Unleashing AI ’s potential in clinical trial recruitment

The onerous cost of clinical trials in time, money and effort is well documented. The average clinical trial process lasts between 7.5 and 12 years, studies estimate, with costs ranging from $161M - $2.6B per drug. Just 14% of clinical trials are successful and only one in ten drugs entering Phase 1 ends up being approved by the FDA.   Recruitment is a significant source of the problem. According to Christina Busmalis, director of global life sciences at IBM Watson Health,80% of clinical trials do not finish on time and the reason for this, in 86% of cases, is that they do not meet target recruitment on time. “It’s a massive problem across the board,” she explains, “which leads to delays and trial costs that can run into the multi-millions.” The growing complexity and volume of data involved in trials is a chief reason for this, says Michelle Longmire, CEO and co-founder of California startup, Medable. The clinical trial world has gradually reached a point over the last decade where the number of patients and the amount of data has increased to such an extent that it has become impossible to deal with it manually.    “When you look at clinical trial protocol, the inclusion/exclusion criteria and the variety of parameters one needs to meet to be eligible, it is a significant challenge for both patients and companies,” says Longmire. “Whereas a clinical trial in the past would have had 20 variables, it will now have an average of 150.” For Robert Jan-Si...
Source: EyeForPharma - Category: Pharmaceuticals Authors: Source Type: news