Efficiency Analysis of Tehran Hospitals’ Emergency Departments through Data Envelopment Analysis with Undesirable Factors

Authors

  • Abbasali Monzeli * Department of Mathematics, South Tehran Branch, Islamic Azad University, Tehran, Iran.
  • Behrouz Daneshian Department of Mathematics, South Tehran Branch, Islamic Azad University, Tehran, Iran.

https://doi.org/10.22105/ahse.v2i2.34

Abstract

In this paper, the efficiency of Tehran hospital emergency departments is measured using Data Envelopment Analysis (DEA) when undesirable input and output factors are present. As traditional DEA models cannot directly handle undesirable factors, this research has overcome this limitation through a redefined framework. The proposed approach first determines the production possibility set under the given problem assumptions. Then it develops a new DEA model to evaluate Decision Making Unit (DMU) efficiency, taking into account the impact of undesirable factors on the efficiency frontier. The utilization of the model with real data from Tehran Hospital's emergency departments confirms the need to account for undesirable factors when measuring efficiency and developing improvement plans. This research helps measure emergency department efficiency and manage unwanted factors.

Keywords:

Data envelopment analysis, Undesirable inputs and outputs, Efficiency, Efficient frontier

References

  1. [1] Koopmans, T. C. (1951). An analysis of production as an efficient combination of activities. In Activity analysis of production and allocation. Wiley. https://cir.nii.ac.jp/crid/1572824499992043008
  2. [2] Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the royal statistical society series a: Statistics in society, 120(3), 253–281. https://doi.org/10.2307/2343100
  3. [3] Cooper, W. W., Deng, H., Huang, Z., & Li, S. X. (2002). Chance constrained programming approaches to technical efficiencies and inefficiencies in stochastic data envelopment analysis. Journal of the operational research society, 53(12), 1347–1356. https://doi.org/10.1057/palgrave.jors.2601433
  4. [4] Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
  5. [5] Chen, Y., & Iqbal Ali, A. (2002). Output–input ratio analysis and DEA frontier. European journal of operational research, 142(3), 476–479. https://doi.org/10.1016/S0377-2217(01)00318-6
  6. [6] Golany, B., & Roll, Y. (1989). An application procedure for DEA. Omega, 17(3), 237–250. https://doi.org/10.1016/0305-0483(89)90029-7
  7. [7] Seiford, L. M., & Thrall, R. M. (1990). Recent developments in DEA: The mathematical programming approach to frontier analysis. Journal of econometrics, 46(1), 7–38. https://doi.org/10.1016/0304-4076(90)90045-U
  8. [8] Pittman, R. W. (1983). Multilateral productivity comparisons with undesirable outputs. The economic journal, 93(372), 883–891. https://doi.org/10.2307/2232753
  9. [9] Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica, 50(6), 1393–1414. https://doi.org/10.2307/1913388
  10. [10] Färe, R., Grosskopf, S., Lovell, C. A. K., & Yaisawarng, S. (1993). Derivation of shadow prices for undesirable outputs: A distance function approach. The review of economics and statistics, 75(2), 374–380. http://www.jstor.org/stable/2109448
  11. [11] Chung, Y. H., Färe, R., & Grosskopf, S. (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of environmental management, 51(3), 229–240. https://doi.org/10.1006/jema.1997.0146
  12. [12] Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European journal of operational research, 197(1), 243–252. https://doi.org/10.1016/j.ejor.2008.05.027
  13. [13] Akazili, J., Adjuik, M., Chatio, S., Kanyomse, E., Hodgson, A., Aikins, M., & Gyapong, J. (2008). What are the technical and allocative efficiencies of public health centres in Ghana? Ghana medical journal, 42(4), 149–155. https://pmc.ncbi.nlm.nih.gov/articles/PMC2673839/
  14. [14] Banker, R. D., & Morey, R. C. (1986). Efficiency analysis for exogenously fixed inputs and outputs. Operations research, 34(4), 513–521. https://doi.org/10.1287/opre.34.4.513
  15. [15] Scheel, H. (2001). Undesirable outputs in efficiency valuations. European journal of operational research, 132(2), 400–410. https://doi.org/10.1016/S0377-2217(00)00160-0
  16. [16] Cook, W. D., Kress, M., & Seiford, L. M. (1996). Data envelopment analysis in the presence of both quantitative and qualitative factors. Journal of the operational research society, 47(7), 945–953. https://doi.org/10.1057/jors.1996.120
  17. [17] Kim, S. H., Park, C. G., & Park, K. S. (1999). An application of data envelopment analysis in telephone officesevaluation with partial data. Computers & operations research, 26(1), 59–72. https://doi.org/10.1016/S0305-0548(98)00041-0
