Agentic AI Workflows for Clinical Trials
Accelerate complex trial workflows through governed, human-guided AI execution.
Delivery Model
AI Workflows Built for Clinical Trial Execution
Clinical trials depend on a constant flow of decisions across data, sites, safety, operations, and regulatory processes. Many of those workflows are still slowed by manual triage, repeated review, fragmented systems, and delayed follow-up.
IQA’s Agentic AI Workflows are designed to help clinical teams move faster by coordinating multi-step processes across monitoring, assessment, routing, draft generation, escalation, and follow-up. Rather than treating AI as a chatbot or a stand-alone automation tool, we apply it as a governed workflow capability that supports real clinical execution.
What Agentic AI Means in Clinical Research
Agentic AI refers to AI systems that can work toward a defined goal across multiple steps, using data, tools, and workflow logic to move a process forward with less manual coordination.
This is more than task automation. It is workflow-aware AI designed to support complex, decision-heavy trial operations.
In clinical trial environments, that can mean AI workflows that:
Why Agentic Workflows Matter in Clinical Trials
Traditional automation can reduce repetitive effort, but clinical trials also depend on workflows where timing, context, and cross-functional coordination matter. Data issues, site signals, safety events, document updates, and operational exceptions often require more than a single automated step.
The goal is not to replace clinical teams. It is to reduce workflow friction and improve execution speed across the trial processes where delays most often begin.
By combining real-time awareness, workflow logic, and governed action, agentic AI can help teams:
Framework
The IQA Agentic AI Framework
Use Cases
Where IQA Applies Agentic AI in Clinical Trials
Data Query Management
Support earlier discrepancy detection, contextual query drafting, severity classification, routing, and follow-up across EDC-driven review workflows.
Site Performance Monitoring
Continuously analyze enrollment pace, protocol deviation rates, data entry lag, and quality signals to highlight site-level risks and support proactive intervention.
Safety Signal Workflows
Scan incoming adverse event patterns, compare them with historical context, and flag potential safety concerns for governed review and follow-up.
CDISC-Oriented Data Transformation
Coordinate transformation steps across raw data, standards-driven mapping, validation checks, and review support for downstream SDTM and ADaM workflows.
Recruitment and Enrollment Coordination
Support identification, screening orchestration, enrollment pacing, and site-level recruitment visibility using structured operational workflows.
Regulatory Document Preparation
Assist with compiling study-specific content, populating structured templates, identifying gaps, and routing documents for medical or regulatory review.
AI-First Execution with Human-Guided Governance
Agentic AI does not mean unchecked autonomy. In regulated clinical environments, governance matters as much as speed.
This model helps sponsors and CROs gain the speed of AI-supported execution while maintaining the control and accountability required for clinical research.
At IQA, agentic workflows are designed with configurable human oversight so organizations can define:
Why IQA
Why IQA for Agentic AI Workflows
Designed to Work Within Your Clinical Ecosystem
IQA’s Agentic AI Workflows are designed to integrate with existing clinical and operational environments rather than replace them.
This makes it possible to embed agentic workflows into the systems and processes teams already use.
They can connect with:
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See Agentic AI in Action
Explore how governed agentic workflows can improve trial speed, operational visibility, and cross-functional execution across your clinical programs.