Designing a Predictive Analytics Data Warehouse for Modern Hospital Management
DOI:
https://doi.org/10.32628/CSEIT251112337Keywords:
Data Warehouse Architecture, Healthcare Analytics, Machine Learning Optimization, Predictive Analytics, Real-time ProcessingAbstract
This technical article explores the design and implementation of predictive analytics data warehouses in modern hospital management systems. The article examines the transformation of healthcare operations through sophisticated data management architectures, focusing on the integration of real-time analytics, machine learning, and advanced storage solutions. The article investigates four key areas: architectural framework design, storage layer optimization, analytics layer capabilities, and performance optimization strategies. The article analyzes how healthcare organizations are leveraging multi-layered architectural approaches to improve patient care outcomes, optimize resource allocation, and enhance operational efficiency. It also addresses critical challenges in managing structured and unstructured medical data, implementing security frameworks, and ensuring scalability while maintaining performance standards for critical healthcare operations.
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