Pharma: RWE

Pharmaceutical companies need an accurate, detailed picture of patient experience and outcomes in both clinical trials and the real world to develop and deliver effective drugs. However, analyzing real-world data for differentiated biomedical insight requires dealing with tremendous scale and complexity, as well as potential privacy concerns. Claims data, for example, is on the order of billions of rows, and genomics data can contain thousands of features per patient.

Investigate drug development data in real-world context.

Palantir Foundry enables the integration and end-to-end analysis of internal drug development data alongside external data sources (such as PubMed, TCGA, and ChemBL) and real-world data. Our clients are using the platform to:

  • Understand patient population dynamics and drug outcomes with unprecedented granularity
  • Identify promising gene and biomarker combinations for clinical trial stratification and analysis
  • Demonstrate cost-effectiveness of drug therapy
  • Identify molecular correlates of drug response based on internal and external screening data

Bring all subject matter experts onto a single, secure platform.

Under the hood, subject matter experts ranging from bioinformaticians to real-world evidence analysts to health economists are collaborating to:

  • Analyze pre-clinical, clinical, manufacturing, sales, and marketing data in a single environment for R&D and commercial purposes
  • Conduct both visual and code-based cohort analysis with complex inclusion and exclusion criteria
  • Map complex treatment pathways over millions of prescription claims
  • Automate the reporting of population baseline characteristics and outcome metrics

For pharma companies around the world, Palantir Foundry is improving the fidelity of drug development, from clinical trial planning, to market access, to post-market safety monitoring. With robust privacy measures baked into the platform, including full auditability of all activity, these companies are maintaining rigorous standards of patient data privacy and analytical veracity along the way.