Measuring Productivity Changes of Hospitals in Tehran: The Fuzzy Malmquist Productivity Index

Document Type: Research Paper

Authors

1 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

2 Faculty of Mathematics, Science and Research Branch, Islamic Azad University,Tehran, Iran

3 Faculty of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract

Background and Objectives: The purpose of this paper is to suggest a novel method to measure the productivity changes of hospitals over time in the presence of linguistic variables along with fuzzy data.
Methods: Applying the popular and applicable approaches including data envelopment analysis (DEA), Malmquist productivity index (MPI) and possibilistic programming, the fuzzy Malmquist productivity index (FMPI) is proposed.
Results: In this study, the proposed fuzzy MPI is implemented for measuring productivity changes of 10 hospitals in Tehran. Notably, the input variables include the number of beds, the number of doctors, equipment & infrastructures and hospital location. Also, the output variables include the number of inpatient days, the number of outpatient, and overall patient satisfaction. According to the obtained results, the productivity of 5 hospitals has increased in 2014 in comparison to 2013.
Conclusions: The obtained results have shown the capability of the proposed index to calculate the changes in productivity of hospitals in the presence of ambiguity in data.

Keywords


  1. Bahadori M, Izadi AR, Ghardashi F, Ravangard R, Hosseini SM. The evaluation of hospital performance in Iran: a systematic review article. Iranian Journal of Public Health. 2016;45(7):855.
  2. Kohl S, Schoenfelder J, Fügener A, Brunner JO. The use of data envelopment analysis (DEA) in healthcare with a focus on hospitals. Health Care Management Science. 2019;22(2):245-86.
  3. Mahdiyan S, Dehghani A, Tafti AD, Pakdaman M, Askari R. Hospitals' efficiency in Iran: A systematic review and meta-analysis. Journal of Education and Health Promotion. 2019;8(1):126.
  4. Farrell MJ. The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General). 1957;120(3):253-290.
  5. Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research. 1978;2(6):429-44.
  6. Banker RD, Charnes A, Cooper WW. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science. 1984;30(9):1078-92.
  7. Peykani P, Mohammadi E, Seyed Esmaeili FS. Stock evaluation under mixed uncertainties using robust DEA model. Journal of Quality Engineering and Production Optimization. 2019.
  8. Peykani P, Mohammadi E, Jabbarzadeh A, Jandaghian A. Utilizing robust data envelopment analysis model for measuring efficiency of stock, a case study: Tehran Stock Exchange. Journal of New Research in Mathematics. 2016;1(4):15-24.
  9. Hatam N, Moslehi S, Askarian M, Shokrpour N, Keshtkaran A, Abbasi M. The efficiency of general public hospitals in Fars Province, Southern Iran. Iranian Red Crescent Medical Journal. 2010;12(2):138.
  10. Lotfi F, Kalhor R, Bastani P, Zadeh NS, Eslamian M, Dehghani MR, Kiaee MZ. Various indicators for the assessment of hospitals' performance status: differences and similarities. Iranian Red Crescent Medical Journal. 2014;16(4).
  11. Torabipour A, Najarzadeh M, Mohammad AR, Farzianpour F, Ghasemzadeh R. Hospitals productivity measurement using data envelopment analysis technique. Iranian Journal of Public Health. 2014;43(11):1576.
  12. Raei B, Yousefi M, Rahmani K, Afshari S, Ameri H. Patterns of productivity changes in hospitals by using Malmquist–DEA Index: a panel data analysis (2011–2016). Australasian Medical Journal. 2017;10(10):856-64.
  13. Alinezhad A. Malmquist productivity index using two-stage DEA model in heart hospitals. Iranian Journal of Optimization. 2018;10(2):81-92.
  14. Ebrahimnejad A. Cost efficiency measures with trapezoidal fuzzy numbers in data envelopment analysis based on ranking functions: application in insurance organization and hospital. International Journal of Fuzzy System Applications. 2012;2(3):51-68.
  15. Hatami-Marbini A, Tavana M, Emrouznejad A. Productivity growth and efficiency measurements in fuzzy environments with an application to health care. International Journal of Fuzzy System Applications. 2012;2(2):1-35.
  16. Khaki AR, Sadjadi SJ, Gharakhani M, Rashidi S. Data envelopment analysis under uncertainty: A case study from public healthcare. African Journal of Business Management. 2012;6(24):7096-105.
  17. Costantino N, Dotoli M, Epicoco N, Falagario M, Sciancalepore F. Using cross-efficiency fuzzy Data Envelopment Analysis for healthcare facilities performance evaluation under uncertainty. IEEE International Conference on Systems, Man, and Cybernetics. 2013;912-917.
  18. De Nicola A, Gitto S, Mancuso P. Evaluating Italian public hospital efficiency using bootstrap DEA and CART. International Journal of Applied Decision Sciences. 2013;6(3):281-92.
  19. Kalantary Z, Azar A. A robust data envelopment analysis model for ranking: A case of hospitals of Tehran. Data Envelopment Analysis and Performance Measurement. 2014;Proceedings of the 11th International Conference of DEA:21-28.
  20. Karadayi MA, Karsak EE. Imprecise DEA framework for evaluating the efficiency of state hospitals in Istanbul. Proceedings of the World Congress on Engineering. 2014.
