Workflow chains
AuraScore 81/100

Pharmacovigilance Adverse Event Intake and Triage Workflow Plan

Design an end-to-end multi-agent workflow chain for adverse event intake, MedDRA coding, and regulatory triage.

Use this template when architecting an automated safety reporting pipeline to ingest, validate, and escalate pharmacovigilance cases under GxP standards. It structures agent handoffs from initial multi-channel intake to regulatory urgency routing.

Template

Role: Principal Pharmacovigilance Automation Architect with 15+ years designing GxP-compliant safety reporting pipelines.

Context

  • Life sciences sponsor: {{sponsor_name}}
  • Therapeutic focus area: {{therapeutic_area}}
  • Ingestion channels to support: {{ingestion_channels}}
  • Primary regulatory authority: {{regulatory_agency}}
  • Compliance and validation standard: {{validation_framework}}
  • Human review trigger threshold: {{human_oversight_threshold}}

Task

Develop a comprehensive architectural workflow plan that orchestrates an autonomous multi-agent chain for adverse event intake, source document parsing, MedDRA coding, seriousness evaluation, and compliant case routing.

Method

  1. Map multi-channel intake handlers across {{ingestion_channels}} to standardize unstructured narrative text into structured case payloads.
  2. Design the medical entity extraction sub-agent to isolate patient demographics, suspect medications, dosages, and adverse event terms.
  3. Define the MedDRA auto-coding agent logic, incorporating semantic similarity scoring, synonym tables, and strict confidence thresholds.
  4. Construct the seriousness and causality assessment agent to evaluate 7-day and 15-day expedited reporting criteria under {{regulatory_agency}} guidelines.
  5. Establish duplicate detection and case reconciliation rules against existing safety database records.
  6. Specify the deterministic routing matrix that triggers human-in-the-loop review whenever confidence falls below {{human_oversight_threshold}} or when fatal/life-threatening outcomes occur.
  7. Detail the audit logging, data lineage tracking, and validation protocols required to satisfy {{validation_framework}}.

Constraints

  • MUST maintain strict 21 CFR Part 11 and GxP data integrity standards with immutable event logging.
  • MUST NOT route any potential expedited adverse event to auto-submission without a mandatory qualified person safety review.
  • Agent handoffs must implement deterministic retry logic and dead-letter queues for unparseable input payloads.
  • All terminology mappings must explicitly reference standard MedDRA hierarchy levels (SOC, HLGT, HLT, PT, LLT).

Output format

  • Phase 1: Ingestion & Extraction Architecture (max 250 words)
  • Phase 2: Agent Handoff & Logic Specifications (4-step detailed breakdown)
  • Phase 3: Triage Matrix & Human Escalation Rules (table format with 4 columns: Trigger, Severity, Agent Action, Human Role)
  • Phase 4: Regulatory Compliance & Audit Controls (max 200 words)

Self-review

  • Ensure all channels in {{ingestion_channels}} have dedicated parsing and sanitization steps.
  • Confirm the human-in-the-loop threshold matches {{human_oversight_threshold}} exactly.
  • Verify the triage workflow adheres strictly to the timeline requirements of {{regulatory_agency}}.
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.

ai-agents
agents-workflows
healthcare-life-sciences
pharmacovigilance
workflow-chains
regulatory-compliance