Researchers Submit Patent Application, “Predictive and Prescriptive Analytics for Managing High-Cost Claimants in Healthcare”, for Approval (USPTO 20230222597): Patent Application

2023 JUL 27 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Washington, D.C., NewsRx journalists report that a patent application by the inventors Hao, Yifan (Foster City, CA, US); Liu, Nan (San Jose, CA, US); Qiao, Mu (Belmont, CA, US); Routray, Ramani R. (San Jose, CA, US); Seery, Colman (Claremont, CA, US); Zhang, Lixiang (State College, PA, US), filed on January 10, 2022, was made available online on July 13, 2023.

No assignee for this patent application has been made.

News editors obtained the following quote from the background information supplied by the inventors: “The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for predictive and prescriptive analytics for managing high-cost claimants in healthcare.

“United States healthcare spending grew 4.6% to $3.8 trillion in 2019, or $11,582 per person, and accounted for 17.7% of Gross Domestic Product (GDP), according to the Centers for Medicare and Medicaid Services (CMS). The American Health Policy Institute defines a high-cost claimant (HCC) as an individual who costs $50,000 or more annually. The group’s analysis of twenty-six large employers’ claims data found that the average high-cost claimant costs $122,382 each year, or 29.3 times as much as the average member. Even though they represent just 1.2% of all members, high-cost claimants make up 31% of total healthcare spending for the surveyed employers. Reducing costs for HCCs requires a focus on impactable members where intervention can yield a change in future cost, utilization, and outcome.”

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “This Summary is provided to introduce a selection of concepts in a simplified form that are further described herein in the Detailed Description. This Summary is not intended to identify key factors or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

“In one illustrative embodiment, a method is provided in a data processing system, for predictive and prescriptive analytics for managing high-cost claimants. The method comprises training a machine learning model using the set of de-identified claims data to predict high-cost claimants using the training data. The method further comprises applying transfer learning by retraining the machine learning model using a first set of customized client data to generate a client-specific machine learning model. The method further comprises applying the client-specific machine learning model to a second set of customized client data to identify a set of predicted high-cost claimants within the second set of customized client data. The method further comprises generating association rules for determining recommendations for preventing the set of predicted high-cost claimants from becoming high-cost claimants. The method further comprises applying the association rules to the second set of customized client data to generate a set of recommendations.

“In another illustrative embodiment, a computer program product comprises a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to train a machine learning model using the set of de-identified claims data to predict high-cost claimants using the training data. The computer readable program further causes the computing device to apply transfer learning by retraining the machine learning model using a first set of customized client data to generate a client-specific machine learning model. The computer readable program further causes the computing device to apply the client-specific machine learning model to a second set of customized client data to identify a set of predicted high-cost claimants within the second set of customized client data. The computer readable program further causes the computing device to generate association rules for determining recommendations for preventing the set of predicted high-cost claimants from becoming high-cost claimants. The computer readable program further causes the computing device to apply the association rules to the second set of customized client data to generate a set of recommendations.

“In yet another illustrative embodiment, an apparatus comprises a processor and a memory coupled to the processor, the memory comprises instructions which, when executed by the processor, cause the processor to train a machine learning model using the set of de-identified claims data to predict high-cost claimants using the training data. The instructions further cause the processor to apply transfer learning by retraining the machine learning model using a first set of customized client data to generate a client-specific machine learning model. The instructions further cause the processor to apply the client-specific machine learning model to a second set of customized client data to identify a set of predicted high-cost claimants within the second set of customized client data. The instructions further cause the processor to generate association rules for determining recommendations for preventing the set of predicted high-cost claimants from becoming high-cost claimants. The instructions further cause the processor to apply the association rules to the second set of customized client data to generate a set of recommendations.

“The illustrative embodiments provide mechanisms for not only predicting likely future high-cost claimants but also generating prescriptive recommendations for preventing members from becoming high-cost claimants.

“In one example embodiment, generating the association rules comprises finding frequent common features among the set of predicted high-cost claimants; filtering the de-identified claims data for individuals having the frequent common features; and applying association rule mining on the filtered de-identified claims data to generate a set of association rules, wherein each rule in the set of association rule associates a measure with an individual who is no longer a high-cost claimant. This embodiment generates rules for creating recommendations for reducing healthcare costs by mining existing claims data.

“In another example embodiment, generating the association rules further comprises generating a confidence value for each measure and ranking the measures by confidence value. In yet another example embodiment, applying the association rules to the second set of customized client data comprises outputting a predetermined number of recommendations with the highest confidence value. These embodiments provide recommendations that are most likely to result in reduction in healthcare costs.

“In another example embodiment, generating the association rules comprises applying a Frequent Pattern (FP) Growth algorithm to the second set of customized client data. This embodiment uses Association Rule Learning for discovering frequent items in a transaction database without any generation of candidates.

“In one example embodiment, the machine learning model comprises a bidirectional Recurrent Neural Network with attention. This embodiment allows the neural network to exhibit temporal dynamic behavior and to focus on a subset of its inputs (or features).

