A holistic model based on Bayesian BWM and VIKOR for hospital disaster preparedness

Hospitals are places where people want to apply safely in the face of disasters that hinder daily functioning and endanger both their health and social life. All preparations should be made by considering the worst conditions that may occur, and the service to be provided should continue without interruption. In this study, the preparedness levels of six hospitals in times of disasters were examined. Firstly, six main 34 sub-criteria were determined, which are required for hospitals to continue their services without any problems. Weights of these criteria were calculated by five decision-makers who are experts in their fields. The extent to which six hospitals selected from different regions of the country met these specified criteria was learned through interviews with their managers. All these data obtained were evaluated with a decision model developed using Bayesian BWM, VIKOR and TOPSIS MCDM methods. The Bayesian BWM calculated weights of the criteria and sub-criteria evaluated by the experts with a pairwise comparison manner. According to these results, "Personnel" has been determined as the most important criterion. It is followed by "Equipment" with a slight difference. This result indicates that personnel and equipment cannot be considered separately from each other in emergencies. Then, the weight values calculated with Bayesian BWM were combined with the data received from the hospitals, and input was provided for VIKOR. According to the results obtained with VIKOR, Hospital-2 is the most prepared hospital against disasters. The VIKOR Q value of this hospital was obtained as 0,000. This hospital is followed by Hospital-4 (Q = 0.5661) and Hospital-5 (Q = 0.7464). Other hospitals are Hospital-6, Hospital-3, and Hospital-1, respectively. When TOPSIS evaluates the solidity of the results, Hospital-2 again stands out as the readiest hospital. The use of Bayesian BWM ensured that the views of the expert group were combined without the loss of information, and the weights of the criteria and sub-criteria with a probabilistic perspective were determined with less pairwise comparison. The interpretation of hierarchy between each criterion with the "Credal Ranking", which is the contribution of the Bayesian BWM to the literature, was made more precisely.