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

Abstract

Efficient querying for medication information in Electronic Health Record (EHR) datasets is crucial for effective patient care and clinical research. To address the complexity and data volume challenges involved in efficient medication information retrieval, we propose an ontology-driven medication query (ODMQ) optimization approach, leveraging the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Integrating semantic ontology structures from the OMOP CDM can help enhance query accuracy and efficiency by broadening the scope of relevant medication terms like drug names, National Drug Codes, and generics, resulting in more comprehensive query outcomes than traditional methods. ODMQ significantly reduces manual search time and enhances query capabilities. We validate ODMQ's efficacy using real-world COVID-19 EHR data, demonstrating improved query performance. Through a comprehensive manual review, ODMQ ensures that expanded search terms are relevant to user inputs. It also includes an intuitive query interface and visualizes patient history for result validation and exploration.