AI-Powered Medical Coding: A Multi-Agent GenAI System for Clean Claims

Abstract

This article presents an innovative multi-agent GenAI system architected for medical coding automation that addresses fundamental challenges in healthcare revenue cycle operations. The solution features purpose-built agents handling distinct functions: clinical documentation parsing, appropriate code assignment, validation against documentation, and comprehensive summary generation—all coordinated through a sophisticated prompt-chaining framework maintaining contextual understanding while ensuring process transparency. Deployment across varied clinical environments demonstrates remarkable adaptability, functioning effectively in settings ranging from time-sensitive acute care to specialized outpatient practices, with flexible implementation models supporting either real-time documentation assistance or retrospective encounter processing. Comprehensive evaluation reveals marked enhancements in automation capabilities, error mitigation, and financial performance indicators when compared with conventional manual coding approaches. The platform incorporates extensive auditability features, providing thorough documentation references and accessible natural language explanations for coding decisions, satisfying compliance obligations while supporting ongoing quality improvement initiatives. The solution's impact transcends operational efficiencies to encompass superior documentation completeness, decreased claim rejection frequency, and heightened professional satisfaction through thoughtfully designed human-AI collaborative workflows.