Keywords
digital health - digital capability - digital maturity - digital hospitals - digital
transformation - health information management - organizational characteristics
Background and Significance
Background and Significance
Health care delivery is increasingly challenging as demand for care escalates against
a static resourcing profile. Digital health care platforms are seen as a solution
to deliver health care at scale.[1]
[2] Large fiscal investments are required to implement these digital solutions. However,
it can be confusing for health care organizations to plan and justify such large investments.
Digital maturity is “the extent to which health IT is an enabler of high-quality care
through supporting improvements to service delivery and patient experience.”[3] Digital health maturity evaluations help identify the level of capability across
a series of “dimensions” to understand different aspects from business processes and
organizational characteristics, to information and people.[4] Digital maturity models (MM) provide structure to a maturity or capability assessment
and have risen in use[4] in response to the increased drive to digitize health care. Limitations of current
MMs include challenges over which dimensions should be assessed,[5] overly technologically focused,[6] narrow focus, and lack of peer-reviewed evidence base.[7] MMs are often developed for specific clinical areas or information systems,[8] yet there is a growing need for MMs to include all areas and subsystems in a health
care organization[9] to capture the complex reality of digital transformations.[10]
The digital health indicator (DHI) from the Healthcare Information and Management
Systems Society (HIMSS) documents the digital capability of health care services (beyond
simple assessments of the presence or absence of electronic medical record systems)
using outcome driven, specific and balanced measures.[11] The DHI is a self-assessment tool[11] which assesses four key dimensions of digital transformation: Interoperability:
Person-Enabled Health; Predictive Analytics; and Governance and Workforce ([Fig. 1]). These four dimensions recognize the need for data to be mobilized across the health
system to enable advanced analytics for outcome tracking, risk management, and cue
health care teams to focus on preventative action that helps keep people and populations
well.[11]
Fig. 1 Digital health indicator dimensions.
The DHI aims to provide a baseline understanding of where the health care organization
has strengths and opportunities to improve as they progress in digital transformation.
Launched in the global market in 2019, the DHI was developed from a critical analysis
of published, peer-reviewed digital health literature, and was tested in health care
organisations.[11] The DHI is currently adopted by many global health systems across jurisdictions
such as Canada, United States of America, England, Saudi Arabia, Australia, New Zealand,
Korea, Indonesia, Japan, Taiwan, and Hong Kong. It was chosen over other MMs or capability
frameworks because the dimensions measured extend beyond technological implementations
and include the often underrepresented patient-centeredness in existing models,[12] and provide the ability for global benchmarking. As a new tool, there has been limited
published results of how the DHI has been applied to health services to shape digital
transformation strategies and investment decisions.
In the state of Queensland, Australia, an ambitious digital transformation of the
public health care system is underway, aiming to provide a single electronic record
to all consumers as a key foundational component.[13] In a quest to understand progress made toward this digital health vision, an evaluation
was undertaken with the research question: What is the digital health capability of the Queensland health system? Our hypothesis was digital capability would vary widely across the jurisdiction.
Objective
The aim of this research was to apply the DHI[11] to evaluate the digital health capability in Queensland to inform digital health
strategy and investment.
Methods
A cross-sectional survey design involving the DHI was employed in the state of Queensland.
This study has received a multisite ethics approval from the Royal Brisbane and Women's
Hospital [ID: HREC/2020/QRBW/66895], followed by site-specific research governance
approvals.
Setting
Queensland Health (QH) provides state-wide health care to over 5 million people, covering
a geographical area 2.5 times the size of Texas, United States. At an annual cost
of over AUD$29billion, QH funds universal free health care across acute inpatient
care; emergency care; mental health and alcohol and other drug services; outpatient
care; prevention, primary and community care; ambulance services, and; sub and non-acute
care.[14] Queensland is also serviced by multiple private hospitals, non-government organizations,
Aboriginal and Torres Strait Islander Community Controlled Health Organizations, general
practices, primary health networks, and charitable organisations.[15] In 2018 to 2019, 57% of inpatient care was in Queensland's public hospitals.[15]
The population is unevenly dispersed within metropolitan areas in the Southeast corner
of the state, regional sites with large base hospitals to considerably isolated remote
and ultra-remote communities with a fly-in fly-out health workforce. To effectively
manage health services across a large geographically dispersed state, QH decentralized
their operations into 16 regionally divided independent statutory bodies called health
care systems. Each health care system has variable numbers of health services covering
the full spectrum of complexity from quaternary academic hospitals to small rural
hospitals.[13]
[16] Publicly funded health care systems were assessed in this study with a focus on
digital capabilities of the hospitals contained within, and not the private hospitals
across the systems.
