by Microsoft (http://office.microsoft.com/en-us/ excel/default.aspx). It features calculation, graphing tools, pivot tables and a macro programming language called VBA (Visual Basic for Applications). The comma-separated values (CSV) file format, also known as a comma-separated list or comma-separated variables, is a file type that stores tabular data. More robust approaches might

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include making use of the HL7 data exchange standard, or optionally using a customized XML format. Exchange criteria could also be designed that complied with the CDISC standards for clinical data exchange. In the circumstance of the Women’s Cardiovascular Outcomes study, we chose to employ an approach that leveraged both the expert knowledge of the DCC director, study co-principal investigators, and the ubiquitous familiarity of the Microsoft Office suite of applications. We selected Microsoft Excel, a tool that we could reasonably assume would be available at each institution and that required little, if any, training to use. We then identified the risk factors that would likely limit our ability to collect quality data and systematically addressed each topic. The primary concerns addressed were: •

•

•

Lack of pre-defined rules for data collection. Each institution was given the flexibility to determine their own data collection forms and data management systems. Rules were developed that were unambiguous to all involved. This was true for the purpose of the participating investigation sites, and for creating the data transfer code. Enforcement of rules was not to be done within the DCC. The participating institutions (i.e., those closest to the data) would be responsible for submitting “clean” data.

The basic principles of this approach were designed to counter our perceived obstacles to receiving quality data. The first obstacle, the lack of pre-defined rules for data collection, was addressed by a panel of Women’s Cardiovascular Outcomes study investigators, led by the UC Davis DCC Director. Through a series of conference calls, the investigators jointly decided on a standard set of data points that would ultimately be conveyed to the Data Coordinating Center. Perhaps most importantly, the investigators decided upon a mutually

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agreed upon definition and delimiting of each data point in the data set described. Ideally, such definitions could be tied to a standard reference model for this area of study. However, this was beyond the scope of this project. Nonetheless, a series of rules were developed. A component of the rules was allowable filtered data values. The output of these meetings was a data upload template spreadsheet that described the rules of the data transfer application, see Figure 1A. In the figure, column headings display fields allowed. The actual description of the rules, however, is hidden. The spreadsheet of rules was then converted into a series of rules (as described in Figure 1A). As development continued on the data transfer tool, the data definition process was iteratively revisited to account for nuanced discrepancies within the data sets of each institution. This iterative cycle was critical to the success of the Data Coordinating Center effort in that all data outliers could be accommodated retrospectively, and prior to final data submission to the DCC.(See Figure 1)

New Use of Excel Construct for Data Transfer within Healthcare Data Management Although Excel has been used to transfer health information for many years, Excel is poorly suited to manage health information without significant customization. Originally designed to benefit home finance calculations, Excel does not consider the rigors required by the federal HIPAA standards, Part 11, of Title 21 of the Code of Federal Regulations; Electronic Records; Electronic Signatures (21CFR Part 11) guidelines for health data systems (http://www.fda.gov/cdrh/ comp/guidance/938.html & U.S. Food and Drug Administration, 2003), and basic usability of a standardized form-entry interface. As a health data collection tool, Excel has limitations. FDA requirements for electronic data storage (U.S. Food and Drug Administration, 2003) will not be met if Excel is used for data capture and storage,

Novel Data Interface for Evaluating Cardiovascular Outcomes in Women

Figure 1. Date upload template spreadsheet (1A) and data entry and validation (1B)

due to Excel’s inherent inability to audit data in individual fields. Because formatting changes to cells, such as alignment and font, do not have to be audit trailed, an ideal audit trail for Excel would capture changes to both the data and to formulas (http://21cfrpart11.com/pages/faq/index.htm). Therefore, to use Excel effectively, it was determined that we would need to constrain the flexibility of the spreadsheet, and enforce both the institutional investigators’ rules and the relational structure envisioned for the DCC database for the demographic, survey and clinical data variables. Essentially we implemented a series of complex clinical rules and data-associated constraints within Excel to enforce referential and qualitative integrity within a series of semi-structured data sets. Thus, what is generally considered a technically challenging task became accessible to a much broader audience. The clinical research coordinators and data managers on each project team were able to manipulate the data they collected using an intuitive and transparent tool in

preparation for upload and transfer to the Data Coordinating Center, without requiring assistance from IT personnel. Database management incorporates technologies that support rules-based handling and processing of information. Accordingly, rules were developed to counter any of the most likely errors on the part of the participating investigating institutions. However, we determined that our data management counterparts at the participating investigation sites would remain responsible for the submitted data, and therefore we did not see a need to further protect the Excel application from data management best practices until the point of data transfer. However, to additionally ensure the quality of the data submitted, we opted to run further validation checks during our custom extract, transform and load (ETL) process that would catch any unintentional outliers in the data with types of notification and level of check (data validation, range checks, x- field and x-form validation, etc.).

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Advantage of the Excel Interface Developed The approach we selected to employ leveraged the availability and existing knowledge of Excel, but reduced or eliminated the risk that generally accompanies its use in clinical data management (e.g., no enforcement restrictions, unbound data, omission errors, potential invalid data entry, etc.). The novel Excel/XML database interface construct developed is illustrated schematically in Figure 2. The figure illustrates the features of Excel and XML that were combined and married together to create the novel Excel interface construct described in this chapter.

DESCRIPTION OF SYSTEM (DATABASE ARCHITECTURE) AND FRAMEWORK Relational Database Design, Data Schema and Database Normalization The database design process plays an extremely important role in order for a database to be implemented effectively, accurately and efficiently. Identifying the tables needed, the fields in the

tables (including unique/key fields), and the relationships between the table, are some of the basic steps involved in database design (Date, 1975). The goal is to design a simple, normalized, and relational database. The overall approach to the design of the database utilized for the Women’s Cardiovascular Outcomes DCC is illustrated in the Database Construction Schematic Diagram detailed in Figure 3. The process of identifying and subsequently reducing or eliminating any data redundancies is called database normalization (Codd, 1971). Database normalization involves a series of steps to be followed to obtain a database design that allows for efficient access and storage of data in a relational database. When a database is normalized you are optimizing the tables, protecting the database against data anomalies, and minimizing the chances of data inconsistency in the database’s schema. The Data Coordinating Center’s joint database was submitted to these procedures.

Data Capture, Flow, and Transfer Data collection at each study investigation site was undertaken by following best practices. As previously mentioned, the methodology for data capture by each site was not pre-defined.

Figure 2. Schematic of novel custom Excel interface construct for data exchange

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Figure 3. Process and timeline used to construct a joint database

Similarly, investigation sites were free to make use of any desired data management program (e.g., Excel, Access, etc.) they wished. Sites also individually decided how they would upload their data to the DCC- e.g. copy and paste column by column from their data sheet(s) to the DCC data upload template, upload data in blocks, or other methodology. However, the mechanism for data transfer and data flow was uniform, and consisted of the new Excel construct described in Figure 2 and FTP described in Figure 3, together.

Definition of Data Dictionary This chapter describes the role of the Coordinating Center for a six-site program focusing on prevention and treatment of cardiovascular disease in women. The program was conducted at different types of health-care delivery sites having diverse

populations of women at high risk for cardiovascular disease. Fundamentally, the Center’s functions hinged on creating a joint database for analysis for the overall project. The Coordinating Center helped to develop a database committee structure within the program, established a communications and data transmission internet interface, arranged conference calls, committee meetings, and paper writing groups. To these ends, the Coordinating Center also identified opportunities for collaboration, encouraged communication among the project investigators, and facilitated the adoption of common measures. The most pivotal common measure determined by the Coordinating Center pertained to the data dictionary. The data dictionary contained a list of each of the study outcomes and sub outcomes, and a definition for each study outcome and sub outcome. In some instances these were provided by the grantor (e.g.

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hypertension= systolic blood pressure > 140 mm Hg and/or diastolic blood pressure > 90 mmHg), and in others the outcomes and definitions were mutually agreed upon in conference calls with the participating study investigation sites. For example, the sites mutually agreed to the following definitions and valid ranges for each of the following study outcomes listed: •

•

•

•

Age: Determined the age of participating study subjects as age at entry into the study (MM/YYYY). Race/Ethnicity: Defined Race/Ethnicity as 1: Caucasian (non-Hispanic), 2: Black (non-Hispanic), 3: Hispanic, 4: Asian/ Pacific Islander, 5: American Indians/ Alaska Natives, 6: Other, and 999: Unknown/Missing. Exercise: Defined exercise as moderate exercise at least 30 minutes per day by 1: Yes, 2: No, and 999 Unknown/Missing; and Body mass index (BMI): Determined valid range to be a BMI of 17-54.

A data dictionary describing the logical structure of the database was then created. Table 1 provides additional examples of the data diction-

ary we utilized for defining clinical and survey outcome study variables. The data dictionary included descriptions of variables, and the respective normative range values. The data dictionary also defined the relationships between the variables and outcomes for demographic, clinical and survey data. The same data dictionary developed for the relational database was then used to define the variables for the analysis of each corresponding study outcome.

Process for Data Gathering Requirements, Implementing Requirements, and Training of User The data gathering component of a database project is very crucial as it is essential that the database be populated with accurate and current information. The information we needed to include in the database for the DCC were only those fields/ variables pertaining to the desired outcomes. The data dictionary itself also helps in designing the database structure, detailing the information being collected and stored in the tables. It is essential that design processes, procedures, and tools be put into place to help ensure that the data is kept current and accurate.

Table 1. Data dictionary Range/Value

Format

PatientID

Unique Identifier

Alphanumeric (25)

VisitTimepoint (Visit Timepoint?)

1: Baseline 2: 6 Months 3: 12 Months

Alphanumeric (1)

EduPhysicalActivity (Received Education on Physical Activity?)

1: Yes 2: No 999: Unknown/Missing

Alphanumeric (3)

EduDiabetes (Received Education on Diabetes?)

1: Yes 2: No 999: Unknown/Missing NA: Not Applicable

Alphanumeric (3)

TG (Serum Triglycerides in mg/dl)

10 to 1000, 999

Number (Decimal)

WC (Waist Circumference in inches)

18 to 60, 999

Number (Decimal)

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Data Entry and Validation A draft version of the Excel spreadsheets were made available online for all investigation sites during a conference call, for live training purposes, and to demonstrate how to enter data into the Excel spreadsheets. Any concerns with range checks, data input, etc., expressed during the conference call, were immediately addressed by making the necessary changes to the Excel template. The actual process of data entry is a simple cut/copy and paste of each site’s data onto the templates. Users extract (copy) data from their sources and paste it onto the appropriately labeled columns in Excel. The user-friendly data entry forms had integrated range checking, logic checks, and structural checks. Microsoft Excel was chosen as the data entry tool, because of its ease of use and availability. Excel is a user-friendly software easily learned by users familiar with data processing systems. All six investigation sites involved in our DCC were familiar with the use of Excel. Thus, two custom designed easy to use spreadsheet templates for data entry were created in Excel. One spreadsheet was used for entering variables that were to be collected only once (e.g., Demographics). The other spreadsheet was for entering Clinical/ Survey variables that were collected over several study time points. In addition to performing validation checks across columns, cross spreadsheet validation checks were also implemented. The cross-spreadsheet validations were implemented to ensure that each Clinical/Survey patient row in one spreadsheet had a corresponding Patient Demographic row in the other spreadsheet. Data validation also included duplicate entry checks, range, and logical checking. A user guide was made available to all users with thorough details on how to enter data, execute the built-in validation checks, and interpret the various error messages. Screenshots and color coding of fields were included to facilitate use, see Figure 1B.

Data validation is essentially a combination of length, range, format and type checks. Range checks are implemented to verify that data falls between specified lower and upper boundaries, e.g. ranges within dates, pairs of numbers, alphanumeric characters, etc. An example of the data entry and validation fields developed for our DCC is illustrated in Figure 1 B. In the example provided in the figure, range check for “Age at entry into Study” must validate that the age is between 20 and 90 years of age. Numbers less than 20 and greater than 90 will be rejected. Data validation for length ensures that the correct number of characters have been entered into a data entry field. A minimum and maximum length of the field can be specified. For example, “Month and Year of Birth” must have at least 6 or a maximum of 7 characters in it e.g. “1/1956” or “12/1961”. If less than 6 or more than 7 characters have been entered, a validation error message will be generated. Format and type checks were also in place to ensure that each input-data item did not contain invalid characters. For example, a format and type check on the “Month and Year of Birth” data item will check to ensure that only numbers or a “/” (forward slash) character are accepted. Buttons were enabled in the template and clicking on these buttons executed logic and input validation checks on the data. The logic checks program alerted users to problems in the data, if any existed, by highlighting the appropriate cell entries. Cross spreadsheet validation checks were developed to confirm that a data record in the Demographics data spreadsheet has at least one corresponding matching key field entry in the spreadsheet with Clinical entries. All programming for input validation was done using the VBA (Visual Basic for Applications) tool, which is built into Microsoft Excel. An example VBA code utilized for the DCC is demonstrated in Figure 4. Once UC Davis received the data, further checks were conducted on the data received. It was important to ensure that data received was

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Figure 4. VBA code example

correct and accurate. A brief summary report was submitted to each site, highlighting potential problems with data received. The summary report included the total number of records uploaded for each time point (baseline, 6 months, and 12 months), missing responses if any, and the frequency of uploads for each outcome at each time point. Site-specific frequency for each categorical variable and mean and range for each continuous variable was also provided to the investigation sites. SQL (Structured Query Language) (Chamberlin, 1974) queries were used to produce a statistical summary of the data received. SQL is a standard interactive and programming language for querying and modifying data and managing databases. An SQL query is one of the most common operations in SQL databases and helps users retrieve specific data from a table, or a group of related tables. Validation checks similar to the ones run during input validation were executed again on the data to comprehensively ensure the validity of the data received. If any problems with the data received were identified, vis-à-vis duplicate entries, out of range data, invalid date, missing or blank data, etc., then the investigation site was notified of the corrections that needed to be made so that the data could then be corrected and re-transmitted. After all data received had been validated and checked for accuracy, the

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data was imported into a joint database, hosted on Microsoft SQL Server. The joint database was then considered to be final and ready for statistical analysis to be performed. All study required and optional outcomes were then analyzed and stratified for all of the required demographics and clinical variables.

Database Security and Maintenance File Transfer Protocol (FTP) (Postel, 1985) is a simple way of exchanging data between two computers over the internet or using a network. A secure FTP (SFTP) provides encryption to ensure confidentiality and integrity of the data and enabled investigation sites in our Data Coordinating Center to transmit their data securely. WinSCP, a free windows client with SFTP capability, was used for this purpose. The main function of WinSCP is to provide secure file transfer between a local and remote computer. Database maintenance is essentially a series of manual or automated tasks performed to keep the database updated, backed up, and secure. As part of the database maintenance process, the entire database was backed up daily. Backing up the database essentially created a snapshot of the database and in case of an emergency, the database administrator could choose to restore a previously backed up database or individual files.

Novel Data Interface for Evaluating Cardiovascular Outcomes in Women

Also, maintenance tasks (such as periodically compacting the database tables) ensured that the tables took up less space and helped database operations run more quickly.

ADVANTAGES, ALTERNATIVES, AND LIMITATIONS Advantages The approach we selected for this project was intended to employ the simplest method available to both the DCC and the “client” investigation sites. We selected Microsoft Excel as the data exchange platform because we wanted to divorce the complexity of the exchange mechanism from the data itself, yet we needed to provide a familiar interface by which to collect the data. Each investigation site was required to make interpretations about their data in order to accommodate the exchange standards determined by the Principal Investigator’s working group. This required staff from each investigator site to manually convert individual data points from their local point-of-care proprietary format into the agreed-upon standard format. In many cases, this data conversion was straightforward and required little specialized knowledge of the clinical interactions this data represented. However, no dataset was identical between any of the six investigation sites, and each set of conversion circumstances was completely unique across the consortium. In order for each site to reliably convert its data to the new standard format without invalidating the integrity of the aggregated dataset, we needed to ensure that the data transfer mechanism would not be a barrier to this conversion process. Transparency, simplicity, and visualization of the data exchange format were of primary importance in this effort. Excel offered exactly this benefit, provided the data structure could be enforced at each contributing site, where the details of local point-of-care data assumptions

and the limits of available interpretations were best understood. We assumed that there would be very little training required of the end users to use the Excel application, and indeed little was required. The rules we enforced were intended to be self-explanatory and therefore self-reconcilable. In doing so, we effectively implemented features that will be desirable for next-generation data exchange, but in a scaled down framework.

Alternatives There exist several options for post-collection data exchange. These options each have disadvantages that could be characterized by the following criteria: • •

Too flexible in accepting data and loose in defining rules Too technically complex for a non-IT professional to manage or implement effectively

A simple delimited text file is an example of a data transfer mechanism that offers little guarantee of built-in compliance with defined parameters. Such a file can be evaluated for its structure, for example whether the amount and location of delimiters is accurate, but little else can be validated on such a file without an additional validation tool. Similarly, a flat Excel file (without custom VBA validation included) offers little more than a delimited text file. Excel has a robust and userfriendly graphical user interface, but is essentially the same as a delimited text file. In fact, most delimited text files can be easily opened, edited and saved through the Excel interface. But one significant limitation of Excel is that it is so flexible in how it accepts data, that the integrity of a large data set can be corrupted with errant data if no custom validation is used. XML using XSD for validation is widely recognized as a robust and flexible data exchange

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platform. Many healthcare data systems routinely exchange data in XML format as recommended by the Health Level Seven (HL7) standards development organization. Tools such as Microsoft InfoPath (http://office.microsoft.com/en-us/infopath/FX100487661033.aspx), Altova’s XMLSpy (http://www.altova.com/simpledownload2.ht ml?gclid=CMzMu9T9rpECFRk0awodLk5Uew), and others exist to simplify the development of XML data structures, and provide an interface that allows users to define reasonably complex rules for just this purpose. The XML/XSD alternative is indeed a more scalable approach to solving the problem of data transfer. However, the use of XML is not commonplace in individual clinical research studies, especially where clinical research coordinators are the primary handlers of data, nor is it practical to expect research coordinators to master the complexities inherent in XML/XSD data exchange for the purpose of a single, or even several studies. In today’s age of internet data collection tools, all investigation sites in the study could have utilized the same electronic data collection forms with embedded validation logic that interfaced with a central data repository. However, it is not always practical to require real-time data entry in a clinical setting at the point of patient contact and care. Nor is it practical to expect patients to enter their survey information electronically, convenient though it may be to the study investigators. Further barriers to this ideal approach include the cost and expertise required to design, build, maintain, and support such complex distributable logic systems. Such software tools and services are indeed available in the clinical research domain, but the cost of employing any such service may be prohibitive for a research study (Kush, 2007, & Shah, 2005). Therefore often, study data may be initially collected in paper format (e.g., surveys), and the data later transcribed to an electronic system for analysis or transfer. Ideally this transfer is automated to improve efficiency and workflows, and reduce errors associated with multiple iterations of

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data entry and transcription. Therefore, a system that is both simple and permits protections of data integrity, is an ideal alternative. The construct described herein is one such model.

Limitations One limitation to the approach we describe, and one that is accounted for in great detail within the XML framework, is that when alterations were made to the schema definition, it required a programmer to make changes to the underlying validation code, test the changes, and then redistribute the new version of the application to each investigation site to replace the previous version. Ideally such rules could be extracted from the transmission framework, as indeed they are when an XSD file is used. Should it be possible to locate the VBA code that was embedded in each data file on a centrally accessible Web server, our application framework would be greatly improved. Another limitation of our construct was that although it handled simple data structures well, the relational nodes of cardinality (i.e., relational data tables) were limited.

Data Harmonization vs. Data Standardization Data harmonization between disparate data sets that occurs after data collection might also be referred to as “data salvage”. When similar points of data are collected at different locations without a common plan for semantic inter-operability (Wen-Shan, 2007) (i.e., deriving common meaning from different data sets), the result is that one or all sets of data must be altered in some fashion to be incorporated into that collective analysis. In the harmonization process, some measure of the integrity of the data is inevitably lost, and the semantic interoperability threatened. Such is frequently the case when compiling disparate data sets for clinical research.

Novel Data Interface for Evaluating Cardiovascular Outcomes in Women

Data standardization, on the other hand, is best expressed prior to data capture to avoid the complications of data harmonization. The Clinical Data Interchange Standards Consortium (CDISC) and HL7 are actively pursuing development of standards for clinical data exchange intended to minimize such data management complications. One result of this effort, the Clinical Document Architecture (CDA) Standard (Dolin, 2001 & Dolin, 2006) offers one possible vehicle by which standardized clinical data exchange can be employed. The HL7 CDA is an XML-based markup standard intended to specify the encoding, structure and semantics of clinical documents for exchange. In the case of the Women’s Cardiovascular Outcomes project, a standard data model would have required all investigation sites to adhere to specified data collection methods. However, in the case of the Women’s Cardiovascular Outcomes project data collection methodologies were not predetermined, but rather evolved during the process of data collection. This provided flexibility to each site. Furthermore, our approach allowed the harmonization of data, based on previously agreed upon standards, to be assessed at the local point-of-care level (i.e. each site), and not at the level of the Data Coordinating Center.

Post Coordination of Data An abstract data model was not developed for this effort, although one could improve upon the employed effort in this way for more complicated data sets. The functions of the DCC were such that three tables could suffice in Excel, and therefore we determined a more rigorous framework was unnecessary. Should one decide to employ a more complicated data exchange application than was developed here, a more formal data structure could be developed to represent the domain of interest. Such a domain model could be developed in a topic map application such as Topic Maps 4 e-Learning (tm4l; http://compsci.wssu.edu/iis/nsdl/) or in an ontology development tool (Cimino, 2006) such

as Protégé (http://protege.stanford.edu/)], a free open source ontology editor and knowledge-base framework (http://protege.stanford.edu/). Protégé ontologies can be exported into a variety of formats, including XML schema.

APPLICATIONS There are a number of potential applications to the Excel/XML data transfer interface construct described herein. These include applicability to information technology, clinical and health care settings, data coordinating centers, and educational venues as follows:

Information Technology (IT) Applications From an IT perspective, Excel is relatively inexpensive and simple to use compared to many other database programs. Excel is an excellent tool to chart data and it includes many features that can easily be used to create customized data analysis solutions (e.g., editing, formulas, formatting, etc). It is therefore a good tool to import data from various formats, notably XML, and subsequently analyze and share data. Also, because of its relative ease of use, a customized Excel spreadsheet database is an ideal choice to enter, store and analyze data sets that are relatively small (approximately 64,000 row entries or less).

Clinical Applications We have described and defined critical success factors in the design, implementation, and utility of a new construct for data transfer utilizing Excel with broad applicability to clinical data set management. These include utility for: (a) multiple clinical and/or population research end-users irrespective of specialty or type of study, (b) collaborative activities between end users, informaticists, and institutional IT support

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services, (c) maintaining active engagement of clinical scientists and a research team throughout a project extending over time, and (d) electronic transfer of clinical and laboratory data for data exchange that minimizes human data entry errors and reduces investigator burden.

Education and Knowledge Exchange Applications The data exchange interface and construct model developed and described in this chapter instructs on a new technology that may be used in a variety of data management settings. In addition, the construct serves as a building block for higher throughput data exchange systems, and as a training/educational tool for IT personnel and professionals.

Data Coordinating Centers and CTSCs In the context of a Data Coordinating Center for small to medium sized data sets, the novel custom construct we describe has the advantage of providing the same electronic interface to multiple users. In addition, it provides for a central location for data repository, utilizing agreed upon standard(s) for multiple investigation sites participating in a DCC. With the advent of the expansion of clinical and translational sciences, our construct has potential immediate applicability and utility to Clinical and Translational Science Centers (CTSCs) where a well functioning and integrated biomedical informatics program is a crucial element in realizing support for all aspects of clinical and translational research, including efficient dissemination of research advances. Effective management of knowledge resources is essential to advancing the pursuit of new research findings in clinical and translational research. Therefore, a well defined data exchange construct may be an important element to meet the data collection, processing, and sharing needs of a CTSC.

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LESSONS LEARNED Approach to Relational Tables in Excel for Data Validation Although, by using Excel it is possible to create a customized solution for a simple set of relational tables, Excel is not a database management system. Hence, utilizing a database management system such as Access, MS SQL Server, FileMaker Pro (www.filemaker.com/products/fmp/index.html), etc., is an easier and far superior way of managing data. Relational tables can be represented as linked spreadsheets in Excel, but this approach has its limitations which are non-existent when using a database program to handle relational tables, wherein the different tables can easily be linked together and presented visually to the user as one comprehensive table.

Method to Extract Data from Excel to XML File and Translation of Logic There are a variety of commercial products, or VBA modules, available in the market to assist in converting Excel data to XML. VBA programs, or macros, can be written or used to export Excel data into unformatted XML data. In most cases, when converting a spreadsheet into XML, not all of the features and attributes are retained, therefore the resulting XML document is not quite what is expected as there is not corresponding translating definition and structure. If the fields in Excel are laid out differently, with clear and concise labels, then the resulting table structure in XML will be well formed and without structural errors.

Enforcing Referential Integrity of Excel Validation Modules Ensuring data integrity is one of the important factors to consider during database design. A relational database design model enforces referential integrity. Cross-spreadsheet validation

Novel Data Interface for Evaluating Cardiovascular Outcomes in Women

modules were implemented in our model to ensure that each Clinical/Survey patient data row in one spreadsheet had a corresponding Patient Demographic data row in the other spreadsheet. These cross-spreadsheet validation modules enforced referential integrity between the spreadsheets.

Chamberlin, D. D., & Boyce, R. F. (1974). SEQUEL (SQL): A Structured English Query Language. Paper presented at the Proceedings of the 1974 Association for Computing Machinery SIGFIDET Workshop on Data Description, Access, and Control, Ann Arbor, MI.

CONCLUSION

Cimino, J. J., & Zhu, X. (2006). The practical impact of ontologies on biomedical informatics. Methods of Information in Medicine, 45(1), 124–135.

During the last two decades, advances in IT, particularly in database, internet, and telecommunication technologies, have permitted a revolution in healthcare and health research data management. These advances have enabled clinicians and researches alike to have unprecedented access to clinical data. This trend has spurred the growing importance of the role of health information and the influence of fields such as bioinformatics, biomedical and genetic engineering. We provide a discussion of critical success factors in the design, implementation, and utility of a new custom construct for data transfer and exchange utilizing Excel, with broad applicability to clinical data set management.

ACKNOWLEDGMENT This work was made possible by a grant to AV from the United States Department of Health and Human Services Office on Women and Health (1 HHCWH050005-01-00) and by the University of California, Davis Clinical and Translational Science Center (CTSC) by Grant Number UL1 RR024146 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research.

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Codd, E.F. (1971). Normalized database structure: A brief tutorial (IBM Res. Rep. RJ935). Date, C. J. (1975). An introduction to database systems (first ed.). Addison Wesley. Dolin, R. H., Alschuler, L., Beebe, C., Biron, P. V., Boyer, S. L., & Essin, D. (2001). The HL7 clinical document architecture (CDA). Journal of the American Medical Informatics Association, 8, 552–569. Dolin, R. H., Alschuler, L., Boyer, S., Beebe, C., Behlen, F. M., & Biron, P. V. (2006). HL7 clinical document architecture (CDA), release 2. Journal of the American Medical Informatics Association, 13, 30–39. doi:10.1197/jamia.M1888 Excel. Retrieved on February 11, 2008, from http:// office.microsoft.com/en-us/excel/default.aspx Extensible Markup Language-XML Schema. World Wide Web consortium (W3C). Retrieved on February 11, 2008, from http://www.w3.org/XML/ XML Schema Definition (XSD) General Principles of Software Validation. (2002). Final guidance for industry and FDA staff. FDA, Center for Devices and Radiological Health, Center for Biologics Evaluation and Research. Retrieved from http://www.fda.gov/cdrh/comp/ guidance/938.html Health level seven (HL7) standards organization. Accessed on February 11, 2008, from http://www. hl7.org/

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Holm, M. B., Rogers, J. C., Burgio, L. D., & McDowell, B. J. (1999). Observational data collection using computer and manual methods: Which informs best? Topics in Health Information Management, 19, 15–25. Jordan, K., Porcheret, M., Kadam, U. T., & Croft, P. (2006). Use of general practice consultation databases in rheumatology research. Rheumatol., 45, 126–128. doi:10.1093/rheumatology/kei265 Kush, R., Alschuler, L., Ruggeri, R., Cassells, S., Gupta, N., & Bain, L. (2007). Implementing single source: The STARBRITE proof-of-concept study. Journal of the American Medical Informatics Association, 14(5), 662–673. doi:10.1197/ jamia.M2157 Lusignan, S., & van Weel, C. (2005). The use of routinely collected computer data for research in primary care: Opportunities and challenges. Family Practice, 23, 253–263. doi:10.1093/ fampra/cmi106 Microsoft® Visual Basic for Applications (VBA) code. Retrieved on February 11, 2008, from http:// msdn2.microsoft.com/en-us/isv/bb190540.aspx Miller, P. Interoperability what is it and why should I want it? Accessed on February 11, 2008, from http://www.ariadne.ac.uk/issue24/interoperability/intro.html Mosca, L., Appel, L. J., Benjamin, E. J., Berra, K., Chandra-Strobos, N., & Fabunmi, R. P. (2004). Evidence-based guidelines for cardiovascular disease prevention in women- American Heart Association. Circulation, 109(5), 672–693. doi:10.1161/01.CIR.0000114834.85476.81 Musen, M. A., Carlson, R. W., Fagan, L. M., Deresinski, S. C., & Shortliffe, E. H. (1992). T-HELPER: Automated support for communitybased clinical research. Proceedings of the Annual Symposium on Computer Applications in Medical Care, 719–723.

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Pipino, L. L., Lee, Y. W., & Wang, R. Y. (2002). Data quality assessment. Communications of the ACM, 45, 211–218. doi:10.1145/505999.506010 Postel, J. B., & Reynolds, J. K. (1985). File transfer protocol (FTP). Internet RFC 959. Retrieved on February 11, 2008, from http://tools.ietf.org/ html/rfc959 Protege ontology editor [Electronic Version]. Retrieved on February 11, 2008, from http:// protege.stanford.edu/ Shah, M. R., Claise, K. A., Bowers, M. T., Bhapkar, M., Little, J., & Nohria, A. (2005). Testing new targets of therapy in advanced heart failure: The design and rationale of the strategies for tailoring advanced heart failure regimens in the outpatient setting: Brain natriuretic peptide vs. the clinical congestion score E (STARBRITE) trial. American Heart Journal, 150, 893–898. doi:10.1016/j. ahj.2005.01.003 U.S. Department of Health and Human Services. (2004). Clinical research and the HIPAA privacy rule [Electronic Version]. NIH publication number 04-5495, pp.1-16. Retrieved on February 11, 2008, from http://privacyruleandresearch.nih.gov/ clin_research.asp U.S. Department of Health and Human Services. (2004a). Clinical research and the HIPAA privacy rule [Electronic Version]. Retrieved on February 11, 2008, from http://privacyruleandresearch.nih. gov/pdf/clin_research.pdf U.S. Department of Health and Human Services. (2004b). Research repositories, databases, and the HIPAA privacy rule [Electronic Version]. NIH publication number 05-5489, pp.1-12. Retrieved on February 11, 2008, from http:// privacyruleandresearch.nih.gov/pdf/research_repositories_final.pdf

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U.S. Food and Drug Administration. (2003). Guidance for industry part 11, electronic records; electronic signatures-scope and application [Electronic Version]. Retrieved on February 11, 2008, from http://www.fda.gov/Cder/guidance/5667fnl. pdf Wen-Shan, J., Chien-Yeh, H., Te-Hui, H., HsyienChia, W., Min-Huei, H., & Yen-Liang, L. (2007). Building a portable data and information interoperability infrastructure—framework for a standard Taiwan electronic medical record template. Computer Methods and Programs in Biomedicine, 88, 102–111. doi:10.1016/j.cmpb.2007.07.014

World Wide Web Consortium. (W3C). (1998, February 10). Extensible markup language (XML), 1.0. Recommendation. Retrieved on February 11, 2008, from http://www.w3c.org/TR/REC-xml/ World Wide Web Consortium. (W3C). Extensible markup language (XML). Retrieved on February 11, 2008, from http://www.w3.org/XML/Schema Health Insurance Portability and Accountability Act (HIPAA)

This work was previously published in Handbook of Research on Information Technology Management and Clinical Data Administration in Healthcare, edited by Ashish N. Dwivedi, pp. 34-53, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 8.7

New Developments in Intracoronary Ultrasound Processing Christos V. Bourantas Michailideion Cardiology Center, Greece & University of Hull, UK Katerina K. Naka Michailideion Cardiology Center, Greece Dimitrios I. Fotiadis Michailideion Cardiology Center, Greece Lampros K. Michalis Michailideion Cardiology Center, Greece

ABSTRACT Intracoronary Ultrasound (ICUS) imaging is an intravascular catheter-based technique which provides real-time, high resolution, cross-sectional images of coronary arteries. In these images the lumen, the media-adventitia border, the plaque burden and the composition of the plaque can be identified. Conventionally, ICUS border detection is performed manually. However, this process is laborious and time consuming. To enhance the clinical applicability of ICUS, several automated algorithms have been developed for fast ICUS segmentation and characterisation of the type of the plaque. In this chapter the authors present an overview on the developments in ICUS processing and they describe advanced methodologies

which fuse ICUS and X-ray angiographic data in order to overcome indigenous limitations of ICUS imaging and provide complete and geometrically correct coronary reconstruction.

INTRODUCTION Accurate assessment of luminal pathology is useful for the diagnosis and treatment of coronary artery disease. The traditional method used for the depiction of coronary artery morphology is coronary angiography, which provides two–dimensional (2-D) views of the luminal silhouette. Major limitation of this modality is its inability to provide information regarding the plaque burden and the composition of the plaque, data which are useful to guide treatment and estimate prognosis.

DOI: 10.4018/978-1-60960-561-2.ch807

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

New Developments in Intracoronary Ultrasound Processing

To overcome these limitations intracoronary ultrasound (ICUS) has been introduced. ICUS requires the insertion of a catheter, within the coronary artery. At the tip of the catheter there is a transducer which transmits ultrasound signal perpendicular to its axis. There are two types of ICUS systems: the “mechanical” and the “electronic” ICUS systems. In mechanical systems a single rotating transducer, at 1800 rpm (30 revolutions per second), sweeps a high frequency (20 – 40 MHz) ultrasound signal perpendicular to the axis of the catheter, while in electronic systems there is an array of small crystals which have been programmed so that one set of crystals to transmit and the other to receive simultaneously. In both systems cross–sectional images of the coronary artery are produced by detecting the reflections of the ultrasound signal while this is passing through the vessel. As the ICUS catheter is pulled–back (either manually or by a motorized pull–back device) a series of images is generated. In each image, the luminal border, the outer vessel wall border (in the text the term media–adventitia border is used), the stent border, the plaque and the composition of the plaque can be identified and accurate measurements can be obtained (Mintz et al., 2001). In ICUS images there are often several artefacts which may reduce the ability to identify the regions of interest (Figure 1). These artefacts include: the non–uniform rotational distortion which appears only in the mechanical ICUS systems, the ring down artefact (a bright halo surrounding the transducer) that is due to a high amplitude of the ultrasound signal, the guide wire artefact, the near field artefact, the blood speckles artefact, etc. (Nissen et al., 1993). Initially, ICUS border detection was performed manually. However, it became apparent that this process is laborious, time consuming and can be unreliable in the hands of inexperienced operators. Therefore, there was an emerging interest in the development of fast and accurate (semi-) automated segmentation algorithms which would

Figure 1. Structures and artefacts observed in an ICUS image

enhance the clinical applicability of ICUS. These algorithms had to face several challenges mostly caused by the high noise content, the low image resolution, the non–uniform luminance and contrast as well as the presence of the above mentioned artefacts. Another problem that needed to be addressed was the reliable identification of the type of the plaque as well as the integration of the detected ICUS borders into a 3-D object which would represent the coronary vessel. Some of the earlier work on the 3-D reconstruction and visualization of the ICUS sequence assumed that the vessels were straight. However, with this assumption, ICUS could not provide any information on the 3-D arterial geometry or the spatial orientation of the plaque onto the artery. To overcome these limitations fusion of biplane angiographic data and ICUS has been proposed. In this chapter we attempt to present an overview of the developments in ICUS processing. This review is organised as follows: in the next section we describe the segmentation algorithms which have been introduced for ICUS border detection and plaque characterization. We also

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present methodologies which have been initially proposed for 3-D coronary reconstruction. In the main part of the chapter we describe novel techniques able to fuse ICUS and angiographic data in order to generate geometrically correct coronary segments. In addition, we present two advanced user–friendly systems, that incorporate these data fusion techniques and allow reliable and comprehensive coronary representation with the general goal to show future trends and interesting potentialities of these systems.

