Using simulation to investigate impact of different approaches to coordination on a healthcare system’s resilience to disasters - Tippong, Danuphon (2023)
Many disasters that have happened in the last decades have caused a shortage of healthcare resources and change in healthcare activities. Coordination of healthcare facilities is one of the emergency medical response strategies to ensure the continued provision of medical services during disasters. The importance of coordination in healthcare systems during disasters is well recognised in the literature, but to the best of our knowledge there has been no review of the published research in this area. In this thesis, a focused literature review of models for the coordination in the healthcare system is provided. Additionally, measures of coordination effectiveness that denote resilience are discussed. In the field of medical management, there are two types of coordination including integrative care and collaborative care. Both types of coordination aim to improve the emergency medical response by ensuring the continuity of medical services and improving healthcare capability during disasters. Integrative care mainly investigates the resource allocation within a common governance, whereas collaborative care is mainly focused on the sharing of healthcare resources across governances. Thus, integrative care is mainly implemented within a healthcare provider setting, while collaborative care is mainly implemented between the settings. However, resilience is usually perceived at community level rather than at an individual institution when responding to disasters. Improving resilience during disasters requires the capability of different healthcare providers, which can be achieved by collaborative care, rather than integrative care. In addition, the literature has commonly addressed collaborative care using optimisation approach, not simulation approach. In this regard, this study presents simulation models for resilience of the healthcare network during disasters. In collaboration with the health authorities and medical staff in Thailand who experienced a number of disasters we investigated real-world activities that took place in emergency medical responses. We developed novel discrete event simulation models of collaboration in an emergency medical response in a healthcare network during disasters with the aim to improve the resilience of the healthcare network. Three strategies for collaboration in the healthcare network were defined including non-collaborative care, semi-collaborative care, and a new proposed collaborative care. Non-collaborative care strategy was in place in response to Tsunami in Phuket in 2004, while semi-collaborative care strategy is the current strategy which was implemented during the boat capsizing in Phuket in 2018. We propose a new collaborative care strategy which is defined by considering the disadvantages of the current semi-collaborative care strategy. It addresses a new collaboration in the network that enables information sharing and the classification of healthcare providers. The strategies differ with respect to the first treatment provision of patients, sharing of resources, and patient transportation The simulation models were validated and verified by using the boat capsizing real-world event. The model validations were in line with the available system outputs including the number of patients in different categories, resource allocation, patient allocation and average patient waiting times at healthcare providers. A generic metric of resilience proposed in the literature was adapted to be used in healthcare context. Our analysis yielded managerial insights into the emergency planning as follows. In all defined scenarios, the new collaborative care strategy had a considerable impact on improving the resilience and enabled faster return to the pre-disaster state of healthcare network than other strategies. The semi-collaborative care strategy frequently provided the worst resilience in almost all the defined scenarios. However, it provided better resilience than the non-collaborative care strategy when the number of affected patients was relatively small. Even though simulation enabled investigation of the impact of different strategies for collaboration in the network on the resilience, the patient allocation might not be optimal. We developed a mixed integer programming model to address the allocation of patients in collaborative care in which ambulances transport multiple patients to healthcare providers in one trip. The developed model will provide further insights into the collaborative care in disasters management.