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Scenario analysis on medical treatments of patients with knee osteoarthritis

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Abstract

In developed countries, osteoarthritis (OA) has been among the ten most severe disability diseases in recent years. Approximately, 10% of the world’s population struggle with knee OA symptoms at the age of 60. OA causes significant pain, disability, lifestyle decline, and financial burdens. Therefore, the discussion regarding the treatment of OA is crucial. In this study, we presented the use of scenario and incident models as frameworks, and divide patients into groups by cluster analysis. Subsequently, we identified the key factors of the initial state and analyzed the medical scenarios. Accordingly, we inferred that the characteristics of a patient influence the outcome of treatments, and administering different treatments to patients triggers different outcomes. The method to carry out this study was using cluster analysis and scenario analysis, and the result shows patients with higher effectiveness of improvement while taking supplement treatment, that is glucosamine. Patients should control their weight appropriately, because high BMI will impact the treatment outcome. Patients who exercise frequently have better treatment outcome, the age and gender rarely influence the treatment outcome. The findings of this study can provide beneficial suggestions for hospitals and physicians in adjusting the supply of treatments, improving medical quality, and providing knee OA patients with a way to adopt appropriate treatment.

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Authors and Affiliations

  1. Department of Business Administration, National Chiayi University, Chiayi City, 600023, Taiwan, ROC

    Shih-Chin Tan & Jia-Jeng Hou

  2. Department of Multimedia Game Development and Application, Hungkuang University, Taichung, 433304, Taiwan, ROC

    Chun-Liang Lai

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  1. Shih-Chin Tan
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  2. Chun-Liang Lai
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  3. Jia-Jeng Hou
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Correspondence to Chun-Liang Lai.

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Tan, SC., Lai, CL. & Hou, JJ. Scenario analysis on medical treatments of patients with knee osteoarthritis. J Supercomput 78, 10310–10325 (2022). https://doi.org/10.1007/s11227-021-04206-4

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