Big Data Integration in Hospital Networks

Abstract

Hospital networks across Europe generate enormous quantities of clinical data across diverse source systems - electronic health records, laboratory and imaging systems, physiological monitoring, pharmacy, operational and financial systems -- that remain largely fragmented at the infrastructure level despite widespread recognition of big data integration value. Effective big data integration enables population health management, operational efficiency improvement, clinical research infrastructure, and AI model development at scales that individual hospitals cannot support. Integration challenges include technical heterogeneity across legacy systems, data governance complexity under GDPR, terminology harmonisation across institutions, and organisational coordination requirements. We evaluated five big data integration frameworks applied to 42 European hospital networks across 24 healthcare systems in Berlin, Amsterdam, Stockholm, Milan, and London between 2018 and 2024. Frameworks ranged from point-to-point legacy integration through integrated enterprise data platforms with federated analytics. Performance was benchmarked using our Hospital Big Data Integration Effectiveness Score (HBDIES), integrating data completeness, query responsiveness, analytical enablement, governance compliance, and implementation sustainability. The integrated enterprise platform framework achieved the highest HBDIES (0.914), reducing query response time from median 42 days to 2 days for cross-system analytical requests, enabling 4.8-fold increase in clinical research protocols executed per year, and achieving full GDPR compliance across integrated analytics. Clinical research, operational analytics, and AI model development infrastructure benefited most from integrated platform approaches