Timely identification of intensive care unit (ICU) patients likely to exit the unit can support anticipatory workflows such as chart review, eligibility screening, and patient outreach prior to transfer. Most ICU discharge prediction studies report discrimination and calibration, but these metrics do not quantify the decision consequences of acting on predictions. Using adult ICU admissions from MIMIC-IV, we represented each ICU stay as a sequence of daily clinical summaries and trained logistic regression, random forest, and XGBoost models to predict next day ICU transfer. Models achieved ROC AUC of 0.80–0.84 with differing calibration. We evaluated decision utility using decision curve analysis (DCA), where positive predictions trigger proactive review. Across thresholds, model guided strategies outperformed review-all, review-none, and a simple clinical rule. To translate net benefit into implementable operations, we modeled a clinical trial recruitment workflow with an 8 hour daily time constraint, incorporating chart review and consent effort. At a feasible operating threshold (0.23), the model flagged ∼23 charts/day and yielded ∼1.23 enrollments/day under conservative eligibility and consent assumptions. These results demonstrate that DCA provides a transparent framework for determining when ICU transfer predictions are worth using and how thresholds should be selected to align with real world workflow constraints.

Data and Code Availability This research has been conducted using data from MIMIC-IV. Researchers can request access via PhysioNet. Implementation code is available upon request.

The authors have declared no competing interest.

This study was supported the Huntsman Mental Health Foundation, the National Institute of Aging of the National Institutes of Health under Award Number L70AG096751, and the University of Utah's Digital Health Initiative (W.W.P.). National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Number R01HL169521 and the Intermountain Fund under Project Number 20400306 (BWL).

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study used (or will use) ONLY openly available human data through MIMIC-IV that were originally located at PhysioNet.

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↵* Co-senior author.

u0635678utah.edu, Abigail.Papehsc.utah.edu, brian.lockeimail.org