Pharmaceuticals & Life Sciences · Pharmacovigilance & Drug Safety

Adverse Event Case Processing & Reporting

Automates→Stable

Available Now

Production-ready. Commercial solutions exist and organizations are actively deploying.

Trajectories describe the observable direction of human effort — not a prediction about specific roles, headcount, or individual careers.

What You Do Today

Receive, triage, and process adverse event reports from clinical trials, spontaneous reports, literature, and patient support programs. Code events using MedDRA, assess causality, and submit expedited reports (ICSRs) to regulatory agencies within mandated timelines — 15 days for serious unexpected events, 7 days for fatal/life-threatening.

AI Technologies

Case Intake Automation NLPMedDRA Auto-CodingCausality Assessment AIDuplicate Detection ML

Roles Involved

Who works on this

Pharmacovigilance SpecialistData AnalystCompliance Analyst

Individual Contributor

How It Works

NLP extracts adverse event information from unstructured sources — emails, call transcripts, social media mentions, literature. AI auto-codes events to MedDRA terms and performs initial causality assessment. Duplicate detection algorithms identify repeat reports of the same event across databases.

What Changes

Case processing throughput increases 3-5x as AI handles intake and initial coding. Compliance with reporting timelines improves as automation eliminates manual bottlenecks.

What Stays the Same

Medical assessment of serious cases, causality determination for complex events, and the judgment to escalate safety signals to senior medical officers require trained pharmacovigilance professionals.

Cross-Industry Concepts

event processing regulatory reporting classification

Evidence & Sources

•ICH E2B(R3) individual case safety report guidelines
•FDA FAERS database statistics

Sources listed are directional references, not formal citations. Verify against primary sources before using in business cases or presentations.

Last reviewed: March 2026

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.

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for adverse event case processing & reporting, document your current state in pharmacovigilance & drug safety.

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Map your current process: Document how adverse event case processing & reporting works today — who does what, how long each step takes, and where the bottlenecks are. Use your compliance monitoring platform data to establish a factual baseline.

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Identify the judgment calls: Medical assessment of serious cases, causality determination for complex events, and the judgment to escalate safety signals to senior medical officers require trained pharmacovigilance professionals. — these are the boundaries AI won't cross. Know them before you start.

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Check your data readiness: AI tools for pharmacovigilance & drug safety need clean, accessible data. Check whether your compliance monitoring platform has the historical data, integrations, and quality to support Case Intake Automation NLP tools.

Without a baseline, you can't tell whether AI actually improved adverse event case processing & reporting or just changed who does it.

Define Your Measures

What to track and how to calculate it

findings per audit cycle

How to calculate

Measure findings per audit cycle for adverse event case processing & reporting before and after AI adoption. Pull from your compliance monitoring platform.

Why it matters

This is the most direct indicator of whether AI is adding value to pharmacovigilance & drug safety.

time to remediate

How to calculate

Track time to remediate using the same methodology you use today. Don't change how you measure just because you changed how you work.

Why it matters

Speed without quality is just faster mistakes. Measure both together.

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 goal. Measure outcomes. If the tool helps with adverse event case processing & reporting, people will use it.

Start These Conversations

Who to talk to and what to ask

Chief Compliance Officer

“What's our plan for AI in pharmacovigilance & drug safety? Are we piloting, planning, or waiting?”

This tells you whether to experiment quietly or push for formal investment in adverse event case processing & reporting.

your compliance monitoring platform administrator or vendor

“What AI capabilities exist in our current compliance monitoring platform that we're not using? Most platforms are adding AI features faster than teams adopt them.”

The cheapest AI adoption is the features already included in your existing license.

a practitioner in pharmacovigilance & drug safety at another organization

“Have you deployed AI for adverse event case processing & reporting? What worked, what didn't, and what would you do differently?”

Peer experience is more useful than vendor demos. Find someone who has actually done this.