Authors: Eshan Karkhanis
Abstract: The exponential growth of healthcare data driven by electronic health records (EHRs), medical imaging, genomic sequencing, Internet of Things (IoT) devices, and insurance claim databases has created unprecedented opportunities for healthcare systems to deliver more personalized, efficient, and cost-effective care. However, this explosion of structured and unstructured data also presents significant challenges in storage, integration, governance, and real-time access. Data warehousing in healthcare serves as the backbone of modern analytics, enabling organizations to consolidate disparate data into a unified platform for research, operational optimization, and clinical decision-making. The future of data warehousing in healthcare systems lies in embracing cloud-native architectures, real-time streaming integration, advanced artificial intelligence (AI)-driven analytics, and compliance with stringent privacy regulations. Moreover, the fusion of data warehouses with data lakes and hybrid models promises flexibility to manage both structured and semi-structured datasets, supporting predictive modeling and precision medicine. As healthcare providers shift toward value-based care models, data warehouses are evolving from retrospective analysis tools into proactive, intelligent engines that inform patient care in real time. This evolution requires new approaches to data governance, interoperability, and ethical use of patient data while ensuring cost-effectiveness and scalability for diverse healthcare organizations. Ultimately, the future of healthcare data warehousing is not merely about storing vast amounts of information but about transforming that data into actionable insights that directly enhance patient outcomes, operational resilience, and system-wide innovation.
DOI: https://doi.org/10.5281/zenodo.16981284
