Using EHR Data to Identify Clinical Trial Patient Cohorts

Sept. 9, 2021

Researchers to deploy OMOP Common Data Model to make information retrieval approach easier to generalize across institutions

David Raths

Researchers at Oregon Health & Science University, Mayo Clinic and the University of Texas have received a 5-year, $3 million grant from the National Library of Medicine to continue their work developing and evaluating methods to identify patient cohorts for clinical research studies based on patient data in the electronic health record.

Many clinical research studies struggle to reach patient enrollment goals. One way to increase enrollment is identify individuals who might be candidates for such studies by processing the data in their EHR. OHSU faculty members Steven Bedrick, Ph.D., and William Hersh, M.D., have been collaborating with colleagues from Mayo Clinic (Hongfang Liu, Ph.D.) and University of Texas Houston Health Science Center at Houston (Kirk Roberts, Ph.D.) on this work.

The new grant builds on the researchers’ previous work and adds a new dimension to make their methods easier to generalize across institutions by adhering to the data being in a common data model, according to a blog post on the OHSU web site. While actual patient data will not leave the premises of the participating institutions, each will maintain their own data in the Observational Medical Outcomes Partnership (OMOP) Common Data Model so that algorithms can be developed and trained in a more generalizable manner.

Once the foundational systems and OMOP-formatted data are in place, each site will use common information queries and evaluate the output of their systems internally. Different methods, including those applying machine learning, will be applied across the different sites and compared for their efficacy. Other sites will be able to implement, train, and use these models at their own sites.

In their description of the project, the researchers said that “if successful, the proposed project will advance informatics research on cohort discovery and identification, which impacts many applications based on EHR data such as learning healthcare systems, predictive modeling, or AI in healthcare.”