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Estimating Hospital Inefficiency: Does Case Mix Matter?

Journal of Medical Systems volume 23, pages 57–71 (1999)Cite this article

Abstract

A two-stage approach is used in a stochastic frontier analysis of the factors affecting hospital efficiency. In the first stage, a translog cost-function is used to estimate inefficiency scores. In the second stage, inefficiency scores are regressed against independent variables to test hypotheses that come from X-inefficiency Theory. The study was based on 1989 data for 195 Pennsylvania acute care hospitals. This data base was chosen because of the availability of patient-level severity of illness data, a measure of output that is not available from most data sources. The stochastic frontier analysis models estimated mean inefficiency scores that ranged from 0.075 to 0.180. The addition of the DRG case mix index (CMI) reduced estimated inefficiency by more than 50%. The incremental effect of a severity of illness variable to an equation with CMI was very small. The second-stage results suggest inefficiency and are inversely associated with regulatory pressures and industry concentration.

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REFERENCES

  1. ProPAC., Prospective Payment Assessment Commission Report and Recommendations to the Congress. ProPAC, Washington, D.C., 1995.

    Google Scholar 

  2. Aaron, H., Serious and Unstable Condition: Financing America's Health Care, Brookings Institution, Washington, D.C., 1991.

    Google Scholar 

  3. Rosko, M.D., Measuring technical efficiency in health care organizations. J. Med. Syst. 14(4):307–22, 1990.

    Google Scholar 

  4. Farrell, M.J., The measurement of productive efficiency. J. Roy. Stat. Soc. Series A Gen. 120(3):253–281, 1957.

    Google Scholar 

  5. Aigner, D., K. Lovell, and Schmidt, P., Formulation and estimation stochastic frontier production models. J. Econometr. 6:21–37, 1977.

    Google Scholar 

  6. Hofler, R.A., and Folland, S.T., Technical and Allocative inefficiencies of United States hospitals under a stochastic frontier approach. Presentation at the Midwest Economics Associations Fifty-Fifth Annual Meeting, St. Louis, MO, 1991.

  7. Zuckerman, S., Hadley, J., and Iezzoni, L., Measuring hospital efficiency with frontier cost functions. J. Health Econ. 13:255–80, 1994.

    Google Scholar 

  8. Grannemann, T.W., Brown, R.S., and Pauly, M.V., Estimating hospital costs: A multiple-output analysis, J. Health Econ. 5(2):107–127, 1986.

    Google Scholar 

  9. Cowing, T.G., and Holtmann, A.G., Multiproduct short-run hospital cost functions: Empirical evidence and policy implications from cross-section data. South. Econ. J. 49(3):637–653, 1983.

    Google Scholar 

  10. Rosko, M.D., and Broyles, R., The Economics of Health: A Reference Handbook, Greenwood, New York, 1988.

    Google Scholar 

  11. Newhouse, J., Frontier estimation: How useful a tool for health economics? J. Health Econ. 13(3):317–322, 1994.

    Google Scholar 

  12. Rosko, M.D., and Carpenter, C.E., Development of a scalar hospital-specific severity of illness index. J. Med. Syst. 17(1):25–36, 1993.

    Google Scholar 

  13. Leibenstein, H., Beyond Economic Man. Harvard University Press, Cambridge, 1976.

    Google Scholar 

  14. Lovell, C.K., Production frontiers and productive efficiency. In: Fried, H.O., Lovell, C.K., and Schmidt, S.S., editors. The Measurement of Productive Efficiency. Oxford University Press, New York, 1993, pp. 3–67.

    Google Scholar 

  15. Charnes, A., Cooper, W., Lewin, A., and L. Seifford, eds., Data Envelopment Analysis: Theory, Methodology and Applications. Kluwer, Boston, 1994.

    Google Scholar 

  16. Timmer, C., Using a probabilistic frontier production function to measure technical efficiency. J. Pol. Econ. 79(4):776–794, 1971.

