Clinical trials represent the forefront of medicine bringing new treatment options to patients and caregivers. Unfortunately, close to 90% of clinical trials fail to meet recruitment goals. This results in expensive delays that often result in early trial termination, or simply the inability to gather sufficient data to draw efficacy conclusions. These clinical trials failures slow down research, delay patient access to life-saving treatments, and contribute to rising drug costs. Enrollment Challenges · Most patients are unaware of which clinical trials are being conducted and if they qualify to participate.
Analyzing and understanding social media and patient discussions at forums provide additional leading indicators of potential study issues. This functionality will enable us to predict the likelihood of meeting the required recruitment number at sites and the most optimal match to make the study a success in terms of time and budget. This learning model can predict the success probability of a specific site for the target study. A recent McKinsey Global Institute (MGI) report,6 "The Age of Analytics: Competing in a Data-Driven World," explains the role of analytics for enhanced decision making, disruptive business models, and organizational challenges.
Artificial intelligence (AI) technology, combined with automatically collected big data hold the potential to solve many key clinical trial challenges. These include increasing trial efficiency through better protocol design, patient enrollment and retention, and study start-up, which were each named as prime candidates for improvement by nearly 40% of sponsors in a recent ICON-Pharma Intelligence survey.1 With clinical trials accounting for 40% of pharma research budgets2, sponsors need new ways to accelerate timelines and reduce costs. Data-driven protocols and strategies powered by advanced AI algorithms processing data automatically collected from mobile sensors and apps, electronic medical and administrative records, and other sources have the potential to significantly cut trial costs. They achieve this by improving data quality, increasing patient compliance and retention, and identifying treatment efficacy more efficiently and reliably than ever before.
Technology that can mine the clinical patient data that already exists is finally available - now is the time to speculate to accumulate. The intersection of technology and health has thrown up almost as many questions as it has solutions. "Innovation through collaboration: Bringing disruption to the patient" was the title of the recent Financial Times Digital Health Summit Europe, which took place in London in June. Speaking at the event was ICON's Chief Information Officer, Tom O'Leary. Since its inception in 1990, ICON has been offering a range of drug development services to the pharmaceutical industry, running clinical trials worldwide across all therapeutic areas, diseases, and indications, on behalf of pharmaceutical, biotechnology, and medical device companies.
As the pharmaceutical and medical care industries move toward adoption of artificial intelligence technologies, new possibilities are arising for improvements in drug trials through streamlined clinical operations processes. Worldwide Clinical Trials' recent partnership with Deep Lens could provide evidence of how future drug studies can benefit from AI-driven technologies. A recent study estimates the average dropout rate for all clinical trials at 30%.1 Such patient discontinuation can necessitate exponential increases in patient numbers to achieve required levels of statistical significance. The goal is to minimize additional recruitment costs and delays by improving efficiencies during study execution. As clinical trial stakeholders seek to streamline clinical processes, artificial intelligence emerges as an innovative approach to improving patient monitoring and clinical care, as well as enhancing and accelerating end point detection.