Application of Fuzzy Method to Predict Patient's Belief in Doctor

Qualifications

Rudiyanto, Ermi Girsang, Ali Napiah Nasution

Faculty of Medical, Universitas Prima Indonesia, Indonesia

Keywords: Patient Confidence, Doctor Qualifications, Prediction, Fuzzy, Modeling.

Abstract: It was found that the majority of hospitals lately tend to experience a decrease in visits, especially at the

nursery, presumably because some patients are not sure about the practicing doctor. The aim of this study is

to identify factors related to patient belief in practicing doctors. For the data, from 9,919 respondents, 260

samples were used. Data identification using univariate, bivariate with chi-square test, and multivariate with

multiple logistic regression at 95% confidence level ( = 0.05) were selected. The results of the identification

are then modeled using the fuzzy method to predict the patient's confidence in the qualifications of doctors.

The design of the independent variable factor relationship model with the dependent variable of 90% is

achieved. It is hoped that the results of the model prediction will greatly assist management in making policies.

Identification result showed that the factors of doctor reliability, doctor's communication style, and doctor-

patient relationship were the most significant factors (p <0.05) in increasing patient confidence in doctors.

The dominant variable was found in the doctor-patient relationship (having a weight of confidence in the

practicing doctor of 11.1 times higher).

1 INTRODUCTION

An important human resource in a hospital is a doctor.

Doctors are not only needed when sick, but also when

healthy to prevent illness or maintain and improve the

physical and psychological health of a patient.

Patients can choose medical services that suit

themselves (Setyawan, 2017).

In India, over the past decade, a series of failures

in service in the health system, especially in the

medical profession, has caused a great lack of trust in

hospitals and the medical profession. In many low

and middle income countries, the situation is similar

and has reached crisis proportions (Kane & Calnan,

2017).

Some important factors that influence the

selection of medical services as explained by Shaikh

& Hatcher (2004) are: demographic, gender,

economic factors, availability of treatment facilities

and the severity of the disease. Other factors were

also stated by Ahmed (2005), such as; medical costs,

education level, ethnicity, age, and distance of the

patient's residence from the treatment facility.

Sarafino (2006) also added another important factor

was the patient's belief or trust.

Hall, Dugan, Zheng, and Mishra (2001) believe

that belief is the optimism of the patient in a

vulnerable condition where the patient trusts the

doctor for the patient's interests. In medical settings,

belief or trust can be divided into two forms, namely

institutional trust and interpersonal trust. Institutional

trust is trust in medical institutions or systems and

doctors in general, influenced by the media and

institutions related to the health sector, such as the

reputation of hospitals, completeness of

infrastructure, and others. Whereas what is meant by

interpersonal trust is trust in a health provider, such

as a doctor who is built through repetition of

interactions and expectations about behavior,

reliability, kindness (honesty), communication,

physical appearance, empathy from a trusted person

can be tested from time to time (Hall, Dugan, Zheng,

& Mishra, 2001; Pearson & Raeke, 2000).

According to the Indonesian Medical Ethics

Honorary Council (MKEK), a doctor tries to cure a

patient of his illness and restore his health. One way

to cure the disease by a doctor is to cause and

strengthen the patient's confidence and belief that he

can recover. Turning his attention to things that are

hopeful, and optimistic. Psychological / mental state

of the patient must be considered so that an

118

Rudiyanto, ., Girsang, E. and Nasution, A.

Application of Fuzzy Method to Predict Patient’s Belief in Doctor Qualiﬁcations.

DOI: 10.5220/0010291001180125

In Proceedings of the International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical (HIMBEP 2020), pages 118-125

ISBN: 978-989-758-500-5

explanation of the patient's illness, must be able to

generate confidence that the disease can heal or the

symptoms of the disease can be reduced (MKEK IDI,

2004).

The relationship between a doctor and a patient is

a relationship based on trust, in which the patient

places his belief in his doctor that his illness can be

cured (Guwandi, 2015). Patients' trust in the services

of doctors in hospitals becomes the basis in the

ongoing relationship between health care providers

with patients. A doctor who has a good track record

will attract more patients as consumers because he

believes that the doctor has good quality and can be

trusted for healing his illness (Soetjiningsih, 2016;

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

The accuracy of the examination carried out by

the doctor will affect the level of patient confidence

in the reliability of the hospital in providing services.

