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Efficiency of Diabetes Treatment

In: Operations Research Applications in Health Care Management

Author

Listed:
  • Peter Wanke

    (The Federal University of Rio de Janeiro)

  • Emel Aktas

    (Cranfield University)

Abstract

Diabetes is an emerging global epidemic linked to increases in physical inactivity, overweight, and obesity. The total number of deaths from diabetes is expected to rise by more than 50% in the next decade with a notable increase by more than 80% in upper-middle income countries. This paper proposes an integrated methodology to assess the efficiency of diabetes treatment and presents its application on data from the diabetes care providers in the UK. In this research, we use TOPSIS first in a two-stage approach to assess the relative efficiency of diabetes care providers. Then, in the second stage, we build neural networks and process TOPSIS results to construct a predictive model for diabetes treatment efficiency. The results reveal that variables related to hospital and patient demographics have a prominent impact on and predictive power for the efficiency assessment in diabetes treatment. Findings also indicate that the medical routines and treatment dynamics are quite standardized within different sites examined in this paper. To improve the efficiency of diabetes treatment, health care providers should focus on contextual variables such as prevalence of diabetes and management of diabetes.

Suggested Citation

  • Peter Wanke & Emel Aktas, 2018. "Efficiency of Diabetes Treatment," International Series in Operations Research & Management Science, in: Cengiz Kahraman & Y. Ilker Topcu (ed.), Operations Research Applications in Health Care Management, chapter 0, pages 351-377, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-65455-3_14
    DOI: 10.1007/978-3-319-65455-3_14
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    Cited by:

    1. Thorsen, Maggie & McGarvey, Ronald & Thorsen, Andreas, 2020. "Diabetes management at community health centers: Examining associations with patient and regional characteristics, efficiency, and staffing patterns," Social Science & Medicine, Elsevier, vol. 255(C).
    2. Agata Sielska, 2020. "Stability of hospital rankings," Operations Research and Decisions, Wroclaw University of Science Technology, Faculty of Management, vol. 30(4), pages 95-112.

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