Clinical Trial Compliance at Document Speed: Verified Extraction for Protocols, Consents, and IRB Approvals

Clinical trial operations run on documents:

• protocols and amendments
• informed consent forms (ICFs)
• IRB/EC approvals
• delegation logs
• safety reporting guidance
• monitoring reports and follow-up letters

And compliance depends on details that must be correct:

• which protocol version was active on which date
• whether the correct consent version was used
• what the reporting timelines are for certain events
• whether approvals cover specific changes

The cost of “almost right” is high:

• audit findings
• delays
• re-consenting
• and potential regulatory exposure

Citation-backed extraction is high value here because it creates verifiable document control data rather than untraceable interpretations.

Where document AI actually helps in trials

Not by replacing medical judgment.

By accelerating:

• document indexing and control
• verification of critical fields
• audit readiness
• site startup and monitoring workflows

High-value fields to extract (with citations)

Protocol and amendments

• protocol number
• protocol title (optional)
• version number
• version date
• key change indicators (optional, careful)
• effective dates (if stated)

Informed consent forms (ICFs)

• consent version/date
• site name (if included)
• sponsor name (if included)
• signature requirements sections (presence/structure)

IRB/EC approvals

• approval date
• expiration date (if stated)
• approved documents list (names/versions if included)
• site identifier

Safety reporting obligations (if you manage them centrally)

• reporting timelines (e.g., “within X days”)
• contact details / reporting channels
• definitions sections (often nuanced)

The key is not to “summarize compliance.” It’s to extract the control fields that let humans prove compliance.

Why citations matter more in regulated clinical workflows

In audits and monitoring visits, the question is often:

• “Show me the supporting document.”

A system that outputs “ICF version: 3.1” without showing where it’s written creates friction.

A system that outputs “ICF version: 3.1” + highlighted evidence makes verification immediate.

That reduces time in:

• monitoring preparation,
• QA checks,
• and query resolution.

Practical workflow: trial document indexing + verification

Step 1: Ingest a binder (document pack)

Trial documentation is naturally a packet. Treat it like one:

• folder upload (or batch) by site/study
• classify document types (protocol, ICF, IRB letter, etc.)

Step 2: Extract a “document control” schema per document

For each document, generate:

• control metadata
• citations for each control field

Step 3: Build a timeline view

Once you have version dates and approval dates, build:

• a timeline per site/study
• alerts for missing approvals or inconsistent versions

Step 4: Route exceptions to QA

Examples:

• consent version extracted but no matching IRB approval found
• protocol amendment exists but no approval letter found
• expiration dates missing or inconsistent

For every exception, show evidence links.

Schema sketch: trial document control

{
  "schema": {
    "document_type": { "type": "string", "description": "protocol, protocol_amendment, icf, irb_approval, other" },
    "study_id": { "type": "string", "description": "Study or protocol identifier if present" },
    "document_title": { "type": "string" },
    "version_number": { "type": "string", "description": "Version identifier as written" },
    "version_date": { "type": "date", "description": "Version date if present" },
    "approval_date": { "type": "date", "description": "IRB/EC approval date if present" },
    "expiration_date": { "type": "date", "description": "IRB/EC expiration date if present" },
    "site_name": { "type": "string" }
  },
  "options": { "confidence_threshold": 0.85 }
}

If a field is ambiguous (common!), surface that explicitly as “ambiguous” and route to QA rather than forcing a value.

The biggest adoption secret: don’t hide uncertainty

Clinical teams distrust “confident wrong.”

Design for explicit states:

• present
• not present
• ambiguous

And back every “present” with evidence.

What to measure

High-value operational metrics:

• time to assemble “audit-ready” documentation
• time to verify active document versions for a site
• reduction in monitoring prep hours
• reduction in document-related findings (where applicable)

Clinical trial operations aren’t improved by prettier PDFs. They’re improved by faster verification and cleaner control.

Evidence-backed extraction turns binders into a verifiable record you can defend.