Faster adverse event detection with AI—boost safety, cut costs by 40%.
Q-Safe’s AI swiftly detects adverse drug reactions from structured and unstructured sources like PubMed, regulatory reports, and social media. It auto-classifies reports as valid or invalid using key identifiers, assesses medical significance, and runs rigorous validation checks for data reliability.
Automate case processing and submission workflows to global regulatory authorities like the FDA and EMA. Q-Safe enhances compliance by reducing manual reporting errors, streamlining validation processes, and accelerating approval timelines.
With AI-driven signal detection, risk prioritization, and Role-Based Access Management, Q-Safe enables swift action on safety signals. It ensures early detection, efficient risk mitigation, and secure data handling by assigning high-severity cases to the right stakeholders, keeping humans in the loop for critical oversight.
Tailor Q-Safe for seamless integration with pharmacovigilance systems, supporting configurable case intake, data extraction, MedDRA coding, severity classification (CTCAE Grades 1-5), and causal evaluation using WHO-UMC and Naranjo Algorithm.
Q-Safe simplifies regulatory compliance by automating data extraction, deduplication, and standardization. The system integrates open-source datasets and medical taxonomies like MedDRA and WHO-UMC, ensuring consistency in case reporting and validation.
Q-Safe processes pharmacovigilance data in real-time, providing organizations with actionable insights for faster, data-driven decision-making. The platform’s predictive analytics module identifies emerging safety risks before they escalate, enhancing proactive risk management.
Tailored, user-friendly UI that adapts to specific client workflows and requirements.
Offers configurable access levels aligned with user roles for secure data handling
Seamlessly manages and switches between datasets across multiple client environments
Supports document uploads to train the system, ensuring adaptability to evolving datasets.
Converts articles from various languages into English for comprehensive analysis
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Continuously enhances accuracy and performance by integrating reviewer feedback