BACKGROUND (Semi-) Automated Border Detection Algorithms Over the last two decades a multitude of methodologies has been introduced for the segmentation of the ICUS images. The first semi–automated method was described by Dhawale et al. (1993). Their approach required an expert observer to approximate the regions of interest in each ICUS image. These estimations were then used to guide a 1-D dynamic search algorithm. Two years later, Sonka et al. (1995) developed a faster segmentation methodology. His approach incorporated a priori knowledge of the ICUS images’ characteristics and utilised a minimum cost algorithm for border detection. In this method an expert observer had to approximate the regions of interest only in the first image and afterwards the algorithm found automatically the regions of interest in the next frames. Bouma et al. (1996) were the first to introduce a fully automated methodology for luminal border detection. This approach processed images obtained at the same coronary segment and at the same phase of the cardiac circle to detect the region with different luminosity. This region corresponded to the blood speckles and thus to the lumen. Even though this method was fully automated, it had limited clinical applicability since it

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failed to identify the media–adventitia border and required an increased number of ICUS frames. Von Birgelen et al. (1996) developed a semi– automated, clinically applicable segmentation methodology. Their approach utilised a minimum cost algorithm for border detection. Initially, they modelled the ICUS sequence in a 3-D cylinder–like object. In a next step, a minimum cost algorithm was applied to identify the regions of interests in longitudinal cut planes of the object. These borders were then transferred onto the ICUS images and used to guide the segmentation of each image. Lobregt and Viergever (1995) suggested the use of deformable models (snakes) for ICUS border detection. Snakes are parametrical curves that are described by an energy function. In an ICUS image the deformable models have the ability to be attracted by the regions of interest which minimize their energy function. This desirable feature renders them useful in ICUS processing. Thus, over the last years many methodologies have incorporated deformable models for ICUS border detection (Plissiti et al., 2004; Giannoglou et al., 2007; Sanz-Requena et al., 2007). An interesting approach was introduced by Plissiti et al. (2004). In their method a Hopfield neural network was utilised to expedite the minimisation of the deformable model’s energy function. They also incorporated efficient algorithms to overcome common artefacts observed in ICUS images (e.g. the blood speckle artefact and the guide wire artefact) and to address ordinary problems in ICUS segmentation such as the increased noise and the computation of the media–adventitia border in images where calcified lesions are present. In 1999 Shekhar et al. (1999) modelled the ICUS sequence into a 3-D cylinder–like object and then they used for first time a 3-D deformable model to identify the regions of interest. A year later Kovalski et al. (2000) suggested a modification in the energy function of the 3-D model introducing the balloon snakes while in 2002 Klingensmith and Vince (2002) proposed a segmentation approach based on 3-D B-spline surfaces.

New Developments in Intracoronary Ultrasound Processing

Today, there is variety of segmentation algorithms and user–friendly systems (Koning et al., 2002) that are available for ICUS border detection. However, ICUS segmentation still remains a challenging and heated issue since none of the existed methodologies appears to be as reliable as the expert observers (Klingensmith et al., 2000; Bourantas et al., 2005). This is due to the fact that the increased noise and artefacts seen in ICUS frames often mislead these algorithms. Therefore, most systems require an expert observer to inspect the segmentation process, introduce the appropriate corrections whenever the method fails and restart the procedure from the corrected image.

Plaque Characterization Algorithms There is direct and indirect evidence that the architecture and the histological characteristics of the atherosclerotic plaque play an important role in outcome and natural evolvement of the atherosclerotic process (Falk et al., 1995). Recognition of the type of the plaque may also be a key in the lesion–specific treatment strategy. Several studies have showed that ICUS can be a useful tool in assessing various morphologic features and identifying the composition of the plaque (Gussenhoven, 1989). Traditionally, plaque’s characterization was performed by expert observers. The categorization of an atheroma was depended on its echogenicity in ICUS frames. According to Mintz et al. (2001) an atheroma could be classified as: calcific (a bright echogenic plaque which obstructs the penetration of the ultrasound), soft (an echolucent plaque with high lipid content), fibrous (a plaque with intermediate echogenicity between soft and calcific) or mixed (a plaque including more than one acoustic subtype, e.g. fibrocalcific, fibrofatty etc.). Rasheed et al. (1995) and Zhang et al. (1998) developed the first computerized methods for plaque characterisation. Both they compared the gray–scale intensity of the plaque

with the adventitial region of the artery to classify the plaque as calcified, mixed, or soft. Nair et al. (2002) and Kawasaki et al. (2002) proposed more advanced methodologies to identify the type of the plaque. These approaches are called “virtual histology” and are superior to those introduced by Rasheed et al. (1995) and Zhang et al. (1998) since the latter often fail to differentiate the soft from the mixed plaque. Virtual histology today constitutes the gold standard for plaque characterisation and is based on spectral analysis of the radiofrequency back–scattered ICUS signals. In these methods the average power of the back– scattered signal from a small volume of tissue is calculated and used to identify the composition of the plaque which is then depicted in each ICUS image using a comprehensive colour–coded map.

3-D Reconstruction of ICUS Images To visualize the vessel’s morphology and to measure the plaque volume integration of the ICUS sequence into a 3-D object has been suggested. Conventional 3-D reconstruction methodologies stack all the ICUS frames to form a cylinder– shaped object (Rosenfield et al., 1991; Roelandt et al., 1994). However, this process did not take into account the luminal and outer vessel wall changes during the cardiac circle, resulting in a “sawtooth” artefact that was more prominent in normal vessels with increased diameter and preserved compliance. This problem was overcome by gating the image acquisition and selecting only the ICUS frames that corresponded to the same phase of the cardiac circle (e.g. to the end–diastolic phase). There are two methodologies for ICUS gating: the prospective electrocardiographic gating (Bruining et al., 1998) and the retrorespective gating (Zhang et al., 1998). In the first case the ICUS images are acquired only at a predefined phase of the cardiac circle (e.g. at the peak of R wave). After the acquisition of the first frame the catheter is pulled–back at a specific distance, by an automated ECG gated pull–back device, to acquire

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the next frame and so on. Thus, immediately after acquisition, a volumetric dataset is obtained, ready for volumetric analysis. In retrorespective gating the ICUS images are obtained continuously while the ICUS catheter is pulled–back at a constant speed (e.g. 0.5mm/sec or 1mm/sec) by an automated pull–back device. Then, the ICUS frames are selected on the basis of the electrocardiogram, to prepare the final volumetric dataset. Even though these straight 3-D reconstruction methods managed to overcome the “sawtooth” artefact they still have two significant limitations. The first is that they fail to provide reliable information about the spatial orientation of the plaque onto the artery and the second is that they do not take into account the vessel curvature. These approximations may lead to miscalculation of the plaque volume especially in segments with increased curvature where the error can be up to 5% (Schuurbiers et al., 2000).

FUSION OF ICUS AND ANGIOGRAPHIC DATA Developed Methodologies for Geometrically Correct Arterial Reconstruction To overcome the above mentioned limitations reliable combination of the 3-D arterial geometry (obtained from biplane angiographic images) with the luminal and vessel wall data (obtained from ICUS images) has been proposed. The first methodology able to fuse ICUS and X–ray angiographic data was proposed by Klein et al. (1992). A limitation of this methodology was the fact that they used the vessels’ midline to approximate the ICUS catheter path. In addition, Klein et al. did not estimate the spatial orientation of the ICUS images onto the reconstructed vessel. Three years later Lengyel et al. (1995) implemented a snake algorithm to extract the vessel’s midline in a more accurate fashion. Lengyel et al. also proposed a

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methodology which used anatomical landmarks, that were visible in both ICUS and angiographic images (e.g. side branches, calcium plaques etc.), to estimate the spatial orientation of the ICUS images. A different approach was introduced by Cothren et al. (2000) who used dynamic biplane angiographic images to reconstruct the pull–back of the ICUS catheter. In a next step, they placed the ICUS images onto the reconstructed path and then, they back–projected each frame onto the angiographic images to find its spatial orientation. Although, this approach appeared to provide geometrically correct coronary arteries it had limited clinical use since it required an increased number of biplane angiographic images to reconstruct the catheter path. In addition, it became apparent that the methodology used to identify the absolute orientation of the ICUS images was not accurate in the common cases of vessels’ overlapping or angiographic artefacts. Finally, the validation of this method showed that the cardiac and respiratory movements reduce its accuracy. These limitations were overcome by Slager et al. (2000) who developed a clinically applicable methodology. Slager et al. used a parametric 3-D curve to extract the ICUS catheter path from two biplane angiographic images. In a next step, they placed the ICUS images perpendicular onto the path. To find the spatial orientation of each frame they first determined their relative twist using the Frenet–Serret formula. Then, they back–projected the reconstructed artery onto the angiographic images and compared these projections with the outline of the artery as shown in biplane angiographies. Afterwards, they rotated the first frame around its axis and the angle of the first frame, at which these outlines best match, represented the correct absolute orientation. A similar reconstruction method was proposed by Wahle et al. (1999), while Bourantas et al. (2005) introduced a methodology which used B-spline surfaces to extract the ICUS catheter path in a more accurate fashion.

New Developments in Intracoronary Ultrasound Processing

User–Friendly Systems for Geometrically Correct Arterial Reconstruction In order to enhance the clinical and research applicability of the previously described data fusion techniques the development of user–friendly systems is required. These systems have to be fast, stable, and able to overcome common difficulties such as the presence of arterial overlapping or foreshortening or the partial visualisation of the ICUS catheter in the X–ray images. Moreover, they should not require visualisation of additional calibration objects to define image geometry and to correct distortion. Finally, they should include a visualisation platform for comprehensive representation of the final objects and a quantitative analysis sub–system which will provide the operator with accurate measurements. Today, there are two systems available. The first was developed by Wahle et al. (2004). This includes the 3-D reconstruction methodology introduced by Wahle et al. (1999) which provides 3-D or 4-D (3-D + time) objects from ICUS and X–ray angiographic images. It also incorporates a module that can create a finite–element mesh into the final objects. This mesh is used to estimate the local plaque thickness and to compute the local haemodynamics. To visualise the final objects, Wahle et al. used the standardised Virtual Reality Modelling Language (VRML). More specifically, the visualisation module utilised the 2-D texture mapping capabilities of VRML to render a 3-D volume as a set of 2-D surfaces. In these images the operator can see the luminal and the media–adventitia surface and visually assess the severity of the atherosclerosis since the plaque is depicted in a colour–coded map (where the blue corresponds to the low amount of plaque and the red to the increased amount, Figure 2A1). In addition, the system allows visual estimation of the vessel’s local curvature showed in a colour–coded map (the red indicates the inner curvature while the

blue indicates the outer curvature, Figure 2A2) and assessment of the correlation between the vessel’s curvature and the plaque burden (Figure 2A3). Apart from the VRML 2-D texture mapping technique, a 3-D graphical user interface was incorporated that allows virtual endoscopy of the reconstructed vessel. Thus, the operator can see the coronary artery from inside, estimate the type of the plaque and the local shear stresses and assess in detail the extent of atherosclerosis (Figure 2B1, 2B2). The system introduced by Wahle et al. (2004) has features which render it as a practical tool in research but it has limited clinical applicability since it fails to provide arterial reconstruction in real time. This limitation was addressed by Bourantas et al. (2008) who developed a novel system able to perform expedite coronary reconstruction. The fact that this novel system is fast and manages to provide all diagnostic data that are valuable for treatment planning, while the patient is in the catheterisation table, renders it useful in clinical practice. This system, named by the inventors ANGIOCARE, incorporates a well–established and validated methodology (Bourantas et al., 2005) for arterial reconstruction and a quantitative analysis sub–system able to process the detected ICUS borders and the final 3-D objects and to provide reliable measurements. ANGIOCARE operates in a user–friendly environment (Figure 3) facilitating fast learning by the operator and includes an advanced 3-D photorealistic visualisation environment which allows comprehensive 3-D representation of the final object. In this visualisation platform the operator can examine the arterial morphology and visually assess the distribution of the plaque (depicted in a colour– coded map, Figure 4A). In addition, he can interact with the object in real time, select a segment of his interest and obtain quantitative measurements (Figure 4C). He can also back–project the object onto the angiographic images and assess the distribution of the plaque by performing virtual endoscopy (Figure 4D).

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Figure 2. Image A shows a left coronary artery with part of the left main and a segment of the left anterior descending artery; the branch to the left circumflex artery (red line) has not been followed and was discarded: A1) Plaque thickness showed in a rainbow colour–map from low (blue) to high (red) amounts of plaque. The media–adventitia border is depicted with a grey colour; A2) representation of the local curvature in a colour–coded map. The red indicates “inner” curvature, the blue indicates “outer” curvature while the green marks the sides; A3) Correlation analysis between local vessel’s curvature and circumferential plaque distribution: blue regions indicate a high correlation and red a low correlation. B1) Endoscopy of the reconstructed artery. ICUS data showed in semi–transparent fashion. In this images it is possible to look behind of the stenosis (arrow); B2) Reconstructed luminal surface of the same view with depiction of the shear stress in colour–coded fashion. The increased shear stresses correspond to the red areas while the low shear stresses to the dark blue. (From Wahle et al., 2004)

These two systems today constitute the gold standard in coronary imaging since they provide complete, accurate and comprehensive representation of coronary artery’s morphology. The obtained 3-D objects are superior to those, derived by all the other systems which process data from different imaging modalities (e.g. X-ray angiography, multi slices computed tomography, magnetic resonance imaging) and allow more accurate assessment of plaque burden.

FUTURE TRENDS Nowadays, there is an augmented interest in the role of blood flow in the atherosclerotic process

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since it seems that local haemodynamics create an environment which determines the behaviour of the endothelium and affects plaque’s formation. The above mentioned systems can be used to further investigate this relationship. They can be utilised to implement complex haemodynamics, determine the local endothelial shear stress and predict changes in arterial morphology and blood flow after stent implantation. In addition, the already developed systems may be used to process data regarding the composition of the plaque (given by virtual histology) and create objects which will allow the identification of the vulnerable and ruptured plaque. These objects will provide information that is necessary to study in

New Developments in Intracoronary Ultrasound Processing

Figure 3. Snapshot of the user–friendly interface used for the extraction of the catheter path (A) and the segmentation of the ICUS images (B). (From Bourantas et al., 2008).

Figure 4. A) Luminal surface with colour–coded delineation of the plaque thickness (red indicates increased plaque while blue reduced); B) depiction of the luminal and the media–adventitia borders. The outer vessel wall is showed in a semi–transparent fashion; C) Quantitative measurements (displayed at the bottom-left side of the screen) from a segment with significant luminal stenosis; D) Virtual endoscopy of the reconstructed vessel. (From Bourantas et al., 2008).

detail the role of local haemodynamics in the process of vulnerable plaque formation and rapture. Finally, the present systems can be implemented to process other imaging modalities such as the optical coherence tomographic images (OCT). OCT images have higher resolution (axial capacity 15μm) than ICUS and allow the identification of neointimal coverage in stents (Takano et al., 2007). Data suggests that neointimal coverage is

incomplete after drug eluting stent implantation resulting possibly in late thrombosis. Fusion of OCT and angiographic images will allow us to further understand the mechanisms that are involved in this process and decide about the type and duration of antiplatelet treatment.

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CONCLUSION New developments in ICUS processing allow fast, reliable and reproducible segmentation of the ICUS images and accurate quantification of the type of the plaque. Novel methodologies able to fuse X–ray and ICUS data provide fully and geometrically correct coronary representation and nowadays constitute the state of the art in arterial reconstruction. The newly developed user–friendly systems for 3-D reconstruction appear to have increased research applicability as they allow blood flow simulation and direct evaluation of the correlation between the local haemodynamics and the plaque thickness. In addition, these systems constitute a useful tool in clinical practice since they provide in real time comprehensive coronary representation and offer measurements useful to guide treatment.

Bourantas, C. V., Plissiti, M. E., Fotiadis, D. I., Protopappas, V. C., Mpozios, G. V., & Katsouras, C. S. (2005). In vivo validation of a novel semiautomated method for border detection in intravascular ultrasound images. The British Journal of Radiology, 78(926), 122–129. doi:10.1259/ bjr/30866348 Bruining, N., von Birgelen, C., de Feyter, P. J., Ligthart, J., Li, W., & Serruys, P. W. (1998). ECG-gated versus nongated three-dimensional intracoronary ultrasound analysis: implications for volumetric measurements. Catheterization and Cardiovascular Diagnosis, 43(3), 254–260. doi:10.1002/ (SICI)1097-0304(199803)43:3<254::AIDCCD3>3.0.CO;2-8

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Bourantas, C. V., Kalatzis, F. G., Papafaklis, M. I., Fotiadis, D. I., Tweddel, A. C., & Katsouras, C. S. (2008). ANGIOCARE: an automated system for fast three dimensional coronary reconstruction using angiographic and intracoronary ultrasound data. Catheterization and Cardiovascular Interventions, 72(2), 166–175. doi:10.1002/ccd.21527

Falk, E., Shah, P. K., & Fuster, V. (1995). Coronary plaque disruption. Circulation, 92(3), 657–671.

Bourantas, C. V., Kourtis, I. C., Plissiti, M. E., Fotiadis, D. I., Katsouras, C. S., & Papafaklis, M. I. (2005). A method for 3-D reconstruction of coronary arteries using biplane angiography and intravascular ultrasound images. Computerized Medical Imaging and Graphics, 29(8), 597–606. doi:10.1016/j.compmedimag.2005.07.001

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New Developments in Intracoronary Ultrasound Processing

Gussenhoven, E. J., Essed, C. E., Lancée, C. T., Mastik, F., Frietman, P., & van Egmond, F. C. (1989). Arterial wall characteristics determined by intravascular ultrasound imaging: an in vitro study. Journal of the American College of Cardiology, 14(4), 947–952. Kawasaki, M., Takatsu, H., Noda, T., Sano, K., Ito, Y., & Hayakawa, K. (2002). In vivo quantitative tissue characterization of human coronary arterial plaques by use of integrated backscatter intravascular ultrasound and comparison with angioscopic findings. Circulation, 105(21), 2487– 2492. doi:10.1161/01.CIR.0000017200.47342.10 Klein, H. M., Gunther, R. W., Verlande, M., Schneider, W. D., Vorwerk, D., & Kelch, J. (1992). 3D-surface reconstruction of intravascular ultrasound images using personal computer hardware and a motorized catheter control. Cardiovascular and Interventional Radiology, 15(2), 97–101. doi:10.1007/BF02734099 Klingensmith, J. D., Sheknar, R., & Vince, D. G. (2000). Evaluation of three dimensional segmentation algorithms for the identification of luminal and medial-adventitial borders in intravascular ultrasound images. IEEE Transactions on Medical Imaging, 19, 996–1011. doi:10.1109/42.887615 Klingensmith, J. D., & Vince, D. G. (2002). B-spline methods for interactive segmentation and modeling of lumen and vessel surfaces in three-dimensional intravascular ultrasound. Computerized Medical Imaging and Graphics, 26(6), 429–438. doi:10.1016/S0895-6111(02)00025-3 Koning, G., Dijkstra, J., Von Birgelen, C., Tuinenburg, J. C., Brunette, J., & Tardif, J. C. (2002). Advanced contour detection for three–dimensional intracoronary ultrasound: a validation in–vitro and in vivo. The International Journal of Cardiovascular Imaging, 18, 235–248. doi:10.1023/A:1015551920382

Kovalski, G., Beyear, R., Shofti, R., & Azhari, H. (2000). Three–dimensional automatic quantitative analysis of intravascular ultrasound images. Ultrasound in Medicine & Biology, 26(4), 527–537. doi:10.1016/S0301-5629(99)00167-2 Lengyel, J., Greenberg, D. P., & Pop, R. (1995). Time–dependent three-dimensional intravascular ultrasound. In: Robert Cook, (Ed). The SIGGRAPH 95 Conference on Comp Graphics, (pp. 457-64). Lobregt, S., & Viergever, M. A. (1995). Discrete dynamic control model. IEEE Transactions on Medical Imaging, 14, 12–24. doi:10.1109/42.370398 Mintz, G. S., Nissen, S. E., Anderson, W. D., Bailey, S. R., Elber, R., & Fitzgerald, P. J. (2001). American college of cardiology clinical expert consensus document on standards for acquisition, measurement and reporting of intravascular ultrasound studies: a report of the American College of Cardiology task force on clinical expert consensus documents (committee to develop a clinical expert consensus on standards for acquisition, measurement and reporting of intravascular ultrasound studies [IVUS]). Journal of the American College of Cardiology, 37, 1478–1492. doi:10.1016/ S0735-1097(01)01175-5 Nair, A., Kuban, B. D., Obuchowski, N., & Vince, D. G. (2001). Assessing spectral algorithms to predict atherosclerotic plaque composition with normalized and raw intravascular ultrasound data. Ultrasound in Medicine & Biology, 27(10), 1319– 1331. doi:10.1016/S0301-5629(01)00436-7 Nissen, S. E., Di Mario, C., & Tuzcu, E. M. (1998). Intravascular Ultrasound Angioscopy, Doppler and Pressure Measurements. In E. J. Topol (Ed.), Comprehensive Cardiovascular Medicine (pp. 2471-2501). Lippincott-Raven Publishers, Philadelphia.

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Plissiti, M. E., Fotiadis, D. I., Michalis, L. K., & Bozios, G. E. (2004). An automated method for lumen and media-adventitia border detection in a sequence of IVUS frames. IEEE Transactions on Information Technology in Biomedicine, 8(2), 131–141. doi:10.1109/TITB.2004.828889 Rasheed, Q., Dhawale, P. J., Anderson, J., & Hodgson, J. M. (1995). Intracoronary ultrasounddefined plaque composition: computer-aided plaque characterization and correlation with histologic samples obtained during directional coronary atherectomy. American Heart Journal, 129(4), 631–637. doi:10.1016/0002-8703(95)90307-0

Shekhar, R., Cothern, R. M., Vince, D. G., Chandra, S., Thomas, J. D., & Cornhill, J. F. (1999). Three–dimensional segmentation of luminal and adventitial borders in serial intravascular ultrasound images. Computerized Medical Imaging and Graphics, 23, 299–309. doi:10.1016/S08956111(99)00029-4 Slager, C. J., Wentzel, J. J., Schuurbiers, J. C. H., Oomen, J. A. F., Kloet, J., & Krams, R. (2000). True 3-Dimensional reconstruction of coronary arteries in patients by fusion of angiography and IVUS (ANGUS) and its quantitative validation. Circulation, 102, 511–516.

Roelandt, J. R., di Mario, C., Pandian, N. G., Wenguang, L., Keane, D., & Slager, C. J. (1994). Three-dimensional reconstruction of intracoronary ultrasound images. Rationale, approaches, problems, and directions. Circulation, 90(2), 1044–1055.

Sonka, M., Zhang, X., Siebes, M., Bissing, M., DeJong, S., Collins, S., & McKay, C. (1995). Segmentation of Intravascular Ultrasound Images: A knowledge-Based approach. IEEE Transactions on Medical Imaging, 14(4), 719–732. doi:10.1109/42.476113

Rosenfield, K., Losordo, D. W., Ramaswamy, K., Pastore, J. O., Langevin, R. E., & Razvi, S. (1991). Three dimensional reconstruction of human coronary and peripheral arteries from images recorded during two-dimensional intravascular ultrasound examination. Circulation, 84(5), 1938–1956.

Takano, M., Inami, S., Jang, I. K., Yamamoto, M., Murakami, D., & Seimiya, K. (2007). Evaluation by optical coherence tomography of neointimal coverage of sirolimus-eluting stent three months after implantation. The American Journal of Cardiology, 99(8), 1033–1038. doi:10.1016/j. amjcard.2006.11.068

Sanz-Requena, R., Moratal, D., García-Sánchez, D. R., Bodí, V., Rieta, J. J., & Sanchis, J. M. (2007). Automatic segmentation and 3D reconstruction of intravascular ultrasound images for a fast preliminar evaluation of vessel pathologies. Computerized Medical Imaging and Graphics, 31(2), 71–80. doi:10.1016/j.compmedimag.2006.11.004 Schuurbiers, J. C., Von Birgelen, C., Wentzel, J. J., Bom, N., Serruys, P. W., & De Feyter, P. J. (2000). On the IVUS plaque volume error in coronary arteries when neglecting curvature. Ultrasound in Medicine & Biology, 26(9), 1403–1411. doi:10.1016/S0301-5629(00)00295-7

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Von Birgelen, C., van der Lugt, A., Nicosia, A., Mintz, G. S., Gussenhoven, E. J., & de Vrey, E. (1996). Computerized assessment of coronary lumen and atherosclerotic plaque dimensions in three-dimensional intravascular ultrasound correlated with histomorphometry. The American Journal of Cardiology, 78(11), 1202–1209. doi:10.1016/S0002-9149(96)00596-6 Wahle, A., Olszewski, M. E., & Sonka, M. (2004). Interactive virtual endoscopy in coronary arteries based on multimodality fusion. IEEE Transactions on Medical Imaging, 23(11), 1391–1403. doi:10.1109/TMI.2004.837109

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Wahle, A., Prause, G. P. M., DeJong, S. C., & Sonka, M. (1999). Geometrically correct 3-D reconstruction of intravascular ultrasound images by fusion with biplane angiography–methods and validation. IEEE Transactions on Medical Imaging, 18(8), 686–698. doi:10.1109/42.796282 Zhang, X., McKay, C. R., & Sonka, M. (1998). Tissue characterization in intravascular ultrasound images. IEEE Transactions on Medical Imaging, 17(6), 889–899. doi:10.1109/42.746622

KEY TERMS AND DEFINITIONS B-splines: Parametrical curves which are described by the following formula: n

C (u ) = ∑ N i ,p (u )PA,i , 4.10where, u is B-spline’s i =0

C(u) knot vector, Ni,p(u) is B-spline’s basis functions and PA,i is the sum of the control points that have been used to approximate spline’s morphology. Frennet–Serret Formula: A mathematical formula developed by Jean Frédéric Frenet and Joseph Alfred Serret to calculate the curvature and torsion of a 3-D curve.

Neural Network: A modelling technique based on the observed behaviour of biological neurons and used to mimic the performance of a system. It consists of a set of elements (neurons) that start out connected in a random pattern, and, based upon operational feedback, are modelled into the pattern required to generate the optimal results. Neointima Coverage: A new or thickened layer of arterial intima formed especially on a prosthesis (e.g. stents) by migration and proliferation of cells from the media. Optical Coherence Tomography: Intravascular imaging modality which provides cross–sectional images of the vessel. It is similar to ICUS since it requires the insertion of a catheter within the artery, which transmits infrared light. The light is back–reflected as passing through the vessel. These reflections are obtained and analysed to finally generate high resolution cross–sectional images. Drug Eluting Stent: A metal tube, which is placed into diseased vessels to keep them open and releases a drug that blocks cells’ proliferation Vulnerable Plaque: Atherosclerotic plaque which is prone to thrombosis or has high probability of undergoing rapid progression and causing coronary events.

This work was previously published in Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications, edited by Themis P. Exarchos, Athanasios Papadopoulos and Dimitrios I. Fotiadis, pp. 48-59, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 8.8

Pulse!!:

Designing Medical Learning in Virtual Reality Claudia L. McDonald Texas A&M University-Corpus Christi, USA Jan Cannon-Bowers University of Central Florida, Orlando, USA Clint Bowers University of Central Florida, Orlando, USA

ABSTRACT Pulse!! The Virtual Clinical Learning Lab is designed to transfer and further develop stateof-the-art game design and technology to create subject matter for teaching critical thinking skills in experiential medical learning in virtual reality. The underlying design principles of Pulse!! include real-time feedback, repetitive practice, controlled environment, individualized learning, defined outcomes and educational validity. Pulse!! development incorporated evaluation issues early in the design cycle. The Pulse!! evaluation process ensures that the learning platform meets standards as rigorous as that required by military simulation systems and educational accrediting bodies. The Pulse!! method of case and technology development indicates that virtual-world educational platforms must be not only user-friendly, visually DOI: 10.4018/978-1-60960-561-2.ch808

effective, interactively immersive and fluid but also procedurally rooted in curriculum, rich in readily-accessible information and cognizant of actual practice in the real world.

INTRODUCTION Pulse!! The Virtual Clinical Learning Lab is a research project designed to transfer and further develop state-of-the-art game design and technology to create subject matter for clinical medical learning in virtual reality. Pulse!! is a high-tech response to a coalescing host of adverse factors compelling innovative means to provide clinical experience and practical knowledge rooted in critical thinking, not only for degree-based education but also continuing education for medical practitioners. Pulse!! problem-based case scenarios are designed for degree-based education and post-degree certification, as well as continuing

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education and training for health-care professionals that is pedagogically structured for deep and rapid experience-based learning. The underlying design principles of Pulse!! include immediate feedback, repetitive practice, controlled environment, individualized learning, defined outcomes and educational validity. An important feature of the Pulse!! development strategy incorporates evaluation issues early in the design cycle. This iterative approach begins with usability analyses and proceeds to incorporate learner reactions, cognitive change, behavior and transfer of knowledge. To this end, Pulse!! field research began with beta testing of the learning platform in medical-education institutions to establish the system’s functionality and usability. This chapter focuses on whether the apparatus and conventions of this new paradigm in medical education can be made into an effective tool that is generally acceptable to those who undergo medical training.

BACKGROUND Pulse!! originated as a research project to determine whether sophisticated medical learning could be achieved in virtual space created by cuttingedge videogame technologies. The platform must prove itself valid and reliable according to the highest academic standards; otherwise, the medical profession will justifiably bypass virtual-world technologies as viable media for experiential learning in critical thinking and differential diagnosis.

The Crises and the Research The need for low-cost, portable educational media for medical education is driven by a host of contemporary factors, including but not limited to: •

Deaths due to medical error estimated between 44,000 and 98,000 annually with related costs estimated at $17 billion to

•

•

•

•

$29 billion, according to the Institute of Medicine (Kohn, Corrigan & Donaldson, 1999, pp. 1-2); Baby-boom retirements from academic faculties and other demographic factors, which are creating looming shortages of medical personnel, especially physicians and nurses (Rasch, 2006, pp. 29-35); Shorter hospital stays and medical residents’ workweeks, which are reducing clinical training opportunities and expertise development (e.g., Verrier, 2004, p. 1237); Continuously changing warfare and terrorist technology and methods, which drive a need for rapid deployment of training for continuously evolving medical treatment (Zimet, 2003, 40); Two-thirds of battlefield fatalities from potentially survivable injuries that might have been prevented with through more effective training of U.S. armed forces’ Tactical Combat Casualty Care (TCCC) guidelines (Holcombe, et al., 2006, p. 36).

Responding to the Institute of Medicine report, a partnership of the National Academy of Engineering and the Institute of Medicine formed to conduct a study of health-care mistakes and to identify future remedies. The report concludes that the U.S. health-care industry neglected engineering strategies and technologies that have revolutionized quality, productivity, and performance in many other industries and calls for an array of powerful new tools in medicine (Reid et al., 2005, p. 1). Virtual-world simulations are among technologies being explored for use in various formats. The Pulse!! project posits that these technologies are a means of an inevitable paradigm shift in health-care education. Entertainment has been the main use of virtual reality, but virtual-world training in various formats – the field of “serious games” – also has been explored. In the academy, there has been inci-

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dental development of human-simulation trainers anchored to computer-assisted case presentations for surgeons, U.S. Army combat medics and electronic role-playing games aimed at clinical soft-skills education. The military originally developed the state-of-the-art America’s Army online war game for recruitment, using the “first-person shooter” videogame genre. The U.S. Military Academy successfully used a version of the game to test the effectiveness of new computer-based, interactive curricula for several levels of map reading and land navigation (Farrell, 2003, p. 5). “The result is excited and motivated cadets who take ownership of their military development, in terms of what they study, when they study it, and how both cadets and instructors receive feedback on performance” (p. 1). Issenberg et al (2005) conclude “that highfidelity medical simulations facilitate medical learning under the right conditions” (p. 3). The “right conditions” noted by Issenberg ratify the underlying principles of the Pulse!! learning platform development, including immediate feedback, repetitive practice, controlled environment, individualized learning, defined outcomes and educational validity (p. 26). This first comprehensive review of 34 years of research on the efficacy of simulation in medical training found that “high-fidelity medical simulations are educationally effective and simulation-based education complements education in patient care settings” (p. 4). Issenberg notes that the medical community’s increasing interest in simulation and virtual reality is driven in part by “the pressures of managed care” that lead to fewer clinical opportunities for medical learners, including practicing physicians constrained by shrinking financial resources to spend less time keeping abreast of developing medical topics. A 1997 study by Mangione and Nieman found a decline in bedside acumen and urged “the use of simulation systems for training,” according to Issenberg (p. 8).

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Watters et al (2006) propose that substantial learning occurs through what has become standard entertainment-game architecture, including instantaneous feedback, a rising scaffold of challenges, visible goal indicators, personalization and customization, fluidity and contextual grounding. Watters and Duffy (2004) propose “a framework of motivational constructs” found in games that are applicable in developing interactive health-related software: self-regulation, or autonomy; relatedness that includes role-playing, narrative and personalization; and competency, or self-efficacy built upon completion of meaningful tasks. Reznick and MacRae (2006) observe that well-established learning theory (Fitts & Posner, 1967), as it applies to surgical training, ratifies the use of simulators in the acquisition of motor skills through three stages – cognitive, integrative and autonomous. Ericsson (1997) applied a thicker description directly to surgical training with the concept of “deliberate practice,” defined in Reznick & MacRae as “repeated practice along with coaching and immediate feedback on performance” (p. 2665). Simulation and virtual reality appear to be options for medical learning as rare opportunities for deliberate practice become more so. Reznick and MacRae note: “A Food and Drug Administration panel recently recommended the use of virtual reality simulation as an integral component of a training package for carotid artery stenting” (p. 2667). No project so far has created a high-fidelity, persistent-world platform with a template and process providing ease of use in authoring and assembling training and educational content. Lack of such a system prevents creation of complete end-to-end solutions for distance health education. While professionals will still need to demonstrate clinical thinking and skills in an actual clinical environment, Pulse!! provides a pathway to shorten time spent in a live environment. This will save money and reduce errors in the real world.

Pulse!!

Addressing the Issues As a virtual-reality learning platform, Pulse!! addresses the following issues in military and civilian health-care education and training. •

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•

Flexibility, timeliness and economy: Medical education in virtual reality will stimulate a paradigm shift in clinical education and treatment that is not possible with live training. Learning from “trial and error” will move to a new level with assured safety and increased timeliness for actual development and easy deployment. For professionals in need of cross-training into new environments or post-graduation practice, this system will provide rapid, experience-based, just-in-time learning and updated specialization. Portability, adaptability, reliability and consistency: This system will allow for rapid development of fully-immersive training for relevant health-care professionals to provide training that is appropriate, realistic and experiential. For example, this system will provide the reserve military with a means for creating applications that allow for the verification, validation and accreditation training on field procedures in a justin-time fashion before or after deployment. Scheduled live training, with its required logistics, costs and time frames make these training exercises error-ridden, fragmented and inconsistent. Independent, marketable learning: Caregivers require an environment to learn and practice treatments with dynamic feedback, resulting in rapidly-acquired competencies. Virtual simulations create opportunities to rehearse, replay and sharpen the critical skills needed in events such as bioterrorism, and from new patterns of complex injuries.

•

Efficiency and future application: There exists a dual problem in career counseling and recruitment. Pulse!! will hasten recruitment by immersing prospective career applicants in a high-fidelity virtual environment that simulates specific skills and situations for various health-care professionals. This system enable a significant upgrade in the future quality of health professions in the nation and in the world.

The Center for Virtual Medical Education The Texas A&M University System has created a Center for Virtual Medical Education at Texas A&M University-Corpus Christi with Pulse!! as its signature project. The center has been created to provide crossdisciplinary expertise and resources to educational, governmental and business entities engaged in meeting looming health-care crises with threedimensional virtual learning platforms that are iterative, providing unlimited, repeatable clinical experience without risk to patients; portable, for training anywhere there is a computer; asynchronous, for training anytime; and immersive, providing first-person experience leading to critical thinking and practical knowledge. Center products will be grounded in research and equipped with tools and generators that enable clients to author their own cases and create their own scenarios within a variety of virtual environments. The Pulse!! platform is being rigorously researched and tested extensively for reliability and validity, which will yield a product for delivering curricula with confidence for medical and other health professions. New products will be created on that sound foundation. Virtual medical education research will be continuous at the center with further refinement and development of the Pulse!! platform, including voice-recognition technology and further research

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in how to replicate true-to-life physiological and patho-physiological states in three-dimensional virtual space. The center also will develop an entrepreneurial dimension through collaboration with other entities engaged in developing virtual medical education platforms, which in turn will generate revenue for the center’s continuing support. The center is expected to become a pool of resources for medical training, professional certification and credentialing, professional development and graduate medical education. By their very nature, center products will produce efficiencies of operations and economies of scale as a source and distribution point for clinical training materials transmitted electronically anywhere in the world. The field of medical simulation is expected to provide a virtually inexhaustible array of projects for the center. Pulse!! fundamental technology – its proprietary engines and features – has almost limitless applications, and its continued development will keep the Pulse!! learning platform at the cutting edge of development. The Pulse!! project has created a unique development interface between academe and a growing sector of videogame industry – the so-called “serious games” sector – and has resulted in a licensing agreement for its private-sector partner, BreakAway Ltd. of Hunt Valley, Md.

The Pulse!! Platform The Pulse!! learning platform looks and acts like a videogame. Users navigate the platform’s threedimensional space using a standard computer “mouse” and keyboard in a manner familiar to videogame players. The virtual space is totally navigable: Users can “walk around” within it and “look” at its various features. The original virtual environment replicates in detail the intensive care unit of a major military hospital.

