ArticleMayo Clinic proceedings. Digital health2025
Evaluating the Quality and Safety of Ambient Digital Scribe Platforms Using Simulated Ambulatory Encounters.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Sustaining High Reliability Amid Artificial Intelligence Adoption in Oncology.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Article
- For the record: a narrative review of ambient voice technology in clinical documentation for dentistry and wider healthcare.British dental journal · 2026Review
- A Conceptual Model for Ambient AI Adoption: Perspectives From Academia and Industry.JMIR medical informatics · 2026Article
- Please document the nature of the medical emergency.BMJ quality & safety · 2026Article
- Nurse-Led Ambient AI Scribe for Patient Safety Incident Investigation Reports (Project NARRATE): Retrospective Pre-Post Comparative Document-Quality Study.JMIR nursing · 2026Article
- Evaluation of Socio-Technical Mechanisms Shaping AI Scribe Documentation Failures: A Netnographic Study.Journal of evaluation in clinical practice · 2026Article
- Propagation of Interpreter Errors by Ambient AI Scribes: Study Using Simulated Clinical Encounters.JMIR medical informatics · 2026Article
- Feasibility of Tailoring Artificial Intelligence-Assisted Ambient Scribes for Intensive Care Unit Rounds: Algorithm Development and Validation.JMIR medical informatics · 2026Article
- From Clinical Encounter to Draft Documentation: A Mechanistic Narrative Review of Ambient Scribe Technology.Cureus · 2026Review
- Ethical considerations for clinical adoption of ambient digital scribe technology.Journal of the American Medical Informatics Association : JAMIA · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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
What OpenQuestion holds
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.