Causal Effects and Optimal Policy Learning for Intensive Care Unit Discharge Decisions to Solve Hospital Process Bottlenecks: Approach, Methods, and First Results

Intensive care units (ICUs) operate with fixed capacities and face uncertainty such as demand variability, leading to demand-driven, early discharges to free up beds. These discharges can increase readmission rates, negatively impacting patient outcomes and aggravating ICU bottleneck congestion. This study investigates how ICU discharge timing affects readmission risk, with the goal of developing policies that minimize ICU readmissions, managing demand variability and bed capacity. To define a binary treatment, we randomly assign hypothetical discharge days to patients, comparing these with actual discharge days to form intervention and control groups. We apply two causal machine learning techniques (generalized random forest, modified causal forest). Assuming unconfoundedness, we leverage observed patient data as sufficient covariates. For scenarios where unconfoundedness might fail, we discuss an IV approach with different instruments. We further develop decision policies based on individualized average treatment effects (IATEs) to minimize individual patients' readmission risk. We find that for 72% of our sample (roughly 12,000 cases), admission at point in time 𝑡 as compared to 𝑡+1 increases their readmission risk. Vice versa, 28% of cases profit from an earlier discharge in terms of readmission risk. To develop decision policies, we rank patients according to their IATE, and compare IATE rankings for instances, when demand exceeds the available capacity. Finally, we outline how we will assess the potential reduction in readmissions and saved bed capacities under optimal policies in a simulation, offering actionable insights for ICU management. We aim to provide a novel approach and blueprint for similar operations research and management science applications in data-rich environments.