Email campaigns
AuraScore 83/100

Healthcare Professional MLR Review Email Readiness Checklist

Audit promotional and scientific HCP email campaigns against MLR compliance, fair balance, and clinical claim standards.

Use this template prior to submitting HCP email assets to Medical, Legal, and Regulatory (MLR) review boards. It ensures promotional claims, ISI placement, and referenced clinical literature comply with strict regional regulatory codes.

Template

Role: Principal Medical Communications & MLR Email Compliance Lead

Context

  • Therapeutic Indication: {{drug_indication}}
  • Recipient Cohort: {{target_hcp_specialty}}
  • Fair Balance & Risk Profile: {{fair_balance_guidelines}}
  • Core Clinical Claims: {{promotional_claims}}
  • Governing Regulatory Framework: {{regional_regulatory_body}}
  • Campaign Type: {{unbranded_vs_branded_mode}}

Task

Generate a comprehensive pre-submission MLR readiness checklist to audit every component of an upcoming healthcare professional email campaign, ensuring zero regulatory deviations, compliant claim-to-evidence linkage, and airtight safety disclosures before formal review.

Method

  1. Cross-reference all subject lines, preheaders, and header copy against {{promotional_claims}} to confirm no misleading efficacy assertions exist.
  2. Evaluate header and body layout to confirm {{unbranded_vs_branded_mode}} distinction guidelines are strictly maintained without premature commercial exposure.
  3. Map every clinical claim to its primary source citation, auditing footnote formatting against {{regional_regulatory_body}} validation standards.
  4. Audit the prominence, readability, and placement of Important Safety Information (ISI) and Boxed Warnings relative to {{fair_balance_guidelines}}.
  5. Inspect all destination links, Prescribing Information (PI) anchors, and Adverse Event reporting pathways for single-click accessibility.
  6. Review tone and medical terminology to ensure appropriateness for {{target_hcp_specialty}} without trivializing risk profiles.
  7. Establish email template technical fallbacks, ensuring safety content renders legibly across desktop, mobile, and plain-text clients.

Constraints

  • Checkpoints MUST be categorized into distinct functional review stages.
  • Checkpoints MUST NOT use ambiguous verification criteria; every item must have a binary pass/fail condition.
  • MUST include explicit citation validation rules for all clinical claims.
  • MUST include mandatory checks for Black Box warning typography and contrast ratios.

Output format

Return a markdown checklist formatted into exactly 4 sections:

  1. Section A: Header, Subject Line & Claim Scrutiny (4-5 checks)
  2. Section B: Fair Balance, ISI & Prescribing Information (4-5 checks)
  3. Section C: Source Substantiation & Link Integrity (3-4 checks)
  4. Section D: Viewport & Client Rendering Verification (3-4 checks) Each item must follow the syntax: [ ] **[Check ID] [Component]**: [Verification Criterion] | **Remediation Trigger**: [Action if failed]

Self-review

  • Confirm all 6 variables ({{drug_indication}}, {{target_hcp_specialty}}, {{fair_balance_guidelines}}, {{promotional_claims}}, {{regional_regulatory_body}}, {{unbranded_vs_branded_mode}}) are accounted for.
  • Verify every checklist item contains both a binary verification criterion and an explicit remediation trigger.
  • Ensure the output contains no vague marketing platitudes and is strictly MLR-compliant.
AuraScore breakdown
83/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness5/5 · Strong

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

marketing
marketing-email-campaigns
healthcare-life-sciences
mlr-review
hcp-marketing
pharma-compliance