scholarly journals Student Flow Scheduling System for Student Affairs By Using Queuing Theory

2020 ◽  
pp. 583-591
Author(s):  
Soe Moe Aye ◽  
Aye Aye Thant ◽  
Soe Soe Nwe
Keyword(s):  
Computer Simulation ◽  
Student Affairs ◽  
Student Satisfaction ◽  
Waiting Time ◽  
Queuing Theory ◽  
Waiting Times ◽  
Scheduling System ◽  
Queuing Analysis ◽  
Actual Operation ◽  
Student Flow

This Student registration at University involved students being registered in Student Affairs Department and make a deposit in Finance Department within the University, where they would present a form which had previously been filled in by the student. Students often wait for minutes, hours, half day or days to receive registration service for which they were waiting. Delays in the registration may result in difficulties of scheduling at speciality units and decrease in student satisfaction. This system examines the wide-spread problem of extended waiting times for registration. This system implements as student flow scheduling system and can help staff of student affairs department to reduce student congestion in department. This system uses Queuing analysis and Computer Simulation in Operation Research (OR) field. OR is a scientific approach to analyse problem and reduce waiting time. Simulation is the use of a system model that has the mapped characteristics of existence in order to produce the essence of actual operation. This system presents stand-alone application to help student registration using queuing analysis and computer simulation whose are finding appropriate waiting time for student affairs department.

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.

Enhancing outpatient appointment scheduling system performance when patient no-show percent and lateness rates are high

2018 ◽  
Vol 31 (4) ◽  
pp. 309-326 ◽  
Author(s):  
Mahmoud Barghash ◽  
Hanan Saleet
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Multiple Objectives ◽  
Outpatient Clinics ◽  
Arrival Process ◽  
Content Type ◽  
Scheduling System ◽  
Modeling Procedure ◽  
Basic Interval ◽  
Patient Arrival

Purpose High lateness and no-show percentages pose great challenges on the patient scheduling process. Usually this is addressed by optimizing the time between patients in the scheduling process and the percent of extra patients scheduled to account for absent patients. However, since the patient no-show and lateness is highly stochastic we might end up with many patients showing up on time which leads to crowded clinics and high waiting times. The clinic might end up as well with low utilization of the doctor time. The purpose of this paper is to study the effect of scheduled overload percentages and the patient interval on the waiting time, overtime, and the utilization. Design/methodology/approach Actual data collection and statistical modeling are used to model the distribution for common dentist procedures. Simulation and validation are used to model the treatment process. Then algorithm development is used to model and generate the patient arrival process. The simulation is run for various values of basic interval scheduled time between arrivals for the patients. Further, 3D graphical illustration for the objectives is prepared for the analysis. Findings This work initially reports on the statistical distribution for the common procedures in dentist clinics. This can be used for developing a scheduling system and for validating the scheduling algorithms developed. This work also suggest a model for generating patient arrivals in simulation. It was found that the overtime increases excessively when coupling both high basic interval and high overloading percentage. It was also found that: to obtain low overtime we must reduce the basic interval. Waiting time increases when reducing the basic scheduled appointment interval and increase the scheduled overload percentage. Also doctors’ utilization is increased when the basic interval is reduced. Research limitations/implications This work was done at a local clinic and this might limit the value of the modeled procedure times. Practical implications This work presents a statistical model for the various procedures and a detailed technique to model the operations of the clinics and the patient arrival time which might assist researches and developers in developing their own model. This work presents a procedure for troubleshooting scheduling problems in outpatient clinics. For example, a clinic suffering from high patient waiting time is directly instructed to slightly increase their basic scheduled interval between patients or slightly reduce the overloading percentage. Social implications This work is targeting an extremely important constituent of the health-care system which is the outpatient clinics. It is also targeting multiple objectives namely waiting times, utilization overtime, which in turn is related to the economics and doctor utilization. Originality/value This work presents a detailed modeling procedure for the outpatient clinics under high lateness and no-show and addresses the modeling procedure for the patient arrivals. This 3D graphical charting for the objectives includes a study of the multiple objectives that are of high concern to outpatient clinic scheduling interested parties in one paper.

scholarly journals COMPARISON OF THE EFFECTIVENESS ONLINE AND OFFLINE REGISTRATION SYSTEM ON PATIENT WAITING TIME IN PRIMARY HEALTH CARE SEMARANG USING QUEUING THEORY