  18. [18] Toloo, M., & Nalchigar, S. (2011). On ranking discovered rules of data mining by data envelopment analysis: Some new models with applications. In New fundamental technologies in data mining (pp. 425). BoD-Books on Demand. https://doi.org/10.5772/13659
  19. [19] Amin, G. R., & Toloo, M. (2007). Finding the most efficient DMUs in DEA: An improved integrated model. Computers & industrial engineering, 52(1), 71–77. https://doi.org/10.1016/j.cie.2006.10.003
  20. [20] Toloo, M., & Nalchigar, S. (2011). A new DEA method for supplier selection in presence of both cardinal and ordinal data. Expert systems with applications, 38(12), 14726–14731. https://doi.org/10.1016/j.eswa.2011.05.008
  21. [21] Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V, Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European journal of operational research, 132(2), 245–259. https://doi.org/10.1016/S0377-2217(00)00149-1
  22. [22] Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European journal of operational research, 142(1), 16–20. https://doi.org/10.1016/S0377-2217(01)00293-4
  23. [23] Kao, C. (2017). Network data envelopment analysis: Foundations and extensions. Springer. https://doi.org/10.1007/978-3-319-31718-2
  24. [24] Yang, M., Li, Y., Chen, Y., & Liang, L. (2014). An equilibrium efficiency frontier data envelopment analysis approach for evaluating decision-making units with fixed-sum outputs. European journal of operational research, 239(2), 479–489. https://doi.org/10.1016/j.ejor.2014.05.013
  25. [25] Zhu, Q., Wu, J., Song, M., & Liang, L. (2017). A unique equilibrium efficient frontier with fixed-sum outputs in data envelopment analysis. The journal of the operational research society, 68(12), 1483–1490. https://doi.org/10.1057/s41274-017-0181-z
  26. [26] Liu, F. H. F., & Hsuan Peng, H. (2008). Ranking of units on the DEA frontier with common weights. Computers & operations research, 35(5), 1624–1637. https://doi.org/10.1016/j.cor.2006.09.006
  27. [27] Wu, J., An, Q., Yao, X., & Wang, B. (2014). Environmental efficiency evaluation of industry in China based on a new fixed sum undesirable output data envelopment analysis. Journal of cleaner production, 74, 96–104. https://doi.org/10.1016/j.jclepro.2014.03.054
  28. [28] Li, F., Zhu, Q., & Liang, L. (2019). A new data envelopment analysis based approach for fixed cost allocation. Annals of operations research, 274(1), 347–372. https://doi.org/10.1007/s10479-018-2819-x
  29. [29] Kao, C., & Hwang, S.-N. (2019). Efficiency evaluation in the presence of undesirable outputs: The most favorable shadow price approach. Annals of operations research, 278(1), 5–16. https://doi.org/10.1007/s10479-017-2399-1
  30. [30] Wang, N., Chen, J., Yao, S., & Chang, Y. C. (2018). A meta-frontier DEA approach to efficiency comparison of carbon reduction technologies on project level. Renewable and sustainable energy reviews, 82, 2606–2612. https://doi.org/10.1016/j.rser.2017.09.088
  31. [31] Dakpo, K. H., Jeanneaux, P., & Latruffe, L. (2016). Modelling pollution-generating technologies in performance benchmarking: Recent developments, limits and future prospects in the nonparametric framework. European journal of operational research, 250(2), 347–359. https://doi.org/10.1016/j.ejor.2015.07.024
  32. [32] Pham, M. D., & Zelenyuk, V. (2019). Weak disposability in nonparametric production analysis: A new taxonomy of reference technology sets. European journal of operational research, 274(1), 186–198. https://doi.org/10.1016/j.ejor.2018.09.019
  33. [33] Podinovski, V. V. (2019). Direct estimation of marginal characteristics of nonparametric production frontiers in the presence of undesirable outputs. European journal of operational research, 279(1), 258–276. https://doi.org/10.1016/j.ejor.2019.05.024
  34. [34] Shi, H., Wang, Y., & Zhang, X. (2019). A cross-efficiency evaluation method based on evaluation criteria balanced on interval weights. Symmetry, 11(12), 1503. https://doi.org/10.3390/sym11121503

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Published

2025-04-18

Issue

Vol. 2 No. 2 (2025): Annals of Healthcare Systems Engineering

Section

Articles

How to Cite

Monzeli, A. ., & Daneshian, B. . (2025). Efficiency Analysis of Tehran Hospitals’ Emergency Departments through Data Envelopment Analysis with Undesirable Factors. Annals of Healthcare Systems Engineering, 2(2), 65-75. https://doi.org/10.22105/ahse.v2i2.34

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