J Med Syst - Employing post-DEA cross-evaluation and cluster analysis in a sample of Greek NHS hospitals.

Tópicos

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Resumo

To increase Data Envelopment Analysis (DEA) discrimination of efficient Decision Making Units (DMUs), by complementing "self-evaluated" efficiencies with "peer-evaluated" cross-efficiencies and, based on these results, to classify the DMUs using cluster analysis. Healthcare, which is deprived of such studies, was chosen as the study area. The sample consisted of 27 small- to medium-sized (70-500 beds) NHS general hospitals distributed throughout Greece, in areas where they are the sole NHS representatives. DEA was performed on 2005 data collected from the Ministry of Health and the General Secretariat of the National Statistical Service. Three inputs -hospital beds, physicians and other health professionals- and three outputs -case-mix adjusted hospitalized cases, surgeries and outpatient visits- were included in input-oriented, constant-returns-to-scale (CRS) and variable-returns-to-scale (VRS) models. In a second stage (post-DEA), aggressive and benevolent cross-efficiency formulations and clustering were employed, to validate (or not) the initial DEA scores. The "maverick index" was used to sort the peer-appraised hospitals. All analyses were performed using custom-made software. Ten benchmark hospitals were identified by DEA, but using the aggressive and benevolent formulations showed that two and four of them respectively were at the lower end of the maverick index list. On the other hand, only one 100% efficient (self-appraised) hospital was at the higher end of the list, using either formulation. Cluster analysis produced a hierarchical "tree" structure which dichotomized the hospitals in accordance to the cross-evaluation results, and provided insight on the two-dimensional path to improving efficiency. This is, to our awareness, the first study in the healthcare domain to employ both of these post-DEA techniques (cross efficiency and clustering) at the hospital (i.e. micro) level. The potential benefit for decision-makers is the capability to examine high and low "all-round" performers and maverick hospitals more closely, and identify and address problems typically overlooked by first-stage DEA.

Resumo Limpo

increas data envelop analysi dea discrimin effici decis make unit dmus complement selfevalu effici peerevalu crosseffici base result classifi dmus use cluster analysi healthcar depriv studi chosen studi area sampl consist small mediums bed nhs general hospit distribut throughout greec area sole nhs repres dea perform data collect ministri health general secretariat nation statist servic three input hospit bed physician health profession three output casemix adjust hospit case surgeri outpati visit includ inputori constantreturnstoscal crs variablereturnstoscal vrs model second stage postdea aggress benevol crosseffici formul cluster employ valid initi dea score maverick index use sort peerapprais hospit analys perform use custommad softwar ten benchmark hospit identifi dea use aggress benevol formul show two four respect lower end maverick index list hand one effici selfapprais hospit higher end list use either formul cluster analysi produc hierarch tree structur dichotom hospit accord crossevalu result provid insight twodimension path improv effici awar first studi healthcar domain employ postdea techniqu cross effici cluster hospit ie micro level potenti benefit decisionmak capabl examin high low allround perform maverick hospit close identifi address problem typic overlook firststag dea

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