Evidence map›Paper›PMID 41625485›Full record

ArticleNEJM AI2025

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

Majid Afshar, Mary Ryan Baumann, Felice Resnik, Josie Hintzke, Anne Gravel Sullivan, Graham Wills, Kayla Lemmon, Jason Dambach, Leigh Ann Mrotek, Mariah Quinn and 12 more

4 registry-linked trialsAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

NCT06517082 nacompletednot on this map

An EHR-embedded Pragmatic Stepped-wedge Individual Randomized Clinical Trial of Ambient Artificial Intelligence to Improve Provider Well-Being

TypeinterventionalSponsorUniversity of Wisconsin, MadisonRan2024 to 2025Enrolled66ConditionsHealthcare Provider Well-beingArmsArtificial Intelligence
NCT07302906 narecruitingnot on this map

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

TypeinterventionalSponsorPedro Angelo Basei de PaulaRan2025 to 2026Enrolled300ConditionsBurnout, Professionals, Medical Records Systems, Computerized, Physician-Patient Relations, Ambulatory CareArmsAmbient AI scribe for clinical documentation (Voa Health), Usual documentation without AI scribe (standard care)
NCT07456241 narecruitingnot on this mapstarted 2026, after this paper: background citation

An EHR-Embedded Pragmatic Stepped-Wedge Clinical Trial of Ambient Artificial Intelligence to Reduce Nursing Staff Documentation Time

TypeinterventionalSponsorUniversity of Wisconsin, MadisonRan2026 to 2026Enrolled250ConditionsNursing Documentation BurdenArmsArtificial Intelligence
NCT07742761 naactive not recruitingnot on this mapstarted 2026, after this paper: background citation

Emergency Medicine Practitioners Overall Well-being Enhancement With Ambient AI Scribes

TypeinterventionalSponsorWashington University School of MedicineRan2026 to 2027Enrolled55ConditionsWell-being at Work, BurnoutArmsAmbient AI Scribe
3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  18. State of clinical AI in 2026.BMJ digital health & AI · 2026
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Majid AfsharInstitute for Clinical and Translational Research, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.ORCID 0000-0002-6368-4652
Mary Ryan BaumannInstitute for Clinical and Translational Research, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.ORCID 0000-0002-9137-1549
Felice ResnikInstitute for Clinical and Translational Research, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.ORCID 0000-0003-1494-1357
Josie HintzkeInstitute for Clinical and Translational Research, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.ORCID 0009-0001-6840-1340
Anne Gravel SullivanInstitute for Clinical and Translational Research, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.ORCID 0009-0009-7972-6907
Graham WillsUniversity of Wisconsin Hospitals and Clinics, Madison, WI, USA.ORCID 0009-0005-3284-873X
Kayla LemmonInstitute for Clinical and Translational Research, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.ORCID 0009-0001-9965-5351
Jason DambachDepartment of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.ORCID 0009-0001-2354-230X
Leigh Ann MrotekInstitute for Clinical and Translational Research, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.ORCID 0000-0001-6559-934X
Mariah QuinnDepartment of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.ORCID 0000-0001-7534-739X
Kirsten AbramsonDepartment of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.ORCID 0000-0001-8491-3138
Peter KleinschmidtDepartment of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.ORCID 0000-0003-1672-6845
Thomas B BrazeltonUniversity of Wisconsin Hospitals and Clinics, Madison, WI, USA.ORCID 0000-0003-2840-3285
Margaret A LeafUniversity of Wisconsin Hospitals and Clinics, Madison, WI, USA.ORCID 0000-0001-8076-7110
Heidi TwedtDepartment of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.ORCID 0000-0002-7091-0152
David KunstmanUniversity of Wisconsin Hospitals and Clinics, Madison, WI, USA.ORCID 0009-0003-2816-2175
Brian PattersonUniversity of Wisconsin Hospitals and Clinics, Madison, WI, USA.ORCID 0000-0002-4584-3808
Frank LiaoUniversity of Wisconsin Hospitals and Clinics, Madison, WI, USA.ORCID 0000-0002-7577-7530
Stacy RasmussenUniversity of Wisconsin Hospitals and Clinics, Madison, WI, USA.ORCID 0009-0009-2202-0911
Elizabeth S BurnsideInstitute for Clinical and Translational Research, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.ORCID 0000-0002-6600-435X
Cherodeep GoswamiUniversity of Wisconsin Hospitals and Clinics, Madison, WI, USA.ORCID 0009-0008-9805-4694
Joel GordonUniversity of Wisconsin Hospitals and Clinics, Madison, WI, USA.ORCID 0000-0001-7619-9384

Funding

University of Wisconsin Institute for Clinical and Translational ResearchUL1TR002373 · NCATS · UNIVERSITY OF WISCONSIN-MADISON · PI ELIZABETH S BURNSIDE, Allan R. Brasier · 2017 to 2026
$75.9M
Institutional Career Development CoreKL2TR002737 · NCATS · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI RUNDEK, TATJANA, SCOTT, GWENDOLYN BARBARA · 2018 to 2022
$3.6M
NCATS NIH HHS KL2 TR002737NCATS NIH HHS UL1 TR002373
6 · The paper itself

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

PMID41625485
PMCPMC12858090

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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.