Predictive Risk Modelling for Forecasting High-Cost Patients: A Real-World Application Using Medicaid Data
Source: Inderscience Enterprises
Approximately two - thirds of healthcare costs are accounted for by 10% of the patients. Identifying such high-cost patients early can help improve their health and reduce costs. Data from the Arizona Health Care Cost Containment System provides a unique opportunity to exploit state-of-the-art data analysis algorithms to mine data and provide actionable findings that can aid cost containment. A novel data mining approach is proposed for this challenging healthcare problem of predicting patients who are likely to be high-risk in the future. This paper indicates that the proposed approach is highly effective and can benefit further research on cost containment.