Predictive risk modelling for forecasting high-cost patients: a real-world application using Medicaid data
by Sai T. Moturu, William G. Johnson, Huan Liu
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 3, No. 1/2, 2010

Abstract: 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 study indicates that the proposed approach is highly effective and can benefit further research on cost containment.

Online publication date: Mon, 30-Nov-2009

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