Systems and methods for providing digital health services
The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.
1 . A method of remotely programming an implantable medical device that provides therapy to a patient, comprising:
establishing a first communication between a patient controller (PC) device and the implantable medical device, wherein the implantable medical device provides therapy to the patient according to one or more programmable parameters, the PC device communicates signals to the implantable medical device to set or modify the one or more programmable parameters, and the PC device comprises a video camera;
establishing a video connection between the PC device and a clinician programmer (CP) device of a clinician for a remote programming session in a second communication that includes an audio/video (A/V) session;
communicating a value for a respective programmable parameter of the implantable medical device from the CP device to the PC device during the remote programming session; and
modifying, by the PC device, the respective programming parameter of the implantable medical device according to the communicated value from the CP device during the remote programming session;
wherein the method further comprises:
presenting video data from the A/V session at the CP device;
presenting a pop-up window with the video data at the CP device, the pop-up window including one or more graphical user interface elements (GUI) for, upon receiving a user input from the clinician, selecting a type of analytics to perform on the video data;
analyzing, by one or more processors and in response to the user input at the pop-up window, the video data from the A/V session to calculate one or more metrics related to a neurological condition of the patient; and
presenting a level or classification of the one or more metrics related to the neurological condition of the patient.
2 . The method of claim 1 wherein the neurological condition of the patient is related to a motor disorder of the patient.
3 . The method of claim 1 wherein the neurological condition of the patient is related to chronic pain of the patient.
4 . The method of claim 1 wherein the one or more GUI elements are superimposed over or surrounding bodily regions automatically analyzed for patient movement.
5 . The method of claim 1 wherein the one or more GUI elements are indicative of tremor of the patient.
6 . The method of claim 1 wherein the one or more GUI elements are indicative of rigidity of the patient.
7 . The method of claim 1 wherein the one or more GUI elements are modified according to an artificial intelligence (AI) classification of patient movement.
8 . The method of claim 1 wherein the one or more GUI elements are modified according to an artificial intelligence (AI) quantification of patient movement.
9 . The method of claim 1 wherein the analyzing comprises applying patient movement data to a trained neural network.
10 . The method of claim 1 further comprising:
displaying calculated anatomical features that track patient movement over a display of the patient.
11 . The method of claim 10 wherein the calculated anatomical features comprise one or more features that follow limb movement of the patient.
12 . The method of claim 10 wherein the calculated anatomical features comprise one or more features that follow torso movement of the patient.
13 . The method of claim 10 wherein the calculated anatomical features comprise one or more features that follow head movement of the patient.
14 . The method of claim 1 wherein the one or more GUI components comprise a plurality of GUI components for respective patient conditions detected from the video data.
15 . The method of claim 1 , wherein the one or more GUI elements of the pop-up window include a first GUI element for selecting a kinematic type of analysis and a second GUI element for selecting an auditory type of analysis.
16 . The method of claim 1 , wherein the one or more GUI elements of the pop-up window include at least one GUI element for anonymizing at least some data of the A/V session.
17 . The method of claim 16 , wherein the at least one GUI element includes a first GUI element for anonymizing voice data associated with the A/V session and a second GUI element for anonymizing the video data of the A/V session.