Dexcom, Inc.

Stephen J Vanslyke

Peter C Simpson

Apurv Ullas Kamath

Naresh Chandra Bhavaraju

Arturo Garcia

Hari Hampapuram

Leif N Bowman

Anna Leigh Davis

Andrew Attila Pal

Lauren Hruby Jepson

Katherine Yerre Koehler

Jonathan Hughes

Aarthi Mahalingam

Thomas Arnold Peyser

Michael J Estes

Aditya Sagar Mandapaka

Alexandra Elena Constantin

Christopher Robert Hannemann

Daniel Smith

Eli Reihman

Rian W Draeger

Laura J Dunn

Sebastian Böhm

Gary A Morris

Nicholas Polytaridis

Philip Thomas Pupa

Thomas Hall

Scott M Belliveau

Benjamin Elrod West

Subrai Girish Pai

Daiting Rong

Disha B Sheth

Jeff Jackson

Rui Ma

Shawn Larvenz

Mark Dervaes

Jose Hector Hernandez-Rosas

Tom Miller

Matthew D Wightlin

Sebastian Bohm

Jack Pryor

Eric Cohen

Brian Christopher Smith

Ted Tang Lee

Jason M Halac

Stephen D Patek

Douglas William Burnette

Richard Chien Yang

Becky L Clark

Jason Mitchell

David DeRenzy

Matthew T Vogel

Atiim Joseph Wiley

Anna Claire Harley-Trochimczyk

Tomas C Walker

Nathaniel David Heintzman

Andrea Jean Jackson

Samuel Jere Marsh

Sofie Wells Schunk

Chris W Dring

Zebediah L McDaniel

Matthew Lawrence Johnson

Shanger Wang

Anna Leigh Rack-Gomer

David I-Chun Lee

Lucas Bohnett

Dmytro Sokolovskyy

Thomas Stuart Hamilton

Alexandra Lynn Carlton

Vincent Peter Crabtree

Patrick Wile McBride

Michael Levozier Moore

Stephen Alan Reichert

Jiong Zou

Sumitaka Mikami

Rita M Castillo

Gary Brian Gable

James Stephen Amidei

Ashley Anne Rindfleisch

Philip Mansiel Pellouchoud

Dmytro Sokolovsky

Liang Wang

Kamuran Turksoy

Wenjie Lan

Minda McDorman Grucela

Michael A Bloom

Alexander Leroy Teeter

Sarah Kate Pickus

Darin Edward Chum Dew

Steven David King

Robert Patrick Van Tassel

Leah Ziegler

Ghazaleh R Esmaili

Todd N Haseyama

Apurv Ullas Karnath

Kho Braun

Abhishek Sharma

Dana Charles Cambra

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 method are described for determining if a decision support recommendation is to be presented to a user for treatment of a diabetic state, including receiving a plurality of input data items impacting a diabetic state of a user of continuous glucose monitor, the input data items serving as input data to a process for determining a decision support recommendation; assigning a reliability level to each of the input data items; calculating a reliability metric based on the reliability levels assigned to each of the input data items; determining a decision support recommendation based on the process and the input data and presenting the decision support recommendation to the user on a user interface only if the reliability metric exceeds a threshold.

Safety tools for decision support recommendations made to users of continuous glucose monitoring systems

Systems and methods for analyte monitoring, particularly systems and methods for monitoring and managing life of a battery in an analyte sensor system worn by a user, are provided.

Systems and methods for monitoring and managing life of a battery in an analyte sensor system worn by a user

Systems and methods described provide dynamic and intelligent ways to change the required level of user interaction during use of a monitoring device. The systems and methods generally relate to real time switching between a first or initial mode of user interaction and a second or new mode of user interaction. In some cases, the switching will be automatic and transparent to the user, and in other cases user notification may occur. The mode switching generally affects the user's interaction with the device, and not just internal processing. The mode switching may relate to calibration modes, data transmission modes, control modes, or the like.

System and method for mode switching

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 calibrate an analyte concentration sensor within a biological system, generally using only a signal from the analyte concentration sensor. For example, at a steady state, the analyte concentration value within the biological system is known, and the same may provide a source for calibration. Similar techniques may be employed with slow-moving averages. Variations are disclosed.

Transcutaneous analyte sensors and monitors, calibration thereof, and associated methods

Systems and methods are provided that address the need to frequently calibrate analyte sensors, according to implementation. In more detail, systems and methods provide a preconnected analyte sensor system that physically combines an analyte sensor to measurement electronics during the manufacturing phase of the sensor and in some cases in subsequent life phases of the sensor, so as to allow an improved recognition of sensor environment over time to improve subsequent calibration of the sensor.

Automatic analyte sensor calibration and error detection

An amount of glycemic dysfunction associated with mis-timing (e.g., delay) of meal boluses based on replay analysis is determined. The amount of dysfunction of historical or estimated bolusing as compared to an optimally timed bolus based on the replay analysis is quantified and visualized. Inferences may be made about diabetes meal management regarding inputs from a patient.

Evaluation and visualization of glycemic dysfunction

Systems and methods for applying time-dependent algorithmic compensation functions to data output from a continuous analyte sensor. Some embodiments determine a time since sensor implantation and/or whether a newly initialized sensor has been used previously.

Systems and methods for processing analyte sensor data

Systems and methods disclosed here provide ways to discriminate fault types encountered in analyte sensors and systems and further provide ways to process such discriminated faults responsively based on sensor data, clinical context information, and other data about the patient or patient's environment. The systems and methods thus employ clinical context in detecting and/or responding to errors or faults associated with an analyte sensor system, and discriminating the type of fault, and its root cause, particularly as fault dynamics can appear similar to the dynamics of physiological systems, emphasizing the importance of discriminating the fault and providing appropriate responsive processing. Thus, the disclosed systems and methods consider the context of the patient's health condition or state in determining how to respond to the fault.

Fault discrimination and responsive processing based on data and context

Systems and methods are disclosed which provide for a 'factory-calibrated' sensor. In doing so, the systems and methods include predictive prospective modeling of sensor behavior, and also include predictive modeling of physiology. With these two correction factors, a consistent determination of sensitivity can be achieved, thus achieving factory calibration.

System and method for factory calibration or reduced calibration of an indwelling sensor based on sensitivity profile and baseline model of sensors

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Carlsbad, CA, United States of America

Stephen J Vanslyke