Site Network

Agentic AI for Clinical Trial Site Networks

Site networks managing multiple studies face a core constraint: enrollment inefficiency driven by high screen failure rates and coordinator overload.

Screen failure rates average ~30% and can exceed 70% in complex protocols, while manual screening and protocol complexity increase workload and delay enrollment.

The issue is not access to patients. It is identifying and enrolling the right patients efficiently.

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Agentic AI Offerings

Our platform strengthens execution across key clinical workflows:

Patient Matching and Screening

Automated patient-to-protocol matching
Faster identification of eligible patients

Protocol Compliance Support

Real-time protocol guidance
Reduced protocol deviation risk

Enrollment Monitoring and Oversight

Continuous enrollment tracking
Early identification of at-risk sites

Coordinator Workflow Support

Reduced manual coordination effort
Unified dashboards across studies

Where Site Network Execution Often Slows

Execution constraints typically emerge due to:

Manual patient screening workflows

High coordinator workload

Limited enrollment visibility

Protocol deviation risks

Fragmented operational systems

The constraint is not effort. It is scalable execution capacity.

What Improves with Agentic AI

With agentic AI for small CRO clinical operations teams, delivery becomes more scalable:

Faster patient screening and enrollment

Reduced coordinator workload

Earlier identification of enrollment risks

Improved protocol compliance

Recommended approach: deploy AI-driven patient pre-screening first. This reduces time spent on ineligible patients and allows coordinators to focus on eligible patient deploy AI-driven patient pre-screening first. This reduces time spent on ineligible patients and allows coordinators to focus on eligible patient

Where Small Pharma Execution Slows

Execution challenges commonly occur due to:
Limited internal operational capacity
Heavy dependence on CRO delivery
Delays in biometrics workflows
Limited real-time execution visibility
Small teams managing complex workflows
These constraints slow execution predictability.

What Improves with Agentic AI

With agentic AI for small pharma, execution becomes more scalable:
Faster database readiness timelines
Reduced dependency on manual workflows
Earlier risk detection
Improved operational visibility
Stronger execution predictability
Recommended approach: deploy supervised execution capacity at the Phase 1 to Phase 2 transition - when complexity increases but teams remain lean. Organizations that do this advance to database lock 40–50% faster while preserving runway and avoiding internal team expansion.
FAQ

All You Need to Know

How does Maxis AI’s agentic AI improve site network enrollment?

It automates patient matching and improves enrollment monitoring.

Does this replace site coordinators?

No. It supports coordinators and reduces workload.

How does this improve protocol compliance?

Real-time monitoring helps detect risks earlier.

Where do sites see impact first?

Screening speed and enrollment consistency.

Is this suitable for multi-site networks?

Yes. It supports scalable site network execution.

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