Leverage data and insights to drive efficient and effective clinical trial design and identify new portfolio opportunities.
- Optimize patient recruitment strategies
- Learn from historical trials
- Validate biomarker strategy
- Build virtual cohorts to validate hypotheses
- Reduce clinical trial planning time
- Facilitate faster decision-making
- Consolidate data across CROs
- Terminate "low success" projects earlier
- Identify opportunities for drug repositioning and indication expansion
- Develop insights into patient outcomes
- Mitigate the risk of costly protocol amendments
- Remove single points of failure
- Avoid the purchase of duplicate datasets
- Improve regulatory submissions
DISQOVER's Clinical Trial Design application supports clinical trial designers and other relevant profiles in accessing all the data needed for optimized decision making within clinical development. With the power of linked data, users can ensure their design decisions will drive trial efficiencies and success.
What can be accomplished in the Clinical Trial Design application?
DISQOVER's Clinical Insights applications helps translational researchers and other profiles access the necessary data to support them in improving the efficacy and safety of treatments and generating repurposing opportunities. Easily examine clinical and associated data, identify biomarkers, analyze clinical trial outcomes, and more.
What can be accomplished in the Clinical Insights application?
From the vast amount of data sources, it can be hard to identify biomarkers optimally. In this demo video learn how DISQOVER can help you and your team with your biomarker process.
With DISQOVER's enhanced NLP Clinical Packs, users can extract valuable insights and knowledge from both structured and unstructured text data, including scientific literature, news articles, clinical trials, and more.
Quickly find and analyze information about specific biological entities (such as diseases, genes, proteins, and more), populations, interventions, and outcomes that are mentioned in large volumes of text data.