It's important to note that we view EnsoSleep as the first of many products built on our core machine learning technology. Our true vision is to become the leading machine learning and artificial intelligence engine for all health signal data. Put simply, we've implemented hundreds of thousands of lines of code to build custom machine learning algorithms that are massively parallelized both across CPU/GPU and distributed cloud systems, capable of running optimizations on terabytes of healthcare data daily. This system has immediate applications for event detection in conditions like Epilepsy, Cardiology (e.g. CHF, atrial fibrillation, arrhythmia), ICU physiological monitoring for decompensation, home health monitoring for chance patients, and a broad variety of other medical, diagnostic, therapeutic, and EMR data sources. Similar to sleep, our goal is to provide artificial intelligence powered software services that create massive cost and time savings for frontline clinicians, allowing them to spend more time with patients and less time with data; improving both patient and provider satisfaction, and patient outcomes.
I can't recommend the EnsoData team more highly. In addition to being technical ninjas, they are collaborative, persistent, and highly emotionally intelligent -- all the things you want in a group of founders. It is teams like EnsoData that will change the future of health care.
The EnsoData team has fully immersed themselves in the sleep medicine world as they look to match up their data science/machine learning expertise with making the lives of sleep clinicians and their patients better. After watching sleep technicians spend an hour or more manually scoring complicated test results consisting of dozens of wave forms, Chris and Sam made it their focus to get the clinicians away from the computer and back with the patients. They have very quickly been able to replicate the quality of manual scoring while cutting the time needed per test by about 90%.
They are hungry to get their product through the required regulatory hurdles and into the hands of sleep clinicians, while also keeping an eye on the next opportunity where their platform can analyze and create actionable insights from the exponentially increasing amount of healthcare data being collected by wearables, bedside devices, and EMRs.