Advertisement

Journal of Medical Systems

, Volume 34, Issue 3, pp 397–405 | Cite as

Separating Managerial Inefficiency from Influences of the Operating Environment: An Application in Dialysis

  • Nick Kontodimopoulos
  • Nikolaos D. Papathanasiou
  • Yannis Tountas
  • Dimitris Niakas
Original Paper

Abstract

In any production unit, the ability to achieve technical efficiency is influenced by characteristics of the external operating environment. This study uses the Greek dialysis sector to employ a previously reported frontier procedure to obtain a measure of managerial inefficiency that controls for exogenous features. The sample consisted of 124 dialysis facilities. Two inputs —nursing staff and dialysis machines— and one output —dialysis sessions— were used in an input-oriented, variable-returns-to-scale DEA model. Input slacks were regressed against environmental characteristics such as ownership, location, operating years and facility size, and parameter estimates were used to adjust primary input data. New efficiency scores were generated to measure managerial inefficiency. Older, public, regional facilities were operating under unfavorable circumstances, whereas newer, private, Athens-based facilities under favorable conditions. This respectively generated lower and higher efficiency scores than would have been attained on a level “playing field”. After adjustment, scores reflected only management inefficiency and could be compared fairly. This study emphasizes the importance of efficiency comparisons, which take into account external conditions beyond the influence of management, as these have been shown to under— or overstate true management inefficiency.

Keywords

Data envelopment analysis Slack adjustment Managerial efficiency Operating environment Dialysis 

