Patent Issued for Computer Network Architecture With Benchmark Automation, Machine Learning And Artificial Intelligence For Measurement Factors (USPTO 10,643,751)

2020 MAY 19 (NewsRx) -- By a News Reporter-Staff News Editor at Hospital & Nursing Home Daily -- According to news reporting originating from Alexandria, Virginia, by NewsRx journalists, a patent by the inventors Drouin, Jean P. (San Francisco, CA); Bauknight, Samuel H. (San Francisco, CA); Gottula, Todd (San Francisco, CA); Wang, Yale (San Francisco, CA); Rogow, Adam F. (San Francisco, CA); Warner, Justin (San Francisco, CA), filed on September 26, 2019, was published online on May 18, 2020.

The assignee for this patent, patent number 10,643,751, is Clarify Health Solutions Inc. (San Francisco, California, United States).

Reporters obtained the following quote from the background information supplied by the inventors: “As physicians, insurance companies, hospitals, and other healthcare facilities identify areas for potential improvement in resource utilization and quality of care, the healthcare providers have a need for applicable industry benchmarks to determine the scale of improvement that might be reasonable to achieve.

“New tools may now identify pockets (i.e., specific patient cohorts) of under-performance in healthcare at a level of granularity that healthcare providers have not previously seen. Therefore, there are often no publicly available benchmarks for how the industry as a whole performs for these specific cohorts of patients. There is a longstanding frustrated need to automatically measure related healthcare factors.

“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 accurately forecast aspects of healthcare to provide practical applications for healthcare providers. Systems that generate such forecasts automatically without human intervention would, of course, operate more efficiently, reliably, and faster than systems requiring constant human input to make such practical applications.”

In addition to obtaining background information on this patent, NewsRx editors also obtained the inventors’ summary information for this patent: “The present disclosure describes a computer network architecture with machine learning and artificial intelligence to provide the automated production and distribution of custom healthcare performance benchmarks for specific patient cohorts, and to automatically measure related healthcare factors.

“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 in practical applications to healthcare. Various embodiments perform certain steps automatically without human intervention, and hence are more efficient, reliable and faster than systems without these automatic aspects.

“Embodiments of the present invention allow specification and automated production of benchmarks using any of many dozens of patient, disease process, facility, and physical location attributes. This enables healthcare providers to automatically measure their performance for distinct patient cohorts against industry benchmarks for each specific cohort. This automatic measurement enables healthcare providers to get a true quantitative sense of what level of healthcare performance is achievable for any given population of patients, regardless of how specifically or novelly that patient population may be defined.”

The claims supplied by the inventors are:

“What is claimed is:

“1. A computer network architecture with benchmark automation, artificial intelligence and machine learning, comprising: a prediction module with a prediction generator and updated system databases a learning module with a training submodule, in electronic communication with the prediction module and the updated system database, a prediction web application in electronic communication with the prediction module and a user device, and an analytic module web application (AMWA) in electronic communication with the prediction module, the prediction web application, and a user device, and a benchmark service module web application (BSMWA) in electronic communication with the AMWA and the user device, and wherein, the AMWA is configured to: a. receive a user request for analysis and performance benchmarks, with patient clinical episode data, b. analyze the patient clinical episode data, c. identify underperforming patient cohorts, and d. request the BSMWA to generate performance benchmarks for the identified cohorts, and wherein the BSMWA is configured to: a. identify updated system databases and third-party databases with relevant patient claims data, b. generate and distribute requests for data to the identified databases, c. receive the requested data and store the data received from the user requests and the data requests in the updated system databases, d. generate a benchmark response report for the identified patient cohorts using the received data, e. transfer the benchmark response report to the user device, and f. access the prediction web application to predict the performance of the identified patient cohorts and impacts of meeting the benchmarks, and wherein, the learning module is configured to: receive a list of algorithm definitions and datasets for prediction, automatically calibrate one or more defined algorithms with the identified updated system databases, test the calibrated algorithms with a plurality of evaluation metrics, store the tested algorithms and the evaluation metrics in a library, automatically select a tested algorithm, wherein the selected tested algorithm is utilized by the prediction web application to predict the performance of the identified patient cohorts and the impacts of meeting the benchmarks; update further the identified updated system databases with third party data, and with user episode data, and upon occurrence of an event or periodically, re-execute the calibrate, test, store, and select steps after the update of the identified updated system databases step.

