Optimizing Clinical Trials with Machine Learning and Robotic Process Optimization GEN Genetic Engineering & Biotechnology News - Biotech from Bench to Business GEN

In my earlier post, 'The Era of Cognitive Systems', I discussed how the understanding of data determines their value and the way data are experienced. In this article, I will elaborate on a specific use of data that the pharma industry is grappling with and how cognitive computing could come to the rescue. Randomized clinical trials (RCTs) are the "Gold Standard" for testing new therapeutics for safety and efficacy in human subjects. However, their success rates range between 40–80% across phases. 1 Significant failure rates can be attributed to patient recruitment, which is influenced by a number of factors.2 About 90% of successful trials are delayed by at least six weeks due to the failure of meeting enrollment timelines.1 At today's high cost of conducting RCTs, extending a study timeline by as little as a month can result in significant budget overruns, not to mention the potential revenue and opportunity loss from delayed drug commercialization.