Clinical Trial Benchmarking with Competitive Intelligence

SITUATION

A global pharmaceutical company needed a more efficient and scalable way to monitor competitor trials. Manually reviewing large volumes of trial documentation was slow, error-prone, and hindered real-time strategy shifts. The client’s goal was to build a competitive intelligence engine that could automatically surface benchmarking insights from clinical literature.

CHALLENGE

Most trial data exists in semi-structured or unstructured form buried within PDFs, registries, and medical publications. Traditional tools couldn’t extract comparator drugs, patient cohorts, or trial endpoints at scale. As a result, the client struggled to answer key questions like:

• What drugs are competitors using in similar indications?
• What trial designs are gaining traction?
• How do our protocols compare?

APPROACH

Mu Sigma deployed an NLP-driven solution that structured clinical trial literature into an interactive benchmarking dashboard:

• Text Mining & Entity Recognition: Extracted study arms, drugs, outcomes, and eligibility criteria from clinical trial descriptions.
• Knowledge Graph Construction: Identified relationships between comparator drugs, indications, and outcomes using a smart NLP algorithm.
• Custom Dashboard: Built a user-friendly interface to compare similar studies by therapy area, trial design, inclusion/exclusion criteria, and
  endpoints.
• Validation Framework: Ensured high precision recall rates for benchmark study identification using Mu Sigma’s iterative training and feedback loop.

IMPACT

• Reduced time to identify comparator studies by 80%, replacing manual search with automated extraction.
• Enabled real-time trial strategy adjustments based on competitor moves and protocol trends.
• Empowered teams across R&D, HEOR, and regulatory with on-demand benchmarking intelligence to support feasibility, protocol design, and value communication.