Optimal Resource and Demand Redistribution for Healthcare Systems Under Stress from COVID-19

Abstract

When facing an extreme stressor, such as the COVID-19 pandemic, healthcare systems typically respond reactively by creating surge capacity at facilities that are at or approaching their baseline capacity. However, creating individual capacity at each facility is not necessarily the optimal approach, and redistributing demand and critical resources between facilities can reduce the total required capacity. Data shows that this additional load was unevenly distributed between hospitals during the COVID-19 pandemic, requiring some to create surge capacity while nearby hospitals had unused capacity. Not only is this inefficient, but it also could lead to a decreased quality of care at over-capacity hospitals. In this work, we study the problem of finding optimal demand and resource transfers to minimize the required surge capacity and resource shortage during a period of heightened demand. We develop and analyze a series of linear and mixed-integer programming models that solve variants of the demand and resource redistribution problem. We additionally consider demand uncertainty and use robust optimization to ensure solution feasibility. We also incorporate a range of operational constraints and costs that decision-makers may need to consider when implementing such a scheme. Our models are validated retrospectively using COVID-19 hospitalization data from New Jersey, Texas, and Miami, yielding at least an 85% reduction in required surge capacity relative to the observed outcome of each case. Results show that such solutions are operationally feasible and sufficiently robust against demand uncertainty. In summary, this work provides decision-makers in healthcare systems with a practical and flexible tool to reduce the surge capacity necessary to properly care for patients in cases when some facilities are over capacity.

Mitigating the COVID‐19 pandemic through data‐driven resource sharing

“…This paper formulates a new mathematical optimization model to inform data-driven optimal allocation and relocation of scarce resources (in particular, mechanical ventilators). To the best of our knowledge, our paper extends the literature by modeling the following practical features that were not considered in earlier studies (e.g., Bertsimas et al, 2020;Mehrotra et al, 2020;Parker et al, 2020).…”

“…Queiroz et al (2020) provided a comprehensive review of various approaches for optimizing the allocation of critical resources during the COVID-19 pandemic. In this regard, the recent studies include Ahn et al (2021), Bertsimas et al (2020), Lacasa et al (2020), Mehrotra et al (2020), and Parker et al (2020), among others.…”

Deploying ADVISER: Impact and Lessons from Using Artificial Intelligence for Child Vaccination Uptake in Nigeria

“…Combinatorial resource allocation for optimizing health outcomes has been applied to several problems, such as streamlining patient admissions in hospitals (Hulshof et al 2013), managing patients among hospitals in case demand surge (e.g., in case of a pandemic) (Parker et al 2020), delivering medical and social services to patients in their homes (Aiane, El-Amraoui, and Mesghouni 2015), and data-driven probabilistic frameworks for clinical decisionmaking (Tsoukalas, Albertson, and Tagkopoulos 2015).…”

COVID-19: Optimal Allocation of Ventilator Supply under Uncertainty and Risk

“…Billingham et al (2020) present a network optimization model to tackle the problem of scarce ventilator distribution. Parker et al (2020) develop mixed-integer programming and robust optimization models to redistribute patients instead of resources, such as ventilators among different hospitals under demand uncertainty. Govindan et al (2020) develop a practical decision support system hinge on the knowledge of the physicians and the fuzzy interference system (FIS) to help manage the demands of essential hospital services in a healthcare supply chain, to break down the pandemic propagation chain, and reduce the stress among the health care workers.…”