At the time of the assessment, 15 individual hospitals across nine health care systems
were digital hospitals with the single instance Cerner integrated Electronic Medical
Record (EMR) system. The full stack of advanced EMR capability covers the patient
journey across various health care sites, and is integrated with computerized provider
order entry, ePrescribing, and clinical decision support systems.[17] The remaining hospitals use paper-based clinical documentation with various levels
infrastructure, connectivity, and point of care technologies for integration of business,
patient administration, diagnostics and virtual care systems.
Data Collection
Data were collected using the DHI electronic self-assessment questionnaire. The DHI
consists of 121 indicator statements measured on a five-point scale ranging from not
enabled to fully enabled covering the dimensions of digital transformation. Organizational
data are collected using 10 demographic questions which do not contribute to the overall
DHI score.
The survey was administered electronically to each individual site and completed between
February and July 2021. Survey respondents were voluntary staff representatives from
each site, who (1) had an awareness of digital health across the health care system,
(2) the ability to network with local workforce to complete the survey accurately,
and (3) provide informed consent. The survey respondents included chief information
officers (n = 8), chief digital officers (n = 2), clinical directors of digital health (n = 2), director of information communication technologies (n = 2), executive director of medical services (n = 1), and chief digital director medical services (n = 1). Respondents required at least 2 hours to complete the survey, receiving support
and clarification from HIMSS to avoid partial completions.
Data Analysis
The DHI score was calculated for each health care system using pre-built algorithms;
proprietary of HIMSS. Through application of this algorithm each DHI dimension (i.e.,
interoperability; person-enabled health; predictive analytics; and governance and
workforce) can be scored from zero to 100. A proprietary algorithm is then applied
to calculate a total score (i.e., the total score is not the sum of the dimension
scores). The dimension level scores and the overall DHI scores were exported to IBM
SPSS Statistics (Version 28) where a series of analyses were performed.
Dimension Capability
A dimension level analysis was performed to provide granular insights into the strengths and weaknesses
in digital capability. The scores of each DHI dimension per site were aggregated,
with descriptive statistics and visualized using box and whisker plots.
Regional Capability
A region level analysis was conducted to examine differences in digital capability due to the geographical
spread of Queensland. This required the regionality of each health care system to
be determined. Applying the Modified Monash Model 2019 tool (MMM) in the Health Workforce
Locator,[18] six sites were determined to be metro (MMM1), four regional (MMM2), and six rural
(MMM3–7). A Kruskal–Wallis test was performed to identify if there were any statistical
differences among the regions. Mann-Whitney U tests were then performed to identify
differences between all combinations of groups (i.e., metro vs. regional; metro vs.
rural; regional vs. rural).
Regional Dimension Analysis
A region dimension level analysis was conducted to examine if regional areas differed in their evaluations
of the digital capability dimensions. For each region classification, the DHI dimension
scores (i.e., interoperability; person-enabled health; predictive analytics; and governance
and workforce) were extracted. A Kruskal–Wallis test was performed to identify if
there were any statistical differences among regions.
Digital Hospital Analysis
A digital hospital analysis was conducted to examine if EMR implementation impacted digital capability
between health care systems. The DHI score for each health care system was grouped
(EMR vs. non-EMR) and a Mann-Whitney U test performed to identify any statistically
significant differences between EMR sites and non-EMR sites.
External Benchmarking
External comparisons were performed to benchmark the digital capability of QH globally.
This involved examining archival data provided by HIMSS for the DHI scores reported
by other private and public health systems across Oceania (n = 7), and North America (n = 10). For the first comparison, a Kruskal–Wallis test was performed to identify
if there were any differences in DHI scores among the continental areas. A Mann-Whitney
U test was performed to identify differences between all combinations of groups (i.e.,
North America vs. QH; North America vs. Oceania; QH vs. Oceania). For the second comparison,
Mann-Whitney U tests were performed to identify if there were any statistical differences
between QH's DHI score and global DHI scores, global public DHI scores, and global
private DHI scores. For the final comparison, a Global regionality level analysis was performed; Kruskal–Wallis and Mann-Whitney U tests were performed to identify
if there were any differences between global metro and rural DHI scores with QH's
global, rural, and regional DHI scores.
