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ICSR Drug Coding Automation (WHODrug + NLP)

Automates a large share of case-level drug coding to reduce manual pharmacovigilance workload Evidence basis: WHODrug Koda evaluation on 4.8 million VigiBase entries reported major automation gains with high coding accuracy; additional NLP work supports scalable coding from unstructured narratives with expert oversight for exceptions

The Problem

“Automate ICSR drug coding with WHODrug and NLP to reduce pharmacovigilance case-processing effort”

Organizations face these key challenges:

1

Drug names appear in inconsistent formats, languages, abbreviations, and misspellings

2

Manual WHODrug search and selection is slow and dependent on coder experience

3

Narrative text often contains incomplete or ambiguous medication references

4

Legacy cases may require archived WHODrug entries that are not easily visible

5

High case volumes create backlogs and increase compliance risk

6

Transmission setup errors can cause failed or delayed regulatory submissions

7

Post-approval safety intake sources are fragmented and difficult to monitor at scale

8

Quality review effort rises when coding decisions are inconsistent or poorly documented

Impact When Solved

Reduces manual drug coding workload for high-volume ICSR processingImproves consistency of WHODrug coding across processors, affiliates, and countriesAccelerates case intake and downstream pharmacovigilance workflowsSupports coding from unstructured narratives and noisy medicine namesPreserves human review for low-confidence, legacy, and archived-record scenariosImproves reporting readiness by validating transmission routing and configurationEnhances post-approval surveillance through multi-source monitoring and relevance ranking

The Shift

Before AI~85% Manual

Human Does

  • •Review each ICSR and identify reported drug names from case details
  • •Manually code drugs to WHODrug terms and resolve ambiguous matches
  • •Coordinate case follow-up and track coding status in spreadsheets
  • •Perform retrospective quality checks and correct coding inconsistencies

Automation

  • •No meaningful automation in routine drug coding
  • •Limited rule-based lookup support for reference checking
  • •Minimal prioritization of cases needing urgent coding attention
With AI~75% Automated

Human Does

  • •Review low-confidence or ambiguous coding suggestions and make final coding decisions
  • •Approve coded cases that require expert validation before submission
  • •Handle exceptions from incomplete narratives, unusual products, or conflicting source data

AI Handles

  • •Extract reported drug information from structured fields and unstructured case narratives
  • •Match case drugs to WHODrug terms and propose standardized codes at scale
  • •Prioritize straightforward cases for rapid processing and flag uncertain cases for review
  • •Monitor coding consistency and surface potential discrepancies for human follow-up

Operating Intelligence

How ICSR Drug Coding Automation (WHODrug + NLP) runs once it is live

AI runs the first three steps autonomously.

Humans own every decision.

The system gets smarter each cycle.

Confidence93%
ArchetypeRecommend & Decide
Shape6-step converge
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 shapeconverge

Step 1

Assemble Context

Step 2

Analyze

Step 3

Recommend

Step 4

Human Decision

Step 5

Execute

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 handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

The Loop

6 steps

1 operating angles mapped

Operational Depth

Technologies

Technologies commonly used in ICSR Drug Coding Automation (WHODrug + NLP) implementations:

GatewayOther
4 mentions
Service DeskOther
4 mentions
WebTraderOther
4 mentions
EV Production/XCOMPOther
3 mentions
EVPOSTOther
3 mentions

Key Players

Companies actively working on ICSR Drug Coding Automation (WHODrug + NLP) solutions:

ArisGlobal safety gateway toolingcustom pharmacovigilance integration servicesOracle Argus E2B gateway integrations

Real-World Use Cases

Archived-record aware drug coding for legacy pharmacovigilance cases

When coders search for a medicine, the system now marks old archived drug records so they can tell legacy entries apart from current ones.

decision support in human-in-the-loop codingdeployed ui and data-model enhancement in production safety software.
10.0

Medicine-name normalization support for national pharmacovigilance reporting

When countries send drug-safety reports, UMC helps them match medicine names correctly using national drug databases or another identification method so the reports can be understood consistently.

entity normalizationdeployed support capability described as part of ongoing collaboration.
10.0

AI-assisted source surveillance for post-approval safety signal intake

AI watches many places where safety issues might appear after a drug is sold and flags possible cases for the safety team to review.

Multi-source monitoring, relevance ranking, and case findingproposed but credible: the guideline explicitly broadens source categories, which supports ai monitoring workflows even though the document does not prescribe ai.
10.0

Transmission-mode setup and validation for EVPOST/WebTrader/Gateway reporting

A setup workflow decides how a company will send safety data to EudraVigilance and gathers the technical details needed to activate that connection.

routing and configuration validationdeployed operational setup process with documented transmission-mode updates in revision 16.
10.0

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