Lead: Feifan Liu; Collaborators: Ben Gerber, Adrian Zai, Celine Larkin

Promising suicide risk prediction algorithms in research may not be readily translated into clinical practice due to data heterogeneity and healthcare system complexity. Lack of external validation across clinical contexts has delayed their adoption and wider implementation. One of the barriers to multi-site validation is clinical data sharing. Observational Health Data Sciences and Informatics (OHDSI) is an international collaborative whose goal is to create and apply open-source data analytic solutions to a large network of health databases. Based on the Observational Medical Outcomes Partnership (OMOP) common data model, it provides a rigorous and elegant way to validate an algorithm’s generalizability across different institutions without a need for actual data sharing. This pilot study aims to build suicide risk prediction models of the OMOP common data model so that the OHDSI research network can be leveraged for a multi-site validation study.