I work in the tech healthcare industry. I wonder why they only went with (or focused on?) ML/NLP text analysis to analyze data. There is a wealth of discrete data available in EMRs (pharmacy, lab, eMAR, etc.). Yes, there is plenty of diagnosis text but that is almost always associated with ICD10 codes. The only area where I believe text analysis would be useful is documentation and microbiology data, and in many case…
The focus on NLP is because in 20-30 years, discrete data won't be entered into EMRs by providers at all.
Here's the pitch: Providers hate EMRs. They begrudge the time they spend working in EMRs instead of focusing on patient care. They loathe spending their office hours working on documentation. They hate spending their time in meetings with IT staff talking about EMR workflows and form-building. They hate getting lectured by accounting staff about how their documentation affects billing. They couldn't care less about this stuff and in an ideal (or even just a slightly more efficient) world, they wouldn't have to.
Every EMR that depends on webform-like inputs that map to database fields is going to be seen as a roadblock to patient care, which is what providers see as their primary concern.
I think the focus on NLP makes a ton of sense if you frame the problem with EMRs that way. EMR software will continue to be the bane of providers' existence right up to the day that providers can simply talk to their EMR in whatever way they want and have the computer interpret their speech and reliably spit out the discrete data that the accountants and number-crunchers want.
Obviously, I'm talking about a monumentally complicated undertaking and it's a bit on the ridiculous side. But I honestly believe we're going to go there, if only because providers won't be happy until we do.