US-12626305-B2 - Systems and methods for prediction and estimation of medical claims payments

US12626305B2US 12626305 B2US12626305 B2US 12626305B2US-12626305-B2

Abstract

Systems and methods for calculating medical claims payment estimates may receive a medical claim for a first patient including billing code(s) and demographic data, apply the demographic data to identify payer(s) for the first patient, access a data universe including patient data collection(s) of patient data records for a second group of patients, payer data collection(s) of data records for payers, and a financial history data collection including financial data record(s) for the first patient, identify, for each billing code, a payer payment pattern based on a combination of the patient data records and payer data record(s) corresponding to the payer for the first patient, identify a patient payment pattern based on the financial data record(s), and apply the payer payment pattern and the patient payment pattern to the medical claim for the first patient to calculate a payment estimation for the medical claim.

Inventors

Kevin M. Zahora
Jennifer A. Carlson
Charles E. Fuller, III
Jessica P. Deschane
Paul D. Canino

Assignees

ZOLL MEDICAL CORPORATION

Dates

Publication Date: 20260512
Application Date: 20220325

Claims (20)

1 . A system for generating medical claims payment data, the system comprising: a predictive analytics platform communicatively coupled via an application programming interface (API) to an external claims processing system, wherein the predictive analytics platform excludes claim generation capabilities and comprises hardware logic and/or software logic configured to execute a process for generating a recommended pre-payment amount in real-time, the process when executed causing the hardware logic and/or software logic to: receive, via the API from the external claims processing system, medical claim information request for a first patient prior to generation of a medical claim, the medical claim information request comprising at least one billing code and at least one item of demographic data for the first patient, access a hybrid plurality of distributed databases comprising a de-identified database comprising: at least one patient data collection comprising a plurality of de-identified patient data records for a plurality of second patients, wherein the plurality of deidentified patient data records for the plurality of second patients comprise demographic data for the second patients, at least one health insurer data collection comprising a plurality of health insurer data records for a plurality of health insurers, and a credit agency database comprising a credit history data collection comprising at least one credit history data record for the first patient, identify a portion of the plurality of de-identified patient data records for the plurality of second patients where respective demographic data for the second patients is similar to the at least one item of demographic data for the first patient, identify, for each billing code of the at least one billing code, a health insurer payment pattern for a respective billing code based on a combination of (a) the portion of the plurality of de-identified patient data records for the plurality of second patients where respective demographic data for the second patients is similar to the at least one item of demographic data for the first patient, and (b) the plurality of health insurer data records, and identify, using machine learning analysis, a patient payment pattern for the first patient based on the at least one credit history data record and patient payment outcomes for one or more other patients among the plurality of second patients where respective demographic data for the second patients is similar to the at least one item of demographic data for the first patient, generate aggregated payment pattern data based on the health insurer payment pattern and the patient payment pattern, based on the aggregated payment pattern data, calculate a plurality of likelihoods that the first patient will pay a particular payment amount and a confidence score for each particular payment amount, the plurality of likelihoods comprising at least a first likelihood that the first patient will pay a first particular payment amount and a second likelihood that the first patient will pay a second particular payment amount, generate the recommended pre-payment amount based on the first and second likelihoods, the first and second particular payment amounts, and a threshold for the confidence score for each particular payment amount, convert the medical claim information request to a medical claim information response comprising the recommended pre-payment amount for display at a graphical user interface (GUI) of the external claims processing system, and transmit, via the API to the claims processing system, the medical claim information response comprising the recommended pre-payment amount for display at the GUI of the external claims processing system.
2 . The system of claim 1 , wherein identifying the health insurer payment pattern for the respective billing code comprises identifying, in the plurality of health insurer data records, remittance data for each billing code of the at least one billing code; calculating, using the remittance data, a remittance estimate; and calculating a confidence in similarity of the remittance estimate to an actual future remittance value.
3 . The system of claim 1 , wherein identifying the patient payment pattern comprises matching a credit score of the first patient to a credit score range of a portion of the plurality of second patients.
4 . The system of claim 1 , wherein generating the aggregated payment pattern data comprises applying at least one deductible amount corresponding to a deductible level of an active health insurer plan of first patient; and automatically determining a first deductible amount of the at least one deductible amount by contacting an external computing system of an active health insurer via a network to request a current balance of a remaining maximum deductible.
5 . The system of claim 1 , wherein the process when executed causes the hardware logic and/or software logic to identify a liability health insurer for the first patient based on the at least one item of demographic data for the first patient.
6 . The system of claim 5 , wherein generating the aggregated payment pattern data comprises determining coverage coordination between the liability health insurer and a primary health insurer.
7 . The system of claim 1 , wherein the predictive analytics platform is configured to identify eligibility for one or more medical payment assistance programs based on the at least one item of demographic data for the first patient.
8 . The system of claim 7 , wherein the process when executed causes the hardware logic and/or software logic to initiate enrollment of the first patient in the one or more medical payment assistance programs.
9 . The system of claim 1 , further comprising: a procedure trends analysis engine comprising hardware logic and/or software logic configured to analyze a plurality of historic claims related to the plurality of second patients to identify a plurality of sets of commonly paired procedures, wherein claim submission of a first procedure of the plurality of sets of commonly paired procedures precedes claim submission of a second procedure of the plurality of sets of commonly paired procedures by up to a threshold period of time, for each set of commonly paired procedures, identifying comprises determining that the first procedure is followed by the second procedure for at least a threshold percentage of a portion of the plurality of historic claims including the first procedure and identify, within the plurality of sets of commonly paired procedures, a likelihood of the second procedure following the first procedure based on a set of demographic groups.
10 . The system of claim 1 , wherein the process when executed causes the hardware logic and/or software logic to analyze the at least one item of demographic data in view of identification criteria to identify insufficiency of the demographic data, and, based on the insufficiency of the demographic data, locate patient information regarding the first patient in at least one additional data source to supplement the at least one item of demographic data.
11 . The system of claim 10 , wherein the process when executed causes the hardware logic and/or software logic to identify at least one of a contradiction or an ambiguity in patient information by comparing the at least one item of demographic data to known demographic data for the first patient, and locate patient information regarding the first patient in at least one additional data source resolve the at least one of the contradiction or the ambiguity in the at least one item of demographic data.
12 . The system of claim 10 , wherein the insufficiency of the demographic data comprises one or more inconsistent elements in an address, spelling of name, and/or date of birth for the first patient.
13 . The system of claim 12 , wherein supplementing the at least one item of demographic data comprises presenting the demographic data for acceptance by a user.
14 . The system of claim 1 , wherein the predictive analytics platform is communicatively coupled with one or more of a computer-aided dispatch (CAD) and an electronic patient care record (ePCR) application hosted by an ePCR system and configured to: receive initial patient demographics for a patient from the CAD and/or the ePCR application; identify supplemental patient demographics based on the initial patient demographics; identify insurance coverages for the patient based on the supplemental patient demographics; provide the supplemental patient demographics and the insurance coverages to the CAD and/or the ePCR application; receive the initial patient demographics for the patient from the CAD; and provide the supplemental patient demographics and insurance coverages to the ePCR application.
15 . The system of claim 1 , wherein at least a portion of input is received through the API accessed by a source of input data.
16 . The system of claim 1 , wherein the medical claim information request comprises a JavaScript Object Notation (JSON) or Extensible Markup Language (XML) format.
17 . The system of claim 1 , wherein the at least one credit history data record comprises past payment history, personal or family income and credit history information, medical credit history, a medical spending plan available, and/or a deductible amount remaining for the first patient.
18 . The system of claim 1 , wherein the process when executed causes the hardware logic and/or software logic configured to apply the at least one item of demographic data for the first patient to identify at least one health insurer for the first patient.
19 . The system of claim 18 , wherein identifying the at least one health insurer comprises automatically verifying, with an external computing system of the at least one health insurer via a network, active coverage of the first patient; and contacting an external computing system of each health insurer of most likely health insurers, via a network, to query existence of active coverage of the first patient with a respective health insurer.
20 . The system of claim 18 , wherein identifying the at least one health insurer comprises determining, for each billing code of the at least one billing code, a pre-authorization status corresponding to the respective billing code; and wherein determining the pre-authorization status comprises analyzing at least a portion of i) the plurality of health insurer data records and/or ii) the plurality of de-identified patient data records using the respective billing code to identify evidence of authorization requests from one or more of the plurality of second patients.

