The Application of Data Envelopment Analysis in Healthcare Performance Evaluation of Rehabilitation Departments in Hungary

Rita Veronika Dénes (1), Judit Kecskés (2), Tamás Koltai (3), Zoltán Dénes (4)
(1) Budapest University of Technology and Economics, Hungary,
(2) Department of Mangement and Corporate Economics of Budapest University of Technology and Economics, Hungary,
(3) Budapest University of Technology, Hungary,
(4) National Institute for Medical Rehabilitation Budapest, Hungary

Abstract

Purpose: Performance evaluation is a general problem both in production and service systems. Generally, operation performance is determined based on input resource utilization and on outputs related data. Performance evaluation is especially complicated when both financial and nonfinancial indicators must be considered in the evaluation of the efficiency of healthcare system. The purpose of this paper is to apply data envelopment analysis (DEA) in order to measure the efficiency of rehabilitation departments curing musculoskeletal diseases.

Methodology/Approach: The evaluation of the efficiency of rehabilitation departments includes several parameters. Performance evaluation becomes complicated when several evaluation criteria must be taken into consideration at the same time. In these cases, scoring methods are generally used, which transform performance data into a common scale and an aggregate score is calculated with subjective weights. Using DEA the subjective element of evaluation is eliminated when the weights of inputs and outputs are determined.

Findings: The applied DEA model evaluates the performance of rehabilitation departments. The presented analysis highlights the differences between the efficiency of the studied departments, and explores inefficiencies related to economies of scale. The slack values directly show the operational shortcomings in specific areas, and indicate the exact amount of the required changes.

Research Limitation/implication: The applied DEA model evaluates the performance of rehabilitation departments. The presented analysis highlights the differences between the efficiency of the studied departments, and explores inefficiencies related to economies of scale. The slack values directly show the operational shortcomings in specific areas, and indicate the exact amount of the required changes.

Originality/Value of paper: The originality of the paper lies on the identification of inputs and outputs for the applied DEA model as only nonfinancial indicators were taken into consideration. The analysis involves all rehabilitation departments of the Hungarian healthcare system; consequently, conclusions related to the general state of this area can be drawn.

Full text article

Generated from XML file

References

Akazili, J., Adjuik, M., Jehu-Appiah, C. and Zere, E., 2008. Using data envelopment analysis to measure the extent of technical efficiency of public health centres in Ghana. BMC International Health and Human Rights, [e-journal], pp.8-11. http://dx.doi.org/10.1186/1472-698X-8-11.

Asandului, L., Roman, M. and Puiu, F., 2014. The efficiency of health systems in Europe: a Data Envelopment Analysis Approach. Procedia Economics and Finance, 10, pp.261-268.

Boussofiane, A., Dyson, R.G. and Thanassoulis, E., 1991. Applied data envelopment analysis. European Journal of Operations Research, 53, pp.1-15.

Charnes, A., Cooper, W.W. and Rodes, A., 1978. Measuring the efficiency of decision making units. European Journal of Operations Research, 2, pp.429-444.

Csákvári, T., Turcsányi, K., Ágoston, I., Endrei, D. and Boncz, I., 2014. Az aktív fekvőbeteg-szakellátás hatékonysága és mérési lehetőségei (Efficiency and measuring possibilities of active in-patient care). IME egészség-gazdaságtan különszám, 13(special issue), pp.29-32.

Dénes, R., 2015. Indicators in the quality development of health care. In: SGEM, 2nd International Multidisciplinary Scientific Conference on Social Sciences and Arts, Albena, Bulgaria, 26 August - 01 September 2015. Book 1 (Psychology, Psychiatry, Education, Sociology & Healthcare). pp.605-612.

Donabedian, A., 1980. The definition of quality and approaches to its management. Vol 1: Explorations in Quality Assessment and Monitoring. Ann Arbor, Mich: Health Administration Press.

Hadad, S., Hadad, Y. and Simon-Tuval, T., 2011. Determinants of health system’s efficiency in OECD countries. European Journal of Health Economy, 14, pp.253-265.

