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Efficiency and productivity assessment of public hospitals in Greece during the crisis period 2009–2012

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Background: This study is an initial effort to examine the dynamics of efficiency and productivity in Greek public hospitals during the first phase of the crisis 2009–2012. Data were collected by the Ministry of Health after several quality controls
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  Xenos et al. Cost Eff Resour Alloc (2017) 15:6 DOI 10.1186/s12962-017-0068-5 RESEARCH Efficiency and productivity assessment of public hospitals in Greece during the crisis period 2009–2012 P. Xenos 1 , J. Yfantopoulos 2* , M. Nektarios 1 , N. Polyzos 3 , P. Tinios 1  and A. Constantopoulos 2 Abstract   Background:  This study is an initial effort to examine the dynamics of efficiency and productivity in Greek public hospitals during the first phase of the crisis 2009–2012. Data were collected by the Ministry of Health after several quality controls ensuring comparability and validity of hospital inputs and outputs. Productivity is estimated using the Malmquist Indicator, decomposing the estimated values into efficiency and technological change. Methods:  Hospital efficiency and productivity growth are calculated by bootstrapping the non-parametric Malmquist analysis. The advantage of this method is the estimation efficiency and productivity through the corre-sponding confidence intervals. Additionally, a Random-effects Tobit model is explored to investigate the impact of contextual factors on the magnitude of efficiency. Results:  Findings reveal substantial variations in hospital productivity over the period from 2009 to 2012. The eco-nomic crisis of 2009 had a negative impact in productivity. The average Malmquist Productivity Indicator (MPI) score is 0.72 with unity signifying stable production. Approximately 91% of the hospitals score lower than unity. Substantial increase is observed between 2010 and 2011, as indicated by the average MPI score which fluctuates to 1.52. Moreo-ver, technology change scored more than unity in more than 75% of hospitals. The last period (2011–2012) has shown stabilization in the expansionary process of productivity. The main factors contributing to overall productivity gains are increases in occupancy rates, type and size of the hospital. Conclusions:  This paper attempts to offer insights in efficiency and productivity growth for public hospitals in Greece. The results suggest that the average hospital experienced substantial productivity growth between 2009 and 2012 as indicated by variations in MPI. Almost all of the productivity increase was due to technology change which could be explained by the concurrent managerial and financing healthcare reforms. Hospitals operating under decreasing returns to scale could achieve higher efficiency rates by reducing their capacity. However, certain social objectives should also be considered. Emphasis perhaps should be placed in utilizing and advancing managerial and organizational reforms, so that the benefits of technological improvements will have a continuing positive impact in the future. Keywords:  Efficiency, Hospitals, Productivity, Malmquist, Tobit, Greece, Crisis JEL classification:  C14, D24, H51 © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the srcinal author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated. Background Analysis of efficiency and productivity of the hospital sector has become a considerable concern in Europe. It presents a challenging area of relevant studies because hospitals absorb a large amount of public healthcare spending. In OECD countries and in the European Union of the 27 member states, hospital expenditure represents on the average around 30 and 37% of the total health expenditures in 2012 respectively [1]. e correspond- ing share in Greece is 47%, indicating a hospital based Open Access Cost Effectiveness and Resource Allocation *Correspondence: yfantopoulos@gmail.com 2  School of Economics and Political Science, University of Athens, 6  Themistokleous Str., 10678 Athens, GreeceFull list of author information is available at the end of the article  Page 2 of 12Xenos et al. Cost Eff Resour Alloc (2017) 15:6 healthcare system and the need to give greater emphases on operational efficiency and cost containment in order to balance healthcare expenditure at a feasible level [2, 3]. e Greek health system is a combination of an National Health System (NHS) model in the supply-side (consisting of an extensive hospital sector of 138 pub-lic hospitals and an underdeveloped primary healthcare sector) and a social insurance system in the demand side (consisting of many health funds that all merged in one, EOPPY (National Organization for the Provision of Healthcare), in 2010. Grants