A Practical Guide to Using Machine Learning for RBQM in a New Clinical Trial Landscape

Artificial intelligence technology, including machine learning (ML), is an increasingly hot topic in clinical research, but there are still important questions to be answered when putting these systems into action. This practical webinar will help set your organization up for success by offering a fresh perspective on the key goals of risk-based quality management (RBQM) and how you can best implement ML as part of this process for streamlined and effective data exploration.

In this webinar, Remarque Systems experts will discuss:

• How to align your team on the basic RBQM framework to reorient your trial management strategy toward your goals
• Where in the RBQM process ML can be leveraged most easily
• Common examples of ML in our day-to-day lives
• Practical ways to successfully utilize and leverage ML in your day-to-day clinical research processes, including real-world examples

Speakers

Amanda Coogan, Senior Customer Partnership Manager, Remarque Systems

Amanda Coogan worked for 12 years in university hospitals and medical device firms supporting domestic and international studies across a range of disease states, then transitioned to the software industry. There she serves as a consultant, subject matter expert and project manager to small and large pharma, medical device and CRO customers supporting the implementation of risk-based quality management. In addition to working with customers to optimize their use of technology within their RBQM strategy, she provides risk-based clinical trial operation expertise to support software product development.

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Rhonda Roberts, MSPH, Manager, Data Scientist, Remarque Systems

Rhonda Roberts is a biostatistician by training and has been in the clinical research industry for 13 years after migrating out of infectious disease hard science. She is passionate about empowering people through the use of their own data.

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