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Measuring the efficiency of hospitals: a fully-ranking DEA–FAHP approach

Author

Listed:
  • Babak Daneshvar Rouyendegh

    (Atilim University
    Auburn University)

  • Asil Oztekin

    (University of Massachusetts Lowell)

  • Joseph Ekong

    (Auburn University)

  • Ali Dag

    (University of South Dakota)

Abstract

The goal of this study is to present a DEA-based fuzzy multi-criteria decision making model for firms in the health care industry in order to enhance their business performance. The study demonstrates a real-life use of the proposed model, mainly designed for hospitals. Data envelopment analysis enhanced with fuzzy analytic hierarchy process are collectively utilized to quantify the data and structure the model in decision-making. The juxtaposition of the two methods is used to compile a ranked list of multiple proxies containing diverse input and output variables which occur in two stages. This hybrid model provides several benefits, one of which is the ability to make the most appropriate decision considering the value of the weights determined by the data from the hybrid model.

Suggested Citation

  • Babak Daneshvar Rouyendegh & Asil Oztekin & Joseph Ekong & Ali Dag, 2019. "Measuring the efficiency of hospitals: a fully-ranking DEA–FAHP approach," Annals of Operations Research, Springer, vol. 278(1), pages 361-378, July.
    • Handle: RePEc:spr:annopr:v:278:y:2019:i:1:d:10.1007_s10479-016-2330-1
    DOI: 10.1007/s10479-016-2330-1
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    References listed on IDEAS

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    1. Friedman, Lea & Sinuany-Stern, Zilla, 1997. "Scaling units via the canonical correlation analysis in the DEA context," European Journal of Operational Research, Elsevier, vol. 100(3), pages 629-637, August.
    2. Joe Zhu, 2015. "DEA Based Benchmarking Models," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 10, pages 291-308, Springer.
    3. Juan Du & Justin Wang & Yao Chen & Shin-Yi Chou & Joe Zhu, 2014. "Incorporating health outcomes in Pennsylvania hospital efficiency: an additive super-efficiency DEA approach," Annals of Operations Research, Springer, vol. 221(1), pages 161-172, October.
    4. Thomas L. Saaty & Luis G. Vargas, 2006. "Decision Making with the Analytic Network Process," International Series in Operations Research and Management Science, Springer, number 978-0-387-33987-0, September.
    5. Merja Halme & Tarja Joro & Pekka Korhonen & Seppo Salo & Jyrki Wallenius, 1999. "A Value Efficiency Approach to Incorporating Preference Information in Data Envelopment Analysis," Management Science, INFORMS, vol. 45(1), pages 103-115, January.
    6. Rouselle Lavado & Emilyn Cabanda, 2009. "The efficiency of health and education expenditures in the Philippines," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 17(3), pages 275-291, September.
    7. Cook, Wade D. & Tone, Kaoru & Zhu, Joe, 2014. "Data envelopment analysis: Prior to choosing a model," Omega, Elsevier, vol. 44(C), pages 1-4.
    8. Adler, Nicole & Friedman, Lea & Sinuany-Stern, Zilla, 2002. "Review of ranking methods in the data envelopment analysis context," European Journal of Operational Research, Elsevier, vol. 140(2), pages 249-265, July.
    9. Babak Daneshvar Rouyendegh, 2011. "The DEA and Intuitionistic Fuzzy TOPSIS Approach to Departments' Performances: A Pilot Study," Journal of Applied Mathematics, Hindawi, vol. 2011, pages 1-16, December.
    10. Reuben Elan & Verma Bharat Bhushan & Bhat Ramesh, 2001. "Hospital Efficiency: An Empirical Analysis of District and Grant-in-Aid Hospitals in Gujarat," IIMA Working Papers WP2001-07-05, Indian Institute of Management Ahmedabad, Research and Publication Department.
    11. Thomas L. Saaty, 2006. "The Analytic Network Process," International Series in Operations Research & Management Science, in: Decision Making with the Analytic Network Process, chapter 0, pages 1-26, Springer.
    12. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    13. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
    14. Miika Linna, 1998. "Measuring hospital cost efficiency with panel data models," Health Economics, John Wiley & Sons, Ltd., vol. 7(5), pages 415-427, August.
    15. Kontodimopoulos, Nick & Niakas, Dimitris, 2005. "Efficiency measurement of hemodialysis units in Greece with data envelopment analysis," Health Policy, Elsevier, vol. 71(2), pages 195-204, February.
    16. Cook, Wade D. & Liang, Liang & Zhu, Joe, 2010. "Measuring performance of two-stage network structures by DEA: A review and future perspective," Omega, Elsevier, vol. 38(6), pages 423-430, December.
    17. Joe Zhu, 2014. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 1, pages 1-9, Springer.
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    2. Pourmahmoud, Jafar & Bagheri, Narges, 2023. "Uncertain Malmquist productivity index: An application to evaluate healthcare systems during COVID-19 pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    3. Pengyue Wu & Jing Ma & Xiaoyu Guo, 2022. "Efficiency evaluation and influencing factors analysis of fiscal and taxation policies: A method combining DEA-AHP and CD function," Annals of Operations Research, Springer, vol. 309(1), pages 325-345, February.
    4. Esther Jose & Puneet Agarwal & Jun Zhuang & Jose Swaminathan, 2023. "A multi-criteria decision making approach to evaluating the performance of Indian railway zones," Annals of Operations Research, Springer, vol. 325(2), pages 1133-1168, June.
    5. Yu Shi & Anyu Yu & Huong Ngo Higgins & Joe Zhu, 2021. "Shared and unsplittable performance links in network DEA," Annals of Operations Research, Springer, vol. 303(1), pages 507-528, August.
    6. Diogo Ferraz & Enzo Barberio Mariano & Patricia Regina Manzine & Herick Fernando Moralles & Paulo César Morceiro & Bruno Guimarães Torres & Mariana Rodrigues Almeida & João Carlos Soares de Mello & Da, 2021. "COVID Health Structure Index: The Vulnerability of Brazilian Microregions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 158(1), pages 197-215, November.
    7. Jorge Antunes & Abdollah Hadi-Vencheh & Ali Jamshidi & Yong Tan & Peter Wanke, 2022. "Bank efficiency estimation in China: DEA-RENNA approach," Annals of Operations Research, Springer, vol. 315(2), pages 1373-1398, August.

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