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

dc.creatorPeña A.
dc.creatorBonet I.
dc.creatorLochmuller C.
dc.creatorTabares M.S.
dc.creatorPiedrahita C.C.
dc.creatorSánchez C.C.
dc.creatorGiraldo Marín L.M.
dc.creatorGóngora M.
dc.creatorChiclana F.
dc.date2019
dc.date.accessioned2020-04-29T14:53:54Z
dc.date.available2020-04-29T14:53:54Z
dc.identifier.issn14327643
dc.identifier.urihttp://hdl.handle.net/11407/5757
dc.descriptionAdvances 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.
dc.language.isoeng
dc.publisherSpringer Verlag
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85057601325&doi=10.1007%2fs00500-018-3625-8&partnerID=40&md5=40b2fc2ca2a9af49f394d41372d42db2
dc.sourceSoft Computing
dc.subjectBig data
dc.subjectELECTRE method
dc.subjectFuzzy methods
dc.subjectHealthcare
dc.subjectMaturity level
dc.subjectOutranking
dc.subjectDecision making
dc.subjectHealth care
dc.subjectInformation management
dc.subjectClinical data
dc.subjectElectre methods
dc.subjectFuzzy methods
dc.subjectHealthcare sectors
dc.subjectMaturity levels
dc.subjectMulti criteria decision making
dc.subjectOutranking
dc.subjectSmall and medium sized enterprise
dc.subjectBig data
dc.titleA fuzzy ELECTRE structure methodology to assess big data maturity in healthcare SMEs
dc.typeArticleeng
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Sistemas
dc.identifier.doi10.1007/s00500-018-3625-8
dc.relation.citationvolume23
dc.relation.citationissue20
dc.relation.citationstartpage10537
dc.relation.citationendpage10550
dc.publisher.facultyFacultad de Ciencias Básicas;Facultad de Ingenierías
dc.affiliationPeña, A., University EIA, Envigado, Colombia; Bonet, I., University EIA, Envigado, Colombia; Lochmuller, C., University EIA, Envigado, Colombia; Tabares, M.S., Universidad EAFIT, Medellín, Colombia; Piedrahita, C.C., Universidad de Medellín, Medellín, Colombia; Sánchez, C.C., Universidad de Medellín, Medellín, Colombia; Giraldo Marín, L.M., Universidad de Medellín, Medellín, Colombia; Góngora, M., Institute of Artificial Intelligence, De Montfort University, Leicester, United Kingdom; Chiclana, F., Institute of Artificial Intelligence, De Montfort University, Leicester, United Kingdom, Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
dc.relation.referencesAngilella, S., Mazzu, S., The financing of innovative SMEs: a multicriteria credit rating model (2015) Eur J Oper Res, 244 (2), pp. 540-554
dc.relation.referencesAnún, J.P., Alarcón, R., Ranking projects of logistics platforms: a methodology based on the electre multicriteria approach (2014) Proc Soc Behav Sci, 160, pp. 5-14
dc.relation.referencesBana e Costa, C.A., (1990) Readings in multiple criteria decision aid, , Springer, Berlin
dc.relation.referencesBenayoun, R., Roy, B., Sussmann, B., (1966) ELECTRE: Une méthode Pour Guider Le Choix En présence De Points De Vue Multiples, Note De Travail N ? 49 De La Direction Scientifique De La SEMA
dc.relation.referencesBodas-Sagi, D.J., Labeaga Big, J.M., Data and health economics: opportunities, challenges and risks (2018) Int J Interact Multimed Artif Intell, 4 (7), pp. 47-52
dc.relation.referencesBouyssou, D., Marchant, T., An axiomatic approach to noncompensatory sorting methods in MCDM, I: the case of two categories (2007) Eur J Oper Res, 178 (1), pp. 217-245
dc.relation.referencesBrans, J.-P., De Smet, Y., PROMETHEE methods (2016) Multiple criteria decision analysis. International series in operations research & management science, 233, pp. 187-219. , Greco S, Ehrgott M, Figueira J, (eds), Springer, New York
dc.relation.referencesCabrerizo, F.J., Al-Hmouz, R., Morfeq, A., Balamash, A.S., Martínez, M.A., Herrera-Viedma, E., Soft consensus measures in group decision making using unbalanced fuzzy linguistic information (2017) Soft Comput, 21 (11), pp. 3037-3050
dc.relation.referencesCamba, J.D., Contero, M., Company, P., Parametric CAD modeling: an analysis of strategies for design reusability (2016) Comput-Aided Des, 74, pp. 18-31
