Evidence map›Paper›PMID 41071988›Full record

ReviewJMIR AI2025

Real-World Evidence Synthesis of Digital Scribes Using Ambient Listening and Generative Artificial Intelligence for Clinician Documentation Workflows: Rapid Review.

Naga Sasidhar Kanaparthy, Yenny Villuendas-Rey, Tolulope Bakare, Zihan Diao, Mark Iscoe, Andrew Loza, Donald Wright, Conrad Safranek, Isaac V Faustino, Alexandria Brackett and 2 more

Registry-linked trialAbstract readReview
In one paragraph

Review in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07742761 (Emergency Medicine Practitioners Overall Well-being Enhancement With Ambient AI Scribes), which is not on this map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
–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.

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

24 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Effect of use of ambient listening technology on patient-reported communication and satisfaction ratings.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  5. Article
  6. Article
  7. Review
  8. Observational
  9. What do clinicians edit in ambient AI-drafted clinical documentation? A qualitative content analysis.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  10. Article
  11. Article
  12. Observational
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Review
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

12 authors.

Naga Sasidhar KanaparthyDepartment of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, United States.ORCID http://orcid.org/0000-0002-2342-2412
Yenny Villuendas-ReyCentro de Innovación y Desarrollo Tecnológico en Cómputo CIDETEC, Instituto Politécnico Nacional, Mexico City, Mexico.ORCID http://orcid.org/0000-0001-9889-3924
Tolulope BakareDepartment of Biostatistics (Health Informatics Division), Yale School of Public Health, New Haven, CT, United States.ORCID http://orcid.org/0000-0002-8337-3915
Zihan DiaoDepartment of Emergency Medicine, Yale School of Medicine, 464 Congress Avenue, #260, New Haven, CT, 06519, United States, 1 203-737-7694.ORCID http://orcid.org/0000-0003-1571-5386
Mark IscoeDepartment of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, United States.ORCID http://orcid.org/0000-0003-4446-2488
Andrew LozaDepartment of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, United States.ORCID http://orcid.org/0000-0002-1754-5133
Donald WrightDepartment of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, United States.ORCID http://orcid.org/0000-0002-2564-7754
Conrad SafranekDepartment of Emergency Medicine, Yale School of Medicine, 464 Congress Avenue, #260, New Haven, CT, 06519, United States, 1 203-737-7694.ORCID http://orcid.org/0000-0003-1985-9432
Isaac V FaustinoDepartment of Emergency Medicine, Yale School of Medicine, 464 Congress Avenue, #260, New Haven, CT, 06519, United States, 1 203-737-7694.ORCID http://orcid.org/0000-0001-7445-4265
Alexandria BrackettHarvey Cushing/John Hay Whitney Medical Library, Yale University, New Haven, CT, United States.ORCID http://orcid.org/0000-0003-3801-833X
Edward R MelnickDepartment of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, United States.ORCID http://orcid.org/0000-0002-6509-9537
R Andrew TaylorDepartment of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, United States.ORCID http://orcid.org/0000-0002-9082-6644

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Institutional Career Development CoreKL2TR001862 · NCATS · YALE UNIVERSITY · PI CANTLEY, LLOYD G, EDELMAN, E. JENNIFER · 2016 to 2025
$12.2M
Short-Term Reserch Training: Students in Health SchoolsT35HL007649 · NHLBI · YALE UNIVERSITY · PI Sarwat I Chaudhry, Erica L Herzog · 1987 to 2026
$5.7M
NCATS NIH HHS KL2 TR001862NCATS NIH HHS UL1 TR001863NHLBI NIH HHS T35 HL007649
6 · The paper itself

Abstract

Background: As physicians spend up to twice as much time on electronic health record tasks as on direct patient care, digital scribes have emerged as a promising solution to restore patient-clinician communication and reduce documentation burden-making it essential to study their real-world impact on clinical workflows, efficiency, and satisfaction. Objective: This study aimed to synthesize evidence on clinician efficiency, user satisfaction, quality, and practical barriers associated with the use of digital scribes using ambient listening and generative artificial intelligence (AI) in real-world clinical settings. Methods: A rapid review was conducted to evaluate the real-world evidence of digital scribes using ambient listening and generative AI in clinical practice from 2014 to 2024. Data were collected from Ovid MEDLINE, Embase, Web of Science-Core Collection, Cochrane CENTRAL and Reviews, and PubMed Central. Predefined eligibility criteria focused on studies addressing clinical implementation, excluding those centered solely on technical development or model validation. The findings of each study were synthesized and analyzed through the QUEST human evaluation framework for quality and safety and the Systems Engineering Initiative for Patient Safety (SEIPS) 3.0 model to assess integration into clinicians' workflows and experience. Results: Of the 1450 studies identified, 6 met the inclusion criteria. These studies included an observational study, a case report, a peer-matched cohort study, and survey-based assessments conducted across academic health systems, community settings, and outpatient practices. The major themes noted were as follows: (1) they decreased self-reported documentation times, with associated increased length of notes; (2) physician burnout measured using standardized scales was unaffected, but physician engagement improved; (3) physician productivity, assessed via billing metrics, was unchanged; and (4) the studies fell short when compared to standardized frameworks. Conclusions: Digital scribes show promise in reducing documentation burden and enhancing clinician satisfaction, thereby supporting workflow efficiency. However, the currently available evidence is sparse. Future real-world, multifaceted studies are needed before AI scribes can be recommended unequivocally.

Indexed as

artificial intelligence in medicineclinical documentationdigital scribespatient-clinician communicationspeech recognition software

Identifiers

PMID41071988
PMCPMC12513689

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.