TY  - JOUR
AU  - Fong, Allan
AU  - Iscoe, Mark
AU  - Sinsky, Christine A
AU  - Haimovich, Adrian D
AU  - Williams, Brian
AU  - O'Connell, Ryan T
AU  - Goldstein, Richard
AU  - Melnick, Edward
PY  - 2022
DA  - 2022/4/15
TI  - Cluster Analysis of Primary Care Physician Phenotypes for Electronic Health Record Use: Retrospective Cohort Study
JO  - JMIR Med Inform
SP  - e34954
VL  - 10
IS  - 4
KW  - electronic health record
KW  - phenotypes
KW  - cluster analysis
KW  - unsupervised machine learning
KW  - machine learning
KW  - EHR
KW  - primary care
AB  - 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. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2022/4/e34954
UR  - https://doi.org/10.2196/34954
UR  - http://www.ncbi.nlm.nih.gov/pubmed/35275070
DO  - 10.2196/34954
ID  - info:doi/10.2196/34954
ER  -