The (Eccles et al., 2009). Despite the considerable

scientific community continuously produces research findings that can
contribute to more effective and efficient healthcare. However, to have an
effect on healthcare outcomes, these findings need to be adopted by healthcare systems,
organisations and professionals (Eccles
et al., 2009). Despite the considerable amount of
scientific knowledge produced, the implementation of research outcomes in
clinical practice remained challenging and got little attention (Bero
et al., 1998; Eccles et al., 2009). According to Green,
Ottoson, García, & Hiatt (2009), it can take an average of 17 years
for generated knowledge to become implemented in routine clinical practice.


in spite of the rapid development of innovative eHealth technologies and
the growth in published research on eHealth showing positive results, akin to
scientific knowledge in general, the dissemination and implementation of these
new technologies in real-world healthcare settings remains difficult (Rabin & Glasgow, 2012; Varsi, 2016). This gap was
described in a review by Elbert et al. (2014), which advocated
for a shift in research from large controlled studies on effectiveness of eHealth
towards studies focussed on strategies to implement effective eHealth
initiatives in daily practice. The majority of published research showed to be
more concerned with efficacy and effectiveness, 
rather than analysing and evaluating how new interventions are
implemented (Elbert et al., 2014; Murray et al., 2011). This gap between research, development
and implementation is widely acknowledged. It is evident that there was, and
still is, a clear need for research addressing the uptake of research into
practice, namely implementation science.

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1.1  Defining implementation science

In its
broadest sense, implementation means; to
carry into effect (OED, 2017). A more
operational definition was given by Rabin
et al. (2008), who stated that implementation is
the process of putting to use or integrating evidence-based interventions
within a setting. Implementations science considers the determinants,
processes, and results of implementation to understand what, why and how
interventions work in real-world settings (Peters
et al., 2014). Eccles
& Mittman (2006)
define implementation science as: ”The scientific study of methods to promote
the systematic uptake of research findings and other evidence-based practices
into routine practice, and, hence, to improve the quality and effectiveness of
health services”. It is
especially concerned with the context in which implementation takes place, the
users of the new produced knowledge and (maintaining) behavioural change in
organisations and individuals (Eccles
et al., 2009; Peters et al., 2014; Varsi, 2016). In the past two decades,
implementation science has grown into a well-recognized body of science and is
now widely used to gain a better understanding of how to make use and implement
new eHealth interventions in practice (Eccles
et al., 2009; Varsi, 2016).


3.3 Implementation theories

There is a growing interest in the use of frameworks,
models and theories for implementation studies (Nilsen, 2015; Sales et al., 2006; Tabak et al., 2012). Some of the
theories used, have been previously applied in other disciplines such as
psychology and sociology, while others have emerged from inductive research
approaches used within implementation science (Tabak et al., 2012; Varsi, 2016). In a narrative
review, Nilsen (2015) distinguished five
categories of theoretical approaches used in implementation science: process
models,  determinant frameworks, classic
theories, implementation theories and evaluation frameworks.

models are aimed at describing and/or guiding the process of translating
research into practice (Nilsen, 2015). Earlier process
models, so-called research to practice models, characterized implementation as
a linear process of production, diffusion and dissemination. However, as it
became evident that the context in which research is used and implemented is
fundamental, this view shifted to consider a broader spectrum of implementation
aspects (Nilsen, 2015). Planned action
models, another type of process model, provide practical guidance to
organisations or individuals for the planning and execution of implementation (Lehman, Simpson, Knight, & Flynn, 2011; Nilsen,


To help understand and/or explain what factors
influence implementation outcomes, determinant
frameworks describe general types of determinants that (are hypothesized
to) influence implementation outcomes (Nilsen, 2015).  Many frameworks use a multi-level approach
including determinants from individual, organisational and other levels that
are made up of various barriers and/or enablers (Nilsen, 2015; Varsi, 2016). Determinants are
often derived from psychological theories on individual behaviour and
organisational theories on leadership and organisational culture (Nilsen, 2015; Tabak et al., 2012; Varsi, Ekstedt,
Gammon, & Ruland, 2015).


theories refer to theories that have previously been applied
in other research fields (Nilsen, 2015). A widely adopted
classic theory is Rogers’ (2003) theory of
Diffusions of Innovations, which was first used in sociology. He described five
attributes of innovation; complexity, relative advantage, compatibility,
trialability and observability. According to Rogers (2003), these five factors
determine the diffusion of new ideas, products and practices in a population
through social systems.


theories provide a deeper understanding and/or explanation of
certain aspects of implementation (Nilsen, 2015). It supports
researchers in prioritising the most important implementation features for
analysis. The Normalisation Process Theory (NPT) by McEvoy et al. (2014) for example,
identifies coherence, cognitive participation, collective action and reflexive
monitoring as the four main aspects of embedding complex interventions in


frameworks provide structural approaches for the monitoring and
evaluation of specific aspects of implementation initiatives to determine
implementation success (Nilsen, 2015). Evaluation
frameworks also provide means to compare outcomes of complex interventions and
monitor implementation progress (Campbell et al., 2000; Nilsen, 2015).


