Cutting-Edge Clinical Trials
Speeding patients to trials.
Empowering the most precise treatment.
By identifying patients at the time of diagnosis, our advanced AI-enabled patient recruitment platform compresses study timelines by finding the best-suited patients for clinical trials.
Deep Lens gives you a sharp focus on patients
Our AI-driven cloud platform, VIPER, identifies and recruits patients earlier than current methods, at the time of diagnosis, thereby accelerating trial recruitment and bringing game-changing therapies to market sooner.
VIPER - it simply works
- Cloud-based for quick and easy set up at all your participating sites.
- Real-time dashboard capabilities to track site level trial performance.
- Visible indicators when new patient data is received – intuitive data filters to prioritize patients who require action.
- Patient data filtering to match cases against inclusion and exclusion criteria.
- Molecular/genetic testing notifications to increase the number of qualified patients.
- Eligibility and consent status to monitor patient enrollment.
- Role-based alerting and messaging that is ﬂexible and dynamic.
- Accelerate study timelines by identifying and recruiting patients upstream of all current methods (at the time of diagnosis).
- Seamless data integration with anytime, anywhere access through the cloud.
- Strengthen collaboration on groundbreaking cancer research.
- Improve patient engagement to recruit patients to trials before commencing a competing therapy.
- Manage patients from identiﬁcation through to qualiﬁed enrollment.
- Increase recruitment by community oncologists and improve the number of qualiﬁed patients who receive molecular tests.
Abstract #2052: Novel artificial intelligence (AI)-based technology to improve oncology clinical trial fulfillment
Authors: TJ Bowen, Laura Stephens, Mark Vance, Yancui Huang, Deborah Fridman, Chadi Nabhan
- Less than 5% of US adult cancer pts are enrolled on clinical trials. Challenges in clinical trial fulfillment limit available treatment options, slow enrollment and ultimately delay new therapies from reaching market.
- Patient screening requires multiple clinical team members to find pts that meet strict inclusion/exclusion criteria.
- We evaluated the impact of new technology, Deep Lens VIPER, in identifying more qualified pts for clinical studies, and reduction of staff burden.
- 1 novice clinical research coordinator pre-screened 20 studies previously managed by 6 staff
- 4 months of retrospective data triaged in 3 weeks
- 150 previously unidentified potential patients for 16 out of 20 studies were identified
- 11 different tumor types included in analysis across 12 biomarkers (HER2, HER4, EGFR, MET, RET, MTC, ATM, ALK, ROS1, PD-1, RAS, and MSI high)
- 3 basket studies (multi-arm; multi-indication) were analyzed