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Article Abstract
Integrating heterogeneous healthcare datasets, such as claims and electronic health records, is important for making real-world data analytical-ready but poses challenges due to variations in data structures and clinical terminologies. We developed a systematic Extract, Transform, Load (ETL) pipeline to transform Medicare claims data into the Observational Medical Outcomes Partnership Common Data Model, enabling interoperability and standardized analytics. The ETL process was developed using the ETL-CMS framework and enhanced with updated procedures utilizing open-source ETL tools-WhiteRabbit. Rigorous testing ensured data accuracy and consistency, achieving a 99.2% passing rate in quality check assessment using the Data Quality Dashboard. Comparative analysis demonstrated an accurate representation of demographics and clinical comorbidities, with slight variations attributed to syntactic errors in the source data. This scalable framework fosters interoperability, supports global standards, and enables collaborative clinical research, advancing the integration of diverse healthcare datasets for improved analytics.
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| http://dx.doi.org/10.3233/SHTI251106 | DOI Listing |
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