Exploring the Application of the Observational Medical Outcomes Partnership Common Data Model to Multi-site Stroke Rehabilitation Research Data

Background: Emerging artificial intelligence and machine learning (AI/ML) tools can help generate robust knowledge to support precision rehabilitation approaches for varied patient populations. There is a large amount of research-generated and clinical rehabilitation data available for this purpose; however, a pronounced lack of interoperability prevents large-scale data aggregation. Common data models (CDMs) such as Observational Medical Outcomes Partnership (OMOP) have improved data interoperability across healthcare settings, and more recently, for clinical rehabilitation data, specifically. However, the application of these CDMs to research-generated data has not yet been explored. Therefore, as a foundational step, our study evaluated the breadth and depth of OMOP CDM coverage for data in a multi-site repository of harmonized rehabilitation research data: the Enhancing NeuroImaging Genetics through Meta-Analysis Stroke Recovery (ENIGMA-SR) database. Methods: Two raters independently mapped data elements representing 46 demographics and medical history (DMH) ENIGMA-SR variables and 95 distinct ENIGMA-SR rehabilitation assessments to OMOP standard concepts. Initial rater agreement was assessed for data element inclusion in OMOP and for specific OMOP concepts used (primary metric: Gwet's agreement coefficient [AC]). Mapping differences were reconciled, and final mappings were descriptively analyzed to examine (1) overall OMOP inclusion, (2) inclusion of more granular levels (subscales, items) of complex assessments, and (3) mapped OMOP concept characteristics. Results: Initial rater agreement was good/very good for overall OMOP inclusion of DMH and assessment data elements and for OMOP concepts mapped across almost all assessment data elements (Gwet's AC: 0.79-0.89). Initial OMOP concept agreement was more variable for DMH data elements; however, all mapping differences were successfully reconciled to 100%. Overall, DMH data elements had higher OMOP inclusion than rehabilitation assessments: 84.8% (39/46) vs. 58.9% (56/95). OMOP coverage was particularly limited for complex assessment subscale- and item-level data elements (9.4% [3/32]; 19.2% [14/73]) and did not match the granularity level represented in ENIGMA-SR data for 56.2% (41/73) of complex assessments. DMH and top-level assessment data elements were frequently mapped to multiple OMOP concepts (median: 6, 2; range: 1-23, 1-8), and for > 50% of these data elements the concepts spanned 2-3 different OMOP domains. Conclusion: For ENIGMA-SR, the OMOP CDM has good coverage of DMH data, moderate top-level coverage of rehabilitation assessments, and very limited coverage of assessment subscales and items. This uneven coverage, combined with variability in OMOP concepts and domains mapped to equivalent data points, presents challenges for aggregating clinical and research-generated rehabilitation data into AI/ML-ready datasets. Moreover, software tools currently available to facilitate the mapping process do not effectively accommodate content- and structure-related features inherent to research-generated data. Going forward, the utility of the OMOP CDM to aggregate multi-source rehabilitation data may be improved by expanding the catalogue of OMOP rehabilitation-related concepts, building cross-walks to research-oriented data standards, and adapting emerging computational tools to streamline the mapping process.