{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T02:35:13Z","timestamp":1793414113314,"version":"4.5.0"},"reference-count":26,"publisher":"American Medical Association (AMA)","issue":"16","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA"],"published-print":{"date-parts":[[2026,4,28]]},"abstract":"\n Importance<\/jats:title>\n Artificial intelligence (AI)\u2013enabled scribes have been proposed to reduce electronic health record (EHR) burden and improve clinician satisfaction. There is limited evidence about their associated results across multiple sites and relative benefits for different clinician groups.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To assess the association of AI scribe adoption with changes in EHR time expenditure and visit volume and how associations vary by clinician characteristics.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n Multisite, longitudinal cohort study of AI scribe adoption conducted at 5 US academic health care institutions that introduced AI scribes to their clinicians between June 2023 and August 2025. Participants were ambulatory clinicians.<\/jats:p>\n <\/jats:sec>\n \n Exposures<\/jats:title>\n AI scribe adoption, defined as receiving access to an AI scribe. This was determined by opt-in decisions by eligible physicians at 4 of the 5 sites.<\/jats:p>\n <\/jats:sec>\n \n Main Outcome and Measures<\/jats:title>\n Total time spent on the EHR, time spent on documentation, and time spent on the EHR outside scheduled hours or on unscheduled days, all normalized to 8 scheduled patient hours; weekly visit volume.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The sample comprised 8581 clinicians, including 1809 AI scribe adopters. Participants were 57.1% female and were split between primary care (24.4%), medical (62.4%), and surgical (13.2%) specialties. Most (74.1%) were attending physicians, with 18.1% advanced practice clinicians and 7.8% resident physicians. In a difference-in-differences analysis, AI scribe adoption was associated with 13.4 (95% CI, 9.1-17.7) fewer minutes of EHR time, 16.0 (95% CI, 13.7-18.3) fewer minutes of documentation time, and 0.49 (95% CI, 0.17-0.81) additional weekly visits delivered. Electronic health record time outside work hours did not change significantly. Changes associated with AI scribe adoption were greatest for primary care specialists, advanced practice clinicians, female clinicians, and clinicians who used AI scribes in 50% or more of visits.<\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n AI scribe adoption was associated with modest decreases in total EHR time and documentation time and with a modest increase in weekly visit volume.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jama.2026.2253","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T15:30:29Z","timestamp":1775057429000},"page":"1408","source":"Crossref","is-referenced-by-count":65,"title":["Changes in Clinician Time Expenditure and Visit Quantity With Adoption of Artificial Intelligence\u2013Powered Scribes"],"prefix":"10.1001","volume":"335","author":[{"given":"Lisa S.","family":"Rotenstein","sequence":"first","affiliation":[{"name":"Division of Clinical Informatics and Digital Transformation, Department of Medicine, University of California, San Francisco"},{"name":"Division of General Internal Medicine, Department of Medicine, University of California, San Francisco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. 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