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Generative AI-enhanced nomogram predicts left without being seen among ambulance-transported emergency department
Hao Wang1, Emily Drone1, Devin Sandlin1
1Department of Emergency Medicine, JPS Health Network, Integrative Emergency Service, 1500 S. Main St., Fort Worth, TX 76104, United States of America.
The American Journal of Emergency Medicine
|November 25, 2025
Summary
This study developed a nomogram to predict patients likely to leave the emergency department without being seen (LWBS). The tool aids clinicians in managing ambulance-transported patients with less urgent conditions, improving care quality.
Area of Science:
- Emergency Medicine
- Health Services Research
- Artificial Intelligence in Healthcare
Background:
- Left Without Being Seen (LWBS) is a critical metric for emergency department (ED) performance.
- LWBS negatively impacts patient satisfaction, healthcare costs, and outcomes, especially for ambulance-transported patients.
- Predicting LWBS risk in less urgent cases is essential for resource optimization.
Purpose of the Study:
- To develop and validate a predictive nomogram for Left Without Being Seen (LWBS) risk.
- To identify key predictors of LWBS in ambulance-transported patients with non-urgent conditions.
- To enhance clinical decision-making for ED patient management.
Main Methods:
- Utilized a large dataset of 54,380 patient visits, randomly split into training and testing sets.
- Employed a generative AI tool (large language model) to structure chief complaint data.
- Constructed a predictive nomogram using LASSO and multivariable logistic regression.
Main Results:
- Identified seven significant predictors for LWBS.
- The nomogram demonstrated strong predictive performance with AUC values of 0.804 (training) and 0.802 (testing).
- Calibration curves and decision curve analysis confirmed the nomogram's accuracy and clinical utility.
Conclusions:
- A validated nomogram effectively predicts LWBS risk in ambulance-transported patients with less urgent conditions.
- The nomogram offers valuable clinical insights for informed decision-making and optimized patient flow.
- This tool can help improve emergency department quality metrics and patient care.
