The complexity of initiating studies continues to grow, a confluence of complicated protocols, globalization, and regulatory changes, at a time when there is intense pressure to speed clinical trials and restrain costs. Key to reducing these complexities is the ability to be able to leverage operational insights with granular performance metrics and machine learning, which can guide clinical research staff in their daily activities.
Machine learning technologies can help predict outcomes in clinical trials, leading to faster drug approval times, lower costs, and more funding to develop new treatments. More accurate predictions can reduce the uncertainty in study execution by providing greater risk transparency and allowing informed data-driven decisions to be made in the risk assessment and portfolio management of investigational drugs in clinical trials.
How can machine learning deliver business intelligence before starting studies?
Key Learning Objectives:
In this webcast we will explore:
- What is required in order to make machine learning a reality?
- The applicability of machine learning to make more precise predictions in planning studies now
- How machine learning can be leveraged now to improve daily activities for clinical research staff
- Where machine learning/artificial intelligence can provide the most value:
- Impact on study design: traditional site-based trials vs. decentralized trials?
- Impact on country selection?
- Impact on KPIs: balancing enrollment vs. speed?
Who Should Attend:
This webcast is applicable to:
- Sponsor and CRO roles responsible for:
- Site identification, feasibility assessment, selection and activation
- Collecting and evaluating trial metrics
- Project management of studies and operational excellence