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Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation

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  • Barros, Oscar
  • Weber, Richard
  • Reveco, Carlos

Abstract

Demand forecasting and capacity management are complicated tasks for emergency healthcare services due to the uncertainty, complex relationships, and high public exposure involved. Published research does not show integrated solutions to these tasks. Thus, the objective of this paper is to present results from three hospitals that show the feasibility of routinely applying integrated forecasting and capacity management with advanced operations research tools.

Suggested Citation

  • Barros, Oscar & Weber, Richard & Reveco, Carlos, 2021. "Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation," Operations Research Perspectives, Elsevier, vol. 8(C).
    • Handle: RePEc:eee:oprepe:v:8:y:2021:i:c:s2214716021000257
    DOI: 10.1016/j.orp.2021.100208
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Mingzhe Shi & Bahman Rostami-Tabar & Daniel Gartner, 2025. "Looking for the crystal ball in unscheduled care: a systematic literature review of the forecasting process," Health Care Management Science, Springer, vol. 28(3), pages 548-564, September.

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