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Data envelopment analysis applied to quality in primary health care

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  • Javier Salinas-Jimenez
  • Peter Smith

    () (Centre for Health Economics, The University of York)

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    Abstract

    The quality of primary care should ultimately be judged on the effect of health outcome of individual patients. However, for the foreseeable future, it is inconceivable that the necessary data will be available to implement this principle. And in any case, specification of the necessary statistical model is fraught with difficulty. This paper therefore applies data envelopment analysis (DEA) to quality in primary health care administration, in the belief that it offers a consistent and helpful “intermediate technology” for assessing performance. Many of the outputs of primary care are intangible and unfold over a long time period. It is therefore useful to judge the quality of primary care on the basis of process variables, which are particularly well suited to DEA. Moreover, DEA is not vulnerable to the misspecification bias that afflicts statistical models. The principle difficulty DEA gives rise to is the selection of relevant environmental variables. The issues are illustrated with an example from English Family Health Service Authorities.

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    File URL: http://www.york.ac.uk/media/che/documents/papers/discussionpapers/CHE%20Discussion%20Paper%20124.pdf
    File Function: First version, 1994
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    Bibliographic Info

    Paper provided by Centre for Health Economics, University of York in its series Working Papers with number 124chedp.

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    Length: 26 pages
    Date of creation: Jul 1994
    Date of revision:
    Handle: RePEc:chy:respap:124chedp

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    Related research

    Keywords: Family Health Service Authorities;

    References

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    1. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    2. Rajiv D. Banker & Robert F. Conrad & Robert P. Strauss, 1986. "A Comparative Application of Data Envelopment Analysis and Translog Methods: An Illustrative Study of Hospital Production," Management Science, INFORMS, vol. 32(1), pages 30-44, January.
    3. Thompson, Russell G. & Langemeier, Larry N. & Lee, Chih-Tah & Lee, Euntaik & Thrall, Robert M., 1990. "The role of multiplier bounds in efficiency analysis with application to Kansas farming," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 93-108.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Roll, Y & Golany, B., 1993. "Alternate methods of treating factor weights in DEA," Omega, Elsevier, vol. 21(1), pages 99-109, January.
    6. Rosenhead, J., 1978. "Operational research in health services planning," European Journal of Operational Research, Elsevier, vol. 2(2), pages 75-85, March.
    7. Grosskopf, S. & Valdmanis, V., 1987. "Measuring hospital performance : A non-parametric approach," Journal of Health Economics, Elsevier, vol. 6(2), pages 89-107, June.
    8. Valdmanis, Vivian, 1992. "Sensitivity analysis for DEA models : An empirical example using public vs. NFP hospitals," Journal of Public Economics, Elsevier, vol. 48(2), pages 185-205, July.
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    Cited by:
    1. Tsai, P. F. & Mar Molinero, C., 2002. "A variable returns to scale data envelopment analysis model for the joint determination of efficiencies with an example of the UK health service," European Journal of Operational Research, Elsevier, vol. 141(1), pages 21-38, August.

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