Engineering Compliance Automation in Government Health Plans: A Framework for Policy-as-Code Implementation in Medicaid and CHIP Programs

Abstract

Government health plans face unprecedented compliance complexity as regulatory requirements continue to evolve across federal, state, and local jurisdictions. This article presents a comprehensive framework for engineering automated compliance systems that embed regulatory logic directly into healthcare platform operations, transforming traditional manual compliance processes into intelligent, self-monitoring systems. The article examines the implementation of compliance-as-code methodologies in Medicaid, Medicare, and Children's Health Insurance Program environments, demonstrating how automated compliance engines can process complex regulatory rulesets while ensuring continuous adherence to evolving policy requirements. Through analysis of real-world implementations across multiple state jurisdictions, findings reveal that automated compliance systems achieve 99.2% regulatory adherence rates, reduce compliance-related penalties by 85%, and decrease audit preparation time by 70%. The article framework encompasses automated rule validation, real-time compliance monitoring, exception handling protocols, and adaptive policy updates that maintain regulatory alignment without manual intervention. Advanced techniques include natural language processing for regulatory document analysis, machine learning for compliance pattern recognition, and intelligent decision trees for multi-jurisdictional rule reconciliation. Results demonstrate that organizations implementing comprehensive compliance automation reduce regulatory risk exposure by $2.3M annually while improving operational efficiency. These outcomes establish compliance automation as essential infrastructure for sustainable government health plan operations in an increasingly complex regulatory environment.