Medtronic Minimed, Inc.

Sinu Bessy Abraham

Yuxiang Zhong

Pratik J Agrawal

Boyi Jiang

Huzefa F Neemuchwala

Michael P Stone

Chantal M McMahon

Siddharth Arunachalam

Kevin E Velado

Anupam Phukan

Valerie Dormant

Infusion devices and related medical devices, patient data management systems, and methods are provided for monitoring a physiological condition of a patient. An exemplary method of querying a database involves receiving an input query from a client device, identifying a logical layer of a plurality of different logical layers of the database for searching based at least in part on the input query, generating a query statement for searching the identified logical layer of the plurality of different logical layers of the database based at least in part on the input query, querying the identified logical layer of the database using the query statement to obtain result data, and providing a search result influenced by the result data.

Patient data management systems and conversational interaction methods

Techniques for glucose level management are disclosed. In some examples, the techniques may involve obtaining an estimated protein value for a meal and determining an equivalent carbohydrate value for the meal based on the estimated protein value. In some examples, the techniques may involve obtaining an estimated fat value for a meal and determining, based on the estimated fat value, a glucose absorption rate resulting from consumption of the meal. In some examples, the equivalent carbohydrate value and/or the glucose absorption rate may be used to determine an amount of insulin to deliver to a patient to counteract a glucose level increase caused by consumption of the meal. The techniques may further involve generating an output indicative of the amount of insulin to deliver to the patient to counteract the glucose level increase caused by consumption of the meal.

Glucose level management based on fat content of meals

Glucose level management based on protein content of meals

Techniques for detection of occurrence of a meal are provided. In some embodiments, the techniques may involve obtaining a plurality of glucose measurements indicative of a glucose level in a body of a patient during an analysis interval. The techniques may involve determining one or more glucose measurement statistics based on the plurality of glucose measurements. The techniques may involve determining a likelihood of the patient having consumed a meal during the analysis interval based on the one or more glucose measurement statistics that are correlative to an occurrence of a meal for the patient. The techniques may involve detecting an occurrence of the meal during the analysis interval based on the likelihood of the patient having consumed the meal during the analysis interval.

Personalized event detection

Disclosed herein are techniques related to personalized event detection. In some embodiments, the techniques may involve obtaining a plurality of glucose measurements indicative of a glucose level in a body of a patient during an analysis interval, obtaining a patient-specific meal detection model that identifies one or more glucose measurement statistics that are correlative to occurrence of a meal for the patient, determining values for the one or more glucose measurement statistics based on the plurality of glucose measurements, determining a meal consumption metric based on the values for the one or more glucose measurement statistics, detecting an occurrence of the meal during the analysis interval based on the meal consumption metric, and in response to detecting the occurrence of the meal, providing an indication of the occurrence of the meal during with the analysis interval.

A computer-implemented therapy management system is operated to collect patient data associated with medical device users. Treatment of the users is managed by healthcare professionals (HCPs), which include a customer HCP and a plurality of non-customer HCPs. The system receives a first set of attributes for a first individual patient or a first group of patients under care of the customer HCP, and a second set of attributes for a second individual patient or a second group of patients. The system identifies a first set of patient records associated with the customer HCP that satisfy the received first attributes, and a second set of patient records that satisfy the received second attributes, and compares patient outcomes from the first set of records against patient outcomes from the second set of records. A comparative therapy report is generated and communicated to a client device associated with the customer HCP.

Patient therapy management system that leverages aggregated patient population data

Infusion devices and related medical devices, patient data management systems, and methods are provided for monitoring a physiological condition of a patient. An exemplary method of monitoring a physiological condition of a patient involves obtaining current measurement data for the physiological condition of the patient provided by a sensing arrangement, obtaining a user input indicative of one or more future events associated with the patient, and in response to the user input, determining a prediction of the physiological condition of the patient in the future based at least in part on the current measurement data and the one or more future events using one or more prediction models associated with the patient, and displaying a graphical representation of the prediction on a display device.

Patient-specific glucose prediction systems and methods

Medical devices and related patient management systems and event detection methods are provided. An exemplary method of detecting events pertaining to operation of a medical device, such as an infusion device, involves obtaining measurements indicative of a condition in a body of a patient, determining statistics for an analysis interval based on the measurements, and determining an event probability associated with the analysis interval based on historical event data associated with the patient. An event detection model associated with the patient is obtained and applied to the statistics and the event probability to identify occurrence of the event using the event detection model, and in response, an indication of the event associated with the analysis interval is provided, for example, by tagging or marking data in a database, displaying graphical indicia of the event, or the like.

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Gallatin, TN, United States of America

Sinu Bessy Abraham