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The partnership gives RealTime's network of more than 3,000 research sites access to the Archer platform for direct, automated data transfer from eSource to any sponsor electronic data capture system or data destination.

In this video interview, Saamir Pasha and Carole Berini, PhD, of Ontada, identify three persistent obstacles to consistent real-world evidence generation in oncology—inconsistent data capture, structural data limitations that modeling alone can't fix, and the need for earned generalizability through deliberate multi-site validation.

How a phased CRO partnership built on ICH E6(R2) and E6(R3) principles supported a multi-indication biotech portfolio.

The second of a two-part perspective examining why clinical trial enrollment and representation outcomes are shaped long before the first patient is screened.

In this video interview, Saamir Pasha and Carole Berini, PhD, of Ontada, describe what operationalizing regulatory-grade quality frameworks requires—and why fit-for-purpose evidence generation is a transparency and documentation discipline, not just a data cleanliness exercise.

AI can turn complex protocols into first-pass budget inputs, letting sponsors start budgeting sooner while budget builders shift from data entry to review.

Trialbee CEO Matt Walz explores the current impact of AI in delivering practical gains in clinical development and reflects on its future potential, where enrollment predictability may be the biggest prize of all.

In this video interview, Carole Berini, PhD, of Ontada, explains how validated proxies for variables like ECOG performance status can recover not just sample size but the representativeness of hard-to-reach patient populations in real-world oncology studies.

A biomarker test now decides who enters a precision medicine trial, yet results often go unused and assays are not ready when screening opens.

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.

In this video interview, Saamir Pasha and Carole Berini, PhD, of Ontada, explain how building trust into the data foundation—through source lineage, standardization, and reconciliation—is what makes reliable real-world evidence generation in oncology possible.

In today's ACT Brief, we examine how AI surfaces design flaws before enrollment, why site training readiness depends on learning science, and the priorities defining next-generation weight-loss therapies.

Site training effectiveness depends on readiness at first-patient-in, requiring learning-science design principles around performance focus, cognitive load reduction, progressive sequencing, and spaced retrieval practice rather than training completion metrics or content volume.