Corresponding Author: Noverinda Galuh Puspitarani
Sudaryono
E-mail: [email protected]
INTRODUCTION
Patients generally come directly to health
facilities in person to get health services. However, this conventional thing
must also be considered regarding its limitations. Therefore, communication
technology continues to be developed to provide long-distance medical services,
widely known as telehealth or telemedicine (Li, 2020). Telemedicine has been applied to previous events
due to a lack of access to healthcare (Sirintrapun &
Lopez, 2018). However, recent research has shown that
telemedicine has evolved according to the existing infrastructure to continue
being used during and after the COVID-19 pandemic (Thomas et al., 2022). Each telemedicine has its advantages, such as
teleconferencing for registering new services and devices in the biometric setting
of patient health (Cheng et al., 2021). Telemedicine can be used for health systems with
superior capabilities in preventing exposure to the coronavirus. There are
several concerns in telemedicine, such as medical practice, training for health
practitioners, and patient problems in applying telemedicine (Bokolo, 2021).
In some parts of the world, the term telemedicine
may differ depending on a group of people or individuals in the country. One of
the studies in China mentioned mobile medical treatment (MMT) and
E-Consultation, research in Iran and mobile health, mHealth in Arab countries,
China and Spain, and mobile health care in Italian research. Of the many
existing terms, all refer to applications that provide services for patients in
consultation with healthcare providers. With considerations related to the
advantages and problems that exist with the adoption of telemedicine, the
Technology Acceptance Model (TAM) is used for previous research patterns
related to web-based learning and the distribution of masks (Baber, 2021); (Tsai et al., 2021). TAM describes the application of the latest
technology with ease that is directly felt by the individual related to the
attitudes and behavioral intentions of the application of technology (Anderson et al.,
2022).
TAM also explores the perception of use and
perceived ease of use in telemedicine applications, proving the potential to
control network communication (Anderson et al.,
2022). Since the beginning of the pandemic on social
media, research has been carried out on the pattern of adoption of telemedicine
for speech pathology based on several previous studies (Weidner et al.,
2021). The research technique used in this study is a
benefit of telemedicine applications that are relatively recently used by
users, especially in Indonesia since the COVID-19 pandemic, where telemedicine
applications can provide information and health solutions online from doctors
directly to users or prospective patients. This research uses TAM to limit
telemedicine-related issues by differentiating this study from previous
studies, so we can better describe it, such as how telemedicine use is affected
by trust, privacy concern, attitude toward using, perceived usefulness, perceived
ease of use, and intention to use.
Theoretical
Background and Hypothesis Development
���������� Although the use of TAM has proven
the user's acceptance and adoption of the common advantages that telemedicine
has, the tendency of users in the transition period of medical consultations
from offline to online, then identifying factors that affect user acceptance
and the adoption of telemedicine is significant (Li, 2020). Therefore, this study uses the TAM model with the addition of
several external factors, such as privacy concerns and trust, as shown below:
Figure 1. Theoretical Framework
Technology Acceptance Model (TAM)
In
1989, a Technology Acceptance Model (TAM) method was introduced by Fred Davis;
TAM determines the causal relationship between system features, perceived
benefits, ease of use, attitudes towards use, and actual user behavior. Overall,
TAM provides an informative representation of the mechanisms by which design
choices affect user acceptance and should therefore assist in the applied
context for forecasting and evaluating user acceptance of information
technology. TAM places the attitude factor of each user behavior as two
variables, namely usefulness and ease of use.
Privacy Concern
Privacy
concern can be interpreted as a person's authority to choose freely what and to
what extent their personal information will be exposed to others. This is also
a driving factor that hinders the use of telemedicine services through mobile
platforms and hinders the trust of service providers (Li, 2020). A study says that privacy concerns by patients
have a significant negative impact on telemedicine providers. Telemedicine
application users may believe that privacy and security are less critical if
telemedicine system service providers provide superior healthcare (Archer et al., 2021); (Dhagarra et al.,
2020). Based on the definition, here are the hypotheses
put forward:
|
H1:
|
Privacy concerns negatively affected telemedicine
app service trust during the COVID-19 pandemic.
|
|
H2:
|
Privacy concerns negatively affect the perceived
usefulness of telemedicine application services during the COVID-19 pandemic.
