Next-Generation Enterprise Intelligence Systems for Healthcare Combining Generative AI and Automation with Security-Aware Analytics
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Abstract
The rapid digital transformation of healthcare enterprises has created an urgent need for intelligent systems capable of delivering real-time insights while ensuring data security, regulatory compliance, and operational efficiency. Next-generation enterprise intelligence systems address these challenges by integrating generative artificial intelligence, intelligent automation, and security-aware analytics into a unified framework. This paper presents a comprehensive approach for healthcare enterprise intelligence that combines generative AI for contextual reasoning and natural language insight generation with automated workflows for clinical and operational decision support. Security-aware analytics are embedded across the architecture to enable continuous risk assessment, privacy preservation, anomaly detection, and compliance monitoring for sensitive healthcare data. The proposed system supports diverse healthcare applications including predictive patient risk modeling, automated clinical documentation, operational optimization, and cyber threat detection. By incorporating explainable AI mechanisms and human-in-the-loop controls, the framework enhances trust, transparency, and accountability in AI-driven healthcare environments. Experimental evaluation and use-case analysis demonstrate that the integrated approach improves decision accuracy, reduces manual workload, strengthens security posture, and accelerates data-driven healthcare outcomes. This work highlights the potential of secure and automated enterprise intelligence systems to transform healthcare delivery and management in complex, data-intensive environments
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