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Use CasePharmacovigilance Signal Detection

Proactive Safety Intelligence from Every Data SourceAI Agents for Pharmacovigilance Signal Detection

AI agents continuously mine your internal ICSR database, EudraVigilance, FAERS, VigiBase, published literature and social media to detect emerging safety signals — with automated disproportionality analysis, signal validation and regulatory-grade documentation.

About Pharmacovigilance Signal Detection

Pharmacovigilance Signal Detection solutions powered by autonomous AI agents enable enterprises to overcome reactive safety monitoring at scale. Our pharmacovigilance signal detection AI platform provides comprehensive automation for continuous multi-source signal intelligence. Trusted by Fortune 500 companies and leading enterprises worldwide for mission-critical pharmacovigilance signal detection operations. Deploy AI-powered agents that work 24/7 to transform your pharmacovigilance signal detection workflows with enterprise-grade security, compliance, and scalability.

Key capabilities include continuous prr, ror and ebgm calculations across internal and external icsr databases, multi-source signal correlation across eudravigilance, faers, vigibase and who-umc, automated signal validation documents with literature evidence and labelling comparison, real-time alerting for threshold breaches and emerging signal clusters. Organizations achieve Detect Signals Weeks Earlier, Full Database Coverage, and Audit-Ready Signal Files through our intelligent automation platform.

Purpose-built autonomous agents, tailored to your stack.

Problem and Solution Overview

The friction today

Reactive Safety Monitoring at Scale

Signal detection relies on periodic, manual analysis of large datasets. By the time signals are identified and reviewed, patient exposure has grown and regulators may already be investigating.

Common challenges in pharmacovigilance signal detection include high operational costs, slow processing times, manual errors, and scalability limitations. Traditional approaches to pharmacovigilance signal detection struggle with periodic batch analysis misses early signals between review cycles, leading to inefficiencies and missed opportunities. Organizations face increasing pressure to modernize pharmacovigilance signal detectionoperations while maintaining compliance and reducing costs.

  • Periodic batch analysis misses early signals between review cycles
  • Manual disproportionality analysis (PRR, ROR, EBGM) is resource-intensive and error-prone
  • Fragmented data across internal safety database, public databases and literature
  • No systematic integration of social media and patient forum safety signals
With Alomana agents

Continuous Multi-Source Signal Intelligence

Alomana agents run continuous disproportionality analysis across all data sources, cross-reference signals against labelling and prior reviews, and generate validation-ready signal assessments with literature evidence.

Alomana's pharmacovigilance signal detection AI agents provide end-to-end automation with enterprise-grade reliability. Our platform leverages advanced machine learning, natural language processing, and intelligent process automation to deliver Detect Signals Weeks Earlier. The solution integrates seamlessly with existing systems including ERPs, CRMs, and legacy applications. Real-time monitoring, audit trails, and compliance reporting ensure governance and transparency. Scalable architecture supports growing workloads without performance degradation.

  • Continuous PRR, ROR and EBGM calculations across internal and external ICSR databases
  • Multi-source signal correlation across EudraVigilance, FAERS, VigiBase and WHO-UMC
  • Automated signal validation documents with literature evidence and labelling comparison
  • Real-time alerting for threshold breaches and emerging signal clusters

See it in motion

See the agents working on your behalf

Watch how autonomous agents orchestrate data extraction, reasoning and reporting on complex, real-world workloads.

Turn your proprietary data into value

Autonomously analyzes databases, discovers anomalies, and extracts insights from your data

Jade is an autonomous AI data analysis agent that discovers anomalies, generates predictive insights, and automates database analysis across your enterprise systems. Key features include fraud detection, automated reporting, real-time data monitoring, and advanced analytics for business intelligence.
$1M Fraud Prevented

in the last 12 months for one of the largest global servicers across $5B+ in transactions processed.

Data Upload

Upload and process your data files

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Supports Excel (.xlsx, .xls) and CSV files

Outcomes

What teams achieve with this use case

Organizations implementing pharmacovigilance signal detection AI agents achieve significant improvements across key metrics. Benefits include reduced operational costs, faster processing times, improved accuracy, enhanced compliance, and better scalability. Real-world deployments demonstrate measurable ROI within weeks of implementation. Teams report higher productivity, reduced manual work, and ability to focus on strategic initiatives. The platform supports continuous improvement through machine learning and adaptive algorithms. Enterprise customers benefit from dedicated support, custom integrations, and tailored deployment options including on-premises and cloud-based solutions.

01

Detect Signals Weeks Earlier

Continuous monitoring catches emerging patterns between scheduled review cycles, enabling proactive risk management before signals reach regulatory thresholds.

02

Full Database Coverage

Analyse 100% of ICSR records continuously — not just periodic samples — eliminating the signal detection gaps inherent in manual review.

03

Audit-Ready Signal Files

Every signal assessment includes source data, statistical outputs and literature citations — ready for PRAC, REMS or EMA safety committee review.

Questions, answered

Frequently asked questions

Which statistical methods do agents apply for signal detection?

Agents apply PRR (Proportional Reporting Ratio), ROR (Reporting Odds Ratio), EBGM (Empirical Bayes Geometric Mean) and IC (Information Component) methods — configurable by product, class or regulatory market — with automated threshold alerting per your signal management procedure.

How do agents handle social media and patient forum monitoring?

Agents monitor structured patient communities, disease forums and social platforms using medical NLP to identify potentially valid ICSRs, classify them by seriousness and route confirmed cases into your standard ICSR pipeline for processing.

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