Identification and Modeling of Factors That Cause Patient Queue

Sylvana Rianti, Ermi Girsang, Sri Wahyuni Nasution

Faculty of Medical, Universitas Prima Indonesia, Indonesia

Keywords: Patient Queue, Outpatient, Waiting Time.

Abstract: Patient queues occur in various hospitals in the world, especially in outpatients with an average waiting time

of more than 60 minutes. Long queues often occur and become a problem because of the many people who

come to wait beyond the normal limit. The purpose of this study was to identify and model the factors causing

the queue of patients from 5,898 patients with a sample of 260 respondents (accidental sampling). Data

analysis used univariate, bivariate with chi-square test, and multivariate with multiple logistic regression at

the 95% confidence level ( = 0.05) were performed. Statistical test results showed that there was an influence

of patient waiting time at registration until the call, length of time the doctor's examination, human resources

to the patient queue, p <0.05. As the most influential variable, human resource (HR) availability has a 9.6

times higher chance of inadequate HR availability. Accuracy of 91% for the design of the patient queue

variable model using SEM method for all test variables was achieved.

1 INTRODUCTION

Queuing is a common thing in daily life. Queuing is

done in many ways one of which is queuing for health

checks in health facilities such as health centers,

clinics or hospitals (Akbar & Hirawan, 2018).

Hospital queues can occur in outpatient services,

inpatient services and emergency services. Outpatient

service is the first service and is the gate of the

hospital, and is the only part of the medical service

that gives a first impression for patients as consumers

(Kakiay, 2014). In outpatient services, both new

patients and old patients will undergo a series of

service processes that can not be separated from the

queue itself, namely when waiting for a call in the

administrative, polyclinic, or while making a

redemption of drugs or payment in the cashier's

pocket (Barnhart & Laporte, 2015).

One aspect of service quality is the fast and

precise aspect. Leaving consumers waiting for no

apparent reason causes negative perceptions in

service quality (Sugiarto, 2014). Timeliness is the

suitability of medical services provided from what is

needed from time to time (Lovelock & Wright, 2012).

If all services provided have not been able to satisfy

patients this will end in a low level of patient

satisfaction, and end in a low level of patient loyalty

(Septiani, Wigati, & Fatmaasari, 2017).

In hospitals, patient queues occur as a result of

high demand for health services which means that

demand for health care services by residents exceeds

the capacity of service facilities. Queues can be fully

represented by the process of patient arrival (in) and

service provision (out) which are the main

components of each queuing system (Obulor & Eke,

2016; Turnip et al,2018).

Patient queues occur in various parts of the world,

the data obtained from several previous researchers.

Mohebbifar et al. (2014) in several clinics and

hospitals in Iran showed that the waiting time for

ophthalmology clinic outpatients with an average of

245 minutes. The orthopedic clinic has a minimum

average waiting time of 77 minutes per patient, the

average waiting time for each patient in a teaching

hospital is around 161 minutes. Musinguzi Research

(2013) at the Mulago Hospital, Uganda that patients

spend more than 5 hours waiting time, most of the

time spent in the registration and pharmacy. There are

also patients who report> 11 hours, at the beginning

of the week (Monday) increasing by about 3-4

minutes for each patient who is waiting in line.

Likewise what happened in China is that one of the

biggest complaints voiced by the Chinese community

is the amount of time needed to queue for outpatient

registration at the hospital (Yu et al., 2013).

Category of distance between waiting time and

check time which is estimated to be satisfactory or

104

Rianti, S. and Nasution, S.

Identiﬁcation and Modeling of Factors That Cause Patient Queue.

DOI: 10.5220/0010289601040111

In Proceedings of the International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical (HIMBEP 2020), pages 104-111

ISBN: 978-989-758-500-5

unsatisfactory for patients, among others, when the

patient comes from registering to the counter,

queuing and waiting for a call to the general poly to

be planted and examined by a doctor, nurse or

midwife for more than 90 minutes ( old category), 30-

60 minutes (medium category) and ≤ 30 minutes (fast

category) (Esti, 2015). Waiting times in Indonesia are

determined by the Ministry of Health through

minimum service standards. Every hospital must

follow this minimum service standard regarding

waiting time. The minimum standard of care in

outpatient services based on the Ministry of Health

No. 129 / Menkes / SK / II / 2008 is less or equal to

60 minutes (Kemenkes RI, 2008).

