Problem solved

Healthcare organizations often store patient data using a variety of local or proprietary coding systems, making it difficult to share, aggregate, or analyze across institutions. Manually mapping these disparate terminologies to a common standard such as OMOP requires deep domain expertise and is time‐consuming and error‐prone. OMOP MCP Server solves this problem by using large language models to automatically map clinical terms to standard OMOP concepts, significantly reducing the manual effort while maintaining high accuracy. It enables faster and more consistent data harmonisation, which is critical for collaborative research, regulatory submissions, and building large‐scale observational health data networks.

Use cases

• Mapping diagnosis codes from a hospital's custom dictionary to OMOP condition_concept_ids during ETL processes
• Standardizing medication names and dosages to OMOP drug concepts for a clinical research data mart
• Converting laboratory test names to LOINC codes and then to OMOP measurement concepts in real time
• Ingesting free‐text clinical notes and extracting conditions that are then normalized to OMOP standard concepts
• Building a terminology service that allows multiple applications to query LLM‐based mappings via an MCP interface

Audience

• Developers
• Data Scientists
• Researchers

Pros

• Automates tedious manual terminology mapping tasks
• Integrates seamlessly with AI agents through the Model Context Protocol
• Configurable to work with different LLM backends, balancing cost and accuracy
• Open source and self‐hostable, ensuring data privacy
• Docker support simplifies deployment and scaling

Cons

• LLM‐based mappings may require manual review for critical applications
• Effectiveness depends on the chosen LLM model and prompt engineering
• Limited to the terms understood by the configured models; rare or novel terms might fail