Data Management

In rare disease drug development where no registry or natural history dataset exists, real-world evidence quality depends on treating evidence engineering as a design-stage decision, defining intended regulatory use and standardizing endpoints, harmonization, and data provenance upfront rather than reconciling gaps after collection.

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.

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

Regulatory expectations for patient input in drug development have shifted from aspiration to documented methodology across three major jurisdictions.

This episode of The Human Side of Clinical Trials, hosted by Brian S. McGowan, PhD, FACEHP, chief learning officer and co-founder of ArcheMedX, Inc., and Kelly Ritch, chief operating officer of ArcheMedX, Inc., explores why training completion metrics like attendance and quiz scores fail to capture true study readiness, and how measuring both competence and confidence can reveal trial risks before enrollment even begins.

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.

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

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

How to generate reliable Phase I evidence in a capital-constrained environment.

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.

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.

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.

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.

Clinical development operates as an integrated system where dependencies persist across phases and vendors, but treating contracts as execution handoffs creates invisible oversight gaps that surface later as vendor performance problems, timeline slips, or inspection findings.

The European Health Data Space reshapes CRO operations by transitioning them from data custodians to regulated users accessing data within secure, auditable environments, enabling cross-border research but requiring substantial investment in compliance, standardization, and methodological rigor.

Phase III oncology setbacks often reflect incomplete biological understanding rather than target failures, but robust biomarker strategy and multi-dimensional patient selection developed early in development can strengthen pivotal trial design and increase success.

The traditional global-to-local communication model creates fragmented content through sequential adaptation, but a local-first approach using structured data and AI-orchestrated generation can embed stakeholder terminology and preferences from the start, reducing rework while improving clarity and relevance.