from the public budget finance the ‘fixed’ and contractual expenses (mainly sal-aries) of public hospitals, while the revenues from the social insurance funds (now consolidate in the single fund EOPPY) finance the variable expenses.During the crisis period 2009–2012, a reduction of 40% took place in hospital budgets. Additionally, shortages in healthcare workforce and medical supplies have been recorded in the Greek hospital sector [4]. e current efforts of public authorities towards a more efficient allo-cation of financial and human resources to public hospi-tals raised questions about the criteria used to evaluate the performance of the Greek healthcare system in the previous years.Nowadays, Greece has 136 public plus two Non-Gov-ernmental Organizations (NGO) hospitals, managed by 85 NHS Trusts, which belong to the Greek National Health System (ESY). During the crisis period 2009–2012, the Greek Ministry of Health attempted to reform pub-lic hospitals operations and restrain healthcare expendi-ture. One such operation was the initiation of department budgets, which offers better expenditure control, more accurate estimation of hospital products and supports productivity enhancement in hospital departments [5]. Furthermore, two major reforms were implemented regarding Greek public hospitals. e first reform was the operational redeployment of the 136 NHS hospitals into 85 Trusts and the second was the implementation of a Diagnosis Related Groups (DRG) prospective reimburse-ment system, which was introduced in 2011, in order to minimize costs. DRGs are developed in order to identify and price hospital services, based on the diagnosis [6]. Apart from the above measures, additional reforms are in progress in order to restrain healthcare expenditure. For example, the joint purchasing of goods and services by using price–volume agreements can lead to significant decline of healthcare costs. Additionally, the consolidation of NHS hospitals, the adoption of specific policies related to pharmaceuticals and the advancement of public hospi-tals infrastructure and technology can further contribute to expenditure reductions. At this point, it is important to mention that EOPYY, signs contracts with all Greek hos-pitals under the KEN-DRG reimbursement system [2, 5]. To the best of our knowledge, the impact of these hospital reforms has not yet been measured. eoreti-cally, budget cuts are expected to cause a positive shift of efficiency provided that outputs remain stable. Intui-tively, since shortages in workforce and medical equip-ment vary between hospitals, their impact would most probably affect efficiency change rather than technol-ogy. e redeployment of hospitals leads to better management of inputs. erefore, by reducing costs it would most probably increase the overall efficiency of the redeployed hospitals. Moreover, the DRG-based reimbursement system, combined with the pharmaceu-tical pricing reforms, is expected to create economies of scale which would greatly improve hospital effi-ciency [7]. Research objectives e purpose of this paper is to investigate the dynamics of productivity and efficiency in the Greek Hospital sec-tor over the years from 2009 to 2012. e study is limited in this period, due to the unavailability of more recent data. Moreover, data prior to 2009 were not collected and  validated according to international organization princi-ples and guidelines.We make use of Malmquist Productivity Index (MPI) through data envelopment analysis (DEA) augmented by bootstrapping techniques. e study contributes to the current literature in several possible ways. First, it takes into account all Greek public hospitals (excluding the specialized in psychiatry and pediatrics). Homo-geneity is preserved and selection bias is avoided. Sec-ond, the data are collected by the Ministry of Health after several quality controls ensuring comparability and validity of the hospital inputs and outputs. ird, our methodology is based on the non-parametric Malmquist productivity analysis developed by Simar and Wilson [8] not previously applied in Greek hospital sector. e great advantage of this method is the esti-mation of efficiency and productivity change followed by the corresponding confidence intervals. Fourth we decompose the estimated values of productivity into efficiency and technological change components. e above points would provide valuable information to decision makers for effective policy guidance during the crisis period of 2009–2012.e rest of the paper is arranged in three sections as follows. e first section provides efficiency and pro-ductivity measurement concepts, with a brief literature review on healthcare efficiency measurement in Greece and in some other countries. In the following section, the data and the estimated results are presented and dis-cussed. e final section provides the conclusion of the study.  