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ANALYSIS OF THE ASSOCIATED FACTORS OF BOARDING TIME 

IN YELLOW ZONE PATIENTS IN EMERGENCY DEPARTMENT 

Ahsan Ahsan, Fitrio Deviantony, Setyoadi Setyoadi 

School of Nursing, Faculty of Medicine, Universitas Brawijaya 

Email: ahsanfkub@yahoo.com 
ABSTRACT 

Introduction: Patient’s visits to the emergency room increase in number every year. The increasing number of hospital visits is 
directly associated with the increasing number of patients who wait in the emergency department. The yellow zone is a part of the 

emergency room that has become a place for the most increased patients to enter. This situation causes boarding time patient 

longer than usual. The aim of this research was to analyse the various factors that have been associated with boarding time in the 

yellow zone emergency department. Methods: This research was an analytic observational study with a cross-sectional approach. 
The number of samples was 78 respondents who were recruited with a non-probability sampling technique. The univariate and 

bivariate analysis was utilized to test the relationship between the variables. A further statistical test was conducted with linear 

regression to understand the most dominant factor. Results: The results showed a significant relationship between transfer time, 

laboratory turnaround time, diagnostic time, time arrival, insurance coverage, ratio nurse and patient and boarding time. 
Furthermore, multivariate analysis with linear regression showed a significant association between transfer time, laboratory turns 

around, and the ratio of nurses to patients with the boarding time. Conclusions: The findings from this study show that patient 

boarding time in the yellow zone should consider several factors. This research provides the output of the initial data as one of 

the basic considerations for service management and team minimum service standards in hospitals. 

Keywords: boarding time, emergency department, yellow zone 

 

 

INTRODUCTION 

The visitation of patients in the 

Emergency Department (ED) is growing every 

year. The increase of this situation is by about 

30% in hospitals around the world (Bashkin et 

al., 2015). Increasing the number of hospital 

visits clearly affects the increasing number of 

patients who enter the emergency room. The 

emergency room unit is an area located in the 

hospital that is used to perform standard 

emergency, acute and urgent care (Geelhoed and 

de Klerk, 2012). Caring in the emergency phase 

is used to stabilize patients who have disorders 

from injuries and other sources that require 

resuscitation and patients who have a certain 

degree of injury and illness (Australian College 

for Emergency Medicine, 2015). The data entry 

of patients who come into the ED in Indonesia is 

approximately 4,402,205 patients or 13.3% of all 

visits to hospitals (MoH, 2015). Data for the ED 

patients who visited the emergency room in East 

Java in 2014 amounted to 8,201,606 cases. 

Looking at the district level, the data of the visits 

in Tulungagung in 2014 was 29,877 cases 

(Dinkes Provinsi Jatim, 2015).  

The patient condition who wait too long 

in the ER will hamper the service process in the 

emergency department. Fast and precise services 

are most needed in the emergency installation 

(Ducharme et al., 2008). The length of time 

patients in the yellow zone could be due to a 

mismatch between the number of patients, 

patient beds and the number of health personnel. 

An excessive number of patients causes in 

increased check-up time for other patients, 

increased waiting time, and prolonged waiting 

time for returning patients, and patients who will 

be hospitalized. This condition is called 

boarding time (Singer et al., 2011). Quality 

Control Standard of RSUD Dr. Iskak 

Tulungagung explained that the patient waiting 

period after the inpatient decision becomes one 

of the indicators of hospital quality control. So 

this research focuses on it, as a further hospital 

evaluation material. 

 The number of patients per day in the 

yellow zone can reach 36 patients per day, 

coming in during the morning, day and night 

shift. The number of beds in the yellow zone in 

the ED room is 9 beds with the total number of 

nurses in the yellow zone being 21 people 

divided into 5 people per shift. The number of 

patients boarding for more than 6 hours per day 

can reach 10 patients. The results of the 

interview with the team leader in the yellow 

zone can help to identify the causes of boarding 

due to the number of limited resources, not 

being well organised, the results of the 

laboratory requiring a lot of time, the time to 

diagnose the disease being longer than usual, the 



 

262 

number of patients who came in on the day shift 

being more than in other shifts, and the bed 

occupancy time for patients from the ED 

becoming a factor that causes the boarding time 

to be longer than usual. The speed of patient care 

in the Emergency Department can determine the 

prognosis of the next patient, and obtain optimal 

results in the care of the patient. The aim of this 

research was to analyse the various factors that 

have been associated with boarding time in the 

yellow zone emergency department. 

