Evidence map›Paper›PMID 41234546›Full record

ArticleMayo Clinic proceedings. Digital health2025

Evaluating the Quality and Safety of Ambient Digital Scribe Platforms Using Simulated Ambulatory Encounters.

Taylor N Anderson, Vishnu Mohan, David A Dorr, Raj M Ratwani, Joshua M Biro, Jeffrey A Gold

Abstract read
In one paragraph

Article in Mayo Clinic proceedings. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Sustaining High Reliability Amid Artificial Intelligence Adoption in Oncology.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026
    Article
  2. Review
  3. Article
  4. Article
  5. Article
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  7. Article
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  9. Review
  10. Ethical considerations for clinical adoption of ambient digital scribe technology.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
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

6 authors.

Taylor N AndersonDivision of Informatics, Clinical Epidemiology, and Translational Data Science, Oregon Health & Science University, Portland, OR.
Vishnu MohanDivision of Informatics, Clinical Epidemiology, and Translational Data Science, Oregon Health & Science University, Portland, OR.
David A DorrDivision of Informatics, Clinical Epidemiology, and Translational Data Science, Oregon Health & Science University, Portland, OR.
Raj M RatwaniMedstar Health National Center for Human Factors in Healthcare, Washington, DC.
Joshua M BiroMedstar Health National Center for Human Factors in Healthcare, Washington, DC.
Jeffrey A GoldDivision of Informatics, Clinical Epidemiology, and Translational Data Science, Oregon Health & Science University, Portland, OR.

Funding

Evaluation of Impact of EHR Documentation Assistant Modalities on Provider and System Level OutcomesR01LM014255 · NLM · OREGON HEALTH & SCIENCE UNIVERSITY · PI Vishnu Mohan · 2024 to 2026
$2.0M
AHRQ HHS R03 HS030307AHRQ HHS R18 HS029345NLM NIH HHS R01 LM014255
6 · The paper itself

Abstract

Objective: To evaluate and compare the quality and safety of ambient digital scribe (ADS) platforms using simulated ambulatory encounters. Methods: Five ADS platforms were evaluated using audio recordings of fourteen simulated clinical encounters. Audio recordings were played on a laptop computer and captured by ADS platforms on a mobile phone. Generated transcripts were compared to professional transcriptions. Clinical notes were graded using rubrics of key elements for each case. Note errors were classified as omission, commission, or partially correct. Potential clinical harm was assessed using the agency for healthcare research and quality harm scale. Note quality was assessed using the 9-item Physician Documentation Quality Instrument (range 9-45). Statistical comparisons included Friedman and χ Results: Transcripts generated by platforms A through D contained an average of 13.9 (95% CI, 6.0-17.5) errors, with 19.5% of the transcript errors transmitted to the clinical note (95% CI, 6.6%-28.8%). For clinical notes, mean percent error across platforms was 26.3% (95% CI, 17.0%-31.0%) with a significantly higher proportion of errors in notes generated by platform E ( Conclusion: Clinical notes generated by ADS platforms using simulated encounters reports important inter-platform and intra-platform variability in accuracy and quality. These findings indicate a need for standardized, objective evaluation and reporting.

Identifiers

PMID41234546
PMCPMC12605248

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Registered trials

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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.