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A Methodology To Measure Hospital Quality Using Physicians' Choices Over Training Vacancies

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  • Matilde P. Machado,

    ()

  • Ricardo Mora

    ()

  • Antonio Romero- Medina

    ()

Abstract

In this paper, we propose an alternative methodology to rank hospitals based on the choices of Medical Schools graduates over training vacancies. We argue that our measure of relative hospital quality has the following desirable properties: a) robustness to manipulation from the hospital’s administrators; b) comprehensiveness in the scope of the services analyzed; c) inexpensive in terms of data requirements, and d) not subject to selection biases. Accurate measures of health provider quality are needed in order to establish incentive mechanisms, to assess the need for quality improvement, or simply to increase market transparency and competition. Public report cards in certain US states and the NHS ranking system in the UK are two attempts at constructing quality rankings of health care providers. Although the need for such rankings is widely recognized, the criticisms at these attempts reveal the difficulties involved in this task. Most criticisms alert to the inadequate risk-adjustment and the potential for perverse consequences such as patient selection. The recent literature, using sophisticated econometric models is capable of controlling for case-mix, hospital and patient selection, and measurement error. The detailed data needed for these evaluations is, however, often unavailable to researchers. In those countries, such as Spain, where there is neither public hospital rankings nor public data on hospital output measures such as mortality rates our methodology is a valid alternative. We develop this methodology for the Spanish case. In a follow-up paper we will present results using Spanish data. In Spain graduates choose hospital training vacancies in a sequential manner that depends on their average grade. Our framework relies on three assumptions. First, high quality hospitals provide high quality training. Second, graduates are well informed decision makers who are well qualified to assess hospital quality. Third, they prefer to choose a high quality vacancy rather than a low quality one ceteris paribus. If these assumptions hold, then the first physicians to choose are likely to grab the best vacancies while the ones who choose last are stuck with the worst available. Thus, it is possible to infer from physicans’ choices quality differentials amongst hospitals. We model the physician’s decision as a nested-logit a la McFadden. Unlike in standard applications of McFadden’s model, in our application the choice set is not constant across physicians but it shrinks along the sequential hospital choice process

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Bibliographic Info

Paper provided by Universidad Carlos III, Departamento de Economía in its series Economics Working Papers with number we060201.

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Date of creation: Jan 2006
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Handle: RePEc:cte:werepe:we060201

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  1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, April.
  2. Herriges, Joseph A. & Kling, Catherine L., 1996. "Testing the consistency of nested logit models with utility maximization," Economics Letters, Elsevier, vol. 50(1), pages 33-39, January.
  3. Gowrisankaran, Gautam & Town, Robert J., 1999. "Estimating the quality of care in hospitals using instrumental variables," Journal of Health Economics, Elsevier, vol. 18(6), pages 747-767, December.
  4. Andrés Romeu & Marcos Vera-Hern�ndez, 2005. "Counts with an endogenous binary regressor: A series expansion approach," Econometrics Journal, Royal Economic Society, vol. 8(1), pages 1-22, 03.
  5. John Geweke & Gautam Gowrisankaran & Robert J. Town, 2003. "Bayesian Inference for Hospital Quality in a Selection Model," Econometrica, Econometric Society, vol. 71(4), pages 1215-1238, 07.
  6. John Hey & Louise Allsopp, . "Two Experiments to Test a Model of Herd Behaviour," Discussion Papers 99/24, Department of Economics, University of York.
  7. Mingshan Lu, 1999. "Separating the True Effect from Gaming in Incentive-Based Contracts in Health Care," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 8(3), pages 383-431, 09.
  8. Daniel A. Ackerberg & Matilde P. Machado & Michael H. Riordan, 2006. "Benchmarking For Productivity Improvement: A Health-Care Application ," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(1), pages 161-201, 02.
  9. Christopher N. Avery & Mark E. Glickman & Caroline M. Hoxby & Andrew Metrick, 2013. "A Revealed Preference Ranking of U.S. Colleges and Universities," The Quarterly Journal of Economics, Oxford University Press, vol. 128(1), pages 425-467.
  10. Kling, Catherine L. & Herriges, Joseph A., 1995. "Empirical Investigation of the Consistency of Nested Logit Models with Utility Maximization (An)," Staff General Research Papers 1499, Iowa State University, Department of Economics.
  11. Simon Burgess & Denise Gossage & Carol Propper, 2003. "Explaining differences in hospital performance: Does the answer lie in the labour market?," The Centre for Market and Public Organisation 03/091, Department of Economics, University of Bristol, UK.
  12. Roth, Alvin E, 1984. "The Evolution of the Labor Market for Medical Interns and Residents: A Case Study in Game Theory," Journal of Political Economy, University of Chicago Press, vol. 92(6), pages 991-1016, December.
  13. Mark McClellan & Douglas Staiger, 1999. "The Quality of Health Care Providers," NBER Working Papers 7327, National Bureau of Economic Research, Inc.
  14. Holl, Adelheid, 2004. "Manufacturing location and impacts of road transport infrastructure: empirical evidence from Spain," Regional Science and Urban Economics, Elsevier, vol. 34(3), pages 341-363, May.
  15. Daniel McFadden, 1977. "Modelling the Choice of Residential Location," Cowles Foundation Discussion Papers 477, Cowles Foundation for Research in Economics, Yale University.
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