Optimal discharge of patients from intensive care via a data-driven policy learning framework

TL;DR

This paper presents a data-driven framework using Markov decision processes to optimize patient discharge timing from intensive care units, balancing costs and health risks based on electronic health records.

Contribution

It introduces a novel end-to-end framework that models patient discharge decisions as an MDP, integrating physiological data and cost considerations for optimal policy derivation.

Findings

Validated with real ICU data

Achieved improved discharge decision policies

Demonstrated effective trade-off management

Abstract

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.