Data Management

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.

In this Q&A, Rob DiCicco, vice president of portfolio management at TransCelerate Biopharma, discusses the collaborative work underway with FDA and CTTI to explore selective safety data collection as a model for de-risking trial design, reducing site and patient burden, and scaling pragmatic trial approaches across the industry.

In this Q&A, Liz Beatty, co-founder and chief strategy officer at Inato, discusses how real-time patient data is reducing non-enrolling sites, why sponsor-specific technology remains the biggest barrier to adoption, and what a shift toward cross-asset site partnerships could mean for enrollment efficiency.

The FDA's launch of real-time clinical trial proof-of-concept studies signals a fundamental shift in regulatory oversight, one that most sponsors are not yet equipped to meet and that demands urgent investment in unified data infrastructure, quality-by-design practices, and protocol digitization.

In this Q&A, Raj Indupuri, CEO and co-founder of eClinical Solutions, discusses what the FDA's push toward continuous data review actually demands of sponsors operationally, why fragmented systems are the core obstacle, and how AI and real-world evidence fit into a more data-driven regulatory environment.

From real-time evidence generation to federated AI to site-level data integration, ACT spoke with seven experts at DIA 2026 on the trends and challenges defining clinical trial operations today.

In this video interview following the 2026 DIA Global Annual Meeting, Jonathan Andrus, co-CEO of CRIO, explains why integrating site-level data systems with sponsor oversight has remained so difficult, what a central eSource model requires to work for all stakeholders, and why the industry needs to stop waiting for perfection and start taking the step.

In this video interview from the 2026 DIA Global Annual Meeting, Kevin Bugin, head of global regulatory policy and intelligence at Amgen and executive sponsor of TransCelerate's Embedded Pragmatic Trials initiative, reframes the FDA's continuous review expectations around real-time evidence generation and explains why quality by design—not data cleanup—is what regulators are now demanding.

In this video interview, Raj Indupuri, CEO and co-founder of eClinical Solutions, explains why organizations that build infrastructure capable of harmonizing clinical and real-world data in real time will be best positioned for the continuous, data-driven regulatory environment now emerging.

A Tufts CSDD study quantifies risk-based quality management's net financial value at $13.8 million per Phase III oncology trial, with ROI multiples up to 22.7x driven primarily by cycle time reductions rather than monitoring cost savings.

In this video interview, Raj Indupuri, CEO and co-founder of eClinical Solutions, argues that study-by-study RBM is no longer sufficient and describes what enterprise-wide, AI-enabled risk and quality management looks like in practice.

In this video interview, Raj Indupuri, CEO and co-founder of eClinical Solutions, makes the case that governance and quality must be built into every stage of the trial life cycle—not enforced at database lock or submission—and what that requires from teams and technology.

In this video interview, Raj Indupuri, CEO and co-founder of eClinical Solutions, explains why the FDA's push toward continuous data review exposes the fragmentation at the heart of current clinical trial infrastructure and what unified data pipelines need to look like to make it possible.

From planning one Phase III trial at a time to digital standardization on repeat.

In this video interview, Liz Beatty, co-founder and chief strategy officer at Inato, makes the case for moving beyond trial-by-trial planning toward cross-asset site partnerships—and explains why sites are three times more likely to share patient data when sponsors make that shift.

Patient Engagement Strategies in Anti-Obesity Medication Clinical Trials: Addressing Drop Out Rate and Improving Retention

In this Q&A, Sam Hinsley, statistics manager at Phastar, discusses the role statisticians play in raising standards across the clinical development timeline—and why getting early phase decisions right is critical to the entire drug development pathway.

In this video interview, Liz Beatty, co-founder and chief strategy officer at Inato, explains how AI is replacing unreliable feasibility estimates with precise, real-time patient matching—and what that means for reducing non-enrolling sites and screen failure rates.