Description

RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application Ser. No. 63/166,690, entitled “Systems and Methods for Prediction and Estimation of Medical Claims Payments,” filed Mar. 26, 2021, the contents thereof being hereby incorporated by reference in its entirety. BACKGROUND 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. SUMMARY OF ILLUSTRATIVE EMBODIMENTS In one aspect, the present disclosure relates to a system for calculating medical claims payment estimates, the system including a payment pattern application engine including hardware logic and/or software logic configured to receive a medical claim for a first patient, the medical claim including at least one billing code and at least one item of demographic data for the first patient, and apply the at least one item of demographic data for the first patient to identify at least one payer of a number of payers for the first patient. The system may include a payment pattern identification engine including hardware logic and/or software logic configured to access data collections including at least one patient data collection including a number of patient data records for a number of second patients, at least one payer data collection including a number of payer data records for the number of payers, and a financial history data collection including at least one financial data record for the first patient, identify, for each billing code of the at least one billing code, a payer payment pattern for the respective billing code based on a combination of the number of patient data records and at least one payer data record of a portion of the number of payer data records corresponding to the at least one payer for the first patient, and identify a patient payment pattern based on the at least one financial data record. The payment pattern application engine may be further configured to apply the payer payment pattern and the patient payment pattern to the medical claim for the first patient to calculate a payment estimation for the medical claim. In some embodiments, identifying the at least one payer includes automatically verifying, with an external computing system of the payer via a network, active coverage of the first patient. Automatically verifying the active coverage of the first patient may include locating a first plan for the first patient based on the at least one item of demographic data of the patient, the first plan including additional demographic data for the patient, identifying the first plan as an inactive plan, locating a second plan based on the additional demographic data for the first patient, and automatically ve