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:
receiving data pertaining to one or more patient reported outcomes (PRO) from a patient device of the patient prior to the remote programming session;
receiving sensor data from a wearable patient device related to physiological signals and movement of the patient prior to the remote programming session;
receiving sensor data from a wearable patient device during the remote programming session; and
providing patient data, received prior to and during the remote programming session, related to sensor data and PRO data to one or more trained neural networks to determine whether a change in one or more programmable parameters during the remote programming session induces an improvement in one or more patient conditions.
2 . The method of claim 1 further comprising:
training one or more patient specific neural networks for determining one or more patient conditions based on received patient data to determine whether a change in one or more programmable parameters during the remote programming session induces an improvement in one or more patient conditions.
3 . The method of claim 2 wherein the training one or more specific neural networks comprises refining a generalize patient model using patient specific data.
4 . The method of claim 1 wherein the sensor data from the wearable device includes data indicative of cardiac activity.
5 . The method of claim 1 wherein data related to movement of the patient is indicative of a physical activity level of the patient.
6 . The method of claim 1 wherein the determine whether a change in one or more programmable parameters during the remote programming session induces an improvement in one or more patient conditions comprises:
calculating a first patient condition metric for a patient condition before the remote programming session; and
calculating a second patient condition metric for a patient condition during the remote programming session.
7 . The method of claim 1 further comprising:
overlaying one or more graphical user interface (GUI) elements over video of the patient to indicate a level or classification of one or more metrics related to the neurological condition of the patient, wherein the one or more metrics related to the neurological condition are calculated using one or more trained neural networks.
8 . The method of claim 7 wherein the neurological condition of the patient is related to a motor disorder of the patient.
9 . The method of claim 7 wherein the neurological condition of the patient is related to chronic pain of the patient.
10 . The method of claim 7 wherein the one or more GUI elements are superimposed over or surrounding bodily regions automatically analyzed for patient movement.
11 . The method of claim 7 wherein the one or more GUI elements are indicative of tremor of the patient.
12 . The method of claim 7 wherein the one or more GUI elements are indicative of rigidity of the patient.
13 . The method of claim 7 wherein the one or more GUI elements are modified according to an artificial intelligence (AI) classification of patient movement.
14 . The method of claim 7 wherein the one or more GUI elements are modified according to an artificial intelligence (AI) quantification of patient movement.
15 . The method of claim 1 wherein the CP device is programmed to provide one or more pop-up windows to display of one or more GUI components related to one or more neurological conditions of the patient.