Technology

In this episode of Beyond Compliance, Otis Johnson, PhD, MPA, founder and principal consultant at Vantix Operations, speaks with Cecilia Xi, PhD, VP of clinical and scientific affairs at Vivalink, about why defensible trial data depends on traceability, platform ownership, and device strategy long after a study ends.

Decentralized trial models have demonstrated real gains in enrollment performance and patient access, but operational switching costs, fragmented technology stacks, and unresolved gaps in the patient-site relationship raise a question the industry has been slow to confront directly.

Most AI pilots in drug development fail not from poor technology but from lack of strategic prioritization, organizational readiness, integrated data infrastructure, and disciplined governance, making success dependent on business discipline rather than technical capability.

A platform-based approach connects AI across workflows, data and governance, helping life sciences organizations move from isolated wins to real scale.

The clinical research industry talks about patient-centeredness constantly but embeds it too late, too narrowly, and without clear ownership, and the cost shows up in enrollment failures, protocol deviations, and outcomes that don't reflect what patients actually care about.

Why clinical development’s next AI bottleneck is not the model, but the system around it.

Clinical trials have spent decades perfecting data capture, but the execution layer underneath it, including decision workflows, data collection design, and lab connectivity, remains fragmented in ways that limit what AI can realistically deliver and that quietly compromise the scientific validity of the data itself.

In this Q&A, John Worden, chief commercial officer at Javara, discusses why clinical trial recruitment has remained stubbornly one-size-fits-all, how AI can identify patients at scale without displacing the clinician relationships that drive trust, and what care-integrated research engagement needs to look like for health systems, sponsors, and sites.

Participant engagement depends more on clarity and understanding than on gamification or incentives, requiring engagement embedded into trial workflows as a core design component rather than a peripheral consideration

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.

In this video interview, Amber Hill, PhD, founder and CEO of Research Grid, explains why most trial failures are fundamentally administrative problems—and why patient sourcing, site feasibility, and back-office data capture are where AI delivers the fastest and most meaningful wins.

In this video interview, Amber Hill, PhD, founder and CEO of Research Grid, makes the case that AI architecture—who built the model, who owns the data, and how outputs are traced—is the most important due diligence sponsors can do before adopting any AI tool in clinical development.

In this video interview, Amber Hill, PhD, founder and CEO of Research Grid, identifies the administrative back-office processes causing the most friction in Phase I trials and explains why purpose-built, traceable AI models are the right tool for addressing them.

In this video interview, Amber Hill, PhD, founder and CEO of Research Grid, explains what hitting the 6- to 12-month timeline reductions envisioned under Operation TrialBlazer actually demands operationally—and where AI-native automation can make the biggest difference earliest.

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.

Clinical research coordinators face early burnout driven by gaps between job expectations and operational reality, inadequate mentorship structures, and fragmented technology environments that compound insufficient onboarding and deplete resources needed to sustain engagement.