# US 2023/0054513 A1 · Systems and methods for healthcare provider dashboards

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- Number: US 2023/0054513 A1
- Document ID: US20230054513A1
- Status: Published application
- Kind: A1
- Application number: 18/045,427
- Filed: 2022-10-10
- Published: 2023-02-23
- Earliest claimed: 2019-03-01
- Assignees: Cambia Health Solutions, Inc.
- Applicants: Cambia Health Solutions, Inc.
- Inventors: Dean Nida; Michael Brown-Hayes; James Chung; Carolyn Espinoza; Michael Emerson; John Larson; Jennifer Tschirpke; Stephani Driver; Daniel Jaesup Yoo; Jenifer Curry
- Web page: https://patentgrep.com/patent/US20230054513A1

## CPC

- G16H 40/20: for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
- G06F 3/0482: Interaction with lists of selectable items, e.g. menus
- G06F 3/04845: for image manipulation, e.g. dragging, rotation, expansion or change of colour
- G06F 3/04847: Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
- G06N 5/04: Inference or reasoning models
- G06N 20/00: Machine learning
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof
- G16H 40/40: for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
- G16H 40/63: for local operation
- 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 80/00: ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
- H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

Systems and methods for generating dashboards for healthcare providers are provided. In one embodiment, a method comprises receiving, from a user of a client device, a selection of a patient, determining an order of a plurality of display modules, each display module displaying information relating to the patient, generating a dashboard including the plurality of display modules displayed in the order, and transmitting the dashboard to the client device for display to the user. In this way, members may be identified for intervention in a timely manner, and the most relevant information for a healthcare provider may be prioritized for display to a healthcare provider.

## Claims

### Claim 1

A method comprising:
collecting medical claims from one or more healthcare databases, each of the medical claims associated with a patient;
generating a clinical prediction for the patient, using a machine learning model, based on the medical claims associated with the patient;
receiving, from a user of a client device, a selection of the patient;
determining an order of a plurality of display modules based on the clinical prediction, wherein each display module displays information relating to the patient;
generating a dashboard including the plurality of display modules displayed in the order; and
transmitting the dashboard to the client device for display to the user.

### Claim 2 (depends on 1)

The method of claim 1, the method further comprising:
retrieving user preferences from a user profile of the user; and
adjusting the order of the plurality of display modules based on the user preferences.

### Claim 3 (depends on 1)

The method of claim 1, wherein the plurality of display modules includes a visual timeline of the clinical prediction for the patient.

### Claim 4 (depends on 1)

The method of claim 1, wherein the clinical prediction comprises a predicted measure of healthcare consumption.

### Claim 5 (depends on 1)

The method of claim 1, the method further comprising:
receiving, from the client device, feedback regarding the clinical prediction; and training the machine learning model based on the feedback

### Claim 6 (depends on 1)

The method of claim 1, the method further comprising:
adjusting content of one or more display modules of the plurality of display modules based on the clinical prediction.

### Claim 7 (depends on 6)

The method of claim 6, wherein adjusting content of one or more display modules of the plurality of display modules based on the clinical prediction comprises:
adjusting the display of a medical claims history to emphasize medical claims relating to the clinical prediction.

### Claim 8 (depends on 1)

The method of claim 1, wherein determining the order of the plurality of display modules based on the clinical prediction comprises:
displaying along a top of the dashboard one or more medical claims from a medical claims history, wherein the one or more medical claims pertain to the clinical prediction.

### Claim 9

A method, comprising:
collecting patient data from one or more databases, wherein the patient data is associated with a patient;
generating one or more clinical predictions for the patient, using at least a first machine learning model, based on the patient data;
receiving, from a user of a client device, a selection of the patient;
predicting with a second machine learning model an actionability for each display module of a plurality of display modules based on the patient data and a user profile of the user, wherein the plurality of display modules includes a display module comprising a visual timeline of the one or more clinical predictions for the patient;
determining an order of the plurality of display modules based on the actionability for each display module of the plurality of display modules;
generating a dashboard including the plurality of display modules displayed in the order; and
transmitting the dashboard to the client device for display to the user.

### Claim 10 (depends on 9)

The method of claim 9, the method further comprising:
receiving, from the client device, feedback regarding the order of the plurality of display modules; and
training the first machine learning model based on the feedback.

### Claim 11 (depends on 9)

The method of claim 9, the method further comprising:
adjusting the order of the plurality of display modules based on the one or more clinical predictions.

### Claim 12 (depends on 11)

The method of claim 11, wherein adjusting the order of the plurality of display modules based on the one or more clinical predictions comprises:
positioning a display module of the plurality of display modules along a top of the dashboard based on relevancy of the display module to the one or more clinical predictions.

### Claim 13 (depends on 9)

The method of claim 9, wherein the one or more clinical predictions includes a first clinical prediction generated by the first machine learning model, and a second clinical prediction generated by a third machine learning model; and wherein the visual timeline of the one or more clinical predictions for the patient includes the first clinical prediction and the second clinical prediction.

### Claim 14 (depends on 9)

The method of claim 9, the method further comprising:
retrieving a user type from a user profile of the user, wherein the user type is one of a medical doctor (MD), a physician assistant (PA), a certified nursing assistant (CNA), a nurse practitioner (NP), a doctor of osteopathic medicine (DO), a registered nurse (RN), a licensed practical nurse (LPN), an administrative assistant, and an insurance administrator; and
adjusting the order of the plurality of display modules based on the user type.

### Claim 15 (depends on 9)

The method of claim 9, the method further comprising:
displaying, to the user via the client device, a clinical queue comprising a list of individuals prioritized for healthcare intervention based on the one or more clinical predictions generated by at least the first machine learning model, wherein receiving the selection of the patient comprises receiving a selection by the user of the patient from the clinical queue.

### Claim 16 (depends on 9)

The method of claim 9, wherein the plurality of display modules includes a visual timeline of future cost predictions for the patient.

### Claim 17

A computer-readable storage medium including an executable program stored thereon, the program configured to cause a computer processor to:
collect patient data associated with a patient from one or more databases;
generate a first patient care prediction for the patient, using a first machine learning model, based on the patient data;
generate a second patient care prediction for the patient, using a second machine learning model, based on the patient data;
receive, from a user of a client device, a selection of the patient;
predict with a third machine learning model an actionability for each display module of a plurality of display modules based on the patient data and a user profile of the user;
determine an order of the plurality of display modules based on the actionability for each display module of the plurality of display modules;
adjust the order of the plurality of display modules based on the first patient care prediction and the second patient care prediction;
generate a dashboard including the plurality of display modules displayed in the adjusted order; and
transmit the dashboard to the client device for display to the user.

### Claim 18 (depends on 17)

The computer-readable storage medium of claim 17, wherein the program is further configured to cause the computer processor to:
receive, from the client device, feedback regarding the order of the plurality of display modules; and
train the third machine learning model based on the feedback.

### Claim 19 (depends on 17)

The computer-readable storage medium of claim 17, wherein the program is configured to adjust the order of the plurality of display modules based on the first patient care prediction and the second patient care prediction by:
positioning a display module of the plurality of display modules along a top of the dashboard based on the first patient care prediction and the second patient care prediction.

### Claim 20 (depends on 17)

The computer-readable storage medium of claim 17, wherein the program is further configured to cause the computer processor to adjust content of one or more display modules of the plurality of display modules based on one or both of the first patient care prediction and the second patient care prediction.