Using Analytics to Gain Insights on U.S. Prescription Drug Prices: An Inductive Analysis

Abstract

Using data scraping techniques to gather data from a variety of previously disjointed sources—some proprietary and some publicly available—this research applies the analytical techniques of data visualization and machine learning to (1) gain exploratory insights into the drivers of prescription drug list prices and (2) test how well these variables impact prices directly and interact to predict pricing. Specifically, this inductive analysis considers characteristics related to the brand (i.e., manufacturer, brand/generic classification), product attributes (i.e., dosing levels, amount of active ingredient), the condition for which the drug is recommended (i.e., therapeutic class, subclass, and pricing tier), and market factors (i.e., number of drugs in class and approval year). Through these analytic analyses, the authors seek to cut through some of the opacity of pharmaceutical drug list prices to consider the drivers of drug prices, evaluate how these insights might drive marketplace and policy solutions, and spark future research inquiries in this area.