NDLI: Interpretable machine learning models for prolonged Emergency Department wait time prediction

Content Provider	Springer Nature : BioMed Central
Author	Wang, Hao Sambamoorthi, Nethra Sandlin, Devin Sambamoorthi, Usha
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.
Related Links	https://bmchealthservres.biomedcentral.com/counter/pdf/10.1186/s12913-025-12535-w.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	14726963
DOI	10.1186/s12913-025-12535-w
Journal	BMC Health Services Research
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2025-03-18
Access Restriction	Open
Subject Keyword	Public Health Health Administration Health Informatics Nursing Research Emergency department Wait time Machine learning Performance
Content Type	Text
Resource Type	Article
Subject	Health Policy

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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