Literature DB >> 30625502

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

Adam C Dziorny1, Evan W Orenstein2, Robert B Lindell1, Nicole A Hames2, Nicole Washington3, Bimal Desai4.   

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. Georg Thieme Verlag KG Stuttgart · New York.

Entities:  

Mesh:

Year:  2019        PMID: 30625502     DOI: 10.1055/s-0038-1676819

Source DB:  PubMed          Journal:  Appl Clin Inform        ISSN: 1869-0327            Impact factor:   2.342


  6 in total

1.  Using electronic health record audit logs to study clinical activity: a systematic review of aims, measures, and methods.

Authors:  Adam Rule; Michael F Chiang; Michelle R Hribar
Journal:  J Am Med Inform Assoc       Date:  2020-03-01       Impact factor: 4.497

2.  Attributing Patients to Pediatric Residents Using Electronic Health Record Features Augmented with Audit Logs.

Authors:  Mark V Mai; Evan W Orenstein; John D Manning; Anthony A Luberti; Adam C Dziorny
Journal:  Appl Clin Inform       Date:  2020-06-24       Impact factor: 2.342

3.  Temporal Associations Between EHR-Derived Workload, Burnout, and Errors: a Prospective Cohort Study.

Authors:  Sunny S Lou; Daphne Lew; Derek R Harford; Chenyang Lu; Bradley A Evanoff; Jennifer G Duncan; Thomas Kannampallil
Journal:  J Gen Intern Med       Date:  2022-06-16       Impact factor: 6.473

4.  Electronic Health Record Use among Ophthalmology Residents while on Call.

Authors:  Christopher P Long; Ming Tai-Seale; Robert El-Kareh; Jeffrey E Lee; Sally L Baxter
Journal:  J Acad Ophthalmol       Date:  2020-07

Review 5.  Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review.

Authors:  Amanda J Moy; Jessica M Schwartz; RuiJun Chen; Shirin Sadri; Eugene Lucas; Kenrick D Cato; Sarah Collins Rossetti
Journal:  J Am Med Inform Assoc       Date:  2021-04-23       Impact factor: 7.942

6.  Pediatric trainees systematically under-report duty hour violations compared to electronic health record defined shifts.

Authors:  Adam C Dziorny; Evan W Orenstein; Robert B Lindell; Nicole A Hames; Nicole Washington; Bimal Desai
Journal:  PLoS One       Date:  2019-12-12       Impact factor: 3.240

  6 in total

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