US-12626797-B2 - Systems and methods for reducing patient readmission to acute care facilities

US12626797B2US 12626797 B2US12626797 B2US 12626797B2US-12626797-B2

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.

Inventors

Karyn Burnett
Jaikumar Ramanathan
Guy Katsav
Jason Strober

Assignees

Saiva AI, Inc.

Dates

Publication Date: 20260512
Application Date: 20240703

Claims (20)

1 . A method of generating a report about 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 a healthcare recording system of a post-acute care facility, patient data for a plurality of patients, including medical history and textual input from a caregiver; converting, by a risk machine-learning system, at least some of the patient data into expected patient data with predetermined input formats and predetermined features; inputting the patient data for the plurality of patients into the 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 respective patient data associated with each patient, wherein each risk score is based on one or more risk features that contribute to a risk score for a respective patient being readmitted to an acute care facility from the post-acute care facility; generating a report comprising a list of at least a subset of patients from the plurality of patients, the report provides for each patient of the subset of patients: (i) a risk score for readmission of each patient to an acute care facility, (ii) a daily risk indicator of a variable risk score that is changeable relative to a previous day's risk score, and (iii) information about a predetermined number of the risk features; sending the report to a remote device for display; causing display of the report at the remote device; and facilitating a change in care provided to each patient of the subset of patients to address the risk features.
2 . The method of claim 1 , wherein the report includes respective patient risk scores for each of the patients in the subset of patients.
3 . The method of claim 1 , wherein each respective associated risk feature explanation describes a respective risk feature's contribution to the risk score.
4 . 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 the respective patient's one or more risk features and corresponding one or more risk feature scores.
5 . The method of claim 1 , 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 for each patient of the plurality of patients, wherein the report includes, for each of the respective one or more risk features, that respective risk feature's risk feature score.
6 . The method of claim 1 , wherein the risk score for each patient of the subset of patients includes a summary explanation and the report includes a portion of the summary explanation.
7 . The method of claim 6 , wherein the summary 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.
8 . The method of claim 1 , wherein the report includes one or more conditions for a patient.
9 . The method of claim 1 , wherein the report includes patient data for each of the plurality of patients.
10 . The method of claim 1 , wherein the report includes an entry field configured to receive textual input.
11 . The method of claim 10 , further comprising: in response to receiving the textual input at the entry field, determining an updated risk score for each patient of the plurality of patients.
12 . 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.
13 . The method of claim 12 , wherein the notification event includes determining that a subsequent determined risk score for a respective patient is above a user defined risk threshold.
14 . 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.
15 . A system for generating a report about 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 executable instructions that, when executed by the one or more processors, cause the system to: receive, from a healthcare recording system of a post-acute care facility, patient data for a plurality of patients, including medical history and textual input from a caregiver; convert, by a risk machine-learning system, at least some of the patient data into expected patient data with predetermined input formats and predetermined features; input the patient data for the plurality of patients into the 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; determine, by the risk machine-learning system, a risk score for each patient of the plurality of patients based on respective patient data associated with each patient, wherein each risk score is based on one or more risk features that contribute to a risk score for a respective patient being readmitted to an acute care facility from the post-acute care facility; generate a report comprising a list of at least a subset of patients from the plurality of patients, the report provides for each patient of the subset of patients: (i) a risk score for readmission of each patient to an acute care facility, (ii) a daily risk indicator of a variable risk score that is changeable relative to a previous day's risk score, and (iii) information about a predetermined number of the risk features; send the report to a remote device for display; cause display of the report at the remote device; and facilitate a change in care provided to each patient of the subset of patients to address the risk features.
16 . The system of claim 15 , wherein the report includes respective patient risk scores for each of the patients in the subset of patients.
17 . The system of claim 15 , wherein each respective associated risk feature explanation describes a respective risk feature's contribution to the risk score of each patient of the plurality of patients.
18 . The system of claim 15 , the risk score for each patient of the subset of patients includes a summary explanation and the report includes a portion of the summary explanation.
19 . The system of claim 18 , wherein the summary 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.
20 . A non-transitory computer-readable storage medium including executable instructions stored thereon for generating a report about patients at-risk for an unplanned transfer from a post-acute care facility to another medical facility, wherein the executable instructions, when executed by one or more processors of a device, cause the device to perform operations comprising: receiving, from a healthcare recording system of a post-acute care facility, patient data for a plurality of patients, including medical history and textual input from a caregiver; converting, by a risk machine-learning system, at least some of the patient data into expected patient data with predetermined input formats and predetermined features; inputting the patient data for the plurality of patients into the 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 respective patient data associated with each patient, wherein each risk score is based on one or more risk features that contribute to a risk score for a respective patient being readmitted to an acute care facility from the post-acute care facility; generating a report comprising a list of at least a subset of patients from the plurality of patients, the report provides for each patient of the subset of patients: (i) a risk score for readmission of each patient to an acute care facility, ii) a daily risk indicator of a variable risk score that is changeable relative to a previous day's risk score, and iii) information about a predetermined number of the risk features; sending the report to a remote device for display; causing display of the report at the remote device; and facilitating a change in care provided to each patient of the subset of patients to address the risk features.

