AN ANALYSES ON PATIENTS’ QUEUING SYSTEM AT MUHAMMAD ABDULLAHI WASE SPECIALIST HOSPITAL, KANO

2021 ◽  
Vol 5 (2) ◽  
pp. 344-350
Author(s):  
Yunusa Ojirobe ◽  
Abubakar Yahaya ◽  
Muhammad Abdulkarim
Keyword(s):  
Waiting Time ◽  
Performance Indicators ◽  
Goodness Of Fit ◽  
Primary Data ◽  
Single Server ◽  
Utilization Factor ◽  
Average Waiting Time ◽  
Number Of Patients ◽  
Multi Server ◽  
System Average

A major cause for concern in hospitals is congestion, which brings about untoward hardship to patients due to long queues and delay in service delivery. This paper seeks to minimize the waiting time of patients by comparing the performance indicators of a single server and multi-server model at the Paediatrics Department of Muhammad Abdullahi Wase Specialist Hospital Kano (MAWSHK). In order to achieve this, primary data was obtained through direct observation which in turn is subjected to the test of goodness of fit to ascertain the distribution that best describes the data. The performance indicators comprising utilization factor, average number of patients in the queue, average number of patients in the system, average waiting time in queue and average waiting time in system for a single server and multi-server model were computed and analyzed respectively. Our findings indicate that the G/G/4 model performs better compared to the G/G/1 model as it minimizes the waiting time of patients

scholarly journals A Priority Queue with Many Customer Types, Correlated Arrivals and Changing Priorities

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1292
Author(s):  
Seokjun Lee ◽  
Sergei Dudin ◽  
Olga Dudina ◽  
Chesoong Kim ◽  
Valentina Klimenok
Keyword(s):  
Markov Chain ◽  
Waiting Time ◽  
Performance Indicators ◽  
Queueing System ◽  
Priority Queue ◽  
First Aid ◽  
Single Server ◽  
Finite Buffer ◽  
Impatient Customers ◽  
Arrival Times

A single-server queueing system with a finite buffer, several types of impatient customers, and non-preemptive priorities is analyzed. The initial priority of a customer can increase during its waiting time in the queue. The behavior of the system is described by a multi-dimensional Markov chain. The generator of this chain, having essential dependencies between the components, is derived and formulas for computation of the most important performance indicators of the system are presented. The dependence of some of these indicators on the capacity of the buffer space is illustrated. The profound effect of the phenomenon of correlation of successive inter-arrival times and variance of the service time is numerically demonstrated. Results can be used for the optimization of dispatching various types of customers in information transmission systems, emergency departments and first aid stations, perishable foods supply chains, etc.

scholarly journals Deskripsi Sistem Antrian pada Klinik Dokter Spesialis Penyakit Dalam

2012 ◽  
Vol 12 (1) ◽  
pp. 72
Author(s):  
Deiby T Salaki
Keyword(s):  
Direct Observation ◽  
Waiting Time ◽  
Arrival Rate ◽  
Queuing System ◽  
Queuing Model ◽  
Average Waiting Time ◽  
Number Of Patients

