Data Lakehouse Architecture in Healthcare: Implementation and Applications

This paper presents a comprehensive analysis of Data Lakehouse Architecture implementation and applications in healthcare. It addresses the challenges of managing exponential health care data growth while ensuring regulatory compliance, data security, and operational efficiency. The study demonstrates significant improvements, including 42% reduction in data retrieval time, 35% improvement in patient outcomes, and 28% reduction in diagnosis times [34]. The paper outlines an implementation framework with advanced security measures to address cybersecurity threats [2], and explores the integration of AI, machine learning, and real-time processing within the data lakehouse [3], [4]. Examining the impact on key stakeholders, this research provides health care organizations with actionable insights for modernizing their data infrastructure while ensuring HIP AA compliance through automated PHI protection and privacy-preserving techniques [5]. As the healthcare predictive analytics market is projected to reach USD 184.58 billion by 2032 [7], this work positions the data lakehouse as a transformative solution for the future of data-driven healthcare.