公开(公告)号：US20230139811A1

公开(公告)日：2023-05-04

申请号：US18092260

申请日：2022-12-31

Applicant: Express Scripts Strategic Development, Inc.

Inventor： Amit K. Bothra ,  Pritesh J. Shah ,  Christopher G. Lehmuth ,  Bradley D. Flynn ,  Varun Tandra

IPC: G16H40/20 ,  G06N3/08 ,  G06Q50/00 ,  G06Q30/0201 ,  G06Q10/107 ,  G16H20/10 ,  G16H50/30 ,  G16H50/20 ,  G16H50/70 ,  A61B5/00 ,  G16H80/00

Abstract: A method includes generating an intervention model by determining principal components for features of a training set, associating each feature of the training set with a principal component, selecting features of the training set most highly correlated with principal components, training a machine learning model with at least some of the selected features, and saving the verified trained machine learning model as the intervention model. The method includes determining multiple channel-specific intervention expectations. Each channel-specific intervention expectation indicates a likelihood that the user will take action in response to an intervention being executed using the engagement channel corresponding to the channel-specific intervention expectation. The method includes selecting an intervention and scheduling the selected intervention for execution.

公开(公告)号：US11830610B2

公开(公告)日：2023-11-28

申请号：US18092260

申请日：2022-12-31

Applicant: Express Scripts Strategic Development, Inc.

Inventor： Amit K. Bothra ,  Pritesh J. Shah ,  Christopher G. Lehmuth ,  Bradley D. Flynn ,  Varun Tandra

IPC: G16H40/20 ,  G06N3/08 ,  G06Q50/00 ,  G06Q30/0201 ,  G06Q10/107 ,  G16H20/10 ,  G16H50/30 ,  G16H50/20 ,  G16H50/70 ,  A61B5/00 ,  G16H80/00 ,  H04L65/1066

CPC classification number: G16H40/20 ,  A61B5/4833 ,  G06N3/08 ,  G06Q10/107 ,  G06Q30/0201 ,  G06Q50/01 ,  G16H20/10 ,  G16H50/20 ,  G16H50/30 ,  G16H50/70 ,  G16H80/00 ,  H04L65/1066

Abstract: A method includes generating an intervention model by determining principal components for features of a training set, associating each feature of the training set with a principal component, selecting features of the training set most highly correlated with principal components, training a machine learning model with at least some of the selected features, and saving the verified trained machine learning model as the intervention model. The method includes determining multiple channel-specific intervention expectations. Each channel-specific intervention expectation indicates a likelihood that the user will take action in response to an intervention being executed using the engagement channel corresponding to the channel-specific intervention expectation. The method includes selecting an intervention and scheduling the selected intervention for execution.

公开(公告)号：US11545260B1

公开(公告)日：2023-01-03

申请号：US17095504

申请日：2020-11-11

Applicant: Express Scripts Strategic Development, Inc.

Inventor： Amit K. Bothra ,  Pritesh J. Shah ,  Christopher G. Lehmuth ,  Bradley D. Flynn ,  Varun Tandra

IPC: G16H40/20 ,  G06N3/08 ,  G06Q50/00 ,  G06Q30/02 ,  G06Q10/10 ,  G16H20/10 ,  G16H50/30 ,  G16H50/20 ,  G16H50/70 ,  A61B5/00 ,  G16H80/00 ,  H04L65/1066

Abstract: A computer-implemented method includes generating an intervention model for a population of users based on engagement data indicating successfulness of prior interventions for each of the population of users. Each prior intervention corresponds to one of multiple engagement channels, and the intervention model includes multiple channel-specific models. The method includes supplying data related to a first user as input to the intervention model to determine multiple channel-specific intervention expectations. Each channel-specific intervention expectation indicates a likelihood that the first user will take action in response to an intervention being executed using the corresponding engagement channel. The method includes determining a likelihood of a gap in care for the first user, and in response to the gap in care likelihood exceeding a minimum threshold, selecting a first intervention according to the channel-specific intervention expectation that has a highest determined value, and scheduling the selected first intervention for execution.