The rapid evolution of Business Intelligence (BI) has transitioned from static, historical reporting to dynamic, real-time analytics, increasingly augmented by Artificial Intelligence (AI) and Machine Learning (ML). This paper explores the architectural convergence of traditional data warehousing, real-time data integration, and Generative AI (GenAI) within the healthcare sector. We analyze the critical success factors for implementing BI in clinical environments, addressing the challenges of data volume, velocity, and variety—often referred to as the "Big Data" revolution. By examining recent developments in automated data preparation, anomaly detection, and data modeling quality, we propose a comprehensive framework for "Intelligent BI." Furthermore, we conduct a cost-benefit analysis of integrating Large Language Models (LLMs) into BI pipelines, referencing current pricing structures from major providers. The study suggests that while technical hurdles regarding data quality and integration remain, the synergy of real-time BI and AI offers unprecedented opportunities for operational efficiency and improved patient outcomes in healthcare ecosystems.

Business Intelligence, Healthcare Analytics, Generative AI, Real-Time Data Warehousing

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