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Medication market performance analysis with help of Analytic Hierarchy Processing

Author

Listed:
  • Vladislav Trubnikov

    (Worcester Polytechnic Institute, United States)

  • Artur Meynkhard

    (Financial University under the Government of the Russian Federation, Russian Federation)

  • Kristina Shvandar

    (Financial Research Institute of the Ministry of Finance of the Russian Federation, Russian Federation)

  • Oleg Litvishko

    (Plekhanov Russian University of Economics, Russian Federation)

  • Valery Titov

    (Plekhanov Russian University of Economics, Russian Federation)

Abstract

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.

Suggested Citation

  • Vladislav Trubnikov & Artur Meynkhard & Kristina Shvandar & Oleg Litvishko & Valery Titov, 2020. "Medication market performance analysis with help of Analytic Hierarchy Processing," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(1), pages 899-916, September.
    • Handle: RePEc:ssi:jouesi:v:8:y:2020:i:1:p:899-916
    DOI: 10.9770/jesi.2020.8.1(60)
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    References listed on IDEAS

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    1. Dayong Nie & Elena Panfilova & Vadim Samusenkov & Alexey Mikhaylov, 2020. "E-Learning Financing Models in Russia for Sustainable Development," Sustainability, MDPI, vol. 12(11), pages 1-14, May.
    2. Anthony Msafiri Nyangarika & Alexey Yurievich Mikhaylov & Bao-jun Tang, 2018. "Correlation of Oil Prices and Gross Domestic Product in Oil Producing Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 8(5), pages 42-48.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Manuel Sousa & Maria Fatima Almeida & Rodrigo Calili, 2021. "Multiple Criteria Decision Making for the Achievement of the UN Sustainable Development Goals: A Systematic Literature Review and a Research Agenda," Sustainability, MDPI, vol. 13(8), pages 1-37, April.

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    More about this item

    Keywords

    medical contract prices analysis; multi criteria decision making; machine learning approach; innovations; healthcare management; HIV; Russia;
    All these keywords.

    JEL classification:

    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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