Informed Consent Management

Streamlining Informed Consent Data Identification and Validation for Clinical Research

Informed Consent Documents (ICDs) are central to clinical research, as they define how participant data and specimens can be used, reused, or restricted. As clinical programs grow in scale and complexity, identifying and validating consent data across multiple ICDs becomes increasingly difficult. This case study highlights how Princeton Blue helped a global life sciences company to treat informed consent data as a structured, decision-ready asset rather than a manual review exercise. By simplifying how consent information was identified and validated, teams reduced delays, improved consistency, and gained a scalable foundation for supporting research at speed.

Challenges

Our client is a large, global biopharmaceutical company conducting clinical research across multiple therapeutic areas and geographies. Before participant data or specimens could be referenced or used, business teams were required to validate consent information. This made ICD interpretation a foundational step in the research lifecycle and a critical dependency for downstream research and lab teams.

To validate consent for a study request, business users manually reviewed data from the Transfer File Master (PTMF) portal and cross-checked multiple ICDs to determine:

  • Which sample types were permitted
  • Whether future use was allowed and under what restrictions
  • Applicable opt-out conditions
  • Retention periods and limitations

This approach was slow and highly dependent on individual interpretation. Users had to sift through lengthy documents, reconcile differences across versions, and apply judgment to determine whether consent criteria were met.

As study volumes increased, this created several challenges:

High manual effort 1

High manual effort

Reviewing and interpreting ICDs consumed significant time for each request
Risk of inconsistency 1

Risk of inconsistency

Different users could interpret the same consent language differently
Limited scalability 1

Limited scalability

The process did not scale with growing research demand
Downstream delays 1

Downstream delays

Research and lab teams had to wait for consent validation before proceeding
Poor visibility 1

Poor visibility

There was no consolidated, study-level view of validated consent information

The organization needed a structured, repeatable way to identify and validate consent data – one that reduced manual effort while improving accuracy and confidence.

Solution

Princeton Blue implemented a solution using the Process Automation and Agentic AI capabilities of the Appian Platform to accurately identify and validate informed consent data from Informed Consent documents and make it structured, trusted, and easy to work with.

Centralized Clinical Document Repository 1

Centralized Clinical Document Repository

All relevant study and consent documents were consolidated into a single, well-organized repository.
AI Agents for Data Extraction 1

AI Agents for Data Extraction

Agentic AI capabilities were applied to extract key data points such as specimen types, future-use restrictions, opt-out status, and retention periods.
Structured and Contextualized Clinical Data Views 1

Structured & Contextualized Clinical Data Views

Extracted data and documents were organized by study, participating country, and clinical site.
Verification Ready Document Preparation 1

Verification-Ready Document Preparation

Documents were automatically pre-processed, ensuring they were ready for quick review and validation across clinical operations.
Version Control and Traceability 1

Version Control & Traceability

The system maintained full version history and document traceability, ensuring clinical teams always worked with the latest information.
Intuitive Dashboards for Clinical Operations 1

Intuitive Dashboards for Clinical Operations

A user-friendly dashboard provided clear insight into study-level progress, document status, and pending verification tasks.

Impact

The solution delivered measurable improvements across efficiency, accuracy, and scalability:

  • 40,000+ ICD files were processed using AI-powered extraction
  • ~80% extraction accuracy reduced reliance on manual interpretation
  • Consent validation turnaround time was reduced from days to hours
  • Manual review effort was significantly reduced across business teams
  • Study-level visibility improved collaboration with research and lab teams
  • The process scaled effectively to support growing study volumes and additional document types

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