The NIH needed insights into clinical trial data from a trials in Eastern Europe. The clinical trial data was related to different drug efficacy and tuberculosis. The NIH team was looking to get more insights from the clinical data faster and use this data to accelerate their research and to contribute to disease surveillance. The NIH and CDC have been concerned about the increase in drug-resistant tuberculosis.

Avenir staff was granted access to the clinical trial data from NIH. The data was cleansed and prepared. The team installed a couple licenses for data analytics software for the NIH team. This cutting-edge software had many data science tools and methods built-in--as well as a real-time dashboard. The team provided NIH with training on how to use the software and how to pull in their data from clinical trials.

The researchers were able to use complex data science tools even though many of the researchers did not have a background in data science. The team trained NIH on how to get insights from their data using tools to show nearest neighbor, network graphs, Naives Bayes methods, decision trees, and other data science methods. Using the software dashboard, the team was able to pivot quickly between different analysis methods and see almost instantly which drugs were more effective and geographic areas where there was drug resistant tuberculosis.