AI-Driven Site and Investigator Selection

SITUATION

A global pharmaceutical company was facing bottlenecks in clinical trial startup due to inconsistent site selection, unclear investigator performance signals, and delayed feasibility assessments. They needed a scalable, automated solution to select top-performing sites and principal investigators (PIs) using a data-driven approach.

CHALLENGE

• Site selection was heavily manual, relying on anecdotal evidence and outdated performance metrics
• PI contribution and influence were difficult to quantify
• No standardized way to assess digital footprint, prior trial success, or publication impact across geographies
• Users needed the ability to adapt site recommendations dynamically based on trial-specific factors

APPROACH

Mu Sigma built a two-phase solution:

• Phase I:Developed a ranking engine combining historical trial data, publication volume, digital activity, and trial footprint to identify top-
  performing sites and PIs
• Phase II:Deployed a robust, end-to-end tool with machine learning (ML) models that incorporated dynamic trial-level inputs (e.g., indication,
  geography, enrollment goals) to recommend best-fit Site-PI combinations in real-time
• Integrated metrics included investigator engagement, therapeutic expertise, operational capacity, and dropout risk
• Delivered insights through a user-friendly dashboard with customizable filters and model-driven outputs

IMPACT

• Reduced site selection time by 65%, accelerating trial startup cycles
• Improved precision of site-PI pairing, reducing underperforming site risk
• Enabled smarter, geographically-aware site planning, improving feasibility across new markets
• Enhanced strategic decision-making with transparent scoring logic and reproducible results