Assessing the Need of Decision-making Frameworks to Guide the
Adoption of Health Information Systems in Healthcare
Raja Manzar Abbas
1
, Noel Carroll
2
and Ita Richardson
1,3
1
Lero - the Irish Software Research Centre, University of Limerick, Limerick, Ireland
2
Lero - the Irish Software Research Centre, National University of Ireland Galway, Galway, Ireland
3
HRI- Health Research Institute, University of Limerick, Limerick, Ireland
Keywords: Healthcare Information System, Decision-making Theories, Adoption Theories.
Abstract: Hospital Information System (HIS) is important in the healthcare industry as it supports a wide range of highly
specialized health-care tasks, services and provide high-quality patient care. Adoption of HIS is one of the
key decisions by hospital management, yet the function of hospital decision-makers within the area of new
technology adoption, specifically the decision-making processes in the adoption of HIS remains unsupported.
To investigate this phenomenon, this paper identifies HIS decision-making theories, their short-coming of
adoption in healthcare organisations and decision-making facets that influence the adoption. These review
will shed some light for future researchers to conceptualize, distinguish and comprehend the underlying HIS
decision-making models and theories that may affect the future application of HIS adoption. A literature
search was conducted to identify studies presenting HIS decision-making adoption theories/models in a
healthcare environment. From synthesis of 26 studies, we identified five major facets that provides a structure
to organize and capture information on the decision-making and adoption of HIS. The themes presented here
provide a starting point in understanding the decision-making adoption theories, their major facets and their
short-coming in adopting HIS. This will facilitate our future research on decision-making framework for the
adoption of HIS.
1 INTRODUCTION
Healthcare information system (HIS) is defined by
Lippeveld et al. (2000) as “a set of components and
procedures organized with the objective of generating
information which will improve healthcare
management decisions at all levels of the health
system”. HIS has the potential to address many of the
challenges that healthcare is currently confronting.
For example, it can improve information
management, access to health services, quality and
safety of care, continuity of services, and costs
containment (Lippeveld et al., 2000). The adoption
and use of HIS can play an important role in cost
reduction and enhancing hospital performance
(Sulaiman and Wickramasinghe, 2014).
Central to the adoption of any HIS is the
decision-making process and frameworks to guide
decision-making. Thus, following decision guidelines
to support the adoption of (HIS) is vital to take full
advantage of HIS. However, despite an accumulation
of best practices and frameworks or research
identifying success factors, only 50% of HIS adoption
projects succeed (Alipour et al., 2017). Indeed, there
is ample evidence to suggest that despite the proposed
benefits of HIS failing to adopt a suitable decision
framework for the adoption of healthcare information
system can exculpate costs and in some cases lead to
the failure of HIS within a healthcare organisation
(Ahmadi et al., 2017).
2 PROBLEM STATEMENT
Adoption of HIS is one of the key decisions by
hospital management, yet the function of hospital
decision-makers within the area of new technology
adoption, specifically the decision-making processes
in the adoption of a new technology remains
unsupported (Yang et al., 2013).
Many interventions to improve the success of
information systems (IS) decision-making and
implementations are grounded in behavioural science,
using theories and models to identify conditions and
Abbas, R., Carroll, N. and Richardson, I.
Assessing the Need of Decision-making Frameworks to Guide the Adoption of Health Information Systems in Healthcare.
DOI: 10.5220/0007363202390247
In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019), pages 239-247
ISBN: 978-989-758-353-7
Copyright
c
2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
239
determinants of successful use. However, models in
the IS literature have evolved to address specific
theoretical problems of particular disciplinary
concerns, and each model has been tested and has
evolved using restricted set of IS implementation
procedures (Kim et al., 2016). Several theories have
been suggested to describe how hospitals decide and
adopt new technology, yet none of these perspectives
alone has been able to satisfactorily explain
technology adoption decisions (Kim et al., 2016,
Sulaiman and Wickramasinghe, 2014).
3 RESEARCH QUESTIONS
There is an apparent lack of insight into what a
decision-making adoption framework should capture,
and what are its short-comings when applied for
adoption of HIS. To address these gaps, we formulate
the following research questions:
RQ1. What are the current decision-making
theories/models used for the adoption of HIS?
RQ2. What are the short-comings of decision-
making theories/models to support HIS
adoption in the modern healthcare
environment?
4 METHODOLOGY
To explore these questions, we undertook a structured
literature review. A structured literature review may
be described as appraisals of past studies conducted
systematically, purposefully and methodologically
(Armitage and Keeble-Allen, 2008, Petticrew, 2001).
A structured literature review was done in accord with
Preferred Reporting Items for Systematic Reviews
and Meta-Analysis, or PRISMA guidelines for
systematic review and meta-analyses given by
Liberati et al. (2009).
In the research discussed in this article, a literature
search was completed in the bibliographic databases
(CINAHL, Embase, IEEE Xplore, ACM, Scopus,
Springer Link and Web of Science) for relevant
publications using the keyword search phrases
‘decision-making’, ‘decision support’, ‘decision-
making adoption frameworks’, ‘decision-making
adoption models’, ‘technology adoption’,
‘information system adoption’, ‘healthcare’,
‘hospitals’ and ‘health information system adoption’.
Initially 4532 reference sources were found. 580
studies were removed by EndNote software as they
were duplicated. From the remaining 3952 studies,
after screening titles and abstracts, 3789 were deemed
not eligible. Out of remaining 163 research articles,
137 articles were screened out after applying the
exclusion criteria on full text and 26 studies were
selected as primary studies.
5 FINDINGS
5.1 Importance of Decision-making
Frameworks in Healthcare
According to Baker et al. (2002) “decision-making is
regarded as the cognitive process resulting in the
selection of a belief or a course of action among
several alternative possibilities”.
Technology adoption decisions in hospitals may
occur through planned acquisitions or through
uncontrolled changes in medical practice. They reflect
a complex set of dynamics and incentives (Gelijns,
1992). Several theories (mentioned in table 1) have
been suggested to describe hospital behaviour and
adoption of new technology, yet none of these
perspectives alone has been able to satisfactorily
explain technology adoption decisions (Teplensky et
al., 1995).
There have been a number of high profile and
costly HIS failures within hospitals in recent years,
leading to the importance of having a decision making
framework to decrease the costs and failure rates
(Ajami and Mohammadi-Bertiani, 2012).
5.2 Models Used to Support Adoption
of HIS
We have looked into original versions of the theories
rather than the modified ones. We chose this route as
publications on HIS implementation are often based
on case studies that report before-and-after outcomes
and assessments of HIS as an intervention. Although
they can provide rich detail on particular examples,
they are often so focused on the specific aspects of the
cases at hand that they are difficult to use as building
blocks for constructing more generalizable theory. In
addition, because of their focus on the process and
impact of implementation, they offer limited insight
into the underlying factors and conditions that shaped
the outcomes (Ahmadi et al., 2015).
A range of models and theories are used to
evaluate and test the adoption of HIS. The purpose of
theories of adoption for HIS is to understand, explain,
or predict how, why and to what extend individuals
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Table 1: HIS decision-making related theories, its aim and theory facets.
HIS Decision
-
making Theories
Theory Description
Decision
-making
Characteristics
Corresponding
theory facet
Technology diffusion
(Ash, 1997)
Diffusion is the process for assimilating an
innovation by the members of a social system over
time and through certain
communication
channels.
This theory explains how diffusion of an
innovation/technology spreads across a social
system, including individuals, groups and
organisation.
The individual’s decision
to adopt HIS is influenced by five
characteristics of innovation
which include
: relative
advantage, compatibility, complexity,
trialability and
observability.
Environment
Human
Organisation
Technology
Theory of Reasoned
Action
(TRA)
(Fishbein
and Ajzen,
1975)
TRA is a social psychology
theory which attempts
to explain an individual’s behaviour in
acquiring
such an innovation
.