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Users interact with a high-fidelity virtual patient and with other virtual medical personnel to conduct examinations, order tests and administer medication. The “patient” is modeled to respond accordingly in real time. Two core engines drive the Pulse!! platform – the simulation engine for patient traits and environmental reaction (SEPTER) and the visual interface and scaffolding for total acuity (VISTA). Additional engines provide rules, customization and case authoring. A scene editor assembles physical objects in the virtual learning environment. Intelligent tutoring by the learning platform provides feedback and direction. Layered together, these complex core engines generate a virtual world that is persistent and capable of dynamic change to reflect new medical knowledge. Pulse!! compiles a database for medical education, training and continuing education based on medical lessons learned. As the database becomes more sophisticated – broader in scope and deeper in immersive description – its usefulness as an educational/training tool addresses complex issues of military and civilian medical care across the education/training continuum; e.g., fewer military physicians and paramedical personnel will be seeing their first clinical battlefield cases in-theater, as most will have had broad, immersive virtual-world training to supplement traditional didactic curricula. Pulse!! medical cases are generated by cadres of subject matter experts. The first set of cases simulate multiple types of shock resulting from an improvised explosive device (IED) and began rigorous beta testing and evaluation in 2007 by the medical communities at Yale University School of Medicine, The Johns Hopkins School of Medicine and the National Naval Medical Center. Usability and training effectiveness were measured in 2007. Transfer of training and knowledge acquisition with educational outcomes will be assessed in 2008.

Pulse!!

THE EVALUATION PROCESS The Pulse!! evaluation process ensures that the learning platform meets standards as rigorous as that required by military simulation systems. Evaluation is guided by the Accreditation Council for Graduate Medical Education standards (ACGME) and military guidelines for Pre-hospital Trauma Life Support (PTLS) and Tactical Combat Casualty Care (TCCC). Pulse!! training for each protocol tests core competencies by demonstrating that the learner knows, knows how, shows how, and does.

Purposes of Evaluation in Training Training evaluation can serve multiple goals, and the nature of data collected and conclusions drawn from evaluation activities are related to the particular goal at hand. For example, training evaluation may be conducted to: • • • • • • • • • •

Determine whether training (learning) objectives are being met; Assess the efficiency and cost effectiveness of training; Validate new training content; Determine whether training methods are effective/optimal; Evaluate instructor/trainer effectiveness; Assess a trainee’s readiness for training; Provide a basis for instructional decisions: feedback, remediation, lesson progression; Determine a trainee’s readiness for the job (certification); Assess the impact of training on overall organizational outcomes; Determine whether other training-related goals are being met: trainee development and progression, trainee self-efficacy and confidence.

Any one or a combination of these purposes can motivate a training evaluation. Hence the

strategies, measures, data collection methods and resulting conclusions involved in a training evaluation can vary widely according to its purpose and goals. Effective evaluations are those that make their goals explicit, and are planned early and so that measurement activities can be specified in advance. Another distinction common in evaluation is whether the purpose of the evaluation is summative or formative. Formative evaluation collects effectiveness data as the system or program is being developed as a means of providing feedback into the design process, which provides iterative corrections. Summative evaluation collects data indicating the overall effectiveness of the program or intervention by assessing outcomes. Our goals for the evaluation of Pulse!! are multifaceted, employing both formative and summative components. Our overarching goal, and a summative goal, is to demonstrate that simulation-based gaming is an effective method for teaching shock trauma and related skills in medical personnel. The bulk of evaluation activities we are planning feed directly or indirectly into this goal. Beyond this, we are also interested in using early evaluation results to inform subsequent design (a formative goal); determining how best to provide feedback, remediation and lesson progression within the game (a formative goal) and how the system might be used to augment certification classes/exams (a summative goal). We also would like to assess the impact of training on overall organizational outcomes, such as reduced error and better operational performance (a summative goal).

The Measurement Challenge Training evaluation is a difficult process that requires extensive planning and commitment of resources. There are many variables that can affect the success of a training program, including design of the program itself as well as the way it is implemented. Our focus in this chapter concerns mainly the former, as implementation of the

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Pulse!! system is not planned until later phases of effort. Hence we are primarily concerned with how well the system is designed and whether it has the potential to achieve desired learning outcomes. Given the focus of Pulse!! to train complex medical decision-making and procedures, a wellconceived evaluation plan must address issues associated with measuring higher-order skills that manifest themselves in complex environments. Measuring learning is difficult in such settings because such environments typically involve: •

• • • • • •

Complex, multi-component decisions: rapidly evolving, ambiguous scenarios, information overload; Severe time pressure; Severe consequences for error; Adverse physical conditions; Performance pressure; Sustained operations/fatigue; Distributed, multi-operator (team) problems.

These characteristics complicate measurement for several reasons. First, there is often no “right” answer; i.e., performance may proceed down different paths before arriving at a correct decision. Moreover, the specifics of the case used in training will never be identical to the actual episodes that occur in real practice. For this reason, the emphasis on training must be to teach sound, generalizable processes and strategies, so trainees are able to recognize and adapt them to novel situations they may encounter. A second measurement challenge in complex environments is to track ongoing performance meaningfully. In many cases, actions and events occur so rapidly that simply keeping track and making sense of the trainees’ behavior is a challenge. In a system like Pulse!!, the opportunity exists to automatically record performance as trainees interact with the system. The mechanisms for accomplishing this must be well designed and coupled tightly to the measurement strategy. Re-

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lated to this, a full picture of ongoing performance requires that many sources and types of data are recorded and interpreted; e.g., the state of the patient model when the action is taken. In addition, such interpretation must be accomplished quickly if it is to provide a basis for continuous feedback. A final difficulty in measuring complex performance is related to the nature of decisionmaking and other higher order skills that need to be trained. Specifically, it is often difficult to discern a trainee’s knowledge state based solely on observable performance. Simply put, much of the important processing happens in the trainee’s head and is not easily available for inspection or evaluation. Hence, strategies must be developed that help to infer a trainee’s state of mastery based on observed actions. In some cases, carefully designed triggers can be embedded into the game to elicit a particular action or response. The nature of the response can then be used to indicate knowledge state. Taken together, these challenges contribute to the difficulty of evaluating the impact of a training program that targets higher-order skills. However, the use of simulation-based games provides unique opportunities that may actually foster the evaluation process. For example, as noted, performance tracking is relatively easy in a game-based format since trainees’ actions and corresponding system states can be automatically recorded. In the case of Pulse!!, it will also be possible to anticipate and incorporate measurement and evaluation concerns in the initial conception and design of the game, since we are addressing evaluation issues early in the project’s life cycle.

Evaluating Training Effectiveness As noted, our goal is to implement a comprehensive, multi-component approach to evaluating Pulse!! Hence we are crafting a plan that spans a variety of system design and training issues. These involve the following.

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Assess the Soundness of System Design This includes a variety of evaluation questions that are required to ensure that the system is developed as planned and can be easily used by trainees. Among the concerns here are: determining whether the underlying physiological models are valid, and whether they interact with one another in a realistic way; comparing actual system functionality to design specifications to ensure that it performs as intended; and ensuring that basic usability issues are resolved. This latter category involves both an expert review to ensure that usability standards are being met, and collection of empirical data demonstrating that the system and tutorials are easy to use.

Assess the System’s Incorporation of Sound Learning Features Using the extant literature into the science of learning, synthetic experience, game-based learning and scenario-based training as a guide, this part of the plan determines whether the system embodies sound learning strategies and features. These include: • • • • • •

Specific, measurable learning objectives; Appropriate scenario design, including trigger events; Sound instructional strategies/elements; Accurate performance assessment and data recording strategies; Sound diagnosis routines and models; Appropriate feedback and AAR strategies.

Establish Training Effectiveness We advocate a modified version of Kirkpatrick’s (1976) hierarchy. This model is used extensively throughout industry and the military. It includes the following elements.

Pre-training Assessment Determine the degree to which the trainee is prepared to benefit from the training. Research has shown that trainees who lack the cognitive or affective prerequisites for training do not perform as well as trainees who are better prepared (Tannenbaum et al, 1991). Some of these prerequisites include: Aptitudes such as cognitive and physical ability; prior knowledge and experience; and attitudes such as the motivation to train and the instrumentality of training. Assessing readiness for training is valuable because it can indicate whether a trainee needs remedial attention prior to entering training or how training might be tailored to the his or her specific needs; e.g., by starting with less-challenging scenarios. Reactions/Motivation Reaction measures are the most common method of measuring training effectiveness but not necessarily the most informative. Reactions typically take the form of a “did you like it?” survey at the completion of training. While this is a relatively cheap and easy measurement strategy, research has shown that simple reaction measures are not necessarily correlated with actual learning. This does not mean that motivation is unimportant, only that reaction measures, by themselves, are insufficient. In addition, reactions that involve trainees’beliefs about the value or utility of training have been shown to predict performance better than purely affective reactions. Hence, measuring reactions is recommended in this project. Learning For our purposes, learning can be defined as permanent cognitive change. Learning is the second most popular training effectiveness measure, and is typically assessed by paper and pencil tests. However, particularly when dealing with higher-order skills, recognition or recall tests are not sufficient to capture whether appropriate learning has occurred. In fact, evidence suggests that the way experts organize knowledge may

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be crucial to task performance. Recent work into mental model development and measurement has yielded several candidate strategies for assessing knowledge organization in this project. Training Behavior Even when trainees know how to perform a task, it does not necessarily mean that they have developed the skills to actually do it. “Training behavior” in this model refers to the extent to which the trainee can demonstrate mastery of crucial skills. Behavior is more difficult to measure than learning because it usually involves a concrete demonstration of skill mastery, which is not amenable to written tests. Instead, training behavior is typically measured through work sample tests or simulations. In the current case, the nature of the learning system being built is such that this kind of assessment can be done relatively easily by preparing several test scenarios that contain no hints, feedback or other performance enhancing features. Transfer of Training Transfer of training is a complex phenomenon that encompasses a variety of factors (e.g., see Baldwin & Ford, 1988). A consistent finding from past work indicates that even when trainees have mastered the competencies for effective performance, it does not necessarily mean they will use those skills in practice. For example, supervisor support has been found to be related to the degree of transfer of learning to the job (Cromwell & Kolb, 2004); many other such factors exist. These findings complicate the assessment of transfer because it is often not clear why trainees fail to transfer what they learned; i.e., it could be because the training was inadequate or due to some other environmental factor. The best strategy to overcome these challenges is to collect data from different sources (supervisors, peers, archival records) and augment quantitative measures with surveys to determine if barriers to performance exist in the operational setting.

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Organizational Results The final level in Kirkpatrick’s hierarchy involves determining whether the organization’s goals were served due to training. It poses questions such as: “Did we solve the initial problem?” and “Did training help us achieve our goals?” The types of measures that organizations are typically concerned about include safety, productivity, reduced costs, reduction of errors, quality/quantity of performance, profit, job satisfaction, personal growth and the like. However, the link between any given training program and these outcomes is often weak. It is unclear whether results can or should be measured in this program.

USER-CENTERED DESIGN AND USABILITY Developers of serious games are confronted with significant challenges as they try to create useful, usable products. There is very little design guidance regarding game usability in general (Federoff, 2002; Song & Lee, 2007). There is even less guidance regarding the design of educational or other “serious” games (Virvou & Katsionis, 2008). However, the existing data do demonstrate that these games often suffer from usability issues that may limit their effectiveness as training tools (Panesse & Carlesi, 2007), so a user-centered design approach is important in the development of this type of software. In developing Pulse!!, we attempted to follow a user-centered design process similar to that used in other software development approaches (cf. Gould, 1987; Norman & Draper, 1986). In the following sections, we will describe some of the lessons learned from this process and how we incorporated them into the Pulse!! platform.

Design for a Range of Users Although one might assume that today’s young medical professionals are computer-savvy and

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have lots of game experience, our investigations indicate that this is almost certainly not true. In fact, users were almost as likely to be complete novices in gaming as they were to be avid gamers. It became important to not assume that learners using Pulse!! would know how to navigate in the virtual environment. Navigation aids, cues, and hints were frequently added to help novices. Rollovers indicating active elements seemed to be helpful in assisting players to choose their interactions. A tutorial was developed to assist beginning game players in using the interface. (See below.)

Use Fidelity to Create Affordances Although it has been argued that high levels of visual fidelity are not always required in learning systems (e.g., Bowers, Rhodenizer & Salas, 1997), there are certainly some instances where an investment in fidelity can have a high pay off. The Pulse!! platform seemed to present such an opportunity. Because health-care professionals have high levels of knowledge regarding various types of equipment, high-fidelity recreation of these items seemed to create an “affordance”; i.e., the cue is so salient that users know what do to without instruction. For example, a ventilator in the platform was so faithfully recreated that doctors were immediately able to change oxygen saturation without training on a less intuitive interface. Because we anticipated having relatively little time for the tutorial, this seemed to be a good investment.

Time Matters Pulse!! subject-matter experts emphasized the importance of representing time correctly in the game. Consequently, we were confronted with the problem of communicating the amount of time a procedure such as a CT scan would take, without actually making the learner wait that amount of time. We attempted to solve this problem by pre-

senting a platform clock at the top of the display. A procedure requiring a lengthy delay initiates a “cut scene” in which the procedure begins and the screen fades to black. The screen then returns to its normal state, and the clock has advanced by the amount of time suggested by our experts.

Use Story to Communicate Context An interesting aspect of our user analysis was the degree to which medical decision-making relies on a variety of contextual variables. Rather than simply present these as a list of textual information, it was decided to imbed the information into cinematic scenes at the beginning of training scenarios. It was believed that these narrative presentations would help create a sense of “presence,” which is believed, in turn, to lead to better training outcomes (Vora et al, 2002).

Usability Analyses Although a user-centered design approach can be useful in informing design, there is still a need to do a detailed series of usability analyses. The goal of these analyses was to identify design shortcomings that might have reduced the training effectiveness of the platform. Once identified, these elements could be improved in subsequent iterations of the software. These many individual design recommendations can be clustered into the categories that follow.

Interface Issues In general, learners worked with interface elements with little trouble. One aspect that was mildly troublesome was interaction with nonplayer characters. Although experienced gamers are used to interacting with these “NPCs,” most players did not infer that they could ask the NPCs to perform tasks. This was solved effectively by adding visual cues to the game display.

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Another important lesson learned was that faithfully modeling “real life” occasionally led to interface confusion. For example, the original interface required participants to purposefully move a stethoscope over each quadrant of the chest or abdomen because that was a common process discussed in our task analyses. However, participants often found the need to move the mouse over each quadrant annoying and suggested that one click be sufficient to start a process of listening to all quadrants.

Navigation Issues One lesson learned is that hospital rooms are difficult navigational challenges for computer game environments. In real life, health-care professionals navigate around equipment and each other with little difficulty. However, this clearly did not translate well into the simulated environment. Faithful placement of items in the room is probably too much fidelity; rather, it might be more useful to increase the size of the virtual room to something larger than the actual room. It is probably also appropriate to place objects in the virtual room such that they maximize navigability rather than fidelity. Alternatively, it is possible to address this challenge by allowing ample practice time for participants. By providing practice, trainees may be able to master navigation so that it is no longer an issue. Finally, game developers need to attend to navigation issues between characters. In real life, workers rarely physically run into one another or fail to yield. However, this behavior remains common in most computer games, leading to frustration in users trying to move quickly. Although this is a limitation of the current development platforms, it can be averted through thoughtful design of the virtual environment. Pulse!! programmers are working on improving collision algorithms.

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Information Issues Being a health-care provider is a task of managing information. Although this was a point that was heard loud and clear in our user-centered design process, it still proved challenging to provide health-care providers with all of the information they needed to be effective in the platform. This is further complicated by the difference in forms, protocols and displays from hospital to hospital. In general, we learned that more information is typically better. When choosing between forms to use as a model for inclusion in the platform, the most inclusive form is typically the right answer. Further, it is important to replace the corporate knowledge that would exist in an actual treatment room. For example, in real life, doctors could probably quickly get the blood pressure simply by asking. They could obtain this information while continuing to do other tasks. Although this information was available in original iterations of the game, it required stopping a task to retrieve it. Based on user feedback, it might be advisable to make some of the critical information constantly and effortlessly available by displaying it in the background. It should also be noted that embedding information in cinematic scenes met with mixed success. Although participants were generally pleased with the scenes, several did not appreciate their importance, so that they missed critical information presented in these scenes. Consequently, there is a need to explore other techniques for presenting these data in a way that is engaging and salient.

The Tutorial The Pulse!! tutorial grew through several phases in tandem with preparation for preliminary testing. A printed users’ manual was drafted by the industry contractor, but researchers concluded that it would be inadequate across a broad spectrum of study participants. Consistent with user-centered design principles, researchers opted for an interactive,

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in-platform tutorial to introduce basic functional concepts and actually bring participants into the platform itself before they encountered a virtual clinical case. Due to insufficient time to develop such a tutorial prior to scheduled testing at Yale and Bethesda, researchers developed and produced a battery of videos that would show rather than tell participants how to navigate and interact with the Pulse!! platform. Six segments totaling more than 27 minutes proved cumbersome though somewhat more effective than the printed manual. By comparison, the in-platform tutorial in place for tests at Johns Hopkins can be completed in far less than half the time with the added dimension of interactivity. The in-platform tutorial appears as a menu item on the Pulse!! opening screen. Instructions are given orally by voiceover, which is cued to resume when a procedure has been properly executed. Basic training includes movement within the virtual space, interaction with other figures in the virtual treatment room, interaction with the virtual patient and various diagnostic equipment, how to order tests and procedures and how to retrieve those results. The tutorial may be repeated without limit. Conventions of movement and menu-driven interaction are familiar to participants who have used various types of personal-computer based videogames. Simple keyboard/mouse conventions make Pulse!! a two-handed platform for navigating in the virtual-world environment, but a mostly mouse-driven platform for interaction with virtual personnel and equipment. For certain procedures, such as stethoscope examination, the platform becomes demonstrative rather than interactive, showing how a procedure is done rather than allowing users freedom of movement. The relatively brief, in-platform tutorial is key to users’ success in the virtual learning space created by Pulse!!

Summary Overall, we are confident that the user-centered design approach taken in the creation of Pulse!! was successful. By forming a multidisciplinary team and incorporating subject-matter experts as design partners, even early versions of the software received positive user reviews. Through careful application of usability analysis, we were able to identify critical design shortcomings early in the development process and to create solutions that could be incorporated into later versions. The result is a product that now seems usable for a wide variety of health-care professionals.

TESTING THE PLATFORM Usability test results reported here are from preliminary tests of the Pulse!! platform at Yale School of Medicine and National Naval Medical Center Bethesda, which were preliminary to tests at The Johns Hopkins School of Medicine of a modified version of Pulse!! incorporating changes indicated by the previous test sessions.

Preliminary Studies Participants from four samples participated in preliminary studies. The samples represented different specialties and levels of training: Yale Sample 1 comprised six anesthesiologists who used the platform with no experimenters present; Yale Sample 2, four anesthesiologists with experimenters present; Yale Sample 3, six neurosurgeons, no experimenters present; and Bethesda Sample 1, seven trauma surgeons, experimenters present. Participants were given instructions on how to use the platform, then they completed several interactive cases requiring them to diagnose a simulated trauma patient. Participants completed a survey about their perceptions of the Pulse!! platform.

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In general, participants reviewed the platform positively. For the most part, the pattern of responses was similar among the various specialties and levels. Participants also responded to several open-ended questions about the software. A summary of participants’ responses follows.

The Johns Hopkins Study A usability analysis was conducted on an early iteration of the Pulse!! system to evaluate the degree to which users from the targeted population could infer how to use the interface. Attention was also paid to the effectiveness of the system’s tutorial, information needs, and system fidelity. Nine physicians participated in the Johns Hopkins usability study. Each was offered continuing medical education credits for participating. Participants were told about the purpose of the study and were asked to provide informed consent. After agreeing to participate, participants were asked to complete the Pulse!! tutorial. The tutorial instructs the participant in using the interface to accomplish necessary actions such as ordering medications or taking the patient’s temperature. Following the tutorial, participants were asked to accomplish a variety of tasks to assess the degree to which they could extrapolate from information in the tutorial to other necessary user interface tasks. These tasks were designed by experimenters to explicitly determine whether potential trainees would be able to interact with the system well enough to allow learning to occur. Clearly, it would be ill advised to expect trainees to learn targeted material if they are struggling to understand the interface. The performance of each participant on these tasks was observed and recorded by experimenters to identify issues in the design of the user interface. It took approximately two hours for participants to complete the analysis.

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Tasks Fifteen tasks composed Pulse!! basic training immediately following the tutorial. These included: reviewing intake information, ordering tests, ordering and checking intravenous fluids (IV), ordering a second IV, checking the trachea, adjusting oxygen delivery, listening to the abdomen, checking femoral pulse, ordering an x-ray and CT scan, checking blood-test results, checking CT results, checking awareness, turning the patient and checking his back, entering a diagnosis and identifying visual clues toward a diagnosis.

Preliminary Findings Our initial goal for this preliminary study was twofold: to gather specific comments for necessary updates to the system; and second, to make a global assessment of participants’ reactions. The results of this analysis yielded numerous recommendations for changes to the interface and simulation. Reaction data indicated that the overwhelming majority (82%) of participants reacted positively to using the Pulse!! platform. Over 80% also reported that engaging in the platform held their interest, and all but one respondent reported that the platform was visually appealing. Participants specifically commented that the platform provided for fun problem-solving; provided for real-time actions; was realistic and highly interactive and realistic; had excellent graphics; fostered applied problem-solving. Taken together, these comments suggest the motivation to learn on the Pulse!! platform is high. This conclusion is bolstered by comments participants made to experimenters, many of which expressed great enthusiasm for the platform. A few negative comments were also voiced, and we tried to carefully probe these participants so we could address their concerns. With respect to usability, a majority of participants also reported that the system was easy to use. In addition, for the most part, menus were deemed

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acceptable by participants as was the overall user interface. A majority of participants reported that they believed the platform could provide training that was relevant to their jobs and that they would recommend the platform to a colleague. Our expert, anecdotal assessment was that the menu design was relatively easy for participants to master and that most were able to interact effectively with the system after a few minutes of familiarization. We also observed that the biggest challenge for participants was “walking” through the space, i.e., moving around in the virtual world. This was mostly the case for participants who had little or no video-game experience. Overall, our preliminary results indicate that the Pulse!! platform appears to be a viable environment in which to embed instruction. Participants responded well to the technology and expressed enthusiasm regarding its utility as a learning tool. Our approach of iteratively collecting specific usability data and making needed upgrades has helped ensure that the interface is easy to use and not a hindrance to learning. As with initial studies, subsequent evaluation efforts will involve a variety of learners, from medical students through residents and practicing physicians in a variety of specialties. In this way, we will be in a position to assess whether meaningful differences exist among varying group of learners, based on past experience, age, specialty, comfort with video games and the like.

FUTURE TRENDS Preliminary findings and anecdotal success of the Pulse!! learning platform suggest that Pulse!! development methods represent a robust model for future projects of its kind. It is unlikely that medical education or any species of education requiring critical thinking will adopt virtualworld technologies as media for learning without integrated testing and evaluation indicating their validity and reliability. The Pulse!! method of case-

and learning-platform development is well under way to establishing a viable development model. We believe Pulse!! is plotting a new course for educational technology that will change rapidly as virtual-world technologies continue to develop and become more realistic as hardware and software methods continue to develop at their current pace. As these preliminary studies suggest, however, it will be critical to integrate learning-evaluation and user-centered design methods with technological development across all curricular fields. Some usability issues may become less problematic as so-called “digital natives” (e.g., Prensky, 2001; & Gee, 2003) become the dominant in the educational system, but it is our sense that there always will be a significant sector of any student body that will be unfamiliar with the rudiments of virtualworld technology to the degree that, in order to “play the game,” they will have to be taught how. In any case, however, virtual-world technologies are likely to represent a significant paradigm shift in how education is acquired.

CONCLUSION Pulse!! usability studies indicate that sophisticated learners are amenable to using virtual-world technologies in acquiring critical thinking and clinical skills, and that the learning platform provokes learning that is experienced not only as useful and productive but also as fun – recreational in a broad sense. The Pulse!! method of case and technology development indicates that virtual-world educational platforms must be not only broad – user-friendly, visually effective, interactively immersive and fluid – but also deep: procedurally rooted in curriculum, rich in readily-accessible information and cognizant of actual practice in the real world. Furthermore, we believe that our experience in developing Pulse!! reinforces the notion that user-centered design approaches were not only effective for developing this learning technology,

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but for the development of other technologybased learning environments. By working with a multi-disciplinary team, we were able to design an interface that was effective. We were also able to anticipate, and eliminate, usability problems. We recommend this approach for others trying to develop training products for this complex and challenging domain.

REFERENCES Baldwin, T. T., & Ford, J. K. (1988). Transfer of training: A review and directions for future research. Personnel Psychology, 41, 63–105. doi:10.1111/j.1744-6570.1988.tb00632.x Cromwell, S., & Kolb, J. A. (2004). An examination of work-environment support factors affecting transfer of supervisory skills training to the workplace. Human Resource Development Quarterly, 15, 449–471. doi:10.1002/hrdq.1115 Ericsson, K. A. (1996). The acquisition of expert performance: An introduction to some of the issues. In The Road to Excellence: The Acquisition of Expert Performance in the Arts and Sciences, Sports, and Games (pp. 1–50). Mahwah, NJ: Lawrence Erlbaum Associates. Farrell, C. M., Klimack, W. K., & Jacquet, C. R. (2003). Employing Interactive Multimedia Instruction in Military Science Education at the U.S. Military Academy. Proceedings: Interservice/Industry Training, Simulation and Education Conference (I/ITSEC), Orlando FL. Federoff, M. A. (2002). Heuristics and usability guidelines for the creation and evaluation of fun in video games. Unpublished thesis, Indiana University, Bloomington IN. Gee, J. P. (2003). What Video Games Have to Teach Us About Learning and Literacy. New York: Palgrave MacMillan.

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Gould, J. D., & Lewis, C. (1987). Designing for usability: Key principles and what designers think. In Human-computer interaction: a multidisciplinary approach. San Francisco: Morgan Kaufmann Publishers. Holcomb, J. B., Caruso, J., McMullin, N. R., Wade, C. E., Pearse, L., Oetjen-Gerdes, L., et al. (2006). Causes of death in U.S. special operations forces in the global war on terrorism: 2001-2004. MacDill AFB, Tampa, FL: U.S. Special Operations Command. Issenberg, S.B., McGaghie, W.C., & Petrusa, E.R., Gordon, Lee, D., & Scalese, R.J. (2005). Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Medical Teacher, 27(1), 10–28. doi:10.1080/01421590500046924 Kirkpatrick, D. L. (1976). Evaluation in training. In R. L. Craig (Ed.), Training and Development Handbook: A Guide to Human Resource Development (2nd ed., pp. 1-26). New York: McGraw-Hill. Kohn, L., Corrigan, J., & Donaldson, M. (Eds.). (1999). To Err is Human: Building a Safer Health System. Committee on Quality of Health Care in America, Institute of Medicine. Washington, DC: National Academy Press. Norman, D. A., & Draper, S. W. (1986). User Centered System Design; New Perspectives on Human-Computer Interaction. Mahwah NJ: Lawrence Erlbaum Associates. Pannese, L., & Carlesi, M. (2007). Games and learning come together to maximize effectiveness: The challenge of bridging the gap. British Journal of Educational Technology, 38(3), 438–454. doi:10.1111/j.1467-8535.2007.00708.x Prensky, M. (2001). Digital Game-Based Learning. New York: McGraw-Hill. Rasch, R. F. R. (2006). Teaching opens new doors. Men in Nursing, 1(5), 29–35.

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Reid, P. P., Compton, W. D., Grossman, J. H., & Fanjiang, G. (Eds.). (2005). Building a Better Delivery System: A New Engineering/Health Care Partnership. Committee on Engineering and the Health Care System. IOM Press. Reznick, Richard K., & MacRae, H. (2006). Teaching Surgical Skills: Changes in the Wind. The New England Journal of Medicine, 355(25), 2664–2669. doi:10.1056/NEJMra054785 Salas, E., Bowers, C., & Rhodenizer, L. (1998). It is not how much you have but how you use it: Toward a rational use of simulation to support aviation training. The International Journal of Aviation Psychology, 8(3), 197–208. doi:10.1207/ s15327108ijap0803_2 Song, S., & Lee, J. (2007). Key factors of heuristic evaluation for game design: Towards massively multi-player online role-playing game. International Journal of Human-Computer Studies, 65(8), 709–723. doi:10.1016/j.ijhcs.2007.01.001 Tannenbaum, S., Mathieu, J., Salas, E., & CannonBowers, J. (1991). Meeting trainees’ expectations: The influence of training fulfillment on the development of commitment, self-efficacy, and motivation. The Journal of Applied Psychology, 76, 759–769. doi:10.1037/0021-9010.76.6.759 Verrier, E. D. (2004). Who moved my heart? adaptive responses to disruptive challenges. Journal of Thoracic and Cardiovascular Surgery, 127(5), 1235-1244. Retrieved May 4, 2007, at http:// dx.doi.org/10.1016/j.jtcvs.2003.10.016. Virvou, M., & Katsionis, G. (2008). On the usability and likeability of virtual reality games for education: The case of VR-ENGAGE. International Journal of Computers and Education, 50(1), 154–178. doi:10.1016/j.compedu.2006.04.004

Vora, J., Nair, S., Gramopadhye, A. K., Duchowski, A. T., Melloy, B. J., & Kanki, B. (2002). Using virtual reality technology for aircraft visual inspection training: presence and comparison studies. Applied Ergonomics, 33(6), 559–570. doi:10.1016/S0003-6870(02)00039-X Watters, C., & Duffy, J. (2004). Metalevel Analysis of Motivational Factors for Interface Design. In K. Fisher, S. Erdelez, & E.F. McKechnie (Ed.), Theories of Information Behavior: A Researcher’s Guide. Medford, NJ: ASIST (Information Today, Inc.). Watters, C., Oore, S., Shepherd, M., Azza, A., Cox, A., Kellar, M., et al. (2006) Extending the use of games in health care. Proceedings of the 39th Hawaii International Conference on System Sciences (pp. 1-8). Zimet, E., Armstrong, R. E., Daniel, D. C., & Mait, J. N. (2003). Technology, transformation, and new operational concepts. Defense Horizons, 31. Retrieved May 4, 2007, from http://www.ndu. edu/inss/DefHor/DH31/DH_31.htm.

KEY TERMS AND DEFINITIONS Curricula: Established, accredited educational content to be delivered experientially in virtual reality by which yardstick the effectiveness of a learning platform must be evaluated. Usability: A quality inherent in user-centered design of a virtual-reality platform determined by quantitative and qualitative data. User-Centered Design: A concept of platform design virtual space by which users participate in the design process in a variety of ways to ensure four basic principles, (1) ease of determining what actions are possible at any moment, (2) clarity of the visible workings of the system; (3) clarity of the current state of the system, (4) clarity of mappings of intention and action, e.g., visible information and interpretation of the system state.

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Virtual Reality: Three-dimensional space created by various virtual-world computer technologies that is navigable by users as though they were actually within that space.

Virtual-World Technologies: An array of computer programming conventions that compile mathematical algorithms with images to create the likeness of three-dimensional space on a twodimensional surface.

This work was previously published in Handbook of Research on Human Performance and Instructional Technology, edited by Holim Song and Terry T. Kidd, pp. 374-390, copyright 2010 by Information Science Reference (an imprint of IGI Global).

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Chapter 8.9

Visualization and Modelling in Dental Implantology Ferenc Pongracz Albadent, Inc, Hungary

ABSTRACT

INTRODUCTION

Intraoperative transfer of the implant and prosthesis planning in dentistry is facilitated by drilling templates or active, image-guided navigation. Minimum invasion concept of surgical interaction means high clinical precision with immediate load of prosthesis. The need for high-quality, realistic visualization of anatomical environment is obvious. Moreover, new elements of functional modelling appear to gain ground. Accordingly, future trend in computerized dentistry predicts less use of CT (computer tomography) or DVT (digital volume tomography) imaging and more use of 3D visualization of anatomy (laser scanning of topography and various surface reconstruction techniques). Direct visualization of anatomy during surgery revives wider use of active navigation. This chapter summarizes latest results on developing software tools for improving imaging and graphical modelling techniques in computerized dental implantology.

In this chapter the author give his experiences gained developing visualization and graphical modelling tools in applications for computerized dental implantology. Intensive development efforts can be seen worldwide on the field of image-guided navigation systems applied in dental implantology. Several commercial products are available on European market (Simplant®, www.materialise.com; coDiagnostiX®, www.ivssolutions.de; Denx®IGI, www.denx.com; ARTMA Virtual Implant®, www.landsteiner.org; Galileos Implant Guide, www.sirona.de, Dental Planner, Albadent Inc.). Drawbacks and advantages of technology in dentistry and oral surgery for the last decade are surveyed by several laboratories (Siessegger et al, 2001, Ewers et al, 2005). The computerized implantology evolves into two directions: 1/ surgery support with prefabricated templates; 2/ active, image guided navigated drilling. In case of surgical template preparation of both the registration and drill calibration can be made outside of operation room in a stress-free

DOI: 10.4018/978-1-60960-561-2.ch809

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Visualization and Modelling in Dental Implantology

environment. This improves accuracy; however, other problems like template fixing to patient’s jaw, complete absence of interactive control of drill’s orientation in space make the future of this method uncertain. In case of active, intraoperative navigation, the lack of experience, poor software support for synchronization of real and virtual environments sometimes overburden the surgeon. Future trend in computerized dentistry predicts less use of direct diagnostic imaging and more use of 3D visualization of anatomy (laser scanning of topography and various surface reconstruction techniques). Functional modelling is now possible with tooth surface database and simulation of occlusal properties (Pongracz and Bardosi, 2006). Modern visualization methods like resampling the slice sequence in CT and MR diagnostic volumes, combined views of surface topography and mesh surfaces support the modelling. These new approaches help in direct visualization of anatomy during surgery that revives wider use of active navigation in the future. The accuracy of dental hand piece tracking in active navigation highly depends on the alignment method between diagnostic and surgical spaces (REGISTRATION) and the alignment procedure between tool’s own coordinate space and the coordinate space of attached sensor (CALIBRATION). Well designed computational approach is needed for calibration of dental hand piece with small and unique geometry (Bardosi and Pongracz, 2007, Pongracz et al, 2007).

GRAPHICAL MODELLING Data Sources Conventional (CT) and cone beam computer tomography (CBCT) are generally used for 3 dimensional dental imaging. Recent results have controversial results regarding optimization of image characteristics (artifacts reduction versus density resolution) by using CT and CBCT tech-

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nologies (Humpries et al, 2006, Katsumata et al, 2007). In our studies the patient imaging data are read in from both CT and CBCT sequences and stored as volumetric model (Xoran Technologies: i-CAT 3D Dental Imaging, GE Medical Systems: HiSpeed NX/i). Reslicing algorithm is frequently included to generate slice view in arbitrary cutting plane orientation. Bone surface models are created by isosurface raytracing algorithm or marching cubes algorithm. Mesh surfaces can be imported for adding gips model topography and realistic model of implants. The surface models can be clipped to see interaction of surfaces with volumetric data and visualize the details of interocclusal relationships.

Tooth Database This is a really difficult issue because the fine, realistic details of intercusp relations are important for dental planning and their presence in the program gives an impression of the real environment for the dentist. The realistic graphics helps in navigating within the virtual scene for medical personnel even if he doesn’t like working with computers. The CT imaging was performed on teeth of undamaged surfaces taken from the same cadaver skull. The teeth had no metal filling. The surface reconstruction was made by marching cubes (Lorensen and Cline, 1987) and decimator algorithms to find polygons and set their number to a reasonable level (Figure 1). The ideal positions and the 3D surface models of each tooth are stored and added during initialization (read in from compressed file package). Their locations are rendered to the standard dentition curve (represented as cubic spline connecting the surface centers of teeth). The coordinates are based on detailed anatomical description of articulation of dental curves and classification of the permanent dentition which are available for a long time (Massler and Schour, 1958). Local alignment and scaling of each tooth is possible within the ideal occlusal reference frame on separate panels

Visualization and Modelling in Dental Implantology

Figure 1. Tooth surface database from cadaver teeth. Each tooth model was reconstructed from CT data with marching cubes algorithm and decimated to 5000 triangles. After reconstruction they were placed into a global coordinate space defined by occlusal triangles. The triangles for the upper and lower jaws are in the same position and size according to ideal centric occlusion. Spline curves, - crossing the occlusal triangles’ vertices - added to initialize dentition curves. The shape of dentition curves is controlled by moving the triangles’ vertices in 3 dimensions. Each tooth model can be locally moved, rotated relative to their initial location on dental curve. Each tooth has own anatomical coordinate space and their shape can be rescaled along any of local axes

for mesiodistal (transversal) and faciolingual (tangential) inclinations. These are related to the actual tangent and normal planes of the upper or lower dentition curves. Similar database has been created by others (Hassan et al, 2005, Krsek et al, 2007) but without using globally structured datasets based on ideal dental curves.

