| 摘要 |
This invention uses non-parametric statistical measures and probability mathematical techniques to calculate deviations of variable values, on both the high and low side of a data distribution, from the midpoint of the data distribution. It transforms the data values and then combines all of the individual variable values into a single scalar value that is a good-ness score. This good-ness behavior score model characterizes normal or typical behavior, rather than predicting fraudulent, abusive, or bad, behavior. The good score is a measure of how likely it is that the subject's behavior characteristics are from a population representing a good or normal provider, claim, beneficiary or healthcare merchant behavior. The good score can replace or compliment a score model that predicts bad behavior in order to reduce false positive rates. The optimal risk management prevention program should include both a good behavior score model and a bad behavior score model.
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