Lupine Publishers | Scholarly Journal Of Psychology And Behavioral Sciences
Abstract
Current
healthcare outcomes depend on the adoption of valid and latest research
evidence and practicing evidence-based medicine (EBM). EBM is the process of
adaptation of the finest available scientific research evidence into routine
clinical practice. However, literature reports gap between actual and required
clinical practice. This gap will not be bridged by just updating physicians
about EBM. Therefore, it is required to study the motivational factors in a
context to use technology in routine clinical practice as things behave
differently under varying constraints. This study aimed to address this gap by
investigating motivating factors to promote EBM, focusing mainly on developing
country context. Innovation diffusion theory will be used to provide the basic
or theoretical support for the research as this theory states that the adoption
of any innovation is itself facilitated by its certain characteristics.
Cross-sectional quantitative methodology will be used for this research. SPSS
and SEM will be used to analyze data and validation of the tested research
model. The innovation diffusion theory may provide constructive and practical
insights into the factors for the successful implementation of EBM, as well as
it will provide a guideline for those who try to adopt the bestevidences into
their clinical practice.
Keywords: Innovation diffusion theory; Evidence based
medicine; EBM; DOI Diffusion of innovation
Introduction
The
innovation is a new idea that is observed by the individual. Evidence-based medicine
is an innovation in the clinical decisionmaking process, which promises to
improve health care delivery [1]. But at the same time, practicing EBM is a
paradigm shift that is changing the conventional way of the clinical
decision-making process [2]. Acceptance of any change or innovation in
conventional clinical practices is always very hard, and multiple factors play
their part in the adoption procedure [3]. The characteristics of innovation
play a significant role in defining its rate of adoption [4]. For an innovation
to be adopted, it must be perceived as offering relative advantage, i.e.,
simple, compatible, observable and testable. Roger proposes one of the
theoretical approaches addressing the diffusion of innovation (DOI). DOI model
is supportive at describing the acceptance of explicit clinical events, mostly
when determining which components will need additional effort if diffusion is
to ensure [4]. Literature showed applications of DOI in health departments as
well [5-7]. Becker and Mohr worked to identify organizational characteristics
linked with the diffusion process among the health department [6]. One study
found that demographic features age, gender, education level, urban and rural
areas had a great impact on the time for adoption of innovation. It was noted
that the initial adopters of innovations different by age, education and used
information-seeking approaches as compared to their jurisdictions who are
varied by rurality [7,8]. Graduated younger people who had a higher standing in
their graduating class and belonged to urban areas were ready to adopt less
risky interventions. In comparison, older people in countryside areas who had a
normal standing in their graduating class, and they established their
leadership roles were ready to take more risks, but they adopt less
conventional innovations. It is also evident in literature that large health
departments easily adopt innovations than small health departments. The
accessibility of funds and human resources are the reasons which are supporting
this finding [8,9].
The
effective implementation of EBM in the healthcare sector, a better
understanding of the motivating factors is required in detail. Relation and
association of factors with each other are also very important as the context is
very special, and physicians have autonomous authority in the clinical
decision-making process. Context and users of technology matter a lot because
things behave differently under varying constraints. It is evident from
literature that the primary characteristics of the innovations have been
conflicting as these are inherent in the innovation. These primary
characteristics are not dependent on the perceived characteristics of the
intended users. Augury of the potential user’s behavior depends on their perception
about the primary traits as different users may perceive the primary
characteristics of the innovation in a different way, which can cause different
behavior of the users. This is the origin of the problem of using the primary
characteristics of innovation.
