ORGANIZED HEALTHCARE FRAUD DETECTION

• US 20140172439A1
• Filed: 12/19/2012
• Published: 06/19/2014
• Est. Priority Date: 12/19/2012
• Status: Abandoned Application

• Current Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Original Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Inventors: CONWAY, Adrian E., DREW, James H.
• Application Number:
  US13/720,324
• Publication Number:
  US 20140172439A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

A fraud detection system receives information that identifies healthcare claims associated with providers and beneficiaries, and determines, based on the healthcare claims, first fraud sets associated with postulated classes of fraud. The fraud detection system determines, based on the healthcare claims, second fraud sets using one or more data mining techniques, and calculates probabilities that the first fraud sets and the second fraud sets are similar to no fraud observations. The fraud detection system ranks the first fraud sets and the second fraud sets based on the calculated probabilities, and outputs a ranked list of suspected fraud cases, associated with the healthcare claims, based on the ranking of the first fraud sets and the second fraud sets.

Claims

1. A method, comprising:

receiving, by one or more devices of a fraud detection system, information that identifies healthcare claims associated with providers and beneficiaries;

determining, by the one or more devices and based on the healthcare claims, first fraud sets associated with postulated classes of fraud;

determining, by the one or more devices and based on the healthcare claims, second fraud sets using a data mining technique;

calculating, by the one or more devices, probabilities that the first fraud sets and the second fraud sets are similar to no fraud observations;

ranking, by the one or more devices, the first fraud sets and the second fraud sets based on the calculated probabilities; and

outputting, by the one or more devices, a ranked list of suspected fraud cases, associated with the healthcare claims, based on the ranking of the first fraud sets and the second fraud sets.

2. The method of claim 1, further comprising:

setting thresholds for the calculated probabilities based on investigational ability and a specified low probability of fraud; and

identifying rings of fraudulent providers, associated with the healthcare claims, based on the thresholds.

3. The method of claim 2, further comprising:

outputting information identifying the rings of fraudulent providers.

4. The method of claim 2, where identifying the rings of the fraudulent providers includes:

using a model to create a matrix of visits between the providers associated with the healthcare claims;

determining association rules that link the providers in the matrix;

ranking the association rules based on a fraud interest measure;

ranking the providers in the matrix based on a number of remote visits; and

identifying the rings of the fraudulent providers based on a relationship between the ranked association rules and the ranked providers.

5. The method of claim 1, further comprising:

pre-processing the healthcare claims by grouping specialties, associated with the providers, into a plurality of categories.

6. The method of claim 1, where the suspected fraud cases are associated with beneficiaries that visit providers located remotely from each other.

7. The method of claim 1, further comprising:

outputting an unranked list of the suspected fraud cases associated with the healthcare claims.

8. A fraud detection system, comprising:

one or more processors to;

receive information that identifies healthcare claims associated with providers and beneficiaries,

determine, based on the healthcare claims, first fraud sets associated with postulated classes of fraud,

determine, based on the healthcare claims, second fraud sets using one or more data mining techniques,

calculate probabilities that the first fraud sets and the second fraud sets are similar to no fraud observations,

rank the first fraud sets and the second fraud sets based on the calculated probabilities, and

output or store a ranked list of suspected fraud cases, associated with the healthcare claims, based on the ranking of the first fraud sets and the second fraud sets.

9. The system of claim 8, where the one or more processors are further to:

set thresholds for the calculated probabilities based on investigational ability and a specified low probability of fraud, and

identify rings of fraudulent providers, associated with the healthcare claims, based on the thresholds.

10. The system of claim 9, where the one or more processors are further to:

output or store information identifying the rings of fraudulent providers.

11. The system of claim 9, where, when identifying the rings of the fraudulent providers, the one or more processors are further to:

use a model to create a matrix of visits between the providers associated with the healthcare claims,

determine association rules that link the providers in the matrix,

rank the association rules based on a fraud interest measure,

rank the providers in the matrix based on a number of remote visits, and

identify the rings of the fraudulent providers based on a relationship between the ranked association rules and the ranked providers.

12. The system of claim 8, where the one or more processors are further to:

pre-process the healthcare claims by grouping specialties, associated with the providers, into a plurality of categories.

13. The system of claim 8, where the suspected fraud cases are associated with beneficiaries that visit providers located remotely from each other.

14. The system of claim 8, where the one or more processors are further to:

output or store an unranked list of the suspected fraud cases associated with the healthcare claims.

15. One or more computer-readable media, comprising:

one or more instructions that, when executed by one or more processors of a fraud detection system, cause the one or more processors to;

receive information that identifies healthcare claims associated with providers and beneficiaries,

determine, based on the healthcare claims, first fraud sets associated with postulated classes of fraud,

determine, based on the healthcare claims, second fraud sets using one or more data mining techniques,

calculate probabilities that the first fraud sets and the second fraud sets are similar to no fraud observations,

rank the first fraud sets and the second fraud sets based on the calculated probabilities, and

output a ranked list of suspected fraud cases, associated with the healthcare claims, based on the ranking of the first fraud sets and the second fraud sets.

16. The media of claim 15, further comprising:

one or more instructions that, when executed by the one or more processors, cause the one or more processors to;

set thresholds for the calculated probabilities based on investigational ability and a specified low probability of fraud, and

identify rings of fraudulent providers, associated with the healthcare claims, based on the thresholds.

17. The media of claim 16, further comprising:

one or more instructions that, when executed by the one or more processors, cause the one or more processors to;

output information identifying the rings of fraudulent providers.

18. The media of claim 16, where the one or more instructions that cause the one or more processors to identify the rings of the fraudulent providers further include:

one or more instructions that, when executed by the one or more processors, cause the one or more processors to;

use a model to create a matrix of visits between the providers associated with the healthcare claims,

determine association rules that link the providers in the matrix,

rank the association rules based on a fraud interest measure,

rank the providers in the matrix based on a number of remote visits, and

identify the rings of the fraudulent providers based on a relationship between the ranked association rules and the ranked providers.

19. The media of claim 15, further comprising:

one or more instructions that, when executed by the one or more processors, cause the one or more processors to;

pre-process the healthcare claims by grouping specialties, associated with the providers, into a plurality of categories.

20. The media of claim 15, where the suspected fraud cases are associated with beneficiaries that visit providers located remotely from each other.