Dentition Planning Latest effort in software development reveals that functional modelling is possible within the diagnostic environment with some novel graphical features (Pongracz et al, 2005, Pongracz and Bardosi, 2006). The goal is to simulate the function of articulator used in conventional design of prosthesis. The optimal dental occlusion is estimated according to the condition of centric occlusion i.e. after bringing occlusal surfaces of mandible and opposing maxillary arch into identical centered position. This identical position of occlusal sur-

faces is set by triangles in the program, and—after appropriate resampling (interpolation of images according to the triangles’ normal vectors) of the CT volume—the diagnostic estimate for an ideal interocclusal relationship can be created (Figure 2). The original volume is divided into two subvolumes during resampling: first one is defined above the maxillary triangle and the second one is under the mandibular triangle. These two subvolumes are merged into a single one for analysing local relationships. This resampled volume now determines the common reference frame for all manipulation of tooth and implant models and helps in implant placement (Figure 3). Local alignment is made on separate planes for mesiodistal and faciolingual inclinations. These local views display the resampled image together with tooth models (from database) to predict small details for contact areas. The tooth models can be scaled, rotated and shifted in 3D to fit patient’s anatomy.

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Figure 2. Modelling centric occlusion in diagnostic environment. The 3D volumetric resampling is performed separately for the upper and lower subvolumes (maxilla and mandible) above the upper triangle and under the lower triangle, respectively

Figure 3. Modelling local interocclusal contacts in resampled volume (a and b: transversal and tangential planes). Implant planning has been made in parallel with dentition planning. The surface model (c and d) is given according to the original volume sampling in CT and shows the final results of planning

Topographic Modelling The information on the shape of mucous soft tissue is added to the graphical scene after laser scanning the gips model or other negative impression taken from the mouth. The mesh surface of gips model is registered to CT diagnostic data and visualized with or without the bone surface. Important element of the topographic modelling is

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the clipping and contur tracing algorithms together with an appropriate texture mapping onto planes or surface meshes. The effective use of these new methods needs special programming technique based on OpenGL (OpenGL 2.1: www.opengl.org/ documentation/) or DirectX (The DirectX SDK: http://msdn.microsoft.com) graphical libraries and suitable hardware support. The conventional, CPU based programming can slow down the output

Visualization and Modelling in Dental Implantology

causing large delay in visualization that hinders these new methods from surgical planning. The bone surface is first reconstructed by segmentation and marching cubes algorithms which are available in several third party applications (Osirix Mac OSX version is used in this article). After it the exported model of bone surface, which is automatically aligned with CT data, can be included in modelling. The 3D scene now contains several graphical elements, such as surface mesh of the gips model for soft tissue representation, bone surface, CT volumetric model, implants, virtual hand piece for active navigation and clipping planes for interactive visualization of internal structures. Following figures show samples of texture mapping onto the clipping plane defined on the

registered model of soft tissue (Figure 4a). The critical procedure for implant localization can be greatly simplified. The optimum software implementation of previous calculation utilizes the power of 3D texture programming and graphics accelerator card. The real-time calculation and visualization of resampled CT images give a chance for fast, convenient diagnostic analysis.

Bone Clipping The reconstruction of bone surface of the maxillofacial area is usually difficult because of frequent artifacts seen in CT images. The marching cubes algorithm (Lorensen and Cline, 1987) generates noisy surface with abundant components in mesh. Several approaches are known to decrease artifacts

Figure 4. (a) Topographic modelling with vertical and horizontal clipping planes, contur detection with soft tissue surface and CT texture mapping (both clipping planes are locked to implant and can be moved in real-time). (b) and (c): Clipping with transversal and horizontal planes. The clipping is applied for registered models of bone (white) and surface of soft tissue (green). Distance analysis between the bone surface and soft tissue is possible by appropriate compression and expansion algorithms applied to the surface representing soft tissue

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and improve surface reconstruction (Bazalova et al, 2007, Krsek et al, 2007, Yongbin et al, 2007, Beaulieu and Yazdi, 2006). The visibility of complex region at tooth-jaw bone transitions can be greatly enhanced by clipping at critical locations. The relationship between the topographic and bone surfaces can be analysed and small details visualized by plane and volume clippings. This procedure enhances the visibility of critical layer between the bone surface and the surface of mucous soft tissue. The clipping can be performed in real time synchronized or unsynchronized to the implant alignment procedure. The unclipped surface models of bone and soft tissue are usually displayed in noisy 3D scene with disturbing metal artifacts. The hidden elements of the implant base and abutment tool can be made visible with clipping planes. Figures 4B and C illustrate the results after clipping with transversal and horizontal planes. Both clipping interactions are locked to the actual position and orientation of implant. Further clipping is possible by volumes with resizable edges. Graphical manipulations (clipping plane rotation around the implant axis, resizing of volume clip edges near the implant) help in detailed visualization. Surface expansion and compression tools can be easily implemented in this environment to support distance analysis between the bone surface and soft tissue. This analysis can give valuable information for surgery. At this time the real-time clipping technique is more acceptable in practical applications than any filtering or image pre-processing method.

HAND PIECE NAVIGATION CONTROL Previous results of graphical modelling and visualization can be validated in active navigation after the registration of surgical space and drill calibrations have been done. The registration to the CT volume can be performed with the help of surface fiducials located on the template (Figure

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5a). The registration should be handled as SVD (“singular value decomposition”) problem and this way, even with ill-defined marker positions, a least-square estimate always can be found for the registration matrix (Arun et al, 1987, Press et al, 1992). The hand piece calibration has three steps (using optical tracking with passive sensors): 1. Tip offset vector calculation is based on the known pivoting procedure (Bardosi and Pongracz, 2007, Pongracz et al, 2007) and gives back results with accuracy under 0.2 mm (mean sqrt variation from pivoting, 50-100 samples); 2. Drill axis direction was estimated by surface scanning (50-200 samples) with the pointer; the estimated projection error remained in the range of 0.15-0.2 mm; 3. Final orientation of tool’s space is aligned to the tool’s geometry (handle position) by the pointer of the tracking system. This procedure is flexible and accurate enough to use for small dental hand piece and also usable in many cases during research and development period of navigation systems. Tip offset vector calculation. The drill calibration (both for template drilling and active navigation) is based on the known pivoting procedure (tip offset vector calculation) (Figure 5b). Tip offset calculation steps: • • • •

Sampling data by rotating the tool around the tip; All points lie on a tip-centered sphere; Task: find the center point (tip offset) (t); SVD problem

Drill axis calculation. The second phase of the calibration (axis direction) starts with data capture on that part of device which has a cylinder shape with an axis identical with the tip direction. An optimization algorithm (Levenberg-Marquardt

Visualization and Modelling in Dental Implantology

Figure 5. Registration and drill calibration controls in the navigation panel of dental planning and navigation program. (a) Typical arrangement of registration panel. The registration procedure is markerbased with accuracy in the range of 0.3 to 0.6 mm. The patient is scanned in CT lab with a template containing four fiducials. (b) Tip offset calibration for navigated drill. The offset vector points from the sensor’s space origin to the drill tip. (c) and (d): Axis vector calibration of small dental hand piece. The system reads in location and orientation values for tool sensor and pointer sensor during the random move of the pointer on drill head

method; Press et al., 1992) was implemented which uses the output of data capture made by the random move of the pointer on the tool cylinder (Figures 5c and d). This optimization procedure projects first the random surface points onto the plane centered by the tip offset vector which was determined during the pivoting phase. The 3D projection transform for the actual axis direction is calculated from two orientation angles. These angles are the parameters to be optimized. On output the axis calibration procedure gives back the orientation angles for the tip axis, the cost value at optimum and the estimated diameter of the fitted cylinder. Tool axis calibration algorithm: •

2 rotation angles used to define „projection plane”, relevant to the problem (Φ,Ψ);

• •

Center the data origin to the tip offset (preset during tip offset pivoting); Cost function is the empirical variance of the distances of the projected points from the origin, as related to the rotation angles: where is the rotation/projection matrix.

The final orientation of tool’s space can be aligned to the tool’s geometry (handle position) by an up-vector defined by the pointer.

SYSTEM DESIGN FOR ACTIVE NAVIGATION The architecture of the software implementation can follow different principles: 1/ modular design with panels reflecting well distinguished steps in surgical workflow; 2/ highly simplified view

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integrating several steps in workflow. The latest development in computer hardware offers powerful support for real-time graphical programming (Burdea and Coiffet, 2003). The improved quality and fast, direct visualization of anatomy during surgery revives wider use of active navigation. Modern programming techniques with hardware accelerators help to improve applications for surgical planning. Large volumetric or surface models can be moved, clipped or resampled (recalculated according to non-standard sampling directions) in real time. The interaction of different diagnostic models can be visualized with fine details. Communication with new peripherals like 3D mouse can make the graphical manipulation of virtual scenes more and more acceptable for medical personnel and will have an increasing impact on dentistry in the future (Huff et al, 2006). The inadequate drill calibration methods can influence the accuracy of active navigation of small dental hand piece. Therefore, generally usable calibration procedure is needed, which, by means of the methods known in reverse engineering, accurately estimates the tip offset and axis direction of navigated device relative to the space of an attached motion sensor. An implementation of modular calibration for improved accuracy control has been described in this article. This method gives stable output without the use of prefabricated adapter clamps known in commercial systems.

CONCLUSION Novel visualization and modelling techniques have been summarized for use in field of dental implantology. New graphical tools have been presented for functional modelling. Latest modelling incorporates inter-occlusal analysis of teeth contacts and simulation of dentition together with virtual implant positioning. The critical relationship between the jaw bone surfaces and the mucous soft tissue can be visualized. An implant planning and navigation system has been

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developed which implements the new features presented in this article.

ACKNOWLEDGMENT The author thanks to Dr. M. Truppe (Karl Landsteiner Institute, Vienna) for medical advices, Zoltán Bárdosi (Eötvös Lóránd University, Budapest) for help in software development and Northern Digital Europe GmbH (Radolfzell, Germany) for supplying us with the Polaris system. This work was supported in part by the Bureau of National Research and Development in Hungary under the combined EU/hungarian grant GVOP-3.3.305/1.-2005-0002/3.0.

REFERENCES Arun, K. S., Huang, T. S., & Blostein, S. D. (1987). Least square fitting of two 3-D point sets. [PAMI]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9, 698–700. doi:10.1109/ TPAMI.1987.4767965 Bardosi, Z., & Pongracz, F. (2007). Flexible drill/ endoscope calibration method for navigation. Proc.of 4thInternational Conference on Computer Aided Surgery Around the Head, Innsbruck, (pp. 11-13). Bazalova, M., Beaulieu, L., Palefsky, S., & Verhaegena, F. (2007). Correction of CT artifacts and its influence on Monte Carlo dose calculation. Medical Physics, 34(6), 2119–2132. doi:10.1118/1.2736777 Beaulieu, L., & Yazdi, M. (2006). Method and apparatus for metal artifact reduction in computed tomography. Patent Pub. No.: WO/2006/039809 (US), PCT/CA2005/001582 (Int.) Burdea, G. C., & Coiffet, P. (2003). Virtual reality technology, (2nd Ed.) with CD-ROM. New York: Wiley.

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Ewers, R., Schicho, K., Undt, G., Wanschitz, F., Truppe, M., Seemann, R., & Wagner, A. (2005). Basic research and 12 years of clinical experience in computer-assisted navigation technology: a review. International Journal of Oral and Maxillofacial Surgery, 34, 1–8. doi:10.1016/j. ijom.2004.03.018 Hassan, H., et al. (2005) A volumetric 3D model of the human jaw. Proceedings of 19th International Congress and Exhibition of CARS2005, Berlin, Germany, Elsevier ICS 1281, 1244-49. Huff, R., Dietrich, C. A., Nedel, L. P., Freitas, C., Comba, L. D., & Olabarriaga, S. D. (2006). Erasing, digging and clipping in volumetric datasets with one or two hands. VRCIA’06 Proceedings of the 2006 ACM int. conf. on Virtual reality continuum and its application, Hong Kong, 271-278. Humphries, S., Christensen, A., & Bradrick, J. (2006). Cone beam versus conventional computed tomography: comparative analysis of image data and segmented surface models. [Computer Assisted Radiology and Surgery]. International Journal of Computer Assisted Radiology and Surgery, (Suppl. 1), 398–400. Katsumata, A., Hirukawa, A., Okumura, S., Naitoh, M., Fujishita, M., Ariji, E., & Langlais, R. P. (2007). Effects of image artifacts on gray-value density in limited-volume cone-beam computerized tomography. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontics, 104, 829–836. doi:10.1016/j.tripleo.2006.12.005 Krsek, P., Spanel, M., Krupa, P., Marek, I., & Cernochov, P. (2007). Teeth and Jaw 3D Reconstruction in Stomatology. Medical Information Visualisation – BioMedical Visualisation. MediVis, 2007, 23–28. Lorensen, W. E., & Cline, H. E. (1987). Marching cubes: A high resolution 3D surface construction algorithm. Computer Graphics (SIGGRAPH ’87 Proceedings), 21, 163-169.

Massler, M., & Schour, I. (1958). Atlas of the Mouth. Chicago: American Dental Association. Pongracz, F., & Bardosi, Z. (2006). Dentition planning with image-based occlusion analysis [Computer Assisted Radiology and Surgery]. International Journal of Computer Assisted Radiology and Surgery, 1(3), 149–156. doi:10.1007/ s11548-006-0052-6 Pongracz, F., & Bardosi, Z. (2007). Navigated drilling for dental implants based on virtual occlusion analysis: presentation of a new approach. Proc.of 4thInternational Conference on Computer Aided Surgery Around the Head, Innsbruck, (pp. 125-127). Pongracz, F., Bardosi, Z., & Szabo, L. (2005). Dentition planning for image-guided implantology. In Proc. of CARS (Computer Assisted Radiology and Surgery), International Congress Series, Chicago, 1268, (pp. 1168-1173). Pongracz, F., Szabo, L., & Bardosi, Z. (2007). Planning and navigation tools for dental implantology. Proc.of Automatisierungstechnische Verfahren für die Medizin Nr.267, München, (pp. 41-42). Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1992). Numerical Recipes in C. The Art of Scientific Computing. Cambridge: Cambridge University Press. Siessegger, M., Schneider, B. T., Mischkowski, R. A., Lazar, F., Krug, B., & Klesper, B. (2001). Use of an image-guided navigation system in dental implant surgery in anatomically complex operation sites. Journal of Cranio-Maxillo-Facial Surgery, 29, 276–281. doi:10.1054/jcms.2001.0242 Yongbin, Z., Lifei, Z., Zhu, X. R., Lee, A. K., Chambers, M., & Lei, D. (2007). Reducing metal artifacts in cone-beam CT images by preprocessing projection data. International Journal of Radiation Oncology, Biology, Physics, 67, 924–932. doi:10.1016/j.ijrobp.2006.09.045

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KEY TERMS AND DEFINITIONS Active Navigation: The type of surgical interaction where the surgical tool is moved under continuous 3D motion tracking on a motion path which is selected freely by the surgeon. Centric Occlusion: The ideal, closed position of upper and lower jaws. The teeth contacts in this case represent the ideal relationships in the mouth. Hardware Accelerator: The name of an internal element of computer hardware which supports the visualization of complicated 3D graphics. Maxilla and Mandible: Upper and lower jaws, respectively. Open GL: The name of a graphical library which is frequently used during software development with complex 3D visualizations. Optical Tracking: It is a procedure where the motion of a rigid object is continuously followed by cameras through sensors attached rigidly to the object. Surface Model: Mathematical representation of surfaces. Ordered sequence of large number of small polygons (usually triangles) which can be placed in 3D graphical scene created by programming.

Surgical Template: Supporting tool or guide to help surgical interaction by guiding tools into the target position. Computerized dental implantology uses frequently this approach to prepare implant placement into jaw. Tip Offset in Navigation: The 3D vector representing the offset from the origo of an attached motion sensor to the tip of the navigated drill. The tip offset vector is defined in the local coordinate space of the attached sensor. Tip Direction in Navigation: The 3D axis vector representing the direction of the drill axis within the coordinate space of the attached motion sensor. Topographic Surface: The surface which represents 3D variability of shape of an object. Laser topography can be created by laser scanning of the visible surface of an object made from hard material. Volumetric Model: Mathematical representation of 3D grid consisting of voxels (3D equivalents of pixels) in vertices. The model can be filled up by reading in the sequence of grayscale medical images.

This work was previously published in Dental Computing and Applications: Advanced Techniques for Clinical Dentistry, edited by Andriani Daskalaki, pp. 159-169, copyright 2009 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 8.10

Patient Journey Record Systems (PaJR) for Preventing Ambulatory Care Sensitive Conditions: A Developmental Framework

Carmel M. Martin Trinity College Dublin, Ireland

David Topps Northern Ontario School of Medicine, Canada

Rakesh Biswas People’s College of Medical Sciences, India

Rachel Ellaway Northern Ontario School of Medicine, Canada

Joachim Sturmberg Monash University and The University of Newcastle, Australia

Kevin Smith National Digital Research Centre, Ireland

ABSTRACT This chapter articulates key considerations for the translation of the concept of the Patient Journey Record Systems (PaJR) into real world systems. The key concept lies in the ‘discovery’ of the use of patient narratives to locate the phase of illness in a patient journey. We describe our developmental framework of in the context of Ambulatory Care Sensitive Conditions (ACSC) for older patients with multiple morbidity, who are at a high risk DOI: 10.4018/978-1-60960-561-2.ch810

of hospitalizations and other adverse health outcomes. The framework addresses the feasibility and usability of an information technology based solution to avert adverse outcomes of hospitalization when this is potentially avoidable by activities in primary care. Key considerations in the PaJR knowledge systems are the design and implementation of robust expert knowledge and data support systems. The patient, caregiver, physician and care team perspectives drive clinical usability and functionality requirements. Experts from computer science domains in artificial intelligence, expert systems, and decision support systems ensure the

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Patient Journey Record Systems (PaJR) for Preventing Ambulatory Care Sensitive Conditions

requirements for the functionality of underlying systems architecture are met. We explore this transdisciplinary perspective and ways in which coherence might be achieved among the many practitioners and expert domains involved in a developmental framework for PaJR. We make a case for the implementation of PaJR systems as part of a universal move to electronic user driven health care.

for patients with chronic conditions at high risk of decline, or where they or their families have sustained inputs and an ongoing active role in their care.

INTRODUCTION

Understanding Knowledge Translation: A Brief Introduction

In this chapter we explore the translation of explicit and tacit knowledge (J. P. Sturmberg & Martin, 2008) about the patient journey into real world systems – the Patient Journey Record System (PaJR). The exploration described in this chapter is from a clinical perspective. We propose a developmental framework to encompass knowledge translation from implicit information to explicit knowledge utilizing many existing developments in health services use of information technology, supporting self care and clinical care. Increasingly ongoing developments in information technology can, if appropriately designed, provide a way forward for patient-centered user-driven care, particularly

TRANSLATING NEEDS, IDEAS AND KNOWLEDGE FOR AN INFORMATION TECHNOLOGYBASED SOLUTION

Knowledge translation (KT) is the process of transferring research-based knowledge to daily practice. Moving knowledge between users, researchers, inventors, innovators and consumers should benefit society by improving the well being for its members, and enhancing the economic rewards for its goods and services (Graham & Tetroe, 2007). Knowledge comes in many forms and Lane and Flagg (2010) have identified three stages of development from the concept to operational design to implementation and marketing (Figure 1) (Lane & Flagg, 2010).

Figure 1 Stages of knowledge translation from discovery, to invention to innovation.(Lane & Flagg, 2010) with the PaJR knowledge translation steps

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The development of a human information knowledge-based system (Kendal & Creen, 2007), involves: “Assessment of the problem; Development of a knowledge-based system shell/structure; Acquisition and structuring of the related information, knowledge and specific preferences for usability and functionality; Implementation of the structured knowledge into knowledge bases; Testing and validation of the inserted knowledge and Integration and maintenance of the system and Revision and evaluation of the system.” (“http:// en.wikipedia.org/wiki/Knowledge_ engineering#cite_note-3,” 2009). Based on this theoretical framework and the PaJR conceptual framework, (Carmel M Martin, Biswas, Joshi, & Sturmberg, 2010)we are developing a prototype IT-solution to integrate patient, carer and clinician knowledge for ongoing close monitoring and more timely intervention for patients with chronic and/or unstable conditions. A workable prototype implies the development of potential applications that form the basis for intellectual property and claims through patenting. Inventions are more tangible than discoveries, although inventions are still malleable and open to shaping in different ways (Lane & Flagg, 2010). A technology-based solution may be feasible and novel in a controlled setting, but utility is achieved only when the solution addresses the economic and operational constraints of the target user’s problem in the context of the marketplace.

The Problem and Context for the PaJR Developmental Framework for Chronic Illness In Relation To Ambulatory Care Sensitive Conditions (ACSC) “Chronicity” implies ongoing asynchronous and heterogeneous journeys of individuals through disease, illness and care encompassing health promotion, preventative care, diagnosis, self management and self-care support, disease man-

agement and control, treatment and palliation (C. Martin, 2007). We argue that there is an immediate need for a frame of reference in order to design patient journey systems for individually tailored care that can address unmet needs in current and future health and e-health systems, building on existing knowledge in areas such as chronic disease management (Dorr et al., 2007) and complex adaptive chronic care models (Martin CM & Sturmberg JP, 2008). Ambulatory care sensitive conditions (ACSCs) are those conditions for which hospital admission could be prevented by interventions in primary care, the majority of which occur in complex phases of chronic care (U.S. Agency for Healthcare Research and Quality, March 12, 2007). Hospital admission may resolve acute destabilization of current health status, but does not necessarily resolve the underlying problem for which people are admitted, and many admissions result in harm due to unintended consequences of interventions including cognitive decline, mental distress, hospital acquired infections and inappropriate use of hospital resources. (Gillick, Serrell, & Gillick, 1982; Htwe, Mushtaq, Robinson, Rosher, & Khardori, 2007; M. Martin, P. Y. Hin, & D. O’Neill, 2004; Podrazik & Whelan, 2008; Shepperd et al., 2009). The problem of ACSCs is increasing in size, as the population ages, underscoring the importance of developing, adopting and implementing innovative programs (Falik, Needleman, Wells, & Korb, 2001). While an adequate supply of primary care services are required to provide interventions to prevent avoidable ACSC events, even in countries with well-developed universal and comprehensive primary care services such as the UK, Canada and Australia, there is, at the individual level, unpredictability in timing the need for an early intervention. Internationally, health decision makers and service providers are seeking mechanisms for timely interventions to prevent ACSCs using predominately disease-focused guidelines and protocols, selected aged care team programs and/

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or and fairly expensive telehealth models (Cai, Johnson, & Hripcsak, 2000; Celler, Lovell, & Basilakis, 2003; Darkins et al., 2008; Fursse, Clarke, Jones, Khemka, & Findlay, 2008; Kim & Oh, 2003; Krumholz et al., 2006) (Bronagh, Helen, & Jane, 2007; Laditka, Laditka, & Mastanduno, 2003; M. Martin, P. Hin, & D. O’Neill, 2004). Although the concept of early intervention may be promising, many inventive/innovative programs to prevent ACSCs are not cost-effective (Armstrong et al., 2008; Gravelle et al., 2007; Malcolm Battersby, 2007). For instance the Australian Coordinated Care trials, have involved a high level of human resources in order for a team to intensely follow a cohort of high risk individuals and have not proved to be cost effective.(Esterman & BenTovim, 2002) Arguably, this is due to intense interventions being continuously deployed to the at-risk population over long periods of time which include a considerable amount of time when they are relatively well rather than distributing intense resources to people only when they are needed. Using linear models of health services research, there is an ongoing view that innovation is concerned with adding new services, rather than reconfiguring existing services. The result are larger teams are the answer making care more complicated and expensive, and more process orientated rather than focused on the individual journey based on affective as well as cognitive knowledge ((Carmel M. Martin, Biswas, Topps, Joshi, & Sturmberg, 2010) While the use of narrative in electronic records is not in itself new (Kay & Purves, 1998), the PaJR approach is ‘exploring and discovering’ in its combination of multiple narratives around a patient’s journey as a temporal framework into which more or less structured data and services are connected and contextualized. Not only does this provide a more human dimension to the creation and use of health information, it integrates the patient as an active rather than passive participant and helps to reduce some of the inequalities intrinsic to data- and schema-driven models (Kay

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& Purves, 1998). Our framework supports chronic disease self-management and clinical interventions across all phases of the patient journey as a problem solving exercise in relation to the need to provide complex adaptive chronic care for those involved in unstable and multidimensional phases of chronic illness.

Development of a Knowledge-Based System Shell/Structure for the PaJR Developmental Framework We propose that the input of patient and caregiver narratives and semi-structured data will be analyzed with intelligent knowledge systems that provide feedback to physicians and care teams. Identifying the state of “self rated health” or “health” from the patient and care giver narratives is a first step. (See Key Terms and Definitions) (Idler & Benyamini, 1997; J. Sturmberg, Martin, & Moes, 2010) The trajectory of self reported health, and personal support arrangements, provide the basic knowledge base that would be acquired by the analysis of self reports using various input modes, including text (both email and/or SMS), voice and mobile phones. These data would be coded and analyzed by intelligent agents and developed into a system to track the illness.

The Development of an Expert System Expert systems are designed to carry the intelligence and information found in the intellect of experts and provide this knowledge to other members of the organization for problem-solving purposes. Problem solving is a mental process and has been defined as higher-order cognitive process that requires the modulation and control of more routine or fundamental skills. Problem solving occurs when an organism or an artificial intelligence system needs to move from a given state to a desired goal state. In the PaJR project clinical experts, typically primary care physicians and nurses with many

Patient Journey Record Systems (PaJR) for Preventing Ambulatory Care Sensitive Conditions

years of experience on “narratives and symptoms of decompensation”) are will be asked to provide “rules of thumb” on how they evaluate the problems, both explicitly with the aid of experienced systems developers, and implicitly, by evaluating evaluate case vignettes and using computer programs to examine the test data and (in a strictly limited manner) derive rules from their processes. Generally, expert systems are used for problems for which there is no single algorithm or “correct” solution which can be encoded in a conventional algorithm such as in a clinical pathway Simple systems use simple true/false logic to evaluate data. More sophisticated systems are capable of performing at least some evaluation, taking into account real-world uncertainties, using such methods as fuzzy logic and complexity science. Such sophistication is difficult to develop and still highly imperfect. http://en.wikipedia.org/ wiki/Expert_systemaccessed8/03/2010

Expert Systems Shells or Inference Engines A shell is a complete development environment for building and maintaining knowledge-based applications. It provides a step-by-step methodology, and ideally a user-friendly interface such as a graphical interface, for a knowledge engineer that allows the domain experts themselves to be directly involved in structuring and encoding the knowledge base. http://en.wikipedia.org/wiki/ Expert_systemaccessed8/03/2010

Issues to Consider In the Development of the PaJR System Kevin Smith – IT –knowledge translation and systems design expert

Firstly –it is important to develop partnerships among the clinicians and the intelligent systems experts. An intelligent system must be derived from multiple perspectives –initially - according to user requirements that then determine functional requirements that shape system design. This is always a non- linear iterative process. In order to derive the user requirements and functionality requires the clinician to engage with AI specialists and natural language processes. – AI or knowledge engineering specialists don’t see patients, physicians don’t design IT systems. Partnerships and joined up expertise, thus, builds innovation. PaJR represents a journey, not only of the patient and the caregiver and care team, but also through multiple disciplines. The phases involved in the translational research framework are described below. These disciplines involved in these phases range from clinicians and psychologists to linguists and computational linguists to knowledge engineers and clinical evaluators and researchers. Assess Identify physical, psychosocial and practical needs and problems Provide Structural support - personal and practice continuity, access Information/advice on drugs, treatment, lifestyle Emotional reassurance and psychosocial counseling Appraisal and practical support Mobilize Caregiver and or social agency to enhance physical, psychosocial and practical support Coordinated and integrated appropriate sources, facilitate structure of support

User requirements and functionality are always the key drivers in the designing of intelligent systems for health care.

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Training an Expert System for Patients with At Risk of an Ambulatory The PaJR expert system will be ‘trained’ to monitor patient and care giver narratives by physicians and nurses who are experienced in the triage and management of people with multiple morbidity and at risk of an ACSC. The training set will be based on real cases and case vignettes of the patient journey as reported by the patient and/or care giver(s), the decisions made in clinical practice and the outcomes observed. Patients and care givers would be trained to record any concerns as soon as they occur and well before they might consider calling their physician or nurse. The expert system would be trained to monitor these narratives to detect changes predicting the need for early intervention. The advantages of using an expert systems approach in PaJR includes the extension of outreach to multiple patients in an at risk cohort and the provision of early and/or continuous monitoring of individuals by the team on an as needed real time basis, 24 hours a day, 7 days a week. It will achieve this by provision of consistent processing and the maintaining of continual monitoring. However, expert systems are essentially cognitive and they lack ‘common sense’ and the everyday knowledge of care. They cannot draw on the vast patterns of experience from outside the narrowly focused expert system to address atypical profiles or unusual profiles. Experts need to express both explicit knowledge and implicit knowledge which may be difficult to articulate. Hence there is a need to see such systems as providing sophisticated extensions to facilitate timely access to clinical care and thus earlier decision making care rather than replacing the cognitive and affective skills of clinicians. The PaJR framework describes a proposal for an innovative user driven, low cost, real time information adaptive solution for the prevention of ASCS (See Figure 2 for the PaJR ‘discoveries’).

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The long-term patient-physician relationship and organizational structures of General Practice and Primary Care ideally provide the nexus for ongoing linking of multiple trajectories. In particular it has been shown that patients can benefit from information and communications technology (ICT) support of self-management (Green, Fortin, Maclure, Macgregor, & Robinson, 2006). Chapter 15 identified the importance of patient and care giver narratives, as they provide physicians and other health care professionals with knowledge about the patient’s journey in order to provide timely and appropriate user diver health care(Carmel M Martin, et al., 2010). Self-rated health and patient perceptions of illness are better predictors of mortality and morbidity than disease-specific parameters (Bayliss, Ellis, & Steiner, 2009; Hubbard, Inoue, & Diehr, 2009; Idler, Russell, & Davis, 2000). (See Key Terms and Definitions) The patient journey, using a complexity science perspective, can be understood as a journey towards the cusp of a ‘catastrophe’ of an ambulatory care sensitive condition.(Guastello, 2005) The risk of an avoidable ACSC remains low while the individuals’ health state and their overall sense-making are stable. However, if important factors in biopsychosocial domains change slowly, the individual and their social and care networks usually adapt. However, once these destabilizing factors reach a critical point, a sudden change to high risk, and hence the need for an ACSC alert, occurs. Subcritical risk levels are gradients to the high risk state. Because of the unpredictability of the timing of triggers and enablers of decline on the individual trajectory towards an ACSC adverse event, prior “learning of the system” from real-time data are required to “teach the system” to detect triggers and gradients of decline. A successful system will minimize false positives as well as negatives, and identify changes towards improvements or deterioration. The system will mainly function between the chronic and subacute complex phases in order preempt unstable

Patient Journey Record Systems (PaJR) for Preventing Ambulatory Care Sensitive Conditions

Figure 2 PaJR ‘discoveries’ are using the framework of Lane and Flagg. (Lane & Flagg, 2010)

phases of illness and acute care. It will not be involved if change is very rapid and in acute or very stable stages when narratives and patient data are not recorded. Currently, prediction theories related to complex nonlinear systems are infrequently used in health research. In fact, complexity theory states that it is not possible to accurately predict outcomes in complex systems, although methods such as Bayesian probability theory, cusp catastrophe theory and the use of mathematical techniques including catastrophe and growth mixture analyses, vector auto regression (VAR), Bayesian VAR and Variance decomposition provide better estimates than simple linear models (Enders, 2003; Guastello, 2005). Real-time self monitoring of symptoms and experiences is becoming common in specialized areas such as oncology that increasingly relies on sophisticated patient reported outcome measures (Basch et al., 2005; Jones, Snyder, & Wu, 2007). The PROM (patient-reported outcome measures) movement increasing seeks to obtain patient feedback through patient centred measures of experiences of care, in a whole range of areas from prostate surgery to psychological counseling. (Marshall, Haywood, & Fitzpatrick, 2006) Yet, in the US and other countries’ primary care delivery systems are not alerted by real-time patient reported deterioration patterns to intervene cost-effectively in primary care patients who

contribute the bulk of ACSCs. Our conceptual framework provides the concepts to build such systems – in this case PaJR. Expert support and advice, for example on lifestyle changes such as diet and exercise, are central to health promotion, as is practical advice from individuals, family and peer supports (Ball, Costin, & Lehmann, 2008). Ongoing developments in ICT can provide a way forward for physician supported patient-centered user-driven care, particularly for patients with chronic conditions. Patients and caregivers who take a more active role in care can identify early deterioration patterns to assist timely interventions (Bodenheimer & Grumbach, 2003). Informatics, knowledge systems and complexity science (Darkins, et al., 2008; Mikhail Prokopenko, 2008) are developing fields which offer innovative ways to more effectively manage current health system problems.

Identifying Worsening Illness in Patient and Care Giver Narratives Narratives of people at risk of ambulatory care sensitive conditions are to be obtained and qualitatively and semantically analyzed in order to develop algorithms to determine phases of illness using expert inputs. A clinical study will test patient and care giver, and clinical team usability. The development of a semantic engine will trawl

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patient narratives and extract warning signals that indicate that a patient may need early intervention. In order to identify early warning signs of ill health leading to hospital admissions and develop mechanisms to facilitate early intervention requires • •

•

setting up a patient/caregiver narrative and semi-structured database data structuring and semantic analysis of structured data from patients about the journey to emergency medical admissions feedback systems to the care team

Evidence is emerging about care at home through telehealth or other information technology modalities to support those who need ongoing health care as they journey through more unstable and complex phases of illness (Darkins, et al., 2008). In relation to health applications, there has been a broad adoption of Web 2.0 technologies and approaches in the form of professional and personal use of electronic health records - EHR and PHR (see Key Terms and Definitions for discussion of terms). The use of Web 2.0 technologies and/or semantic web and virtual reality approaches can enable social networking and health learning within and between these user groups and clinical care (Eysenbach, 2008). These developments are based on a wide range of research and knowledge management disciplines from knowledge engineering, semantic analysis, social constructionism to mathematical modeling of complex systems, and are usually beyond the scope of the majority of clinicians. Nevertheless, clinicians are beginning to engage, and need a developmental framework as an important starting point.

Developing a Real World System: Functionality There is a requirement to integrate PaJR knowledge generating components into computer sys-

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tems in order to solve complex problems normally requiring a high level of human expertise. How should user driven (patient and care giver) IT monitoring be designed that alerts primary care teams to the need for early interventions in order to prevent avoidable ACSCs? Can it be done without unnecessarily worrying people, alerting physicians and the team unnecessarily, and missing important changes in health? The PaJR expert system prototype will be developed to demonstrate that the intelligent system will be established that would continue to learn, under the supervision of experts and with feedback from the outcomes of the patient journey, as more narratives and structured data are added to the PaJR system. We make the case, building on emerging evidence, that we can, by taking complexity science approaches utilizing semantic analysis techniques, identify patterns of unstable states in the patient journey. We can then identify those phases of the patient journey that are linked to increasing risk for an ACSC in electronically provided self-recorded patient/caregiver information. Katerndahl recently described this approach in the management of patients with anxiety disorders (Katerndahl, 2009). In order to develop a system that puts the patient and their caregiver at the centre of their care, we need to consider the feasibility of the use of electronic input modes like keyboard or voice by older patients and their care givers. Prototypes, such as the Heartphone where patients use their phone to relay their daily weights to a server that pattern processes their data to detect fluid retention for Congestive Heart Failure Patients, indicate the feasibility of the PaJR concept1 from a user perspective. (“National Digital Research Centre - Heartphone,” 2010). People, who would be suitable for the PaJR, submit their daily weightings through a mobile phone and both clinicians and patient/caregivers are comfortable with data entry and feedback mechanisms. Similar successful innovations are emerging everyday in a wide

Patient Journey Record Systems (PaJR) for Preventing Ambulatory Care Sensitive Conditions

range of applications with accumulating evidence of success in major programs associated with cardiovascular, respiratory, diabetes and mental health programs (Darkins, et al., 2008; Department of Fusion Technologies and Nuclear Presidium, 2009; Eysenbach, 2008; Morris, 2005). These programs include disease focused and personcentred approaches. Yet it is often the subjective rather than the objective disease measures which are linked to mortality, morbidity and hospitalization.2 Ongoing design and development is needed to streamline and operationalize these concepts. Social networking analysis and semantic and computational linguistic analysis (Eysenbach, 2008) are providing potent tools for the analysis of natural and disease related language (Morris, 2005; Zeng et al., 2006) and health concepts (Choi, Jenkins, Cimino, White, & Bakken, 2005). These tools have demonstrable potential for the electronic sharing of narratives in chronic illness (Jui-Chih, 2009), detecting instability in disease patterns such as cardiac disease with weight and symptom monitoring (Goldberger et al., 2002; Jordan, McKeown, Concepcion, Feiner, & Hatzivassiloglou, 2001; Salvador et al., 2005) (Molinari et al., 2004; Scherr et al., 2006) and in the care of elderly in the community (Bendixen, Levy, Olive, Kobb, & Mann, 2009; Bottazzi, Corradi, & Montanari, 2006; Chan, Campo, & Esteve, 2002; Tech, Radhakrishnan, & Subbaraj, 2009). Thus technological advances are converging to deliver more sophisticated and adaptive systems to support our patient journey systems - PaJR.

