Researchers Submit Patent Application, “Dynamic Database Updates Using Probabilistic Determinations”, for Approval (USPTO 20230154582): Patent Application

2023 JUN 02 (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 Mac Manus, Lorcan B. (Co Kildare, IE); Neftzger, Amy (Franklin, TN, US), filed on January 4, 2023, was made available online on May 18, 2023.

No assignee for this patent application has been made.

News editors obtained the following quote from the background information supplied by the inventors: “Provider classification is an important topic in relationship management for health insurance companies. Providers cover a diverse range of specializations, from inpatient hospitals to primary care physicians. Inaccurate classification can be due to data entry errors, or more likely, owing to a change in the organizational structure of the provider’s business (such as a merger or acquisition of another practice). Since provider taxonomy is not a data field required for payment of claims, very few providers update this information in corresponding systems. As a result, this data field may be unpopulated or incorrect in most systems, and current operational processes do not rely on the information provided, but instead perform manual searches into each individual provider’s billing history in order to make a taxonomy-based classification. Thus, the time required for this step may range anywhere from a few hours to several days, depending upon the systems being queried and the number of claims.

“Through ingenuity and innovation, various embodiments of the present invention make substantial improvements to the above challenges.”

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “In general, embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like.

“In accordance with one aspect, a computer-implemented method is provided. In one embodiment, the computer-implemented method comprises generating, by one or more processors and one or more machine learning models, (a) a first plurality of predicted taxonomy-based classification scores for a first claim for a first provider and based at least in part on first claim data, wherein the first plurality of predicted taxonomy-based classification scores comprises a predicted taxonomy-based classification score for each classification of a plurality of classifications in a taxonomy, (b) a second plurality of predicted taxonomy-based classification scores for a second claim for a second provider and based at least in part on second claim data, wherein the second plurality of predicted taxonomy-based classification scores comprises a predicted taxonomy-based classification score for each classification of the plurality of classifications in the taxonomy, and © a third plurality of predicted taxonomy-based classification scores for a third claim for a third provider and based at least in part on third claim data, wherein the third plurality of predicted taxonomy-based classification scores comprises a predicted taxonomy-based classification score for each classification of the plurality of classifications in the taxonomy; identifying, by the one or more processors, (a) a first predicted taxonomy-based classification for the first claim from the first plurality of predicted taxonomy-based classification scores, (b) a second predicted taxonomy-based classification for the second claim from the second plurality of predicted taxonomy-based classification scores, and © a third predicted taxonomy-based classification for the third claim from the third plurality of predicted taxonomy-based classification scores; comparing, by the one or more processors, (a) the first predicted taxonomy-based classification for the first provider with a first assigned taxonomy-based classification for the first provider, (b) the second predicted taxonomy-based classification for the second provider with a second assigned taxonomy-based classification for the second provider, and © the third predicted taxonomy-based classification for the third provider with a third assigned taxonomy-based classification for the third provider, wherein (i) the first assigned taxonomy-based classification for the first provider is at least one of null, empty, unknown, or not populated, (ii) the second predicted taxonomy-based classification for the second claim is equivalent to the second assigned taxonomy-based classification for the second provider, and (iii) the third predicted taxonomy-based classification for the third claim is not equivalent to the third assigned taxonomy-based classification for the third provider; assigning, by the one or more processors and based at least in part on the comparisons, (a) an unknown condition to the first claim, (b) a match condition to the second claim, and © a not matched condition to the third claim, wherein (i) the unknown condition is assigned to any claim whose assigned taxonomy-based classification is at least one of null, empty, unknown, or not populated, (ii) the match condition is assigned to any claim whose assigned taxonomy-based classification and predicted taxonomy-based classification are equivalent, and (iii) the not matched condition is assigned to any claim whose assigned taxonomy-based classification and predicted taxonomy-based classification are not equivalent; and updating, by the one or more processors, a first record for the first provider comprising the first assigned taxonomy-based classification to the first predicted taxonomy-based classification.

