AI Article Synopsis
- The COVID-19 outbreak strained health systems due to a surge in severe cases, necessitating a reallocation of resources among hospitals.
- A mathematical programming model is proposed to optimize resource distribution and patient care, specifically focusing on home healthcare services and resource sharing.
- The approach includes advanced optimization techniques and is validated with real-world data from Northern Italy, demonstrating its effectiveness for decision-making in healthcare during emergencies.
Article Abstract
The outbreak of SARS-CoV-2 and the corresponding surge in patients with severe symptoms of COVID-19 put a strain on health systems, requiring specialized material and human resources, often exceeding the locally available ones. Motivated by a real emergency response system employed in Northern Italy, we propose a mathematical programming approach for rebalancing the health resources among a network of hospitals in a large geographical area. It is meant for tactical planning in facing foreseen peaks of patients requiring specialized treatment. Our model has a clean combinatorial structure. At the same time, it considers the handling of patients by a dedicated home healthcare service, and the efficient exploitation of resource sharing. We introduce mathematical programming heuristic based on decomposition methods and column generation to drive very large-scale neighborhood search. We evaluate its embedding in a multi-objective optimization framework. We experiment on real world data of the COVID-19 in Northern Italy during 2020, whose aggregation and post processing is made openly available to the community. Our approach proves to be effective in tackling realistic instances, thus making it a reliable basis for actual decision support tools.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9671698 | PMC |
| http://dx.doi.org/10.1016/j.ejor.2022.11.024 | DOI Listing |
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