Developing a Real World System: Usability Are the users ready? Early adopter physicians and health care professionals have enthusiastically embraced technological and knowledge services (Schoen et al., 2009), although the diffusion, uptake and enthusiasm among the mainstream is uneven and a slower process (Greenhalgh et al., 2004). Will older patients and their caregivers

embrace technologies, which provide secondary and tertiary preventive and/or supportive care in chronic disease management (see Table 1)? There is emerging evidence about acceptance of a range of programs, particularly telehealth in heart failure programs (Ralston et al., 2007; Tang, Ash, Bates, Overhage, & Sands, 2006). When people develop unstable chronic disease and illness, their family usually become highly motivated to participate in their relatives or their own health care (C. M. Martin, Peterson, Robinson, & Sturmberg, 2009). On the other hand “perceived health status” is a second important influence for adoption, and refers to the patient’s expressed physical and mental well being and motivation to improve (Goh, 2008; Venkatesh, Morris, Davis, & Davis, 2004), while a third health ability is defined as the individual ‘resources, skills, or proficiencies’ (Venkatesh, et al., 2004). Thus making PaJR recordings must be coordinated by family members, if the patient does not have the necessary skills or motivation. Ideally all technological solutions need to take account of the patient and caregiver capacity and health ability.

Testing and Validation of the System Having demonstrated the feasibility and usability of an expert system in the clinical sense, there is a need to test the reliability and validity of such a system in a real world context. The final stage in the developmental framework is to determine the robustness, functionality and usability, comparing our system assessment of risk with the presence or absence of admissions, visits to the emergency room and visits to primary care. This will involve comparing the real journeys of participants, caregivers and primary care providers with the predicted patient journeys. Did the PaJR system provide timely and usable feedback? Positive findings would confirm that the system indeed is able to alert primary health care teams in a timely manner to facilitate and prioritize required care to older patients at risk of ACSCs. Negative

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Table 1 Framework for knowledge transfer from theory to practice 1. Assessment of the problem Ambulatory care sensitive conditions occur partly due to problems of timely intervention for older people with multi-morbidity and unpredictable interactions among physical, psychological, social and environmental factors. While disease monitoring can identify changes in disease states, avoidable admissions and adverse outcomes are a result of nonlinear dynamic interactions among many factors. Research indicates that self rated health and health perceptions are better predictors of adverse outcomes than disease parameters. 2. Development of a knowledge-based system shell/structure We propose the input of patient and caregiver narratives and semi-structured data will be analyzed with intelligent knowledge systems that provide feedback to physicians and care teams. Self rated health in the context of patient and care giver narratives provides the basic knowledge base that would be acquired by self reports using mobile phone inputs. This would be coded and analyzed by intelligent agents and developed into a system to track the illness. 3. Acquisition and structuring of the related information, knowledge & specific preferences for usability & feasibility Narratives of people at risk of ambulatory care sensitive conditions are to be obtained and then qualitatively and semantically analyzed in order to develop algorithms to determine phases of illness using expert inputs. A clinical study would test patient and care giver, and clinical team usability. The development of a semantic engine to trawl patient narratives and extract 4. Implementation of structured knowledge into knowledge bases The PaJR expert system prototype would be developed to demonstrate the: - Setting up a patient/caregiver narrative and semi-structured database - Data structuring and semantic analysis of structured data from patients about the journey to emergency medical admissions - Feedback systems to the care team An intelligent system will be established that will continue to learn, under the supervision of experts and with feedback from the outcomes of the patient journey, as more narratives and structured data are added to the PaJR system. 5. Testing and validation of the inserted knowledge and Integration and maintenance of the system The PaJR system would undergo functionality testing - Testing of all features and functions of a system [software, hardware, etc.] to ensure requirements and specifications are met. It focuses solely on the outputs generated in response to selected inputs and execution conditions. Usability testing is a combination of user interface testing and manual support testing to test ease of use, look and feel, ease of access. 6. Revision and evaluation of the system An iterative activity of testing in practice to refine the usability, functionality and reliability in the clinical setting. Because of the unpredictability of the timing of triggers and enablers of decline and the individual trajectory towards an ACSC adverse event, real time intervention around early detection of triggers and enablers of decline will be tested. Such an approach has to deal with early detection, false positives and rapid regression or early resolution.

findings would inform the field that primary care systems require more development or that the concepts underpinning health IT systems need to be refined, or indeed indicate that the direction of health related technology development must be changed. This is an iterative process and we will endeavor to refine the system until it is deemed functional, usable and robust.

Implementation and Commercialization The widespread move to electronic health systems will ensure commercial viability of well developed PaJR systems. Major industry players such as Microsoft have embraced the patient journey idea in the design of electronic health

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information systems - http://www.mscui.net/PatientJourneyDemonstrator/ as have Intel - http:// www.itnews.com.au/Event/166187,the-patientjourney-what-role-for-it.aspx amongst many others. Personal health records are proposed as a key mechanism for patient or user driven or at least person-centred health care http://en.wikipedia. org/wiki/Personal_health_record. Personal health records (Bourgeois, Taylor, Emans, Nigrin, & Mandl, 2008; Detmer, Bloomrosen, Raymond, & Tang, 2008; Winkelman, Leonard, & Rossos, 2005) and mobile phone technology (Bielli et al., 2004; Farmer et al., 2005; Jayaraman, Kennedy, Dutu, & Lawrenson, 2008; Kaplan, 2006; Revere & Dunbar, 2001; Ryan, Cobern, Wheeler, Price, & Tarassenko, 2005; Scherr, et al., 2006) can link to electronic medical records and people

Patient Journey Record Systems (PaJR) for Preventing Ambulatory Care Sensitive Conditions

can send information and ask questions directly through these technologies. After development and implementation, traditional health service feedback and evaluation and continuous improvement of the system will take place to strengthen the PaJR system.

DISCUSSION The PaJR for people with complex chronic care must reconfigure new knowledge from narrative based medicine, chronic disease management, chronic illness care theory and complexity science. Clinical research is emerging to support this new approach using patient experience over disease based information to track the phase of illness and stage of care needs. Currently health information systems are predominantly shaped by professional, organizational– and business-centered ideologies in which the role of the patient is that of a passive subject rather than an active participant (Detmer, et al., 2008). An individual’s care pathway is unique, traversing dynamic experiences of health and illness, disease and treatment (Charon, Wyer, & for the NEBM Working Group, 2008). Empowering individual patients by enabling them to take control of their health, illness and disease, significantly improves their outcomes (Wallerstein, 2006). An ICT system that has direct input from and is constructed around the individual patient with chronic disease should enable them to take greater control of their journey. There are numerous care relationships, predictable and unpredictable, as well as positive and negative influences and feedback loops, through which the patient must pass, hopefully making sense of them as they go. The informational needs of the primary care team in partnership with other primary care and secondary and tertiary care providers should be supported by the PaJR. This meta-system will interdigitate with existing health information systems including the Personal Health Record

and the Electronic Health Record, on the one hand supporting self-management and peer support programs and on the other, enabling patients’ self-care and self-management of their chronic diseases even in the complicated, complex and chaotic stages of care. The integration of the patient narrative into more formal informational processes also seeks to reverse the separation that clinical technologies have created between the clinician, caregivers and patient. Heeding subjective meanings and interpretations of their health journey and integrating them with more positivist and instrumentalist data and techniques (Sochalski et al., 2009) allows a degree of humanity and individuality to be reintroduced to an otherwise impersonal and often dehumanizing environment. Sense making is the pivotal concept that emerges in this developmental framework. Sense making at the level of the individual journey is a human activity conducted by the individual in their networks and context. Sense making takes place at different levels by different agents. At the level of care, sense making is essentially a human activity by clinicians that relies on knowledge and relationships with patients with decision making informed by evidence from medical and other knowledge sources. Sense making processes are tacit and explicit. Intelligent systems seek to build upon and replicate elements of the human processes of sense making to improve care. Intelligent systems can support human sense making, the core activity to detect shifts from stable to unstable phases in the patient’s illness trajectory that includes their resources for adapting. PaJR must identify critical points in a timely fashion to prevent the deterioration into chaotic phases of the patient’s illness journey, so that self management can be enhanced or appropriate and decisive clinical interventions can be taken. Finally, the provision of and participation in patient care needs to be dynamic and adaptive to an individual’s journey through stages of disease and illness. Human support enables patients to make sense of their experiences and optimizes

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their quality of life. Better planned and more human-focused care supported by electronic expert systems can prevent unnecessary costs which do not contribute to improved personal health experience and quality of life in the phases of end stage chronic disease. The development of a PaJR-system requires a transdisciplinary approach to seamlessly integrate many layers of different types of knowledge seamlessly integrated to take knowledge from clinical need, clinical and health related theory and evidence system must be able to make sense of natural language inputs to be a robust, usable, functional and marketable system. This outcome can only be achieved iteratively through continuous feedback and evaluation (see Figure 3).

CONCLUSION Patient Journey Record Systems (PaJR) is an innovative project. The key concept is the use of patient narratives to make sense of the phase of illness of a patient in the context of ambulatory care for older patients, with multiple morbidity, who are at risk for the adverse consequence of

hospitalisation. The framework addresses the feasibility and usability of an information technology based solution to avert Ambulatory Care Sensitive Conditions which are potentially avoidable by activities in primary care. Key features include information feedback to support and enable self management and timely adjustments to care. PaJR architectures, thus, need to encompass self-recorded patient narratives, messages to and from the care team, potentially feeding into electronic health records of different types and configurations. In such a framework patients and clinical experts interact through social networking and sense making occurs through participation in group activities such as learning among hospital teams, primary care teams, patient and caregivers, peer groups and learners at all levels. As an additional layer of knowledge processing to the human networking, intelligent knowledge systems underpin this by sense making using intelligent agents employing advanced pattern processing and reasoning systems to continuously review and analyze the information generated by the narratives and other data in the PaJR.

Figure 3. The developmental phases of PaJR with different inputs

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Key considerations in the implementation and commercialization of PaJR knowledge systems are the design and implementation of robust intelligent knowledge and data support systems. While patient, caregiver and physician and care team perspectives drive usability and functionality requirements, experts from computer science domains such as artificial intelligence, expert systems, and decision support systems ensure the requirements for the underlying systems architecture are met. A transdisciplinary perspective and ways in which coherence might be achieved in the many practitioners and expert domains involved in a developmental framework for PaJR are highlighted. The commercialization of well developed PaJR systems as part of a universal move to electronic user-driven health care is assured if usability, feasibility and functionality are addressed.

Bayliss, E. A., Ellis, J. L., & Steiner, J. F. (2009). Seniors’ self-reported multimorbidity captured biopsychosocial factors not incorporated into two other data-based morbidity measures. J Clin Epidemiol, 62(5), 550-557 e551.

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Jones, J., Snyder, C., & Wu, A. (2007). Issues in the design of Internet-based systems for collecting patient-reported outcomes. Quality of Life Research, 16(8), 1407–1417. doi:10.1007/ s11136-007-9235-z Jordan, D. A., McKeown, K. R., Concepcion, K. J., Feiner, S. K., & Hatzivassiloglou, V. (2001). Generation and Evaluation of Intraoperative Inferences for Automated Health Care Briefings on Patient Status After Bypass Surgery. Journal of the American Medical Informatics Association, 8(3), 267–280. Jui-Chih, C. (2009). Multi-user Narrative Sharing System for Children with Chronic Illness. Kaplan, W. (2006). Can the ubiquitous power of mobile phones be used to improve health outcomes in developing countries? Globalization and Health, 2(1), 9. doi:10.1186/1744-8603-2-9 Katerndahl, D. A. (2009). Power laws in covariability of anxiety and depression among newly diagnosed patients with major depressive episode, panic disorder and controls. Journal of Evaluation in Clinical Practice, 15(3), 565–570. doi:10.1111/j.1365-2753.2009.01166.x Kay, S., & Purves, I. (1998). The Electronic Medical Record and the “Story Stuff”: a narrativistic model. In Greenhalgh, T., & Hurwitz, B. (Eds.), Narrative Based Medicine: dialogue and discourse in medical practice (pp. 185–201). London: BMJ Books. Kendal, S. L., & Creen, M. (2007). An introduction to knowledge engineering. London: Springer. Kim, H. S., & Oh, J. A. (2003). Adherence to diabetes control recommendations: impact of nurse telephone calls. Journal of Advanced Nursing, 44(3), 256–261. doi:10.1046/j.13652648.2003.02800.x

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Morris, M. E. (2005). Social networks as health feedback displays. IEEE Internet Computing, 9(5), 29–37. doi:10.1109/MIC.2005.109 National Digital Research Centre. (2010). Heartphone. Retreived from http://www.ndrc.ie/projects/heartphone/ Podrazik, P. M., & Whelan, C. T. (2008). Acute Hospital Care for the Elderly Patient: Its Impact on Clinical and Hospital Systems of Care. [doi: DOI: 10.1016/j.mcna.2007.11.004]. Medical Clinics of North America, 92(2), 387-406. Ralston, J. D., Carrell, D., Reid, R., Anderson, M., Moran, M., & Hereford, J. (2007). Patient Web Services Integrated with a Shared Medical Record: Patient Use and Satisfaction. Journal of the American Medical Informatics Association, 14(6), 798–806. doi:10.1197/jamia.M2302 Revere, D., & Dunbar, P. J. (2001). Review of Computer-generated Outpatient Health Behavior Interventions: Clinical Encounters “in Absentia”. Journal of the American Medical Informatics Association, 8(1), 62–79. Ryan, D., Cobern, W., Wheeler, J., Price, D., & Tarassenko, L. (2005). Mobile phone technology in the management of asthma. Journal of Telemedicine and Telecare, 11(Suppl 1), 43–46. doi:10.1258/1357633054461714 Salvador, C. H., Pascual Carrasco, M., Gonzalez de Mingo, M. A., Munoz Carrero, A., Marquez Montes, J., & Sosa Martin, L. (2005). Airmedcardio: a GSM and Internet services-based system for out-of-hospital follow-up of cardiac patients. IEEE Transactions on Information Technology in Biomedicine, 9(1), 73–85. doi:10.1109/ TITB.2004.840067 Scherr, D., Zweiker, R., Kollmann, A., Kastner, P., Schreier, G., & Fruhwald, F. M. (2006). Mobile phone-based surveillance of cardiac patients at home. Journal of Telemedicine and Telecare, 12(5), 255–261. doi:10.1258/135763306777889046

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Sochalski, J., Jaarsma, T., Krumholz, H. M., Laramee, A., McMurray, J. J. V., & Naylor, M. D. (2009). What Works In Chronic Care Management: The Case Of Heart Failure. Health Affairs, 28(1), 179–189. doi:10.1377/hlthaff.28.1.179 Sturmberg, J., Martin, C., & Moes, M. (2010). (in press). Health at the Centre of Health Systems Reform – How Philosophy Can Inform Policy. Perspectives in Biology and Medicine. Sturmberg, J. P., & Martin, C. M. (2008). Knowing - in Medicine. Journal of Evaluation in Clinical Practice, 14(5), 767–770. doi:10.1111/j.13652753.2008.01011.x Tang, P. C., Ash, J. S., Bates, D. W., Overhage, J. M., & Sands, D. Z. (2006). Personal Health Records: Definitions, Benefits, and Strategies for Overcoming Barriers to Adoption. Journal of the American Medical Informatics Association, 13(2), 121–126. doi:10.1197/jamia.M2025 Tech, M. M., Radhakrishnan, S., & Subbaraj, P. (2009). Elderly patient monitoring system using a wireless sensor network. Telemedicine Journal and e-Health, 15, 73. doi:10.1089/tmj.2008.0056

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KEY TERMS AND DEFINITIONS Ambulatory Care Sensitive Conditions (ACSCs): ACSCs are those conditions for which hospital admission could be prevented by interventions in primary care, the majority of which occur in complex phases of chronic care (U.S.

Patient Journey Record Systems (PaJR) for Preventing Ambulatory Care Sensitive Conditions

Agency for Healthcare Research and Quality, March 12, 2007). Electronic Medical Record (EMR): A dataset of the patient’s health information that is usually specifically generated, and sometimes owned, by one organization such as a medical clinic, hospital or HMO. This dataset is often more complete is certain areas but tends to be of a less standardized data format. While the patient may to some degree determine who has access to the record, this tends to be coarse grained, with the patient having little control over what is included or excluded from the record. Health Record Systems: The nomenclature around health record systems varies from region to region. For our purposes, we have adopted the following names and abbreviations but recognize that this is not universal. We hope that clarifying this will help to explain how PaJR interacts with some of these systems. Electronic Health Record (EHR): a subset of the patient’s entire information on health related issues. This subset consists of major items that are for general use across multiple systems and organizations, such as allergies, medications, major problems, blood type etc. Because of the broader scope of data systems, this subset is necessarily simplified and adheres more closely to data standards. Multimorbidity: (Bayliss, et al., 2009; Boyd et al., 2007; Fortin et al.) Can be defined as the simultaneous occurrence of several medical conditions in the same person. While definitions vary, prevalence varies from up to 27% in early adult hood to over 90% of GP at tenders in the older age groups. In primary care settings multi-morbidity is the rule rather than the exception.(Carmel M Martin, 1998) Ambulatory Care sensitive conditions predominantly occur in older patients with multiple morbidity.(U.S. Agency for Healthcare Research and Quality, March 12, 2007) Personal Health Record (PHR): Also known as ‘Personal Health Application Platforms’ and ‘Personally Controlled Health Records’, this is a new concept, not yet widely accepted, where

the dataset may be generated by a wide range of providers or organizations but the key feature is that the patient has control over what is included or excluded, usually with the consent of the originating authority, and that the patient has complete control over who has access to this information, with ownership residing in the patient’s hands. Data storage is often managed by a secure thirdparty, not related to the originating authorities. Personal Health Application Platforms and Personally Controlled Health Records’such as Google Health, Microsoft Health Vault, and Dossia. “Medicine 2.0” applications, services and tools, are defined as Web-based services for health care consumers (that is caregivers, patients,) health professionals, and researchers. (Eysenbach, 2008) Self-Rated Health, Illness and Outcome Prediction: Self-rated health and illness perceptions are better predictors of morbidity and mortality than disease specific measures.(Hubbard, et al., 2009; Idler & Benyamini, 1997) Additionally, GPs’ perceptions of their patients’ understanding were accurate in 82% of the consultations, but when the patients had a both physical and psychological understanding of their health problem, the GPs were right in only 26% of the consultations.(Frostholm et al., 2010; Hubbard, et al., 2009) Social Support: Social support can be simply defined as the availability of people on whom patients feel that they can depend.(Diez Roux, 2007; Lett et al., 2005) It comprises the following support domains – intimate, emotional, informational, structural, practical, appraisal and companionship and friendship.(Carmel M Martin, 1998)

ENDNOTES 1



Heartphone is developing a web-based system to support the management of chronic llnesses, using mobile technology in the home to measure and report on vital patient data including blood pressure, weight and medication levels. The project will allow

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medical professionals to get real-time information on patients, presenting the prospect of more timely intervention Personal communication from heartphone is that the self assessed health status better

predicted adverse outcomes than disease based measures in a population with cardiac failure.

This work was previously published in User-Driven Healthcare and Narrative Medicine: Utilizing Collaborative Social Networks and Technologies, edited by Rakesh Biswas and Carmel Mary Martin, pp. 93-112, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

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Chapter 8.11

Picture Archiving and Communication System for Public Healthcare Carrison K. S. Tong Pamela Youde Nethersole Eastern Hospital, Hong Kong, China Eric T. T. Wong The Hong Kong Polytechnic University, Hong Kong, China

BACKGROUND For the past 100 years, film has been almost the exclusive medium for capturing, storing, and displaying radiographic images. Film is a fixed medium with usually only one set of images available. Today, the radiologic sciences are on the brink of a new age. In particular, Picture Archiving and Communication System (PACS) technology allows for a near filmless process with all of the flexibility of digital systems. PACS consists of image acquisition devices, storage archiving units, display stations, computer processors, and database management systems. These components are integrated by a communications DOI: 10.4018/978-1-60960-561-2.ch811

network system. Filmless radiology is a method of digitizing traditional films into electronic files that can be viewed and saved on a computer. This technology generates clearer and easier-to-read images, allowing the patient the chance of a faster evaluation and diagnosis. The time saved may prove to be a crucial element in facilitating the patient’s treatment process. With filmless radiology, images taken from various medical sources can be manipulated to enhance resolution, increasing the clarity of the image. Images can also be transferred internally within hospital departments and externally to other locations such as the office of the patient’s doctor or medical specialist in other parts of the world. This is made possible through the picture-archiving and communication system (Dreyer, Mehta, & Thrall, 2001), which

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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electronically captures, transmits, displays, and saves images into digital archives for use at any given time. The PACS functions as a state-of-theart repository for long-term archiving of digital images, and includes the backup and bandwidth to safeguard uninterrupted network availability. The objective of the picture-archiving and communications system is to improve the speed and quality of clinical care by streamlining radiological service and consultation. With instant access to images from virtually anywhere, hospital doctors and clinicians can improve their work processes and speed up the delivery of patient care. Besides making film a thing of the past, the likely benefits would include reduced waiting times for images and reports, and the augmented ability of clinicians since they can get patient information and act upon it much more quickly. It also removes all the costs associated with hard film and releases valuable space currently used for storage. According to Dr. Lillian Leong, Chairman of the Radiology IT Steering Group of the Hong Kong Medical Authroity, a single hospital can typically save up to 2.5 million Hong Kong dollars (approximately US$321,000) a year in film processing cost (Intel, 2007). The growing importance of PACS on the fight against highly infectious disease such as Severe Acute Respiratory Syndrome (SARS) is also identified (Zhang & Xue, 2003). In Hong Kong, there was no PACS-related project until the establishment of Tseung Kwan O Hospital (TKOH) in 1998. The TKOH is a 600bed acute hospital with a hospital PACS installed for the provision of filmless radiological service. The design and management of the PACS for patient care was discussed in the first edition of this encyclopedia (Tong & Wong, 2005). The TKOH was opened in 1999 with PACS installed. At the beginning, due to immature PACS technologies, the radiology service was operating with film printing. A major upgrade was done in 2003 for the implementation of server clustering, network resilience, liquid crystal display (LCD), smart card, and storage-area-network (SAN) technologies.

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This upgrade has greatly improved the reliability of the system. Since November 2003, TKOH has started filmless radiology service for the whole hospital. It has become one of the first filmless hospitals in the Greater China region (Seto, Tsang, Yung, Ching, Ng, & Ho, 2003; Tsou, Goh, Kaw, & Chee, 2003).

MAIN FOCUS OF THE ARTICLE The design of a PACS for such a system should be high-speed, reliable, and user friendly (Siegel & Kolodner, 2001). While most equipment is designed for high reliability, system or subsystem breakdowns can still occur, especially when equipment is used in a demanding environment. A typical situation is what could be called a “single-point failure.” That is, the entire system fails if only one piece of equipment such as a network switch fails. If some of the processes that the system supports are critical or the cost of a system stop is too high, then implementing redundancy management into the system is the best way to overcome this problem. The continuous operation of a PACS in a filmless hospital for patient care is a critical task. The main purpose of a reliability design is to avoid the occurrence of any single point of failure in the system. This design includes a number of technical features. The technical features of the PACS installed in a local hospital include the archiving of various types of images, clustering of Web servers installed, redundancy provision for image distribution and storage channels, and adoption of bar-code and smart-card systems. All these features are required to be integrated with the electronic patient record system (ePR) for effective system performance and these are described below.

Archiving of Multiple Image Types In order to make connections with different imaging modalities (e.g., Magnetic Resonance

Picture Archiving and Communication System for Public Healthcare

Imaging, or MRI, Computed Tomography, or CT, Positron Emission Tomography, or PET, etc.), a common international standard is important. The Digital Imaging and Communications in Medicine (DICOM) standard developed by the American College of Radiology (ACR) and the National Electrical Manufacturers’ Association (NEMA) is the most common standard used today. It covers the specification image format, a point-to-point connection, network requirements, and the handling of information on networks. The adoption of DICOM by other specialties that generate images (e.g., pathology, endoscopy, dentistry) is also planned. The fact that many of the medical imaging-equipment manufacturers are global corporations has sparked considerable international interest in DICOM. The European standards organization, the Comitâ Europâen de Normalisation, uses DICOM as the basis for the fully compatible MEDICOM standard. In Japan, the Japanese Industry Association of Radiation Apparatus and the Medical Information Systems Development Center have adopted the portions of DICOM that pertain to the exchange of images on removable media and are considering DICOM for future versions of the Medical Image Processing Standard. The DICOM standard is now being maintained and extended by an international, multispecialty committee. Today, the DICOM standard has become a predominant standard for the communication of medical imaging devices.

Web Technology The World Wide Web (WWW) began in March 1989 at CERN (CERN was originally named after its founding body, the Conseil Europeen pour la Recherche Nucleaire, that is now called the European Laboratory for Particle Physics). CERN is a meeting place for physicists from all over the world who collaborate on complex physics, engineering, and information-handling projects. Because of the intuitive nature of hypertext, many inexperienced computer users were able

to connect to the network. The simplicity of the hypertext markup language, used for creating interactive documents, has allowed many users to contribute to the expanding database of documents on the Web. Also, the nature of the World Wide Web provided a way to interconnect computers running different operating systems, and display information created in a variety of existing media formats. In short, the Web technology provides a reliable platform for the distribution of various kinds of information including medical images. Another advantage of Web technology is its low demand on the Web client. Any computer running on a common platform such as Windows or Mac can access the Web server for image viewing just using Internet Explorer or Netscape. Any clinical user can carry out his or her duty anytime and anywhere within a hospital.

Clustering of Web Servers The advantage of clustering computers for high availability (Piedad & Hawkings, 2000) is that if one of the computers fails, another computer in the cluster can then assume the workload of the failed computer at a prespecified time interval. Users of the system see no interruption of access. The advantages of clustering DICOM Web servers for scalability include increased application performance and the support of a greater number of users for image distribution. There is a myth that to provide high availability (Marcus & Stern, 2003), all that is required is to cluster one or more computer-hardware solutions. In practice, no hardware-only solution has been able to deliver trouble-free answers. Providing trouble-free solutions requires complicated software to be written to cope with the myriad of failure modes that are possible with two or more sets of hardware. Clustering can be implemented at different levels of the system, including hardware, operating systems, middleware, systems management, and applications. The more layers that incorporate clustering technology, the more complex the whole system

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is to manage. To implement a successful clustering solution, specialists in all the technologies (i.e., hardware, networking, and software) are required. The authors used the clustering of Web servers by connecting all of the Web servers using a load-balancing switch. This method has the advantage of a low server overhead and requires no computer-processor power.

RAID Technology Patterson, Gibson, and Katz (1988) at the University of California, Berkeley, published a paper entitled “A Case for Redundant Arrays of Inexpensive Disks (RAID).” This paper described various types of disk arrays, referred to by the acronym RAID. The basic idea of RAID was to combine multiple small, inexpensive disk drives into an array of disk drives, which yields performance exceeding that of a single large, expensive drive (SLED). Additionally, this array of drives appears to the computer as a single logical storage unit or drive. The mean time between failure (MTBF) of the array will be equal to the MTBF of an individual drive divided by the number of drives in the array. Because of this, the MTBF of an array of drives would be too low for many application requirements. However, disk arrays can be made fault tolerant by redundantly storing information in various ways. Five types of array architectures, RAID-1 through RAID-5, were defined by the Berkeley paper, each providing disk fault tolerance and each offering different trade-offs in features and performance. In addition to these five redundant array architectures, it has become popular to refer to a nonredundant array of disk drives as a RAID-0 array. In PACS, RAID technology can provide protection for the availability of the data in the server. In RAID level 5, no data are lost even during the failure of a single hard disk within a RAID group. This is essential for a patient-care information system. Extra protection can be obtained by using spare global hard disks for automatic protection of data

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during the malfunctioning of more than one hard disk. Today, most storage-area networks (SANs) for high capacity storage are built on RAID technology.

Storage Area Network A storage area network (Marcus & Stern, 2003; Toigo & Toigo, 2003) is a high-speed, special purpose network (or subnetwork) that interconnects different kinds of data-storage devices with associated data servers on behalf of a larger network of users. Typically, a storage-area network is part of the overall network of computing resources for an enterprise. A storage-area network is usually clustered in proximity to other computing resources such as SUN servers, but it may also extend to remote locations for backup and archival storage using wide-area-network carrier technologies such as ATM or Ethernet. Storage-area networks use fiber channels (FCs) for connecting computers to shared storage devices and for interconnecting storage controllers and drives. Fiber channel is a technology for transmitting data between computer devices at data rates of up to 1 or 2 Gbps and 10 Gbps in the near future. Since fiber channel is three times as fast, it has begun to replace the small computer system interface (SCSI) as the transmission interface between servers and clustered storage devices. Another advantage of fiber channel is its high flexibility; devices can be as far as 10 km apart if optical fiber is used as the physical medium. Standards for fiber channel are specified by the Fiber Channel Physical and Signaling standard, and the ANSI X3.230-1994, which is also ISO 14165-1. Other advanced features of a SAN are its support of disk mirroring, backup, and restoring; archival and retrieval of archived data; data migration from one storage device to another; and the sharing of data among different servers in a network. SANs can also incorporate subnetworks with network attached storage (NAS) systems.

Picture Archiving and Communication System for Public Healthcare

Redundant Network for Image Distribution Nevertheless, all of the PACS devices still need to be connected to the network, so to maximize system reliability, a PACS network should be built with redundancy (Jones, 2000). To build up a redundant network (Marcus & Stern, 2003), two parallel gigabit-optical fibers were connected between the PACS and the hospital networks as two network segments using four Ethernet switches. The Ethernet switches were configured in such a way that one of the network segments was in active mode while the other was in standby mode. If the active network segment fails, the standby network segment will become active within less than 300 ms to allow the system to keep running continuously.

Bar-Code System Recognizing that manual data collection and keyed data entry are inefficient and error prone, bar codes evolved to replace human intervention. Bar codes are simply a method of retaining data in a format or medium that is conducive to electronic data entry. In other words, it is much easier to teach a computer to recognize simple patterns of lines, spaces, and squares than it is to teach it to understand written characters or the English language. Bar codes not only improve the accuracy of entered data, but also increase the rate at which data can be entered. A bar-code system includes printing and reading the bar-code labels. In most hospital information systems, the bar-code system has commonly been adopted as a part of the information system for accurate and fast patient-data retrieval. In PACS, bar-code labels are mostly used for patient identification and DICOM accession. They are used to retrieve records on patient examinations and studies.

Smart-Card System A smart card is a card that is embedded with either a microprocessor and a memory chip or only a memory chip with nonprogrammable logic. The microprocessor card can add, delete, and otherwise manipulate information on the card, while a memory chip card, such as prepaid phone cards, can only undertake a predefined operation. Smart cards, unlike magnetic-stripe cards, can carry all necessary functions and information on the card. Smart cards can also be classified as contact and contactless types. The contactless smart card communicates with the reader using the radio frequency (RF) method. In PACS, a contactless smartcard system was installed for the authentication of the user. The information about the user name, log-in time, and location are stored in a remote server through a computer network. Contactless cards rather than RFID cards are chosen because they are capable of transacting more sensitive and confidential information than RFID cards as they are usually equipped with higher authentication mechanisms and security layers. It should not be confused with RFID technology, which is more popular in instances where less confidential data are transacted and single application is used.

Embedded LCD Monitor To display medical images in the hospital, LCD monitors were installed on the walls in ward areas adjacent to existing light boxes. LCD displays utilize two sheets of polarizing material with a liquid crystal solution between them. An electric current passed through the liquid causes the crystals to align so that light cannot pass through them. Each crystal, therefore, is like a shutter, either allowing light to pass through or blocking the light. Monochrome LCD images usually appear as blue or dark-grey images on top of a greyish-white background. Color LCD displays use two basic techniques for producing color: Passive matrix is the less expensive of the two

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technologies. The other technology, called thin film transistor (TFT) or active matrix, produces color images that are as sharp as traditional CRT displays, but the technology is expensive. Recent passive-matrix displays using new color supertwist nematic (CSTN) and double-layer super-twisted nematic (DSTN) technologies produce sharp colors rivaling active-matrix displays. Most LCD screens used are transmissive to make them easier to read. These are a type of LCD screens in which the pixels are illuminated from behind the monitor screen. Transmissive LCDs are commonly used because they offer high contrast and deep colors, and are well suited for indoor environments and low-light circumstances. However, transmissive LCDs are at a disadvantage in very bright light, such as outdoors in full sunlight, as the screen can be hard to read. In PACS, the LCD monitors were installed in pairs for the comparison of a large number of medical images. They were also configured in portrait mode for the display of chest X-ray CR (computed radiography) images.

viewing for the diagnosis or follow-up of patients. The Web-based X-ray-image viewers were set up on the computers in all wards, intensive care units, and specialist and outpatient departments to provide a filmless radiological service. The design of the computers for X-ray-image viewing in wards is shown in Figure 4. These computers were built using all the above technologies for performance and reliability. After several years of filmless radiological operation in TKOH, less than 1% of the cases required special X-ray film for follow-up. Basically, X-ray image viewing through a computer network was sufficient for the radiological diagnosis and monitoring of patients. Furthermore, filmless radiology (Siegel & Kolodner, 2001) service definitely reduced the chance for spreading highly infectious diseases through health-care staff. No staff member from the radiology department became infected during the outbreak of the severe acute respiratory syndrome in 2003. No film-loss and film-waiting times were recorded.

Implementation of PACS

Integration with Electronic Patient Record System

In the design of the TKOH PACS (Figure 1), all computed tomographic (CT), magnetic resonance (MR), ultrasound (US), and computed radiographic images were archived in image servers of the PACS (Figure 2). During the diagnosis and monitoring of patients with highly infectious diseases, CT and CR scans were commonly used for comparison. A large storage capacity for the present and previous studies was required. The capacity of the image servers designed was about 5 terabytes using 2.3-terabyte SAN technology and a DICOM compression of 2.5. The image distribution to the clinicians was through a cluster of Web servers, which provided high availability of the service. The connection between the PACS and the hospital network was through a cluster of automatic fail-over switches as shown in Figure 3. Our users can use a Web browser for X ray-image

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Patients in Hong Kong are quite mobile and sometimes visit different hospitals for a variety of reasons. Hospitals were repeating scans and patient data entries that were already done elsewhere. To provide high quality care and relieve the hospitals of duplicated work meant integrating the PACS systems with the ePR system across all hospitals within the Hong Kong Hospital Authority (HKMA) network. Patients’ radiological information and scans would then be centrally available at HKHA as part of the patient’s electronic records and easily available to any doctor across any HKHA hospital. In a multiphased plan, 10 major public hospitals are connected and sharing over 30 TB of centralized storage for over two million patient examinations. The GE Enterprise Archiving and Enterprise Web Solution

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Figure 1. X-ray imaging modalities in the TKOH PACS

with an Intel architecture was chosen to provide the flexibility and open architecture for effective digital image management. Treating millions of patients a year meant data storage needs would grow exponentially and this led to the selection of EMC storage systems to meet HKHA’s current needs with the scalability to expand to meet future growth.

FUTURE TRENDS In PACS, most of the hard disks used in the RAID are expensive fiber-channel drives. Some RAID manufacturers are designing their RAID controllers using mixed ATA and fiber-channel drives in the same array with 100% software compatibility. This design has many benefits. It can reduce the

Figure 2. Design of the TKOH PACS

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Figure 3. Design of a PACS and hospital network interface

data backup and restore from seconds to hours, keep more information online, reduce the cost of the RAID, and replace the unreliable tap devices in the future. Another advanced development of PACS was in the application of voice recognition (Dreyer et al., 2001) in radiology reporting, in which the computer system was able to automatically and instantly convert the radiologist’s verbal input into Figure 4. X-ray image viewer in wards

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a textual diagnostic report. Hence, the efficiency of diagnostic radiologists can be further improved.

CONCLUSION It has been reported (Siegel & Kolodner, 2001) that filmless radiological service using PACS could

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be an effective means to improve the efficiency and quality of patient care. Other advantages of filmless radiological service are infection protection for health-care staff and the reduction of the spreading of disease through the distribution of films. In order to achieve the above tasks, many computer and multimedia technologies such as the Web, SAN, RAID, high availability, LCD, bar code, smart card, and voice recognition were applied. In conclusion, the application of computer and multimedia technologies in medicine for efficient and quality health care is one of the important areas of future IT development. There is no boundary and limitation in this application. We shall see more and more doctors learning and using computers in their offices and IT professionals developing new medical applications for health care. The only limitation we have is our imagination.

REFERENCES Alison, B. (1994, December 17). New Scientist. Dreyer, K. J., Mehta, A., & Thrall, J. H. (2001). PACS: A guide to the digital revolution. SpringerVerlag. Huang, H. K. (2004). PACS and imaging informatics: Basic principles and applications (2nd ed.). Wiley. doi:10.1002/0471654787 Intel. (2007). Retrieved May 14, 2008, from http://www.intel.com/business/casestudies/ hong_kong_hospital_authority.pdf Jones, V. C. (2000). High availability networking with Cisco. Addison-Wesley Longman. Marcus, E., & Stern, H. (2003). Blueprints for high availability (2nd ed.). Wiley. Menasce, D. A., & Almeida, V. A. F. (2001). Capacity planning for Web services: Metrics, models, and methods (2nd ed.). Prentice Hall.

