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Recovered 30% Enrollment Gap in 8 Weeks, Avoiding ~6-Month Trial Delay
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Outcomes
What we did
30%
Enrollment gap recovered
8 Weeks
Full recovery timeline
2–3×
Faster site identification
20–40%
Enrollment velocity improvement
About Company
A VP of Clinical Operations managing multi-region trials across the US and EU is responsible for 10–25 active sites, where ~20–30% of sites trend below enrollment targets.
Site performance issues are identified only after CRO reports consolidate data, typically 4–8 weeks after early decline begins.
This delayed visibility creates a ~6-month trial delay risk, driven not by execution gaps but by late intervention.
Site performance issues are identified only after CRO reports consolidate data, typically 4–8 weeks after early decline begins.
This delayed visibility creates a ~6-month trial delay risk, driven not by execution gaps but by late intervention.
Challenges
Key Barriers
to Trial Execution
Late Site Visibility
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Performance issues detected months late
Limited recovery options
Enrollment already impacted
Reactive Interventions
Actions triggered after targets missed
Limited time for correction
Ineffective recovery measures
Fragmented CRO Reporting
Conflicting reports across CROs
No clear view of underperformance
Delayed response due to manual investigation
No Early Warning System
Reliance on periodic reviews
No real-time site performance signals
Late detection of underperformance
Core platform
Operational Solution
Maxis AI Agentic Workflows — Under Human Oversight Throughout
Real-Time Site Monitoring
Continuous tracking of enrollment and dropouts
Underperforming sites flagged early
2–3 weeks earlier identification
2–3× faster site identification
Supervised Site Intervention
Automated intervention workflows
Alerts, escalations, and triggers
Human oversight maintained
20–40% enrollment velocity uplift
Unified Operational View
Integrated signals across sites and vendors
Audit-traceable governance layer
Replaces fragmented CRO reporting
30% enrollment gap recovered in 8 weeks
Maxis AI operated as a governed and supervised execution layer within existing systems throughout.
Core platform
Measured Impact
Quantified Outcomes After Deploying Maxis AI’s Agentic Workflows
| Metric | Before Agentic AI | After Agentic AI |
|---|---|---|
| Time to identify slow sites | Months into enrollment after targets were already materially missed | 2–3 weeks into enrollment, while recovery is still operationally feasible |
| Corrective action speed | Limited and reactive — constrained by time taken to confirm the problem | Rapid interventions; 5 new sites activated, recruitment strategies adjusted within days |
| Enrollment gap recovery | 30% lag with ~6-month delay projected and escalation under review | 30% enrollment gap recovered within ~8 weeks — trial kept on schedule |
| Enrollment velocity | Declining — shortfall compounding with each passing week | 20–40% improvement in enrollment velocity post-intervention |
| Trial outcome | Enrollment targets missed; study delay projected at ~6 months | ~6-month projected study delay avoided entirely; no protocol amendment required |
Outcomes
The study recovered its enrollment trajectory without a protocol amendment — keeping the trial on schedule and avoiding a projected 6-month delay.
30% enrollment gap recovered within ~8 weeks of AI-supervised intervention
2–3× faster identification of underperforming sites vs traditional monitoring
20–40% improvement in enrollment velocity after targeted site interventions
~6-month projected study delay avoided entirely — no protocol amendment required
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