“Bayesian decision making” it’s not very common in med research, as far as I can see. And it is also not very commonly meant in intro statistics books. But the objection remains, I’d say, also in this context. Instead of the likelihood you use a posterior distribution. This may incorporate some of the weights that decisions will eventually be based upon, but getting these weights and priors are just outside the realm of statistics, again. A Bayesian analysis certainly allows us to make probability statements about parameter values (the model and the priors must be given), or how we ought to change priors upon seeing data. This is a form of “learning from data”, as to refine our beliefs about parameter values in given models. That’s valuable, but that is not “decision making”. It may, under very special conditions, contribute to the process of making a decision. Decisions are eventually made outside of statistics, and even if you pack everything into the Baesian model, it’s just put on the selection of model and priors.