Comprehensive liability management platform for calculation of multiple alternative scenarios

Patent metadata

Field	Value
Publication number	US-12586106-B2
Application number	US-202318231934-A
Country	US
Kind code	B2
Filing date	Aug 9, 2023
Priority date	Sep 12, 2022
Publication date	Mar 24, 2026
Grant date	Mar 24, 2026

Abstract

A computer-implemented method for adjusting one or more electronic medical bills for a claimant injured in an accident comprises generating a user interface to be presented to a claims adjuster; receiving a first user input identifying a claimant; responsive to the first user input, retrieving and aggregating multiple electronic medical bills each having at least one line; generating one or more findings and multiple scenarios by providing the aggregated electronic medical bills as inference input to a trained machine learning model, wherein the trained machine learning model has been trained with historical electronic medical bills and corresponding findings and scenarios, wherein responsive to the inference input, the trained machine learning model outputs the one or more findings and the multiple scenarios, wherein the one or more findings represent rationales for approving, denying, or repricing, and wherein the multiple scenarios include cost estimates based on the one or more findings.

First claim

The invention claimed is:

1 . A system for adjusting one or more electronic medical bills for a claimant injured in an accident, comprising:
one or more hardware processors; and a memory coupled with the one or more hardware processors and comprising a set of instructions which, when executed by the one or more hardware processors, causes the one or more hardware processors to:
generate a user interface to be presented to a claims adjuster;
receive a first user input via the user interface, the first user input identifying a claimant;
responsive to receiving the first user input, retrieve multiple electronic medical bills, each electronic medical bill having at least one line, each line representing a medical service provided to the identified claimant;
preprocess the received multiple electronic medical bills, wherein preprocessing the received multiple electronic bills comprises performing an input data transformation on each of the received multiple electronic bills;
aggregate the preprocessed electronic medical bills;
provide the aggregated electronic medical bills as inference input to a trained machine learning model, wherein the trained machine learning model has been trained with historical electronic medical bills and corresponding findings and scenarios;
generate, by the trained machine learning model, one or more findings and multiple scenarios based on the inference input, wherein the one or more findings represent rationales for approving, denying, or repricing at least one of the lines of the electronic medical bills, and wherein the multiple scenarios include cost estimates based on the one or more findings;
present the one or more findings and the one or more scenarios in the user interface;
receive second user input via the user interface, the second user input representing a selected one of the scenarios; and
responsive to the second user input, generate at least one adjusted electronic medical bill.

2 . The system of claim 1 , wherein the instructions further cause the processor to:
receive third user input via the user interface, the third user input representing an acceptance or rejection of one or more lines of one of the electronic medical bills; and responsive to the third user input, modify at least one of the scenarios; and present the modified at least one of the scenarios in the user interface.

3 . The system of claim 1 , wherein the instructions further cause the processor to:
obtain a training data set comprising the historical electronic medical bills and corresponding findings and scenarios; and train the machine learning model using the training data set.

4 . The system of claim 1 , wherein the rationales represented by the one or more findings comprise at least one of:
vertical determinations based on intervals between a date of injury of the claimant and a date of a corresponding treatment identified in the aggregated electronic medical bills; and horizontal determinations based on known effectiveness of a treatment identified in the aggregated electronic medical bills.

5 . The system of claim 1 , wherein the instructions further cause the processor to present, in the user interface, at least one of:
a charged amount representing a total cost corresponding to the aggregated electronic medical bills; a low evaluation amount corresponding to the scenario having the lowest cost estimate; a high evaluation amount corresponding to the scenario having the highest cost estimate; and a recommended amount corresponding to the selected one of the scenarios.

6 . The system of claim 5 , wherein the instructions further cause the processor to:
determine a likelihood of acceptance of the recommended amount based on historical acceptances of recommended amounts by at least one of attorneys and law firms; and present, in the user interface, a representation of the likelihood of acceptance of the recommended amount.

7 . The system of claim 6 , wherein determining a likelihood of acceptance of the recommended amount comprises:
providing the selected one of the scenarios as further inference input to a further trained machine learning model, wherein the further trained machine learning model has been trained with historical scenarios and corresponding acceptances of recommended amounts, wherein responsive to the inference input, the further trained machine learning model outputs the likelihood of acceptance of the recommended amount.

8 . One or more non-transitory machine-readable storage media comprising a set of instructions stored therein which, when executed by a processor, causes the processor to:
generate a user interface to be presented to a claims adjuster; receive a first user input via the user interface, the first user input identifying a claimant; responsive to receiving the first user input, retrieve multiple electronic medical bills, each electronic medical bill having at least one line, each line representing a medical service provided to the identified claimant; preprocess the received multiple electronic medical bills, wherein preprocessing the received multiple electronic bills comprises performing an input data transformation on each of the received multiple electronic bills; aggregate the preprocessed electronic medical bills; provide the aggregated electronic medical bills as inference input to a trained machine learning model, wherein the trained machine learning model has been trained with historical electronic medical bills and corresponding findings and scenarios; generate, by the trained machine learning model, one or more findings and multiple scenarios based on the inference input, wherein the one or more findings represent rationales for approving, denying, or repricing at least one of the lines of the electronic medical bills, and wherein the multiple scenarios include cost estimates based on the one or more findings; present the one or more findings and the one or more scenarios in the user interface; receive second user input via the user interface, the second user input representing a selected one of the scenarios; and responsive to the second user input, generate at least one adjusted electronic medical bill.

9 . The non-transitory machine-readable storage media of claim 8 , wherein the instructions further cause the processor to:
receive third user input via the user interface, the third user input representing an acceptance or rejection of one or more lines of one of the electronic medical bills; and responsive to the third user input, modify at least one of the scenarios; and present the modified at least one of the scenarios in the user interface.

10 . The non-transitory machine-readable storage media of claim 8 , wherein the instructions further cause the processor to:
obtain a training data set comprising the historical electronic medical bills and corresponding findings and scenarios; and train the machine learning model using the training data set.

11 . The non-transitory machine-readable storage media of claim 8 , wherein the rationales represented by the one or more findings comprise at least one of:
vertical determinations based on intervals between a date of injury of the claimant and a date of a corresponding treatment identified in the aggregated electronic medical bills; and horizontal determinations based on known effectiveness of a treatment identified in the aggregated electronic medical bills.

12 . The non-transitory machine-readable storage media of claim 8 , wherein the instructions further cause the processor to present, in the user interface, at least one of:
a charged amount representing a total cost corresponding t

Assignees

• Mitchell Int Inc

Inventors

• Tyrrell Norman
• Williamson Christopher

Classifications

• G06Q40/08

  Insurance · CPC title

• G06Q30/04 Primary

  Billing or invoicing · CPC title