A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence to Improve Health Practitioner Well-Being

Abstract

BACKGROUND: Electronic health record (EHR) documentation is a major contributor to work-related practitioner exhaustion and the interpersonal disengagement known as burnout. Generative artificial intelligence (AI) scribes that passively capture clinical conversations and draft visit notes may alleviate this burden, but evidence remains limited. METHODS: A 24-week, stepped-wedge, individually randomized pragmatic trial was conducted across ambulatory clinics in two states. Sixty-six health care practitioners were randomly assigned to three 6-week sequences of ambient AI. The coprimary outcomes were professional fulfillment and work exhaustion/interpersonal disengagement from the Stanford Professional Fulfillment Index. Secondary measures included time spent on notes, work outside work (WoW), documentation quality with the Provider Documentation Summarization Quality Instrument 9 (PDSQI-9), and billing diagnostic codes reviewed by professional staff coders. Linear mixed models were used for intention-to-treat (ITT) analyses. RESULTS: A total of 71,487 notes were authored, of which 27,092 (38%) were generated using ambient AI. Ambient AI use had a significant reduction in work exhaustion/interpersonal disengagement (-0.44 points; 95% confidence interval [CI], -0.62 to -0.25; P<0.001), and a nonsignificant increase in professional fulfillment (+0.14 points; 95% CI, 0.004 to 0.28; P=0.04) on a five-point Likert scale. Among secondary measures, time spent on notes decreased (-0.36 hours per day; 95% CI, -0.55 to -0.17). The reduction in WoW (-0.50 hours per day; 95% CI, -0.90 to -0.09) was sensitive to exclusion of extreme values and was no longer significant after removing the top 3% of daily observations. Diagnostic billing codes improved with ambient AI use (P<0.001). Documentation quality, assessed with the PDSQI-9, demonstrated mean scores ranging from 3.97 to 4.99 across domains on a five-point scale. No drift in software performance was detected. CONCLUSIONS: In a real-world randomized implementation, ambient AI reduced health care practitioners' work exhaustion/interpersonal disengagement but did not significantly increase professional fulfillment. Documentation time decreased without compromising diagnosis, billing compliance, or note quality. (Funded by the University of Wisconsin Hospital and Clinics and the National Institutes of Health Clinical and Translational Science Award; ClinicalTrials.gov number, NCT06517082.).

The authors' abstract, as published at the source. NEJM AI, 2025 · DOI ↗