Innovation Diffusion Theory (IDT) is used for this research work because IDT is
considered relevant and useful to researchers conducting studies of information
systems innovations in healthcare organizations. The IDT theory tries to
explain the diffusion of new ideas, attitudes, opinions, and behaviors all over
a community. The IDT is an important constituent of the upgrading health
services worldwide, yet the literature illustrates that it is not always an
easy theory to relate empirically [10]. IDT identifies five factors that
influence the diffusion and adoption of an innovative idea or strategy. The IDT
offers a theoretical framework globally to accept information technology. The
role of ethnical background plays an important part in the adoption of new
ideas/technology as it addresses that how, why, and what adoption rate of new
technology can relate with different social backgrounds and settings [11]. IDT
not only addresses the adoption of information technology only, but it also
addresses other diffusion processes through the society, such as the acceptance
of new technology products such as services, style of music, fashion, food,
ideas, or political candidates [11-13].
This study builds upon what is known from past research on the diffusion and
research conducted by Jenine K. Harris [8], which described the significance to
comprehend how health personnel perceives the relative advantage, simplicity,
compatibility, and testability of evidence-based decision making. Specifically,
this research examined the effect of IDT factors, which can play a great
motivational role for the physicians to adopt the new way of the clinical
decision-making process. Results are based on their perceptions and experience
of practicing EBM. Rogers identified five (05) elements of a new or substitute
clinical behavior that can help in determining the adoption of a new activity
such as EBM. These are relative advantage, compatibility, complexity,
trialability, and observability [14]. Definitions of all these four elements
are given in Table 1 along with the hypothesized relations. All these variables
are discussed below.
Independent variables
Relative
advantage: The social prestige, satisfaction and convenience, clinicians are
some important factors for measuring the degree of relative advantage. Relative
advantage can also be measured in economic terms for a new clinical activity
[3]. Objective advantage of innovation is less important as compared to
clinician’s perception of the advantages of innovation. Clinicians, patients,
and the healthcare system together decide the best-evidence practice
implementation. For instance, if a new clinical activity changes the power
distribution balance in professional groups in a negative way, then the
innovation cannot be successfully applied. On the other hand, if the proposed
activity generates more revenue and benefits the clinicians without disturbing
the balance of power distribution, then innovation will be readily accepted and
adopted.
Therefore, it is hypothesized that
H1: Relative advantage of EBM has a noteworthy positive effect on the usage
intention for EBM adoption.
Compatibility: It is necessary
for successful adoption of an innovation that it must tackle an issue that is
perceived as problematic by the clinicians. For instance, a new clinical
activity or procedure will be adopted fast if it helps clinicians to detect
cancer or other life-threatening illness at very early stages [15]. It is a
strong medical belief that early detection of a disease is beneficial for the
patients. Accordingly, clinical activity or procedure offering this capacity
will be adopted quickly. The rapid adoption of mammography screening [16,17]
and testing for prostate cancer are a few real-life examples. Though literature
also has some controversial debates about the therapy mentioned above
effectiveness.
Therefore,
it is hypothesized that
H2: Compatibility of EBM has a significant positive effect on the usage
intention for EBM adoption.
Complexity/Simplicity: Literature proves
that the probability of adoption for a clinical procedure increases when the
procedure is simple, easy, and well defined. For example, the rate of change in
drug regimen for patients by clinicians is high and the reason behind this
phenomenon is that it is easy to adopt. While some precautionary activities as
detecting and handling patients with harmful alcohol consumption [18] have not
been adopted quickly, though reported potential health gain in literature. This
may be due to the complexity of these activities. All preventions at the
primary level are vulnerable due to the patient’s resistance and their lack of
accuracy in self-reporting risk behaviors. Additionally, inadequate expertise
in the consulting skills of clinicians necessary to achieve change may be the
other reason.
Therefore,
it is hypothesized that
H3: Complexity/simplicity of EBM has a significant positive effect usage intention
for EBM adoption.
Trialability: According to
Rogers, “trialability” is the degree of modification of an innovation. In other
words, the capability to test an intervention in medicine on a limited basis
allows clinicians to explore the implementation of the procedure, its
acceptability, and the possible outcomes. Rogers claims that the ability to
undertake limited cost-benefit experiments of an intervention endorses trust
and confidence that the evidence is not only, but its implementation is
logistically promising as well.
Therefore, it is hypothesized that
H4: Trialability of EBM has a significant positive effect on the usage
intention for EBM adoption.
