Implementation of an emergency nursing pathway reduces emergency department length of stay in trauma: Retrospective...

Timely, organized emergency care is critical for patients with severe trauma, particularly in primary hospitals where resources and workflows may be constrained. This study evaluated the effect of implementing an emergency nursing pathway (ENP) on emergency management of injured patients in a county-level hospital and developed a predictive model to identify factors associated with outcomes. We retrospectively reviewed patients with trauma treated at The First People's Hospital of Jiande between August 2023 and July 2025. Patients were allocated at random into a training cohort (70%) and a validation cohort (30%). The Boruta algorithm was applied in the training cohort to select relevant predictors, which were then used to train 10 machine learning (ML) classifiers; model performance was evaluated by area under the receiver operating characteristic curve, calibration curves, and decision curve analysis. Model interpretability was assessed using SHapley Additive exPlanations (SHAP) values to quantify each variable's contribution to predictions. P < .05 considered significant. Implementation of the ENP was associated with a statistically significant reduction in emergency department length of stay compared with routine care (P < .05). Using Boruta-selected predictors, the XgBoost classifier exhibited the best discrimination among tested ML models (area under the curve = 0.669; 95% confidence interval: 0.632-0.705) and was therefore selected as the primary predictive model. SHAP analysis indicated that injury severity score and ENP contributed most to model predictions, followed by mechanism of injury, severity of illness, and method of visiting the hospital. In this single-center retrospective study, application of an ENP for severely injured patients in a primary hospital was associated with shorter emergency department length of stay. These findings support ENP implementation to streamline care and suggest that ML-based tools may help risk-stratify trauma patients in resource-limited settings. Prospective multicenter validation is warranted.