# Predicting Hospital Admissions to Reduce Crowding in the Emergency Departments
## 1 Introduction  
During the last couple of years, the coronavirus pandemic has put healthcare systems from all around the globe under extreme stress. Most hospitals have seen their infrastructure and capacity to offer quality health care collapsing under an unexpected and enormous demand, and despite COVID-19 having obviously been the main actor in this crisis, the whole situation has evidenced some big inefficiencies of the system. The healthcare service delivered in hospitals depends, to a large extent, on the efficiency in the execution of all those medical and non-medical processes aimed at providing, altogether, quality service to the patient. Busy healthcare systems continue to challenge their managers and decision-makers due to high demands, high costs, limited budgets, and health resources. As a result, decision-makers are constantly examining the efficiency of existing healthcare systems and must be able to evaluate the outcomes of any changes they make to those systems [1]. These activities and relationships are often complex and multidisciplinary, making them a major focus of study as not only the development and application of advanced analytical methods but also their optimization. Both of these can result in improvements in many different areas simultaneously, such as the reduction in administrative costs [2], the optimization of hospital resources [1], and the reduction in patient waiting times or a greater degree of service customization [3]. This results in a higher quality service that is appreciated by both institutions and users alike. The emergency department (ED) is the service with the highest demand in a hospital, and this is continuously increasing, reaching a critical point. The number of patients visiting the ED has been steadily rising worldwide for the last few decades, which has ledto situations of overcrowding in emergency rooms all around the globe. Data from the Institute of Medicine in the United States already identified crowding as a critical threat to the quality of the service provided to patients back in 2006 [4]. Similarly, the Spanish Ministry of Health released data regarding ED usage in 2010 which showed an increase of 23.2% in ED visits between 2001 and 2007 [5], with an ED usage frequency rate notoriously higher than that of the UK or the US. The latest report showed a 9% increase in the number of visits attended in hospitals in Spain between 2014 and 2018, without any specific health reasons or population growth to account for that change. ED crowding has been described by the American College of Emergency Physicians (ACEP) [6] as \"a situation in which the need for emergency services outstrips available resources in the ED. This situation occurs in hospital EDs when there are more patients than staffed ED treatment beds and wait times exceed a reasonable period.\"  
A health emergency, on the other hand, has been defined by the WHO as the unexpected appearance of a health issue of any type and cause, of varying degrees of seriousness, that generates an imminent need for attention or treatment by a professional. Therefore, EDs need to offer a multidisciplinary assistance service, complying with functional, structural, and organizational requisites to guarantee safety, quality, and efficient conditions to attend to any possible emergency. Despite this, crowding hinders the ability of EDs around the world to deliver such a service in optimal conditions. Existing studies show the direct correlation of ED crowding with increased ED waiting times, decreased patient satisfaction, inadequately treated pain, higher walkaway rates, and even higher mortality [7; 8; 9; 10]. Furthermore, hospital staff suffer from the consequences of overcrowding, showing dissatisfaction, frustration, and stress, and facing higher exposure to violence and physical aggression [11]. The optimization of this service and all the processes behind it must therefore be a priority in order to guarantee the continuity and improvement of its quality.