ARTIFICIAL INTELLIGENCE AND CLOUD-BASED HEALTHCARE DATA INTEGRATION FOR MEDICATION MANAGEMENT AND CLINICAL DECISION SUPPORT SYSTEMS

Abstract

The rapid digitization of healthcare systems has generated unprecedented volumes of patient data, yet this data remains largely siloed, underutilized, and disconnected from clinical workflows. This paper presents a novel framework that integrates artificial intelligence (AI) with cloud-based healthcare data integration to enhance medication management and clinical decision support systems (CDSS). The proposed architecture leverages a hybrid cloud-edge computing model to ingest, normalize, and analyze multi-modal data from electronic health records (EHRs), pharmacy information systems, wearable devices, and genomic databases. Using a two-stage machine learning pipeline—comprising a gradient-boosted medication reconciliation model and a deep neural network for adverse drug event prediction—the system provides real-time, evidence-based recommendations at the point of care. The framework was evaluated on a de-identified clinical dataset comprising 1.2 million patient encounters across three tertiary care hospitals. Experimental results demonstrate that the proposed system achieves a medication error detection accuracy of 97.4%, an adverse drug event prediction F1-score of 94.2%, and a clinical decision alignment rate of 96.8% with expert physician panels. Furthermore, the cloud-based architecture reduced data retrieval latency by 82% compared to traditional on-premise solutions. The study concludes that AI-driven cloud integration offers a scalable, secure, and intelligent pathway toward precision medication management and proactive clinical governance.