Investigative Sites

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.

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.

Clinical R&D modernization stalls through incremental optimization of individual workflows, but meaningful systemic change requires leaders to visualize structural relationships, understand hidden incentives, and identify leverage points that benefit the whole system rather than parts.

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.

Experience sampling captures real-time signals of site burden and operational friction at the moment they occur, providing earlier and more actionable insight than retrospective assessments and revealing workarounds before they compound into protocol deviations.

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.

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, Joan Chambers, independent senior consultant at Tufts CSDD, examines the operational challenges sites face when implementing DCT components and makes the case that the industry is still in a learning phase—one the data will ultimately need to guide.

A CFO’s perspective on capital allocation, balancing risk and opportunity, and structuring successful China-based clinical programs.

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.

In this video interview, Liz Beatty, co-founder and chief strategy officer at Inato, explains why asking sites to use different technology for every sponsor remains a major barrier to AI adoption and what a site-first approach to technology development looks like in practice.

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.

Study coordinators identify only about 45% of critical and major problems during document reviews, with substantial performance variability across experience levels, but focused training can improve detection rates from 45% to approximately 68%.