Increase Clinical Trials Recruitment with Predictive Analytics using Real-World Data
Globally 80% of clinical trials fail to meet enrollment timelines and 35% of clinical trials are terminated due to under-enrollment. Clinical research coordinators are overwhelmed with activities. In order to mitigate the issues facing clinical trials and improve trial success, companies try to leverage a wealth of readily available data through clinical records to help improve these dismal results, these data are not structured for search and often not queryable without expert support. By incorporating predictive analytics and machine learning, trials can better source potential candidates as well as gain more insights during design to streamline overall success. This interactive webinar will explore how leveraging new technology can help support clinical trial coordination in the mission of meeting the essential recruitment needs and offer opportunities to better assess design options and retention.
Join this expert speaker panel to hear best practices on enhancing patient recruitment.
Learning Objectives:
- Capitalizing on machine learning to qualify candidates, hypothesize on missing data and minimize clinical trial failures
- Streamlining onsite operations with predictive analytics
- Strategizing methods to mitigate potential screen failure, patient dropout or non-adherence through improved collaboration and deeper vetting of candidates
Speakers
Theodora Weisz Patient Advocacy and Public Affairs Lead EMEA/APAC at Ionis Pharmaceuticals
Nina Antoniotti Director of Interoperability and Patient Engagement at St. Jude Children’s Research Hospital
Ori Geva Co-Founder and President of Commercial Strategy at Medial EarlySign
Adam Dooley Vice President Of Business Development at Medial EarlySign