2021 ◽  
Vol 3 (2) ◽  
pp. 181-186
Author(s):  
Evina Widianawati ◽  
Faik Agiwahyuanto ◽  
Trianni Trianni ◽  
Adelia Ayu Sabrina ◽  
Dimas Angga Febian
Keyword(s):  
Health Care ◽  
Patient Satisfaction ◽  
Primary Health Care ◽  
Waiting Time ◽  
Queuing Theory ◽  
Waiting Times ◽  
Time Of Arrival ◽  
Registration System ◽  
Primary Health ◽  
Online Registration

The online registration system at Semarang's Primary Health Care has been implemented in 2018, but online registration users are still small. The purpose of this study was to compare the effectiveness of online and offline registration at waiting times using variables in queuing theory. Research at 3 health centers in the city of Semarang. By observing the time of arrival and time of admission to the patient's clinic then the patient is also given a registration service satisfaction questionnaire sheet. The data were processed using queuing theory variables as well as descriptive and inferential statistical analysis. The number of online registrants is only 12% while 88% registered offline. The total number of online registrant patient arrivals per hour is 0.85 patients and offline registrants are 6.38 patients per hour where many patients arrive at the first 105 minutes to open the Puskesmas. The utility of the online registrant registration server is 7%, while 48% offline is classified as low and the patient queue is only about 1 patient so there is no need for an additional registration server to speed up service. There was a difference between online and offline registration waiting times (p = 0.00) where online patients waited 4.91 minutes while offline patients waited 8.84 minutes. There is an effect of waiting time in the system on patient satisfaction (p = 0.00) so that to increase patient satisfaction, it is hoped that patients will register online.

scholarly journals Interhospital referral of colorectal cancer patients: a Dutch population-based study

2021 ◽  
Author(s):  
A. K. Warps ◽  
◽  
M. P. M. de Neree tot Babberich ◽  
E. Dekker ◽  
M. W. J. M. Wouters ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Patients ◽  
Waiting Time ◽  
Secondary Care ◽  
Advanced Disease ◽  
Waiting Times ◽  
Tertiary Care ◽  
Disease Stage ◽  
Colorectal Cancer Patients ◽  
Tertiary Care Hospitals

Abstract Purpose Interhospital referral is a consequence of centralization of complex oncological care but might negatively impact waiting time, a quality indicator in the Netherlands. This study aims to evaluate characteristics and waiting times of patients with primary colorectal cancer who are referred between hospitals. Methods Data were extracted from the Dutch ColoRectal Audit (2015-2019). Waiting time between first tumor-positive biopsy until first treatment was compared between subgroups stratified for referral status, disease stage, and type of hospital. Results In total, 46,561 patients were included. Patients treated for colon or rectal cancer in secondary care hospitals were referred in 12.2% and 14.7%, respectively. In tertiary care hospitals, corresponding referral rates were 43.8% and 66.4%. Referred patients in tertiary care hospitals were younger, but had a more advanced disease stage, and underwent more often multivisceral resection and simultaneous metastasectomy than non-referred patients in secondary care hospitals (p<0.001). Referred patients were more often treated within national quality standards for waiting time compared to non-referred patients (p<0.001). For referred patients, longer waiting times prior to MDT were observed compared to non-referred patients within each hospital type, although most time was spent post-MDT. Conclusion A large proportion of colorectal cancer patients that are treated in tertiary care hospitals are referred from another hospital but mostly treated within standards for waiting time. These patients are younger but often have a more advanced disease. This suggests that these patients are willing to travel more but also reflects successful centralization of complex oncological patients in the Netherlands.

scholarly journals E-Triage Systems for COVID-19 Outbreak: Review and Recommendations

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2845
Author(s):  
Fahd Alhaidari ◽  
Abdullah Almuhaideb ◽  
Shikah Alsunaidi ◽  
Nehad Ibrahim ◽  
Nida Aslam ◽  
...  
Keyword(s):  
Population Growth ◽  
Disease Severity ◽  
Waiting Time ◽  
Transmission Rate ◽  
Waiting Times ◽  
Triage System ◽  
Disease Symptoms ◽  
Comprehensive Review ◽  
Severity Levels ◽  
Growth And Aging

