Semantic Web for Healthcare Data Interoperability

Abstract

Healthcare systems generate vast amounts of heterogeneous data that often remain fragmented across different platforms, leading to challenges in interoperability and data exchange. This paper presents a Semantic Web-based framework for Healthcare Data Interoperability aimed at enabling seamless integration and sharing of medical information. The proposed system utilizes semantic technologies such as ontologies, Resource Description Framework, and Web Ontology Language to represent healthcare data in a structured and machine-readable format. By establishing standardized vocabularies and relationships among clinical entities, the framework facilitates efficient data integration across diverse healthcare systems. The approach also incorporates reasoning mechanisms to enhance data interpretation and support clinical decision-making. Additionally, integration with machine learning techniques improves data usability and predictive analytics capabilities. Experimental observations indicate that the proposed framework enhances interoperability, reduces data redundancy, and improves the accuracy of information retrieval. The study highlights the potential of semantic web technologies in transforming healthcare ecosystems by enabling efficient, scalable, and intelligent data exchange, particularly in complex and distributed healthcare environments.