Medical device selection in private hospitals by integrated fuzzy MCGDM methods: A case study in choosing MRI (Magnetic Resonance Imaging) system

dc.contributor.authorKundu, Pradip
dc.contributor.authorGorcun, Omer Faruk
dc.contributor.authorKüçükönder, Hande
dc.contributor.authorKüçükönder, Hande
dc.date.accessioned2025-10-18T13:22:24Z
dc.date.created2021
dc.date.issued2021
dc.departmentFakülteler, İktisadi ve İdari Bilimler Fakültesi, İşletme Bölümü
dc.description.abstractThis paper investigates medical device selection problem in healthcare organizations. As compared to numerous research works on supplier/equipment selection problems in diverse areas of applications, surprisingly, the number of works is less in case of the healthcare industry. In this paper, as a case study, we consider MRI (Magnetic Resonance Imaging) system selection problem in private hospitals. Because of non-radiation and advanced technology, MRI has emerged as the imaging modality of choice in diagnostic and monitoring treatment. In this study, we identify 16 brands (alternatives) of MRI systems and select 10 selection criteria that are chosen with the consultation of a group of experts in the field of hospital management. Methodological framework as suggested to deal with the device selection problem includes an integrated MCGDM (multi-criteria group decision-making) approach which is a combination of fuzzy PSI (preference selection index) method and fuzzy MARCOS (measurement of alternatives and ranking according to compromise solution) method. To cope with the vagueness in linguistic evaluation done by the decision makers, fuzzy numbers have been used in the representation of linguistic terms. The integrated fuzzy MCGDM method is applied to evaluate and rank the alternatives, and the results are analyzed through a comprehensive sensitivity analysis. Total 100 scenarios are created by changing the weight of each criterion as computed using fuzzy PSI technique to examine the effects of change of the weights of the criteria on the ranking results. It is observed that out of 100 scenarios, alternative 8 (A8) remains the best option for 96 scenarios, and it is the second-best option only for the remaining 4 scenarios. Also, the results of the hybrid fuzzy MCGDM technique have been compared with the results obtained by using other MCGDM methods.
dc.identifier.doi10.1080/01605682.2021.1960910
dc.identifier.endpage2079
dc.identifier.issn0160-5682
dc.identifier.issn1476-9360
dc.identifier.issue9
dc.identifier.orcidKundu, Pradip/0000-0001-8297-8894
dc.identifier.scopus2-s2.0-85113755641
dc.identifier.scopusqualityQ1
dc.identifier.startpage2059
dc.identifier.urihttps://doi.org/10.1080/01605682.2021.1960910
dc.identifier.urihttps://hdl.handle.net/11772/22296
dc.identifier.volume73
dc.identifier.wosWOS:000686877800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofJournal of the Operational Research Society
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWoS_20251016
dc.subjectMulti-Criteria
dc.subjectGroup Decision Making
dc.subjectMri (Magnetic Resonance Imaging) System
dc.subjectFuzzy Psi
dc.subjectFuzzy Marcos
dc.titleMedical device selection in private hospitals by integrated fuzzy MCGDM methods: A case study in choosing MRI (Magnetic Resonance Imaging) system
dc.typeArticle
dspace.entity.typePublication
relation.isAuthorOfPublication0872bd73-169a-4685-b8af-048c5908b57b
relation.isAuthorOfPublication.latestForDiscovery0872bd73-169a-4685-b8af-048c5908b57b

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