ArticleJAMA network open2025
Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout.
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.
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.
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
Who cites it
96 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Healthcare Professionals' Perceptions of Artificial Intelligence in Healthcare-A Systematic Review of Qualitative Studies.Journal of advanced nursing · 2026Pooled 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
- Artificial intelligence-driven work systems and employee burnout: a systematic review of mechanisms, moderators, and a dynamic meta-system framework.Frontiers in psychology · 2026Pooled it
- Evidence on artificial intelligence-assisted clinical documentation and healthcare workers' emotional wellbeing at work: a scoping review.Frontiers in psychology · 2026Pooled it
- Ambient AI Scribes to Create Educational Feedback Notes for Medical Students: Randomized Trial.JMIR medical education · 2026Trial
- Well, How Did We Get Here? The Current State of Pediatric Endocrinology.Journal of pediatrics. Clinical practice · 2026Article
- Evaluating AI-Enabled Voice to Text for Medical Affairs Documentation and Insight Capture: The RAPID Randomized Crossover Trial.Pharmaceutical medicine · 2026Article
- 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 · 2026Article
- The Ambulatory Patient Experience with Ambient Artificial Intelligence Scribes: A Qualitative Study.Journal of general internal medicine · 2026Article
- Ambient artificial intelligence scribes in urology: a pilot evaluation of user experience and clinical efficiency gains.World journal of urology · 2026Article
- Ambient AI Scribe Technology and Perceived Documentation Efficiency Among Surgeons: Cross-Sectional Study.JMIR medical informatics · 2026Article
- Am I Keeping You from Something Important?Journal of general internal medicine · 2026Article
- AI-Assisted Clinical Documentation in Routine Danish General Practice: Quantitative Pre-Post Study.JMIR human factors · 2026Article
- The Promise and Peril of Generative AI in Nursing: Moving Beyond Hype Toward Responsible Innovation.Journal of clinical nursing · 2026Article
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Article
- Association Between Large Language Model-Derived Mobility Functional Status Assessment and Clinical Outcomes.Journal of the American Geriatrics Society · 2026Article
- Navigating Collective Bargaining Barriers to the Implementation of AI Scribe Technology Among Ambulatory Advanced Practice Providers: Mixed Methods Quality Improvement Study.JMIR formative research · 2026Article
- [Requirements for artificial intelligence scribe systems for medical documentation in ophthalmology].Die Ophthalmologie · 2026Article
- Generative AI Use, Perceived Usefulness, Perceived Risk, and Physician Burnout and Fulfillment Among Chinese Physicians: Mixed Methods Multiregional Study.Journal of medical Internet research · 2026Article
36 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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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.