How to Identify Health Innovation Gaps? Insights from Data on Diseases’ Costs, Mortality, and Funding

This report aims to identify disease categories with the highest economic and social costs and a low level of R&D investment. First, we combine data sets on diseases’ medical expenses, patient counts, death rates, and research funding. We then use text mining and machine learning methods to identify gaps between diseases’ social and economic costs and research investments in therapeutic areas. We find that only 25 percent of disease categories causing high economic and social costs received more than 1 percent of National Institutes of Health (NIH) funding over 12 years. In addition, rare diseases imposing high medical costs per patient collected 0.3 percent of research investments on average. A disease’s cost and impact on society are challenging to assess. Our results highlight that the different measures may lead to different conclusions if considered separately: A disease can have a very high cost per patient but a low death rate. They also show that merging information across data sets becomes more complicated when the sources do not focus on diseases specifically. Our analysis reveals that a formalized procedure to define the correspondence between data sets is needed to successfully develop a metric that allows a systematic assessment of diseases’ cost, impact on society, and investment level. Furthermore, the simplification of the large dimensional decision space will only be useful to the questions at hand if there is a clear order of priorities. In our case, the first was the costs and then funding. These priorities dictate how to merge the data sets.