i need confounding factors from the study


write confounding factors from the studyCHECKLIST FOR ANALYTICAL
Critical Appraisal tools for use in JBI Systematic Reviews
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All systematic reviews incorporate a process of critique or appraisal of the research evidence. The purpose
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Critical Appraisal Checklist for Analytical Cross Sectional Studies – 2
Reviewer ______________________________________ Date_______________________________
Author_______________________________________ Year_________ Record Number_________
1. Were the criteria for inclusion in the sample clearly
□ □ □

2. Were the study subjects and the setting described in
□ □ □

3. Was the exposure measured in a valid and reliable
□ □ □

4. Were objective, standard criteria used for
measurement of the condition?
□ □ □

5. Were confounding factors identified?
□ □ □

6. Were strategies to deal with confounding factors
□ □ □

7. Were the outcomes measured in a valid and reliable
□ □ □

8. Was appropriate statistical analysis used?
□ □ □

Overall appraisal:


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Comments (Including reason for exclusion)
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Critical Appraisal Checklist for Analytical Cross Sectional Studies – 3
How to cite: Moola S, Munn Z, Tufanaru C, Aromataris E, Sears K, Sfetcu R, Currie M, Qureshi R, Mattis P,
Lisy K, Mu P-F. Chapter 7: Systematic reviews of etiology and risk . In: Aromataris E, Munn Z (Editors). JBI
Manual for Evidence Synthesis. JBI, 2020. Available from https://synthesismanual.jbi.global
Analytical cross sectional studies Critical Appraisal Tool
Answers: Yes, No, Unclear or Not/Applicable
1. Were the criteria for inclusion in the sample clearly defined?
The authors should provide clear inclusion and exclusion criteria that they developed prior to recruitment
of the study participants. The inclusion/exclusion criteria should be specified (e.g., risk, stage of disease
progression) with sufficient detail and all the necessary information critical to the study.
2. Were the study subjects and the setting described in detail?
The study sample should be described in sufficient detail so that other researchers can determine if it is
comparable to the population of interest to them. The authors should provide a clear description of the
population from which the study participants were selected or recruited, including demographics, location,
and time period.
3. Was the exposure measured in a valid and reliable way?
The study should clearly describe the method of measurement of exposure. Assessing validity requires that
a ‘gold standard’ is available to which the measure can be compared. The validity of exposure
measurement usually relates to whether a current measure is appropriate or whether a measure of past
exposure is needed.
Reliability refers to the processes included in an epidemiological study to check repeatability of
measurements of the exposures. These usually include intra-observer reliability and inter-observer
4. Were objective, standard criteria used for measurement of the condition?
It is useful to determine if patients were included in the study based on either a specified diagnosis or
definition. This is more likely to decrease the risk of bias. Characteristics are another useful approach to
matching groups, and studies that did not use specified diagnostic methods or definitions should provide
evidence on matching by key characteristics
5. Were confounding factors identified?
Confounding has occurred where the estimated intervention exposure effect is biased by the presence of
some difference between the comparison groups (apart from the exposure investigated/of interest).
Typical confounders include baseline characteristics, prognostic factors, or concomitant exposures (e.g.
smoking). A confounder is a difference between the comparison groups and it influences the direction of
the study results. A high quality study at the level of cohort design will identify the potential confounders
and measure them (where possible). This is difficult for studies where behavioral, attitudinal or lifestyle
factors may impact on the results.
6. Were strategies to deal with confounding factors stated?
Strategies to deal with effects of confounding factors may be dealt within the study design or in data
analysis. By matching or stratifying sampling of participants, effects of confounding factors can be adjusted
for. When dealing with adjustment in data analysis, assess the statistics used in the study. Most will be
some form of multivariate regression analysis to account for the confounding factors measured.
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Critical Appraisal Checklist for Analytical Cross Sectional Studies – 4
7. Were the outcomes measured in a valid and reliable way?
Read the methods section of the paper. If for e.g. lung cancer is assessed based on existing definitions or
diagnostic criteria, then the answer to this question is likely to be yes. If lung cancer is assessed using
observer reported, or self-reported scales, the risk of over- or under-reporting is increased, and objectivity
is compromised. Importantly, determine if the measurement tools used were validated instruments as this
has a significant impact on outcome assessment validity.