Menasce, D. A., & Almeida, V. A. F. (2004). Performance by design: Computer capacity planningby example. Pearson Education. Patterson, D., Gibson, G., & Katz, R. H. (1988). A case for redundant arrays of inexpensive disks (RAID). SIGMOD Record, 17(3), 109–116. doi:10.1145/971701.50214 Piedad, F., & Hawkings, M. (2000). High availability: Design, techniques and processes. Prentice Hall. Seto, W. H., Tsang, D., Yung, R. W., Ching, T. Y., Ng, T. K., & Ho, M. (2003). Effectiveness of precautions against droplets and contact in prevention of nosocomial transmission of severe acute respiratory syndrome (SARS). Lancet, 361, 1519–1520. doi:10.1016/S0140-6736(03)13168-6 Siegel, E. L., & Kolodner, R. M. (2001). Filmless radiology. Springer-Verlag. Toigo, J. W., & Toigo, M. R. (2003). The holy grail of network storage management. Prentice Hall. Tong, C. K. S., & Wong, E. T. T. (2005). Picture archiving and communication system in healthcare. In Pagani, M. (Ed.), Encyclopedia of multimedia technology and networking. Idea Group Publishing. Tsou, I. Y. Y., Goh, J. S. K., Kaw, G. J. L., & Chee, T. S. G. (2003). Severe acute respiratory syndrome: Management and reconfiguration of a radiology department in an infectious disease situation. FRCR Radiology, 229, 21–26. doi:10.1148/ radiol.2291030789 Zhang, X.Z., & Xue, A.H. (2003). The role of radiological imaging in diagnosis and treatment of severe acute respiratory syndrome. CMJ, 116(6), 831–833.: 831-833

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KEY TERMS AND DEFINITIONS Clustering: A cluster is two or more interconnected computers that create a solution to provide higher availability, higher scalability, or both. Computed Radiography (CR): Computed radiography is a method of capturing and converting radiographic images into a digital form. The medium for capturing the X-ray radiation passing through the patient and generated by a standard X-ray system is a phosphor plate that is placed in a standard-sized cassette, replacing the regular radiographic film. The X-ray exposure forms a latent image on a phosphor plate that is then scanned (read or developed) using a laser-beam CR reader. The CR unit displays the resultant digital image on a computer monitor screen. By the end of the short process, the phosphor plate is erased and ready for another X-ray image exposure. Computed Tomography (CT): Also known as Computed Axial Tomography (CAT), is a painless, sophisticated X-ray procedure. Multiple images are taken during a CT or CAT scan, and a computer compiles them into complete, crosssectional pictures (“slices”) of soft tissue, bone, and blood vessels. Digital Imaging and Communications in Medicine (DICOM): The international standard for generating, transferring, processing, and storing of digital images and is used mainly in hospitals, clinics, and large radiology practices. Disk Array: Common name for a RAID unit. A term for a unit that groups hard drives (sometimes called striping) to provide increased storage capacity, speed, and data security. Load Balancing: A technique to spread work between a large number of computers, processes, hard disks, or other resources in order to get optimal resource utilization and decrease computing time. Magnetic Resonance Imaging (MRI): An advanced diagnostic procedure that makes detailed images of internal bodily structures such as the spine, joints, brain, and other vital organs without the use of X-rays or other forms of radiation.

MR images are produced with a large, powerful magnet, radio waves, and a computer. This technology enables physicians to detect diseases or abnormalities in early stages of development. Mean Time Between Failures (MTBF): The mean (average) time between failures of a system, and is often attributed to the “useful life” of the device, that is, not including “wear-in” or “wear-out” periods. Calculations of MTBF assume that a system is renewed, that is, fixed, after each failure, and then returned to service immediately after failure. Positron Emission Tomography (PET): A nuclear medicine procedure that produces pictures of the body’s biological functions. This is important because functional change, such as tissue metabolism and physiological functions, often predate structural change. PET can save time and costs in diagnosis and treatment of many significant disease conditions in the fields of oncology, cardiology, and neurology. Utilizing PET improves patient care by helping physicians select more effective therapies. Redundant Arrays of Inexpensive Disks (RAID): RAID is a method of accessing multiple individual disks as if the array were one larger disk, spreading data access out over these multiple disks, thereby reducing the risk of losing all data if one drive fails and improving access time. Radio-frequency Identification (RFID): An automatic identification method, relying on storing and remotely retrieving data using devices called RFID tags or transponders. Severe Acute Respiratory Syndrome (SARS): Severe acute respiratory syndrome is a newly emerged infectious disease with moderately high transmissibility that is caused by a coronavirus. Storage-Area Network (SAN): A storage-area network is a networked storage infrastructure (also known as a fabric) that provides the any-to-any connectivity between servers and storage devices, such as RAID disk systems and tape libraries.

This work was previously published in Encyclopedia of Multimedia Technology and Networking, Second Edition, edited by Margherita Pagani, pp. 1162-1170, copyright 2009 by Information Science Reference (an imprint of IGI Global). 2182

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Imaging Advances of the Cardiopulmonary System Holly Llobet Cabrini Medical Center, USA Paul Llobet Cabrini Medical Center, USA Michelle LaBrunda Cabrini Medical Center, USA

INTRODUCTION A technological explosion has been revolutionizing imaging technology of the heart and lungs over the last decade. These advances have been transforming the health care industry, both preventative and acute care medicine. Ultrasound, nuclear medicine, computed tomography (CT), and magnetic resonance imaging (MRI) are examples of radiological techniques which have allowed for more accurate diagnosis and staging (determination of severity of disease). The most notable advances have occurred in CT and MRI. Most medical subspecialties rely on CT and MRI as the dominant diagnostic tools an exception being cardiology. CT and MRI are able to provide a detailed image of any organ or tissue in the body DOI: 10.4018/978-1-60960-561-2.ch812

without the necessity of invasive or painful procedures. Virtually any individual could be tested as long as they are able to remain immobile for the duration of the study. Image generation traditionally has been limited by the perpetual motion of the human body. For example, the human heart is continually contracting and relaxing without a stationary moment during which an image could be obtained. Lung imaging has been more successful than cardiac imaging, but studies were limited to the length of time an ill person is able to hold his or her breath. Historically, imaging technology was limited by inability to take a picture fast enough of a moving object while maintaining a clinically useful level of resolution. Recent technologic innovation, resulting in high speed electrocardiogram-gated CT and MRI imaging, now allows the use of these imaging modalities for evaluation of the heart and

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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lungs. These novel innovations provide clinicians with new tools for diagnosis and treatment of disease, but there are still unresolved issues, most notably radiation exposure. Ultrasound and MRI studies are the safest of the imaging modalities and subjects receive no radiation exposure. Nuclear studies give an approximate radiation dose of 10mSv and as high as 27mSv (Conti, 2005). In CT imaging, radiation dose can vary depending on the organ system being imaged and the type of scanner being used. The average radiation dose for pulmonary studies is 4.2mSv (Conti, 2005). The use of multi-detector CT (MDCT) to evaluate the heart can range from 6.7—13mSv. To put it into perspective, according to the National Institute of Health, an average individual will receive a radiation dose of 360mSv per year from the ambient environment. It is unlikely that the radiation doses received in routine imaging techniques will lead to adverse reactions such as cancer, but patients should be informed of the risks and benefits of each procedure so that they can make informed decisions. It is especially important that patients be informed when radioactive material is to be injected into their bodies. The reasons for this will be discussed later on in the chapter.

BACKGROUND Coronary artery disease (CAD) is the leading cause of death in the U.S.. It is also the most common cause of the most costly heart disease in the U.S.—heart failure (Thomas & Rich, 2007). With a prevalence of almost 13,000,000, it carries an estimated cost of US$130 billion per year (Sanz & Poon, 2004). The American Heart Association calculates the cost of caring for those with cardiovascular disease at $300 billion per year (Raggi, 2006). According to the American Lung Association, lung disease is the third most common cause of death in America, responsible for one in seven deaths every year. Infections, particularly those of the lungs, are the number one killer of

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infants. An estimated 35 million Americans are currently living with lung disease such as emphysema, asthma, or chronic bronchitis. Even small technological advancement can lead to dramatic improvements in health care. Raggi (2006), the American Heart Association/American College of Cardiology and the National Cholesterol Education program III have describe simple screening tools to risk stratify patients (group patients into prognostic categories). These stratification tools involve assessment of lifestyle factors, genetic factors, and simple blood tests. For example, patients who smoke, are obese, inactive, have high cholesterol, and a family history of heart disease are more likely to have heart disease than people who lack these risk factors. Based on the results of the screening, recommendations can be made for further diagnostical procedures, treatments, and lifestyle interventions. Some of the more advanced diagnostic procedures include echocardiography, nuclear medicine (NM) scans, CT, and MRI.

ECHOCARDIOGRAPHY Echocardiography uses the principle of ultrasound (sound wave) reflection off cardiac structures to generate images of the heart just as it does in producing images of an unborn baby in a pregnant woman. In the past three decades, echocardiography has rapidly become a fundamental component of the cardiac evaluation. Measuring the same feature from different angles (windows) with different types of sound wave detectors (transducer) is done to produce an image. The entire heart and its major blood vessels can be displayed in real time and in various 2-D planes. Transthoracic echocardiogram (TTE) imaging is performed with a hand-held transducer placed directly on the chest wall. In select situations in which a more direct image needs to be taken without the interference of the chest wall, chest muscles, and chest fat, a transesophageal echocardiogram (TEE) may be performed. In TEE, an ultrasound transducer is

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mounted on the tip of an endoscope and placed into the esophagus of a patient. The transducer is directed toward the heart so that high-resolution images can be attained with minimal interference. TEE is a riskier and more invasive procedure but is able to provide details on the parts of the heart distant from the transducer on TEE. Newer echocardiographic machines have the advantage of being lightweight and portable (Figure 1). Some machines weigh less than 6 pounds and can be carried like a briefcase. The benefit of these advances is the ability to immediately assess heart function without the risk of having to move unstable patients (Beaulieu, 2007). This will soon become an essential part of the physical examination both hospitals and the outpatient setting. The echocardiograph permits quantification of the overall ability of the heart to supply blood to the body, description of the appropriate opening and closing of the heart valves, determination of leaks within the heart valves, visualization of abnormal fluid around the heart, visualization fluid trapped in the lungs and detection of structural abnormalities that disturb the blood flow through the heart (Figure 2). Although one of the older modalities

Figure 1. Portable echocardiogram machine (Sonosite MicroMaxx, product photographs reprinted with permission from SonoSite; Sonosite trademarks owned by SonoSite, Inc.)

in cardiopulmonary imaging, it still is the test of choice for certain conditions. TTE is advantageous in emergency situations when time is limited and a patient may be too unstable to transport (Shiga, Wajima, Apfel, Inoue, & Ohe, 2006). The most significant limitation is in the quality of the image that TTE delivers. A large amount of information can be generated from TTE, but the images may be particularly limited in obese individuals or those severe lung disease. In addition echocardiography is highly operator dependant for generating and interpreting images. Doppler echocardiography is a newer modality in heart imaging. It uses ultrasound reflecting off moving red blood cells to measure the velocity of blood flow across several different parts of the heart. Normal and abnormal blood flow patterns can be assessed and by adding color. Blood flow can be evaluated for smoothness or turbulence. The addition of color to Doppler echoes is an important advancement yielding more accurate readings.

NUCLEAR MEDICINE NM techniques consist of the injection of a radioactive isotope into the blood stream of an individual. This isotope emits a photon (usually a gamma ray) Figure 2. Echocardiogram of the heart (product photographs reprinted with permission from SonoSite)

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generated during radioactive decay as the nucleus of one isotope changes to a lower energy level. Special cameras are placed over the chest of the individual and capture the photons released. Nuclear studies of the heart and lungs have numerous applications. One can assess blood flow to specific areas of the heart. Blood provides a fresh supply of oxygen and nutrients required for the proper cardiac function. NM scans occurs in two parts, one while the patient is at rest and the second after the heart has been exercised. While at rest, the isotope is injected into the blood stream while the individual lies flat. A specialized camera rests over the heart capturing the emitted photons. The second phase of the test occurs just after the heart rate has been increased, either by medication or exercise. The camera then captures the photons emitted during increased cardiac stress and a comparison is made between the two studies. In a healthy heart, the photons emitted during both tests should be equal creating identical pictures. If any region of the heart that has poor circulation an image produced from the nuclear scan shows a black area from which few or no photons are emitted. This signifies that there is a reduced blood flow to that area of the heart and allows treatment to be initiate before the decreased blood flow leads to a heart attack. Similar techniques are used when examining the lungs. A lung scan is most commonly used to detect a blood clot obstructing normal blood flow to part of a lung (pulmonary embolism). The test is broken down into two parts—a ventilation scan (V) and a perfusion scan (Q)—which combined comprise the V/Q scan. The ventilation scan involves inhaling of a known amount of radioactive tracer by an individual. Cameras are then placed over the chest to detect the emitted photons. An image representative of the movement of air through the lung is generated. Areas with inadequate ventilation appear dark because few photons arrive to those areas. Conversely, areas of the lung containing too much air emit an increased number of photons. The perfusion

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scan works in a similar fashion to that of the NM heart scan. The radioactive tracer is injected into the bloodstream as the blood passes through to the lungs. The scan will show areas of the lungs that are not receiving enough blood. Normally the tracer should be evenly distributed throughout the lung. When both tests are completed, a comparison of the images is done and any mismatch may indicate a that not enough blood is arriving or not enough air is arriving to a specific region of the lung. A newer modality in nuclear medicine is positron emission tomography (PET). A PET scan is a diagnostic examination that involves the acquisition of physiologic images based on the detection of radiation from the emission of positrons. Positrons are tiny particles emitted from a radioactive substance injected into a patient’s blood stream. PET scans of the heart and lungs can be done to examine blood flow to the heart, viability of the heart muscle, detect cancer in the lungs, and determine the effectiveness of cancer therapy using chemotherapeutics. Unlike the previous tests, which are only able to describe structure, PET scans are able to relay information on the functionality of the tissues being studied. Current research is investigating the possibility of using PET scan to routinely measure cardiac blood flow (deKemp, Yoshinaga & Beanlands, 2007; Di Carli, Dorbala, Meserve, El Fakhri, Sitek, & Moore, 2007). One major problem in generating images from this technique is that the photons are emitted in a random array of directions from the origin. False readings can occur if the cameras are not placed correctly. A second problem occurs when tissues other than those targeted absorb photons. When this occurs insufficient numbers of photos reach the camera to create an interpretable image. The use of higher energy isotopes, most commonly technetium 99m (99m Tc) and thallium 201 (201 Tl) helps avoid these difficulties (Hoheisel, 2006). Both isotopes are frequently used and many times

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choices of isotope are made solely on the cost or experience of the operator.

COMPUTED TOMOGRAPHY CT has emerged as a cutting edge technique able to evaluate the structure and function of both the heart and lungs. Often, both lung and heart imaging can be done simultaneously with no added patient inconvenience. Advancements in imaging speed have allowed for more accurate evaluation of both relatively stationary structures, such as large arteries as well as rapidly moving structures, such as the heart. Traditional mechanical CT scanners produce images by rotating an x-ray tube around a circular gantry through which the patient advances on a moving bed. The basic principle of CT is that a fan-shaped, thin x-ray beam passes through the body at many angles providing cross-sectional images. The corresponding x-ray transmission measurements are collected by a detector array. Information entering the detector array and x-ray beam itself is collimated to produce thin sections and avoid unnecessary photon scatter. The transmission measurements recorded by the detector array are digitized into picture elements with known dimensions and are reconstructed. What this provides is a 3-D 360 degree examination of not only the heart, lungs, and its arteries, but also the lumen and walls of the arteries and airways. These images can provide detailed information on the size of the blood vessels lumen (the hole through the blood vessel). If the lumen of the blood vessels of the heart are significantly reduced and the individual is at high risk for a heart attack and may already be having chest pain resulting from decreased blood flow to the heart. In some institutions, cardiac CT is replacing cardiac catheterization for the diagnosis of coronary artery disease. Although x-ray angiography remains the “gold standard” in diagnosing changes in artery lumen size, it is an invasive procedure. Non-invasive modalities such as CT and MRI are becoming

routine in the diagnostic examination of patients with vascular disease (Dowe, 2007; Raff & Goldstein, 2007). Conventional CT scanners have been modified and now use a different approach to gather data. Spiral (helical) CT and now MDCT are now replacing older scanners providing detailed information in one sweep. Since spiral CT became a part of the routine diagnostic imaging in the early 1990’s, CT as a whole has matured. In spiral CT scanners, the x-ray tube rotates continuously around the patient as the bed moves through the scanner eliminating stops in between traditional shots. This prevents data misread and reduces the time needed to acquire images. The recent development and refinement of MDCT systems is a pivotal advance in CT technology (Chartland-Lefebvere et al., 2007). MDCT can take a complete 3-D picture of the heart within one heartbeat simultaneously imaging pulmonary structures. Many researchers now consider CT angiography/CT venography the test of choice when evaluating lungs for pulmonary embolism (blood clots) (Garcia, Lessick, & Hoffmann, 2006; Stein et al., 2006). As with cardiac CT imaging, the test involves placing a patient on the bed of the scanner. The bed moves through the doughnut-shaped scanner. In a cardiac CT, the patient is attached to a cardiac monitor so that it can follow the heart rate. An optimal study occurs if the heart rate remains regular and kept a maximal rate of 65 beats per minute (Poon, 2006). Many times a short acting medication is given to slow the heart rate. An iodinated contrast is injected into the blood stream of the patient, and the scanner is set on a timer. The timer predicts when the contrast will most likely reach the heart and images are recorded accordingly. Mis-timing can lead to poor images and an uninterpretable test. The patient is asked to hold his or her breath for 15 to 20 seconds as the scan is being done. The breath hold prevents the heart from moving as the chest rises and falls. Finally, the images are reconstructed able to be reviewed. Not only have these advancements

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changed how heart disease is measured, but have also substantially improved the quality of CT angiography of extracranial, thoracic, abdominal, pulmonary, and peripheral vasculature.

Figure 3. MRI system (reprinted with permission from Siemens)

MAGNETIC RESONANCE IMAGING MRI is a technique based on the magnetic properties of hydrogen protons present in water molecules. A large magnetic field can be used to induce, nuclear spin transitions from the ground state to excited states. As the nuclei lose energy, they return to their ground state, releasing energy in the form of electromagnetic radiation, which can be detected and processed into an image. Different tissues have a different concentration of water protons and molecular environments, therefore generating signals with different characteristics, which is the basis of the high quality tissue-contrast resolution of MRI (Sanz & Poon, 2004). The development of cardiac MRI has been particularly challenging because of motion of the heart and coronary arteries. Long scanning times needed in old MRI techniques made it technically difficult to get interpretable results. Much like the CT, an individual is placed on a sliding table and positioned in the MRI scanner. It also is doughnut-shaped with the table sliding back in forth through the hole (Figure 3). Depending on the number of images needed, the scan time can take 15 to 45 minutes. Contrast material can be injected into the blood stream but unlike CT contrast or nuclear medicine scans, there is no radiation or radioactive isotopes used. The high-resolution 3-D images without the use of radiation of cardiac MRI sets it apart from other imaging modalities, but it also had its limitations (Figure 4). Disadvantages included long scanning times preventing acutely ill individuals and those who suffer from claustrophobia from tolerating the test. Also, anyone with metallic foreign bodies embedded in their bodies such as pacemakers, surgical clips, or bullet fragments

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are typically unable to undergo MRIs. Despite the advantages, MRI is rarely used as the initial imaging technique because of lack of availability, time delay, incompatibility with implanted metal devices, and monitoring difficulties during the study (Shiga et al., 2006). The most common patient complaint is remaining still during the study, which can last 30 minutes or more.

FUTURE TRENDS Ultrasound and nuclear medicine have undergone technological breakthroughs, and newer devices provide more accurate and detailed results, which manufactures and researchers continue to focus

Figure 4. MRI of the heart (reprinted with permission from Siemens)

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on developing improved MDCT and MRI scanners. MDCT scanners will become faster with greater precision and detail allowing for improved diagnostic accuracy and reproducibility (Sanz & Poon, 2004). Similar advances are taking place with MRI. Scanners are being designed utilizing stronger magnetic fields, improving data acquisition technology, and improving contrast agents to produce higher quality images. The use of CT may decline as MRI becomes better, faster, and cheaper. The advancements of all these imaging modalities will change the field of medicine for the better.

CONCLUSION Each imaging modality provides a specific type of information and when used appropriately can aid physicians in making rapid diagnoses, and each also has its limitations. At present it may appear as multi-slice CT imagers are taking center stage especially with the development of 16-slice, 32-slice, 64-slice, and 256-slice imagers now available. Presently, MR is the imaging modality of choice for peripheral vasculature and is more suited for elective diagnosis rather than clinical emergencies. CT plays a large role for pulmonary and cardiac imaging. Since technological advances and clinical research continue to improve all modalities, it is impossible to know which will be of most utility in the future. Whatever the future of cardiopulmonary imaging brings, there will always be a need to understand basic principles of ultrasound technology, nuclear medicine, CT, and MRI.

REFERENCES Beaulieu, Y. (2007). Bedside echocardiography in the assessment of the critically ill. Critical Care Medicine, 35(5Suppl), S235–S249. doi:10.1097/01.CCM.0000260673.66681.AF

Chartrand-Lefebvre, C., Cadrin-Chênevert, A., Bordeleau, E., Ugolini, P., Ouellet, R., Sablayrolles, J., & Prenovault, J. (2007). Coronary computed tomography angiography: Overview of technical aspects, current concepts, and perspectives. Canadian Association of Radiologists Journal, 58(2), 92–108. Conti, C. (2005). One-stop cardiovascular diagnostic imaging (and radiation dose). Clinical Cardiology, 28(10), 450–453. doi:10.1002/ clc.4960281002 deKemp, R., Yoshinaga, K., & Beanlands, R. (2007). Will 3-dimensional PET-CT enable the routine quantification of myocardial blood flow? Journal of Nuclear Cardiology, 14(3), 380–397. doi:10.1016/j.nuclcard.2007.04.006 Di Carli, M., Dorbala, S., Meserve, J., El Fakhri, G., Sitek, A., & Moore, S. (2007). Clinical Myocardial Perfusion PET/CT. Journal of Nuclear Medicine, 48(5), 783–793. doi:10.2967/jnumed.106.032789 Dowe, D. (2007). The case in favor of screening for coronary artery disease with coronary CT angiography. Journal of the American College of Radiology, 4(5), 295–299. doi:10.1016/j. jacr.2007.01.003 Garcia, M., Lessick, J., & Hoffmann, M. (2006). Accuracy of 16-row multidetector computed tomography for the assessment of coronary artery stenosis. Journal of the American Medical Association, 296(4), 403–411. doi:10.1001/ jama.296.4.403 Hoheisel, M. (2006). Review of medical imaging with emphasis on x-ray detectors. Nuclear Instruments & Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment, 563(1), 215–224. doi:10.1016/j.nima.2006.01.123 Poon, M. (2006). Technology insight: Cardiac CT angiography. Nature Clinical Practice. Cardiovascular Medicine, 3(5), 265–275. doi:10.1038/ ncpcardio0541

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Raff, G., & Goldstein, J. (2007). Coronary angiography by computed tomography: Coronary imaging evolves. Journal of the American College of Cardiology, 49(18), 1827–1829. doi:10.1016/j. jacc.2007.01.074 Raggi, P. (2006). Nonivasive imaging of atherosclerosis among asymptomatic individuals. Archives of Internal Medicine, 166(10), 1068–1070. doi:10.1001/archinte.166.10.1068 Sanz, J., & Poon, M. (2004). Evaluation of ischemic heart disease with cardiac magnetic resonance and computed tomography. Expert Review of Cardiovascular Therapy, 2(4), 601–615. doi:10.1586/14779072.2.4.601 Shiga, T., Wajima, Z., Apfel, C., Inoue, T., & Ohe, Y. (2006). Diagnostic accuracy of transesophageal echocardiography, helical computed tomography, and magnetic resonance imaging for suspected thoracic aortic dissection: Systematic review and meta-analysis. Archives of Internal Medicine, 166(13), 1350–1356. doi:10.1001/ archinte.166.13.1350 Stein, P., Woodard, P., Weg, J., Wakefield, T., Tapson, V., & Sostman, H. (2006). Diagnostic pathways in acute pulmonary embolism: Recommendations of the PIOPED II investigators. The American Journal of Medicine, 119(12), 1048–1055. doi:10.1016/j.amjmed.2006.05.060 Thomas, S., & Rich, M. (2007). Epidemiology, pathophysiology and prognosis of heart failure in the elderly. Clinics in Geriatric Medicine, 23, 1–10. doi:10.1016/j.cger.2006.08.001

KEY TERMS AND DEFINITIONS Atherosclerosis: A condition in which fatty material collects along the walls of arteries. This fatty material thickens, hardens, and may eventually block the arteries. Computed Tomography: An imaging technology completed with the use of a 360-degree x-ray beam and computer production of images. These scans allow for cross-sectional views of body organs and tissues. Coronary Heart Disease: A narrowing of the small blood vessels that supply blood and oxygen to the heart. CHD is also called coronary artery disease (CAD). Echocardiography: A procedure that evaluates the structure and function of the heart by using sound waves recorded on an electronic sensor producing a moving image of the heart and heart valves. Magnetic Resonance Imaging: The use of a nuclear magnetic resonance spectrometer to produce electronic images of specific atoms and molecular structures in solids, especially human cells, tissues, and organs. Multidetector Computed Tomography: An imaging system which incorporates approximately 1,500 solid-state ceramic X-ray detector elements, each approximately 1 mm wide, arranged in rows. This detector array has been combined with improved X-ray tube design, allowing faster, multilevel scanning with high spatial resolution. Nuclear Medicine Scan: The use of a camera to image certain tissue in the body after a radioactive tracer accumulates in the tissue. Spiral (Helical) Compute Tomography: A newer version of CT scanning which is continuous in motion and allows for three-dimensional image generation.

This work was previously published in Encyclopedia of Information Science and Technology, Second Edition, edited by Mehdi Khosrow-Pour, pp. 1824-1829, copyright 2009 by Information Science Reference (an imprint of IGI Global).

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The Study of Transesophageal Oxygen Saturation Monitoring Zhiqiang Zhang Sichuan University, China Bo Gao Sichuan University, China Guojie Liao Sichuan University, China Ling Mu West China Hospital, China Wei Wei West China Hospital, China

ABSTRACT In this chapter, the transesophageal oxygen saturation (SpO2) monitoring system was proposed based on the early experiments, to provide a new program of SpO2 acquisition and analysis and avoid the limitation of traditional methods. The PPG (photoplethysmographic) signal of descendDOI: 10.4018/978-1-60960-561-2.ch813

ing aorta and left ventricular was monitored in the experiment. The analysis of the peak-to-peak values, the standard deviation and the position of peaks in signal waveforms showed that in vivo signal was more stable and sensitive; and the physiological information was reflected in the left ventricular PPG waveform. Therefore, it can be concluded that the transesophageal SpO2 monitoring technology has better guidance in clinical applications.

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The Study of Transesophageal Oxygen Saturation Monitoring

INTRODUCTION Oxygen saturation (SpO2) is the percentage of oxyhemoglobin (HbO2) with respect to the sum of hemoglobin (Hb) and HbO2 in blood, which is an important physiological parameter to assess human health condition. Therefore, oximetry has become an indispensable guardianship and diagnostic equipment and there is a wide range of applications in clinical practice, such as surgery, anaesthesia and intensive care units (ICU) (Kyriacou, 2006). There are some reports that Oximeters are placed in fingers, foreheads (Kim et al., 2007), tongues (Jobes & Nicolson, 1988), faces (O’Leary et al., 1992) and other parts of body surface to monitor oxygen saturation. However, in some cases, such as trauma (burn), surgery, or the unstable peripheral circulation, there are some limitations in clinical applications (Kyriacou et al., 2002; Ahrens, 1999; Pal et al., 2005). Before this study, Zhu et al. (2005) had verified the feasibility of transesophageal pulse oximetry through the animal experiments, then, the human experiments that monitoring pulmonary artery through trachea could be achieved (Wei et al., 2005). In this work, based on the anatomical relationship that the esophagus was close to the descending aorta (Kyriacou et al., 2003), a method of transesophageal SpO2 monitoring was proposed,

to provide a new method for in vivo monitoring. As the blood vessels of descending aorta and left ventricular are larger than surface vessels, which means that the light absorption of internal vessels is larger, so we expected that the signals from these parts would be more stable and sensitive, to make the SpO2 monitoring accurate and timely. Also, more biological information was expected to be obtained from the signal waveforms.

SPO2 MONITORING SYSTEM Theoretical Principle of SpO2 Monitoring Timely monitoring of blood oxygen saturation is an important indicator to determine human respiratory system, circulatory system, or whether there are anoxic obstacles in the surrounding environment. The measurement of SpO2 is based on the Hb and HbO2 with different light absorption characteristics (Sola et al., 2006), as shown in Figure 1. Studies have shown that human blood is sensitive to the light in the range of 600 nm to 1000 nm wavelength (Sola et al., 2006). In Figure 1, HbO2 and Hb have different light absorption coefficients in different wavelength regions. In

Figure 1. The light absorption coefficients of HbO2 and Hb in the red and infrared spectrum

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the infrared spectrum, their absorption curves changed smoothly and are close to each other, so there is little difference in absorption of Hb and HbO2. However, in the red spectrum, HbO2 and Hb are more sensitive to the changes in blood oxygen, because of their large difference in absorption coefficient, especially around 660 nm where the difference between HbO2 and Hb absorption coefficient is the greatest. With these factors considered, light sources at 660 nm and 940 nm were selected for the oxygen saturation measurement. Generally, the human’s blood SpO2 is determined by measuring photoplethysmographic (PPG) signal, which is caused by the fluctuations of vessels volume. The SpO2 calculation is shown in empirical Equation (1). SpO2 = AS − BS •

λ

λ

λ

λ

(I AC1 / I DC1 ) (I AC2 / I DC2 )



(1)

blood vessel. And then, current to voltage, amplification and analog to digital. At last, the signal will be put into the computer to filter, real-time display and calculate the SpO2, pulse and other physiological information by MATLAB software. Figure 2 is the block diagram of SpO2 measurement system.

EXPERIMENT This study had been approved by the Hospital Ethics Committee and agreed by patients. Basic vital signs such as BP (Blood Pressure), HR (Heart Rate) and SpO2 were monitored when patients went into the operating room. After anesthesia Figure 2. The block diagram of SpO2 measurement system

AS, BS are the coefficients of empirical equation, andλ1, λ2 are red and infrared light wavelengths, respectively, and IAC, IDC are light intensity. In the experiment, IAC is calculated by the peak-topeak values of PPG waveforms, whereas IDC is a constant. Therefore, as the blood volume alters, the changes of light intensity will be shown in the PPG signal waveforms. For example, when blood backward flow caused by heart beating occurs, the changes of blood volume will be reflected in the PPG waveforms.

Methodology In order to overcome the limitation of traditional SpO2 measurement in some circumstances, the transesophageal SpO2 measurement method was proposed in this work. It uses red light (660 nm) and infrared light (940 nm) as the light sources, which are controlled by FPGA, at the same time, the sensors are inspired with the trigger pulse to gather the reflected light intensity through the

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Figure 3. The diagram of transesophageal monitoring SpO2 method

was induced, the self-made probe (Patent No. ZL200320115080.2) against narcotic-induced tube was inserted into the esophagus and adjusted to the proper position. Method is shown in Figure 3. In the clinical experiment, the researchers first gathered the PPG signal from fingers using selfmade equipment. Then, the measurements were place into esophagus to monitor the PPG signal of descending aorta and left ventricle. It should be emphasized that it was not in the same time when the PPG signal was collected in different parts, so the pulses calculated from the waveforms were not same with each other, whereas they were same with the hospital’s patient monitor, respectively, which was place on finger.

Finger PPG Signal In the experiment 1, the researchers used the device to collect finger PPG signal, as shown in Figure 4. In Figure 4, the finger PPG signal waveform is smooth, but has a small peak-to-peak value. There are mostly capillary vessels in fingers, which have small blood volume, so the changes of blood volume are small. As a result, it can be concluded the sensitivity of the body surface PPG is lower.

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Descending Aorta PPG Signal In the experiment 2, the SpO2 acquisition tube was placed into the lower esophagus to collect descending aorta PPG signal. The red and infrared rays were launched by self-made probe through esophagus mucosa and directly into the descending aorta. Because the light absorptions of Hb and HbO2 are different, so the red and infrared rays’ intensity change in varying degrees, which are collected by the probe sensors. The waveform is shown in Figure 5. In Figure 5, the PPG signal waveform is continuous, smooth and clean. The distance between peak 1 is calculated and normalized, then calcuFigure 4. Finger PPG signal (a is red and b is infrared)

The Study of Transesophageal Oxygen Saturation Monitoring

Figure 5. Descending aorta PPG signal (a is red and b is infrared)

Figure 6. Left ventricle PPG signal (a is red and b is infrared)

lates the position of peak 2 between peak 1. After analysis, the location of peak 2 is very stable, which concentrates in 0.618-0.632, but its physiological significance need to be studied in the subsequent experiments.

DISCUSSION

Left Ventricle PPG Signal Figure 6 is the left ventricle PPG signal waveform. From this waveform, the “double peak” phenomenon is found, which is unique characteristic of heart. As we know, heart keeps up the circulation of the blood by alternately contracting and expanding. The waveform peak is corresponding to ejection time, which the heart’s blood volume is least, and the amount of light absorbed is smallest. After ejection, the measured signal value decreases in a period of time, then the value is fluctuating between heart and vessel, which will make a process of repeated shocks, as shown in Figure 6 circle tag. This phenomenon can be seen intuitively, however, the relationship between this phenomenon and the cardiovascular status still need further experiments.

In order to calculate SpO2, the peak-to-peak values are calculated, and larger value could be more sensitive to the SpO2 in different parts. The researchers select 30 seconds PPG waveforms of the left ventricle, descending aorta and finger to calculate the average of peak-to-peak values, which are shown in Figure 7. The peak-to-peak values of infrared and red of left ventricle are 0.269 and 0.503, and the values of descending aorta are 0.154 and 0.274, while the values of finger are 0.019 and 0.050, respectively. Consequently, the peak-to-peak values in vivo are larger than in finger, and that is more than 5 times. The reason is that heart and descending aorta are close to the esophagus, SpO2 in aorta can be monitored directly. However, finger blood vessels are mostly terminal capillary vessels, which contain less blood than the aorta, so the absorption of light is less than the aorta. As a result, the peak-to-peak values of PPG signal in vivo are much larger than body surface’s, which means in vivo signal is more sensitive to evaluate the SpO2 timely and accurate. In order to measure the stability of waveforms, standard deviation was used to assess the degree of fluctuation in PPG waveforms in this work.

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Figure 7. Comparison of left ventricle, descending aorta and finger PPG peak-to-peak value

Standard deviation is proposed to measure the difference between individuals and their average. Therefore, the greater the standard deviation is, the more different the individual would be from their average, which may impact the stability of the pulse calculation. The researchers choose the 30 seconds PPG waveforms in finger, descending aorta and left ventricle to calculate the standard deviation, to assess the stability of PPG waveforms. The standard deviation is shown in Figure 8. In Figure 8, the standard deviation of left ventricular and descending aorta is much smaller than finger’s. That means in vivo PPG waveform

is more stable, so it is better to analyze the SpO2 and pulse. The descending aorta and left ventricle signal were monitored in this study, so the degree of interference was minimal. In Figure 5, 30 seconds PPG signal waveform was selected and 70% of the peak values are concentrative, whereas the peak values out of this range are significantly larger. The patient breathes through the breathing machine in the same frequency as the peak value out of range occurrence. Therefore, the peak values out of range are caused by the breathing machine. Excluding this impact of breathing machine, the signal waveforms are stable.

Figure 8. Comparison of left ventricle, descending aorta and finger PPG standard deviation

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CONCLUSION As the SpO2 measurement on the body surface is difficult to achieve in some clinical applications, the in vivo PPG signal is monitored directly in this chapter, which can achieve continuous, noninvasive assessment of the oxygen saturation. The experimental analysis shows that the PPG waveforms of descending aorta and left ventricular are more stable and sensitive, so the application of this study may allow the measurement of body oxygen supply directly and accurately. Meanwhile, the echo information of heart can be seen in the left ventricular waveform. This information merits further evaluation and development. Therefore, this study is significant to clinical assessment, and may act as a better guide to surgery and the SpO2 monitoring.

ACKNOWLEDGMENT We would like to thank Prof. Min GONG for proofreading of this chapter and his constructive feedback and comments. This study was supported by the National Natural Science Foundation of China.

