Evidence map›Paper›PMID 41497288›Full record

ArticleNEJM AI2025

Ambient AI Scribes in Clinical Practice: A Randomized Trial.

Paul J Lukac, William Turner, Sitaram Vangala, Aaron T Chin, Joshua Khalili, Ya-Chen Tina Shih, Catherine Sarkisian, Eric M Cheng, John N Mafi

3 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 3 registered trials, which are not on this map. Cited by 42 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 2 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.

NCT06792890 nacompletednot on this map

A Randomized Controlled Trial of Two Ambient Artificial Intelligence Scribe Technologies to Improve Documentation Efficiency and Reduce Physician Burnout

TypeinterventionalSponsorUniversity of California, Los AngelesRan2024 to 2025Enrolled238ConditionsPhysician Workflow, Artificial Intelligence (AI)ArmsUse Nabla AI Scribe tool provided, Use AI Scribe tool provided by Vendor B
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)
NCT07438743 naactive not recruitingnot on this mapstarted 2026, after this paper: background citation

Epic Generative Artificial Intelligence Chart Summarization Tool to Reduce Ambulatory Provider Cognitive Task Load: A Randomized Controlled Trial

TypeinterventionalSponsorUniversity of California, Los AngelesRan2026 to 2026Enrolled284ConditionsQuality ImprovementArmsGenAI Chart Summarization
3 · Its place in the literature

Who cites it

42 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Teaching Note Writing in the Age of AI: Three Practical Steps.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026
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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

9 authors.

Paul J LukacDepartment of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0002-3315-6613
William TurnerDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0009-0002-9773-1290
Sitaram VangalaDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0002-8842-5702
Aaron T ChinUCLA Health Information Technology, UCLA Health, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0001-9490-6580
Joshua KhaliliDepartment of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0009-0009-3559-4332
Ya-Chen Tina ShihDepartment of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0001-7290-3864
Catherine SarkisianDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0003-2768-2323
Eric M ChengUCLA Health Information Technology, UCLA Health, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0002-2568-5603
John N MafiDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0002-0322-7636

Funding

UCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1M
NRSA Training CoreTL1TR001883 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ALEX BUI, Nina Thawata Harawa · 2016 to 2026
$8.6M
Midcareer Award in Patient-Oriented Community-Academic Partnered Aging ResearchK24AG047899 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SARKISIAN, CATHERINE A · 2014 to 2025
$2.0M
Biomedical Data Science Training Program for Precision Health EquityT15LM013976 · NLM · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ALEX BUI · 2022 to 2026
$1.9M
Leveraging Electronic Health Records to Measure and Reduce Harmful, Low-Value Care Among Older AdultsK76AG064392 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI MAFI, JOHN · 2020 to 2024
$1.2M
NCATS NIH HHS TL1 TR001883NCATS NIH HHS UL1 TR001881NIA NIH HHS K24 AG047899NIA NIH HHS K76 AG064392NLM NIH HHS T15 LM013976
6 · The paper itself

Abstract

backgroundAmbient artificial intelligence (AI) scribes record patient encounters and rapidly generate visit notes, representing a promising solution to documentation burden and physician burnout. However, the scribes' impacts have not been examined in randomized clinical trials.

methodsIn this parallel three-group pragmatic randomized clinical trial, 238 outpatient physicians, representing 14 specialties, were assigned 1:1:1 via covariate-constrained randomization (balancing on time-in-note, baseline burnout score, and clinic days per week) to either one of two AI scribe applications - Microsoft Dragon Ambient eXperience (DAX) Copilot or Nabla - or a usual-care control group from November 4, 2024, to January 3, 2025. The primary outcome was the change from baseline log writing time-in-note. Secondary end points measured by surveys included the Mini-Z 2.0, a four-item physician task load (PTL), and Professional Fulfillment Index - Work Exhaustion (PFI-WE) scores to evaluate aspects of burnout; work environment; stress; and targeted questions addressing safety, accuracy, and usability.

resultsDAX was used in 33.5% of 24,696 visits; Nabla was used in 29.5% of 23,653 visits. Nabla users experienced a 9.5% (95% confidence interval [CI], -17.2% to -1.8%; P=0.02) decrease in time-in-note versus the control group, whereas DAX users exhibited no significant change versus the control group (-1.7%; 95% CI, -9.4% to +5.9%; P=0.66). Increases in total Mini-Z (scale 10-50; DAX 2.83 [95% CI, +1.28 to +4.37]; Nabla +2.69 [95% CI, +1.14 to +4.23]) and reductions in PTL (scale 0-400; DAX -39.9 [95% CI, -71.9 to -7.9]; Nabla -31.7 [95% CI, -63.8 to +0.4]), and PFI-WE (scale 0-4; DAX 0.32 [95% CI,-0.55 to -0.08]; Nabla -0.23 [95% CI, -0.46 to +0.01]) scores suggest improvement for users of either scribe versus the control. One grade 1 (mild) adverse event was reported, while clinically significant inaccuracies were noted "occasionally" on five-point Likert questions (DAX 2.7 [95% CI, 2.4 to 3.0]; Nabla 2.8 [95% CI, 2.6 to 3.0]).

conclusionsNabla reduced time-in-note versus the control. Both DAX and Nabla resulted in potential improvements in burnout, task load, and work exhaustion, but these secondary end point findings need confirmation in larger, multicenter trials. Clinicians reported that performance was similar across the two distinct platforms, and occasional inaccuracies observed in either scribe require ongoing vigilance. (Funded by the University of California, Los Angeles, Department of Medicine and others; ClinicalTrials.gov number, NCT06792890.).

Identifiers

PMID41497288
PMCPMC12768499

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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.