Abstract: Embodiments relate to system for automatically predicting payer response to claims. In an embodiment, the system receives claim data associated with a claim. The system identifies a set of claim features of the claim data, and generates an input vector with at least a portion of the set of claim features. The system applies the input vector to a trained model. A first portion of the neural network is configured to generate an embedding representing the input vector with a lower dimensionality than the input vector. A second portion of the neural network is configured to generate a prediction of whether the claim will be denied based on the embedding. The system provides the prediction for display on a user interface of a user device. The prediction may further include denial reason codes and a response date estimation to indicate if, when, and why a claim will be denied.

Abstract: A method of predicting an outcome of a prior-authorization, claim, or appeal includes receiving, at a server, a natural language data file representing doctors notes from a provider visit related to a service instance; receiving, at the server, a structured data set including patient profile data, diagnosis and procedure codes, and quantitative data related to a payment requested; processing at least the natural language data file using a medical dictionary to output a set of key medical terms; processing, using a supervised machine learning algorithm, the structured data set and the set of key medical terms to predict an outcome of the payment requested; and outputting an indication of the predicted outcome of the payment requested.

Abstract: A method of predicting an outcome of a prior-authorization, claim, or appeal includes receiving, at a server, a natural language data file representing doctors notes from a provider visit related to a service instance; receiving, at the server, a structured data set including patient profile data, diagnosis and procedure codes, and quantitative data related to a payment requested; processing at least the natural language data file using a medical dictionary to output a set of key medical terms; processing, using a supervised machine learning algorithm, the structured data set and the set of key medical terms to predict an outcome of the payment requested; and outputting an indication of the predicted outcome of the payment requested.

Abstract: Embodiments relate to system for automatically predicting payer response to claims. In an embodiment, the system receives claim data associated with a claim. The system identifies a set of claim features of the claim data, and generates an input vector with at least a portion of the set of claim features. The system applies the input vector to a trained model. A first portion of the neural network is configured to generate an embedding representing the input vector with a lower dimensionality than the input vector. A second portion of the neural network is configured to generate a prediction of whether the claim will be denied based on the embedding. The system provides the prediction for display on a user interface of a user device. The prediction may further include denial reason codes and a response date estimation to indicate if, when, and why a claim will be denied.

Abstract: Embodiments relate to system for automatically predicting payer response to claims. In an embodiment, the system receives claim data associated with a claim. The system identifies a set of claim features of the claim data, and generates an input vector with at least a portion of the set of claim features. The system applies the input vector to a trained model. A first portion of the neural network is configured to generate an embedding representing the input vector with a lower dimensionality than the input vector. A second portion of the neural network is configured to generate a prediction of whether the claim will be denied based on the embedding. The system provides the prediction for display on a user interface of a user device. The prediction may further include denial reason codes and a response date estimation to indicate if, when, and why a claim will be denied.

Abstract: Embodiments relate to system for automatically predicting payer response to claims. In an embodiment, the system receives claim data associated with a claim. The system identifies a set of claim features of the claim data, and generates an input vector with at least a portion of the set of claim features. The system applies the input vector to a trained model. A first portion of the neural network is configured to generate an embedding representing the input vector with a lower dimensionality than the input vector. A second portion of the neural network is configured to generate a prediction of whether the claim will be denied based on the embedding. The system provides the prediction for display on a user interface of a user device. The prediction may further include denial reason codes and a response date estimation to indicate if, when, and why a claim will be denied.

Abstract: A scaleable abstraction control solution including an abstraction process and an abstraction system. The abstraction solution permits extraction, tracking, and management of data defined in one or more documents. The abstraction process can include double blind data abstraction. The abstracted data can be verified using a quality assurance process that can include statistical sampling, tracked random error insertion, and abstraction auditing. Data from documents can be abstracted into one or more customizable databases, forms, templates, or software.

Abstract: A scaleable abstraction control solution including an abstraction process and an abstraction system. The abstraction solution permits extraction, tracking, and management of data defined in one or more documents. The abstraction process can include double blind data abstraction. The abstracted data can be verified using a quality assurance process that can include statistical sampling, tracked random error insertion, and abstraction auditing. Data from documents can be abstracted into one or more customizable databases, forms, templates, or software.