  Understanding decision-making in clinical environments is of paramount
importance if we are to bring the strengths of machine learning to ultimately
improve patient outcomes. Several factors including the availability of public
data, the intrinsically offline nature of the problem, and the complexity of
human decision making, has meant that the mainstream development of algorithms
is often geared towards optimal performance in tasks that do not necessarily
translate well into the medical regime; often overlooking more niche issues
commonly associated with the area. We therefore present a new benchmarking
suite designed specifically for medical sequential decision making: the
Medkit-Learn(ing) Environment, a publicly available Python package providing
simple and easy access to high-fidelity synthetic medical data. While providing
a standardised way to compare algorithms in a realistic medical setting we
employ a generating process that disentangles the policy and environment
dynamics to allow for a range of customisations, thus enabling systematic
evaluation of algorithms' robustness against specific challenges prevalent in
healthcare.