Having established the objectivity of the outcome measurement (e.g. lung cancer) instrument, it’s
important to establish how the measurement was conducted. Were those involved in collecting data
trained or educated in the use of the instrument/s? (e.g. radiographers). If there was more than one data
collector, were they similar in terms of level of education, clinical or research experience, or level of
responsibility in the piece of research being appraised?
8. Was appropriate statistical analysis used?
As with any consideration of statistical analysis, consideration should be given to whether there was a more
appropriate alternate statistical method that could have been used. The methods section should be
detailed enough for reviewers to identify which analytical techniques were used (in particular, regression or
stratification) and how specific confounders were measured.
For studies utilizing regression analysis, it is useful to identify if the study identified which variables were
included and how they related to the outcome. If stratification was the analytical approach used, were the
strata of analysis defined by the specified variables? Additionally, it is also important to assess the
appropriateness of the analytical strategy in terms of the assumptions associated with the approach as
differing methods of analysis are based on differing assumptions about the data and how it will respond.
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Critical Appraisal Checklist for Analytical Cross Sectional Studies – 5
Original research
Digital health transformation in Saudi Arabia: A
cross-sectional analysis using Healthcare
Information and Management Systems Society’
digital health indicators
Digital Health
Volume 8: 1–9
© The Author(s) 2022
Article reuse guidelines:
DOI: 10.1177/20552076221117742
Nouf Al-Kahtani1, Sumaiah Alrawiai1, Bnan Mohammed Al-Zahrani1,
Rahaf Ali Abumadini1, Afnan Aljaffary1, Bayan Hariri1, Khalid Alissa2,
Zahra Alakrawi1 and Arwa Alumran1
Background: The digital revolution has had a huge impact on healthcare around the world. Digital technology could dramatically improve the accuracy of diagnosis, treatment, health outcomes, efficiency of care, and workflow of healthcare
operations. Using health information technology will bring major improvements in patient outcomes.
Purpose: This study aims to measure the readiness for digital health transformation at different hospitals in the Eastern Province,
Saudi Arabia in relation to Saudi Vision 2030 based on the four dimensions adopted by the Healthcare Information and
Management Systems Society: person-enabled health, predictive analytics, governance and workforce, and interoperability.
Methods: The study was conducted with a cross-sectional design using data collected through an online questionnaire from
10 healthcare settings, the questionnaire consists of the four digital health indicators. The survey was developed by
Healthcare Information and Management Systems Society for the purpose of assessing the level of digital maturity in healthcare settings.
Results: Ten healthcare facilities in the Eastern Province, both private and governmental, were included in the study.
The highest total scores for digital health transformation were reported in private healthcare facilities (median score for private
facilities = 77, public facilities = 71). The ‘governance and workforce’ was the most implemented dimension among the healthcare facilities in the study (median = 80), while the dimension that was least frequently implemented was predictive analytics
(median score = 70). In addition, tertiary hospitals scored the least in digital transformation readiness (median = 74) compared
to primary and secondary healthcare facilities in the study.
Conclusion: The results of the study show that private healthcare facilities scored higher in digital health transformation indicators. These results will be useful for promoting policymakers’ understanding of the level of digital health transformation in
the Eastern Province and for the creation of a strategic action plan.
Health information systems, Data Science, Health Information Interoperability, Health Status Indicators
Submission date: 23 June 2022; Acceptance date: 18 July 2022
Health Information Management and Technology, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
Department of Networks and Communication, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University,
Dammam, Saudi Arabia
Corresponding author:
Arwa Alumran, Health Information Management and Technology Department, College of Public Health, Imam Abdulrahman Bin Faisal University, P.O. Box
40140, Alkhobar 31952, Dammam, Saudi Arabia.
