Clinical intelligence platform strengthens data-driven clinical research

Clinical trial information was distributed across multiple systems and data sources, making it difficult to integrate, interpret, and use the information effectively.

The data included unstructured content such as protocols, administrative correspondence, schedules, and supporting documents, along with structured data from IRB systems, CTMS platforms, and operational databases.

This fragmented data landscape limited the ability to generate timely insights, streamline workflows, and scale AI-driven decision-making across clinical research operations.

• Iris analyzed the requirements and developed a scalable intelligence platform to bring disparate clinical trial data together and transform it into trusted, actionable information.
• The solution included robust data ingestion pipelines capable of processing more than 80,000 protocol documents at scale and converting unstructured content into high-quality structured data. This established a reliable data foundation for AI/ML models and supported the expansion of the platform's knowledge base.
• We also built human-in-the-loop (HITL) validation workflows to enable human review and oversight of AI-generated outputs. The workflows helped refine results while strengthening governance, compliance, and quality controls required for clinical research.

The platform created a reliable data backbone for clinical research and helped the client make better use of its growing data landscape. Key outcomes included:

• Accelerated AI-driven insights through a reliable and structured data foundation
• Reduced manual effort involved in data preparation
• Faster onboarding of new data sources
• Improved accuracy and trust in AI-generated insights
• Strengthened compliance and quality controls for clinical research use cases