Healthcare risk modeling for medicaid patients the impact of sampling on the prediction of high-cost patients

Healthcare data from 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 data and provide actionable findings that can aid cost containment. Our work addresses specific challenges in this real-life healthcare application to build predictive risk models for forecasting future high-cost patients. We survey the literature and propose novel data mining approaches customized for this compelling application with specific focus on non-random sampling. Our empirical study indicates that the proposed approach is highly effective and can benefit further research on cost containment in the healthcare industry.