Evaluating Patient Flow Based on Waiting Time and Travel Distance for Outpatient Clinic Visits

2011 ◽  
pp. 376-401
Author(s):  
S.Reza Sajjadi ◽  
Jing Shi ◽  
Kambiz Farahmand
Keyword(s):  
Waiting Time ◽  
Queuing Theory ◽  
Patient Flow ◽  
Travel Distance ◽  
Network Systems ◽  
Queuing Models ◽  
Patient Travel ◽  
Single Station ◽  
Patient Waiting Time

Patient flow greatly affects the quality of service delivered to the patients. Among various performance measures identified for patient flow, this chapter focuses on the analytical modeling of two key measures, namely, patient waiting time and travel distance. Waiting time is analyzed by a promising yet simple analytical tool – queuing theory. Three queuing models, including single station, multiple serial stations, and network systems are presented. Moreover, patient travel distance is investigated by an analytical model to evaluate the patient flow. For both measures, the applicability of models is illustrated with numerical examples.

Evaluation of Patient Flow and Waiting Time for the First Physician Visit in an Overcrowded Emergency Department

2020 ◽  
Vol 24 (3) ◽  
Author(s):  
Hadi Yousefi ◽  
Fariba Asadi Noghabi ◽  
Samere Farhani Nejad ◽  
Mohsen Yousefi
Keyword(s):  
Emergency Department ◽  
Waiting Time ◽  
Physician Visit ◽  
Patient Flow ◽  
Demographic Data ◽  
Adverse Outcomes ◽  
Cross Sectional ◽  
Average Waiting Time ◽  
Significant Difference ◽  
Patient Waiting Time

Background: The velocity of providing services in health centers is crucial to reduce mortality and adverse outcomes. Objectives: The present study aimed at determining the waiting time from entering the emergency department (ED) up to physician visiting based on congestion in the triple shift at Shahid Mohammadi Hospital in Bandar Abbas, Iran. Methods: The current cross-sectional, analytical study was conducted in 2019 on 1285 subjects selected from three shifts. The data collection tools included demographic data and standard triage forms, as well as a timetable with a stopwatch. The time between patient arrival and physician visit was recorded. SPSS software version 21 was employed to analyze the data. Results: The highest percentage of patients, 65.1% (n = 837), was non-traumatic, 38.98% (n = 501) referred during the evening shift, and 47.54% (n = 611) were related to the triage level 3. The maximum average waiting time from the beginning to the end of the triage was 4.46, and up to the physician, the visit was 12.8 minutes. Waiting time from entering ED up to physician first visit in terms of gender, refer to ED, and cause of referral statistically divulged a significant difference (P < 0.05). Estimation of the maximum congestion in the department was from 16:00 to 20:00, which showed a significant difference with other day times (P < 0.05). Conclusions: The average waiting time for patients was higher than the global standard. The interventions based on the maximum congestion in ED can be effective in reducing patient waiting time.

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.

Queuing Theory and Customer Satisfaction: A Review of Terminology, Trends, and Applications to Pharmacy Practice

2001 ◽  
Vol 36 (3) ◽  
pp. 275-279 ◽  
Author(s):  
Ronald Anthony Nosek ◽  
James P. Wilson
Keyword(s):  
Customer Satisfaction ◽  
Waiting Time ◽  
Operations Management ◽  
Queuing Theory ◽  
Pharmacy Practice ◽  
High Volume ◽  
Working Environment ◽  
Patient Counseling ◽  
Staffing Levels ◽  
Patient Waiting Time

Queuing theory is the formal study of waiting in line and is an entire discipline in operations management. This article will give the reader a general background into queuing theory, its associated terminology, and it relationship to customer satisfaction. Queuing theory has been used in the past to assess such things as staff schedules, working environment, productivity, customer waiting time, and customer waiting environment. In pharmacy, queuing theory can be used to assess a multitude of factors such as prescription fill-time, patient waiting time, patient counseling-time, and staffing levels. The application of queuing theory may be of particular benefit in pharmacies with high-volume outpatient workloads and/or those that provide multiple points of service. By better understanding queuing theory, service managers can make decisions that increase the satisfaction of all relevant groups – customers, employees, and management.

scholarly journals Adaptive staff scheduling at Outpatient Department of Ntaja Health Center in Malawi - A queuing theory application