Hwang, S.N. and Chang, T.Y., 2003. Using data envelopment analysis to measure hotel managerial efficiency change in Taiwan. Tourism Management, 24(4), pp.357-369.

Kirigia, J.M., Emrouznejad, A. and Sambo, L.G., 2002. Measurement of technical efficiency of public hospitals in Kenya: using data envelopment analysis. Journal of Medical Systems, 26(1), pp.39-45.

Koltai, T., Lozano, S., Uzonyi-Kecskés, J. and Moreno, P., 2017. Evaluation of the results of a production simulation game using a dynamic DEA approach. Computers and Industrial Engineering, 10, pp.1-11.

Portafke, N., 2010. The growth of public health expenditures in OECD countries: Do government ideology and electoral motives matter?. Journal of Health Economy, 29(6), pp.797-810.

Reynolds, D. and Thompson, G.M., 2007. Multiunit restaurant productivity assessment using three-phase data envelopment analysis. International Journal of Hospitality Management, 26(1), pp.20-32.

Rivera, B., 2010. The effects of public health spending on self-assessed health status: an ordered probit model. Applied Economics, 33(10), pp.1313-1319.

Rosko, M., 1990. Measuring technical efficiency in health care organizations. Journal of Medical Systems, 14(5), pp.307-322.

Schoenberg, N.E., Kim, H., Edwards, W. and Fleming, S.T., 2007. Burden of Common Multiple Morbidity Constellations on Out-of-Pocket Medical Expenditures Among Older Adults. The Gerontologist, 47(4), pp.423-437.

Sherman, H., 1984. Hospital efficiency measurement and evaluation: empirical test of a new technique. Medical Care, 22(10), pp.922-938.

Spinks, J. and Hollingsworth, B., 2009. Cross-country comparisons of technical efficiency of health production: a demonstration of pitfalls. Applied Economics, 41, pp.417-427.

Tone, K., 2001. A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130, pp.498-509.

Vitrai, J. and Vokó, Z., 2012. A hazai egészségmonitorozás lehetséges szerepe az egészségügyi rendszer teljesítményének mérésében és alkalmazásának aktuális problémái (The possible role of healthy monitoring in measuring the of output of Hungarian health system and the problems of implementation). Egészségügyi Gazdasági Szemle, 50(2), pp.33-36.

Vos, T., Murray, Ch. and Barber, R., 2015. Global Burden of Disease Study, Global, regional and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet, 386(9995), pp.743-800.

Authors

Rita Veronika Dénes
denes.rita.veronika@gmail.com (Primary Contact)
Judit Kecskés
Tamás Koltai
Zoltán Dénes
Author Biographies

Rita Veronika Dénes, Budapest University of Technology and Economics

PhD student at the Budapest University of Technology and Economics (Department of Management and Corporate Economics).

Research field: Tools of the quality management in the healthcare 

Tamás Koltai, Budapest University of Technology

Department of Management and Corporate Economics of the Budapest University of Technology
Dénes, R. V., Kecskés, J., Koltai, T., & Dénes, Z. (2017). The Application of Data Envelopment Analysis in Healthcare Performance Evaluation of Rehabilitation Departments in Hungary. Quality Innovation Prosperity, 21(3), 127–142. https://doi.org/10.12776/qip.v21i3.920

Article Details

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.

Suggestions for Integrated BSC-DEA Implementation in the Industry 4.0 Company

Michaela Kočišová, Milan Fiľo, Jaroslava Kádárová
Abstract View : 236
Download :53

Assessing Public Water Management Efficiency in North-western Mexico with a Two-stage Bootstrap Data Envelopment Analysis Model

Martin Flegl, David Güemes-Castorena, Marien Morán-Valencia, Aldo I. Ramírez
Abstract View : 822
Download :129

Comparative Analysis of Innovation Districts to Set Up Performance Goals for Tec Innovation District

Jaime Eduardo Alarcón-Martínez, David Güemes-Castorena, Martin Flegl
Abstract View : 1059
Download :483