Four teams of researchers have won a prize competition aimed at integrating eye care and ocular imaging data into studies using large healthcare data sets in biomedical research. The selected teams participated in the $1 million challenge called Expand OHDSI Initiative for Eye Care and Ocular Imaging Challenge, hosted by the National Eye Institute (NEI), part of the National Institutes of Health.
Each of the teams developed ways to investigate eye health and vision preservation using the Observational Health Data Sciences and Informatics (OHDSI) Network. Pronounced “Odyssey,” the OHDSI network is an open-science multidisciplinary collaborative working to improve health outcomes based on real-world evidence derived from observational health data in health records.
The network supports a common data model that standardizes the structure and content of health care databases.
“The common data model is like a Rosetta stone,” said Kerry Goetz, associate director of the Office of Data Science and Health Informatics at the NEI, in a statement. “Collaborating healthcare sites map their data to the model. Researchers can then develop common cohort definitions and agree on what questions to ask to get reproducible answers across disparate systems.”
It’s a federated model, meaning that each healthcare site can run the query in their own environment and report back the findings, without data needing to leave the source.
The NEI Challenge was launched to incentivize researchers to submit innovative ideas for leveraging the OHDSI network to maximize its usefulness for eye-related health outcomes research. The awarded funds will also help cover developer costs of adopting the common data model at their sites.
“The winning projects demonstrate how standardizing ophthalmic data within global research networks like OHDSI can transform how we study, treat, and ultimately prevent vision loss. We’re proud to support these efforts that push the boundaries of what’s possible in collaborative, data-driven health research,” Goetz added.
The four teams selected to receive $250,000 each in prize funding are:
Mass Eye and Ear (MEE)
Project: Expanding the Availability of Ophthalmic Data in the Observational Medical Outcomes Partnership (OMOP) Data Model to Catalyze Eye and Vision
Research
Team Lead: Michael Boland, M.D., Ph.D.
Team Members: Lucia Sobrin, M.D.; Ines Lains, M.D., Ph.D.; Tobias Elze, Ph.D.; and Pearse Keane, M.D., consultant ophthalmologist, Moorfields Eye Hospital, U.K.
Objective: Standardize ophthalmic data in the OMOP Common Data Model (CDM) across U.S. and U.K. institutions using Epic Clarity and INSIGHT models. By mapping key eye care data elements and creating supportive documentation, it will fill OMOP gaps and enable scalable, global vision research.
Oregon Health & Science University (OHSU)
Project: Data Coordinating Center in the OHDSI Ophthalmic Network
Team Lead: Michelle Hribar, Ph.D.
Team Members: Mohammad Adibuzzaman, Ph.D.; Mitchell Brinks, M.D.; Aiyin Chen, M.D.; David Huang, M.D., Ph.D.; Hiroshi Ishikawa, M.D.; Yali Jia, Ph.D.; Elizabeth Silbermann, M.D.; Xubo Song, Ph.D.; and Oa Tan, Ph.D.
Objective: Develop an independent data coordinating center to harmonize ophthalmic data across OHDSI. The team will develop tools for integrating eye exams and imaging into OMOP.
Columbia University
Project: Eye Care and Vision Drug Characterization
Team Lead: George Hripcsak, M.D.
Objective: Examine ophthalmic medication use, adherence, and adverse events across the OHDSI network to improve patient care.
Stanford University
Project: Comprehensive Data Network for Pediatric Eye Research
Team Lead: Gayathri Srinivasan, O.D.
Team Members: Alan Schroeder, M.D., Nathan Cheung, O.D., Duke University; Angela Chen, M.D., University of California, Los Angeles; Kristine Huang, O.D., Southern California College of Optometry; and Michelle Hribar, Julian Ponsetto, M.D., Allison Summers, O.D., and Courtney Nall, M.D., (OHSU)
Objective: Create a pediatric-focused ophthalmic data network by mapping claims and EHR data into OMOP for longitudinal analysis using diverse health system data from the PEDSNet health system.
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