Formal Model for e-Healthcare Readiness Assessment in Developing Country Context
Ojo S.O. 1, Olugbara O.O.2, Ditsa G.3, Adigun M. O.2 and Xulu S.S. 2
1
Department of Computer Science, University of Botswana,
2
Department of Computer Science, University of Zululand, South Africa,
3
College of Information Technology, United Arab Emirates University
Email: [email protected], [email protected], [email protected]
Abstract
The main objective of this paper is to report on a pilot test
for a proposed formal model for e-Healthcare readiness
assessment. The model provides a tool for determining critical
factors of e-Healthcare readiness such as need-change
readiness, engagement readiness, structural readiness and,
acceptance and use readiness. These factors constitute the
main constructs of the model which are formalized as
Hierarchical e-Healthcare Readiness Index System. The model
was operationalized and pilot tested to determine the eHealthcare readiness status of healthcare practitioners, the
public and patients from communities associated with two
healthcare facilities in the Uthungulu Health District of
KwaZulu/Natal province of Republic of South Africa (RSA).
The result of the pilot testing shows that (i) readiness with
acceptance and use appeared to be the most important
attribute, followed by structural and then engagement while
need-change is the least important, (ii) healthcare
practitioners agreed to be e-Healthcare ready while the public
and patients fairly agreed and (iii) the attitude of healthcare
practitioners can be determined as a function of their
preference for technology usefulness to ease of use. The
theoretical framework for the model is drawn from change
and change management theories, and IT acceptance and use,
and innovation adoption theories.
1. Introduction
There is great potential in e-Healthcare to address a
number of pressing challenges facing healthcare systems in
developing countries, including clear inequalities in health
status, quality of care and access. With healthcare equity
divide which is skewed against rural communities, these
challenges are more pronounced in rural areas in developing
countries. Information & Communication Technology (ICT)
projects in developing countries are also generally associated
with failures. E-Healthcare represents a substantial ICT
investment and as such, its failure can result in huge losses in
time, money and effort [1].
978-1-4244-1841-1/08/$25.00 ©2008 IEEE
Established innovation adoption and change theories
suggest that multiple factors are at play when an innovation is
successful or failing [2]. The successful introduction of eHealthcare requires the examination of such complex social,
political, organizational and infrastructure factors, which
include the readiness factor [3]. That is, the degree to which a
community is ready to participate and succeed in e-Healthcare
adoption. The assessment of readiness for an innovation in
healthcare can reduce the risk of its failure after introduction
[3]. It is therefore, imperative that all e-Healthcare
stakeholders have tools and mechanisms to understand the
readiness concept and to determine the readiness status of
rural communities before implementing e-Healthcare
innovations. The remainder of the paper is organized as
follows. Section 2 provides the background for this study.
Section 3 reviews literature and Section 4 presents the
theoretical framework for the study Section 5 presents the
research methodology and a discussion of the model. Section
6 presents analysis and results of the pilot study. The paper
finally concludes in Section 7.
2. Background
Major socio-economic development challenges facing
most African countries include economic diversification,
poverty and unemployment, diseases and unsustainable use of
natural resources. The challenge of quality healthcare service
provisions is compounded by the HIV/AIDS pandemic,
notably in the sub-Saharan Africa. The pandemic has huge
strain on the national healthcare system, increased number of
orphans which stretches family support systems, reduced
productive human capital & productivity, eroding knowledge
and skills, pressure on the national budget, increased povertystricken populace and reduced quality of life. Attainment of
socio-economic objectives is also greatly affected [4, 5]. The
usual socio-economic & infrastructural divide between rural
and urban communities makes the effects of these challenges
to be more pronounced in the rural areas. Hence, bridging the
rural-urban equity divide in healthcare service provisions has
become a key issue of concern in developing countries.
In RSA context, a Govt White Paper on the transformation of
the public health system reveals that: (i) majority of RSA
population has inadequate access to basic health services, (ii)
larger percentage of the population live in rural communities,
the vast majority of whom are poor, (iii) poverty is widely
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recognized as a major determinant of health status of
individuals, households and communities and (iv) gross
disparity between health status and nation’s economy. The
country spends 8.5% of her Gross Domestic Product (GDP),
which is greater than the European Union (EU) average of 8%
on health [6]. However, according to Thomas et al [7], the
most deprived health districts, that is, those with highest
deprivation indices have the lowest per capital expenditure on
Primary Health Care (PHC) services. A constant theme of the
health policy document [6] is the recognition of the need to
reduce the large level of social inequality in healthcare service
provisions towards overcoming the inadequacies of hospitals
and clinics in rural areas.
