OMOP common data model transformation: leveraging a nationwide.

OBJECTIVE: To demonstrate a large-scale EHR data transformation to the Observational Medical Outcomes Partnership (OMOP) Common Data model in the OCHIN network to support AI/ML analyses within the AIM-AHEAD national research consortium. We highlight customized workflows and infrastructure development processes designed to support OCHIN database use by AI/ML researchers of varied academic backgrounds and skillsets. MATERIALS AND METHODS: Automated and manual mappings were used to ingest and transform OCHIN's i2b2-formatted database to the OMOP Common Data Model. RESULTS: 360 million encounters across 10+ million OCHIN patients were mapped over the course of a single calendar year, with custom concepts created to represent social drivers of health. DISCUSSION: The successful transformation of the OCHIN Research Data Warehouse benefitted from incorporating both customized and legacy data workflows. CONCLUSION: OCHIN's efforts will facilitate parallel analyses across AIM-AHEAD datasets and support AI/ML model representativeness of affected populations.