Analysis of the effect of knowledge, attitude, and skill related to the preparation of doctors in facing industrial revolution 4.0

Open access: www.balimedicaljournal.org and ojs.unud.ac.id/index.php/b j Analysis of the effect of knowledge, attitude, and skill related to the preparation of doctors in facing industrial revolution 4.0 Ayu Sudiwedani1*, Gede Sri Darma1 Backgrounds: Human resource development in terms of the knowledge, attitude, and skills of a doctor is important in a digital era and to face the industrial revolution 4.0. This study aims to analyze the effect of knowledge, attitude, and skills on doctor behavior and the impact of doctor behavior on its readiness in facing the industrial revolution 4.0. Methods: This study uses descriptive-quantitative methods with regression analysis techniques. Results: The results of the study based on Regression Weight (γ) show that knowledge has no significant negative effect on behavior; attitude does not have a significant positive impact on behavior; skill does not have a significant positive effect on behavior, and behavior significantly has a positive effect on the readiness of doctors. Conclusion: The results and discussion show that hospitals can use the structural equation model in analyzing knowledge, attitude, skill, and behavior problems to increase the doctor’s readiness, and strengthening the doctor’s behavior needs to develop doctors’ attitudes and skills.


INTRODUCTION
The industrial revolution 4.0 era is a new development period where several technologies, including physical technology, digital technology, and biological technology were the main technology drivers for the industrial revolution 4.0. 1 One aspect that embodies digital technology is the Internet of Things (IoT). The Internet of Things is a powerful driver, which can find, identify, track, and monitor a subject, and even trigger related events in realtime.
The health sector is a sector that takes benefits from the combination of physical, digital, and technological systems. Currently, mobile phones consumer has the potential to transform, not only for their health and medical needs but also for health research. Existing data have been processed conventionally, sometimes encounter problems caused by human error (human error). Thus, it is necessary to create an information support system that processes data effectively and efficiently, to achieve a competitive advantage. The system must be designed to be developed and applied to create a data information system. 2 Many health care providers, for example, hospitals are exploring the potential of telemedicine.
One of the applications of hospital integration systems is electronic prescription (e-prescribing) to be able to make maximum use of technological developments to improve better patient service. One hospital that has implemented a Hospital Integration Management System (SIMARS) in Denpasar is Sanglah Hospital.
Technology development in Sanglah Hospital requires doctors' adaptation to be ready to face a new work model in the future. Human resources development in terms of the knowledge, attitude, and skills of a doctor is very important in preparing this digital era without forgetting the professional ethics of a doctor. Doctors, as human resources at Sanglah Hospital, are at the forefront service. Doctors are required to be able to always work quickly, precisely, safely, friendly, and comfortable in serving patients. The large number of patients handled by doctors at Sanglah Hospital makes doctors not optimally run the SIMARS program. If the input data is carried out by a non-doctor in charge, it can certainly cause fatal errors that result in unwanted events for the patient. According to Kandou et al., the behavior of doctors as human resources in hospital organizations can thus be influenced by the knowledge, attitude, and skills ORIGINAL ARTICLE of doctors themselves in responding to changes in conventional to digital work model. 3 Thus, this research aims to: 1) Analyzed the knowledge impact on doctor behavior, 2) Analyzed the effect of attitude on doctor behavior, 3) Analyzed the skill impact on doctor behavior, and 4) Analyzing the doctor behavior impact on readiness to face the industrial revolution 4.0 Gibson, Ivancevich, and Donnely in Koesmono suggested that skill is a proficiency that is related to the tasks that are owned and used by someone at the proper time. 4 According to Lian, in Kandou's research, skill (proficiency) is a person's ability to do an activity or job. 3 More about skills, Dunnett's in Kandou's research stated that skill is the capacity needed to carry out a series of tasks that develops from the results of training and experience. 3

METHODS
The thinking framework is based on a theoretical study, which then forms a research model. This theoretical research model illustrates the causal relationship between variables of knowledge, attitude, skill, behavior, and physician readiness.
From the thinking framework above, three independent variables influence behavior directly. They are knowledge, attitude, and skill. Furthermore, intervening behavior is the variable that directly affects doctor readiness as the dependent variable.
This research is want to find out the causal relationship between variables using a quantitative study with a descriptive and causative associative survey method. This study will identify a causal relationship between variables and predict these relationships and hypothesis testing. This research was conducted at Sanglah Hospital in Denpasar, where the hospital has implemented a Sistem Informasi Manajemen Rumah Sakit (SIMARS) that changed the doctor's working model in serving patients. This study uses quantitative data sourced from the doctor's response at Sanglah Hospital.
The study population was all 368 doctors working at Sanglah Hospital Denpasar. The sample size calculation uses the Slovin formula with a 5% error level. Based on the data of the total population (N) = 368 and the error rate of sample (e) = 5%, the size of the sample size needed in this study was determined, namely: Figure 1. Research framework Furthermore, the sample was determined using purposive accidental random sampling.
This research used quantitative analysis, including data processing, organizing data, and result finding. Data analyzed use SPSS software and Structural Equation Modeling (SEM) assisted by AMOS software packages. This model is used to see the size of the direct impact of independent variables on the dependent variable. 5

RESULT AND DISCUSSION
The research result will explain the descriptive analysis, which includes respondent identity description and respondent score description according to the research variable as well as analysis of the impact between variables using SEM AMOS.

Descriptive Analysis
This descriptive analysis was carried out on the respondents' answers related to the characteristics of the 193 respondents and the description of the respondent's answer scores. The answers include two main points, namely: characteristics of respondents and answer scores about knowledge variables (X 1 ), attitude (X 2 ), skills (X 3 ), behavior (Y), and readiness of the doctor (Z), each has three items questionnaire.