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Tachibana, C., Fukada, T., Hasegawa, R., Satoh, K., Furuya, Y., & Ohe, Y. (1996). Accuracy of A Pulse Oximeter during Hypoxia. Masui, 45(4), 479–482. Tong, D. A., Bartels, K. A., & Honeyager, K. S. (2002). Adaptive Reduction of Motion Artifact in the Electrocardiogram. EMBS/BMES Conference, 2, 1403-1404. Tremper, K. K., & Barker, S. J. (1989). Pulse Oximetry. Anesthesiology, 70(1), 98–108. doi:10.1097/00000542-198901000-00019 Vicenzi, M. N., Gombotz, H., Krenn, H., Dorn, C., & Rehak, P. (2000). Tran esophageal versus Surface Pulse Oximetry in Intensive Care Unit Patients. Critical Care Medicine, 28(7), 2268–2270. doi:10.1097/00003246-200007000-00014 Widrow, B., & Glover, J. R. (1975). Adaptive Noise Canceling: Principles and Applications. Proceedings of the IEEE, 63(12), 1692–1716. doi:10.1109/PROC.1975.10036 Wood, L. B., & Asada, H. H. (2006). Noise Cancellation Model Validation for Reduced Motion Artifact Wearable PPG Sensors Using MEMS Accelerometers. Conference Proceedings; ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 1, 3525–3528.

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KEY TERMS AND DEFINITIONS Esophagus: Is a channel formed by the muscles connecting the throat to the stomach. SpO2: Is the percentage of HbO2 to the sum of hemoglobin Hb and HbO2 in blood. PPG: Photoplethysmographic, which is caused by the fluctuations of vessels volume. Oximetry: By detecting the absorbance of the finger or other parts of the body, in red and infrared wavelength, calculate human SpO2. AC/ DC: The PPG waveform is composed of two parts. One is changing with the pulse change, that we call AC, the other one is not changing with the pulse change, that we call DC. Left Ventricle: Is one of the four ventricular of human heart ventricles, which include two atrium and two ventricles. It receives oxygenated blood from the left atrium, and then pumps into the arteries to supply oxygenated blood to the body. Descending Aorta: Is a part of the circulation of the artery. Standard Deviation: Is proposed to measure a difference between data and its average. Peak-to-Peak Value: The distance between maximum and minimum.

This work was previously published in Interdisciplinary Research and Applications in Bioinformatics, Computational Biology, and Environmental Sciences, edited by Limin Angela Liu, Dongqing Wei and Yixue Li, pp. 173-182, copyright 2011 by Medical Information Science Publishing (an imprint of IGI Global).

2200

1

Index

Symbols 3D arrays 948 3D centerline extraction 1340, 1341, 1342, 1357 3D colon isosurface generation 1340, 1342 3D colon segmentation 1341, 1354, 1355 3D imaging 885, 886, 890 3D motion capture 1327, 1328, 1329, 1337 3D motion capture systems 1327, 1328, 1329, 1337 3D volumetric image 887, 888, 893 3D volumetric image registration 887, 888 3D/4D images 885 911 call data 1393, 1395, 1398 911 calls 1393, 1394, 1395, 1396, 1397, 1398, 1399

A A Query Analyzer (AQUA) 1018 absolute force 901, 915 academic hospital 1913, 1914 academic knowledge 1504 access control policy 391, 394, 395, 397, 399 access to healthcare 572 Accreditation Council for Graduate Medical Education standards (ACGME) 2131 acoustic-phonetic aspects 1555 action line 1154, 1169, 1170 action research 445, 446, 448, 449, 450 active contour model 362, 363, 365, 367 active navigation 2152 active phase 1155, 1156, 1157, 1160, 1162, 1166, 1170 active phase of labour 1155, 1156, 1160, 1162, 1166, 1170 activities of daily living (ADLs) 212, 215, 216, 242 Activity Based Costing (ABC) 574

acute myocardial infarctions (AMI) 1592, 1593 adaptive algorithm 617 adaptive altered auditory feedback (A-AAF) 1286, 1310, 1312, 1313, 1314, 1315 adaptive e-health systems 119 adaptive level sets 1340, 1350, 1357 adaptive systems 263 adaptive technology 1530, 1537 Adult Day Health Care (ADHC) 212 advanced network data mining 423 adverse drug events (ADE) 26, 28, 29, 40, 42, 46, 826, 1264, 1283, 1492, 1497, 1499, 1500 adverse event 1516 affect modeling 675 aged care 1539, 1540, 1547, 1549 ageing 1539, 1540, 1542, 1543, 1546 alert line 1154, 1169, 1170 altered auditory feedback (AAF) 1284-1288, 12931297, 1303-1315, 1319-1326 ambulance diversion 1393, 1394, 1395, 1396, 1400, 1401 ambulatory care centres 510 ambulatory care sensitive conditions (ACSCs) 2153, 2155, 2156, 2158, 2159, 2160, 2161, 2162, 2170 America’s Army 2128 American Health Information Management Association (AHIMA) 226, 242, 243 American Medical Informatics Association (AMIA) 27, 41, 42, 43, 44, 45, 46, 47, 48, 109, 111, 2014, 2015 American National Standards Institute (ANSI) 1677, 1683 American Nurses Association (ANA) 1682 American Nurses Informatics Association (ANIA) 109, 111, 115

Volume I pp. 1-743; Volume II pp. 744-1490; Volume III pp. 1491-2200

Index

American Society for Testing and Materials International (ASTM) 1677, 1680, 1681, 1682 American Telemedicine Association (ATA) 1843, 1844, 1845, 1847, 1848, 1849, 1850, 1851 aminolevulinic acid (ALA) 866, 867, 868, 869, 872, 874, 876 amplitude-integrated EEG (aEEG) 1216, 1217, 1218 anatomic pathology 1235-1241, 1244-1253, 1257, 1258 anatomical repere 800 angiography 368, 369, 370, 371, 373, 374, 375, 376 ANN model 1814, 1817, 1818, 1819, 1820, 1821, 1823 answering multidimensional information needs (AMIN) 2004 Anti-Retroviral Treatment (ART) 444, 445, 446, 447, 448, 450 apnea 295, 296, 297, 301, 302, 303, 304 Apnea Hypopnea Index (AHI) 295, 296, 297, 301 applications of biotelemetry 687, 689, 692 applications of telemedicine 680, 689, 692 Applied Strategies for Improving Patient Safety (ASIPS) 2057, 2070 approach behavior 2092 arachnophobia 2077, 2092 arrhythmic beat 306, 310, 311 arrhythmic episode 306, 308, 310, 311 artery 373, 374, 375, 376 artial premature contractions (APC) 310, 313 articulating paper 896, 913, 915 Artificial Intelligence (AI) 315, 353, 355, 358, 1017, 1329, 1337, 1721, 2049 Artificial Neural Network (ANN) 307-315, 323, 744748, 753-764, 1812-1829 Assembly of European Regions (AER) 575 assisted living facilities (ALFs) 215, 226, 243 assisted reproductive technologies (ART) 1866 assistive technologies 2048, 2049, 2050, 2051, 2052, 2053 assistive technology (AT) 780, 781, 784, 789, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537 asthma 720 asynchronous telehealth 702 atherosclerosis 2190 Atrial Fibrillation (AF) 306, 308, 310, 313, 1263, 1269, 1283 atrial tachycardia (AT) 308 audiology 693, 694, 695, 696, 697, 698, 699, 700, 702 audiology clinics 693 audiometer 696, 697, 702

2

Australian Incident Monitoring System (AIMS) 2057 Australian Patient Safety Foundation (APSF) 2057, 2070 automatic classifiers 964 automatic diagnosis 325 automatic text processing 1360 Average Length of Stay (ALOS) 1463, 1464, 1465, 1466, 1469 avoidance behavior 2092

B baby boomers 2049 backpropagation 1812, 1816, 1817, 1818, 1819, 1822, 1823, 1829 balanced scorecard 508, 509, 513, 517, 529, 530, 531 bar coding 2031 Barcoded Medication Administration (BCMA) 826, 835, 836, 837, 839, 840, 843, 845, 847 barriers 1900, 1903, 1904, 1906, 1907, 1908, 1909, 1915, 1917, 1918, 1920, 1921 biliary tumors 746, 753 biobanks 73, 89 biochip 73, 83, 90 biocontrol system 973 bioelectric signals 973 Bioethics Advisory Committee (BAC) 1854-1873 bioinformatics 412, 414, 419, 421, 478, 479, 483, 484, 1018 BioLiterature 1361, 1362, 1363, 1364, 1370, 1371 biological endpoints 877 biological information 412, 413, 416 biological information processing 412 biomarker 479, 480, 484, 485 biomarker analysis 480 biomechanical interactions 1874 biomedical data 682, 685, 692 biomedical engineering 305, 718 biomedical image 356, 766, 767, 771, 772, 775 biomedical image registration 766, 767, 771, 772, 775 Biomedical imaging 353 biomedical information 1360, 1361, 1370 biomedical instrumentation 677, 687, 692 biomedical knowledge 676 biomedical knowledge management 676 biomedical phenotypes 423 biomedical research 1235, 1250, 1853-1859, 18621864, 1868-1872 Biomedical Sciences Initiative (BMS Initiative) 1854, 1871

Index

biomedical signals 413 Biomedical Technology 1676, 1681 bio-medical time 1213 biomimetic 581, 586, 610, 617 biomimetic algorithm 581, 610 BioOntology 1360, 1363, 1364, 1370 bio-psycho-social model 1875, 1889, 1892 biotelemetry 676, 677, 682-692 biotelemetry system 676, 682, 683, 684, 685, 686, 688 Blogs 194, 208 blood pressure (BP) 2193 blood smear 325-331, 337, 341-351 Bluetooth 1508, 1516 bone marrow biopsies 327 bone marrow smears 325, 326, 327, 328, 331, 334, 336, 346, 348 brain–computer interface (BCI) 617 brain–machine interface (BMI) 581, 582, 584, 585, 604, 606, 607, 608, 609, 617 breast cancer 314, 315, 320, 321, 322, 323 Breast Imaging Reporting and Data System (BIRADS) 319, 321, 322, 324 breast ultrasound 377, 378, 383, 387, 390 Brian Hodges 453 British Heart Foundation (BHF) 1592, 1596 British Medical Association (BMA) 1954, 1958 broadband networks 1047, 1048 broker 1151 B-splines 2125 bug-in-the-ear 1639, 1640, 1654 bug-in-the-eye 1639, 1640, 1641, 1654

C Canadian Institutes of Health Research (CIHR) 148, 156, 158 cancer staging 421 cardiac arrhythmia , 305, 306, 310, 311, 312719 cardiac output 917, 918, 925 cardiac preload 917, 925 Cardio Pneumonary Resuscitation (CPR) 1676 cardiomegaly 1263, 1270, 1283 cardiovascular diseases 305, 313, 368 Care Process Pathways (CPP) 1093, 1095, 1096, 1097, 1098, 1102, 1103, 1104 cell classification 325, 349 cell detection 325 cellular processes 423, 424 cellular telecommunication 717, 736 Center for Aging Services Technologies (CAST) 2051

Center for Disease Control (CDC) 2015, 2017, 2019, 2025 Center for Medicare and Medicaid Services (CMS) 134, 213, 215, 217, 225, 244, 246 centerline extraction 1340, 1341, 1342, 1357 central service level 2037 Central Venous Pressure (CVP) 917, 918, 919, 920, 921, 922, 923, 925 Centre for Global Education and Research (CGER) 1771 centric occlusion 2152 cephalometric analysis 926, 943 cephalometric measurements 927, 938 cephalometric radiographs 926, 946 cervicogram 1154, 1155, 1160, 1170 cervicograph 1154, 1155, 1160, 1162, 1163, 1170 cervimetry 1155, 1156 Chair of Medical Engineering (mediTEC) 554, 555, 560, 562, 565 charting 110, 111, 113, 115, 116 Chatham-Kent project 1938, 1939 Chief Privacy Officer (CPO) 1943 cholangiocarcinoma 744, 745, 759, 762, 764, 765 cholangiography 746 cholangiopancreatography 744, 746 chronic care models 2155 chronic disease 1048-1050, 1053, 1057-1060, 10751083, 1089-1091, 1539, 1543-1545, 15501553 chronic disease management (CDM) 50, 52, 53, 54, 1075, 1076, 1077, 1079, 1080, 1081, 1082, 1089, 1090 chronic heart failure (CHF) 1049, 1592 chronic illness 1540, 1542, 1543, 1544, 1546, 1550, 1552, 1553 chronic medical conditions 717, 719 chronic obstructive pulmonary disease (COPD) 719, 720, 1049, 1676 chronic-degenerative pathologies 572, 573 chronicity 2155 ciphertext re-encryption 393, 402 Circular coil 855 classification rules 486, 491, 495, 496, 497, 503 Clinical Care Classification (CCC) 1678, 1682 clinical care gaps 147 clinical data 703, 706 clinical data coordinating center (DCC) 2095, 2096, 2098, 2099, 2100, 2101, 2102, 2103, 2104, 2105, 2106, 2107, 2109, 2110 clinical datawarehouses 1235, 1237, 1250 clinical decision making (CDM) 27, 28, 1721

3

Index

clinical decision support system (CDSS) 25-31, 41, 44, 162, 413-416, 421, 1505, 1510, 1516, 1784, 1785, 1797, 1923-1931, 2054, 2065, 2066 clinical decision-support tools 1078, 1091 clinical deterioration 1393, 1394 clinical determinants 1760, 1761, 1762, 1763, 1765, 1768 Clinical Document Architecture (CDA) 1678, 1682 clinical expert 1008, 1016 clinical expertise 1900, 1901, 1902, 1909, 1911, 1913, 1916, 1917 clinical gait analysis 1328, 1336, 1337 clinical governance 1093, 1094, 1096, 1100, 1104 clinical guidelines 172, 183, 184, 185, 186, 187, 1721, 1735, 1737 Clinical Guidelines Acquisition Manager (CG-AM) 1723 Clinical Guidelines Database (CG-DB) 1723 Clinical Guidelines Knowledge Representation Manager (CG-KRM) 1723 clinical HIS 843, 852 clinical history 1624, 1633 clinical informatics 1600, 1606, 1608, 1610, 1611, 1613 clinical information system (CIS) 1581, 1582, 1583, 1924, 1925, 2029 clinical knowledge 1093, 1098, 1099, 1103, 1104, 1784 clinical manifestations 1264, 1274, 1278 clinical pharmacist 1492, 1497 clinical placements 93, 94, 95, 96, 97, 99, 104, 106 clinical practice 250, 261, 1093, 1098-1101, 1504, 1505, 1508-1512, 1516 Clinical Practice Guidelines (CPGs) 1721-1737 clinical professions 1623, 1629 clinical proteomics 479, 484, 485 clinical routines 1912, 1916 clinical simulation 532, 533, 537, 538, 539, 540, 541, 542, 543, 548, 550, 553 clinical skills 93, 95, 96, 98, 102, 104, 105 clinical supervision 1638-1642, 1646, 1650, 16541660, 1663-1665, 1668-1673 clinical work 95, 1623 closing quotient 1011 cluster analysis 421 clustering 2175, 2182 clustering coefficient 424, 425, 427, 428 coagulation molecules 1813 coding enzymes 866, 874 cognitive architecture 779, 780, 786

4

cognitive biases 779, 787 cognitive domains 1555 cognitive engineering 567, 568, 570, 571 cognitive functioning 781, 784 cognitive modelling 559, 562, 567, 571 cognitive rehabilitation 780, 781, 788 cognitive task analysis (CTA) 1880, 1881 cognitive-socio-technical fit 532-537, 540, 542, 545548, 553 coil shapes 855 collaborative procedures 1192 collaborative strategy 510 colon isosurface generation 1340, 1341, 1342, 1357 colon segmentation 1340, 1341, 1342, 1350, 1352, 1354, 1355, 1357 colonic polyp 1340, 1341, 1342, 1347, 1355, 1356, 1357 colonoscopy 1340, 1341, 1342, 1356, 1357, 1358 color wavelet covariance (CWC) 951 colorectal cancer 1340, 1341, 1356 Common Object Request Broker Architecture (CORBA) 707, 716 Commonwealth Medical Benefits (CMB) 512, 514 communication technology 1656, 1657, 1660, 1669 community care home 1050, 1052, 1053, 1054, 1055, 1060 Community Health Information Network (CHIN) 1473, 1474, 1487, 1490 community healthcare trusts 572 community of practice 1623 competence centre 975, 978, 980, 981, 982, 987, 994 Competitiveness and Innovation Framework Programme (CIP) 1832, 1833 Compilation process 1995 compliance 918, 921, 922, 925 Composition process 1995 computed axial tomography (CAT) 2182 computed radiography (CR) 2178, 2182 computed tomographic colonography (CTC) 13401342, 1352-1359 Computer Aided Diagnosis (CAD) 305, 306, 311, 314, 325, 355, 358, 359, 367, 1341, 1342, 1356, 1357 computer aided risk estimation 314 computer aided surgery 73, 78 Computer human interaction (CHI) 2000 computer network 553 computer tomography (CT) 162, 354, 359, 376, 378, 746, 761, 763, 767, 769, 773-778, 886, 892, 926-948, 951, 963, 1017-1029, 1074, 2175, 2178, 2182, 2183, 2190

Index

computer-aided diagnosis (CAD) 744, 746, 749, 755, 761, 949, 950, 951, 960-964 computer-aided mammography 315 Computerised Medical Records (CMR) 1581 Computerised Physician Order Entry (CPOE) 826, 834, 847 computerized clinical decision support systems 25, 44 Computerized Occlusal Analysis 895, 896, 907, 912, 913, 914 Computerized patient care documentation (CPD) 1121-1128, 1134-1141 Computerized Patient Record System (CPRS) 11221126, 1131-1136, 1139, 1141 computerized provider order entry (CPOE) 25, 31, 41, 43, 46, 133, 143, 145, 162-164, 168, 170, 226, 243, 248, 553, 1105, 1110, 1111, 1116, 1120, 1492, 1497 Computerized Speech Laboratory (CSL) 1012 computing applications 1624 Conditioning Stimulus (CS) 860, 865 conductive elastomer composites (CEs) 794, 800 cone-beam computed tomography (CBCT) 886-893, 927-937, 940-948 cone-shaped kernel distribution (CSKD) 308 confidentiality 13, 14, 15, 16, 18, 22, 23 consumer health informatics 705 Continuity of Care Record (CCR) 1677-1683 contraceptive pill 1521 coronary angiography 367 coronary artery 1592 coronary artery disease (CAD) 2184, 2190 coronary heart disease (CHD) 1543, 1593, 1597, 2190 coronary tree 369, 371, 373, 374, 376 cost-benefit 25, 35, 39 cost-effectiveness 25, 27, 28, 31 cost-effectiveness analysis 3, 4, 10, 12 Council on Graduate Medical Education (COGME) 622, 623, 630 cradle to grave 1518, 1520 crawler 995, 999, 1000, 1001 Creative Commons 190, 192, 196, 202 critical thinking 620, 624, 629 CT colonoscopy 1342, 1356 cultural change 1235 Cumulative Index to Nursing and Allied Health Literature (CINAHL) 94 curricula 2141 curse of dimensionality 878, 879, 884 customer-centric production system 264

cybersupervision 1637, 1638, 1641-1653

D daily living activities (DLA) 1772 data integration 413, 419, 421 data management 2036, 2037, 2039, 2042 data mining 73, 83, 89, 90, 413-415, 421, 423, 435, 437, 486, 487, 491, 495, 496, 503-507, 16841688, 1694, 1697, 1700 database management systems (DBMS) 1419, 1583, 1586, 1588 dataset 936, 943, 948 data-sharing system 1491, 1492, 1493, 1498, 1500 D-dimer assay 1813, 1814, 1815, 1817, 1818, 1822, 1823 D-dimer molecules 1813 deceleration phase 1155 decision making 1721-1723, 1730, 1736-1743, 1754, 1812, 1822, 1828 decision support systems 25, 41, 44, 1492, 1721 decision theory 1721, 1723, 1729-1736 deep brain stimulation (DBS) 609, 1194-1197, 1213 deep vein thrombosis (DVT) 1813-1818, 1822-1825 degree centrality 1151 degree distribution 424, 426, 427 Delayed Auditory Feedback (DAF) 1286, 1293, 1294, 1302-1313, 1318, 1319 Department Information Systems (DIS) 634 Department of Veterans Affairs (DVA) 162 descending aorta 2194, 2200 developing countries 449, 450, 1419-1423, 14271435 developmental disabilities 1554, 1555 diagnosis automatic intelligent systems 1015 diagnosis-related tasks 1782 Diagnostic Related Group (DRG) 509, 517, 518, 523-528, 1677-1683 diagnostic signs 1783 diagnostic systems 359 diagnostic technologies 359 didactic curricula 620 differential diagnostics 620, 629 diffusion of innovation 830, 847, 852 digital divide 1540, 1541, 1543, 1544, 1545, 1546 digital imaging 75, 767 Digital Imaging and Communications in Medicine (DICOM) 1235, 1246-1261, 1850, 21752178, 2182 digital pathology 1235-1238, 1242, 1243, 1251, 1255-1257, 1260, 1262 digital rectal exam 1341

5

Index

digital signal processor (DSP) 605 digital slide 1235-1257, 1260 digitalis toxicity (DT) 1263, 1264, 1269-1277, 1280 digitized blood smears 326 digitized smears 326 Digoxin (DGX) 1263, 1264, 1275-1278, 1280 digoxin plasmatic concentration 1263, 1269, 1270 Directed Acyclic Graph (DAG) 1364 Disclusion Time (DT) 902, 904, 905, 911, 912, 914, 916 disclusion time reduction 916 Discovery Research Laboratory (DRL) 1770, 1771, 1773, 1778, 1779 discrete cosine transform (DCT) 309 discrete wavelet transformation (DWT) 480 disease management 1075-1082, 1089-1091 disease management initiatives 510 disease modeling 73, 84, 92 Disease Monitor (DISMON) 995, 996, 998, 999, 1000, 1002, 1003 disease registry 1080, 1091 diseases 995, 996, 998, 999, 1000, 1001, 1002, 1003 diseconomies of scale 1, 3, 4, 5, 6, 12 disk array 2182 disphonia 1014, 1016 distributed knowledge 1784 DNA microarrays 877, 883 domain knowledge 1360, 1364, 1366, 1370 Dorsolateral Prefrontal Cortex (DLPFC) 861, 865 double-contrast barium enema 1341 drug eluting stent 2125 drug related problem 1283

E early intervention services 1537 Early Referrals Application (ERA) 1784 eating disorder 781 e-business 252 ECG signal 306, 313 echocardiography 2184, 2190 ecological validity 540, 541, 553 e-commerce 1541, 1800, 1802, 1803, 1804, 1810 e-commerce paradigm 1802 Economic and Social Research Council (ESRC) 456 edge detection 355, 358 EEG frequency bands 304 EEG-arousal (EEGA) 296, 301, 302, 303, 304 e-files 2032 e-forms 2032 Egyptian era 1782

6

e-health applications 118, 125, 572, 573, 574, 575 e-health automation management 119 e-health Grids 118, 705 e-health human resources 1403, 1413, 1416 e-health paradigm 1800 e-health services 573, 1802, 1807, 1808 eHealth technologies 1831 EHR initiatives 1948, 1949, 1950, 1952, 1953, 1958 elastic modulus 1376, 1377, 1386, 1388, 1391 e-learning 252 elective surgery 1759, 1760, 1768 electrocardiograph (ECG) 306-313, 1593-1595 electroencephalographic (EEG) 296, 301, 302, 303, 304, 354, 356, 358, 617 electromedical devices 703, 710 electromyogram (EMG) 467-476, 895, 907-916, 965, 967, 971-973, 1327, 1328, 1332, 1335, 1338 electromyogram (EMG) signal 467-476 electron paramagnetic resonance imaging (EPRI) 886 electronic health (e-health) 118-122, 125, 174-176, 187, 210, 224, 242, 243, 635, 653, 703, 709, 710, 718, 740-743, 1030, 1044, 1048, 1052, 1060, 1403-1417, 1800-1811, 1831-1844 electronic health record (EHR) 13-17, 20, 22, 25, 31-38, 44-47, 52, 55, 56, 64, 66, 71, 118, 132-146, 160-163, 166-170, 226, 234, 235, 246, 634, 704, 716, 852, 1105-1120, 1235, 1403-1408, 1413, 1418, 1487, 1490, 1518, 1581-1589, 1678-1683, 1934-1945, 19481961, 2009 electronic measurement 1092 electronic media 696, 702 electronic medical consultation 1843 electronic medical or health records 116 Electronic Medical Record (EMR) 160-167, 170, 392, 487, 704, 716, 852, 1030, 1033, 10431045, 1076, 1080, 1081, 1091, 1490, 1511, 1517, 1875, 1884, 1892, 1935, 2171 Electronic Medication Administration Record (eMAR) 544, 553 Electronic Patient Record (EPR) 826, 852, 1048, 1094, 1097, 1935, 2036, 2045 electronic prescribing (e-prescribing) 1875, 1885, 1890-1892 email supervision 1673 e-medical information 173, 176 e-medicine 172-176, 187, 189, 486-491, 501-503 eMedicine.com 174 eMedicineHealth 174 emergency cardiac care 1595, 1598

Index

Emergency Departments (EDs) 1394, 1395, 1396, 1397, 1400 Emergency Management System (EM Systems) 1393 emergency medical care 1394 Emergency Medical Services (EMS) 974-982, 986994 Emergency Medical Services (EMS) physician 974981, 986-989, 994 emergency surgery 1768, 1769 EMG recordings 467 emotional functioning 779, 780 endoscopic retrograde cholangiopancreatography (ERCP) 746, 758 endoscopic ultrasound (EUS) 746 endoscopy imaging 949, 950, 954 end-to-end care 1093, 1094 Engineering and Physical Sciences Research Council (EPSRC) 456 environmental practices 2014 eosinophil 327 e-procurement 1541 erythrocytes 327-331, 334-339, 342-346 esophagus 2199, 2200 ethical awareness 1831, 1832, 1839 ETHICS 457, 464 etiology 1284, 1285, 1287, 1291, 1315, 1325 Evaluation Vocal Assistee (EVA) 1012 Everyday Technologies for Ahzheimer’s Care (ETAC) 2051 evidence-based medicine (EBM) 172, 173, 704, 705, 833, 834, 852, 1721, 1722, 1900-1918, 1921 Evidence-based Practice (EBP) 1900 Excel 2094-2096, 2099-2112 exercise-based interventions 801, 802, 809, 812 Expectation-Maximization (EM) 356, 357 expectations-confirmation theory (ECT) 814, 815 experiential learning 620, 623 expert system 376 expressive communication 1299, 1300, 1301, 1304, 1305, 1315, 1326 extensible markup language (XML) 1678-1683, 2094-2102, 2107-2113 Extension for Community Healthcare Outcomes (ECHO) model 2003

F face-to-face interaction 1839 Failure Mode and Effect Analysis (FMEA) 555-558, 562, 565-570 fall detection 805, 809, 812, 817, 819, 824

feature selection 879, 880, 881, 883, 884 fecal occult blood test 1341 feeling of presence 2092 ferrochelatase (FECH) 866-868, 871-876 fiduciary 1923 fight or flight 918 Figure of eight coil 855 finite element analysis 1376-1380, 1383-1391 Fitts’ law 1876, 1878, 1879, 1896 fonator system 1008, 1013, 1014 Food and Drug Administration (FDA) 555, 826, 827, 2031 force location 896 force movie 915 force-based occlusal adjustments 896, 915 force-mapping 897, 904, 905, 906, 908, 911, 912, 913, 915, 916 forward modeling 871, 876 Four-dimensional (4D) 885, 886, 889, 890, 891, 892, 893 Four-dimensional (4D) medical imaging 885, 893 Frank-Starling Mechanism 917, 925 Frennet-Serret formula 2125 Frequency Altered Feedback (FAF) 1286, 1293, 1294, 1304-1313 frequency bands 302, 303, 304 full-time equivalents (FTEs) 622 functional electrical stimulation (FES) 608 functional imaging 354 functional magnetic resonance imaging (FMRI) 361, 367 functional MRI 886 fuzzy adaptive resonance theory mapping (fuzzy ARTMAP) 310, 312 fuzzy classifiers 307 fuzzy clustering 356 fuzzy c-means (FCM) 361 fuzzy Kohonen network 310 fuzzy logic 307, 311, 313, 1329 fuzzy set theory 313

G gait analysis 792-794, 799, 800, 1327-1333, 13361338 gait cycle 1328-1330, 1334 gastroendoscopy 949 gene expression 877-884 gene expression data 414, 416, 420 gene ontology (GO) 1361-1372 general practitioner (GP) 511, 514, 515, 519, 520, 528

7

Index

Generalized learning vector quantizer (GLVQ) 481 Generic Occurrence Classification (GOC) 2057, 2058, 2059 Generic Reference Model (GRM) 2057 genetic exceptionalism 1853, 1854, 1858, 1859, 1860, 1864 genetic information 413, 1853, 1854, 1858-1864, 1867 genetic testing 1853, 1854, 1857-1869, 1872 Genetic Testing Report 1854, 1857, 1858, 1860, 1864, 1866, 1871, 1872 genome sequencing 1018 genomic medicine 414, 419, 420 genomics 73, 83, 84, 85, 89, 92 geometric morphometric (GM) 926, 927, 928, 932, 938, 944 gestational age 1216, 1220 global positioning systems (GPS) 2048 Globus Pallidus external (GPE) 1193 globus pallidus internus (GPI) 1192, 1193 glycoproteomics 478 Goal interdependence (GO) 1980 goals, operators, methods, and selection rules (GOMS) 1876- 1883, 1886, 1887, 1893, 1895 GOAnnotator (GOA) 1360-1371 government Accountability Office (GAO) 213, 245, 246 graphic symbols 1554, 1555, 1560, 1561, 1563, 1564 graphical user interface (GUI) 1123 grasp 969, 972, 973 Gray level difference statistics (GLDS) 950, 951, 955, 957, 958, 959, 961 gray literature 2014 gross national product (GNP) 1107 Group assimilation 1974, 1976 Group of Pharmacists of the European Union (PGEU) 574 GuideLine Acquisition, Representation and Execution (GLARE) 1721-1724, 1727-1729, 17331737 guidelines for medical practice 172 gynaecological cancer 949, 950, 951, 960, 961

H handheld computers 1504-1508, 1511-1516 hardware accelerator 2152 HD sensor 898, 899, 915 health care 13, 14, 16, 17, 18, 19, 20, 22, 23, 24 health care disciplines 94 health data 1623, 1627

8

health domain 1623, 1624, 1628, 1630, 1631, 1634, 1635 health emergency 703, 704, 708 health evidence 147 health history 15 Health ICT 174 health informatician 1403 health informatics (HI) , 25, 43, 160, 1403-1419, 1581, 1588, 1624, 1625, 1628, 1631, 1635, 1636, 1684-1690, 1694-1700 health informatics professional 1403, 1410 health information exchange (HIE) 1471-1490 health information management (HIM) 1403-1408, 1411-1418 health information management professional 1403 health information management systems 25, 30 health information sharing 1033 health information systems (HIS) 445-450, 532-548, 553, 1030, 1044, 1105-1107, 1110, 1120 health insurance 211, 221 Health Insurance Portability and Accountability Act (HIPAA) 392, 404, 1940, 1945 health knowledge management 705 Health Level Seven (HL7) 1235, 1250, 1251, 1253, 1259, 1262, 1678, 1683 Health Management and Research Institute (HMRI) 1438-1460 health managers 1739, 1757 health record systems 2171 health status 1623 health system 1706, 1707, 1716, 1717 health systems engineering 1684, 1691, 1692, 1696 health systems informatics 1684 healthcare 508-519, 530, 1438-1446, 1449-1460, 1706-1709, 1714-1717, 1720, 1831-1841, 2013-2026, 2054-2072 healthcare administration 1843 healthcare communications healthcare control levels 262 healthcare customer 172 healthcare decision support systems 1721 healthcare equipment 95 healthcare group 508 healthcare information systems 705 healthcare information systems, integrated 2095 healthcare information technology (HIT) 225, 226, 233-235, 242, 243, 1600-1610, 1613, 1619, 2094, 2095 healthcare institutions 508 healthcare integrated delivery system 1461, 1469 healthcare IT 132

Index

healthcare organizations 1800 healthcare professional 13, 93, 95 healthcare services 1438, 1439, 1440, 1443, 1445, 1450, 1456 healthcare system 1706, 1707, 1709, 1716, 1717, 1739, 1740, 1741 healthcare technology 210 healthcare technology interface hearing impairment 1554, 1555, 1559 heart failure 917, 918, 919, 925 heart rate (HR) 2193 Heart Rate Variability (HRV) 308, 313 help systems for diagnosis 1008, 1016 hematological cytology 325 hematopoietic stem cells 327 heme 866, 867, 868, 869, 871, 872, 873, 874, 875, 876 Hermite basis functions (HBF) 310 High Dependency Unit (HDU) 1094 High Integration Aligned (HIA) 1462, 1463, 1464, 1465, 1466 Hippocrates-mst , 314, 319, 320 histopathological examination 950, 954, 961 HMRI Model 1438-1443, 1448-1452, 1456-1458 Hodges’ Health Career Model (h2cm) 453, 454, 455, 456, 458, 459, 461, 463 Hodges’ model 451, 452, 453, 454, 455, 456, 459, 461, 462, 463, 466 home monitoring systems 2049 hospital environments 1624 Hospital Information Management System (HIMS) 1771, 1773, 1778, 1779 Hospital Information System (HIS) 634, 638-642, 645, 646, 651-653, 826, 830, 834, 835, 843, 852, 1033, 2029, 2030, 2032, 2033 human computer interaction (HCI) 1183, 1189, 1190, 1874-1886, 1889-1898 human ecology 2013, 2014, 2015 Human Resource Administration System (HRAS) 2046 Human Resource Information System (HRIS) 2046 human resource management (HRM) 1937, 1944, 2046 human resources 1419, 1422, 1436 human technology 1759, 1768 human-computer interface 780 Human-Machine Interaction (HMI) 556, 557, 571 hybrid heuristic algorithm 486, 503 hypertrophy 1283 hypervolemia 918 hypopnea 295, 296, 301

hypovolemia 918 hysteroscopy examination 961, 964 hysteroscopy imaging 949, 952

I ICT professionals 1831, 1832, 1835, 1838, 1839 ICT system 2163 ICU models 262 identity-based encryption 391, 402, 403 ill-posedness 355, 358 image integration 355, 358 image processing 885, 886, 890, 893 image registration 766-778, 886-893 image segmentation 355, 377-379, 386-390, 886, 890-893, 1340, 1342, 1345, 1359 image visualization 887, 894 imaging modality 893 immersive video-oculography 2078, 2092 implementation 1900, 1901, 1906, 1909, 1913-1915, 1918, 1920 in-degree 1144, 1146, 1151 Independent Practitioner Associations (IPAs) 19251930 Indian healthcare system 1439 Indiana Health Information Exchange (IHIE) 1482, 1483, 1486, 1490 individual-cognitive interactions 1874 infectious disease profiles 2014 Information and Privacy Commissioner (IPC) 1936, 1939 information communication technology (ICT) 13, 52, 93, 95, 98, 104, 147, 150-155, 160-163, 167, 168, 573-576, 634, 653, 1047, 1048, 1052, 1106, 1107, 1171, 1172, 1176, 11831187, 1403-1410, 1530, 1532, 1535, 1537, 1770-1775, 1831-1842 information management 2036, 2038, 2043 information management systems 877 information network 1146, 1149, 1151 information philanthropy 190, 193, 208 information society 1923, 1931 information systems (IS) 109-117, 445-449, 825852, 1875, 1964-1969, 1981-1986, 1995 information technology (IT) 13, 19, 24, 74, 77-81, 109, 110, 114-116, 132-136, 213, 224, 225, 243, 247, 248, 264, 704-707, 995, 1075-1078, 1081, 1082, 1085, 1089, 1090, 1106, 1107, 1110-1112, 1119, 1143-1145, 1148, 1149, 1518, 1520, 1527-1530, 1601-1606, 16141617, 1622-1627, 1630, 1634-1636, 19221926, 1929, 1933

9

Index

innovative technologies 1 Institute of Medicine (IOM) 26, 622, 623, 631, 1492, 1499, 1501, 1502, 1685, 1692, 1702, 1704 institutional review board (IRB) 1856 instructional technology 1530 integrated delivery system (IDS) 1461-1468 Integrated Service Digital Network (ISDN) 488, 1063, 1065, 1074 Integrating the Healthcare Enterprise (IHE) 1235, 1250-1255, 1259-1262 intellectual disabilities 1555-1564 intellectual impairment 1554-1559 Intellectual Property (IP) 192 intelligent agents 263, 293, 1419, 1435 intended consequences , 532 Intensive Care Unit (ITU) 1094 internal jugular (IJ) 918, 920, 921, 922, 923, 924 international 10/20 electrode system 304 International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM) 1286 international classification of patient safety (ICPS) 2054, 2064, 2065, 2071, 2072 International Council on Nursing (ICN) 109, 110, 113 International Health Terminology Standards Development Organisation (IHTSDO) 1235, 1253, 1254 International Statistical Classification of Diseases and Related Health Problems (ICD) 1683 International Telecommunications Union (ITU) 1850 Internet Explorer 2031 Internet Protocol (IP) 1063, 1069, 1074 interorganizational network 1461-1464, 1467, 1469 Interstimulus Interval (ISI) 860, 865 inter-subject registration 778 interventional informatics 1600, 1601, 1610 intracoronary ultrasound (ICUS) imaging 21142122, 2125 intra-subject registration 778 intravascular ultrasound (IVUS) 360-367, 377, 378, 384-390 intravenous cannulation 919, 925 intravenous nutrition (IVN) 1219, 1220, 1225 Ippokrateio 314, 315, 319 I-Scan system 901, 915 isosurface generation 1340, 1341, 1342, 1357

