Abstract: Techniques for automatically reconfiguring medical devices are provided. According to some embodiments, the techniques may involve determining that a first medical device is being placed into service to replace a second medical device that was previously in service in accordance with user-specific configuration data. The techniques may further involve communicating data indicative of the first medical device being placed into service to replace the second medical device. The techniques may further involve obtaining the user-specific configuration data used by the second medical device. The techniques may further involve configuring the first medical device based on the obtained user-specific configuration data.

Abstract: Techniques disclosed herein relate to safe correction boluses. In some embodiments, the techniques involve obtaining a plurality of sensor glucose readings for a patient. The techniques may further involve determining, based on the plurality of sensor glucose readings that the patient has consumed a meal. The techniques may further involve in response to determining the patient has consumed the meal, reducing a hypoglycemia threshold from a first value to a second value lower than the first value. The techniques may further involve determining a correction bolus dosage based on the reduced hypoglycemia threshold.

Abstract: 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.

Abstract: 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.

Abstract: Techniques disclosed herein relate to managing operations of a dual-mode insulin delivery device that can operate in a manual insulin delivery mode and an automated closed-loop insulin delivery mode. In one example, a processor-implemented method includes obtaining insulin delivery data of an insulin delivery device collected while the insulin delivery device operates in a closed-loop mode, determining an updated value of a parameter of the insulin delivery device in a manual mode based on the insulin delivery data, and causing the insulin delivery device to deliver insulin in the manual mode based on the updated value of the parameter of the insulin delivery device, where the parameter of the insulin delivery device has separate values in the manual mode and the closed-loop mode.

Abstract: Techniques disclosed herein involve automatically adjusting a control parameter for an operating mode of a medical device. In some embodiments, the techniques involve determining a value for the control parameter using data pertaining to a physiological condition of a patient by disproportionately penalizing a physiological parameter when it is below a target range in comparison to when the physiological parameter is above the target range.

Abstract: Disclosed herein are techniques for prediction based delivering or guiding of therapy for diabetes. In some embodiments, the techniques may involve predicting that a meal event is to occur. The techniques may further involve in response to predicting that the meal event is to occur, determining a partial therapy dosage to be delivered prior to the meal event occurring. The techniques may further involve determining, after a duration of time subsequent to delivery of the partial therapy dosage has elapsed, that meal consumption has not yet begun. The techniques may further involve prompting a patient to begin consumption of the meal.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Techniques for automatic network configuration based on biometric authentication are described herein. In some embodiments, the techniques may involve obtaining first biometric data from a first device that is substantially attachable to or substantially worn on a body of a user. The techniques may further involve obtaining second biometric data from a second device that is substantially attachable to or substantially worn on the body of the user. The techniques may further involve comparing the first biometric data and the second biometric data. The techniques may further involve determining that the first biometric data and the second biometric data correspond to the same user based on the comparison. The techniques may further involve establishing a communication link with the second device based on the determination that the second device is attached to the body of the user.

Abstract: Techniques for automatically configuring a medical device with user-specific configuration data are provided. In some embodiments, the techniques may involve determining, by a first medical device, that the first medical device is being removed from service on a patient based at least in part on detecting removal of a portion of the first medical device from contact with or being coupled to the patient. The techniques may further involve in response to determining that the first medical device is being removed from service on the patient, communicating user-specific configuration data used by the first medical device to a second medical device, wherein the second medical device is a replacement medical device for the first medical device.

Abstract: 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.

Abstract: Techniques disclosed herein relate generally to diabetes therapy management. In some examples, the techniques involve obtaining therapy-related data (e.g., including sensor glucose data and meal data) of a user of an insulin delivery device for a time period of a plurality of time periods, determining whether the therapy-related data associated with the time period is well-fit data by determining whether a physiological model for the user can be fitted to the therapy-related data associated with the time period, determining one or more parameters of the physiological model by fitting the physiological model to well-fit data associated with the time period, and causing the insulin delivery device to deliver insulin to the user based on the one or more parameters of the physiological model.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed herein are techniques for delivery of diabetes therapy. In some embodiments, the techniques may involve receiving, using one or more processors of an insulin pump, gesture data indicative of a gesture of a patient using the insulin pump. The techniques may further involve determining, by the one or more processors of the insulin pump and based on a connection to a short-range communication system of a vehicle, that the patient is in the vehicle and determining, based at least in part on the gesture data, that the patient is operating the vehicle. The techniques may further involve determining, using the one or more processors of the insulin pump, a driving therapy protocol based on the determination that the patient is operating the vehicle.