Evaluation of Patient-Level Retrieval from Electronic Health Record Data for a Cohort Discovery Task

ABSTRACT Objective Growing numbers of academic medical centers offer patient cohort discovery tools to their researchers, yet the performance of systems for this use case is not well-understood. The objective of this research was to assess patient-level information retrieval (IR) methods using electronic health records (EHR) for different types of cohort definition retrieval. Materials and Methods We developed a test collection consisting of about 100,000 patient records and 56 test topics that characterized patient cohort requests for various clinical studies. Automated IR tasks using word-based approaches were performed, varying four different parameters for a total of 48 permutations, with performance measured using B-Pref. We subsequently created structured Boolean queries for the 56 topics for performance comparisons. In addition, we performed a more detailed analysis of 10 topics. Results The best-performing word-based automated query parameter settings achieved a mean B-Pref of 0.167 across all 56 topics. The way a topic was structured (topic representation) had the largest impact on performance. Performance not only varied widely across topics, but there was also a large variance in sensitivity to parameter settings across the topics. Structured queries generally performed better than automated queries on measures of recall and precision, but were still not able to recall all relevant patients found by the automated queries. Conclusion While word-based automated methods of cohort retrieval offer an attractive solution to the labor-intensive nature of this task currently used at many medical centers, we generally found suboptimal performance in those approaches, with better performance obtained from structured Boolean queries. Insights gained in this preliminary analysis will help guide future work to develop new methods for patient-level cohort discovery with EHR data.