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

dc.authoridKundu, Pradip/0000-0001-8297-8894
dc.authoridGörçün, Ömer Faruk/0000-0003-3850-6755
dc.authorwosidKundu, Pradip/O-3598-2016
dc.authorwosidGorcun, Omer Faruk/ADF-0541-2022
dc.authorwosidGörçün, Ömer Faruk/ABG-9628-2020
dc.contributor.authorGörçün, Ömer Faruk
dc.contributor.authorGorcun, Omer Faruk
dc.contributor.authorKucukonder, Hande
dc.date.accessioned2023-10-19T15:12:51Z
dc.date.available2023-10-19T15:12:51Z
dc.date.issued2022
dc.department-temp[Kundu, Pradip] Birla Global Univ, Bhubaneswar, India; [Gorcun, Omer Faruk] Kadir Has Univ, Istanbul, Turkey; [Kucukonder, Hande] Bartin Univ, Bartin, Turkeyen_US
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.en_US
dc.identifier.citation10
dc.identifier.doi10.1080/01605682.2021.1960910en_US
dc.identifier.endpage2079en_US
dc.identifier.issn0160-5682
dc.identifier.issn1476-9360
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85113755641en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage2059en_US
dc.identifier.urihttps://doi.org/10.1080/01605682.2021.1960910
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5547
dc.identifier.volume73en_US
dc.identifier.wosWOS:000686877800001en_US
dc.identifier.wosqualityQ2
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofJournal of The Operational Research Societyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDecision-Making MethodEn_Us
dc.subjectSupplier SelectionEn_Us
dc.subjectTodim MethodEn_Us
dc.subjectCriteriaEn_Us
dc.subjectMcdmEn_Us
dc.subjectDecision-Making Method
dc.subjectMulti-criteriaen_US
dc.subjectSupplier Selection
dc.subjectgroup decision makingen_US
dc.subjectTodim Method
dc.subjectMRI (Magnetic Resonance Imaging) systemen_US
dc.subjectCriteria
dc.subjectfuzzy PSIen_US
dc.subjectMcdm
dc.subjectfuzzy MARCOSen_US
dc.titleMedical device selection in private hospitals by integrated fuzzy MCGDM methods: A case study in choosing MRI (Magnetic Resonance Imaging) systemen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublication4d0f6004-dbe4-4e79-befd-457af3bb133f
relation.isAuthorOfPublication.latestForDiscovery4d0f6004-dbe4-4e79-befd-457af3bb133f

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