MATERIALS AND METHODS 

 This research used a cross-sectional 

study approach. The aim of this study was to 

identify the determinants factors related to 

health-related events and problems. Data 

collection was taken from June to July 2017, and 

this research was conducted at the Emergency 

Unit of the RSUD Dr. Iskak Tulungagung. The 

population of this study was the patients who 

came to the yellow zone emergency room; 1,080 

patients per month. The sampling technique used 

non-probability sampling which used the rule of 

thumb with the number of samples, which was 5 

to 50 times the number of independent variables 

(Sastroasmoro and Ismael, 2011). The sample 

size for this research was 78 respondents. The 

subjects in this study have inclusion criteria; 

patients who were in the yellow zone of the ED 

> 6 hours. The exclusion criteria for this 

research were patients who were forced to leave 

the hospital, patients who had not been 

examined by the doctor and patients who were 

discharged or referred to another hospital. 

This research was conducted by direct 

observation method using the instrument of data 

collection sheet and observation guidance sheet 

Table 1. Demographic characteristic of the respondents 

 n % 

Gender 
Men 43 55.1 

Women 35 44.9 

Insurance coverage 

General  43 55.1 

BPJS 30 38.8 

Jasa Raharja 5 6.4 

    

Number patient 
Mean  16 

Min-max  9-28 

Age 
Mean  52.4 

Min-max  13-76 

Table 2. Result of the bivariate analysis 

Variable Test p-value r 

Boarding Time-Transfer Time Pearson 0.000 0,747 

Boarding Time-Laboratory Turn-around Time Pearson 0.000 0,693 

Boarding Time-Diagnostic Time Pearson 0.000 0,462 

Boarding Time-Time of arrival Spearman 0.263 -0,128 

Boarding Time-Insurance Coverage Spearman 0.980 -0,003 

Boarding Time-Ratio between nurse-patient Spearman 0.000 -0,638 

Table 3. Multivariate analysis among the variables 

 β SE t p F Adj R2 

Boarding time     16.342 .134 

Transfer time -.300 212.157 -3.784 .000   

Laboratory turnaround time  .452 .199 4.367 .000   

Diagnostic time .129 .153 1.207 .231   

Ratio nurse and patient .155 1.136 2.163 .034   

 



 

263 

and validated by medical record. We analyzed 

the dependent and independent variable 

correlation using bivariate analysis with 

Spearman test and multivariate analysis with 

linear regression test. P-value ≤0.05 was 

considered statistically significant. Statistical 

analysis was performed under SPSS (Statistical 

Package for the Social Sciences) 14.0 (SPSS Inc. 

Chicago, IL, USA). 

The study was carried out in accordance 

with the Code of Ethics of the World Medical 

Association (Declaration of Helsinki) for 

experiments in humans and approved by the 

Ethics Committee on Faculty of Medicine, 

Universitas Brawijaya. Written informed 

consent was obtained from all study participants. 

RESULTS 

Distribution of respondent characteristic 

The demographic characteristics showed 

that the respondents were mostly male (55.1%) 

and the rest female (44.9%). The types of 

insurance coverage held by the respondents were 

also identified. Half of the respondents had no 

insurance (55.1%). The remaining respondents 

used BPJS (37.5%), and Jasa Raharja (6.4%). 

The average number of patients coming to the 

ER was 16 patients per shift, with the minimum 

number recorded at the time of the shift guard 

change being 9 patients and the highest number 

of patients on one shift being 28 patients. The 

age of the respondents ranged from 13 years old 

to 76 years old, with the average age of the 

respondents being 52.4 years (Table 1). 

Variable of patient transfer time in 

RSUD dr Iskak Tulungagung mostly had about 

634.55 minutes or 10 hours 57 minutes. Thus, 

the fastest time was 3 hours 41 minutes and the 

longest time was 22 hours. The return time of 

laboratory results was mostly done for 7 hours 9 

minutes with the fastest time was 1.8 hours and 

the longest time is 13.6 hours. Most diagnostic 

timings were performed for 57.77 minutes with 

the fastest diagnostic timing being 20 minutes 

and the longest diagnosis time of 98 minutes. 

