Hi everyone,
I’m developing a new tool designed to dramatically accelerate the conversion of source databases to the OMOP CDM, leveraging the power of large language models (LLMs). The goal is to minimize the time from gaining access to a source database to generating the first OMOPified result—typically within 30 minutes, including an initial Data Quality Dashboard (DQD) run.
Key Features:
- LLM-First Approach:
We use an LLM trained to understand a our custom syntax for structural mapping. This allows users to describe mappings using natural language prompts. - Mapping as a code Approach:
We developed declarative special language, optimized for the structural mapping (alike terraform) to get all benefits from coding: collaboration, version control, code as documentation, tests, possibility to use LLM - Declarative Mapping Syntax:
Each OMOP field is defined with an elementary SQL expression for data extraction. Fields can also be flagged for semantic mapping. - Automated Semantic Mapping:
The system performs initial vocabulary (concept) mapping automatically. Users can then review and fine-tune the mappings as needed. - Security by design: sends only statistical information to backend (like WhiteRabbit report) and may work with synthetic version of database and produce ETL to be applied to real data
Outcome:
- Rapid onboarding: From database connection to initial OMOP CDM + DQD results in ~30 minutes.
- Iterative refinement: Quickly adjust mappings and improve output over time.
I’m currently looking for test users who are willing to try the product for free and provide feedback. If you’re working on an OMOP conversion project and interested in reducing the time and effort required, I’d love to hear from you!