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Integration of Data Envelopment Analysis and Clustering Methods

Author

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  • Hassan Najadat

    (Jordan University of Science & Technology, Jordan2Texas A& M, San Antonio, USA)

  • Ahmad Alaiad

    (Jordan University of Science & Technology, Jordan2Texas A& M, San Antonio, USA)

  • Sanaa Abu Alasal

    (Jordan University of Science & Technology, Jordan2Texas A& M, San Antonio, USA)

  • Ghadeer Anwar Mrayyan

    (Jordan University of Science & Technology, Jordan2Texas A& M, San Antonio, USA)

  • Izzat Alsmadi

    (Jordan University of Science & Technology, Jordan2Texas A& M, San Antonio, USA)

Abstract

Data Envelopment Analysis (DEA) has been applied creatively in various study domains to compare and evaluate different Decision Making Units (DMUs) based on multiple input–output attributes. In this paper, the performance of Jordanian public hospitals is assessed via a methodology combining DEA with data mining methods, specifically, clustering. Initially, inputs of inefficient hospitals were altered to check for waste in the allocated resources. Then, the number of inputs–outputs was manipulated to test if the number is strongly influencing the productivity of the DMUs. The number of DMUs used was 27 public hospitals and the applicable efficiency measurements used were constant return to scale (CRS) and variable return to scale (VRS) through the DEAP software. Experiments showed that the efficiency of a hospital might be more meaningfully assessed if it is compared with a group of hospitals that are similar in some factors. More specifically, results of applying the CRS model proved that 77% of the hospitals were efficient. Additionally, we found that the inefficiencies of some hospitals are linked to weak resource utilization. It is concluded that number of inputs–outputs inserted in the efficiency evaluation process impacts the resulted values.

Suggested Citation

  • Hassan Najadat & Ahmad Alaiad & Sanaa Abu Alasal & Ghadeer Anwar Mrayyan & Izzat Alsmadi, 2020. "Integration of Data Envelopment Analysis and Clustering Methods," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-19, March.
    • Handle: RePEc:wsi:jikmxx:v:19:y:2020:i:01:n:s0219649220400067
    DOI: 10.1142/S0219649220400067
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    References listed on IDEAS

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    1. Robert Stefko & Beata Gavurova & Kristina Kocisova, 2018. "Healthcare efficiency assessment using DEA analysis in the Slovak Republic," Health Economics Review, Springer, vol. 8(1), pages 1-12, December.
    2. Carlos Barros & António Gomes de Menezes & Nicolas Peypoch & Bernardin Solonandrasana & José Vieira, 2008. "An analysis of hospital efficiency and productivity growth using the Luenberger indicator," Health Care Management Science, Springer, vol. 11(4), pages 373-381, December.
    3. Linna, Miika & Hakkinen, Unto & Magnussen, Jon, 2006. "Comparing hospital cost efficiency between Norway and Finland," Health Policy, Elsevier, vol. 77(3), pages 268-278, August.
    4. Butler, Timothy W. & Li, Ling, 2005. "The utility of returns to scale in DEA programming: An analysis of Michigan rural hospitals," European Journal of Operational Research, Elsevier, vol. 161(2), pages 469-477, March.
    5. Joseph G. Hirschberg & Jenny N. Lye, 2001. "Clustering in a Data Envelopment Analysis Using Bootstrapped Efficiency Scores," Department of Economics - Working Papers Series 800, The University of Melbourne.
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    1. Kosycarz, Ewa & Dędys, Monika & Ekes, Maria & Wranik, Wiesława Dominika, 2023. "The effects of provider contract types and fiscal decentralization on the efficiency of the Polish hospital sector: A data envelopment analysis across 16 health regions," Health Policy, Elsevier, vol. 129(C).

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