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Optimizing Hospital Capacity During Pandemics: A Dual-Component Framework for Strategic Patient Relocation

arXiv:2603.15960v1 Announce Type: new Abstract: 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 tem

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Sadaf Tabatabaee, Hicham El Baz, Mohammed Khalil Ghali, Nagendra N. Nagarur · · 1 min read · 8 views
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arXiv:2603.15960v1 Announce Type: new Abstract: 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.

Executive Summary

This article proposes a dual-component framework to optimize hospital capacity during pandemics through strategic patient relocation. By developing a time series prediction model to forecast patient arrival rates and a simulation model to evaluate the impact of different patient relocation strategies, the framework combines predictive analytics and simulation modeling to provide hospital administrators with a comprehensive decision-support tool. This framework enables proactive planning of resource allocation and patient flow, anticipating demand, simulating relocation strategies, and implementing optimal policies to distribute patients and resources. The proposed framework aims to enhance the resilience of healthcare systems in the face of COVID-19 and future pandemics.

Key Points

  • ▸ Develops a time series prediction model to forecast patient arrival rates
  • ▸ Creates a simulation model to evaluate the impact of different patient relocation strategies
  • ▸ Combines predictive analytics and simulation modeling to provide a comprehensive decision-support tool

Merits

Strength

The proposed framework addresses a critical challenge in healthcare management, namely optimizing hospital capacity during pandemics. By leveraging machine learning and simulation modeling, the framework provides a data-driven approach to decision-making in high-pressure situations.

Demerits

Limitation

The article does not provide a detailed discussion of the data requirements and potential biases associated with the time series prediction model, which could impact the accuracy of forecasted patient volumes.

Expert Commentary

The proposed framework represents a significant contribution to the field of healthcare management, particularly in the context of pandemics. However, the article's limitations, such as the lack of discussion on data requirements and potential biases, highlight the need for further research to refine the framework. Additionally, the article's focus on acute care settings may limit its applicability to other healthcare settings, such as long-term care facilities. Nevertheless, the proposed framework has the potential to make a meaningful impact on healthcare system resilience and patient outcomes during pandemics.

Recommendations

  • ✓ Future research should focus on refining the time series prediction model to account for potential biases and data quality issues.
  • ✓ The framework should be tested in diverse healthcare settings to evaluate its applicability and generalizability.

Sources

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