NLP-Driven Automation of Pharmacovigilance Workflows
Reducing expert time in pharmacovigilance through NLP – based automation was a complex and high – impact engagement for Alumnus, delivered for a leading pharmaceutical company.
In compliance with FDA regulations, the client was required to perform continuous post-market surveillance to identify adverse drug events. Data sources were heterogeneous and included structured and unstructured inputs — such as prescriptions, hospital discharge summaries, and FDA MedWatch forms – often expressed in free-text clinical language.
Alumnus engineered an intelligent automation solution that integrated Natural Language Processing (NLP) pipelines to extract medically relevant information, normalize terminology, and map entities to standardized coding systems (e.g., MedDRA, WHO-DD). The system also inferred temporal relationships between clinical events to construct patient timelines and auto-generate draft Case Narratives for safety review and regulatory submission.
The solution significantly reduced cycle times for identifying and escalating valid safety cases. While final decisions remained with human experts, the automation optimized their involvement, improved compliance, and scaled up case throughput.
The platform remains in active production today.