Patients will feel safe and comfortable receiving the

services provided if what they receive is as expected

(Maksum, Maidin, & Anggareni, 2013). This is

consistent with the opinion of Bowers, Swan, &

Kochler (2014) states that in looking at the quality of

health services mentioned factors that determine the

quality of services, namely the ability, skills and

knowledge of officers must be in accordance with

service providers and doctors who are well trained.

This will increase the patient's confidence in the

doctor who handles the disease.

Several previous studies that examined the factors

that influence patient confidence, Djohan's study

(2015) found that the reliability of doctors and

perceived benevolence (operational benevolence) had

a significant positive effect on the confidence of

hospitalized patients in private hospitals in

Banjarmasin. Research conducted by Croker et al.

(2013) found that belief in the doctor was because the

doctor gave the patient enough time, the doctor asked

about the symptoms of the patient's illness, the doctor

listened to the patient, the doctor explained the

examination and treatment, the doctor involved the

patient in the decision about treatment, the doctor

treated the patient with care and attention and the

doctor takes the patient's problem seriously. Another

study by Jiang & Street (2017), that patients' trust in

doctors positively moderate the relationship between

patient activation and doctor-patient communication

online, and between perceptions of health status and

doctor-patient communication online.

Research conducted by Chandra,

Mohammadnezhad, & Ward (2018) on 17 selected

articles from electronic databases including PubMed,

Hinari, Cochrane, Scopus, and Google Scholar

obtained results that in addition to doctor

communication skills, interpersonal care levels and

knowledge about patient illnesses, respect for

patients' illnesses, respect for doctors and care

guarantees increase patient confidence or confidence.

Another study by Gopichandran & Chetlapalli (2013)

in Tamil Nadu, South India found that patients' beliefs

or trust in doctors are influenced by doctor behavior,

perceived comfort levels, personal involvement with

patients, and to a lesser extent by cultural competence

and physical appearance doctor. Based on the results

of some of these studies indicate that the patient's

belief or trust in the doctor is an important concern

and need to know what factors influence the patient's

belief or trust.

The preliminary survey conducted by researchers

interviewed 20 patients who received doctor's

services by asking their beliefs / trust about doctors

practicing their illness or their families showing as

many as 11 people said they were very confident in

their ability, 5 people said they were quite sure, and 4

people said they were not sure . Patients who believe

in the doctor's practice are caused by good doctor's

behavior, friendly, polite communicative. Doctors

understand about illnesses suffered by patients and

family members. The doctor shows empathy to the

patient and looks neat. Conversely, patients who are

not sure say that doctors are not friendly, doctors are

considered less communicative in explaining

illnesses suffered by patients, in addition there are

also some who say that they are not satisfied with the

answers given by practicing doctors when asked by

patients. The patient's beliefs or trust are influenced

by several factors that will be the topic of this

research.

2 METHOD

This type of research was a quantitative analytic study

with a cross sectional study design. This research was

conducted at the Stella Maris Hospital in Medan in

November 2019. The population of the study was

9,919 people, and the samples were 260 people. The

research sampling technique was done by accidental

sampling. The sample in this study was a portion of

the population whose size was taken using the

Lemeshow formula as follows:

 









12/1

1)1(

PoPa

PaPaZPoPoZ











where, n is the sample size, Z



is the standard

deviation of the alpha for  = 0.05  Z = 1,96, Z-



is the standard deviation of beta for  = 0,10 

Z = 1,282, Po is the proportion of the prevalence of

Application of Fuzzy Method to Predict Patient’s Belief in Doctor Qualiﬁcations

119

SC non-medical indications, Pa is the estimated

maternal cesarean section without medical indication,

Pa-Po is the proportion difference of 0.10. Inclusion

criteria used include: Inpatient / Inpatient status the

way, get a doctor's service, can communicate well,

willing to be a respondent, while the exclusion criteria

include not being able to talk (deaf / deaf) and not

willing to be a respondent.

