The data model is a big part of the problem. There are lots of different ways to encode the same information within HL7. People refer to different "flavors" of HL7, different ones for different implementations (and implementations are not just vendor specific, but site specific — EHR software is very heavily customized for each health system). Add to that doctors and nurses entering information in their own ways within a given health system (since the software isn't very clear or usable), and the data that's getting transferred is a huge mess.

Beyond that, a lot of the most important information is encoded in free text fields, and so isn't directly analyzable. And even when information is mapped to codes from standard medical ontologies, there's no guarantee that when that information is transferred in HL7 formats it includes the code from the ontology.

It's not at all clear what the optimal way to structure medical information should be, so it's no surprise that there's a huge amount of variance out in the world. HL7 is quite old (v2 was made in 1989), and every new variant has to support the existing variants. EHRs were originally designed around billing and administrative workflows, so it's also not surprising that the data structures aren't great for analyzing data or treating patients.