SYSTEM AND METHOD FOR MODE SWITCHING

• US 20160113558A1
• Filed: 12/18/2015
• Published: 04/28/2016
• Est. Priority Date: 09/22/2014
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Bhavaraju, Naresh C., Bloom, Michael A., Bowman, Leif N., Carlton, Alexandra Lynn, Koehler, Katherine Yerre, Hampapuram, Hari, Jepson, Lauren Hruby, Hughes, Jonathan, Rack-Gomer, Anna Leigh, Simpson, Peter C., Vanslyke, Stephen J., Karnath, Apurv Ullas
• Granted Patent: US 11,903,697 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2560/0487 Special user inputs or interfaces
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: A61B 5/14503 invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors A61B5/1459, A61B5/1464, A61B5/1473, A61B5/1482, A61B5/14865 take precedence
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/1486 using enzyme electrodes, e.g. with immobilised oxidase
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/72 Signal processing specially adapted for physiological signals or for diagnostic purposes character recognition, recognising digital ink, document-oriented image-based pattern recognition G06V30/00; recognition or understanding of postal images G06V30/424
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7435 Displaying user selection data, e.g. icons in a graphical user interface
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 5/7475 User input or interface means, e.g. keyboard, pointing device, joystick
• 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 5/1723 : using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: G16H 40/40 : for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Systems and methods described provide dynamic and intelligent ways to change the required level of user interaction during use of a monitoring device. The systems and methods generally relate to real time switching between a first or initial mode of user interaction and a second or new mode of user interaction. In some cases, the switching will be automatic and transparent to the user, and in other cases user notification may occur. The mode switching generally affects the user'"'"'s interaction with the device, and not just internal processing. The mode switching may relate to calibration modes, data transmission modes, control modes, or the like.

Claims

1-20. -20. (canceled)

21. A method of glucose monitoring in which a diabetes management system is in signal communication with a glucose sensor through sensor electronics, the method providing decision support, the method comprising:

a. receiving measurement data indicative of a subject glucose concentration measured by the glucose sensor and displaying on a user interface a representation of the glucose concentration in a first decision-support mode;

b. determining usability data indicative of a clinical usability of the glucose concentration data;

c. transitioning to a second decision support mode based on a comparison of the determined usability data against one or more decision mode transition criteria; and

d. receiving additional measurement data and displaying on the user interface a representation of the glucose concentration in the second decision-support mode, whereby the clinical usability of the data affects the display on the user interface based on the determined decision support mode.

22. The method of claim 21, further comprising displaying an indication of the decision-support mode, and/or an indicator of the usability data, on the user interface.

23. The method of claim 21, wherein the usability data includes a calibration mode selected from the group consisting of:

factory calibration and device self-calibration.

24. The method of claim 21, wherein the usability data includes diagnostic data.

25. The method of claim 21, wherein the usability data includes parameters entered by a user in a setup mode.

26. The method of claim 25, wherein the setup mode is for a bolus calculator.

27. The method of claim 21, wherein the usability data corresponds to a level of confidence in the measurement data.

28. The method of claim 27, wherein the level of confidence corresponds to a certainty level or confidence interval of the measurement data.

29. The method of claim 28, wherein the certainty level corresponds to a level of confidence in the calibrated sensor data.

30. The method of claim 28, wherein the level of confidence corresponds to a level of confidence in a calibration, and wherein the calibration is performed by device self-calibration.

31. The method of claim 28, wherein the level of confidence corresponds to a level of confidence in a calibration, and wherein the calibration is performed by:

a. receiving a priori calibration distribution information;

b. receiving one or more real-time inputs that may influence calibration of the glucose sensor;

c. forming a posteriori calibration distribution information based on the one or more real-time inputs; and

d. converting, in real-time, at least one measurement data point based on the a posteriori calibration distribution information.

32. The method of claim 21, wherein the usability data includes internal data selected from the group consisting of:

data from diagnostic tests, calibration slope and baseline data, impedance data, data about faults and artifacts, drift data, noise data, pattern data, time rate of change of signal data, metadata about sensor identity, environmental data corresponding to a sensor, frequency of calibration data, and transmission mode data.

33. The method of claim 21, wherein the usability data includes external data selected from the group consisting of:

user perceived error data, external meter data, temperature data, clock data, location data, user response to prompt data, activity data, exercise data, sleep data, data from a medicament pump, data from an insulin delivery device, data from a smart pen, data about a desired use of the monitoring device, meal data, data about illness or menstrual cycle or pregnancy, user preference or profile data, user goal data, user knowledge data, and data from other connected devices.

34. The method of claim 21, wherein the first decision-support mode is configured for a patient with type I diabetes, and is therapeutic or non-therapeutic, where therapeutic refers to the first decision-support mode providing information on which to base insulin dosing.

