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Abstract
The increasing complexity of healthcare data and the need for real-time decision support necessitate scalable and interoperable data architectures. This paper explores the fundamental principles, challenges, and best practices for designing healthcare data architectures that support seamless system integration, secure data exchange, and real-time analytics. It highlights the role of cloud computing, AI, and standardized interoperability frameworks such as HL7 FHIR in optimizing healthcare workflows. The study further examines existing literature on healthcare data integration and presents practical implementation strategies, along with performance evaluations of various architectural models. The proposed design framework is validated using case studies and empirical data. The findings underscore the importance of adopting modular, cloud-based, and AI-driven architectures to enhance healthcare delivery and patient outcomes.
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