Hi Erin - I like the way you are thinking about this. Ioannidis is somewhat off base here because he is tacitly assuming that treatments don’t evolve over time. He wants to use a “for all time in the history of drug development in sepsis” prior. The prior must be tailored to what is currently known. Priors should be settled before the results are available, so re-analysis is always a little dangerous. But a formal process as you’ve described, even if done after the fact, can still be helpful. Or one can leave it all up to the reader by plotting y=posterior probability of efficacy vs. y=degree of skepticism of the prior (best described as prior P(efficacy > c) for some large c.
The best paper I’ve ever seen on prior elicitation is https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1854
One thing to feel good about: those who disdain Bayesian statistics are in effect using poorly documented procedures for turning data into conclusions, and are basing their decisions on the wrong metric - P(data|hypothesis) instead of P(effect|data).