Final Human Review Before Use

Structured final review process for AI-assisted deliverables before submission or publication.

~30 min with AI support, ~45 min without The reviewer must be qualified for the content type and risk tier.

Deliverable → Source verification → Compliance screen → Review checklist → Sign-off

Best for

• Final QC of any AI-assisted deliverable nearing completion
• Signing off content that has been through development workflows
• Preparing content for MLR review, client submission, or publication
• Reviewing content as the named reviewer accountable for quality

Inputs

• The deliverable to be reviewed (near-final version)
• All source materials used in its development
• Workflow documentation showing how the content was developed (which AI tools and workflows were used)
• Any approved messaging framework, claims matrix, or brief
• The applicable review checklist (from the workflow card or the review checklist template)

Steps

1. Read the deliverable in full

   Before checking details, read the entire piece to assess overall quality, flow, and coherence. Note anything that feels off — then verify systematically.

2. Check the workflow trail

   Review which workflows and AI tools were used. This tells you where to focus your verification effort, since AI-generated sections need closer scrutiny.

3. Verify accuracy against sources

   Use RefCheckr and manual checking to confirm that every claim, data point, and conclusion is supported by the cited sources. Confirm the right references are cited, not just that claims match their cited references.

4. Run compliance pre-screening

   For promotional or compliance-sensitive content, use MedCheckr as one input to your compliance assessment. This supplements but does not replace your own compliance read.

5. Complete the review checklist

   Work through the applicable checklist systematically. Do not skip sections. Check specifically for hallucinated content, meaning drift, unsourced claims, and merged findings from different sources.

6. Make corrections and sign off

   Fix any issues found and document changes. If the deliverable meets all requirements, sign off with reviewer name and date. If not, return for revision with specific, actionable feedback.

Output

The output is a review decision: Approved, Approved with minor changes (corrections documented), Returned for revision (specific feedback provided), or Rejected (fundamental issues requiring full rework). Good final review produces documented evidence that the deliverable was systematically checked against sources, reviewed for compliance where applicable, and assessed for quality, with a named individual accountable for the outcome.

Prompt pattern

The final human review workflow is primarily manual. However, AI can support the process:

You are a medical writing QC assistant. Your task is to help me review the following deliverable against its source materials.

Deliverable:
[INSERT DELIVERABLE TEXT]

Source materials:
[INSERT SOURCE TEXTS]

Please check:
1. Does every factual claim in the deliverable have support in the source materials?
2. Are all data points (numbers, statistics, endpoints) accurately reproduced?
3. Are there any statements in the deliverable that appear to go beyond what the sources support?
4. Are qualifiers present in the sources (subgroup, post-hoc, exploratory, specific population) preserved in the deliverable?
5. Is safety information present and proportionate to the efficacy content?

For each issue found, quote the relevant text from the deliverable and explain the concern.

Note: This is a support tool for my review. I will verify all findings manually.

Customisation: Adjust the checklist emphasis based on content type. Promotional content needs heavier compliance focus; publication summaries need heavier accuracy focus. Limit AI-assisted QC to 3 documents per session to avoid review fatigue.

Why this works

Final review is fundamentally a human activity — AI supports it but cannot replace the judgement calls involved. Automated tools like RefCheckr and MedCheckr catch pattern-level issues at scale, freeing the human reviewer to focus on what AI cannot assess: contextual accuracy, completeness (what is absent, not just what is present), appropriateness of tone and framing, and the accountability that comes with a named sign-off.

Common mistakes

Review fatigue across multiple documents

A reviewer QCs five AI-generated summaries in one session. By the fourth, they scan for flow rather than verifying data points. An incorrect confidence interval passes through into a client slide deck. Use the structured checklist for every review and limit AI-assisted QC to 3 documents per session.

Over-reliance on automated verification

RefCheckr returns no flags on a manuscript summary. The reviewer concludes reference accuracy is confirmed. But one claim cites the wrong reference entirely: a mismatch the tool did not detect because it checks claims against cited references, not whether the right reference was cited. Automated tools are one layer, not the whole review.

Reviewing without full source access

The reviewer has the paper’s abstract but not the full text. They cannot verify a subgroup result cited in the deliverable. They pass it, assuming it is correct. It is not. Before starting, confirm full-text access to every cited reference.

Confirmation bias from project involvement

The reviewer wrote the brief and has been involved for weeks. They expect the content to be correct and read to confirm rather than to challenge. AI-generated errors that “look right” pass through unchallenged. Approach final review as adversarial verification — assume errors are present.

Unclear accountability for the sign-off

A deliverable was AI-assisted by one writer, edited by another, and reviewed by a third. No one is clearly accountable. Document the reviewer name, review date, and review outcome. The sign-off reviewer is accountable regardless of who produced the draft.

Tool stack

Tool	Role
RefCheckr	Automated reference accuracy check as one input to the review
MedCheckr	Automated compliance signal scan for promotional content

Review checklist

Human review checklist

Accuracy

• Every factual claim is supported by the cited source
• All numerical data matches the source exactly
• Study design, populations, and endpoints are correctly described
• Conclusions match the authors’ stated conclusions
• No hallucinated content, data, or citations

Completeness

• All key messages from the brief are addressed
• Safety information is present and proportionate
• Limitations are noted where appropriate
• Qualifiers are preserved (subgroup, post-hoc, exploratory)

Compliance (where applicable)

• Claims are within the approved messaging framework
• Fair balance is maintained
• No off-label implications
• References are correctly cited
• Prescribing information requirements are met

Quality

• Language is appropriate for the specific audience and channel (not just generically “professional”)
• Structure and narrative flow support the deliverable’s objective — a reader should follow the logic without needing to backtrack
• Length meets the brief requirements and is proportionate across sections (efficacy does not dominate at the expense of safety or context)
• Medical terminology is used correctly, consistently, and at the right level for the audience
• No grammatical, spelling, or formatting errors — including reference numbering, table formatting, and figure callouts

AI-specific checks

• Sections that used AI assistance are identified and specifically reviewed
• No unsourced content from AI training data
• No meaning drift from AI transformation or adaptation
• No merged findings from different sources or study arms
• Automated verification results (RefCheckr, MedCheckr) have been reviewed and acted on

Documentation

• AI tools and workflows used are documented
• Changes made during review are tracked
• Review outcome is recorded
• Reviewer identity and date are documented

---

Next steps: This review workflow applies to every AI-assisted deliverable. Common preceding workflows include Verify Claims Against References, Check Promotional Compliance, and Create a Plain Language Summary.

---

Last reviewed: 15 April 2026