Architectural health data standards and semantic interoperability: a comprehensive review in the context of integrating medical data into big data analytics

Abstract

The integration of medical data into Big Data analytics holds significant potential for advancing healthcare practices and research. However, achieving semantics interoperability, wherein data is exchanged and interpreted accurately among diverse systems, is a critical challenge. This study explores the impact of existing architectures on semantics interoperability in the context of integrating medical data into Big Data analytics. The study highlights the complexities involved in integrating medical data from various sources, each using different formats, data models, and vocabularies. Without a strong emphasis on semantic interoperability, data integration efforts can result in misinterpretations, inconsistencies, and errors, adversely affecting patient care and research outcomes. The significance of data standards and ontologies in establishing a common vocabulary and structure for medical data integration is underscored. Additionally, the importance of data mapping and transformation is discussed, as data discrepancies can lead to data loss and incorrect analysis results. The success of integrating medical data into Big Data analytics is heavily reliant on existing architectures that prioritize semantics interoperability. A well-designed architecture addresses data heterogeneity, promotes semantic consistency, and supports data standardization, unlocking the transformative capabilities of medical data analysis for improved healthcare outcomes.