Using RBQM and New Technologies to Enhance Data Analysis and Enable Faster Review and Decision Making
An increase in clinical trial data is driving demand for enhanced clinical trial processes. Sponsors want to be able to review data which has been analyzed quickly and efficiently so they can make informed decisions faster.
Advancing Technology in Clinical Trials
With increasing pressure to overcome drug development challenges and advance drugs to market faster and with greater efficiency, sponsor expectations of CROs and their services are running high.
CRO Industry Report
Today’s drug development landscape demands contract research organizations (CROs) provide flexible models that leverage the latest technologies to support sponsor needs.
DIA 2024: Pat Hughes of CluePoints Highlights Renewed Agreement With FDA, Utilizing AI in RBQM
In an interview with ACT editor Andy Studna at DIA 2024, Hughes, co-founder and chief commercial officer of CluePoints touches on collaboration with FDA and using artificial intelligence in clinical research.
Does Risk-Based Quality Management (RBQM) Actually Improve Quality?
Over the past 10 years we have seen a lot of progress in risk-based quality management (RBQM) adoption across the industry. This has rightly led to questions about its impact.
Quality Tolerance Limits: An Updated View of Industry Trends
We presented an initial review of quality tolerance limit (QTL) adoption trends in the March 2023 issue of Applied Clinical Trials, based on information compiled from the CluePoints RBQM platform through the end of 2022.
FDA and CluePoints Extend Existing Collaboration to Include AI/ML for Quality Assessment
Under the new extension, FDA and CluePoints will enhance CluePoints’ SMART software to address a broader range of regulatory concerns.
The Risk-Based Data Management (RBDM) Revolution
The clinical trials landscape is evolving with more data, increased investment in personalized medicines and a shift toward decentralized and hybrid clinical trials.
Leveraging Machine Learning and Deep Learning for Natural Language Processing in Clinical Data Management
Nicolas Huet, CluePoints Machine Learning Manager, discusses developments in the use of machine learning and deep learning for natural language processing and how it can be pragmatically implemented in clinical data management.
The Future of RBQM Adoption
In the fifth and final part of this video interview series, Ken Getz, executive director and research professor, Tufts Center for the Study of Drug Development, Tufts University School of Medicine and Steve Young, chief science officer, CluePoints look ahead to the future of risk-based quality management and what adoption will look like.
