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The aim of the study was to study the capabilities and perspectives of using big data analysis method to assess rationality and quality of pharmacotherapy in patients with arterial hypertension. Materials and Methods. Analysis of data on supply of medicinal products (MP) to benefit-entitled categories of citizens (federal and regional) was carried out using the software written in Python 3.6 and OLAP-system. Pharmacoepidemiological methods were based on the defined daily dose (DDD) methodology. Results. Rational pharmacotherapy and high adherence to treatment of arterial hypertension were not recorded in all cases. It was revealed that in the investigated areas of the Republic of Tatarstan, the average of 86 days passed from the initial to the subsequent visit and 25% of patients had the next appointment in 90 days or more in 2013. In addition to MPs for the cardiovascular system pertaining to category C of ATC classification (anatomic therapeutic chemical classification of drugs (the international system)), in 10% of cases, drugs of other categories were administered (acetylsalicylic acid, Piracetam, Cerebrolysin, etc.). Inhibitors of renin-angiotensin system were the most widely used MPs (196 DDD/person/year), while calcium channel antagonists came second in the volume of consumption (50.4 DDD/ person/year). There were recorded significant differences in total consumption of antihypertensive MPs between areas (up to 3.5 times). Conclusion. Big data analysis method is a promising tool to assess rationality and quality of pharmacotherapy providing the possibility to evaluate qualitative and quantitative indicators of pharmacotherapy at the general population level.

Prospective Value of Big Data Analysis Method for Assessment of Pharmacotherapy Quality and Efficacy in Patients with Arterial Hypertension