From Relational Models to Intelligent Platforms: Evolving Data Management Systems in Healthcare

Abstract

With the scale and complexity of healthcare data expanding more than ever, the need for efficient, secure, and intelligent data management systems is more important than ever. This paper provides a comprehensive overview of traditional and contemporary medical data management methods. The paper begins with traditional relational database management methods and provides an overview of these classical methods and discusses some advantages of relational databases such as structured data integrity and consistency. It then reviews emerging methods such as NoSQL databases, cloud computing methods, and AI-enabled systems that address issues associated with relational listing methods, including weaknesses handling unstructured data with structured schemas, managing scalability secondary to growth, and enabling advanced analytics based on querying and ingest. It also separately reviews hybrid approaches and forward-looking technologies, including blockchain, federated learning, edge computing, and semantic knowledge graphs that are largely shaping the next generation of healthcare data demography. It then contrasts the core features, trade-offs and challenges of implementing each system, and outlines current trends and for future guidance objectives for design of medical data systems. The aim is to provide useful references for practitioners in the design and selection of adaptable systems for the broad applicability of clinical needs.