Clinical Discovery PlatformBuilt on Microsoft for Healthcare
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Clinical Trial Data Management

AI-assisted data management. Built for control.

Move from reactive cleanup to continuous data readiness with governed workflows that improve quality, accelerate timelines, and keep people in control.

Let's talk strategy Explore the approach
Built on Microsoft for Healthcare Human-in-the-loop AI Inspection-ready
A clinical data management leader presenting analysis to colleagues during a team meeting.
Clinical data sources flow through the governed Clinical Discovery Platform into operational efficiency and AI-assisted discovery.
Clinical Trial Data Management operating model. Unified electronic data capture brings traditional EDC, ZDE eSource, real-world data, devices, and partner systems into the Clinical Discovery Platform. Governed data management and Microsoft platform services support operational efficiency, evidence synthesis, and AI-assisted discovery while preserving traceability, security, and human oversight.

The data management challenge

More data. More sources. Higher expectations.

The challenge is no longer simply collecting & cleaning data. It is transforming growing volumes from fragmented systems into a trusted, analysis-ready asset—without compromising regulatory confidence.

CDP moves clinical teams from reactive data cleanup to continuous data readiness.

The CDP approach

Continuous, Controlled,
and Inspection-Ready.

CDP establishes a new operating model in which clinical data management is no longer a reactive phase. Quality is created continuously, work remains controlled, and readiness stays visible throughout the study.

01 · CONTINUOUSQUALITY AT INGESTION

Validate continuously

Apply quality controls as data enters CDP so issues are surfaced while they are still actionable.

  • Validation at ingestion
  • Real-time discrepancy detection
  • Continuous quality improvement
Earlier quality signalsIssues surfaced closer to their source
02 · CONTROLLEDGOVERNED RESOLUTION

Reconcile and resolve

Coordinate discrepancies across every source, prioritize attention, and keep qualified reviewers in control.

  • Ongoing cross-source reconciliation
  • Risk-based work prioritization
  • Human review and approval
Governed decisionsAccountable · attributable · traceable
03 · INSPECTION-READYALWAYS-ON VISIBILITY

Maintain readiness

Maintain a current view of data quality, outstanding work, and readiness for analysis and database lock.

  • Always-on quality visibility
  • Traceable workflow evidence
  • Continuous database readiness
Readiness by designVisible throughout the study—not only at lock

AI assists throughout this operating model.Qualified people remain responsible for every regulated decision.

UNDERLYING DATA ARCHITECTUREMicrosoft Fabric Medallion Lakehouse
BronzeSource-alignedSilverValidated & reconciledGoldAnalysis-ready
Microsoft Fabric + OneLake

AI-assisted workflows

Purpose-built agents.
People remain in control.

Governed AI agents support regulated workflows by automating repetitive work, prioritizing attention, and surfacing risk—without replacing qualified human judgment.

Five-stage AI-assisted workflow from data ingestion and normalization through validation, reconciliation, coding, monitoring, and continuous database readiness.
AI-assisted clinical data management workflow. The workflow progresses through ingest and normalize, validate and query, reconcile and code, monitor and resolve, and readiness and lock. AI agents assist with repetitive work, qualified people approve clinical decisions, and every input, output, rule, prompt, and action remains traceable. Database readiness reaches 100 percent before lock.
Agents assistRepetitive work is automated and attention is prioritized.
People approveQualified reviewers remain accountable for clinical decisions.
Everything is traceableInputs, outputs, prompts, rules, and actions remain auditable.

AI governance & control

Innovation without compromising regulatory confidence.

AI is valuable in clinical research only when it is governed, traceable, and accountable. CDP surrounds every AI-assisted workflow with enterprise controls and human oversight.

21 CFR Part 11HIPAAGDPRGxP

Traceable

Every action is logged, attributable, and reviewable.

Controlled

Role-based access and workflow enforcement protect every decision.

Versioned

Models, prompts, rules, and logic remain managed and auditable.

Observable

Outputs are monitored, evaluated, and explainable.

Designed for measurable impact

Turn data readiness into clinical momentum.

Shorterpath to database lock
Reducedmanual effort
Earlierrisk visibility
Ongoinginspection readiness

Better data management is not simply about cleaning data faster. It is about making trustworthy data available sooner—so your teams can make better decisions throughout the study.

Start a strategic conversation

What could your team accomplish with continuously ready clinical data?

Let's map CDP to your current data management workflows, systems, and study priorities.

Let's talk strategy 603.673.1900