ArticleJAMIA open2025
Artificial intelligence-generated encounter summaries: early insights from ambulatory clinicians at a large academic health system.
Article in JAMIA open, 2025. 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.
- Sentence-Level Provenance for AI Medical Record Summarization in a Click-to-Inspect Interface: Formative Usability Evaluation.JMIR human factors · 2026Article
- For the record: a narrative review of ambient voice technology in clinical documentation for dentistry and wider healthcare.British dental journal · 2026Review
- Please document the nature of the medical emergency.BMJ quality & safety · 2026Article
- Randomized Trial Protocol: Epic Generative AI Chart Summarization Tool to Reduce Ambulatory Provider Cognitive Task Load.medRxiv : the preprint server for health sciences · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Assess time impact and provider perception of AI-generated encounter summaries. Materials and Methods: An artificial intelligence (AI) clinical note summarization tool was deployed in ambulatory practices for 22 providers. A 6-month pre-post analysis evaluated changes in EHR time, and survey feedback assessed tool utility. Results: Among 17 providers with complete Epic Signal time data, average time spent in clinical review per visit decreased from 3:22 minutes pre-intervention (8345 visits) to 3:04 minutes post-intervention (7924 visits). Higher baseline review time was significantly associated with greater post-implementation reductions, with each additional minute at baseline linked to an 8.6-second decrease in review time ( Discussion: AI-generated summaries appear most beneficial for clinicians who initially spend the longest time in chart review, suggesting a targeted efficiency gain. Providers expressed optimism about the tool's potential to streamline clinical workflows. Conclusion: Early experience with AI-generated summaries shows promise in improving provider workflow.
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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.