Evaluating EHR Data Availability for Cohort Selection in Retrospective Studies

Electronic health record (EHR) systems store longitudinal medical data that allows for retrospective studies in healthcare. An ideal EHR would clearly designate when patient data starts and ends, allowing maximal data utility in retrospective studies. However, with differences in EHR implementation, and patients receiving treatment across many healthcare systems, timelines for reliable longitudinal medical data are often unclear, and retrospective studies include suboptimal data. To better identify reliable EHR data for retrospective studies, we built metrics to restrict and weight data based on availability. Our metrics measure the rise and persistence of three different datatypes in an EHR: billing codes, medication events, and tumor registry diagnoses. We implemented our metrics in a generalized cohort creation heuristic to select cohorts with reliable data. We applied our heuristic to select a cohort of stage I-III breast cancer patients at Vanderbilt University Medical Venter (VUMC) for a retrospective study on five-year adjuvant endocrine therapy adherence. Recent clinical trials report five-year adherence at 85%, but studies in the general patient population report lower five-year adherence rates. With our heuristic, we determined a five-year adherence rate bounded between 55% and 78%.