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Related Experiment Video

Updated: Jun 20, 2026

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

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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.

Journal of the American Medical Informatics Association : JAMIA
|June 19, 2026
PubMed
Summary
This summary is machine-generated.

A large-scale transformation of electronic health record (EHR) data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model was completed for the OCHIN network. This effort supports AI/ML analyses for the AIM-AHEAD consortium.

Keywords:
artificial intelligencecommunity health servicesdata warehousingelectronic health recordsmachine learning

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Area of Science:

  • Biomedical Informatics
  • Health Services Research

Background:

  • Large-scale electronic health record (EHR) data transformation is crucial for advancing AI/ML in healthcare.
  • The OCHIN network's data needed standardization for use in national research consortia like AIM-AHEAD.

Purpose of the Study:

  • To demonstrate a large-scale EHR data transformation to the OMOP Common Data Model within the OCHIN network.
  • To support AI/ML analyses for the AIM-AHEAD national research consortium.
  • To highlight customized workflows and infrastructure for diverse AI/ML researchers.

Main Methods:

  • Automated and manual mappings were employed for data ingestion and transformation.
  • OCHIN's i2b2-formatted database was converted to the OMOP Common Data Model.
  • Custom concepts were developed to represent social drivers of health.

Main Results:

  • Over 360 million encounters from 10+ million OCHIN patients were mapped within one year.
  • The transformation process successfully integrated customized and legacy data workflows.
  • The OCHIN Research Data Warehouse was successfully transformed.

Conclusions:

  • The data transformation facilitates parallel analyses across AIM-AHEAD datasets.
  • This initiative enhances the representativeness of AI/ML models for affected populations.
  • Standardized data infrastructure supports collaborative research and AI/ML development.