This week Applied Clinical Trials published an interesting case study of the risk-based quality management learning curve. The provision of a set of training modules helped Merz Pharma to initiate a smooth RBQM rollout.
Four RBQM case studies illustrate how our intelligent EarlyBird system forecasts future events and detects suspicious activity, to subsequently trigger on time preventive and corrective actions.
An effective way to control risks during a clinical trial is to anticipate events induced by human factor before the study even begins. Dr Johann Proeve's third chapter on Adaptive Monitoring talks about how human factor is complementary to numeric data.
Adaptive Monitoring is not a “status quo”, it is a dynamic response to clinical research that drives monitoring scope and activities to the evolving areas of greatest need which have the most potential to positively impact. Each clinical study requires its own tailored monitoring approach ensuring risks are minimized.
It is widely broadcasted that pharma companies will have to accelerate adoption of adaptive clinical trial designs to reduce study timelines and costs while increasing success rates. Risk-based Monitoring “Real RBM” integrates the Adaptive Monitoring (AM) process, which addresses all aspects of Quality Risk Management.
Case Study Clinical trial site engagement has been advocated as a critical component relating to a study’s performance and success, however, a minimum amount of data supports this connection. In this article a reader will find quantitative and qualitative approach based on risk-based [...]