Given the five categories of theoretical approaches
for implementation research, the Consolidated Framework for Implementation
Research (CFIR) was selected and used to guide this study. According to Nilsen (2015), the CFIR can be
categorised under determinant frameworks, which is in line with this study’s
aim to identify the main barriers and facilitators of the PHC eHealth programme
in South Africa. Moreover, in a systematic review by Ross et al. (2016) on the factors that
influence eHealth implementation, the outcomes were said to fit the CFIR
remarkably well and its domains were very well defined. The broad and
comprehensive nature of the framework allowed for a complete and detailed
description of the implementation, covering all aspects of implementation
without limiting the depth of the study.



1.2  The Consolidated Framework for Implementation Research

The consolidated framework for Implementation research
(CFIR) was introduced by Damschroder et al. (2009) and is increasingly
being used in implementation research. The CFIR encompasses a set of general
domains comprised of multiple constructs that are synthesized and consolidated from
19 theories about innovation, dissemination, organisational change, knowledge
translation, implementation and research uptake (Damschroder et al., 2009; Ilott, Gerrish, Booth,
& Field, 2013). The
meta-theoretical nature of the framework allows it to be used in various
contexts and acknowledges the multi-layered complexity of implementation (Damschroder et al., 2009; Ross et al., 2016). The included
constructs are all believed to influence implementation either positively or
negatively. However, no distinction is made between the importance of different
constructs and causal relationships are not specified, making it a descriptive
framework (Damschroder et al., 2009). The CFIR includes
a total of 38 constructs divided under five major domains: intervention
characteristics, outer setting, inner setting, characteristics of individuals,
and process (Damschroder et al., 2009). Each domain is
shortly explained below. An overview of all constructs by Damschroder et al. (2009) is attached (appendix 1).


The first domain is focused on the characteristics of
the intervention that is being implemented. It addresses the perceptions of key
stakeholders on the origin, quality and validity of supporting evidence, costs,
design and adaptability of the intervention. It also includes the complexity
and relative advantage associated with the intervention. The clearer the
understanding of the advantage of an intervention, the easier it is to
implement (Rogers, 2003). According to the
CFIR, interventions have core components (essential elements) and an adaptable
periphery (adaptable elements related to the intervention and its context).
Adaptation is fundamental to avoid resistance to implementation of
organisations or individuals (Damschroder & Hagedorn, 2011).


The outer setting describes the external determinants
that promote the implementation of an intervention including policies and
regulations, external mandates, and guidelines (Damschroder et al., 2009). Competitive
pressure for implementation, and patient needs are also taken in consideration.
It generally includes the broader social, economic and political context in
which an intervention is implemented (Damschroder et al., 2009; Varsi, 2016). With the PHC
eHealth programme being a governmental initiative, the outer setting is
believed to enable a better understanding of the effect of political incentives
on the implementation.  


The inner setting is mainly focused on the structural
characteristics, communication channels, culture, readiness form
implementation, and the overall implementation climate (Damschroder et al., 2009). This includes
compatibility, learning climate and leadership engagement. According to Damschroder & Hagedorn (2011), this is arguably
the most complex domain because of the dynamic and interrelated nature of
elements within organisations. A good understanding of the organisational
structure in which an intervention is introduced is essential to be able to
take into account the multiple levels at which barriers and facilitators may be
of influence (Damschroder et al., 2009; Varsi et al., 2015).


Characteristics of individuals describe the attitudes
of involved stakeholders, self-efficacy, motivation, values and competences. Individuals
are not simply passive receivers of new innovations, they have agency, and can
influence themselves or others which may have consequences for implementation (Damschroder et al., 2009). This domain is
especially concerned with the knowledge, beliefs and skills of the individuals
involved, as the effect of their interests, norms, values and mindset on the
implementation process should not be underestimated (Damschroder et al., 2009; Varsi, 2016).

The final domain, process, describes the practical
elements related to the implementation process, such as the planning, execution
and evaluation. It also addresses the engagement of stakeholders, including
opinion leaders and champions which are involved in decision making and/or
promoting implementation (Damschroder & Hagedorn, 2011). A high-quality
implementation plan for example, comprising clear phases or distinct steps,
increases the chance of successful implementation. The process domain can refer
to multiple processes and sub-process running sequentially or simultaneously at
multiple levels (Damschroder et al., 2009).


Multiple reviews have shown that the CFIR (figure 3)
is suitable and most frequently used to identify barriers and facilitators for
the implementation of an innovation during or post implementation (Kirk et al., 2016; Ross et al., 2016). While the
framework is often solely used to guide data analysis, early adoption of the
framework during research question formulation and data collection strengthens this
research and the applicability of its finding (Damschroder & Hagedorn, 2011; Kirk et al., 2016;
Ross et al., 2016).