35. The method of claim 21, wherein the first decision-support mode is adjunctive or non-adjunctive, where adjunctive refers to the first decision-support mode providing information not used to base insulin dosing.

36. The method of claim 21, wherein the first and second decision-support modes correspond to different phases of operation of an artificial pancreas, and wherein the phases of operation of an artificial pancreas correspond to a series of sequential phases defined by:

phase 0 or adjunctive only, phase 1 where a system including an insulin pump is controlled so as to turn off at times when the user is encountering low glucose levels, phase 2 where hypoglycemia predictions are allowed and result in alarms, phase 3 where insulin dosing is allowed if a user'"'"'s glucose level passes above a high threshold, phase 4 where the system is closed loop except for meal time manual assist bolusing, phase 5 where the system is closed loop for insulin, and phase 6 where the system is closed loop for insulin and other hormones.

37. The method of claim 36, wherein the first decision-support mode and the second decision-support mode are sequentially adjacent.

38. The method of claim 36, wherein the first decision-support mode and the second decision-support mode are sequentially non-adjacent.

39. A system for glucose monitoring in which a diabetes management system is in signal communication with a glucose sensor through sensor electronics, the method comprising:

a. means for receiving measurement data indicative of a subject glucose concentration measured by the glucose sensor and displaying on a user interface a representation of the glucose concentration in a first decision-support mode;

b. means for determining usability data indicative of a clinical usability of the glucose concentration data;

c. means for transitioning to a decision support mode different from the first based on a comparison of the determined usability data against one or more decision mode transition criteria; and

d. wherein the means for receiving and displaying is further configured for receiving additional measurement data and displaying on the user interface a representation of the glucose concentration in the decision-support mode different from the first, whereby the clinical usability of the data affects the display on the user interface based on the determined decision support mode.

40. The system of claim 39, wherein the first and second decision-support modes correspond to different phases of operation of an artificial pancreas, and wherein the phases of operation of an artificial pancreas correspond to a series of sequential phases defined by:

phase 0 or adjunctive only, phase 1 where a system including an insulin pump is controlled so as to turn off at times when the user is encountering low glucose levels, phase 2 where hypoglycemia predictions are allowed and result in alarms, phase 3 where insulin dosing is allowed if a user'"'"'s glucose level passes above a high threshold, phase 4 where the system is closed loop except for meal time manual assist bolusing, phase 5 where the system is closed loop for insulin, and phase 6 where the system is closed loop for insulin and other hormones.

41. A method of glucose monitoring in which a diabetes management system is in signal communication with an insulin delivery system through sensor electronics, the method providing decision support, the method comprising:

a. receiving measurement data indicative of a subject glucose concentration measured by a glucose sensor and transmitting an indication of the glucose concentration and a first clinical usability of the glucose concentration in a first mode of decision support to an insulin delivery system;

b. evaluating usability data indicative of a clinical usability of the glucose concentration data;

c. transitioning to a second decision support mode based on a comparison of the usability data against one or more decision mode transition criteria; and

d. receiving additional measurement data and transmitting to the insulin delivery system an indication of the glucose concentration and a second clinical usability of the glucose concentration based on the second decision-support mode, wherein the second clinical usability is different from the first clinical usability, and whereby an indication of the clinical usability of the glucose concentration data is provided to the insulin delivery device based on the decision support mode.

42. The method of claim 41, wherein the first decision-support mode is therapeutic, and further comprising driving a medicament pump at least in part based on the measurement data.

43. The method of claim 41, wherein the usability data includes parameters entered by a user in a setup mode.

44. The method of claim 43, wherein the setup mode is for a bolus calculator.

45. The method of claim 41, wherein the first decision-support mode is configured for a patient with type I diabetes, and is therapeutic or non-therapeutic, where therapeutic refers to the first decision-support mode providing information on which to base insulin dosing.

46. The method of claim 45, wherein the first decision support mode is therapeutic and wherein the information is transmitted to the insulin delivery device.

47. The method of claim 41, wherein the first and second decision-support modes correspond to different phases of operation of an artificial pancreas, and wherein the phases of operation of an artificial pancreas correspond to a series of sequential phases defined by:

phase 0 or adjunctive only, phase 1 where a system including an insulin pump is controlled so as to turn off at times when the user is encountering low glucose levels, phase 2 where hypoglycemia predictions are allowed and result in alarms, phase 3 where insulin dosing is allowed if a user'"'"'s glucose level passes above a high threshold, phase 4 where the system is closed loop except for meal time manual assist bolusing, phase 5 where the system is closed loop for insulin, and phase 6 where the system is closed loop for insulin and other hormones.

48. The method of claim 47, wherein the first decision-support mode and the second decision-support mode are sequentially adjacent.

49. The method of claim 47, wherein the first decision-support mode and the second decision-support mode are sequentially non-adjacent.