Public hospitals in major cities in Indonesia such

as Jakarta, patients can queue up at 5 in the morning.

Based on observations made between March and June

2017 in outpatients, patients queue from 9 am to 10

am queues at internal medicine clinics around 110

patients, queues at cardiac clinics around 104 patients

and lung clinics 60 patients (Dachyar, Farizal, & Yafi,

2018). Mayasari's study (2016) in the AMC Metro

RSIA clinic most of the outpatients waited for

queuing for more than 60 minutes to be served by

nurses and doctors so they felt dissatisfied, a small

portion waited less than 60 minutes.

The cause of the accumulation of queues at the

outpatient reception is the human factor, which is

slow service, the use of manual systems and patients

who often do not carry registration requirements.

Machine factor (machine), which is electricity that

often dies due to inadequate power and the use of

queue number machines that are used to speed up

service actually experience problems in operation due

to machine factors (machine error). Factor methods

(methods) namely incomplete Standard Operating

Procedures (SPO) or work procedures and the

absence of media information or communication to

patients (Sari, 2016).

The impact of long queues on hospital services

will cause patients to feel bored. As a result, patients

will go and look for other hospitals. This is

considered a loss for the organization because

patients become disloyal (Fitzsimmons &

Fitzsimmons, 2014). Long waiting times at health

centers can increase disease severity and cause socio-

economic costs. The results of several studies on the

assessment of patient satisfaction show a direct

correlation between patient satisfaction and waiting

time, and show the negative effect of long waiting

times on total patient perception of service quality

(Bahadori et al., 2014; Kusumandari et al, 2018;

Turnip et al, 2018).

The best service for patients is to provide fast

service to patients, so they don't wait too long to get

services. However, the impact of this rapid service

delivery will incur costs for organizations such as

hospitals, because they have to add expensive service

facilities and technology (Dachyar et al., 2018).

Stella Maris Hospital in Medan is a specialized

hospital that provides services for maternal and child

health. Stella Maris Medan RSIA has several

integrated polyclinics that can help serve family

health. By presenting the best doctors, as well as

medical services provided, as a form of commitment

of Stella Maris Medan Women's and Children's

Hospital to reliable health services for all maternal

and child health problems. Based on the performance

indicators achieved by the Stella Maris Hospital in

Medan in 2017, the Bed Occupancy Rate (BOR) is

80.2%, the Average Length of Stay (ALOS) is 2.7

days and the Turn Over Interval (TOI) is 1 day

whereas in 2018, the number of BOR was 72.9%,

ALOS was 2.8 days and TOI was 0.6 days (RSIA

Stella Maris, 2019a). The ideal BOR parameter value

is 60-85%, the ALOS value is between 6-9 days, the

TOI value is in the range of 1-3 days (MOH RI,

2005).

The latest data that researchers obtained from the

Stella Maris Mother and Child Hospital Medan that

the number of recurrent outpatients during the last 8

months (the period January 2019-August 2919) was

37,294 patients. While the total number of outpatients

as many as 47,179 people, namely in January 2019 as

many as 6,074 people, February 2019 as many as

5,271 people, March 2019 as many as 5,828 people,

April 2019 as many as 5,474 people, in May 2019 as

many as 5,783 people, in June 2019 as many as 5,694

people, July 2019 as many as 6,224 people, and in

August 2019 there were 6,831 people. The average

number of outpatients every month is 5,898 (RSIA

Stella Maris, 2019b). In statistical techniques we

know what is called Structural Equation Modeling

(SEM). SEM is a statistical technique that is able to

analyze the pattern of relationships between latent

constructs and indicators, latent constructs with one

another, and direct measurement errors. SEM is a

multivariate dependent statistical family. SEM allows

analysis between several dependent and independent

variables directly (Yamin, 2009).

