Scalable Healthcare Data Warehousing for Advanced Data Science and Predictive Analytics

Abstract

The networking and storage requirements of healthcare have to scale more efficiently while maintaining detail, security, and accessibility as the recorded data of digital health, medical imaging, genomics, and real-time patient monitoring have grown exponentially over the years. This paper presents a scalable healthcare data warehousing system that can provide advanced data science and predictive analytics support to complex clinical environments. The proposed method merges the data sources EHR, laboratory systems, and external health datasets in a single cloud-based warehouse architecture that is optimized for processing the high-volume and high-velocity data. Data reliability, system efficiency, and scalability are guaranteed through current extract, transform, and load (ETL) pipelines, schema design strategies, and distributed storage technologies. Supervised learning and deep learning techniques are applied to the different healthcare challenges such as disease risk estimation, patient readmission prediction, and resource utilization optimization. The real-world case study provides evidence of how the proposed architecture could enhance query performance, enable large-scale analytics, and make timely insights possible compared to traditional, monolithic data systems. The results point to the fact that a well-designed scalable data warehouse is a great instrument for elevating data science workflows as it significantly shortens the time of data preparation and makes it possible to achieve more accurate predictive models. The findings accentuate the necessity of scalable data warehousing as the core of data-driven decision-making in healthcare being a source of advantages to clinicians, administrators, and researchers. Finally, this work states that the implementation of scalable healthcare data warehousing solutions is the way through which clinical raw data can be transformed into actionable insights, which will not only result in better patient outcomes but will also enable healthcare organizations to meet the future demands of predictive and personalized ​‍​‌‍​‍‌medicine.