Results
Analysis of Queensland's Digital Capability
The overall digital capability, denoted by the mean DHI score across the health care
systems, was 143 (/400), ranging from the lowest digital capability health care system
of 78 with a maximum digital capability health care system of 193.
The dimension level analysis ([Fig. 2]) identified that Governance and Workforce was on average the highest scoring dimension
(x̅ = 54), although there was a potential outlier present. This was followed by interoperability
(x̅ = 46), person-enabled health (x̅ = 36), and predictive analytics (x̅ = 30).
Fig. 2 DHI dimensional scores for 16 health care services in Queensland, Australia. DHI,
digital health indicator.
[Fig. 3] illustrates the region level analysis comparing DHI scores of metro, regional, and rural areas. A Kruskal − Wallis test
showed that the regionality of the site affects the DHI scores (p <0.05). Post-hoc Mann-Whitney U tests ([Table 1]) indicated that the differences in DHI scores of health care systems in metro regions
did not significantly differ from those in regional regions (p> 0.05), although, metro and regional regions received higher DHI scores than rural
regions (p <0.05).
Table 1
Region level analysis
|
Metro vs. regional
|
Metro vs. rural
|
Regional vs. rural
|
Metro
|
Regional
|
Metro
|
Rural
|
Regional
|
Rural
|
N
|
6
|
4
|
6
|
6
|
4
|
6
|
Mean
|
161
|
159
|
161
|
114
|
159
|
114
|
Median
|
163
|
166
|
163
|
111
|
166
|
111
|
Mann-Whitney result
|
U = 11, z = −0.213
|
U = 4, z = 2.242
|
U = 2.5, z = −2.032
|
p-Value
|
p> 0.05
|
p <0.05
|
p <0.05
|
Outcome
|
Metro = Regional
|
Metro> Rural
|
Regional> Rural
|
Fig. 3 DHI scores comparing health care systems across geographic regions. DHI, digital
health indicator.
The region dimension level analysis ([Fig. 4]) identified similar results to the dimension level analysis ([Fig. 2]) with Governance and Workforce scoring higher than interoperability, person-enabled
health and predictive analytics, although the Kruskal − Wallis test ([Table 2]) indicated that the scores for each dimensions were comparable across regions (p >0.05).
Table 2
Region-dimension level analysis
|
Governance and workforce
|
Interoperability
|
Person-enabled health
|
Predictive analytics
|
Metro
|
Reg.
|
Rural
|
Metro
|
Reg.
|
Rural
|
Metro
|
Reg.
|
Rural
|
Metro
|
Reg.
|
Rural
|
N
|
6
|
4
|
6
|
6
|
4
|
6
|
6
|
4
|
6
|
6
|
4
|
6
|
Mean
|
59
|
66
|
40
|
56
|
46
|
37
|
42
|
36
|
30
|
30
|
39
|
25
|
Median
|
62
|
60
|
44
|
58
|
47
|
35
|
41
|
36
|
29
|
30
|
40
|
27
|
Kruskal–Wallis result
|
H(2) = 4.724
|
H(2) = 4.923
|
H(2) = 3.333
|
H(2) = 1.821
|
p-Value
|
p> 0.05
|
p> 0.05
|
p> 0.05
|
p> 0.05
|
Abbreviation: Reg, regional.
Fig. 4 DHI dimensional scores comparing health care systems across different geographic
regions. DHI, digital health indicator.
The digital hospital analysis is illustrated in [Fig. 5]. The Mann-Whitney U test ([Table 3]) indicated that sites with an EMR have a higher DHI score than sites that do not
have the EMR (p <0.001).
Table 3
Digital hospital analysis
|
EMR site
|
Non-EMR site
|
N
|
8
|
8
|
Mean
|
170
|
115
|
Median
|
167
|
119
|
Mann-Whitney result
|
U = 1, z = −3.258
|
p-Value
|
p <0.001
|
Outcome
|
EMR site >non-EMR site
|
Fig. 5 DHI scores comparing health care systems stratified by EMR use. DHI, digital health
indicator; EMR, electronic medical record.
External Comparisons of QH's Digital Capability with Global DHI Scores
A Kruskal–Wallis test indicated that there are differences in DHI scores across locations
([Fig. 6]) (p <0.001). Post-hoc Mann-Whitney U tests indicated that the DHI scores of health systems
in North America were significantly higher than QH (p <0.001) and Oceania (p <0.001). There was no significant difference in DHI scores reported between QH and
Oceania (p > 0.05) ([Table 4]).
