Medication market performance analysis with help of Analytic Hierarchy Processing
This study proposes the concept of Analytic Hierarchy Processing (AHP) on the market of active substances used in treatment of HIV and checks the control factors and criteria interconnection and implements Random Forest forecasting model. The new method must help to improve the management decision-making process in the fields of healthcare government budget planning. It has become a prime concern for understanding and comparing of publicly available information with internal market data and the consequences of companies` and government`s actions in choosing the best approach for correct construction of to reduce HIV incidence in Russia. The paper develops the forecasting model of one of the parameters, which has a substantial role in decision-making process. The medication market data in this study represents the cumulative daily concluded contracts, used in treatment of HIV in Russia, the level of HIV incidence (yearly) and federal budget on healthcare (yearly). The proposed approach have more than 82% average accuracy at predicting the sum of medication contract prices at the 3-year time period. The received figures are effective in predicting the factors` behavior in future. It can be used for improved modulation of AHP and consequently, the overall accuracy of the model structure.
medical contract prices analysis, multi criteria decision making, machine learning approach, innovations, healthcare management, HIV, Russia
D40 , I11 , I18
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