Patent Issued for Computer Network Architecture With Benchmark Automation, Machine Learning And Artificial Intelligence For Measurement Factors (USPTO 10,910,113)

2021 FEB 17 (NewsRx) -- By a News Reporter-Staff News Editor at Hospital & Nursing Home Daily -- From Alexandria, Virginia, NewsRx journalists report that 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 April 13, 2020, was published online on February 15, 2021.

The patent’s assignee for patent number 10,910,113 is Clarify Health Solutions Inc. (San Francisco, California, United States).

News editors 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.”

As a supplement to the background information on this patent, NewsRx correspondents 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 method utilizing benchmark automation, artificial intelligence and machine learning, comprising: receiving, by an analytic module web application (AMWA), a user request for analysis and performance benchmarks, with patient clinical episode data, wherein the AMWA is in electronic communication with a prediction module, a prediction web application, and a user device, wherein the prediction module includes a prediction generator and updated system databases, wherein the prediction web application is in electronic communication with the prediction module and the user device, analyzing, by the AMWA, the patient clinical episode data, identifying, by the AMWA, underperforming patient cohorts, requesting, by the AMWA, a benchmark service module web application (BSMWA) to generate performance benchmarks for the identified cohorts, wherein the BSMWA is in electronic communication with the prediction module, the prediction web application, the AMWA, and the user device, identifying, by the BSMWA, updated system databases and third-party databases with relevant patient claims data, generating and distributing, by the BSMWA, requests for data to the identified databases, receiving, by the BSMWA, the requested data and storing the data received from the user requests and the data requests in the updated system databases, generating, by the BSMWA, a benchmark response report for the identified patient cohorts using the received data, transferring, by the BSMWA, the benchmark response report to the user device, accessing, by the BSMWA, the prediction web application to predict the performance of the identified patient cohorts and impacts of meeting the benchmarks, receiving, by a learning module, a list of algorithm definitions and datasets for prediction, wherein the learning module includes a training submodule and is in electronic communication with the prediction module and the updated system databases, automatically calibrating, by the learning module, one or more defined algorithms with the identified updated system databases, testing, by the learning module, the calibrated algorithms with a plurality of evaluation metrics, storing, by the learning module, the tested algorithms and the evaluation metrics in a library, automatically selecting, by the learning module, 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, updating further, by the learning module, the identified updated system databases with third party data, and with user episode data, and upon occurrence of an event or periodically, re-executing, by the learning module, the calibrating, testing, storing, and selecting steps after the updating of the identified updated system databases step.

“2. The method of 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 method 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 method 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 method of 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 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, and telecommunications usage data.

“6. A method utilizing benchmark automation, artificial intelligence and machine learning, comprising: receiving, by an analytic module web application (AMWA), a user request for analysis and performance benchmarks, with patient clinical episode data, wherein the AMWA is in electronic communication with a prediction module, a prediction web application, and a user device, wherein the prediction module includes a prediction generator and updated system databases, wherein the prediction web application is in electronic communication with the prediction module and the user device, analyzing, by the AMWA, the patient clinical episode data, identifying, by the AMWA, underperforming patient cohorts, requesting, by the AMWA, a benchmark service module web application (BSMWA) to generate performance benchmarks for the identified cohorts, wherein the BSMWA is in electronic communication with the prediction module, the prediction web application, the AMWA, and the user device, identifying, by the BSMWA, updated system databases and third-party databases with relevant patient claims data, generating and distributing, by the BSMWA, requests for data to the identified databases, receiving, by the BSMWA, the requested data and storing the data received from the user requests and the data requests in the updated system databases, generating, by the BSMWA, a benchmark response report for the identified patient cohorts using the received data, transferring, by the BSMWA, the benchmark response report to the user device, accessing, by the BSMWA, the prediction web application to predict the performance of the identified patient cohorts and impacts of meeting the benchmarks, receiving, by a learning module, a list of algorithm definitions and datasets for prediction, wherein the learning module includes a training submodule and is in electronic communication with the prediction module and the updated system databases, automatically calibrating, by the learning module, one or more defined algorithms with the identified updated system databases, testing, by the learning module, the calibrated algorithms with a plurality of evaluation metrics, storing, by the learning module, the tested algorithms and the evaluation metrics in a library, automatically selecting, by the learning module, 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, updating further, by the learning module, the identified updated system databases with third party data, and with user episode data, and upon occurrence of an event or periodically, re-executing, by the learning module, the calibrating, testing, storing, and selecting steps after the updating of the identified updated system databases step, 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.”

For additional information on this patent, see: 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 10,910,113, filed April 13, 2020, and published online on February 15, 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=10,910,113.PN.&OS=PN/10,910,113RS=PN/10,910,113

(Our reports deliver fact-based news of research and discoveries from around the world.)