Electronic Data Capture

A collaborative study by the Tufts Center for the Study of Drug Development and CRIO identifies protocol interpretation and source document preparation as an understudied yet significant bottleneck in study start-up timelines that may hold key opportunities for efficiency gains.

Why rigorous testing and validation matter more than ever.

As eSource adoption expands, industry leaders are confronting new questions around AI oversight, unstructured data activation, institutional readiness, and regulatory trust. Here’s how experts say the next phase will unfold.

Individual-level attribution modeling reveals that real-world semaglutide effectiveness is not a fixed property of the molecule, but an interaction between pharmacological exposure and modifiable care and support conditions, carrying implications for real-world evidence interpretation and clinical development.

Persistent delays and inefficiencies in COA licensing and translation stem from limited pre-license access and fragmented processes, making a strong case for providing outcome assessment measures earlier to reduce risk and accelerate trial start-up.

Only with recent advances in cloud computing, data standards, and interoperable platforms has it become feasible to realize the full potential of a digital thread.

New research finds that while eSource adoption is advancing through EHR-to-EDC workflows, scaling its impact will depend on integrating unstructured clinical data using AI, shared standards, and collaborative validation models across sites, sponsors, and vendors.

Strong relational governance between technology vendors and sponsor–CRO teams is becoming a critical foundation for eCOA trial success, enabling faster study launches, clearer communication, proactive issue management, and sustained quality across entire trial portfolios.

AI-driven discovery, EHR-based real-world evidence, and synthetic patient modeling are rapidly reshaping drug repurposing, reducing development timelines, expanding therapeutic applications, and accelerating regulatory acceptance of computational approaches.

The full value of clinical data will only be unlocked by redesigning how it’s captured, connected, and applied to accelerate decisions, improve trials, and better serve patients.

An overview of key considerations shaping the BGM and CGM decision, why both often coexist in trials today, and how eCOA platforms play a critical role in supporting this data collection at scale.

How the application of artificial intelligence, broader use of real-world evidence, decentralized clinical trials, master protocols, and risk-based quality monitoring, together with strong ethical oversight and increased collaboration, are contributing to better healthcare delivery and strengthening the role of clinical research in driving global health progress.

Why future-ready pharma companies must embrace AI-driven, real-time decision-making.

How human-centered AI that is focused on customer, user, and employee experience can drive real transformation in clinical trials and beyond by aligning intelligent technologies with the people who use them.

Clinical research evolves to prioritize patient-centered outcomes, integrating accessibility standards in electronic data capture for inclusive clinical trials.

Companies share their experience in future-proofing clinical data technology.

Strategies for ensuring that innovation with PROs remains parallel with advancements in disease detection and progression.

The challenges, opportunities, and strategic outlook for oncology research centers.

COVID-19 not only advanced scientific boundaries, but also transformed research methodologies and accelerated adaptive clinical trial design.

This year’s conference highlighted a number of critical areas in clinical R&D including financial management, representation, and eCOA.

The ongoing evolution of real-world evidence from a novel concept to a cornerstone of modern medical research signifies its growing importance and vast potential to improve personalized medicine, overall healthcare outcomes, and eventually democratization of scientific facts by general accessibility.

Machine learning can help investors dive deeper into trial data to evaluate the true potential of an asset and uncover new hidden opportunities.

Use of decentralized approach in a Phase 1 pharmacokinetic trial shows the ability to enable remote data collection and monitoring, which could improve patient access and enhance the efficiency of clinical research.