Malmquist Productivity Index Using Two-stage DEA Model in Heart Hospitals
Subject Areas : Data Envelopment Analysis
علی رضا
علینژاد
1
(دانشیار،گروه مهندسی صنایع، دانشکده مهدسی صنایع و مکانیک،واحد قزوین، دانشگاه آزاد اسلامی،قزوین، ایران)
میرپویا
میرمظفری
2
(PhD Candidate, Department of Computer Science, Montana State University, Bozman)
Keywords: Keywords: DEA, MPI, Intermediate element, Two-stage model,
Abstract :
Abstract Heart patients displays several symptoms and it is hard to point them. Data envelopment analysis (DEA) provides a comparative efficiency degree for each decision-making units (DMUs) with several inputs and outputs. Evaluating of hospitals is one of the major applications in DEA. In this study, a comparison of additive model with standard input oriented and output oriented Malmquist productivity index (MPI) are used. The MPI is calculated to measure productivity growth relative to a reference technology. Two primary subjects are addressed in computation of MPI growth. What are generally referred to as a “catching-up” effect or technical efficiency change (TEC) and a “frontier shift” effect or technological change (TC). The data covers a six-year span from 2011 to 2016 for 15 local heart hospitals. Two inputs, one intermediate element and two outputs are chosen in two-stage model and these factors reflect the main function of hospitals. Conversion of two-stage to single-stage model is introduced. This model is proposed to fix the efficiency of a two-stage process, and avoid the dependence to various weights. Finally, the results indicated that geometry average of MPI in input oriented pure technical efficiency (PTE) in the tenth Hospital (2.1517) is introduced as the highest performance hospital with highest productivity growth.
Aboueljinane, L., Sahin, E., & Jemai, Z. (2013). A review on simulation models applied to emergency medical service operations. Computers & Industrial Engineering, 66(4), 734-750.
Bhattacharjee, P., & Ray, P. K. (2014). Patient flow modelling and performance analysis of healthcare delivery processes in hospitals: A review and reflections. Computers & Industrial Engineering, 78, 299-312.
Bilsel, M., & Davutyan, N. (2011). Hospital efficiency with risk adjusted mortality as undesirable output: the Turkish case, Annals of Operations Research, 1-16.
Bwana, K. M. (2015). Measuring technical efficiency of faith based hospitals in Tanzania: An application of data envelopment analysis (DEA). Research in Applied Economics, 7(1), 1-12.
Caballer-Tarazona, M., Moya-Clemente, I., Vivas-Consuelo, D., & Barrachina-Martínez, I. (2010). A model to measure the efficiency of hospital performance. Mathematical and computer modelling, 52(7), 1095-1102.
Chang, H., Cheng, M. A., & Das, S. (2004). Hospital ownership and operating efficiency: evidence from Taiwan. European Journal of Operational Research, 159(2), 513-527.
Chen, Y., & Ali, A. I. (2004). DEA Malmquist productivity measure: New insights with an application to computer industry. European Journal of Operational Research, 159(1), 239-249.
Chen, Y., Cook, W. D., Li, N., & Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operational Research, 196(3), 1170-1176.
Chen, Y., Cook, W. D., Li, N., & Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operational Research, 196(3), 1170-1176.
Chen, Y., Liang, L., & Zhu, J. (2009b). Equivalence in two-stage DEA approaches. European Journal of Operational Research, 193(2), 600-604.
de Castro Lobo, M. S., Ozcan, Y. A., da Silva, A. C., Lins, M. P. E., & Fiszman, R. (2010). Financing reform and productivity change in Brazilian teaching hospitals: Malmquist approach. Central European Journal of Operations Research, 18(2), 141-152.
Giokas, D. I. (2001). Greek hospitals: how well their resources are used. Omega, 29(1), 73-83.
Gul, M., & Guneri, A. F. (2015). A comprehensive review of emergency department simulation applications for normal and disaster conditions. Computers & Industrial Engineering, 83, 327-344.
Jehu-Appiah, C., Sekidde, S., Adjuik, M., Akazili, J., Almeida, S. D., Nyonator, F., ... & Kirigia, J. M. (2015). Ownership and technical efficiency of hospitals: evidence from Ghana using data envelopment analysis. Cost Effectiveness and Resource Allocation, 12(1), 1-13.
Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European journal of operational research, 185(1), 418-429.
Kawaguchi, H., Tone, K., & Tsutsui, M. (2014). Estimation of the efficiency of Japanese hospitals using a dynamic and network data envelopment analysis model. Health care management science, 17(2), 101-112.
Köse, T., Uçkun, N., & Girginer, N. (2014). An efficiency analysis of the clinical departments of a public hospital in Eskisehir by using DEA. Glob J Adv Pure Appl Sci, 4, 252-258.
Lotfi, F. H., Eshlaghy, A. T., Saleh, H., Nikoomaram, H., & Seyedhoseini, S. M. (2012). A new two-stage data envelopment analysis (DEA) model for evaluating the branch performance of banks. African Journal of Business Management, 6(24), 7230.
Mendis, S., Puska, P., & Norrving, B. (2011). Global atlas on cardiovascular disease prevention and control. World Health Organization.
Mirmozaffari, M., Alinezhad, A., & Gilanpour, A. (2017a). Data Mining Classification Algorithms for Heart Disease Prediction, Int'l Journal of Computing, Communications & Instrumentation Engg., 4(1), 11-15.
Mirmozaffari, M., Alinezhad, A., & Gilanpour, A. (2017b). Heart Disease Prediction with Data Mining Clustering Algorithms, Int'l Journal of Computing, Communications & Instrumentation Engg., 4(1), 16-19.
Mirmozaffari, M., Alinezhad, A., & Gilanpour, A. (2017c). Data Mining Apriori Algorithm for Heart Disease Prediction, Int'l Journal of Computing, Communications & Instrumentation Engg., 4(1), 20-23.
Wang, Y. M., & Chin, K. S. (2010). Some alternative DEA models for two-stage process. Expert Systems with Applications, 37(12), 8799-8808.