Development of a Semi-Automated Decision-Making Method for the Resilience of Urban Healthcare Systems in Crisis Situations

This study is dedicated to solving the problem of how urban healthcare systems function in crisis situations. Cases where crisis situations lead either to population migrations or to a rapid increase in demand for medical services are the focus. There are often cases of the overloading of medical staff within institutions or the entire healthcare system in the city itself during new situations for which there are no clearly developed response protocols, such as the COVID-19 epidemic or man-made disasters. These situations can lead to the uneven access of resources for the population. This study develops a semi-automated decision-making method combining Wald world analysis and fuzzy logic. The method optimizes resource allocation and determines the priority of medical care, and, as a result, reduces the burden on the healthcare system by integrating socio-demographic and medical data. The results of experimental verification confirmed the ability of the method to adapt to dynamic changes, increase the accuracy of decision-making, and reduce response time. Importantly, the proposed method allows for a more equitable and efficient distribution of resources in the context of urbanization and population density growth.