Data analysis uses the Structural Equation

Modeling (SEM) approach using the AMOS

program. Structural Equation Modeling (SEM)

equation model based on components or variants.

AMOS can help researchers for predictive purposes.

The formal model defines latent variables as linear

aggregates of the indicators. Weight estimates for

Identiﬁcation and Modeling of Factors That Cause Patient Queue

105



creating the component score of latent variables are

obtained based on how the outer model (measurement

model is the relationship between indicators and

constructs) and inner model (structural models that

connect between latent variables) (Ghazali, 2013;

Turnip et al, 2020; Wijaya et al, 2019).

In statistical techniques we know what is called

Structural Equation Modeling (SEM). SEM is a

statistical technique that is able to analyze the pattern

of relationships between latent constructs and

indicators, latent constructs with one another, and

direct measurement errors. SEM is a multivariate

dependent statistical family. SEM allows analysis

between several dependent and independent variables

directly (Yamin, 2009).

Data analysis uses the Structural Equation

Modeling (SEM) approach using the AMOS

program. Structural Equation Modeling (SEM)

equation model based on components or variants.

AMOS can help researchers for predictive purposes.

The formal model defines latent variables as linear

aggregates of the indicators. Weight estimates for

creating the component score of latent variables are

obtained based on how the outer model (measurement

model is the relationship between indicators and

constructs) and inner model (structural models that

connect between latent variables) (Ghazali 2013;

Turnip et al, 2020; Wijaya et al, 2019).

Preliminary survey conducted by researchers

conducted observations on 20 outpatients by asking

them, how long they waited for a queue in the

outpatient room. A total of 12 people said to wait less

than 1 hour (≤60 minutes) while 8 others said to wait

for a queue for more than 1 hour (> 60 minutes). They

said that they were bored and caused more time to be

consumed because they had to queue and did not

know when the time would be called for inspection.

2 METHOD

This type of research is a quantitative analytic study

with a cross sectional study design. This research was

conducted at Stella Maris Hospital in Medan in

November 2019. The study population was all

outpatients at the Polyclinic of the Stella Maris

Hospital in Medan with an average number of 5,898

people per month, and samples were 260 people. The

research sampling technique was accidental

sampling. Univariate data analysis, bivariate using

chi-square test, and multivariate using multiple

logistic regression tests with a confidence level of

95% ( = 0.05).

In this study data analysis using the Structural

Equation Modeling (SEM) approach using the

AMOS program. Structural Equation Modeling

(SEM) equation model based on components or

variants. AMOS can help researchers for predictive

purposes. The underlying reason used by SEM is

First, SEM has the ability to estimate relationships

between variables that are multiple relationships. This

relationship is formed in the structural model (the

relationship between dependent and independent

constructs). Second, it has the ability to describe the

pattern of relationships between latent (Unobserved)

and manifest variables (manifest variables or

indicator variables). In its development, processing

data for SEM analysis has become easy with the help

of several statistical software, such as Lisrel, AMOS,

and Smart PLS. The reliability test in SEM is

calculated using the following formula (Puncopo,

2015).

The variables in this study consisted of queuing

variables as exogenous variables. Human resources

and arrival as endogenous variables. Data analysis in

this study uses descriptive analysis and Structural

Equation Modeling (SEM) analysis. Descriptive

analysis aims to describe the characteristics of the

respondents studied as well as each variable so as to

give a clear picture of the patient queue. Structural

Equation Modeling (SEM) analysis is used to test the

models and relationships developed in this study.

SEM is a statistical technique used to measure

research questions that are regression or dimensional.

