Computable Eligibility Criteria through Ontology-driven Data Access: A Case Study of Hepatitis C Virus Trials.

The increasing adoption of electronic health record (EHR) systems and proliferation of clinical data offer unprecedented opportunities for cohort identification to accelerate patient recruitment. However, the effort required to translate trial eligibility criteria to the correct cohort identification queries for clinical investigators is substantial, at least in part due to the lack of clear definitions in both the free-text eligibility criteria and the data models used to structure the available data elements in target patient databases. We propose to adopt an ontology-driven data access approach that generates formal representations of the connections between the entities in eligibility criteria and the available data elements to (1) narrow the semantic gap between researchers' cohort identification needs and the underlying database nuances, and (2) render the eligibility criteria computable. We implemented our approach based on an analysis of the eligibility criteria from 77 Hepatitis C trials. We found that 4 major types of data manipulation queries and 4 temporal patterns covered all eligibility criteria that were computable. We built a prototype system that helps researchers write computable eligibility criteria and execute them against clinical data in real-time to find potential trial cohorts.