US 12,437,343 B2
Method of personalizing, individualizing, and automating the management of healthcare fraud-waste-abuse to unique individual healthcare providers
Akli Adjaoute, Mill Valley, CA (US)
Assigned to Brighterion, Inc., Purchase, NY (US)
Filed by Brighterion, Inc., Purchase, NY (US)
Filed on Feb. 9, 2024, as Appl. No. 18/437,315.
Application 18/437,315 is a continuation of application No. 17/364,450, filed on Jun. 30, 2021, granted, now 11,900,473.
Application 17/364,450 is a continuation of application No. 16/677,458, filed on Nov. 7, 2019, granted, now 11,080,793, issued on Aug. 3, 2021.
Application 16/677,458 is a continuation of application No. 14/986,534, filed on Dec. 31, 2015, granted, now 10,546,099, issued on Jan. 28, 2020.
Application 14/986,534 is a continuation in part of application No. 14/815,848, filed on Jul. 31, 2015, abandoned.
Application 14/815,848 is a continuation in part of application No. 14/514,381, filed on Oct. 15, 2014, abandoned.
Application 14/986,534 is a continuation in part of application No. 14/521,667, filed on Oct. 23, 2014, abandoned.
Application 14/986,534 is a continuation in part of application No. 14/815,934, filed on Jul. 31, 2015, abandoned.
Application 14/986,534 is a continuation in part of application No. 14/517,771, filed on Oct. 17, 2014, abandoned.
Application 14/986,534 is a continuation in part of application No. 14/517,872, filed on Oct. 19, 2014, abandoned.
Application 14/986,534 is a continuation in part of application No. 14/935,742, filed on Nov. 9, 2015, abandoned.
Prior Publication US 2024/0185354 A1, Jun. 6, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 40/08 (2012.01); G06Q 10/10 (2023.01); G16H 40/20 (2018.01); G06N 20/00 (2019.01)
CPC G06Q 40/08 (2013.01) [G06Q 10/10 (2013.01); G16H 40/20 (2018.01); G06N 20/00 (2019.01); Y02A 90/10 (2018.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for healthcare fraud detection, comprising, via one or more processors:
receiving a healthcare payment request corresponding to a healthcare provider;
matching the healthcare payment request to a smart agent having a profile comprising a representation of historical data of the healthcare provider;
analyzing the healthcare payment request for fraud with a predictive model constructed according to one or more of: data mining logic, a neural network, case-based-reasoning, clustering, fuzzy logic, a genetic algorithm, a decision tree, and business rules;
comparing the contents of at least one data field of the healthcare payment request against the profile of the corresponding smart agent and, based at least in part on the comparison and on the analysis under the predictive model, generating a first output indicative of a determination of likely fraud;
generating a second smart agent having a second profile by supervised clustering of a group of healthcare providers based on at least one claim attribute;
matching the healthcare payment request to the second smart agent having the second profile;
comparing the contents of at least one data field of the healthcare payment request against the second profile of the corresponding second smart agent and, based at least in part on the comparison, generating a second output indicative of a determination of unlikely fraud;
analyzing the first output and the second output and generating a decision on the healthcare payment request based at least in part on the analysis, the analysis including determining that the first output meets a first criteria indicating fraud or abuse, determining that the second output meets a second criteria indicating acceptable behavior within the corresponding group, and determining that the second output overrides the first output,
wherein the decision comprises an approval decision for the healthcare payment request.