Most personal healthcare devices and applications today have one-size-fits-all performance that work for as many people as possible, but don’t work optimally for anyone. These devices are often unchanging and are unable to adapt to the user or their environments. In this era of personalized medicine, where we understand that each patient is unique and deserves tailored, dynamic care, why aren’t healthcare devices — our most personal devices — dynamic as well?
We exist to enable the medical device of the future. At Enabyl, we have developed a cloud-based artificial intelligence (A.I.) framework called Foresight that allows IoT-connected devices and apps to update their functionality in real-time in response to patient data. By integrating A.I. and advanced analytics, devices that track our health can not only learn to predict a patient’s needs and health risks, but also calibrate themselves to each user to collect more accurate data.
Using this framework, we have developed a first-of-its-kind application for predicting falls in Parkinson’s patients called Balance. Due to each patient’s unique movement patterns, symptoms, and noisy environments, predicting falls an extremely difficult task by conventional means. With an adaptive framework, however, Balance learns patients’ unique movement patterns over time, improving its ability to predict falls as the patient walks with their device.
To truly make healthcare devices more effective, our devices must learn about us — the patients — and not just the disease itself. At Enabyl, this idea is our driving passion.