# US 12,009,087 B2 · Predictive modeling for mental health management

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- Number: US 12,009,087 B2
- Document ID: US12009087B2
- Status: Granted
- Kind: B2
- Application number: 17/386,223
- Filed: 2021-07-27
- Published: 2022-05-19
- Granted: 2024-06-11
- Earliest claimed: 2020-11-18
- Assignees: Evernorth Strategic Development, Inc.
- Applicants: Evernorth Strategic Development, Inc.
- Inventors: Gang Wu; Christopher G. Lehmuth; Amit K. Bothra; Pritesh J. Shah
- Web page: https://patentgrep.com/patent/US12009087B2

## CPC

- G16H 20/70: relating to mental therapies, e.g. psychological therapy or autogenous training
- G06N 20/00: Machine learning
- G06Q 40/08: Insurance
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G16H 70/40: relating to drugs, e.g. their side effects or intended usage
- G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof
- G16H 40/20: for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
- G16H 40/67: for remote operation
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

Systems and methods in this document describe a mental health management system. The mental health management system accesses patient data associated with a patient from a database, determines that the patient is associated with a trigger event, generates a prediction, using a predictive modeling system trained to analyze the patient data, the prediction corresponding to a probability that the patient's current medication data will be modified, stores the prediction in association with the patient data, and transmits the prediction to a computing device.

## Claims

### Claim 1

A method comprising:
accessing, using a processor, member data associated with a patient from a database, the member data comprising a current medication data;
determining, based on the member data, that the patient is at risk for progression of a mental health illness associated with the patient;
in response to a determination that the patient is associated with the risk of progression of the mental health illness, generating a prediction based on a machine learning model trained to analyze the member data, the prediction corresponding to a probability that the current medication data will be supplemented with additional medication data corresponding to an additional medication that the patient is not currently using within a predetermined period of time;
based on the prediction, generating a standard score comprising an indication of a treatment efficiency of a physician associated with the patient; and
causing presentation of the standard score on a graphical user interface of a computing device.

### Claim 2 (depends on 1)

The method of claim 1, wherein the machine learning model is further trained on claims data that comprises medical prescription data.

### Claim 3 (depends on 1)

The method of claim 1, wherein the member data comprises demographic data, medication data and medical history data associated with the patient.

### Claim 4 (depends on 2)

The method of claim 2, wherein the prediction corresponds to probability of an increased usage of a current medication associated with the current medication data.

### Claim 5 (depends on 1)

The method of claim 1, wherein determining that the patient is associated with the risk for progression of the mental health illness further comprises:
validating the member data based on one or more aspects of the member data.

### Claim 6 (depends on 1)

The method of claim 1, wherein transmitting the prediction to the computing device further comprises:
generating a notification comprising the prediction; and
causing display of the notification on a graphical user interface of the computing device.

### Claim 7 (depends on 1)

The method of claim 1, wherein the machine learning model is trained using training data, the training data comprising claim data and the member data.

### Claim 8

A system comprising:
a processor; and
a memory storing instructions that, when executed by the processor, configure the system to perform operations comprising:
accessing member data associated with a patient from a database, the member data comprising a current medication data;
determining, based on the member data, that the patient is at risk for progression of a mental health illness associated with the patient;
in response to a determination that the patient is associated with the risk of progression of the mental health illness, generating a prediction based on a machine learning model trained to analyze the member data, the prediction corresponding to a probability that the current medication data will be supplemented with additional medication data corresponding to an additional medication that the patient is not currently using within a predetermined period of time;
based on the prediction, generating a standard score comprising an indication of a treatment efficiency of a physician associated with the patient; and
causing presentation of the standard score on a graphical user interface of a computing device.

### Claim 9 (depends on 8)

The system of claim 8, wherein determining that the patient is associated with the risk for progression of a mental health illness further comprises:
validating the member data based on one or more aspects of the member data.

### Claim 10 (depends on 8)

The system of claim 8, wherein the machine learning model is trained using training data, the training data comprising claim data and the member data.

### Claim 11 (depends on 10)

The system of claim 10, wherein the claim data comprises information on prescription claims associated with the patient.

### Claim 12 (depends on 10)

The system of claim 10, wherein the risk for progression of the mental health illness is based on aspects of the claim data and the member data.

### Claim 13 (depends on 8)

The system of claim 8, wherein transmitting the prediction to the computing device further comprises:
generating a notification comprising the prediction; and
causing display of the notification on a graphical user interface of the computing device.

### Claim 14 (depends on 8)

The system of claim 8, wherein the prediction corresponds to probability of an increased usage of a current medication associated with the current medication data.

### Claim 15

A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to
perform operations comprising:
accessing member data associated with a patient from a database, the member data comprising a current medication data;
determining, based on the member data, that the patient is at risk for progression of a mental health illness associated with the patient;
in response to a determination that the patient is associated with the risk of progression of the mental health illness, generating a prediction based on a machine learning model trained to analyze the member data, the prediction corresponding to a probability that the current medication data will be supplemented with additional medication data corresponding to an additional medication that the patient is not currently using within a predetermined period of time;
based on the prediction, generating a standard score comprising an indication of a treatment efficiency of a physician associated with the patient; and
causing presentation of the standard score on a graphical user interface of a computing device.

### Claim 16 (depends on 15)

The computer-readable storage medium of claim 15, wherein determining that the patient is associated with the risk for progression of a mental health illness further comprises:
validating the member data based on one or more aspects of the member data.

### Claim 17 (depends on 15)

The computer-readable storage medium of claim 15, wherein the machine learning model is trained using training data, the training data comprising claim data and the member data.

### Claim 18 (depends on 17)

The computer-readable storage medium of claim 17, wherein the claim data comprises information on prescription claims associated with the patient.

### Claim 19 (depends on 17)

The computer-readable storage medium of claim 17, wherein the risk for progression of the mental health illness is based on aspects of the claim data and the member data.

### Claim 20 (depends on 15)

The computer-readable storage medium of claim 15, wherein transmitting the prediction to the computing device further comprises:
generating a notification comprising the prediction; and
causing display of the notification on a graphical user interface of the computing device.