Enhancing healthcare system resilience: Optimization of strategic investments portfolio

Healthcare systems are facing growing challenges such as the shortage and unequal distribution of healthcare professionals, a rise in demand due to an aging population and a rise in chronic diseases but also daily disruptions. At a facility level, various mitigation strategies against everyday challenges can be implemented. Each mitigation strategy comes with its own costs and outcomes. These investments will strengthen the day-to-day resilience of healthcare facilities, thereby reducing the risk of service disruptions, but not all mitigation strategies are possible due to limited budget. Our research focuses on developing a model to identify an optimal investment strategy aimed at enhancing the resilience of healthcare systems. The aim of this bi-objective model is to simultaneously minimize the distances traveled by patients and the number of treatments deferred due to system disruptions. The probability of a patient accessing a facility on his preferred list is strongly impacted by the investment portfolio. To meet this challenge, we propose a new approach for evaluating the probability that a patient will choose a facility based on a Markov chain model. Moreover, the problem uses level-based fortification and probabilistic facility failures. The addressed problem is solved using a dedicated Non-dominated Sorting Genetic Algorithm (NSGA-II). The effectiveness and the robustness of the proposed approach are analyzed through a large experimental and a sensitivity analysis campaign.