Pre-emptive chronic obstructive pulmonary disease risk notifications based on medicament device monitoring
A system and method for providing chronic obstructive pulmonary disease (COPD) exacerbation risk notifications in real time or near-real time is described. Rescue and controller medication events are detected by sensors associated with the patient's medicament device/s, and provide a basis to determine to rescue and controller medication use trends for the patient. This data is analyzed to determine the patient's risk for COPD exacerbation after each event, and is used to send notifications to one or both of the patient and their health care provider.
1. A computer executed method comprising:
receiving, by a remote server over a network, at least one historical chronic obstructive pulmonary disease (COPD) rescue medication event from a client computing device associated with a first patient, wherein the at least one historical COPD rescue medication event is detected by a sensor of a medicament device associated with the first patient during a first time window;
storing, in a storage device of the remote server, the at least one historical COPD rescue medication event in association with a patient profile of the first patient;
receiving, by the remote server over the network, a current COPD rescue medication event at the remote server, wherein the current COPD rescue medication event is detected by the sensor of the medicament device during a second time window occurring later in time than the first time window, wherein the current COPD rescue medication event includes a geographical location of the current COPD rescue medication event detected by the sensor; and
responsive to receiving the current COPD rescue medication event,
applying, at the remote server, a machine-learned model to the at least one historical COPD rescue medication event and the current COPD rescue medication event together to determine a real-time risk of COPD exacerbation for the first patient;
performing, at the storage device of the remote server, a query for one or more nearby COPD rescue medication events detected in a geographical region inclusive of the geographical location of the current COPD rescue medication event, wherein each of the one or more nearby COPD rescue medication events is detected by a sensor of a medicament device associated with a second patient; and
transmitting, by the remote server over the network, a real-time notification to the client computing device associated with the first patient, the real-time notification comprising the real-time risk of COPD exacerbation and information describing the one or more nearby COPD rescue medication events in the geographical region.
2. The method of claim 1 , wherein applying the machine learned model to the at least one historical COPD rescue medication event comprises:
determining a rescue medication baseline based on the at least one historical COPD recue medication event;
determining a rescue medication trend by comparing the rescue medication baseline to a count of current COPD rescue medication events received during the second time window; and
determining the real-time risk of COPD exacerbation based on the rescue medication trend.
3. The method of claim 2 , further comprising:
storing, in the storage device of the remote server, a COPD controller medication adherence plan for the first patient;
receiving, over a third time window occurring earlier in time than the second time window, at least one historical COPD controller medication event from the client computing device associated with the first patient; and
determining controller medication adherence based on the at least one historical COPD controller medications event and the COPD controller medication adherence plan; and
determining the real-time risk of COPD exacerbation further based on the COPD controller medication adherence.
4. The method of claim 3 , wherein determining controller medication adherence comprises:
dividing a count of the historical COPD controller medication events occurring during the first time window by a number of controller medication doses prescribed in the COPD controller medication adherence plan.
5. The method of claim 3 , wherein determining the real-time risk of COPD exacerbation comprises:
determining a numerical risk score based on the controller medication adherence and the rescue medication trend, wherein the controller medication adherence and the rescue medication trend are weighted relative to each other such that the rescue medication trend is more determinative of the numerical risk score than the controller medication adherence.
6. The method of claim 2 , wherein determining the rescue medication baseline comprises:
summing a count of the historical COPD rescue medication events occurring during the second time window; and
dividing the count of the historical COPD rescue medication events occurring during the second time window by a duration of the second time window.
7. The method of claim 2 , wherein determining the rescue medication trend comprises:
labeling the patient as at risk of COPD exacerbation responsive to a function of the count of current COPD rescue medication events received during the second time window and the rescue medication baseline being above a threshold value; and
labeling the patient as not at risk of COPD exacerbation responsive to the function of the count of current COPD rescue medication events received during the second time window and the rescue medication baseline being below the threshold value.
8. The method of claim 1 , wherein the real-time notification comprises at least one of a numerical risk of COPD exacerbation, and a categorical risk of COPD exacerbation.
9. The method of claim 1 , wherein the patient profile of the first patient comprises an identifier of the client computing device associated with the first patient.
10. The method of claim 1 , wherein the patient profile of the first patient comprises a type of COPD controller medication and a type of COPD rescue medication prescribed to the first patient.
