A Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak

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  • SDG 3 · Sağlık ve Kaliteli Yaşam

Özet

The use of emergency departments (EDs) has increased during the COVID-19 outbreak, thereby evidencing the key role of these units in the overall response of healthcare systems to the current pandemic scenario. Nevertheless, several disruptions have emerged in the practical scenario including low throughput, overcrowding, and extended waiting times. Therefore, there is a need to develop strategies for upgrading the response of these units against the current pandemic. Given the above, this paper presents a hybrid fuzzy multicriteria decision-making model (MCDM) to evaluate the performance of EDs and create focused improvement interventions. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique is used to estimate the relative priorities of criteria and sub-criteria considering uncertainty. Then, the intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL) is employed to calculate the interdependence and feedback between criteria and sub-criteria under uncertainty, Finally, the combined compromise solution (CoCoSo) is implemented to rank the EDs and detect their weaknesses to device suitable improvement plans. The aforementioned methodology was validated in three emergency centers in Turkey. The results revealed that the most important criterion in ED performance was ER facilities (14.4%), while Procedures and protocols evidenced the highest positive D + R value (18.239) among the dispatchers and is therefore deemed as the main generator within the performance network.

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https://rdlopen.rawdatalibrary.net/r/munzur/1627Resmi atıflarda kullanılan kalıcı adres
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https://doi.org/10.3390/ijerph20054591Yayıncı resmi DOI adresi
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dc.contributor.authorOrtíz-Barrios, Miguel
dc.contributor.authorJaramillo-Rueda, Natalia
dc.contributor.authorGul, Muhammet
dc.contributor.authorYucesan, Melih
dc.contributor.authorJiménez-Delgado, Genett
dc.contributor.authorAlfaro-Saíz, Juan-José
dc.date.accessioned2026-10-28T14:54:25.013512491Z
dc.date.available2026-10-28T14:54:25.013512491Z
dc.date.issued2023
dc.description.abstractThe use of emergency departments (EDs) has increased during the COVID-19 outbreak, thereby evidencing the key role of these units in the overall response of healthcare systems to the current pandemic scenario. Nevertheless, several disruptions have emerged in the practical scenario including low throughput, overcrowding, and extended waiting times. Therefore, there is a need to develop strategies for upgrading the response of these units against the current pandemic. Given the above, this paper presents a hybrid fuzzy multicriteria decision-making model (MCDM) to evaluate the performance of EDs and create focused improvement interventions. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique is used to estimate the relative priorities of criteria and sub-criteria considering uncertainty. Then, the intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL) is employed to calculate the interdependence and feedback between criteria and sub-criteria under uncertainty, Finally, the combined compromise solution (CoCoSo) is implemented to rank the EDs and detect their weaknesses to device suitable improvement plans. The aforementioned methodology was validated in three emergency centers in Turkey. The results revealed that the most important criterion in ED performance was ER facilities (14.4%), while Procedures and protocols evidenced the highest positive D + R value (18.239) among the dispatchers and is therefore deemed as the main generator within the performance network.
dc.identifier.articlenumber4591
dc.identifier.doi10.3390/ijerph20054591
dc.identifier.issn1661-7827
dc.identifier.issue5
dc.identifier.pmcidPMC10001734
dc.identifier.pmid36901601
dc.identifier.scopus2-s2.0-85149993249
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/munzur/1627
dc.identifier.volume20
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofInternational Journal of Environmental Research and Public Health
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectcombined compromise solution (CoCoSo)
dc.subjectemergency departments (EDs)
dc.subjectintuitionistic fuzzy analytic hierarchy process (IF-AHP)
dc.subjectintuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL)
dc.subjectperformance evaluation
dc.subject.otherCOVID-19
dc.subject.otherDecision Making
dc.subject.otherFuzzy Logic
dc.subject.otherHumans
dc.subject.otherTurkey
dc.subject.otherUncertainty
dc.subject.otheranalytical hierarchy process
dc.subject.otherfuzzy mathematics
dc.subject.otherhealth services
dc.subject.otherperformance assessment
dc.subject.otherArticle
dc.subject.otherclinical protocol
dc.subject.othercoronavirus disease 2019
dc.subject.otheremergency health service
dc.subject.otheremergency ward
dc.subject.otherfeedback system
dc.subject.otherfuzzy system
dc.subject.otherhuman
dc.subject.otherlaboratory test
dc.subject.othermajor clinical study
dc.subject.othermathematical model
dc.subject.othermulticriteria decision analysis
dc.subject.otherpandemic
dc.subject.otherturkey (bird)
dc.titleA Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak
dc.typeArticle
rdl.citation.openalex29
rdl.citation.scopus28
rdl.departmentDepartment of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 081001, Colombia
rdl.departmentSchool of Transportation and Logistics, Istanbul University, Istanbul 34320, Turkey
rdl.departmentDepartment of Emergency Aid and Disaster Management, Munzur University, Tunceli 62000, Turkey
rdl.departmentDepartment of Industrial Engineering, Institución Universitaria de Barranquilla IUB, Barranquilla 080002, Colombia
rdl.departmentResearch Centre on Production Management and Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
rdl.oa.statusgold
rdl.oa.statusgreen
rdl.oa.urlhttps://www.mdpi.com/1660-4601/20/5/4591/pdf?version=1678165000
rdl.scopus.quartileQ2
rdl.sdgSDG03
rdl.source.indexSCOPUS
rdl.source.indexPUBMED