The average value of the waiting time variable 

of patients after inpatient decision was decided 

about 909 minutes or 15.15 hours and the lowest 

patient waiting time after the inpatient decision 

was 365 minutes or 6 hours and the highest 

waiting time was 14.22 minutes or 23.7 hours. 

 In table 2 the variables most closely 

related to the patient waiting time after the 

inpatient decision was the patient transfer time 

(0.747), laboratory return time (0.693) and 

diagnosis time (0.462). Table 2 explained that 

there is a significant correlation between the 

dependent variable (boarding time) with the 

three independent variables (patient transfer 

time, laboratory turn-around time and diagnosis 

time), where the p-value = 0.00. A simple linear 

regression was calculated to estimate the 

association between boarding time and the other 

variables. Table 3 explained that A significant 

regression equation found that transfer time 

(beta=-0.30 (212.15), p<0.000), laboratory 

turnaround time (beta=0.452 (0.199), p<0.000), 

and the ratio nurse and patient (beta=0.155 

(1.136), p<0.034). 

In the multivariate test the following 

equations are obtained: 

Y= constanta+a1X1+a2X2...............................+aiXi 

Y= 681.212+0.620 (patient transfer time)+2.636 

(diagnostic time)-861.011 (comparison of nurses and 

patients) (see in the discussion below). 

DISCUSSION 

This study found out that the longer time 

it takes to order boarding time in the yellow 

zone, the faster it takes the patient's to move to 

the room. The boarding time recommended by 

the hospital is no more than six hours. This is in 

accordance with research which states that the 

decision-making process related to medical 

decisions in Indonesia is determined by culture, 

where decision-makers related to medical 

treatment is the oldest member of the family. A 

similar study conducted by Hodgins et al. (2011) 

mentioned that 41,256 patients treated in the ER 

during full conditions that resulted in patient 

boarding time leading to death, decreased by 

more than 2 hours (Hodgins, Moore and Legere, 

2011). 

The univariate analysis of the laboratory 

turnaround time showed that the minimum time 

for the laboratory results to the ED was 110 

minutes and the maximum value was 920 

minutes. All patients treated should have 

laboratory tests carried out related to the 



 

264 

condition experienced by the patient. Prior 

research in accordance with these results is 

shown in a study conducted by Steindel & 

Howanitz (2001) which states that laboratory 

tests were performed in > 50% of patients 

visiting the ER and who were hospitalized, 

including those who were discharged (Steindel 

and Howanitz, 2001). The return time of the 

laboratory results is the period of time from the 

physician's order for a blood test until the results 

arrive at the ER, with a target time of <60 

minutes (Hawkins, 2007).  

The study found that both variables also 

have a strong relationship with positive value. It 

means that the longer the diagnosis time is raised 

to, then the boarding time of the patient in the 

yellow zone will be longer, and vice versa when 

the diagnostic time is faster, then the patient 

waiting time in the yellow zone of the ED will 

be shorter as well. This is in accordance with the 

research put forward by Boyle A et al., (2012), 

stating that the delay of diagnosis is defined as 

the time from the patient arriving up until the 

medical diagnosis arises(Boyle et al., 2012). 

This variable also enters the framework 

described by (Rabin et al., 2012), which is 

where this variable enters the throughput factor 

component in the described framework 

overcrowding model. 

The result of the bivariate test using a 

Spearman test on arrival time with boarding time 

showed no statistical significance. Differences in 

the results of the study as revealed by Powell et 

al., (2012) are where the study explains that the 

arrival time during the day has the possibility of 

patient waiting time after the decision of 

hospitalisation being higher(Powell et al., 2012). 

Bashkin et al. (2015) in his study also explained 

the related matters where he found the result that 

52% of patients enter the ED in the morning 

shift and 48% are divided into the day shift and 

night shift. The difference in this result is 

probably due to the homogenized sample size of 

the subject so that the variable has no significant 

relationship in relation to each shift. Another 

thing that can cause the absence of a relationship 

between the variable of arrival time and 

boarding time is the number of patients 

experiencing boarding time on each shift. 

