# Ambulance Locations in a Tiered Emergency Medical System in a City
## 3 Results
### Results for the LandScan Demand Model
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Indicator** & \begin{tabular}{c} **Current** \\ **Locations** \\ \end{tabular} & \(p\)**MP** & \
\begin{tabular}{c} **MEXCLP** \\ **Binary** \\ \end{tabular} &
\begin{tabular}{c} **MEXCLP** \\ **Integer** \\ \end{tabular} & **ERTM** & **MCLP** \\ \hline Average response time & 6.12 & 5.34 & 5.13 & 5.30 & **5.08** & 5.35 \\ to all patients (min) & (6.07; 6.17) & (5.28; 5.40) & (5.03; 5.23) & (5.19; 5.41) & (5.02; 5.15) & (5.24; 5.46) \\ \hline \% of calls responded to & 95.21 & 95.22 & 95.34 & 95.41 & 95.30 & **95.48** \\ within 15 min & & & & & & \\ \hline Average response times to high-priority & 5.86 & 4.64 & 4.73 & 5.00 & 4.56 & **4.54** \\ patients (min) & (5.78; 5.93) & (4.51; 4.76) & (4.63; 4.83) & (4.84; 5.15) & (4.47; 4.65) & (4.40; 4.68) \\ \hline \% of high-priority calls & & & & & & \\ responded to within & 88.18 & 90.96 & **93.15** & 93.08 & 91.90 & 91.91 \\
8 min & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance indicators for different distributions of the EMS stations in the city of Presov, LandScan model of demand. best model from the response time point of view. The locations of the stations proposed by this model are displayed in Figure 5.  
Simulated response times are about one minute longer than the expected times produced by mathematical models. The reason behind this is that the simulation model reflects real operations where the ambulances are occupied not only by the calls arising in the city, but also due to the calls coming from the surrounding villages. In addition, secondary transports reduce the ambulance availability as well. The coverage in the city is very good at present; therefore, the possibilities of location models are limited. The overall coverage meets the national target of reaching 95% of patients within 15 min. The 8-min coverage rate for high-priority calls exceeds the EU average of 66.9% [31]. However, the simulation study has shown that improvements in coverage is still possible, especially regarding high-risk patients. The highest increase in this indicator (by 5%) was achieved by the MEXCLP model with binary variables. This is an unexpected result because the integer MEXCLP outperforms the binary MEXCLP in the static criterion of the 8-min expected coverage. Figure 6 shows that the binary MEXCLP model concentrates the stations close to the city center. Because the distribution of the stations in the city influences the provision of the services in the surrounding areas, it is wise to also evaluate the performance indicators at the district level. The results of the simulation experiment for the district of Presov are in Table 3. Here we can see that the best improvement was achieved by the \(p\)MP model. It is not surprising, because the distribution of the stations in the \(p\)MP model is the same as in the MCLP model, and it is more spread out over the city than in the case of the coverage-based models. Figure 6: Ambulance locations using the binary MEXCLP model with the LandScan demand model.