With population growth and aging, the emergence of new diseases and immunodeficiency, the demand for emergency departments (EDs) increases, making overcrowding in these departments a global problem. Due to the disease severity and transmission rate of COVID-19, it is necessary to provide an accurate and automated triage system to classify and isolate the suspected cases. Different triage methods for COVID-19 patients have been proposed as disease symptoms vary by country. Still, several problems with triage systems remain unresolved, most notably overcrowding in EDs, lengthy waiting times and difficulty adjusting static triage systems when the nature and symptoms of a disease changes. In this paper, we conduct a comprehensive review of general ED triage systems as well as COVID-19 triage systems. We identified important parameters that we recommend considering when designing an e-Triage (electronic triage) system for EDs, namely waiting time, simplicity, reliability, validity, scalability, and adaptability. Moreover, the study proposes a scoring-based e-Triage system for COVID-19 along with several recommended solutions to enhance the overall outcome of e-Triage systems during the outbreak. The recommended solutions aim to reduce overcrowding and overheads in EDs by remotely assessing patients’ conditions and identifying their severity levels.

493 Evaluation of Patient Waiting Times for Plain Radiographs Whilst Attending Outpatient Clinics in A Tertiary Orthopaedic Centre

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
Z Hayat ◽  
E Kinene ◽  
S Molloy
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Time Frame ◽  
Outpatient Clinics ◽  
Contributing Factors ◽  
Time Data ◽  
Work Related ◽  
Mean Waiting Time ◽  
Appointment Time ◽  
Related Stress

Abstract Introduction Reduction of waiting times is key to delivering high quality, efficient health care. Delays experienced by patients requiring radiographs in orthopaedic outpatient clinics are well recognised. Method To establish current patient and staff satisfaction, questionnaires were circulated over a two-week period. Waiting time data was retrospectively collected including appointment time, arrival time and the time at which radiographs were taken. Results 84% (n = 16) of radiographers believed patients would be dissatisfied. However, of the 296 patients questioned, 56% (n = 165) were satisfied. Most patients (89%) felt the waiting time should be under 30 minutes. Only 36% were seen in this time frame. There was moderate negative correlation (R=-0.5); higher waiting times led to increased dissatisfaction. Mean waiting time was 00:37 and the maximum 02:48. Key contributing factors included volume of patients, staff shortages (73.7%), equipment shortages (57.9%) and incorrectly filled request forms. Eight (42.1%) had felt unwell from work related stress. Conclusions A concerted effort is needed to improve staff and patient opinion. There is scope for change post COVID. Additional training and exploring ways to avoid overburdening the department would benefit. Numerous patients were open to different days or alternative sites. Funding requirements make updating equipment, expanding the department and recruiting more staff challenging.

Some general results for many server queues

1973 ◽  
Vol 5 (01) ◽  
pp. 153-169 ◽  
Author(s):  
J. H. A. De Smit
Keyword(s):  
Integral Equations ◽  
Waiting Time ◽  
Queue Length ◽  
Joint Distribution ◽  
Waiting Times ◽  
Virtual Waiting Time ◽  
Server Queue ◽  
Many Server Queues ◽  
The Many ◽  
Busy Cycle

Pollaczek's theory for the many server queue is generalized and extended. Pollaczek (1961) found the distribution of the actual waiting times in the model G/G/s as a solution of a set of integral equations. We give a somewhat more general set of integral equations from which the joint distribution of the actual waiting time and some other random variables may be found. With this joint distribution we can obtain distributions of a number of characteristic quantities, such as the virtual waiting time, the queue length, the number of busy servers, the busy period and the busy cycle. For a wide class of many server queues the formal expressions may lead to explicit results.

HOW TO REPORT AND MONITOR THE PERFORMANCE OF WAITING LIST MANAGEMENT

2002 ◽  
Vol 18 (3) ◽  
pp. 611-618
Author(s):  
Markus Torkki ◽  
Miika Linna ◽  
Seppo Seitsalo ◽  
Pekka Paavolainen
Keyword(s):  
Hallux Valgus ◽  
Waiting Time ◽  
Varicose Vein ◽  
Elective Surgery ◽  
Waiting Times ◽  
Waiting List ◽  
Mean Waiting Time ◽  
Number Of Patients ◽  
Potential Problems ◽  
Queue Discipline