Table 4
International comparison
|
North America vs. QH
|
North America vs. Oceania
|
Oceania vs. QH
|
North America
|
QH
|
North America
|
Oceania
|
Oceania
|
QH
|
N
|
10
|
16
|
10
|
7
|
7
|
16
|
Mean
|
252
|
143
|
252
|
135
|
135
|
143
|
Median
|
238
|
155
|
238
|
132
|
132
|
155
|
Mann-Whitney result
|
U = 3, z = −4.059
|
U = 0, z = −3.416
|
U = 50, z = −0.401
|
p-Value
|
p <0.001
|
p <0.001
|
p >0.05
|
Outcome
|
North America> QH
|
North America> Oceania
|
Oceania = QH
|
Fig. 6 Comparison of DHI scores against global location. DHI, digital health indicator.
The global-dimension level analysis ([Fig. 7]), indicates a similar pattern across Oceania and QH with governance and workforce
being the highest scoring dimensions, followed by interoperability, person-enabled
health, and predictive analytics. In North America, however, interoperability scored
equally with governance and workforce, with person-enabled health being the lowest
scoring dimension.
Fig. 7 Comparison of DHI dimensions scores against global location. DHI, digital health
indicator.
Subsequently, a global comparison was performed with a Mann-Whitney U test ([Fig. 8], [Table 5]) indicating that the QH DHI score was lower than those reported globally (p< 0.05). To identify if the difference at the global level is partially explained
by the public or private nature of the health care system follow-up, Mann-Whitney
U tests were performed, which indicated that QH's DHI scores were comparable to global
public DHI scores (p >0.05), and lower than global private DHI scores (p <0.01).
Table 5
Global health provider status comparison
|
Global vs. QH
|
Global private vs. QH
|
Global public vs. QH
|
|
Global
|
QH
|
Global private
|
QH
|
Global public
|
QH
|
N
|
17
|
16
|
9
|
16
|
|
16
|
Mean
|
204
|
143
|
234
|
143
|
|
143
|
Median
|
202
|
155
|
244
|
155
|
|
155
|
Mann-Whitney result
|
U = 68, z = −2.558
|
U = 24, z = −2.718
|
U = 41, z = −1.409
|
p-Value
|
p <0.05
|
p <0.01
|
p >0.05
|
Outcome
|
Global> QH
|
Global private> QH
|
Global Public = QH
|
Fig. 8 DHI scores comparing global private and public health systems. DHI, digital health
indicator.
A Kruskal–Wallis test ([Fig. 9], [Table 6]) indicated that QH's DHI scores in regional and rural areas were comparable to global
rural DHI scores (p >0.05). A Mann-Whitney analysis ([Table 7]) further indicated that QH's DHI scores in metro areas were comparable to global
metro DHI scores (p >0.05).
Table 6
Global rural/regional comparison
|
Global-rural
|
QH-regional
|
QH-rural
|
N
|
5
|
4
|
6
|
Mean
|
166
|
159
|
114
|
Median
|
168
|
166
|
111
|
Kruskal–Wallis result
|
H(2) = 5.291
|
p-Value
|
p> 0.05
|
Table 7
Global metro comparison
|
Global-metro
|
QH-metro
|
N
|
12
|
6
|
Mean
|
220
|
161
|
Median
|
227
|
164
|
Mann-Whitney result
|
U = 18, z = −1.686
|
p-Value
|
p> 0.05
|
Outcome
|
Global-Metro = QH-Metro
|
Fig. 9 DHI scores comparing global and QH data stratified by geographic location (regional,
metro, and rural). DHI, digital health indicator.
Discussion
There is a significant drive to digitize health care to enable new models of care,
data, and analytics and to form the foundation to apply emerging technologies such
as artificial intelligence and precision medicine. To digitally transform health care
delivery in a strategic and informed manner, understanding and benchmarking the current
digital capability is essential to drive and monitor progress.
Summary of Key Findings
Queensland's results reveal a variation in DHI scores reflecting the diverse stages
of health care digitization across the state, with the findings indicating that sites
that have adopted an integrated EMR system possess higher levels of digital capability
than non-digital sites. The DHI score is similar to other systems in the Oceania region
and global public systems but below the global private average. Dimension-specific
results were explored to understand digital capability across four categories: Interoperability;
Person Enabled Health; Predictive Analytics; and Governance and Workforce. Relevant
findings are informing the updated state-wide digital health strategic plan.
