85% of clinical trials end with less than expected enrollment, while 19% are terminated or fail to meet accrual goals at all. This scenario has significant impact on the viability of trials, leading to insufficient results, loss of time and financial resources and ultimately a reduction in innovation.
One of the biggest challenges associated with clinical trial enrollment is identifying candidates due to the vast amounts of siloed and unstructured data needed. In fact, over half of eligibility criteria is unstructured, making it incredibly complex to search and access. Attempting to make sense of this content, combined with searching across data silos such as patient records and genomics data is incredibly time intensive, with key insights and candidates missed.
A leading U.S. cancer research center is turning to Vyasa to solve this problem. By establishing an intelligent data fabric with Vyasa Layar, the research center has created a unified data platform to search and identify relationships between clinical trials and its patient population to improve trial enrollment and viability. With Vyasa, the research center can:
- Unify siloed data sources including patient records, genomics files and clinical trials into a single platform without moving or replicating files.
- Search across multiple data sources and quickly identify insights with the power of deep learning.
- Explore data in natural language. No coding or data science experience required.
- Discover relationships in their data through dynamic knowledge graphs.
- Visualize their data in smart spreadsheets and real-time dashboards.
With Vyasa, organizations empower users across skill sets to leverage deep learning and gain new value from their data. In turn, users have experienced a 25% improvement in productivity around data analysis tasks, all without compromising on accuracy.