Abstract
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.
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References
Nunamaker, T. R., Measuring routine nursing service efficiency: a comparison of cost per patient day and data envelopment analysis models. Health Serv. Res. 18:183–205, 1983.
Sherman, H. D., Hospital efficiency measurement and evaluation. Med. Care 22:922–938, 1984.
Baker, R. C., and Talluri, S., A closer look at the use of data envelopment analysis for technology selection. Comput. Ind. Eng. 32:101–108, 1997.
Boussofiane, A., Dyson, R. G., and Thanassoulis, E., Applied data envelopment analysis. Eur. J. Oper. Res. 52:1–15, 1991.
Doyle, J., and Green, R H., Efficiency and cross efficiency in DEA: derivations, meanings and uses. J. Oper. Res. Soc. 45:567–578, 1994.
Sexton, T.R., Silkman, R.H., and Hogan, A.J., Data envelopment analysis: critique and extensions, in: R.H. Silkman (eds.) Measuring Efficiency: An Assessment of Data Envelopment Analysis, Jossey-Bass, San Francisco, CA. pp. 73–105, 1986.
Hollingsworth, B., and Wildman, J., Efficiency and Cross Efficiency Measures: A Validation Using OECD Data. Working paper 132, Centre for Health Program Evaluation (CHPE), 2002.
Adler, N., Friedman, L., and Sinuany-Stern, Z., Review of ranking methods in the data envelopment analysis context. Eur. J. Oper. Res. 140:249–265, 2002.
Anderson, T. R., Hollingsworth, K. B., and Inmam, L., The fixed weighting nature of a cross-evaluation model. J. Prod. Anal. 17:249–255, 2002.
Mukherjee, A., Nath, P., and Pal, M., Performance benchmarking and strategic homogeneity of Indian banks. Int. J. Bank Market. 20:122–139, 2002.
Braglia, M., and Petroni, A., A quality assurance-oriented methodology for handling trade-offs in supplier selection. Int. J. Phys. Distrib. Logist. Manag. 30:96–111, 2000.
Shang, J., and Sueyoshi, T., A unified framework for the selection of a flexible manufacturing system. Eur. J. Oper. Res. 85:297–315, 1995.
Sarkis, J., Evaluating flexible manufacturing systems alternatives using data envelopment analysis. Eng. Economist 43:25–48, 1997.
Sarkis, J., and Talluri, S., Performance based clustering for benchmarking of US airports. Transport. Res. Part A 38:329–346, 2004.
Martin, J. C., and Roman, C., A benchmarking analysis of Spanish commercial airports: a comparison between SMOP and DEA ranking methods. Networks Spatial Econ. 6:111–134, 2006.
Wu, J., Liang, L., and Chen, Y., DEA game cross-efficiency approach to Olympic rankings. Omega 37:909–918, 2009.
Wu, J., Liang, L., and Yang, F., Achievement and benchmarking of countries at the Summer Olympics using cross efficiency evaluation method. Eur. J. Oper. Res. 197:722–730, 2009.
Sarkis, J., and Talluri, S., Eco-efficiency measurement using DEA: research and practitioner issues. J. Environ. Assess. Pol. Manag. 6:91–123, 2004.
Sarkis, J., and Weinrach, J., Using data envelopment analysis to evaluate environmentally conscious waste treatment technology. J. Cleaner Prod. 9:417–427, 2001.
Chen, T. Y., An assessment of technical efficiency and cross-efficiency in Taiwan’s electricity distribution sector. Eur. J. Oper. Res. 137:421–43, 2002.
Sajeev, A. G., and Narayan, R., A performance benchmarking study of Indian railway zones. Benchmark. Int. J. 15:599–617, 2008.
Basso, A., and Funari, S., A Quantitative approach to evaluate the relative efficiency of museums. J. Cult. Econ. 28:195–216, 2004.
Banker, R. D., Charnes, A., and Cooper, W. W., Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag. Sci. 30:1078–1092, 1984.
Charnes, A., Cooper, W., and Rhodes, E., Measuring the efficiency of decision-making units. Eur. J. Oper. Res. 3:429–444, 1978.
Emrouznejad, A., Ali Emrouznejad’s data envelopment analysis homepage, 1995–2003, http://www.deazone.com/.
Emrouznejad, A., Parker, B. R., and Tavares, G., Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Soc. Econ. Plann. Sci. 42:151–157, 2008.
