The OMOP Common Data Model (CDM) is a standardized data model designed to organize healthcare data into a common structure. It enables systematic analysis across disparate observational databases and facilitates collaborative research in the healthcare domain.
The Model Context Protocol (MCP) is a framework that enables structured interaction between Large Language Models (LLMs) and databases. OMCP combines this protocol with the OMOP data model to create a powerful system for healthcare data analysis.
OMCP (OMOP Model Context Protocol) is an open-source server that enables Large Language Models (LLMs) to interact with healthcare databases that follow the OMOP Common Data Model. It provides a structured way for AI systems to:
- Query healthcare data with appropriate security and privacy controls
- Perform cohort discovery and selection
- Generate statistical analyses and insights from clinical data
- Maintain data lineage and provenance tracking
- Access standardized healthcare terminologies and concept mappings
- Clinical research and cohort discovery
- Population health analytics
- Healthcare quality measurement
- Drug safety surveillance
- Clinical decision support
- Medical knowledge extraction
Our first project, OMCP, is a Model Context Protocol server that sits between LLMs and OMOP CDM databases, providing a structured, secure interface for AI models to query and analyze healthcare data without direct database access.
+-------+ +--------------+ +----------------+
| LLM | <-> | OMCP Server | <-> | OMOP Database |
+-------+ +--------------+ +----------------+
/ \
Natural Language Structured Queries
Requests & Data Validation