Authenticx has expanded its pharmacovigilance artificial intelligence platform to automate more of the downstream workflow involved in identifying and processing potential adverse drug events.
The new capabilities build on the company’s existing AI technology for detecting safety events within patient-service conversations.
The platform can now use information extracted from those conversations to pre-populate configurable pharmacovigilance forms and route records directly into pharmaceutical manufacturers’ existing safety systems.
Human teams remain responsible for reviewing and qualifying the cases.
Pharmacovigilance organisations traditionally rely on manual processes to identify, document and transfer potential adverse events reported through patient-support and contact-centre interactions.
These workflows can involve reviewing large volumes of unstructured conversations before relevant information is entered into formal safety systems.
Authenticx is applying AI to reduce the amount of manual work required between initial detection and formal safety review.
The system analyses healthcare conversations for information potentially relevant to adverse event reporting.
Once a potential event is identified, the platform can extract relevant information, populate structured safety documentation and route the record into the manufacturer’s existing workflow.
The company said the approach is intended to improve consistency and reduce administrative effort while maintaining human oversight in safety qualification.
The development reflects broader adoption of artificial intelligence across healthcare operations where large volumes of unstructured patient information need to be converted into structured clinical or regulatory data.
Unlike AI applications aimed directly at diagnosis or treatment, pharmacovigilance automation focuses on improving the infrastructure supporting post-market drug safety.
As pharmaceutical companies increasingly interact with patients through digital support programmes, contact centres and other channels, the volume of safety-relevant conversational data is also increasing.
Automating the early stages of adverse event processing could help safety teams focus more of their attention on clinical review and regulatory decision-making rather than manual data entry.
The expansion also demonstrates how healthcare AI is moving beyond individual prediction tools towards multi-step workflow automation integrated with existing enterprise systems.
