EHR + Narrative ADE Detection Engine for Signal Triage
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
“EHR + Narrative ADE Detection Engine for Signal Triage”
Organizations face these key challenges:
Surfaces probable ADEs from unstructured records to prioritize pharmacovigilance review
Impact When Solved
The Shift
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
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 EHR + Narrative ADE Detection Engine for Signal Triage runs once it is live
AI surfaces what is hidden in the data.
Humans do the substantive investigation.
Closed cases sharpen future detection.
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.
Step 1
Scan
Step 2
Detect
Step 3
Assemble Evidence
Step 4
Investigate
Step 5
Act
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI scans and assembles evidence autonomously. Humans do the substantive investigation. Closed cases improve future scanning.
The Loop
6 steps
Scan
Scan broad data sources continuously.
Detect
Surface anomalies, links, or emerging signals.
Assemble Evidence
Pull related records into a working case file.
Investigate
Humans interpret evidence and make case judgments.
Authority gates · 1
The system must not confirm that an adverse drug event has occurred without pharmacovigilance reviewer judgment [S1].
Why this step is human
Investigative judgment involves ambiguity, legal considerations, and stakeholder impact that require human expertise.
Act
Carry out the human-directed next step.
Feedback
Closed investigations improve future detection.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in EHR + Narrative ADE Detection Engine for Signal Triage implementations: