“Systems and Methods for Medical Claims Analytics and Processing Support” in Patent Application Approval Process (USPTO 20230317260): ZOLL Medical Corporation

2023 OCT 19 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent application by the inventors Canino, Paul D. (Miller Place, NY, US); Deschane, Jessica P. (Thornton, CO, US); Forester, Fredrick (San Diego, CA, US); Fuller, III, Charles E. (Westminster, CO, US); Sanchez, JR., Mario (Longmont, CO, US); Whitt, Kristin N. (Dayton, OH, US); Williams, Tyler L. (Sarasota, FL, US); Zahora, Kevin M. (Liberty Twp., OH, US), filed on March 30, 2023, was made available online on October 5, 2023, according to news reporting originating from Washington, D.C., by NewsRx correspondents.

This patent application is assigned to ZOLL Medical Corporation (Chelmsford, Massachusetts, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “Payers of health services and products, such as health insurance companies, federal and state health coverage providers including Medicare and Medicaid, and liability insurance companies provide payment coverage to a wide variety of medical providers. The medical providers include traditional brick and mortar service providers such as, in some examples, hospitals, physician practices, emergency rooms, urgent care facilities, and surgical centers. Further, medical providers can include mobile providers such as emergency medical services (EMS) providers, ambulance companies, and medical evacuation providers. Medical providers can include specialty care providers such as dental care providers, chiropractors, and physical therapists. Further, medical providers include prescription medical device companies such as contact lens retailers, wheelchair companies, and prosthetic device manufacturers. As technological solutions expand, more and more wearable prescription medical device companies are included within the span of health product coverage, including, in some examples, wearable glucose monitors, wearable nerve stimulation devices, and the ZOLL LifeVest® wearable cardioverter defibrillator. Each of the medical providers seek reimbursement from payers for services and products provided to patients, and each of the medical providers maintains records related to patients and payer remittance.

“Rather than submitting reimbursement directly to each medical provider, payers are often billed via a billing company or a medical provider administration platform used by the medical provider for managing the claims and patient invoicing. The billing company or medical provider administration platform, for example, may streamline the complexities of claims processing and records keeping on behalf of the medical provider.

“Medical claims submission and processing involves many complex steps to move from initiating claims preparation to receiving payment and involves numerous entities and databases. Claim verification, for example, oftentimes involves confirmation of patient information as well as payer information. In another example, determining the correct billing amount can involve numerous factors, such as patient co-pay amount, patient deductible, multiple insurers and/or liability insurance, and location where the medical services were rendered.”

In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventors’ summary information for this patent application: “In one aspect, the present disclosure relates to a system for providing automation and virtual assistance to medical claims processing, the system including a networked storage region including non-transitory computer readable media storing patient data corresponding to a number of patients, and payer data corresponding to a number of payers, and a predictive analytics computing platform including hardware logic and/or software logic configured for execution on processing circuitry, where the predictive analytics computing platform is configured to receive, from a claims processing system, claims data corresponding to a medical claim for a first patient, access, from the predictive analytics platform, a set of requirements corresponding to at least one first payer of the number of payers to which the medical claim is directed, where the set of requirements was generated at least in part through training at least one machine learning classifier with claims data corresponding to a subset of the number of patients having coverage provided by one or more payers of the at least one first payer, verify the claims data in view of the set of requirements, and provide predictive analytic output to the claims processing system responsive to verifying the claims data, the predictive analytic output including an indication of at least one of missing claims information or invalid claims information for the medical claim for the first patient.

“In some embodiments, the predictive analytics platform includes a portal configured to receive the predictive analytic output, where the portal is configured to interoperate with a data resource access interface for claims and billing via an integration platform. The portal and the data resource access interface may interoperate via a Substitutable Medical Applications and Reusable Technologies (SMART®) on Fast Healthcare Interoperability Resources (FHIR®) integration platform. The integration platform may be configured to enable a user to access the claims and billing system and the predictive analytic output with a single sign-on operation by the user.