DESKRIPSI SISTEM ANTRIAN PADA KLINIK DOKTER SPESIALIS PENYAKIT DALAM ABSTRAK Penelitian ini dilakukan untuk mengetahui deskripsi sistem antrian pada klinik dokter internist. Pengumpulan data dilakukan secara langsung pada klinik dokter internist JHA selama 12 hari, selama 2 jam waktu pengamatan tiap harinya pada periode sibuk.. Model antrian yang digunakan adalah model (M/M/1) : (FIFO/~/~), tingkat kedatangan bersebaran poisson, waktu pelayanan bersebaran eksponensial, dengan jumlah pelayanan adalah seorang dokter, disiplin antrian yang digunakan adalah pasien yang pertama datang yang pertama dilayani, jumlah pelayanan dalam sistem dan ukuran populasi pada sumber masukan adalah tak berhingga.  Sistem antrian pada klinik ini memiliki kecepatan kedatangan pelayanan anamnesa rata-rata  menit 1 orang pasien datang, kecepatan kedatangan pelayanan pemeriksaan fisik rata-rata  menit 1 orang pasien datang, rata-rata waktu pelayanan anamnesa untuk  seorang pasien  menit, rata-rata waktu pelayanan pemeriksaan fisik untuk  seorang pasien  menit, peluang kesibukan  pelayanan anamnesa sebesar , peluang kesibukan  pelayanan pemeriksaan fisik sebesar , dan peluang pelayanan anamnesa menganggur sebesar , peluang pelayanan pemeriksaan fisik menganggur sebesar . Rata-rata banyaknya pengantri untuk anamnesa adalah  pasien sedangkan untuk pemeriksaan fisik  pasien, rata-rata banyaknya pengantri dalam sistem adalah  pasien, waktu rata-rata seorang pasien dalam klinik adalah  menit, waktu rata-rata seseorang pasien untuk antri adalah  menit. Kata kunci: Sistem Antrian, Klinik Penyakit Dalam  DESCRIPTION OF QUEUING SYSTEM AT THE INTERNIST CLINIC ABSTRACT This research determines the description of queuing system at the internist Clinic. Data collected by direct observation during 12 days and in 2 hours. Queuing model that used is model of (M/M/1): (FIFO /~/~). Based on the research, the clinic has 3.256 minutes per patient in average arrival rate for anamnesys, the average arrival rate for diaagnosys is 3.255 minutes per patient, average service speed for anamnesys is 2.675 minutes per patient, average service speed for diagnosys is 12.635 minutes, the probability of busy periods for anamnesys is 0.864, the probability of busy periods for diagnosys is 0.832 and probability of all free services or no patient in the anamnesys equal to 0.136, probability of all free services or no patient in the anamnesys equal to 0.168. The average number of patients in anamnesys queue is 5 patients, the average number of patients in diagnosys queue is 4 patients, the average number of patients in the system is 10 patients, the average waiting time in the system is 47.078 minutes and the average queuing time is 31.660 minutes. Keywords: Queuing system, internist clinic

Performance measures of parallel tandem open queueing network

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Banu Priya ◽  
Rajendran P.
Keyword(s):  
Performance Analysis ◽  
Waiting Time ◽  
Queueing Network ◽  
Performance Measure ◽  
Tandem Queue ◽  
Content Type ◽  
Average Waiting Time ◽  
Open Queueing Network ◽  
And Performance ◽  
System Average

Purpose The authors consider parallel four-state tandem open queueing network. The queue capacity is infinite. Passenger arrival rate is Poisson distribution and service rate is exponential distribution. The queue is constructed in the form of tandem queue, and each and every queue of tandem queue is single server (M/M/1) queue. In tandem queue, passengers will leave the system once they receive service from both the states. The purpose of this paper is to provide performance analysis for four-state tandem open queue network, and a governing equation is formulated with the help of transition diagram. Using Burke theorem, the authors formulated equation for average number of passenger in the system, average waiting time of passenger in the system, average number of passenger in the queue and average waiting time of passenger in the queue. Design/methodology/approach This paper used Burke’s theorem. Findings In this paper, performance analysis is done for parallel four-state tandem open queueing network and performance measure solved using Burkes theorem formula. K. Sreekanth et al. has done performance analysis for single tandem queue with three states. In this paper, the authors have done performance analysis for two tandem queues parallel with four states. This four-state tandem open queueing network is suitable for real world applications. This paper can extend for more number of service states and multi-server states according to the application, and in such case, the authors have to prove and explain with numerical examples. This analysis is more useful for the applications such as airports, railway stations, bus-stands and banks. Originality/value In this paper, parallel four-state tandem open queueing network and performance measure has been solved using Burke’s theorem formula.

