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Flexible bed allocations for hospital wards

Author

Listed:
  • René Bekker

    (VU University Amsterdam)

  • Ger Koole

    (VU University Amsterdam)

  • Dennis Roubos

    (HOTflo Company)

Abstract

Flexibility in the usage of clinical beds is considered to be a key element to efficiently organize critical capacity. However, full flexibility can have some major drawbacks as large systems are more difficult to manage, lack effective care delivery due to absence of focus and require multi-skilled medical teams. In this paper, we identify practical guidelines on how beds should be allocated to provide both flexibility and utilize specialization. Specifically, small scale systems can often benefit from full flexibility. Threshold type of control is then effective to prioritize patient types and to cope with patients having diverse lengths of stay. For large scale systems, we assert that a little flexibility is generally sufficient to take advantage of most of the economies of scale. Bed reservation (earmarking) or, equivalently, organizing a shared ward of overflow, then performs well. The theoretical models and guidelines are illustrated with numerical examples. Moreover, we address a key question stemming from practice: how to distribute a fixed number of hospital beds over the different units?

Suggested Citation

  • René Bekker & Ger Koole & Dennis Roubos, 2017. "Flexible bed allocations for hospital wards," Health Care Management Science, Springer, vol. 20(4), pages 453-466, December.
  • Handle: RePEc:kap:hcarem:v:20:y:2017:i:4:d:10.1007_s10729-016-9364-4
    DOI: 10.1007/s10729-016-9364-4
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    References listed on IDEAS

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    Cited by:

    1. Wu, Xiaodan & Li, Juan & Chu, Chao-Hsien, 2019. "Modeling multi-stage healthcare systems with service interactions under blocking for bed allocation," European Journal of Operational Research, Elsevier, vol. 278(3), pages 927-941.
    2. 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.
    3. Navid Izady & Israa Mohamed, 2021. "A Clustered Overflow Configuration of Inpatient Beds in Hospitals," Manufacturing & Service Operations Management, INFORMS, vol. 23(1), pages 139-154, 1-2.
    4. Aili (Alice) Zou & Douglas G. Down, 2018. "Asymptotically Maximal Throughput in Tandem Systems with Flexible and Dedicated Servers," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(05), pages 1-15, October.
    5. Jian-Jun Wang & Zongli Dai & Ai-Chih Chang & Jim Junmin Shi, 2022. "Surgical scheduling by Fuzzy model considering inpatient beds shortage under uncertain surgery durations," Annals of Operations Research, Springer, vol. 315(1), pages 463-505, August.
    6. Silviya Valeva & Guodong Pang & Andrew J. Schaefer & Gilles Clermont, 2023. "Acuity-Based Allocation of ICU-Downstream Beds with Flexible Staffing," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 403-422, March.

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