scholarly journals Employment of Emergency Advanced Nurses of Turkey: A Discrete-Event Simulation Application

Processes ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 48 ◽  
Author(s):  
Abdulkadir Atalan ◽  
Cem Donmez
Keyword(s):  
Length Of Stay ◽  
Discrete Event Simulation ◽  
Emergency Services ◽  
Treatment Time ◽  
Waiting Times ◽  
Discrete Event ◽  
Weekly Basis ◽  
Event Simulation ◽  
Number Of Patients ◽  
Efficiency Rate

In the present study, problems in emergency services (ESs) were dealt with by analyzing the working system of ESs in Turkey. The purpose of this study was to reduce the waiting times spent in hospitals by employing advanced nurses (ANs) to treat patients who are not urgent, or who may be treated as outpatients in ESs. By applying discrete-event simulation on a 1/24 (daily) and 7/24 (weekly) basis, and by employing ANs, it was determined that the number of patients that were treated increased by 26.71% on a 1/24 basis, and by 15.13% on a 7/24 basis. The waiting time that was spent from the admission to the ES until the treatment time decreased by 38.67% on a 1/24 basis and 53.66% on a 24/7 basis. Similarly, the length of stay was reduced from 82.46 min to 53.97 min in the ES. Among the findings, it was observed that the efficiency rate of the resources was balanced by the employment of ANs, although it was not possible to obtain sufficient efficiency from the resources used in the ESs prior to the present study.

scholarly journals Discrete event simulation for problem solving in the context of an emergency department

2020 ◽  
Vol 11 (5) ◽  
pp. 1515
Author(s):  
Letícia Ali Figueiredo Ferreira ◽  
Igor Leão dos Santos ◽  
Ana Carla De Souza Gomes dos Santos ◽  
Augusto Da Cunha Reis
Keyword(s):  
Length Of Stay ◽  
Discrete Event Simulation ◽  
Waiting Times ◽  
Discrete Event ◽  
Saturation Point ◽  
Event Simulation ◽  
Emergency Care System ◽  
Patient Arrival ◽  
The Subject

Emergency departments (ED) are responsible for the immediate care and stabilization of patients in critical health conditions. Several factors have caused overcrowding in the emergency care system, but the variability of patient arrival and the triage process requires special attention. The criticality of these components and their configuration directly impact the waiting times, length of stay and quality of service, being the subject of several studies. So, this paper aims to understand by means of Discrete Event Simulation how ED works with the variation of patient arrival and how this variation highlights the bottlenecks of the triage process. Varying the patient arriving interval between 0.1 and 7.6 in a 4-hour scenario,  the system saturation point was established in β = 1.1. Besides, with the variation in the number of triages points, a considerable decrease in the total length of stay spent and the waiting times were noticed, mainly when there was two triage points operating simultaneously.

scholarly journals Optimizing Emergency Medical Service Structures Using a Rule-Based Discrete Event Simulation—A Practitioner’s Point of View

2021 ◽  
Vol 18 (5) ◽  
pp. 2649
Author(s):  
Christoph Strauss ◽  
Günter Bildstein ◽  
Jana Efe ◽  
Theo Flacher ◽  
Karen Hofmann ◽  
...  
Keyword(s):  
Discrete Event Simulation ◽  
Emergency Services ◽  
Prehospital Care ◽  
Discrete Event ◽  
Point Of View ◽  
Public Healthcare ◽  
Healthcare Organizations ◽  
Rule Based ◽  
Event Simulation ◽  
Emergency Medical

Many studies in research deal with optimizing emergency medical services (EMS) on both the operational and the strategic level. It is the purpose of this method-oriented article to explain the major features of “rule-based discrete event simulation” (rule-based DES), which we developed independently in Germany and Switzerland. Our rule-based DES addresses questions concerning the location and relocation of ambulances, dispatching and routing policies, and EMS interplay with other players in prehospital care. We highlight three typical use cases from a practitioner’s perspective and go into different countries’ peculiarities. We show how research results are applied to EMS and healthcare organizations to simulate and optimize specific regions in Germany and Switzerland with their strong federal structures. The rule-based DES serves as basis for decision support to improve regional emergency services’ efficiency without increasing cost. Finally, all simulation-based methods suggest normative solutions and optimize EMS’ performance within given healthcare system structures. We argue that interactions between EMS, emergency departments, and public healthcare agencies are crucial to further improving effectiveness, efficiency, and quality.

