US2023084146A1PendingUtilityA1

Machine learning systems and methods for processing data for healthcare applications

Assignee: DOCVOCATE INCPriority: Jun 15, 2018Filed: Nov 22, 2022Published: Mar 16, 2023
Est. expiryJun 15, 2038(~11.9 yrs left)· nominal 20-yr term from priority
G06N 20/20G06Q 10/10G06Q 40/08
61
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Claims

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.

Claims

exact text as granted — not AI-modified
1 . A method of prioritizing denied insurance claims for appeal to one or more payers, the method comprising:
 training a first machine learning algorithm to determine a percentage likelihood that an appeal of an insurance claim to a first payer will result in a paid insurance claim;   training a second machine learning algorithm to determine a percentage likelihood that an appeal of an insurance claim to a second payer will result in a paid insurance claim;   receiving a first set of data associated with a first insurance claim denied by the first payer;   receiving a second set of data associated with a second insurance claim denied by the second payer;   inputting at least a portion of the first set of data into the first machine learning algorithm;   using the first machine learning algorithm, determining a first percentage likelihood that an appeal of the first insurance claim to the first payer will result in a first paid claim;   inputting at least a portion of the second set of data into the second machine learning algorithm; and   using the second machine learning algorithm, determining a second percentage likelihood that an appeal of the second insurance claim to the second payer will result in a second paid claim.   
     
     
         2 .- 44 . (canceled)

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