SYSTEMS AND METHODS FOR MANAGING CHRONIC DISEASE USING ANALYTE AND PATIENT DATA
Devices, systems, and methods herein relate to managing a chronic condition such as diabetes. These systems and methods may obtain patient data from a plurality of devices, integrate the data for analysis of trends that may be presented to the patient and/or health care professional along with an actionable suggestion. In some variations, a method may include the steps of receiving analyte data generated by an analyte measurement device and patient data generated by a patient measurement device. One or more data trends may be generated by analyzing the analyte data against the patient data using a computing device. The device settings of one or more of the analyte measurement device and the computing device may be modified in response to one or more of the data trends.
1 . A method of monitoring a chronic condition of a patient, comprising:
receiving analyte data generated by an analyte measurement device and patient data generated by a patient measurement device;
generating one or more data trends by analyzing the analyte data against the patient data using a computing device comprising a processor and memory; and
modifying device settings of one or more of the analyte measurement device and the computing device in response to one or more of the data trends.
2 . The method of claim 1 , further comprising outputting at least one prompt to modify patient behavior in response to one or more of the data trends.
3 . The method of claim 2 , wherein the prompt may comprise encouragement to comply with one or more of a testing, diet, and exercise regimen.
4 . The method of claim 1 , further comprising outputting at least one prompt to modify the device settings in response to one or more of the data trends.
5 . The method of claim 4 , further comprising outputting a set of prompts to modify the device settings at predetermined intervals.
6 . The method of claim 1 , wherein modifying the device settings comprises modifying one or more of frequency, timing, and content of patient notification.
7 . The method of claim 1 , further comprising notifying a set of one or more predetermined contacts based on a characteristic of the one or more data trends.
8 . The method of claim 7 , wherein the set of one or more predetermined contacts comprises one or more of a health care professional, a patient's partner, family member, and support group.
9 . The method of claim 7 , further comprising notifying the set of predetermined contacts of the patient's condition in response to one or more of the data trends being a high risk condition.
10 . The method of claim 7 , further comprising notifying the set of predetermined contacts of the patient's condition in response to one or more of the data trends being an improving health condition.
11 . The method of claim 1 , further comprising determining that health care professional attention is urgent in response to one or more of the data trends being a high risk condition.
12 . The method of claim 1 , further comprising scheduling an appointment between the patient and a health care professional using the computing device in response to one or more of the data trends being a high risk condition.
13 . The method of claim 1 , further comprising outputting at least one prompt to modify health care professional device settings in response to one or more of the data trends.
14 . The method of claim 1 , further comprising establishing a communication channel between the computing device and a health care professional device in response to one or more of the data trends being a high risk condition.
15 . The method of claim 14 , further comprising receiving the analyte data, the patient data, and the one or more data trends at the health care professional device, and outputting a prompt to modify patient behavior and the device settings at the health care provider device.
16 . The method of claim 14 , further comprising transmitting at least one prompt comprising a suggestion from the health care professional device to the computing device using the communication channel.
17 . The method of claim 1 , wherein the analyte measurement device comprises a blood glucose monitor and the patient measurement device comprises one or more of an activity tracker, a heart rate monitor, a blood pressure monitor, a scale, an A1c monitor, and a cholesterol monitor.
18 . The method of claim 1 , wherein the analyte data comprises blood glucose data and blood glucose testing history.
19 . The method of claim 1 , wherein the patient data comprises one or more of activity data, nutrition data, drug data, hydration data, sleep data, blood pressure data, heart rate data, cholesterol data, A1c data, weight data, geolocation data, mental health data, and patient data.
20 . The method of claim 1 , wherein generating the one or more data trends comprises performing one or more of time synchronization and range normalization of the analyte data and the patient data.
21 . The method of claim 1 , wherein generating the one or more data trends comprises generating a wellness indicator based at least in part on the analyte data and the patient data.
22 . The method of claim 21 , wherein the wellness indicator is governed by the equation:
Ws
=
100
-
a
(
s
.
d
(
glucose
over
30
days
)
)
-
b
(
target
glucose
-
avg
(
glucose
over
30
days
)
)
-
c
(
number
of
hypoglycemic
readings
over
30
days
)
-
d
(
number
of
hyperglycemic
readings
over
30
days
)
+
e
(
%
of
readings
in
target
range
-
60
%
)
+
f
(
number
of
glucose
measurements
over
30
days
)
+
g
(
minutes
of
activity
over
previous
7
days
60
)
-
h
(
minutes
of
activity
-
target
minutes
of
activity
)
-
i
(
grams
of
carbohydrates
consumed
over
previous
day
)
-
j
(
grams
of
carbohydrates
consumed
over
previous
day
above
target
grams
of
carbohydrates
consumed
over
previous
day
)
-
k
(
BMI
)
+
l
(
number
of
meals
marked
)
+
m
(
number
of
hours
of
sleep
over
7
days
7
)
+
n
(
number
of
doctor
visits
over
previous
365
days
)
+
p
(
number
of
eye
exams
over
previous
365
days
)
+
q
(
number
of
diabetic
foot
exams
over
previous
365
days
)
,
where a, b, c, d, e, f, g, h, i, j, k, l, m, n, p, and q are scale factors, s.d. is standard deviation, and BMI is Body Mass Index.
23 . The method of claim 1 , further comprising determining a high risk condition based on a comparison between at least one of blood glucose data of the analyte data and activity data of the patient data relative to at least one predetermined threshold.
24 . The method of claim 1 , wherein generating the one or more data trends comprises estimating a risk of a hypoglycemic event based at least in part on at least one of the analyte data and the patient data, wherein the analyte data comprises blood glucose data and the patient data comprises one or more of activity data and nutrition data.
25 . The method of claim 24 , wherein the risk of the hypoglycemic event is governed by the equation:
(
AvgGlu
Current
Glucose
)
*
(
Act
*
Exe
)
-
(
Carbs
*
4
)
100
,
where AvgGlu is an average blood glucose value over the 90 preceding days, Current Glucose is a current blood glucose value, Act is a number of minutes of patient activity over a predetermined time interval, Exe is an exertion level based on heart rate, and Carbs is a number of grams of carbohydrates consumed in the 90 preceding minutes.
26 . The method of claim 24 , wherein the risk of the hypoglycemic event is governed by the equation:
Glu<150 mg/DL and Act*Exe>200, where Glu is a current blood glucose value, Act is a number of minutes of patient activity over a predetermined time interval, and Exe is an exertion level based on heart rate.
27 . The method of claim 1 , further comprising receiving a patient query and outputting at least one prompt to modify at least one of patient behavior and device settings in response to one or more of the data trends.
28 . The method of claim 1 , further comprising transferring the analyte data from the analyte measurement device to the computing device at predetermined intervals.
29 . The method of claim 1 , further comprising outputting the one or more data trends using the computing device.
30 - 36 . (canceled)
37 . A device, comprising:
a transceiver configured to receive analyte data generated by an analyte measurement device and patient data generated by a patient measurement device; and
a controller coupled to the transceiver, the controller comprising a processor and a memory, and the controller configured to:
generate one or more data trends by analyzing the analyte data against the patient data;
generate a prompt to modify patient computing device settings in response to one or more of the data trends; and
output the prompt to a patient computing device using the transceiver.