Download PDF
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
1   Division 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
2   Division of Hospital Medicine, Department of Pediatrics, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, Georgia, United States
,
Robert B. Lindell
1   Division 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
2   Division of Hospital Medicine, Department of Pediatrics, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, Georgia, United States
,
Nicole Washington
3   Pediatrics Residency Program, Department of Pediatrics, The Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
,
Bimal Desai
4   Division 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
Further Information

Publication History

06 August 2018

19 November 2018

Publication Date:
09 January 2019 (online)

Also available at
   Permissions and Reprints

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

Protection of Human and Animal Subjects

This study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects, and was reviewed and approved by the Children's Hospital of Philadelphia Institutional Review Board.


Supplementary Material

 

  • References

  • 1 Shanafelt TD, Dyrbye LN, West CP. Addressing physician burnout: the way forward. JAMA 2017; 317 (09) 901-902
    CrossrefPubMedGoogle Scholar
  • 2 West CP, Dyrbye LN, Shanafelt TD. Physician burnout: contributors, consequences and solutions. J Intern Med 2018; 283 (06) 516-529
    CrossrefPubMedGoogle Scholar
  • 3 Roberts DL, Shanafelt TD, Dyrbye LN, West CP. A national comparison of burnout and work-life balance among internal medicine hospitalists and outpatient general internists. J Hosp Med 2014; 9 (03) 176-181
    CrossrefPubMedGoogle Scholar
  • 4 Carayon P, Wetterneck TB, Alyousef B. , et al. Impact of electronic health record technology on the work and workflow of physicians in the intensive care unit. Int J Med Inform 2015; 84 (08) 578-594
    CrossrefPubMedGoogle Scholar
  • 5 Babbott S, Manwell LB, Brown R. , et al. Electronic medical records and physician stress in primary care: results from the MEMO Study. J Am Med Inform Assoc 2014; 21 (e1): e100-e106
    CrossrefPubMedGoogle Scholar
  • 6 Coleman M, Dexter D, Nankivil N. Factors affecting physician satisfaction and Wisconsin Medical Society strategies to drive change. WMJ 2015; 114 (04) 135-142
    PubMedGoogle Scholar
  • 7 Gregory ME, Russo E, Singh H. Electronic health record alert-related workload as a predictor of burnout in primary care providers. Appl Clin Inform 2017; 8 (03) 686-697
    Article in Thieme ConnectPubMedGoogle Scholar
  • 8 Shanafelt TD, Dyrbye LN, Sinsky C. , et al. Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction. Mayo Clin Proc 2016; 91 (07) 836-848
    CrossrefPubMedGoogle Scholar
  • 9 Desai SV, Asch DA, Bellini LM. , et al; iCOMPARE Research Group. Education outcomes in a duty-hour flexibility trial in internal medicine. N Engl J Med 2018; 378 (16) 1494-1508
    CrossrefPubMedGoogle Scholar
  • 10 Destino LA, Valentine M, Sheikhi FH, Starmer AJ, Landrigan CP, Sanders L. Inpatient hospital factors and resident time with patients and families. Pediatrics 2017; 139 (05) e20163011
    CrossrefPubMedGoogle Scholar
  • 11 Fletcher KE, Visotcky AM, Slagle JM, Tarima S, Weinger MB, Schapira MM. The composition of intern work while on call. J Gen Intern Med 2012; 27 (11) 1432-1437
    CrossrefPubMedGoogle Scholar
  • 12 Neri PM, Redden L, Poole S. , et al. Emergency medicine resident physicians' perceptions of electronic documentation and workflow: a mixed methods study. Appl Clin Inform 2015; 6 (01) 27-41
    Article in Thieme ConnectPubMedGoogle Scholar
  • 13 Gordon JA, Alexander EK, Lockley SW. , et al; Harvard Work Hours, Health, and Safety Group (Boston, Massachusetts). Does simulator-based clinical performance correlate with actual hospital behavior? The effect of extended work hours on patient care provided by medical interns. Acad Med 2010; 85 (10) 1583-1588
    CrossrefPubMedGoogle Scholar
  • 14 Landrigan CP, Rothschild JM, Cronin JW. , et al. Effect of reducing interns' work hours on serious medical errors in intensive care units. N Engl J Med 2004; 351 (18) 1838-1848
    CrossrefPubMedGoogle Scholar
  • 15 Lopetegui M, Yen PY, Lai A, Jeffries J, Embi P, Payne P. Time motion studies in healthcare: what are we talking about?. J Biomed Inform 2014; 49: 292-299
