Governance-Driven Federated Cloud Intelligence For Secure Healthcare Analytics Under Distributed Data Constraints

Abstract

Background:Healthcare institutions face significant challenges in managing and analyzing vast amounts of data, with many systems remaining fragmented and siloed. Traditional centralized data architectures struggle with scalability, privacy concerns, and regulatory compliance, particularly under evolving data protection laws such as HIPAA and GDPR. There is a need for efficient, privacy-preserving solutions that enable secure data analytics across distributed systems.

Objective:To design and implement a federated cloud data architecture for secure, scalable, and self-service healthcare analytics.

Methods:The federated cloud architecture integrates multiple healthcare nodes, each maintaining control over its data. The system includes a federation layer for query execution, a central metadata registry for data discovery, and a governance layer to ensure compliance with privacy regulations. The architecture was tested using AWS, Tableau, and Power BI for data visualization, measuring query latency, data aggregation accuracy, and usability.

Results:The federated architecture significantly reduced query latency, achieving faster response times compared to centralized systems. Data aggregation accuracy was 98%, ensuring reliable results. Usability tests demonstrated that healthcare analysts and clinicians could easily perform self-service analytics, enhancing decision-making. The system also met HIPAA and GDPR compliance requirements, providing robust data security.

Conclusion:The federated cloud architecture successfully addresses healthcare analytics challenges by enabling secure, scalable, and compliant self-service analytics. It offers a promising alternative to traditional centralized systems for managing distributed healthcare data.