Cloud Enabled Intelligent Enterprise Healthcare Framework with Machine Learning Based on Artificial Intelligence and Blockchain Governance

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Praveen Kumar Reddy Gujjala

Abstract

The evolution of digital healthcare ecosystems demands intelligent, scalable, and secure infrastructures capable of proactive risk management and patient-centric service delivery. This study proposes a cloud-enabled intelligent enterprise healthcare framework integrating machine learning (ML), artificial intelligence (AI), and blockchain governance to enhance predictive analytics, operational efficiency, and regulatory compliance. The framework leverages cloud-native architectures to provide scalable computing resources, distributed data management, and real-time interoperability across healthcare stakeholders. Machine learning models analyze multimodal healthcare data—including electronic health records, medical imaging, wearable sensor streams, and financial claims—to detect clinical risks, predict disease progression, optimize hospital operations, and identify fraudulent transactions. Blockchain governance mechanisms ensure data integrity, decentralized identity management, secure consent handling, and immutable audit trails through smart contracts. The integration of AI-driven analytics with blockchain-based trust frameworks enhances transparency, strengthens cybersecurity, and promotes ethical data usage. The proposed architecture adopts a layered enterprise model encompassing data acquisition, cloud orchestration, AI analytics engines, governance protocols, and application interfaces. A comprehensive research methodology is designed to validate system performance, scalability, compliance adherence, and predictive effectiveness. The framework supports proactive healthcare decision-making, reduces systemic risk, and establishes a resilient, intelligent digital health enterprise ecosystem

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How to Cite

Cloud Enabled Intelligent Enterprise Healthcare Framework with Machine Learning Based on Artificial Intelligence and Blockchain Governance. (2025). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(5), 12873-12882. https://doi.org/10.15662/IJRPETM.2025.0805025

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