Evidence map›Paper›PMID 41037268›Full record

ArticleJAMA network open2025

Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout.

Kristine D Olson, Daniella Meeker, Matt Troup, Timothy D Barker, Vinh H Nguyen, Jennifer B Manders, Cheryl D Stults, Veena G Jones, Sachin D Shah, Tina Shah and 1 more

Registry-linked trialAbstract read
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07302906 (The SOAR Trial), which is not on this map. Cited by 96 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
96citing papers in PubMed, 4 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.

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)
3 · Its place in the literature

Who cites it

96 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Well, How Did We Get Here? The Current State of Pediatric Endocrinology.Journal of pediatrics. Clinical practice · 2026
    Article
  7. Article
  8. Transforming Cardiology with AI: Ambient AI scribes in Epic.Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2026
    Article
  9. Article
  10. Article
  11. Article
  12. Am I Keeping You from Something Important?Journal of general internal medicine · 2026
    Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

36 more citing papers are in PubMed but not listed here.

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

11 authors.

Kristine D OlsonDepartment of Medicine, Yale School of Medicine, New Haven, Connecticut.
Daniella MeekerDepartment of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut.
Matt TroupAbridge AI, Inc, Pittsburg, Pennsylvania.
Timothy D BarkerClinical Excellence Division, CHRISTUS Health, Irving, Texas.
Vinh H NguyenDepartment of Family Medicine, MemorialCare Health System, Huntington Beach, California.
Jennifer B MandersDepartment of Surgery, The Christ Hospital Health Network, Cincinnati, Ohio.
Cheryl D StultsCenter for Health System Research, Sutter Health, San Francisco, California.
Veena G JonesDepartment of Digital Health, Sutter Health, San Francisco, California.
Sachin D ShahDepartments of Medicine and Pediatrics, University of Chicago, Chicago, Illinois.
Tina ShahDivision of Pulmonary and Critical Care, RWJBarnabus Health, Newark Beth Israel Medical Center, Newark, New Jersey.
Lee H SchwammDepartment of Bioinformatics and Data Sciences, Yale School of Medicine and Digital and Technology Solutions, Yale New Haven Health System, New Haven, Connecticut.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: While in short supply and high demand, ambulatory care clinicians spend more time on administrative tasks and documentation in the electronic health record than on direct patient care, which has been associated with burnout, intention to leave, and reduced quality of care. Objective: To examine whether ambient AI scribes are associated with reducing clinician administrative burden and burnout. Design, Setting, and Participants: This quality improvement study used preintervention and 30-day postintervention surveys to evaluate the use of the same ambient AI platform for clinical note documentation among ambulatory care physicians and advanced practice practitioners of 6 academic and community-based health care systems across the US. Clinicians were recruited by the health systems' digital health leaders; participation was voluntary. The study was conducted between February 1 and October 31, 2024. Exposure: Use of an ambient AI scribe for 30 days. Main Outcomes and Measures: The primary outcome was change in self-reported burnout, estimated using hierarchical logistic regression. Secondary outcomes of burnout evaluated were changes in note-related cognitive task load, focused attention on patients, patient understandability of notes, ability to add patients to the clinic schedule if urgently needed, and time spent documenting after hours. Outcome measures were linearly transformed to 10-point scales to ease interpretation and comparison. Differences between preintervention and postintervention scores were determined using paired t tests. Results: Of the 451 clinicians enrolled, 272 completed the preintervention and postintervention surveys (60.3% completion rate), and 263 with direct patient care in ambulatory clinics (mean [SD] years in practice, 15.1 [9.3]; 141 female [53.6%]) were included in the analysis. The sample included 131 primary care practitioners (49.7%), 232 attending physicians (88.2%), and 168 academic faculty (63.9%). After 30 days with the ambient AI scribe, the proportion of participants experiencing burnout decreased significantly from 51.9% to 38.8% (odds ratio, 0.26; 95% CI, 0.13-0.54). On 10-point scales, the ambient AI scribe was associated with significant improvements in secondary outcomes of burnout (mean [SE] difference, 0.47 [0.12] points), note-related cognitive task load (mean [SE] difference, 2.64 [0.13] points), ability to provide undivided attention (mean [SE] difference, 2.05 [0.18] points), patient understandability of their care plans from reading the notes (mean [SE] difference, -0.44 [0.17] points), ability to add patients to the clinic schedule if urgently needed (mean [SE] difference, 0.51 [0.24] points), and time spent documenting after hours (mean [SE] difference, 0.90 [0.19] hours). Conclusions and Relevance: This multicenter quality improvement study found that use of an ambient AI scribe platform was associated with a significant reduction in burnout, cognitive task load, and time spent documenting, as well as the perception that it could improve patient access to care and increase attention on patient concerns in an ambulatory environment. These findings suggest that AI may help reduce administrative burdens for clinicians and allow more time for meaningful work and professional well-being.

Indexed as

Burnout, ProfessionalDocumentationElectronic Health RecordsAdultAmbulatory CareFemaleHumansMaleMiddle AgedQuality ImprovementUnited States

Identifiers

PMID41037268
PMCPMC12492056

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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.