Optimal hospital capacity management during demand surges.

Area of Science:

• Operations Research
• Healthcare Management
• Public Health

Background:

• Effective hospital capacity management is crucial for patient care, operational efficiency, and healthcare system resilience, especially during demand surges like the COVID-19 pandemic.
• Devising optimal capacity strategies is challenging due to fluctuating demand, competing objectives, and practical constraints.

Purpose of the Study:

• To present a data-driven framework for optimizing hospital capacity management decisions during surge events.
• To optimize the allocation of dedicated capacity for surge patients and the transfer of patients between emergency departments (EDs) to balance demand.

Main Methods:

• Formulation of optimization models as robust mixed-integer linear programs to handle demand uncertainty.
• Incorporation of practical constraints such as setup times/costs for surge capacity and ED patient transfer restrictions.
• Retrospective evaluation of the methodology in a hospital system during the COVID-19 pandemic.

Main Results:

• Optimal allocation of beds and strategic patient transfers (approx. 1 transfer/2 days) could reduce surge capacity needs by nearly 90% during a 63-day peak period.
• Demonstrated the potential impact of data-driven capacity management recommendations on hospital system performance.
• Identified that transferring only 32 patients could significantly decrease the need for surge beds.

Conclusions:

• The study introduces a practical, data-driven tool to transform hospital capacity management decision-making during surge events.
• Proactive planning and optimized resource allocation can significantly improve healthcare system outcomes and resilience.
• Highlights the effectiveness of integrating bed allocation and patient transfer strategies for efficient surge management.