Email: aalumran@iau.edu.sa
Creative Commons NonCommercial-NoDerivs CC BY-NC-ND: This article is distributed under the terms of the Creative Commons AttributionNonCommercial-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction
and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on
the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Introduction and background
Health Information Technology has been contributing to dramatic changes in healthcare by reshaping the health service
delivery system and introducing new modalities of patient
care. In fact, health care as a specialized field of an industry
cannot survive in such a dynamic and rapidly changing environment without accelerated adoption of new and innovative
technology and investing in further development of digital
health infrastructure. Digital health as an emerging specialization focuses on the use of information technology and electronic communication services, tools, and processes to
deliver health care services and facilitate better health.1 The
technological revolution has had a huge impact on healthcare
in the world. Moreover, advances in information and communication technologies in all sectors have raised the ambitions of
the healthcare sector around the world for providing highquality services.2
In 2013, the World Health Organization (WHO) issued a
global strategy on digital health to improve healthcare by
developing and adopting digital health for appropriate,
accessible, and affordable healthcare for all. The strategy
calls for developing infrastructure that enables countries
to support healthcare delivery through digital health technologies.3 It is evident from the literature that there is the
heterogeneity of the results across countries in terms of
digital transformation.4 For instance, Catalonia in Spain
has been considered a forerunner of eHealth adoption in
Europe. Since 2009, Catalonia achieved robust information
exchange deployment, which allowed health care workers
in the public health system to share clinical information.5
This indeed supported Catalonia’s health system to
sustain healthcare delivery during the coronavirus crisis
and helped to a high extent decreasing the unnecessary
visits to the hospitals during the pandemic.5
In the case of Saudi Arabia, the country has experienced
rapid socioeconomic changes, contributing to major shifts in
public health priorities and leading to extensive health
reform.6,7 As part of Saudi Vision 2030, the kingdom launched
the National Transformation Program (NTP) in 2015 which is
an executive program designed to transform the healthcare
delivery system through technology and innovation. Having
a solid foundation of digital health infrastructure will surely
contribute to achieving the country’s vision in the health of
facilitating access to equitable, affordable, and universal highquality healthcare for all.3 However, one of the major challenges identified by the NTP is developing an effective strategy
for digital health transformation of the Saudi Ministry of Health
(MoH) due to the lack of an integrated IT system for healthcare
services.8 Therefore, the MoH has generated several programs
and systems to overcome this challenge. Three main solutions
are being implemented: automating patient care, electronic
health records, and billing; standardizing regulations and procedures for the quality and exchange of information, and establishing the National Health Observatory.9
Since 2015, Saudi Arabia has made remarkable progress
in the implementation of digital health. However, only a
few studies have assessed the current status of Saudi
Arabian digital health efforts. One study analysed the readiness of Saudi Arabian healthcare facilities to change in
accordance with the Saudi National Healthcare Plan of
Saudi Vision 2030.10 Based on a review of different
resources on organizational readiness for change, the
study concluded that many factors would facilitate the efficient implementation of the Saudi healthcare transformation
plan. These factors mainly depend on the determination of
the organization, the effort of the members of the organization, and the availability of resources.10
Another study assessed the state of digital health maturity
in Saudi Arabia compared to other countries.11 The study
used the Global Digital Health Index Platform (GDHI),
which has seven main dimensions: strategy and investment,
workforce, legislation and policy, leadership and governance, standards and interoperability, infrastructure, and
services and applications. The study concluded that there
are many digital health initiatives in the country; multiple
key implementation solutions have been launched, and
digital health in Saudi Arabia is evolving steadily.11
Moreover, a recent study published in 2021 measured
the status of HIS implementation in 18 hospitals in the
Eastern Province in Saudi Arabia – which is the same geographical setting as this study – the results showed a
variety in implementation stages, however, most of the
hospitals indicated using the basic functionalities such as
clinical documentation.12
The Saudi MoH strongly supports continuous progress in
digital health transformation by focusing on building digital
infrastructure and improving healthcare quality. However, it
is unclear whether the current level of digital health implementation fulfils the expectations of Saudi Vision 2030.
This study aims to measure the readiness for digital health
transformation at different hospitals in the Eastern
Province, Saudi Arabia in relation to Saudi Vision 2030
based on the four dimensions adopted by the Healthcare
Information and Management Systems Society (HIMSS):
person-enabled health, predictive analytics, governance
and workforce, and interoperability. Further discussion of
this instrument will be provided under the Methods
section. The results of this study could help health care policymakers at MoH and the National Transformation Unit to
identify and address the gap between the aims of Saudi
Vision 2030 and the current situation. The study could
also inform the general literature on digital transformation
by providing information on the actual level of digitalization
in a setting with rapid changes in health system reforms.
Materials and methods
This is a quantitative cross-sectional study. The study
included government and private healthcare facilities in
Al-Kahtani et al.
three major cities in the Eastern Province of Saudi Arabia
(Al-Khobar, Dammam, and Dhahran).
