CLOUD-NATIVE MLOPS FRAMEWORK FOR PRODUCTION-SCALE HEALTHCARE CLAIMS ANALYTICS

Abstract

The rapid digitization of healthcare systems has generated massive volumes of claims data that require intelligent processing, predictive analysis, and scalable computational infrastructure. Healthcare claims analytics plays a crucial role in improving fraud detection, cost optimization, reimbursement accuracy, patient service management, and operational decision-making. However, traditional Machine Learning approaches often face challenges related to scalability, model deployment, monitoring, data governance, and continuous performance improvement when applied to production-scale healthcare environments. This paper proposes a CloudNative MLOps Framework for ProductionScale Healthcare Claims Analytics that integrates cloud computing, Machine Learning Operations (MLOps), automated model lifecycle management, distributed data processing, and intelligent analytics pipelines. The proposed framework enables automated data ingestion, feature engineering, model training, deployment, monitoring, and continuous optimization of healthcare claims prediction models. Cloud-native technologies provide scalability, resilience, and efficient resource utilization, while MLOps practices ensure reliable model governance and operational stability. Experimental analysis demonstrates that the proposed framework improves claims processing efficiency, prediction accuracy, scalability, deployment reliability, and real-time analytical capabilities compared with traditional healthcare analytics approaches. The proposed solution provides an intelligent, secure, and scalable foundation for next-generation healthcare claims management systems. Keywords: Cloud-Native Computing, MLOps, Healthcare Claims Analytics, Machine Learning, Predictive Analytics, Model Deployment, Healthcare Data Management, Cloud Infrastructure, Artificial Intelligence, Automated Workflows.