## 4 Results
### Experimental Result
#### 4.3.1 Comparison between Real Data and Simulation  
The first experiment conducted assesses the difference in length of stay (LoS) between real operational data and simulated results across three distinct emergency department scenarios. The comparative analysis focuses on understanding how well different scheduling strategies perform in aligning simulated outcomes with real-world data. The experimental results are shown in Figure 5.  
The current scenario includes all KTAS levels, thus showing a lower LoS. The real data show an average of 295.93 min, while the simulation results show a duration of 330.35 min. The final error is observed as an average of 10.41%, indicating an accuracy of 89.59%. Since the ED simulation shows a similarity of about 90%, it can be considered useful for simulations. In the case of Scenario A, the error rate is 9.46%, and because there are many low-severity patients and a large staff, the accuracy is higher. For Scenarios B and C, the error rates are 10.83% and 10.41%, respectively, showing that accuracy decreases when there are more high-severity patients.  
The second experiment compares the results of the simulation with the actual data according to the KTAS of the actual data. It shows the results according to each KTAS in the three regions. The experimental results are shown in Table 7. This experiment inputs patients with the same severity. ED simulation measures the performance of predictions for each severity level.  
Figure 4: Result of machine learning algorithms (%).  
The experimental results showed a high accuracy of 92.31% overall. This is because patients with the same severity were entered. Scenario A: Revealed an accuracy ranging from 92.14% for KTAS levels 1 and 2, decreasing slightly for KTAS 3 at 91.18%, and showing the best simulation performance at KTAS levels 4 and 5 with 94.75%. This pattern suggests that while the simulation is quite robust in predicting outcomes for less severe cases, it struggles slightly with the most severe cases, possibly due to the unpredictable nature and complexity of the treatments required. Scenario B: Mirrored similar trends with the lowest accuracy for KTAS 1 and 2 at 93.04%, and slightly better accuracy for KTAS 3 and 4 + 5 at 92.11% and 94.56%, respectively. This consistency across scenarios underscores a systematic issue with simulating higher acuity levels accurately. Scenario C: Exhibited the most significant drop in accuracy for mid-level acuity (KTAS 3) at 88.90%, while maintaining relatively high accuracy for the least severe cases at 93.28%.  
Given the high level of accuracy demonstrated across various KTAS levels and scenarios, and considering the close alignment between simulated and real data outcomes, it is evident that the ED simulation has been well constructed and is appropriately suited for practical application. The simulation's ability to mirror real-world conditions with a substantial degree of accuracy averaging over 90% confirms its utility and effectiveness in predicting operational dynamics within emergency departments. This makes it a valuable tool for strategic planning and operational adjustments in emergency care settings.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **KTAS Level** & **Real Data** & **Simulation Result** & **Accuracy** \\ \hline \multirow{4}{*}{A} & KTAS1 + 2 & 417.45 & 453.08 & 92.14\% \\  & KTAS3 & 353.81 & 388.02 & 91.18\% \\  & KTAS4 + 5 & 225.49 & 237.99 & 94.75\% \\ \hline \multirow{4}{*}{B} & KTAS1 + 2 & 463.62 & 498.30 & 93.04\% \\  & KTAS3 & 404.75 & 439.40 & 92.11\% \\  & KTAS4 + 5 & 231.02 & 244.32 & 94.56\% \\ \hline \multirow{4}{*}{C} & KTAS1 + 2 & 350.93 & 386.20 & 90.87\% \\  & KTAS3 & 278.43 & 313.18 & 88.90\% \\ \cline{1-1}  & KTAS4 + 5 & 175.7 & 188.37 & 93.28\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of actual data and simulation data of KTAS.  
Figure 5: Compares of average LoS between real data and simulation result (min).