Observational studyApplied clinical informatics2025
The Effect of Ambient Artificial Intelligence Scribes on Trainee Documentation Burden.
Observational study in Applied clinical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis 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.
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, 1 synthesis or guideline pooled 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
- Ambient Artificial Intelligence Scribes: A Scoping Review With Implications for Otolaryngology.The Laryngoscope · 2026Article
- The Promise of Ambient AI Technology in Medical Education: Opportunities and Guardrails.JMIR medical informatics · 2026Article
- Impact of an Ambient AI Scribe on Medical Student Objective Structured Clinical Examination Notes: Nonrandomized Clinical Trial.JMIR medical education · 2026Article
- Article
- Ambient Artificial Intelligence Scribes in Pediatric Hematology-Oncology: Early Implementation of DAX Copilot.Applied clinical informatics · 2026Article
- Ethical considerations for clinical adoption of ambient digital scribe technology.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- AI Scribe Use in Residency Training: A Call for Specialty Society Guidance in Graduate Medical Education.Advances in medical education and practice · 2026Article
- AI Scribes in Health Care: Balancing Transformative Potential With Responsible Integration.JMIR medical informatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ambient artificial intelligence scribes have become widespread commercial products in the era of generative artificial intelligence. While studies have examined the effect of these tools on the experience of attending physicians, little evidence is available regarding their use by resident physician trainees.To assess trainee experience with an ambient artificial intelligence scribe using measures of usability, acceptability, and documentation burden.This prospective observational study enrolled 47 trainees in a 2-month pilot. Pre/postsurveys were conducted with the NASA Task Load Index (NASA-TLX, raw unweighted form, pre/post, for cognitive load during the documentation), the System Usability Scale (post; general usability), the Net Promoter Score (post; acceptability), and the AMIA TrendBurden Survey (pre/post; documentation burden). Electronic health record utilization metrics were obtained from Epic Signal for both the pilot period and a 6-month baseline.In total, 43/47 (91.5%) of participants adopted the intervention in practice. NASA-TLX scores improved from 56.3 to 43.3 (
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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.