US 12,481,869 B1
Machine learning systems for predicting unenrollment in claims processing
Morgan J. Finley, St. Louis Park, MN (US); Garret L. Anderson, Arden Hills, MN (US); Nihitha Davis, Bengaluru (IN); Heather Durosko, New Castle, PA (US); and Camille Patel, New York, NY (US)
Assigned to Express Scripts Strategic Development, Inc., St. Louis, MO (US)
Filed by Express Scripts Strategic Development, Inc., St. Louis, MO (US)
Filed on Jul. 8, 2020, as Appl. No. 16/923,270.
Int. Cl. G06N 3/08 (2023.01); G06F 40/205 (2020.01); G06N 3/042 (2023.01); G06N 3/044 (2023.01); G06N 3/048 (2023.01); G06Q 10/10 (2023.01); G06Q 40/08 (2012.01); G16H 20/10 (2018.01); G16H 40/20 (2018.01)
CPC G06N 3/08 (2013.01) [G06F 40/205 (2020.01); G06N 3/042 (2023.01); G06N 3/044 (2023.01); G06N 3/048 (2023.01); G06Q 10/10 (2013.01); G06Q 40/08 (2013.01); G16H 20/10 (2018.01); G16H 40/20 (2018.01)] 17 Claims
OG exemplary drawing
 
1. A machine learning server for trained prediction of unenrollment in claim processing systems, said machine learning server comprising:
a memory for storing data; and a processor in communication with the memory, said processor configured to:
construct a layered neural network configured to be trained by claim features;
receive a plurality of historic claims, at least one of the historic claims including historic claim data, the historic claim data including a claim enrollment status of enrolled or unenrolled;
extract a set of historic claim features from the historic claim data;
determine the claim enrollment status that is associated with the set of historic claim features;
train the layered neural network with the set of historic claim features and the claim enrollment status such that the trained layered neural network is configured to predict a claim enrollment status of a another claim;
receive a pending claim including pending claim data, wherein the pending claim lacks a claim enrollment status as the pending claim has not been processed by a claim processing server;
extract a set of pending claim features from the pending claim data;
apply the pending claim features to the trained layered neural network to determine a predicted claim enrollment status for the pending claim, wherein the predicted claim enrollment status is determined to be one of an enrolled status, an unenrolled status, or a not clear status, wherein the unenrolled status is associated with a failure to fill a prescription of the pending claim while the enrolled status is associated with a successful fulfillment of the prescription;
determine whether to fulfill the pending claim based on the predicted claim enrollment status;
on condition that the predicted claim enrollment status is determined to be the enrolled status, automatically submit the pending claim for processing by the claim processing server for fulfilling the pending claim;
on condition that the predicted claim enrollment status is determined to be the unenrolled status, automatically reject the pending claim; and
on condition that the predicted claim enrollment status is determined to be the not clear status, automatically submit the pending claim for processing by the claim processing server for fulfilling the pending claim,
define the layered neural network to include at least a recurrent neural network and a multilayer perceptron, wherein the set of pending claim features includes a therapy type, a location of service and claimant demographics.