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Commentary|Videos|July 29, 2026

What Needs to Be True for the FDA's Real-Time Pilot Model to Work Beyond Big Pharma

In this video interview, Raviv Pryluk, CEO and co-founder of PhaseV, explains why the FDA's real-time clinical trial pilot was promising but not yet scale ready—and what technology infrastructure needs to be in place for the model to work broadly.

Full interview summary

In a recent video interview with Applied Clinical Trials, Raviv Pryluk, CEO and co-founder of PhaseV, discussed the "dead time" problem in clinical data—the gap between when data is measured and when it is clean enough to analyze—and what it will actually take to make real-time trial monitoring a practical reality at scale. He opened by framing the core challenge: the data collection, cleaning, validation, and standardization processes that stand between raw trial data and analysis-ready data create delays that undermine the promise of continuous oversight, unless the right technologies are in place to compress or eliminate that gap.

Pryluk described how data flowing into clinical trials from central labs, CRA queries, EDC systems, and CTMS arrives in formats that require substantial transformation before it meets standards like ADAM and STDM. Until recently, that transformation required weeks of manual work from statistical programmers. The emergence of LLMs and AI-driven tools is changing that, enabling the kind of near-automated standardization that is a prerequisite for real-time monitoring at scale. At the same time, he acknowledged a tension sponsors are actively raising: the risk of surfacing false signals from data that hasn't been properly cleaned. Balancing speed with rigor, he argued, is the central challenge.

On the FDA's real-time pilot with two large pharma companies, Pryluk was measured—acknowledging it as a meaningful step while noting that even at that scale, the work was not yet scale ready. The enabler that could change that is the same LLM-driven capability that is beginning to automate ADAM and STDM generation. He stressed that scaling this model will require an ecosystem approach, bringing together regulators, sponsors, CROs, and technology vendors in a transparent, shared-learning environment rather than allowing learnings to remain siloed within individual companies. He closed by emphasizing the importance of quick wins and a gradual rollout across phases and therapeutic areas to build confidence and maintain momentum.


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