Patent Issued for Machine Learning Clinical Decision Support System For Risk Categorization (USPTO 10,650,927)

2020 MAY 26 (NewsRx) -- By a News Reporter-Staff News Editor at Disease Prevention Daily -- According to news reporting originating from Alexandria, Virginia, by NewsRx journalists, a patent by the inventors Are, Sasanka (Kansas City, MO); Sugden, John Matthew (Lee’s Summit, MO), filed on November 14, 2016, was published online on May 25, 2020.

The assignee for this patent, patent number 10,650,927, is Cerner Innovation Inc. (Kansas City, Kansas, United States).

Reporters obtained the following quote from the background information supplied by the inventors: “With medical care expenses compounding every year, effective population health risk management is essential. Risk stratification systems, for computer assisted clinical decision support, are necessary for determining risks of patient populations in regard to certain conditions and facilitating health management. But present systems for managing population health risks do not harness valuable electronic health record data and claims experience for categorizing patient risks. As a consequence, inaccurate and imprecise risk assignment often results, rendering these systems less effective at understanding their population of patients. This contributes to decreased quality of care, increased risk of medical errors, and increased cost of healthcare. Additionally, specific knowledge of patient risk strata can enable health care administrators to develop wellness programs with population-specific conditions in mind, more accurately forecast future spend levels, and anticipate resource needs. It is of great significance, then, to improve upon conventional technological approaches to achieve a greater degree of accuracy and dependability, especially as applied to a target individual as opposed to a population as a whole--a drawback that conventional approaches have not been able to effectively overcome.”

In addition to obtaining background information on this patent, NewsRx editors also obtained the inventors’ summary information for this patent: “This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

“Embodiments of the present disclosure relate to systems, methods, and user interfaces for providing improved risk categorization for clinical decision support. In particular, a risk index is provided that improves on other risk stratification models by synthesizing electronic medical records and health questionnaires with an individual patient’s claim histories. One or more machine learning algorithms catalogue patients into distinct group clusters, based on risk which may be associated with annual health care spending, thereby enabling administrators to forecast future spending on the individual and population level. Further, by synthesizing member claims with electronic health records, a more holistic view is provided of patient health. With an improved understanding of the patient population, administrators can more efficiently target individual patients for intervention and develop wellness programs targeting population-specific conditions.

“In some embodiments, one or more risk index models are provided that may use information from claims, clinical data, electronic medical records (EMR), and wellness information, which may include personal health assessments (PHA) to classify individuals into risk groups thereby enabling future spending levels to be predicted. Machine learning algorithms may be used in conjunction with historic patient data to determine the risk model(s). In some embodiments, the population risk categories may be displayed to an administrator or clinician to facilitate decision making. Similarly, in some embodiments, a patient may be indexed (or categorized) according to a determined risk index.”

The claims supplied by the inventors are:

“What is claimed is:

“1. A computerized system comprising: a risk index classifier configured to classify members of a population of humans into one or more risk groups from a set of risk groups; one or more processors; and computer storage memory having computer-executable instructions stored thereon which, when executed by the processor, implement a method of predicting likely future health care spend for the population members, the method comprising: receiving a set of member data for a plurality of members of the population; determining one or more risk index models used by the risk index classifier based on a type of the set of member data, the one or more risk index model determined using a set of machine learning algorithms; classifying, using the risk index classifier, one or more members of the population into one or more risk groups from the set of risk groups, based on the set of member data; determining a future health care spend forecast for the classified one or more members of the population; and modifying a health care event based on the determined forecast, wherein the set of member data comprises data related to claims and data related to at least one of (i) wellness or personal-health-assessment (PHA) information and (ii) electronic medical record (EMR) information.

“2. The computerized system of claim 1, wherein the modified health care event comprises selecting or modifying a computer health care treatment program associated with the classified one or more members of the population.

“3. The computerized system of claim 1, wherein the set of member data comprises data related to claims, wellness or personal-health-assessment (PHA) information and electronic medical record (EMR) information.

“4. The computerized system of claim 1, wherein determining a future health care spend forecast is based at least in part on the received set of member data.

“5. The computerized system of claim 1, wherein one or more index models are determined at least based on the received set of member data.

“6. The computerized system of claim 1, wherein the set of machine learning algorithms comprises an ensemble of alternating decision trees.

“7. The computerized system of claim 6 wherein the set of machine learning algorithms further comprises a cost-sensitive classifier, a multi-class classifier, and a rotational forest ensemble method.

“8. The computerized system of claim 1 wherein the set of machine learning algorithms comprises a cost-sensitive classifier.

“9. The computerized system of claim 1 further comprising determining the number of risk groups in the set of risk groups, based in part on the received set of member data.

“10. The computerized system of claim 1, wherein the set of member data comprises data related to claims and behavior or lifestyle information.

“11. A method for predicting likely future health care spend for individual members of a population of humans using a computing system, the method comprising: receiving electronic data over a communications network for the members of the population, the data comprising historical information related to claims and data related to at least one of (i) wellness or personal-health-assessment (PHA) information and (ii) electronic medical record (EMR) information; determining, via the computing system, one or more risk index models used by risk index classifier based on a type of the received data, the one or more risk index models determined using a set of machine learning algorithms; classifying, via the computing system, members of the population into a set of risk groups, based on the received data and a risk index model, the risk index model using one or more machine learning algorithms; and determining, via a computing system, a set of future health care costs over a duration of time associated with the classified members based on the classification and the received data.

“12. The method of claim 11 further comprising modifying a health care event based on the determined set of future health care costs.

“13. The method of claim 11, wherein the one or more machine learning algorithms comprises an ensemble of alternating decision trees.

“14. The method of claim 13, wherein the set of machine learning algorithms further comprises a cost-sensitive classifier.

“15. The method of claim 11 further comprising determining a future health care cost the over a duration of time for the population based on the set of future health care costs associated with the classified members.

“16. One or more non-transitory computer-storage media having computer-executable instructions embodied thereon that, when executed by a computing device, cause the computing device to perform a method of determining future health care costs for members in a population of human patients, the method comprising: receiving raw data for the members in the population, the raw data comprising claims information and at least one of (i) wellness or personal-health-assessment (PHA) information and (ii) electronic medical record (EMR) information for each of the members; determining a risk index model based on the raw data, a type of the raw data, and a set of machine learning algorithms; classifying the members of the population into one or more risk groups using the risk index model; determining a future health care costs over a duration of time for the members based on the risk groups for which the members are classified; and modifying a health care event based on the determined future health care cost.

“17. The media claim 16, wherein the set of machine learning algorithms comprises an ensemble of alternating decision trees.

“18. The media claim 16, wherein the set of machine learning algorithms comprises a cost-sensitive classifier.”

For more information, see this patent: Are, Sasanka; Sugden, John Matthew. Machine Learning Clinical Decision Support System For Risk Categorization. U.S. Patent Number 10,650,927, filed November 14, 2016, and published online on May 25, 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=10,650,927.PN.&OS=PN/10,650,927RS=PN/10,650,927

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