|
Trust
����������� �Trust
demonstrates user perceptions of information technology service trust and has
been a significant factor influencing the intention to use in receiving and
using the service (Guo et al., 2016); (Li, 2020). The existence of risks and uncertainties with the
ability to use the application requires trust. A trust can also be defined as a
party's willingness to be vulnerable or take risks with the other party's
actions based on the expectation that the other party will perform the actions
that the user needs, regardless of whether the user can monitor or control the
other party (Beldad & Hegner,
2018). The study examined the level of trust that is
important in reducing uncertainty when people use telemedicine services (Akter et al., 2013). Trust also pushes Perceived Usefulness to a higher
level in telemedicine services. Based on this definition, the target trust of
the user is not the application but the application's developer (Beldad & Hegner,
2018). Through the study, the proposed hypothesis:
|
H3:
|
Trust positively affected the Perceived Usefulness
of telemedicine app services during the COVID-19 pandemic.
|
Perceived
Ease of Use
����������� Perceived Ease of Use is when a potential user believes that a
particular application is valid and that the system is easy to use. The
performance benefits of using it outweigh the effort of using the system. An
information system can help someone's work if it is easy to use. Perceived ease
of use can also be defined as the extent to which people believe in using an
information system or application without effort (Qi et al., 2021). The application's ease of use is predicted to increase the
user's ability. The study by Jeon & Park, and Cho shows the effect of
Perceived Ease of Use on telemedicine applications and Perceived Usefulness (El-Amrawy & Nounou, 2015); (Cho, 2016). Additional studies examine the effect of Perceived Ease of Use
on Perceived Usefulness which affects the decision to use telemedicine
applications (Palos-Sanchez et al., 2021); (Palos-Sanchez et al., 2021). Therefore, the proposed hypothesis is as follows:
|
H4:
|
Perceived Ease of Use positively affects the Perceived
Usefulness of telemedicine application services during the COVID-19 pandemic.
|
|
H5:
|
Perceived Ease of Used positively affects Attitudes toward using
telemedicine application services during the COVID-19 pandemic.
|
Perceived Ease of Use
Perceived
Ease of Use is when a potential user believes that a particular application is
practical and that the system is easy to use. The performance benefits of its
use outweigh the efforts to use that system. An information system can help a
person's work if it is easy to use. Perceived ease of use can also be defined
as the extent to which believers use an information system or application
without effort (Qi et al., 2021). The ease of using the application is predicted to
increase users' ability. The study by Jeon & Park and Cho shows the
influence of perceived ease of use on telemedicine applications and their
influence on perceived usefulness (Jeon & Park,
2015), (Cho, 2016). Another study by Palos-Sanchez et
al. and Li examined the influence of perceived ease of use on perceived
usefulness that influences the decision to use telemedicine applications (Palos-Sanchez et al.,
2021);. Therefore, the hypotheses proposed are:
|
H4:
|
Perceived Ease of Use positively affects the
Perceived Usefulness of telemedicine application services during the COVID-19
pandemic.
|
|
H5:
|
Perceived Ease of Used positively affects
attitudes toward telemedicine application services during the COVID-19
pandemic.
|
Perceived Usefulness
Perceived
usefulness plays a significant role in explaining one's habits toward the use
of technology (Bettiga et al.,
2020). This shows how much people tend to use or not use
the app to the extent that they believe it will help them do their job better (Bokolo, 2021). The study researched individual
Perceived Usefulness, which positively influences attitudes toward using MMT
(Mobile Medical Treatment) services (Bokolo, 2021). This was also revealed who found a positive
influence of Perceived Usefulness on the attitude toward using the mHealth
application (Palos-Sanchez et
al., 2021). Perceived usefulness is defined as far away as the
patient knows telemedicine technology information technology that can provide
convenience to health monitoring (Qi et al., 2021). Therefore, the hypotheses proposed are:
|
H6:
|
Perceived usefulness positively affects attitude
toward using telemedicine applications during the COVID-19 pandemic.
|
Attitude Toward Using
According
to the Theory of Reasoned Action (TRA) and Technology Acceptance Model or TAM,
attitude toward using is the actual behavior of an individual toward a
particular action determined by his Behavioral Intention or BI. Refers to a
measure of the strength of his willingness to try and exert effort when
performing certain behaviors or activities. Attitude toward using refers to the
level of profitability or not of the behavior or effort to use a particular
information technology (Pan et al., 2019). The study by Davis and Li considers
that the intention to use the service reflects the acceptance and adoption of
technological applications allegedly influenced by the attitude toward using
the relevant service. Based on the study, the hypotheses proposed are:
|
H7:
|
Attitude toward using positively affects the
intention to use telemedicine applications during the COVID-19 pandemic.
|
Intention to Use
Intention
to Use is generally used to measure user intent by using information technology
as a function in the future (Bokolo, 2021). Consistent with previous studies, this study also
considers that the intention to use a service reflects the acceptance and
adoption of telemedicine applications (Li, 2020). Then further data analysis can help in
generalizing end-user traits and usage patterns and identifying end-user needs
in describing the relationship between intention to use and actual use (Kim et al., 2015).
