# Predicting Hospital Admissions to Reduce Crowding in the Emergency Departments
## 4 Results  
### Sample Characteristics  
During the year 2018, the EDs in the Integral Healthcare System for Public Use in Catalonia received a total of 3,561,936 individual visits, 3,434,130 of which were included in the dataset provided by the Catalan Healthcare System to perform the study. After cleansing the data, 3,189,204 observations that had been correctly identified and classified were included. These accounted for 1,805,096 unique patients. The accumulated number of visits per patient ranged from 1 to 244, with a median of 1, and a mean equal to 1.767. A box plot of the absolute frequencies of visits per patient can be found in Figure 4, in which the outlier values regarding the number of visits are clearly visible. The overall admission rate was 11.018%, with 351,391 admissions by 275,875 unique patients, and 2,837,813 discharges by 1,690,092 different patients. Looking for an indicator of admission in the frequency of visits, a correlation analysis was performed using the Pearson correlation method. This yielded a result of 0.01, an extremely weak correlation indicating that both variables are hardly related. Further, a \(p\)-value < 0.001 was obtained, giving undeniable significance to the test result. To observe the behavior of these two variables against each other, Figure 5 is presented. Figure 4: Accumulated number of visits per unique patient. The gender distribution of the sample was 46.44% male and 53.56% female, with a rate of admission slightly higher for the former, 11.41% against 10.68%, which proved to be statistically significant with alpha equal to 0.001 (\(p\)-value \(<\) 0.001). The average age of the patients included in the study was 43.08 years old, slightly higher for females (44.90), than for males (41.91). Charts for the gender-specific age distribution can be found in Figure 6. The average age for those visits that ended in an admission to the hospital was 59.57 years old, while that of those visits that ended in discharge was 41.04 years old, a more than 18 points difference that was clearly significant with a \(p\)-value \(<\) 0.001 for alpha equal to 0.001. Regarding the triage level frequencies, it is concluded that one accounts for the most urgent and priority cases and five for the least urgent ones. Moreover, it is worth noticing the statistically significant higher severity (\(p\)-value \(<\) 0.001, alpha = 0.001) of the classification of patients admitted in front of patients discharged; the former had an average triage level of 3.03, while the latter had a 3.74 average triage level. Figure 7 shows the admission rates for all five triage levels. Based on absolute numbers, it is apparent that only a small percentage of patients are classified as extremely acute (level 1), and that the majority are classified as either level 3 or level 4. The number of patients for each triage level was as follows: level one, 5774 observations; level two, 161,714 observations; level three, 1,048,227 observations; level four, 1,650,429 observations; and finally, level five, 323,060 observations. Figure 5: Variable importance. All these previous statistical analyses were performed with the Student's _t_-tests method to compare means from different samples with two-sided critical area (two-tail _t_-test). The most predominant symptoms classified at arrival of the patient in the ED were (Figure 8): spondylosis, intervertebral disk disorder, and other back complaints (4.43%, 141,357 observations), followed by superficial wound and concussion (4.23%, 134,970 observations), abdominal pain (4.12%, 131,261 observations), non-classified codes for causes that were unclear, not included in the CCS or that the patient could not describe (3.96%, 126,190 observations), and other respiratory infections in the upper tract (3.65%, 116,420 observations). Figure 6: Age distribution patients: **(a)** male patients; **(b)** female patients. Figure 7: Admission frequency per triage level.