REVOLUTIONIZING DHSS ELIGIBILITY RULE TESTING: AN AI AND DATA MINING APPROACH

Abstract

This article explores the transformative potential of Artificial Intelligence (AI) and data mining techniques in automating and enhancing eligibility rule testing for Department of Health and Social Services (DHSS) programs. It examines the challenges of manual eligibility testing, including the complexity of rules, high volume of applications, and potential for human error. The study proposes an AI-driven framework that leverages machine learning and data mining to improve accuracy, efficiency, and scalability in eligibility determinations. Key components of this framework including rule extraction, data preparation, model training, and validation are discussed. The article also presents a case study of the Centers for Medicare & Medicaid Services' Fraud Prevention System, demonstrating the real-world impact of advanced analytics in government services. Benefits such as improved accuracy, increased efficiency, and enhanced insights are weighed against challenges including data quality concerns, model transparency issues, and the need for legacy system integration. Finally, the article outlines future research directions, emphasizing the need for more sophisticated AI algorithms, improved interpretability, and strategies for ensuring fairness and mitigating bias in these systems.