Objectives: Potential problems concerning waiting list management are often monitored using mean waiting times based on empirical samples. However, the appropriateness of mean waiting time as an indicator of access can be questioned if a waiting list is not managed well, e.g., if the queue discipline is violated. This study was performed to find out about the queue discipline in waiting lists for elective surgery to reveal potential discrepancies in waiting list management. Methods: There were 1,774 waiting list patients for hallux valgus or varicose vein surgery or sterilization. The waiting time distributions of patients receiving surgery and of patients still waiting for an operation are presented in column charts. The charts are compared with two model charts. One model chart presents a high queue discipline (first in—first out) and another a poor queue discipline (random) queue. Results: There were significant differences in waiting list management across hospitals and patient categories. Examples of a poor queue discipline were found in queues for hallux valgus and varicose vein operations. Conclusions: A routine waiting list reporting should be used to guarantee the quality of waiting list management and to pinpoint potential problems in access. It is important to monitor not only the number of patients in the waiting list but also the queue discipline and the balance between demand and supply of surgical services. The purpose for this type of reporting is to ensure that the priority setting made at health policy level also works in practise.

scholarly journals Preferences and Perceptions of Patients Attending Emergency Departments with Low Acuity Problems in Hong Kong

2009 ◽  
Vol 16 (3) ◽  
pp. 148-154 ◽  
Author(s):  
CA Graham ◽  
WO Kwok ◽  
YL Tsang ◽  
TH Rainer
Keyword(s):  
Health Care ◽  
Hong Kong ◽  
Health Care Providers ◽  
Waiting Time ◽  
Health Care Professionals ◽  
Waiting Times ◽  
Medical Advice ◽  
Care Providers ◽  
Convenience Sample ◽  
Waiting Area

Objective To explore why patients in Hong Kong seek medical advice from the emergency department (ED) and to identify the methods by which patients would prefer to be updated on the likely waiting time for medical consultation in the ED. Methods The study recruited 249 semi-urgent and non-urgent patients in the ED of Prince of Wales Hospital from 26th September 2005 to 30th September 2005 inclusive. A convenience sample of subjects aged ≥15 years old in triage categories 4 or 5 were verbally consented and interviewed by research nurses using a standardized questionnaire. Results From 1715 potential patients, 249 were recruited ad hoc (mean age 44 years [SD18]; 123 females). About 63% indicated that an acceptable ED waiting time was less than or equal to two hours, and 88% felt that having individual number cards and using a number allocation screen in the ED waiting area would be useful. Perceived reasons for attending the ED rather than other health care providers such as primary health care or the general outpatient clinic (GOPC) included: a desire for more detailed investigations (56%); a perception that more professional medical advice was given in the ED (35%); patients were under the continuing care of the hospital (19%); and patients were referred to the ED by other health care professionals (11%). Notably, 26% of participants had considered attending the GOPC prior to attending the ED. Patients educated to tertiary level expected a shorter waiting time than those educated to lesser degrees (p=0.026, Kruskal-Wallis test). Suggestions were made on how to provide a more pleasant ED environment for the wait for consultations, which included the provision of a television screen with sound in the waiting area (43%), more comfortable chairs (37%) and health care promotion programs (32%). Conclusion Patients chose ED services because they believed they would receive more detailed investigations and more professional medical advice than available alternatives. Clear notification of the likely waiting times and enhancement of comfort before consultation are considered desirable by patients. Enhanced public education about the role of the ED and making alternatives to ED care more accessible may be useful in reducing inappropriate ED attendances in Hong Kong.

Announcing Waiting Time in Emergency Departments

2019 ◽  
pp. 1-6
Author(s):  
Martin Lariviere ◽  
Sarang Deo
Keyword(s):  
Waiting Time ◽  
Emergency Departments ◽  
Waiting Times ◽  
Large Network ◽  
Regional Networks ◽  
Marketing Campaign ◽  
National Healthcare ◽  
Time Estimates ◽  
Patient Waiting Time ◽  
Patient Waiting

First National Healthcare (FNH) runs a large network of hospitals and has worked to systematically reduce waiting times in its emergency departments. One of FNH's regional networks has run a successful marketing campaign promoting its low ED waiting times that other regions want to emulate. The corporate quality manager must now determine whether to allow these campaigns to be rolled out and, if so, which waiting time estimates to use. Are the numbers currently being reported accurate? Is there a more accurate way of estimating patient waiting time that can be easily understood by consumers?

Sign in / Sign up

Export Citation Format

Share Document