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ADE Signal Triage Engine

Surfaces probable ADEs from unstructured records to prioritize pharmacovigilance review Evidence basis: Recent Drug Safety reviews report consistent progress in NLP and ML ADE detection from free-text narratives; evidence supports faster triage and broader coverage while transportability varies by dataset quality

The Problem

“Detect probable adverse drug events from EHR narratives to accelerate pharmacovigilance triage”

Organizations face these key challenges:

1

Critical ADE evidence is buried in long unstructured clinical narratives

2

Structured diagnosis and billing codes undercapture true adverse events

3

Manual review is slow, costly, and inconsistent across reviewers

4

Drug-event causality and temporal linkage are difficult to infer at scale

5

Dataset quality and note style variation reduce model transportability

6

Regulated workflows require explainability, auditability, and controlled updates

7

Safety teams need high recall without overwhelming reviewers with false positives

Impact When Solved

Improves recall of probable ADEs missed by ICD-code-only workflowsReduces manual chart review volume by prioritizing likely positive casesSurfaces evidence spans and source notes to support reviewer trustAccelerates retrospective safety studies and postmarket signal triageSupports oncology and immune-related adverse event detection from free textEnables governed model updates for AI-enabled postmarket monitoring workflows

The Shift

Before AI~85% Manual

Human Does

  • •Review unstructured records manually for possible adverse drug events
  • •Coordinate case findings and follow-up actions in spreadsheets
  • •Prioritize records for pharmacovigilance review based on manual judgment
  • •Perform retrospective quality checks on completed reviews

Automation

  • •No AI-supported triage or detection in the legacy workflow
  • •No automated prioritization of probable adverse drug events
  • •No continuous monitoring of narrative records for review signals
With AI~75% Automated

Human Does

  • •Review AI-prioritized records and confirm probable adverse drug events
  • •Decide escalation priority for pharmacovigilance follow-up
  • •Handle ambiguous, incomplete, or high-risk cases requiring expert judgment

AI Handles

  • •Scan unstructured records to identify probable adverse drug event mentions
  • •Prioritize and queue records for pharmacovigilance review based on likely signal relevance
  • •Flag patterns and higher-risk narratives for earlier human attention
  • •Support broader and more consistent triage coverage across incoming records

Operating Intelligence

How ADE Signal Triage Engine runs once it is live

AI surfaces what is hidden in the data.

Humans do the substantive investigation.

Closed cases sharpen future detection.

Confidence95%
ArchetypeDetect & Investigate
Shape6-step funnel
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapefunnel

Step 1

Scan

Step 2

Detect

Step 3

Assemble Evidence

Step 4

Investigate

Step 5

Act

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI scans and assembles evidence autonomously. Humans do the substantive investigation. Closed cases improve future scanning.

The Loop

6 steps

1 operating angles mapped

Operational Depth

Technologies

Technologies commonly used in ADE Signal Triage Engine implementations:

Marketing submission documentationOther
4 mentions
Performance monitoring planOther
4 mentions
Predetermined change control planOther
4 mentions
Ongoing monitoring workflowOther
3 mentions
Electronic health recordsOther
2 mentions
+1 more technologies(sign up to see all)

Key Players

Companies actively working on ADE Signal Triage Engine solutions:

AI device manufacturers with regulated update pipelinesDigital health firms building postmarket monitoring capabilities

Real-World Use Cases

Bias and transparency management workflow for AI-enabled medical devices

Before and after launch, the device maker checks whether the AI is fair, explains important information to users, and watches for problems that could hurt certain patient groups.

Fairness-aware classification or prediction with human-facing transparency controlsearly-to-mid stage; fda is formalizing expectations and explicitly seeking comment on adequacy for emerging technologies such as generative ai.
10.0

LLM-based detection of severe immune-related adverse events from oncology EHRs

An AI reads hospital notes for cancer patients on immune checkpoint inhibitors and flags serious treatment side effects more accurately and much faster than relying on billing codes alone.

clinical document classification with evidence retrievalvalidated research-stage workflow with external validation at a second institution; not yet described as routine production deployment.
10.0

LLM-based comorbidity extraction from oncology EHR free text

An AI reads doctors’ narrative history notes and turns mentions of patient comorbidities into structured fields a database can use.

information extraction and classification from unstructured clinical textpilot/validated workflow with benchmark evidence against specialists, but not yet shown as routine production deployment in the source.
10.0

NLP extraction of safety insights from unstructured medical text

Teach AI to read messy medical notes and documents so safety teams can find important drug-risk clues without reading everything by hand.

information extraction and entity/relation recognition from clinical and safety textdeployed enabling capability; iqvia explicitly markets an advanced nlp platform for production-scale insight extraction.
10.0

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