So, the last startup I was a fulltime engineer at actually worked in this area. What I would suggest to be maximally useful would be to focus on physiological

So, the last startup I was a fulltime engineer at actually worked in this area. What I would suggest to be maximally useful would be to focus on physiological data: EKG/ECG, EMG, glucose, SPO2, maybe various blood work counts. All of those are data that are both well-understood and are thrown away regularly, and that if fed into a computer with modern ML methods we could maybe see some really cool stuff. I'd suggest staying away from unstructured data and things that are primarily of interest to only the business side of healthcare--insurance figures, billing codes, EMR/EHR shit. If you really wanted to get in there, putting up a minimal and standardized format for representing labs and medications would go a looooong way. ~ The problem in healthcare isn't the medical stuff--it's that people get bogged down in the inefficiencies of the system and zoom off solving problems that are removed from the immediate task of "what the fuck is wrong with this patient from the instruments I have at hand?"