AI can turn complex protocols into first-pass budget inputs, letting sponsors start budgeting sooner while budget builders shift from data entry to review.
|Podcasts|December 15, 2011
Montrium CEO Paul Fenton Discusses the Need for Electronic Document Workflow
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Montrium CEO Paul Fenton Discusses the Need for Electronic Document Workflow and How It Positively Impacts Clinical Trial and Life Science Business Processes
Paul Fenton, President and CEO of Montrium, discusses how electronic workflow changes business process and brings efficiencies to eTMP, eCTD, and overall document awareness.
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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.
In today's ACT Brief, we examine proportionate analysis frequency in RBQM, how to build trust into real-world evidence foundations for cancer trials, and how rare disease developers monetize regulatory success.
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.
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