Optum Services (ireland) Limited

Dominik Dahlem

Rick A Hamilton, Ii

Damian Kelly

Bruno Ohana

Yangcheng Huang

Raja Mukherji

Thomas R Gilbertson

Mallory B Van Horn

Scott D Johnson

David A Chennisi

Michael B Wentzien

Joshua L Taylor

Without timely reminders, certain individuals may be highly unlikely to complete various tasks scheduled for completion. For example, medicinal therapy regimen patients often have difficulty in remembering to take their prescribed medications in accordance with care provider specified instructions. By providing a reminder prompt generation system that utilizes machine-learning and/or artificial intelligence to determine appropriate reminder times and modalities for providing automated-prompting notifications to users, the users' adherence to scheduled task completions can be significantly increased.

Machine-learning for state determination and prediction

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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Dublin, Ireland

Dominik Dahlem