Electronic health record use factors linked to efficiency and productivity: an explainable machine learning analysis

Abstract

Objective: To explore the relationship between ambulatory physician electronic health record (EHR) use characteristics and proxies for physician efficiency. Materials and Methods: A longitudinal cohort study was conducted to examine physician-month EHR use metadata in 413 US organizations between May 2019 and April 2022. A multi-model machine learning classifier was developed to predict physician efficiency. The main outcomes of the study were physician efficiency, measured as the proportion of same-day chart completion by specialty, and productivity, measured as daily patient visit volume, both segmented into quintiles. Results: The study included 218610 unique physicians with 5193385 physician-month observations from 413 organizations with an average chart completion efficiency of 72.9% and 10.8 visits per scheduled day. The primary ML analysis achieved an accuracy of 0.74 in classifying physician-months with high chart completion efficiency and highlighted associations with key features, such as inbox message turnaround time <1.5 days and after-hours documentation <25 min/scheduled day. A secondary analysis achieved an accuracy of 0.84 in classifying physician-months with high visit volumes, indicating that factors such as EHR time outside scheduled hours <4.1 min/visit and clinical review time <3.2 min/visit were associated with higher visit volumes. Discussion and Conclusion: Implementing specific EHR use measures with distinct thresholds, such as inbox management and after-hours documentation, could help target interventions to enhance productivity, providing actionable insights to create balanced and efficient work environments that improve patient care and reduce EHR time.