AI Transforms Clinical Trials: Design Certainty & Peak Execution
Watch the 2-Part Webinar Series: Accelerating Trials from Design Through Execution
Both drug and device development pipelines face significant pressures, as escalating protocol and design complexity contribute to over 80% of trials missing their enrollment targets. This series reveals how purpose-built AI, leveraging validated, global, cross-sponsor data from over 36,000 studies and 11 million patients, is redefining the clinical trial operating model. Learn how AI accelerates trials from designing smarter protocols to achieving peak operational execution.
Webinar Part 1: AI-Powered Design for Protocol Certainty
Learn how AI ensures scientific rigor and operational feasibility during the design and planning phases:
Scientific Design & Sample Size Reduction
- Covariant Adjustment with Virtual Twins (CAVT): AI and statistical algorithms combine baseline features into a “super covariant” to reduce unexplained variability.
- This adjustment decreases the required sample size, potentially reducing patient counts from 100 to about 85 per arm in some settings.
- Synthetic Control Arm (SCA)/External Controls: Used to provide supportive evidence for regulatory submissions. Regulators are most open to accepting ECAs in diseases that are severe and lack an adequate standard of care.
Operational Protocol Optimization & Budgeting
- Protocols are digitized from Word/PDF into a digital artifact for benchmarking.
- The Patient Burden Index score objectively quantifies study complexity, and higher complexity is associated with decreased enrollment and increased study dropout rates.
- Automated extraction and standardization of the Schedule of Activities (SOA) content fuels automated budget builds by assigning standardized codes (e.g., CPT library). This automation can save approximately 70% of time spent in budget building.
Webinar Part 2: Unlock Peak Performance in Start-Up & Execution
This session explores how AI unlocks peak operational performance during study start-up and continuous execution:
Study Start-Up (SSU) Acceleration
- Predictive Site Selection: AI models use study characteristics (indication, phase, disease severity) to recommend high-performing sites.
- Sites predicted to be high performers enroll on average five times faster than low performers. Leveraging these sites can yield an average three-month acceleration in cumulative enrollment timelines.
- Database Build Automation: AI is automating the EDC study build (forms, edit checks, test cases), moving toward a “one-click study build”. This is projected to yield a 75% improvement in time savings, reducing the typical 12-to-16-week database build process down to three or four weeks.
Continuous Execution and Automation
- Live Enrollment Forecasts: AI establishes a baseline and then dynamically updates the enrollment trajectory based on real-time performance to enable course correction.
- Payment Automation: Integrated clinical and operational data drives automated calculation and disbursement of site and patient stipends, handling global tax calculations and country-specific invoice regulations.
- Streamlined Monitoring Workflows: Automation, including auto-filing of letters and reports to the eTMF, increases CRA efficiency, resulting in a 40% reduction in time spent on site monitoring administration.