Few computational approaches exist for abstracting electronic health record (EHR) log files into clinically meaningful phenomena like clinician shifts. Because shifts are a fundamental unit of work recognized in clinical settings, shifts may serve as a primary unit of analysis in the study of documentation burden. We conducted a proof- of-concept study to investigate the feasibility of a novel approach using time series clustering to segment and infer clinician shifts from EHR log files. From 33,535,585 events captured between April-June 2021, we computationally identified 43,911 potential shifts among 2,285 (74.2%) emergency department nurses. On average, computationally-identified shifts were 10.6±3.1 hours long. Based on data distributions, we classified these shifts based on type: day, evening, night; and length: 12-hour, 8-hour, other. We validated our method through manual chart review of computationally-identified 12-hour shifts achieving 92.0% accuracy. Preliminary results suggest unsupervised clustering methods may be a reasonable approach for rapidly identifying clinician shifts.
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