The healthcare industry is undergoing a significant transformation, shifting from a fee-for-service model to value-based care (VBC). One of the core components of this transition is Hierarchical Condition Category (HCC) coding, which plays a vital role in risk adjustment, reimbursement optimization, and regulatory compliance. Accurate HCC coding ensures that providers receive appropriate reimbursements based on patient risk profiles. However, manual coding processes are time-consuming, prone to errors, and create compliance risks, leading to financial losses for healthcare providers.
This whitepaper explores how Artificial Intelligence (AI)-driven analytics and AI-powered agents enhance HCC coding accuracy, efficiency, and compliance. AI solutions such as Natural Language Processing (NLP), Machine Learning (ML), and automated coding systems have transformed risk adjustment, reducing human errors while optimizing reimbursement. The paper also highlights a real-world case study demonstrating the effectiveness of AI-driven risk adjustment solutions and introduces the role of AI agents in further advancing HCC coding and risk adjustment compliance.
