  In time-critical decisions, human decision-makers can interact with
AI-enabled situation-aware software to evaluate many imminent and possible
scenarios, retrieve billions of facts, and estimate different outcomes based on
trillions of parameters in a fraction of a second. In high-order reasoning,
"what-if" questions can be used to challenge the assumptions or pre-conditions
of the reasoning, "why-not" questions can be used to challenge on the method
applied in the reasoning, "so-what" questions can be used to challenge the
purpose of the decision, and "how-about" questions can be used to challenge the
applicability of the method. When above high-order reasoning questions are
applied to assist human decision-making, it can help humans to make
time-critical decisions and avoid false-negative or false-positive types of
errors. In this paper, we present a model of high-order reasoning to offer
recommendations in evidence-based medicine in a time-critical fashion for the
applications in ICU. The Large Language Model (LLM) is used in our system. The
experiments demonstrated the LLM exhibited optimal performance in the "What-if"
scenario, achieving a similarity of 88.52% with the treatment plans of human
doctors. In the "Why-not" scenario, the best-performing model tended to opt for
alternative treatment plans in 70% of cases for patients who died after being
discharged from the ICU. In the "So-what" scenario, the optimal model provided
a detailed analysis of the motivation and significance of treatment plans for
ICU patients, with its reasoning achieving a similarity of 55.6% with actual
diagnostic information. In the "How-about" scenario, the top-performing LLM
demonstrated a content similarity of 66.5% in designing treatment plans
transferring for similar diseases. Meanwhile, LLMs managed to predict the life
status of patients after their discharge from the ICU with an accuracy of 70%.