Purpose of This Study
The
purpose of this study is to find out how the user's points of view on using
telemedicine services during the COVID-19 pandemic in Indonesia and what
factors affect the acceptance of telemedicine service users during the COVID-19
pandemic based on the Technology Acceptance Model (TAM) with privacy concerns
and trust for observation of the relationship with factors influencing people's
tendency to use telemedicine.� The
benefits of this research are to enhance understanding of user perspectives on
the use of telemedicine services during the COVID-19 pandemic in Indonesia. It
is also beneficial for identifying the factors that influence user acceptance
of telemedicine services during the COVID-19 pandemic.
METHOD
Previous
studies evaluated individuals' experiences using mHealth applications with
various external factors (Mahmood et al.,
2019). Thus, the study adopts a quantitative approach.
Hypothesis testing method using online questionnaires. This research hypothesis
was developed based on the technology acceptance model (TAM). An online survey
was conducted with a questionnaire filling platform; this survey consisted of
35 questions using a Likert interval scale of 1-5 with a total of 570
respondents.
Instrument Development
Research
instruments are tools used to observe each variable under study. These research
instruments are adopted from previous studies on telemedicine applications and
technologies in the health sector. The questionnaire was made with as many as
35 questions, including the screening questions, respondent profile, and
questions related to the use of telemedicine. The questionnaire was translated
from English into Indonesian. The questionnaire is divided into three parts,
the first is a respondent screening question to determine the suitability of
the sample criteria with the research conducted, and the second part contains seven
questions related to the respondent's profile and questions related to the use
of telemedicine. The third part contains 26 questions consisting of Privacy
Concern (n=4), Trust (n=4), Perceived Ease Of Use (n=6),� Perceived usefulness (n=6), Attitude Toward
Using (n=3), and Intention to Use (n=3). Question items are tailored to the
needs and suitability of the research. All question items except profile
questions are measured using a 5-point Likert scale with a range of 1 strongly
disagree and five strongly agree.
Table 1. Distribution of Construct
Items in Question Items
|
Constructs
|
Definition
|
Item
|
No.
Item
|
|
Trust
|
User perceptions of the trust of telemedicine
services are the main factors influencing the intention to use in receiving
and using health services.
|
a.
The services of this telemedicine provider are reliable
b. Services
from this telemedicine provider can provide reliable information
c.
Telemedicine service keeps its promise and commitment
d. This
telemedicine service can meet my expectations (Li, 2020)
|
1
2
3
4
|
|
Privacy Concern
|
The
authority of a person to freely choose what and to what extent their personal
information will be shown to others.
|
a.
Telemedicine will make me lose control of my privacy
b. Using
telemedicine services will not cause privacy problems
c.
Registering and using telemedicine services will cause a loss of
privacy for me because my personal information can be used without my
knowledge
d. Other
people may take control of my information if I use the telemedicine services
[
|
5
6
7
8
|
|
Perceived
Ease of Use
|
Users believe that telemedicine is valuable, then at the same
time, the application system is easy to use, and the performance benefits of
using it outweigh the effort of using the application.
|
a.
Telemedicine apps can make it easier to interact with healthcare
providers
b. Telemedicine
apps can make it easier to get health services
c.
Telemedicine apps can make it easier to remember how to get
health services
d. Telemedicine
apps can be learned easily
e.
Telemedicine apps can minimize the effort of getting health
services
f.
Telemedicine application is easy to use (Qi et al., 2021)
|
9
10
11
12
13
14
|
|
Perceived Usefulness
|
As far as users know, telemedicine is an information technology
that can provide convenience to health monitoring.
|
a.
Using a telemedicine app makes consulting a doctor easier.
b. Using the
services of a telemedicine app makes it possible to understand the disease
and what treatment is needed more quickly.
c.
