Optimizing Hospital Capacity During Pandemics: A Dual-Component Framework for Strategic Patient Relocation
This work addresses capacity challenges in healthcare systems during pandemics like COVID-19, offering a tool for hospital administrators, but it appears incremental as it combines existing predictive and simulation methods.
The research tackled hospital capacity optimization during pandemics by proposing a dual-component framework for patient relocation, combining time series prediction for patient arrival forecasts and simulation modeling to evaluate relocation strategies, aiming to provide a decision-support tool for administrators.
The COVID-19 pandemic has placed immense strain on hospital systems worldwide, leading to critical capacity challenges. This research proposes a two-part framework to optimize hospital capacity through patient relocation strategies. The first component involves developing a time series prediction model to forecast patient arrival rates. Using historical data on COVID-19 cases and hospitalizations, the model will generate accurate forecasts of future patient volumes. This will enable hospitals to proactively plan resource allocation and patient flow. The second com- ponent is a simulation model that evaluates the impact of different patient relocation strategies. The simulation will account for factors such as bed availability, staff capabilities, transportation logistics, and patient acuity to optimize the placement of patients across networked hospitals. Multiple scenarios will be tested, including inter-hospital trans- fers, use of temporary care facilities, and adaptations to discharge protocols. By combining predictive analytics and simulation modeling, this research aims to provide hospital administrators with a comprehensive decision-support tool. The proposed framework will empower them to anticipate demand, simulate relocation strategies, and imple- ment optimal policies to distribute patients and resources. Ultimately, this work seeks to enhance the resilience of healthcare systems in the face of COVID-19 and future pandemics.