# Predicting Hospital Admissions to Reduce Crowding in the Emergency Departments
## 2 Literature Review  
Prediction models in healthcare seek to increase logistical efficiency, enhance resource allocation, reduce patient waiting time, and improve patient care. During the last fifteen years, researchers have put attention to data collected early in ED encounters to help existing triage systems more quickly to identify and prioritize patients with critical conditions from the volumes of those with less urgent needs to tackle and possibly alleviate ED overcrowding. Triage is usually performed by a member of the nursing staff based on the patient's demographics, chief complaint, and vital signs. The patient is then seen by a healthcare provider, who develops the initial plan of care and ultimately recommends triage [21]. There are many different triage systems. Among them, the most widely accepted is the Australian Triage Scale (ATS) [22], the Canadian Triage Acuity Scale (CTAS) [23], and the Manchester Triage System (MTS) [24]. However, there are other approved systems such as the Andorran Triage Model (MAT) [25], which is used in almost all the hospitals in the Catalan healthcare system. Researchers have been implementing machine learning algorithms, in different forms, to extract valuable information from the huge amounts of data existing in hospital and ED databases. These algorithms are capable of understanding patterns in data and building corresponding models that use the identified patterns to classify new observations. Some basic studies have been researched on the topic with the development of models to predict future ED visits, thus facilitating the provision and preparation of ED staff to avoid overcrowding. [PERSON] and [PERSON] (2015) used ARIMA and Holt-Winters models to demonstrate that such predictions were possible with mean errors of 2.5% and 3.84%, respectively, when predicting visits at a monthly level [26]. More complex models have attempted to predict, not the number of visits, but the percentage of these that will turn into an admission to the hospital. The first studies were centered in trauma level I centers, in which the reasons for visiting are not as varied as in a general hospital ED, and the severity of disease (or injury in this case) can be more objectively assessed at triage. Probabilistic systems in the form of a Bayesian network for early prediction of admission in ED in these centers, with data available early in the ED visit, proved to be useful for predicting patient admissions already in 2005 (AUC = 0.894) [19]. (The specificity in these models is to be understood as the capacity of the algorithm to correctly predict the discharges, i.e., a specificity of 90% means 90 out of 100 discharges were predicted as such with the model, while 10 out of 100 were wrongly predicted as admissions). The same researchers concluded in 2006 that artificial neural networks (ANN) could also be used to predict hospital admissions in pediatric ED encounters, again in trauma level I centers, showing an almost equal performance (AUC = 0.897) [27]. More recently, logistic regression and Naive Bayes analysis have been used extensively to make predictions in the healthcare field. [PERSON] et. al. (2017) applied logistic models on triage administrative data to estimate admissions to the ED (AUC = 0.78), which in turn is used to predict the number of hours of bed requirements for the ED. The logistic regression models obtained had a sensitivity of 23% and a specificity of 97%. Even though these results were satisfactory for particular admission predictions, the hourly pooled probabilities of bed requirements showed better results, which are consistent with historical demand [28]. [PERSON], [PERSON], & [PERSON] (2017) [12] have investigated to improve accuracy (percentage of predictions, of both admissions and discharges, that conform to the real observed value) on the combination of different analytical tools with the logistic regression approach.