Using the purposive sampling technique, the current
study questionnaire was sent via email to the head of the
Information Technology at the selected study sites. A
total of 10 facilities participated in this study by completing
the questionnaire.
The independent variables are the Healthcare Facility
Type (Private or Governmental), and Healthcare Facility
Level (Primary, Secondary or Tertiary). The dependent
variables are the four dimensions of digital health as presented in the survey: Interoperability, Governance &
Workforce, Predictive Analytics, and Person-Enabled
Health. The outcome variable in the research is the
overall digital health transformation score of each facility.
The data was collected using an online survey that was
sent to hospitals in the Eastern Region of Saudi Arabia.
The questionnaire used an existing open-source questionnaire developed and validated by the Healthcare
Information and Management Systems Society Healthcare
Information and Management Systems Society
(HIMSS).13 The study took place from January 2021 to
June 2021, while data was collected between March 2021
and May 2021.
The questionnaire includes two main sections, general
questions about the facility, and the digital transformation
four indicators. General questions are related to the facility
characteristics, such as the healthcare facility type (private
or governmental), and the healthcare facility level (secondary or tertiary).
The digital transformation indicators section comprises
four dimensions. Each dimension has three indicators, and
the answers were measured on a scale of 0 to 10 rate of
Interoperability of the foundational, structural, semantic,
and organizational features of the healthcare facility. The
second dimension is the Governance & Workforce, which
measures the stewardship, policy and decision-making,
transparency, and workforce capacity and competency of
the facility. The third dimension is Person-Enabled
Health, which has three indicators, personalized care delivery, proactive risk management, and predictive population
health. The fourth dimension is Predictive Analytics,
which measures personalized analytic tools, predictive analytics tools, and operational analytics. Face validity of the
four dimensions of digital health was tested using qualitative content analysis.13
All participants were asked for their voluntary participation and consent before filling out the questionnaire and
were free to withdraw from the study at any time.
The study complies with the Declaration of Helsinki and
was approved by the Institutional Review Board (IRB) at
Imam Abdulrahman Bin Faisal University, Saudi Arabia
in December 2020, ethical approval number is
All of the numerical data collected by the questionnaire
were analysed using the statistical package for social
sciences (SPSS).14 healthcare facilities’ characteristics and
the four digital health dimensions (interoperability, governance & workforce, person-enabled health, and predictive
analytics) were analysed using descriptive analysis.
Furthermore, to detect statistical significance a normality
test was conducted on these dimensions. Mann-Whitney
test was used to assess the statistical differences between
the digital health dimensions and type of healthcare facility,
whereas the Kruskal-Wallis test was used to assess the statistical differences between the digital health indicators and
level of a healthcare facility.
Ten healthcare facilities in the study were assessed regarding their perceived digital transformation readiness. Half of
the facilities in the study were governmental and the other
were private. The healthcare facilities in the study varied
in their level of care, 60% were tertiary hospitals (n = 6),
30% were secondary (n = 3), and 10% were primary healthcare centres (n = 1) (Figure 1).
The digital transformation dimensions measured in the
study are interoperability, Governance & Workforce,
Person-Enabled Health, and Predictive Analytics. Scores
of the indicators were calculated using the tool provided
by HIMSS DHI rapid assessment,13 where a higher score
indicates better readiness.
Descriptive analysis of the hospital’s responses regarding these dimensions is shown in Tables 1 and 2.
Governance & workforce has the highest median score
among the rest of the dimensions (median = 80.00), followed by interoperability (median = 78.5), then
person-enabled health (median = 71.50). When the normality of the dimensions was assessed using the skewness and
kurtosis criteria of normality,15 all dimensions appeared
normal except the interoperability. In addition, specific
item rating is available in Table 2, which it shows that the
average score for all items ranges from 6.20 to 8.20.
When the digital transformation readiness was compared
across different healthcare facility levels, no statistically
significant associations were found. However, as shown
in Table 3, secondary hospitals in the study have the
lowest mean score in the interoperability dimension. On
the other hand, secondary hospitals appear to have the
highest mean score in the governance & workforce and predictive analytics dimensions. In addition, tertiary hospitals
in the study had the lowest mean score in the
person-enabled health dimension. Finally, the mean score
of the total digital health readiness across different healthcare facility levels is almost the same.
