Rule-based Prediction of Medical Claims' Payments

• US 20130054259A1
• Filed: 02/22/2012
• Published: 02/28/2013
• Est. Priority Date: 02/22/2011
• Status: Abandoned Application

• Current Assignee: Janusz Wojtusiak, John M. Shiver
• Original Assignee: Janusz Wojtusiak, John M. Shiver
• Inventors: Wojtusiak, Janusz, Shiver, John M.
• Application Number:
  US13/401,942
• Publication Number:
  US 20130054259A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

Some embodiments of the present invention evaluate claim submissions. Prediction(s) are generated that employ machine learning engine(s) and/or expert models executing on processor(s). The prediction(s) may forecast if claim data when submitted to a payer will result in at least one of the following: an approved submission; a denied submission; and an apparent payment variation. The machine learning engine(s) may be updated using use the prediction. Labeled data may be generated by classifying patient claim data residing in a database of claim records and histories with at least one of the following: an anomaly detection label; a contract based label; and a combination of the above. Claims classification model(s) may be trained using the labeled data. Predictive paid amount model(s) may be created that employ at least the labeled data and/or an amount paid on a claim.

Claims

1. ) A non-transitory computer readable medium including a series of computer readable instructions configured to cause one or more processors to execute a method comprising:

a) generating a prediction employing a machine learning engine executing on the one or more processors, the prediction forecasting if first patient claim data when submitted to a payer will result in at least one of the following;

i) an approved submission;

ii) a denied submission; and

iii) an apparent payment variation; and

b) updating the machine learning engine using the prediction; and

wherein the machine learning engine is trained by;

i) generating labeled data by classifying at least one second patient claim data residing in a database of claim records and histories with at least one of the following;

(1) an anomaly detection label;

(2) a contract based label; and

(3) a combination of the above;

ii) employing the labeled data to train a claims classification model; and

iii) creating a predictive paid amount model employing at least one of the following;

(1) the labeled data;

(2) an amount paid on a claim; and

(3) a combination of the above.

2. ) The non-transitory computer readable medium according to claim 1, further including comparing the first patient claim data against the database of claim records and histories for completeness and consistency.

3. ) The non-transitory computer readable medium according to claim 1, further including employing the database of claim records and histories to resolve any of at least one missing value in the first patient billing data.

4. ) The non-transitory computer readable medium according to claim 3, wherein at least one of the at least one missing value includes a visit date.

5. ) The non-transitory computer readable medium according to claim 3, wherein at least one of the at least one missing value includes claim information.

6. ) The non-transitory computer readable medium according to claim 1, further including proving at least one of the following with a manual model:

a) the claims classification model;

b) the predictive paid amount model;

or

c) a combination of the above.

7. ) The non-transitory computer readable medium according to claim 1, wherein the predictive paid amount model is created after filtering claims classified as at least one of the following:

a) the approved submission;

b) the denied submission; and

c) the apparent payment variation.

8. ) The non-transitory computer readable medium according to claim 1, wherein the predictive paid amount model is created while filtering claims classified as at least one of the following:

a) the approved submission;

b) the denied submission; and

c) the apparent payment variation.

9. ) The non-transitory computer readable medium according to claim 1, wherein the labeled data includes an expert specified label.

10. ) The non-transitory computer readable medium according to claim 1, further including predicting an amount for the apparent payment variation.

11. ) The non-transitory computer readable medium according to claim 1, further including the machine learning engine determining a pattern.

12. ) The non-transitory computer readable medium according to claim 1, wherein the first patient claims data is de-identified.

13. ) The non-transitory computer readable medium according to claim 1, further including formatting the first patient billing data into a machine learning data format.

14. ) The non-transitory computer readable medium according to claim 1, further including updating a contract using at least one of the following:

a) the claims classification model;

b) the predictive paid amount model;

c) at least one of the anomaly detection label; and

d) a combination of the above.

15. ) The non-transitory computer readable medium according to claim 1, further including updating a claim employing at least one of the following:

a) the claims classification model;

b) the predictive paid amount model;

c) at least one of the anomaly detection label; and

d) a combination of the above.

