MedTech Post-Market Safety Signal Dashboard Evaluation
Assess medical device surveillance dashboards to detect adverse event clustering and reporting compliance risks.
Use this template when pharmacovigilance and quality teams need to audit safety signal detection dashboards against regulatory expectations. It exposes detection latencies and helps teams reconfigure statistical process control charts.
Role: Lead Medical Device Safety Informaticist and Post-Market Surveillance Consultant.
Context
- Device Classification: {{device_classification}}
- Regulated Market: {{reporting_geography}}
- Signal Update Rhythm: {{complaint_ingestion_frequency}}
- Statistical Methodology: {{signal_detection_algorithm}}
- Incident Log Scale: {{adverse_event_volume}}
- Competent Authority: {{regulatory_oversight_body}}
Task
Deliver a regulatory-grade dashboard evaluation that determines whether the post-market surveillance interface for {{device_classification}} products effectively flags adverse safety signals across {{adverse_event_volume}} records under {{regulatory_oversight_body}} mandates.
Method
- Evaluate the mathematical sensitivity of {{signal_detection_algorithm}} when applied to complaint data ingested at {{complaint_ingestion_frequency}}.
- Trace regulatory submission trigger points to determine if threshold crossings generate compliant reports for {{regulatory_oversight_body}}.
- Map how adverse event code groupings mask micro-clusters of component failures in {{device_classification}} hardware.
- Audit geographic filtering functionality across {{reporting_geography}} to verify that regional failure surges remain detectable.
- Critique visual alarm hierarchies, focusing on false-positive alert fatigue versus unflagged critical safety anomalies.
- Formulate statistical process control dashboard specifications that reduce time-to-signal discovery.
Constraints
- MUST explicitly evaluate compliance against standard vigilance timeframes enforced by {{regulatory_oversight_body}}.
- MUST NOT propose diagnostic algorithms incompatible with {{device_classification}} risk categories.
- Findings MUST directly address data scale challenges associated with {{adverse_event_volume}} entries.
- Recommendations must preserve audit-trail validation standards.
Output format
Produce an evaluation document formatted into these mandatory headings:
- Safety Surveillance Dashboard Overview (100-140 words)
- Signal Detection Methodological Gaps (3 detailed points reviewing {{signal_detection_algorithm}})
- Regulatory Compliance & Reporting Vulnerabilities (focusing on {{regulatory_oversight_body}} requirements)
- Metric & Layout Refactoring Matrix (bulleted action list for UI/UX and alert redesign)
Self-review
- Checked that {{signal_detection_algorithm}} is critically evaluated for both sensitivity and false-positive rates.
- Verified that regional jurisdictional nuances for {{reporting_geography}} are explicitly factored into the reporting audit.
- Ensured the review does not violate medical device vigilance recordkeeping principles.
Explicit role, a named task, and discrete steps the model can follow.
Background, inputs and variables the model needs before it starts.
Hard boundaries — what the model must and must not do.
A named, field-level shape for the response.
Ordered work items that force analysis before an answer.
Length and structure that travel across frontier models.
Signal density — instruction weight without padding.
Documented variables so the scaffold adapts to new inputs.
Quality bar, assumptions and behaviour when inputs are thin.
How much real usage the template has behind it.