Evidence map›Paper›PMID 34803219›Full record

ArticleNatural hazards (Dordrecht, Netherlands)2022

A Bayesian BWM and VIKOR-based model for assessing hospital preparedness in the face of disasters.

Halit Serdar Saner, Melih Yucesan, Muhammet Gul

Abstract read
In one paragraph

Article in Natural hazards (Dordrecht, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. A Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak.International journal of environmental research and public health · 2023
    Article
4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Halit Serdar SanerDepartment of Engineering Management, Munzur University, 62000 Tunceli, Turkey.
Melih YucesanDepartment of Emergency Aid and Disaster Management, Munzur University, 62000 Tunceli, Turkey.
Muhammet GulDepartment of Emergency Aid and Disaster Management, Munzur University, 62000 Tunceli, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hospitals are the first point of contact for people in the face of disasters that interfere with the daily functioning of life and endanger health and social life. All preparations should be made considering the worst possible conditions and the provided service should continue without interruption. In this study, a multi-criteria decision-making model was developed to evaluate disaster preparedness of hospitals. This decision model includes Bayesian best-worst method (BBWM), the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and technique for order preference by similarity to ideal solution (TOPSIS) methods. With the proposed decision model, six main criteria and 34 sub-criteria related to disaster preparedness of hospitals were considered. The criteria and sub-criteria evaluated in pairwise comparison manner by the experts were weighted with BBWM. These weight values and the data obtained from the six Turkish hospitals were combined to provide inputs for VIKOR and TOPSIS. In addition, a comparative study and sensitivity analysis were carried out using weight vectors obtained by different tools. BBWM application results show that the "Personnel" criterion was determined as the most important criterion with an importance value of 26%. This criterion is followed by "Equipment" with 25%, "Transportation" with 14%, "Hospital building" and "Communication" with 12%, and "Flexibility" with 11%. Hospital-2 was determined as the most prepared hospital for disasters as a result of VIKOR application. The VIKOR Q value of this hospital was obtained as 0.000. According to the results of the comparative study, Hospital-2 was determined as the most disaster-ready hospital in all six different scenarios. This hospital is followed by Hospital-4 (Q = 0.5661) and Hospital-5 (Q = 0.7464). The remaining rankings were Hospital-6, Hospital-3 and Hospital-1. The solidity of the results was checked with TOPSIS. Based on TOPSIS application results, Hospital-2 was again found the most-ready hospital. The usage of BBWM in this study enabled the expert group's views to be combined without loss of information and to determine the criteria and sub-criteria weights with less pairwise comparisons in a probabilistic perspective. Via the "Credal ranking", which is the contribution of BBWM to the literature, the interpretation of the hierarchy between each criterion has been performed more precisely.

Indexed as

Bayesian best–worst methodCredal rankingHospital disaster preparednessTOPSISVIKOR

Identifiers

PMID34803219
PMCPMC8593641

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.