Performance measurement is essential for improvement.Although techniques for collecting, analyzing, and report-ing data fall within the quantitative skill set of theorganization, translating data into information that manag-ers need to promote performance improvement requires adifferent and more subtle skill set. Health services sector isa complex area that is unique in all its characteristics. It hastoo many dimensions to be fitted into a simple singular unitand it is therefore essentially very difficult to approach themeasurement of the performance of healthcare services byusing one method or another [7]. Frontier EfficiencyMethodologies and Multi-criteria Decision Making havebeen used rigorously in recent years. Data Envelopmentanalysis [1–3] is proven to be a useful tool in measuringefficiency and productivity of hospitals and health carerelated units [4, 5]. Multi-criteria decision making approachhas also been adopted extensively for performance analysisin healthcare sector [6, 8–10].The 9 papers comprising this special volume of theJournal of Medical Systems contribute to the theory andapplication of Data Envelopment Analysis (DEA). Paperswere included in the special volume after a rigorousrefereeing process, and represent only a small fraction ofthe total number of submitted manuscripts.The volume opens with an application focused on theperformance of dialysis facilities. Nick Kontodimopoulos,Nikolaos D. Papathanasiou, Angeliki Flokou, YannisTountas and Dimitris Niakas examine the impact of non-discretionary factors on DEA and SFA technical efficiencydifferences. They investigated the effect of factors such asoperating environment on the efficiency. In a sample of124 dialysis facilities, technical efficiency was comparedaccording to ownership, region, years in operation andsize. With second-stage Tobit regression, DEA and SFAefficiency was regressed against these environmental factorstodeterminetheirpotentialforpredictingtechnicalefficiency,as well as the efficiency differences between the two frontiermethods. DEA expectedly generated lower mean efficiencyscores than SFA, due to the “random effects” term computedby the latter, in addition to “true” inefficiency. This findingwas consistent for the subgroups formed on the basis of theenvironmentalfactors.ThyfoundthathalfthevariationintheDEA-SFA efficiency differences was explained by environ-mental factors. This suggests that in addition to marketinstabilities, luck, and other related phenomena, decision-makersintheirefforttodetermineoptimalresourceallocation,should point their attention to the potentially useful insightprovided by environmental factors.In another application in measuring technical efficiency inprimary health care in the Spanish region of Extremadura,Jose Manuel Cordero Ferrera, Eva Crespo Cebada and LuisR. Murillo Zamorano investigated the effect of exogenousvariables on efficiency. In this context, the exogenousvariables are represented by the main characteristics of thecovered population. They argued that with using multi-stageprocess in DEA allows calculating more accurate efficiencyscores that can reflect the performance of units moreproperly. Their results show that the inclusion of theexogenous variables in the evaluation has a great impacton both the values of efficiency scores and the rank of units.In the same area of searching for factors that affect onefficiency, Jos L.T. Blank and Bart L. Van Hulst investigatedgovernance characteristics that could explain the performance