Evidence map›Paper›PMID 42518625›Full record

ArticlePRiMER (Leawood, Kan.)2026

The Impact of Ambient AI on Resident Documentation and Well-Being: A Pilot Study.

Wayne Anderson, Jessica B Koran-Scholl

Abstract read
In one paragraph

Article in PRiMER (Leawood, Kan.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

2 authors.

Wayne AndersonUniversity of South Florida, BayCare Health System, Clearwater, FL.
Jessica B Koran-SchollUniversity of South Florida, BayCare Health System, Clearwater, FL.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Burnout among family medicine residents remains a significant challenge, often exacerbated by electronic health record (EHR) documentation burden.1 Emerging technologies such as ambient artificial intelligence (AI), may reduce clinical documentation time while simultaneously enhancing work satisfaction. We looked to evaluate the impact of ambient AI on resident well-being, satisfaction, and documentation time. Methods: This prospective pilot study was conducted at two community-based family medicine residency programs. Sixteen third-year residents were invited to participate in the study. Residents used ambient AI software for outpatient clinical documentation. Pre- and postintervention data included objective EHR usage metrics and well-being assessments using the Mini ReZ survey. Primary outcomes included subjective well-being, changes in documentation time per patient, and total EHR time. Results: Fifteen residents enrolled in the study and 13 completed pre/postsurveys. The documentation time per patient decreased significantly postintervention (-3.09 min, Conclusion: The use of ambient AI software in a family medicine resident population was associated with improved well-being, satisfaction, and documentation efficiency. This study indicates that a larger study focused on resident efficiency and subsequent satisfaction is warranted.

Identifiers

PMID42518625
PMCPMC13384460

What OpenQuestion holds

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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.