## 6 Discussion
### Practical Implications  
Recent studies highlight the use of ML models to refine emergency triage processes. For example, integrating NLP with machine learning for Emergency Severity Index (ESI) acuity assignment has shown improved accuracy over traditional approaches [86]. Additionally, benchmark platforms leveraging large-scale electronic health record (EHR) datasets have been developed to evaluate triage prediction models' performance in diverse clinical scenarios [87].  
The prediction of admission or discharge is important for patients, the ED, and the hospital. Firstly, knowledge of the probability of admission should allow the patient to inform family members accordingly. Secondly, medical supervisors seek to optimize the flow of patients in the ED. Using the triage data, the supervisors dispatch patients to different sectors as a function of the current workload and capacity and anticipate possible additional examinations (lab tests, biomedical imaging, etc.). Automatic, systematic knowledge of the probability of admission would enable organ specialists or specialist hospital units to be contacted earlier. Thirdly, knowing the number of beds needed in real time helps the hospital's bed manager to adopt a targeted strategy and to free up beds in the required units only. All these aspects might help to reduce the length of stay and thus the mortality rate in the ED [8; 9].  
At present, the 3P-U model predicts the probability of hospital admission based on triage data collected shortly after a patient's arrival. Patients are categorized as having a high probability of admission if the predicted probability exceeds 0.6, while others are classified as \"not high probability of admission.\" This information supports bed managersin prioritizing patients likely to require admission, enabling more informed resource allocation decisions in coordination with ward clinicians (Figure 6). However, adoption of the 3P-U model has been limited, partly due to systemic challenges such as the availability of hospital beds and some skepticism toward AI-based approaches. During the COVID-19 pandemic, the model's predictions were used to estimate bed requirements during periods of increased patient flow, demonstrating its potential utility in resource planning [28].  
To address concerns about transparency and trust, XAI techniques such as SHAP and PDP have been integrated into the model. These methods enhance the interpretability of the ANN model's predictions, providing clinicians with insights into how decisions are made while maintaining its predictive performance. In clinical practice, local explanations for each patient are more useful than the global explanations, which help physicians understand the model's overall logic and behavior. Although the explainability features of the model had not been fully introduced to emergency physicians before this study, future efforts will focus on validating the model across individual specialties and involving clinicians in practical evaluations. These steps are expected to encourage greater confidence and adoption of the 3P-U model in clinical workflows. Because emergency medicine is a sensitive domain, local explanations should help to clarify the confidence in predictions given by the model. The final decision should remain the responsibility of the clinician, who considers additional information and ethical considerations [88].