Utilizing ETL Processes to Enhance Healthcare Education: Migrating MIMIC-IV to OpenEMR

The integration of real-world clinical data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database into the Open Electronic Medical Records (OpenEMR) system presents a unique opportunity to develop and implement innovative Extract, Transform, and Load (ETL) methodologies. This study focuses on the complex process of migrating 40,000 patient records from 33 tables in MIMIC-IV to the intricate schema of OpenEMR, which comprises 264 tables. The primary objective is to outline the novel approaches employed in the ETL process to ensure data fidelity, integrity, and usability within OpenEMR’s frontend interface. The methodology involves a three-stage process: extraction, transformation, and loading. During the extraction phase, relevant data is carefully selected from MIMIC-IV, taking into account data privacy considerations. The transformation phase involves intricate data mapping and manipulation to align the extracted data with OpenEMR’s schema, addressing challenges such as schema mismatches and data format inconsistencies. Finally, the loading phase ensures the accurate and efficient population of OpenEMR with the transformed data. The ETL process is designed to maintain data quality and integrity throughout the migration, employing robust validation and error-handling mechanisms. This study contributes to the advancement of ETL methodologies in the context of integrating real-world clinical data into electronic medical record systems. The detailed description of the innovative approaches employed in this project might serve as a valuable resource for researchers and practitioners working on similar data migration tasks.