As Risk-Based Monitoring (RBM) has evolved into modern Risk-Based Quality Management (RBQM), one thing is clear: process alone isn’t enough. While design, risk assessments, and KRIs remain essential, organizations now understand that impactful RBQM depends on elevating data and analytics to the same level as process.
In this RBQM Live keynote, leaders from Pfizer, GSK, and Veeva share how advanced analytics, AI, and machine learning are reshaping clinical data management and what this means for the future of RBQM.
They discuss how organizations are modernizing data infrastructures, unifying platforms, running datathons and hackathons, and enabling teams with the technology needed to detect risk earlier, accelerate database locks, and support more agile study design and execution.
You’ll learn how:
- Pfizer built a multi-year transformation roadmap and used large-scale hackathons to validate real-world AI/ML solutions.
- GSK unified its data ecosystem, standardized workflows, and leveraged a global datathon to accelerate data cleaning and RBQM activities.
- High-quality, interconnected data, not just large volumes, is essential for successful AI and machine learning models.
- AI/ML is redefining data cleaning and risk detection, enabling teams to manage rapidly expanding datasets that traditional methods can’t handle.
- Technology and strong partnerships enable more agile, concurrent study execution and faster, more confident decision-making.