TRA defines the links
to adopt HIS
between the beliefs,
attitudes, norms, intentions and behaviours of
hospitals
individuals. An individual’s decision adoption behaviour is
determined by his/her behavioural intention, which is itself
determined by his/her attitudes and subjective norms towards
the behaviour
of HIS
.
Human
Environment
Theory of planned
behaviour
(TPB)
(Ajzen, 1991)
TPB was developed based on the
TRA; however,
TRA was related to voluntary behaviour which
appears not to be 100% voluntary in certain
circumstances. This
resulted in the addition of
another
construct which is perceived behavioural
control in TRA.
Perceived behavioural control is the individual's perception
with regard to how easy or difficult a particular behaviour
of
HIS is to be performed. The decision
-making intention of an
individual to a
dopt HIS is determined by attitudes, subjective
norms and perceived behavioural control.
Human
Environment
Technology
acceptance model
(TAM)
(Davis,
1989)
TAM is an IT theory that explains how people
come to accept and use a technology. TAM is an
adaptation of the Theory of TRA.
TAM posits two factors that determine an individual’s
decision
-making intention
for the adoption of HIS;
these are
Perceived Usefulness and Perceived Ease of Use. A personal
behavioural intention to use
HIS is directly influenced by
perceived usefulness and perceived ease of use.
Human
Technology
Unified theory of
acceptance and use of
technology
(UTAUT)
(Venkatesh et al.,
2003)
UTAUT was a result of a review and
consolidation of eight theories that earlier studies
had employed to explain technology usage
behaviour like TRA, TAM etc. Its main aim was
to explain users’ intentions to use a
technology
and their subsequent behaviour.
It deals with i
ndividual’s perceptions of whether
they have the ability to decide whether or not to
adopt the technology
.
UTAUT
posits two main decision
-making factors
for
adopting HIS
including
dependent variables (
behavioural
intention
of and usage behaviour) and independent constructs
(which
are performance expectancy
of HIS
, effort
expectancy, social influence, facilitating conditions, gender,
age, experience and voluntariness of use).
Simpli
fying the
model by using age as the only moderating factor
significantly increases the model’s ability to predict HIS
adoption
Human
Technology
Task-technology fit
model (TTF)
(Goodhue, 1998
)
TTF describes
interaction of task and technology
and how well
technology
fits within individuals.
TTF theorizes that
HIS utilization depends on the degree to
which a
HIS assists an individual in performing the
individual’s tasks, i.e. the task
-technology fit. The TTF
framework adds new insight into decision
-making of
HIS
adoption by incorporating the element of task and also
the
fitness of the task and HIS
.
Human
Technolo
gy
Assessing the Need of Decision-making Frameworks to Guide the Adoption of Health Information Systems in Healthcare
241
Table 1: HIS decision-making related theories, its aim and theory facets (cont.).
HIS Decision
-
making Theories
Theory Description
Decision
-making
Characteristics
Corresponding
theory facet
Evaluation
Framework
al., 2016)
requirements, monitors human interaction (end
-
strategy.
interventions and
opportunities for improvement.
CHEF
is comprised of four main layers for HIS decision
-
users and systems with a view to determine how these co
-
Environment
Human
Organisation
Technology
Connected Health
Delivery Framework
(Kuziemsky et al.,
2018)
Connected Health Delivery framework identifies
pain points, business model development,
analytics, and evaluation as four main linkages
between users (e.g. patients and providers) and
technology.
The central point to C
onnected Health Delivery Framework is
the use of the Design Thinking approach to understand the
relationship between and explorative
interplay between
people, processes, technology and business needs.
Business
Human
Organisation
Technology
HOT-fit (Yusof et
al., 2008
)
HOT-fit theory
covers h
uman perspective
issues
encountered by information technology
staff in
an
organisation.
Yosof et al., (2008
) proposed the Human,
Organis
ation and
Technology
-fit (HOT
-fit framework) which was developed
from a literature
review on
HIS evaluation studies.
