The CRO managed 38 active clinical trials across 210 research sites in 14 countries, primarily in oncology and cardiovascular indications. Trial enrollment was consistently running 4.7 months behind protocol timelines — each month of delay costing sponsors an average of $370K per trial in extended operational costs and delayed market exclusivity.
Patient screening was a manual nightmare. Clinical research coordinators at each site reviewed electronic health records against inclusion/exclusion criteria using printed protocol checklists. The average screening-to-enrollment cycle took 34 days per patient, with a screen failure rate of 62% — meaning nearly two-thirds of screened patients were ultimately ineligible, wasting thousands of coordinator hours.
Site selection for new trials relied on historical relationships and investigator reputation rather than data-driven performance metrics. The CRO had no systematic way to predict which sites would enroll on target and which would underperform, leading to chronic over-reliance on a small cluster of high-performing sites while 40% of activated sites enrolled fewer than 3 patients per trial.
We were activating 210 sites knowing that 40% of them would barely enroll anyone. But we had no model to predict performance before spending $85K to activate each one. We were burning cash on sites that would never deliver.
Intelliblitz deployed a dual-engine clinical trial intelligence platform. Engine one: an AI patient matching system that ingested de-identified EHR data from participating sites and automatically screened patient populations against trial inclusion/exclusion criteria. The NLP layer parsed complex medical histories, lab results, concomitant medications, and prior treatment lines to generate eligibility probability scores — reducing manual screening from 34 days to 9 days per patient.
Engine two: a site performance prediction model trained on 12 years of historical enrollment data across 3,400 completed trials. The model analyzed 74 variables per site — including investigator publication history, site staff turnover, geographic patient density, competing trial load, and historical enrollment velocity — to generate enrollment probability forecasts before site activation. Sites scoring below the viability threshold were flagged for replacement or enhanced support before a single dollar was spent on activation.
A unified trial command dashboard gave sponsors real-time visibility into enrollment velocity, site-level performance, and predictive completion timelines across all active studies.
The AI flagged a site in Munich that our team had ranked as high-potential based on the investigator's reputation. The model detected that three competing trials at the same institution would cannibalize enrollment. We redirected to an alternative site that enrolled 340% faster.
— Director of Site Strategy, Mid-Market CRO
Within two quarters, average screening-to-enrollment time dropped from 34 days to 9 days — a 73% reduction. Screen failure rates fell from 62% to 28% as the AI pre-qualification layer filtered out ineligible patients before coordinators invested screening time. The site performance model achieved 86% accuracy in predicting enrollment velocity, enabling the CRO to eliminate $4.2M in wasted site activation costs by redirecting resources to high-probability sites.
Overall trial enrollment timelines improved by an average of 3.1 months per study, recovering an estimated $14M in annual revenue that had been lost to sponsor penalties, extended operations, and delayed milestones. Two top-20 pharmaceutical sponsors expanded their contract portfolios by 60% after reviewing the enrollment intelligence dashboards. The CRO's win rate on new trial bids increased from 22% to 38% as the predictive enrollment data became a core differentiator in sponsor RFP responses.
We went from losing bids because sponsors questioned our enrollment projections to winning them because we could show real-time predictive data no other CRO was offering. The intelligence platform changed our competitive position entirely.
— CEO, Mid-Market CRO