Web-Based Budget Impact And Pricing Scenarios Planning Model: Example From The United Kingdom


      Develop a generic modelling approach to estimate and visualize optimal pricing levels of various drug classes in the UK. Present economic model as interactive web-based app and develop tool to inform regional healthcare budgeting and decision making.


      The model utilizes detailed IMS Sales data as a primary data source. Future healthcare budget projections and extent of savings from introduction of new drugs are estimated with linear extrapolation of historic monthly 2010-2015 sales data. Model simulation time horizon is 5 years. The model defines optimal uptake, rate of introduction and price levels based on a set of economic objectives and constraints entered by the decision maker. Linear programming and excel solver is replicated in the web browser environment to generate the optimal solution for the specified decision problem under constraints entered by the model user.


      Results are presented for 12 UK regions together with direct comparison of sales dynamics and disease prevalence metrics across region pairs. Historical savings realised to date due to introduction of different drug classes, and extent of future potential savings, are presented viainteractive and interconnected charts illustrating detailed time dependent relations between price, market shares and savings. Detailed results are presented by year, region, drug class and type of specified health objective. D3.js framework is used to visualise data and present results.


      A pricing scenarios planning optimization model may inform decision making and regional budget planning. An economic model powered with web capabilities and dedicated data visualization libraries is a more effective tool to convey detailed economic data. Models deployed as web apps remove data presentation limitations of conventional modelling packages like MS Excel and utilize advantages of dedicated data visualization libraries and web environment to more effectively present complex healthcare data.