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Use of data envelopment analysis for efficiency evaluation of czech hospitals

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  • Martin Dlouhý
  • Josef Jablonský
  • Ivana Novosádová
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    Abstract

    Efficiency of hospitals belongs among frequently discussed topics of public policy. This paper deals with a nonparametric model for efficiency evaluation of hospitals - data envelopment analysis (DEA). DEA evaluates efficiency of production units with the help of mathematical programming. DEA specifies the production frontier as the most pessimistic piecewise linear envelopment of the data. In case that a hospital is inefficient DEA determines the sources of inefficiency and defines corresponding target values. In this study, authors analyze the sample of 22 Czech acute-care hospitals with the constant-returns-to-scale model (the CCR model) and the variable-returns-to-scale model (the BCC model). The data comes from regional yearbooks of the Institute of Health Information and Statistics of the Czech Republic (ÚZIS ČR). A fundamental problem of evaluation in health care is a definition of inputs and outputs, especially a measurement of case-mix of patients. Interpretation of the results is described on one selected hospital (Hospital of Prachatice). One hospital was identified as efficient by the CCR model and six hospitals were efficient according to the BCC model.

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    Bibliographic Info

    Article provided by University of Economics, Prague in its journal Politická ekonomie.

    Volume (Year): 2007 (2007)
    Issue (Month): 1 ()
    Pages: 60-71

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    Handle: RePEc:prg:jnlpol:v:2007:y:2007:i:1:id:590:p:60-71

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    Related research

    Keywords: efficiency; data envelopment analysis; health services; hospitals;

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    Cited by:
    1. Brailsford, Sally & Vissers, Jan, 2011. "OR in healthcare: A European perspective," European Journal of Operational Research, Elsevier, vol. 212(2), pages 223-234, July.
    2. Ippoliti, Roberto & Falavigna, Greta, 2012. "Efficiency of the medical care industry: Evidence from the Italian regional system," European Journal of Operational Research, Elsevier, vol. 217(3), pages 643-652.

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