Method and system using artificial intelligence to monitor user characteristics during a telemedicine session
A computer-implemented system may include a treatment device configured to be manipulated by a user while the user is performing a treatment plan and a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session. The computer-implemented system may also include a first computing device configured to: receive treatment data pertaining to the user while the user uses the treatment device to perform the treatment plan; write to an associated memory, for access by an artificial intelligence engine, the treatment data; receive, from the artificial intelligence engine, at least one prediction; identify a threshold corresponding to the at least one prediction; and, in response to a determination that the at least one prediction is outside of the range of the threshold, update the treatment data pertaining to the user to indicate the at least one prediction.
1. A computer-implemented system, comprising:
a treatment device configured to be manipulated by a user while the user is performing a treatment plan;
a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session; and
a first computing device configured to:
receive treatment data pertaining to the user while the user uses the treatment device to perform the treatment plan, wherein the treatment data comprises at least one of characteristics of the user, baseline measurement information pertaining to the user, measurement information pertaining to the user while the user performs the treatment plan, characteristics of the treatment device, and at least one aspect of the treatment plan;
write to an associated memory, for access by an artificial intelligence engine, the treatment data, the artificial intelligence engine being configured to use at least one machine learning model, wherein the machine learning model uses at least one aspect of the treatment data to generate at least one prediction;
receive, from the artificial intelligence engine, the at least one prediction;
identify a threshold corresponding to the at least one prediction;
in response to a determination that the at least one prediction is within a range of the threshold, provide via an interface, at a second computing device of a healthcare provider, the at least one prediction and the treatment data; and
in response to a determination that the at least one prediction is outside of the range of the threshold, update the treatment data pertaining to the user to indicate the at least one prediction.
2. The computer-implemented system of claim 1 , wherein the first computing device is further configured to receive, from the interface of the second computing device of the healthcare provider, treatment plan input, wherein the treatment plan input includes at least one modification to the treatment plan.
3. The computer-implemented system of claim 2 , wherein the first computing device is further configured to modify the treatment plan using the at least one modification indicated in the treatment plan input.
4. A method comprising:
receiving treatment data pertaining to a user who uses a treatment device to perform a treatment plan, wherein the treatment data comprises at least one of characteristics of the user, baseline measurement information pertaining to the user, measurement information pertaining to the user while the user performs the treatment plan, characteristics of the treatment device, and at least one aspect of the treatment plan;
writing to an associated memory, for access by an artificial intelligence engine, the treatment data, the artificial intelligence engine being configured to use at least one machine learning model that uses at least one aspect of the treatment data to generate at least one prediction;
receiving, from the artificial intelligence engine, the at least one prediction;
identifying a threshold corresponding to the at least one prediction;
in response to a determination that the at least one prediction is within a range of the threshold, communicating with an interface, at a computing device of a healthcare provider, the at least one prediction and the treatment data; and
in response to a determination that the at least one prediction is outside of the range of the threshold, updating the treatment data pertaining to the user to indicate the at least one prediction.
5. The method of claim 4 , further comprising receiving, from the interface of the computing device of the healthcare provider, treatment plan input, wherein the treatment plan input includes at least one modification to the treatment plan.
6. The method of claim 5 , further comprising modifying the treatment plan using the at least one modification indicated in the treatment plan input.
7. The method of claim 6 , further comprising controlling, while the user uses the treatment device during a telemedicine session and based on the modified treatment plan, the treatment device.
8. The method of claim 4 , wherein at least some of the treatment data corresponds to sensor data from a sensor associated with the treatment device.
9. The method of claim 4 , wherein at least some of the treatment data corresponds to sensor data from a sensor associated with the user while using the treatment device.
10. The method of claim 9 , wherein the sensor associated with the user is comprised of a wearable device worn by the user.
11. The method of claim 4 , wherein the baseline measurement information includes, while the user is at rest, at least one of a vital sign of the user, a respiration rate of the user, a heartrate of the user, a temperature of the user, a blood pressure of the user, a blood oxygen saturation level of the user, a blood glucose level of the user, the eye dilation level of the user, a biomarker level of the user, and wherein the measurement information includes, while the user performs the treatment plan, at least one of a vital sign of the user, a respiration rate of the user, a heartrate of the user, a temperature of the user, a blood pressure of the user, a blood oxygen saturation level of the user, a blood glucose level of the user, the eye dilation level of the user, and a biomarker level of the user.
12. The method of claim 4 , wherein the at least one machine learning model includes a deep network comprising more than one level of non-linear operations.
13. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:
receive treatment data pertaining to a user who uses a treatment device to perform a treatment plan, wherein the treatment data comprises at least one of characteristics of the user, baseline measurement information pertaining to the user, measurement information pertaining to the user while the user performs the treatment plan, characteristics of the treatment device, and at least one aspect of the treatment plan;
write to an associated memory, for access by an artificial intelligence engine, the treatment data, the artificial intelligence engine being configured to use at least one machine learning model that uses at least one aspect of the treatment data to generate at least one prediction;
receive, from the artificial intelligence engine, the at least one prediction;
identify a threshold corresponding to the at least one prediction;
in response to a determination that the at least one prediction is within a range of the threshold, communicate with an interface, at a computing device of a healthcare provider, to provide the at least one prediction and the treatment data; and
in response to a determination that the at least one prediction is outside of the range of the threshold, update the treatment data pertaining to the user to indicate the at least one prediction.
14. The computer-readable medium of claim 13 , wherein the instructions further cause the processing device to receive, from the interface of the computing device of the healthcare provider, treatment plan input, wherein the treatment plan input includes at least one modification to the treatment plan.
15. The computer-readable medium of claim 14 , wherein the instructions further cause the processing device to modify the treatment plan using the at least one modification indicated in the treatment plan input.
16. The computer-readable medium of claim 15 , wherein the instructions further cause the processing device to control, while the user uses the treatment device during a telemedicine session and based on the modified treatment plan, the treatment device.
17. The computer-readable medium of claim 13 , wherein at least some of the treatment data corresponds to sensor data from a sensor associated with the treatment device.
18. The computer-readable medium of claim 13 , wherein at least some of the treatment data corresponds to sensor data from a sensor associated with the user while using the treatment device.
19. The computer-readable medium of claim 18 , wherein the sensor associated with the user is comprised of a wearable device worn by the user.
20. The computer-readable medium of claim 13 , wherein the baseline measurement information includes, while the user is at rest, at least one of a vital sign of the user, a respiration rate of the user, a heartrate of the user, a temperature of the user, a blood pressure of the user, a blood oxygen saturation level of the user, a blood glucose level of the user, the eye dilation level of the user, and a biomarker level of the user, and wherein the measurement information includes, while the user performs the treatment plan, at least one of a vital sign of the user, a respiration rate of the user, a heartrate of the user, a temperature of the user, a blood pressure of the user, a blood oxygen saturation level of the user, a blood glucose level of the user, the eye dilation level of the user, and a biomarker level of the user.
21. The computer-readable medium of claim 13 , wherein the at least one machine learning model includes a deep network comprising more than one level of non-linear operations.
22. A system comprising:
a processing device; and
a memory including instructions that, when executed by the processor, cause the processor to:
receive treatment data pertaining to a user who uses a treatment device to perform a treatment plan, wherein the treatment data comprises at least one of characteristics of the user, baseline measurement information pertaining to the user, measurement information pertaining to the user while the user performs the treatment plan, characteristics of the treatment device, and at least one aspect of the treatment plan;
write to an associated memory, for access by an artificial intelligence engine, the treatment data, the artificial intelligence engine being configured to use at least one machine learning model that uses at least one aspect of the treatment data to generate at least one prediction;
receive, from the artificial intelligence engine, the at least one prediction;
identify a threshold corresponding to the at least one prediction;
in response to a determination that the at least one prediction is within a range of the threshold, communicate with an interface, at a computing device of a healthcare provider, to provide the at least one prediction and the treatment data; and
in response to a determination that the at least one prediction is outside of the range of the threshold, update the treatment data pertaining to the user to indicate the at least one prediction.
23. The system of claim 22 , wherein the instructions further cause the processing device to receive, from the interface of the computing device of the healthcare provider, treatment plan input, wherein the treatment plan input includes at least one modification to the treatment plan.
24. The system of claim 23 , wherein the instructions further cause the processing device to modify the treatment plan using the at least one modification indicated in the treatment plan input.
25. The system of claim 24 , wherein the instructions further cause the processing device to control, while the user uses the treatment device during a telemedicine session and based on the modified treatment plan, the treatment device.
26. The system of claim 22 , wherein at least some of the treatment data corresponds to sensor data from a sensor associated with the treatment device.
27. The system of claim 22 , wherein at least some of the treatment data corresponds to sensor data from a sensor associated with the user while using the treatment device.
28. The system of claim 27 , wherein the sensor associated with the user is comprised of a wearable device worn by the user.
29. The system of claim 22 , wherein the baseline measurement information includes, while the user is at rest, at least one of a vital sign of the user, a respiration rate of the user, a heartrate of the user, a temperature of the user, a blood pressure of the user, a blood oxygen saturation level of the user, a blood glucose level of the user, the eye dilation level of the user, and a biomarker level of the user, and wherein the measurement information includes, while the user performs the treatment plan, at least one of a vital sign of the user, a respiration rate of the user, a heartrate of the user, a temperature of the user, a blood pressure of the user, a blood oxygen saturation level of the user, a blood glucose level of the user, the eye dilation level of the user, and a biomarker level of the user.
30. The system of claim 22 , wherein the at least one machine learning model includes a deep network comprising more than one level of non-linear operations.