ArticleDigital health
Ambient listening implementation in primary care and changes in electronic health record documentation metrics: Pre-post study of an ambient listening tool.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- For the record: a narrative review of ambient voice technology in clinical documentation for dentistry and wider healthcare.British dental journal · 2026Review
- Evaluation of Socio-Technical Mechanisms Shaping AI Scribe Documentation Failures: A Netnographic Study.Journal of evaluation in clinical practice · 2026Article
- From Clinical Encounter to Draft Documentation: A Mechanistic Narrative Review of Ambient Scribe Technology.Cureus · 2026Review
- Communicating for optimal cardiovascular prevention: understanding the clinician's role.Archives of medical science : AMS · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Ambient listening with subsequent artificial intelligence enabled medical record documentation is changing how physicians interact with the electronic health record (EHR). We studied primary care physician use, documentation time, and changes in note metrics associated with ambient listening technology. Methods: We calculated the percentage of physicians adopting the ambient listening tool, Abridge. Note input contributed by Abridge was determined by the percentage of total characters in generated notes. Using the Epic EHR Signal metrics, we analyzed the change in note documentation time and note length for Abridge users before and after they implemented Abridge in their note generation process. Results: The percentage of physicians using Abridge increased from 15% to 50% in 8 weeks. Adoption was also reflected in the percentage of the note attributable to the use of ambient listening technology. Note content generated by Abridge tripled, from 5% to 15% over the same 8 weeks. After Abridge adoption, 332 primary care physicians had a mean time of 4.16 minutes [confidence interval (CI) 95%; 3.86, 4.46] time in notes per note in a paired comparison to 5.11 minutes [CI 95%; 4.75, 5.47] per note prior to Abridge. Mean time saved per note after Abridge was 0.95 minutes [CI 95%; 0.48, 1.42, p < 0.0001], an 18.6% reduction in time. Mean note length for Abridge adopters increased by 238 characters [CI 95%; 89, 387, p < 0.0001] from a pre-implementation mean of 4404 characters per note (+5.4%). Conclusion: Ambient listening had rapid uptake and widespread use by physicians in primary care. It was also associated with a significant decrease in time in notes.
Indexed as
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.