Automation Workflow for Intelligent Triage and Prioritization of ICSRs

Manual triage creates a queue where high-priority cases can be delayed by routine ones, risking a breach of the critical 7- or 15-day regulatory reporting clock. An AI agent evaluates seriousness, expectedness, and reporter type at ingestion, applying configurable business rules to assign a defensible priority score (e.g., P1-P4) and route to the appropriate reviewer queue within minutes. This systematic prioritization virtually eliminates the risk of a missed expedited report deadline due to manual backlog.

Pharmacovigilance associates spend hours daily reviewing unstructured reports to assign basic priority and reviewer. This workflow automates that initial clinical and regulatory assessment using a hybrid rules-based and ML scoring engine. The agent extracts key fields (seriousness criteria, outcome, drug role), checks against product-specific expectedness lists, and references historical triage patterns. Associates are elevated to exception handling and complex case review, allowing a single team to manage significantly higher case volumes without adding headcount.

Manual assignment leads to uneven workloads and suboptimal use of medical expertise. The AI router uses dynamic load balancing based on real-time reviewer capacity, specialty (e.g., oncology, cardiology), and language skills. It integrates directly with case management systems (e.g., ARISg, Veeva Safety) to update case ownership. This ensures the right case reaches the right reviewer faster, reducing overall case processing time and improving medical review quality by aligning complexity with expertise.

Manual prioritization decisions are opaque and difficult to defend during audits. This workflow generates a complete, immutable audit trail for every case, logging the ingested data, applied rules, scoring factors, and final routing decision. This evidence package is stored alongside the case, providing instant justification for priority assignments and demonstrating a controlled, consistent process to regulators. It turns triage from a subjective step into a governed, explainable operation.

When a new safety signal is identified, triage rules must adapt immediately. A manual process requires retraining entire teams. This workflow centralizes control in a configuration dashboard where PV scientists can instantly update product-specific expectedness lists, seriousness criteria weightings, and routing destinations. New rules apply to all incoming cases within minutes, allowing the organization to pivot rapidly during a safety crisis or label change without operational disruption.

Intelligent triage is the essential first node in a fully automated PV workflow. By standardizing and digitizing the initial case assessment, it creates clean, structured data that downstream agents can act upon—automating MedDRA coding, narrative generation, and causality assessment. Implementing this workflow is not just a point solution; it's the architectural prerequisite for building a scalable, multi-agent pharmacovigilance operating model that continuously reduces cost per case.

This agent processes the initial influx from emails, faxes, web forms, and EMR feeds. Using LLMs and document understanding, it extracts patient demographics, drug names, event narratives, and reporter details, normalizing them into structured ICSR fields. It validates against business rules and flags incomplete data for immediate human review, cutting data-entry time by over 70% and serving as the single source of truth for downstream agents.

The core decision agent applies configurable rule-based and ML scoring logic to each normalized case. It evaluates seriousness criteria (death, hospitalization), expectedness against the reference safety information (RSI), and calculates country-specific regulatory deadlines (7-day, 15-day). The agent outputs a priority score (Critical, High, Standard) and a recommended routing path to a reviewer queue or an expedited reporting workflow, preventing deadline misses.

This integration component is the bridge between the AI workflow and the enterprise safety database (e.g., Oracle Argus, Veeva Safety). It uses secure APIs to create case shells, push enriched data, update priority flags, and move cases between workflow states (e.g., Triage Complete -> Awaiting Medical Review). It ensures all AI-driven actions are auditable within the primary system of record, maintaining compliance and traceability.

This agent manages the human-in-the-loop layer. Based on the priority score, product expertise, reviewer capacity (pulled from the CMS), and workload, it assigns cases to the appropriate pharmacovigilance associate or physician queue. It can escalate cases that age in a queue and re-route work during absences, optimizing team throughput and ensuring high-priority cases are reviewed first.

A critical control agent that intercepts cases where scoring confidence is low, data conflicts exist, or rules cannot be applied. It routes these exceptions to a dedicated remediation queue with context on the ambiguity. It also manages the approval loop for the triage agent's decisions, allowing a human supervisor to confirm or override the automated priority and routing before the case proceeds, ensuring safety and governance.

Not an agent, but a required system component. This real-time dashboard monitors the entire triage workflow: intake volume, agent processing times, queue lengths, priority distribution, and exception rates. It provides KPIs on triage accuracy (vs. human gold standard) and cycle time savings, offering operational visibility for PV managers and data to continuously refine scoring rules and routing logic.

The primary business owner. This team defines the clinical rules for seriousness (e.g., hospitalization, death), expectedness (vs. Reference Safety Information), and regulatory deadlines (7-day, 15-day clocks). They provide the labeled historical case data for model training, validate the agent's priority scores, and ultimately own the risk of a missed deadline. Their buy-in is critical for defining the acceptable error rate and designing the human-in-the-loop review queue for borderline cases.

Responsible for the production environment, integrations, and data governance. This team provisions the infrastructure (often cloud-based for elastic NLP processing), manages API connections to the core safety database (e.g., Oracle Argus, Veeva Safety), and ensures the workflow adheres to data privacy (GDPR, HIPAA) and system validation (GxP) standards. They decide whether to containerize the LangGraph orchestration layer or host it as a managed service.

Designs and builds the intelligent agents. This team selects the model (e.g., fine-tuned clinical BERT for MedDRA coding, LLM for narrative summarization), architects the multi-agent system using LangGraph for state management, and implements the scoring logic that combines rule-based flags with ML confidence scores. They are accountable for the model's performance, drift monitoring, and the design of the explainability layer that shows why a case was prioritized.

Ensures the automated workflow is defensible to auditors and health authorities. This team writes the validation plan (IQ/OQ/PQ), reviews the algorithm's change control procedures, and audits the audit trail. They mandate features like immutable logging of all agent decisions, configurable thresholds for escalation, and regular back-testing of the triage logic against a gold-standard dataset to prove consistent performance.

Manages the commercial relationship with the implementation partner (e.g., Inference Systems). This team negotiates the statement of work based on defined deliverables—number of integrated data sources, agent types, and reviewer dashboards—and structures the pricing model (e.g., fixed-scope build with annual support). They align payment milestones with successful user acceptance testing (UAT) sign-off from PV Operations.

The end-users who interact with the system daily. These PV associates receive the pre-sorted, prioritized case queue. Their feedback on UI/UX—such as the clarity of the agent's priority rationale and the efficiency of the override function—is vital for adoption. Successful implementation includes training them to trust the AI for clear-cut cases while maintaining vigilance for exceptions, turning their role from manual sorters to focused clinical validators.