The digital transformation readiness was measured
across two healthcare facility types (governmental and
private) (Table 4). None of the digital transformation
Figure 1. Characteristics of the healthcare facilities in the study.
Table 1. Descriptive statistics about the digital transformation dimensions in the study (n = 10).
Governance and workforce
Person enabled health
Predictive analytics
Inter-quartile range (IQR)
25th percentile
50th percentile
75th percentile
dimensions appeared to be significantly related to the facility type. However, the mean score of the dimensions is
higher in private healthcare facilities in the study compared
to governmental ones.
The main aim of this study is to investigate and assess the
digital health transformation capacity at healthcare facilities
in the eastern province of Saudi Arabia. This was accomplished
by measuring and analysing the total score for digital health
indicators. The authors used the HIMSS DHI rapid assessment
which focuses on four digital health dimensions:
Interoperability, Governance & Workforce, Person-Enabled
Health, and Predictive Analytics.
The study findings reveal that Governance & workforce
has the highest mean score among the rest of the dimensions. In addition, secondary hospitals in the study have
Al-Kahtani et al.
Table 2. Indicators summary.
1. Interoperability – x̄ (SD) = 71.70 (26.192)
1.1. Individuals have access to their personal health records, health system services, educational tools and
health navigation tools to support health decisions, navigate access to care and services from their own
homes. Includes fully integrated virtual care and remote patient monitoring with intervention.
1.2. Clinicians use secure devices in daily practice routines, to enable collaboration with other clinicians,
including secure messaging, consultations, and real-time access to patient data, securely managed to
protect privacy.
1.3. Security breaches and alerts are tracked using machine learning technologies to identify accuracy and
risk of alerts, cost to manage breaches, and track compliance with security legislation.
2.1. Staff are accountable for supporting care that is personalized to the unique needs, circumstances, and
choices of the individual informed by evidence of value and person-reported outcomes.
2.2. Organizational strategy and performance outcomes are shared publicly to inform the community of
impact and value achieved by the organization or health system.
2.3. Organizational policies are responsive to value for patients, informed by patient participation at all levels
of governance, to inform and support digital healthcare systems.
3.1. Data is mobilized to track population health outcomes to inform personalized care strategies that support
and sustain population health and wellness.
3.2. Care delivery focuses on keeping people well by proactively intervening to reduce risk using predictive
analytic tools.
3.3. Individuals are the primary decision-makers and use digital tools to self-manage their health and
4.1. Analytic tools at the point of care track individual outcomes to inform care decisions that mitigate health
risks and optimize health outcomes.
4.2. Predictive analytic tools segment the population based on risks and outcomes for population segments to
identify the conditions under which best outcomes are achieved, to inform proactive interventions that
strengthen population health.
4.3. Analytic tools track operational performance in real-time to inform leadership decisions to strengthen
quality, safety, and cost outcomes across the organization/system.
2. Governance and workforce – x̄ (SD) = 79.60 (11.616)
3. Person enabled health – x̄ (SD) = 73.50 (9.778)
4. Predictive analytics – x̄ (SD) = 71.80 (7.757)
the lowest mean score in the interoperability dimension and
highest mean score in two dimensions, governance & workforce, and the predictive analytics dimensions, whereas tertiary hospitals in the study had the lowest mean score in the
person-enabled health dimension. Interestingly, there was
no significant relationship between the digital transformation dimensions and facility type. However, the mean
score of the dimensions is higher in private healthcare facilities in the study compared to government healthcare
The ‘Governance & Workforce’ dimension got the
highest mean score compared to the other dimensions.
Marcelo et al.,16 believe that digital governance is one of
the pillars for implementing digital health solutions, which
Table 3. Digital transformation readiness in relation to the healthcare facility level.
Healthcare facility level
Median (IQR)
Kruskal-Wallis test (P-value)
77.0 (26)
.644 (.725)
Governance & Workforce
80.0 (21)
.806 (.668)
Person Enabled Health
70.0 (10)
.889 (.641)
Predictive Analytics
68.5 (13)
.689 (.709)
Total digital transformation readiness
74.0 (12.5)
.069 (.966)
Inter-quartile range cannot be calculated since there isn’t enough data in the group.
Table 4. Digital transformation readiness in relation to the healthcare facility type.