Governance and Workforce received the highest comparable dimension score. The dimension
captures organizational elements of governance, risk, culture, leadership, and accountability
which have always existed within organizations. Modernizing these policies is possible
to reflect the broader institutional environment which has shifted over the past decades
to recognize the criticality of digital health.[19] However, the large variance in scores indicates that not all health care systems
have data strategy, security and privacy, and safety and quality outcome tracking
in place. To develop the capability from within a site, mirroring other digital health
implementation strategies from other sites may not transfer successfully across due
to context-specific factors.[20]
[21] Informed by these results, the strategic focuses include: clinician workflow improvement
and increasing the digital health competence of staff (workforce), and transparency
of health system performance, improvements in service planning and an increase in
artificial intelligence capability (governance).
Interoperability is a relatively strong dimension across QH with the large variation
in scores linked to regionality; metropolitan and regional sites are more likely to
have higher scores. These sites are more likely to have implemented digital tools
including the EMR. To improve interoperability and the connectivity between health
care stakeholders, health care systems can invest in technology advancements and centralization
of patient information. The state-wide digital health investment strategy outlined
the key investment of EMR implementations at large tertiary and quaternary care hospitals
in metropolitan and regional sites. The rollout in rural areas is currently under
review for funding allocation.
Person-enabled health is an area for development across all geographic regions. Absence
of digitally enabled self-management platforms, patient access to medical documents
or care plans, connectivity between clinicians and patients, and underutilization
of patient-reported outcomes emerged as limitations in this dimension. Standardized
design features in wide-scale EMR implementations do not necessarily account for well-established
person-centered care delivery approaches[21] which will need management. The acceptance of telehealth options for consumers,
clinicians, and system administrators during the COVID−19 pandemic uncovered through
the analysis means that the QH will continue to promote the use of virtual care services.
These service options not only ensure that consumers have the option to receive care
safely and effectively in their homes, and be with family and carers, whenever possible,
they also increase the efficiency of the health system to cope with ever increasing
demand.
The predictive analytics dimension scores the lowest of the four dimensions, which
is to be expected given this is an emerging technology. Predictive tools employing
machine learning and artificial intelligence are not currently used, yet real-time
descriptive analytics are being leveraged in pockets across the state. Adoption of
predictive analytics tools is a major step toward making the health care system prevention-focused
through targeted interventions and resourcing.[22] The Predictive Analytics assessment provided an opportunity to modernize data strategy
and infrastructure aiming to: (1) enable business areas to have timely access to high
quality data; (2) effectively integrate across the system, achieving horizontal collaboration
through establishing a single source of truth; (3) coproduce knowledge and insights
with key research and industry partners to drive health service and system improvements,
and; (4) establish the underpinnings of precision medicine to effectively reduce low
value-based care, waste, and harm.
Comparison to Existing Literature
This is the first large simultaneous application of the DHI across a single jurisdiction
reported in the academic literature. While HIMSS describes the DHI as a “capability
assessment,” the applications of comprehensive MMs or capability assessments in health
care are scarcely described.[23] MMs are often conducted without academic input by national or supranational organizations,
corporations, and national health organizations limiting the peer reviewed evidence.[8] Existing literature is focused on MM design over deployment,[24] with few reporting on evaluations of multifunctional systems within complex health
care organisations.[3] Measuring single technological implementations alone is unlikely to capture the
full spectrum of capability required for system transformation. Evidence of practical
application and efficacy of implementations are needed to address the practice-research
gaps[23] with this study contributing to the growing evidence base.
The closest comparison of a large scale digital capability assessment was the English
National Health Service's Clinical Digital Maturity Index (CDMI). The CDMI uses readiness,
capability, and infrastructure to contribute to a total score /1,400. When 136 hospitals
were assessed in 2016, the mean aggregate of the total CDMI score was 797 (range 324–1,253;
SD 174).[3] Significant variation among hospitals in all domains was uncovered,[3] similar to the findings from Queensland and demonstrating the possibility for improvement
in dimension and overall scores for both the DHI and CDMI tools.
The most widely reported maturity application is HIMSS's seven stage Electronic Medical
Record Adoption Model (EMRAM).[25] EMRAM is a technology-focused staged MM rather than a comprehensive assessment across
governance and workforce, interoperability, person-enabled health and predictive analytics,
which is what the DHI assesses. Large applications of EMRAM were reported in two jurisdictions.
