Optimizing Medication Querying Using Ontology-Driven Approach with OMOP: with an application to a large-scale COVID-19 EHR dataset

Presentation Time: 10:00 AM - 10:15 AM

Abstract Keywords: Information Extraction, Clinical Decision Support, Information Visualization, Usability
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

Efficient medication querying in Electronic Health Record (EHR) datasets is crucial for effective patient care and clinical research. However, the complexity and volume of such datasets present significant challenges in extracting relevant medication information accurately. In this study, we propose an ontology-driven medication query optimization approach, named ODMQ, leveraging the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to enhance medication querying capabilities. By integrating semantic ontology structures of OMOP CDM, our method provides a simpler and more convenient way to obtain a comprehensive list of drug names, National Drug Codes, and generic names. This enhancement reduces the time required for clinical researchers to manually search for medication information and improves query capability. We validate the efficacy and scalability of our methodology by conducting evaluations and experiments on an extensive real-world COVID-19 EHR dataset. The experimental results demonstrate that ODMQ can effectively improve medication query outcomes. Through a comprehensive manual review of all expansion results, ODMQ not only covers the medication terms provided by domain experts but also ensures that the expanded search terms (ranging from several times to a dozen times more than those provided by the domain experts) are relevant to the user's input. Our study contributes to the advancement of ontology-driven techniques aimed at optimizing medication querying processes.

Speaker(s):
Xiaojin Li
UTHealth

Author(s):
Yan Huang - UT Health Science Center; Licong Cui, PhD - The University of Texas Health Science Center at Houston (UTHealth Houston) School of Biomedical Informatics; Shiqiang Tao, PhD - The University of Texas Health Science Center at Houston; GQ Zhang, PhD - The University of Texas Health Science Center at Houston;