ArticleNEJM AI2025
A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence to Improve Health Practitioner Well-Being.
Article in NEJM AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 4 registered trials, which are not on this map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
An EHR-embedded Pragmatic Stepped-wedge Individual Randomized Clinical Trial of Ambient Artificial Intelligence to Improve Provider Well-Being
The SOAR Trial (Scribe Optimization for Ambulatory Records): Ambient Artificial Intelligence Scribe With Voa Health for Generating Medical Documentation From Outpatient Visit Audio - A Randomized Controlled Trial
An EHR-Embedded Pragmatic Stepped-Wedge Clinical Trial of Ambient Artificial Intelligence to Reduce Nursing Staff Documentation Time
Emergency Medicine Practitioners Overall Well-being Enhancement With Ambient AI Scribes
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cognitive Workload and Mental Burden in Health Care Professionals Interacting With AI: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Exploring AI scribes in Norwegian general practice: a qualitative individual interview study.Scandinavian journal of primary health care · 2026Article
- Bridging the trust-adoption gap for AI scribes in rural communities: A machine learning approach using the 2024 Canadian digital health survey.International journal of medical informatics · 2026Article
- Actions Reported by Physicians With Large Improvements in Burnout and Professional Fulfillment.JAMA network open · 2026Article
- A Conceptual Model for Ambient AI Adoption: Perspectives From Academia and Industry.JMIR medical informatics · 2026Article
- A Conceptual Model for Ambient AI Adoption: Perspectives From Academia and Industry.JMIR medical informatics · 2026Article
- Evidence, use cases, and implementation safeguards of large language models in primary care.Communications medicine · 2026Review
- Accelerating ambient AI scribe enterprise-scale deployment: Cleveland Clinic's novel approach to health system-industry partnership.npj health systems · 2026Review
- Artificial Intelligence and Clinician Burnout in the United States: A Narrative Review.Cureus · 2026Review
- Physician-Reported Safety Outcomes of AI-Generated Hospital Course Summaries.JAMA network open · 2026Article
- Physician-Reported Safety Outcomes of AI-Generated Hospital Course Summaries.JAMA network open · 2026Article
- Ambient Artificial Intelligence Use and Clinician Documentation Burden, Productivity, and Efficiency.JAMA network open · 2026Article
- Article
- Review
- Impact of an Ambient AI Scribe Among Clinicians and Patients: Real-World Prospective Observational Time-Motion Study.JMIR medical informatics · 2026Observational
- Leveraging AI to reduce operational healthcare costs: lessons from other industries.npj health systems · 2026Article
- Rethinking sepsis prediction in the era of large language models.npj health systems · 2026Article
- State of clinical AI in 2026.BMJ digital health & AI · 2026Review
- Shifting bottleneck: AI capability and healthcare adaptation.BMJ digital health & AI · 2026Article
- Digital morphine: why AI scribes are symptomatic relief for a broken system.BMJ digital health & AI · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
Abstract
backgroundElectronic 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.
methodsA 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.
resultsA 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.
conclusionsIn 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.).
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.