Page 3 of 12Xenos et al. Cost Eff Resour Alloc (2017) 15:6 Hospital efficiency and productivity measurement e measurement of efficiency and productivity is cru-cial for hospitals because it allows them to compare the performance of their own organization with that of other hospitals in the same NHS and establish a reciprocal pol-icy of “best practices” in order to improve their own per-formance [9–13]. Jenu-Appiah et al. [14] and Kirigia and Asbu [15] used two-stage analysis using DEA efficiency measure-ment and Tobit model in order to examine relationships between hospital inefficiencies and environmental fac-tors. Both studies used cross-sectional data.Zavras et al. [16], by using DEA, assessed the relative efficiency of 133 primary healthcare services, between 1998 and 1999; the results indicated that the primary healthcare centers that had the appropriate technologi-cal capacity to carry out laboratory or radiological exami-nations had the highest efficiency scores, whereas the medium-sized centers that covered population areas of 10,000–50,000 people performed better than the other primary healthcare units.In another study, Tsekouras et al. [17], by using Boot-strap DEA, measured the productive efficiency of 39 intensive care units (ICUs) of the Greek Healthcare sys-tem for 2004. e purpose of the study was to reveal if new medical technology investment into ICUs had a positive impact; the findings demonstrated that techni-cal efficiency improved but scale efficiency remained unchanged.Certain studies employ the Malmquist Index method-ology and then decompose total factor productivity into technical efficiency and technology change. In Greece, the application of DEA in efficiency and productivity measurement has gained considerable attention by both researchers and policy makers [18, 19]. In a recent study Karagiannis and Velentzas [20] estimated productivity growth for Greek public hospitals for the period 2002–2007 including quality variables in their analysis. ey create a quality-adjusted Malmquist productivity index. eir findings indicate reductions both in productiv -ity and quality as well as significant variations between hospitals.Androutsou et al. [21] measured the performance in seven homogenous specialty clinics across all National Health System hospitals in the Regional Health Author-ity (RHA) of essaly, over the period 2002–2006 with Malmquist Index. Overall productivity progressed in all clinics. Technical change progressed except the general medicine clinics, and diachronically the size of the clin-ics influences the overall effects on hospital performance. Polyzos [22] analyzed the performance of 117 Greek NHS hospitals by means of DEA, for years 2009–2011. All hospitals, especially middle-sized hospitals showed performance improvements on technical efficiency terms.is study attempts to make an early assessment of the health reforms in the period 2009–2012 by exploiting the Malmquist methodology which provides a dynamic approach to the assessment of efficiency and productiv -ity of the hospital sector. Additionally, a Random-effects Tobit regression model is explored to investigate the impact of several contextual factors on the magnitude of efficiency in public hospitals. Methods Data envelopment analysis Charnes, Cooper and Rhodes (CCR) [23] calculated the efficiency frontier basing their estimates on best practices rather than the average performance in a given sample. Based on their research, Banker et al. [24] introduced the “ Banker, Charnes and Cooper (BCC) model” of effi-ciency measurement. is model assumes a production technology of variable returns to scale, implying that any proportional change in inputs usage results in variable proportional change in outputs [25]. Specifically, we used the input-oriented approach, since inputs are more eas-ily controlled by hospital administrations, compared to outputs.According to Simar and Wilson [26], two-stage approach results are inconsistent and biased unless the DEA efficiency scores are corrected by a bootstrap-ping procedure. Bootstrapping estimates a more robust regression model in order to determine the effect of con-textual factors on efficiency [27].e DEA model can only be applied to multiple DMUs (Decision Making Units: hospitals in our case) on a per-year basis. erefore, DEA cannot estimate the efficiency change over time. e Malmquist Produc-tivity Index (MPI), which is presented in the next sub-section, overcomes this limitation. In a non-parametric framework the MPI evaluates the efficiency change over time [28]. The malmquist productivity index Assuming a list of p inputs and q outputs, the production set is defined in the Euclidean space R p + q +  as follows:We can define the input requirement set V(y) as the set of all input vectors that can produce the output vector  y  ∈ R + :Fare et al. [29] determined the