    CrossrefPubMedGoogle Scholar
  • 16 Sinsky C, Colligan L, Li L. , et al. Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties. Ann Intern Med 2016; 165 (11) 753-760
    CrossrefPubMedGoogle Scholar
  • 17 Zheng K, Guo MH, Hanauer DA. Using the time and motion method to study clinical work processes and workflow: methodological inconsistencies and a call for standardized research. J Am Med Inform Assoc 2011; 18 (05) 704-710
    CrossrefPubMedGoogle Scholar
  • 18 Chen L, Guo U, Illipparambil LC. , et al. Racing against the clock: internal medicine residents' time spent on electronic health records. J Grad Med Educ 2016; 8 (01) 39-44
    CrossrefPubMedGoogle Scholar
  • 19 Wu DTY, Smart N, Ciemins EL, Lanham HJ, Lindberg C, Zheng K. Using EHR audit trail logs to analyze clinical workflow: A case study from community-based ambulatory clinics. AMIA Annu Symp Proc 2018; 2017: 1820-1827
    PubMedGoogle Scholar
  • 20 Arndt BG, Beasley JW, Watkinson MD. , et al. Tethered to the EHR: primary care physician workload assessment using EHR event log data and time-motion observations. Ann Fam Med 2017; 15 (05) 419-426
    CrossrefPubMedGoogle Scholar
  • 21 Hribar MR, Read-Brown S, Goldstein IH. , et al. Secondary use of electronic health record data for clinical workflow analysis. J Am Med Inform Assoc 2018; 25 (01) 40-46
    CrossrefPubMedGoogle Scholar
  • 22 Hirsch AG, Jones JB, Lerch VR. , et al. The electronic health record audit file: the patient is waiting. J Am Med Inform Assoc 2017; 24 (e1): e28-e34
    CrossrefPubMedGoogle Scholar
  • 23 Goldstein IH, Hribar MR, Read-Brown S, Chiang MF. Association of the presence of trainees with outpatient appointment times in an ophthalmology clinic. JAMA Ophthalmol 2018; 136 (01) 20-26
    CrossrefPubMedGoogle Scholar
  • 24 Gilleland M, Komis K, Chawla S, Fernandez S, Fishman M, Adams M. Resident duty hours in the outpatient electronic health record era: inaccuracies and implications. J Grad Med Educ 2014; 6 (01) 151-154
    CrossrefPubMedGoogle Scholar
  • 25 Vawdrey DK, Wilcox LG, Collins S. , et al. Awareness of the care team in electronic health records. Appl Clin Inform 2011; 2 (04) 395-405
    Article in Thieme ConnectPubMedGoogle Scholar
  • 26 Hom J, Richman I, Chen JH, Singh B, Crump C, Chi J. Fulfilling outpatient medicine responsibilities during internal medicine residency: a quantitative study of housestaff participation with between visit tasks. BMC Med Educ 2016; 16: 139
    CrossrefPubMedGoogle Scholar
  • 27 Shine D, Pearlman E, Watkins B. Measuring resident hours by tracking interactions with the computerized record. Am J Med 2010; 123 (03) 286-290
    CrossrefPubMedGoogle Scholar
  • 28 Byrne JM, Loo LK, Giang DW. Duty hour reporting: conflicting values in professionalism. J Grad Med Educ 2015; 7 (03) 395-400
    CrossrefPubMedGoogle Scholar
  • 29 Carpenter RO, Spooner J, Arbogast PG, Tarpley JL, Griffin MR, Lomis KD. Work hours restrictions as an ethical dilemma for residents: a descriptive survey of violation types and frequency. Curr Surg 2006; 63 (06) 448-455
    CrossrefPubMedGoogle Scholar
  • 30 Saunders DL, Kehoe KC, Rinehart VH, Berg BW. Self-reporting of internal medicine house staff work hours. Hawaii Med J 2005; 64 (01) 14-16
    PubMedGoogle Scholar
  • 31 R. Core Team. R: A Language and Environment for Statistical Computing [program]. Vienna, Austria: R Foundation for Statistical Computing; 2017
    Google Scholar
  • 32 Health Insurance Portability and Accountability Act. Security Standards for the Protection of Electronic Protected Health Information. 45 C.F.R. 2003
    PubMed
  • 33 Gonzalo JD, Yang JJ, Ngo L, Clark A, Reynolds EE, Herzig SJ. Accuracy of residents' retrospective perceptions of 16-hour call admitting shift compliance and characteristics. J Grad Med Educ 2013; 5 (04) 630-633
    CrossrefPubMedGoogle Scholar
  • 34 Bennett CL, Finch A, Vuong K, McDonald D, Rennie S. Surgical resident duty hours. N Engl J Med 2016; 374 (24) 2399-2401
    CrossrefPubMedGoogle Scholar
  • 35 Shanafelt TD, Hasan O, Dyrbye LN. , et al. Changes in burnout and satisfaction with work-life balance in physicians and the general US working population between 2011 and 2014. Mayo Clin Proc 2015; 90 (12) 1600-1613
    CrossrefPubMedGoogle Scholar