Enhancing the Observational Medical Outcomes Partnership Common Data Model to Support Health Equity Research

BACKGROUND: Common Data Models (CDMs) standardize health data from disparate observational sources, enabling the use of real-world data in more efficient, collaborative observational research studies. Until recently, the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) had limited infrastructure and ontology options to store a person's race and ethnicity data. This made using these data challenging for patient-centered outcomes research (PCOR) and health equity studies. OBJECTIVE: The primary goals are to outline the Observational Health Data Sciences and Informatics (OHDSI) community's collaborative efforts to improve race and ethnicity representation in the OMOP CDM and to explain how these enhancements enable a more detailed and comprehensive representation of individuals' racial and ethnic identities. These improvements ensure that research findings are both relevant to everyday clinical practice and applicable across diverse demographic groups. METHODS: The OHDSI community collaboratively addressed limitations in race and ethnicity representation within the OMOP CDM through a structured, inclusive process. Key workgroups worked together, engaging diverse stakeholders. Patient input played a pivotal role in shaping enhancements like multivalue storage. The multistep enhancement process included community-wide feedback at every step. RESULTS: The OHDSI community significantly enhanced the OMOP CDM's ability to represent race and ethnicity data, improving its granularity, inclusivity, and flexibility. These updates expand the race and ethnicity value sets, address multiracial and multiethnic identities, and enable more accurate and granular PCOR and health equity studies. CONCLUSION: This enhancement to the OMOP CDM reduces the gap between data stored in source systems and data converted to the OMOP CDM. The enhanced data model enables more detailed, nuanced health equity research and eliminates biases previously associated with limited demographic representation.