  21. Haji-Sami E, Ebrahimi Khameneh M, Rezaeisaray M, Esmaeili S. Robust DEA under discrete uncertain data: An application for Iranian hospital emergency. 8th International Conference of Iranian Operations Research Society, Iran. 2015.
  22. Kheirollahi H, Matin BK, Mahboubi M, Alavijeh MM. Chance constrained input relaxation to congestion in stochastic DEA. An application to Iranian hospitals. Global Journal of Health Science. 2015;7(4):151.
  23. Mitropoulos P, Talias ΜA, Mitropoulos I. Combining stochastic DEA with Bayesian analysis to obtain statistical properties of the efficiency scores: An application to Greek public hospitals. European Journal of Operational Research. 2015;243(1):302-11.
  24. Rabbani M, Heidari N, Farrokhi Asl H. A bootstrap interval robust data envelopment analysis for estimate efficiency and ranking hospitals. Journal of Industrial Engineering and Management Studies. 2016;3(2):107-22.
  25. Arya A, Yadav SP. A fuzzy dual SBM model with fuzzy weights: an application to the health sector. Proceedings of 6th International Conference on Soft Computing for Problem Solving, Singapore. 2017;230-238.
  26. Karsak EE, Karadayi MA. Imprecise DEA framework for evaluating health-care performance of districts. Kybernetes. 2017;46(4):706-27.
  27. Kheirollahi H, Hessari P, Charles V, Chawshini R. An input relaxation model for evaluating congestion in fuzzy DEA. Croatian Operational Research Review. 2017;8(2):391-408.
  28. Wu D, Wu DD. Risk-Based Robust Evaluation of Hospital Efficiency. IEEE Systems Journal. 2018;13(2):1906-14.
  29. Hatefi SM, Haeri A. Evaluating hospital performance using an integrated balanced scorecard and fuzzy data envelopment analysis. Journal of Health Management and Informatics. 2019;6(2):66-76.
  30. Peykani P, Mohammadi E, Emrouznejad A, Pishvaee MS, Rostamy-Malkhalifeh M. Fuzzy Data Envelopment Analysis: An Adjustable Approach. Expert Systems with Applications. 2019;136:439-452.
  31. Färe R, Grosskopf S. Malmquist productivity indexes and Fisher ideal indexes. The Economic Journal. 1992;102(410):158-60.
  32. Emrouznejad A, Tavana M. Performance Measurement with Fuzzy Data Envelopment Analysis. Springer; 2014.
  33. Hatami-Marbini A, Emrouznejad A, Tavana M. A taxonomy and review of the fuzzy data envelopment analysis literature: two decades in the making. European Journal of Operational Research. 2011;214(3):457-72.
  34. Lertworasirikul S, Fang SC, Joines JA, Nuttle HL. Fuzzy data envelopment analysis (DEA): a possibility approach. Fuzzy Sets and Systems. 2003;139(2):379-94.
  35. Peykani P, Mohammadi E, Pishvaee MS, Rostamy-Malkhalifeh M, Jabbarzadeh A. A novel fuzzy data envelopment analysis based on robust possibilistic programming: possibility, necessity and credibility-based approaches. RAIRO-Operations Research. 2018;52(4):1445-63.
  36. Peykani P, Mohammadi E, Rostamy-Malkhalifeh M, Hosseinzadeh Lotfi F. Fuzzy data envelopment analysis approach for ranking of stocks with an application to Tehran stock exchange. Advances in Mathematical Finance and Applications. 2019;4(1):31-43.
  37. Zadeh LA. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems. 1978;1(1):3-28.
  38. Emrouznejad A, Yang GL. A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences. 2018;61:4-8.
  39. Peykani P, Mohammadi E. Interval network data envelopment analysis model for classification of investment companies in the presence of uncertain data. Journal of Industrial and Systems Engineering. 2018;11(Special issue: 14th International Industrial Engineering Conference):63-72.
  40. Peykani P, Mohammadi E. Window network data envelopment analysis: an application to investment companies. International Journal of Industrial Mathematics. 2019.
  41. Hosseini-Motlagh SM, Ghatreh Samani MR, Cheraghi S. Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-Economic Planning Sciences. 2019.
  42. Hosseini-Motlagh SM, Ghatreh Samani MR, Homaei S. Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real-life case). Journal of Ambient Intelligence and Humanized Computing. 2019:1-20.
  43. Ghatreh Samani MR, Hosseini-Motlagh SM, Ghannadpour SF. A multilateral perspective towards blood network design in an uncertain environment: Methodology and implementation. Computers & Industrial Engineering. 2019;130:450-71.
  44. Ghatreh Samani M, Hosseini-Motlagh SM. A hybrid algorithm for a two-echelon location-routing problem with simultaneous pickup and delivery under fuzzy demand. International Journal of Transportation Engineering. 2017;5(1):59-85.
  45. Cheraghi S, Hosseini-Motlagh SM, Ghatreh Samani M. Integrated planning for blood platelet production: a robust optimization approach. Journal of Industrial and Systems Engineering. 2017;10(special issue on healthcare):55-80.
  46. Sedady F, Beheshtinia MA. A novel MCDM model for prioritizing the renewable power plants’ construction. Management of Environmental Quality: An International Journal. 2019;30(2):383-99.
  47. Beheshtinia MA, Omidi S. A hybrid MCDM approach for performance evaluation in the banking industry. Kybernetes. 2017;46(8):1386-407.
  48. Torkzad A, Beheshtinia MA. Evaluating and prioritizing hospital service quality. International Journal of Health Care Quality Assurance. 2019;32(2):332-46.