Adaptive Pharmacovigilance under Model Drift, Product Evolution, Data-Source Change, and Emerging Patterns of Medicine Use across Global Health Systems

Pharmacovigilance systems operate within changing medicinal-product, population, data, analytical, and organizational environments. Static surveillance configurations may consequently become misaligned with the products being monitored, the populations exposed, the information captured, or the decisions supported. This article develops an original adaptive-governance framework for pharmacovigilance under model drift, product evolution, data-source change, and emerging patterns of medicine use. The framework conceptualizes surveillance drift as a multidomain change in the relationship among a medicinal product, its use context, observed safety data, analytical processes, and decision environment. It distinguishes product drift, use-pattern drift, data-source drift, model-performance drift, and governance drift without assuming that these categories are independently observable or causally separable. The proposed Adaptive Pharmacovigilance Governance Lifecycle connects versioned baseline specification, multidomain change sensing, change-impact hypothesis formation, consequence-proportionate evidence assignment, targeted revalidation, human-authorized action, rollback, and post-change monitoring. Emerging use patterns and off-label exposure are treated as changing exposure contexts requiring stratified interpretation rather than presumptive evidence of harm. Change-impact assessment is separated from revalidation, and both are distinguished from causal assessment, clinical recommendation, and regulatory acceptability. Human oversight is defined through attributable decision ownership, auditability, override documentation, and recoverable prior states rather than nominal reviewer involvement. The framework requires retrospective temporal evaluation, external and subgroup validation, data-lineage assessment, source-transition analysis, prospective silent-mode testing, human-factors research, and controlled rollback exercises. It is an original conceptual synthesis rather than a validated model, universal standard, regulatory pathway, or deployment-ready system.