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Errors in Survey Based Quality Evaluation Variables in Efficiency Models of Primary Care Physicians

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Author Info

  • Kittelsen, Sverre A.C.

    ()
    (Frisch Centre)

  • Kjæserud, Guri Galtung

    (Frisch Centre)

  • Kvamme, Odd Jarle

    (Department of General Practice)

Abstract

Efficiency analyses in the health care sector are often criticised for not incorporating quality variables. The definition of quality of primary health care has many aspects, and it is inevitably also a question of the patients’ perception of the services received. This paper uses variables derived from patient evaluation surveys as measures of the quality of the production of health care services. It uses statistical tests to judge if such measures have a significant impact on the use of resources in various Data Envelopment Analysis (DEA) models. As the use of survey data implies that the quality variables are measured with error, the assumptions underlying a DEA model are not strictly fulfilled. This paper focuses on ways of correcting for biases that might result from the violation of selected assumptions. Firstly, any selection bias in the patient mix of each physician is controlled for by regressing the patient evaluation responses on the patient characteristics. The corrected quality evaluation variables are entered as outputs in the DEA model, and model specification tests indicate that out of 25 different quality variables, only waiting time has a systematic impact on the efficiency results. Secondly, the effect on the efficiency estimates of the remaining sampling error in the patient sample for each physician is accounted for by constructing confidence intervals based on resampling. Finally, as an alternative approach to including the quality variables in the DEA model, a regression model finds different variables significant, but not always with a trade-of between quality and quantity.

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Bibliographic Info

Paper provided by Oslo University, Health Economics Research Programme in its series HERO On line Working Paper Series with number 2001:12.

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Length: 25 pages
Date of creation: 30 Jun 2009
Date of revision:
Handle: RePEc:hhs:oslohe:2001_012

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Postal: HERO / Institute of Health Management and Health Economics P.O. Box 1089 Blindern, N-0317 Oslo, Norway
Phone: 2307 5309
Fax: 2307 5310
Email:
Web page: http://www.hero.uio.no/eng.html
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Related research

Keywords: DEA; Health economics; Quality; Patient evaluation; Efficiency; Errors in variables; Resampling; Bootstrap; Selection bias; Sampling error;

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References

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  1. Kittelsen,S.A.C., 1999. "Monte Carlo simulations of DEA efficiency measures and hypothesis tests," Memorandum 09/1999, Oslo University, Department of Economics.
  2. A. Charnes & W. W. Cooper & E. Rhodes, 1981. "Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through," Management Science, INFORMS, vol. 27(6), pages 668-697, June.
  3. repec:fth:louvco:9639 is not listed on IDEAS
  4. Simar, L., . "Aspects of statistical analysis in DEA-type frontier models," CORE Discussion Papers RP -1226, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. Kneip, A & Park, B-U & Simar, L, 1996. "A Note on the Convergence of Nonparametric DEA Efficiency Measures," Papers 9603, Catholique de Louvain - Institut de statistique.
  6. Banker, Rajiv D., 1984. "Estimating most productive scale size using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 17(1), pages 35-44, July.
  7. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
  8. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
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