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Abstract

An automated method and system for predicting the likelihood that a patient will acquire high medical service utilization characteristics, thereby becoming a high-cost patient to a managed care organization or the like, relative to other patients includes selecting a predictive subset of variables from a larger set of variables corresponding to patient claims data based on the results of multivariate statistical modeling, such as logistical regression analysis. Predetermined weighing coefficients derived from the statistical modeling are applied to each of the claims variables of the predictive subset and a probability equation is developed based upon the weighing coefficients and claims variables of the predictive set. The probability equation is applied to patient claims data to determine a probability value indicative of the likelihood that the given patient will have a high utilization of health care resources in a given period of time, and thereby become a higher-cost patient relative to other patients. Once identified, high-use patients can be targeted for preventative medical interventions.

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