Healthcare facility type
Median (IQR)
Mann-Whitney U-test (P-value)
77.00 (76)
83.00 (27)
4.500 (.093)
Governance & Workforce
80.00 (14)
87.00 (25)
7.500 (.289)
Person Enabled Health
70.00 (20)
73.00 (15)
9.500 (.527)
Predictive Analytics
70.00 (12)
73.00 (15)
9.000 (.458)
Total digital transformation readiness
71.00 (20)
77.00 (13.5)
5.500 (.141)
will in turn manage different health systems processes, such
as electronic health records, health insurance payment processes, and systems evaluation. The lack of governance or
low implantation of governance in digital health systems
will lead to the inefficient implementation of strategic and
smart investment decisions.16 In addition, Tanniru et al.17
relate the reason behind the movement toward digital governance to the fact that global digital transformation is touching all work fields, and therefore health organization faces
pressure to adapt to this rapid change as well as it has to
raise to the customer expectations.
In Saudi Arabia, a governance program has been
implemented among healthcare facilities to fulfil the
2030 vision.8 This program enforced policies focusing
on enabling patients to be more active in managing
their care. It also publicly advertises the different applications designed by the MoH and ensures the confidentiality and security of their data. These could be some of
the reasons this dimension achieved such a score.
Further, the attention given by MoH to make sure that
all these changes are value-based could also be contributing to the high score.
On the other hand, the study findings have shown that
implementation of interoperability as a distinctive aspect
of digital health transformation is relatively low compared
to the other dimensions of the digital health indicators.
The Interoperability dimension received a low mean
score in implementation rate, especially at the secondary
governmental healthcare facilities. This could possibly be
a result of having the data go through multiple systems,
which increases the possibility of errors and incomplete
data. In fact, privately owned hospitals have a higher rate
of implementation and adoption compared to their public
counterparts. In concordance, Alghamdi18 believed that
the difference in interoperability between public and
private healthcare facilities can be attributed to the higher
adoption of Electronic Health Records in general and
Personal Health Records in particular which increases
their interoperability score based on the rapid assessment.
Furthermore, Luna et al.19 concluded that data are usually
Al-Kahtani et al.
managed by one entity, using one healthcare system at the
private facilities making it easier to be interoperable
Also, the limited interoperability implementation can
be traced back to other different reasons including the
cost of initiation and maintenance of the technical infrastructure, privacy and security of the data being
exchanged, and regulatory agencies’ involvement.20
Persons et al.20 have discussed three attempts for interoperability implementation in three different countries
including Canada, Austria, and the USA. The study
found there is a greater chance for interoperability in
national health systems supported by a national governing body in terms of financially sponsoring the initiatives
and regulating the health information exchange between
different organizations. What we have seen in this study,
however, is the opposite with public hospitals scoring
relatively lower in interoperability and this simply can
be attributed to having more pressing public health priorities compared to the private sector that could limit
funding of Health IT initiatives.
In fact, Alghamdi18 concluded that cost is a major barrier
to the implementation of EHRs and Health IT in the Saudi
public healthcare system. Other factors also include high
maintenance costs, lack of technical skills and capabilities,
perceived security and privacy threats, and resistance to
new technology. Furthermore, having to assume the responsibility of population health also is the same factor that
makes the private sector less invested in interoperable technical infrastructure for public health purposes as suggested
by Persons et al.20 Also, Persons et al.20 concluded that
interoperability is less common in private silo healthcare
institutions compared to private multi-hospital systems
and this could further describe the lower interoperability
score as all participating private facilities are individual
In addition, the Person-Enabled Health dimension
shows some similarity to the Interoperability dimension,
where private facilities got a higher implementation rate
compared to governmental facilities. Similarly, a study conducted by Lu et al.21 in public hospitals in China revealed
that information technology infrastructure, system reliability, and government policy are considered as barriers to
rapid adoption of a person-enabled health, that is,
mhealth, in Chinese public hospitals. This could be
because private facilities are keen on finding different
ways to enhance patients’ satisfaction regarding the care
provided to them. Which ensures the facility’s continuous
patronage with the patients, expecting more involvement
in the decision-making process, considering that the
patients are paying for the services. Private hospitals are
making efforts to use patients’ data to provide more personalized and customized care.22 Similar efforts are needed in
private and public hospitals in order to attain digital transformation in healthcare facilities.
Tertiary hospitals have the lowest score in
person-enabled health. This could be because these hospitals treat patients with the most severe cases, thus they do
not feel the need to focus on the personalization of patient
care, and the overall wellness and how it could be achieved.