The result of the bivariate test analysis 

shows that there is no close relationship between 

the insurance ownership variable with patient 

boarding time. Differences occurred when this 

result was compared with the research conducted 

by Kennedy et al. (2004), where in his research 

described that in about 7.7% of the number of 

visits during the 12 months, there was a delay or 

a delay in the services in ER (Kennedy et al., 

2004). This occurs due to problems such as 

service charges which are swollen, and 

insurance coverage will also cause delayed 

service, causing the patient’s boarding time to 

increase. In this study, another thing that also 

became an obstacle was if the patient did not 

have health insurance. Patients who already have 

health insurance do not have to bother giving 

their data forward, saving time. 

This study found out that there was a 

strong correlation between the ratio of the nurses 

to the patients with the patient boarding time in 

which the direction of the relationship is 

negative. This means that when the ratio of 

nurses with patients per shift is greater, then the 

patient boarding time is smaller, and vice versa 

if the ratio of nurses with patients per shift is 

smaller then the patient waiting time value after 

the decision of becoming an inpatient is longer. 

A study described by Wiler et al., (2012) 

explains how the ratio of nurses with patients on 

each shift becomes a thing that can affect the 

duration of service time and the workload of the 

nurses also increases(Wiler et al., 2012). These 

results are in accordance with the journal articles 

published by Zarea (2014) in Iran, mentioning 

that 78.2% of nurses are not satisfied with their 

performance due to the increased workload, 

safety and salary (Zarea et al., 2014). 

Based on the results of the multivariable 

analysis, where this research used linear 

regression to predict patient boarding time in the 

yellow zone, there were the variables of patient 

transfer time, laboratory turnaround time, 

diagnostic time and the comparison of nurses 

with patients per shift. Transfer time, diagnostic 

time and the proportion of the number of nurses 

with the patients became the significant factors 

of the patient's boarding time. This is because 

long diagnostic timing can lead to long transfer 

times since transfer decisions are decided when 

diagnoses are raised. This can be seen from the 

length of time of diagnosis and the length of 



 

265 

transfer time. This finding is in accordance with 

the research described by Lo et al. (2014).  

Based on the result above (the equations 

of the multivariate test) indicates that any delay 

in transferring the patient to an inpatient room 

within 60 minutes will increase the patient's 

boarding time by as much as 0.620 minutes in a 

patient with a strong correlation strength. This is 

due to the limitations of the treatment room, 

where it is characterised by a significant number 

of patients and a BOR of 75%. These results 

indicate that bed utilisation in the inpatient 

wards is higher than the national standard of 

60%. This will certainly affect the duration of 

the patient's boarding time in the ER. With this 

equation model, it can estimate patient boarding 

time using the variable time of patient transfer, 

time of diagnosis determination and ratio of a 

nurse. The meaning of constant value is that if 

there is no trust value or variable value, then the 

participation value is equal to 681.212 minutes. 

For the variable time of patient transfer, for 

every addition of 1 minute then there will be an 

addition of 0,620 minutes. For the variable of the 

determination time of each diagnosis, there is the 

addition of 1 minute with an addition of 2,636 

minutes. For the nurse comparison variables 

with patients, the number per guard must be 

calculated in advance of the number of nurses in 

one shift then divided by the number of patients 

in one shift. The bigger the ratio of nurses with 

patients per shift, is the p-value or waiting time 

of the patient and whether or not the 

hospitalisation decision will be smaller. 

The results of this study obtained 

information about the factors relating to patient 

boarding time in the yellow zone. The 

limitations in this study are that the researchers 

only look at the time when the patient was in the 

ER without considering the amount of space 

taken and the type of illness that the patient 

suffers from, so the researchers cannot see which 

room is used the most to move patients. Another 

limitation of this research is that the researcher 

only conducted research in one hospital only, so 

this research should go on to take time elsewhere 

with the same characteristics to be able to see 

the boarding time of patients in the yellow zone 

in other hospitals. 

 

CONCLUSIONS 

The comparison factors of nurses with 

patients per shift, diagnostic time, and transfer 

time have a significant correlation to patient 

boarding time in the yellow zone. The benefits 

of this research are that it has identified multi-

factor causes that have a proximity relationship 

with patient boarding time in the yellow zone, in 

particular the time factor of patient transfer, the 

timing of the diagnosis and the comparison of 

the nurses and number of patients. It provides 

the output of the initial data as one of the basic 

considerations for service management and team 

minimum service standards for hospitals to 

enable them to improve the quality of their 

services and strategies that can be used as a basis 

for quality development. 

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