Abstract: 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.

Abstract: 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.

Abstract: Subject matter disclosed herein relates to a method and/or system for tailoring insulin therapies to physiological characteristics of a patient. In particular, observations of a blood glucose concentration of a patient responsive to a meal profile and an insulin profile may be used for estimating one or more physiological parameters.

Abstract: Technologies are provided for classifying detected events as labeled event combinations. Input data for different combinations of detected events are received at a server system, and each combination of detected events is processed at the server system. Processing can include classifying that combination of detected events at a classifier to map that combination of detected events to a probability of an increased insulin delivery demand or a probability of a decreased insulin delivery demand, and labeling each classified combination of detected events with a label to generate a corresponding labeled event combination that has the probability of the increased insulin delivery demand or the probability of the decreased insulin delivery demand. Each labeled event combination can be stored in storage as a database of labeled event combinations, and selected ones of the labeled event combinations can be processed at a client application to generate an output result.

Abstract: A processor-implemented method includes obtaining measurement data indicative of a physiological condition measured by a sensing arrangement located at a site on a body, determining a lag associated with the sensing arrangement based on a relationship between the measurement data and reference data, and identifying, based on the lag, the site at which the sensing arrangement is located from a plurality of sites on the body.

Abstract: Techniques related to automatically generating a super bolus may include determining an amount of an augmented meal bolus to be delivered to a patient for regulating the patient's glycemic response to a meal. The amount of the augmented meal bolus may exceed a sufficient amount for counteracting a glucose level increase caused by the meal. In some embodiments, the techniques may further include determining a duration of a postprandial reduction period during which basal dosage deliveries to the patient are to be reduced. In some other embodiments, the techniques may further include delivering the augmented meal bolus to the patient prior to determining whether or not to cause reduction of basal dosage deliveries. More specifically, a glucose level of the patient may be obtained after delivery of the augmented meal bolus, and the obtained glucose level may be used to determine whether to reduce basal dosage deliveries.

Abstract: A system includes one or more processors and one or more processor-readable storage media storing instructions which, when executed by the one or more processors, cause performance of receiving, via a user interface, confirmation input that indicates user acceptance of a blood glucose (BG) measurement generated by a BG meter device, in response to receiving the confirmation input, using the BG measurement for performing both glucose sensor calibration and bolus calculation, and causing delivery of a bolus based on outputting a delivery command in accordance with the bolus calculation.

Abstract: A method for retrospective calibration of a glucose sensor uses stored values of measured working electrode current (Isig) to calculate a final sensor glucose (SG) value retrospectively. The Isig values may be preprocessed, discrete wavelet decomposition applied. At least one machine learning model, such as, e.g., Genetic Programing (GP) and Regression Decision Tree (DT), may be used to calculate SG values based on the Isig values and the discrete wavelet decomposition. Other inputs may include, e.g., counter electrode voltage (Vcntr) and Electrochemical Impedance Spectroscopy (EIS) data. A plurality of machine learning models may be used to generate respective SG values, which are then fused to generate a fused SG. Fused SG values may be filtered to smooth the data, and blanked if necessary.

Abstract: Disclosed herein are techniques related to automatic real-time meal detection. In some embodiments, the techniques involve obtaining a plurality of glucose concentration values and a plurality of plasma insulin concentration estimations, each glucose concentration value corresponding with a respective plasma insulin concentration estimation; generating an output based on applying a meal detection model to the plurality of glucose concentration values and the plurality of plasma insulin concentration estimations; and determining, based on the output, that a glucose concentration value of the plurality of glucose concentration values corresponds to an ongoing glycemic response to a meal.

Abstract: Techniques disclosed herein relate to infusion devices and related meal bolus adjustment methods. In some embodiments, the techniques may involve determining an initial bolus amount. The techniques may further involve predicting a value for a first physiological condition based at least in part on the initial bolus amount. The techniques may further involve when the predicted value for the first physiological condition violates a threshold: identifying an adjusted bolus amount that results in the predicted value for the first physiological condition satisfying the threshold, and causing delivery of the adjusted bolus amount.

Abstract: Techniques disclosed herein relate to operating a fluid delivery device in a personalized manner based at least in part on historical data of a patient. In some embodiments, the techniques involve determining a predicted physiological condition of a patient in response to a future activity of the patient, based at least in part on historical data corresponding to the future activity for the patient; determining, based at least in part on the predicted physiological condition of the patient, an adjustment to fluid delivery to the patient by a medical device to prospectively account for the future activity; and operating the medical device to deliver a fluid to the patient in accordance with the adjustment.