Description

RELATED APPLICATIONS This application is a continuation of U.S. patent application Ser. No. 17/165,842, filed on Feb. 2, 2021, entitled “Systems and Methods for Reducing Patient Readmission to Acute Care Facilities,” which claims priority from U.S. Provisional Application Ser. No. 62/969,593, filed Feb. 3, 2020 and from U.S. Provisional Application Ser. No. 63/069,674, filed Aug. 24, 2020, which are incorporated by reference herein for all purposes. TECHNICAL FIELD The disclosed embodiments relate generally to risk identification and reduction of patient re-admittance to an acute care facility. BACKGROUND Patients discharged from an acute care facility, such as an emergency room or hospital, and placed in a post-acute care facility, such as a nursing or rehabilitation home, are at risk of being readmitted to the acute care facility. This is especially true for older patients and patients with more or complex medical issues. At times, the initial reason for going to the acute care facility may result in a number of undiagnosed or unidentified conditions that worsen after the patient is discharged. These undiagnosed or unidentified conditions can cause serious harm to a patient and force him or her to be returned to an acute care facility. Similarly, patients can be treated for one condition while left untreated for another that was not readily known. As such, improving the ability of post-acute care facilities to identify the patients most at risk for returning to the acute care facility improves the chances for patients to make a full recovery. However, determining which patients are most likely to be readmitted to an acute care facility is not as simple as examining the patients' medical history, as there are numerous unpredictable reasons why some patients are readmitted, and others not. As such, a need exists for identifying patients with the highest likelihood of readmission to an acute care facility so that special care can be given to those patients. SUMMARY A system generates a report that ranks patients at risk for readmission to an acute care facility from a post-acute care facility. In some embodiments, the system receives, from the post-acute care facility, patient data for a plurality of patients. In some embodiments, the system inputs 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. In some embodiments, the system determines, by the risk machine-learning system, a risk score for each patient of the plurality of patients based on each patient's patient data. Each risk score represents risk of a respective patient being readmitted to an acute care facility from the post-acute care facility. In some embodiments, the system generates, for display, the report. In some embodiments, the report includes a list of at least a subset of patients from the plurality of patients. In some embodiments, the list is further ranked from the patient with the highest risk of readmission to the patient with the lowest risk of readmission. The methods and systems described herein identify and report the risks for a patient of a post-acute care facility to be readmitted to an acute care facility. In some embodiments, the reports are generated using historical and current data for the patients and provided to medical practitioners (e.g., physicians or nurses). In some embodiments, the historical data is used to generate machine-learning systems that are able to identify risks, rank the risks, and provide human readable explanations for the risks. In some embodiments, the generated machine-learning systems use current patient data to generate and provide the reports to the medical practitioners. In some embodiments, the provided reports allow for medical practitioners to focus on the patient with the highest risk of being readmitted to an acute care facility. In some embodiments, the reports further allow medical practitioners to efficiently input notes, diagnosis, or actions taken into the report, via an electronic device. In some embodiments, input received at the report by the medical practitioners is used as feedback to update the machine-learning systems, future reports, or the rankings of the patients. In some embodiments, the method includes identifying the patients risks and presenting the information into a report for medical practitioners to use reducing the amount of data required by providing a centralized repository for monitoring patients. Further, the ability to monitor patients and their respective risk of being readmitted to an acute care facility enables medical practitioners to provide better and more focused treatment to patients at higher risk of readmission, to help them make a full recovery. In accordance with some embodiments, a method is performed at a computer (e.g., associated with a me