Risk assessment tools assume that the impact of risk factors is linear and cumulative.
This is an erroneous assumption.
We will demonstrate that surgical risk is not linear.
Across industries, risk management groups seek to identify factors that will predict
and quantify risk.
Insurance firms seek to predict underwriting risk.
Banks seek to predict loan default.
In the healthcare setting, physicians seek to quantify the likelihood of mortality or
morbidity prior to conducting surgery to guide decision making.
The developed models that physicians rely on are called clinical decision support tools.
In this module, we demonstrate the advantages of using machine learning based optimal classification
trees to build clinical decision support tools over the current state logistic regression
model.
Specifically, we show how optimal classification trees do not make the erroneous assumption
that risk factors are linear and cumulative.
Over and above the machine learning discussion, we will also introduce a number of failure
modes that must be addressed in order to increase the likelihood of adoption of decision support
tools.