NIH-funded Observational Health Data Sciences Initiative (OHDSI) (pronounced “odyssey”) links over 3,000 collaborators across approximately 80 countries with electronic health records data repositories that collectively contain over 900 million unique patient records.
Advanced machine learning methods can now help predict and understand health risks and outcomes. These methods use large sets of clinical data, including electronic health records, socio-demographic data and medical imaging. Until recently, Penn State researchers had limited access to big biomedical and health research data, such as electronic health records (EHR). This has now changed with the establishment of the Penn State Digital Collaboratory for Precision Health Research (DCPHR), an initiative led by Penn State Clinical and Translational Sciences Institute (CTSI) and the Penn State Center for Artificial Intelligence Foundations and Scientific Applications (CENSAI).
DCPHR offers the infrastructure and research capacity to allow Penn State researchers to pursue collaborative data-intensive research projects using large clinical data sets and high-performance Artificial Intelligence/Machine Learning (AI/ML) data analytic workflows.
The DCPHR currently provides access to de-identified Penn State Health EHR data organized according to the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) developed by the NIH-funded OHDSI consortium. The OMOP common data model powers many large-scale biomedical data science efforts such as the NIH’s National COVID Cohort Collaborative (N3C).
OHDSI access is the newest addition to the DCPHR initiated by the CTSI Informatics Core. OHDSI aims “to improve health by empowering a community to collaboratively generate the evidence that promotes better health decisions and better care.” OHDSI links over 3000 collaborators across approximately 80 countries with OMOP-based EHR data repositories that collectively contain over 900 million unique patient records (representing about 12% of the world’s population). Each member of OHDSI maintains EHR data for its patient population in an OMOP-based institutional EHR data repository, like the one maintained by DCPHR at Penn State.
Any institution that is a member of the OHDSI consortium can propose a multi-site study with a defined study protocol and solicit participation from collaborators across the OHDSI consortium.
Read the full study details here.