Abstract: Disclosed herein are techniques related to automatic real-time meal detection. In some embodiments, the techniques involve obtaining a plurality of glucose concentration values and a plurality of plasma insulin concentration estimations, each glucose concentration value corresponding with a respective plasma insulin concentration estimation; generating an output based on applying a meal detection model to the plurality of glucose concentration values and the plurality of plasma insulin concentration estimations; and determining, based on the output, that a glucose concentration value of the plurality of glucose concentration values corresponds to an ongoing glycemic response to a meal.

Abstract: Techniques related to automatically generating a super bolus may include determining an amount of an augmented meal bolus to be delivered to a patient for regulating the patient's glycemic response to a meal. The amount of the augmented meal bolus may exceed a sufficient amount for counteracting a glucose level increase caused by the meal. In some embodiments, the techniques may further include determining a duration of a postprandial reduction period during which basal dosage deliveries to the patient are to be reduced. In some other embodiments, the techniques may further include delivering the augmented meal bolus to the patient prior to determining whether or not to cause reduction of basal dosage deliveries. More specifically, a glucose level of the patient may be obtained after delivery of the augmented meal bolus, and the obtained glucose level may be used to determine whether to reduce basal dosage deliveries.

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: Medical devices and related systems and operating methods are provided. A method of operating an infusion device capable of delivering fluid influencing a physiological condition to a patient involves obtaining an event indication, such as a meal indication, determining an initial bolus amount based on the event indication, and determining predicted values for the physiological condition of the patient during a time window into the future based at least in part on the initial bolus amount. When the predicted values violate a threshold during the time window, the control system identifies an adjusted bolus amount that results in the predicted values for the physiological condition satisfying the threshold during the time window from within a search space defined by the initial bolus amount and operates an actuation arrangement of the infusion device to deliver the adjusted bolus amount of the fluid to the patient.

Abstract: Techniques related to temporary setpoint values are disclosed. The techniques may involve causing operation of a fluid delivery device in a closed-loop mode for automatically delivering fluid based on a difference between a first setpoint value and an analyte concentration value during operation of the fluid delivery device in the closed-loop mode. Additionally, the techniques may involve obtaining a second setpoint value. The second setpoint value may be a temporary setpoint value to be used for a period of time to regulate fluid delivery, and the second setpoint value may be greater than the first setpoint value. The techniques may further involve causing operation of the fluid delivery device for automatically reducing fluid delivery for the period of time based on the second setpoint value.

Abstract: Infusion systems, infusion devices, and related operating methods are provided. An exemplary method of operating an infusion device capable of delivering fluid to a patient involves obtaining, by a control system associated with the infusion device, measurement values indicative of a condition of the patient provided by a sensing device, identifying an event pattern based at least in part on the measurement values for the condition and historical data associated with the patient, generating a notification indicative of the event pattern in response to identifying the event pattern, and adjusting operation of the infusion device to deliver the fluid to the patient in a manner that is influenced by the event pattern in response to receiving user input confirming the event pattern.

Abstract: Infusion systems, infusion devices, and related operating methods are provided. An exemplary method of operating an infusion device capable of delivering fluid to a patient involves obtaining, by a control system associated with the infusion device, an input meal indication, obtaining historical data for the patient associated with the input meal indication, determining an estimated carbohydrate amount corresponding to the input meal indication based at least in part on the historical data, determining a bolus dosage of the insulin based at least in part on the estimated carbohydrate amount, and operating an actuation arrangement of the infusion device to deliver the bolus dosage of the insulin to the patient.

Abstract: Infusion systems, infusion devices, and related operating methods are provided. An exemplary method of operating an infusion device capable of delivering fluid to a patient involves obtaining, by a control system associated with the infusion device, user input indicating an activity by the patient, obtaining historical data for the patient corresponding to the activity, determining a probable patient response corresponding to the activity based at least in part on the historical data for the patient, determining an adjustment for delivering the fluid by the infusion device based at least in part on the probable patient response, and operating the infusion device to deliver the fluid to the patient in accordance with the adjustment.

Abstract: Disclosed herein are techniques related to automatic real-time meal detection. In some embodiments, the techniques involve obtaining a plurality of glucose concentration values and a plurality of plasma insulin concentration estimations, each glucose concentration value corresponding with a respective plasma insulin concentration estimation; generating an output based on applying a meal detection model to the plurality of glucose concentration values and the plurality of plasma insulin concentration estimations; and determining, based on the output, that a glucose concentration value of the plurality of glucose concentration values corresponds to an ongoing glycemic response to a meal.