input distance function: (1) =  x,y   | x  ∈ R p + ,y   ∈ R q + ,  x,y   is feasible  (2)   y    =  x  ∈ R p + |  x,y    ∈ Ξ   (3) D ti  x t ,y  t   =  sup {   :  ( x t /  ,y  t )  ∈  S t }  Page 4 of 12Xenos et al. Cost Eff Resour Alloc (2017) 15:6 where S t =  x t ,y  t   :  x t can produce y  t  . Malmquist Total Factor Productivity change index between period t and t + 1 as:Equation 4 represents the geometric mean of the two Malmquist indices for periods t and t + 1. e first index employs reference technology, which corresponds to period t, while the second index performs the same func-tion, as the first one, for period t + 1.Fare et al. [29] factor the expression (4) into the product of technical efficiency and technological change (frontier shift) as:orwhere “M” symbolizes Total Factor Productivity Growth index between periods t and t + 1, and “E” and “T” repre-sent the technical efficiency change, and the technological change respectively for the same period. Full interpreta-tion of these indices specified to health sector can be found in Jacobs et al. [30] and Adenso-Diaz [31]. By combining each DMUs distance from the efficiency frontier (efficiency change) and the overall shift of the frontier over time (technology change), the Malmquist Productivity Index offers a dynamic approach on han-dling panel datasets [32].However, Eq. (4) limits our ability of determining whether changes in productivity, efficiency and tech-nology, really exist or they are merely appearing as such because of the fact that we do not know the actual pro-duction frontiers, in which case we must estimate them from the finite sample [26, 33, 34]. For the above reason, a bootstrap estimation procedure for obtaining confi-dence intervals and correcting the Malmquist Index and its components was employed. e estimation is imple-mented through the data generating process procedure (DGP), by using a series of pseudo datasets to create a bootstrap estimate. e problems that occur when bootstrapping DEA models are discussed by Simar and (4) M I  X t ,Y t ,X t + 1 ,Y t + 1  =  M tI  X t + 1 ,Y t + 1 ,X t ,Y t  × M t + 1I  X t + 1 ,Y t + 1 ,X t ,Y t  1 / 2 =  D tI  X t + 1 ,Y t + 1  D tI  X t ,Y t  D t + 1I  X t + 1 ,Y t + 1  D t + 1I  X t ,Y t  1 / 2 (5) M I  X t ,Y t ,X t + 1 ,Y t + 1 = D t + 1I  X t + 1 ,Y t + 1  D tI  X t ,Y t   D tI  X t + 1 ,Y t + 1  D t + 1I  X t + 1 ,Y t + 1  D tI  X t ,Y t  D t + 1I  X t ,Y t  1 / 2 M  = E  ×  T Wilson [35]. e bootstrapping procedure concerning Malmquist indices is described in detail at Simar and Wilson [8]. us, by obtaining a confidence interval for the Malmquist index and its components it becomes pos-sible to validate whether productivity changes are signifi-cant at the desired level of confidence.However, Simar and Wilson have expressed doubts about the former methodology. ey argue that the usual semi-parametric framework is inconsistent in some cases [34]. Using Monte Carlo simulations, they show that since the data generating process cannot be estimated the Tobit regression is inadequate. ey propose a truncated regression model and perform single and double boot-strapping, finding that the latter produces better results. Regression analysis between inefficiencies and contextual factors e point of a two-stage analysis of hospital efficiency, is to shed more light on the impact of contextual factors beyond the control of the hospitals on efficiency. Such factors are the operating status of the hospital, the region that is located, etc. In cases where differences across the panel variable have influence on the dependent variable, random-effects models are often used in relevant litera-ture [36–43]. erefore, in order to explore the potential effect of time as the panel variable, which in this case is expressed in years, we used random- rather than fixed-effects. Besides that, fixed-effects models control for all cannot variables constant across years, such as hospital type, size and RHA, and are therefore unable to measure their effect [44].e Tobit model ensures lower tail censoring of the dis-tribution that DEA creates. e use of OLS estimation is not appropriate for determining the desired factors of hospital efficiency, because of the nature of the depend-ent variable (efficiency), which is constrained in the 0–1 interval.Greene [43] proposed a censoring point at zero for computation purposes and transformed DEA efficiency scores into inefficiency scores left-censored at zero using the equation as follows:where DEA eff.score  = 1  D ti  x t ,y  t  .Consider the linear regression model with panel-data random-effects: (6) ineff score = 1DEA eff.score  − 1 (7)  y  ∗ it  = β i z it +  v  i + ε it  y  it  =  y  ∗ it  if y  ∗ it  <  0 y  it  = 0 if y  ∗ it  ≤ 0i =  1,2, . . . ,N  Page 5 of 12Xenos et al. Cost Eff Resour Alloc (2017) 15:6 where i =  1,…,N is the number of DMU’s and t is time, β i  