Reorganization of a medical service network to manage pandemic waves: A real case study

Abstract

During Covid-19, medical service networks (MSNs) faced new challenges, such as an impressive increase in hospital visits, a shortage of hospital beds and staff, and insufficient information to estimate the number of mild and critical cases. In addition, governments were encountered to implement appropriate quarantine policies. Dealing with these problems became more complex and challenging when a new wave of disease occurred. This study develops a mixed-integer linear programming model for reorganizing an MSN to manage future pandemic waves. The model aims at reallocation medical staff to prevent a shortage of hospital beds. A fuzzy approach is employed to estimate the uncertain number of patients in each period. As a result, direct hospital visits are decreased by 60% on average, and shortages of beds are avoided by adding the fewest beds possible in each period. The model can also optimize several performance ratios, e.g., the ratio of hospitalized patients to the specialized personnel assigned to each hospital, which is decreased by approximately 40% in our case.