Implementation of Large Language Models in Electronic Health Records

Electronic Health Records (EHRs) have greatly improved access to clinical documents which also resulted in new challenges for clinicians, with some spending over 50% of their time on non-clinical tasks. While Large Language Models (LLMs) offer promise for reducing this burden, most implementations focus on synthetic benchmarks rather than clinical deployment. This paper presents a secure, fully on-premises, GDPR-compliant LLM chatbot integrated into the Epic EHR system at a European university hospital. The system utilizes Qwen3-235B with Retrieval Augmented Generation (RAG) for contextual awareness across internal records, regional eHealth documents, and medical literature. During a one-month pilot with 28 physicians from nine specialties, over 400 multi-turn conversations were initiated, with 64% of participants using the tool daily. Primary use cases included chart summarization, targeted retrieval, and support for clinical reasoning. Clinicians emphasized the model’s utility in rapidly synthesizing dispersed information, while diagnostic use remained limited. Our results demonstrate the technical feasibility and adoption patterns of integrating LLMs into production EHR systems, providing a replicable framework for secure clinical AI deployment.