The challenges of healthcare needs in the developing
countries in general underscore the fact that healthcare
administrators need to increase their operations, efficiency and
effectiveness in healthcare provisions. Therefore, appropriate
ICT solution adoption for strengthening various activities such
as information and communication exchange between
healthcare managers, providers and the community, optimal
allocation of resources and managing the roll out of the
chronic diseases programs has been advocated. According to
Brewer et al [8], assuming researchers can find a suitable and
appropriate healthcare management solutions for developing
regions, then technology and specifically ICT can play
enabling roles in disease control, improving doctor's
efficiency, offering low cost diagnostics, improving data
collection and providing patient management tool. EHealthcare is increasingly considered an important tool for
enhancing healthcare service provisions, particularly in rural
communities [9]. E-Healthcare represents a substantial ICT
investment and as such, its failure can result in huge losses in
resources, which are scarce in developing countries. Hence,
there is the need to provide a tool for handling factors that
provide successful e-Healthcare adoption, including eReadiness.
3. Literature review
3.1 E-Readiness and e-Healthcare
Multiple factors are at play when an innovation is
successful or failing [2]. Readiness is one such factor and it is
the cognitive precursor to behaviors to either resist or support
a change effort [10]. Readiness for change is an integral and
preliminary step in the successful adoption of innovation [11].
E-Readiness is defined as the degree to which a community is
prepared to participate in the networked world [2]. It is usually
measured by assessing the community’s relative advancement
in the areas that are most critical for ICT the adoption and
most important applications of ICT [12]. This notion is
different from classical needs assessment, which identifies the
real problems. E-Readiness is a strategy to identify fissures in
the ability of a community or organization to implement
essential ICT solution to pre-identified problems.
Extrapolating from the general concept of e-Readiness, eHealthcare Readiness is the degree to which a community is
ready to participate and succeed in e-Healthcare adoption.
Fundamentally, e-Healthcare involves moving information
without moving the information owner using ICTs to deliver
and support healthcare services. A successful introduction of
e-Healthcare requires examination of the readiness of the
community among other complex organizational, social,
political, and infrastructure factors.
3.2 E-Readiness assessment models and approaches
The goal of this study is to develop a formal model for eHealthcare Readiness Assessment (eHRA) appropriate for
developing countries. This section examines some of the
exiting e-Readiness assessment models.
There are numerous case studies that aim at rating
countries on various measures held to indicate e-Readiness.
Based on their underlying goals, existing assessment tools and
models can generally be divided into three main categories: EEconomy Readiness Assessment models, E-Society Readiness
models, & E-Systems Readiness Assessment models [12]. EEconomy Readiness Assessment models focus on basic
infrastructure or a nation’s readiness for ICT to enable
economic activities towards economic growth. Examples are
WITSA e-Commerce survey, Mosaic’s Global Diffusion of
Internet Framework and EIU’s e-Business readiness rankings.
E-Society Readiness Assessment models, focus on the ability
of the overall society to benefit from ICT at work and personal
lives. Examples are CSPP’s e-Readiness Assessment Guide,
ASEAN Readiness Assessment and World Bank’s Knowledge
Assessment Methodology (KAM). E-Systems Readiness
Assessment models, examine the underlying technology
infrastructure that is a prerequisite for both e-Economy and eSociety. Example includes ITU’s World Telecom Indicators.
Some researchers [11, 13] have critique these models.
