Breaking Data Silos in Healthcare: A Novel Framework for Standardizing and Integrating NHS Medical Data for Advanced Analytics

Abstract

The rapid expansion of medical data within the National Health Service (NHS) presents both opportunities and challenges in leveraging healthcare analytics for improved pa- tient outcomes and research. However, disparate data sources, inconsistent formats, and the lack of standardized integration mechanisms hinder effective data utilization. This study proposes a novel framework for standardizing and integrating NHS medical data by addressing structural heterogeneity, semantic in- consistencies, and interoperability gaps. The framework leverages machine learning techniques for data harmonization and Natural Language Processing (NLP) to extract insights from unstructured clinical notes. Additionally, I introduce a hybrid model that combines ontology-based mapping with federated learning to enhance data interoperability across healthcare institutions while ensuring data security and compliance with privacy regulations. The proposed approach is validated using real-world NHS datasets to assess its effectiveness in improving data accessibility and analytical performance. This research aims to bridge the gap between fragmented healthcare data and actionable insights, paving the way for more efficient, data-driven decision-making in clinical and research settings.