The data analysis technique used to discuss the

problems in this study is the Structural Equation

Model (SEM). Structural Equation Model (SEM)

models are statistical techniques that enable the

testing of a series of relatively complex relationships

simultaneously (Ghozali, 2007). Complex

relationships can be established between one or

several dependent variables with one or several

independent variables. There may also be a variable

that has a dual role, that is, as an independent variable

in a relationship, but becomes a dependent variable in

another relationship given the tiered causal

relationship. Conceptual framework or thought flow

in this study can be seen in Figure 1 which shows the

research flowchart model.

HIMBEP 2020 - International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical

106

Figure 1: Model diagram of the patient queue path.

3 RESULTS AND DISCUSSIONS

Characteristics of respondents most of the majority of

respondents aged <43 years were 54.6%. Based on

gender, the majority of respondents were female as

many as 52.7%. Based on education, most of the

respondents had high education as much as 78.5%,

the rest had secondary education as much as 21.5%.

Based on work, the majority of respondents work as

entrepreneurs / traders as much as 37.7%, the rest are

civil servants as much as 13.0%. Based on the reason

for the visit, the majority of respondents to the

hospital for treatment were 50.8%, the rest were for

controls (routine checks) as much as 49.2%.

Based on the results of bivariate analysis, all

independent variables are significantly related to

maternal satisfaction, namely physical evidence (p =

0,000), reliability (p = 0,000), responsiveness (p =

0,000), guarantee (p = 0,000), and empathy (p =

0,000) ), Full Chi-Square statistical test results can be

seen in Table 1.

Table 1: Relationship of Each Independent and Dependent

Variable.

Variables

Keantrian Pasien

Jumlah

-value

Tidak

Antri

f f F

Arrival:

On time

Not on time

121

109

172

155

105

0,027

Waiting time

for

registration:

Suitable

Not Suitable

0,047

Waiting Time

for Doctor

Services:

Suitable

Not Suitable

108

105

150

146

114

149

111

191

0,002

Waiting Time

for

Pharmaceutic

al Services:

Sesuai

Tidak sesuai

0,049

Human

Resources:

Adequate

Inadequate

0,000

Multivariate analysis results with multiple logistic

regression tests (Table 2) show that of the 5 variables

as model candidates, 3 variables are obtained that are

related to the cause of patient queue, namely waiting

time for registration until the call, waiting time for

doctor services and HR availability.

The variable that has the greatest relationship with

output is the HR availability variable having a value

of Exp (B) / OR = 9,618 meaning that patients who

state that human resources (HR) are adequate are

available, and the chances of patient queue being

resolved are 9.6 times higher.

Table 2: Multiple Logistic Regression Test Results

Variabel B Sig. Exp(B)

95% CI for

Exp(B)

Waiting time

Service time

Availability SDM

Constants

0,867

1,081

2,264

-2,024

0,007

0,001

0,000

2,380

2,946

9,618

1,272-4,452

1,584-5,480

4,965-18,630

3.1 Waiting Time Factors

Based on the results of the study showed that there

was a relationship between the time of registration

and call with the cause of the patient queue, p = 0.007

<0.05. Variable length of time of registration until the

summon has a value Exp (B) / OR = 2.380 means that

patients who state the length of time of registration

until the summon was appropriate, the patient

queuing chance was resolved by 2.3 times higher than

that which was not appropriate.

Patients or customers come to a health facility

with random, irregular time and cannot be served

immediately so they have to wait long enough. The

queue process starts when customers who need

service start coming (registration) to get health care

facilities according to their needs. They come from a

Identiﬁcation and Modeling of Factors That Cause Patient Queue

107



population that is referred to as an input source. The

input source of a queuing system consists of a

population of people, goods, paperwork components

that come to the service system.

The results of this study prove that the queue or

waiting time of patients from registration to the call is

the cause of the patient queue. The average time

required by patients in the registration section is 10.57

minutes, with the fastest time (minimum) of 5.08

minutes, and the longest time is 14.59 minutes. It was

also found that the majority of patient waiting times

were appropriate ie below the average time even

though there were still 40% of respondents with

waiting times above the average time. This is due to

the large number of patient visits so they have to

queue and wait for the examination of patients who

have come first. The Hospital must strive for the

waiting time (queuing) of patients in the registration

until the calling is increased again, so that more

patients are waiting in line below the average waiting

time.

