Skip to content

A Real-World Evaluation of Large Language Model-Generated Hospital Courses in Pediatrics.

Sep 2026 · Hospital Pediatrics · 0 citations · 25 references
Medicine

TL;DR

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.

View source

Similar papers

Open access Sep 2026

Comparison of physician-authored and artificial intelligence-generated after-visit summaries: A blinded comparative study.

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. · 0 citations
Review May 2026

Evaluation of a Large Language Model Discharge Summary Hospital Course Tool: Improved Quality but Longer Documentation Time

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. · 0 citations
#natural language process... Preprint Oct 2026

Clinician use of language models diverges from how the models are evaluated

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
Open access Aug 2026

Mapping Gaps and Improvement Targets in Large Language Model-Generated Melanoma Patient Education in a Non-English Setting

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 · 0 citations
Review Open access Aug 2026

A Comparative Analysis of Large Language Models (LLMs) in Generating High-Quality Pharmacology Questions for Undergraduate Medical Education

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 · 0 citations
Review Open access Sep 2026

Benchmarking Large Language Model Performance in Generating and Assessing Radiology Objective Structured Clinical Examination

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. · 0 citations

Related blog posts

MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

Microsoft Research Blog Oct 22, 2026

Flint-interactive: A language for creating interactive data visualizations

Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.