Clinical Trial Data Management

AI-Assisted Clinical Data Management—Built for Control, Traceability, and Scale. Establishing a governed foundation for analytics and AI.

The Clinical Discovery Platform (CDP) transforms clinical data management into a continuous, governed process—combining Zero Data Entry (ZDE™) with AI-assisted workflows to improve data quality, accelerate timelines, and maintain full regulatory confidence.

Built on Microsoft for Healthcare and powered by Azure AI Foundry, CDP modernizes clinical data management with automation, governance, and intelligent AI agents.

Let’s Talk Strategy

CDP helps clinical research organizations move from reactive data cleanup to continuous data readiness.

Clinical data managers are being asked to manage more data, from more sources, with greater speed and higher regulatory expectations. Traditional approaches often depend on manual review, spreadsheet-based reconciliation, delayed query cycles, and fragmented visibility across systems.

The CDP Approach

Continuous, Controlled, and Inspection-Ready

The Clinical Discovery Platform (CDP) establishes a new operating model:

  • Continuous validation at ingestion
  • Real-time discrepancy detection
  • Ongoing reconciliation across all data sources
  • AI-assisted workflows with human oversight
  • Always-on visibility into data quality and readiness

This transforms data management from a reactive phase into a continuous capability.

AI-Assisted Data Management Workflows

Built on Azure AI Foundry

CDP extends traditional clinical data management with AI agents built in Azure AI Foundry. These agents help data management teams automate repetitive activities, identify risks earlier, prioritize work, and accelerate review processes while maintaining human oversight and auditability. CDP uses purpose-built AI agents to support regulated workflows—not replace them.

All agents operate within a governed control framework with:

  • Human-in-the-loop validation
  • Role-based access controls
  • Full auditability and traceability

Governed AI agents include standardized as well as custom-built options to support your existing data management processes. Below are some examples:

1) Query Management & Data Validation

What it does:

  • Detects anomalies, missing values, and protocol deviations in near real time
  • Generates and prioritizes queries based on rules and historical patterns
  • Routes queries through controlled workflows

Measured Impact:

  • 30–50% reduction in avoidable queries
  • 25–40% faster query resolution
  • Earlier identification of data quality issues

2) Continuous Data Reconciliation

What it does:

  • Reconciles data across EDC, labs, EHR/EMR, and external sources
  • Flags discrepancies as they occur
  • Supports cross-functional resolution workflows

Measured Impact:

  • 40–60% reduction in reconciliation cycle time
  • Significant reduction in end-of-study reconciliation backlog

3) Medical Coding Acceleration (AI-Assisted)

What it does:

  • Extracts clinical terms from structured and unstructured data
  • Suggests standardized codes (e.g., MedDRA, WHO Drug)
  • Ranks suggestions based on confidence and prior patterns
  • Routes to coders for review and approval

Governance:

  • Human review required for all coding decisions
  • Full audit trail of suggestions and approvals

Measured Impact:

  • 30–50% faster coding workflows
  • Improved consistency across studies

4) Data Review & Risk Detection

What it does:

  • Identifies outliers, trends, and risk signals across sites and patients
  • Supports centralized monitoring and risk-based strategies
  • Surfaces issues earlier in the study lifecycle

    Measured Impact:

    • 25–40% reduction in late-stage data issues
    • Earlier detection of site- and patient-level risks

    5) Database Readiness & Lock Preparation

    What it does:

    • Continuously evaluates data completeness and quality
    • Tracks readiness for interim analysis and database lock
    • Automates validation against predefined criteria

    Measured Impact:

    • 30–50% reduction in time to database lock
    • Real-time visibility into study readiness

    AI Governance & Control

    AI in clinical trials must be governed—not just deployed.

    AI is only valuable in clinical research when it is governed, traceable, and accountable. CDP combines Azure AI Foundry, Foundry Control Plane, Microsoft Purview, Microsoft Fabric, Azure SQL, and enterprise security controls to ensure AI-assisted workflows remain transparent, auditable, secure, and compliant.

    CDP leverages a controlled framework within Azure AI Foundry to ensure all AI-assisted workflows are:

    • Traceable — every action is logged and attributable
    • Controlled — role-based access and workflow enforcement
    • Versioned — models, prompts, and logic are managed and auditable
    • Observable — outputs are monitored, evaluated, and explainable
    • Aligned to validation expectations — supporting enterprise and regulatory requirements (GxP, HIPAA, GDPR, and 21 CFR Part 11)

    Result: AI-driven efficiency without compromising compliance or inspection readiness.

    Key Outcomes for Sponsors and CROs

    • 30–50% reduction in database lock timelines
    • 25–50% reduction in manual data management effort
    • Significant reduction in avoidable queries and rework
    • Improved data quality and consistency across studies
    • Continuous inspection readiness

    The Clinical Discovery Platform (CDP) combines Zero Data Entry (ZDE™) with governed, AI-assisted data management to deliver continuous data readiness—accelerating clinical development while maintaining full regulatory confidence.

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