“In some embodiments, the predictive analytics computing platform is configured to access the patient data corresponding to the number of patients and the payer data corresponding to the number of payers via a data exchange interface with an integration platform configured provide a Health Level 7 (HL7®) standard integration. The predictive analytics computing platform may be configured to access the patient data corresponding to the number of patients and the payer data corresponding to the number of payers via at least one application programming interface (API).

“In some embodiments, the predictive analytics computing platform may be configured to receive, from a claims processing system, patient information corresponding to the medical claim, where the patient information includes at least one item of demographic data, cross-reference the patient information with the stored patient data to identify a patient record, apply at least a portion of the patient information to a machine learning classifier to estimate a likelihood of an individual identified by the patient information matching a patient identified by the patient record, and provide the predictive analytic output to the claims processing system, the predictive analytic output including at least an identifier corresponding to the patient record and an indication of the likelihood of matching. Providing the indication of the likelihood of matching includes indicating match or no match. Providing the indication of the likelihood of matching may include identifying at least one mismatching data element between the patient information and the patient record.

“In some embodiments, receiving the claims data includes receiving the claims data from an electronic medical record. Receiving the claims data may include receiving the claims data responsive to data entry of at least a portion of the claims data by a user of the claims processing system. Receiving the claims data may include iteratively receiving at least portions of the claims data responsive to corresponding portions of data entry one or more of a number of fields of a medical claim form and iteratively verifying the at least portions of the claims data. Iteratively verifying the at least portions of the claims data may include identifying, based upon data entry in a given field of the number of fields, another field of the number of fields requiring information corresponding to the data entry in the given field. Based on the indication of the at least one of the missing claims information or the invalid claims information, the claims processing system may provide a prompting message to the user regarding the missing claims information and/or invalid claims information.

“In some embodiments, verifying the claims data includes determining a likelihood of acceptance of the one or more procedure codes by the at least one payer. Determining the likelihood of acceptance of the one or more procedure codes may include cross-referencing the one or more procedure codes with a set of procedure codes accepted by the at least one payer. Determining the likelihood of acceptance of the one or more procedure codes may include executing at least one machine learning classifier to obtain a likelihood of approval of the one or more procedure codes. The predictive analytics computing platform may be configured to, when determining the likelihood of acceptance of a given procedure code of the one or more procedure codes is low, identify an alternative procedure code to given procedure code. The claims processing system may provide a prompting message to the user presenting, at a user interface, the alternative procedure code for acceptance by a user.

“In some embodiments, the set of requirements was generated at least in part through training at least one machine learning classifier with a plurality of claims denied by the at least one first payer.

“In some embodiments, verifying the claims data includes confirming receipt of information corresponding to a set of required data fields of a number of fields of a medical claim form. Confirming receipt of information corresponding to the set of required data fields may include executing at least one machine learning classifier trained to identify information corresponding to the set of required fields.

“In some embodiments, the predictive analytics computing platform is configured to identify, using the claims data, at least one second payer for paying the medical claim. The predictive analytics computing platform may be configured to confirm patient eligibility for coverage via a payer account corresponding to at least a portion of the claims data.

“In some embodiments, verifying the claims data includes determining pre-approval status corresponding to at least one procedure code of one or more procedure codes of the claims data. The pre-approval status may correspond to at least one of needed, not needed, pending, or obtained. Responsive to the pre-approval status of a given procedure code of the at least one procedure code i) corresponding to needed and ii) not corresponding to pending or obtained, the predictive analytics computing platform may be configured to request, from the at least one first payer via a network, pre-approval for the given procedure code. Determining the pre-approval status may include executing at least one machine learning classifier trained to determine a likelihood of pre-approval requirement based on the at least one first payer and the one or more procedure codes.

