Optimizing NoSQL Data Models for Large-Scale Health Insurance Claims Processing

Abstract

Conventional relational database systems, which struggle to satisfy their performance and scalability criteria, are being taxed by the increasing amount and the complexity of health insurance claims resulting from increased patient populations, changing regulatory frameworks, and more thorough clinical information. Rising as a practical choice in the evolving data environment, NoSQL databases provide the required scalability and the adaptability to manage unstructured and semi-structured healthcare information. The optimization of NoSQL data models is investigated in this work in order to meet the specific needs of processing massive health insurance claims: We analyze basic challenges such as schema evolution, high-throughput ingestion, and actual time analytics and provide tailored solutions for creating effective data models employing document-oriented, columnar, and key-value NoSQL paradigms. We show how processing latency and system cost might be significantly reduced by precisely matching data structures with access patterns and using best practices in denormalization, sharding, and indexing. Emulating workloads typical of claims adjudication and fraud detection, a viable solution is shown using a synthetic health claims dataset organized in MongoDB and Cassandra. Comparatively to conventional relational setups, the results show significant increases in query performance, storage efficiency, and the system scalability. Our findings highlight the architectural and the operational advantages of using NoSQL in this industry and provide a structure for data architects and health tech developers trying to replace antiquated claims systems. This article emphasizes the need of intelligent NoSQL architecture in the future of health data processing as it offers useful insights on structuring healthcare information for performance while keeping integrity and compliance