US-20260123860-A1 - SYSTEM AND METHOD FOR DECISION SUPPORT

US20260123860A1US 20260123860 A1US20260123860 A1US 20260123860A1US-20260123860-A1

Abstract

Systems and methods are provided to provide guidance to a user regarding management of a physiologic condition such as diabetes. The determination may be based upon a patient glucose concentration level. The glucose concentration level may be provided to a stored model to determine a state. The guidance may be determined based at least in part on the determined state.

Inventors

Alexandra Elena Constantin
John Michael Gray
Hari Hampapuram
Nathaniel David Heintzman
Lauren Hruby Jepson
Matthew Lawrence Johnson
Apurv Ullas Kamath
Katherine Yerre Koehler
Phil Mayou
Patrick Wile McBride
Michael Robert Mensinger
Scott M. Belliveau
Sumitaka Mikami
Andrew Attila Pal
Nicholas Polytaridis
Philip Thomas Pupa
Eli Reihman
Peter C. Simpson
Tomas C. Walker
Daniel Justin Wiedeback
Subrai Girish Pai
Matthew T. Vogel
Naresh C. Bhavaraju
Jennifer D. BLACKWELL
Eric S. Cohen
Basab Dattaray
Anna Leigh Davis
Rian W. DRAEGER
Arturo Garcia

Assignees

DEXCOM, INC.

Dates

Publication Date: 20260507
Application Date: 20251231

Claims (15)

