A fuzzy ELECTRE structure methodology to assess big data maturity in healthcare SMEs

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dc.citation.journalTitleSOFT COMPUTINGeng
dc.contributor.authorPeña A.
dc.contributor.authorBonet I.
dc.contributor.authorLochmuller C.
dc.contributor.authorTabares M.S.
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería de Sistemasspa
dc.contributor.researchgroupI+D+I en Tecnologías de la Información y las Comunicacionesspa
dc.creatorPeña A.
dc.creatorBonet I.
dc.creatorLochmuller C.
dc.creatorTabares M.S.
dc.date.accessioned2021-04-12T20:55:47Z
dc.date.available2021-04-12T20:55:47Z
dc.date.issued2019-10-01
dc.description.abstractAdvances in technology and an increase in the amount and complexity of data that are generated in healthcare have led to an indispensable revolution in this sector related to big data. Analytics of information based on multimodal clinical data sources requires big data projects. When starting big data projects in the healthcare sector, it is often necessary to assess the maturity of an organization with respect to big data, i.e., its capacity in managing big data. The assessment of the maturity of an organization requires multicriteria decision making as there is no single criterion or dimension that defines the maturity level regarding big data but an entire set of them. Based on the ISO 15504, this article proposes a fuzzy ELECTRE structure methodology to assess the maturity level of small- and medium-sized enterprises in the healthcare sector. The obtained experimental results provide evidence that this methodology helps to determine and compare maturity levels in big data management of organizations or the evolution of maturity over time. This is also useful in terms of diagnosing the readiness of an organization before starting to implement big data initiatives or technologies. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8496
dc.identifier.doi10.1007/s00500-018-3625-8
dc.identifier.issn14327643
dc.identifier.issn14337479
dc.identifier.otherWOS;000487038100048
dc.identifier.otherSCOPUS;2-s2.0-85057601325
dc.identifier.urihttp://hdl.handle.net/10784/28620
dc.language.isoengeng
dc.publisherSpringer Verlag
dc.relationDOI;10.1007/s00500-018-3625-8
dc.relationWOS;000487038100048
dc.relationSCOPUS;2-s2.0-85057601325
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85057601325&doi=10.1007%2fs00500-018-3625-8&partnerID=40&md5=40b2fc2ca2a9af49f394d41372d42db2
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/1432-7643
dc.sourceSOFT COMPUTING
dc.subjectDecision makingeng
dc.subjectHealth careeng
dc.subjectInformation managementeng
dc.subjectClinical dataeng
dc.subjectElectre methodseng
dc.subjectFuzzy methodseng
dc.subjectHealthcare sectorseng
dc.subjectMaturity levelseng
dc.subjectMulti criteria decision makingeng
dc.subjectOutrankingeng
dc.subjectSmall and medium sized enterpriseeng
dc.subjectBig dataeng
dc.titleA fuzzy ELECTRE structure methodology to assess big data maturity in healthcare SMEseng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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