An Intelligent Scheduling of Non-Critical Patients Admission for Emergency Department

Abstract

The combination of the progressive growth of an aging population, increased life expectancy and a greater number of chronic diseases all contribute significantly to the growing demand for emergency medical care, and thus, causing saturation in Emergency Departments (EDs). This saturation is usually due to the admission of non-urgent patients, who constitute a high percentage of patients in an ED. The Agentbased Model (ABM) is one of the most important tools that helps to study complex systems and explores the emergent behavior of this type of department. Its simulation more accurately reflects the complexity of the operation of real systems. Our proposal is the design of an ABM to schedule the access of these noncritical patients into an ED, which can be useful for the service management dealing with the actual growing demand for emergency care. We suppose that a relocation of these non-critical patients within the expected input pattern, provided initially by historical records, enables a reduction in waiting time for all patients, and therefore, it will lead to an improvement in the quality of service. It would also allow us to avoid long waiting times. This research offers the availability of relevant knowledge for Emergency Department managers in order to help them make decisions to improve the quality of the service, in anticipation of the expected growing demand of the service in the very near future. INDEX TERMS Agent-based modeling and simulation (ABMS), Emergency Department (ED), response capacity, decision support systems (SDS), length of stay (LoS).

Atención progresiva del paciente adulto crónico descompensado, desde el servicio de emergencia a la atención en las unidades críticas, Hospital II-1 Rioja

“…Hoy en día incluso existen programas basados en inteligencia artificial que permiten la gestión para una adecuada identificación de pacientes críticos y no críticos que pretendan acceder al servicio de emergencia, de esta manera se priorizaría a los adultos que presenten un estado de descompensación donde estos también estarían incluidos en el análisis de la sintomatología que presenta y sea una adecuada selección, la aplicación de dicho sistema permite una adecuada gestión de las herramientas tecnologías en los servicios críticos (6).…”

Response Time Improvement in Medical Emergency Departments Through Evolutionary Optimization

“…Resource management inside the ED can be either static or dynamic [4], [5], [6]. The first category is characterised from simple resource-allocating procedures where the constantly changing runtime environment is ignored.…”

Investigating Impacts of Telemedicine on Emergency Department Through Decreasing Non-Urgent Patients in Spain

“…According to the Spanish triage system, ED visits are divided into five acuity levels (based on patients' clinical condition) in order to determine the priority of treatments and/or optimize the use of physical resources in an ED [2]. statistical analysis of real data set (Collect from Parc Tauli Hospital) consistent with another study [3], however, has reported that ∼ 70% of patients visiting ED are not in an urgent clinical condition and can be considered as outpatients. Use of ED services for non-urgent medical care leads to hospital overcrowding, high medical expenses, delays in clinical service delivery, and a low service efficiency for urgent patients who truly need emergency care.…”