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Data Management & Oversight

Source Document Review Agent

H1ST-AI-MON-003 · Task 2.3

Your monitors are reviewing blind and sampled

When review is sampled and slow, the finding you miss is the one that surfaces at the worst possible time. The Source Document Review Agent applies OCR and a clinical language model in the BioBERT/ClinicalBERT family to read every source document the way a monitor does — spotting inconsistencies, transcription mismatches, and undocumented events — and attaches a confidence score to each finding, so your monitors spend their limited hours where it actually matters instead of reviewing blind.

The grind you know

Your monitors are reviewing blind and sampled

Your monitors sit with source documents and read them line by line against the CRF, on-site or remote, with only so many hours per subject. So you sample instead of covering everything, what gets caught depends on who happened to review it, and the issues that matter most tend to surface late in the cycle — when they're hardest to unwind.

Part of the Risk-Based Monitoring family

Risk-based monitoring & safety oversight.

When review is sampled and slow, the finding you miss is the one that surfaces at the worst possible time. The Source Document Review Agent applies OCR and a clinical language model in the BioBERT/ClinicalBERT family to read every source document the way a monitor does — spotting inconsistencies, transcription mismatches, and undocumented events — and attaches a confidence score to each finding, so your monitors spend their limited hours where it actually matters instead of reviewing blind.

  • ICH E6(R3)
Explore the Risk-Based Monitoring family

How it works

  1. 1

    Upload source documents

    Provide native or scanned source files; OCR converts image-based documents into readable text.

  2. 2

    Review with clinical NLP

    The model interprets the clinical content, checks it against the CRF, and identifies discrepancies and undocumented items.

  3. 3

    Triage by confidence

    Findings arrive scored, letting monitors act on high-confidence issues and route the rest for human judgment.

Capabilities

What it takes off your plate

OCR + clinical NLP review engine

Reads scanned and native source documents with Paddle OCR, then applies a ClinicalBERT-style model to interpret clinical language, abbreviations, and context.

Source-to-CRF consistency checks

Compares source content against corresponding CRF entries to flag transcription mismatches, missing documentation, and values that don't reconcile.

Confidence-scored findings

Assigns a calibrated confidence score to each finding so monitors triage high-certainty issues first and dismiss noise quickly.

Complete-coverage review

Reviews every uploaded source document rather than a sample, giving centralized monitoring full-population visibility ahead of on-site visits.

What you get

  • Source document review report with per-finding confidence scores
  • Source-to-CRF discrepancy list
  • Prioritized review queue for on-site or remote monitors
  • Audit trail of documents reviewed and findings raised

In practice

What this looks like on a real study

Where the agent shows up in the day-to-day of a live trial — the moments the grind usually lives in.

You can only sample so much source

With limited monitor hours per subject, you sample source review and accept that coverage depends on who reviewed what. The agent reads every uploaded source document, giving centralized monitoring full-population visibility ahead of the on-site visit.

A transcription mismatch in a note

A value on the CRF doesn't quite match what the source note says, and that kind of mismatch is easy to miss line by line. Source-to-CRF consistency checks flag the discrepancy, so it surfaces early rather than at the worst possible time.

Which findings to chase first

A long list of potential issues is only useful if you know where to start. Each finding carries a calibrated confidence score, so your monitors act on high-certainty flags first and dismiss the noise quickly instead of reviewing blind.

Proof

The impact on your study

100%
Source documents reviewed
93%
Discrepancy detection accuracy
60%
Less monitor review time

Figures shown are pre-launch targets based on internal benchmarks, not guaranteed outcomes.

Works with your stack

  • Paddle OCR

    OCR

  • Gemini / Anthropic LLM

    LLM

Who it's for

Built for the teams who run trials

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Peace of mind

Built to the standards inspectors expect

Every output is generated inside a validated, audit-ready platform, kept under human-in-the-loop control, and mapped to the regulatory and CDISC standards this agent supports.

  • ICH E6(R3)

Frequently asked questions

See the Source Document Review Agent on your study

Walk through it on your own workflow with a clinical-trials expert — no pressure, no obligation, and honest answers, including on the limits.