These findings support the use of LLM-generated hospital courses as starting drafts when paired with clinician review and institutional safeguards, and errors were common, but perceived potential harm was low, even assuming use without clinician correction.
Abstract
Background
Large language model (LLM)-generated hospital courses are increasingly integrated into electronic health records (EHRs), yet their accuracy and safety in pediatric populations remain poorly characterized.
Objective
To evaluate the accuracy, text quality, and perceived potential harm of EHR-integrated and LLM-generated hospital courses in pediatric inpatient care during early clinical implementation.
Methods
We conducted a descriptive evaluation from June 10 to August 8, 2025, at an academic freestanding children's hospital using an Epic EHR with an integrated LLM tool (GPT-4o and GPT-4.1). Clinicians across multiple roles, including attending physicians, residents, and advanced practice providers, reviewed LLM-generated hospital courses for their own patients. Clinicians identified and categorized errors (hallucinations, inaccuracies, or omissions). They also rated text quality (comprehensiveness, conciseness, coherence) on a 5-point scale and perceived harm on an 8-point scale.
Results
A total of 129 LLM-generated hospital courses were reviewed (median length of stay, 3 days; IQR, 2-7) by 50 involved clinicians. Hallucinations occurred in 21% (95% CI, 14%-29%) of the hospital courses, inaccuracies in 41% (53/129; 95% CI, 33%-50%), and omissions in 24% (31/129; 95% CI, 17%-32%). Overall, perceived harm ratings were low (median, 0; IQR, 0-1). Text quality ratings were high (median [IQR]: comprehensiveness, 4 [3-5]; conciseness, 4 [4-5]; coherence, 4 [4-5]) and comparable with prior literature.
Conclusion
In this pediatric evaluation of LLM-generated hospital courses reviewed by frontline clinicians, errors were common, but perceived potential harm was low, even assuming use without clinician correction. These findings support the use of LLM-generated hospital courses as starting drafts when paired with clinician review and institutional safeguards.
In this blinded evaluation, LLM-generated AVSs were clearer, more comprehensive, and more empathetic than physician-authored AVSs and were associated with lower physician-rated potential for harm.
Milla Kviatkovsky, Caden Stewart, Annalise J. McDonald et al.· Journal of Hospital Medicine· 0 citations
Abstract Background No studies have examined the effect of artificial intelligence (AI)-generated hospital courses on discharge summary documentation time. Objective This study aimed to evaluate the effect of an AI-generated hospital course tool on discharge summary edit time, quality, and clinician perceptions. Method...
Alexander Dobek, Fawaz Naeem, Christina DeBenedictus et al.· Applied Clinical Informatics· 0 citations
Large language model (LLM) assistants are being deployed to clinicians across health systems, and judgments about their readiness rest largely on benchmark scores, most of them derived from examination questions or curated cases. A benchmark predicts performance in deployment only to the extent that its items resemble...
K. Vishwanath, Hai-Tong Lin, A. Alyakin et al.· 0 citations
How well large language models (LLM) handle Turkish melanoma patient education varies widely from one model to the next, and findings suggest that LLM-generated Turkish melanoma materials may be useful as preliminary educational drafts.
Nıyazı Çetın, A. Atılan· European Journal of Therapeu...· 0 citations
The ability of three advanced LLMs to generate pharmacology assessment questions for Phase II MBBS students was examined and the clinical relevance of questions was strong, and human reviewers identified minor factual inaccuracies.
Gurusamy Sivagnanam, Parasuraman Jayabharathy, Ganesan Justina Princess· The Journal of medical resea...· 0 citations
This study provides a benchmark of LLM performance for radiology OSCE-style content generation and evaluation during a specific snapshot of artificial intelligence development (August 2024).
Ankush Ankush, Samriddhi Burman, Sydney Smith et al.· Radiology Advances· 0 citations
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