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TRV-2026-0977Version 1 · Certified

Written 2026-09-04 06:04:41 UTC · current record

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TRUVACE RECORD VERSION
record: TRV-2026-0977
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-04T06:04:41.859622Z
status: published
lens: p_space
sector: health
headline: A Real-World Evaluation of Large Language Model-Generated Hospital Courses in Pediatrics
dek: 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 freestandi…
gain_title: (none)
problem_title: 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.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: 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.
problem_evidence: (none)
quick_read: 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.
limitation: 
tag: Evidence-backed problem
key_points: 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.
rundown: 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.
sources:
- peer_reviewed | Hospital Pediatrics | https://doi.org/10.1542/hpeds.2026-009216 | 2026-09-03
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