There are only very limited ready-to-use tools and models
available to assess e-Readiness. Each assessment tool or
model has a different goal and definition of e-Readiness. The
first generation of e-Readiness models assumes a fixed, onesize-fits-all set of requirements, regardless of the
characteristics of individual countries, the investment context,
or the demands of specific applications. Many e-Readiness
models provide little information on how their indices were
constructed, or how they might be adjusted to analyze
particular e-Opportunities. The details and methodologies of
assessment, if any, are not always publicly available and there
is a general tendency to provide single standard views and
values. Ambiguities in methodology compound uncertainties
of analyses and results. More to the point, the prevailing one
size fits all feature obscures the very differences that investors
or policy analysts require in order to reduce uncertainties or,
possibly even make more educated decisions. Finally, there is
no attention to the most fundamental questions, namely: eReadiness for what? Furthermore, these earlier e-Readiness
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assessment models all focus on too general measures for
assessing a nation’s e-Readiness without particular focus on
rural communities. We believe that efforts to assess overall
society’s e-Readiness need to focus specific attention on rural
communities given their particularities with regards to lack of
equity in rural-urban infrastructural provisions. In particular,
any attempt to address the digital divide problem must place
strong emphasis on rural communities.
Recently, rural community focused healthcare related eReadiness assessment tools have emerged [11, 14].
Additionally, some studies look at telehealth readiness
assessment prior to implementation as being an important
consideration [15]. However, existing e-Healthcare readiness
assessment tools are in the context of the developed world.
Given the contextual differences between the two worlds, this
raises the possibility of model mismatch in e-Readiness model
adoption. Also, the models and tools are limited in eHealthcare adoption scope as they are specifically designed
for telehealth technology adoption. There is the necessity
therefore to develop a model for eHRA in a developing world
context with potential for extrapolation to countries with
contextual similarities.
the understandable structure of being-in-the-world [16]. The
model accepted that human beings are self-interpreting and
recognized that meaning can be limited by language, culture
and history [17]. The analysis of the transcripts from each of
the domains led to the construction of the essence of the
eHRA model.
A questionnaire instrument was designed containing four
principal model constructs with their 94 measurement items.
The Hierarchical e-Healthcare Readiness Index System (HeHRIS) Models 1 and 2 shown in Figures 2 and 3 depict the
general structure of the instrument with the distribution of the
94 measurement items among the four principal constructs.
Response to these measurement items are designed based on
five-point Likert scale: strongly disagree (1), disagree (2),
neural (3), agree (4) and strongly agree (5). The communities
around some healthcare facilities within the Uthungulu Health
District of the Kwazulu/Natal Province of RSA were
randomly chosen as the sample population areas for the pilot
testing of the model. The questionnaire instrument
complemented with interviews was then administered. The
population sampling frame comprises healthcare practitioners,
public, patients and managers associated with healthcare
facilities in the selected communities.
4. Theoretical framework for eHRA model
The theoretical framework for the eHRA model draws on
the ideas from change and change management theories,
information technology acceptance and use, and innovation
adoption theories. The principal constructs of the model are
Need-change readiness (NCR) and Engagement readiness
(ER) both of which have their foundation in change theories
[10], Structural readiness (SR) which has its root from
perceived behavioral control element of the theory of planned
behavior [23], and Acceptance and Use readiness (AUR)
which is an adaptation of the technology acceptance and use
theories [18, 19, 20]. The NCR, ER and SR are adaptations of
the concepts of Core readiness, Engagement Readiness and
Structural readiness respectively defined in [21], though their
work did not link these concepts with any established theories.
The AUR component draws on elements of the Technology
Acceptance and Use (TAU) model introduced in [18], and
which has been subjected to rigorous theoretical and empirical
validation [19, 20]. Figure 1 shows the principal components
(constructs) of the model with their definitions and
characteristics. Figure 2 shows a refinement of this model with
these constructs and the sub-constructs depicted.
5. Research methodology and pilot testing
This study took a phenomenological approach that allows
one to discover the semantics for people in situations by
interpreting their self-descriptions. This allowed the
exploration of semantics of e-Healthcare readiness from the
following domains: practitioner, public, patient and
management using discourse as the meaningful articulation of
Figure 1: Model construct and characteristics
To facilitate revision of indexes, the He-HRIS Models 1
and 2 shown in Figures 2 and 3 respectively were developed.
Figure 2 is a six-level more complex model, which describes
the relationship between constructs in terms of opinions
evaluated for healthcare practitioners. Figure 3 is a three-level
simpler model that describes opinions evaluated for public,
patient and management respectively. The items at level 3 of
the He-HRIS in Figure 3 indicate the number of measurement
predictors for public, patient and management respectively.