In The Netherlands in 2014, 48% (n = 32) of hospitals sampled (80% total non-academic Dutch hospitals) received a score
of five on the EMRAM and no hospital received the highest score of seven.[26] In the same year in the United States over 5,200 hospitals (86% total U.S. hospitals)
completed the EMRAM, demonstrating an incremental increase in scores from previous
years.[27] More than 96% of hospitals were identified to be in Stage 3 or below in 2006, while
this number decreased to approximately 31% in 2014.[27] In comparison to single technology and implementation focused MMs such as EMRAM,
a holistic capability assessment such as the DHI provides contextual detail, highlights
weaknesses, and documents specific actions for system wide improvements.
Limitations
The DHI was the chosen method for assessing digital capability in this instance and
results may vary using another tool due to the methodologically diverse approaches
to assessing digital maturity. The individual indicator statements and algorithm to
calculate the DHI scores are proprietary of HIMSS, and the weighting of the dimension
scores to generate the total DHI is unknown. We do not believe this will discredit
the approach as it still provides a useful benchmark for others employing the DHI.
Although our findings indicate that DHI scores are higher with the presence of an
EMR within the health systems examined, due to limitations in the archival dataset
it was not possible to evaluate whether this finding also manifests at the global
level. It is certainly possible given EMRs can be associated with improved interoperability
and are a precursor to predictive analytics, however, there is no guarantee as these
technology systems can be implemented and adopted by users in a myriad of ways. The
ability to assess the digital capability at an individual hospital level, longitudinally
or objectively was not possible using this study design and provides opportunity for
future research. The state health system was assessed by aggregating multiple site
analyses, and therefore subject to the accuracy of localized assessments. Global comparisons
were limited to DHI scores, with no accompanying comparison of health care systems
or point-of-care digital health capabilities. Some possibilities could be differences
in EMR implementation, health care expenditure, and nature of health funding, which
will benefit from future research.
Future Research
Future research involves building the evidence-base for advancing digital capabilities
in practice, including validation of the DHI tool. We are underway with an analysis
of routinely collected hospital performance and clinical quality and safety data to
correlate digital capability with outcomes mapped to the quadruple aims of health
care,28 including a longitudinal analysis of digitizing health care systems. Measuring the
impact of the digitization of health care is necessary to quantify the meaningful
impacts on health outcomes, cost of care, and the patient and clinician experience.
Conclusion
For health care organizations to drive digital transformation in a strategic and informed
manner, it is critical they understand and benchmark their current digital health
capability. Queensland, a large state in Australia, undertook a capability assessment
of public health services using the DHI. The results reveal a variation in DHI scores
reflecting the diverse stages of health care digitization across the state which is
consistent with global trends. Governance and Workforce was on average the highest
scoring dimension, followed by interoperability, person-enabled health, and predictive
analytics. The findings helped derive specific insights for future digital health
planning. As the first large scale application of the DHI globally and the first published
state-wide digital capability report in Australia, the findings also offer insights
for policy makers and organizational managers. Understanding and monitoring digital
transformation at scale is critical for strategic and evidence-based digital transformation
investments.
Clinical Relevance Statement
Clinical Relevance Statement
A digital health maturity or capability assessment involves clinicians and managers
completing a self-assessment by answering a series of indicator statements describing
the current system and workforce capability. Reports are generated helping staff to
identify strengths and weaknesses in the current state, and provide recommendations
into how this might be addressed in the future state. The process generates insights
beyond the common focus on technology implementation by health care teams, affording
the opportunity to guide the digital transformation to meet organizational goals for
patient care. Business cases or reports can be generated for health care executives,
decision-makers, and policy makers for targeted digital health planning, resourcing,
and investment.
Multiple Choice Questions
Multiple Choice Questions
-
The Digital Health Indicator is a capability assessment of which of the following
dimensions?
-
Business; organization; information; people.
-
People; process; information; technology.
-
Interoperability; person-enabled health; predictive analytics; governance and workforce.
-
Interoperability; patient centered care; quality and safety; leadership.
Correct Answer: The correct answer is option c. The Digital Health Indicator assesses the digital
health capability of the health system across the dimensions of: interoperability;
person-enabled health; predictive analytics; governance and workforce.
-
The benefits of conducting a digital health capability assessment in Queensland include
all the following except:
-
Identify key directions for future resourcing.
-
Name who was responsible for suboptimal implementation efforts.
-
Benchmark against global counterparts.
-
Assess the current state of digital health.
Correct Answer: The correct answer is option b. There are many reasons for conducting a digital health
capability assessment including: identification of key directions for future resourcing;
benchmarking against global counterparts; and assess the current state of digital
health. Naming individuals who were responsible for suboptimal implementation efforts
was not a benefit.