scholarly journals A Paradox of the Average Waiting Time for the Case of a Single Bottleneck on the Commuters’ Route

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hakan Özaktaş ◽  
Nureddin Kırkavak ◽  
Ayşe Nilay Alpay
Keyword(s):  
Waiting Time ◽  
Performance Indicators ◽  
Random Variable ◽  
Interarrival Time ◽  
Deterministic Case ◽  
Exponential Distributions ◽  
Average Waiting Time ◽  
Statistical Verification ◽  
Basic Performance ◽  
Interarrival Times

Average waiting time is considered as one of the basic performance indicators for a bottleneck zone on a route for commuter traffic. It turns out that the average waiting time in a queue remains paradoxically unchanged regardless of how fast the queue dissolves for a single bottleneck problem. In this study, the paradox is verified theoretically for the deterministic case with constant arrival and departure rates. Consistent results with the deterministic case have also been obtained by simulation runs for which vehicle interarrival time is a random variable. Results are tabulated for interarrival times which have uniform, triangular, normal, and exponential distributions along with a statistical verification of the average waiting time paradox.

scholarly journals EP.WE.338Ambulatory Ultrasound Scans Reduce Inpatient Admissions During COVID-19: A Need for the Expansion of Ambulatory Services

2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
L Cornett ◽  
S Davidson ◽  
K McElvanna
Keyword(s):  
Emergency Department ◽  
Waiting Time ◽  
Retrospective Review ◽  
First Line ◽  
Average Waiting Time ◽  
Number Of Patients ◽  
Line Imaging ◽  
Ambulatory Services ◽  
The Impact

Abstract Aim With the increased need to manage patients out of hospital during COVID-19, it was anticipated that need for ambulatory imaging would increase. This study aimed to assess the demand for ambulatory ultrasounds (US) during the COVID-19 pandemic and the impact on inpatient admissions. Methods A retrospective review of patients presenting to the Emergency Department (ED) between 12th July – 23rd August 2020 who required an US as first line imaging. Electronic Care Records were used to collect data regarding type of US i.e., inpatient, or ambulatory, time taken for ambulatory US and outcome after imaging. The same period in 2019 was assessed for comparison. Results In 2020, 100 patients required an US compared to 88 in 2019. 37% (37/100) of which were discharged for an ambulatory US, compared to 14.8% (13/88) in 2019 (p = 0.006). The average waiting time for an ambulatory US in 2019 was 2 days, this increased to 7 days in 2020. Following ambulatory US in 2020 43.2% (16/37) required further outpatient imaging or assessment; similar outcomes were seen in 2019 with 46.2% (6/13). Overall, there was a 150% increase in the use of ambulatory US, with a 26% decrease in admissions in 2020 vs. 2019. Conclusions There was a significant increase in the number of patients discharged from ED to undergo an ambulatory US resulting in reduced inpatient admissions. This increase in demand is reflected by the prolonged waiting time highlighting the requirement for expansion of ambulatory services to meet this clinical need.

EVALUASI WAKTU TUNGGU PELAYANAN RESEP PASIEN RAWAT JALAN DI RUMAH SAKIT ANWAR MEDIKA

2020 ◽  
Vol 2 (1) ◽  
pp. 80
Author(s):  
Erviani Dinda Mahmudah Sari ◽  
Khurin In Wahyuni ◽  
Puspita Raras Anindita
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Primary Data ◽  
Cross Sectional ◽  
Grace Period ◽  
Average Waiting Time ◽  
Outpatient Prescription ◽  
Time Required ◽  
Service Standards ◽  
Prescription Services

