Patent Issued for Intelligently linking payer/health plan combinations to specific employers (USPTO 11763390): Cerner Innovation Inc.

2023 OCT 06 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Delano, III, Raymond G. (Leawood, KS, US), Jensen, Julie Ann (Olathe, KS, US), Poteet, III, James L. (Overland Park, KS, US), filed on March 9, 2020, was published online on September 19, 2023, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11763390 is assigned to Cerner Innovation Inc. (Kansas City, Missouri, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “Health care providers are dependent on accurate insurance information to ensure their medical claim reimbursements are timely paid. Such timeliness enables health care providers to predict cash flow. Often patient insurance eligibility and health care claims are sent to the wrong payer or health plan, resulting in denied claims. This situation may happen if the health plan on the insurance card cannot be matched to a health plan in the electronic health record system (EHR). Or, perhaps the health plan was built in the EHR using a slightly different name or is abbreviated or confused with or similarly named plan. Similar situations may arise if the patient is from a different state or locality and the provider organization has not built a particular health plan in the EHR. Sometimes, the health insurance card is scanned correctly but still manually matched to the wrong health plan and/or the electronic routing is configured incorrectly. In each of these scenarios, the health care provider is not likely to receive timely reimbursement and a budget shortfall results.”

In addition to the background information obtained for this patent, NewsRx journalists 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 invention relate to utilizing machine learning to verify payers and/or health plans. More particularly, the present invention utilizes HIPAA transactions to train a machine learning model to intelligently link payers and/or health plans to specific employers. Initially, transaction data is received from electronic data interchange (EDI) insurance transactions. The transaction data comprises data corresponding to a plurality of employers, a plurality of payers, and a plurality of health plans provided by the plurality of payers. A machine learning model is trained with the transaction data to build a mapping of the plurality of employers, the plurality of payers contracted with each employer of the plurality of employers, and the plurality of health plans provided by the plurality of payers for each employer of the plurality of employers. The machine learning model can be utilized to verify the scan data is mapped in accordance with the mapping.”

The claims supplied by the inventors are:

“1. A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a computer including a processor, cause the computer to perform functions comprising: training a machine learning model to predict a payer/health plan combination for a patient by: receiving transaction data from electronic data interchange (EDI) insurance transactions, the transaction data comprising data corresponding to a plurality of employers, a plurality of payers contracted with each employer of the plurality of employers, and a plurality of health plans provided by the plurality of payers for each employer of the plurality of employers; extracting a first dataset of features from the received transaction data: and training the machine learning model to predict a payer/health plan combination for a patient by inputting the first dataset into the machine learning model to build a mapping of the plurality of employers, the plurality of payers contracted with each employer of the plurality of employers, and the plurality of health plans provided by the plurality of payers for each employer of the plurality of employers; receiving additional transaction data from the EDI insurance transactions, the additional transaction data comprising reimbursements or denials; extracting a second dataset of features from the additional transaction data; and inputting the second dataset into the machine learning model to update the mapping; obtaining employer and work location data associated with a patient who does not present an insurance card; creating a patient dataset from the obtained employer and work location data: and inputting the patient dataset into the machine learning model to predict, for the patient who does not present an insurance card, one or more payer/health plan combinations based on the employer and work location information.

“2. The non-transitory computer-readable medium of claim 1, further comprising instructions that, when executed by at least the processor, cause the processor to: provide a user interface that displays fields for the identification of the payer, the identification of the health plan of the payer, and the identification of the employer.

“3. The non-transitory computer-readable medium of claim 2, further comprising instructions that, when executed by at least the processor, cause the processor to: automatically extract, at a card scanning service, scan data into appropriate fields of the user interface, the scan data corresponding to an insurance card and comprising the identification of the payer and the identification of the health plan of the payer.

“4. The non-transitory computer-readable medium of claim 3, further comprising instructions that, when executed by at least the processor, cause the processor to: enable a user, via the user interface, to review or revise the extracted scan data prior to saving the extracted scan data in an electronic health record of a patient.

“5. The non-transitory computer-readable medium of claim 3, wherein the extracted scan data comprises the identification of the payer, the identification of the health plan of the payer, and the identification of the employer.

“6. The non-transitory computer-readable medium of claim 3, further comprising instructions that, when executed by at least the processor, cause the processor to: display, at the user interface, a view of a front image capture and a view of a back image capture of the insurance card.

“7. The onc non-transitory computer-readable medium of claim 3, further comprising instructions that, when executed by at least the processor, cause the processor to: display, at the user interface, employer and work location address information imported from a registration system.

