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LEVERAGING EPIC'S NATIVE ETL INFRASTRUCTURE FOR OMOP CDM IMPLEMENTATION: A COLLABORATIVE EXPERIENCE.

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Researchers created a collaborative research pipeline using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and Epic

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Area of Science:

  • Health Informatics
  • Biomedical Research
  • Data Management

Background:

  • Collaborative research across institutions is crucial for advancing medical knowledge.
  • Standardizing electronic health record (EHR) data is essential for multi-institutional studies.
  • The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) facilitates data harmonization.

Purpose of the Study:

  • To implement a reproducible research pipeline using the OMOP CDM with Epic EHR data.
  • To enable collaborative research within the OHDSI (Observational Health Data Sciences and Informatics) network.
  • To assess the feasibility and data quality of using Epic's native ETL system for OMOP CDM mapping.

Main Methods:

  • Mapped University of Texas Southwestern Medical Center (UTSW) and Texas Health Resources (THR) EHR data to 25 OMOP CDM tables.
  • Utilized Epic's existing extract, transform, and load (ETL) system for data transfer to a shared OMOP database.
  • Conducted weekly and daily ETL processes at THR and UTSW, respectively.

Main Results:

  • Achieved high data quality assessment scores: 97% for THR and 98% for UTSW.
  • Successfully established a functional and reproducible data pipeline.
  • Expanded the OHDSI research network with harmonized data from UTSW and THR.

Conclusions:

  • The OMOP CDM, integrated with Epic's native ETL framework, provides a robust solution for collaborative research.
  • This approach enhances data quality and generalizability for future observational studies.
  • The established pipeline supports broader participation in the OHDSI network, accelerating medical discoveries.