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Simplified Trial Protocol by 25% and Reduced Study Duration by ~6 Months
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Outcomes
What we did
25%
Protocol complexity reduction
~6 Months
Shorter projected study timeline
15–20%
Participant burden reduction
30–40%
Fewer protocol amendments
About Company
A Clinical Development Director based in the United States is working on a Phase III study with 150–200+ endpoints and 25–30 CRFs, resulting in high protocol complexity.
Interim analysis cycles extend to several weeks, limiting the ability to make timely adaptive decisions.
As complexity increases, the study faces a 30–40% likelihood of protocol amendments, each introducing additional delays and operational burden.
Interim analysis cycles extend to several weeks, limiting the ability to make timely adaptive decisions.
As complexity increases, the study faces a 30–40% likelihood of protocol amendments, each introducing additional delays and operational burden.
Challenges
Key Barriers
to Trial Execution
Protocol Over-Engineering
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30 CRFs and 200+ endpoints
High operational complexity
Increased amendment risk
Slow Interim Analysis
Biostat team at capacity
2–3 week turnaround times
Delayed adaptive decisions
Amendment Cycle Cost
6+ week delays per amendment
Significant budget impact
Recurring across program
Data Volume Burden
Large, complex datasets (eCRF, lab, device)
Manual analysis overload
Delays in deliLimited analytical capacityvery
Core platform
Operational Solution
Maxis AI Agentic Workflows — Under Human Oversight Throughout
AI Protocol Simplification
Identifies redundant endpoints
Reduces low-value assessments
Expert-reviewed recommendations
25% protocol complexity reduction
Accelerated Interim Analysis
Automated statistical pipelines
Survival curves and interim outputs generated
Defined validation checkpoints
85–90% faster interim analysis
RBQM + Data Oversight
Unified eCRF, lab, device, genomic data
Frees analyst capacity for high-value work
Audit-traceable governance
20–30% analytical capacity reclaimed
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 |
|---|---|---|
| Protocol complexity | 30 CRFs, ~200 endpoints — high amendment risk and investigator burden | 22 CRFs, ~150 endpoints — streamlined design validated under supervised governance |
| Projected study duration | 24 months with significant amendment risk | 18 months with optimized protocol design — ~6 months shorter |
| Participant burden | High — complex data collection requirements increasing dropout risk | 15–20% reduction in participant burden, improving retention and data completeness |
| Protocol amendments | Frequent — each adding 6+ weeks of delay and budget impact | 30–40% fewer protocol amendments — regulatory exposure significantly reduced |
| Interim analysis speed | 2–3 weeks manual statistical programming per cycle — adaptive decisions delayed | 85–90% faster turnaround (weeks → days); 2–3× faster decision cycles |
Outcomes
Protocol complexity reduced by 25%, projected study duration cut from 24 to 18 months, with fewer amendments and improved participant retention.
25% reduction in protocol complexity (CRFs and endpoints) — amendment risk significantly lowered
~6 months shorter projected study timeline through optimized protocol design
15–20% reduction in participant burden — improved retention and data completeness
30–40% fewer protocol amendments — regulatory exposure and budget risk reduced
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