Medtronic Minimed, Inc.

Patrick E Weydt

Louis J Lintereur

Benyamin Grosman

Anirban Roy

Neha J Parikh

Di Wu

Ali Dianaty

David Dunleavy

Pratik J Agrawal

Afshin Bazargan

Hans K Wenstad

Samuel Finney

Seung C Shin

Adam S Trock

Lavie Golenberg

Yuxiang Zhong

Huzefa F Neemuchwala

Chantal M McMahon

Francine R Kaufman

Ulrich H Rankers

Scott W Lee

Mahta Sadeghzadeh

Steven J Vargas

David Legray

Catherine Tan Fogel

A method for automatically configuring a first medical device with user-specific configuration data comprises: determining that the first medical device is being placed into service to replace a second medical device that was previously in service in accordance with the user-specific configuration data, communicating data indicative of the first medical device being placed into service to replace the second medical device, obtaining, by the first medical device, the user-specific configuration data used by the second medical device, and configuring the first medical device based on the obtained user-specific configuration data.

Automatic configuration of user-specific data based on networked charger devices

Techniques disclosed herein relate to safe correction boluses. In some embodiments, the techniques involve predicting a future glucose level that would result from delivery of a correction bolus. The techniques may also involve comparing the future glucose level to a threshold level for hypoglycemia. The techniques may further involve causing delivery of the correction bolus when the future glucose level is above the threshold level for hypoglycemia.

Safe correction boluses

Disclosed herein are techniques related to determination of adjustments to fluid delivery settings. In some embodiments, the techniques may involve accessing insulin delivery data for a user of an insulin delivery device. The insulin delivery data may be indicative of an amount of insulin delivered by the insulin delivery device to the user during a time period. The techniques may also involve analyzing the insulin delivery data to generate an updated insulin sensitivity factor (ISF) value. The techniques may further involve generating, based on the updated ISF value, a recommendation to adjust an ISF value of the insulin delivery device. Additionally, the techniques may involve causing adjustment of the ISF value to the updated ISF value in accordance with the recommendation.

Determination of adjustments to fluid delivery settings

An example system for therapy delivery includes one or more processors configured to in response to a prediction indicating that the meal event is to occur, output instructions to an insulin delivery device to deliver a partial therapy dosage, to a device to notify the patient to use the insulin delivery device to take the partial therapy dosage, or to the insulin delivery device to prepare the partial therapy dosage prior to the meal event occurring, and in response to a determination indicating that the meal event is occurring (e.g., based on movement characteristics of a patient arm), output instructions to the insulin delivery device to deliver a remaining therapy dosage, to the device to notify the patient to use the insulin delivery device to take the remaining therapy dosage, or to the insulin delivery device to prepare the remaining therapy dosage.

Prediction based delivering or guiding of therapy for diabetes

Techniques disclosed herein involve automatically adjusting a control parameter for an operating mode of a medical device. In some embodiments, the techniques involve obtaining data pertaining to a physiological condition of a patient during operation of the medical device. In some embodiments, the techniques further involve determining an optimized value for the control parameter using the data pertaining to the physiological condition of the patient and a cost function, wherein the cost function disproportionately penalizes for an amount of time that a physiological parameter is below a lower bound of a target range and includes a first weighting factor that is dependent on an amount of time that the physiological parameter is outside of the target range, and a second weighting factor, different from the first weighting factor, dependent on the amount of time that the physiological parameter is below the lower bound of the target range.

Personalized closed loop optimization systems and methods

Infusion devices and related medical devices, patient data management systems, and methods are provided for monitoring a physiological condition of a patient. An exemplary infusion device includes an actuation arrangement operable to deliver fluid to a user, a communications interface to receive measurement data indicative of a physiological condition of the user, a sensing arrangement to obtain contextual measurement data, and a control system coupled to the actuation arrangement, the communications interface and the sensing arrangement to determine a command for autonomously operating the actuation arrangement in a manner that is influenced by the measurement data and the contextual measurement data and autonomously operate the actuation arrangement in accordance with the command to deliver the fluid to the user.

Context-sensitive infusion devices, systems and methods

Devices, systems, and techniques for automatic network configuration based on biometric authentication are described herein. In one example, one or more processors may obtain first biometric data derived from one or more sensor signals generated by one or more sensors of a first device coupled to a user. The one or more processors may obtain second biometric data derived from one or more sensor signals generated by one or more sensors of a second device. The one or more processors may compare the first biometric data and the second biometric data, determine that the second device is coupled to the user based on the comparison, and establish a communication link with the second device based on the determination that the second device is coupled to the user.