Using the telemedicine app gives access to complete
communication with doctors.
d. Telemedicine
apps make it possible to discover how to prevent and treat disease.
e.
Using telemedicine apps makes it possible to make better
treatment decisions.
f.
It is easy to get information regarding telemedicine. (Qi et al., 2021)
|
15
16
17
18
19
20
|
|
Attitude
Toward Using
|
The actual behavior of the user toward specific actions,
determined by Behavioral Intention, refers to the level of benefits or not
the behavior or efforts made in the use of telemedicine technology.
|
a.
Using a telemedicine
service would be a good idea
b. Using telemedicine
services is a wise move
c.
I like the services that exist in the telemedicine application. (Li, 2020)
|
21
22
23
|
|
Intention
to Use
|
Measurement of users' intention to use
telemedicine as a function of the future.
|
a.
I intend to use telemedicine services in the future
b. I believe I
will use telemedicine services in the future
c.
I plan to use telemedicine services in the future (Li, 2020)
|
24
25
26
|
Sample and Data Collection
Population refers to the entire group of people,
events, or things of interest that the researcher wants to investigate (Sekaran & Bougie, 2016). The population in this study were people who had
used telemedicine application services during the COVID-19 pandemic in
Indonesia. Data collection will be carried out from May-December 2022. The
questionnaire responses received were 570 respondents with 406 valid
questionnaires and 164 invalid questionnaires due to the first two things,
which are respondents needing experience in using telemedicine applications and
needing more understanding related to the intent of the questions.
Data Analysis
Data analysis and hypothesis testing using a
structural equation model (SEM) with variants allow statistical testing to
measure the relationship of dependence between latent variables (dependents)
and variable indicators of the research model by directly measuring the
observed variables. SEM is used with partial least squares (PLS). PLS-SEM is
used if the research's purpose is exploratory or the development of an existing
structural theory (Hair Jr et al., 2021). The application used for data analysis and
hypothesis testing is SmartPLS 3.
RESULTS AND DISCUSSION
Participants Profile
All respondents came from 5 provinces on the island
of Java, namely DKI Jakarta, Banten, West Java, Central Java, and East Java.
The selection of Java Island was carried out because Java is the island with
the highest population in Indonesia; economic equality and digital literacy are
also evenly distributed and adequate. The sample collected consisted of men at
51% and women at 49%. The age range of respondents was 16-25 years old 41%,
26-35 years old 42%, 36-45 years old n 13%, and over 45 years old as much as
4%. Most respondents had the last education Diploma/Bachelor's Degree with a
percentage of 63%, followed by Master graduates at 18% and high school at 16%.
Most respondents to this study worked as private employees with a percentage of
49%, 22% as students, 12% as ASN, 14% as entrepreneurs, and 3% as housewives.
Furthermore, the average monthly expenses of respondents to this study were IDR
5-10 million, with the most significant percentage of 46%, below IDR 5 million
by 39%, and above IDR 10 million by 15%. The following is a socio-demographic
table of respondents to this study.
Table
2. Socio-Demographic (n=406)
|
|
Frequency
|
Percentage (%)
|
|
Gender
|
Male
|
206
|
51%
|
|
Female
|
200
|
49%
|
|
Age
|
16-25
|
166
|
41%
|
|
26-35
|
171
|
42%
|
|
36-45
|
52
|
13%
|
|
>45
|
17
|
4%
|
|
Education
|
Senior High School
|
66
|
41%
|
|
Diploma/Bachelor Degree
|
257
|
42%
|
|
Master Degree
|
73
|
13%
|
|
Doctoral Degree
|
10
|
4%
|
|
Job
|
Student
|
90
|
22%
|
|
Civil Servant
|
47
|
12%
|
|
Employee
|
199
|
49%
|
|
Entrepreneur
|
57
|
14%
|
|
Housewife
|
13
|
3%
|
|
Monthly Expenses
|
< 5.000.000
|
158
|
39%
|
|
5.000.000 - 10.000.000
|
188
|
46%
|
|
> 10.000.000
|
60
|
15%
|
Research
respondents habitually used telemedicine applications during the COVID-19
pandemic that hit Indonesia. Among these respondents, 94.7% reported using
telemedicine services during the COVID-19 pandemic. The most widely used
telemedicine application, with 59.7% of respondents, chose Halodoc, followed by
Alodokter at 21.8%, KlikDokter at 12.9%, and other telemedicine services at a
percentage below 5%.
