It's a truism in machine learning that curating and massaging your dataset is the most labor-intensive and error-prone part of any project. I don't why that would stop being true in healthcare just because lives are on the line.
There’s multiple countries with socialized medicine and no profit motive and it’s still not solved.
I think it’s just really complex with high negative consequences from a mistake. It takes lots of investment with good coordination to solve and there’s an “easy workaround” with pdfs that distributes liability to practitioners.
Layering any sort of data standard into that environment (and evolving it in a timely manner!) is nigh impossible without an external impetus forcing action (read: government payer mandate).
Eventually the question will be, how far do we really need to go, i.e. how much profit do we allow ourselves before it's morally untenable and we should plow it back into R&D? Unfortunately, as long as health care is for-profit, and absent effective regulation, companies will always err on the side of profit.