Customers
AuraScore 83/100

Chronic Therapy Patient Adherence Email System Specification

Design a clinical adherence email sequence specification for chronic disease patients on specialized biopharma therapies.

Use this template when building or updating automated, compliant patient-engagement email sequences for ongoing prescription therapy management. It guides patient onboarding, dose reminders, and clinical support escalation workflows while adhering to health data privacy standards.

Template

Role: Senior Director of Patient Experience and Digital Therapeutics with 15+ years architecting patient communication systems.

Context

  • Target Therapy: {{therapy_name}}
  • Patient Cohort: {{target_patient_cohort}}
  • Indication and Disease Stage: {{clinical_indication}}
  • Regulatory Jurisdiction: {{regulatory_jurisdiction}}
  • Compliance Standard: {{compliance_framework}}
  • Delivery Platform: {{digital_health_platform}}

Task

Author a comprehensive email system specification defining a triggered, personalized patient adherence communication architecture that drives therapy persistence and safe administration without breaching clinical privacy standards.

Method

  1. Analyze {{target_patient_cohort}} behavioral friction points across the {{therapy_name}} onboarding journey.
  2. Map trigger logic and cadence windows across the initial 90-day onboarding lifecycle on {{digital_health_platform}}.
  3. Draft exact email copy blueprints for four core triggers: Welcome/Unboxing, Injection/Administration Day, Refill Pre-notification, and Missed Dose Guidance.
  4. Embed clinical safety disclaimers, black-box warnings (if applicable to {{clinical_indication}}), and regulatory opt-out mechanisms matching {{regulatory_jurisdiction}} rules.
  5. Define personalized dynamic tokens, fallback defaults, and conditional content blocks based on adherence telemetry.
  6. Specify accessibility requirements (WCAG 2.1 AA) and plain-language health literacy guidelines (6th-to-8th grade reading level).
  7. Formulate escalation trigger logic routing high-risk adverse event signals to live clinical nurse educators.

Constraints

  • MUST adhere strictly to {{compliance_framework}} and eliminate any unencrypted Protected Health Information (PHI) in subject lines.
  • MUST NOT provide diagnostic advice or replace direct prescribing physician instructions.
  • Tone must be empathetic, encouraging, and clinically precise without alarmist language.
  • Every email template in the spec must define explicit trigger conditions, subject line variants, and primary call-to-action.

Output format

Provide the specification in four structured markdown sections:

  1. Architecture Overview (triggers, timing cadence, and technical data dependencies).
  2. Email Trigger Specifications (4 discrete message blueprints with subject, preheader, body copy, and metadata).
  3. Privacy, Regulatory & Medical Review Matrix (risk controls under {{compliance_framework}}).
  4. Clinical Escalation & Telemetry Routing Logic. Total word count must be between 800 and 1,200 words.

Self-review

  • Are all trigger timing rules unambiguous and clinically safe for {{clinical_indication}}?
  • Are subject lines free of sensitive diagnosis and health telemetry data?
  • Does every email specify a distinct, actionable fallback path for vulnerable patients?
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.

emails
emails-customers
healthcare-life-sciences
patient-adherence
digital-health
email-specification