AI-Driven Site Selection & Patient Feasibility for Clinical Trials
Clinical trial planning was hindered by fragmented clinical and real-world data, limiting visibility into patient availability and site performance. This led to suboptimal site selection, inaccurate enrollment forecasting, delays in trial timelines, and increased operational costs—especially when targeting diverse and hard-to-reach patient populations
Trial success is no longer unpredictable—it is modeled, optimized, and scalable
The Challenge
Fragmented Data and Limited Visibility Impacting Site Selection and Enrollment Planning
Clinical trial planning relied on disconnected clinical and real-world data sources, limiting visibility into patient availability and site performance. This resulted in suboptimal site selection, inaccurate enrollment forecasts, delays in trial timelines, and challenges in recruiting diverse and hard-to-reach patient populations
Solution
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Leveraged integrated clinical and real-world data for data-driven site selection and feasibility analysis
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Developed patient cohort models to assess availability across geographies and identify hard-to-reach populations
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Built AI-driven enrollment prediction models to forecast timelines and optimize site mix
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Enabled scenario-based planning to determine optimal number of sites and improve enrollment success
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Delivered interactive analytics and visualization dashboards for faster, insight-driven decision-making
Impact
Improved Trial Planning, Site Selection, and Patient Recruitment Outcomes
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Improved site selection accuracy and reduced underperforming trial sites
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Accelerated trial planning and enhanced enrollment predictability
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Reduced effort in site setup, contracting, and activation
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Increased success in recruiting targeted and diverse patient populations
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Enabled faster, data-driven decision-making across clinical teams
Measurable Impact
30–40%
improvement in site selection accuracy
25–35%
faster trial planning timelines
20–30%
reduction in site setup and activation effort