Speeding patients to trials.
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
An AI-based platform, VIPER, triaged study participants across 20 different cancer studies simultaneously, allowing identification of patients for interventional studies (previously not attempted at hospital due to resource constraints). 150 patients were identified for potential fit that were previously not identified over a four-month period.
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VIPER identified 1,045 pts (18.3%) with malignant neoplasms that would qualify for further analysis for clinical trials enrollment. Further triage based on inclusion and exclusion criteria led to the identification of 150 previously unidentified pts for 16 of the 20 studies. The VIPER system increased monthly candidate pt catchment for 16 of the 20 studies under investigation, which is approximately 600 patients annually added for final triage for studies being conducted.
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