Observability: Observability
means the idea of innovation is visible to others. The visibility of
innovation’s results motivates colleagues to discuss that particular
innovation. Discussions on the method or innovation by the influential
physician will further enhance the adoption rate. More clinicians will tend to
adopt the change in their clinical behavior if the role model practitioner is
more influential and charismatic. New techniques are often adopted very quickly
in the surgery department because of a common belief that there are
disadvantages in being “left behind” by not adopting new technology.
Therefore, it is hypothesized that
H5: Observability of EBM has a significant positive effect on the usage
intention for EBM adoption.
Dependent variable
UI:
In IS research, the system usage intention is a vital construct. Self-reported
usage is the only aspect of UI addressed by most of the researchers in
quantitative studies. These studies adapted self-reported usage to
operationalize the actual system usage, in the absence of usage metrics. In
technology acceptance studies intentions leads towards behavior [19]. The
reason behind this is high values of correlation reported in the literature for
the relation of intention and behavior. This research framework focused on the
use of UI as a dependent variable because the use of UI as a dependent variable
has literature support relative advantage and compatibility equivalent to PEOU
and PU constructs. Under conditions of incomplete volitional control, the
intention cannot act as a sufficient predictor of the behavior [20].
Diagrammatic representation of the research framework along with dependent and
independent variables is given as Figure 1.
Methodology
Sampling
A
simple random sampling method was used to collect data. Random numbers were
generated through the computer after having access to the staff records of the
physicians in the selected hospitals. The self-administered questionnaire used
as a data collection tool. A description of the questionnaire is provided in
the next section (Figure 2). A total of 350 questionnaires were distributed,
and 290 responses were returned. Thus, the response rate was 82.85%. Total of
twenty responses was discarded due to the same reply, missing responses or left
blank and thus the usable response rate was 77.1% of the 270 respondents, 61.1%
were female doctors (Mean age= 38.3 years, SD= 10.7) and 38.9% were male
doctors (Mean age= 40.1 years, SD= 9.83).
Data collection tool
A
structured close-ended questionnaire was used. At the beginning of the
questionnaire definition of evidence-based medicine was provided to have a
better understanding of the concept. Demographic questions (were age, gender,
organization, and working experience) were included at the beginning of the
questionnaire. It was requested to the participants to give feedback by
thinking that practice evidence-based medicine will be a requirement in the
future for their routine clinical practice. The standard variable for
innovation diffusion theory (technical compatibility, simplicity, relative
advantage, and intentions) was used in the current research model. The variables
derived from other related studies on the extension of the IDT (Trialability
and Observability) were also included to have a deeper understanding of the
phenomenon. Seven (07) points Likert scale (1=strongly disagree, 7=strongly
agree) was used to code the responses. English was the language of the
questionnaire. All the constructs were measured through the validated items
confirmed by the literature.
Results and Discussion
Data
screening was performed to identify missing data. The expectation-maximization
technique was applied by using little’s MCAR test, and it was found that the
missing data was less than 10 percent, and it is missing completely at random.
Missing data were handled by using the mean substitution imputation method.
Skewness and kurtosis were checked to detect data normality at a univariate
level while Kolmogorov Smirnov and Shapiro Wilks tests were performed to check
multivariate normality. Results suggested that the data was normally
distributed both at univariate and multivariate level. The linear regression
method by using the Mahalanobis distance test was performed to detect the
presence of outliers. Results revealed that the presence of the few outliers;
it was, decided to retain all the cases, as there was insufficient evidence to
suggest that these outliers were not part of the entire population (Hair et al.
2006). Construct reliability for each construct is calculated with alpha value
and tabulated in Table 2 all values are within an acceptable range. After basic
data screening process factor analysis is performed.
Factor analysis (FA)
FA
is a multivariate regression analysis statistical method used to analyze the
correlation structure between different variables/constructs. In FA, after
identifying latent dimensions of the constructs, data reduction was performed.
Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are
two steps in FA. The data is explored in EFA, while hypothesis is tested in CFA
[21]. For the current analysis, the researcher performed both steps of factor
analysis. EFA: There are two main steps in EFA, which are extraction and
rotation processes. The extraction process identifies the underlying factors or
constructs, while the rotation process yields an easy presentation of a factor
loading pattern. In the current analysis, the researcher used the orthogonal
rotation method for factor loading. Before performing factor analysis, we must
check the suitability of current research data for sample adequacy. For this
purpose, we performed KMO and Bartlett’s Test.
Kaiser-Meyer Olkin (KMO) and Bartlett’s Test
For
the current research study, the value of the KMO test is 0.853, which shows the
confidence about sample size adequacy to proceed for further steps of factor
analysis. The result for Bartlett’s test can be interpreted on the value of
significance. The value of significance is .000 confirmed that the analysis
could continue by using factor analysis as tabulated in Table 3. Furthermore,
for exploratory factor analysis (EFA), the principal components analysis (PCA)
and orthogonal model with varimax rotation method were applied. The result of
principal component analysis also confirms the presence of 6 factors explaining
the total variance of the components as shown in Table 4. The results
recommended the deletion of the item SP4 of Complexity/ Simplicity construct,
as it was highly cross loaded on another latent factor, OB (observability). The
graphical presentation of latent constructs based on eigenvalues in EFA is
shown in the scree plot (Figure 3). Scree plot plots all the eigenvalues in
their decreasing order, where eigenvalues on the vertical axis and factors on
the horizontal axis. The scree plot confirms the choice of 6 components.
Confirmatory factor analysis (CFA)
CFA
consists of the measurement and structural model. The measurement model used to
evaluate the model validity and reliability. The reliability includes
Cronbach’s alpha, while validity includes discriminant validity. Whereas
structural model is used to test the relationship between constructs. The
result for discriminant validity is shown in Table 5. The off-diagonal values
represents correlation squared while diagonal values represent AVE In order to
meet the criteria for discriminant validity, it is required that the AVE square
root for each construct should be greater than the interconstruct correlation
as tabulated in Table 6. It is also found that the inter-construct correlation
value was not above the square-root of the AVE, so the model satisfies the
discriminant validity criterion. In Table 7, the fit indexes for both
measurement and structural model are given. Hypothesis testing is done by using
the structural model. It is noted that four out of five hypotheses are
supported either at .001 (***) or .05 level of p-value; only one hypothesis BIßOB is rejected. Path diagram showing all
the hypothesis is presented in Figure 4. The most probable reason for the
insignificant hypothesis between observability and usage intention was that the
software and information system had less observability by physicians, hence
less rate of adoption as compare to hardware innovation [22]. Consequently, the
more potential user can see the innovation, the more likely he will adopt it.
This
study has following limitations:
a) Cross-sectional self-reported data limit this study.
b) Only one healthcare system, which might not reflect the factors for the
successful diffusion of EBM in other health care settings.
c) Using an anonymous survey for data collection.
d) The small sample sizes.
Conclusion
Evidence-based
medicine holds promise in improving health care quality and efficacy by
improving the clinical decision-making process. So far, health care is decades
behind other industries to adoption and use of information technology (IT).
Though, stakeholders in the health care sector have highlighted the urgent need
to adopt IT systems. This research work advocates that the resources presently
accessible to practice EBM are unlikely to attain full diffusion in the
preferred time frames. Regardless of the existing resources, the factors
influencing adoption patterns are also unlikely to change absent significant
incentives that have a positive impact on the new paradigm of clinical
practice. Already a lot of time elapsed between the introduction of sustainable
EBM technologies and today. There is growing recognition that the EBM diffusion
process is multidimensional and that no single dimension will effectively
address all the barriers and challenges to EBM adoption by physicians. Future
research that draws on cross-national comparisons of government programs and
their effect on diffusion factors could help shape policy maker’s attempts to
accelerate EBM adoption among health providers. It would be informative to have
in-depth information on how physicians and other providers react to the
government-introduced standards.
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