Interpretable machine learning models for prolonged Emergency Department wait time prediction
Abstract Objective Prolonged Emergency Department (ED) wait times lead to diminished healthcare quality. Utilizing machine learning (ML) to predict patient wait times could aid in ED operational management. Our aim is to perform a comprehensive analysis of ML models for ED wait time prediction, iden...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-03-01
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| Series: | BMC Health Services Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12913-025-12535-w |
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| Summary: | Abstract Objective Prolonged Emergency Department (ED) wait times lead to diminished healthcare quality. Utilizing machine learning (ML) to predict patient wait times could aid in ED operational management. Our aim is to perform a comprehensive analysis of ML models for ED wait time prediction, identify key feature importance and associations with prolonged wait times, and interpret prediction model clinical relevance among ED patients. Methods This is a single-centered retrospective study. We included ED patients assigned an Emergency Severity Index (ESI) level of 3 at triage. Patient wait times were categorized as <30 minutes and ≥30 minutes (prolonged wait time). We employed five ML algorithms - cross-validation logistic regression (CVLR), random forest (RF), extreme gradient boosting (XGBoost), artificial neural network (ANN), and support vector machine (SVM) - for predicting patient prolonged wait times. Performance assessment utilized accuracy, recall, precision, F1 score, false positive rate (FPR), and false negative rate (FNR). Furthermore, using XGBoost as an example, model key features and partial dependency plots (PDP) of these key features were illustrated. Shapley additive explanations (SHAP) were employed to interpret model outputs. Additionally, a top key feature interaction analysis was conducted. Results Among total 177,665 patients, nearly half of them (48.20%, 85,632) experienced prolonged ED wait times. Though all five ML models exhibited similar performance, minimizing FNR is associated with the most clinical relevance for wait time predictions. The top features influencing patient wait times and gaining the top ranked interactions were ED crowding condition and patient mode of arrival. Conclusions Nearly half of the patients experienced prolonged wait times in the ED. ML models demonstrated acceptable performance, particularly in minimizing FNR when predicting ED wait times. The prediction of prolonged wait times was influenced by multiple interacting factors. Proper application of ML models to clinical practice requires interpreting their predictions of prolonged wait times in the context of clinical significance. |
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| ISSN: | 1472-6963 |