# US 2021/0241871 A1 · Systems and Methods for Reducing Patient Readmission to Acute Care Facilities

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2021/0241871 A1
- Document ID: US20210241871A1
- Status: Published application
- Kind: A1
- Application number: 17/165,842
- Filed: 2021-02-02
- Published: 2021-08-05
- Earliest claimed: 2020-02-03
- Assignees: Saiva, Inc.
- Applicants: Saiva, Inc.
- Inventors: Karyn Burnett; Jaikumar Ramanathan; Guy Katsav; Jason Strober
- Web page: https://patentgrep.com/patent/US20210241871A1

## CPC

- G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 40/20: for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
- 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 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- G16H 70/60: relating to pathologies
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Methods and systems for generating a report that ranks patients at risk for readmission to an acute care facility from a post-acute care facility. A system receives, from the post-acute care facility, patient data for a plurality of patients. The system inputs the patient data into a risk machine-learning system that was previously trained using historical patient data and data reflecting any readmissions from one or more post-acute care facilities to one or more acute care facilities. The system determines, by the risk machine-learning system, a risk score for each patient. Each risk score represents risk of a respective patient being readmitted to an acute care facility from the post-acute care facility. The system further generates the report. The report including a list of at least a subset of patients from the plurality of patients ranked from the patient with the highest risk to the lowest risk of readmission.

## Claims

### Claim 1

A method of generating a report that ranks patients at risk for readmission to an acute care facility from a post-acute care facility, the method comprising:
at a computer system having one or more processors and memory storing one or more programs that are executable by the computer system:
receiving, from the post-acute care facility, patient data for a plurality of patients;
inputting the patient data for the plurality of patients into a risk machine-learning system that was previously trained using historical patient data and data reflecting any readmissions from one or more post-acute care facilities to one or more acute care facilities;
determining, by the risk machine-learning system, a risk score for each patient of the plurality of patients based on each patient's patient data, wherein each risk score represents risk of a respective patient being readmitted to an acute care facility from the post-acute care facility; and
generating, for display, the report comprising a list of at least a subset of patients from the plurality of patients, where the list is ranked from the patient with the highest risk of readmission to the patient with the lowest risk of readmission.

### Claim 2 (depends on 1)

The method of claim 1, wherein the report includes respective patient risk scores for each of the patients in the subset of patients.

### Claim 3 (depends on 2)

The method of claim 2, further comprising: for each patient of the subset of patients, determining one or more risk features that contributed to that patient's respective risk score, wherein the report includes one or more respective risk features for each of the subset of patients.

### Claim 4 (depends on 3)

The method of claim 3, further comprising for each of the one or more risk features, determining a risk feature score indicating how much that respective risk feature contributed to the risk score, wherein the report includes, for each of the respective one or more risk features, that respective risk feature's risk feature score.

### Claim 5 (depends on 3)

The method of claim 3, wherein the one or more risk features that contributed to that patient's respective risk score include a respective explanation and the report includes a portion of the respective explanation for the one or more risk features.

### Claim 6 (depends on 5)

The method of claim 5, wherein the respective human readable explanation is based on one or more of the one or more risk features, patient data trends, patient data patterns, and other sources of data.

### Claim 7 (depends on 1)

The method of claim 1, wherein the report includes one or more conditions for each of the plurality of patients.

### Claim 8 (depends on 1)

The method of claim 1, wherein the report includes patient data for each of the plurality of patients.

### Claim 9 (depends on 1)

The method of claim 1, wherein the report includes one or more note entry fields configured to receive input from a caregiver device.

### Claim 10 (depends on 9)

The method of claim 9, further comprising;
receiving an input into a note entry field of the one or more note entry fields; and
in response to receiving the input into the note entry field, updating the patient data.

### Claim 11 (depends on 1)

The method of claim 1, wherein the report includes one or more notification entry fields for receiving user defined notification requests; and
the method further comprises:
receiving a user defined notification request into a notification entry field of the one or more notification entry fields; and
in response to receiving the user defined notification request, generating a notification event based on the user defined notification request, wherein a notification is provided upon occurrence of the notification event.

### Claim 12 (depends on 11)

The method of claim 11, wherein the notification event includes determining that a subsequent determined risk score for a respective patient is above a user defined risk threshold.

### Claim 13 (depends on 3)

The method of claim 3, wherein generating the report comprises, preparing a separate detailed report for each patient of the subset of patients, where each detailed report for a respective patient of the subset of patients comprises that respective patient's one or more risk features and corresponding one or more risk feature scores.

### Claim 14 (depends on 1)

The method of claim 1, wherein the subset of patients comprises a predetermined number of patients with the highest risk scores from the plurality of patients.

### Claim 15

A system for generating a report that ranks patients at-risk for readmission to an acute care facility from a post-acute care facility, the system comprising:
one or more processors; and
memory storing instructions for execution by the one or more processors, the instructions including instructions for:
receiving, from the post-acute care facility, patient data for a plurality of patients;
inputting the patient data for the plurality of patients into a risk machine-learning system that was previously trained using historical patient data and data reflecting any readmissions from one or more post-acute care facilities to one or more acute care facilities;
determining, by the risk machine-learning system, a risk score for each patient of the plurality of patients based on each patient's patient data, wherein each risk score represents risk of a respective patient being readmitted to an acute care facility from the post-acute care facility; and
generating, for display, the report comprising a list of at least a subset of patients from the plurality of patients, where the list is ranked from the patient with the highest risk of readmission to the patient with the lowest risk of readmission.

### Claim 16 (depends on 15)

The system of claim 15, wherein the report includes respective patient risk scores for each of the patients in the subset of patients.

### Claim 17 (depends on 16)

The system of claim 16, further comprising: for each patient of the subset of patients, determining one or more risk features that contributed to that patient's respective risk score, wherein the report includes one or more respective risk features for each of the subset of patients.

### Claim 18 (depends on 17)

The system of claim 17, wherein the one or more risk features that contributed to that patient's respective risk score include a respective human readable explanation and the report includes a portion of the respective explanation for the one or more risk features.

### Claim 19 (depends on 18)

The system of claim 18, wherein the respective human readable explanation is based on one or more of the one or more risk features, patient data trends, patient data patterns, and other sources of data.

### Claim 20

A non-transitory computer-readable storage medium for generating a report that ranks patients at-risk for readmission to an acute care facility from a post-acute care facility having one or more processors and memory storing instructions for execution by the one or more processors, the instructions including instructions for:
receiving, from the post-acute care facility, patient data for a plurality of patients;
inputting the patient data for the plurality of patients into a risk machine-learning system that was previously trained using historical patient data and data reflecting any readmissions from one or more post-acute care facilities to one or more acute care facilities;
determining, by the risk machine-learning system, a risk score for each patient of the plurality of patients based on each patient's patient data, wherein each risk score represents risk of a respective patient being readmitted to an acute care facility from the post-acute care facility; and
generating, for display, the report comprising a list of at least a subset of patients from the plurality of patients, where the list is ranked from the patient with the highest risk of readmission to the patient with the lowest risk of readmission.