Big data analytics are now also transforming the way the FDA’s clinical trials are performed. Instead of just the traditional randomized clinical trials, the FDA is now encouraging the incorporation of electronic health records (EHRs), AI-driven machine learning, and other non-traditional resources to support scientific investigations.

Jennifer Bresnick reports on the FDA’s new thrust towards hybrid clinical trials in this article from Healthcare IT Analytics:

“It’s a recognition that new approaches and new technologies can help expand the sources of evidence that we can use to make more reliable treatment decisions. And it’s a recognition that this evidence base can continue to build and improve throughout the therapeutic life of an FDA approved drug or medical device.”

“Hybrid” clinical trials that combine traditional research approaches with real-world evidence (RWE) can support agile discovery and greater efficiency, speeding up the process of bringing new therapies and devices to market, (FDA Commissioner Dr. Scott Gottlieb) continued.

RWE is important for ensuring that clinical trials capture accurate and comprehensive data about diverse participants.

“We believe that more accessible clinical trials can facilitate participation by more diverse patient populations within diverse community settings where patient care is delivered, and in the process can generate information that’s more representative of the real world and may help providers and patients make more informed treatment decisions,” he said.

“This approach, called decentralized clinical trials, can help move prospective collection of data from the real-world—including randomization—outside of the brick and mortar boundaries of traditional clinical research facilities, tapping into not only EHRs but additional digital health tools like wearable devices.”

Some of these strategies are already being deployed in the research and clinical settings. “Providers and other stakeholders are already exploring effective ways to leverage electronic tools to gather vast amounts of health-related data from EHRs and other sources,” Gottlieb noted. “And they’re working on ways to use advanced analytics, including machine learning algorithms, to transform data into evidence that can be used to help guide clinical decision-making or inform innovators during the development of medical products.”