The HOT
-
Fit has three decision
-making
aspects for HIS adoption linked
with Human (clinical users), Organisation (healthcare
organisation) and Technology (HIS functionality and
characteristics).
Human
Organisation
Technology
Kreuter, 1999)
planning health
IT promotion programs.
The goals of the model are to explain health
-related decision
-
Environment
Human
Technology
Temporality
Strategic triangle
model
(Pearlson and
Saunders,
2006)
The Strategic triangle
model explains
importance
for organisations of having an alignment between
strategic perspectives
.
The strategic triangle is a model used to establish the
competitive position of
healthcare organisation
in relation to
its competitors. It emphasises the importance for
healthcare
organisations of having an alignment between three strategic
perspectives
that play important role for decision
-making
adoption of HIS
(business, organisation and information)
.
Business
Organisation
TOE
Framework
(Tornatzky
and
Fleischer, 1990)
TOE focuses on the process by which a firm
adopts and implements technological innovations
.
TOE identifies
three aspects of healthcare organization's
context that can influence HIS adoption decision making i.e.,
technological aspec
t of HIS, organizational context of health
organisation, and environmental influence on the adoption of
HIS.
Environment
Organisation
Technology
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242
or organizations will adopt and decide to deploy HIS.
To look into underlying factors of decision-making
adoption of HIS, we need to look into HIS
applicability of these major theories and models that
predict outcomes and to identify the important facets
relating to success of adopting. Table 1 lists the
theories, description, characteristics and major facets
Based upon our understanding of the HIS field and
the key theory-based components highlighted in Table
1, there are six major decision-making facets but we
have included only five and excluded temporality
facet as it is about diffusion and not about adoption.
These five facets are:
1. Business—The business facet represents
the consideration of business issues related to the HIS
adoption decision. Business competition was found to
stimulate HIS adoption as healthcare organizations
strive to earn increased revenues by improving
efficiency (Hsiao et al., 2009).
2. Environment—elements relating to the
context influencing the decision-making and use of
HIS. The environment facet captures categories that
influence the implementation and use of the
technology like regulation of use for HIS.
3. Human—elements capturing the decision-
making and end results of the HIS in use by the user.
Its importance can be explained by the following
example: Physicians were told they had to use
the Computerized Provider Order Entry (CPOE)
systems and were not involved in the selection of the
system or the development of order sets. When the
system was implemented, many of the physicians did
not use the predefined order sets, ordering took a
significant time, and resistance dramatically increased
when errors were discovered. There was no ownership
or sense of responsibility to solve problems that arose,
and the CPOE system was subsequently abandoned
(Rippen et al., 2013).
4. Organisation—Decision-making elements
relating to internal factors of healthcare organisations
that are controlled by the organisation itself. The
organisational factors refer to the decision-making
characteristics and resources of hospital, including
intra-hospital communication processes, hospital size
and top management support within hospitals.
5. Technology—Decision-making elements
relevant to the HIS functionality and characteristics
like compatibility, complexity etc.
5.3 Short-comings from the HIS
Decision-making Adoption Models
and Theories
HIS decision-making adoption has largely been
studied at two levels, the individual and the
organisational. However, much of the HIS decision-
making adoption research has focused on the
individual by explaining what influences their
decision to use HIS. The most used decision-making
theories are the Technology Acceptance Model
(TAM), Theory of Planned Behaviour (TPB), Unified
Theory of Acceptance and Use of Technology
(UTAUT). For the relatively fewer studies on
organisation or group-level decision-making
adoption, the important theoretical perspectives
include the diffusion of innovation (DOI) theory,
HOT-fit, CHEF and the technology–organisation–
environment (TOE) perspective. Individually and
collectively, these theories make valuable
contributions by calling attention to the role of a range
of key decision-making factors influencing the
implementation and use of healthcare information
systems beyond the features of the technology itself
(Sulaiman and Wickramasinghe, 2014). While these
theoretically driven approaches are broader and often
richer than case studies, they are still highly focused,
which allows them to deeply explore the impact of a
limited number of factors. However, this prevents
them from explaining the effects of others factors.