    Google Scholar 

  17. Nyman, J., and Bricker, D., Profit incentives and technical efficiency in the production of nursing home care. Rev. Econ. Stat. 71(3):586–594, 1989.

    Google Scholar 

  18. Vitaliano, D.F., and Toren, M., Hospital cost and efficiency in a regime of strict regulation. East. Econ. J. 22(2):161–175, 1996.

    Google Scholar 

  19. Burgess, J., and Wilson, P., Variation in inefficiency among U.S. hospitals. INFOR 36(3):84–102, 1998.

    Google Scholar 

  20. Garnick, D.W., Luft, H.S., Robinson, J.C., and Tetreault, J., Appropriate measures of hospital market areas. Health Serv. Res. 22:69–89, 1987.

    Google Scholar 

  21. Greene, W.H., LIMDEP: User's Manual and Reference Guide. Econometric Software, Inc., Bellport, N.Y., 1991.

    Google Scholar 

  22. Jondrow, J., Lovell, K., Materov, I., and Schmidt, P., On the estimation of technical inefficiency in the stochastic frontier production function model. J. Econometr. 19:233–8, 1982.

    Google Scholar 

  23. Christensen, L.R., Jorgenson, D.W., and Lau, L.J., Transcendental logarithmic utility functions. Am. Econ. Rev. 65(3):367–383, 1975.

    Google Scholar 

  24. Rosko, M.D., DRGs and severity of illness measures: An analysis of patient classification systems. J. Me.l Sys. 12(4):257–74, 1988.

    Google Scholar 

  25. Iezzoni, L.I., Ash, A.S., Cobb, J.L., and Moskowitz, M.A., Admission MedixGroup score and the cost of hospitalizations. Med. Care 26(10:1068–80, 1988.

    Google Scholar 

  26. 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(9):1–20, 1995.

    Google Scholar 

  27. Rosko, M.D., All-payer and rate-setting and the provision of hospital care to the uninsured: The New Jersey experience. J. Health Polit. Policy Law 15(4):815–831, 1990.

    Google Scholar 

  28. Diehr, P., Madden, C., Cheadle, A., et al., Estimating county percentages of people without health insurance. Inquiry 28(4):413–19, 1991.

    Google Scholar 

  29. Rosko, M.D., and Carpenter, C.E., The impact of severity of illness on hospital profits: Implications for payment reform. J. Health Polit. Policy Law 19(4):729–52, 1994.

    Google Scholar 

  30. Sloan, F.A., Feldman, R.D., and Steinwald, A.B., Effects of teaching on hospital costs. J. Health Econ. 2(1):1–28, 1983.

    Google Scholar 

  31. Thorpe, K.E., The use of regression analysis to determine hospital payment: The case of Medicare's indirect teaching adjustment. Inquiry 25(2):219–31, 1988.

    Google Scholar 

  32. Caves, R.E., Industrial Efficiency in Six Nations, MIT Press, Cambridge, 1992.

    Google Scholar 

  33. Leibenstein, H., Inside the Firm: The Inefficiency of Hierarchy, Harvard University Press, Cambridge, MA, Cambridge, 1987.

    Google Scholar 

  34. Leibenstein, H., Inflation, Income Distribution and X-efficiency Theory, Croom Helm, London, 1980.

    Google Scholar 

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Affiliations

  1. Graduate Program in Health and Medical Services Administration, Widener University, Chester, Pennsylvania, 19013

    Michael D. Rosko

  2. Heller School for Advanced Studies in Social Welfare Policy, Brandeis University, Waltham, Massachusetts, 02154

    Jon A. Chilingerian

Authors
  1. Michael D. Rosko
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  2. Jon A. Chilingerian
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Rosko, M.D., Chilingerian, J.A. Estimating Hospital Inefficiency: Does Case Mix Matter?. Journal of Medical Systems 23, 57–71 (1999). https://doi.org/10.1023/A:1020823612156

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  • DOI: https://doi.org/10.1023/A:1020823612156

  • hospital efficiency
  • stochastic frontier analysis
  • X-efficiency