Data Management

Webinar Date/Time: Thursday, November 5, 2026 11:00 AM EST

Real-time RBQM requires proportionate analysis frequency matched to data arrival and issue criticality, not continuous reanalysis, because signal churn from over-frequent runs creates alert fatigue and operational noise that obscures meaningful deterioration.

Radiopharmaceutical therapy is exposing a broader challenge for clinical development: scientific innovation can advance only as fast as the infrastructure, data, and cross-functional decisions required to deliver it.

Mixed FSP and FSO models are gaining ground as sponsors seek agility and resource flexibility, but the oversight infrastructure, audit trails, and accountability structures needed to run them well and survive regulatory inspection are consistently lagging behind the model itself.

Hierarchical composite endpoints analyzed through pairwise comparisons more accurately reflect multifaceted treatment benefit than time-to-first-event composites, but transparent outcome prioritization, patient involvement in ranking, and reporting of Net Treatment Benefit remain underutilized despite their importance to interpretation.

RWE is finding a role earlier in drug development than ever before, but the data quality, integration, and organizational alignment required to make it regulatory-grade are still catching up to the ambition.

In this Q&A, Sylviane de Viron of CluePoints and Abigail Dirks, MS, of Tufts CSDD discuss the findings of a collaborative study quantifying the financial value of RBQM, why time savings emerged as the largest driver, and what sponsors struggling to justify adoption now have that they didn't before.

Sponsor oversight of outsourced CRO work is often robust in practice but fails inspection because oversight decisions are fragmented across systems and lack an audit trail, requiring sponsors to document oversight as a connected operating system with clear decision records, escalation pathways, and issue resolution from start to finish.

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.

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

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.

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.