Although, these are very widely used and
implemented theories, there seems no single theory of
decision-making that can be fitted to all the
technology adoption in healthcare (Ahmadi et al.,
2015).
5.3.1 Short-coming from Individual
Decision-making Adoption Theories
In 1975, Ajzen and Fishbein proposed the Theory of
Reasoned Action (TRA), which mainly illustrates a
person's behavioural tendency, for the purpose of
predicting, changing and interpreting an individual's
particular behaviour. TRA posits that individual
decision-making is driven by behavioural intentions
where behavioural intentions are a function of an
individual's attitude toward the behaviour and
subjective norms surrounding the performance of the
behaviour. In this theory, attitude and subjective
norms are independent of each other and they could
each exert indirect influence on an individual's
behaviour through behavioural intention.
In 1985, Ajzen proposed the Theory of planned
behaviour (TPB). It is an extension of the TRA that
Assessing the Need of Decision-making Frameworks to Guide the Adoption of Health Information Systems in Healthcare
243
strived for a more appropriate prediction and
interpretation of behavioural theory. The difference
between TPB and TRA is that the former predicts
decision-making under comparatively less
controllable circumstances for adoption of HIS, while
the latter predicts decision-making of HIS adoption
based on the assumption that all behaviours and
behavioural motivations are under control.
In order to explore the relationship between the
perceived emotions factor and the use of technology,
Davis developed the Technology Acceptance Model,
TAM that shows how users decides to accept and use
a technology and is based on the TRA and TPB. TAM
assumes that there are two specified beliefs that
determine HIS usage: perceived usefulness and
perceived ease of use, eliminating subjective norms
and normative beliefs.
Studies on TAM have generated conflicting
findings and have led to the confusion over
moderating and external variables (Chen and Tan,
2004). Hence, the TAM model should be generalized
with caution. Further, TAM measures perceived
adoption and self-reports on future behaviour rather
than measurement of actual behaviour. TAM contains
restricted constructs and thus cannot handle the issue
of adopting new HIS services or solutions. Also, TAM
is known for its limited possibility of explanation and
prediction, triviality and lack of practical value (Kim
et al., 2016). Venkatesh and Bala (2008) highlighted
that TAM-based empirical studies do not produce
totally consistent or clear results. Hence, significant
factors are needed to be identified and included in the
models especially for the adoption of HIS. The
extensive focus of TAM on technology to the neglect
of social and psychological parameters on the usage
of HIS limits its explanatory and predictive utilities,
and therefore demands its integration with other
frameworks.
Venkatesh et al. (2003) reviewed and consolidated
eight theories that earlier studies had employed to
explain technology decision-making behaviour like
TRA and TAM. They incorporated four key
determinants (performance expectancy, effort
expectancy, social influence and facilitation
conditions) and four key moderators (gender, age,
voluntariness and experience) in the UTAUT model.
According to Bagozzi (2007), UTAUT might be a
powerful model due to its parsimonious structure and
higher explanatory power (R²) compared to TAM.
However, the model does not examine direct effects
which might reveal new relationships and important
factors which were left out by subsuming under the
existing predictors only. Kim et al. (2016) added that
for HIS adoption, UTAUT lacks expansion in new
settings such as new technology, new users, and/or
new culture. They also suggested that UTAUT lacks
some constructs required for HIS adoption which is
echoed by Bagozzi findings. Although these theories
are well known and used for individual adoption, they
may not be well suited for organisational level
(Maillet et al., 2015).
Other perspectives, such as technology diffusion,
seek to assess HIS decision-making in a broader
context of the relationship of individuals, groups,
organisational features and other elements to the
technology. These perspectives underscore the
complex, interactive, and often subtle range of
influences that shape HIS decision-making and that
must be considered in evaluating its adoption. Still
other perspectives, such as PRECEDE/PROCEED
underscore temporal dimensions as initial HIS
implementation and use over time is affected by
change over time in the environment or other factors.
