Overview
What is AI pharmacovigilance?
Pharmacovigilance is the science of monitoring the safety of medicines after they reach patients. As data sources multiply — spontaneous reports, literature, real-world and social data — manual signal detection and case processing can't keep pace.
AI pharmacovigilance automates the intake, triage, and coding of adverse-event reports and continuously scans diverse sources for emerging safety signals — surfacing them to safety scientists faster, with full traceability, while keeping human experts in control of safety decisions.
What the system analyzes
The Challenge
Safety data is outgrowing manual review
Adverse-event volumes and data sources are growing faster than safety teams can review them manually \u2014 raising cost and the risk that important signals are detected late.
Rising Case Volumes
Manual intake and coding of adverse-event reports is slow and costly at scale.
Fragmented Signals
Safety signals hide across reports, literature, and real-world data.
Late Detection Risk
Manual review delays signal detection, with patient-safety implications.
Regulatory Burden
Reporting requirements are complex, strict, and time-sensitive.
Education AI Solutions
How Solnix strengthens pharmacovigilance
Case Intake
Ingest adverse-event reports from multiple channels automatically.
Triage & Coding
Auto-triage and code cases (e.g., MedDRA) for review.
Literature Monitoring
Continuously scan scientific literature for safety information.
Real-World Signals
Monitor real-world and social data for emerging signals.
Signal Prioritization
Surface and rank potential signals for safety scientists.
Reporting Support
Assist with regulatory reporting and documentation.
End-to-End Implementation
How Solnix implements pharmacovigilance AI
A phased rollout from case-intake automation through validated signal-detection models with human oversight \u2014 measured on processing throughput and detection timeliness.
Discovery & AI Opportunity Mapping
We start by understanding your operations, data landscape, and goals, then map where AI delivers measurable value and where it does not. Every engagement begins with a prioritized opportunity backlog, not a technology pitch.
Data Foundation & Readiness
AI is only as good as the data behind it. We assess data quality, connect fragmented sources, and build the secure, governed pipelines that production AI depends on, with privacy and compliance designed in from the start.
Model & Agent Development
We build the models, retrieval systems, and AI agents tailored to your use cases, selecting the right approach (fine-tuning, RAG, multi-agent orchestration) for accuracy, cost, and latency, and validating against your real-world edge cases.
Integration & Workflow Embedding
AI only creates value when it lives inside the tools your teams already use. We embed models and agents into existing systems, surfaces, and workflows, so adoption is natural and human-in-the-loop controls stay in place.
Deployment, Security & Compliance
We deploy to production with the security, monitoring, and compliance controls enterprises require, including bias and fairness testing, audit logging, and the observability needed to operate AI responsibly at scale.
Optimization & Continuous Improvement
AI systems improve with use. We measure outcomes against the goals set in Phase 01, retrain and tune from live feedback, and expand to the next set of use cases, turning a single deployment into a compounding capability.
Key Capabilities
Key capabilities
Automated Case Processing
Intake, triage, and coding at scale.
Benefits
Signal Detection
Scan diverse sources for signals.
Benefits
Literature Mining
Continuous literature surveillance.
Benefits
Prioritization
Rank potential signals for review.
Benefits
Regulatory Support
Assist reporting workflows.
Benefits
Human-in-the-Loop
Experts make safety decisions.
Benefits
Who Benefits
Outcomes for safety teams, regulatory, and patients
For Safety Scientists
Focus on judgment, not data entry.
For Regulatory & Compliance
Stronger, more timely compliance.
For Patients
Safer medicines.
Education Segments
Explore related healthcare AI
Drug Discovery Screening
Connect safety insight back to discovery.
Clinical Trial Patient Matching
Link safety monitoring to trial populations.
Care Gap Identification
Use real-world data for population safety.
Clinical Documentation AI
Ground real-world evidence in structured data.
Enterprise Automation
Automate safety operations.
Agent Ecosystem
Orchestrate multi-step safety agents.
Methodology
How Solnix builds for healthcare
01, Clinical Workflow Assessment
01, Clinical Workflow Assessment
We shadow real clinical and administrative workflows to design AI around how teams actually work.
02, HIPAA-Compliant Architecture
02, HIPAA-Compliant Architecture
All PHI is processed within HIPAA-compliant infrastructure under signed BAAs, with encryption and access logging.
03, EHR & System Integration
03, EHR & System Integration
We integrate with Epic, Cerner, and Athenahealth via FHIR R4 / HL7 so AI lives inside existing workflows.
04, Clinical Validation
04, Clinical Validation
Systems are validated against retrospective data with clinician oversight before production.
05, Continuous Monitoring
05, Continuous Monitoring
Post-deployment monitoring tracks performance and outcomes; drift triggers retraining.
FAQ
Questions from drug-safety and regulatory leaders
Get Started
Detect Safety Signals Before They Spread.
Talk to a Solnix specialist about a pharmacovigilance pilot scoped to your products, sources, and reporting requirements.
Talk to a Drug-Safety AI Specialist \u2192