| Sumario: | The article focuses on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and its significance in enhancing health research through large-scale observational data. Developed by the Observational Health Data Sciences and Informatics (OHDSI) consortium, the CDM standardizes data from electronic health records (EHRs) and other sources, facilitating consistent analyses across various institutions. The article highlights the critical role of health information (HI) professionals in ensuring data quality and governance, particularly as healthcare increasingly relies on artificial intelligence and automation. It also discusses the challenges faced in implementing the CDM, including potential loss of clinical detail and the need for upskilling in data science, while presenting findings from a study that demonstrated improved patient identification through enhanced mapping techniques in the ETL process.
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