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Neonatal Physician Scheduling at the University of Tennessee Medical Center

Author

Listed:
  • Melissa R. Bowers

    (Department of Business Analytics and Statistics, The University of Tennessee, Knoxville, Tennessee 37996)

  • Charles E. Noon

    (Department of Business Analytics and Statistics, The University of Tennessee, Knoxville, Tennessee 37996)

  • Wei Wu

    (Department of Business Analytics and Statistics, The University of Tennessee, Knoxville, Tennessee 37996)

  • J. Kirk Bass

    (Regional Neonatal Associates, Knoxville, Tennessee 37920)

Abstract

A common issue faced by physician groups is how to schedule 24-7 coverage for hospital units such as an emergency department. The first step is to determine the shifts to be covered. The second step, assigning physicians to specific shifts, is complicated because shifts vary with respect to duration, day of week, time of day, and desirability. To ensure workload fairness, physician groups often create “equality” schedules in which they evenly divide shifts, by type, among physicians. This problem can be readily modeled and solved via optimization. This paper presents a novel approach that incorporates individual physician shift-type preference and seeks, for each physician, a schedule that is superior to his or her equality schedule. We formulate and solve the problem as a binary, mixed-integer program designed to maximize relative gains in individual physician preference. We describe the methodology and real-world implementation within a neonatal intensive care group, and present a hybrid version of the model that is capable of simultaneously accommodating physicians who prefer an equality schedule and physicians willing to deviate from the equality schedule in pursuit of a schedule that better fits their shift preferences. Increases in schedule preference for the latter group ranged from 6.3 to 8.5 percent.

Suggested Citation

  • Melissa R. Bowers & Charles E. Noon & Wei Wu & J. Kirk Bass, 2016. "Neonatal Physician Scheduling at the University of Tennessee Medical Center," Interfaces, INFORMS, vol. 46(2), pages 168-182, April.
  • Handle: RePEc:inm:orinte:v:46:y:2016:i:2:p:168-182
    DOI: 10.1287/inte.2015.0839
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    References listed on IDEAS

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    Cited by:

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    3. Xu, Shuling & Hall, Nicholas G., 2021. "Fatigue, personnel scheduling and operations: Review and research opportunities," European Journal of Operational Research, Elsevier, vol. 295(3), pages 807-822.
    4. Renata Mansini & Roberto Zanotti, 2020. "Optimizing the physician scheduling problem in a large hospital ward," Journal of Scheduling, Springer, vol. 23(3), pages 337-361, June.
    5. David Scheinker & Margaret L. Brandeau, 2020. "Implementing Analytics Projects in a Hospital: Successes, Failures, and Opportunities," Interfaces, INFORMS, vol. 50(3), pages 176-189, May.
    6. Yücel Öztürkoğlu, 2020. "A Different Approach to Nurse Scheduling Problem: Lagrangian Relaxation," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(2), pages 237-248, December.
    7. Christopher N. Gross & Jens O. Brunner & Manfred Blobner, 2019. "Hospital physicians can’t get no long-term satisfaction – an indicator for fairness in preference fulfillment on duty schedules," Health Care Management Science, Springer, vol. 22(4), pages 691-708, December.
    8. Shaowen Lan & Wenjuan Fan & Kaining Shao & Shanlin Yang & Panos M. Pardalos, 2022. "A column-generation-based approach for an integrated service planning and physician scheduling problem considering re-consultation," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3446-3476, December.

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