ORIGINAL ARTICLE
Based on the figure above, an analysis of measurement models with lambda parameters for the relationship between Knowledge, Attitude, Skill, Behavior with Doctor Readiness, structural model analysis, determination analysis, Goodness of fit analysis can be done.

Analysis of Model Measurement Test with
Lamda Parameters (λi) From the data processing result, we found that all latent variable indicators have a standardized estimate (regression weight) in the form of a loading factor or lambda (λi)> 0.50, a critical value C.R> 2.00 and probability smaller than 0.05 (***). Thus, it can be stated that all of the latent variable indicators are valid/significant.

Structural Equation Model Analysis
Model testing is performed using regression coefficients for Knowledge (X 1 ), Attitude (X 2 ), Skill (X 3 ), Behavior (Y), and Doctor Readiness (Z) variables through table output of the submenu view/set. Based on the calculation results, the regression coefficient (regression weight) and Standardized Regression Weights can be made as output tables as presented in the following.

Doctor Readiness (Z)
Regarding the standardized estimate for the variables Knowledge (X 1 ), Attitude (X 2 ), Skill (X 3 ), Behavior (Y), and behavior effect (Y) on Doctor Readiness (Z), then structural equation models can be made as follows: a. The structural equation of X 1 , X 2 , and X 3 impact on Y Impact of X 1 , X 2 and X 3 on Y: Y = γ y.x1 X1 + γ y.x2 X2 + γ y.x3 X3 + e1 Y = -2,164 X 1 + 4,151 X 2 -1,377 X 3 + e 1 b. The structural equation Y impact on Z Effect of Y on Z: Z = γ z.y Y + e2 = 0.834 Y + e 2 From the two tables above, it can be seen that Knowledge (X 1 ) impact on Behavior (Y) has a standardized estimate (regression weight) of -2,164, with Cr (Critical ratio = identical to the t-count value) of -0.352 at a probability of 0.725. CR value= 0.352 <2,000 and Probability = 0.725> 0.05 indicating that Knowledge variable (X 1 ) impact on the Behavior variable (Y) is negatively insignificant. This shows Hypothesis 1 (H 1 ), which stated knowledge has a positive impact on behavior is untested. Thus, it can be stated that the doctor's knowledge does not significantly improve the doctor's behavior. Instead, there is a tendency to decrease the doctor's behavior.
The Attitude (X 2 ) impact on Behavior (Y) has a standardized estimate (regression weight) of 4.151, with a Cr of 0.362 on a probability of 0.717. CR value = 0.362 <2,000 and Probability = 0.717> 0.05 indicating that the Attitude (X2) variable impact on the Behavior variable (Y) positively insignificant. This shows Hypothesis 2 (H 2 ), which stated that attitude has a positive impact on behavior is untested. Thus, it can be stated that the doctor's attitude does not significantly improve doctor behavior.
The Skill variable (X 3 ) impact on the Behavior variable (Y) has a standardized estimate (regression weight) of -1,377, with a Cr of -0,211 on a probability of 0.833. CR value = -0.211 <2,000 and Probability = 0.833> 0.05 shows that the Skill variable (X 3 ) impact on the Behavior variable (Y) is negatively insignificant. This shows Hypothesis  Regarding the cut-of-value and Goodness of fit results of the model in the table above, it appears that there are no criteria met the eight criteria that were used. Thus, the above model can be stated as a bad model. 6 From the above analysis, it can be concluded that all indicators of Knowledge (X 1 ), Attitude (X 2 ), Skill (X 3 ), Behavior (Y), and Doctor Readiness (Z) are valid. From the structural equation model above, which shows Regression Weight (γ), it can be seen that two exogenous variables have a negative relationship, and two exogenous variables have a positive relationship. The Goodness of Fit evaluation results indicates that none has met the criteria. Thus, the model cannot be stated as a good model, and it is necessary to modify the model to improve the model's fitness (Goodness of fit). 6 The Goodness of fit value can be improved by correlating several indicators that have a big Modification Index (M.I.) value or by excluding indicators that have a relatively small λ. 5

Model Modification
This modification model is done by correlating some errors that have big Indices Modification (M.I) coefficients. Modification of the model is done by correlating error that has a Modification Indices (M.I.) value > 10,000 in order to improve the Goodness of fit. After processing the model modification, SEM processing results are obtained, as shown below.  Judging from the Goodness of fit, it looks like the modified model shows improvements in all existing indicators. The main model that originally had no Goodness fits indicators fulfilled turns into five indicators that meet the requirements of Relative Chi-square (χ2/df), RMSEA, GFI, TLI, and CFI. Furthermore, from the regression weight of the exogenous variable to the endogenous variable when the model is modified, there is an increase in the three regression coefficients (standardized regression weight). Squared Multiple Correlation seems to increase. From the overall results of the above analysis, it can be stated that by doing a modification model can improve the Goodness of fit model. Since there are already five (more than two) indicators that meet the requirements of Goodness of fit, the model is already good (the good of fit). 6 Sanglah Hospital can use a modification result of a structural analysis model to analyze the knowledge, attitudes, skills, and behavior problems related to doctors' readiness improvement efforts. In addition, in the recruitment process of new doctors at Sanglah Hospital, should be emphasized to their Attitudes and Skills than their knowledge. Last, in an effort to improve the doctors' readiness, behavioral strengthening should be done, which is preceded by improvement in Attitudes and Skills.

CONFLICT OF INTERESTS
There is no competing interest regarding the manuscript.

FUNDING
None.