Improving Completeness of Electronic Problem Lists through Clinical Decision Support: A Randomized, Controlled Trial
Maloney, Francine L.
Wilcox, Allison R.
McLoughlin, Karen Sax
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CitationWright, Adam, Justine Pang, Joshua C. Feblowitz, Francine L. Maloney, Allison R. Wilcox, Karen Sax McLoughlin, Harley Ramelson, Louise Schneider, and David W. Bates. 2012. Improving completeness of electronic problem lists through clinical decision support: A randomized, controlled trial. Journal of the American Medical Informatics Association: JAMIA 19(4): 555-561.
AbstractBackground: Accurate clinical problem lists are critical for patient care, clinical decision support, population reporting, quality improvement, and research. However, problem lists are often incomplete or out of date. Objective: To determine whether a clinical alerting system, which uses inference rules to notify providers of undocumented problems, improves problem list documentation. Study Design and Methods: Inference rules for 17 conditions were constructed and an electronic health record-based intervention was evaluated to improve problem documentation. A cluster randomized trial was conducted of 11 participating clinics affiliated with a large academic medical center, totaling 28 primary care clinical areas, with 14 receiving the intervention and 14 as controls. The intervention was a clinical alert directed to the provider that suggested adding a problem to the electronic problem list based on inference rules. The primary outcome measure was acceptance of the alert. The number of study problems added in each arm as a pre-specified secondary outcome was also assessed. Data were collected during 6-month pre-intervention (11/2009–5/2010) and intervention (5/2010–11/2010) periods. Results: 17,043 alerts were presented, of which 41.1% were accepted. In the intervention arm, providers documented significantly more study problems (adjusted OR=3.4, p<0.001), with an absolute difference of 6,277 additional problems. In the intervention group, 70.4% of all study problems were added via the problem list alerts. Significant increases in problem notation were observed for 13 of 17 conditions. Conclusion: Problem inference alerts significantly increase notation of important patient problems in primary care, which in turn has the potential to facilitate quality improvement.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:10121116