  Clinical decision support tools (DST) promise improved healthcare outcomes by
offering data-driven insights. While effective in lab settings, almost all DSTs
have failed in practice. Empirical research diagnosed poor contextual fit as
the cause. This paper describes the design and field evaluation of a radically
new form of DST. It automatically generates slides for clinicians' decision
meetings with subtly embedded machine prognostics. This design took inspiration
from the notion of "Unremarkable Computing", that by augmenting the users'
routines technology/AI can have significant importance for the users yet remain
unobtrusive. Our field evaluation suggests clinicians are more likely to
encounter and embrace such a DST. Drawing on their responses, we discuss the
importance and intricacies of finding the right level of unremarkableness in
DST design, and share lessons learned in prototyping critical AI systems as a
situated experience.
