Evidence map›Paper›PMID 42527476›Full record

Articlenpj health systems2026

Evaluation of electronic health record-integrated artificial intelligence chart review.

Nicolas M Kahl, Marshall J Frieden, Zach R Pope, Marlene M Millen, Vaishal M Tolia, Theodore C Chan, Christopher A Longhurst, Karandeep Singh, Alan X You

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in npj health systems, 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

9 authors.

Nicolas M KahlDepartment of Emergency Medicine, University of California, San Diego, CA, USA. nmkahl@health.ucsd.edu.
Marshall J FriedenDepartment of Family Medicine, University of California, San Diego, CA, USA.
Zach R PopeDepartment of Family Medicine, University of California, San Diego, CA, USA.
Marlene M MillenDivision of Biomedical Informatics, Department of Medicine, University of California, San Diego, CA, USA.
Vaishal M ToliaDepartment of Emergency Medicine, University of California, San Diego, CA, USA.
Theodore C ChanDepartment of Emergency Medicine, University of California, San Diego, CA, USA.
Christopher A LonghurstDivision of Biomedical Informatics, Department of Medicine, University of California, San Diego, CA, USA.
Karandeep SinghDivision of Biomedical Informatics, Department of Medicine, University of California, San Diego, CA, USA.
Alan X YouDepartment of Emergency Medicine, University of California, San Diego, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study evaluates the quality of artificial intelligence (AI) clinical note summarization by analyzing physician qualitative feedback on a large language model (LLM) chart review tool integrated into the electronic health record (EHR). Physicians provided free-text feedback on AI-generated chart summaries, which physician informaticists analyzed using MAXQDA. Feedback from 10 physicians was collected on 147 AI-generated summaries. Positive feedback was common (n = 71), but users identified omissions (n = 46), confusing content (n = 20), token limitations (n = 27), hallucinations (n = 5), and bias (n = 1). Cohen's Kappa was 0.64, indicating substantial reviewer agreement. Physician feedback on the tool revealed overall positive impressions, though omissions raised concerns about summary completeness. AI-assisted chart review technology is not infallible, but physicians found this tool acceptable for use in clinical workflows.

Identifiers

PMID42527476
PMCPMC13354172

What OpenQuestion holds

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Registered trials

None linked

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.