A novel drug selection decision support model based on real-world medical data by the hybrid entropic weight TOPSIS method

Abstract

BACKGROUND: The medicine selection method is a critical and challenging issue in medical insurance decision-making. OBJECTIVES: This study proposed a real-world data-based multi-criteria decision analysis (MCDA) model with a hybrid entropic weight Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithms to select satisfactory drugs. METHODS: The evaluation index includes two levels: primary criteria and sub-criteria. Firstly, we proposed six primary criteria to form the value health framework. The primary criteria’s weights were derived from the policymakers’ questionnaire. Meanwhile, clinically relevant sub-criteria were derived from high-quality (screened by GRADE scores) clinical-research literature. Their weights are determined by the entropy weight (EW) algorithm. Secondly, we split the primary criteria into six mini-EW-TOPSIS models. Then, we obtained six ideal closeness degree scores (ICDS) for each candidate drug. Thirdly, we get the total utility score by linear weighting the ICDS. The higher the utility score, the higher the ranking. RESULTS: A national multicenter real-world case study of the ranking of four generic antibiotics validated the proposed model. This model is verified by comparative experiments and sensitivity analysis. The whole ranking model was consistent and reliable. Based on these results, medical policymakers can intuitively and easily understand the characteristics of each drug to facilitate follow-up drug policy-making. CONCLUSION: The ranking algorithm combines the objective characteristics of medicine and policy makers’ opinions, which can improve the applicability of the results. This model can help decision-makers, clinicians, and related researchers better understand the drug assessment process.