A fuzzy hybrid decision-making framework for increasing the hospital disaster preparedness: The colombian case

Abstract

The recent increase in the number of disasters over the world has once again brought to the agenda the question of preparedness of the hospitals, which are the most necessary units of healthcare pillar to resist these disasters. The COVID-19 epidemic disease, which has affected the whole world, has caused a large number of people to die in some countries simply because of the inadequate and incomplete planning and lack of readiness of hospitals. For this reason, determining the disaster preparedness level of hospitals is an important issue that needs to be studied and it is important in terms of disaster damage reduction. In this study, a fuzzy hybrid decision-making framework is proposed to assess hospital disaster preparedness. The framework covers three important decision-making methods. For the first phase, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) is used to assign relative weights for several disaster preparedness criteria considering uncertainty. Secondly, Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL) is applied to identify interrelations among these criteria and feedback. Finally, via the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, priorities of hospitals regarding disaster readiness are obtained. A case study involving the participation of 10 Colombian tertiary hospitals is carried out to show the applicability of this fuzzy hybrid approach.

Disaster Preparedness in Hospitals

“…EMS saves lives by providing immediate medical attention, stabilizing patients, and transporting them to appropriate healthcare facilities. The efficiency and effectiveness of emergency services are essential to ensure timely intervention and increase survival chances for those in need [ 22 ]. EMS is essential to get patients to trauma centers for additional testing and care.…”

Time Series Cleaning Methods for Hospital Emergency Admissions

“…In the healthcare field, in addition to the emergency department density estimation [18] [19], covid-19 [20], the number of calls to the 112emergency call center [21], the average cost per prescription [22], the need for medical supplies [23], serum set consumption [24], Electrocardiogram (ECG) signal analyzes [25] and hospital disaster preparedness [26] have also been used for estimation purposes. LSTM (Long Short Time Memory) with recent success in deep learning approaches [27] [28] [29] has been used in many fields such as [30] financial [31], energy [32], health [33] [34] [35] [36]. In addition, the LSTM shows high performance in areas such as handwriting recognition [37] [38], translation [39].…”