Cardiac Monitoring Reinvented: Patient Centricity, Data Collection, and the Role of AI

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.

In this Q&A, Claire Riches, VP of clinical solutions at Citeline, discusses how AI is transforming endpoint selection and protocol pressure-testing, what data inputs matter most for realistic enrollment planning, and why the tools are more accessible than most sponsors assume.

In today's ACT Brief, we examine the FDA's multi-domain transparency priorities, why radiopharmaceutical trials need redesigned infrastructure, and how AI can make enrollment more predictable.

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.

A new FDA Voices post from the agency's acting chief of staff details completed milestones and upcoming priorities across complete response letter disclosure, application lifecycle transparency, new approach methodologies, labeling, and inspectional records.