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How Supervised AI Reduced Manual Review by 90% in Pharmacovigilance
March 24, 2026 by Molly Connor
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As a global pharmaceutical manufacturer expanded its use of AI-powered chatbots and voicebots, the goal was clear: deliver faster, always-on patient support while diverting high-volume questions away from live agents.
But in pharmacovigilance (PV), scale comes with responsibility. Regulatory policy required every AI interaction to be monitored for potential safety events. As volumes increased, this meant highly trained agents were manually pulling transcripts, reviewing conversations, and routing findings downstream.
The challenge wasn’t just inefficiency; it was feasibility. The team did not have the capacity to manually monitor the growing volume of AI interactions, and the work itself was not the best use of internal expertise. Without a scalable way to oversee safety, the organization could not confidently launch or expand chatbot and voicebot programs at all.
AI was ready to grow—but without supervision, scale wasn’t possible.
The A-Ha Moment
Listening across conversations with Authenticx revealed a critical insight: reviewing every interaction manually
did not improve patient safety.
During a proof of concept, the team found that agents were labeling safety events incorrectly nearly 40% of the time, skewed toward false positives. That caution was understandable—call center agents are trained to err on the side of safety, but it also created
unnecessary noise and downstream workload.
The insight clarified the real challenge. The goal wasn’t just to reduce manual effort; it was to establish a more consistent, unbiased, and standardized approach to identifying true safety risk.
That’s where supervised AI came in.
Using supervised AI with human-in-the-loop validation focused on continuous improvement, Authenticx automatically flagged potential adverse events and product complaints. This allowed agents to focus their review on interactions that truly mattered.
Behind the scenes, data scientists and data labelers continuously monitored model performance, applying a rubric developed in collaboration with the manufacturer to ensure consistency, reduce false positives, and identify opportunities for ongoing model refinement.
The impact was immediate: AI alone wasn’t the answer. Supervised, continuously improving AI was.
The Intervention
By combining automated detection with human review, the manufacturer established a process trusted by PV, legal, and regulators alike:
AI surfaced potential safety signals at scale
Humans validated edge cases and false negatives
Full transcripts and audit trails were preserved
PII was securely redacted and routed downstream
The result was a system the organization felt firmly in control of—even as AI adoption accelerated.
The Impact
With supervision in place, results quickly followed:
~90% reduction in manual AE/PC review immediately, with 100% elimination following validated model performance
~20 FTEs of monitoring effort eliminated at full scale
99% accuracy over 372k+ interactions
The chatbot and voicebot review processes yielded favorable regulatory audit findings due to the safeguards built into the review framework.
The supervised approach not only improved efficiency, but also strengthened audit readiness—earning praise during a regulatory audit by authorities for the rigor and control of the AI review process.
What began in patient services expanded across PV and regulatory compliance teams, becoming a trusted part of the manufacturer’s compliance infrastructure.
The New Normal
AI could now scale without sacrificing safety.
Instead of choosing between innovation and control, the manufacturer established a model where every AI interaction was visible, accountable, and supervised—before issues impacted patients or inaccurately reported events.
In pharmacovigilance, autonomy requires supervision to remain safe and compliant and Authenticx serves as the supervision layer that makes safe scale possible.