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.

Incorporating Discrete Event Simulation Into Quality Improvement Efforts in Health Care Systems

2013 ◽  
Vol 30 (1) ◽  
pp. 31-35 ◽  
Author(s):  
Matthew Harris Rutberg ◽  
Sharon Wenczel ◽  
John Devaney ◽  
Eric Jonathan Goldlust ◽  
Theodore Eugene Day
Keyword(s):  
Health Care ◽  
Quality Improvement ◽  
Discrete Event Simulation ◽  
Health Care Systems ◽  
Discrete Event ◽  
Event Simulation ◽  
Care Systems

Using lean techniques and discrete-event simulation for performance improvement in an outpatient clinic

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kudret Demirli ◽  
Abdulqader Al Kaf ◽  
Mecit Can Emre Simsekler ◽  
Raja Jayaraman ◽  
Mumtaz Jamshed Khan ◽  
...  
Keyword(s):  
Health Care ◽  
Discrete Event Simulation ◽  
Waiting Time ◽  
Outpatient Clinic ◽  
Waiting Times ◽  
Discrete Event ◽  
Content Type ◽  
Event Simulation ◽  
Patient Waiting Time ◽  
Patient Waiting

Purpose Increased demand and the pressure to reduce health-care costs have led to longer waiting time for patients to make appointments and during the day of hospital visits. The purpose of this study is to identify opportunities to reduce waiting time using lean techniques and discrete-event simulation (DES). Design/methodology/approach A five-step procedure is proposed to facilitate the effective utilization of lean and DES to improve the performance of the Otolaryngology Head and Neck Surgery Outpatient Clinic at Cleveland Clinic Abu Dhabi. While lean techniques were applied to reduce the potential sources of waste by aligning processes, a DES model was developed to validate the proposed solutions and plan patient arrivals under dynamic conditions and different scenarios. Findings Aligning processes resulted in an efficient patient flow reducing both waiting times. DES played a complementary role in verifying lean solutions under dynamic conditions, helping to plan the patient arrivals and striking a balance between the waiting times. The proposed solutions offered flexibility to improve the clinic capacity from the current 176 patients up to 479 (without violating the 30 min waiting time policy) or to reduce the patient waiting time during the visit from the current 33 min to 4.5 min (without violating the capacity goal of 333 patients). Research limitations/implications Proposing and validating lean solutions require reliable data to be collected from the clinic and such a process could be laborious as data collection require patient and resource tracing without interfering with the regular functions of the clinic. Practical implications The work enables health-care managers to conveniently conduct a trade-off analysis and choose a suitable inter-arrival time – for every physician – that would satisfy their objectives between resource utilization (clinic capacity) and average patient waiting time. Social implications Successful implementation of lean requires a supportive and cooperative culture from all stakeholders involved. Originality/value This study presents an original and detailed application of lean techniques with DES to reduce patient waiting times. The adopted approach in this study could be generalized to other health-care settings with similar objectives.

Improving patient waiting time of centralized front office service in a regional hub hospital using the discrete event simulation model

2020 ◽  
Vol 28 (5) ◽  
pp. 487-494
Author(s):  
D. Cocchi ◽  
E. Ciagli ◽  
A. Ancora ◽  
P. Tortoli ◽  
C. Carpini ◽  
...  
Keyword(s):  
Simulation Model ◽  
Discrete Event Simulation ◽  
Waiting Time ◽  
Waiting Times ◽  
Discrete Event ◽  
University Hospital ◽  
Event Simulation ◽  
Discrete Event Simulation Model ◽  
Front Office ◽  
Patient Waiting

BACKGROUND: Today, hospital rankings are based not only on basic clinical indicators, but even on quality service indicators such as patient waiting times. Improving these indicators is a very important issue for hospital management, so finding a solution to achieve it in a simple and effective way is one of the greatest goals. OBJECTIVES: The aim of this article is to evaluate the use of a discrete event simulation model to improve healthcare processes and reduce waiting time of patients and hospital costs. METHODS: The case study proposed in this paper is the reorganization of non-clinical front office operation for the patients (i.e. booking of exams, delivering medical reports, etc.) of the Careggi University Hospital of Florence, to optimize the utilization of the human resources and to improve performances of the process. RESULTS: The development and validation of the model was made according to an analysis of real processes and data, pre and post implementation of model outcomes. The new organization shows a decrease of waiting times from an average value of 10 minutes and 37 seconds to 5 minutes and 57 seconds (-44%). CONCLUSIONS: This paper shows that discrete event simulation could be a precise, cost-limited tool to optimize hospital processes and performance.

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.

Discrete Event Simulation and Real Time Locating Systems

2012 ◽  
Vol 4 (4) ◽  
pp. 16-28
Author(s):  
T. Eugene Day ◽  
Ajit N. Babu ◽  
Steven M. Kymes ◽  
Nathan Ravi
Keyword(s):  
Health Care ◽  
Real Time ◽  
Discrete Event Simulation ◽  
Medical Center ◽  
Discrete Event ◽  
Healthcare Delivery ◽  
The United States ◽  
Careful Consideration ◽  
Health Administration ◽  
Event Simulation

The Veteran’s Health Administration (VHA) is the largest integrated health care system in the United States, forming the arm of the Department of Veterans Affairs (VA) that delivers medical services. From a troubled past, the VHA today is regarded as a model for healthcare transformation. The VA has evaluated and adopted a variety of cutting-edge approaches to foster greater efficiency and effectiveness in healthcare delivery as part of their systems redesign initiative. This paper discusses the integration of two health care analysis platforms: Discrete Event Simulation (DES), and Real Time Locating systems (RTLS) presenting examples of work done at the St. Louis VA Medical Center. Use of RTLS data for generation and validation of DES models is detailed, with prescriptive discussion of methodologies. The authors recommend the careful consideration of these relatively new approaches which show promise in assisting systems redesign initiatives across the health care spectrum.

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.

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