Validity test is used to measure the validity of an

instrument used, meaning that it is able to reveal

what is measured. Validity test was carried out at

Sarah Medan General Hospital 30 patients. The test

conducted is to determine the correlation between

the questions with the total construct score or

variable. A construct is declared valid if there is a

positive and significant correlation. The correlation

value must be greater than 0.361 or the Corrected

Indicator-Total Correlation value in the SPSS output

greater than 0.361 using the Pearson Product

Moment correlation test (Ghozali, 2015). Univariate

data analysis, bivariate using chi-square test, and

multivariate using multiple logistic regression tests

with a confidence level of 95% ( = 0.05).

Soft Computing is a new innovation in building

intelligent systems. This intelligent system is a

system that has expertise like humans in a particular

domain, is able to adapt and learn so that it can work

better in the event of environmental changes. The

basic elements in Soft Computing are: Fuzzy

Systems, Artificial Neural Networks, Probabilistic

Reasoning, Evolutionary Computing.

Fuzzy systems generally consist of 5 steps in

reasoning: Enter fuzzy input, apply the fuzy operator,

apply the implication method, the composition of all

outputs, and defuzifikasi process. Fuzzy logic is an

appropriate way to map an input space into an output

space. For very complex systems, the use of fuzzy

logic (fuzzy logic) is one solution (Kusumandari et al,

2018; Turnip et al, 2018). In general, fuzzy systems

are very suitable for reasoning approaches, especially

for systems that deal with problems that are difficult

to define using mathematical models. For example,

the input values and parameters of a system are less

accurate or unclear, making it difficult to define

mathematical models.

A membership function is a curve that shows the

mapping of data input / output points into their

membership values. One method that can be used to

get membership values is through the function

approach such as triangular, trapesoidal, gaussian,

and others. The diagram of fuzzy modeling is shown

in Figure 1.

Figure 1: Scheme of Fuzzy Model

If in a fuzzy system there is a fuzzy rule with

several inputs, then those inputs can be connected

with Zadeh operators, for example AND and OR.

When the inputs in the fuzzy rule are connected using

the AND operator, the degree of membership of the

set of fuzzy outputs is taken from the minimum

degree of membership of the inputs. Conversely, if

the input operator used is the OR function, then the

degree of membership of the fuzzy output set is taken

from the maximum membership degree of the input

fuzzy set (Turnip et al 2018). Besides being formed

based on fuzzy operations on input, the output graph

is cut or scaled using the MIN or DOT implication

function. Furthermore, decisions can be taken

through the defuzzification process.

3 RESULTS AND DISCUSSION

Respondent characteristics include age, sex, last

education, occupation, and reason for the visit. Table

1 shows that the majority of respondents aged <43

years (54.6%), a small proportion aged> 43 years

(45.4%). Based on gender, all respondents were

female (100.0%). Based on education, the majority of

respondents were highly educated (diploma / higher)

(78.5%), a small proportion were middle educated

(high school) (21.5%). Based on work, most

respondents work as entrepreneurs / traders (37.7%),

a small proportion of respondents work as civil

servants (13.0%). Based on the reason for visiting

Stella Maris Hospital in Medan, the majority of

respondents went to the hospital for treatment

(50.8%), a small portion went to the hospital for

control (routine check) (49.2%).

Based on the results of the bivariate analysis

obtained a significant correlation of independent

variables with confidence / trust in lecturers who

practice at RSIA Stella Maris in 2019 namely

physician behavior (p = 0.003), physician honesty (p

= 0.005), physician reliability (p = 0,000), style

doctor communication (p = 0,000), doctor empathy (p

= 0,000), choice recommendations (p = 0,000), and

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

120

doctor-patient relationship (p = 0,000). Unrelated

variables are gender (0.879), and education (0.582).

The complete Chi-Square statistical test results can be

seen in Table 1.

Table 1: Relationship of Each Independent and Dependent

Variable.

