P207: **Signal Detection Methodology Across Small, Mid Size, and Large Pharmaceutical Organizations
Poster Presenter
Mohit Raizada
Director - Signal Management Services
IQVIA India
Objectives
To present a leadership-level comparison of signal detection methodologies across small, midsize, and large pharmaceutical organizations, highlighting how scale, data complexity, and governance influence fit-for-purpose approaches while maintaining regulatory compliance.
Method
A comparative framework was developed assessing qualitative, hybrid, and analytics-enabled signal detection models across organizational sizes, integrating methodology, governance structures, and enabling technologies, with emphasis on human-in-the-loop decision-making.
Results
Clear differentiation in signal detection approaches was observed across organizational scales. Small pharmaceutical and biotech organizations primarily relied on qualitative, physician-led case-by-case medical review, focusing on serious and unexpected adverse events, biological plausibility, and close clinical oversight. While this model supported strong medical judgment, scalability and automation remained limited.
Midsize organizations demonstrated a hybrid approach, combining structured qualitative assessment with routine quantitative screening methods, including disproportionality analyses and descriptive trend evaluations. Formal signal validation and evaluation steps, supported by signal review committees and semi-automated dashboards, enabled balanced regulatory readiness, though effective prioritization was required to manage signal volume.
Large pharmaceutical organizations adopted multilayered, analytics-enabled models supported by automated statistical screening, advanced stratification, and enterprise signal detection platforms. These models enabled scalability, consistency, and early detection across global datasets but required robust governance to mitigate signal noise and ensure transparency. Across all organizational sizes, technology functioned as a decision-support enabler rather than a replacement for expert clinical assessment.
Conclusion
Signal detection methodologies must evolve in alignment with organizational scale, portfolio complexity, and data volume, while remaining grounded in consistent regulatory principles. There is no single optimal model applicable to all organizations; instead, effective signal detection requires fit-for-purpose methodologies supported by appropriate levels of automation and analytics. Regardless of organizational size, expert medical judgment, transparent documentation, and clearly defined governance remain foundational to compliant and effective signal management. Leadership oversight is critical to ensuring that technology enhances efficiency, consistency, and inspection readiness without eroding clinical accountability. A human-in-the-loop approach enables organizations to adapt to increasing data complexity while preserving scientifically sound and regulator-trusted decision-making.
Key Takeaways
• Signal detection methodologies evolve with organizational scale and data complexity
• Technology should enable efficiency and consistency, not replace expert judgment
• A human in the loop model remains central across all organizational sizes
• Fit for purpose technology selection is essential for regulatory compliance and inspection readiness
