  Laboratory testing is an integral tool in the management of patient care in
hospitals, particularly in intensive care units (ICUs). There exists an
inherent trade-off in the selection and timing of lab tests between
considerations of the expected utility in clinical decision-making of a given
test at a specific time, and the associated cost or risk it poses to the
patient. In this work, we introduce a framework that learns policies for
ordering lab tests which optimizes for this trade-off. Our approach uses batch
off-policy reinforcement learning with a composite reward function based on
clinical imperatives, applied to data that include examples of clinicians
ordering labs for patients. To this end, we develop and extend principles of
Pareto optimality to improve the selection of actions based on multiple reward
function components while respecting typical procedural considerations and
prioritization of clinical goals in the ICU. Our experiments show that we can
estimate a policy that reduces the frequency of lab tests and optimizes timing
to minimize information redundancy. We also find that the estimated policies
typically suggest ordering lab tests well ahead of critical onsets--such as
mechanical ventilation or dialysis--that depend on the lab results. We evaluate
our approach by quantifying how these policies may initiate earlier onset of
treatment.
