Right Sized RBM Solution For Your Trials
This paper is the first in a short series of two papers written by the same authors taking you through:
1. Implementing RBM from an Operational Standpoint – your journey through the early stages of RBM, critical decisions to be made in identifying the right approach for your particular needs, de-risking your protocol, centralized monitoring, and more.
2) Implementing RBM from a Technological Standpoint – and on to implementation challenges and lessons learned, including data integration, automation of processes using AI and machine learning (AI/ML) with advanced and predictive analytics.
The move to RBM was initially fueled by the International Council for Harmonisation (ICH) E6 – Good Clinical Practice (GCP) guideline, 1 requesting sponsors to maintain greater trial oversight and use a more formal approach to quality management, combining new technology and timely data.
While the ICH’s 2016 Guidelines encouraged companies to adopt RBM as a strategy and to focus more on formalized quality management and timely data, many companies are uncertain about how to execute the requirements.
Visit the second installment to dive deeper into this topic.
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