Variables

Patient's Belief /

Trust

value

Believ

Unbelieve

Sex:

Male

Female

109

0,879

Education :

Midle

High

159

0,582

Doctor Behavior:

Good

Not Good

176

0,003

Doctor Honesty:

Honest

Not Honest

163

0,005

Doctor reliability:

Reliable

Not Reliable

172

0,000

Doctor's

Communication

Style:

Communicative

Not

Communicative

168

0,000

Doctor Empathy:

Empathy

Not Em

ath

167

0,000

Recommended

Choice:

Personally

Others

160

0,000

Doctor-Patient

Relationship:

Good

Not Good

170

0,000

The results of multivariate analysis (Table 2) with

multiple logistic regression tests showed that from 7

model candidate variables, only 3 variables were

associated with patients' beliefs / trust in practicing

physicians namely reliability, communication style,

and doctor-patient relationship. The most significant

variable was the doctor-patient relationship variable

which had an Exp (B) / OR value = 11,180 meaning

that the patient who stated the doctor's relationship

with the patient was good, had confidence in the

practicing doctor by 11.1 times higher than the

unfavorable one.

Table 2: Multiple Logistic Regression Test Results

(Significant).

Variabel B Sig. Exp(B)

95%CI for

Exp(B)

Reliability

Communication style

Doctor-Patient

Relationship

Constan

2,220

1,750

2,414

-9,979

0,00

9,207

5,753

11,180

4,148-20,437

2,479-13,349

4,978-25,106

The results of the multiple logistic regression tests

also showed variables that were not related to the

beliefs / trust of patients in practicing doctors because

they had a significant value> 0.05, namely behavior

(p = 0.056), honesty (p = 0.110), empathy (p = 0.055)

and choice recommendations (p = 0.051). More can

be seen in Table 3.

Table 3: Multiple Logistic Regression Test Results (not

significant).

No. Variables Si

-value

)

Behaviour

Honesty

Empathy

Recommended

Choice

0,056

0,110

0,055

0,051

3.1 Doctor Reliability

The results showed that there was a relationship

between physician reliability and patient confidence /

trust in the practice physician, p = 0,000 <0.05.

Reliability variable that has a value of Exp (B) / OR

= 9,207 means that patients who state that doctors

have confidence in doctors who practice were 9.2

times higher than less reliable ones.

Based on the assumptions it was found that

physician reliability was significantly related to

patient confidence / belief. Patients who say that

doctors are reliable tend to feel more confident about

these doctors than patients who state that doctors

were less reliable and feel less sure or lack trust in

doctors. The reliability of the doctor in conducting the

examination was the key to the patient's confidence.

The reliability of doctors was also judged by the

doctor's rapid response in providing services to

patients. In addition, doctors also show readiness to

always serve patients, because by providing the

services that patients want, patients will be satisfied

and confident of the services they receive from

doctors. To increase patient confidence, doctors must

also explain in detail if there are questions raised by

patients. Patients' beliefs also get stronger when the

doctor states there is always for the patient.

Application of Fuzzy Method to Predict Patient’s Belief in Doctor Qualiﬁcations

121

3.2 Communication Style

The relationship between doctor's communication

style and patient's belief / trust was found, p = 0,000

<0.05. The doctor communication style variable that

has the value Exp (B) / OR = 5.753 means that

patients who declare communicative doctors have

confidence in doctors who practice 5.7 times higher

than those who are less communicative.

Communication in the process of history taking is

not only done to the patient (auto history taking), but

also to other people who know the patient's disease

history. A significant contribution of the

communication process in the doctor's history to the

patient shows that communication skills were an

absolute requirement for a doctor to get complete

information about the symptoms experienced by the

patient. If a doctor is unable to carry out the

communication process, this inability can result in an

inaccurate diagnosis that is made because information

cannot be extracted from the patient. Communication

delivered in a style that is easy to understand, polite,

relaxed will make it easier for patients to understand

what is delivered by the doctor. Improving the

communication skills of doctors in hospitals must

also be a concern of hospital management because by

establishing good communication between doctors

and patients will be able to create a comfortable

atmosphere and will arise mutual trust, mutual respect

and respect.

According to the researchers' assumptions, this

study proves that the doctor's communication style

was related to the patient's belief / trust. Patients who

state that the doctor has a communicative style of

communication tend to believe in the doctor. The

communication style of the doctor was related to the

style of speaking in a clear voice. When

communicating, provide information to patients and

always smile. Doctors also use language that is easily

understood by patients so that patients feel confident

that the information provided adds to the knowledge

and insight of patients about the disease they are

experiencing. Doctors also act interactively by

inviting patients to ask things that are not understood

or not understood.

