AUTOMATED CLAIM RISK FACTOR IDENTIFICATION AND MITIGATION SYSTEM

• US 20160055589A1
• Filed: 08/20/2014
• Published: 02/25/2016
• Est. Priority Date: 08/20/2014
• Status: Abandoned Application

• Current Assignee: Midwest Employers Casualty Company (W.R. Berkley Corp.)
• Original Assignee: Midwest Employers Casualty Company (W.R. Berkley Corp.)
• Inventors: Billings, Brian Andre
• Application Number:
  US14/464,288
• Publication Number:
  US 20160055589A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

A system to predict and identify claims that have a high likelihood of exceeding a predetermined limitation in a given excess workers'"'"' compensation insurance policy and to present the automated indication of possible intervention strategies to mitigate potential claims costs. The processing system includes a computer server, database engine, computer programming instructions, network connectivity, associated claims, payment, medical, pharmacy and other relevant data, a plurality of statistical and machine learning algorithms and a method for electronically displaying and attaching the results to a business process. The system will use all available data to analyze the medical treatment pattern of a claimant and based on automated findings make recommendations as to appropriate interventions to positively impact claims costs.

Claims

1. A computer-executable method of identifying elevated risk claims and providing suggestions for mitigating ongoing claim risk, said method comprising the steps of:

retrieving input data stored in a database, said input data determined to be relevant to a claim;

rendering variables for use by a predictive model, said variables comprising the input data and each having an importance score associated therewith based at least in part on a predictive model to which the variables are to be applied;

accessing the predictive model, said predictive model stored on a memory storage device associated with a processor;

executing, by the processor, the predictive model as a function of the accessed variables to yield a risk score for the claim; and

generating a report identifying the claim as an elevated risk claim when the risk score exceeds a predetermined threshold and providing suggestions for mitigating ongoing claim risk based on the variables.

2. The computer-executable method as recited in claim 1, wherein the input data stored in the database relates to one or more of the following types of data or tables:

ICD9 code data, workers compensation claim data, claim payment data, medical/prescription billing data, U.S. census data, Social Security disability data, state regulatory issues data, medical coding data, pharmacy database data, chronic condition data, comorbidity data, ICD9 cross reference tables, NCCI cross reference tables, target variable manipulation tables, claim exclusion tables, and an evidence-based medical treatment crosswalk for comparison to medical bill data.

3. The computer-executable method as recited in claim 1, wherein the predictive model comprises one or more of the following types of predictive models:

a model that identifies claims likely to exceed a self-insured retention or deductible, and a model that identifies claims likely to exceed a predetermined total cost.

4. The computer-executable method as recited in claim 1, further comprising transforming the input data into model scoring records, said model scoring records representing all relevant variables for the predictive model.

5. The computer-executable method as recited in claim 1, wherein the elevated risk claims comprise catastrophic claims and migratory claims.

6. The computer-executable method as recited in claim 1, wherein the variables are categorized into 1 of 8 risk factor categories.

7. The computer-executable method as recited in claim 6, wherein providing suggestions for mitigating ongoing claim risk based on the variables comprises retrieving specific suggested interventions stored in an intervention database according to risk factor category.

8. A computer-implemented method of mitigating elevated risk workers'"'"' compensation claims, the method comprising:

retrieving, by a processor, input data stored in a database, said input data determined to be relevant to mitigating a claim;

rendering, by the processor, variables for use by a predictive model, said variables comprising the input data and each having an importance score based at least in part on a predictive model to which the variables are to be applied;

accessing the predictive model, said predictive model stored on a memory storage device associated with the processor;

executing, by the processor, the predictive model as a function of the accessed variables to yield a risk score for the claim;

retrieving, by the processor, one or more specific suggested interventions stored in an intervention database according to a risk factor category associated with the claim;

generating a report on a computer user interface, said report identifying the claim as a predicted elevated risk claim when the risk score exceeds a predetermined threshold, wherein the report includes the retrieved suggestions for mitigating ongoing claim risk for the elevated risk claim.

9. The computer-implemented method as recited in claim 8, further comprising implementing said one or more suggestions to mitigate ongoing claim risk.

10. The computer-implemented method as recited in claim 9, wherein the data stored in the database relates to one or more of the following types of data or tables:

ICD9 code data, workers compensation claim data, claim payment data, medical/prescription billing data, U.S. census data, Social Security disability data, state regulatory issues data, medical coding data, pharmacy database data, chronic condition data, comorbidity data, ICD9 cross reference tables, NCCI cross reference tables, target variable manipulation tables, claim exclusion tables, and an evidence-based medical treatment crosswalk for comparison to medical bill data.

11. The computer-implemented method as recited in claim 10, wherein the predictive model comprises one or more of the following types of predictive models:

a model that identifies claims likely to exceed a self-insured retention or deductible, and a model that identifies claims likely to exceed a predetermined total cost.

12. The computer-implemented method as recited in claim 11, wherein the elevated risk workers'"'"' compensation claims comprise catastrophic claims and migratory claims.

13. A computer system for predicting and mitigating elevated risk workers'"'"' compensation claims comprising:

a computer-implemented user interface, executed by at least one computer system having at least one computer processor, said processor electronically retrieving data stored in a database and displaying the data on the interface, said data determined to be relevant to an elevated risk workers'"'"' compensation claim mitigation;

a variable rendering computer processing module, executed by the at least one computer processor, rendering at least one variable for use by the system, wherein said at least one variable comprises both the data relevant to the identification and mitigation model, and an importance score in part based upon predetermined risk predictions to which the variables are to be applied;

a predictive model computer processing module, executed by at least one computer processor, accessing at least one predictive model stored on a memory storage device and executing the predetermined risk predictions as a function of the at least one variable rendered by the variable rendering computer processing module to yield a risk score for a claim; and

a report generation computer processing module, executed by the at least one computer processor, generating a report identifying a predicted elevated risk claim based on the risk score and providing suggestions for mitigating ongoing claim risk based on the variables.

14. The system as recited in claim 13, wherein the data stored in the database relates to one or more of the following types of data or tables:

ICD9 code data, workers compensation claim data, claim payment data, medical/prescription billing data, U.S. census data, Social Security disability data, state regulatory issues data, medical coding data, pharmacy database data, chronic condition data, comorbidity data, ICD9 cross reference tables, NCCI cross reference tables, target variable manipulation tables, claim exclusion tables, and an evidence based medical treatment crosswalk for comparison to medical bill data.

15. The system as recited in claim 14, wherein the predictive model comprises one or more of the following types of predictive models:

a model that identifies claims likely to exceed a self-insured retention or deductible, and/or a model that identifies claims likely to exceed a predetermined total cost.

16. The system as recited in claim 15, further comprising an identification and mitigation model database containing the data transformed into model scoring records, said model scoring records representing all relevant variables for the predictions.

17. The system as recited in claim 16, wherein the elevated risk workers'"'"' compensation claims comprise catastrophic claims and migratory claims.

18. The system as recited in claim 13, wherein the variables are categorized into 1 of 8 risk factor categories.

19. The system as recited in claim 18, further comprising additional explanatory predictive models to grade the risk of the risk factor category of the variables.

20. The system as recited in claim 19, further comprising specific suggested interventions stored in the intervention database according to risk factor category.