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Updated: Jun 20, 2026

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases
Published on: March 19, 2018
OMOP common data model transformation: leveraging a nationwide
Taona P Haderlein1, Claudia Der-Martirosian1, Wyatt P Bensken1
1OCHIN, Inc, Portland, OR, United States.
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.
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