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.
Learning Objective 1: Understand the current issues in cohort identification informatics approaches and tools, especially the gap between researchers’ mental models of what they want to query and the actual queries they need to construct.
Learning Objective 2: Formulate an approach to the adoption of ontology-driven data access frameworks for handling semantic gaps in the free-text eligibility criteria and the data models used to structure the available data elements in target patient databases.
Hansi Zhang (Presenter)
University of Florida
Zhe He, Florida State University
Xing He, University of Florida
Yi Guo, University of Florida
David Nelson, University of Florida
François Modave, University of Florida
Yonghui Wu, University of Florida
William Hogan, University of Florida
Mattia Prosperi, University of Florida
Jiang Bian, University of Florida