CASPIAN JOURNAL
MANAGEMENT AND HIGH TECHNOLOGIES
FORECASTING THE TIMING OF DRUG USE TO ACHIVE A STABLE PATIENT’S CONDITION
 |
Read | Loshmanov Vadim I., Kravets Alla G. FORECASTING THE TIMING OF DRUG USE TO ACHIVE A STABLE PATIENT’S CONDITION // Caspian journal : management and high technologies. — 2019. — №4. — pp. 51-59. |
/51-59.jpg)
Loshmanov Vadim I. - post-graduate student, Volgograd State Technical University, 28 Lenin Ave., Volgograd, 400005, Russian Federation, loshmanov.vadim17@gmail.com
Kravets Alla G. - Doct. Sci. (Engineering), Professor, Volgograd State Technical University, 28 Lenin Ave., Volgograd, 400005, Russian Federation, agk@gde.ru
Currently, there are a huge number of analogues of drugs. In this regard, it is necessary to improve the methods of pharmacoeconomic analysis. The article provides the rationale for the automation of pharmacoeconomic analysis and the application of the developed decision support system for prescribing drugs by a doctor in the treatment of patients. The purpose of this article is to verify and evaluate the accuracy of forecasting models built using the decision support system in the process of conducting pharmacoeconomic analysis prescribed by a doctor. The article presents the algorithm of the decision support system for conducting pharmacoeconomic analysis, which is based on the speed at which the patient reaches a stable state during the treatment of pathology. Calculation of the cost of using drugs to achieve a patient's stable state will allow, without conducting any research, only based on existing statistics, to obtain another criterion for comparing analogous drugs. As a result of the study, we obtained a comparison of pharmacoeconomic analysis indicators, conducted by the traditional method and using the decision support system for conducting pharmacoeconomic analysis.
Key words: фармакоэкономический анализ, прогнозирование, статистика, поддержка принятия решений, статистический анализ данных, pharmacoeconomic analysis, forecasting, statistics, decision support, statistical data analysis