Dexcom, Inc.

Matthew Lawrence Johnson

Apurv Ullas Kamath

Peter C Simpson

Hari Hampapuram

Katherine Yerre Koehler

Rian W Draeger

Anna Leigh Davis

Philip Thomas Pupa

Scott M Belliveau

Alexandra Elena Constantin

John Michael Gray

Eli Reihman

Andrew Attila Pal

Subrai Girish Pai

Aarthi Mahalingam

Brian Christopher Smith

Esteban Cabrera, Jr

Gary A Morris

Michael Robert Mensinger

Eric Cohen

Naresh Chandra Bhavaraju

Robert James Boock

Matthew D Wightlin

Arturo Garcia

Phil Mayou

Nicholas Polytaridis

David Price

Lauren Hruby Jepson

Laura J Dunn

Jason M Halac

Stephen J Vanslyke

Mark Dervaes

Jennifer Blackwell

Douglas William Burnette

Richard Chien Yang

Aditya Sagar Mandapaka

Benjamin Elrod West

David DeRenzy

Zebediah L McDaniel

Tomas C Walker

Sumitaka Mikami

Peter Galuardi

Nathaniel David Heintzman

Basab Dattaray

Matthew T Vogel

Mark Douglas Kempkey

Vincent Peter Crabtree

Patrick Wile McBride

Michael Levozier Moore

Stephen Alan Reichert

Daniel Justin Wiedeback

James Stephen Amidei

Philip Mansiel Pellouchoud

Minda McDorman Grucela

Darin Edward Chum Dew

Margaret A Crawford

Shawn Clay Sanders

Mark Wu

Steven David King

Robert Patrick Van Tassel

Chad Patterson

Kazanna Calais Hames

Samuel Isaac Epstein

Mark Edward Selander

Ryan Richard Ruehl

Devon M Headen

Alexander Michael Diener

Kho Braun

Jr Cabrera Esteban

Rian Draeger

Jr Esteban Cabrera

Daniel Justin Weideback

Abhishek Sharma

Dana Charles Cambra

Katerine Yerre Koehler

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

Techniques for data analysis and user guidance are provided. One or more current measurements of one or more current analyte levels for the user are received from a sensor. A pattern is generated based on the one or more current measurements and the one or more past measurements. A first alignment with a first user target is then determined based on the pattern, where the first user target relates to one or more of a mental state or physical state of the user. A first result is output to the user, based on the determined first alignment.

Evaluation of data to provide decision support for a ketogenic lifestyle

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.

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.

Disclosed are systems and methods for secure and seamless set up and modification of bolus calculator parameters for a bolus calculator tool by a health care provider (HCP). In one aspect, a method for enabling HCP set up of a bolus calculator includes providing a server accessible by both an HCP and a patient; upon login by the HCP, displaying, or transmitting for display, a fillable form, the fillable form including one or more fields for entry of one or more bolus calculator parameters; receiving data from the fillable form, the data corresponding to one or more bolus calculator parameters; and upon login by the patient, transmitting data to a device associated with the patient, the transmitted data based on the received data, where the transmitted data corresponds to one or more of the bolus calculator parameters in a format suitable for entry to a bolus calculator.

Systems and methods for CGM-based bolus calculator for display and for provision to medicament delivery devices

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.

Provided are systems and methods using which users may learn and become familiar with the effects of various aspects of their lifestyle on their health, e.g., users may learn about how food and/or exercise affects their glucose level and other physiological parameters, as well as overall health. In some cases the user selects a program to try; in other cases, a computing environment embodying the system suggests programs to try, including on the basis of pattern recognition, i.e., by the computing environment determining how a user could improve a detected pattern in some way. In this way, users such as type II diabetics or even users who are only prediabetic or non-diabetic may learn healthy habits to benefit their health.

System and method for educating users, including responding to patterns

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Encinitas, CA, United States of America

Matthew Lawrence Johnson