An Empirical Comparison of DEA and SFA Method to Measure Hospital Units’ Efficiency

George Katharakis, Maria Katharaki, Theofanis Katostaras

Abstract

Although frontier techniques have been used to measure healthcare efficiency, their utility in decision making process is limited by both methodological questions concerning their application. The present paper aims to examine the data envelopment analysis (DEA) and stochastic frontier analysis (SFA) results in order to facilitate a common understanding about the adequacy of these methods. A two-stage bootstrap DEA method and the Translog formula of the SFA were performed. Multi-inputs and multi-outputs were used in both of the approaches assuming two scenarios either including environmental variables or not. Thirty-two Greek public hospital units constitute the sample. The main output of the analysis was that the efficiency scores increased with the incorporation of environmental variables. Moreover, environmental variables being hospital status and geographical position were found significantly correlating with inefficiency, while patient mobility was not found strongly correlating. DEA and SFA were found to yield divergent efficiency estimates due to the nature of the environmental variables and the measurement error. The analysis concludes that there is a need for careful attention by stakeholders since the nature of the data and its availability influence the measurement of the efficiency and thus it is necessary to be specific when choosing the mathematical form.

References

  1. Aigner, D., Lovell, C., Schmidt, P., Formulation and estimation of stochastic frontier production functions. Journal of Econometrics, 6, pp. 21-37.
  2. Admassie, A., Matambalya, F., 2002. Technical Efficiency in Small and Medium Scale Enterprises: Evidences from a Survey of Enterprises in Tanzania - Easter Africa. Eastern Africa Social Science Research Review, 17(2), pp. 1-29.
  3. Assaf, A., Matawie, K., 2008. Cost efficiency modeling in healthcare food service operations. International Journal of Hospitality Management, 27(4), pp. 604- 613.
  4. Banker, R., Charnes, A., Cooper, W.W., 1984. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30, pp. 1078-1092.
  5. Bryce, C., Engberg, J., Wholey, D., 2000. Comparing the Agreement among Alternative Models in Evaluating Hmo Efficiency. Health Service Journal, 35(2), pp. 509-528.
  6. Charnes, A., Cooper, W.W., 1985. Preface to topics in Data Envelopment Analysis. Annals of Operations Research, 2, pp. 59-94.
  7. Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision-making units. European Journal of Operational Research, 2, pp. 429-44.
  8. Chen, A., Hwang, Y., Shao, B., 2005. Measurement and sources of overall and input inefficiencies: Evidences and implications in hospital services. European Journal of Operational Research, 161, pp. 447-468.
  9. Chilingerian, J. A., Sherman, H. D., 2004. Health care applications. From Hospitals to Physicians, from productive efficiency to quality frontiers. In: Cooper WW, Seiford LM, Zhu J, editors. Handbook on data envelopment analysis. Boston/London: Kluwern Academic Publisher.
  10. Chirikos, T., Sear, A., 2000. Measuring hospital efficiency: a comparison of two approaches. Health Services Research, 34(6), pp. 1389-1408.
  11. Coelli, T., 2007. A guide to FRONTIER version 4.1: A computer program for stochastic frontier production and cost function estimation. CEPA Working Paper 96/07, Department of Econometrics, University of New England, Armidale, Australia.
  12. Coelli, T., Rao, D., O'Donnell, C., Battese, G., 2005. An introduction to efficiency and productivity analysis, New York: Springer 2nd edition.
  13. Cooper, W. W., Seiford, M., Zhu, J., 2004. Handbook on Data Envelopment Analysis, Boston: Kluwer Academic Publisher.
  14. Cordero, J., Pedraja, F., Santin, D., 2009. Alternative approaches to include exogenous variables in DEA measures: A comparison using Monte Carlo. Computers and Operations Research, 36, pp. 2699- 2706.
  15. Desaia, A., Ratick, S., Schinnar, A., 2005. Data envelopment analysis with stochastic variations in data. Socio-Economic Planning Science, 39, pp. 147- 164.
  16. Farrell, M., 1957. The measurement of productive efficiency. Journal of the Royal Statistical Society, 120(3), pp. 253-281.
  17. Giuffrida, A., Gravelle, H., 2001. Measuring performance in primary care: Econometric analysis and DEA. Applied Economics, 33(2), pp. 163-175.
  18. Gong, B., Sickles, R., 1992. Finite sample evidence on the performance of stochastic frontiers and data envelopment analysis using panel data. Journal of Econometrics, 51, pp. 259-284.
  19. Hollingsworth, B., 2008. The measurement of efficiency and productivity of health care delivery. Health Economics, 17(10), pp. 1107-1128.
