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A novel inverse DEA-R model with application in hospital efficiency

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  • Ghiyasi, Mojtaba
  • Soltanifar, Mehdi
  • Sharafi, Hamid

Abstract

The inverse Data Envelopment Analysis (DEA) method is primarily used to analyze the changing relationship between the inputs and outputs of production units while preserving efficiency levels. This paper aims to perform an efficiency and post-efficiency analysis of 130 public hospitals in Iran. However, we faced with some hospital data that are only available in form of ratios. This motivates us to develop a novel and proper inverse DEA methodology capable of dealing with such cases. We develop the theoretical foundation of the inverse DEA-R model as a post-efficiency analysis approach. Both input-oriented and output-oriented inverse models are developed. In the end, we apply the proposed models for efficiency assessment and moreover inverse analysis of 130 public hospitals in Iran.

Suggested Citation

  • Ghiyasi, Mojtaba & Soltanifar, Mehdi & Sharafi, Hamid, 2022. "A novel inverse DEA-R model with application in hospital efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
  • Handle: RePEc:eee:soceps:v:84:y:2022:i:c:s0038012122002282
    DOI: 10.1016/j.seps.2022.101427
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    References listed on IDEAS

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    1. Mojtaba Ghiyasi & Sahar Khoshfetrat, 2019. "Preserve the relative efficiency values: an inverse data envelopment analysis with imprecise data," International Journal of Procurement Management, Inderscience Enterprises Ltd, vol. 12(3), pages 243-257.
    2. Puig-Junoy, Jaume, 2000. "Partitioning input cost efficiency into its allocative and technical components: an empirical DEA application to hospitals," Socio-Economic Planning Sciences, Elsevier, vol. 34(3), pages 199-218, September.
    3. M. Eyni & G. Tohidi & S. Mehrabeian, 2017. "Applying inverse DEA and cone constraint to sensitivity analysis of DMUs with undesirable inputs and outputs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(1), pages 34-40, January.
    4. Sebastian Kohl & Jan Schoenfelder & Andreas Fügener & Jens O. Brunner, 2019. "The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals," Health Care Management Science, Springer, vol. 22(2), pages 245-286, June.
    5. Wei, Quanling & Zhang, Jianzhong & Zhang, Xiangsun, 2000. "An inverse DEA model for inputs/outputs estimate," European Journal of Operational Research, Elsevier, vol. 121(1), pages 151-163, February.
    6. Khushalani, Jaya & Ozcan, Yasar A., 2017. "Are hospitals producing quality care efficiently? An analysis using Dynamic Network Data Envelopment Analysis (DEA)," Socio-Economic Planning Sciences, Elsevier, vol. 60(C), pages 15-23.
    7. Gholam R. Amin & Ali Emrouznejad & Said Gattoufi, 2017. "Modelling generalized firms’ restructuring using inverse DEA," Journal of Productivity Analysis, Springer, vol. 48(1), pages 51-61, August.
    8. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    9. Ozren Despić & Mladen Despić & Joseph Paradi, 2007. "DEA-R: ratio-based comparative efficiency model, its mathematical relation to DEA and its use in applications," Journal of Productivity Analysis, Springer, vol. 28(1), pages 33-44, October.
    10. Lim, Dong-Joon, 2016. "Inverse DEA with frontier changes for new product target setting," European Journal of Operational Research, Elsevier, vol. 254(2), pages 510-516.
    11. Ali Emrouznejad & Guo-liang Yang & Gholam R. Amin, 2019. "A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(7), pages 1079-1090, July.
    12. See, Kok Fong & Md Hamzah, Nurhafiza & Yu, Ming-Miin, 2021. "Metafrontier efficiency analysis for hospital pharmacy services using dynamic network DEA framework," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
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    Cited by:

    1. Zhang, Cai Wen & Yang, Yuanhui, 2023. "Appraisal of regional hospital efficiency and healthcare quality in China: Impacts of subsidies and marketization," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).

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