Intelligently linking payer/health plan combinations to specific employers

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.