FDA plans to fully tap real-world data and evidence

EHRs, lab tests, wearable devices, insurance claims and social media provide information not gleaned from clinical trials, says Commissioner Scott Gottlieb, MD.

The Food and Drug Administration plans to exploit claims, clinical and patient-generated data—information not captured through traditional clinical trials—for post-marketing safety surveillance.

“New streams of real-world data gathered from electronic health records, lab tests, wearable devices, insurance claims and even social media can provide important evidence on product safety and effectiveness in settings or populations that may be very different than the information gleaned from registration trials that are used for FDA approval of medical products,” said Commissioner Scott Gottlieb, MD, during a keynote presentation on Monday at the Bipartisan Policy Center in Washington.

“Digital technologies, from our perspective, are one of those promising tools we have for making healthcare more efficient and more patient-focused,” Gottlieb added, while emphasizing that “this isn’t an indictment of randomized controlled clinical trials—far from it—it’s a recognition that new approaches and new technologies can help expand the sources of evidence that we use to make more reliable treatment decisions.”

To support the seamless integration of digital technologies in clinical trials, Gottlieb announced that the FDA plans to convene a stakeholder meeting in 2019 to develop a framework on how digital systems can be used to enhance the efficient oversight of clinical trials, including remote monitoring.

“These technologies present important opportunities to streamline drug trials and improve data site integrity by remotely monitoring data trends, accrual of data and the integrity of the data over the course of a trial,” he said. “By working collaboratively with the clinical trial community and patient groups, we can develop scientific and technical standards for incorporating new technologies into clinical trials to make them more agile and accessible to patients and regulators.”

Real-world data and real-world evidence are critical to improving the FDA’s regulatory decisions, according to Gottlieb, who noted that “advancing RWD into RWE” is a key strategic priority for the agency.

He contended that RWD and RWE are already being leveraged “extensively” for post-market monitoring by the FDA, citing the fact that the agency’s Sentinel system eliminated the need for post-marketing studies on nine potential safety issues involving five products.

Sentinel, a national electronic system for monitoring the safety of FDA-approved drugs and other medical products through active surveillance, has been fully operational since 2016. However, the agency has adopted a five-year plan to make the system more robust by accelerating access to and broadening the use of real-world data for real-world evidence.

Also See: FDA issues 5-year plan to make its Sentinel system more robust

“We’re just getting started incorporating these methods and tools into more routine elements of our review process,” said Gottlieb. “Traditional post-market studies typically require years to design and complete and cost millions of dollars. By using RWD and RWE, we may be able to provide patients and providers with important answers much sooner, identifying a broader range of safety signals more quickly and following up on them much more efficiently and effectively.”

Gottlieb made the case that using Sentinel and similar tools will increasingly enable the shift of some post-market studies and data collection to the point of care.

He also noted that advanced analytics, such as machine learning algorithms, are transforming real-world data into real-world evidence to help guide clinical decision making and to inform innovators during the development of medical products.

When it comes to artificial intelligence and machine learning, Gottlieb announced that the FDA’s Information Exchange and Data Transformation (INFORMED) program—a science and technology incubator designed to harness the power of big data and analytics—will develop the agency’s curriculum on AI. He said the purpose of the curriculum is to “improve the ability of FDA reviewers and managers to evaluate products and incorporate advanced algorithms.”

Under INFORMED, the FDA will also pilot a competitive fellowship program in AI and machine learning to allow post-doctoral fellows from leading academic centers to join the agency for two-year fellowships to develop “high impact” regulatory science tools, according to Gottlieb.

Overall, Gottlieb contends that real-world evidence has the potential to make the U.S. healthcare system more competitive and efficient “as validated outcome measures based on real-world data are incorporated into value-based payment contracts.”

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