CONTINUOUS MODEL MONITORING FOR RELIABLE HEALTHCARE REVENUE CYCLE PREDICTION

Abstract

The increasing complexity of healthcare revenue cycle management (RCM) has created a strong demand for intelligent predictive systems capable of improving claim processing, reimbursement forecasting, payment optimization, and financial decisionmaking. Machine Learning models have been widely adopted in healthcare revenue cycle prediction to analyze historical claims data, identify payment patterns, predict denials, and optimize operational workflows. However, deployed ML models often experience performance degradation due to changing healthcare regulations, evolving payer policies, shifting patient behaviors, and variations in claim data distributions. This paper proposes a Continuous Model Monitoring Framework for Reliable Healthcare Revenue Cycle Prediction that integrates MLOps practices, real-time model monitoring, data drift detection, performance evaluation, automated retraining, and predictive analytics mechanisms. The proposed framework continuously evaluates Machine Learning model behavior by monitoring prediction accuracy, data quality, model drift, operational performance, and financial outcomes. Automated monitoring pipelines enable early identification of model degradation and initiate optimization processes to maintain prediction reliability. Experimental analysis demonstrates that the proposed framework improves revenue cycle prediction accuracy, model stability, operational efficiency, and adaptability compared with traditional static ML deployment approaches. The proposed solution provides a scalable, intelligent, and reliable foundation for next-generation healthcare revenue cycle analytics. Keywords: Healthcare Revenue Cycle Management, Continuous Model Monitoring, MLOps, Machine Learning, Predictive Analytics, Model Drift Detection, Healthcare Claims, Automated Retraining, Artificial Intelligence, Revenue Optimization.