16. ) The non-transitory computer readable medium according to claim 1, further including not submitting a claim because of at least one of the following:

a) the claims classification model;

b) the predictive paid amount model;

c) at least one of the anomaly detection label; and

d) a combination of the above.

17. ) A method comprising:

a) generating a prediction employing a machine learning engine executing on one or more processors, the prediction forecasting if first patient claim data when submitted to a payer will result in at least one of the following;

i) an approved submission;

ii) a denied submission; and

iii) an apparent payment variation; and

b) updating the machine learning engine using the prediction; and

wherein the machine learning engine is trained by;

i) generating labeled data by classifying at least one second patient claim data residing in a database of claim records and histories with at least one of the following;

(1) an anomaly detection label;

(2) a contract based label; and

(3) a combination of the above;

ii) employing the labeled data to train a claims classification model; and

iii) creating a predictive paid amount model employing at least one of the following;

(1) the labeled data;

(2) an amount paid on a claim; and

(3) a combination of the above.

18. ) A non-transitory computer readable medium including a series of computer readable modules configured to cause one or more processors to execute a method, the modules comprising:

a) a payer-specific screening module configured to cause the one or more processors to generate a prediction employing a payer-specific machine learning engine and a payer-specific expert model, the prediction forecasting if first patient claim data when submitted to a payer will result in at least one of the following;

i) an approved submission;

ii) a denied submission; and

iii) an apparent payment variation; and

b) an update module configured to update the payer-specific machine learning engine using the prediction; and

wherein the payer-specific machine learning engine is trained by;

i) generating payer-specific labeled data by classifying at least one second patient claim data residing in a database of claim records and histories with at least one of the following;

(1) a payer-specific anomaly detection label;

(2) a payer-specific contract based label; and

(3) a combination of the above;

ii) employing the labeled data to train a claims classification model; and

iii) creating a payer-specific predictive paid amount model employing at least one of the following;

(1) the payer-specific labeled data;

(2) a payer-specific amount paid on a claim; and

(3) a combination of the above.

19. ) The non-transitory computer readable medium of claim 18, further including:

a) a service-specific screening module configured to cause the one or more processors to generate a service-specific prediction employing a service-specific machine learning engine and a service-specific expert model, the service-specific prediction forecasting if the first patient claim data when submitted to the payer will result in at least one of the following;

i) the approved submission;

ii) the denied submission; and

iii) the apparent payment variation; and

b) a service-specific update module configured to update the service-specific machine learning engine using the service-specific prediction; and

wherein the service-specific machine learning engine is trained by;

i) generating service-specific labeled data by classifying at least one second patient claim data residing in a database of claim records and histories with at least one of the following;

(1) a service-specific anomaly detection label;

(2) a service-specific contract based label; and

(3) a combination of the above;

ii) employing the service-specific labeled data to train a service-specific claims classification model; and

iii) creating a service-specific predictive paid amount model employing at least one of the following;

(1) the service-specific labeled data;

(2) a service-specific amount paid on a claim; and

(3) a combination of the above.

20. ) The non-transitory computer readable medium of claim 18, further including:

a) a general screening module configured to cause the one or more processors to generate a general prediction employing a general machine learning engine and a general expert model, the general prediction forecasting if first patient claim data when submitted to the payer will result in at least one of the following;

i) the approved submission;

ii) the denied submission; and

iii) the apparent payment variation; and

b) a general update module configured to update the general machine learning engine using the general prediction; and

wherein the payer-specific machine learning engine is trained by;

i) generating general labeled data by classifying at least one of the second patient claim data residing in the database of claim records and histories with at least one of the following;

(1) a general anomaly detection label;

(2) a general contract based label; and

(3) a combination of the above;

ii) employing the general labeled data to train a general claims classification model; and

iii) creating a general predictive paid amount model employing at least one of the following;

(1) the general labeled data;

(2) a general amount paid on a claim; and

(3) a combination of the above.