MACHINE LEARNING SYSTEM AND METHOD TO DETERMINE STEP UP AND STEP DOWN OF TREATMENTS
A system and method to determine a recommendation to change a treatment regimen of a respiratory ailment. The treatment regimen includes multiple steps. Use data of a respiration medicament device to deliver controller or rescue respiration medicament to a patient is collected via a communication interface. The use data is transmitted to a storage device. The use data in the storage device is made accessible to a data analysis module. A patient value associated with the treatment regimen is determined based on the collected use data and patient context data. A comparison of the patient value is made to a threshold level. A recommendation to change the step of the treatment is made based on the comparison to the threshold level. A notification of recommendation of the change of the step of the treatment regimen is provided.
1 . A method of determining a recommendation to change a treatment regimen of a respiratory ailment, the treatment regimen including a plurality of steps, the method comprising:
collecting use data of a respiration medicament device to deliver controller or rescue respiration medicament to the patient via a communication interface;
transmitting the use data to a storage device;
storing the use data in the storage device accessible to a data analysis engine; based on the collected use data and patient context data, determining a patient value associated with the treatment regimen via the data analysis engine;
making a comparison of the patient value to a threshold level;
providing a recommendation to change the step of the treatment regimen based on the comparison to the threshold level; and
providing a notification of recommendation of the change of the step of the treatment regimen.
2 . The method of claim 1 , wherein the respiratory ailment is asthma or COPD.
3 . The method of claim 1 , wherein each step of the treatment regimen includes an associated controller respiration medicament or a rescue respiration medicament.
4 . The method of claim 1 , wherein the treatment regimen is defined by Global Initiative for Asthma (GINA) guidelines or the National Heart Lung Blood Institute guidelines.
5 . The method of claim 1 , wherein the threshold level is one of an adherence percentage over a period of time, a number of rescue medicaments over a period of time, a score on a respiratory test, or a visit to a treatment facility.
6 . The method of claim 5 , wherein the respiratory test is one of the COPD assessment test (CAT) or the asthma control test (ACT).
7 . The method of claim 1 , wherein the recommendation is a step-up in treatment when the comparison is above the threshold level.
8 . The method of claim 1 , wherein the recommendation is a step-down in treatment when the comparison is below the threshold level.
9 . The method of claim 1 , wherein the patient context data is one of demographic data, environmental data, weather data, and social determinants of health.
10 . The method of claim 9 , wherein the demographic data is collected from interfacing with an electronic health record system or self-reported by the patient.
11 . The method of claim 1 , wherein the recommendation of the change is made at a predetermined interval over a period of time.
12 . The method of claim 1 , further comprising inputting the collected data to a machine learning model to output the threshold value, wherein the machine learning model is trained from collected context data, use data, and outcomes from the treatment regimen from a population of patients.
13 . The method of claim 12 , wherein the machine learning model includes a clinical decision label for the threshold output.
14 . The method of claim 12 , wherein the machine learning model is based on one of a generalized linear model, a tree-based model or a neural network.
15 . The method of claim 1 , further comprising categorizing the use between preemptive or emergency use.
16 . The method of claim 1 , further comprising outputting a report of the collected use and context data.
17 . The method of claim 1 , further comprising collecting the context data through a survey displayed by an application executed by a mobile computing device operated by the patient.
18 . The method of claim 1 , wherein the notification is provided electronically to one of the patient, a caregiver, or a healthcare provider.
19 . The method of claim 1 , further comprising ordering additional medication for the patient through a medication supply system based on the recommendation.
20 .- 21 . (canceled)
22 . A system to determine changing a treatment regimen having a plurality of treatment steps, the system comprising:
a communication interface to collect use data of a respiration medicament device to deliver controller or rescue respiration medicament to a patient;
a storage device to store the collected use data; and
a data analysis engine configured to:
input the use data and context data of patient to determine a patient value; comparing the patient value with a threshold value;
based on the comparison, making a recommendation to change the treatment regimen; and
providing a notification of the recommendation.
23 . The system of claim 22 , wherein the respiratory ailment is asthma or COPD.
24 . The system of claim 22 , wherein each step of the treatment regimen includes an associated controller respiration medicament or a rescue respiration medicament.
25 . The system of claim 22 , wherein the treatment regimen is defined by Global Initiative for Asthma (GINA) guidelines or the National Heart Lung Blood Institute guidelines.
26 . The system of claim 22 , wherein the threshold level is one of an adherence percentage over a period of time, a number of rescue medicaments over a period of time, a score on a respiratory test, or a visit to a treatment facility.
27 . The system of claim 26 , wherein the respiratory test is one of the COPD assessment test (CAT) or the asthma control test (ACT).
28 . The system of claim 22 , wherein the recommendation is a step-up in treatment when the comparison is above the threshold level.
29 . The system of claim 22 , wherein the recommendation is a step-down in treatment when the comparison is below the threshold level.
30 . The system of claim 22 , wherein the patient context data is one of demographic data, environmental data, weather data, and social determinants of health.
31 . The system of claim 30 , wherein the demographic data is collected from interfacing with an electronic health record system or self-reported by the patient.
