Evaluation of electronic health record-integrated artificial intelligence chart review

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Authors: Nicolas M. Kahl, Marshall Frieden, Zach Pope, Marlene Millen, Vaishal Tolia, T. Chan, Chris Longhurst, Karandeep Singh, Alan X. You
Cited by: 7
References: 9

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.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Electronic Health Records Systems Clinical Reasoning and Diagnostic Skills

Publication details

Journal / source
npj Health Systems
Publisher
Springer Science and Business Media LLC
Volume
3
Issue
1
Publication date
28 January 2026
Language
EN