Revolutionizing Remote Patient Monitoring with AI

The Problem

Remote Patient Monitoring (RPM) has been around for some time now. Conventional RPM involves using a remote camera to monitor patients remotely. While this can assist in monitoring a patient remotely, staff need to manually monitor video feeds 24×7. Such systems also lack any kind of analysis capabilities using the stream data. How can AI be used to improve RPM?

Solution

Computer Vision and Machine Learning models deployed on Kranium can revolutionize RPM. Computer Vision models can perform analysis such as fall detection, tracking patient movements, monitoring people and much more. The models can also alert caretakers of specific events for timely attention.

Also, Machine Learning models can analyse data from remote sensors and medical devices to enable the physician to understand how a patient is doing. In the case of an adverse event where the patient needs to be immediately moved to the emergency room, the physician can intervene and alter care based on the patient’s symptoms.

Such models can ensure timely availability of care for patients and avoids unnecessary inpatient utilization.