Cloud-Based Pharmacovigilance Analytics Platforms

Abstract

Pharmacovigilance has entered a new era as healthcare systems increasingly generate large-scale digital data through electronic health records, spontaneous reporting systems, wearable devices, and telemedicine platforms. Although cloud computing offers scalable infrastructure for processing these heterogeneous datasets, concerns interoperability, and regarding secure patient privacy, multi-institutional collaboration continue to limit its adoption. This study identifies a research gap in the absence of privacypreserving cloud-native pharmacovigilance architectures capable of collaborative machine learning without centralized patient-level data sharing. To address this limitation, a federated cloud-based pharmacovigilance analytics framework is proposed for distributed adverse drug event (ADE) signal detection. The framework integrates cloud orchestration, federated learning, explainable artificial intelligence, and automated data harmonization to improve scalability while maintaining regulatory compliance. The proposed approach is designed to support real-time safety surveillance across geographically distributed healthcare institutions