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Data Envelopment Analysis models with imperfect knowledge of input and output values: An application to Portuguese public hospitals

2023-11-30Journal article Open access
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dc.contributor.authorFerreira, Diogo
dc.contributor.authorFigueira, José Rui
dc.contributor.authorGreco, Salvatore
dc.contributor.authorMarques, Rui Cunha
dc.date.accessioned2024-04-04T16:34:09Z
dc.date.available2024-04-04T16:34:09Z
dc.date.issued2023-11-30
dc.description.abstractAssessing the technical efficiency of a set of observations requires that the associated data composed of inputs and outputs are perfectly known. If this is not the case, then biased estimates will likely be obtained. Data Envelopment Analysis (DEA) is one of the most extensively used mathematical models to estimate efficiency. It constructs a piecewise linear frontier against which all observations are compared. Since the frontier is empirically defined, any deviation resulting from low data quality (imperfect knowledge of data or IKD) may lead to efficiency under/overestimation. In this study, we model IKD and, then, apply the so-called Hit & Run procedure to randomly generate admissible observations, following some prespecified probability density functions. Sets used to model IKD limit the domain of data associated with each observation. Any point belonging to that domain is a candidate to figure out as the observation for efficiency assessment. Hence, this sampling procedure must run a sizable number of times (infinite, in theory) in such a way that it populates the whole sets. The DEA technique is used during the execution of each iteration to estimate bootstrapped efficiency scores for each observation. We use some scenarios to show that the proposed routine can outperform some of the available alternatives. We also explain how efficiency estimations can be used for statistical inference. An empirical case study based on the Portuguese public hospitals database (2013–2016) was addressed using the proposed method.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.eswa.2023.120543pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.5/30528
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationThe authors recognize the generous support of the project ‘hSNS: Portuguese public hospital performance assessment using a multi-criteria decision analysis framework’ (PTDC/EGE-OGE/30546/2017), funded by the Portuguese Foundation for Science and Technology (FCT, from the Portuguese abbreviation of Fundação para a Ciência e a Tecnologia). Diogo Ferreira, José Rui Figueira, and Rui Marques are also grateful for the FCT support through funding UIDB/04625/2020 and UIDB/00097/2020 from the research units CERIS and CEGIST, respectively. Salvatore Greco acknowledges support from the Ministero dell’Istruzione, dell’Universitá e della Ricerca (MIUR) - PRIN 2017, project “Multiple Criteria Decision Analysis and Multiple Criteria Decision Theory”, grant 2017CY2NCA. This research also contributes to the Italian PNRR GRInS Project.pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectData Envelopment Analysis; Imperfect knowledge of data; Robustness concerns; Stochastic multicriteria acceptability analysis.pt_PT
dc.titleData Envelopment Analysis models with imperfect knowledge of input and output values: An application to Portuguese public hospitalspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.startPage120543pt_PT
oaire.citation.titleExpert Systems with Applicationspt_PT
oaire.citation.volume231pt_PT
person.familyNameCunha Ferreira
person.givenNameDiogo Filipe
person.identifier.ciencia-idCF13-9741-551E
person.identifier.orcid0000-0001-5418-9337
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication7ee3508c-76f3-4d20-9c76-b3b727b02f93
relation.isAuthorOfPublication.latestForDiscovery7ee3508c-76f3-4d20-9c76-b3b727b02f93

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