De-identified data fuels AI-driven drug differentiation

De-identified data fuels AI-driven drug differentiation

Client Context

  • Global pharma company consolidating large volumes of patient data into a diverse data.
  • The goal is to improve drug development by unleashing the power of artificial intelligence.

Business Challenge

  • Unlocking the value of clinical study data for internal use with highly automated de-identification.

Privacy Analytics Solution

  • Using Eclipse, the company will de-identify thousands of clinical studies, achieving high-utility data that is compliant with global privacy regulations.
  • Eclipse enterprise-class anonymization software provides auditable proof that the company is taking legally defensible steps to protect the privacy of clinical trial participants.
  • In addition to trial-specific features for real world evidence, Eclipse provides capability to anonymize other data types (e.g. DICOM headers) and measure the risk of re-identification for various disclosure contexts (e.g. external researchers).

Business Impact

  • The company is now well positioned to transform life sciences and change the way medicines are developed.
  • Competitive advantage by leveraging best-of-breed anonymization software that is proven across diverse tech environments globally.

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