Causal Effects and Optimal Policy Learning for Intensive Care Unit Discharge Decisions to Solve Hospital Process Bottlenecks: Approach, Methods, and First Results - Justus Vogel; Johannes Cordier; Miodrag Filipovic (University of St.Gallen, School of Medicine, Chair of Health Economics, Policy and Management, 2025)
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 ICU 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. Our sample comprises 12,950 ICU stays (11,873 unique cases) from the Department of Surgical Intensive Medicine of the Cantonal Hospital of St. Gallen admitted between January 01, 2016, and December 31, 2023. We find that for 72% of our sample discharge at point in time 𝑡 as compared to 𝑡+1 increases patients' readmission risk. Vice versa, 28% of cases profit from an earlier discharge in terms of readmission risk. The range of IATEs is quite large: For 91.4% of ICU stays, an earlier ICU discharge changes a patient's readmission risk between -0.05 and 0.05 percentage points (-55% and 55% relative change as compared to the average readmission rate of 9.04%). To develop decision policies, we will exploit this treatment heterogeneity and rank patients according to their IATEs and compare IATEs of optimal and actual discharges across all decision points in our observation period. 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.
Keywords: I10; C44; 330; Causal Machine Learning; Intensive Care Unit Management; Hospital Operations; Policy Learning