Measurement
Model
The results of the PLS-SEM are presented in Table 3, where each
variable with Cronbach alpha has a value above 0.7 except for the attitude
toward using a variable that has a value of 0.616 but still meets the criteria
with a value above 0.5 and intention to use with a value of 0.662. Cronbach
alpha values range from 0.616 to 0.942. The composite reliability of each
variable has a value greater than 0.7, as recommended, and a range from 0.796
to 0.958. In Cronbach alpha and composite reliability, a reliability
measurement value ranges from 0 to 1, where values of 0.60 to 0.70 can still be
accepted as the lower limit value (Hair et al., 2013). The average variance extracted (AVE) has a smaller value than
Cronbach alpha and composite reliability, but values range from 0.526 to 0.851.
For AVE, values equivalent to 0.5 or more are a rule of thumb and signify
adequate convergence (Hair et al., 2013). Each variable in the AVE has a number above 0.5, as recommended.
According to the rule of thumb, a suitable loading factor is the standard
estimated loading value of 0.5 or higher and the outstanding value of 0.7 or
higher (Hair et al., 2013). The loading factor in Table 3 shows numbers ranging from 0.700
to 0.934, so the results are as recommended.
Table
3. Factor Loading, Cronbach Alpha, Reliability, and Average Variance Extracted
|
Construct
|
Item
|
Factor Loading
|
Cronbach Alpha
|
CR
|
AVE
|
|
Trust
|
TR1
|
0.748
|
0.755
|
0.842
|
0.571
|
|
TR2
|
0.7
|
|
TR3
|
0.802
|
|
TR4
|
0.77
|
|
Privacy Concerns
|
PC1
|
0.934
|
0.942
|
0.958
|
0.851
|
|
PC2
|
0.917
|
|
PC3
|
0.929
|
|
PC4
|
0.909
|
|
Perceived Ease of Use
|
PEOU1
|
0.735
|
0.821
|
0.870
|
0.526
|
|
PEOU2
|
0.726
|
|
PEOU3
|
0.743
|
|
PEOU4
|
0.718
|
|
PEOU5
|
0.719
|
|
PEOU6
|
0.711
|
|
Perceived Usefulness
|
PU1
|
0.824
|
0.882
|
0.911
|
0.629
|
|
PU2
|
0.768
|
|
PU3
|
0.813
|
|
PU4
|
0.771
|
|
PU5
|
0.797
|
|
PU6
|
0.785
|
|
Attitude Toward Using
|
ATU1
|
0.796
|
0.616
|
0.796
|
0.565
|
|
ATU2
|
0.711
|
|
ATU3
|
0.746
|
|
Intention to Use
|
ITU1
|
0.78
|
0.662
|
0.816
|
0.597
|
|
ITU2
|
0.742
|
|
ITU3
|
0.794
|
AVE:
Average variance extracted
CR:
Composite Reliability
Discriminant validity measures the degree to which a construct differs
from another. Thus, high discriminant validity provides evidence that a
construct captures phenomena other constructs do not have (Hair et al., 2013). Discriminant validity is derived from the square
root value of the average variance extracted (AVE) for each construct and the
correlation between constructs in the research model (Li, 2020). The overall AVE square root value is more than
0.7; this value is the highest in several relationships between constructs (Hair Jr et al., 2021)�the following table discriminant validity in this
study.
Table 4. Discriminant Validity
|
|
ATU
|
ITU
|
PC
|
PEOU
|
PU
|
TR
|
|
ATU
|
0.752
|
|
|
|
|
|
|
ITU
|
0.534
|
0.772
|
|
|
|
|
|
PC
|
-0.200
|
-0.192
|
0.922
|
|
|
|
|
PEOU
|
0.515
|
0.476
|
-0.178
|
0.726
|
|
|
|
PU
|
0.423
|
0.456
|
-0.141
|
0.389
|
0.793
|
|
|
TR
|
0.451
|
0.458
|
-0.207
|
0.446
|
0.355
|
0.756
|
Structural
Model
It is further developed using structural methods to
ensure an explanatory relationship through tests using the PLS-SEM application (Hair Jr et al., 2021). Results from the PLS-SEM analysis in (Figure 2
and Table 5) show that users' Perceived Ease of Use, Privacy Concern, and Trust
accounted for 45.8% of their� Perceive
Use variance in telemedicine service usage (R2= 0.458); 31.8%
Attitude Toward Use variance towards telemedicine service usage described
by� Perceive Ease of Use and Perceived
Usefulness (R2= 0.318); 26.6% Trust variance to telemedicine service
usage is described by Privacy Concern (R2= 0.266); finally, the
Attitude Toward Using of users describes 31.7% of the variance in a user's
Intention to Use on telemedicine services (R2= 0.317).
