Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms

The strong performance of Random Forest (RF) and Artificial Neural Network (ANN) models in predicting PACU discharge timing upon admission highlights their potential as effective tools for evaluating discharge readiness, as compared to staff assessments and the Aldrete checklist. This study focused on assessing these models, showing their ability to produce consistent predictions, though differences between top models were not statistically significant due to overlapping confidence intervals. Practical application of these findings to improve patient outcomes or hospital efficiency requires further investigation.