BREAKING HEALTHCARE SILOS: AN AI-DRIVEN FRAMEWORK FOR HEALTHCARE DATA INTEGRATION
DOI:
https://doi.org/10.34218/IJCET_16_01_142Keywords:
Data Harmonization, Healthcare Interoperability, Federated Learning, Clinical Data Integration, AI-driven Healthcare AnalyticsAbstract
Data integration remains one of the most formidable challenges in modern healthcare systems, particularly as organizations strive to leverage artificial intelligence for improved patient care. This article presents a comprehensive article on healthcare data silos and introduces an AI-driven framework for seamless integration across disparate systems. The technical complexities of harmonizing heterogeneous data sources while maintaining strict compliance with healthcare privacy regulations and security standards. This architecture employs advanced natural language processing and federated learning techniques to overcome traditional integration barriers, enabling real-time data synchronization and semantic interoperability. Through multiple case studies across various healthcare settings, this article demonstrates significant improvements in data quality, accessibility, and analytical capabilities. The results show enhanced clinical decision support, more accurate predictive analytics, and streamlined population health management. This article also addresses critical policy considerations and ethical implications, proposing a collaborative roadmap for industry-wide adoption. This article contributes to the evolving landscape of healthcare informatics by providing a scalable, secure, and practical approach to breaking down data silos while ensuring equitable access to AI-enhanced healthcare systems.
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