Figure
2. PLS Results and Structural Model
Based on the data processing results using PLS-SEM,
it was found that the H1 hypothesis that focuses on the relationship between
privacy concerns and trust in telemedicine applications during the COVID-19
pandemic was accepted with a value of p=0, t=4.362. The data processing results
also found that the H2 hypothesis was rejected. This hypothesis focuses on the
relationship between privacy concerns and perceived usefulness in telemedicine
applications during the COVID-19 pandemic, with a p-value=0.173, t=1.363.
Another thing happened to H3, where this hypothesis was accepted with a value
of p=0, t=4.387 relationships between trust and perceived usefulness in
telemedicine applications during the COVID-19 pandemic. The reference value for
p-value < 0.05 and t-value > 1.96 (Hair et al., 2013).
In addition, the results of the study also found
that respondents' perceived ease of use had a significant effect on respondents'
perceived usefulness with p=0, t = 4.763, and respondents' attitude toward
using telemedicine applications during the COVID-19 pandemic with
p-value=0,� t=7.758; this supports the H4
and H5 hypotheses. Respondents' perceived usefulness had a significant
influence on respondents' attitudes toward using telemedicine applications
during the COVID-19 pandemic with p-value=0, t=5.634; this supports the H6
hypothesis. The respondent's attitude toward using had a significant influence
on respondents' intention to use telemedicine applications during the COVID-19
pandemic with p-value=0, t=10.595; this supports the H7 hypothesis. The
following is a table of the results of testing the hypothesis of this study.
Table 5.
Hypothesis Testing Results
|
Hypothesis
|
Path
|
Path Coefficient
|
t-Statistic
|
p-Value
|
Judgment
|
|
H1
|
PC -> TR
|
-0.207
|
4.362
|
0
|
Accepted
|
|
H2
|
PC -> PU
|
-0.045
|
1.363
|
0.173
|
Rejected
|
|
H3
|
TR -> PU
|
0.219
|
4.387
|
0
|
Accepted
|
|
H4
|
PEOU -> PU
|
0.284
|
4.763
|
0
|
Accepted
|
|
H5
|
PEOU -> ATU
|
0.413
|
7.758
|
0
|
Accepted
|
|
H6
|
PU -> ATU
|
0.263
|
5.634
|
0
|
Accepted
|
|
H7
|
ATU -> ITU
|
0.34
|
10.595
|
0
|
Accepted
|
PC������ :
Privacy Concern
TR������ :
Trust
PU������ :
Perceived Usefulness
PEOU :
Perceived Ease of Use
ATU��� :
Attitude Toward Using
ITU���� :
Intention to Use
Telemedicine services have become popular, especially in the health
sector, because of the convenience offered, especially during the COVID-19
pandemic (Cheng et al., 2021). This certainly makes an idea that arises in
everyone's mind related to the adoption of telemedicine use, especially in
Indonesia. Telemedicine services certainly make people ask how to get renewable
health services. They can change people's behavior to do medical consultations
online. With that, this research studies how certain factors, such as privacy
concerns and trust, affect the acceptance of telemedicine services. From the
data obtained, privacy concerns do not impact adoption and use. However, trust
has an impact on the use of telemedicine in Indonesia. In addition, the
limitations of this research must also be further studied as a more mature
consideration.
CONCLUSION
This assessment proves that there is no
relationship between privacy concerns about the adoption of use. However, trust
has a relationship with the adoption of telemedicine in Indonesia due to the
behavior of people who attach importance to trust in telemedicine brands during
the COVID-19 pandemic. During the pandemic, the public only used telemedicine
services because the government required them to get health services such as
consultations and drug acceptance through telemedicine services. This is a
significant concern related to the use of telemedicine used during the need for
health services during the COVID-19 pandemic. Most people use telemedicine
services to review anxiety factors related to privacy and trust in a
telemedicine brand as a reference in the use of telemedicine in Indonesia.
However, telemedicine in the future may have more impact to be used so that
young people can get health services from home.
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