AUTOMATED MODEL GOVERNANCE FOR HEALTHCARE REVENUE CYCLE PREDICTION SYSTEMS

Abstract

Healthcare revenue cycle management has become increasingly dependent on artificial intelligence and machine learning systems for predicting claims outcomes, reimbursement trends, payment delays, denial risks, and operational inefficiencies. However, maintaining accuracy, transparency, compliance, and reliability of deployed machine learning models remains a major challenge in healthcare environments. This paper proposes an automated model governance framework for healthcare revenue cycle prediction systems by integrating MLOps, machine learning lifecycle management, explainable artificial intelligence, cloud computing, data quality monitoring, automated validation, and compliance-aware analytics. The proposed framework enables continuous model monitoring, performance evaluation, bias detection, version management, audit tracking, and automated retraining workflows. Experimental evaluation demonstrates improvements in prediction reliability, governance efficiency, regulatory compliance, operational transparency, and healthcare revenue optimization. The proposed framework provides a scalable solution for managing intelligent healthcare prediction systems while ensuring trustworthy, secure, and continuously optimized AI-driven revenue cycle operations. Keywords— Automated Model Governance, Healthcare Revenue Cycle Management, MLOps, Predictive Analytics, Explainable AI, Machine Learning Lifecycle, Healthcare AI, Model Monitoring.