Task-technology fit theory can be used to address
task variables critical for successful implementation,
but it will neither predict nor explain an
implementation that fails because the technology does
not work (e.g., shuts down unexpectedly or does not
scale). In addition, many of the measures used to
substantiate variables have not been validated in the
HIS context (Kim et al., 2016).
5.3.2 Short-coming from Organisational
Decision-making Adoption Theories
The TOE framework was developed by Tornatzky
and Fleischer (1990) to examine firm-level adoption
of various IS/IT products and services. It has emerged
as a widespread theoretical perspective on IS adoption
(Zhu et al., 2004). Inclusion of technological,
organizational and environmental variables has made
TOE advantageous over other adoption models in
studying technology adoption, technology use and
value creation from technology innovation (Zhu et al.,
2004).
The TOE framework is consistent with the DOI
theory, in which Rogers (1995) emphasized
individual characteristics, and both the internal and
external characteristics of the organization, as drivers
for organizational innovativeness. These are identical
to the technology and organization context of the TOE
framework, but the TOE framework also includes a
new and important component, environment context.
The environment context presents both constraints
and opportunities for technological innovation. The
TOE framework makes Rogers’ innovation diffusion
theory better able to explain intrafirm innovation
diffusion (Hsu et al., 2006).
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But according to Dedrick and West (2003) the
TOE framework is just a taxonomy for categorizing
variables and it does not represent an integrated
conceptual framework or a well-developed theory,
hence, there is a requirement of a more robust
framework to study organizational adoption. The
TOE framework has been used to study the adoption
of inter-organizational systems, but only from the
perspective of a single focal firm. Extant research
does not examine how decisions are made when
multiple firms must collectively reach a decision
about a new system. It was highlighted by Yang et al.
(2013) that TOE framework is limited in its
explanatory power of technology adoption as well as
it can be seen in case of EHR adoption where around
half of the percentages of EHR adoption variance
remain unexplained. Wang et al. (2010) mentioned
that TOE framework has limited major constructs and
the variables of TOE framework may need to expand
to cover human aspects especially in small or medium
level organisations.
6 CONCLUSION
In this paper, we examine the literature on decision-
making adoption theories for HIS. We also explore
the short-comings of the current decision-making
adoption theories used for HIS. Considering the
broad and vast nature of investment and stake in HIS
adoption in healthcare sector, we identify the key
decision-making adoption theory facets (business,
environment, human, organisation and technology)
that stakeholders need to look into for the adoption of
HIS.
There is no panacea for selecting any particular
decision-making adoption theory for HIS. We have
tried to explain short-comings of the HIS decision-
making theories to enlighten the researchers about
designing the new framework to cover these
weaknesses to facilitate the development of more
comprehensive frameworks for effective HIS
implementation.
One limitation of this study is that we did not
assess the extent to which proposed facets addressed
decision-making adoption of HIS. The relative
importance of each facet in specific HIS contexts
remains to be explored by studies using prospective
designs.
In this study, we focused on decision-making
adoption in HIS by healthcare organisations, but we
have to acknowledge that adoption of HIS in
healthcare organizations is a multifaceted process
since various stakeholders are involved (Menachemi
et al., 2004). Also, decision-making is just the first
step to consider for the adoption of the HIS. As noted
by Menachemi et al. (2009), it is important to consider
the viewpoints of all key adopter groups, because
resistance in any of these groups could slow the
overall adoption and would not provide essential
information for decision-makers.
6.1 Future Research
Although this review is preliminary, the five decision-
making facets provide a high level checklist of
decision-making for adoption of HIS to consider in
healthcare environment. One of our future research
topics will be to explore the interrelationship between
the different facets.
We plan to undertake a structured literature
review to synthesize evidence, consider the strength
of evidence in assessing the extent to which factors
addressed the decision-making adoption of HIS in
healthcare organisations and implement these factors
and facets for developing organisational framework to
help decision-makers in adopting HIS.
ACKNOWLEDGEMENT
This work was supported with the financial support of
the Science Foundation Ireland grant 13/RC/2094.
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