Automatic network configuration based on biometric authentication

A method for automatically configuring a medical device with user-specific configuration data includes determining, by a first medical device, that the first medical device is being removed from service, wherein the first medical device was previously placed into service to provide therapy to a patient in accordance with user-specific configuration data stored on the first medical device, and wherein the first medical device is to be replaced with a second medical device that is a replacement medical device for the first medical device, and causing, by the first medical device in response to determining that the first medical device is being removed from service, configuration of the second medical device to provide therapy to the patient in accordance with the user-specific configuration data, wherein causing configuration of the second medical device comprises communicating the user-specific configuration data toward the second medical device.

Automatic configuration of user-specific data based on removal from service

Disclosed herein are techniques related to configurable target values. The techniques may involve identifying a target glucose value obtained based on user input. Additionally, the techniques may involve determining a fluid delivery command based on a difference between a measured glucose value and the target glucose value. The techniques may further involve causing fluid delivery by a fluid infusion device in accordance with the fluid delivery command to regulate the measured glucose value to the target glucose value.

Configurable target values

Disclosed herein are techniques related to automatically adjusting at least one parameter of an insulin delivery controller of an insulin delivery device to regulate delivery of insulin to a user. In some embodiments, the techniques may involve obtaining therapy-related data associated with operation of the insulin delivery device for a number of days in the past. The therapy-related data including sensor glucose data for the user and meal data for the user. The techniques may also involve determining at least one adjusted parameter for the insulin delivery controller based on the therapy-related data. Additionally, the techniques may involve causing the insulin delivery controller to adjust at least one setting in accordance with the determined at least one adjusted parameter.

One or more processors may be configured to detect whether a patient with diabetes is operating a vehicle based on one or more detected gestures. Based on the detection of operating the vehicle, the one or more processors may cause a patient device to output alerts according to a driving alert protocol. In another example, based on the detection of operating the vehicle, one or more processors may cause an insulin pump to operate according to a driving therapy protocol.

Adjusting diabetes alerts and therapy based on gesture-based detection of vehicle operation

A medical device system and related methods of automatically adjusting control parameters of a medical device are disclosed. One method involves obtaining data pertaining to a physiological condition of a patient during operation of the medical device in accordance with the operating mode, determining a plurality of adjusted values for the control parameter based at least in part on the data, determining a respective cost associated with each respective adjusted value for the control parameter based at least in part on the data using a cost function, identifying, from among the plurality of adjusted values, an optimized value from among the plurality of adjusted values, wherein the optimized value has a minimum cost associated therewith from among the plurality of costs, and updating the control parameter at the medical device to the optimized value.

A method for automatically configuring a medical device with user-specific configuration data includes obtaining, by a charger device from one or more server computing devices, user-specific configuration data stored on a first medical device that is configured to provide therapy to a patient in accordance with the user-specific configuration data, and causing, by the charger device, configuration of a second medical device based on communicating the user-specific configuration data to the second medical device while the second medical device is being charged by the charger device, wherein the second medical device is a replacement device for the first medical device.

Devices, systems, and techniques for therapy delivery and healthcare management is described. In one example, a system comprises a delivery device configured to execute therapy information comprising a treatment schedule specifying one or more treatments over a time period, wherein for each of the one or more treatments, the therapy information comprises a treatment type and a treatment amount; and processing circuitry configured to: determine a modification to the therapy information based on a recommendation of a digital twin, wherein the digital twin determines an estimated glucose level expected to be within a healthy range if the delivery device executes the modification to therapy information; and in response to physician review of the modification to the therapy information, output, to a healthcare provider system for the patient, data indicating completion of a healthcare service corresponding to the physician review.

Healthcare service management via remote monitoring and patient modeling

A processor-implemented method for closed-loop control in steady-state conditions includes determining, based on data including historical bolus information but excluding historical basal information, an amount of unmetabolized therapeutic substance in a patient; determining, based on a difference between a most recent measurement of a physiological condition of the patient and a target value for the physiological condition, a first amount or rate of a basal dosage for delivery to the patient; adjusting, based on the amount of unmetabolized therapeutic substance, the first amount or rate of the basal dosage to determine a second amount or rate of the basal dosage; and causing delivery of the second amount or rate of the basal dosage based on communicating the second amount or rate in a delivery command.

Growing community of inventors

Moorpark, CA, United States of America

Patrick E Weydt