Patent Issued for Computer Network Architecture With Machine Learning And Artificial Intelligence And Dynamic Patient Guidance (USPTO 10,923,233)

2021 FEB 26 (NewsRx) -- By a News Reporter-Staff News Editor at Hospital & Nursing Home Daily -- A patent by the inventors Wang, Yale (San Francisco, CA); Bauknight, Samuel H. (San Francisco, CA); Rogow, Adam F. (San Francisco, CA); Larson, Jeffrey D. (San Francisco, CA); Drouin, Jean P. (San Francisco, CA), filed on November 25, 2019, was published online on March 1, 2021, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 10,923,233 is assigned to Clarify Health Solutions Inc. (San Francisco, California, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “There is a longstanding frustrated need to have programmable hardware function in a better manner, and particularly to have hardware be smarter and learn from its own experience, with machine learning and artificial intelligence.

“The longstanding frustrated need is particularly acute in the healthcare field. Historically, patient care plans have been static. Patients undergoing medical procedures may have been given an educational binder with materials and checklists. But, if something is not covered in the documents, or if the patient does not adhere to the prescribed checklists, this prior art approach cannot adapt to bring the patient back on track with the patient care plan.

“There is a frustrated need for a computer system with artificial intelligence and dynamic patient guidance to ensure patients are on track with the patient care plan. This would avoid the need for care coordinators to first call or e-mail patients to simply double check that everything is going well, rather than being able to focus their work on patients who are off track.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “The present disclosure is related generally to computer network architectures for machine learning and artificial intelligence that enable a computer to learn by its own experience with users and by the passage of time, thus enabling superior performance by the computer hardware. Various embodiments apply to the healthcare field and constitute a healthcare robot with artificial intelligence.

“The present disclosure describes a computer network architecture with machine learning and artificial intelligence to allow for a patient’s behavior and characteristics to continually inform and update how a patient is to be guided through their care plan, and how much expensive, direct intervention by care coordinators is required.

“Embodiments automatically adapt patient guidance in the care plan based on the effectiveness of the guidance to date and attributes of the patient.”

The claims supplied by the inventors are:

“What is claimed is:

“1. A computer network system with artificial intelligence and machine learning, comprising: a prediction module with a prediction generator and an updated database, and a learning module with a training submodule, in electronic communication with the prediction module, a dynamic patient guidance web application (DPGWA) in electronic communication with both the prediction module, and a user device, wherein, the DPGWA is configured to: a. log a patient into a the dynamic patient guidance web application, b. receive data from the patient, updating the data in the system, about the patient behavior and/or compliance with a patient care plan, c. log out the patient, d. automatically re-score the patient’s risk scores by accessing the patient risk scoring web application, e. based on the new risk scores, update the patient care plan, f. send an automated message for the patient to the user mobile device requesting that the patient complete a check-in with the DPGWA and receive the updated patient care plan, g. create a reminder task for a patient clinical care coordinator to contact the patient, if the patient has not responded within a time period, h. if the patient has a designated caregiver, send a notification to the designated caregiver either (1) to prompt the patient to respond or (2) to respond on behalf of the patient to provide patient check-in information, and receive the updated patient care plan, and i. if the patient adheres to their patient care plan, reduce the cadence of interventions by a care coordinator for that patient, wherein, the learning module is configured to: receive a list of algorithm definitions and datasets for patient risk scoring, automatically calibrate one or more defined algorithms with the database, test the calibrated algorithms with a plurality of evaluation metrics, store the calibrated algorithms and evaluation metrics in a library, automatically select an algorithm for patient risk scoring based on the evaluation metrics, update further the database with third party data, and with user episode data, and re-execute the calibrate, test, store, and select steps after the update of the database step, wherein, the prediction generator is configured to: receive a user prediction request for patient risk scoring, including episode data and a client model, run the currently selected algorithm corresponding to the user of the episode data, and generate patient risk scoring prediction output, generate a patient risk scoring prediction report based on the algorithm output, and transmit the patient risk scoring prediction report to the user, wherein, the algorithm definitions are of types that are members of the group comprising: multi-level models, random forest regression, logistical regression, gamma-distributed regression, and linear regression; the third party data is from a party that is a member of the group comprising: hospitals, medical practices, insurance companies, credit reporting agencies, and credit rating agencies; the database includes patient medical data, patient personal data, patient outcome data, and medical treatment data; the episode data includes individual patient medical data and personal data; and the user is a member of the group comprising: hospitals, medical practices, and insurance companies, wherein the user device is remote from the prediction module, and the user device is a member of the group comprising: a computer, a desktop PC, a laptop PC, a smart phone, a tablet computer, and a personal wearable computing device, wherein the web application communicates with the user device by the Internet, or an extranet, or a VPN, or other network, and the web application is generic for any user, or customized for a specific user, or class of user, and wherein, the user prediction request requests calibration of the correlation of demographic, social and medical attributes of the patient, to the outcome of a specific patient clinical episode type.

