International Journal on Science and Technology

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A Fresh Look: The Role of a Healthcare Data Fabric in AI-Driven Predictions

Author(s) Ms. Mahalakshmi Nathan, Prof. Dr. Shankar Srinivasan
Country United States
Abstract Integration of heterogeneous healthcare data has become vital in furthering patient outcomes, accelerating clinical research, and enabling predictive analytics. Current healthcare systems generate an unprecedented volume of information from electronic health records, medical images, laboratory findings, claims records, genomic profiles, and wearable sensor technologies. Despite such abundance, key problems remain. Most of the data resides in siloed systems with low interoperability, uneven quality, and fragmented governance. These disadvantages prevent clinicians and researchers from deriving full value from data-driven insights and induce inefficiencies of delivering care and blocking innovation. A data fabric for healthcare provides a single and scalable architecture solution for such problems. Instead of necessitating all data being physically centralized, a data fabric ties together distributed sources with active metadata, semantic models, and techniques of virtualization. This creates safe and real-time access to curated data while imposing governance rules and regulatory compliance. Lineage tracking, provenance, and policy enforcement are also supported from the architecture while making sure data use complies with HIPAA, GDPR, and others of privacy standards. The article elaborates upon the fundamental capabilities of a healthcare data fabric, namely metadata-driven catalogs, semantic integration layers, data virtualization services, orchestration pipelines, and governance frameworks. We also illustrate its value through typical use cases, i.e., clinical decision support, population health management, precision medicine, and operational analytics. We also discuss the data fabric's substantial aid towards improved AI-driven predictions, i.e., curated and context-enriched datasets resulting in improved accuracy, explainability, and trust. Finally, the article also covers fundamental challenges—varying from data silos and compliance to technical challenges—and recommends methods of successful implementation at healthcare facilities..
Keywords Healthcare data fabric, Data integration, Interoperability, Predictive analytics, Electronic health records, Data governance
Field Computer > Data / Information
Published In Volume 16, Issue 4, October-December 2025
Published On 2025-10-03
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