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An exact approach for relating recovering surgical patient workload to the master surgical schedule

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  • P T Vanberkel

    (1] University of Twente, Enschede, the Netherlands[2] Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, the Netherlands)

  • R J Boucherie

    (University of Twente, Enschede, the Netherlands)

  • E W Hans

    (University of Twente, Enschede, the Netherlands)

  • J L Hurink

    (University of Twente, Enschede, the Netherlands)

  • W A M van Lent

    (1] University of Twente, Enschede, the Netherlands[2] Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, the Netherlands)

  • W H van Harten

    (1] University of Twente, Enschede, the Netherlands[2] Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, the Netherlands)

Abstract

No other department influences the workload of a hospital more than the Department of Surgery and in particular, the activities in the operating room. These activities are governed by the master surgical schedule (MSS), which states which patient types receive surgery on which day. In this paper, we describe an analytical approach to project the workload for downstream departments based on this MSS. Specifically, the ward occupancy distributions, patient admission/discharge distributions and the distributions for ongoing interventions/treatments are computed. Recovering after surgery requires the support of multiple departments, such as nursing, physiotherapy, rehabilitation and long-term care. With our model, managers from these departments can determine their workload by aggregating tasks associated with recovering surgical patients. The model, which supported the development of a new MSS at the Netherlands Cancer Institute–Antoni van Leeuwenhoek Hospital, provides the foundation for a decision support tool to relate downstream hospital departments to the operating room.

Suggested Citation

  • P T Vanberkel & R J Boucherie & E W Hans & J L Hurink & W A M van Lent & W H van Harten, 2011. "An exact approach for relating recovering surgical patient workload to the master surgical schedule," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(10), pages 1851-1860, October.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:10:p:1851-1860
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    Citations

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    Cited by:

    1. Veneklaas, W. & Leeftink, A.G. & van Boekel, P.H.C.M. & Hans, E.W., 2021. "On the design, implementation, and feasibility of hospital admission services: The admission lounge case," Omega, Elsevier, vol. 100(C).
    2. Anjomshoa, Hamideh & Dumitrescu, Irina & Lustig, Irvin & Smith, Olivia J., 2018. "An exact approach for tactical planning and patient selection for elective surgeries," European Journal of Operational Research, Elsevier, vol. 268(2), pages 728-739.
    3. Brittney Benchoff & Candace Arai Yano & Alexandra Newman, 2017. "Kaiser Permanente Oakland Medical Center Optimizes Operating Room Block Schedule for New Hospital," Interfaces, INFORMS, vol. 47(3), pages 214-229, June.
    4. Santos, Daniel & Marques, Inês, 2022. "Designing master surgery schedules with downstream unit integration via stochastic programming," European Journal of Operational Research, Elsevier, vol. 299(3), pages 834-852.
    5. Thomas Schneider, A.J. & Theresia van Essen, J. & Carlier, Mijke & Hans, Erwin W., 2020. "Scheduling surgery groups considering multiple downstream resources," European Journal of Operational Research, Elsevier, vol. 282(2), pages 741-752.
    6. Michael Samudra & Carla Van Riet & Erik Demeulemeester & Brecht Cardoen & Nancy Vansteenkiste & Frank E. Rademakers, 2016. "Scheduling operating rooms: achievements, challenges and pitfalls," Journal of Scheduling, Springer, vol. 19(5), pages 493-525, October.
    7. Fügener, Andreas & Hans, Erwin W. & Kolisch, Rainer & Kortbeek, Nikky & Vanberkel, Peter T., 2014. "Master surgery scheduling with consideration of multiple downstream units," European Journal of Operational Research, Elsevier, vol. 239(1), pages 227-236.
    8. Steffen Heider & Jan Schoenfelder & Thomas Koperna & Jens O. Brunner, 2022. "Balancing control and autonomy in master surgery scheduling: Benefits of ICU quotas for recovery units," Health Care Management Science, Springer, vol. 25(2), pages 311-332, June.
    9. Loïc Deklerck & Babak Akbarzadeh & Broos Maenhout, 2022. "Constructing and evaluating a master surgery schedule using a service-level approach," Operational Research, Springer, vol. 22(4), pages 3663-3711, September.
    10. Xiangyong Li & N. Rafaliya & M. Fazle Baki & Ben A. Chaouch, 2017. "Scheduling elective surgeries: the tradeoff among bed capacity, waiting patients and operating room utilization using goal programming," Health Care Management Science, Springer, vol. 20(1), pages 33-54, March.
    11. van den Broek d’Obrenan, Anne & Ridder, Ad & Roubos, Dennis & Stougie, Leen, 2020. "Minimizing bed occupancy variance by scheduling patients under uncertainty," European Journal of Operational Research, Elsevier, vol. 286(1), pages 336-349.
    12. Hessam Bavafa & Charles M. Leys & Lerzan Örmeci & Sergei Savin, 2019. "Managing Portfolio of Elective Surgical Procedures: A Multidimensional Inverse Newsvendor Problem," Operations Research, INFORMS, vol. 67(6), pages 1543-1563, November.
    13. John Bowers, 2013. "Balancing operating theatre and bed capacity in a cardiothoracic centre," Health Care Management Science, Springer, vol. 16(3), pages 236-244, September.
    14. Julian Schiele & Thomas Koperna & Jens O. Brunner, 2021. "Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 65-88, February.

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