Chapter 03 · Data Substrate

Data Substrate as a reviewable surface.

The data substrate for Clinical Trials determines what the model can know, what it misses, what it amplifies, and what remains reviewable later.

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Focus: trial evidence · GCP · recordsRisk: untraceable influenceBridge: protocol · data · decision record
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Data Substrate chapter.

Clinical Trials

The data substrate for Clinical Trials determines what the model can know, what it misses, what it amplifies, and what remains reviewable later.

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What this page maps.

operating content
Source data

Source data

Defines origin, consent or authorization, quality status, context, exclusions, and transformation history.

Lineage

Lineage

Keeps the chain visible from source record to model input, output, human review, and final action.

Monitoring fields

Monitoring fields

Captures use, overrides, errors, drift, incidents, feedback, and review outcomes.

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Governance questions.

review logic
Question

What decision or record does this data substrate surface influence, and who owns that decision?

Question

Which evidence is needed before routine use in Clinical Trials, and where is it retained?

Question

What signal triggers review, restriction, escalation, or retirement?

/ evidence

Evidence-ready minimum record.

iFeed use
Minimum record
OwnerNamed operational, clinical, technical, and governance owners.
UseClear intended use, user group, workflow point, and excluded use.
RiskRisk tier, rationale, residual risks, controls, and escalation route.
EvidenceSource claims, validation basis, limitations, approval decision, and review date.
/ sources

Source anchors and claim boundary.

official first

These anchors support the source layer for this page. iFeed interpretation remains separate from source facts and does not replace legal, regulatory, clinical, or product-specific advice.

Source anchors
ICH E6(R3) Good Clinical Practice, EMA scientific guideline →EU Clinical Trials Regulation, EMA →FDA Electronic Systems, Electronic Records, and Electronic Signatures in Clinical Investigations →FDA Artificial Intelligence for Drug Development →CDISC Standards →
← Use CasesModel Lifecycle →