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Leveraging Observational Medical Outcomes Partnership (OMOP) Data to Populate Disease Registries

Authors:
James McGlothlin
Tim Martens

Keywords: population health; OMOP; congenital heart disease; thoracic surgery; artificial intelligence.

Abstract:
Health care disease registries and procedural registries serve a vital purpose in support of research and patient quality. However, it requires a significant level of clinician effort to collect and submit the data required by each registry, and there are over 1000 common patient registries. In previous research, we have evaluated using supervised learning in conjunction with generative artificial intelligence to generate accurate content for disease registries. However, one of the largest challenges was to extract complete and meaningful data from the electronic medical record in a format that enabled the generative Artificial Intelligence (AI) tools. Standards like HL7 and Fast Healthcare Interoperability Resources (FHIR) were insufficient and burdensome. In this project, we propose using the new Observational Medical Outcomes Partnership (OMOP) data standard to acquire this data. Our Electronic Medical Record (EMR) software provides access to this data in the cloud without requiring extraction and transformation. The goal of this project is to utilize this data and technology to improve the population of disease registry records.

Pages: 70 to 71

Copyright: Copyright (c) IARIA, 2025

Publication date: October 26, 2025

Published in: conference

ISSN: 2519-8491

ISBN: 978-1-68558-312-5

Location: Barcelona, Spain

Dates: from October 26, 2025 to October 30, 2025