OBJECTIVES: To develop and present a data science model to support the supply chain management for the rational selection of essential drugs based on drug consumption patterns at the primary healthcare centres (PHCs) across Karnataka.

METHODS: A stepwise quantitative analysis was performed to analyse the quantity and rate of drugs for defining the primary healthcare level consumption rates in the state. A random sample of 640 primary healthcare centres was drawn using PPS sampling. Data on consumption analysis was collected as two different sets - warehouse supplied drugs and locally purchased drugs. ABC-VED analysis assisted by FSN (fast-moving, slow-moving and non-moving inventory method) and HML (High-cost items, Medium cost items and Low-cost items) analysis was employed for the rationalisation of essential drugs list. A “what if” analysis of the anticipated list of drugs was conducted for assessing the rate of savings to quantity of different classes of drugs to estimate the economic burden.

RESULTS: A state-wise consumption pattern of 10 most commonly used drugs at the sampled PHCs showed that Broad-spectrum antibiotics (Amoxicillin, Ciprofloxacin, Azithromycin, Cefadroxil) were the fast-moving drugs supplied from the warehouses. Non-Steroidal Anti-Inflammatory drugs (Paracetamol and Diclofenac) were the next common class of drugs supplied through warehouses across the state. Comparing with the State essential drug list Oseltamivir though fast moving was not included in the list of Vital Drugs and not centrally supplied to the PHC, 28 drugs included in Essential Drug list for PHCs were not at all consumed and hence were marked for removal form Essential Drug List. The removal of 28 drugs from essential drug list was estimated to positively impact the exchequer financially with savings of 2 million USD annually.

CONCLUSIONS: Data science in drug rationalisation helps in identifying the necessity of the drug thus supplying accordingly to cater those needs by attaining enough budget savings.*