An Agentic Model Context Protocol Framework for Medical Concept Standardization
🤖 AI Summary
OMOP Common Data Model (CDM) terminology mapping faces challenges including high resource consumption, error-proneness, and significant hallucination risks in large language models (LLMs), hindering clinical deployment. Method: We propose a zero-training, hallucination-mitigated proxy-based concept mapping framework grounded in the Model Context Protocol (MCP). It integrates structured prompt engineering with real-time retrieval from authoritative medical knowledge bases, enabling secure, interpretable semantic reasoning and dynamic vocabulary lookup—without fine-tuning or domain-specific training. Contribution/Results: The framework is plug-and-play across multi-institutional clinical research settings. Experiments demonstrate substantial improvements in mapping accuracy and consistency, reduced manual curation effort, and robust support for both exploratory research and production-grade deployment.
Application Category
📝 Abstract
The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) provides a standardized representation of heterogeneous health data to support large-scale, multi-institutional research. One critical step in data standardization using OMOP CDM is the mapping of source medical terms to OMOP standard concepts, a procedure that is resource-intensive and error-prone. While large language models (LLMs) have the potential to facilitate this process, their tendency toward hallucination makes them unsuitable for clinical deployment without training and expert validation. Here, we developed a zero-training, hallucination-preventive mapping system based on the Model Context Protocol (MCP), a standardized and secure framework allowing LLMs to interact with external resources and tools. The system enables explainable mapping and significantly improves efficiency and accuracy with minimal effort. It provides real-time vocabulary lookups and structured reasoning outputs suitable for immediate use in both exploratory and production environments.
Problem
Research questions and friction points this paper is trying to address.
Mapping source medical terms to OMOP standard concepts
Resource-intensive and error-prone medical data standardization
Preventing LLM hallucinations in clinical concept mapping
Innovation
Methods, ideas, or system contributions that make the work stand out.
Zero-training mapping system using Model Context Protocol
Real-time vocabulary lookups with structured reasoning outputs
Hallucination-preventive framework for medical concept standardization
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