Implementation of large language models in electronic health records
Problem
Electronic health records have increased clinicians' documentation workload, and real-world LLM deployments in live hospital systems remain limited.
Why it matters
Documentation burden takes clinicians' time away from patient care in hospitals.
What the AI did
A secure, GDPR-compliant, on-premises LLM assistant with retrieval augmented generation was integrated into the Epic EHR to give context-aware answers from patient data, clinical documents, and medical literature.
Results
One-month pilot with 28 physicians across nine specialties: 64% used it daily, 482 multi-turn conversations; summarization, information retrieval, and note drafting were over 70% of interactions; hospital-wide deployment followed.
Impact
The pilot showed high physician engagement and the system was subsequently deployed hospital-wide.
Record
| Venue | PLOS Digital Health |
|---|---|
| Published | 2025 |
| Work type | article |
| Authors | Maxime Griot, Jean M. Vanderdonckt, Demet Yüksel |
| Citations | 22 (OpenAlex, at import time) |
| References | 27 |
| Model or method | Qwen3-235B with Retrieval Augmented Generation |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1371/journal.pdig.0001141 |
| Open access | gold |
| Keywords | LLM, EHR, RAG, clinical documentation, hospital deployment, Qwen3 |
Importance rating
significant — thousands to millions affected
Documentation burden takes clinician time from patient care; abstract quantifies deployment (1,028 users, 14,910 conversations) but not problem scale. Hospital-wide deployment shows real, significant benefit at one hospital.