Dexcom, Inc.

Sumitaka Mikami

Apurv Ullas Kamath

John Michael Gray

Peter C Simpson

Michael Robert Mensinger

Eli Reihman

Katherine Yerre Koehler

Hari Hampapuram

Rian W Draeger

Arturo Garcia

Eric Cohen

Leif N Bowman

Brian Christopher Smith

Naresh Chandra Bhavaraju

Matthew D Wightlin

Stephen J Vanslyke

Jennifer Blackwell

Phil Mayou

Mark Dervaes

Philip Thomas Pupa

Scott M Belliveau

Esteban Cabrera, Jr

Paul D Kramer

Douglas William Burnette

Anna Leigh Davis

Aditya Sagar Mandapaka

Richard Chien Yang

Andrew Attila Pal

Laura J Dunn

Gary A Morris

Nicholas Polytaridis

Paul Noble-Campbell

Lauren Hruby Jepson

Benjamin Elrod West

Subrai Girish Pai

Alexandra Elena Constantin

David DeRenzy

Atiim Joseph Wiley

Zebediah L McDaniel

Matthew Lawrence Johnson

Tomas C Walker

Basab Dattaray

Nathaniel David Heintzman

Timothy Joseph Goldsmith

Andrea Jean Jackson

Lauren Danielle Armenta

Shawn Clay Sanders

Matthew T Vogel

Vincent Peter Crabtree

Patrick Wile McBride

Daniel Justin Wiedeback

Michael Levozier Moore

Stephen Alan Reichert

James Stephen Amidei

Philip Mansiel Pellouchoud

Minda McDorman Grucela

Darin Edward Chum Dew

Steven David King

Margaret A Crawford

Hemant Mahendra Nirmal

Robert Patrick Van Tassel

Dana Charles Cambra

Kho Braun

Daniel Justin Weideback

Abhishek Sharma

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

Various examples are directed to systems and methods for measuring a parameter related to patient health. An analyte sensor system may detect that the analyte sensor system has been applied to a host and may store analyte data describing the host. The analyte sensor system may determine that sensor use at the analyte sensor system has terminated and upload stored analyte data to an upload computing device.

Intermittent monitoring

Various examples are directed to systems and methods for patient monitoring. An example method comprises receiving an estimated glucose concentration level of the patient from a continuous glucose monitoring (CGM) system for a first time period. The method may also include receiving non-glucose information relating to the patient for the first time period and determining a relationship between the estimated glucose concentration level and the non-glucose information. The method may also include receiving non-glucose information relating to the patient for a second time period and determining diabetic information about the patient for the second time period based upon the determined relationship and the non-glucose information. The method may include electronically delivering a notification about the diabetic information.

Disclosed are systems and methods for generating graphical displays of analyte data and/or health information. In some implementations, the graphical displays are generating based on a self-referential dataset that are modifiable based on identified portions of the data. The modified graphical displays can indicate features in the analyte data of a host.

Systems and methods for health data visualization and user support tools for continuous glucose monitoring

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

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 data sensed by a glucose concentration sensor. A host state change associated with the host glucose concentration data may be determined. A guidance message based at least in part on the host state change may also be determined. The guidance message may be delivered through a user interface.

Various examples are directed to systems and methods for monitoring a patient. For example, patient data of a first type may be monitored with a low-fidelity monitoring technique over a first time period. Patient behavior may be evaluated based on the monitored first type of patient data. Patient data of a second type may be monitored over a second time period with a continuous glucose monitor device. A parameter of the continuous glucose monitor device may be based on the evaluated patient behavior during the monitoring.

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San Diego, CA, United States of America

Sumitaka Mikami