Adam Wright1, Dean F Sittig2, Joan S Ash3, Jessica L Erickson4, Trang T Hickman4, Marilyn Paterno1, Eric Gebhardt3, Carmit McMullen5, Ruslana Tsurikova4, Brian E Dixon6, Greg Fraser7, Linas Simonaitis8, Frank A Sonnenberg9, Blackford Middleton10. 1. Brigham & Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Partners HealthCare, Boston, MA, United States. 2. The University of Texas Health Science School of Biomedical Informatics at Houston, Houston, TX, United States. 3. Oregon Health & Science University, Portland, OR, United States. 4. Brigham & Women's Hospital, Boston, MA, United States; Partners HealthCare, Boston, MA, United States. 5. Kaiser Permanente Center for Health Research, Portland, OR, United States. 6. Regenstrief Institute, Inc., Indianapolis, IN, United States; Indiana University Fairbanks School of Public Health, Indianapolis, IN, United States; Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service CIN 13-416, Richard L. Roudebush VA Medical Center, Indianapolis, IN, United States. 7. WVP Health Authority, Salem, OR, United States. 8. Regenstrief Institute, Inc., Indianapolis, IN, United States; Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service CIN 13-416, Richard L. Roudebush VA Medical Center, Indianapolis, IN, United States. 9. Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, United States. 10. Vanderbilt University, Nashville, TN, United States. Electronic address: awright5@partners.org.
Abstract
OBJECTIVE: To identify challenges, lessons learned and best practices for service-oriented clinical decision support, based on the results of the Clinical Decision Support Consortium, a multi-site study which developed, implemented and evaluated clinical decision support services in a diverse range of electronic health records. METHODS: Ethnographic investigation using the rapid assessment process, a procedure for agile qualitative data collection and analysis, including clinical observation, system demonstrations and analysis and 91 interviews. RESULTS: We identified challenges and lessons learned in eight dimensions: (1) hardware and software computing infrastructure, (2) clinical content, (3) human-computer interface, (4) people, (5) workflow and communication, (6) internal organizational policies, procedures, environment and culture, (7) external rules, regulations, and pressures and (8) system measurement and monitoring. Key challenges included performance issues (particularly related to data retrieval), differences in terminologies used across sites, workflow variability and the need for a legal framework. DISCUSSION: Based on the challenges and lessons learned, we identified eight best practices for developers and implementers of service-oriented clinical decision support: (1) optimize performance, or make asynchronous calls, (2) be liberal in what you accept (particularly for terminology), (3) foster clinical transparency, (4) develop a legal framework, (5) support a flexible front-end, (6) dedicate human resources, (7) support peer-to-peer communication, (8) improve standards. CONCLUSION: The Clinical Decision Support Consortium successfully developed a clinical decision support service and implemented it in four different electronic health records and four diverse clinical sites; however, the process was arduous. The lessons identified by the Consortium may be useful for other developers and implementers of clinical decision support services.
OBJECTIVE: To identify challenges, lessons learned and best practices for service-oriented clinical decision support, based on the results of the Clinical Decision Support Consortium, a multi-site study which developed, implemented and evaluated clinical decision support services in a diverse range of electronic health records. METHODS: Ethnographic investigation using the rapid assessment process, a procedure for agile qualitative data collection and analysis, including clinical observation, system demonstrations and analysis and 91 interviews. RESULTS: We identified challenges and lessons learned in eight dimensions: (1) hardware and software computing infrastructure, (2) clinical content, (3) human-computer interface, (4) people, (5) workflow and communication, (6) internal organizational policies, procedures, environment and culture, (7) external rules, regulations, and pressures and (8) system measurement and monitoring. Key challenges included performance issues (particularly related to data retrieval), differences in terminologies used across sites, workflow variability and the need for a legal framework. DISCUSSION: Based on the challenges and lessons learned, we identified eight best practices for developers and implementers of service-oriented clinical decision support: (1) optimize performance, or make asynchronous calls, (2) be liberal in what you accept (particularly for terminology), (3) foster clinical transparency, (4) develop a legal framework, (5) support a flexible front-end, (6) dedicate human resources, (7) support peer-to-peer communication, (8) improve standards. CONCLUSION: The Clinical Decision Support Consortium successfully developed a clinical decision support service and implemented it in four different electronic health records and four diverse clinical sites; however, the process was arduous. The lessons identified by the Consortium may be useful for other developers and implementers of clinical decision support services.
Keywords:
Clinical decision support systems; Computerized; Distributed systems; Electronic health records; Medical record systems; Service-oriented architecture
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