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

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.