A Discrete Event Simulation Model to Evaluate Operational Performance of a Colonoscopy Suite

2009 ◽  
Vol 30 (3) ◽  
pp. 380-387 ◽  
Author(s):  
Bjorn Berg ◽  
Brian Denton ◽  
Heidi Nelson ◽  
Hari Balasubramanian ◽  
Ahmed Rahman ◽  
...  
Keyword(s):  
Waiting Time ◽  
Discrete Event ◽  
Turnaround Time ◽  
Event Simulation ◽  
Discrete Event Simulation Model ◽  
Number Of Patients ◽  
Patient Waiting Time ◽  
Patient Arrival ◽  
Key Resources ◽  
Procedure Room

Background and Aims. Colorectal cancer, a leading cause of cancer death, is preventable with colonoscopic screening. Colonoscopy cost is high, and optimizing resource utilization for colonoscopy is important. This study’s aim is to evaluate resource allocation for optimal use of facilities for colonoscopy screening. Method. The authors used data from a computerized colonoscopy database to develop a discrete event simulation model of a colonoscopy suite. Operational configurations were compared by varying the number of endoscopists, procedure rooms, the patient arrival times, and procedure room turnaround time. Performance measures included the number of patients served during the clinic day and utilization of key resources. Further analysis included considering patient waiting time tradeoffs as well as the sensitivity of the system to procedure room turnaround time. Results. The maximum number of patients served is linearly related to the number of procedure rooms in the colonoscopy suite, with a fixed room to endoscopist ratio. Utilization of intake and recovery resources becomes more efficient as the number of procedure rooms increases, indicating the potential benefits of large colonoscopy suites. Procedure room turnaround time has a significant influence on patient throughput, procedure room utilization, and endoscopist utilization for varying ratios between 1:1 and 2:1 rooms per endoscopist. Finally, changes in the patient arrival schedule can reduce patient waiting time while not requiring a longer clinic day. Conclusions. Suite managers should keep a procedure room to endoscopist ratio between 1:1 and 2:1 while considering the utilization of related key resources as a decision factor as well. The sensitivity of the system to processes such as turnaround time should be evaluated before improvement efforts are made.

Hospital “Self-Prophylaxis” Strategies for Efficient Protection of the Workforce in the Face of Infectious Disease Threats

2007 ◽  
Vol 28 (05) ◽  
pp. 618-621 ◽  
Author(s):  
Wei Xiong ◽  
Eric Hollingsworth ◽  
Jack Muckstadt ◽  
Jaclyn Van Lieu Vorenkamp ◽  
Eliot J. Lazar ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Waiting Time ◽  
Communicable Disease ◽  
Discrete Event ◽  
Management Strategies ◽  
Event Simulation ◽  
Discrete Event Simulation Model ◽  
Hospital Preparedness ◽  
The Face ◽  
Efficient Protection

Hospital preparedness for nosocomial or community-wide outbreaks of communicable disease includes the capability for rapid, self-reliant administration of prophylaxis to its workforce, with the goal of minimal disruption of patient care, here called hospital “self-prophylaxis.” We created a new discrete-event simulation model of a hypothetical hospital wing to compare the operational charateristics of standard single-line, “first-come, first-served” dispensing clinics with those of 2 staff management strategies that can dramatically reduce staff waiting time while centralizing dispensing around existing pharmacy-distribution points.

scholarly journals Simulation and Improvement of Patients’ Workflow in Heart Clinics during COVID-19 Pandemic Using Timed Coloured Petri Nets

2020 ◽  
Vol 17 (22) ◽  
pp. 8577
Author(s):  
Masoomeh Zeinalnezhad ◽  
Abdoulmohammad Gholamzadeh Chofreh ◽  
Feybi Ariani Goni ◽  
Jiří Jaromír Klemeš ◽  
Emelia Sari
Keyword(s):  
Petri Nets ◽  
Waiting Time ◽  
Patient Flow ◽  
Discrete Event ◽  
Healthcare Providers ◽  
Coloured Petri Nets ◽  
Event Simulation ◽  
Simulation Techniques ◽  
Patient Waiting Time ◽  
Multiple Challenges

The COVID-19 epidemic has spread across the world within months and creates multiple challenges for healthcare providers. Patients with cardiovascular disease represent a vulnerable population when suffering from COVID-19. Most hospitals have been facing difficulties in the treatment of COVID-19 patients, and there is a need to minimise patient flow time so that staff health is less endangered, and more patients can be treated. This article shows how to use simulation techniques to prepare hospitals for a virus outbreak. The initial simulation of the current processes of the heart clinic first identified the bottlenecks. It confirmed that the current workflow is not optimal for COVID-19 patients; therefore, to reduce waiting time, three optimisation scenarios are proposed. In the best situation, the discrete-event simulation of the second scenario led to a 62.3% reduction in patient waiting time. This is one of the few studies that show how hospitals can use workflow modelling using timed coloured Petri nets to manage healthcare systems in practice. This technique would be valuable in these challenging times as the health of staff, and other patients are at risk from the nosocomial transmission.

scholarly journals Performance Improvement of the Yellow Zone in Emergency Department using Discrete Event Simulation Approach

2018 ◽  
Vol 7 (4.33) ◽  
pp. 102 ◽  
Author(s):  
Ireen Munira Ibrahim ◽  
Choong-Yeun Liong ◽  
Sakhinah Abu Bakar ◽  
Ahmad Farid Najmudd
Keyword(s):  
Emergency Department ◽  
Discrete Event Simulation ◽  
Waiting Time ◽  
Performance Indicator ◽  
Discrete Event ◽  
Observation Unit ◽  
The Public ◽  
Event Simulation ◽  
Number Of Patients ◽  
Total Average

Overcrowding is a major concern for the Emergency Department (ED) management at the public hospital under study. Although the number of patients in the Yellow Zone (YZ) of the department represents only 30% of the total visiting patient per day, the Key Performance Indicator (KPI) of the zone’s patients’ LOS (LOS) as well as waiting time are not achievable due to the resources constraints. Therefore, this paper discusses the application of Discrete Event Simulation (DES) approach on modeling the YZ’s daily operations. The model was developed using Arena software to assist the ED management to better understand their system behavior and causes for the high patients’ LOS and waiting time. The simulation outputs show that the bottleneck of the system is waiting for an available resource. A few scenarios were designed based on the discussion made with the ED management for possible improvement. The results show a significant reduction of 25% and 35% in the total average of patients’ LOS for the patients of the observation unit and the intensive unit respectively. Meanwhile, for the total average patients’ waiting time, the results show a reduction of 34% for the observation unit and 29% reduction for the intensive unit.   

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.

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.

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