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43
For example (5, 4, 2) means 5, 4 and 2 predictors are used to
measure the opinion of individuals drawn from public, patient
and management group respectively for the readiness
construct indicated.
measurement variables. Level 4 comprises 8 items and
perceived ease of use, which is determined by 7 items in level
6. Details of all 57 items (x1 – x57) used in He-HRIS model 1
and 37 items (x58 – x94) in He-HRIS model 2 is not released in
this paper for the sake of publication space requirement.
6. Data analysis, results and discussion
Figure 2: Hierarchical e-Healthcare readiness index
system (Model 1)
A total number of 500 questionnaires were administered
out of which 323 valid responses were received for analysis,
giving a response rate of 64.6%. Evaluations were given in
terms of opinion elicited on Likert scale. The result of the
overall evaluation of all the samples for He-HRIS model 1
revealed that healthcare practitioners in Uthungulu Health
District of RSA agreed with e-Healthcare readiness, with the
mean score (standard deviation) of 3.85(0.31), which is far
from the midpoint of the five-point scale. Similarly, the
overall evaluation for He-HRIS model 2 revealed that both
public and patients in Uthungulu Health District of RSA fairly
agreed with e-Healthcare readiness with mean scores slightly
above the midpoint of the scale as evidence in Table 1. The
result for management was not concluded because fewer data
were available to ascertain the effectiveness of the model.
Figures 2 and 3 provide the readiness model for the
hierarchical multiple regression analysis by means of
commercial SPSS 14 software package. Evaluations of higher
level attributes were regressed by the evaluations of the lower
attributes. For the He-HRIS model 1, the resulting equations
system was obtained using a 5-level formula (excluded due to
space limitation- details are obtainable from the authors).
Table 1: Model scores
Domain
Practitioner
Figure 3: Hierarchical e-Healthcare readiness index
system (Model 2)
In the He-HRIS model 1, e-Healthcare readiness depends on
four principal constructs: need-change readiness, engagement
readiness, structural readiness and acceptance and use
readiness as shown in level 2. The attributes of level 2 depend
on opinions measured using 21 items, attitude, social influence
and facilitating condition as shown in level 3. According to the
model, acceptance and use readiness can be measured by 4
items, attitude, social influence and facilitating condition or
directly measured by perceived ease of use as in level 5. Since
attitude in level 3 can be measured in terms of 4 items and
perceived usefulness or directly in terms of perceived ease of
use then acceptance and use readiness has three alternative
Public
Patient
Construct
e-Healthcare
Need-change
Engagement
Structural
Acceptance and Use
Attitude
Social Influence
Facilitating
Condition
Perceived Usefulness
Perceived Ease of
Use
e-Healthcare
Need-change
Engagement
Structural
e-Healthcare
Need-change
Engagement
Structural
Mean
Score
3.85
3.60
4.05
3.58
3.84
4.33
3.35
3.49
Standard
Deviation
0.31
0.77
0.50
0.67
0.38
0.40
0.47
0.66
4.08
3.82
0.50
0.62
3.18
2.98
3.25
3.38
3.28
3.26
3.32
3.26
0.47
0.42
0.92
0.70
0.44
0.60
0.72
1.02
The relative importance of each readiness attribute was
given by standardized regression coefficient β. The readiness
with “acceptance and use” appeared to be the most important
attribute (β=0.82), then came the attributes of “structural”,
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44
“engagement”, and “need-change” readiness with β=0.33,
β=0.24 and β=0.10 respectively. The regression parameters of
all 94 attributes to explain e-Healthcare readiness were
obtained following the American Psychological Association
guidelines for reporting multiple regressions [22]. Table 2
shows the constructs regression parameters with repeated
values denoted by an entry.
The model fitness was determined by the proportion of
explained variance r2, which can vary from 0-100%, with
100% indicating perfect fitness. The analysis results show that
the models perfectly fit well and the hierarchical multiple
attributes model applied in this study offer a promising and
valuable theoretical framework for modeling e-Healthcare
readiness assessment.
