Technology

In this Q&A, Patrick Mizer, chief technology officer at Ledger Run, discusses how payment reliability has become a competitive differentiator in site selection, why decades of disconnected workflows have made payment inconsistency a structural problem, and where AI is delivering real value in the high-friction parts of clinical trial payments.

As sponsors push for faster, more representative studies, AI-enabled matching and EHR integration are emerging as operational levers, but bias, explainability, and governance will determine whether the technology scales.

Agentic AI is arriving in clinical operations with genuine capability to absorb administrative work, but the organizations that will realize lasting value are those willing to redesign accountability structures, financial workflows, and operating models before deploying the tools on top of them.

Biomarker-informed trial enrollment appears straightforward for identifying responders and optimizing efficiency, but failures to account for demographic variation in cut-off selection, causality versus correlation, and composite biomarker complexity risk excluding viable patients and exacerbating disparities.

Agentic AI will put the CRO operating model to the test—and force the efficiency and accountability questions the industry can no longer avoid.

Faster, more efficient clinical trials depend on structural alignment in how sponsors, CROs, and research sites plan and execute studies together.

In this Q&A, Raviv Pryluk, PhD, CEO and co-founder of PhaseV, discusses the data standardization bottleneck that sits between raw trial data and real-time analysis, what the FDA's continuous monitoring pilot will require to scale beyond large pharma, and why an ecosystem approach is the only path to making real-time trial oversight a practical reality.

Clinical research associates are evolving from compliance-focused manual monitors to data-enabled strategic site partners, but organizations need analytics training, clear operating models, and active change management to make the shift stick.

From point-of-care sample collection to real-world data curation, wearable device integration, and continuous review infrastructure, data quality is not a single problem in clinical trials—it is a layered challenge that manifests differently at every stage of development and demands a different set of solutions at each one.

AbbVie’s head of immunology clinical development, Kori Wallace, MD, PhD, discusses how combination therapies, translational science, and emerging immune-reset approaches could usher in a new era of treatment for inflammatory bowel disease and other autoimmune conditions.

In this Q&A, Richard Graham, PhD, co-founder and chairman of the board at TruTechnologies, discusses why six decades of manual site-level processes continue to undermine clinical trial execution, what national initiatives like Operation TrialBlazer leave unaddressed, and why meaningful modernization has to start with data collected at the point of care.

Real-time AI-enabled decision-making only accelerates development when trials have coordinated operational infrastructure for randomization, supply management, and workflows capable of responding to new insights without disrupting execution or compliance.

Under-enrollment isn't news to teams running oncology trials, and the instinct is to spend more on reach. The evidence, however, says the biggest leak is something else entirely and that most recruitment budgets focus on the least valuable part of the problem.

Real-world oncology data requires hybrid curation combining clinical expertise with technology to address variability in documentation, missing data, and complex concepts like lines of therapy that are often implicit rather than explicitly recorded in EHRs.

Consumer-grade wearables offer promise for reducing trial burden and improving engagement, but accuracy and reliability vary substantially by device, measurement, and population, requiring rigorous fit-for-purpose validation and careful endpoint selection before integration into regulated research.

In this video interview, Raviv Pryluk, CEO and co-founder of PhaseV, outlines a gradual, win-driven strategy for making real-time monitoring stick—starting in early phases and building toward pivotal trials with the right technology partners and a network effect that scales.

As agentic AI, automated data harmonization, and real-time monitoring reshape clinical development, the organizations seeing meaningful results are those that have invested in unified data infrastructure and disciplined governance rather than cycling through pilots without the foundation to scale them.

In this video interview, Raviv Pryluk, CEO and co-founder of PhaseV, makes the case that scaling real-time data review requires an ecosystem approach—bringing regulators, sponsors, CROs, and technology vendors together in a shared learning environment rather than letting progress remain siloed.

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.

In this video interview, Raviv Pryluk, CEO and co-founder of PhaseV, breaks down the standardization and mapping work that must happen before streaming data can be analyzed meaningfully—and how AI is beginning to make that process fast enough to enable real-time monitoring at scale.

AI agents operate only within workflows, but most organizations lack unified workflow management systems, making it difficult to identify where agents should be deployed or ensure they integrate effectively across connected business processes in clinical research.

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.

AI churn—repeatedly restarting initiatives before scaling them—stems from organizational execution gaps rather than technology limitations, but agentic AI amplifies these gaps by requiring connected systems, trustworthy data, and disciplined governance from the start.