Lam, K. F., In the determination of weight sets to compute cross-efficiency ratios in DEA. J. Oper. Res. Soc. 61:134–143, 2010.
Despotis, D. K., Improving the discriminating power of DEA: Focus on globally efficient units. J. Oper. Res. Soc. 53:314–323, 2002.
Liang, L., Wu, J., Cook, W. D., and Zhu, J., Alternative secondary goals in DEA cross efficiency evaluation. Int. J. Prod. Econ. 113:1025–1030, 2008.
Talluri, S., and Sarkis, J., Extensions in efficiency measurement of alternate machine component grouping solutions via data envelopment analysis. IEEE Trans. Eng. Manag. 44:299–304, 1997.
Doyle, J. R., Multiple correlation clustering. Int. J. Man-Machine Studies 37:751–765, 1992.
Aletras, V., Kontodimopoulos, N., Zagouldoudis, A., and Niakas, D., The short-term effect on technical and scale efficiency of establishing Regional Health Systems and General Management in Greek NHS hospitals. Health Policy 83:236–245, 2007.
Magnussen, J., Efficiency measurement and the operationalization of hospital production. Health Serv. Res. 31:21–37, 1996.
Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., and Shale, E. A., Pitfalls and protocols in DEA. Eur. J. Oper. Res. 132:245–259, 2001.
Ozcan, Y. A., and Luke, R. D., A national study of the efficiency of hospitals in urban markets. Health Serv. Res. 27:719–739, 1993.
Harrison, J., Coppola, N., and Wakefield, M., Efficiency of federal hospitals in the United States. J. Med. Sys. 28:411–422, 2004.
Helmig, B., and Lapsley, I., On the efficiency of public, welfare and private hospitals in Germany over time—A sectoral DEA-Study. Health Serv. Manag. Res. 14:263–274, 2001.
Renner, A., Kirigia, J., Zere, E., Barry, S., Kirigia, D., Kamara, C., and Muthuri, L., Technical efficiency of peripheral health units in Pujehun district of Sierra Leone: a DEA application. BMC Health Serv. Res. 5:77, 2005.
Osei, D., d'Almeida, S., George, M.O., Kirigia, J.M., Mensah, A.O., and Kainyu, L.H., Technical efficiency of public district hospitals and health centers in Ghana: a pilot study. Cost Eff. Resour. Alloc. 3:9, 2005.
Chilingerian, J., and Sherman H.D., Health care applications: from hospitals to physicians, from productive efficiency to quality frontiers. In: Cooper, W., Seiford W., Lawrence M., and Zhu J., (Eds.) Handbook on Data Envelopment Analysis, Springer US, 495, 2004
Rovithis, D., Health economic evaluation in Greece. Int. J. Techno. Assess. Health Care. 22:388–395, 2006.
Mossialos, E., Allin, S., and Davaki, K., Analyzing the Greek health system: A tale of fragmentation and inertia. Health Econ. 14:151–168, 2005.
Mersha, T., Output performance measurement in outpatient care. OMEGA Int. J. Manag. Sci. 17:159–167, 1989.
Roemer, M. I., Moustafa, A. T., and Hopkins, C. E., A Proposed hospital quality index: Hospital Death Rates Adjusted for Case Severity. Health Serv. Res. 3:96–118, 1968.
O'Neill, L., Rauner, M., Heidenberger, K., and Krau, M., A cross-national comparison and taxonomy of DEA-based hospital efficiency studies. Soc. Econ. Plann. Sci. 42:158–189, 2008.
Kontodimopoulos, N., Nanos, P., and Niakas, D., Balancing efficiency of health services and equity of access in remote areas in Greece. Health Policy 76:49–57, 2006.
Giokas, D. I., Greek hospitals: how well their resources are used. Omega 29:73–83, 2001.
EMS Homepage [http://www.holger-scheel.de/ems/].
Angulo-Meza, L., and Lins, M. P. E., Review of methods for increasing discrimination in data envelopment analysis. Ann. Oper. Res. 116:225–242, 2002.
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Flokou, A., Kontodimopoulos, N. & Niakas, D. Employing post-DEA Cross-evaluation and Cluster Analysis in a Sample of Greek NHS Hospitals. J Med Syst 35, 1001–1014 (2011). https://doi.org/10.1007/s10916-010-9533-9
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DOI: https://doi.org/10.1007/s10916-010-9533-9