<em>The waiting time for finished drug service is the grace period from the time the patient submits the prescription until receiving the finished drug with a minimum standard set by the Ministry of Health is ≤ 30 minutes, while the waiting time for concoction drug service is the grace period from the time the patient submits the prescription until receiving the concoction drug that is ≤ 60 minutes. This study aims to determine the average waiting time for outpatient prescription services at Anwar Medika Hospital. This research was conducted with a cross sectional descriptive method. Primary data was collected through direct observation and recording of prescription service waiting times in the waiting time recording form on February 10, 2020 to February 11, 2020. Data taken were 384 outpatient prescriptions consisting of 346 non-concoctions and 38 prescription concoctions. In this study, 384 recipes were used as the research sample consisting of 346 for non-concoction recipes and 38 for recipe concoctions. The average time needed to complete a non-concoction recipe is 18.7 minutes and the time required to complete a recipe concoction is 30.9 minutes. Based on the results of research that has been done, it is found that the average waiting time for prescription services at Anwar Medika Hospital has fulfilled the requirements according to the minimum service standards at the hospital.</em>

scholarly journals ANALISIS SISTEM ANTREAN PADA LOKET PENDAFTARAN POLIKLINIK SPESIALIS DI RS BALIMED

2021 ◽  
Vol 10 (2) ◽  
pp. 70
Author(s):  
KADEK DITA SUGIARI ◽  
I WAYAN SUMARJAYA ◽  
KETUT JAYANEGARA
Keyword(s):  
Steady State ◽  
Waiting Time ◽  
Queuing Theory ◽  
Queuing System ◽  
State Condition ◽  
Steady State Condition ◽  
Average Waiting Time ◽  
Number Of Patients ◽  
Measures Of Performance

Hospital is one of the service facilities that is not free from queue problem. One example of this hospital is Balimed Hospital. At certain times, especially in the morning, there is a lineup of patients at the Balimed Hospital’s Specialist Polyclinic. In order to maximize service, it is necessary to analyze the queuing system by applying the queuing theory. This study focuses on queues at the Balimed Hospital’s Specialist Polyclinic in Internal Disease. After conducting the research, it was found that the model used at the Specialist Polyclinic in Internal Disease is . With this model, the queuing system at Balimed Hospital's Specialist Polyclinic in Internal Disease is in steady state condition because ???? < 1. The measures of performance for queuing system at Balimed Hospital’s Specialist Polyclinic in Internal Disease is the average number of patients in queue  is 0,1 patient or it can be said that there is almost no patient in queue because the value of  is close to 0, the average number of patients in system  is 1 patient, the average waiting time for patients in queue  is 1 minute, and the average time spent by patients start from queuing until being served  is 2,5 minutes. The queuing system has been effective, it can be seen from the short waiting time for patients.

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.

THE IMPORTANCE OF PSYCHOLOGICAL CLIMATE’S EFFECTS TOWARD EMPLOYEE PERFORMANCE

2021 ◽  
Vol 15 (2) ◽  
pp. 143-158
Author(s):  
Andriyastuti Suratman
Keyword(s):  
Job Satisfaction ◽  
Job Stress ◽  
Goodness Of Fit ◽  
Employee Performance ◽  
Sem Analysis ◽  
Psychological Climate ◽  
Primary Data ◽  
Validity And Reliability ◽  
Relationship Model ◽  
Minimum Requirements

The purpose of this study is to determine a relationship model between Psychological Climate (PC) toward performance within job stress and job satisfaction as a mediator. Primary data were obtained by distributing online questionnaires. The population in this research is the teachers of educational institutions in Indonesia. Obtained 161 people with minimum requirements have worked for one year at the institution (purposive sampling). This research uses validity and reliability test, goodness-of-fit model assumption, and hypothesis testing with SEM analysis of Amos 18.0. Based on data analyzed, four of eight hypotheses proposed are have significant influence; 1) PC on job satisfaction, 2) stress on performance, 3) job satisfaction on performance, and 4) there is a role for job satisfaction in mediating the influence of PC on performance. In addition, there are insignificant effects; 1) PC on stress, 2) PC on performance, 3) job stress on job satisfaction, and 4) job stress have no a significant effect as a mediator between PC on performance.

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