“8. The non-transitory computer-readable medium of claim 3, further comprising instructions that, when executed by at least the processor, cause the processor to: display, at the user interface, a filtered list of valid payer/health plan combinations for specific employers.

“9. The non-transitory computer-readable medium of claim 8, further comprising instructions that, when executed by at least the processor, cause the processor to: enable, via the user interface, a user to select a payer/health plan combination for a patient.

“10. A method for predicting a payer/health plan combination for a patient comprising: training a machine learning model to predict a payer/health plan combination for a patient by: receiving transaction data from electronic data interchange (EDI) insurance transactions, the transaction data comprising data corresponding to a plurality of employers, a plurality of payers contracted with each employer of the plurality of employers, and a plurality of health plans provided by the plurality of payers for each employer of the plurality of employers; extracting a first dataset of features from the received transaction data: and training a machine learning model to predict a payer/health plan combination for a patient by inputting the first dataset into the machine learning model to build a mapping of the plurality of employers, the plurality of payers contracted with each employer of the plurality of employers, and the plurality of health plans provided by the plurality of payers for each employer of the plurality of employers; receiving additional transaction data from the EDI insurance transactions, the additional transaction data comprising reimbursements or denials; extracting a second dataset of features from the additional transaction data; and inputting the second dataset into the machine learning model to update the mapping; obtaining employer and work location data associated with a patient who does not present an insurance card; creating a patient dataset from the obtained employer and work location data: and inputting the patient dataset into the machine learning model to predict, for the patient who does not present an insurance card, one or more payer/health plan combinations based on the employer and work location information.

“11. The method of claim 10, further comprising receiving scan data corresponding to an insurance card, the scan data comprising an identification of a payer and an identification of a health plan of the payer.

“12. The method of claim 10, wherein the method further comprises: utilizing the machine learning model to verify that the scan data is mapped in accordance with the mapping.

“13. The method of claim 10, wherein the method further comprises: receiving an identification of an employer and a work location.

“14. The method of claim 13, wherein the method further comprises: utilizing the machine learning model, to predict one or more payer/health plan combinations based on the identification of the employer and the work location.

“15. The method of claim 14, wherein the method further comprises: enabling a user, via a user interface, to select a payer/health plan combination of the one or more payer/health plan combinations.

“16. The method of claim 15, wherein the method further comprises: saving the selected payer/health plan combination in an electronic health record of a patient.

“17. A computcrizcd system comprising: one or more processors; and a non-transitory computer storage media storing computer-useable instructions that, when used by the one or more processors, cause the one or more processors to: train a machine learning model to predict a payer/health plan combination for a patient by: receiving transaction data from electronic data interchange (EDI) insurance transactions, the transaction data comprising data corresponding to a plurality of employers, a plurality of payers contracted with each employer of the plurality of employers, and a plurality of health plans provided by the plurality of payers for each employer of the plurality of employers; extracting a first dataset of features from the received transaction data: and training a machine learning model to predict a payer/health plan combination for a patient by inputting the first dataset into the machine learning model to build a mapping of the plurality of employers, the plurality of payers contracted with each employer of the plurality of employers, and the plurality of health plans provided by the plurality of payers for each employer of the plurality of employers; receive additional transaction data from the EDI insurance transactions, the additional transaction data comprising reimbursements or denials: extract a second dataset of features from the additional transaction data; input the second dataset into the machine learning model to update the mapping; obtain employer and work location data associated with a patient who does not present an insurance card; create a patient dataset from the obtained employer and work location data; and input the patient dataset into the machine learning model, predict to predict, for the patient who does paticnts that do not present an insurance card, one or more payer/health plan combinations based on the employer and work location information.

“18. The system of claim 17, wherein the computer-executable instructions are further configured to cause the at least one or more processors to: provide a user interface that displays fields for the identification of the payer, the identification of the health plan of the payer, and the identification of the employer; automatically extract, at a card scanning service, scan data into appropriate fields of the user interface, the scan data corresponding to an insurance card and comprising the identification of the payer and the identification of the health plan of the payer; and enable, via the user interface, a review or a revision of the extracted scan data prior to saving the extracted scan data in an electronic health record.”

URL and more information on this patent, see: Delano, III, Raymond G. Intelligently linking payer/health plan combinations to specific employers. U.S. Patent Number 11763390, filed March 9, 2020, and published online on September 19, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11763390)&db=USPAT&type=ids

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