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Pharmacovigilance Specialist

Process incoming Individual Case Safety Reports (ICSRs)

Enhances✓ Available Now

What You Do Today

Receive AE reports from healthcare professionals, patients, literature — enter into safety database, code with MedDRA, assess causality, determine seriousness and expectedness

AI That Applies

NLP auto-extracts case details from unstructured reports (emails, faxes, call transcripts), codes MedDRA terms, and pre-assesses causality

Technologies

How It Works

The system ingests unstructured reports (emails as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output is a structured view that highlights exceptions, trends, and items requiring attention — available in the existing tools without switching systems.

What Changes

Case intake time drops from 30-45 minutes to 5-10 minutes; AI handles the data entry and coding, you focus on medical assessment

What Stays

You make the final causality assessment, determine seriousness, and decide if the case requires expedited reporting

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for process incoming individual case safety reports (icsrs), understand your current state.

Map your current process: Document how process incoming individual case safety reports (icsrs) works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: You make the final causality assessment, determine seriousness, and decide if the case requires expedited reporting. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support IQVIA Argus tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long process incoming individual case safety reports (icsrs) takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your VP Operations or COO

Which of our current reports are manually assembled, and how much time does that take each cycle?

They're prioritizing which operational processes to automate

your process improvement or lean lead

What questions do stakeholders actually ask that our current reporting doesn't answer?

They understand the workflow dependencies that AI tools need to respect

a frontline supervisor

Which steps in this process are fully rule-based with no judgment required?

They see the daily reality that AI tools need to fit into

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.