  20. Ippoliti, R., Falavigna, G., 2012. Efficiency of the medical care industry: Evidence from the Italian regional system. European Journal of Operational Research, 217, pp. 643-652.
  21. Jacobs, R., 2001. Alternative methods to examine hospital efficiency: Data Envelopment Analysis and Stochastic Frontier Analysis. Health Care Management Science, 4, pp. 103-115.
  22. Katharaki, M., 2008. Approaching the management of hospital units with an operation research technique: The Case of thirty two Greek Obstetric and Gynaecology Public Units. Health Policy, 85(1), pp.19-31.
  23. Katharakis, G., Katostaras, T., 2012. SFA vs. DEA for measuring healthcare efficiency: A systematic review. Health Policy, (under review).
  24. Lee, R., Bott, M., Gajewski, B., Taunton, R., 2009. Modelling Efficiency at the Process Level: An Examination of the Care Planning Process in Nursing Homes. Health Services Research, 44(1), pp. 15-32.
  25. Linna, M., 1998. Measuring hospital cost efficiency with panel data models. Health Economics, 7, pp. 415-427.
  26. McDonald, J., 2009. Using least squares and tobit in second stage DEA analyses. European Journal of Operational Research, 197, pp. 792-8.
  27. Meeusen, W., Van den Broeck, J., 1977. Efficiency estimation from Cobb-Douglas production function with composed error. International Economic Review, 18, pp. 435-444.
  28. Minvielle, E., Dervaux, B., Retbi, A., Aegerter, Ph., Boumendil, A., Guincestre, M., Tenaillon, A., Guidet, B., 2005. Culture, Organizations, and Management in Intensive Care: Construction and Validation of Multidimensional Questionnaire. Journal of Critical Care, 20(2), pp. 126-38.
  29. Minvielle, E., Phillipe, A., Dervaux, B., 2008. Assessing Organizational Performance in Intensive Care Units: A French Experience. Journal of Critical Care, 23, pp. 236-44.
  30. Mortimer, D., 2002. A Systematic Review of Direct DEA vs SFA/DFA Comparisons, Working Paper 136, Centre for Health and Evaluation, Australia.
  31. Mutter, L., Rosko, D., Greene, H., Wilson, W., 2011. Translating frontiers into practice: taking the next steps toward improving hospital efficiency. Medical Care Research and Review, 68(1), pp. 35-195.
  32. Nedelea, C., Fannin, J., 2012. Efficiency Analysis of Rural Hospitals: Parametric and Semi-parametric Approaches. In Southern Agricultural Economics Association Annual Meeting, 4-7 February 2005, Birmingham, Alabama.
  33. Newhouse, J., 1994. Frontier estimation: how useful a tool for health economics? Journal of Health Economics, 13, pp. 317-322.
  34. Ondrich, J., Ruggiero, J., 2001. Efficiency measurement in the stochastic frontier model. European Journal of Operational Research, 129, pp. 434-442.
  35. Prochazkova, J., 2011. Efficiency of Hospitals in the Czech Republic: DEA & SFA Applications. Ph.D. Faculty of Social Sciences, Institute of Economic Studies, Charles University in Prague.
  36. Rosko, D., Mutter, L., 2011. What have we learned from the application of stochastic frontier analysis to U.S. hospitals? Medical Care Research and Review, 68(1), pp. 75-100.
  37. Simar, L., Wilson, P., 1998. Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science. 44, pp. 49-61.
  38. Simar, L., Wilson, P., 1999. Estimating and bootstrapping Malmquist indices. European Journal of Operational Research, 115, pp. 459-471.
  39. Simar, L., Wilson, P., 2000a. A General methodology for bootstrapping in non-parametric frontier models. Journal of Applied Statistics, 27, pp. 779-802.
  40. Simar, L., Wilson, P., 2000b. Statistical inference in nonparametric frontier models: The state of the art. Journal of Productivity Analysis, 13, pp. 49-78.
  41. Simar, L., Wilson, P., 2007. Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136, pp. 31-64.
  42. Wilson, P., 2010. FEAR 1.15 User's Guide. Available at: <http://www.clemson.edu/economics/faculty/wilson/S oftware/FEAR/FEAR-1.15/fear-user-guide.pdf> [Accessed 25 July 2012].
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Paper Citation


in Harvard Style

Katharakis G., Katharaki M. and Katostaras T. (2013). An Empirical Comparison of DEA and SFA Method to Measure Hospital Units’ Efficiency . In Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-8565-40-2, pages 242-251. DOI: 10.5220/0004274502420251


in Bibtex Style

@conference{icores13,
author={George Katharakis and Maria Katharaki and Theofanis Katostaras},
title={An Empirical Comparison of DEA and SFA Method to Measure Hospital Units’ Efficiency},
booktitle={Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2013},
pages={242-251},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004274502420251},
isbn={978-989-8565-40-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - An Empirical Comparison of DEA and SFA Method to Measure Hospital Units’ Efficiency
SN - 978-989-8565-40-2
AU - Katharakis G.
AU - Katharaki M.
AU - Katostaras T.
PY - 2013
SP - 242
EP - 251
DO - 10.5220/0004274502420251