Implementation of large language models in electronic health records.

ABSTRACT: Electronic Health Records (EHRs) have improved access to patient information but substantially increased clinicians' documentation workload. Large Language Models (LLMs) offer a potential means to reduce this burden, yet real-world deployments in live hospital systems remain limited. We implemented a secure, GDPR-compliant, on-premises LLM assistant integrated into the Epic EHR at a European university hospital. The system uses Qwen3-235B with Retrieval Augmented Generation to deliver context-aware answers drawing on structured patient data, internal and regional clinical documents, and medical literature. A one-month pilot with 28 physicians across nine specialties demonstrated high engagement, with 64% of participants using the assistant daily and generating 482 multi-turn conversations.

SUBMITTER: Griot M 

PROVIDER: S-EPMC12716761 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature