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Characterizing Healthcare Provider, Claim, Beneficiary and Healthcare Mercant Normal Behavior Using Non-Parametric Statistical Outlier Detection Scoring Techniques
Characterizing Healthcare Provider, Claim, Beneficiary and Healthcare Mercant Normal Behavior Using Non-Parametric Statistical Outlier Detection Scoring Techniques
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机译:使用非参数统计离群值检测评分技术表征医疗保健提供者,索赔,受益人和医疗保健商人的正常行为
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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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