How AI Transforms Decentralized Trials in Clinical Research

FREMONT, CA: For decades, efforts to improve clinical trial outcomes have been centered on minimizing time and expense. This focus, along with the problems posed by a global pandemic, has sparked interest in digitizing the clinical trial life cycle. Clinical research is transforming due to the convergence of decentralized clinical trials (DCTs), which place the patient at the center of the trial experience and use digital technology and artificial intelligence (AI). Organizations are using DCT and AI in novel ways to alter workflows across the clinical lifecycle, from trial design and patient recruiting to evidence generation—and achieving dramatic reductions in both the time and expense of clinical research.

At the moment, at least 25,000 clinical trials provide open-source data. With the correct analytical tools and artificial intelligence, the industry can leverage this wealth of data to optimize patient recruitment, retention, and timelines and maximize research results in future trials. Additionally, AI can aid in the validation of targets, trial design, and patient identification.

Additionally, DCTs can help clinical studies run more efficiently and cost-effectively by increasing patient access and retention by allowing patients to engage safely and comfortably in their homes. Additionally, DCTs decrease geographic, transportation, and financial obstacles, which can increase participant diversity. The net consequence is increased data quality due to the elimination of site-based transcribing and increased efficiency for patients, sites, and study teams. When combined, AI and DCTs can be a formidable force.

It is past time for purposeful AI deployment. AI engines require more than mounds of data to accomplish this. To be accurate, data must be highly representative of the intended population. As has been broadly documented in recent months, traditional clinical trials frequently enroll people from urban regions predominantly white. DCTs power AI engines because they record vast amounts of real-world, real-time data from varied patient populations, making them more representative of the heterogeneous world they live in. Combining the two inventions will enable organizations to improve three critical aspects of clinical trials: trial design, patient recruiting, and evidence collection.