Jeff Elton, Ph.D., Chief Executive Officer, ConcertAI
A: With more clinical data being available from more sources with greater veracity and velocity, we can apply new tools — especially those derived from machine learning and artificial intelligence (AI) methodologies.
We will see AI support the understanding of adverse events, help identify new patient cohorts, optimize clinical trial design, guide us to new clinical sites to accelerate accruals, and actually to avoid the prior generations of health inequities and under-representations. We can create models and AI-enabled optimizations to overcome historical biases. Of course, AI predicated on legacy data can perpetuate bias, but we are getting more sophisticated and purposeful about avoiding this.
In the coming 3–5 years, AI will not be making clinical decisions, but it will be an integral part of why new medicines are available more rapidly; why we will have more confidence in trial design, realize shorter study execution times, and be able to translate study outcomes into evidence and rules guiding clinical practice decisions.
But you can’t have AI unless you have data — a lot of it, with depth and confidence that it lacks bias. This will cause a change in operating models that the United States is posed for, one that’s already starting within the biopharma innovators. The front-edge of the ‘next big thing’ emerged during the pandemic out of necessity. However, now seen, experienced, and assessed, it is clear the new ways can bend the historical curves of time, cost, and precision for the benefit of biomedical innovators, providers, and patients.
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Jeff Elton, Ph.D., Chief Executive Officer, ConcertAI
A: The story less told is that the U.S market has the strongest foundation for advanced AI and other insight tools of any in the world. It has 100% consistent digital clinical infrastructure in place as a result of the full implementation of the HITECH Act, giving us more access to clinical data in electronic form than any other country. This is why the real-world data (RWD) market is larger and growing faster here. If you have data derived according to consistent standards, you have the basis for AI and machine learning solutions.
We saw the benefits of this market with COVID-19 research initiatives –– the mobilization and speed of clinical research was breathtaking. We were able to accelerate our understanding of primary modes of transition; catalyze an almost entirely digital and decentralized trial infrastructure ‘incubating’ for years; and make determinations of benefit and risk for narrow populations in record time.
RWD is being used to plan almost every trial. It is supporting the growth of the digital armamentarium of evidence-generation approaches — extending and augmenting the legacy approaches of randomized controlled trials — by bringing more evidence together, faster, for provider, biomedical innovator, payer, and regulatory decisions. While the F.D.A. may have issued preliminary perspective on RWD in 2018, the real advances in use and utility have been in the last 18 months. Even with the debates of late around accelerated approval processes, it is clear that standard of care analyses and complementary views on the effectiveness and safety of current therapies will become part of almost every regulatory submission and integrated into every decision.
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