Hi! The NER interface is really optimised for actual named entity recognition tasks where the goal is to predict single token-based tags. One option would be to make two passes over the data and use the first pass to focus on getting the boundaries right (e.g. DiseaseEntity) and then annotate all additional attributes in the next round (e.g. using a multiple choice interface). If you're doing two passes, you can also use the first round to make sure all entities are labelled and that the annotation scheme is implemented correctly, before going deeper.
I've also seen approaches that designed a very similar annotation task as a relation annotation task (maybe that was somewhere in the medical tag) – but I think in that case, the goal was to also capture the indicator for the polarity (i.e. "not"). If that's not so important for you, that might be overkill.