3.3 Doctor-Patient Relationship

Based on the results of the study showed that there

was a doctor-patient relationship with the patient's

belief / trust in the doctor, p = 0,000 <0.05. The

doctor-patient relationship variable that has an Exp

value (B) / OR = 11.180 means that the patient who

states that the doctor-patient relationship is good, has

confidence in the doctor practicing 11.1 times higher

than the unfavorable one.

In principle, in the relationship between doctors

and patients there are two important things that must

be considered, namely how doctors place patient

autonomy as individuals, especially in making

medical decisions and how doctors build harmony

through effective communication. During this time

doctors put themselves in medical decisions as a

guardian and the most versatile, so that patient

autonomy has less place.

The results of this study prove that the patient's

doctor relationship is significantly related to the

patient's confidence in the doctor. Patients who state

that a good relationship between the doctor and the

patient makes them believe and trust with the doctor.

According to the majority of respondents surveyed

stated that patients feel familiar with doctors who

examine them especially those who often seek

treatment. This makes the patient feel comfortable

and close to the doctor so that patients do not hesitate

to ask questions related to the disease they are

experiencing. The closeness of the doctor and patient

is caused by the doctor often giving input when the

patient confides about his health condition.

3.4. Fuzzy Model

After the formation of the fuzzy set, the fuzzy rules

are formed. Rules were formed to state the relation

between input and output. Each rule was an

implication. The operator used to connect between

the two inputs is the AND operator, and the one

mapping the input-output is IF-THEN. Propositions

that follow IF are called antecedents, while

propositions that follow THEN are called consistent.

The following are 15 out of 80 built rules in the model

design were:

[R1] IF Behavior is SD AND Honesty is SD AND

Reliability is SD AND Communication style is SD

AND Emapti is SD AND Recommended Choice is

SD AND Patient Doctor Relationship is SD THEN

Patient Confidence in Doctors is SD.

[R2] IF Behavior is SA AND Honesty is A AND

Reliability is A AND Communication Style is SA

AND Emapti is A AND Recommended Choice is A

AND Patient Doctor Relationship is SA THEN

Patient Confidence in Doctors is A.

[R3] IF Behavior is A AND Honesty is A AND

Reliability is A AND Communication Style is SA

AND Emapti is A AND Recommended Choice is D

AND Patient Doctor Relationship is SA THEN

Patient Confidence in Doctors is A.

[R4] IF Behavior is A AND Honesty is D AND

Reliability is D AND Communication style is A AND

Emapti is SD AND Recommended Choice is D AND

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122

Patient Doctor Relationship is A THEN Patient

Confidence in Doctors is A.

[R5] IF Behavior is A AND Honesty is D AND

Reliability is SA AND Communication style is D

AND Emapti is A AND Recommended Choice is SA

AND Patient Doctor Relationship is A THEN Patient

Confidence in Doctors is A.

[R6] IF Behavior is D AND Honesty is SA AND

Reliability is A AND Communication style is A AND

Emapti is D AND Recommended Choice is D AND

Patient Doctor Relationship is D THEN Patient

Confidence in Doctors is D.

[R7] IF Behavior is A AND Honesty is A AND

Reliability is D AND Communication style is SA

AND Emapti is A AND Recommended Choice is A

AND Patient Doctor Relationship is A THEN Patient

Confidence in Doctors is A.

[R8] IF Behavior is A AND Honesty is D AND

Reliability is D AND Communication style is A AND

Emapti is D AND Recommended Choice is A AND

Patient Doctor Relationship is D THEN Patient

Confidence in Doctors is D.

[R9] IF Behavior is D AND Honesty is SA AND

Reliability is A AND Communication style is A AND

Emapti is A AND Recommended Choice is A AND

Patient Doctor Relationship is SA THEN Patient

Confidence in Doctors is A.

[R10] IF Behavior is SA AND Honesty is SA AND

Reliability is SA AND Communication style is A

AND Emapti is A AND Recommended Choice is A

AND Patient Doctor Relationship is A THEN Patient

Confidence in Doctors is A.