dc.relation.referencesCapuano, N., Chiclana, F., Fujita, H., Herrera-Viedma, E., Loia, V., Fuzzy group decision making with incomplete information guided by social influence (2018) IEEE Trans Fuzzy Syst, 26 (3), pp. 1704-1718
dc.relation.referencesChandarana, P., Vijayalakshmi, M., Big data analytics frameworks (2014) Proceedings of the 2014 International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA), pp. 430-434. , Mumbai
dc.relation.referencesDehe, B., Bamford, D., Development, test and comparison of two multiple criteria decision analysis (MCDA) models: a case of healthcare infrastructure location (2015) Exp Syst Appl, 42 (19), pp. 6717-6727
dc.relation.referencesDíaz-Ley, M., García, F., Piattini, M., MIS-PyME software measurement capability maturity model supporting the definition of software measurement programs and capability determination (2010) Adv Eng Softw, 41 (10-11), pp. 1223-1237
dc.relation.referencesDong, Y., Li, C.-C., Chiclana, F., Herrera-Viedma, E., Average-case consistency measurement and analysis of interval-valued reciprocal preference relations (2016) Knowl-Based Syst, 114, pp. 108-117
dc.relation.referencesDong, Y., Liu, W., Chiclana, F., Herrera-Viedma, E., Cabrerizo, F.J., Group decision-making based on heterogeneous preference relations with self-confidence (2017) Fuzzy Optim Decis Mak, 16 (4), pp. 429-447
dc.relation.referencesEric, J.-L., An application of the UTA discriminant model for the evaluation of R & D projects (1995) Advances in multicriteria analysis. Nonconvex optimization and its applications, 5. , Pardalos PM, Siskos Y, Zopounidis C, (eds), Springer, Boston
dc.relation.referencesFigueira, J., Roy, B., Determining the weights of criteria in the ELECTRE type methods with a revised Simos procedure (2002) Eur J Oper Res, 139 (2), pp. 317-326
dc.relation.referencesFigueira, J.R., Mousseau, V., Roy, B., ELECTRE methods (2016) Multiple criteria decision analysis. International series in operations research & management science, 233, pp. 155-182. , Greco S, Ehrgott M, Figueira J, (eds), Springer, New York
dc.relation.referencesGarousi, V., Felderer, M., Hacaloglu, T., Software test maturity assessment and test process improvement: a multivocal literature review (2017) Inf Softw Technol, 85, pp. 16-42
dc.relation.referencesGarzás, J., Pino, F.J., Piattini, M., Fernández, C.M., A maturity model for the Spanish software industry based on ISO standards (2013) Comput Stand Interf, 35 (6), pp. 616-628
dc.relation.referencesGoksen, Y., Cevik, E., Avunduk, H., A case analysis on the focus on the maturity models and information technologies (2015) Proc Econ Fin, 19, pp. 208-216
dc.relation.referencesGonzález-Ferrer, A., Seara, G., Cháfer, J., Mayol, J., Generating big data sets from knowledge-based decision support systems to pursue value-based healthcare (2018) Int J Interact Multimed Artif Intell, 4 (7), pp. 42-46
dc.relation.referencesGörög, M., A broader approach to organisational project management maturity assessment (2016) Int J Proj Manag, 34 (8), pp. 1658-1669
dc.relation.references(2017) Healthcare Smes Lead the Way with GS1 Standards, , https://www.gs1ie.org/Healthcare/Resources/Case-Studies/Healthcare-SMEs-Lead-the-Way-with-GS1-Standards.html
dc.relation.referencesHalper, F., Stoler, D., (2014) TDWI Analytics Maturity Model Guide Transforming Data with Intelligence, , https://tdwi.org/whitepapers/2014/10/tdwi-analytics-maturity-model-guide.aspx, White Paper
dc.relation.references(2017) Big Data Maturity Assessment Tool, , https://www.infotech.com/research/ss/leverage-big-data-by-starting-small/it-big-data-maturity-assessment-tool
dc.relation.referencesJacquet-Lagrèze, E., Siskos, J., Assessing a set of additive utility functions for multicriteria decision-making, the UTA method (1982) Eur J Oper Res, 10 (2), pp. 151-164
dc.relation.referencesJian, W., Xiong, R., Chiclana, F., Uninorm trust propagation and aggregation methods for group decision making in social network with four tuples information (2016) Knowl-Based Syst, 96, pp. 29-39
dc.relation.referencesKeeney, R.L., Raiffa, H., (1993) Decisions with multiple objectives: preferences and value tradeoffs, , Cambridge University Press, Cambridge
