End-to-End Pharmacovigilance Automation for Real-Time Safety Insights
Product Overview
Client’s goals
The provider required a modernized ecosystem that could enable automatic literature monitoring and NLP-assisted triage while maintaining their full audit trail. Their main aim was to consolidate the outputs into a coherent and compliant system to make sure both EMA and FDA inspections were traceable.
They also wanted AI-fueled relevance scoring to improve case detection accuracy and allow reviewers to bypass the monotonous labor that consumed a significant amount of operational time.
They wanted an architecture that facilitated cross-system interoperability through a flexible API layer, enabling them to extend and integrate case management environments in the future. To accomplish long-lasting compatibility with downstream signal detection workflows, which rely on structured, high-quality inputs from literature monitoring systems.
Implementation
The implementation advanced through sequential phases designed to deliver incremental value while maintaining regulatory alignment. Jelvix began by analyzing the client’s existing workflows using methods standard in long-term healthcare software development, revealing the inconsistencies introduced by manual citation handling and unstructured triage steps.
During the architecture phase, the team designed a modular blueprint built for cloud-native deployment. Expertise drawn from AI development services guided the design of NLP pipelines optimized for domain-specific language, enabling relevance scoring based on contextual cues unique to PV literature. Backend orchestration established ingestion pipelines that synchronized retrieval tasks and eliminated asynchronous fetch issues that previously caused gaps in weekly reviews.
As soon as the foundational blocks were established, the engineers developed automated search scheduling and NLP-based classification, as well as the deduplication logic to standardize inconsistencies between PubMed and regional authorities. The front-end added a reviewer dashboard to make it easier for reviewers to make decisions about triage orders in a structured manner, as opposed to a series of email threads and spreadsheets.
The MVP allowed the team to check automated retrieval accuracy, classifier performance, and audit logs. QA testing confirmed regulatory traceability by making sure the system kept records of every triage decision and automated step. After approval, the solution was launched on AWS with encrypted data and IAM-controlled access, and PV scientists received training.
Value Delivered
The new platform created a single system for literature retrieval, abstract review, and triage decisions, all in a compliant environment. Automated relevance scoring reduced reviewer fatigue and let them focus on scientific work instead of repetitive tasks. Deduplication logic stopped duplicate work, making sure each abstract was reviewed once and tracked in line with GVP rules.
Real-time dashboards improved operational transparency, allowing safety leads to track workload distribution and identify bottlenecks before they impacted weekly timelines. The modular architecture positions the organization for future enhancements, including automated case intake and downstream signal processing, setting the stage for more advanced AI-driven robotic process automation in pharmacovigilance
Project Results
70% reduction in manual workload.
This was achieved by automating repetitive search, triage, and classification tasks using retrieval pipelines and NLP models.
60% faster literature processing.
The review cycle accelerated significantly across all monitored substances.
22% improvement in abstract classification accuracy.
This resulted from BioBERT enhancements and targeted machine learning fine-tuning.
Audit-ready traceability.
The system now provides complete evidence chains that regulators can validate without manual reconstruction.
Scalable architecture.
It supports rapid onboarding of new drugs or regions without requiring major redesign.
Lower operational cost per monitored product.
Teams can now redirect effort toward higher-value safety analysis.
