| 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. |
| 主权项 |
1. A computer implemented method comprising:
receiving, by a computer, a healthcare observation in healthcare industry relating to: a healthcare claim, a group of healthcare claims, a healthcare provider, a healthcare beneficiary and a healthcare merchant; receiving a plurality of raw characterization variables related to the healthcare observation, the plurality of raw characterization variables consisting of: health of the healthcare beneficiary, co-morbidity of the healthcare beneficiary, an amount of healthcare effort expended by the healthcare provider, distance from the healthcare provider to the healthcare beneficiary, fee amount submitted for the healthcare claim, sum of all dollars submitted for reimbursement in the healthcare claim, number of procedures in the healthcare claim, number of modifiers in the healthcare claim, change over time for amount submitted in the healthcare claim, a number of the healthcare claims submitted over time, total dollar amount of the healthcare claims submitted over time, comparisons of daily trends for amount billed for the healthcare claim, a time between a date of service and a date of the healthcare claim, a ratio of the healthcare effort required to treat a diagnosis compared to the amount billed on the healthcare claim; using, by the computer, non-parametric statistical measures to calculate corresponding G-values representing deviations for each of the plurality of raw characterization variables related to the healthcare observation, wherein each of the G-values are calculated by subtracting from each of the raw characterization variables an overall midpoint value of the raw characterization variables further divided by a difference between two percentiles corresponding to each of said raw characterization variables; transforming, by the computer, each of the G-Values into corresponding T-Values by calculating T=2/(1+e), wherein e represents Euler's number, λ represents a scaling coefficient and g represents each of the corresponding G-Values and; combining, by the computer, all of the corresponding T-values together into a single scalar value to calculate the inlier identification score to identify fraud, abuse or waste in the healthcare observation, and sending the inlier identification score to an investigations analysis display so an investigations analyst can further review. |
