PRODUCTION-SCALE MLOPS FRAMEWORK FOR INTELLIGENT HEALTHCARE CLAIMS PROCESSING AND REVENUE OPTIMIZATION

Abstract

Healthcare payer organizations process millions of medical claims every day, making claims adjudication, fraud detection, denial prediction, prior authorization, and revenue cycle optimization increasingly complex. Traditional claims-processing systems rely on rule-based workflows and legacy enterprise architectures that struggle to handle rapidly growing healthcare data, evolving regulatory requirements, and the need for real-time decision support. Recent advancements in Machine Learning Operations (MLOps) have enabled healthcare organizations to deploy scalable, automated, and continuously monitored machine learning solutions capable of improving operational efficiency, prediction accuracy, and financial performance. A production-scale MLOps framework integrates data engineering, feature management, automated model training, validation, deployment, monitoring, governance, and continuous feedback into a unified lifecycle, ensuring reliable deployment of predictive models in enterprise healthcare environments. This research presents a comprehensive production-scale MLOps framework designed specifically for intelligent healthcare claims processing and revenue optimization. The proposed framework incorporates automated Extract-Transform-Load (ETL) pipelines, secure healthcare data integration, feature stores, version-controlled datasets, CI/CD-based model deployment, Kubernetes-enabled scalable serving, explainable artificial intelligence techniques, and continuous model monitoring for concept drift detection. Additionally, predictive analytics are utilized for claim denial forecasting, fraud identification, reimbursement optimization, and resource allocation. The framework emphasizes regulatory compliance, security, explainability, scalability, and continuous operational improvement while minimizing manual intervention throughout the machine learning lifecycle. Experimental evaluation demonstrates improvements in processing efficiency, model deployment speed, prediction consistency, operational transparency, and revenue optimization compared with conventional machine learning deployment approaches. The proposed production-scale MLOps architecture provides healthcare organizations with a robust, scalable, and intelligent platform capable of supporting nextgeneration digital healthcare transformation while ensuring sustainable operational excellence and continuous business value generation. Keywords— MLOps, Healthcare Claims Processing, Revenue Cycle Management, Machine Learning Operations, Healthcare Analytics, Predictive Analytics, Explainable AI, Cloud Computing, CI/CD, Intelligent Healthcare Systems.