2021 ◽  
Author(s):  
Kambombo Mtonga ◽  
Antoine Gatera ◽  
Kayalvizhi Jayavel ◽  
Mwawi Nyirenda ◽  
Santhi Kumaran
Keyword(s):  
Health Facility ◽  
Queuing Theory ◽  
Patient Flow ◽  
Waiting Times ◽  
Health Care Service ◽  
Sub Saharan Africa ◽  
Local Health ◽  
Staff Scheduling ◽  
Staffing Ratios

Accurate staff scheduling is crucial in overcoming the problem of mismatch between staffing ratios and demand for health services which can impede smooth patient flow. Patient flow is an important process towards provision of improved quality of service and also improved utilization of hospital resources. However, extensive waiting times remains a key source of dissatisfaction with the quality of health care service among patients. With rarely scheduled hospital visits, the in-balance between hospital staffing and health service demand remains a constant challenge in Sub-Saharan Africa. Accurate workload predictions help anticipate financial needs and also aids in strategic planning for the health facility. Using a local health facility for a case study, we investigate problems faced by hospital management in staff scheduling. We apply queuing theory techniques to assess and evaluate the relationship between staffing ratios and waiting times at the facility. Specifically, using patient flow data for a rural clinic in Malawi, we model queue parameters and also approximate recommended staffing ratios to achieve steady state leading to reduced waiting times and consequently, improved service delivery at the clinic.

Study of Patient Waiting Time in the Emergence Department: An Example of a Medical Center in New Taipei County, Taiwan

2014 ◽  
Vol 519-520 ◽  
pp. 1581-1584
Author(s):  
Chen Shie Ho ◽  
Min Li Yeh ◽  
Yu Sheng Liao
Keyword(s):  
Emergency Department ◽  
Waiting Time ◽  
Queuing Theory ◽  
Flow Model ◽  
Medical Center ◽  
Waiting Times ◽  
Literature Survey ◽  
Theory Analysis ◽  
Patient Waiting Time ◽  
Patient Waiting

Patients who receive care in an emergency department (ED) are usually unattended while waiting in queues. This study attempted to determine whether the application of queuing theory analysis might shorten the waiting times of patients admitted to emergency wards. After the literature survey phase, the flow model to evaluate the patient waiting time in the emergence department corresponding to the target hospital is presented, then the waiting time under some circumstance are simulated. By allocating the human and space resource dynamically, the waiting time can be reduced then patient satisfaction is improved.

scholarly journals The Impact of Longer Consultation Time: A Simulation-Based Approach

2020 ◽  
Vol 11 (05) ◽  
pp. 857-864
Author(s):  
Abdulrahman M. Jabour
Keyword(s):  
Waiting Time ◽  
Quality Care ◽  
Working Hours ◽  
Discrete Events ◽  
Simulation Based ◽  
Patient Waiting Time ◽  
Free Environment ◽  
The Impact ◽  
Consultation Time ◽  
Patient Waiting

Abstract Background Maintaining a sufficient consultation length in primary health care (PHC) is a fundamental part of providing quality care that results in patient safety and satisfaction. Many facilities have limited capacity and increasing consultation time could result in a longer waiting time for patients and longer working hours for physicians. The use of simulation can be practical for quantifying the impact of workflow scenarios and guide the decision-making. Objective To examine the impact of increasing consultation time on patient waiting time and physician working hours. Methods Using discrete events simulation, we modeled the existing workflow and tested five different scenarios with a longer consultation time. In each scenario, we examined the impact of consultation time on patient waiting time, physician hours, and rate of staff utilization. Results At baseline scenarios (5-minute consultation time), the average waiting time was 9.87 minutes and gradually increased to 89.93 minutes in scenario five (10 minutes consultation time). However, the impact of increasing consultation time on patients waiting time did not impact all patients evenly where patients who arrive later tend to wait longer. Scenarios with a longer consultation time were more sensitive to the patients' order of arrival than those with a shorter consultation time. Conclusion By using simulation, we assessed the impact of increasing the consultation time in a risk-free environment. The increase in patients waiting time was somewhat gradual, and patients who arrive later in the day are more likely to wait longer than those who arrive earlier in the day. Increasing consultation time was more sensitive to the patients' order of arrival than those with a shorter consultation time.

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