Automatic Detection of Front-Line Clinician Hospital Shifts: A Novel Use of Electronic Health Record Timestamp Data

Adam C. Dziorny; Evan W. Orenstein; Robert B. Lindell; Nicole A. Hames; Nicole Washington; Bimal Desai

doi:10.1055/s-0038-1676819

Applied Clinical Informatics, Table of Contents

Appl Clin Inform 2019; 10(01): 028-037
DOI: 10.1055/s-0038-1676819

Research Article

Georg Thieme Verlag KG Stuttgart · New York

Automatic Detection of Front-Line Clinician Hospital Shifts: A Novel Use of Electronic Health Record Timestamp Data

Adam C. Dziorny

¹Division of Critical Care Medicine, Department of Anesthesia and Critical Care, The Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States

,

Evan W. Orenstein

²Division of Hospital Medicine, Department of Pediatrics, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, Georgia, United States

,

Robert B. Lindell

¹Division of Critical Care Medicine, Department of Anesthesia and Critical Care, The Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States

,

Nicole A. Hames

²Division of Hospital Medicine, Department of Pediatrics, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, Georgia, United States

,

Nicole Washington

³Pediatrics Residency Program, Department of Pediatrics, The Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States

,

Bimal Desai

⁴Division of General Pediatrics, Department of Pediatrics, The Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States

› Author Affiliations

Abstract

Objective Excess physician work hours contribute to burnout and medical errors. Self-report of work hours is burdensome and often inaccurate. We aimed to validate a method that automatically determines provider shift duration based on electronic health record (EHR) timestamps across multiple inpatient settings within a single institution.

Methods We developed an algorithm to calculate shift start and end times for inpatient providers based on EHR timestamps. We validated the algorithm based on overlap between calculated shifts and scheduled shifts. We then demonstrated a use case by calculating shifts for pediatric residents on inpatient rotations from July 1, 2015 through June 30, 2016, comparing hours worked and number of shifts by rotation and role.

Results We collected 6.3 × 10⁷ EHR timestamps for 144 residents on 771 inpatient rotations, yielding 14,678 EHR-calculated shifts. Validation on a subset of shifts demonstrated 100% shift match and 87.9 ± 0.3% overlap (mean ± standard error [SE]) with scheduled shifts. Senior residents functioning as front-line clinicians worked more hours per 4-week block (mean ± SE: 273.5 ± 1.7) than senior residents in supervisory roles (253 ± 2.3) and junior residents (241 ± 2.5). Junior residents worked more shifts per block (21 ± 0.1) than senior residents (18 ± 0.1).

Conclusion Automatic calculation of inpatient provider work hours is feasible using EHR timestamps. An algorithm to assess provider work hours demonstrated criterion validity via comparison with scheduled shifts. Differences between junior and senior residents in calculated mean hours worked and number of shifts per 4-week block were also consistent with differences in scheduled shifts and duty-hour restrictions.

Keywords

electronic health records - physicians - burnout - algorithms

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References

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Supplementary Material

Supplementary Material