Cluster Analysis of Primary Care Physician Phenotypes for Electronic Health Record Use: Retrospective Cohort Study.

Autor:	Fong A; National Center for Human Factors in Healthcare, MedStar Health, Washington, DC, United States., Iscoe M; Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States., Sinsky CA; American Medical Association, Chicago, IL, United States., Haimovich AD; Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States., Williams B; Northeast Medical Group, Yale New Haven Health, Stratford, CT, United States., O'Connell RT; Northeast Medical Group, Yale New Haven Health, Stratford, CT, United States., Goldstein R; Northeast Medical Group, Yale New Haven Health, Stratford, CT, United States., Melnick E; Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States.
Jazyk:	angličtina
Zdroj:	JMIR medical informatics [JMIR Med Inform] 2022 Apr 15; Vol. 10 (4), pp. e34954. Date of Electronic Publication: 2022 Apr 15.
DOI:	10.2196/34954
Abstrakt:	Background: Electronic health records (EHRs) have become ubiquitous in US office-based physician practices. However, the different ways in which users engage with EHRs remain poorly characterized. Objective: The aim of this study is to explore EHR use phenotypes among ambulatory care physicians. Methods: In this retrospective cohort analysis, we applied affinity propagation, an unsupervised clustering machine learning technique, to identify EHR user types among primary care physicians. Results: We identified 4 distinct phenotype clusters generalized across internal medicine, family medicine, and pediatrics specialties. Total EHR use varied for physicians in 2 clusters with above-average ratios of work outside of scheduled hours. This finding suggested that one cluster of physicians may have worked outside of scheduled hours out of necessity, whereas the other preferred ad hoc work hours. The two remaining clusters represented physicians with below-average EHR time and physicians who spend the largest proportion of their EHR time on documentation. Conclusions: These findings demonstrate the utility of cluster analysis for exploring EHR use phenotypes and may offer opportunities for interventions to improve interface design to better support users' needs. (©Allan Fong, Mark Iscoe, Christine A Sinsky, Adrian D Haimovich, Brian Williams, Ryan T O'Connell, Richard Goldstein, Edward Melnick. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 15.04.2022.)
Databáze:	MEDLINE
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