Register Now - Beyond Recruitment: The Future of Patient Site Engagement in Oncology Clinical Trials
Commentary|Videos|July 27, 2026

What the "Dead Time" Problem Actually Is and Why It Matters for Real-Time Monitoring

In this video interview, Raviv Pryluk, CEO and co-founder of PhaseV, explains why the gap between data collection and analysis-ready data undermines the promise of continuous trial monitoring and what it will take to close it.

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.


Related to this article

Where the Industry Is Headed as Clinical, Operational, and Real-World Data Connect
In this video interview, Rajneesh Patil, global head of AI, digital, and innovation in clinical development at Syneos Health, describes the industry’s trajectory toward a unified intelligence orchestration environment—and explains why the future isn’t one model that knows everything, but a trusted system that brings the right evidence to the right human decision.
© photon_photo - © photon_photo - stock.adobe.com
Standardized measurement across five oncology centers found that clinical research coordinators rated eSource-enabled workflows positively (Overall Mean Satisfaction 5.79 of 7), with reduced manual entry and electronic data transfer earning strong marks, while perceived reduction in sponsor and monitor queries lagged as the one domain below threshold.
What Expanding AI Ecosystems Mean for Sponsor Complexity and Oversight
In this video interview, Rajneesh Patil, global head of AI, digital, and innovation in clinical development at Syneos Health, argues that CROs must act as orchestrators rather than aggregators—and that the real test of any AI ecosystem is whether it reduces sponsor coordination burden or just hands it back to them.
Where AI Governance in Clinical Development Falls Short
In this video interview, Rajneesh Patil, global head of AI, digital, and innovation in clinical development at Syneos Health, identifies three recurring failure modes in AI governance frameworks—and explains why responsible AI must be an ongoing operating discipline, not a one-time approval gate.