“2. The computer network architecture in claim 1, wherein the identified databases include data that is from a party that is a member of the group consisting of: hospitals, medical practices, insurance companies, credit reporting agencies, and credit rating agencies, and the identified databases include patient medical data, patient personal data, patient outcome data, and medical treatment data; the user is a member of the group comprising: hospitals, medical practices, and insurance companies.

“3. The computer network architecture of claim 1 wherein the user device is remote from the AMWA and the BSMWA, and the user device is a member of the group consisting of: a computer, a desktop PC, a laptop PC, a smart phone, a tablet computer, and a personal wearable computing device.

“4. The computer network architecture of claim 1, wherein the BSMWA communicates with the third-party databases by the Internet, or an extranet, or a VPN, or other network, and the AMWA and BSMWA are generic for any user, or customized for a specific user, or class of user.

“5. The computer architecture in claim 1, wherein the identified databases contain data from at least one third party, containing data of a plurality of types consisting of: medical claims data, prescription refill data, publicly available social media 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, and telecommunications usage data.

“6. A computer network architecture with benchmark automation, artificial intelligence and machine learning, comprising: a prediction module with a prediction generator and updated system databases, a learning module with a training submodule, in electronic communication with the prediction module and the updated system database, a prediction web application in electronic communication with the prediction module and a user device, and an analytic module web application (AMWA) in electronic communication with the prediction module, the prediction web application, and a user device, and a benchmark service module web application (BSMWA) in electronic communication with the prediction module, the prediction web application, the AMWA and the user device, and wherein, the AMWA is configured to: a. receive a user request for analysis and performance benchmarks, b. analyze patient clinical episode data, c. identify underperforming patient cohorts, and d. request the BSMWA to generate performance benchmarks for the identified cohorts, and wherein the BSMWA is configured to: a. identify updated system databases and third-party databases with relevant patient claims data, b. generate and distribute requests for data to the identified databases, c. receive the requested data and store the received data in the updated system databases, d. generate a benchmark response report for the identified patient cohorts using the received data, and e. transfer the benchmark response report to the user device; and f. access the prediction web application to predict the performance of the identified patient cohorts and impacts of meeting the benchmarks, and wherein the identified databases include data that is from a party that is a member of the group consisting of: hospitals, medical practices, insurance companies, credit reporting agencies, and credit rating agencies, and the identified databases include patient medical data, patient personal data, patient outcome data, and medical treatment data; and the user is a member of the group comprising: hospitals, medical practices, and insurance companies; and wherein the user device is remote from the AMWA and the BSMWA, and the user device is a member of the group consisting of: a computer, a desktop PC, a laptop PC, a smart phone, a tablet computer, and a personal wearable computing device; and wherein the BSMWA communicates with the third-party databases by the Internet, or an extranet, or a VPN, or other network, and the AMWA and BSMWA are generic for any user, or customized for a specific user, or class of user; and wherein the identified databases contain data from at least one third party, containing data of a plurality of types consisting of: medical claims data, prescription refill data, publicly available social media 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, and telecommunications usage data; and wherein, the learning module is configured to: receive a list of algorithm definitions and datasets for prediction, automatically calibrate one or more defined algorithms with the identified updated system databases, test the calibrated algorithms with a plurality of evaluation metrics, store the tested algorithms and the evaluation metrics in a library, automatically select a tested algorithm, wherein the selected tested algorithm is utilized by the prediction web application to predict the performance of the identified patient cohorts and the impacts of meeting the benchmarks; update further the identified updated system databases with third party data, and with user episode data, and upon occurrence of an event or periodically, re-execute the calibrate, test, store, and select steps after the update of the identified updated system databases step.”

For more information, see this patent: Drouin, Jean P.; Bauknight, Samuel H.; Gottula, Todd; Wang, Yale; Rogow, Adam F.; Warner, Justin. Computer Network Architecture With Benchmark Automation, Machine Learning And Artificial Intelligence For Measurement Factors. U.S. Patent Number 10,643,751, filed September 26, 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=10,643,751.PN.&OS=PN/10,643,751RS=PN/10,643,751

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