“In accordance with another aspect, one or more non-transitory computer-readable storage media are provided. In one embodiment, the one or more non-transitory computer-readable storage media includes instructions that, when executed by one or more processors, cause the one or more processors to generate, by one or more machine learning models, (a) a first plurality of predicted taxonomy-based classification scores for a first claim for a first provider and based at least in part on first claim data, wherein the first plurality of predicted taxonomy-based classification scores comprises a predicted taxonomy-based classification score for each classification of a plurality of classifications in a taxonomy, (b) a second plurality of predicted taxonomy-based classification scores for a second claim for a second provider and based at least in part on second claim data, wherein the second plurality of predicted taxonomy-based classification scores comprises a predicted taxonomy-based classification score for each classification of the plurality of classifications in the taxonomy, and © a third plurality of predicted taxonomy-based classification scores for a third claim for a third provider and based at least in part on third claim data, wherein the third plurality of predicted taxonomy-based classification scores comprises a predicted taxonomy-based classification score for each classification of the plurality of classifications in the taxonomy; identify (a) a first predicted taxonomy-based classification for the first claim from the first plurality of predicted taxonomy-based classification scores, (b) a second predicted taxonomy-based classification for the second claim from the second plurality of predicted taxonomy-based classification scores, and © a third predicted taxonomy-based classification for the third claim from the third plurality of predicted taxonomy-based classification scores; compare (a) the first predicted taxonomy-based classification for the first provider with a first assigned taxonomy-based classification for the first provider, (b) the second predicted taxonomy-based classification for the second provider with a second assigned taxonomy-based classification for the second provider, and © the third predicted taxonomy-based classification for the third provider with a third assigned taxonomy-based classification for the third provider, wherein (i) the first assigned taxonomy-based classification for the first provider is at least one of null, empty, unknown, or not populated, (ii) the second predicted taxonomy-based classification for the second claim is equivalent to the second assigned taxonomy-based classification for the second provider, and (iii) the third predicted taxonomy-based classification for the third claim is not equivalent to the third assigned taxonomy-based classification for the third provider; assign, based at least in part on the comparisons, (a) an unknown condition to the first claim, (b) a match condition to the second claim, and © a not matched condition to the third claim, wherein (i) the unknown condition is assigned to any claim whose assigned taxonomy-based classification is at least one of null, empty, unknown, or not populated, (ii) the match condition is assigned to any claim whose assigned taxonomy-based classification and predicted taxonomy-based classification are equivalent, and (iii) the not matched condition is assigned to any claim whose assigned taxonomy-based classification and predicted taxonomy-based classification are not equivalent; and update a first record for the first provider comprising the first assigned taxonomy-based classification to the first predicted taxonomy-based classification.”

There is additional summary information. Please visit full patent to read further.”

The claims supplied by the inventors are:

“1. A computer-implemented method comprising: generating, by one or more processors and one or more machine learning models, (a) a first plurality of predicted taxonomy-based classification scores for a first claim for a first provider and based at least in part on first claim data, wherein the first plurality of predicted taxonomy-based classification scores comprises a predicted taxonomy-based classification score for each classification of a plurality of classifications in a taxonomy, (b) a second plurality of predicted taxonomy-based classification scores for a second claim for a second provider and based at least in part on second claim data, wherein the second plurality of predicted taxonomy-based classification scores comprises a predicted taxonomy-based classification score for each classification of the plurality of classifications in the taxonomy, and © a third plurality of predicted taxonomy-based classification scores for a third claim for a third provider and based at least in part on third claim data, wherein the third plurality of predicted taxonomy-based classification scores comprises a predicted taxonomy-based classification score for each classification of the plurality of classifications in the taxonomy; identifying, by the one or more processors, (a) a first predicted taxonomy-based classification for the first claim from the first plurality of predicted taxonomy-based classification scores, (b) a second predicted taxonomy-based classification for the second claim from the second plurality of predicted taxonomy-based classification scores, and © a third predicted taxonomy-based classification for the third claim from the third plurality of predicted taxonomy-based classification scores; comparing, by the one or more processors, (a) the first predicted taxonomy-based classification for the first provider with a first assigned taxonomy-based classification for the first provider, (b) the second predicted taxonomy-based classification for the second provider with a second assigned taxonomy-based classification for the second provider, and © the third predicted taxonomy-based classification for the third provider with a third assigned taxonomy-based classification for the third provider, wherein (i) the first assigned taxonomy-based classification for the first provider is at least one of null, empty, unknown, or not populated, (ii) the second predicted taxonomy-based classification for the second claim is equivalent to the second assigned taxonomy-based classification for the second provider, and (iii) the third predicted taxonomy-based classification for the third claim is not equivalent to the third assigned taxonomy-based classification for the third provider; assigning, by the one or more processors and based at least in part on the comparisons, (a) an unknown condition to the first claim, (b) a match condition to the second claim, and © a not matched condition to the third claim, wherein (i) the unknown condition is assigned to any claim whose assigned taxonomy-based classification is at least one of null, empty, unknown, or not populated, (ii) the match condition is assigned to any claim whose assigned taxonomy-based classification and predicted taxonomy-based classification are equivalent, and (iii) the not matched condition is assigned to any claim whose assigned taxonomy-based classification and predicted taxonomy-based classification are not equivalent; and updating, by the one or more processors, a first record for the first provider comprising the first assigned taxonomy-based classification to the first predicted taxonomy-based classification.

