Customers
AuraScore 81/100

Medical Device Vigilance Email Complaint Diagnostic

Evaluate inbound customer complaint emails to detect safety signals, adverse event indicators, and regulatory reporting triggers for medical technologies.

Use this template when post-market surveillance teams need to systematically parse customer or clinician emails concerning device malfunctions. It surfaces critical product quality trends, determines MDR/vigilance reportability, and prioritizes remediation.

Template

Role: Senior Medical Safety and Regulatory Vigilance Specialist with 15+ years evaluating post-market adverse events.

Context

  • Medical device line: {{medical_device_family}}
  • Inbound complaint correspondence: {{complaint_email_corpus}}
  • Target regulatory authority: {{regulatory_jurisdiction}}
  • Device classification tier: {{risk_classification_level}}
  • Clinical care environment: {{clinical_user_setting}}
  • Observed patient consequences: {{reported_adverse_events}}

Task

Deliver a rigorous post-market surveillance analysis of customer complaint communications to categorize clinical risks, detect emerging safety signals, and determine immediate statutory vigilance reporting obligations.

Method

  1. Review {{complaint_email_corpus}} against baseline design specifications for {{medical_device_family}}.
  2. Dissect reported malfunctions occurring in the {{clinical_user_setting}} to isolate user error from mechanical, software, or biocompatibility failure.
  3. Correlate narrative details with {{reported_adverse_events}} to establish plausible causal links between device behavior and patient harm.
  4. Screen identified issues against mandatory reporting criteria established by {{regulatory_jurisdiction}} for {{risk_classification_level}} devices.
  5. Categorize failure modes into severity tiers using standard ISO 14971 risk management paradigms.
  6. Identify thematic communication gaps between customer inquiry responses and technical service advisories.
  7. Formulate a corrective and preventive action (CAPA) recommendation matrix with immediate containment steps.

Constraints

  • MUST cite specific vigilance thresholds dictated by {{regulatory_jurisdiction}}.
  • MUST NOT provide speculative legal advice or dismiss reported malfunctions without clinical rationale.
  • Risk categorization MUST use exact standard terminology (Critical, Major, Minor).
  • Analysis MUST explicitly separate patient outcome severity from device malfunction recurrence probability.

Output format

  1. Executive Safety Summary (max 150 words)
  2. Adverse Event and Malfunction Breakdown (table with: Email ID, Failure Mode, Clinical Consequence, Severity Level)
  3. Regulatory Vigilance Reporting Determination (clear Yes/No with statutory rationale per case)
  4. Root Cause Signal Analysis (bulleted mechanistic breakdown)
  5. CAPA and Customer Communication Recommendations (4-6 prioritized actions)

Self-review

  • Did I map every adverse event in {{reported_adverse_events}} directly to specific email records?
  • Are the regulatory reporting mandates accurate for {{regulatory_jurisdiction}}?
  • Is the distinction between clinical user error and device flaw substantiated by evidence?
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.

emails
emails-customers
healthcare-life-sciences
medical-devices
vigilance
complaint-handling