{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T08:05:13Z","timestamp":1772093113204,"version":"3.50.1"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":51,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"American Medical Association Practice Transformation Initiative grant and Gruber Foundation science fellowship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To explore the relationship between ambulatory physician electronic health record (EHR) use characteristics and proxies for physician efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The study included 218\u2009610 unique physicians with 5\u2009193\u2009385 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 &amp;lt;1.5 days and after-hours documentation &amp;lt;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 &amp;lt;4.1 min\/visit and clinical review time &amp;lt;3.2 min\/visit were associated with higher visit volumes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion and Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  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