EFFICIENT DECISION SUPPORT SYSTEMS – PRACTICE AND CHALLENGES IN BIOMEDICAL RELATED DOMAIN Edited by Chiang S. Jao Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain Edited by Chiang S. Jao Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. 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ISBN 978-953-307-258-6 free online editions of InTech Books and Journals can be found at www.intechopen.com Contents Preface IX Part 1 Barriers, Challenges, Impacts, and Success Factors of System Adoption 1 Chapter 1 Challenges in Developing Effective Clinical Decision Support Systems 3 Kamran Sartipi, Norman P. Archer and Mohammad H. Yarmand Chapter 2 Impacts and Risks of Adopting Clinical Decision Support Systems 21 Wilfred Bonney Chapter 3 Success Factors and Barriers for Implementation of Advanced Clinical Decision Support Systems 31 Anne-Marie J.W. Scheepers-Hoeks, Rene J. Grouls, Cees Neef, Eric W. Ackerman and Erik H. Korsten Part 2 Guideline-Based Clinical Decision Support System 45 Chapter 4 Information Extraction Approach for Clinical Practice Guidelines Representation in a Medical Decision Support System 47 Fernando Pech-May, Ivan Lopez-Arevalo and Victor J. Sosa-Sosa Chapter 5 Guideline-Based Decision Support Systems for Prevention and Management of Chronic Diseases 67 Niels Peek Part 3 Applications for Disease Management 87 Chapter 6 Emerging Information Technologies to Provide Improved Decision Support for Surveillance, Prevention, and Control of Vector-Borne Diseases 89 Saul Lozano-Fuentes, Christopher M. Barker, Marlize Coleman, Michael Coleman, Bborie Park, William K. Reisen and Lars Eisen VI Contents Chapter 7 Optimization Models, Statistical and DSS Tools for Prevention and Combat of Dengue Disease 115 Marcos Negreiros, Adilson E. Xavier, Airton F. S. Xavier, Nelson Maculan, Philippe Michelon, José Wellington O. Lima and Luis Odorico M. Andrade Chapter 8 A Decision Support System Based on Artificial Neural Networks for Pulmonary Tuberculosis Diagnosis 151 Carmen Maidantchik, José Manoel de Seixas, Felipe F. Grael, Rodrigo C. Torres, Fernando G. Ferreira, Andressa S. Gomes, José Márcio Faier, Jose Roberto Lapa e Silva, Fernanda C. de Q Mello, Afrânio Kritski and João Baptista de Oliveira e Souza Filho Part 4 Applications for Medical Procedures 167 Chapter 9 Temporal Knowledge Generation for Medical Procedures 179 Aida Kamišalić, David Riaño and Tatjana Welzer Chapter 10 Predicting Pathology in Medical Decision Support Systems in Endoscopy of the Gastrointestinal Tract 195 Michael Liedlgruber and Andreas Uhl Chapter 11 Workflow and Clinical Decision Support for Radiation Oncology 215 Daniel L McShan Chapter 12 Computerized Decision Support Systems for Mechanical Ventilation 227 Fleur T. Tehrani Chapter 13 Decision Support Systems in Anesthesia, Emergency Medicine and Intensive Care Medicine 239 Thomas M. Hemmerling Chapter 14 Decision Support by Visual Incidence Anamneses for Increased Patient Safety 263 Kerstin Ådahl and Rune Gustavsson Part 5 Miscellaneous Case Studies 287 Chapter 15 Pharmacoepidemiological Studies Using the Veterans Affairs Decision Support System 289 Benjamin Wolozin, Austin Lee, Nien-Chen Li and Lewis E. Kazis Chapter 16 Decision Support Systems in Animal Health 299 Nguyen Van Long, Mark Stevenson and Bryan O’Leary Contents VII Chapter 17 Development of an Image Retrieval Model for Biomedical Image Databases 311 Achimugu Philip, Babajide Afolabi, Adeniran Oluwaranti and Oluwagbemi Oluwatolani Preface Series Preface This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers. This book series is dedicated to support professionals and series readers in the emerging field of DSS. Preface Clinical decision support systems (CDSSs) are computer-based applications that can effectively assist clinical practitioners and healthcare providers in decision making to improve their clinical practice skills and reduce preventable medical errors. A popular example of CDSSs is computerized physician order entry (CPOE) systems that provide patient-specific recommendations, collaborate active problems on the problem list with prescribed medications on the medication list, attach care reminders and alerts to the charts of patients in electronic health records, link laboratory test data to alert physicians while atypical values are detected. Patient safety was once an emerging field when the Institute of Medicine's (IOM) report (“To Err is Human: Building a Safer Health System") was first released and captured the attention of the healthcare community in late 1999. Many of the errors in the biomedical domain result from a culture and a fragmented system. Evidences from research studies indicated that mistakes were not due to clinicians not trying hard X Preface enough; they resulted from inherent shortcomings in the health caring system. Appropriate design of CDSSs assists in reducing such kinds of mistakes and promoting patient safety. Book Volume 2 extends the concepts and methodology of decision support systems (DSS) mentioned in Book Volume 1 to the applications of CDSS in the biomedical- related domain. This book collects a variety of topics that cover design and development of CDSS applications. It can be used as a textbook in formal courses or a reference book for practitioners. The readers will gain in-depth knowledge about the applications of CDSSs to detect/prevent specific diseases, to facilitate medical procedures during operations, and to collaborate the knowledge of biomedical domain experts for making better decisions. Section 1, including Chapter 1 through 3, illustrates challenges, impacts, risks and success factors in developing and adopting a DSS in the clinical domain. Chapter 1 and 2 explore challenges, impacts and risks in developing and adopting effective CDSS. Chapter 3 presents potential success factors and barriers of implementing advanced CDSS. These findings assist decision makers in identifying potential bottlenecks about the development and assessment of a useful CDSS. It is evident that the appropriate use of CDSSs with emerging technologies could enhance the adoption and acceptance rate of CDSS in clinical practice. Section 2, including Chapter 4 and 5, illustrates the applications