3.2 Service Time

Based on the results of the study showed that there is

a relationship between the length of time the doctor's

service and the cause of the patient queue, p = 0.001

<0.05. The length of time the doctor's service variable

has the value Exp (B) / OR = 2,946 means that

patients who state the length of time the doctor's

service is appropriate, the patient queuing chance is

resolved by 2.9 times higher than that which is not

appropriate.

The results of this study prove that the length of

time a patient's health examination by a doctor is one

of the causes of patient queue. If seen from the

waiting time or the average doctor's service time the

patient is 16.58 minutes exceeding the standard

average waiting time which is 15 minutes. The

shortest service time is 8.59 minutes while the longest

service time is 29.54 minutes. As many as 56.2% of

patients get appropriate services in the sense that less

than 15 minutes get doctor's services, while the other

43.8% get services more than 15 minutes.

Doctor's examination in outpatients also depends

on the patient's condition at the time of the

examination, whether the patient has a disease in the

category of mild, moderate or severe. A quick and

thorough examination requires a longer time,

especially for patients who come with moderate and

severe disease, whereas patients with mild illness

conditions, the examination is carried out with a faster

time.

3.3 Availability

Based on the results of the study showed that there is

a relationship between the availability of human

resources (HR) with the cause of the queue of patients

at Stella Maris Hospital in 2019, p = 0,000 <0.05.

Variable availability of human resources has a value

of Exp (B) / OR = 9,618 meaning that patients who

claim the availability of adequate human resources

have a chance of queuing for patients to be 9.6 times

higher than patients who claim the availability of

human resources is inadequate.

The results of this study prove that human

resources were related to the patient queue. The

sufficient number of employees in the registration

section has an impact on reducing the number of

queues in the registration section. Human resources

or officers who work skillfully and skillfully will be

able to make the queue not pile up. Likewise friendly

and communicative officers make it easy for patients

to understand the stages of service. As many as 73.5%

of respondents stated that human resources in the

registration section were sufficient. Respondents who

said that HR was inadequate because they had to wait

longer, especially in the registration until the

summons. For this reason, the hospital must innovate

by providing free wifi for all visitors, especially

outpatients so that when waiting in line, they can

spend time surfing the internet. In addition, the

Hospital must provide regular training to each

employee in the registration section so that the ability

and competency of employees increases.

3.4 Modeling using SEM

Figure 2 showing the model after analysis and

modification, the chi-square value of 196,714 with a

p-value greater than the specified criteria (0.0477

<0.05), so that the overall fit size of the model based

on the chi-square value has a good matching level.

RMSEA value of 0.028 (close fit 0.05 good fit 0.08

bad fit) with a p-value of .0477 <0.05 and the

RMSEA value is within 90 percent confidence

interval for RMSEA of 0.020; 0.083, so the RMSEA

is in good precision. Differentiate the results of the

RMSEA match size to have a good fit.

Indicators on the arrival variable are X1.1, X1.2,

X1.3, X1.4, and X1.5. Shows the results of the biggest

value loading factor of 1.47 indicated by the value of

X1.5 to the arrival variable. this shows that the X1.5

variable has a greater influence than the other

variables on the arrival variable. It can also be

concluded that to find out the patient's flow in the

hospital, X1.5 is a very important factor.

HIMBEP 2020 - International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical

108

Indicators on the HR variable are X5.1, X5.2,

X5.3, X5.4, and X5.5. Shows the results of the biggest

value loading factor of 8.34 indicated by the value of

X5.3 to the HR variable. this shows that the X5.3

variable has a greater influence than the other

variables on the arrival variable. It can also be

concluded that to find out the patient's flow in the

hospital, X5.3 is a very important factor.

Figure 2. Patient Queue Model

Evaluation of the model is done through

suitability and statistical tests, and reliability testing.

