Dexcom, Inc.

Matthew T Vogel

Apurv Ullas Kamath

Lauren Hruby Jepson

Alexandra Elena Constantin

Peter C Simpson

Eli Reihman

Katherine Yerre Koehler

Hari Hampapuram

Rian W Draeger

Naresh Chandra Bhavaraju

Arturo Garcia

Anna Leigh Davis

Andrew Attila Pal

Nicholas Polytaridis

Philip Thomas Pupa

Scott M Belliveau

Subrai Girish Pai

Nathaniel David Heintzman

Tomas C Walker

Stephen J Vanslyke

Christopher Robert Hannemann

Daniel Smith

Leif N Bowman

Aditya Sagar Mandapaka

Laura J Dunn

Gary A Morris

Thomas Hall

Benjamin Elrod West

Atiim Joseph Wiley

Anna Claire Harley-Trochimczyk

Andrea Jean Jackson

Rita M Castillo

Gary Brian Gable

Samuel Jere Marsh

Ashley Anne Rindfleisch

Sofie Wells Schunk

Michael Robert Mensinger

Eric Cohen

Phil Mayou

John Michael Gray

Jennifer Blackwell

Basab Dattaray

Matthew Lawrence Johnson

Patrick Wile McBride

Daniel Justin Wiedeback

Sumitaka Mikami

Kamuran Turksoy

Sarah Kate Pickus

Leah Ziegler

Ghazaleh R Esmaili

Todd N Haseyama

Daniel Justin Weideback

Various examples are directed to systems and methods for generating a bolus dose for a host. A bolus application may display a first bolus configuration parameter question at a user interface and receive, through the user interface, a first answer to the first bolus configuration parameter question. The first answer may describe a previous bolus determination technique of the host. The bolus application may select a second bolus configuration parameter question using the first answer and provide the second bolus configuration parameter question at the user interface. The bolus application may determine a set of at least one bolus configuration parameter using the first answer and a second answer to the second bolus configuration parameter question.

Managing bolus doses

Systems and methods are disclosed that provide smart alerts to users, e.g., alerts to users about diabetic states that are only provided when it makes sense to do so, e.g., when the system can predict or estimate that the user is not already cognitively aware of their current condition, e.g., particularly where the current condition is a diabetic state warranting attention. In this way, the alert or alarm is personalized and made particularly effective for that user. Such systems and methods still alert the user when action is necessary, e.g., a bolus or temporary basal rate change, or provide a response to a missed bolus or a need for correction, but do not alert when action is unnecessary, e.g., if the user is already estimated or predicted to be cognitively aware of the diabetic state warranting attention, or if corrective action was already taken.

System and method for providing alerts optimized for a user

Systems and methods are provided to determine a time to provide guidance to a user regarding management of a physiologic condition such as diabetes. The determination may be based upon a model or pattern. The time to deliver guidance may be calculated to be useful to a user in the management of a glucose concentration level.

System and method for providing personalized guidance to diabetes patients

Systems and methods are provided to provide guidance to a user regarding management of a physiologic condition such as diabetes. The determination may be based upon a patient glucose concentration level. The glucose concentration level may be provided to a stored model to determine a state. The guidance may be determined based at least in part on the determined state.

System and method for decision support

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Encinitas, CA, United States of America

Matthew T Vogel