## 6 Discussion
### Study Limitations  
The present study has several limitations that should be acknowledged. First, while the explainability results were generated independently, the algorithm was developed by physicians, which may introduce bias reflecting their clinical reasoning. Although the validation aligns with findings reported in the literature, independent validation by other physicians would strengthen the robustness of the results. Additionally, future studies should explore the clinical impact of these findings, particularly by quantitatively assessing the contribution of each variable to decision-making from a medical perspective, by evaluating the perceived weight of individual patient features in clinicians' judgments  
Figure 6: 3P-U dashboard with prediction of admission (blue progress bar) that impacts the disposition status (probably admitted). All statuses are presented in the global view. Second, the dataset used in this study was derived from a single center, which limits the generalizability of the findings to broader patient populations. The training data likely reflect the specific characteristics of the local population and admission policies. While the model's successful application within this center suggests potential for replication, further multi-center studies are necessary to validate its robustness in varied healthcare settings and patient cohorts. We are currently exploring collaborations with hospitals in England to develop such a multi-center validation study. This raises additional questions: should the 3P-U model be adapted or retrained to align with local admission strategies? For instance, in some EDs, hand injuries may be admitted immediately for surgical intervention, while in others, they may be scheduled for next-day ambulatory care. These differences in local clinical practice could significantly affect model generalizability. It is important to note that the model was trained on admission policies specific to a single emergency department, influenced by local practices, hospital structure, and staffing. Since SHAP explanations are model-dependent, the resulting explanations are inherently tied to this particular model and setting. Future work should aim to systematically compare SHAP attributions across multiple models and clinical centers to assess the stability of the explanations and to better distinguish between model-specific and context-specific variations. Third, the binary nature of the outcome (i.e., admission or discharge) restricts the granularity of predictions, whereas predicting the specific ward required for each patient would provide more actionable insights. However, this is inherently more complex, as patients are sometimes admitted to inappropriate wards due to bed shortages (e.g., patients with decompensated diabetes admitted to nephrology rather than endocrinology). Additionally, inadequate documentation of ward assignment in the ED further complicates this task. Fourth, the XAI methodology presented in this study represents an initial clinical application and has several limitations. Notably, we did not examine the potential impact of collinearity among input features on SHAP explanations, nor did we apply proxy or statistical evaluation techniques to assess the reliability of SHAP and PDP outputs. Following the recommendations of [PERSON] et al. [89], future work could enhance the robustness of our explainability framework by incorporating quantitative evaluation methods--such as permutation, feature removal with retraining, and perturbation-based assessments--as well as formally addressing variable collinearity. Although SHAP is widely used in healthcare AI, it has several limitations that require careful consideration. A key issue is the assumption of feature independence, which can lead to inaccurate attributions when input features are correlated, which is a frequent scenario in clinical datasets. Research has shown that even modest correlations can distort SHAP values, misrepresenting the true importance of features [88; 89].