“2. The computer-implemented method of claim 1, further comprising identifying the taxonomy.

“3. The computer-implemented method of claim 1 further comprising, responsive to assigning the match condition to the second claim, assigning the second claim to an electronic bypass queue.

“4. The computer-implemented method of claim 3 further comprising updating a user interface to indicate assignment of the second claim to the electronic bypass queue.

“5. The computer-implemented method of claim 1 further comprising, responsive to assigning the not matched condition to the third claim, assigning the third claim to an electronic review queue.

“6. The computer-implemented method of claim 5 further comprising updating a user interface to indicate assignment of the third claim to the electronic review queue, wherein updating the user interface comprises generating a notification.

“7. The computer-implemented method of claim 1 further comprising automatically reassigning a fourth claim assigned to an electronic review queue to a standard queue.

“8. One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to: generate, by one or more machine learning models, (a) a first plurality of predicted taxonomy-based classification scores for a first claim for a first provider and based at least in part on first claim data, wherein the first plurality of predicted taxonomy-based classification scores comprises a predicted taxonomy-based classification score for each classification of a plurality of classifications in a taxonomy, (b) a second plurality of predicted taxonomy-based classification scores for a second claim for a second provider and based at least in part on second claim data, wherein the second plurality of predicted taxonomy-based classification scores comprises a predicted taxonomy-based classification score for each classification of the plurality of classifications in the taxonomy, and © a third plurality of predicted taxonomy-based classification scores for a third claim for a third provider and based at least in part on third claim data, wherein the third plurality of predicted taxonomy-based classification scores comprises a predicted taxonomy-based classification score for each classification of the plurality of classifications in the taxonomy; identify (a) a first predicted taxonomy-based classification for the first claim from the first plurality of predicted taxonomy-based classification scores, (b) a second predicted taxonomy-based classification for the second claim from the second plurality of predicted taxonomy-based classification scores, and © a third predicted taxonomy-based classification for the third claim from the third plurality of predicted taxonomy-based classification scores; compare (a) the first predicted taxonomy-based classification for the first provider with a first assigned taxonomy-based classification for the first provider, (b) the second predicted taxonomy-based classification for the second provider with a second assigned taxonomy-based classification for the second provider, and © the third predicted taxonomy-based classification for the third provider with a third assigned taxonomy-based classification for the third provider, wherein (i) the first assigned taxonomy-based classification for the first provider is at least one of null, empty, unknown, or not populated, (ii) the second predicted taxonomy-based classification for the second claim is equivalent to the second assigned taxonomy-based classification for the second provider, and (iii) the third predicted taxonomy-based classification for the third claim is not equivalent to the third assigned taxonomy-based classification for the third provider; assign, based at least in part on the comparisons, (a) an unknown condition to the first claim, (b) a match condition to the second claim, and © a not matched condition to the third claim, wherein (i) the unknown condition is assigned to any claim whose assigned taxonomy-based classification is at least one of null, empty, unknown, or not populated, (ii) the match condition is assigned to any claim whose assigned taxonomy-based classification and predicted taxonomy-based classification are equivalent, and (iii) the not matched condition is assigned to any claim whose assigned taxonomy-based classification and predicted taxonomy-based classification are not equivalent; and update a first record for the first provider comprising the first assigned taxonomy-based classification to the first predicted taxonomy-based classification.

“9. The one or more non-transitory computer-readable storage media of claim 8, wherein the one or more processors are further configured to identify the taxonomy.

“10. The one or more non-transitory computer-readable storage media of claim 8, wherein the one or more processors are further configured to, responsive to assigning the match condition to the second claim, assign the second claim to an electronic bypass queue.

“11. The one or more non-transitory computer-readable storage media of claim 10, wherein the one or more processors are further configured to update a user interface to indicate assignment of the second claim to the electronic bypass queue.

“12. The one or more non-transitory computer-readable storage media of claim 8, wherein the one or more processors are further configured to, responsive to assigning the not matched condition to the third claim, assign the third claim to an electronic review queue.

“13. The one or more non-transitory computer-readable storage media of claim 12, wherein the one or more processors are further configured to update a user interface to indicate assignment of the third claim to the electronic review queue, wherein updating the user interface comprises generating a notification.

“14. The one or more non-transitory computer-readable storage media of claim 8, wherein the one or more processors are further configured to automatically reassign a fourth claim assigned to an electronic review queue to a standard queue.”

There are additional claims. Please visit full patent to read further.

For additional information on this patent application, see: Mac Manus, Lorcan B.; Neftzger, Amy. Dynamic Database Updates Using Probabilistic Determinations. U.S. Patent Application Number 20230154582, filed January 4, 2023 and posted May 18, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230154582)&db=US-PGPUB&type=ids

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