Predicting the Uptake of a New Medicine in England using Classification

Abstract

The National Healthcare Service (NHS) is experiencing delays of the uptake of a new medicine within their formularies, despite the National Institute for Health and Care Excellence (NICE) recommendations. Such delays not only affect pharmaceutical companies’ during the launch stage but also contribute to potential harm in patients’ health, and low global competitiveness in the life sciences sector. This study investigates the viability of predicting the speed of uptake of a new drug on a formulary level using classification algorithms. Three types of machine learning models: XGBoost, random forest, and logistic regression were employed and evaluated. The results suggest the predictive model, XGBoost, is operating on a market entry level, showing generalized predictions across various formularies. The findings also indicate there is no correlation between the formulary medicine uptake and the number of partnered organizations of a formulary, or the size of patient population.