dc.relation.referencesKim, H.D., Lee, I., Lee, C.K., Building web 2.0 enterprises: a study of small and medium enterprises in the united states (2011) Int Small Bus J, 31 (2), pp. 156-174
dc.relation.referencesKuhrmann, M., Ternité, T., Friedrich, J., Rausch, A., Broy, M., Flexible software process lines in practice: a metamodel-based approach to effectively construct and manage families of software process models (2016) J Syst Softw, 121, pp. 49-71
dc.relation.referencesKuwata, Y., Takeda, K., Miura, H., A study on maturity model of open source software community to estimate the quality of products (2014) Proc Comput Sci, 35, pp. 1711-1717
dc.relation.referencesLian, J.-W., Ke, C.-K., Using a modified ELECTRE method for an agricultural product recommendation service on a mobile device (2016) Comput Electr Eng, 56, pp. 277-288
dc.relation.referencesLismont, J., Vanthienen, J., Baesens, B., Lemahieu, W., Defining analytics maturity indicators: a survey approach (2017) Int J Inf Manag, 37 (3), pp. 114-124
dc.relation.referencesLiu, Y., Liang, C., Chiclana, F., Jian, W., A trust induced recommendation mechanism for reaching consensus in group decision making (2017) Knowl-Based Syst, 119, pp. 221-231
dc.relation.referencesMarr, B., (2015) How Big Data is Changing Healthcare, Forbes, , https://www.forbes.com/sites/bernardmarr/2015/04/21/how-big-data-is-changing-healthcare/#39b365dd2873
dc.relation.referencesMousseau, V., Figueira, J.R., Naux, J.-P., Using assignment examples to infer weights for ELECTRE TRI method: some experimental results (2001) Eur J Oper Res, 130 (2), pp. 263-275
dc.relation.referencesPalacio, L.H., Cálculo de los Parámetros de la Distribución de Weibull (2015) Mantenimiento En Latinoamérica, 7 (1), pp. 42-44. , http://mantenimientoenlatinoamerica.com/pdf/ML%20Volumen%207-1.pdf
dc.relation.referencesPerez, L.G., Mata, F., Chiclana, F., Kou, G., Herrera-Viedma, E., Modelling influence in group decision making (2016) Soft Comput, 20 (4), pp. 1653-1665
dc.relation.referencesProença, D., Borbinha, J., Maturity models for information systems a state of the art (2016) Proc Comput Sci, 100, pp. 1042-1049
dc.relation.referencesQinghua, L., Li, Z., Zhang, W., Yang, L.T., Autonomic deployment decision making for big data analytics applications in the cloud (2017) Soft Comput, 21 (16), pp. 4501-4512
dc.relation.referencesRöglinger, M., Pöppelbuß, J., Becker, J., Maturity models in business process management (2012) Bus Process Manag J, 18 (2), pp. 328-346
dc.relation.referencesRouyendegh, B.D., Erol, S., Selecting the best project using the fuzzy ELECTRE method (2012) Math Prob Eng
dc.relation.referencesSantos, M., (2014) Las Pymes Ya Están Usando Big Data E Inteligencia De Datos, , http://www.enter.co/especiales/enterprise/big-data-tecnologia-pymes/
dc.relation.referencesSchaeffer, D.M., Olson, P.C., Big data options for small and medium enterprises (2014) Rev Bus Inf Syst, 18 (1), pp. 41-46
dc.relation.referencesSevkli, M., An application of the fuzzy ELECTRE method for supplier selection (2009) Int J Prod Res, 48 (12), pp. 3393-3405
dc.relation.referencesTarhan, A., Turetken, O., Reijers, H.A., Business process maturity models: a systematic literature review (2016) Inf Softw Technol, 75, pp. 122-134
dc.relation.referencesVélez, R., (2012) Alta Gerencia: Horarios Flexibles En El Trabajo Motivan a Los Empleados Y Aumentan La Productividad
dc.relation.referencesvon Scheel, H., von Rosing, G., Skurzak, K., Hove, M., BPM and maturity models (2015) The complete business process handbook, pp. 399-430. , Rosing M, Scheer A-W, Scheel H, (eds), Morgan Kaufmann, Burlington
dc.relation.referencesZhang, H., Dong, Y., Herrera-Viedma, E., Consensus building for the heterogeneous large-scale GDM with the individual concerns and satisfactions (2018) IEEE Trans Fuzzy Syst, 26 (2), pp. 884-898
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dc.type.driverinfo:eu-repo/semantics/article


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