[R11] IF Behavior is SA AND Honesty is A AND

Reliability is D AND Communication style is A AND

Emapti is A AND Recommended Choice is SA AND

Patient Doctor Relationship is A THEN Patient

Confidence in Doctors is A.

[R12] IF Behavior is A AND Honesty is SA AND

Reliability is A AND Communication style is A AND

Emapti is A AND Recommended Choice is A AND

Patient Doctor Relationship is A THEN Patient

Confidence in Doctors is SA.

[R13] IF Behavior is A AND Honesty is SA AND

Reliability is A AND Communication Style is D

AND Emapti is A AND Recommended Choice is D

AND Patient Doctor Relationship is A THEN Patient

Confidence in Doctors is A.

[R14] IF Behavior is SA AND Honesty is SA AND

Reliability is D AND Communication style is A AND

Emapti is A AND Recommended Choice is A AND

Patient Doctor Relationship is SA THEN Patient

Confidence in Doctors is SA.

[R15] IF Behavior is A AND Honesty is A AND

Reliability is A AND Communication style is A AND

Emapti is SA AND Recommended Choice is A AND

Patient Doctor Relationship is SA THEN Patient

Confidence in Doctors is SA.

In the rules, SD is strongly disagree, D is disagree, A

is agree, SA is strongly agree.

Next up toFor training, 30% of the training data

wasused. The input and output design is shown in

Figure 2. Fugure 3 is the capture of the 80 built rules

on fuzzy. Figure 4 is the surface form of the fuzzy

program which shows that if the Behavior value is

12.5 and the Honesty value is 12.5, then the Patient

Confidence value is at 25. Data Test results with

about 100 data training data are used to test the

accuracy of the fuzzy model. Figure 5 is a plot of the

measurement data and fuzzy results. From the test

results obtained a model with an accuracy of 92.82%

(blue is the measured data and orange is fuzzy model

results). From Figure 5 it is noted that accuracy

decreases from data to 41. This is because the data is

the first data recognized by the model. However, the

decrease is still not very significant with the meaning

that the model still managed to predict patient

confidence with an accuracy of 92.82%. Without

having to take measurements at any time, the

management can predict the level of patient

confidence with the next to control the related

variables.

Figure 2: Input and output design.

Figure 3: The capture of 80 rules.

Application of Fuzzy Method to Predict Patient’s Belief in Doctor Qualiﬁcations

123

Figure 4: Viewer surface of patient confidence toward

honesty and behaviour.

Figure 5: The comparison of patient confidence obtained

from fuzzy model toward measured data.

4 CONCLUSIONS

The results showed that reliability, communication

style, and doctor-patient relationship were related to

the patient's belief / trust in the doctor. The variable

with the greatest relationship in this study is the

doctor-patient relationship variable which has a value

of Exp (B) / OR = 11,180 which mean patients who

stated that they had a good relationship with the

doctor have an odds of 11,1 times to believe more in

their doctor than those who didn't.

REFERENCES

Ahmed, S. M. (2005). Exploring health-seeking behavior of

disadvantaged populations in rural Bangladesh.

Division of International Health(IHCAR), Department

of Health Public Sciences of Karolinska Institutet.

Bowers, M., Swan, J. E., & Kochler, W. F. (2014). What

Atibutes Determine Quality and Satisfaction With

Health Care Delivery. Health Care Management

Review, Aspen Publisher, Inc, 4(1), 49–55.

Chandra, S., Mohammadnezhad, M., & Ward, P. (2018).

Trust and Communication in a Doctor- Patient

Relationship: A Literature Review. Journal of

Healthcare Communications, 03(03), 1–6.

https://doi.org/10.4172/2472-1654.100146

Croker, J. E., Swancutt, D. R., Roberts, M. J., Abel, G. A.,

Roland, M., & Campbell, J. L. (2013). Factors affecting

patients’ trust and confidence in GPs: Evidence from

the English national GP patient survey. BMJ Open,

3(5), e002762. https://doi.org/10.1136/bmjopen-2013-

002762

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

Departemen Kesehatan Republik Indonesia.

