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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 1 of 1 Research Studies DisplayedMoy AJ, Cato KD, Withall J
Using time series clustering to segment and infer emergency department nursing shifts from electronic health record log files.
Clinical shifts are an essential unit of work recognized in clinical settings and may function as a primary unit of analysis in the study of documentation burden. The purpose of this proof- of-concept study was to investigate the feasibility of a new approach utilizing time series clustering to segment and infer clinician shifts from electronic health record (HER) log files. The researchers recorded 33,535,585 events between April-June 2021 and computationally identified 43,911 potential shifts among 2,285 emergency department nurses. On average, shifts were 10.6±3.1 hours in duration. Researchers classified the shifts based on type: day, evening, night; and length: 12-hour, 8-hour, other. The preliminary results of the study found that unsupervised clustering methods may be a feasible approach for quickly identifying clinician shifts.
AHRQ-funded; HS028454.
Citation: Moy AJ, Cato KD, Withall J .
Using time series clustering to segment and infer emergency department nursing shifts from electronic health record log files.
AMIA Annu Symp Proc 2023 Apr 29; 2022:805-14..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Emergency Department, Workforce