Supporting that, a study conducted in two tertiary hospitals
located in Nigeria and South Africa revealed that healthcare
workers perceived that the use of person-enabled health,
such as mhealth might be negatively disruptive while
engaging with patients.23 Thus, it is important to take into
account the type of work activity and the contextual
factors, such as the type of healthcare facility, that might
negatively affect digital transformation.23 In future
studies, it is recommended to identify the barriers that
hinder the implementation of digital transformation in tertiary hospitals.
As for the Predictive Analytics dimension, it shows a
low implementation rate compared with the other three
dimensions with private hospitals scoring slightly higher.
This is expected due to the complexity of healthcare and
the need for some pre-existing system requirements to be
in place.24 In fact, predictive analytics requires higher
implementation status of the EHR functional and technical
capabilities. This could be a significant barrier to implementation, especially in the Saudi public healthcare system that
is still building its health IT infrastructure.18 Furthermore,
the same complex dynamics involved in interoperability
implementation could play a role in the adoption of predictive analytics as well including the cost of initiation and
maintenance, lack of skills, and resistance to technology.18
However, efforts are currently made to improve such a
critical area of the healthcare system by stressing the
importance of collecting and using the data to ensure a
better outcome for the population.8 Furthermore, the same
complex dynamics involved in interoperability implementation could play a role in the adoption of predictive analytics as well.
The highest total scores reported for digital health capacity in this study, as assessed by the DHI Rapid
Assessment tool, were reported by the private healthcare
facilities. Private facilities are commonly more technologically advanced compared to governmental (public) healthcare facilities. This could be a result of the resources
private hospitals have, especially large ones, which allow
them to allocate a considerable amount of funding to
advance their digital health status. In a contrast, governmental facilities in the study have lower total digital,
which could be due to more financial, organizational, and
regulatory challenges compared to private facilities.25
However, this is changing now with Vision 2030, which
aims to improve the needed indicators to ensure a digital
health transformation. Also, the baseline needed for such
transformation, which is Governance and Workforce, is
one of the highest scored dimensions in the current study.
This shows a real possibility of achieving digital health
transformation across Saudi healthcare facilities in the near
The first limitation of this study is the use of HIMSS’
rapid assessment tool rather than the original instrument
due to lack of funding. However, the rapid assessment
instrument is still considered a reliable and valid instrument
to summarize the digital health indicators and can be generally used as a precursor for the original instrument as suggested by HIMSS.
The second limitation of this study is that it is restricted
to the healthcare facilities in the Eastern Region of Saudi
Arabia which can affect the generalizability of the findings.
Another perceived limitation of this research is the low
sample size of the participating facilities. The researchers
believe this could not be a flaw of this study because all
major acute care facilities in the Eastern Region were
included and therefore, results can be valid to be utilized
within this geographical area.
The main strength of this study is that it is the first of
its kind in the country and the results will be very informative in identifying the current status of digital health
adoption and future opportunities for improvement in
the region.
This study aimed to measure the digital health transformation in multiple healthcare facilities across Eastern Saudi
Arabia using digital health indicators.
It was found that there are high implementation rates in
general, and the total digital transformation score in the
private healthcare facilities was higher compared to governmental hospitals. The study showed that from all the dimensions, the ‘Governance & Workforce’ was the highest
implemented dimension, while ‘Predictive analytics’ was
the lowest implemented dimension.
This study’s findings could help policymakers to understand the level of transformation of digital health in Eastern
Saudi Arabia. It could also help in knowing which indicators are the most applied in the Eastern Province’s healthcare facilities, and which need more attention. Moreover,
the result of this study could help health strategic planners
to focus on understanding why the governmental facilities
got a lower rate in Governance & Workforce dimension,
so they can improve it.
Author contributions: All authors contributed equally to the
conceptualization, data acquisition, project administration, data
analysis, writing the first draft, and reviewing of the final draft.
Declaration of conflicting interests: The authors declared no
potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
Ethical approval: The study complies with the Declaration of
Helsinki and was approved by the Institutional Review Board
(IRB) at Imam Abdulrahman Bin Faisal University, Saudi
Arabia in December 2020, ethical approval number is IRB
Funding: The authors received no financial support for the
research, authorship, and/or publication of this article.
ORCID iD: Arwa Alumran
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