1 . A method of delivering physiologic glucose concentration management guidance comprising: measuring, determining, or receiving a first real-time datum associated with a patient; determining a state the patient is in using at least in part a model and the first real-time datum, wherein the model includes a patient physiology model and a behavior model, and determining a state the patient is based at least on applying the first real-time datum to the patient physiology model, wherein the first real-time datum is a change of state detected by a time of day; determining a personalized guidance message, wherein the personalized guidance message is based at least in part on the determined state; and providing the determined personalized guidance message through a user interface at a time calculated to enable intervention prior to a transition to an undesirable physiologic state wherein the personalized guidance message is based at least in part on a projected transition to the undesirable physiologic state.
2 . The method of claim 1 , wherein determining a personalized guidance message is further based on a timing of the determining the personalized guidance message or a time associated with the determined state.
3 . The method of claim 1 , wherein the model includes a state indicative of a convenience or availability of the patient to participate in an intervention.
4 . The method of claim 1 , wherein the behavior model is based on a machine-learned characteristic of the patient, the machine-learned characteristic based on a behavioral or contextual pattern.
5 . The method of claim 1 , wherein the behavior model is based on a set of one or more steps determined to be likely to be performed by the patient.
6 . The method of claim 1 , wherein the behavior model is a pattern.
7 . The method of claim 1 , wherein the patient physiology model is based on a physiological pattern and the behavior model is based on a behavioral pattern.
8 . The method of claim 1 , wherein determining a state is further based on a measurement model, wherein the measurement model is based on a continuous glucose concentration monitoring system associated with the patient, further comprising measuring glucose concentration data subsequent to the determining a personalized guidance message and using the measured subsequent data to improve one or more models, and wherein the glucose concentration data measured subsequent to the determining is fed back to a measurement model or the behavior model or the patient physiology model, or to a combination thereof.
9 . The method of claim 1 , wherein the personalized guidance message includes an actionable prompt, and/or, wherein the personalized guidance message includes an actionable prompt that is calculated to cause the patient's glucose concentration level to move towards a target level or a target range.
10 . The method of claim 1 , wherein the first real-time datum is measured, received, or determined by a smart phone a wearable device, a wearable device in combination with a smart phone, and/or an external device, wherein the external device is an accelerometer.
11 . The method of claim 1 , wherein the first real-time datum is a time of day, a characteristic or signature signal measured by an accelerometer, or a location determined by a GPS circuit, and/or, wherein the first real-time datum is a user request for a decision support prompt, wherein the decision support prompt is a user request for pre-sleep guidance, and/or, wherein the first real-time datum is received from a continuous glucose concentration monitoring system.
12 . The method of claim 1 , wherein providing the determined personalized guidance message occurs at a time calculated to be useful in management of a patient glucose concentration level.
13 . The method of claim 1 , further comprising receiving sleep information detected using an accelerometer or physiologic sensor, based on temporal patterns, or supplied from a user via a user interface.
14 . The method of claim 1 , further comprising determining a pattern in nighttime blood glucose concentrations levels or trends and delivery guidance to change a behavior or therapy.
15 . The method of claim 1 , wherein the projected transition is predicted to occur during a sleep period.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. patent application Ser. No. 18/913,191, filed Oct. 11, 2024, which is a continuation of U.S. patent application Ser. No. 16/269,531, filed Feb. 6, 2019, now issued as U.S. Pat. No. 12,171,547, which is a continuation of U.S. patent application Ser. No. 16/269,480, filed Feb. 6, 2019, now issued as U.S. Pat. No. 11,766,194, which claims benefit of and priority to U.S. Provisional Application No. 62/628,895, filed on Feb. 9, 2018. The aforementioned applications are incorporated by reference herein in their entirety, and are hereby expressly made a part of this specification. TECHNICAL FIELD The present development relates generally to medical devices such as analyte sensors, including systems and methods for using the same to provide support for treatment decision-making. BACKGROUND Diabetes is a metabolic condition relating to the production or use of insulin by the body. Insulin is a hormone that allows the body to use glucose for energy, or store glucose as fat. When a person eats a meal that contains carbohydrates, the food is processed by the digestive system, which produces glucose in the person's blood. Blood glucose can be used for energy or stored as fat. The body normally maintains blood glucose levels in a range that provides sufficient energy to support bodily functions and avoids problems that can arise when glucose levels are too high, or too low. Regulation of blood glucose levels depends on the production and use of insulin, which regulates the movement of blood glucose into cells. When the body does not produce enough insulin, or when the body is unable to effectively use insulin that is present, blood sugar levels can elevate beyond normal ranges. The state of having a higher than normal blood sugar level is called “hyperglycemia.” Chronic hyperglycemia can lead to a number of health problems, such as cardiovascular disease, cataract and other eye problems, nerve damage (neuropathy), and kidney damage. Hyperglycemia can also lead to acute problems, such as diabetic ketoacidosis—a state in which the body becomes excessively acidic due to the presence of blood glucose and ketones, which are produced when the body cannot use glucose. The state of having lower than normal blood glucose levels is called “hypoglycemia.” Severe hypoglycemia can lead to acute crises that can result in seizures or death. A diabetes patient can receive insulin to manage blood glucose levels. Insulin can be received, for example, through a manual injection with a needle. Wearable insulin pumps are also available. Diet and exercise also affect blood glucose levels. Diabetes conditions are sometimes referred to as “Type 1” and “Type 2”. A Type 1 diabetes patient is typically able to use insulin when it is present, but the body is unable to produce sufficient amounts of insulin, because of a problem with the insulin-producing beta cells of the pancreas. A Type 2 diabetes patient may produce some insulin, but the patient has become “insulin resistant” due to a reduced sensitivity to insulin. The result is that even though insulin is present in the body, the insulin is not sufficiently used by the patient's body to effectively regulate blood sugar levels. This Background is provided to introduce a brief context for the Summary and Detailed Description that follow. This Background is not intended to be an aid in determining the scope of the claimed subject matter nor be viewed as limiting the claimed subject matter to implementations that solve any or all of the disadvantages or problems presented above. SUMMARY This document discusses, among other things, systems and methods to determine a time for delivery or determination of decision-support guidance for a patient or caregiver. An example (e.g., “Example 1”) of subject matter (e.g., a method or system) may include measuring, determining, or receiving a first real-time datum associated with a patient, determining a state the patient is in using at least in part a model and the first real-time datum, determining a guidance message, wherein the guidance message is based at least in part on the determined state, and providing the determined personalized guidance message through a user interface at a time calculated to enable intervention prior to a transition to an undesirable physiologic state. In Example 2, the subject matter of Example 1 may be configured such that determining a guidance message is further based on a timing of the determining the guidance message or a time associated with the determined state. In Example 3, the subject matter of Example 1 or 2 may be configured such that the model includes a state indicative of a convenience or availability of the patient to participate in an intervention. In Example 4, the subject matter of any one or any combination of Examples 1-3 may be configured such that the guidance message is based at least in part on a projected transition to the