  Clinician-facing predictive models are increasingly present in the healthcare
setting. Regardless of their success with respect to performance metrics, all
models have uncertainty. We investigate how to visually communicate uncertainty
in this setting in an actionable, trustworthy way. To this end, we conduct a
qualitative study with cardiac critical care clinicians. Our results reveal
that clinician trust may be impacted most not by the degree of uncertainty, but
rather by how transparent the visualization of what the sources of uncertainty
are. Our results show a clear connection between feature interpretability and
clinical actionability.
