Adam Wright1, Joan S Ash2, Skye Aaron3, Angela Ai4, Thu-Trang T Hickman5, Jane F Wiesen2, William Galanter6, Allison B McCoy7, Richard Schreiber8, Christopher A Longhurst9, Dean F Sittig10. 1. Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, United States; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States; Information Systems, Partners HealthCare, Boston, MA, United States. Electronic address: awright@bwh.harvard.edu. 2. Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, United States. 3. Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, United States. 4. School of Medicine and Public Health, University of Wisconsin at Madison, Madison, WI, United States. 5. Information Systems, Partners HealthCare, Boston, MA, United States. 6. Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States. 7. Department of Global Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States. 8. Physician Informatics and Department of Internal Medicine, Geisinger Holy Spirit, Camp Hill, PA, United States. 9. Department of Biomedical Informatics, University of California San Diego, San Diego, CA, United States. 10. School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States.
Abstract
OBJECTIVE: Developing effective and reliable rule-based clinical decision support (CDS) alerts and reminders is challenging. Using a previously developed taxonomy for alert malfunctions, we identified best practices for developing, testing, implementing, and maintaining alerts and avoiding malfunctions. MATERIALS AND METHODS: We identified 72 initial practices from the literature, interviews with subject matter experts, and prior research. To refine, enrich, and prioritize the list of practices, we used the Delphi method with two rounds of consensus-building and refinement. We used a larger than normal panel of experts to include a wide representation of CDS subject matter experts from various disciplines. RESULTS: 28 experts completed Round 1 and 25 completed Round 2. Round 1 narrowed the list to 47 best practices in 7 categories: knowledge management, designing and specifying, building, testing, deployment, monitoring and feedback, and people and governance. Round 2 developed consensus on the importance and feasibility of each best practice. DISCUSSION: The Delphi panel identified a range of best practices that may help to improve implementation of rule-based CDS and avert malfunctions. Due to limitations on resources and personnel, not everyone can implement all best practices. The most robust processes require investing in a data warehouse. Experts also pointed to the issue of shared responsibility between the healthcare organization and the electronic health record vendor. CONCLUSION: These 47 best practices represent an ideal situation. The research identifies the balance between importance and difficulty, highlights the challenges faced by organizations seeking to implement CDS, and describes several opportunities for future research to reduce alert malfunctions.
OBJECTIVE: Developing effective and reliable rule-based clinical decision support (CDS) alerts and reminders is challenging. Using a previously developed taxonomy for alert malfunctions, we identified best practices for developing, testing, implementing, and maintaining alerts and avoiding malfunctions. MATERIALS AND METHODS: We identified 72 initial practices from the literature, interviews with subject matter experts, and prior research. To refine, enrich, and prioritize the list of practices, we used the Delphi method with two rounds of consensus-building and refinement. We used a larger than normal panel of experts to include a wide representation of CDS subject matter experts from various disciplines. RESULTS: 28 experts completed Round 1 and 25 completed Round 2. Round 1 narrowed the list to 47 best practices in 7 categories: knowledge management, designing and specifying, building, testing, deployment, monitoring and feedback, and people and governance. Round 2 developed consensus on the importance and feasibility of each best practice. DISCUSSION: The Delphi panel identified a range of best practices that may help to improve implementation of rule-based CDS and avert malfunctions. Due to limitations on resources and personnel, not everyone can implement all best practices. The most robust processes require investing in a data warehouse. Experts also pointed to the issue of shared responsibility between the healthcare organization and the electronic health record vendor. CONCLUSION: These 47 best practices represent an ideal situation. The research identifies the balance between importance and difficulty, highlights the challenges faced by organizations seeking to implement CDS, and describes several opportunities for future research to reduce alert malfunctions.
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