“In some embodiments, the predictive analytics computing platform is configured to determine, responsive to a request from the claims processing system, a deductible status corresponding to a deductible value of at least one payer account corresponding to at least a portion of the claims data. Determining the deductible status may include determining whether the deductible value is below a threshold amount. Determining the deductible status may include determining an unpaid deductible amount corresponding to one or more open invoices and updating the deductible value by the unpaid deductible amount. Determining the deductible status may include accessing, via a network, the deductible value from a computing system of at least one payer corresponding to the at least one payer account. Determining the deductible status may include executing at least one machine learning classifier trained to determine a deductible update pattern based at least in part on at least one payer corresponding to the at least one payer account, and, based on the deductible update pattern indicating a stored deductible value has likely been updated, accessing, via a network, the deductible value from a computing system of the at least one payer. The predictive analytics computing platform may be configured to automatically identify the at least one first payer based on a subscriber identifier from the first patient.”

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

The claims supplied by the inventors are:

“1.-129. (canceled)

“130. A system for generating predictive analytics for patient medical records, the system comprising: a networked storage region comprising non-transitory computer readable media storing data resources comprising: patient data corresponding to a plurality of first patients, and payer data corresponding to a plurality of payers; and a predictive analytics computing platform communicatively coupled to the networked storage region and comprising hardware logic and/or software logic configured for execution on processing circuitry, wherein the predictive analytics computing platform is configured to receive at least one subscriber identifier associated with a second patient, analyze the at least one subscriber identifier based on machine learning derived from the patient data corresponding to the plurality of first patients and the payer data corresponding to the plurality of payers, based on the analysis of the at least one subscriber identifier, identify one or more payers associated the at least one subscriber identifier as one or more predicted payers associated with the at least one subscriber identifier, and generate predictive analytic output comprising the one or more predicted payers associated with the at least one subscriber identifier.

“131. The system of claim 130, wherein: receiving the at least one subscriber identifier comprises receiving the demographic information; and analyzing the at least one subscriber identifier comprises analyzing the at least one subscriber identifier in view of demographic information associated with a respective subscriber identifier.

“132. The system of claim 131, wherein the demographic information comprises at least one of a subscriber’s geographic region, a geographic region of a subscriber’s employer, a subscriber’s age, or a subscriber’s military status.

“133. The system of claim 130, wherein analyzing the at least one subscriber identifier based on the machine learning comprises applying one or more machine learning classifiers to the at least one subscriber identifier, wherein each machine learning classifier of the one or more machine learning classifiers is trained using a plurality of subscriber identifiers, each subscriber identifier being associated with a respective confirmed payer identifier of the plurality of payers.

“134. The system of claim 133, wherein: the plurality of subscriber identifiers is associated with at least a subset of the plurality of first patients; and each machine learning classifier of the one or more machine learning classifiers is further trained using a demographic information portion of the patient data.

“135. The system of claim 130, wherein the generated predictive analytic output comprises a confidence level or rating associated with each payer of the one or more predicted payers as generated by the machine learning.

“136. The system of claim 135, wherein generating the predictive analytic output comprises ranking the one or more predicted payers by the confidence level or rating associated with each payer.

“137. The system of claim 130, wherein: receiving the at least one subscriber identifier comprises receiving a set of subscriber identifiers; and generating the predictive analytic output comprises generating a set of correlations comprising a respective correlation between each subscriber identifier of the set of subscriber identifiers and at least one respective payer of the one or more predicted payers identified for each subscriber identifier of the set of subscriber identifiers.

“138. The system of claim 130, wherein the predictive analytic output comprises, for each subscriber identifier of the at least one subscriber identifier, up to three predicted payers of the plurality of payers.

“139. The system of claim 130, wherein the data resources comprise provider data corresponding to a plurality of providers.

“140. The system of claim 139, wherein the predictive analytics platform is configured to: receive medical expense information for a third patient; analyze the medical expense information for the third patient based on machine learning derived from one or more of the patient data corresponding to the plurality of first patients, the payer data corresponding to the plurality of payers, and the provider data corresponding to the plurality of providers; and based on the analysis of the medical expense information, generate another predictive analytic output comprising prior authorization information for a payer associated with the third patient.

