Method and apparatus for assessing credit for healthcare patients

• US 8,930,216 B1
• Filed: 05/24/2013
• Issued: 01/06/2015
• Est. Priority Date: 09/01/2004
• Status: Active Grant

• Current Assignee: Search America Incorporated (Search)
• Original Assignee: Search America Incorporated (Search)
• Inventors: Johnson, Steven G., Busch, Christopher G.
• Primary Examiner(s): Le, Michelle L
• Application Number:
  US13/902,528
• Time in Patent Office: 592 Days
• Field of Search: 705 2- 3
• US Class Current: 705/2
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof

Abstract

According to one example embodiment, there is provided a method and an apparatus to evaluate the credit of a healthcare patient. The example embodiment provides methods and computer systems programmed to use multiple variables that are known about a patient prior to a service being rendered to segment the patient population into finer grained groupings. These finer grained groupings allow financial factors, such as a credit score, to be a more accurate predictor. Also, according to another example embodiment, the model is not a generic model for all patients, but the variables and their parameters are specific to a particular healthcare organization'"'"'s or facility'"'"'s patient population. This creates a custom model that further enhances its predictiveness.

Claims

1. A computing system comprising:

one or more processors configured to execute instructions, wherein the instructions are configured to cause the computing system to;

receive attribute information for a patient of a healthcare facility;

determine a patient population segment for the patient based at least in part on the received attribute information and a custom model for the healthcare facility, wherein the custom model groups patients of the healthcare facility based on attributes of the patients of the healthcare facility; and

generate a prediction of payment for the patient based at least in part on the determined patient population segment.

2. The computing system of claim 1, wherein the instructions are further configured to cause the computing system to:

determine whether additional financial information would increase the accuracy of the prediction of payment; and

in response to determining that additional financial information would increase the accuracy of the prediction of payment;

access one or more financial information sources to obtain at least some of the additional financial information;

determine one or more patient categories for the patient based on the at least some of the additional financial information; and

update the prediction of payment for the patient based at least in part on the one or more determined patient categories.

3. The computing system of claim 1, wherein the instructions are further configured to cause the computing system to:

determine whether the patient is eligible for a discount based at least in part on the prediction of payment for the patient and one or more rules defined by the healthcare facility.

4. The computing system of claim 2, wherein the instructions are further configured to cause the computing system to at least:

determine whether the patient is eligible for a discount based at least in part on an estimated income of the patient and an estimated household size of the patient.

5. The computing system of claim 4, wherein the estimated income of the patient is based on additional financial information accessed from one or more financial information sources.

6. The computing system of claim 4, wherein the estimated household size of the patient is based on marketing demographic data.

7. The computing system of claim 1, wherein the instructions are further configured to cause the computing system to at least:

access actual patient payment outcome information for the healthcare facility; and

update a custom payment prediction model configured to segment patients of the healthcare facility, wherein the updating is based at least in part on the actual patient payment outcome information.

8. The computing system of claim 7, wherein the actual patient payment outcome information for the healthcare facility includes at least one of an indication of whether the patient paid, an indication of how long it took the patient to pay, or a method of payment.

9. The computing system of claim 2, wherein the one or more financial information sources are accessed based on accuracy of data stored by respective financial information sources.

10. The computing system of claim 9, wherein the accuracy of data stored by respective financial information sources is based at least in part on accuracy of regional or demographic information stored by the respective financial information sources.

11. The computing system of claim 1, wherein determining the patient population segment for the patient is based at least in part on segmentation variables of the healthcare facility.

12. A method comprising:

accessing, by a computing system having one or more hardware processors, attribute information for a patient of a healthcare facility;

determining, by the computing system, a patient population segment for the patient based at least in part on the attribute information and a custom model for the healthcare facility, wherein the custom model is configured to group patients of the healthcare facility based on attributes of the patients of the healthcare facility; and

generating, by the computing system, a prediction of payment for the patient based at least in part on the determined patient population segment.

13. The method of claim 12, wherein determining the patient population segment for the patient is based at least in part on segmentation variables of the healthcare facility.

14. The method of claim 12, further comprising:

accessing, by the computing system, actual patient payment outcome information for the healthcare facility; and

updating, by the computing system, a custom payment prediction model configured to segment patients of the healthcare facility, wherein the updating is based at least in part on the actual patient payment outcome information.

15. The method of claim 12, further comprising:

determining, by the computing system, whether additional financial information would increase accuracy of the prediction of payment; and

in response to determining that additional financial information would increase the accuracy of the prediction of payment;

accessing, by the computing system, one or more financial information sources to obtain at least some of the additional financial information;

determining, by the computing system, one or more patient categories for the patient based on the at least some of the additional financial information; and

updating, by the computing system, the prediction of payment for the patient based at least in part on the one or more determined patient categories.

16. The method of claim 15, wherein the one or more financial information sources are accessed based on accuracy of data stored by respective financial information sources.

17. Non-transitory computer storage having stored thereon computer-executable instructions configured for execution on a processing system to cause the processing system to:

access attribute information for a patient of a healthcare facility;

determine a patient population segment for the patient based at least in part on the attribute information and a custom model for the healthcare facility, wherein the custom model is configured to group patients of the healthcare facility based on attributes of the patients of the healthcare facility; and

generate a prediction of payment for the patient based at least in part on the determined patient population segment.

18. The non-transitory computer storage of claim 17, wherein the computer-executable instructions are further configured to cause the processing system to:

determine whether the patient is eligible for a discount based at least in part on the prediction of payment for the patient and one or more rules defined by the healthcare facility.

19. The non-transitory computer storage of claim 17, wherein the computer-executable instructions are further configured to cause the processing system to:

determine whether the patient is eligible for a discount based at least in part on an estimated income of the patient and an estimated household size of the patient.

20. The non-transitory computer storage of claim 17, wherein the computer-executable instructions are further configured to cause the processing system to:

access actual patient payment outcome information for the healthcare facility; and

update a custom payment prediction model configured to segment patients of the healthcare facility, wherein the updating is based at least in part on the actual patient payment outcome information.