is the vector of unknown parameters, Z i  is the vector of explanatory variables. e random-effects v  i  are inde-pendent and identically distributed (i.i.d.), N ( 0, σ 2 v  )  and ɛ  it  are i.i.d. N ( 0, σ ε )  independently of v  i . e observed data  y  *it  represents possibly censored versions of y  it .e estimated empirical model is specified in the fol-lowing equation:where “ineff” is the inefficiency score and Z i  are the fol-lowing contextual factors: (i) average length of stay (ALS), (ii) bed occupancy rate (OCP), (iii) number of diagnostic procedures (DIAG), (iv) number of patients adjusted by the Roemer index (PAT), (v) type of hospital (1 =  Teach-ing, 0 =  Non-Teaching) (TYPE), (vi) three dummy vari-ables concerning hospital size based on the number of beds. Large hospitals are the ones with more than 400 beds (L), medium hospital are the ones containing between 100 and 400 beds (M) and small hospitals are all the rest, having less than 100 beds (S), (vii) seven dummy  variables representing each of the seven Regional Health Authorities (RHA) in which Greece is divided (YPE1–YPE7). e RHAs are responsible for planning, coor-dinating supervising and inspecting all Health Services within the limits of their region. eir aim is to disperse the health sector in order to address problems related to inefficiency in the delivery of healthcare. (viii) four dummy variables signifying the year (YEAR09–YEAR12).e average length of stay (ALS) is the number of days that an inpatient occupies a bed in the hospital. Posi-tive ALS coefficient would indicate a negative impact on efficiency, since hospital resources remain committed on the same patient. Bed occupancy rate has the oppo-site impact, because hospitals operate utilizing all avail-able resources. “Diagnostic procedures” include technical and diagnostic procedures, such as blood tests, MRIs, CTs and biochemical exams. If diagnostics are appointed a negative coefficient, it would indicate a positive effect on efficiency. Teaching hospitals are expected to have a positive coefficient, contributing negatively to efficiency. is occurs because healthcare is not their only aim and therefore some resources are spent on the teaching procedure. Sampling On the base of reforms initiated by the memorandum policies, the Ministry of Health has developed a web-based data repository called “ESY-net”. e base includes all Greek hospitals, covering the period 2009–2012 and several variables concerning organizational, medical and financial information. e sample consists of 108 general hospitals for four years (4 years ×  108 hospitals =  432 (8) Tobit ( ineff  ) = α + β i Z i +···+  v  i + ε i observations). In order to ensure homogeneity of the sample the specialty hospitals (psychiatric, maternity, dermatological and cardiological hospitals) are excluded. ESY-net has been compatible with the international standards of organizations such as World Health Organi-zation, OECD and Eurostat. Grant of access was officially offered to researchers in 2011.Based on a study by the Centre of Health Economics of the University of York [45], each pair of adjacent years is called “link” throughout the paper. is way, by perceiv-ing consecutive pairs of years as links of a chain, it is eas-ier to explore changes made over time. Links and Fiscal  years are shown in Fig. 1.Given the limitations of the data, the outputs used are: (i) the number of patient discharges adjusted for case-mix with Roemer Index [46]. Roemer et al. provide an adjusted estimate for the average length of stay taking into account the occupancy rate of the hospitals. (ii) the number of diagnostic procedures. e inputs include: (a) the number of doctors, (b) the number of beds, (c) the number of other personnel employed and (d) non-labour expenditures (i.e. pharmaceutical and health technology supplies, etc.) (see Table 1).e expenditure variable has been deflated by the GDP price deflator (2012 =  100). Following Vassiloglou and Giokas [47], the number of DMUs is greater than three times the number of inputs plus outputs. Model specifications e distance functions that are required in order to obtain Malmquist indices were measured using DEA, assuming constant returns to scale. In order to decom-pose further the efficiency change into pure efficiency change and scale efficiency change, a variable returns to scale technology (VRS) was considered. Because public hospitals are considered to have smaller ability to con-trol their outputs and more opportunities to lower their inputs, we employed an input-oriented DEA. Moreover, a benchmarking approach was used where the most effi-cient DMUs were estimated regarding their significance as benchmarks for the inefficient DMUs in the sample data. Fiscal Year2009 2010 2011 2012Link 1Link 2Link 3 Fig. 1  Fiscal years and links
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