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.
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Wanke, P., Aktas, E. (2018). Efficiency of Diabetes Treatment. In: Kahraman, C., Topcu, Y. (eds) Operations Research Applications in Health Care Management. International Series in Operations Research & Management Science, vol 262. Springer, Cham. https://doi.org/10.1007/978-3-319-65455-3_14
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