Dynamic Display of Glucose Information

• US 20170193184A1
• Filed: 03/24/2017
• Published: 07/06/2017
• Est. Priority Date: 12/19/2007
• Status: Active Grant

• Current Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Original Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Inventors: Hayter, Gary Alan, Dunn, Timothy Christian
• Granted Patent: US 11,749,410 B2
• Time in Patent Office: Days
• CPC Class Codes: A61M 2005/14208 with a programmable infusion control system, characterised by the infusion program
• CPC Class Codes: A61M 2205/18 with alarm
• CPC Class Codes: A61M 2205/3584 using modem, internet or bluetooth
• CPC Class Codes: A61M 2205/50 with microprocessors or computers
• CPC Class Codes: A61M 2205/502 User interfaces, e.g. screens or keyboards
• CPC Class Codes: A61M 2205/52 with memories providing a history of measured variating parameters of apparatus or patient
• CPC Class Codes: A61M 2205/582 by tactile feedback
• CPC Class Codes: A61M 2230/005 Parameter used as control input for the apparatus
• CPC Class Codes: A61M 2230/201 Glucose concentration
• CPC Class Codes: A61M 5/142 Pressure infusion, e.g. using pumps
• CPC Class Codes: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: G06F 2218/16 by matching signal segments
• CPC Class Codes: G06F 3/04847 Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
• CPC Class Codes: G16H 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: H04W 4/80 Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication

Abstract

Method and system including displaying a first representation of a medication treatment parameter profile, displaying a first representation of a physiological profile associated with the medication treatment parameter profile, detecting a modification to a segment of the medication treatment parameter profile, displaying a modified representation of the medication treatment parameter profile and the physiological profile based on the detected modification to the segment of the medication treatment parameter profile, modifying an attribute of the first representation of the medication treatment parameter profile, and modifying an attribute of the first representation of the physiological profile are provided.

Claims

1-16. -16. (canceled)

17. A non-transitory computer readable medium, containing instructions for causing a computing environment to perform a method of operating a customized bolus calculator, the method including learning a personalized meal interpretation for a host, the method comprising:

during a meal time period, receiving host data about a meal consumed during the meal time period, receiving data about insulin provided to the host during the meal time period, and receiving data about the host glucose concentration during the meal time period;

associating the host meal data with the insulin data and the glucose data during the meal time period, and learning a personalized meal interpretation for the host based on the associating; and

customizing a bolus calculation based on the glucose data, the meal data, and the personalized meal interpretation.

18. The medium of claim 17, wherein the received mea data comprises a meal size, and wherein the meal size corresponds to small, medium, or large.

19. The medium of claim 17, wherein the received meal data comprises a meal diversity and/or makeup.

20. The medium of claim 19, wherein the meal diversity and/or makeup includes data about high, low, or medium amounts of carbohydrates, fats, and proteins.

21. The medium of claim 20, wherein the meal diversity and/or makeup comprises a selection from the group consisting of:

all carbs, high-fat, high carbs, low carbs, or balanced carb/fat/protein.

22. The medium of claim 17, wherein the customizing is performed over a number of meals, and further comprising developing a relationship of a personalized meal interpretation to a customized bolus amount, such that upon host entry of meal data into the bolus calculator, the bolus calculator provides a bolus amount based on the mapping.

23. The medium of claim 22, wherein the developing a relationship further comprises determining a typical meal size for the host.

24. The medium of claim 22, wherein the developing a relationship further comprises determining a typical meal diversity and/or makeup for the host.

25. The medium of claim 17, wherein the customizing further comprises customizing a time for bolus delivery, the time for bolus delivery based on at least one of a meal time and an insulin delivery time.

26. The medium of claim 17, further comprising:

receiving exercise data for determining whether the host has exercised, the data received from one or more external or internal devices, and wherein the customizing a bolus calculation is further based on the received exercise data.

27. The medium of claim 17, further comprising displaying a graphic if display criteria are met.

28. The medium of claim 27, wherein the graphic comprises a prompt for a host to enter data corresponding to meal, insulin, or exercise, and following the prompted-for data being entered, the customizing is further based on the entered data.

29. The medium of claim 27, wherein the graphic comprises a prompt for a host to perform an action.

30. The medium of claim 17, wherein the receiving insulin data includes receiving data from a pump.

31. An insulin pump, programmed to provide a customized bolus calculator, the bolus calculator incorporating learned patient data, the programming performing a method comprising:

during a meal time period, receiving host data about a meal consumed during the meal time period, receiving data about insulin provided to the host during the meal time period, and receiving data about the host glucose concentration during the meal time period;

associating the host meal data with the insulin data and the glucose data during the meal time period, and learning a personalized meal interpretation for the host based on the associating; and

customizing a bolus calculation based on the glucose data, the meal data, and the personalized meal interpretation.

32. The pump of claim 31, wherein the customizing a bolus calculation further comprises customizing a time to action of the delivery of the bolus.