J java 263, 268, 269, 270, 273, 274, 292, 293, 294 Java Mutimedia Framework (JMF) 707

10

Joint Commission on Accreditation on Healthcare Organisations (JCAHO) 826, 2055, 2065, 2069

K Kantian ethics 1831, 1834 keystroke level model (KLM) 1876, 1877, 1878, 1879, 1889, 1890, 1891 kinematic 1329, 1331, 1332, 1338 kinetic 1327, 1329, 1331, 1332, 1338 knee-band (KB) 794, 795, 796, 797, 798 knockout-experiment 876 knowledge application 148 knowledge creation 1706, 1713, 1714, 1717, 1719, 1720 knowledge discovery 766, 767 knowledge exchange 1094 knowledge management (KM) 676, 767, 1594-1598, 1706-1720, 1784 knowledge management infrastructure 1706, 1707, 1708, 1711, 1715, 1716, 1717 knowledge network 1192 knowledge translation (KT) 147, 148, 149, 150, 153, 155, 156, 158, 2154 knowledge-based decisions 1721, 1724 knowledge-driven decisions 1685 Kohonen Self Organizing Map 1017, 1020 Kolmogorov-Smirnoff test (KS-test) 480, 482, 483

L laboratory information system (LIS) 2029, 2034 labour management 1153, 1154, 1157, 1158, 1160, 1164, 1165, 1166, 1167, 1169 language impairment 1554, 1555, 1556, 1559, 1560, 1562 laryngeal pathology 1016 latent phase 1155, 1157, 1160, 1162, 1163, 1170 latent phase of labour 1157, 1162, 1170 learning algorithm 879, 884 learning disabilities 1531, 1534, 1536, 1537 learning platform 620, 625, 626, 630 left ventricle 2195, 2200 length of stay (LOS) 509, 518, 524, 527 leukocytes 327, 328, 330, 331, 332, 333, 334, 335, 337, 338, 339, 340, 341, 342 Lewin Group Report 1963 Licensed Professional Nurse (LPN) 1127, 1134, 1141 lifelong learning 454, 456 Lightweight Directory Access Protocol (LDAP) 638, 641, 642, 644, 646, 647, 648, 653

Index

limited environments 995, 996, 999 live supervision 1662, 1663, 1671, 1673 live vital data transmission 994 liver disease 744 load balancing 2182 local field potential (LFP) 585, 588, 589, 595, 596, 599 logistic regression 1393, 1395, 1397 logistical determinants 1760, 1762, 1763, 1764, 1765, 1768 long run 1, 2, 3, 4, 5, 6, 8, 10, 12 long-term care (LTC) 210, 211, 216, 222, 225, 226, 232, 233, 234, 235, 241, 243, 244, 245, 246, 248 Low Integration Aligned (LIA) 1462, 1463, 1464, 1465, 1466 low risk technologies 2031 low-fidelity prototype 1190 lymphocyte 327

M Magnetic Resonance (MR) 359, 361, 767, 774, 776, 777, 778 magnetic resonance cholangiopancreatography (MRCP) 744-764 Magnetic Resonance Imaging (MRI) 162, 354-378, 744, 746, 761, 764, 886, 891, 1074, 2175, 2182, 2183, 2190 Magnetoencephalography (MEG) 354, 357, 358 make-to-order systems malignant arrhythmia 307 mammographic images 360, 362, 367 mammographic imaging 315 mammography 314, 315, 318, 319, 320, 321, 322, 323, 364, 367 Mandible 2152 mashups 190, 192, 209 Masked Auditory Feedback (MAF) 1286, 1304, 1306, 1307, 1308, 1312 mass customization 263, 293 mass spectrometry (ms) 479, 482, 483, 484, 485 Master Patient Index (MPI) 1480, 1490 mathematical model 1295, 1305, 1314, 1315, 1326 Maxilla 2152 maximum likelihood estimation (MLE) 1342, 1349, 1352 mean time between failures (MTBF) 2182, Med-e-Tel 174 MedHelp 174 Medicaid 211, 213, 217, 219, 221, 222, 225, 244, 245, 247

medical artificial intelligence 1017 medical classification 1678, 1683 medical conditions 1812, 1813, 1814 medical costs 172, 174 medical curricula 620, 624, 625, 626, 629 medical data 2030, 2031 medical decision support 119 medical decision-making 1739, 1740 Medical Device Directive (MDD) 555 medical diagnosis 412, 414 medical education 160, 162, 166, 168, 190, 191, 195, 199-204, 620-630 medical error reporting systems 2055, 2056 medical errors 160, 1219, 1223, 1234, 2055-2059, 2066-2071 medical expert system 486, 487, 489, 503 medical history 1814 medical image 1783 medical image processing 744 medical image segmentation 360, 366, 367 medical image technology 73, 78 medical imaging 885, 893 medical informatics 250, 262, 634, 653, 1235, 1257 medical information 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 1770, 1774 medical information system 995, 1006, 1219 medical infrastructures 2035 medical insights 1143 medical knowledge 766, 767, 1143, 1150, 1520, 1521 medical knowledge discovery 766 medical knowledge management 767 medical networks 2035, 2036, 2042 medical office assistant (MOA) 1882, 1892 medical practice 172, 185, 186 medical prediction 995 medical problems 1812 medical records 14, 15, 16, 20, 23, 1017, 1018, 1019, 1020, 1022 medical science 173 medical societies 1844 Medical Subject Headings (MeSH) 1362 medical technology 1143, 1148, 1530, 1539 medical terminology 1504 Medicare 211, 213, 214, 215, 217, 219, 221, 222, 225, 245 Medicare patients 211 medication administration 111, 117 Med-on-@ix , 974, 975, 976, 977, 978, 979, 980, 981, 985, 986, 989, 990, 993 mega-voltage CT (MVCT) 886, 889, 892

11

Index

mental health professional (MHP) 1656-1661, 16641670, 1673 MER signals 1197, 1198, 1199, 1201, 1203, 1204, 1205, 1209, 1211, 1213 metabolomics 478 Metropolitan Ambulance Services Trust (MAST) 1393, 1395, 1399 m-health services 717, 720 m-health system 717, 718 microarray 878, 879, 880, 881, 882, 883, 884 microelectrode recordings (MER) 1191, 1196, 1197, 1198, 1199, 1200, 1201, 1203, 1204, 1205, 1209, 1210, 1211, 1213 micropower analog circuit 581, 610 Micropower Neural Decoding System 585 microscopic image analysis 325 microscopical examination 326 misdiagnosis 15 mobile agents 260, 262 mobile computing 119, 653, 654 mobile computing technologies 94 mobile devices 1876 mobile e-health 118, 119 MOBile ELectronic patient record (MOBEL) 1171, 1172, 1173, 1174, 1180, 1183, 1184, 1190 mobile health (m-health) 633, 634, 635, 636, 637, 648, 649, 651, 652, 653, 719, 722, 724, 725, 1844 mobile health emergency 704 mobile healthcare 717, 719, 720, 721, 724, 729, 736 mobile information and communication technologies 93, 104 mobile medical informatics 250, 251, 253, 258, 259 mobile monitoring 717, 719, 720 mobile or wireless computing 117 mobile platform 703 mobile technologies 93, 719, 720, 724, 1504, 1513, 1516 mobility monitoring 801, 802, 804, 807, 810, 811, 816 modern healthcare 148 modern medicine 1031 molecular biology 413, 414, 419, 1360, 1362, 1364, 1370, 1371 molecular imaging 73 monitorization 703 monomodal images 772, 778 Moore’s Law 1772 morbidity 1264, 1275, 1277, 1283 Motion Analysis Lab (MAL) 793 motion sensor 824 motion-corrected PET 886 12

motion-sensing 801 motion-sensing technology 801 motor control 581, 585, 596, 607, 617 motor control signals 581 motor cortex 855, 857, 858, 859, 860, 861, 862, 863, 864, 865 Motor Evoked Potential (MEP) 857, 859, 860, 865 Motor threshold (MT) 857, 859, 865 motor unit action potential train (MUAPTs) 468 motor units (MUs) 467, 468, 470, 474, 475 motor-unit action potentials (MUAPs) 467, 468, 469, 470, 471, 472, 473, 474, 475, 476 Mozilla 2031 multi-agent systems (MAS) 262, 1419, 1420, 1421, 1423, 1424, 1426, 1427, 1428, 1430, 1431, 1432, 1433, 1434, 1435, 1436 multidetector computed tomography 2190 Multi-Dimensional Voice Program (MDVP) 1012 multi-layer perceptron (MLP) 744, 754, 755, 756, 757, 1266, 1270, 1275, 1283 multimedia messaging system (MMS) 1063, 1065, 1074 multimodal images 770, 774, 778 multimodal intelligent system 1285, 1326 multimorbidity 2171 multiple testing problem 879, 884 multi-resolution analysis (MRA) 480 multisensory learning 1537 mutation 418, 421, 871, 873, 874, 876 myeloblast 327 myelocyte 327 myocardial infarction 1596, 1597, 1598 myoelectric control 966, 967, 968, 971, 972, 973 Myoelectric signal (MES) 965, 966, 967, 968, 969, 971 Myofascial Pain Dysfunction Syndrome (MPDS) 914, 916

N nanotechnology 73, 75, 84, 85 narrative data 1017 National Alliance for Health Information Technology (NAHIT) 162, 169 national average length of stay (NLOS) 509, 527, 529 National Center for Patient Safety (NCPS) 2056 National e-Health Transition Authority (NEHTA) 826, 1950 National Health Service (NHS) 826, 833, 1047, 1048, 1051, 1059, 1060, 1950, 1954, 1956, 1958, 1959

Index

National Institute of Clinical Excellence (NICE) 2 National Institute of Standards and Technology (NIST) 1844, 1845, 1847 National Medical Ethics Committee (NMEC) 1855, 1856, 1857, 1870, 1871, 1872 National Nursing Home Survey (NNHS) 211, 216, 225, 243 National Service Framework (NSF) 1593 Neointima coverage 2125 Netscape 2031 network analysis 423, 424, 441 network data mining 423 network fragmentation 1151 network mining 423 networks of action 445, 446, 447, 448, 449, 450 neural cell ensemble 581, 583, 585, 617 neural cell ensemble signals 581, 585 neural decoding 582, 583, 584, 585, 588, 599, 602, 606, 607, 608, 609, 610, 617, 618 Neural Gas (NG) 478, 479, 481 neural networks 1264-1270, 1273-1283, 1329, 1337, 1338, 2125 neural prosthesis 608, 617 neural prosthetic 581, 583, 584, 598, 607, 608, 609, 614, 617 neural prosthetic devices 581, 583, 584, 598, 607, 609, 614 neural signals 583, 585, 586, 588, 589, 590, 595, 599, 603, 604, 606, 607, 608, 610, 617 neuromuscular disorders 467, 476 neuromuscular system 468 neuronal ensemble 617 neutrophil 327 non-elective surgery 1769 noninvasive interfaces 966, 971, 973 nonlinear dynamics 2089, 2092 non-stationary signals 1197, 1213 normal sinus rhythm (NSR) 306, 307, 308, 310 Norwegian University of Science and Technology (NTNU) 1171, 1172, 1188, 1189, 1190 nuclear imaging 359 nuclear magnetic resonance (NMR) 358 nuclear medicine (NM) 2184, 2185, 2186 nuclear medicine scan 2190 nucleus subthalamicus (STN) 1192, 1197, 1198, 1204, 1205 Nurse Education 93, 106, 107 nurse mentors 95 Nurse Practitioner (NP) 1127, 1141 Nursing , 108, 109, 110, 111, 113, 115, 116, 117 nursing courses 1624

nursing education 1504, 1505, 1506, 1507, 1511, 1512, 1513, 1514, 1515, 1516 nursing facilities (NF) 211, 226, 236 nursing home 210-225, 230-247 nursing informatics 451, 463 nursing informatics theory 451

O object of interest (OOI) 1106, 1114, 1115, 1116, 1118, 1119 obstructive sleep apnea (OSA) 295, 296, 297, 299, 300, 301, 303 occlusal abnormalities 895, 913 occlusal adjustments 896, 906, 911, 915, 916 Occlusal Analysis 895, 896, 897, 907, 912, 913, 914 occlusal data 895, 897, 900, 909 occlusal event 896, 897, 901, 902, 903, 904, 906, 909, 911, 915, 916 occlusal force 896, 897, 898, 900, 901, 905, 906, 907, 912, 915, 916 occlusal parameter optimization 916 occlusal time parameters 916 Occlusion Time (OT) 902, 904, 905, 916 online healthcare 572 on-line learning 1541 on-line lectures 1541 online medical information 13, 17, 18, 19, 21, 22 online relationship 1637, 1643 ontology 73, 85 ontology 995, 997, 998, 1000, 1001, 1002, 1004, 1005, 1006 Ontology Web Language (OWL) 997 open and mobile medical environment 119 open field MRI 886 Open GL 2152 open quotient 1011 operating room (OR) 556, 557, 559, 560, 568 optical coherence tomography 2125 optical colonoscopy 1341 optical tracking 2152 optimal nursing 1121 organisational structures 2035, 2036, 2042 organization learning 1706, 1708 organization memory 1706 organizational adoption of IT 132 organizational assimilation 1971, 1972, 1974, 1976, 1977, 1981, 1982, 1983, 1986, 1987, 1994 out patient department (OPD) 1033, 1034 out-degree 1144, 1151 over-fitting 879, 880, 884

13

Index

oximetry 2198, 2199, 2200 oxygen desaturation 296, 304

P panic disorder 781, 789 paramedic 1593, 1594, 1596, 1598 paraphilia 2092 parietal cortex 582, 583, 585, 596, 598, 610, 613, 614, 618 Parkinson’s disease 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1208, 1209, 1210, 1211, 1213 partogram 1154, 1167, 1168, 1169, 1170 partograph 1153, 1154, 1155, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170 pathological genotypes 412 patient 926, 928, 929, 933, 938, 940, 941, 942, 948 patient accessible health records (PAEHRs) 52 patient care 108, 109, 110, 111, 113, 114, 115, 117 patient care information system (PCIS) 1121, 1122, 1123, 1124, 1136, 1139, 1141 patient care process 108, 114 patient education 160, 161, 165, 166 patient electronic health records 1235 patient empowerment 50, 62, 67 Patient Empowerment and Environmental Control Support System (PEECSS) 1771, 1773, 1778, 1779 patient information management 119 patient journey 1094, 1095 patient journey record systems (PaJR) 2153, 2154, 2155, 2156, 2157, 2158, 2159, 2160, 2161, 2162, 2163, 2164, 2165, 2169, 2171 patient management 767, 773 patient safety 825, 826, 836, 845, 846, 847, 848, 852, 160, 161, 162, 172, 180, 181, 185 patient safety information system (PSIS) , 2054, 2056, 2057, 2063, 2064, 2065, 2066, 2068, 2072 patient safety reporting system (PSRS) 2054, 2063, 2064, 2072 patient-centered care 149, 160, 170 patient-centered e-health 1800, 1807, 1808, 1809 patient-doctor communication 160, 166, 170 patient-doctor relationship 160 patient-patient relationship 13, 20, 21 patient-provider relationship 13 pattern, range and timing (PRT) 1329, 1331, 1334 pc-based audiometer 702 peak-to-peak value 2200

14

percutaneous transhepatic cholangiography (PTC) 746 performance shaping factors (PSF) 2058 Periodontal Ligaments 916 peripheral neuromuscular system 468 Persistent Developmental Stuttering (PDS) 12841305, 1313, 1315, 1318, 1324, 1326 personal assessment tools 1081, 1091 personal digital assistants (PDA) 93-98, 104-107, 1505-1517, 1879, 2032, 2048 personal health informatics (PHI) 1800, 1803, 1804, 1805, 1806, 1808, 1809, 1811 personal health information 50, 51, 57, 59, 61, 64, 69 Personal Health Record (PHR) 162, 391-402, 18001802, 1809, 1810, 2006, 2007, 2160, 2171 Personal Information Protection and Electronic Documents Act (PIPEDA) 1943, 1945 personalized e-health 118, 120 pervasive computing 637, 654 pharmacodinamics 1283 pharmacogenomics 478 pharmacokinetics 1267, 1276, 1283 Photodynamic Therapy (PDT) 866, 867, 868, 874 photoplethysmographic (PPG) 2191, 2193, 2194, 2195, 2196, 2197, 2198, 2199, 2200 photosensitizer (PS) 866, 867, 868, 875 physical activity 808, 809, 810, 812, 817, 818, 820, 821, 822, 823, 824 physicians 132, 134, 135, 138, 139, 140, 143, 144, 145, 146 Picture Archiving and Communication System (PACS) 162, 1583, 1844 piezoresistive effect 796, 800 pitch 1010, 1011, 1013, 1016 pitch frequency 1010, 1011, 1013 pitch period 1011 Pittsburgh Regional Healthcare Initiative (PRHI) 1491, 1494 platelets 327, 328, 337, 339 plethysmograph 920, 925 podcasts 190, 191, 192, 194, 195, 204, 207, 209 point cloud 948 point of care (POC) 226 point of care clinical systems (PoCCS) 1171, 1173, 1177, 1190 point of care technology 1516 Polarity theory 455 Policy Support Programme (PSP) 1832, 1833 polymerization shrinkage 1374, 1375, 1377, 1382, 1383, 1385, 1386, 1387, 1388, 1389, 1390, 1391

Index

polyp detection 1340, 1341, 1342, 1356, 1357 polyp formation 1341 Polysomnography (PSG) 296, 297, 298, 300, 301, 302 porphyrin 867, 869, 870, 871, 874, 876 positron emission tomography (PET) 354, 358, 767, 769, 773, 774, 775, 776, 777, 778, 886, 887, 888, 890, 891, 892, 2182 posttraumatic stress disorder 781 practical learning 1917 predictive modeling software 1077, 1078, 1091 Pre-hospital Trauma Life Support (PTLS) 2131 Premature Ventricular Contractions (PVCs) 306, 309, 310, 313 prereferral process 1538 primary care delivery 1438, 1439, 1441, 1457, 1458 Primary Health Organisations 1925 private key 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 406, 408 Probabilistic Neural Network (PNN) 950, 951, 955, 957, 959 problem oriented medical records (POMR) 1521 problem-based learning (PBL) 199, 200, 201, 206, 209 process based education 1505, 1517 professional development 1637, 1648 professional identity 1759, 1763, 1769 profile matching 1469 prolonged labour 1153, 1154, 1156, 1157, 1158, 1164, 1165, 1166, 1170 prostate specific antigen (PSA) 414 prostate ultrasound 389, 390 Prosthetic (AAF) Device 1326 prosthetic device 1320, 1326 proteomics 73, 83, 85, 86, 87, 92 proteomics-genomics 413 Protoporphyrin IX (PpIX) 866, 867, 868, 869, 871, 872 prototype classifiers 481, 485 provider order entry 25, 31, 41, 43, 46, 47 proxy re-encryption 391, 392, 393, 396, 397, 398, 399, 401, 402, 403, 406, 407, 409 psychological disorders 781, 789, 790 psychophysiological computing 666, 675 psychophysiology 665, 666, 668, 672, 675 psychotic disorder 781 public health nursing 2013, 2014, 2016, 2019, 2020 public key 393, 395, 396, 397, 399, 400, 401, 403 public-private partnership 1438, 1439, 1441, 1455, 1456, 1458 PubMed Central (PMC) 1363

pulmonary arterial circulation 1813 pulmonary embolism (PE) 1812, 1813, 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1826, 1828, 1829 Pulse!! The Virtual Clinical Learning Lab , 2126, 2127, 2128, 2129, 2130, 2131, 2132, 2134, 2135, 2136, 2137, 2138, 2139 PyBioS , 866, 870, 871, 876

Q QRS detection 306, 313 quality indicators 172, 178, 179, 181, 187, 189 quality management 976, 982, 990, 994 quality management system 172, 177, 178 quality of care issues 73, 1843 quality of patient care 73, 1843 quantitative gait analysis 800 quantitative research 1031 Queensland Smart Home Initiative (QSHI) 1539, 1540, 1548, 1549, 1550

R radial basis functions neural network (RBFNN) 308 Radio Frequency Identification (RFID) 825-829, 835-851, 1831, 1832, 2182 radiology information system (RIS) 2029, 2034 randomized clinical trial (RCT) 1738, 1740, 1741, 1742, 1743, 1744, 1745, 1751, 1752, 1755 real-time information 703, 706 real-time interaction 1663, 1673 real-time m-health 717 receptive field 618 recording sensitivity 915 recording sensor 897, 898, 900, 915 redundant arrays of inexpensive disks (RAID) 2176, 2179, 2180, 2181, 2182, re-encrypted ciphertexts 393 re-encryption key 393, 394, 396, 397, 398, 399, 400, 401, 407, 408, 410 Region Of Interest (ROI) 316, 317, 767, 950, 951, 952, 954, 955, 956, 957, 958, 959, 960, 961 Regional Health Information Organization (RHIO) 1474, 1484, 1485, 1486, 1487, 1488, 1490 registered nurse (RN) 211, 234, 241, 1122, 1127, 1134, 1141 rehabilitation 967, 971, 973 relative occlusal force 896, 897, 901, 912, 915 relevance learning 483, 485 remote assistance 704 remote computing 701, 702

15

Index

remote consultation 1063, 1074 Remote Method Invocation (RMI) 707, 716 remote patient monitoring (RPM) 1047, 1048, 1049, 1050, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1061 remote telemonitoring 1081, 1089, 1092 Repetitive TMS (rTMS) 857, 858, 859, 860, 861, 862, 863, 865 requirements engineering (RE) 1171, 1172, 1176, 1177, 1180, 1187, 1189 Resident Assessment Instrument (RAI) 225 Resource Description Framework (RDF) 997, 1000 respiratory distress 975 Respiratory Disturbance Index (RDI) 297, 301, 304 returns to scale 3, 4, 5, 6, 7, 8, 9, 11, 12 RFID devices 1836, 1838 risk estimation 314, 317, 318 RMI Java Runtime Environment 707 RNA expression 878 role play 1173, 1181, 1182, 1183, 1189, 1190 routinization 1969, 1971, 1972, 1974, 1975, 1976, 1982, 1983, 1986, 1992 RR interval signal 307, 308, 310, 313 rule induction 486, 487, 489, 491, 495, 496, 497, 503 rural health telecare 1566

S SA Tabu Miner 486, 487, 495, 497, 498, 499, 500, 501, 502, 503, 504 safety culture 73, 86, 88, 89, 90 scaling 445, 446, 450 second opinion 1064, 1074 segmentation 355, 358, 368, 369, 374, 375, 376 segmentation process 326, 334 self-management tools 1080, 1090, 1092 self-organizing map (SOM) 356, 357, 1017, 1020, 1021, 1022, 1023, 1026, 1027 semantic markups 195, 209 semantic publishing 209 semantic technologies 995, 996, 997, 998, 999, 1003 semantic Web 190, 209, 997, 998, 999, 1004 semi-automatic segmentation 377, 379, 380, 385, 387 sequential hypothesis testing (SHT) 307, 308, 312 serious game 620, 2127, 2130, 2134 severe acute respiratory syndrome (SARS) 2181, 2182, shared decision-making 50, 52, 53 short run 1, 2, 3, 4, 5, 6, 8, 9, 10, 12 short-time Fourier transform (STFT) 308

16

shrinkage model 1382, 1384, 1386, 1388, 1389, 1391 shrinkage relaxation 1391 shrinkage stress 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1391 shrinkage value 1375, 1377, 1381, 1382, 1383, 1384, 1385, 1387, 1391 SIe-Health , 703, 706, 708, 711, 712, 714 silico analysis 866, 871 simulated annealing 486 simulation engine for patient traits and environmental reaction (SEPTER) 2130 Single Nucleotide Polymorphism (SNP) 418, 419, 420, 421 single photon emission computed tomography (SPECT) 354, 357, 767, 886, 887, 890 skeleton extraction 1340, 1352 skilled nursing facilities (SNFs) 226, 236 Sleep Laboratory 296 slice 933, 934, 935, 942, 948 Smart Systems for Health agency (SSH) 1936 smartphones 93, 94, 97, 98, 99, 104, 105 smoothed pseudo Wigner Ville distribution (SPWVD) 308 social change 1539, 1540 social cognitive ontological constructs (SCOCs) 1996, 1999, 2000, 2001, 2007, 2009 social dimension 995 social informatics 453 social interactions 1874, 1892 social network 1143, 1150, 1152 Social network analysis (SNA) 1142, 1143, 1144, 1145, 1148, 1149, 1150, 1151, 1152 social networking 1030, 1044 social phobia 781, 785, 786, 789 Social Sciences and Humanities Research Council of Canada (SSHRC) 148, 157 social support 2171 Social Web (SW) 995, 996, 997, 998, 999, 1003, 1005, 1006 Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) 1844, 1851 Socio-Economic and Health Care Support System (SEAHCSS) 1771, 1773, 1778, 1779, 1780 socio-technical approach 1174, 1190 socio-technical design 456 socio-technical literature 451, 452, 457 socio-technical structure 451, 452, 454, 455, 456, 458, 459, 460, 461, 462, 463 sociotechnical systems framework (STS) 109, 110

Index

software developer 1927, 1928, 1929, 1930 somato-psycho-socio-semiotic framework 1998 Spatial Gray Level Dependence Matrices (SGLDM) 950, 951, 955, 957, 958, 959, 961 special education 1531, 1532, 1533, 1536, 1538 Special Interest Groups (SIGs) 1847, 1848, 1850 spectral data 478, 479, 480, 481, 482 Speech Generating Devices (SGDs) 1554, 1555, 1557, 1558, 1559, 1560, 1561 speech language professionals (SLPs) 1285 speech recording 1009, 1013 speed quotient 1011 sphygmomanometer 918, 925 spiral (helical) compute tomography 2190 standard deviation 2200 Standard Operation Procedures (SOP) 2045, 2046 standardized patient 655, 656, 658, 661, 664, 669, 670, 675 statistical learning 356, 358 stenosis 368, 373, 376 storage-area network (SAN) 2178, 2181, 2182, strategic behaviour 509 strategic fit 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469 strategic HIS 852 stress concentration 1392 structural imaging 354 structured observation 1172, 1180, 1190 student nurses 93, 94, 95, 96, 104, 105, 106 stuttering 1284-1326 stuttering etiology 1325 subharmonics 1012 Substantia Nigra (NS) 1192, 1193 Subthalamic Nucleus (SN) 1193, 1195, 1204, 1205 subthreshold conditioning stimulus 860, 865 supervised learning 881, 884 Supervised Neural Gas (SNG) 478, 479, 480, 481, 482, 483 Supplementary Motor Area (SMA) 861, 862, 865 supply chain 279, 281, 285, 286, 292, 293, 294 supply chain management 263, 293, 1541 support networks 2049, 2050, 2051 Support Vector Machine (SVM) 307, 310, 950, 951, 955, 957, 959, 961, 963 supra-threshold test stimulus 860, 865 supraventricular rhythm (SVR) 309 supraventricular tachycardia (SVT) 307, 310 surface model 2152 surgical patients 1813, 1817, 1818 surgical template 2152 sustainability 445, 446, 447, 448, 450

sustained sound 1009, 1010 sympathetic activation 918 synchronous telehealth 695, 702 synthetic speech 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565 system 1 1916, 1917 system 2 1916, 1917 System for Sigle Voice Analysis (SSVA) 1011 Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) 1235, 1253, 1260 systemic interoperability 114, 117 systems engineering (SE) 1684, 1685, 1686, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1699, 1705

T tabu miner 486 tabu search 486, 506 tacit knowledge 1031, 1143, 1152 Tactical Combat Casualty Care (TCCC) 621, 629 task interdependence (TI) 1979, 1980 technology acceptance model (TAM) 225, 245, 247 technology enabled knowledge translation (TEKT) 147, 148, 152, 153, 154, 155 technology facilitated errors 532 technology in health care 12 technology productivity 1530 Technology-driven medicine 995 technology-enhanced clinical supervision 1657, 1665 technology-induced error 532, 534, 537, 539, 540, 548, 553 technology-mediated supervision 1656 teleassistance 703 tele-audiology 694, 696, 697, 698, 699, 700, 702 telecommunication technologies 486, 487, 491, 502, 1517, 1567, 1576 teleconsult 1191, 1192, 1214 teleconsultation 975, 977, 994, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074 telehealth 693, 694, 695, 696, 697, 698, 700, 701, 702, 718 Telehealth Intervention Project (TIP) 1568, 1569 telehealth technology 693, 1566, 1572 teleheath systems 1962 telemedicine 174, 176, 189, 210, 223, 224, 232, 233, 243, 486-493, 502, 505, 507, 634, 652, 653, 676-682, 687-692, 703-705, 718-724, 736743, 977-980, 986-990, 994, 1062-1074, 1657, 1670-1672, 1843-1851 telemedicine deontology 1068, 1074

17

Index

telemedicine infrastructure 692 telemedicine technology 692, 1566 Telemedicine Work Station (TWS) 1064, 1065, 1066, 1068, 1072, 1074 telemetry types 676, 692 Telenotarzt 975, 979, 980, 981, 986, 988, 989, 994 teleobstetrics 1153, 1166, 1167 telepathology 1192, 1208, 1212, 1214, 1235, 1236, 1237, 1239, 1243, 1244, 1247, 1253, 1255, 1256, 1257, 1258, 1259, 1260, 1261 telepractice models 1566, 1574, 1575 telesupervision 1210, 1214 template 943, 948 temporo-spatial data 1328, 1334 Test Stimulus (TS) 860, 865 Texas A&M University 2126, 2129 text-based communication 1660, 1661, 1673 text-mining 1017, 1360, 1362, 1363, 1364, 1368, 1369, 1370, 1372 thalamus 582, 583, 585, 595, 597, 610, 618 the chaos theory 250 theoretical learning 1917 Therapeutic (AAF) Device 1326 therapeutic device 1315, 1326 Three-dimensional (3D) 885, 886, 887, 888, 889, 890, 891, 893, 894 Three-dimensional (3D) digital medical images 885, 893 threshold crossing interval (TCI) 307 thresholding 369, 376 thrombolysis 1592, 1593, 1594, 1596, 1597, 1598 thrombolytic 1592, 1593, 1594, 1597 throughput flow 111, 117 time regions 904, 916 timed-based occlusal adjustments 915 time-frequency (TF) 306, 307, 308, 309, 310, 313 time-sequence 896, 897, 906, 909, 911, 913, 915, 916 tip direction in navigation 2152 tip offset in navigation 2152 tomography 354, 355, 356, 358 topographic surface 2152 Total Access Care and Medical Information System (TACMIS) 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1778, 1779, 1780 Transcranial Magnetic Stimulation (TMS) , 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865 transdiagnostic approach 780, 781, 789, 790 transdiagnostic processes 779, 780, 781, 785, 786, 787, 788

18

transesophageal echocardiogram (TEE) 2184, 2185 transrectal ultrasound (TRUS) 377, 378, 379, 381, 387 transthoracic echocardiogram (TTE) 2184, 2185 triage 110, 117 triage of doctors 1900, 1918 trust 1922, 1923, 1924, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933 Trust Management Systems (TMS) 1939 T-Scan I system 897, 898, 915 T-Scan III system 896, 898, 899, 900, 901, 908, 911, 915, 916 tumor 744, 745, 746, 747, 748, 751, 752, 753, 754, 755, 757, 758, 759, 760 tumour suppressor gene 421 two-dimensional (2D) 886, 887, 889, 890, 893, 894 two-dimensional (2D) images 886, 948

U U.S. armed forces tactical combat casualty care (TCCC) 2127, 2131 U.S. Medical Licensing Examination (USMLE) 656, 669, 670 ubiquitous computing 532, 533, 654, 1184, 1190, 1876 ubiquitous computing devices (UCD) 532, 533, 534, 535, 536, 537, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 553 ubiquitous health information (UHI) 50, 51, 52, 54, 56, 57, 60, 62, 63, 65, 66, 67 UC Davis Women’s Cardiovascular Medicine Program 2095 ultrasound 359, 365, 366, 367, 2183, 2184, 2188 ultrasound imaging 377, 378, 381, 387, 388, 390, 918, 925 underserved patient populations 1566 Unified Medical Language System (UMLS) 2014, 2020 unintended consequences 532, 534, 535, 537, 540, 543, 548, 552 universal design 1531, 1534, 1535, 1538 University of California, Davis (UC Davis) 2094, 2095, 2096, 2097, 2100, 2105, 2106 unplanned surgery 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766, 1767, 1769 unsupervised learning 884 usability 2130, 2134, 2135, 2137, 2141 use error 571 use-oriented risk analysis 555, 571 user driven health care 1030, 1038, 1044

Index

user-centered design 567, 571, 1171, 1172, 1173, 1190, 2134, 2141 user-centered design approach 1190 user-Dentist 896, 897, 900, 902, 905, 906, 907, 908, 909, 910, 911, 913, 916 Utah Health Information Network (UHIN) 1470, 1480, 1481, 1482, 1484, 1489, 1490

V variational approach 1340 ventricular fibrillation (VF) 306, 307, 308, 310, 312 ventricular flutter/fibrillation (VFF) 309, 310, 311 ventricular rhythm (VR) 309 ventricular tachyarrhythmias 306, 313 ventricular tachycardia (VT) 306, 307, 308, 310, 311, 313 Veterans Affairs 1121, 1122, 1123, 1124, 1126, 1127, 1128, 1129, 1131, 1135, 1136, 1137, 1139, 1141 veterinary medicine 2014 videocasts 190 video-conferencing supervision 1673 videogame design 620 videogame technology 620 virtual audiology clinics 693 virtual characters 657, 658 virtual clinic 698, 699, 700, 702 virtual colonoscopy (VC) 1340, 1341, 1357, 1358 Virtual environments (VE) 658, 667, 675, 780, 781, 785 virtual health networks 52 virtual healthcare teams 705 virtual human agents 655, 656, 675 Virtual humans (VH) 655, 656, 657, 658, 661, 662, 664, 665, 666, 670, 671, 675 virtual immersion 2074, 2075, 2076, 2082, 2083, 2085, 2090, 2092 virtual medical service centres 2035, 2036, 2037, 2038, 2039, 2040 virtual medical services 2035, 2036 virtual microscopy 1235, 1236, 1237, 1238, 1239, 1242, 1244, 1245, 1253, 1257, 1258, 1259, 1261 virtual oncology centres 2037 virtual patient 1106, 1118, 1119 virtual reality (VR) 620-632, 779-781, 785-791, 2073, 2074, 2077, 2082, 2083, 2092, 2093, 2126-2129, 2141, 2142 virtual slide 1235, 1243, 1247, 1260, 1261

virtual space 620, 622, 624, 627 Virtual Standardized Patients (VSPs) 655, 656, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 675 virtual support systems 2049 virtual world 620 virtual world technology 620, 2142 visual basic for applications (VBA) 2094, 2096, 2099, 2105, 2106, 2107, 2108, 2110, 2112 visual interface and scaffolding for total acuity (VISTA) 2130 voice quality 1008, 1012, 1014, 1015, 1016 volume 926, 930, 933, 934, 935, 936, 937, 938, 942, 946, 948 volumetric image 887, 888, 893 volumetric model 2152 vulnerable plaque 2125

W wavelet analysis 480, 485 wavelet encoding 478, 479 wavelet transform (WT) 307, 308, 309, 968, 973 Wearable Health Care Systems 793 wearable monitoring systems 801 wearable systems 793, 794, 800 Web 2.0 50, 55, 56, 57, 61, 64, 69, 190, 192, 193, 195, 203, 205, 207, 208, 209, 996, 997, 998, 999, 1003, 1004, 1005, 1006 Web Service 646 Web-based communication 1637, 1643, 1651 Web-based educational technologies 191 web-based group supervision 1673 web-based HIS 1033 web-based information 1215, 1225 web-based interface 1216, 1228 Web-based medical simulation 190 Web-based PHR systems 391, 392, 393, 395, 400, 402 web-based platforms 1673 web-based resource 1216 web-based technology 1219, 1224, 1226, 1227 Web-enabled healthcare services 1800 WebMD 174, 1802 wikis 191, 194, 209 WiMAX 486, 489, 490, 491, 492, 493, 494, 502 wireless communications 118 wireless computer 94 wireless computing 115, 117 wireless mobile monitoring 717, 719

19

Index

wireless network 1051, 1055, 1061 wireless technology 1511, 1514, 1517 women’s health 1581, 1582, 1587 Working to Add Value through E-information (WAVE) 826, 831, 832, 838, 851 World Health Organization (WHO) 1154, 1157-1170, 1286, 1287, 1683, 1856, 1870

20

X XML schema definition file (XSD) 2094, 2096, 2098, 2107, 2108, 2111 XML schemas 2094, 2109 X-ray 354, 356, 358, 359, 367, 378, 384 x-ray radiation 360 X-ray tomography 356