“2. A computer network system with artificial intelligence and machine learning, comprising: a prediction module with a prediction generator and an updated database, a learning module with a training submodule, in electronic communication with the prediction module, a patient risk scoring web application in electronic communication with the prediction module and a user device, and a dynamic patient guidance web application (DPGWA) in electronic communication with the prediction module, the patient risk scoring web application, and a user device, and wherein, the dynamic patient guidance web application is configured to: a. log a patient into a the dynamic patient guidance web application, b. receive data from the patient, updating the data in the system, about the patient behavior and/or compliance with a patient care plan, c. log out the patient, d. automatically re-score the patient’s risk scores by accessing the patient risk scoring web application, e. based on the new risk scores, update the patient care plan, f. send an automated message for the patient to the user mobile device requesting that the patient complete a check-in with the DPGWA and receive the updated patient care plan, g. create a reminder task for a patient clinical care coordinator to contact the patient, if the patient has not responded within a time period, h. if the patient has a designated caregiver, send a notification to the designated caregiver either (1) to prompt the patient to respond or (2) to respond on behalf of the patient to provide patient check-in information, and receive the updated patient care plan, and i. if the patient adheres to their patient care plan, reduce the cadence of interventions by a care coordinator for that patient.

“3. The computer network system of claim 2, wherein, the learning module is configured to: receive a list of algorithm definitions and datasets for patient risk scoring, automatically calibrate one or more defined algorithms with the database, test the calibrated algorithms with a plurality of evaluation metrics, store the calibrated algorithms and evaluation metrics in a library, automatically select the best an algorithm for patient risk scoring based on the best evaluation metrics, update further the database with third party data, and with user episode data, and re-execute the calibrate, test, store, and select steps after the update of the database step.

“4. The computer network system of claim 2, wherein the prediction generator is configured to: receive a user prediction request for patient risk scoring, including episode data and a client model, run the currently selected algorithm corresponding to the user of the episode data, and generate patient risk scoring prediction output, generate a patient risk scoring prediction report based on the algorithm output, and transmit the patient risk scoring prediction report to the user.

“5. The computer network system of claim 2, wherein the algorithm definitions are of types that are members of the group comprising: multi-level models, random forest regression, logistical regression, gamma-distributed regression, and linear regression; the third party data is from a party that is a member of the group comprising: hospitals, medical practices, insurance companies, credit reporting agencies, and credit rating agencies; the database includes patient medical data, patient personal data, patient outcome data, and medical treatment data; the episode data includes individual patient medical data and personal data; and the user is a member of the group comprising: hospitals, medical practices, and insurance companies.

“6. The computer network system of claim 2, wherein the user device is remote from the prediction module, and the user device is a member of the group comprising: a computer, a desktop PC, a laptop PC, a smart phone, a tablet computer, and a personal wearable computing device.

“7. The computer network system of claim 2, wherein the web application communicates with the user device by the Internet, or an extranet, or a VPN, or other network, and the web application is generic for any user, or customized for a specific user, or class of user, and wherein, the user prediction request requests calibration of the correlation of demographic, social and medical attributes of the patient, to the outcome of a specific patient clinical episode type.

“8. A computer network system with a web application for artificial intelligence and machine learning and dynamic patient guidance comprising: a dynamic patient guidance web application (DPGWA) in electronic communication with a prediction module, a patient risk scoring web application, and a user device, and wherein, the dynamic patient guidance web application is configured to: a. log a patient into a the dynamic patient guidance web application, b. receive data from the patient, updating the data in the system, about the patient behavior and/or compliance with a patient care plan, c. log out the patient, d. automatically re-score the patient’s risk scores by accessing the patient risk scoring web application, e. based on the new risk scores, update the patient care plan, f. send an automated message for the patient to the user mobile device requesting that the patient complete a check-in with the DPGWA and receive the updated patient care plan, g. create a reminder task for a patient clinical care coordinator to contact the patient, if the patient has not responded within a time period, h. if the patient has a designated caregiver, send a notification to the designated caregiver either (1) to prompt the patient to respond or (2) to respond on behalf of the patient to provide patient check-in information, and receive the updated patient care plan, and i. if the patient adheres to their patient care plan, reduce the cadence of interventions by a care coordinator for that patient, wherein, the learning module is configured to: receive a list of algorithm definitions and datasets for patient risk scoring, automatically calibrate one or more defined algorithms with the database, test the calibrated algorithms with a plurality of evaluation metrics, store the calibrated algorithms and evaluation metrics in a library, automatically select an algorithm for patient risk scoring based on the evaluation metrics, update further the database with third party data, and with user episode data, and re-execute the calibrate, test, store, and select steps after the update of the database step, wherein, the prediction generator is configured to: receive a user prediction request for patient risk scoring, including episode data and a client model, run the currently selected algorithm corresponding to the user of the episode data, and generate patient risk scoring prediction output, generate a patient risk scoring prediction report based on the algorithm output, and transmit the patient risk scoring prediction report to the user, wherein, the algorithm definitions are of types that are members of the group comprising: multi-level models, random forest regression, logistical regression, gamma-distributed regression, and linear regression; the third party data is from a party that is a member of the group comprising: hospitals, medical practices, insurance companies, credit reporting agencies, and credit rating agencies; the database includes patient medical data, patient personal data, patient outcome data, and medical treatment data; the episode data includes individual patient medical data and personal data; and the user is a member of the group comprising: hospitals, medical practices, and insurance companies, wherein, the user device is remote from the prediction module, and the user device is a member of the group comprising: a computer, a desktop PC, a laptop PC, a smart phone, a tablet computer, and a personal wearable computing device, wherein the web application communicates with the user device by the Internet, or an extranet, or a VPN, or other network, and the web application is generic for any user, or customized for a specific user, or class of user, and wherein, the user prediction request requests calibration of the correlation of demographic, social and medical attributes of the patient, to the outcome of a specific patient clinical episode type.