11. A non-transitory computer readable storage medium comprising computer program instructions that, when executed by a computer processor, cause the computer processor to:
receive, by a remote server over a network, at least one historical chronic obstructive pulmonary disease (COPD) rescue medication event from a client computing device associated with a first patient, wherein the at least one historical COPD rescue medication event is detected by a sensor of a medicament device associated with the first patient during a first time window;
store, in a storage device at the remote server, the at least one historical COPD rescue medication event in association with a patient profile of the first patient;
receive, by the remote server over the network, a current COPD rescue medication event at the remote server, wherein the current COPD rescue medication event is detected by the sensor of the medicament device during a second time window occurring later in time than the first time window, wherein the current COPD rescue medication event includes a geographical location of the current COPD rescue medication event detected by the sensor; and
responsive to receiving the at least one current COPD rescue medication event,
apply, at the remote server, a machine-learned model to the at least one historical COPD rescue medication event and the current COPD rescue medication event together to determine a real-time risk of COPD exacerbation for the first patient;
perform, at the storage device of the remote server, a query for one or more nearby COPD rescue mediation events detected in a geographical region inclusive of the geographical location of the current COPD rescue medication event, wherein each of the one or more nearby COPD rescue medication events is detected by a sensor of a medicament device associated with a second patient;
transmitting, by the remote server over the network, a real-time notification to the client computing device associated with the first patient, the real-time notification comprising the real-time risk of COPD exacerbation and information describing the one or more nearby COPD rescue medication events in the geographical region.
12. The storage medium of claim 11 , wherein the instructions to applying the machine-learned model to the at least one historical COPD rescue medication event further comprise instructions that cause the computer processor to:
determine a rescue medication baseline based on the at least one historical COPD recue medication event;
determine a rescue medication trend by comparing the rescue medication baseline to a count of current COPD rescue medication events received during the second time window; and
determining the real-time risk of COPD exacerbation based on the rescue medication trend.
13. The storage medium of claim 12 , further comprising instructions that cause the computer processor to:
store, in the storage device of the remote server, a COPD controller medication adherence plan for the first patient;
receive, over a third time window occurring earlier in time than the second time window, at least one historical COPD controller medication event from the client computing device associated with the first patient; and
determining controller medication adherence based on the at least one historical COPD controller medications event and the COPD controller medication adherence plan; and
determining the real-time risk of COPD exacerbation further based on the COPD controller medication adherence.
14. The storage medium of claim 13 , wherein the instructions to determine controller medication adherence further comprise instructions that cause the computer processor to:
divide a count of the historical COPD controller medication events occurring during the first time window by a number of controller medication doses prescribed in the COPD controller medication adherence plan.
15. The storage medium of claim 13 , wherein the instructions to determine the real-time risk of COPD exacerbation further comprise instructions that cause the computer processor to:
determine a numerical risk score based on the controller medication adherence and the rescue medication trend, wherein the controller medication adherence and the rescue medication trend are weighted relative to each other such that the rescue medication trend is more determinative of the numerical risk score than the controller medication adherence.
16. The storage medium of claim 12 , wherein the instructions to determine the rescue medication baseline further comprise instructions that cause the computer processor to:
sum a count of the historical COPD rescue medication events occurring during the second time window; and
divide the count of the historical COPD rescue medication events occurring during the second time window by a duration of the second time window.
17. The storage medium of claim 12 , wherein the instructions to determine the rescue medication trend further comprise instructions that cause the computer processor to:
label the patient as at risk of COPD exacerbation responsive to a function of the count of current COPD rescue medication events received during the second time window and the rescue medication baseline being above a threshold value; and
label the patient as not at risk of COPD exacerbation responsive to the function of the count of current COPD rescue medication events received during the second time window and the rescue medication baseline being below the threshold value.
18. The storage medium of claim 11 , wherein the real-time notification comprises a notification comprises at least one of a numerical risk of COPD exacerbation, and a categorical risk of COPD exacerbation.
19. The storage medium of claim 11 , wherein the patient profile of the first patient comprises an identifier of the client computing device associated with the first patient.
20. The storage medium of claim 11 , wherein the patient profile of the first patient comprises a type of COPD controller medication and a type of COPD rescue medication prescribed to the first patient.