Clinical Knowledge RAG System for Teaching Hospital
Giving clinicians instant access to 50,000+ clinical guidelines through conversational AI
The Challenge
A major public teaching hospital maintained over 50,000 clinical guidelines, protocols, drug references, and policy documents across multiple disconnected systems. Clinicians spent an average of 18 minutes searching for relevant clinical information, and outdated guidelines were frequently referenced because newer versions were difficult to locate.
Our Approach
We built a retrieval-augmented generation system that ingests, chunks, and indexes the hospital's entire clinical knowledge base. Clinicians can ask natural language questions and receive contextual, cited answers drawn from the most current approved documents. The system integrates with the hospital's clinical workstations and includes role-based access controls aligned with existing clinical governance.
The Outcome
Average clinical information retrieval time dropped from 18 minutes to under 45 seconds. Clinicians rated answer relevance at 4.6 out of 5 in post-launch surveys. Usage of outdated guidelines decreased by 89%, and the system handles 3,200+ queries per day with 97.4% uptime.
Retrieval Time
Daily Queries
Outdated Ref Reduction
Technology Stack
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