BACKGROUND: The use of clinical decision support systems to facilitate the practice of evidence-based medicine promises to substantially improve health care quality. OBJECTIVE: To describe, on the basis of the proceedings of the Evidence and Decision Support track at the 2000 AMIA Spring Symposium, the research and policy challenges for capturing research and practice-based evidence in machine-interpretable repositories, and to present recommendations for accelerating the development and adoption of clinical decision support systems for evidence-based medicine. RESULTS: The recommendations fall into five broad areas--capture literature-based and practice-based evidence in machine--interpretable knowledge bases; develop maintainable technical and methodological foundations for computer-based decision support; evaluate the clinical effects and costs of clinical decision support systems and the ways clinical decision support systems affect and are affected by professional and organizational practices; identify and disseminate best practices for work flow-sensitive implementations of clinical decision support systems; and establish public policies that provide incentives for implementing clinical decision support systems to improve health care quality. CONCLUSIONS: Although the promise of clinical decision support system-facilitated evidence-based medicine is strong, substantial work remains to be done to realize the potential benefits.
BACKGROUND: The use of clinical decision support systems to facilitate the practice of evidence-based medicine promises to substantially improve health care quality. OBJECTIVE: To describe, on the basis of the proceedings of the Evidence and Decision Support track at the 2000 AMIA Spring Symposium, the research and policy challenges for capturing research and practice-based evidence in machine-interpretable repositories, and to present recommendations for accelerating the development and adoption of clinical decision support systems for evidence-based medicine. RESULTS: The recommendations fall into five broad areas--capture literature-based and practice-based evidence in machine--interpretable knowledge bases; develop maintainable technical and methodological foundations for computer-based decision support; evaluate the clinical effects and costs of clinical decision support systems and the ways clinical decision support systems affect and are affected by professional and organizational practices; identify and disseminate best practices for work flow-sensitive implementations of clinical decision support systems; and establish public policies that provide incentives for implementing clinical decision support systems to improve health care quality. CONCLUSIONS: Although the promise of clinical decision support system-facilitated evidence-based medicine is strong, substantial work remains to be done to realize the potential benefits.
Authors: L B Goldstein; A J Bonito; D B Matchar; P W Duncan; G H DeFriese; E Z Oddone; J E Paul; D R Akin; G P Samsa Journal: Stroke Date: 1995-09 Impact factor: 7.914
Authors: Cameron G Shultz; Jean M Malouin; Lee A Green; Melissa Plegue; Grant M Greenberg Journal: Am J Public Health Date: 2015-08-13 Impact factor: 9.308
Authors: Kristen Miller; Muge Capan; Danielle Weldon; Yaman Noaiseh; Rebecca Kowalski; Rachel Kraft; Sanford Schwartz; William S Weintraub; Ryan Arnold Journal: Int J Med Inform Date: 2018-05-21 Impact factor: 4.046
Authors: Meenal B Patwardhan; Kensaku Kawamoto; David Lobach; Uptal D Patel; David B Matchar Journal: Clin J Am Soc Nephrol Date: 2009-01-28 Impact factor: 8.237