Automated SAE (Serious Adverse Event) Detection and Triage

Manual SAE monitoring is a high-stakes operational bottleneck, relying on clinical staff to spot critical safety signals across fragmented EDC, lab, and patient-reported data. This reactive process delays regulatory reporting, increases compliance risk, and ties up pharmacovigilance teams in data sifting. A custom automated workflow replaces this with continuous, algorithm-driven surveillance, detecting potential SAEs in near real-time. The business value is clear: faster detection reduces patient risk, accelerates mandatory reporting to meet FDA/EMA deadlines, and reallocates PV analyst effort from search to high-value case assessment and medical review.

Implementation requires orchestrating detection agents, a rules engine, and human-in-the-loop gates. The core architecture ingests real-time data from Veeva EDC, Medidata Rave, and wearables via secure APIs. Detection logic combines MedDRA-coded rule-based triggers with ML models trained on historical SAEs. A LangGraph orchestrator manages the workflow, routing high-confidence signals to pharmacovigilance systems like Oracle Argus for case creation, while flagging ambiguous events for automated site queries or batch review. Critical controls include an immutable audit trail for all automated decisions, configurable severity thresholds, and mandatory medical reviewer approval before any regulatory submission is generated.

This workflow automates the detection of potential SAEs by continuously monitoring structured EDC data, lab reports, and unstructured patient narratives. It replaces manual, batch-based surveillance with real-time signal processing, reducing the critical window between event occurrence and pharmacovigilance team awareness. The architecture ingests data from clinical systems like Oracle Clinical and Medidata Rave, applying a multi-agent detection layer where specialized models evaluate signals for severity, causality, and expectedness based on the reference safety information.

The triage engine routes signals based on agent consensus and configurable business rules, creating confirmed cases in Argus or Oracle Argus Safety and flagging ambiguous ones for physician review in platforms like Veeva Vault Safety. Implementation requires robust exception handling for data quality issues, audit trails for all automated decisions, and integration with EDC and safety databases via orchestration frameworks like LangGraph. This creates a closed-loop system that improves reporting compliance, reduces manual triage labor by over 70%, and provides measurable oversight through detailed activity logs.

The workflow automates the continuous monitoring of EDC, lab, and patient-reported data for potential Serious Adverse Events, eliminating manual surveillance delays. It ingests structured and unstructured data via API agents, applies detection algorithms for severity and causality, and flags candidate cases. This initial automation reduces pharmacovigilance team review load by 60-80%, directly cutting the time from event occurrence to initial assessment and mitigating compliance risk from delayed reporting.

Implementation follows a phased, validated approach. Phase 1 establishes the data ingestion pipeline and rule-based detection within a sandboxed environment, using synthetic data for validation. Phase 2 integrates NLP for narrative analysis and deploys the triage logic in a pilot study, with all outputs routed through a mandatory human review gate. Phase 3 rolls out the full autonomous workflow with continuous monitoring, integrating with the primary safety database (e.g., Oracle Argus) and maintaining a complete audit trail for regulatory inspection.

This workflow automates the continuous monitoring of EDC, lab, and patient-reported data for potential SAEs, replacing manual chart reviews and batch-based surveillance. The operational upside is faster detection and regulatory reporting, which mitigates patient safety risk and reduces the labor-intensive overhead of pharmacovigilance teams. Implementation requires integrating with clinical data warehouses (e.g., Veeva Vault EDC, Oracle Clinical) and deploying detection algorithms—ranging from rule-based logic for known patterns to ML models for anomaly detection—within a secure, containerized orchestration layer like LangGraph.

Governance is enforced through mandatory human review gates for low-confidence signals and all initial cases before regulatory submission. A phased rollout starts with a single study and high-specificity rules, monitoring false-positive rates before expanding to complex trials and ML models. Controls include immutable audit trails for all automated decisions, integration with safety databases (e.g., Oracle Argus), and real-time dashboards for PV leads to oversee throughput and exception queues, ensuring the system enhances—rather than compromises—regulatory compliance.

This workflow automates the continuous detection and initial assessment of potential Serious Adverse Events (SAEs) from fragmented clinical trial data streams. By replacing manual, batch-based monitoring with real-time orchestration, it reduces the critical window between event occurrence and regulatory reporting, directly mitigating patient risk and compliance exposure. The architecture ingests data from Electronic Data Capture (EDC) systems, lab feeds, and ePRO platforms, applying rule-based and ML-driven detection algorithms to flag anomalies for triage.

Implementation requires integrating detection logic with clinical data warehouses and pharmacovigilance systems like ARISg or Argus. The triage agent scores event severity, expectedness, and causality, routing high-confidence cases directly into case management workflows while flagging ambiguous events for medical monitor review via a dedicated dashboard. Governance is enforced through immutable audit trails, explanation reports for AI flags, and configurable approval gates before any regulatory submission is triggered, ensuring the workflow meets 21 CFR Part 11 and GVP standards.