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.
|Articles|June 18, 2020
Parexel and TriNetX Partner to Advance Real-world Data Use
Advertisement
Parexel and TriNetX have announced a strategic effort through which they will connect healthcare data to support the development of insights intended to accelerate the execution of clinical research. Working with Parexel’s strategic partner Datavant, the integrated offering will leverage Datavant’s privacy-protecting linking technology to connect Parexel’s study operations data with de-identified data from TriNetX’s network of global healthcare organizations.
For more information, click
Advertisement
Related to this article
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.
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.
Advertisement
Advertisement
Trending on Applied Clinical Trials Online
1
Real-Time Data, Right-Time Decisions: Rethinking Analysis Frequency in RBQM
2
What It Takes to Harmonize AI Across Fragmented Oncology Health Systems
3
Using AI to Design Better Trials Before the First Patient Enrolls: Q&A with Claire Riches, Citeline
4
ACT Brief: FDA Transparency Roadmap, Radiopharmaceutical Trial Operations, and AI-Powered Enrollment Prediction
5