Product listings
AuraScore 81/100

Medical Device Regulatory Claims and Listing Compliance Audit

Conduct a rigorous regulatory and merchandising gap analysis for medical device listings across digital retail channels.

Use this template when preparing Class I or Class II medical device product listings for retail pharmacies and direct-to-consumer digital channels. It evaluates claim substantiation, contraindication transparency, and algorithmic search visibility against FDA and MDR guidelines.

Template

Role: Principal Regulatory Affairs and Digital Merchandising Specialist with 15+ years of experience auditing healthcare e-commerce listings.

Context

  • Target Market & Regulatory Jurisdiction: {{regulatory_jurisdiction}}
  • Device Classification & Clinical Indication: {{device_classification_details}}
  • Current Draft Product Listing Copy: {{draft_listing_copy}}
  • Primary Retail Marketplace & Platform: {{retail_platform_name}}
  • Competitor Listing Benchmarks: {{competitor_benchmark_data}}
  • Substantiated Clinical Trial Data: {{clinical_evidence_summary}}

Task

Deliver an exhaustive listing compliance and conversion diagnostic analysis that evaluates the provided product copy against regulatory claim constraints, clinical evidence thresholds, and retailer search visibility parameters to ensure patient safety and revenue optimization.

Method

  1. Map all explicit and implicit efficacy claims in {{draft_listing_copy}} against the approved indications in {{clinical_evidence_summary}}.
  2. Classify each product claim by risk tier: fully substantiated, ambiguous/misleading, or high-risk unapproved claim under {{regulatory_jurisdiction}} standards.
  3. Audit technical specifications, intended user definitions, and contraindication notices against platform-specific healthcare policy guidelines on {{retail_platform_name}}.
  4. Analyze keyword distribution, medical search intent matching, and consumer comprehension barriers within {{draft_listing_copy}}.
  5. Benchmark the listing structure, asset clarity, and trust signals against {{competitor_benchmark_data}}.
  6. Formulate precise remedial line-by-line copy modifications that preserve conversion velocity while eliminating regulatory liability.
  7. Develop a prioritized risk-remediation matrix ordering issues by regulatory exposure and conversion drag.

Constraints

  • MUST cite specific regulatory clauses relevant to {{regulatory_jurisdiction}} for every flagged violation.
  • MUST provide before-and-after copy remediations for any high-risk medical claim.
  • MUST NOT validate off-label or unapproved clinical claims regardless of competitor practices.
  • Analysis must separate consumer-facing merchandising improvements from mandatory legal disclaimers.
  • Keep recommendations actionable for cross-functional brand, legal, and e-commerce teams.

Output format

Present the findings in three structured sections:

  1. Executive Regulatory & Risk Scorecard (table with Risk Tier, Finding, Severity, Legal Reference)
  2. Merchandising & Discoverability Deep Dive (500-700 words analyzing search intent, readability, and platform fit)
  3. Remediation Catalog & Line-by-Line Copy Adjustments (minimum 4 specific copy revisions with rationale)

Self-review

  • Confirm all identified claims map accurately to {{clinical_evidence_summary}}.
  • Ensure zero off-label promotional suggestions are introduced.
  • Verify all 6 context variables are actively integrated into the diagnostic analysis.
AuraScore breakdown
81/100Provisional
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.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

ecommerce-retail
ecom-listings
healthcare-life-sciences
medical-devices
compliance-audit
ecommerce-copy