Abstract: MAIA Outcome Feedback Computing System provides users with a real-time, document guidance, interface to upload medical claims, in multiple different formats. Document classification is performed on the uploaded data to determine a document type. Claim features are identified and extracted specific to each contextual document type, forwarded to a pre-approval and feedback manager and used to perform semantic and keyword searches on knowledge databases specific to each document type. The claims, and relevant data are input into a machine learning model, trained to predict medical billing codes using medical claims and generate: a confidence score and results summary for each billing code. Based on a comparison of the confidence score to a threshold, one of a plurality of validation processes is performed on each billing code, results are output to a user. Validated codes may be automatically submitted to third-party insurance providers, monitored for denials, and automatically appealed.

Abstract: MAIA Multi-Validation Computing System provides users with a real-time, document guidance, interface to upload medical claims, in multiple different formats. Document classification is performed on the uploaded data to determine a document type. Claim features are identified and extracted specific to each contextual document type, forwarded to a pre-approval and feedback manager and used to perform semantic and keyword searches on knowledge databases specific to each document type. The claims, and relevant data are input into a machine learning model, trained to predict medical billing codes using medical claims and generate: a confidence score and results summary for each billing code. Based on a comparison of the confidence score to a threshold, one of a plurality of validation processes is performed on each billing code, results are output to a user. Validated codes may be automatically submitted to third-party insurance providers, monitored for denials, and automatically appealed.