This article examines the implementation and evaluation of an AI-driven healthcare analytics platform designed to address critical challenges in patient data integration, real-time monitoring, and predictive analytics within a large healthcare provider organization. The article presents how cloud-based technology solutions, specifically utilizing Databricks and Azure infrastructure combined with Apache Spark and Delta Lake, can transform healthcare delivery through unified data management and intelligent clinical decision support. The platform successfully integrated disparate healthcare data sources, including electronic health records, Internet of Things-generated vital signs, and laboratory results, into a centralized analytics engine capable of real-time patient monitoring and risk assessment. Key findings demonstrate significant improvements in patient risk evaluation efficiency, enhanced regulatory compliance through automated data governance, and successful early detection of critical conditions such as sepsis and cardiac events through advanced machine learning algorithms. The article incorporated generative AI capabilities to provide personalized treatment recommendations and natural language interfaces for clinical staff, while maintaining strict privacy protections and regulatory compliance requirements. The article reveals that proactive patient care models enabled by continuous monitoring and predictive analytics can substantially reduce emergency interventions and optimize healthcare resource allocation. Despite notable achievements in clinical decision-making enhancement and patient outcome improvements, the research identifies ongoing challenges related to data quality management, legacy system integration complexity, and the need for continuous model validation across diverse patient populations. The article contributes valuable insights into the practical implementation of large-scale healthcare analytics platforms and demonstrates the transformative potential of AI-powered systems in advancing evidence-based, personalized healthcare delivery while highlighting important considerations for future deployments in similar healthcare environments.
Lingareddy Alva (Mon,) studied this question.