“9. A computer network system with a dynamic patient guidance web application with artificial intelligence and machine learning, comprising: a dynamic patient guidance web application (DPGWA) in electronic communication with a prediction module, a patient risk scoring web application and a user device, and wherein, the dynamic patient guidance web application is configured to: a. log a patient into a the dynamic patient guidance web application, b. receive data from the patient, updating the data in the system, about the patient behavior and/or compliance with a patient care plan, c. log out the patient, d. automatically re-score the patient’s risk scores by accessing the patient risk scoring web application, e. based on the new risk scores, update the patient care plan, f. send an automated message for the patient to the user mobile device requesting that the patient complete a check-in with the DPGWA and receive the updated patient care plan, g. create a reminder task for a patient clinical care coordinator to contact the patient, if the patient has not responded within a time period, h. if the patient has a designated caregiver, send a notification to the designated caregiver either (1) to prompt the patient to respond or (2) to respond on behalf of the patient to provide patient check-in information, and receive the updated patient care plan, and i. if the patient adheres to their patient care plan, reduce the cadence of interventions by a care coordinator for that patient.

“10. The computer network system of claim 9, wherein, the learning module is configured to: receive a list of algorithm definitions and datasets for patient risk scoring, automatically calibrate one or more defined algorithms with the database, test the calibrated algorithms with a plurality of evaluation metrics, store the calibrated algorithms and evaluation metrics in a library, automatically select an algorithm for patient risk scoring based on the evaluation metrics, update further the database with third party data, and with user episode data, and re-execute the calibrate, test, store, and select steps after the update of the database step.

“11. The computer network system of claim 9, wherein: the prediction generator is configured to: receive a user prediction request for patient risk scoring, including episode data and a client model, run the currently selected algorithm corresponding to the user of the episode data, and generate patient risk scoring prediction output, generate a patient risk scoring prediction report based on the algorithm output, and transmit the patient risk scoring prediction report to the user.

“12. The computer network system of claim 9, wherein the algorithm definitions are of types that are members of the group comprising: multi-level models, random forest regression, logistical regression, gamma-distributed regression, and linear regression; the third party data is from a party that is a member of the group comprising: hospitals, medical practices, insurance companies, credit reporting agencies, and credit rating agencies; the database includes patient medical data, patient personal data, patient outcome data, and medical treatment data; the episode data includes individual patient medical data and personal data; and the user is a member of the group comprising: hospitals, medical practices, and insurance companies.

“13. The computer network system of claim 9, wherein the user device is remote from the prediction module, and the user device is a member of the group comprising: a computer, a desktop PC, a laptop PC, a smart phone, a tablet computer, and a personal wearable computing device.

“14. The computer network system of claim 9, wherein the web application communicates with the user device by the Internet, or an extranet, or a VPN, or other network, and the web application is generic for any user, or customized for a specific user, or class of user, and wherein, the user prediction request requests calibration of the correlation of demographic, social and medical attributes of the patient, to the outcome of a specific patient clinical episode type.”

URL and more information on this patent, see: Wang, Yale; Bauknight, Samuel H.; Rogow, Adam F.; Larson, Jeffrey D.; Drouin, Jean P. Computer Network Architecture With Machine Learning And Artificial Intelligence And Dynamic Patient Guidance. U.S. Patent Number 10,923,233, filed November 25, 2019, and published online on March 1, 2021. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=10,923,233.PN.&OS=PN/10,923,233RS=PN/10,923,233

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