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Optimization of hospital ward resources with patient relocation using Markov chain modeling

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  • Andersen, Anders Reenberg
  • Nielsen, Bo Friis
  • Reinhardt, Line Blander

Abstract

Overcrowding of hospital wards is a well-known and often revisited problem in the literature, yet it appears in many different variations. In this study, we present a mathematical model to solve the problem of ensuring sufficient beds to hospital wards by re-distributing beds that are already available to the hospital. Patient flow is modeled using a homogeneous continuous-time Markov chain and optimization is conducted using a local search heuristic. Our model accounts for patient relocation, which has not been done analytically in literature with similar scope. The study objective is to ensure that patient occupancy is reflected by our Markov chain model, and that a local optimum can be derived within a reasonable runtime.

Suggested Citation

  • Andersen, Anders Reenberg & Nielsen, Bo Friis & Reinhardt, Line Blander, 2017. "Optimization of hospital ward resources with patient relocation using Markov chain modeling," European Journal of Operational Research, Elsevier, vol. 260(3), pages 1152-1163.
    • Handle: RePEc:eee:ejores:v:260:y:2017:i:3:p:1152-1163
    DOI: 10.1016/j.ejor.2017.01.026
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    References listed on IDEAS

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    1. J K Cochran & K Roche, 2008. "A queuing-based decision support methodology to estimate hospital inpatient bed demand," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(11), pages 1471-1482, November.
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    Cited by:

    1. Jaime González & Juan-Carlos Ferrer & Alejandro Cataldo & Luis Rojas, 2019. "A proactive transfer policy for critical patient flow management," Health Care Management Science, Springer, vol. 22(2), pages 287-303, June.
    2. 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.
    3. Philippe Cohard, 2018. "Modeling and Markov chains," Post-Print hal-02091773, HAL.
    4. Samuel Davis & Nasser Fard, 2020. "Theoretical bounds and approximation of the probability mass function of future hospital bed demand," Health Care Management Science, Springer, vol. 23(1), pages 20-33, March.
    5. Barbato, Michele & Ceselli, Alberto & Premoli, Marco, 2023. "On the impact of resource relocation in facing health emergencies," European Journal of Operational Research, Elsevier, vol. 308(1), pages 422-435.
    6. Reza Maleki & Mohammadreza Taghizadeh-Yazdi & Rohollah Ghasemi & Samar Rivandi, 2024. "A Hybrid Mathematical-Simulation Approach to Hospital Beds Capacity Optimization for COVID-19 Pandemic Conditions," SN Operations Research Forum, Springer, vol. 5(4), pages 1-33, December.
    7. Topuz, Kazim & Urban, Timothy L. & Yildirim, Mehmet B., 2024. "A Markovian score model for evaluating provider performance for continuity of care—An explainable analytics approach," European Journal of Operational Research, Elsevier, vol. 317(2), pages 341-351.
    8. Aswani, Anil & Kaminsky, Philip & Mintz, Yonatan & Flowers, Elena & Fukuoka, Yoshimi, 2019. "Behavioral modeling in weight loss interventions," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1058-1072.
    9. Maria Hajłasz & Bożena Mielczarek, 2020. "Simulation modelling for predicting hospital admissions and bed utilisation," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 30(3), pages 5-27.

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