“These and other features and advantages of the present invention will be described in, or will become apparent to those of ordinary skill in the art in view of, the following detailed description of the example embodiments of the present invention.”

The claims supplied by the inventors are:

“1. A method, in a data processing system, for predictive and prescriptive analytics for managing high-cost claimants, the method comprising: training a machine learning model using the set of de-identified claims data to predict high-cost claimants using the training data; applying transfer learning by retraining the machine learning model using a first set of customized client data to generate a client-specific machine learning model; applying the client-specific machine learning model to a second set of customized client data to identify a set of predicted high-cost claimants within the second set of customized client data; generating association rules for determining recommendations for preventing the set of predicted high-cost claimants from becoming high-cost claimants; and applying the association rules to the second set of customized client data to generate a set of recommendations.

“2. The method of claim 1, wherein generating the association rules comprises: finding frequent common features among the set of predicted high-cost claimants; filtering the de-identified claims data for individuals having the frequent common features; and applying association rule mining on the filtered de-identified claims data to generate a set of association rules, wherein each rule in the set of association rule associates a measure with an individual who is no longer a high-cost claimant.

“3. The method of claim 2, wherein the measure comprises a procedure, a drug, or a rehabilitation measure.

“4. The method of claim 2, wherein generating the association rules further comprises generating a confidence value for each measure and ranking the measures by confidence value.

“5. The method of claim 4, wherein applying the association rules to the second set of customized client data comprises outputting a predetermined number of recommendations with the highest confidence value.

“6. The method of claim 1, wherein generating the association rules comprises applying a Frequent Pattern (FP) Growth algorithm to the second set of customized client data.

“7. The method of claim 1, wherein the machine learning model comprises a bidirectional Recurrent Neural Network with attention.

“8. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: train a machine learning model using the set of de-identified claims data to predict high-cost claimants using the training data; apply transfer learning by retraining the machine learning model using a first set of customized client data to generate a client-specific machine learning model; apply the client-specific machine learning model to a second set of customized client data to identify a set of predicted high-cost claimants within the second set of customized client data; generate association rules for determining recommendations for preventing the set of predicted high-cost claimants from becoming high-cost claimants; and apply the association rules to the second set of customized client data to generate a set of recommendations.

“9. The computer program product of claim 8, wherein generating the association rules comprises: finding frequent common features among the set of predicted high-cost claimants; filtering the de-identified claims data for individuals having the frequent common features; and applying association rule mining on the filtered de-identified claims data to generate a set of association rules, wherein each rule in the set of association rule associates a measure with an individual who is no longer a high-cost claimant.

“10. The computer program product of claim 9, wherein the measure comprises a procedure, a drug, or a rehabilitation measure.

“11. The computer program product of claim 9, wherein generating the association rules further comprises generating a confidence value for each measure and ranking the measures by confidence value.

“12. The computer program product of claim 11, wherein applying the association rules to the second set of customized client data comprises outputting a predetermined number of recommendations with the highest confidence value.

“13. The computer program product of claim 8, wherein generating the association rules comprises applying a Frequent Pattern (FP) Growth algorithm to the second set of customized client data.

“14. The computer program product of claim 8, wherein the machine learning model comprises a bidirectional Recurrent Neural Network with attention.

“15. An apparatus comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: train a machine learning model using the set of de-identified claims data to predict high-cost claimants using the training data; apply transfer learning by retraining the machine learning model using a first set of customized client data to generate a client-specific machine learning model; apply the client-specific machine learning model to a second set of customized client data to identify a set of predicted high-cost claimants within the second set of customized client data; generate association rules for determining recommendations for preventing the set of predicted high-cost claimants from becoming high-cost claimants; and apply the association rules to the second set of customized client data to generate a set of recommendations.

“16. The apparatus of claim 15, wherein generating the association rules comprises: finding frequent common features among the set of predicted high-cost claimants; filtering the de-identified claims data for individuals having the frequent common features; and applying association rule mining on the filtered de-identified claims data to generate a set of association rules, wherein each rule in the set of association rule associates a measure with an individual who is no longer a high-cost claimant.

“17. The apparatus of claim 16, wherein the measure comprises a procedure, a drug, or a rehabilitation measure.

“18. The apparatus of claim 16, wherein generating the association rules further comprises generating a confidence value for each measure and ranking the measures by confidence value.

“19. The apparatus of claim 15, wherein generating the association rules comprises applying a Frequent Pattern (FP) Growth algorithm to the second set of customized client data.

“20. The apparatus of claim 15, wherein the machine learning model comprises a bidirectional Recurrent Neural Network with attention.”

For additional information on this patent application, see: Hao, Yifan; Liu, Nan; Qiao, Mu; Routray, Ramani R.; Seery, Colman; Zhang, Lixiang. Predictive and Prescriptive Analytics for Managing High-Cost Claimants in Healthcare. U.S. Patent Application Number 20230222597, filed January 10, 2022 and posted July 13, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230222597)&db=US-PGPUB&type=ids

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