of CDSS based on clinical practice guidelines (CPG). Chapter 4 highlights the importance of CPG in documenting the clinical diagnosis, prognosis, and treatment of specific diseases. The information extraction approach can connect relevant information in the clinical documents and is critical in enhancing the knowledge acquisition on CPG in CDSS. An experiment was conducted to obtain an intermediate representation of actions from a textual CPG in XML format by means of an information extraction module. Chapter 5 presents a guideline-based CDSS for prevention and management of chronic diseases. The example of an evidence-based conceptual CDSS framework is illustrated how to identify that CDSS can improve CPG implementation by reducing guideline complexity. Section 3 and 4, including Chapter 6 throughout 14, present extensive applications of CDSS developing to diagnose/treat specific diseases (such as vector-borne diseases and pulmonary tuberculosis) or to operate medical procedures (such as endoscopy and radiation oncology) effectively. In each chapter, the readers are able to identify the use of appropriate CDSS models in individual specialty frameworks. It is noteworthy that Chapter 14 presents the visual incidence anamneses (VIA) tool to improve decision support and process transparency in diagnosing patients using the DSS so as to improve patient safety. The readers are able to recognize the deficiencies of patient safety in health care due to the invisibility of potential causes of incidents, injuries and deaths. The VIA can be supportive to screen out unnecessary alternatives and identify the cause of vulnerable events. [...]... 27 1–2 78 Balas, E A & Boren, S A (2007) Clinical trials of information interventions, in E S Berner (ed.), Clinical Decision Support Systems, Health Informatics, Springer New York, pp 14 0–1 55 Berner, E S (2007) Clinical Decision Support Systems - Theory and Practice, Springer Berner, E S & Lande, T J (2007) Overview of clinical decision support systems, in E S Berner (ed.), Clinical Decision Support Systems, ... of achieving quality clinical decision making by healthcare providers is often facilitated with the use of CDSS as a supportive tool 24 Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain In an attempt to improve the use of CDSS to support quality decision making in clinical practice, Buckingham (2002) proposed a gelatean model with the goal of linking “intuitive... enhance physician decision making activity We also present the supporting standards and infrastructure that allow such collaboration among heterogeneous systems Data mining is the process of analyzing data from different perspectives to extract Challenges in DevelopingDecision SupportClinical Decision Support Systems Challenges in Developing Effective Clinical Effective Systems 7 5 information and hidden... methods, and business models must emerge Clinical Decision Support Systems (CDSS) are defined as computer applications that assist practitioners and healthcare providers in decision making, through timely access to 4 2 Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain Will-be-set-by -IN- TECH electronically stored medical knowledge, in order to improve their medical practices... Systems – Practice and Challenges in Biomedical Related Domain Will-be-set-by -IN- TECH protocols, tracking orders, referrals follow-up, and preventive care; iii) Cost control: monitoring medication orders, avoiding duplicate or unnecessary tests; and iv) Decision support: supporting clinical diagnosis and treatment plan processes and promoting use of best practices, condition-specific guidelines, and population-based... travel times into dialysis planning • Nonlinear programming: used to maximize or minimize an objective function subject to a system of equality and inequality constraints where either the objective function or some of the constraints are nonlinear (Aspden et al., 1981) use a non-linear programming model 16 14 Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain Will-be-set-by -IN- TECH... awareness and context mining technologies have made it possible to extract and analyze implicit inputs These implicit outputs can be integrated with the user environment instead of interrupting the user, allowing users to concentrate on their work (Schmidt, 2002) Challenges in DevelopingDecision SupportClinical Decision Support Systems Challenges in Developing Effective Clinical Effective Systems 15... Translation, International Conference on Complex Medical Engineering, pp 1–7 Johnston, M E., Langton, K B., Haynes, R B & Mathieu, A (1994) Effects of Computer-based Clinical Decision Support Systems on Clinician Performance and Patient Outcome: A Critical Appraisal of Research, Annals of Internal Medicine 120(2): 13 5–1 42 Challenges in DevelopingDecision SupportClinical Decision Support Systems Challenges in. .. for securing personal health data in clinical decision support systems, Journal of Healthcare Information Management 21(3): 3 4–4 0 Santibanez, P., Chow, V., French, J., Puterman, M & Tyldesley, S (2009) Reducing patient wait times and improving resource utilization at british columbia cancer agency’s 20 18 Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain Will-be-set-by -IN- TECH... sophisticated and hence these systems act as effective user assistants by Challenges in DevelopingDecision SupportClinical Decision Support Systems Challenges in Developing Effective Clinical Effective Systems 11 9 providing different types of information to assist users in performing their tasks However, domain knowledge and expertise are still needed by users The next generation of CDSS systems will . extract 6 Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain Challenges in Developing Effective Clinical Decision Support Systems 5 information and hidden patterns. EFFICIENT DECISION SUPPORT SYSTEMS – PRACTICE AND CHALLENGES IN BIOMEDICAL RELATED DOMAIN Edited by Chiang S. Jao Efficient Decision Support Systems – Practice. user support component is mostly designed with web-based Graphical User Interfaces (GUI); however 4 Efficient Decision Support Systems – Practice and Challenges in Biomedical Related Domain Challenges