Discrete Event Simulation Of The COVID-19 Sample Collection Point Operation

2021 ◽  
Author(s):  
Martina Kuncova ◽  
Katerina Svitkova ◽  
Alena Vackova ◽  
Milena Vankova
Keyword(s):  
Discrete Event Simulation ◽  
Waiting Times ◽  
Discrete Event ◽  
Real Data ◽  
Simulation Software ◽  
Sample Collection ◽  
The Real ◽  
Event Simulation ◽  
Collection Process ◽  
Collection Point

The year 2020 was very challenging for everyone due to the COVID-19 pandemic. Many people turn their lives upside down from day to day. Politicians had to impose completely unprecedented measures, and doctors immediately had to adapt to the huge influx of patients and the massive demand for testing. Of course, not all processes could be planned completely efficiently, given that the situation literally changes from minute to minute, but sometimes better planning could improve the real processes. This contribution deals with the application of simulation software SIMUL8 to the analysis of the COVID-19 sample collection process in a drive-in point in a hospital. The main aim is to create a model based on the real data and then to find out the suitable number of other staff (medics) helping a doctor during the process to decrease the number of unattended patients and their waiting times.

scholarly journals Analysis of the Installed Productive Capacity in a Medical Angiography Room through Discrete Event Simulation

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 660
Author(s):  
Félix Badilla-Murillo ◽  
Bernal Vargas-Vargas ◽  
Oscar Víquez-Acuña ◽  
Justo García-Sanz-Calcedo
Keyword(s):  
Resource Utilization ◽  
Discrete Event Simulation ◽  
Current System ◽  
Waiting Times ◽  
Continuous Process ◽  
Discrete Event ◽  
Computer Software ◽  
Productive Capacity ◽  
Continuous Process Improvement ◽  
Event Simulation

The installed productive capacity of a healthcare center’s equipment limits the efficient use of its resources. This paper, therefore, analyzes the installed productive capacity of a hospital angiography room and how to optimize patient demand. For this purpose, a Discrete Event Simulation (DES) model based on historical variables from the current system was created using computer software. The authors analyzed 2044 procedures performed between 2014 and 2015 in a hospital in San José, Costa Rica. The model was statistically validated to determine that it does not significantly differ from the current system, considering the DMAIC stages for continuous process improvement. In the current scenario, resource utilization is 0.99, and the waiting list increases every month. The results showed that the current capacity of the service could be doubled, and that resource utilization could be reduced to 0.64 and waiting times by 94%. An increase in service efficiency could be achieved by shortening maximum waiting times from 6.75 days to 3.70 h. DES simulation, therefore, allows optimizing of the use of healthcare systems’ resources and hospital management.

scholarly journals COMPARING SCHEDULING APPOINTMENT RULES PERFORMANCE IN HEALTH CARE UNITS: A DISCRETE EVENT SIMULATION APPROACH

2021 ◽  
Vol 16 (1) ◽  
pp. 28-41
Author(s):  
Thiago Nunes Klojda ◽  
Antônio Pedro de Britto Pereira Fortuna ◽  
Bianca Menezes Araujo ◽  
Daniel Bouzon Nagem Assad ◽  
Thaís Spiegel
Keyword(s):  
Health Care ◽  
Discrete Event Simulation ◽  
Health Care Systems ◽  
Waiting Times ◽  
Discrete Event ◽  
Idle Time ◽  
Event Simulation ◽  
Care Systems ◽  
Demand Scenario ◽  
Patient Waiting

Health care systems are affected by sudden increases in demand that can be generated by factors such as natural disasters, terrorist attacks, epidemics, among others. Patient demand can be divided between scheduled and walk-in and, in pandemic scenarios, both of them must be managed in order to avoid higher patient waiting times or number in queue. A discrete event simulation model is proposed in order to evaluate critical indicators like: patient waiting times, number in queue, resource utilization (doctors), using four different patient schedule appointment rules. In this study it was also considered patients impunctuality, walk-in patients and no-show in different scenarios. The best schedule appointment rules for each demand scenario were evaluated. After comparing six performance indicators, four schedule appointment rules in nine different scenarios it was found that the most known scheduling rule had the lowest queue sizes at scenarios with low or no walk-in patients, whereas, as the unpredictability of the scenarios rose, other rules outperformed it. It was also presented to exist an inverse relation between queue size and the physician idle time. Keywords: discrete event simulation, idle-time, queue management, appointment scheduling, health care.

scholarly journals Using a discrete event simulation as an effective method applied in the production of recycled material

2020 ◽  
Vol 15 (4) ◽  
pp. 431-440
Author(s):  
L. Knapcikova ◽  
A. Behunova ◽  
M. Behun
Keyword(s):  
Discrete Event Simulation ◽  
Ultrasonic Method ◽  
Waiting Times ◽  
Discrete Event ◽  
Optimal Dose ◽  
Time Frame ◽  
Simulation Software ◽  
Auxiliary Equipment ◽  
Event Simulation ◽  
Waste Tyres

