Built for High Stakes: The Next Evolution in AI for Pharmacovigilance

A patient calls in about a prescription refill. Partway through the call, they mention a rash that hasn’t cleared up — and that they skipped this morning’s dose because of it. The agent on the line is focused on getting the refill processed, but nothing about the call raises an obvious flag.

But somewhere in that ordinary conversation, a reportable safety event just happened.

That’s the reality of pharmacovigilance today. Adverse events, product quality complaints, and special situations rarely announce themselves. They surface in the middle of conversations about something else entirely, described in a patient's own words rather than clinical language — and it's the agent handling that refill or billing question, not a pharmacovigilance expert, who's left to catch it. Some do. The subtler signals, the ones buried in an offhand comment, get missed, or flagged inconsistently depending on who happens to be listening that day. What if there were an expert riding shotgun on every conversation, catching what matters without slowing anyone down?

And once an event is caught, the real cost begins. Case processing consumes an estimated 40 to 85% of a pharmacovigilance team’s budget, and case volumes keep climbing 10 to 15% a year. Case officers — the people trained to make expert medical judgment calls — end up spending a disproportionate amount of their time on data entry instead: writing narratives, filling out forms, reconstructing details from a call that happened days ago. Meanwhile, regulators expect serious events be reported within 15 days of being identified. Every handoff between the agent who took the call, the reviewer who qualifies it, and the case officer who finalizes it is a place where something can be missed, mis-typed, or delayed.

This is the problem we built our Pharmacovigilance AI Solution to solve.

It's the next evolution of Authenticx's safety event models: the same foundation our customers already rely on, expanded into a more complete offering that follows a safety event from the moment it's spoken all the way to your system of record.

Closing the Gaps Between Conversation and Documentation

Capture: Authenticx reviews patient conversations at scale (every call, every chat) with the same standard, every time, so whether a reportable event gets caught doesn't come down to whether one agent happened to notice it. In the example above, Authenticx flags the rash the moment the call ends, regardless of whether the agent was able to capture it or not.

Process: Instead of starting from a blank form, your reviewer opens a safety form that's already populated with the patient, product, and event details, pulled directly from the conversation. Their time goes toward the part of the job that actually needs their judgment: qualifying the event, not typing it in from scratch.

Documentation Delivery: Once a potential event is identified, Authenticx delivers it directly to your system of record — or it can be qualified in Authenticx first before moving downstream. Either way, submission time isn't slowed by data entry or manual handoffs.

The judgment stays human. The busy work doesn't.

Why This Matters Now

This isn't a hypothetical problem. Regulators are paying closer attention to how completely — and how quickly — drug safety events make it from a patient conversation into a formal report, and the market is responding in kind: pharmacovigilance is projected to nearly double, from roughly $9 billion in 2025 to almost $17 billion by 2035, driven by rising adoption of AI-powered case processing, cloud-based safety databases, and real-world evidence to strengthen safety surveillance.

That investment matters because of what's likely still slipping through today. Some estimates suggest only about 1% of serious, unexpected adverse drug reactions ever reach the FDA at all.* The events that don't announce themselves are often the ones that matter most.

This isn't theoretical. A global pharmaceutical manufacturer expanding its AI-powered chatbot and voicebot programs hit the same wall: regulators required every AI interaction to be monitored for safety events, but the team didn't have the capacity to review that volume manually — and without a scalable way to do it, they couldn't confidently expand the programs at all.

A proof of concept revealed why manual review wasn't the fix either: agents were mislabeling safety events nearly 40% of the time, skewed toward false positives. The real goal wasn't less review, but rather more consistent, unbiased detection of the events that actually mattered.

With Authenticx's supervised AI in place, the results followed: a ~90% reduction in manual adverse event and product complaint review, ~40 FTEs of monitoring effort eliminated at full scale, and 99% accuracy across more than 370,000 interactions — plus favorable findings in a live regulatory audit that specifically praised the rigor of the AI review process.

Read the full case study →

Built To Evolve With Your Program

When new products launch or call volume shifts, legacy systems sit still while your pharmacovigilance program changes around them. Our team works alongside yours on an ongoing basis to modernize your pharmacovigilance program holistically, tuning detection and closing gaps as your program scales, so the solution that fits your program today keeps fitting it tomorrow.

And every flagged event still comes to your team before anything moves downstream. Our AI does the volume work: reviewing conversations at scale and surfacing the ones that matter. Your team makes the call on what's actually a case. Automation doesn't replace judgment. It makes sure expert judgment gets directed at the conversations that require it.

Behind every safety event is a patient who trusted someone to listen. Our Pharmacovigilance AI Solution exists to make sure that trust doesn't depend on catching every detail in real time, or on hoping nothing falls through the cracks between handoffs.

Built for high stakes. Visit our website to learn more.

Read the press release here.