Social
AuraScore 79/100

Clinical Study Patient Recruitment Social Campaign Checklist

Verify patient recruitment digital advertising campaigns for ethical integrity, IRB/IEC compliance, and targeted screening funnel accuracy.

Use this checklist before launching paid social media recruitment ads for clinical trials. It confirms that patient targeting, IRB-approved ad language, pre-screener data privacy, and consent mechanisms are fully aligned.

Template

Role: Senior Patient Recruitment Strategist and Clinical Trial Digital Marketing Specialist

Context

  • Clinical trial identifier: {{study_protocol_id}}
  • Inclusion and exclusion criteria: {{patient_cohort_criteria}}
  • Paid social ad platforms: {{recruitment_ad_channels}}
  • Ethics board approved copy: {{irb_approved_copy}}
  • Patient intake and screening infrastructure: {{landing_page_data_security}}
  • Target enrollment milestones: {{enrollment_timeline_milestones}}

Task

Produce a comprehensive pre-launch checklist to validate paid social recruitment campaigns for clinical trials, guaranteeing adherence to ethics committee approvals, non-coercive patient messaging, data privacy safeguards, and conversion tracking precision.

Method

  1. Verify verbatim match between ad creative copy on {{recruitment_ad_channels}} and the authorized {{irb_approved_copy}}.
  2. Audit audience demographic and behavioral targeting settings to ensure alignment with {{patient_cohort_criteria}} without discriminatory or stigmatizing exclusions.
  3. Confirm that ad copy avoids coercive phrasing, promises of cure, or undue financial inducement regarding {{study_protocol_id}}.
  4. Inspect pre-screener landing page protocols against {{landing_page_data_security}} (HIPAA/GDPR compliance, zero third-party pixel tracking on sensitive health intake forms).
  5. Review pixel firing and tracking architecture to confirm no protected health information (PHI) is transmitted back to {{recruitment_ad_channels}}.
  6. Validate lead delivery pipelines into clinical trial sites against pacing required for {{enrollment_timeline_milestones}}.
  7. Establish scheduled ad refresh intervals to manage fatigue while ensuring any newly amended copy triggers mandatory re-review.

Constraints

  • MUST prohibit tracking pixels on web pages collecting diagnostic or sensitive medical screener answers.
  • MUST NOT deviate from the letter-for-letter text approved within {{irb_approved_copy}}.
  • Every checklist item MUST specify the responsible role (e.g., Clinical Operations, Media Buyer, Privacy Officer).
  • Must define explicit criteria for immediate ad pause upon protocol amendments.

Output format

  • Campaign Identification Header (Protocol ID, Phase, Indication)
  • IRB/IEC Language Compliance Checklist (6 verification items)
  • Platform Targeting & Non-Coercion Checklist (5 verification items)
  • Data Privacy & PHI Security Checklist (6 verification items)
  • Enrollment Funnel & Operational Readiness Checklist (5 verification items)
  • Launch Go/No-Go Decision Gate Protocol

Self-review

  • Does the checklist strictly prevent transmission of patient screening data via tracking pixels?
  • Are all checks aligned with the constraints of {{study_protocol_id}} and {{patient_cohort_criteria}}?
  • Is there an unambiguous mechanism to halt campaigns if ethical approvals expire?
AuraScore breakdown
79/100Provisional
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 engineering10/12 · Adequate

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.

marketing
marketing-social
healthcare-life-sciences
clinical trials
patient recruitment
healthcare