32 . The system of claim 22 , wherein the recommendation of the change is made at a predetermined interval over a period of time.
33 . The system of claim 22 , wherein the data analysis engine determines the threshold value from inputting the collected data to a machine learning model to output the threshold value, wherein the machine learning model is trained from collected context data, use data, and outcomes from the treatment regimen from a population of patients.
34 . The system of claim 33 , wherein the machine learning model includes a clinical decision label for the recommendation output.
35 . The system of claim 33 , wherein the machine learning model is based on one of a generalized linear model, a tree-based model or a neural network.
36 . The system of claim 22 , wherein the data analysis engine is further configured to categorize the use between preemptive or emergency use.
37 . The system of claim 22 , wherein the data analysis engine is further configured to output a report of the collected use and context data.
38 . The system of claim 22 , wherein the context data is collected through a survey displayed by an application executed by a mobile computing device operated by the patient.
39 . The system of claim 22 , wherein the notification is provided electronically to one of the patient, a caregiver, or a healthcare provider.
40 . The system of claim 22 , wherein the data analysis engine is further configured to order additional medication for the patient through a medication supply system based on the recommendation.
41 . A method of training a machine learning model to determine a threshold for recommendation of one step of a multiple step treatment regimen for a respiratory ailment, the method comprising:
collecting use data from the use of a respiratory medication application device from each of a population of patients undergoing one of the steps of the multiple step treatment regimen;
collecting contextual data from each of the population of patients;
collecting outcomes from the population of patients for the treatment regimen; determining initial recommendation thresholds for each of the steps of the multiple step treatment regimen; and
adjusting the initial recommendation thresholds based on the collected use data, contextual data, and outcomes until a predetermined level of confidence is reached.
42 . A non-transient computer-readable storage medium having instructions embodied thereon, the instructions being executable by one or more processors to perform a method determining a recommendation to change a treatment regimen of a respiratory ailment, the treatment regimen including a plurality of steps, the method comprising:
collecting use data of a respiration medicament device to deliver controller or rescue respiration medicament to the patient via a communication interface;
transmitting the use data to a storage device;
storing the use data in the storage device accessible to a data analysis engine; based on the collected use data and patient context data, determining a patient value associated with the treatment regimen via the data analysis engine;
making a comparison of the patient value to a threshold level;
providing a recommendation to change the step of the treatment regimen based on the comparison to the threshold level; and
providing a notification of recommendation of the change of the step of the treatment regimen.
43 . The non-transient computer-readable storage medium of claim 42 , wherein the respiratory ailment is asthma or COPD.
44 . The non-transient computer-readable storage medium of claim 42 , wherein each step of the treatment regimen includes an associated controller respiration medicament or a rescue respiration medicament.
45 . The non-transient computer-readable storage medium of claim 42 , wherein the treatment regimen is defined by Global Initiative for Asthma (GINA) guidelines or the National Heart Lung Blood Institute guidelines.
46 . The non-transient computer-readable storage medium of claim 42 , wherein the threshold level is one of an adherence percentage over a period of time, a number of rescue medicaments over a period of time, a score on a respiratory test, or a visit to a treatment facility.
47 . The non-transient computer-readable storage medium of claim 46 , wherein the respiratory test is one of the COPD assessment test (CAT) or the asthma control test (ACT).
48 . The non-transient computer-readable storage medium of claim 42 , wherein the recommendation is a step-up in treatment when the comparison is above the threshold level.
49 . The non-transient computer-readable storage medium of claim 42 , wherein the recommendation is a step-down in treatment when the comparison is below the threshold level.
50 . The non-transient computer-readable storage medium of claim 42 , wherein the patient context data is one of demographic data, environmental data, weather data, and social determinants of health.
51 . The non-transient computer-readable storage medium of claim 50 , wherein the demographic data is collected from interfacing with an electronic health record system or self-reported by the patient.
52 . The non-transient computer-readable storage medium of claim 42 , wherein the recommendation of the change is made at a predetermined interval over a period of time.
53 . The non-transient computer-readable storage medium of claim 42 , further comprising inputting the collected data to a machine learning model to output the threshold value, wherein the machine learning model is trained from collected context data, use data, and outcomes from the treatment regimen from a population of patients.
54 . The non-transient computer-readable storage medium of claim 53 , wherein the machine learning model includes a clinical decision label for the threshold output.
55 . The non-transient computer-readable storage medium of claim 53 , wherein the machine learning model is based on one of a generalized linear model, a tree-based model or a neural network.
56 . The non-transient computer-readable storage medium of claim 42 , further comprising categorizing the use between preemptive or emergency use.
57 . The non-transient computer-readable storage medium of claim 42 , further comprising outputting a report of the collected use and context data.
58 . The non-transient computer-readable storage medium of claim 42 , further comprising collecting the context data through a survey displayed by an application executed by a mobile computing device operated by the patient.
59 . The non-transient computer-readable storage medium of claim 42 , wherein the notification is provided electronically to one of the patient, a caregiver, or a healthcare provider.
60 . The non-transient computer-readable storage medium of claim 42 , further comprising ordering additional medication for the patient through a medication supply system based on the recommendation.