PREDICTIVE ML · SPEQ SYNTHESIS
Pharmacovigilance Signal ML
A machine-learning system that supports pharmacovigilance by detecting potential safety signals across case data and by triaging or prioritizing individual case safety reports for human assessment.
Because the endpoint is patient safety and regulatory reporting, both missed signals and mis-triaged cases carry consequences, so the model’s recall and its behavior on rare events dominate its risk profile.
What a system class is not
An AI system class describes a shape of system, not a product and not an approval pathway. SPEQ does not qualify, validate, or endorse any implementation, and no regulator recognises these classes as a category.
Weighted score 25/32 (decision consequence and model influence weighted most heavily) places this in the high tier.
Computed deterministically from four Context-of-Use factors — decision consequence, model influence, data sensitivity, and change dynamics. A transparent scoping aid, not a validated risk-assessment system.
SCOPE THE ASSURANCE STRATEGY → CSA WORKBENCHUsed by drug-safety teams to surface candidate signals and prioritize case processing, influencing which safety patterns get investigated and how quickly serious cases reach a qualified assessor.
It shapes signal detection and case handling that feed regulated safety assessments and reporting, so a missed signal or a downgraded serious case can delay or distort a required safety action.
human in the loop
Because outputs feed regulated safety assessment and reporting, a qualified safety professional must validate signals and case decisions, and the model may prioritize but never conclude a safety judgment.
- A missed signal or an incorrectly de-prioritized serious case is the defining failure, because under-detection in safety can delay action on a genuine patient-safety issue.
- Model drift from changing reporting patterns, new products, or evolving terminology can erode detection performance while routine metrics still appear acceptable.
- Bias in historical case data can cause the model to under-represent certain populations, products, or event types, systematically weakening detection where reporting was already sparse.
- Automation bias may lead assessors to trust triage prioritization and under-scrutinize cases the model ranked low, defeating the safety net the human review provides.
- Opaque scoring makes it difficult to justify why a signal was or was not surfaced, complicating regulatory defense of the pharmacovigilance system’s decisions.
- Evaluate the model with recall-weighted metrics on serious and rare events, since under-detection in pharmacovigilance is far more consequential than a manageable false-positive rate.
- Assess training data for population and reporting bias so the system does not systematically under-detect signals where historical reporting was already weak.
- Monitor detection and triage performance continuously against changing products, terminology, and reporting volumes, and re-qualify when the case landscape shifts.
- Preserve human validation of every surfaced signal and a defined path for cases the model ranks low, so no serious case bypasses qualified assessment.
- Maintain attributable records of model inputs, scores, and human decisions so the pharmacovigilance system’s conclusions remain reconstructable and defensible to regulators.
Standards SPEQ maps to this class
AI-governance frameworks
The AI-specific shelf that defines “quality AI” — see Good AI Practice.
SPEQ synthesis — applied AI-assurance judgment to help you scope your own Context-of-Use assessment and validation. Not regulatory guidance, not an AI classification service, and not a substitute for your documented risk assessment.