Patent Issued for Computer Network Architecture With Machine Learning And Artificial Intelligence And Automated Insight Generation (USPTO 10,643,749)

2020 MAY 19 (NewsRx) -- By a News Reporter-Staff News Editor at Hospital & Nursing Home Daily -- A patent by the inventors Warner, Justin (San Francisco, CA); Drouin, Jean P. (San Francisco, CA); Gottula, Todd (San Francisco, CA); Sun, Emmet (San Francisco, CA), filed on September 30, 2019, was published online on May 18, 2020, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 10,643,749 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. For example, in healthcare there is a need for computer architecture to automatically identify areas of underperformance and over performance in a healthcare practice for decision makers at healthcare providers and payers. Prior art computer systems require manual work by providers or payers to identify such areas of performance, and lack the artificial intelligence and machine learning necessary for the computer systems to automatically general these insights about performance. Systems that generate such insight automatically without human intervention would, of course, operate more efficiently, reliably, and faster than systems requiring constant human input.”

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. Various embodiments perform certain steps automatically without human intervention, and hence are more efficient, reliable and faster than systems without these automatic aspects.

“The present disclosure describes a computer network architecture with machine learning and artificial intelligence that automatically identifies areas of underperformance and over performance in a healthcare practice for decision makers at healthcare providers and payers.

“Decision makers at healthcare providers or payers may, for example, receive from the system an automatically generated and transmitted report with identification of weaknesses to address in a healthcare practice (for example, a patient subgroup suffering from a specific medical condition that is receiving below benchmark outcomes or above benchmark costs and hospital stays) or strengths to exploit (for example, operating or attending physicians for a specific medical procedure that are achieving above benchmark patient outcomes or below benchmark costs and hospital stays). The decision maker can then respond to these automatic insights to pursue higher quality healthcare.”

The claims supplied by the inventors are:

“What is claimed is:

“1. A computer network architecture 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 and the updated database, and an automated insight generation web application (AIGWA) in electronic communication with both the prediction module, and a user device, wherein, the AIGWA is configured to execute the steps of: a. log a user into an Automated Insight Generation Web Application (AIGWA) embodiment, b. receive a request from the user that automated insight reports be generated and transmitted, for specified topics and requested subgroups of data, c. log out the user from the AIGWA, d. the AIGWA selects a metric to measure the performance insight requested, e. the AIGWA selects benchmarking data for the selected metrics, which may be either (1) available empirical data averages from sources available to the AIGWA, or (2) the forecasted outcomes for the selected metrics from the system’s patient risk scoring web application, f. the AIGWA selects an estimate of error for each metric, g. the AIGWA searches the data available to the AIGWA in the system for combinations of metrics for subgroups of data, and computes statistics summarizing each subgroup, h. the AIGWA automatically generates a list of the subgroups where the selected metrics differ from the benchmarking data by a statistically significant amount of underperformance or over performance, i. the AIGWA filters and ranks the insights by impact on the outcomes and selected metrics, and j. the AIGWA automatically generates an insight report of the underperformance and over performance, and their metrics, and transmits the report to the user, occasionally as requested, or periodically, or when triggered by an event, 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, 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, and wherein the updated database includes data from at least one third party, containing data of one or more types from the group consisting of: medical claims data, prescription refill data, publicly available social media data, socio-economic data, credit agency data, marketing data, travel website data, e-commerce website data, search engine data, credit card data, credit score and credit history data, lending data, mortgage data, financial data, travel data, geolocation data, telecommunications usage data, and other third-party databases.

“2. A computer network architecture with a web application for artificial intelligence and machine learning and automated insight generation comprising: an automated insight generation web application (AIGWA) in electronic communication with a prediction module, a patient risk scoring web application, and a user device, and wherein, the AIGWA is configured to: a. log a user into an Automated Insight Generation Web Application (AIGWA) embodiment, b. receive a request from the user that automated insight reports be generated and transmitted, for specified topics and requested subgroups of data, c. log the user out from the AIGWA, d. the AIGWA selects a metric to measure the performance insight requested, e. the AIGWA selects benchmarking data for the selected metrics, which may be either (1) available empirical data averages from sources available to the AIGWA, or (2) the forecasted outcomes for the selected metrics from the system’s patient risk scoring web application, f. the AIGWA selects an estimate of error for each metric, g. the AIGWA searches the data available to the AIGWA in the system for combinations of metrics for subgroups of data, and computes statistics summarizing each subgroup, h. the AIGWA automatically generates a list of the subgroups where the selected metrics differ from the benchmarking data by a statistically significant amount of underperformance or over performance, i. the AIGWA filters and ranks the insights by impact on the outcomes and selected metrics, and j. the AIGWA automatically generates an insight report of the underperformance and over performance, and their metrics, and transmits the report to the user, occasionally as requested, or periodically, or when triggered by an event, 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.

“3. The computer architecture of claim 2, wherein the updated database includes data from at least one third party, containing data of one or more types from the group consisting of: medical claims data, prescription refill data, publicly available social media data, socio-economic data, credit agency data, marketing data, travel web site data, e-commerce web site data, search engine data, credit card data, credit score and credit history data, lending data, mortgage data, financial data, travel data, geolocation data, telecommunications usage data, and other third-party databases.

“4. A computer network architecture with an automated insight generation web application with artificial intelligence and machine learning, comprising: an automated insight generation web application (AIGWA) in electronic communication with a prediction module, a patient risk scoring web application and a user device, and wherein, the AIGWA web application is configured to: a. log a user into an Automated Insight Generation Web Application (AIGWA) embodiment, b. receive a request from the user that automated insight reports be generated and transmitted, for specified topics and requested subgroups of data, c. log the user out from the AIGWA, d. the AIGWA selects a metric to measure the performance insight requested, e. the AIGWA selects benchmarking data for the selected metrics, which may be either (1) available empirical data averages from sources available to the AIGWA, or (2) the forecasted outcomes for the selected metrics from the system’s patient risk scoring web application, f. the AIGWA selects an estimate of error for each metric, g. the AIGWA searches the data available to the AIGWA in the system for combinations of metrics for subgroups of data, and computes statistics summarizing each subgroup, h. the AIGWA automatically generates a list of the subgroups where the selected metrics differ from the benchmarking data by a statistically significant amount of underperformance or over performance, i. the AIGWA filters and ranks the insights by impact on the outcomes and selected metrics, and j. the AIGWA automatically generates an insight report of the underperformance and over performance, and their metrics, and transmits the report to the user, occasionally as requested, or periodically, or when triggered by an event.

“5. The computer network architecture of claim 4: 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.

“6. The computer network architecture of claim 4, 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.

“7. The computer network architecture of claim 4, 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.

“8. The computer network architecture of claim 4, 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.

“9. The computer network architecture of claim 4, 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.

“10. The computer architecture of claim 4, wherein the updated database includes data from at least one third party, containing data of one or more types from the group consisting of: medical claims data, prescription refill data, publicly available social media data, socio-economic data, credit agency data, marketing data, travel web site data, e-commerce web site data, search engine data, credit card data, credit score and credit history data, lending data, mortgage data, financial data, travel data, geolocation data, telecommunications usage data, and other third-party databases.”

URL and more information on this patent, see: Warner, Justin; Drouin, Jean P.; Gottula, Todd; Sun, Emmet. Computer Network Architecture With Machine Learning And Artificial Intelligence And Automated Insight Generation. U.S. Patent Number 10,643,749, filed September 30, 2019, and published online on May 18, 2020. 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=10,643,749.PN.&OS=PN/10,643,749RS=PN/10,643,749

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