Discrete-event simulation and design of experiments to study ambulatory patient waiting time in an emergency department

2019 ◽  
Vol 70 (12) ◽  
pp. 2019-2038 ◽  
Author(s):  
Chantal Baril ◽  
Viviane Gascon ◽  
Dominic Vadeboncoeur
Keyword(s):  
Emergency Department ◽  
Design Of Experiments ◽  
Discrete Event Simulation ◽  
Waiting Time ◽  
Discrete Event ◽  
Ambulatory Patient ◽  
Event Simulation ◽  
Patient Waiting Time ◽  
Patient Waiting

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.

scholarly journals Lean healthcare integrated with discrete event simulation and design of experiments: an emergency department expansion

2019 ◽  
Author(s):  
Gustavo Teodoro Gabriel ◽  
Afonso Teberga Campos ◽  
Aline de Lima Magacho ◽  
Lucas Cavallieri Segismondi ◽  
Flávio Fraga Vilela ◽  
...  
Keyword(s):  
Emergency Department ◽  
Confidence Interval ◽  
Design Of Experiments ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Optimal Number ◽  
Management Tools ◽  
Lean Principles ◽  
Event Simulation ◽  
Lean Healthcare

Background. Discrete Event Simulation (DES) and Lean Healthcare are management tools that are efficient and assist in the quality and efficiency of health services. In this sense, the purpose of the study is to use lean principles jointly with DES to plan the expansion of a Canadian emergency department and to the demand that comes from small closed care centers. Methods. For this, we used simulation and modeling method. We simulated the emergency department in FlexSim Healthcare® software and, with the Design of Experiments (DoE), we defined the optimal number of locations and resources for each shift. Results. The results show that the ED cannot meet expected demand in the current state. Only 17.2% of the patients were completed treated, and the Length of Stay (LOS), on average, was 2213.7, with a confidence interval of (2131.8 - 2295.6) minutes. However, after changing decision variables, the number of treated patients increased to 95.7% (approximately 600%). Average LOS decreased to 461.2, with a confidence interval of (453.7 - 468.7) minutes, about 79.0%. In addition, the study shows that emergency department staff are balanced, according to Lean principles.

scholarly journals Lean healthcare integrated with discrete event simulation and design of experiments: an emergency department expansion

2019 ◽  
Author(s):  
Gustavo Teodoro Gabriel ◽  
Afonso Teberga Campos ◽  
Aline de Lima Magacho ◽  
Lucas Cavallieri Segismondi ◽  
Flávio Fraga Vilela ◽  
...  
Keyword(s):  
Emergency Department ◽  
Confidence Interval ◽  
Design Of Experiments ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Optimal Number ◽  
Management Tools ◽  
Lean Principles ◽  
Event Simulation ◽  
Lean Healthcare

Background. Discrete Event Simulation (DES) and Lean Healthcare are management tools that are efficient and assist in the quality and efficiency of health services. In this sense, the purpose of the study is to use lean principles jointly with DES to plan the expansion of a Canadian emergency department and to the demand that comes from small closed care centers. Methods. For this, we used simulation and modeling method. We simulated the emergency department in FlexSim Healthcare® software and, with the Design of Experiments (DoE), we defined the optimal number of locations and resources for each shift. Results. The results show that the ED cannot meet expected demand in the current state. Only 17.2% of the patients were completed treated, and the Length of Stay (LOS), on average, was 2213.7, with a confidence interval of (2131.8 - 2295.6) minutes. However, after changing decision variables, the number of treated patients increased to 95.7% (approximately 600%). Average LOS decreased to 461.2, with a confidence interval of (453.7 - 468.7) minutes, about 79.0%. In addition, the study shows that emergency department staff are balanced, according to Lean principles.

scholarly journals Lean thinking by integrating with discrete event simulation and design of experiments: an emergency department expansion

2020 ◽  
Vol 6 ◽  
pp. e284
Author(s):  
Gustavo Teodoro Gabriel ◽  
Afonso Teberga Campos ◽  
Aline de Lima Magacho ◽  
Lucas Cavallieri Segismondi ◽  
Flávio Fraga Vilela ◽  
...  
Keyword(s):  
Emergency Department ◽  
Confidence Interval ◽  
Design Of Experiments ◽  
Discrete Event Simulation ◽  
Health Care Quality ◽  
Discrete Event ◽  
Optimal Number ◽  
Care Quality ◽  
Lean Thinking ◽  
Event Simulation

Background Many management tools, such as Discrete Event Simulation (DES) and Lean Healthcare, are efficient to support and assist health care quality. In this sense, the study aims at using Lean Thinking (LT) principles combined with DES to plan a Canadian emergency department (ED) expansion and at meeting the demand that comes from small care centers closed. The project‘s purpose is reducing the patients’ Length of Stay (LOS) in the ED. Additionally, they must be assisted as soon as possible after the triage process. Furthermore, the study aims at determining the ideal number of beds in the Short Stay Unit (SSU). The patients must not wait more than 180 min to be transferred. Methods For this purpose, the hospital decision-makers have suggested planning the expansion, and it was carried out by the simulation and modeling method. The emergency department was simulated by the software FlexSim Healthcare®, and, with the Design of Experiments (DoE), the optimal number of beds, seats, and resources for each shift was determined. Data collection and modeling were executed based on historical data (patients’ arrival) and from some databases that are in use by the hospital, from April 1st, 2017 to March 31st, 2018. The experiments were carried out by running 30 replicates for each scenario. Results The results show that the emergency department cannot meet expected demand in the initial planning scenario. Only 17.2% of the patients were completed treated, and LOS was 2213.7 (average), with a confidence interval of (2131.8–2295.6) min. However, after changing decision variables and applying LT techniques, the treated patients’ number increased to 95.7% (approximately 600%). Average LOS decreased to 461.2, with a confidence interval of (453.7–468.7) min, about 79.0%. The time to be attended after the triage decrease from 404.3 min to 20.8 (19.8–21.8) min, around 95.0%, while the time to be transferred from bed to the SSU decreased by 60.0%. Moreover, the ED reduced human resources downtime, according to Lean Thinking principles.

Reducing Consultation Waiting Time and Overtime in Outpatient Clinic

2011 ◽  
pp. 229-245
Author(s):  
Zhu Zhecheng ◽  
Heng Bee Hoon ◽  
Teow Kiok Liang
Keyword(s):  
Patient Safety ◽  
Design Of Experiments ◽  
Discrete Event Simulation ◽  
Waiting Time ◽  
Outpatient Clinic ◽  
Performance Indicators ◽  
Discrete Event ◽  
Outpatient Clinics ◽  
Event Simulation ◽  
Growing Population

Outpatient clinics face increasing pressure to handle more appointment requests due to aging and growing population. The increase in workload impacts two critical performance indicators: consultation waiting time and clinic overtime. Consultation waiting time is the physical waiting time a patient spends in the waiting area of the clinic, and clinic overtime is the amount of time the clinic is open beyond its normal opening hours. Long consultation waiting time negatively affects patient safety and satisfaction, while long clinic overtime negatively affects the morale of clinic staff. This chapter analyzes the complexity of an outpatient clinic in a Singapore public hospital, and factors causing long consultation waiting time and clinic overtime. Discrete event simulation and design of experiments are applied to quantify the effects of the factors on consultation waiting time/clinic overtime. Implementation results show significant improvement once those factors are well addressed.

Sign in / Sign up

Export Citation Format

Share Document