Mind the gap: Observation windows to define periods of event ascertainment as a quality control method for longitudinal electronic health record data

Abstract Background Use of electronic health records (EHRs) in health research may lead to the false assumption of complete event ascertainment. We present a systematic approach to estimate “observation windows” (OWs), defined as time periods within which the assumption of complete ascertainment of events is more likely to hold, as a quality control approach to reducing the likelihood of this false assumption. We demonstrate the impact of observation windows on estimating the rates of type 2 diabetes mellitus (diabetes) using EHR diagnosis, medication, and laboratory data from HIV clinical cohorts. Methods Data contributed by 16 HIV clinical cohorts to the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) were used to identify and evaluate OWs for an operationalized definition of diabetes occurrence as a case study. Procedures included: 1) gathering cohort-level data; 2) visualizing and summarizing gaps in observations; 3) systematically establishing start and stop dates during which complete ascertainment of diabetes events was reasonable; and 4) visualizing the diabetes OWs relative to the cohort open and close dates to identify periods of time during which immortal person time was accumulated and events were not fully ascertained. We estimated diabetes occurrence event rates and 95% confidence intervals ([,]) in the most recent decade that data were available (Jan 1, 2007 to Dec 31, 2016). Results The number of diabetes events decreased by 17% with the use of the diabetes OWs; immortal person-time was removed decreasing total person-years by 23%. Consequently, the diabetes rate increased from 1.23 (95% confidence interval [1.20, 1.25]) per 100 person-years to 1.32 ([1.29, 1.35] per 100 person-years with the use of diabetes OWs. Conclusions As the use of EHR-curated data for event-driven health research continues to expand, OWs have utility as a quality control approach to complete event ascertainment, helping to improve accuracy of estimates by removing immortal person-time when ascertainment is incomplete.