“141. The system of claim 140, wherein the prior authorization information comprises an indication that a payer associated with the third patient likely requires an authorization of a medical expense prior to an incurrence of that medical expense by the third patient.

“142. The system of claim 141, wherein the indication comprises contact information for requesting the authorization.

“143. The system of claim 141, wherein: the indication comprises an indication of issuance of a request for the prior authorization; and the predictive analytics computing platform is configured to issue the request for the prior authorization to the payer associated with the third patient.

“144. The system of claim 140, wherein: the second patient and the third patient are a same patient; and the payer associated with the third patient is one of the one or more predicted payers.

“145. The system of claim 139, wherein the predictive analytics computing platform is configured to: receive a request from a medical claims processing system for a deductible status for a payer account corresponding to a third patient; analyze the deductible status based on machine learning derived from one or more of the patient data corresponding to the plurality of first patients, the payer data corresponding to the plurality of payers, and the provider data corresponding to the plurality of providers; based on the analysis of the deductible status, generate predictive analytic output comprising a deductible update pattern based at least in part on a payer corresponding to the payer account corresponding to the third patient; and provide the deductible update pattern to the medical claims processing system.

“146. The system of claim 145, wherein the predictive analytics platform is configured to, based on the deductible update pattern indicating a stored deductible value has likely been updated: access, via a network, an updated deductible value from a computing system of the payer; and provide the updated deductible value to the medical claims processing system.

“147. The system of claim 145, wherein: the second patient and the third patient are a same patient; and the predictive analytics computing platform is configured to identify the payer associated the payer account as at least one of the one or more predicted payers.

“148. The system of claim 130, wherein the predictive analytic output comprises an indication regarding a deductible status of at least one payer account corresponding to the at least one subscriber identifier and a first payer of the one or more predicted payers.

“149. The system of claim 148, wherein the indication regarding the deductible status comprises a deductible value of a first payer account of the at least one payer account.

“150. The system of claim 149, wherein the predictive analytics computing platform is configured to: determine an unpaid deductible amount corresponding to one or more open invoices; and update the deductible value by the unpaid deductible amount.

“151. The system of claim 149, wherein the predictive analytics computing platform is configured to: receive one or more medical billing claims related to the at least one subscriber identifier; calculate an unpaid deductible amount corresponding to the one or more medical billing claims; and adjust the deductible value by the unpaid deductible amount.

“152. The system of claim 130, wherein the predictive analytic computing platform is configured to: generate a set of claims data field requirements for medical billing claims submissions to at least one payer of the one or more predicted payers, wherein the set of requirements was generated at least in part through training at least one machine learning classifier with claims data corresponding to the plurality of payers; generate another predictive analytic output comprising the set of claims data field requirements; and provide the predictive analytic output to a medical claims and billing portal.

“153. The system of claim 152, wherein the medical claims and billing portal is configured to apply the set of requirements to claims data of an in-progress medical claim to verify the claims data.

“154. The system of claim 153, wherein verifying the claims data comprises identifying at least one of missing claims information or invalid claims information.

“155. The system of claim 130, wherein the predictive analytic output comprises patient information associated with the subscriber identifier, wherein the patient information comprises at least one item of patient demographic data.

“156. The system of claim 155, wherein the predictive analytics computing platform is configured to access the patient information from a computing system of a first payer of the one or more predicted payers.

“157. The system of claim 155, further comprising a medical claims and billing portal accessed by the user, wherein the medical claims and billing portal is configured to: compare at least a portion of the patient information with stored patient data to identify a patient record; and update the identified patient record based on the comparison.”

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

URL and more information on this patent application, see: Canino, Paul D.; Deschane, Jessica P.; Forester, Fredrick; Fuller, III, Charles E.; Sanchez, JR., Mario; Whitt, Kristin N.; Williams, Tyler L.; Zahora, Kevin M. Systems and Methods for Medical Claims Analytics and Processing Support. U.S. Patent Application Number 20230317260, filed March 30, 2023 and posted October 5, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230317260)&db=US-PGPUB&type=ids

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