Djohan, A. J. (2015). Faktor-Faktor yang Mempengaruhi

Kepuasan dan Kepercayaan untuk Mencapai Loyalitas

Pasien Rawat Inap pada Rumah Sakit Swasta di Kota

Banjarmasin. Jurnal Aplikasi Manajemen, 13(2), 257–

271.

Goold, S. D. (2012). Trust, distrust and trustworthiness. J

Gen Intern Med, 17(1), 79–81.

Gopichandran, V., & Chetlapalli, S. K. (2013). Factors

influencing trust in doctors: A community

segmentation strategy for quality improvement in

healthcare. BMJ Open, 3(1), 1–7.

Guwandi, J. (2015). Hukum Medik (Medical Law). Jakarta:

FK Universitas Indonesia.

Hall, M. A., Dugan, E., Zheng, B. Y., & Mishra, A. K.

(2001). Trust in physicians and medical institutions:

what is it, can it be measured, and does it matter?

Milbank Quarterly, 79(6), 13–39.

Hall, M. A., Zheng, B., Dugan, E., Camacho, F., Kidd, K.,

Mishra, A., & Balkrishnan, R. (2002). Measuring

patients’ trust in their primary careproviders. Medical

Care Research and Review, 59(3), 293–318.

Jiang, S., & Street, R. L. (2017). Factors Influencing

Communication with Doctors via the Internet: A Cross-

Sectional Analysis of 2014 HINTS Survey. Health

Communication, 32(2), 180–188.

Kane, S., & Calnan, M. (2017). Erosion of trust in the

medical profession in India: Time for doctors to act.

International Journal of Health Policy and

Management, 6(1), 5–8.

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.

Maksum, W., Maidin, A., & Anggareni, R. (2013).

Gambaran Tentang Mutu Pelayanan Dokter

Berdasarkan Persepsi Pasien di Instalasi Rawat Jalan

Rumah Sakit Universitas Hasanuddin Kota Makassar.

Bagian Manajemen Rumah Sakit FKM Universitas

Hasanuddin.

MKEK IDI. (2004). Kode Etik Kedokteran Indonesia Dan

Pedoman Pelaksanaan Kode Etik Kedokteran

Indonesia. Retrieved from

https://luk.staff.ugm.ac.id/atur/sehat/Kode-Etik-

Kedokteran.pdf

Moordiningsih. (2016). Relasi Psikologis Dokter-Pasien

Dalam Layanan Kesehatan : Persepsi, Komunikasi,

Empati Dan Unsur Kepercayaan. Psikologika, 22(11),

85–91.

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

124

Pearson, S. D., & Raeke, L. H. (2000). Patients’ trust in

physicians: many theories, fewmeasures, and little data.

J Gen Intern Med, 15(1), 509–513.

Rahmulyono. (2015). Konsep Rumah Sakit (Cetakan 1).

Jakarta: Gramedia Pustaka Utama.

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.

Sarafino, E. P. (2006). Health Psychology (5th editio).

USA: GTS Companies.

Setyawan, F. E. B. (2017). Komunikasi Medis : Hubungan

Dokter - Pasien. BIKKIK FK Unmuh Malang, 1(4), 51–

57.

Shaikh, B. T., & Hatcher, J. (2004). Health Seeking

Behaviour and Health Service Utilization in Pakistan:

Challenging The Policy Makers. Journal of Public

Health, 27(1), 49–54.

Soetjiningsih. (2016). Modul Komunikasi Pasien Dengan

Dokter (Cetakan 2). Jakarta: EGC.

Supranto. (2015). Pengukuran Tingkat Kepuasan

Pelanggan untuk Menaikkan Pangsa Pasar (Cetakan 2).

Jakarta: Rineka Cipta.

Thom, D. H., Ribisl, K. M., Stewart, A. L., & Luke, D. A.

(1999). Further validation andreliability testing of the

trust in physician scale. Medical Care, 37(5), 510–517.

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.

Yunanto. (2015). Pertanggungjawaban Dokter Dalam

Transaksi Terapeutik (Cetakan 1). Semarang:

Universitas Diponegoro Semarang Press.

Application of Fuzzy Method to Predict Patient’s Belief in Doctor Qualiﬁcations

125