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. The most common tasks were summarization, information retrieval, and note drafting, which together accounted for over 70% of interactions. Following the pilot, the system was deployed hospital-wide and adopted by 1,028 users who generated 14,910 conversations over five months, with more than half of clinicians using it at least weekly. Usage remained concentrated on information access and documentation support, indicating stable incorporation into everyday clinical workflows. Feedback volume decreased compared with the pilot, suggesting that routine use diminishes voluntary reporting and underscoring the need for complementary automated monitoring strategies. These findings demonstrate that large-scale integration of LLMs into clinical environments is technically feasible and can achieve sustained use when embedded directly within EHR workflows and governed by strong privacy safeguards. The observed patterns of engagement show that such systems can deliver consistent value in information retrieval and documentation, providing a replicable model for responsible clinical AI deployment.

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