In the suitability and statistical tests carried out by

using several Fit Indexes to measure the truth of the

proposed model. The results of the evaluation of each

of the modified models are compared with the

statistical limits set in the SEM procedure. The results

of the Goodness Of Fit Indexes test in this study are

as follows:

Table3: Recapitulation of Goodness Evaluation (Fit)

Criteria Fit Cut of

Value

Results Evaluation

Chi Square

0

198,71

Fit

Probability < 0,05 0,047 Fit

CMIN/DF < 2,00 1,190 Fit

GFI

 1

0,931 Fit

RMSEA < 0,08 0,027 Fit

NFI

 1

0.845 Fit

Based on the Table 3 it is known that the

Goodness of Fit Indexes values of this research model

of all the criteria have been fulfilled well, so it can be

concluded that the created model was in accordance

with the observation data.

This hypothesis test was conducted to determine

whether or not exogenous variables affect

endogenous variables. This hypothesis can be

accepted if the probability value is P> 0.05 and the

Critical Ratio (CR) value <1.96.

Table 3: Regression Weights Estimates Software AMOS.

C.R. P

Queue <--- HR .688 .492

Queue <--- Arrival .937 .349

Basis of Decision: If the Probability value (P)>

0.05 then the hypothesis can be accepted, If the

Probability value (P) <0.05 then the hypothesis can

be accepted. Based on the table above obtained

information from the results of hypothesis testing

from HR and arrival to queue has a value of P> 0.05,

then the hypothesis can be accepted. So it can be said

that there is a positive and significant effect between

HR and the arrival of the patient queue at the hospital.

4 CONCLUSIONS

Physical evidence, reliability, responsiveness, and

empathy influence on maternal satisfaction while

guarantee variables have no effect. The most

influential variable in this study is the responsiveness

variable which has a value of Exp (B) / OR = 7,985

meaning that the mother who states the

responsiveness of the hospital is good, have a 7.9

times higher chance of being satisfied with pregnancy

and childbirth services.

The results of the SEM study show that there is a

relationship between the length of time the doctor's

service and the cause of the patient's queue. The

hospital is seen from the value of p = 0.349> 0.05. If

seen from the average patient waiting time or doctor's

service time which is 16.58 minutes exceeding the

standard average waiting time which is 15 minutes, a

fast and thorough examination is needed by the

hospital for the efficiency of doctor's service time so

as to reduce the patient queue length in the hospital .

There is a relationship between the availability of

human resources (HR) and the causes of queues in

hospital patients with a value of p = 0.492> 0.05. This

proves that human resources at the hospital are related

to the occurrence of patient queues. In this case a

Identiﬁcation and Modeling of Factors That Cause Patient Queue

109



sufficient number of employees can affect the speed

of the patient registration process. In addition, the

hospital must make innovations by providing

convenient facilities for all visitors, especially

outpatients so that when waiting in line patients still

feel relaxed.

REFERENCES

A.M. Bruin, R. Bekker, L. Zanten, G.M. Koole

Dimensioning hospital wards using the Erlang loss

model Ann Oper Res, 178 (2010), pp. 23-43

Adeleke, R. A., Ogunwale, O.D. and Halid, O.Y. (2009),

Application of Queuing Theory to Waiting Time of

Out-Patients in Hospitals, The Pacific Journal of Science

and Technology, 10 (2): 270-274.

Akbar, W. F., & Hirawan, D. (2018). Rancang Bangun

Sistem Pencegahan Penumpukan Pasien Berbasis

Internet Of Things. Universities Komputer Indonesia.

Bahadori, M., Mohammadnejhad, S. M., Ravangard, R., &

Teymourzadeh, E. (2014). Using queuing theory and

simulation model to optimize hospital pharmacy

performance. Iranian Red Crescent Medical Journal,

16(3), e16807.

Barnhart, C., & Laporte, G. (2015). Handbook in

Operations Research and Management Service. USA:

Elsiever.

Bustani, N. M., Rattu, A. J., & Saerang, J. S. M. (2015).

