Check Promotional Compliance

Pre-screen content for promotional compliance signals before formal MLR review.

~15 min with AI, ~60 min without Expert compliance-qualified review and formal MLR review required before use.

Near-final draft → MedCheckr pre-screen → Expert compliance review → Corrections → MLR-ready content

Best for

• Pre-screening promotional or semi-promotional content before MLR submission
• Self-checking a draft for compliance-sensitive language before internal review
• Reviewing repurposed content that may have introduced promotional language
• Onboarding writers who are building familiarity with advertising code requirements
• Catching common MLR rejection triggers early in the content development cycle

Inputs

• The document or content to be reviewed (near-final draft)
• The applicable regulatory or advertising code (e.g., ABPI Code, IFPMA Code, FDA promotional regulations)
• The approved prescribing information, SmPC, or USPI for the product
• Any approved messaging framework or claims matrix
• The intended channel and audience for the content

Steps

1. Prepare a near-final draft

   Compliance pre-screening works best on content close to submission. Running it on rough drafts generates noise and wastes review effort.

2. Identify the applicable code and context

   Determine which advertising code or regulatory framework applies. Different markets, audiences, and content types have different requirements.

3. Run automated compliance scanning

   Submit the content to MedCheckr for automated compliance signal detection. Include the product, indication, and channel context.

4. Review flagged items in context

   Assess each flag against the applicable code and approved messaging framework. Not every flag is a genuine issue — a superlative may be appropriate if substantiated.

5. Cross-check claims against references

   Use the Verify Claims Against References workflow or RefCheckr to confirm that flagged claims are supported.

6. Revise and document changes

   Address genuine compliance concerns. Record what was flagged, what was changed, and the rationale for each decision.

7. Submit for formal MLR review

   Submit the revised content through your standard approval process. This pre-screening supports MLR; it does not replace it.

Output

Good output identifies 3–15 potential issues per typical promotional piece, each with the relevant text quoted, a clear description of the concern, and actionable guidance on what revision may be needed. Issues are rated by severity (low/medium/high), and the output explicitly states that formal MLR review is still required.

Prompt pattern

You are a medical communications compliance review assistant. Your task is to pre-screen the following content for potential promotional compliance issues.

Content to review:
[INSERT CONTENT]

Product: [SPECIFY]
Approved indication(s): [SPECIFY]
Target audience: [SPECIFY — e.g., oncologists, general practitioners]
Intended channel: [SPECIFY — e.g., detail aid, leave piece, HCP website, conference stand]

Review the content for:
1. Superlative or comparative claims that may require substantiation
2. Language that implies efficacy or outcomes beyond what is supported by the cited references
3. Potential off-label implications or suggestions
4. Areas where safety information may be insufficient relative to efficacy claims (fair balance)
5. Emotive or promotional language that may not be appropriate for the content type
6. Claims that appear to extend beyond the approved indication
7. Unqualified statements that may need hedging or qualification based on the evidence

For each issue found:
- Quote the relevant text
- Describe the potential concern
- Suggest what type of revision may be needed
- Rate the concern: LOW / MEDIUM / HIGH

Rules:
- Flag potential issues for human review. Do not make definitive compliance determinations.
- Consider the content type and audience when assessing — language appropriate for a scientific publication may not be appropriate for a promotional piece.
- If you cannot determine whether something is an issue without access to the full reference, flag it for verification.

Customisation: Adjust the review criteria based on your market’s specific advertising code. For US content, add FDA-specific requirements (e.g., fair balance under 21 CFR 202.1). For UK content, reference the relevant ABPI Code clauses.

Why this works

AI excels at pattern-matching across text — scanning for superlatives, comparative language, emotive phrasing, and other signals that commonly trigger MLR flags. Humans then interpret each flag in context, applying knowledge of the applicable code, the approved messaging, and the clinical evidence. This division catches issues earlier without replacing the qualified compliance judgement that only MLR professionals can provide.

Common mistakes

Treating a clean scan as compliance clearance

A clean MedCheckr result means the tool found nothing, not that nothing is there. Teams that reduce their own compliance review effort after a clean scan lose credibility when MLR catches contextual issues the tool was never designed to detect. Always complete your own read-through after pre-screening.

Reviewing every false positive in detail

MedCheckr may flag 20 items on a leave piece, including appropriate uses of “demonstrated” and “significant” that are fully substantiated. Calibrate expectations: a flag is a prompt to check, not a finding. With experience, reviewers learn which flag types to prioritise.

Ignoring market-specific requirements

Content intended for a market governed by the PMCPA Code may pass general screening but miss UK-specific fair balance requirements. Always specify the applicable code and have a reviewer with market-specific expertise assess the results.

Not accounting for evolving regulations

A new ABPI Code clause may have taken effect recently. If the pre-screening tool does not reflect the update, a recently prohibited phrasing could pass undetected. Check results against the current published code, not against the tool’s training data.

Skipping the reference cross-check

Flagged claims need verification against the cited references, not just assessment of the language itself. A claim may sound promotional but be fully substantiated, or sound neutral but extend beyond what the reference supports. Always cross-check.

Tool stack

Tool	Role
MedCheckr	Automated compliance signal detection and pre-screening
RefCheckr	Verify that promotional claims are supported by cited references

General-purpose models like Claude or ChatGPT can support early drafting, but are not sufficient for formal compliance pre-screening. MedCheckr is built specifically for pharmaceutical code-based compliance signal detection.

Review checklist

Human review checklist

• All flagged items have been assessed in context by a qualified reviewer
• Superlative and comparative claims are substantiated by cited references
• Fair balance of efficacy and safety information is appropriate for the content type and audience
• No off-label implications or suggestions are present
• Claims are consistent with the approved indication(s)
• Language is appropriate for the content type and channel
• Prescribing information or SmPC references are included where required
• The content is consistent with any approved messaging framework or claims matrix
• Changes made during pre-screening are documented
• The content is ready for formal MLR review

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Next steps: Verify Claims Against References to confirm claims are supported before MLR submission, then Final Human Review for QC before the deliverable enters the approval queue.

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Last reviewed: 15 April 2026