References

  1. 1.
    Ozgen, H., and Ozcan, Y., A national study of efficiency for dialysis centers: an examination of market competition and facility characteristics for production of multiple dialysis outputs. Health Serv. Res. 37:711–732, 2002. doi: 10.1111/1475-6773.00045.CrossRefGoogle Scholar
  2. 2.
    Gerard, K., and Roderick, P., Comparison of apparent efficiency of haemodialysis satellite units in England and Wales using data envelopment analysis. Int. J. Technol. Assess. Health Care. 19:533–539, 2003. doi: 10.1017/S0266462303000473.CrossRefGoogle Scholar
  3. 3.
    Kontodimopoulos, N., and Niakas, D., Efficiency measurement of hemodialysis units in Greece with data envelopment analysis. Health Policy. 71:195–204, 2005. doi: 10.1016/j.healthpol.2004.08.004.CrossRefGoogle Scholar
  4. 4.
    Ozgen, H., and Ozcan, Y., Longitudinal analysis of efficiency in multiple output dialysis markets. Health Care Manage. Sci. 7:253–261, 2004. doi: 10.1007/s10729-004-7534-2.CrossRefGoogle Scholar
  5. 5.
    Kontodimopoulos, N., and Niakas, D., A 12-year analysis of Malmquist total factor productivity in dialysis facilities. J. Med. Syst. 30:333–342, 2006. doi: 10.1007/s10916-005-9005-9.CrossRefGoogle Scholar
  6. 6.
    Kontodimopoulos, N., and Niakas, D., An estimate of lifelong costs and QALYs in renal replacement therapy based on patients’ life expectancy. Health Policy. 86:85–96, 2008.Google Scholar
  7. 7.
    Kaitelidou, D., Ziroyanis, P. N., Maniadakis, N., and Liaropoulos, L. L., Economic evaluation of hemodialysis: implications for technology assessment in Greece. Int. J. Technol. Assess. Health Care. 21:40–46, 2005. doi: 10.1017/S0266462305050051.CrossRefGoogle Scholar
  8. 8.
    Fried, H. O., Schmidt, S. S., and Yaisawarng, S., Incorporating the operating environment into a nonparametric measure of technical efficiency. J. Prod. Anal. 12:249–267, 1999. doi: 10.1023/A:1007800306752.CrossRefGoogle Scholar
  9. 9.
    Charnes, A., Cooper, W. W., and Rhodes, E., Measuring efficiency of decision-making units. Eur. J. Oper. Res. 3:429–444, 1978. doi: 10.1016/0377-2217(78)90138-8.CrossRefMathSciNetGoogle Scholar
  10. 10.
    Banker, R. D., Charnes, A., and Cooper, W. W., Models for estimating technical and scale efficiencies in data envelopment analysis. Manage. Sci. 30:1078–1092, 1984. doi: 10.1287/mnsc.30.9.1078.CrossRefMATHGoogle Scholar
  11. 11.
    Farrell, M. J., The measurement of productive efficiency. J. R. Stat. Soc. [Ser A]. 120:252–281, 1957.Google Scholar
  12. 12.
    Lovell, C. A. K., Production frontiers and productive efficiency. In: Fried, H. O., Lovell, C. A. K., and Schmidt, S. S. (Eds.), The measurement of productive efficiency: techniques and applications. New York: Oxford University Press, 1993.Google Scholar
  13. 13.
    Ruggiero, J., Performance evaluation when non-discretionary factors correlate with technical efficiency. Eur. J. Oper. Res. 159:250–257, 2004. doi: 10.1016/S0377-2217(03)00403-X.CrossRefMATHGoogle Scholar
  14. 14.
    Hoff, A., Second stage DEA: comparison of approaches for modeling the DEA score. Eur. J. Oper. Res. 181:425–435, 2007. doi: 10.1016/j.ejor.2006.05.019.CrossRefMATHGoogle Scholar
  15. 15.
    Wooldridge, J. M., Econometric analysis of cross section and panel data. MIT, Cambridge, MA, 2002.Google Scholar
  16. 16.
    Rosko, M. D., Impact of internal and external environmental pressures on hospital inefficiency. Health Care Manage. Sci. 2:63–74, 1999. doi: 10.1023/A:1019031610741.CrossRefGoogle Scholar
  17. 17.
    Chu, H. L., Liu, S. Z., and Romeis, J. C., Does the implementation of responsibility centers, total quality management, and physician fee programs improve hospital efficiency? Evidence from Taiwan hospitals. Med. Care. 40:1223–1237, 2002. doi: 10.1097/00005650-200212000-00009.CrossRefGoogle Scholar
  18. 18.
    Pilyavsky, A. I., Aaronson, W. E., Bernet, P. M., Rosko, M. D., Valdmanis, V. G., and Golubchikov, M. V., East-west: does it make a difference to hospital efficiencies in Ukraine? Health Econ. 15:1173–1186, 2006. doi: 10.1002/hec.1120.CrossRefGoogle Scholar
  19. 19.
    Kooreman, P., Nursing home care in The Netherlands: a nonparametric efficiency analysis. J. Health Econ. 13:301–316, 1994. doi: 10.1016/0167-6296(94)90029-9.CrossRefGoogle Scholar
  20. 20.
    Rosko, M. D., Chilingerian, J. A., Zinn, J. S., and Aaronson, W. E., The effects of ownership, operating environment, and strategic choices on nursing home efficiency. Med. Care. 33:1001–1021, 1995. doi: 10.1097/00005650-199510000-00003.CrossRefGoogle Scholar
  21. 21.
    Kontodimopoulos, N., Moschovakis, G., Aletras, V., and Niakas, D., The relationship between eligible service population and efficiency in primary health care providers in Greece. Cost Eff. Resour. Alloc. 5:14, 2007. doi: 10.1186/1478-7547-5-14.CrossRefGoogle Scholar
  22. 22.
    Zavras, A. I., Tsakos, G., Economou, C., and Kyriopoulos, J., Using DEA to evaluate efficiency and formulate policy within a Greek national primary health care network. Data Envelopment Analysis. J. Med. Syst. 26:285–292, 2002. doi: 10.1023/A:1015860318972.Google Scholar
  23. 23.
    Linna, M., Nordblad, A., and Koivu, M., Technical and cost efficiency of oral health care provision in Finnish health centres. Soc. Sci. Med. 56:343–353, 2003. doi: 10.1016/S0277-9536(02)00032-1.CrossRefGoogle Scholar
  24. 24.
    Chilingerian, J. A., Evaluating physician efficiency in hospitals: a multivariate analysis of best practices. Eur. J. Oper. Res. 80:548–574, 1995. doi: 10.1016/0377-2217(94)00137-2.CrossRefMATHGoogle Scholar
  25. 25.
    Smith, P., Model misspecification in data envelopment analysis. Ann. Oper. Res. 73:233–252, 1997. doi: 10.1023/A:1018981212364.CrossRefMATHGoogle Scholar
  26. 26.
    Held, P. J., and Pauly, M. V., Competition and efficiency in the end stage renal disease program. J. Health Econ. 2:95–118, 1983. doi: 10.1016/0167-6296(83)90001-2.CrossRefGoogle Scholar
  27. 27.
    Fried, H. O., Lovell, C. A. K., Schmidt, S. S., and Yaisawarng, S., Accounting for environmental effects and statistical noise in data envelopment analysis. J. Prod. Anal. 17:157–174, 2002. doi: 10.1023/A:1013548723393.CrossRefGoogle Scholar
  28. 28.
    Street, A., How much confidence should we place in efficiency estimates? Health Econ. 12:895–907, 2003. doi: 10.1002/hec.773.CrossRefMathSciNetGoogle Scholar
  29. 29.
    Worthington, A. C., Frontier efficiency measurement in health care: a review of empirical techniques and selected applications. Med. Care Res. Rev. 61:135–170, 2004. doi: 10.1177/1077558704263796.CrossRefGoogle Scholar
  30. 30.
    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. doi: 10.1016/j.healthpol.2005.04.006.CrossRefGoogle Scholar
  31. 31.
    Sheldon, T. A., and Smith, P. C., Equity in the allocation of health care resources. Health Econ. 9:571–574, 2000. doi: 10.1002/1099-1050(200010)9:7<571::AID-HEC555>3.0.CO;2-8.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Nick Kontodimopoulos
    • 1
  • Nikolaos D. Papathanasiou
    • 2
  • Yannis Tountas
    • 2
  • Dimitris Niakas
    • 1
  1. 1.Hellenic Open UniversityFaculty of Social SciencesPatrasGreece
  2. 2.Center for Health Services Research, Department of Hygiene and Epidemiology, Medical SchoolUniversity of AthensAthensGreece

Personalised recommendations