Abstract: Methods, computer systems, and computer storage media are provided for utilizing machine learning to predict health plans. A machine learning model is trained to predict valid combinations of employer-payer-health plan in response to one or more missing identifiers based on transaction data from electronic data interchange (EDI) insurance transactions that include valid combinations of employer identifier, payer identifier, and health plan identifier. In response to a request to identify a valid combination based on at least one missing identifier, at least one known identifier corresponding to an employer name, a payer name, or a health plan name is inputted and work location data associated with a patient. The machine learning model generates and displays on a user interface, a predicted set of one or more valid combinations of employer-payer-health plans that correspond to the one known identifier and the work location information that is inputted.

Abstract: Methods, computer systems, and computer storage media are provided for utilizing machine learning to verify payers and/or health plans. HIPAA transactions can be utilized 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 is utilized to verify the scan data is mapped in accordance with the mapping.

Abstract: Methods, computer systems, and computer storage media are provided for utilizing machine learning to verify payers and/or health plans. HIPAA transactions can be utilized 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 is utilized to verify the scan data is mapped in accordance with the mapping.

Abstract: Systems and methods for providing automatic adjudication (auto-adjudication) of medical encounters are provided. For example, specific encounters corresponding to a billable service provided to a patient may bypass insurance verification billing, code validation, and/or claim scrubbing. The billable service may have a program bundle that can be utilized to determine if the encounter is eligible for a fast pass token. The fast pass token prevents insurance verification billing, code validation, and claim scrubbing for the encounter. Additionally, the program bundle can be utilized to determine whether the encounter is eligible to participate in auto-adjudication and electronic funds transfer, enabling real-time financial transactions for the encounter.

Abstract: Systems and methods for providing automatic adjudication (auto-adjudication) of medical encounters are provided. For example, specific encounters corresponding to a billable service provided to a patient may bypass insurance verification billing, code validation, and/or claim scrubbing. The billable service may have a program bundle that can be utilized to determine if the encounter is eligible for a fast pass token. The fast pass token prevents insurance verification billing, code validation, and claim scrubbing for the encounter. Additionally, the program bundle can be utilized to determine whether the encounter is eligible to participate in auto-adjudication and electronic funds transfer, enabling real-time financial transactions for the encounter.

Abstract: Systems and methods for providing automatic adjudication (auto-adjudication) of medical encounters are provided. For example, specific encounters corresponding to a billable service provided to a patient may bypass insurance verification billing, code validation, and/or claim scrubbing. The billable service may have a program bundle that can be utilized to determine if the encounter is eligible for a fast pass token. The fast pass token prevents insurance verification billing, code validation, and claim scrubbing for the encounter. Additionally, the program bundle can be utilized to determine whether the encounter is eligible to participate in auto-adjudication and electronic funds transfer, enabling real-time financial transactions for the encounter.

Abstract: Systems and methods for providing automatic adjudication (auto-adjudication) of medical encounters are provided. For example, specific encounters corresponding to a billable service provided to a patient may bypass insurance verification billing, code validation, and/or claim scrubbing. The billable service may have a program bundle that can be utilized to determine if the encounter is eligible for a fast pass token. The fast pass token prevents insurance verification billing, code validation, and claim scrubbing for the encounter. Additionally, the program bundle can be utilized to determine whether the encounter is eligible to participate in auto-adjudication and electronic funds transfer, enabling real-time financial transactions for the encounter.