Analisis Lama Waktu Tunggu Pelayanan Pasien Rawat

Jalan Di Balai Kesehatan Mata Masyarakat Propinsi

Sulawesi Utara. Jurnal E-Biomedik, 3(3), 872-883.

https://doi.org/10.35790/ebm.3.3.2015.10456

Dachyar, M., Farizal, & Yafi, M. M. (2018). Analysis of

Outpatient Service Queue of Public Hospital in Jakarta.

MATEC, 248(2), 1-5.

Depkes RI. (2005). Indikator Kinerja Rumah Sakit. Jakarta:

Departemen Kesehatan Republik Indonesia.

Esti, A. (2015). Pengaruh Waktu Tunggu dan Waktu

Sentuh Pasien terhadap Tingkat Kepuasan Pasien Poli

Umum di Puskesmas Sukorame Kota Kediri. Prodi

Kesmas Stikes Strada.

F. Gorunescu, S.I. McClean, P.H. Millard

A queueing model for bed-occupancy management and

planning of hospitals J Oper Res Soc, 53 (1) (2002), pp.

19-24

Fitzsimmons, J. A., & Fitzsimmons, M. J. (2014). Service

Management: Operations, Strategy, Information

Technology. New York: McGraw-Hill.

Hill-Smith, I. (1989), Mathematical Relationship Between

Waiting Times and Appointment Interval for Doctors

and Patients, Journal of the Royal College of General

Practitioners, 39 (329): 492-494.

Hospital statistics, 1976, 1981, 1991–2010 editions.

Chicago, IL. (Copyrights 1976, 1981, 1991–2010).

<http://www.cdc.gov/nchs/data/hus/2010/104.pdf>,

<http://www.cdc.gov/nchs/data/hus/2010/113.pdf>.

Husnan, S. (2015). Teori Antrian. Arti dan Kegunaannya

bagi Management (Cetakan 4). Yogyakarta: Bhakti

Profesindo (BPFE).

Kakiay, T. J. (2014). Dasar Teori Antrian untuk Kehidupan

Nyata (Cetakan 2). Yogyakarta: Andi Offset.

Kemenkes RI. (2008). Keputusan Menteri Kesehatan

Republik Indonesia Nomor 129 tentang Standar

Pelayanan Minimal Rumah Sakit. Jakarta: Kementerian

Kesehatan Republik Indonesia.

Kusumandari, D., Risqyawan, M., Yazir, M., Turnip, M.,

Darma, A. and Turnip, A., 2018. Application of

convolutional neural network classifier for wireless

arrhythmia detection, Journal of Physics: Conference

Series, Volume 1080 (2018) 012048 doi:

10.1088/1742-6596/1080/1/012048.

Laeliyah, N., & Subekti, H. (2017). Waktu Tunggu

Pelayanan Rawat Jalan dengan Kepuasan Pasien

Terhadap Pelayanan di Rawat Jalan RSUD Kabupaten

Indramayu. Jurnal Kesehatan Vokasional, 1(2), 102.

https://doi.org/10.22146/jkesvo.27576

Lakshmi, C and Sivakumar, A.I. (2013), Application of

Queuing Theory in Healthcare: A Literature Review,

Operations Research for Healthcare, 2 (1-2): 25-39.

Lovelock, C. H., & Wright, L. K. (2012). Principles Service

Marketing and Management (2nd Editio). New Jersey:

Pearson Education.

Mayasari, F. (2016). Analisis Hubungan Waktu Pelayanan

dan Faktor Total Quality Service Terhadap Kepuasan

Pasien di Poliklinik Kebidanan dan Kandungan RSIA

Anugerah Medical Centre Kota Metro Tahun 2015.

Jurnal ARSI, 2(3), 214-230.

Mohebbifar, R., Hasanpoor, E., Mohseni, M., Sokhanvar,

M., Khosravizadeh, O., & Mousavi Isfahani, H. (2014).

Outpatient waiting time in health services and teaching

hospitals: a case study in Iran. Global Journal of Health

Science, 6(1), 172-180.

