Pharmacovigilance has a volume problem that compounds. Case numbers rise with every product, every market and every new reporting channel, while the regulatory clocks stay exactly where they are. Headcount rarely grows at the same rate.
That makes PV one of the strongest candidates for automation in this industry, and also one where the line between help and harm is drawn with unusual clarity.
Intake, coding and triage
Case intake. Reading an incoming report — an email, a form, a call transcript, a literature abstract — and extracting the four elements that make a valid case: patient, reporter, product, event. This is structured extraction from messy text, which is what these systems do best.
Duplicate detection. Recognising that the same event has arrived twice through different channels, which is harder than exact matching and genuinely tedious by hand.
MedDRA coding suggestions. Proposing preferred terms for a reported event. A coder confirms. Consistency of coding across a large case series is exactly the sort of thing humans do unevenly and machines do steadily.
Seriousness and expectedness triage. Proposing a provisional classification so the cases that drive a 15-day clock surface first. Proposing, not deciding.
Narrative drafting from the structured case data, for a reviewer to finish — see safety narratives.
Why signal detection is harder than it looks
Finding a new safety signal in spontaneous report data is a genuinely difficult statistical problem, and adding a model does not make it easy.
Spontaneous reporting is biased in known ways: under-reporting varies by product, market and how long a drug has been available; media attention creates reporting spikes unrelated to incidence; and you never know the denominator. Disproportionality methods have always lived with this. A model trained on the same data inherits every one of those biases, and expresses them more confidently.
Where it does help is in the sifting that precedes the judgement: grouping related reports, surfacing clusters a quarterly review might miss, and pulling together everything already known about a product-event pair so a safety physician spends their time assessing rather than assembling.
A system that says "here are nine reports sharing an unusual pattern, and here is the existing literature" is useful. One that says "this is a signal" is making a judgement that belongs to a person.
What cannot be delegated
- Causality assessment. Whether the product plausibly caused the event is a medical judgement with regulatory consequences, made by a qualified person.
- Final seriousness determination, because it sets a reporting obligation and a clock.
- Signal validation and the decision to act — label change, risk minimisation, communication.
- Benefit-risk conclusions in periodic reports.
- The QPPV's responsibilities, which attach to a named individual by regulation.
Also worth stating plainly: a missed valid case is a reportable compliance failure, not an efficiency problem. That asymmetry should drive your acceptance criteria — an intake system that occasionally creates a false case is an irritation; one that occasionally drops a real one is a finding. See acceptance criteria for AI.
Wider programme context in pharmacovigilance services and GxP AI.
Where to go next
Explore GxP Copilot for AI-native validation, TraceDraft for source-traceable clinical documentation, or book a demo to see either on your own data.