Optimal discharge of patients from intensive care via a data-driven policy learning framework
This work addresses the critical healthcare challenge of managing intensive care unit resources efficiently while ensuring patient safety, representing an incremental improvement in clinical decision support tools.
The authors tackled the problem of optimizing patient discharge timing from intensive care units by developing a data-driven policy learning framework that balances reducing length of stay and hospitalization costs against risks like readmission or death, achieving validated results through extensive numerical experiments on real patient data.
Clinical decision support tools rooted in machine learning and optimization can provide significant value to healthcare providers, including through better management of intensive care units. In particular, it is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay (and associated hospitalization costs) and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions given a patient's electronic health records. A data-driven approach is used to derive a parsimonious, discrete state space representation that captures a patient's physiological condition. Based on this model and a given cost function, an infinite-horizon discounted Markov decision process is formulated and solved numerically to compute an optimal discharge policy, whose value is assessed using off-policy evaluation strategies. Extensive numerical experiments are performed to validate the proposed framework using real-life intensive care unit patient data.