Main Article Content
The Ontario Brain Institute has developed Brain-CODE, an informatics platform designed to support the collection, storage, federation, sharing and analysis of different neuroscience research data types across several brain disorders. Linking such “deep” research data with “broad” health administrative data allows for improved characterization of disorders and supports the development of related health and social policies (Anderson et al., 2015). A privacy preserving record linkage protocol, developed through the Indoc Consortium, has been used to facilitate such linkages between Brain-CODE and administrative data holdings at the Institute for Clinical Evaluative Sciences (ICES; e.g., emergency department use, inpatient records, prescription drug utilization) (Gee et al., 2018).
Objectives and Approach
Three linkage pilots in the areas of neurodevelopmental disorders, epilepsy, and stroke research have been completed with >99% success match rates across all projects. However, each of these projects required a significant amount of human and computational resources to complete. With other similar data linkages being planned, it was determined that a more permanent solution was required rather than completing linkages on a project-by-project basis. The governance and technical elements to support the creation and maintenance of such a crosswalk between Brain-CODE and ICES were reviewed with an implementation plan subsequently developed.
A methodology for creating a crosswalk between Brain-CODE and ICES has been established. The same privacy preserving record linkage protocol, as used in the previous linkage pilots, will support the creation of this crosswalk. A plan has been established to update this crosswalk annually to account for new study participants on Brain-CODE.
Conclusion / Implications
The creation of this crosswalk will allow for a more streamlined approach of data linkage between Brain-CODE and ICES. Such an approach can significantly reduce overall resourcing requirements, enable more efficient data linkages, and contribute to the coupling of “broad” and “deep” data.
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