AUTOMATED MODEL GOVERNANCE FOR ENTERPRISE HEALTHCARE MACHINE LEARNING PIPELINES

Abstract

The increasing adoption of Machine Learning (ML) solutions in healthcare organizations has created significant challenges related to model governance, regulatory compliance, transparency, performance monitoring, and lifecycle management. Healthcare Machine Learning pipelines process sensitive medical and operational data to support applications such as diagnosis prediction, claims analytics, fraud detection, patient risk assessment, and resource optimization. However, unmanaged ML models may suffer from performance degradation, bias, lack of explainability, security vulnerabilities, and compliance issues during production deployment. This paper proposes an Automated Model Governance Framework for Enterprise Healthcare Machine Learning Pipelines that integrates MLOps practices, automated monitoring, model validation, explainable Artificial Intelligence, compliance management, and lifecycle automation mechanisms. The proposed framework enables continuous tracking of model performance, data quality, fairness metrics, security requirements, version control, and regulatory compliance throughout the ML lifecycle. Automated governance workflows ensure reliable model deployment, transparent decision-making, and controlled model evolution in healthcare environments. Experimental analysis demonstrates that the proposed framework improves model reliability, governance efficiency, compliance management, operational scalability, and trustworthiness compared with conventional manual governance approaches. The proposed solution provides an intelligent, secure, and scalable foundation for enterprise healthcare Machine Learning systems. Keywords: Automated Model Governance, Healthcare Machine Learning, MLOps, Model Lifecycle Management, Explainable AI, Compliance Monitoring, Artificial Intelligence, Healthcare Analytics, Model Validation, Cloud Computing.