Predicting future high-cost patients: A real-world risk modeling application

Health care data from patients in the Arizona Health Care Cost Containment System, Arizona's Medicaid program, provides a unique opportunity to exploit state-of-the-art data processing and analysis algorithms to mine the data and provide actionable results that can aid cost containment. This work addresses specific challenges in this real-life health care application to build predictive risk models for forecasting future high-cost users. Such predictive risk modeling has received attention in recent years with statistical techniques being the backbone of proposed methods. We survey the literature and propose a novel data mining approach customized for this potent application. Our empirical study indicates that this approach is useful and can benefit further research on cost containment in the health care industry.