Table 2: Model regression parameters
Domain
Construct
e-Healthcare
B
0.04, 0.15,
0.15, 0.67
Need-change
0.20
Engagement
0.15, 0.15,
0.00, 0.14,
0.28, 0.15,
0.14
Structural
0.20
Acceptance
and Use
0.28, 0.27,
0.19, 0.04,
0.00, 0.00,
0.12, -0.01,
0.05, 0.00,
0.06
0.50, 0.13,
0.13, 0.00,
0.25
0.13, 0.13,
0.13, 0.13,
0.12, 0.13,
0.13, 0.13
0.14, 0.00,
0.21, 0.10,
0.15, 0.01,
0.07, 0.04,
0.19
0.14, 0.00,
0.00, 0.15,
0.13, 0.14,
0.15, 0.14,
0.15
0.51, 0.06,
0.06, 0.13,
0.12, 0.07,
0.06
-0.01
0.37, 0.20,
0.41
0.20
S.E.B.
0.03,
0.03,
0.04, 0.04
0.00
Practitioner
Attitude
Social
Influence
Facilitating
Condition
Perceived
Usefulness
Perceived Ease
of Use
Constant
e-Healthcare
Public
Need-change
0.00,
0.01,
0.00,
0.01,
0.00, 0.00
0.00
0.0
0.00
0.00
0.00
0.00,
0.00,
0.00,
0.01,
0.02, 0.01
0.18
0.04,
0.02, 0.03
0.00
β
0.10, 0.24,
0.33, 0.82
0.24,
0.34,
0.32
0.28,
0.00,
0.28,
0.20
Patient
Engagement
0.33
0.00
Structural
0.20
0.00
Constant
e-Healthcare
0.15
0.00
Need-change
0.03
0.40, 0.40,
0.20
0.25
0.00
Engagement
0.25
0.00
Structural
Constant
0.50
-
0.00
-
0.45
0.42, 0.44,
0.47
0.35, 0.37,
0.33, 0.34,
0.28
0.55, 0.66,
0.46
0.44, 0.47,
0.41, 0.49
0.43, 0.38,
0.42, 0.44
0.55, 0.58
-
Finally, the determination of AUR and Attitude were
based on equations containing perceived usefulness as variable
since error attained the minimum value for these equations.
The result shows that attitude of healthcare practitioners in
Uthungulu Health District can be determined as a function of
their preference for technology usefulness to ease of use.
0.16,
0.32,
7. Future work
0.13,
0.19,
0.18,
Further study to involve wider population of study from a
range of healthcare facilities in different national contexts will
be conducted to further test the theoretical validity and
empirical applicability of the model. Further test of the
reliability of the measurement scales will be carried out using
relevant statistical reliability measures.
0.47, 0.47,
0.25, 0.21,
0.21
0.38, 0.34,
0.34, 0.10,
0.00, 0.00,
0.17, -0.02,
0.07, 0.00,
0.07
0.62, 0.16,
0.17, 0.00,
0.31
0.25, 0.24,
0.24, 0.26,
0.21, 0.25,
0.32, 0.14
0.20, 0.00,
0.38, 0.18,
0.25, 0.02,
0.10, 0.06,
0.29
0.00, 0.00,
0.25, 0.17,
0.09, 0.24,
0.21, 0.12,
0.17
0.63, 0.06,
0.07, 0.12,
0.13, 0.10,
0.09
0.33, 0.40,
0.61
0.53, 0.52,
0.45, 0.56,
8. Conclusion
In this research, a comprehensive formal model for eHealthcare readiness assessment is developed. The model was
operationalized into a battery of questionnaire instrument and
pilot tested through the case study of some selected healthcare
communities. The model includes hierarchical index systems
that were established by means of questionnaire survey and
statistical analysis. This model is expected to provide a formal
approach for determining the e-Healthcare readiness status of
a rural community in a developing country.
9. References
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[2]. Information Technologies Group, Center for International
Development at Harvard University. Readiness for the
Networked World: A Guide for Developing Countries; 2002.
Accessed at http://cyber.law.harvard.edu/readinessguide/
[3]. Jennett P, Jackson A, Healy T, Ho K, Kazanjian A, Woollard R, .
A study of a rural community's readiness for telehealth. J
Telemed Telecare 2003;9:259-63.
[4]. Adigun M O, Ojo S O, Emuoyibofarhe O. J, & Dehinbo. eHealthcare Mgt : A Partnership & Collaboration Model In
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