Optum, Inc.

Optum Services (ireland) Limited

Thomas R Gilbertson

Raja Mukherji

Gregory J Boss

Bruno Ohana

Yangcheng Huang

Matthew R Versaggi

Dominik Dahlem

Mallory B Van Horn

David A Chennisi

Scott D Johnson

Brad K Sitler

Michael B Wentzien

Haylea Tricia Northcott

Karen Harte

Joshua L Taylor

Colby A Wright

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for production line conformance monitoring. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform production line conformance monitoring by utilizing categorical validation machine learning models that are generated using a plurality of training production line images associated with a related category subset of a plurality of validation categories for a target validation category.

Production line conformance measurement techniques using categorical validation machine learning models

A computing system may receive a verification of benefits (VoB) request that identifies a subscriber and a healthcare provider that submitted the VoB request. The VoB request may comprise a request for the computing system to verify that a health insurance policy of the subscriber is active. In response to determining that the health insurance policy of the identified subscriber is active, the computing system may determine whether to send a subscriber alert to the subscriber. The subscriber alert may notify the subscriber that the healthcare provider has been requested to perform a healthcare action for the subscriber in the future as of a time the healthcare provider submitted the VoB request. Based on a determination to send the subscriber alert, the computing system may initiate a process to send the subscriber alert.

Medical identity theft alert system

There is a need for more effective and efficient predictive data analysis. This need can be addressed by, for example, solutions for performing/executing demographic-aware federated machine learning. In one example, a method includes receiving local machine learning model data objects from model data object provider agents; for each inference-profile pair that is associated with a corresponding inference identifier and a corresponding model profile, generating a global machine learning model data object based at least in part on a related local model subset of the local machine learning model data objects for the inference-profile pair; and providing, based at least in part on each global machine learning model data object, a demographic-aware predictive data analysis application programming interface (API), wherein the demographic-aware predictive data analysis API is accessible by the model data object provider agents.

Demographic-aware federated machine learning

There is a need for solutions that increase efficiency and reliability of medical record processing systems. This need can be addressed, for example, by receiving a free-form primary provider input from a primary provider profile; detecting one or more information deficiencies in the primary provider input, wherein each information deficiency indicates a respective query; for each identified information deficiency of the one or more information deficiencies, identifying a supportive profile associated with the respective information deficiency, causing the supportive profile computing entity associated with the respective supportive provider profile to present an audiovisual interface that includes an indication of the respective query associated with the information deficiency, and receiving a supportive provider input from each supportive provider profile; and generating a collaborative record for the primary provider input based on the primary provider input and each received supportive provider input.

Concepts for iterative and collaborative generation of data reports via distinct computing entities

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Hartford, CT, United States of America

Thomas R Gilbertson