Musinguzi, C. (2013). Patient waiting time and associated

factors at the Assessment Center, General out-patient

Department Mulago Hospital Uganda (Masters of

Health Services Research).

Nadeak, S. (2016). Analisis Antrian Pasien Instalasi Rawat

Jalan Poliklinik Lantai 1 Dan 2 RSUD Cengkareng,

Jakarta. Jurusan Statistika Fakultas Sains Dan

Matematika Universitas Diponegoro.

Nugraheni, R. (2017). Gambaran Waktu Tunggu Pasien dan

Mutu Pelayanan Rawat Jalan di Poli Umum UPTD

Puskesmas Pesantren 1 Kota Kediri Tahun 2017. Jurnal

Wiyata, 4(2), 165-172.

Obulor, R., & Eke, B. O. (2016). Outpatient Queuing Model

Development for Hospital Appointment System.

International Journal of Scientific Engineering and

Applied Science, 2(4), 15-22.

RSIA Stella Maris. (2019a). Data Laporan Kinerja RSIA

Stella Maris Medan Tahun 2017-2018. Medan.

RSIA Stella Maris. (2019b). Data Pegawai dan Kunjungan

Rawat Inap di RSIA Stella Maris Medan Tahun 2019.

Medan.

Sari, D. M. (2016). Analisis Penyebab Penumpukan

Antrian Pasien Di Tempat Penerimaan Pasien Rawat

HIMBEP 2020 - International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical

110

Jalan Di Puskesmas Playen I Gunung Kidul

Yogyakarta. Universitas GAdjah Mada Yogyakarta.

Septiani, A. S., Wigati, P. A., & Fatmaasari, E. Y. (2017).

Gambaran Sistem Antrian Pasien Dalam Optimasi

Pelayanan Di Loket Pendaftaran Instalasi Rawat Jalan

Rumah Sakit Umum Pusat Fatmawati. Jurnal

Kesehatan Masyarakat (e-Journal), 5(4), 1-14.

Sugiarto, E. (2014). Psikologi Pelayanan Dalam Industri

Jasa (Cetakan 2). Jakarta: Gramedia Pustaka Utama.

Turnip, A., Andrian, Turnip, M., Dharma, A., Paninsari, D.,

Nababan, T., Ginting, C.N., 2020. An application of

modified filter algorithm fetal electrocardiogram

signals with various subjects, International Journal of

Artificial Intelligence, vol. 18, no., 2020.

Turnip, A., Ilham Rizqywan, M., Kusumandari, D., et al.,

2018. Classification of ECG signal with Support Vector

Machine Method for Arrhythmia Detection, Journal of

Physics: Conference Series, Vol. 970 (2018) 012012

doi: 10.1088/1742-6596/970/1/012012.

Turnip, A., Kusumandari, D., Pamungkas, D., 2018. Drug

Abuse Identification based EEG-P300 Amplitude and

Latency with Fuzzy Logic Calssifier, IEEE

International Conference on Applied Engineering,

(ICAE), 3-4 Oct. 2018, DOI:

10.1109/INCAE.2018.8579378.

Wijaya, C., Andrian, M., Harahap, M., Turnip, A., 2019.

Abnormalities State Detection from P-Wave, QRS

Complex, and T-Wave in Noisy ECG, Journal of

Physics: Conference Series, Volume 1230, (2019)

012015. doi:10.1088/1742-6596/1230/1/012015.

Yu, W., Yu, X., Hu, H., Duan, G., Liu, Z., & Wang, Y.

(2013). Use of hospital appointment registration

systems in China: a survey study. Global Journal of

Health Science, 5(5), 193-201.

Zhu, Z., Heng, B.H. and Teow, K.L. (2012), Analysis of

Factors Causing Long Patient Waiting Time and Clinic

Overtime in Outpatient Clinics, Journal of Medical

Systems, 36 (2): 707-713.

Identiﬁcation and Modeling of Factors That Cause Patient Queue

111