INTELLIGENT REVENUE CYCLE MANAGEMENT USING SCALABLE MLOPS AND PREDICTIVE ANALYTICS

Abstract

Revenue Cycle Management (RCM) has become a strategic component of modern healthcare organizations due to increasing claim complexity, regulatory compliance requirements, and the need for financial sustainability. Conventional RCM systems primarily rely on rule-based processing and manual intervention, resulting in delayed reimbursements, higher denial rates, operational inefficiencies, and increased administrative costs. Recent advances in Machine Learning Operations (MLOps) and predictive analytics provide scalable solutions for automating claim validation, fraud detection, denial prediction, and payment forecasting while ensuring continuous model governance. This paper proposes an intelligent Revenue Cycle Management framework that integrates scalable MLOps pipelines with predictive analytics to automate the complete healthcare revenue lifecycle. The proposed methodology incorporates data ingestion, feature engineering, automated model training, continuous deployment, real-time inference, monitoring, and governance within a cloud-native architecture. Predictive models proactively identify financial risks and optimize reimbursement workflows while maintaining regulatory compliance. Experimental analysis demonstrates improvements in prediction accuracy, claim processing efficiency, denial reduction, and operational scalability. The proposed framework enables healthcare organizations to achieve faster reimbursements, improved revenue realization, and intelligent financial decision-making through production-ready machine learning systems.