Technological processes play an essential task in the enterprise's production system. The behaviour and functioning of these systems cannot be predicted with certainty as they belong to a group of probable determinate structures. Generally, if we wanted to know precisely the behaviour of this condition in advance, we would have to be able to describe them mathematically or observe the action of the system on a real object. By applying discrete event simulation software, we realize the development of environmentally friendly products and using the simulation, we gain the certainty that the planned tasks can be implemented in a given time frame, while the simulation of the production process can help to clearly clarify and better understand the processes. To choose the optimal manufacturing ways of cleaning the fabrics component from waste tyres, we used the Witness discrete event simulation software to determine the usability and time occupancy of individual machines in the production of new fabric-based material. We simulated the ultrasonic method of cleaning the fabrics component from waste tyres and the subsequent creation of the test specimen. After the simulation, the obtained data can be used by a selection of type and number of machines and auxiliary equipment, by numbers of tools and fixtures, and by numbers of transport equipment. Obtained results bring the best layout of the workplace, the optimal dose of input materials and resources used in production. We have identified bottlenecks in the machines with long waiting times. The research priority was to reduce bottlenecks and increase the effectiveness of the entire of production line.

scholarly journals Planning Capacity for Mental Health and Addiction Services in the Emergency Department: A Discrete-Event Simulation Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Deyvison T. Baia Medeiros ◽  
Shoshana Hahn-Goldberg ◽  
Dionne M. Aleman ◽  
Erin O’Connor
Keyword(s):  
Mental Health ◽  
Emergency Department ◽  
Discrete Event Simulation ◽  
Capacity Planning ◽  
Discrete Event ◽  
Minimal Impact ◽  
Event Simulation ◽  
Number Of Patients ◽  
Ed Visits ◽  
Resource Capacity

Ontario has shown an increasing number of emergency department (ED) visits, particularly for mental health and addiction (MHA) complaints. Given the current opioid crises Canada is facing and the legalization of recreational cannabis in October 2018, the number of MHA visits to the ED is expected to grow even further. In face of these events, we examine capacity planning alternatives for the ED of an academic hospital in Toronto. We first quantify the volume of ED visits the hospital has received in recent years (from 2012 to 2016) and use forecasting techniques to predict future ED demand for the hospital. We then employ a discrete-event simulation model to analyze the impacts of the following scenarios: (a) increasing overall demand to the ED, (b) increasing or decreasing number of ED visits due to substance abuse, and (c) adjusting resource capacity to address the forecasted demand. Key performance indicators used in this analysis are the overall ED length of stay (LOS) and the total number of patients treated in the Psychiatric Emergency Services Unit (PESU) as a percentage of the total number of MHA visits. Our results showed that if resource capacity is not adjusted, ED LOS will deteriorate considerably given the expected growth in demand; programs that aim to reduce the number of alcohol and/or opioid visits can greatly aid in reducing ED wait times; the legalization of recreational use of cannabis will have minimal impact, and increasing the number of PESU beds can provide great aid in reducing ED pressure.

scholarly journals A DISCRETE EVENT SIMULATION (DES) BASED APPROACH TO MAXIMIZE THE PATIENT THROUGHPUT IN OUTPATIENT CLINIC

2020 ◽  
Vol 1 (1) ◽  
pp. 1-11
Author(s):  
Vidanelage L. Dayarathna ◽  
Hebah Mismesh ◽  
Mohammad Nagahisarchoghaei ◽  
Aziz Alhumoud
Keyword(s):  
Discrete Event Simulation ◽  
Nurse Practitioners ◽  
Outpatient Clinic ◽  
Current System ◽  
Discrete Event ◽  
Discrete Distributions ◽  
Arrival Process ◽  
Future Research ◽  
Event Simulation ◽  
Number Of Patients

The healthcare system is a complex system which exhibits conditions of uncertainty, ambiguity emergence that incurs incoming patient congestion. Discrete event simulation (FlexSim) is considered as a viable decision support tool in analyzing a system for improvement. Using a data-driven discrete event simulation approach, this paper portrays a comprehensive analysis to maximize the number of patients in an on-campus clinic, located at Mississippi State University. The outcome of the analysis of current system exhibits that deploying a few nurse practitioners results in bottlenecks which decreases the systems’ throughput substantially due to the overall longer patients’ waiting time.  Access to the laboratory is characterized through multi-server queuing network, arrival process is followed discrete distributions, and batch sizes and arrival times are stochastic in nature. In an effort to plummet inpatient congestion at the outpatient clinic, by using empirically calibrated simulation model, we will figure out the best balance between the number of the lab technician and incoming patient during working hour. An analysis of optimal solutions is demonstrated, which is followed by recommendation and avenues for future research.

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