Cloud-Based Data Governance Architectures for Pharmacovigilance: Ensuring Security, Privacy, Compliance, And Patient Safety in Healthcare Systems
Keywords:
Pharmacovigilance; Cloud Computing; Data Governance; AI in Drug Safety; Regulatory Compliance; Real-World Data Integration; Patient Safety; Data Security; Data Privacy; Signal Detection; Big Data.Abstract
Cloud-based data governance frameworks are increasingly recognized as critical infrastructure for modern pharmacovigilance systems, enabling scalable, secure, and compliant management of real-world safety data. This paper explores the architecture, regulatory alignment, and operational advantages of cloud-integrated pharmacovigilance (PV) models, especially those augmented by artificial intelligence (AI) tools for signal detection and decision support. Emphasis is placed on how such systems can improve the speed and accuracy of identifying adverse drug reactions while ensuring adherence to international privacy and regulatory standards (e.g., HIPAA, GDPR, and FDA 21 CFR Part 11). To validate the theoretical advantages discussed in prior sections, an experimental simulation was conducted using a synthetic dataset of ten adverse event reports. Each entry included patient-level data such as the administered drug, the nature of the reported adverse event, and the seriousness classification. A frequency-based AI algorithm was applied to quantify signal strength for each drug-event combination. Drug-event pairs such as DrugA–Headache, DrugA–Rash, and DrugB–Rash were flagged with higher signal scores, illustrating the utility of even basic AI models in highlighting safety concerns. The experimental findings support the central hypothesis of this study: that the integration of AI-driven analytics within a governed cloud environment can facilitate earlier and more reliable identification of pharmacovigilance signals, reduce the manual burden on PV professionals, and enable continuous, real-time monitoring of drug safety across global data sources. This abstract thus encapsulates both the conceptual framework and empirical demonstration of cloud-AI synergies in improving pharmacovigilance outcomes.
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