Workflow chains
AuraScore 83/100

Clinical Safety Case Intake and Adverse Event Triage Framework

Standardize multi-agent workflow chains for parsing, validating, and routing adverse event reports in pharmacovigilance.

Deploy this framework when designing automated Individual Case Safety Report (ICSR) processing pipelines across diverse healthcare data streams. It coordinates agent extraction, MedDRA hierarchy verification, and human-in-the-loop escalation gates.

Template

Role: Principal Pharmacovigilance Automation Architect with twenty years in regulatory clinical safety systems.

Context

  • Intake Streams: {{source_data_types}}
  • Target Regulatory Body: {{target_regulatory_agency}}
  • Safety Database Platform: {{safety_database_system}}
  • Controlled Terminology: MedDRA version {{meddra_version}}
  • Critical Case SLA: {{escalation_sla_hours}} hours
  • Autonomous Triage Confidence Cutoff: {{human_review_threshold}}

Task

Construct an end-to-end multi-agent workflow chain framework that ingests raw safety narratives, performs automated entity extraction and MedDRA coding validation, calculates case seriousness, and enforces deterministic triage gates for human review.

Method

  1. Map data ingress points and define the parsing agent's normalization schema for unstructured medical records and spontaneous reports.
  2. Configure the Medical Entity Extraction Agent to extract suspect drugs, concomitant medications, adverse events, patient demographics, and onset dates.
  3. Design the Terminology Mapping Agent to map extracted clinical terms against {{meddra_version}} Lowest Level Terms (LLTs) and Preferred Terms (PTs).
  4. Build the Seriousness Evaluation Agent to evaluate criteria such as hospitalization, disability, death, or life-threatening status under {{target_regulatory_agency}} guidelines.
  5. Implement an automated confidence scoring model comparing extraction certainty against {{human_review_threshold}}.
  6. Formulate the routing logic directing high-confidence routine cases directly to {{safety_database_system}} while routing borderline or serious cases to safety physicians within {{escalation_sla_hours}} hours.
  7. Detail failure-mode fallbacks, duplicate detection, and automated audit trail generation for regulatory compliance.

Constraints

  • MUST maintain an immutable, time-stamped audit log for every agent state transition and reasoning output.
  • MUST NOT auto-commit any case classified as fatal or life-threatening without mandatory Human-in-the-Loop (HITL) approval.
  • All terminology mappings MUST adhere strictly to {{meddra_version}} standards without hallucinated terms.
  • System architecture must satisfy 21 CFR Part 11 and GAMP 5 data integrity mandates.

Output format

Present the complete framework using these exact sections:

  1. Workflow Pipeline Architecture (sequence diagram flow and agent boundary definitions)
  2. Agent Hand-off and Data Contract Specifications (input/output payloads in JSON schema)
  3. Triage Decision and Escalation Matrix (table with criteria, confidence score, routing target, SLA)
  4. Quality Control and Validation Gates (verification checklist for safety physician sign-off) Limit the total framework to under 1,500 words.

Self-review

  • Did I define explicit data contracts between each agent in the chain?
  • Are regulatory constraints for {{target_regulatory_agency}} strictly integrated into routing decisions?
  • Is the {{human_review_threshold}} explicitly tied to the escalation logic?
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.

ai-agents
agents-workflows
healthcare-life-sciences
pharmacovigilance
workflow-chains
clinical-safety