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 (NV) session;
communicating a value for a respective programmable parameter of the 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 medical device according to the communicated value from the CP device during the remote programming session;
wherein the method further comprises:
automatically analyzing, by one or more processors, patient movement in video data from the NV to generate a plurality of movement metrics indicative of movement characteristics of the patient;
applying the movement metrics to a trained neural network to calculate a rigidity score for the patient;
comparing the calculated rigidity score to a previously calculated patient-specific threshold value that represents a calculated value when the patient was previously determined to be in a non-rigid state;
displaying a determination of a rigidity state of the patient to the clinician during the remote programming session in response to comparison of the rigidity score to the patient-specific threshold value.
2 . The method of claim 1 wherein the movement characteristics include characteristics related to a motor disorder of the patient.
3 . The method of claim 1 wherein the movement characteristics include characteristics related to chronic pain of the patient.
4 . The method of claim 1 further comprising:
superimposing one or more GUI elements over or surrounding bodily regions automatically analyzed for patient movement.
5 . The method of claim 4 wherein the one or more GUI elements are indicative of tremor of the patient.
6 . The method of claim 5 wherein the one or more GUI elements are indicative of rigidity of the patient.
7 . The method of claim 4 wherein the one or more GUI elements are modified according to an artificial intelligence (AI) classification of patient movement.
8 . The method of claim 4 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 further comprising:
displaying calculated anatomical features that track patient movement over a display of the patient in the first mode of operation.
10 . The method of claim 9 wherein the calculated anatomical features comprise one or more features that follow limb movement of the patient.
11 . The method of claim 9 wherein the calculated anatomical features comprise one or more features that follow torso movement of the patient.
12 . The method of claim 9 wherein the calculated anatomical features comprise one or more features that follow head movement of the patient.
13 . The method of claim 1 wherein the CP device is programmed to provide one or more pop-up windows to control types of patient movement for automatic analysis to generate the plurality of movement metrics.
14 . The method of claim 1 wherein the automatically analyzing comprises processing sensor data from a wearable device of the patient.
15 . The method of claim 1 wherein the automatically analyzing comprises processing sensor data from a smartwatch device of the patient.
16 . The method of claim 1 wherein the automatically analyzing comprises processing sensor data from a device implanted within the patient.