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A Data Scientific Approach to Measure Hospital Productivity

In: Data Science and Productivity Analytics

Author

Listed:
  • Babak Daneshvar Rouyendegh (B. Erdebilli)

    (Ankara Yıldırım Beyazıt University)

  • Asil Oztekin

    (University of Massachusetts Lowell)

  • Joseph Ekong

    (Ohio Northern University)

  • Ali Dag

    (Creighton University)

Abstract

This study is aimed at developing a holistic data analytic approach to measure and improve hospital productivity. It is achieved by proposing a fuzzy logic-based multi-criteria decision-making model so as to enhance business performance. Data Envelopment Analysis is utilized to analyze the productivity and then it is hybridized with the Fuzzy Analytic Hierarchy Process to formulate the decision-making model. The simultaneous hybrid use of these two methods is utilized to compile a ranked list of multiple proxies containing diverse input and output variables which occur in two stages. This hybrid methodology presents uniqueness in that it helps make the most suitable decision with the consideration of the weights determined by the data from the hybrid model.

Suggested Citation

  • Babak Daneshvar Rouyendegh (B. Erdebilli) & Asil Oztekin & Joseph Ekong & Ali Dag, 2020. "A Data Scientific Approach to Measure Hospital Productivity," International Series in Operations Research & Management Science, in: Vincent Charles & Juan Aparicio & Joe Zhu (ed.), Data Science and Productivity Analytics, chapter 0, pages 337-358, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-43384-0_12
    DOI: 10.1007/978-3-030-43384-0_12
    as

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