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Scheduling Elective Surgeries with Emergency Patients at Shared Operating Rooms

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  • Kyung Sung Jung
  • Michael Pinedo
  • Chelliah Sriskandarajah
  • Vikram Tiwari

Abstract

Operating rooms (ORs) are the greatest source of revenues for hospitals and also their largest cost centers. When scheduling surgeries, hospitals face a trade‐off between the need to conduct planned elective surgeries and the need to be responsive to emergency cases. However, scheduling ORs, especially at Level‐1 trauma hospitals, is challenging due to significant uncertainties in the arrivals of patients requiring emergent surgery. The issue of allocating limited capacity to emergent surgery cases while scheduling elective patients has major policy implications. We develop a model for allocating the OR capacity to elective patients in such a way that the emergency patients who arrive randomly can be accommodated without incurring excessive delays. The objective is to develop a framework for aggregate weekly schedules and generate detailed daily schedules that minimize the total cost of the ORs’ expected operating time, idle time, and overtime. We present optimization procedures for generating effective schedules and rescheduling procedures that adjust the schedules of elective patients affected by emergency arrivals. Initially, the procedures assume deterministic surgery times for elective patients; the procedures are then extended to include stochastic surgery times. We show that for a given arrival rate of emergency patients, the total expected cost is convex in the weekly load of elective surgeries being scheduled. Numerical experiments are devised to obtain total expected cost curves for various emergency arrival rates. Using these curves, the optimal capacity allocation of ORs to elective patients can be determined as a function of the emergency arrival rate.

Suggested Citation

  • Kyung Sung Jung & Michael Pinedo & Chelliah Sriskandarajah & Vikram Tiwari, 2019. "Scheduling Elective Surgeries with Emergency Patients at Shared Operating Rooms," Production and Operations Management, Production and Operations Management Society, vol. 28(6), pages 1407-1430, June.
    • Handle: RePEc:bla:popmgt:v:28:y:2019:i:6:p:1407-1430
    DOI: 10.1111/poms.12993
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    Citations

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    Cited by:

    1. Zhang, Jian & Dridi, Mahjoub & El Moudni, Abdellah, 2020. "Column-generation-based heuristic approaches to stochastic surgery scheduling with downstream capacity constraints," International Journal of Production Economics, Elsevier, vol. 229(C).
    2. Ferreira, Cristiane & Figueira, Gonçalo & Amorim, Pedro, 2022. "Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning," Omega, Elsevier, vol. 111(C).
    3. Jian-Jun Wang & Zongli Dai & Wenxuan Zhang & Jim Junmin Shi, 2023. "Operating room scheduling for non-operating room anesthesia with emergency uncertainty," Annals of Operations Research, Springer, vol. 321(1), pages 565-588, February.
    4. Shing Chih Tsai & Wu Hung Lin & Chia Cheng Wu & Shao Jen Weng & Ching Fen Tang, 2022. "Decision support algorithms for optimizing surgery start times considering the performance variation," Health Care Management Science, Springer, vol. 25(2), pages 208-221, June.
    5. Seokjun Youn & H. Neil Geismar & Michael Pinedo, 2022. "Planning and scheduling in healthcare for better care coordination: Current understanding, trending topics, and future opportunities," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4407-4423, December.
    6. Sean Harris & David Claudio, 2022. "Current Trends in Operating Room Scheduling 2015 to 2020: a Literature Review," SN Operations Research Forum, Springer, vol. 3(1), pages 1-42, March.
    7. Yao Xiao & Reena Yoogalingam, 2021. "Reserved capacity policies for operating room scheduling," Operations Management Research, Springer, vol. 14(1), pages 107-122, June.
    8. Martin Bichler & Soeren Merting, 2021. "Randomized Scheduling Mechanisms: Assigning Course Seats in a Fair and Efficient Way," Production and Operations Management, Production and Operations Management Society, vol. 30(10), pages 3540-3559, October.
    9. Saharnaz Mehrani & Carlos Cardonha & David Bergman, 2022. "Models and Algorithms for the Bin-Packing Problem with Minimum Color Fragmentation," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 1070-1085, March.
    10. Gökalp, E. & Gülpınar, N. & Doan, X.V., 2023. "Dynamic surgery management under uncertainty," European Journal of Operational Research, Elsevier, vol. 309(2), pages 832-844.
    11. Esmaeil Keyvanshokooh & Pooyan Kazemian & Mohammad Fattahi & Mark P. Van Oyen, 2022. "Coordinated and Priority‐Based Surgical Care: An Integrated Distributionally Robust Stochastic Optimization Approach," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1510-1535, April.
    12. Wang, Jian-Jun & Li, Zhong-Ping & Shi, Jim (Junmin) & Chang, Ai-Chih (Jasmine), 2021. "Hospital referral and capacity strategies in the two-tier healthcare systems," Omega, Elsevier, vol. 100(C).
    13. Yu Wang & Yu Zhang & Minglong Zhou & Jiafu Tang, 2023. "Feature‐driven robust surgery scheduling," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1921-1938, June.
    14. Arne Schulz & Malte Fliedner, 2023. "Minimizing the expected waiting time of emergency jobs," Journal of Scheduling, Springer, vol. 26(2), pages 147-167, April.
    15. Shuwan Zhu & Wenjuan Fan & Xueping Li & Shanlin Yang, 2023. "Ambulance dispatching and operating room scheduling considering reusable resources in mass-casualty incidents," Operational Research, Springer, vol. 23(2), pages 1-37, June.

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