=Paper=
{{Paper
|id=Vol-3115/paper1
|storemode=property
|title=A Reference Motivation Layer for Smart Health - an Enterprise Architecture Approach
|pdfUrl=https://ceur-ws.org/Vol-3115/paper1.pdf
|volume=Vol-3115
|authors=Helena Alves,Alberto Rodrigues da Silva,André Vasconcelos
|dblpUrl=https://dblp.org/rec/conf/eewc/AlvesS021
}}
==A Reference Motivation Layer for Smart Health - an Enterprise Architecture Approach==
https://ceur-ws.org/Vol-3115/paper1.pdf
A Reference Motivation Layer for Smart Health – an
Enterprise Architecture Approach
Helena Alves1, Alberto Rodrigues da Silva1, André Vasconcelos1
1 INESC-ID, Instituto Superior Técnico, Lisbon, Portugal
Abstract. The concept of smart health has emerged with the aim of improving
citizens’ quality of life and better healthcare services. As the cost of medical
services increases and the population ages, along with time and space con-
straints, existing healthcare systems are facing great challenges. The implemen-
tation of smart health solutions imposes a set of requirement, best practices,
concerns and motivations. We conducted a systematic literature review (SLR)
with the purpose of identifying the key motivation elements that shall be pre-
sent in smart health solutions. Based on this SLR, we propose an enterprise ar-
chitecture for smart health solutions based on the SLR conclusions that can be
used as a reference model and a set of guidelines for city authorities and other
decision makers to follow.
Keywords: Smart city, Smart health, Enterprise architecture, ArchiMate, Moti-
vation layer
1 Introduction
A smart city is a developed city that leverages the advance of intelligent sensor sys-
tems to promote smarter environments, raise awareness of surrounding, and enhance
quality of urban life [1]. A smart city provides a secure, safe, environmental, and
efficient urban center that incorporates advanced infrastructures, combining sensors,
electronic devices, and networks, that can stimulate a higher quality of life and eco-
nomic growth [2].
On the other hand, smart health is seen as a paradigm for smart environments, hav-
ing the potential to improve healthcare systems within smart cities or other geograph-
ic contexts [3]. As recent research states, proper management and development of
smart health is the key to success of smart city ecosystems [4]. For instance, Tian et
al. defines smart healthcare as a service system that uses technology to dynamically
access information, connect people, materials and institutions related to healthcare,
and then actively manages and responds to medical needs in an intelligent manner [6].
Both economic and social challenges related with the ageing and the need for fos-
tering healthy habits amongst the population poses both the public and the private
sector to explore the possibilities of smart health. In Europe, for example, it is esti-
mated that by 2025, 20% of Europeans will be at or over the age of 65 [13]. There are
many other drivers and challenges to the development and uptake of smart health. The
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
2
use of technologies for data acquisition, processing, and analysis of healthcare data
(such as mobile applications and sensors) increases along with the volume of data
being recorded.
This paper proposes a “Smart Health Enterprise Architecture Framework” (or just
“SH-EAF”), where smart health is a way to promote transparency on the health do-
main, as well as to enable efficient data integration and reliable analysis within smart
health systems [7], reduce healthcare costs, among others. This approach would also
favor the development of new applications, strengthening interoperability among
systems. One simple example is described in [8], where patients with respiration
problems use their smart phones to walk on the city with the minimal effect on their
health. For that the application needs to use the context-aware network and sensing
infrastructure of the smart city, taking advantage of data regarding pollution and pol-
len levels, among others. The major contribute of this paper is the proposal and dis-
cussion of an enterprise architecture framework motivation layer for guiding the im-
plementation of smart health solutions by city authorities and other decision makers.
This paper is organized as follows: Section 2 explains the research methodology
followed to obtain the elements presented in the proposed framework; Section 3 de-
scribes the framework, by introducing ArchiMate and motivation elements, explain-
ing each element and exploring the relationships between them; Section 4 discusses
the proposed framework and compares it based on related work. Finally, Section 5
presents the conclusion and future work.
2 Research Methodology
To define the relevant motivation elements for a successful implementation of a smart
health solution, we follow the systematic literature review (SLR) methodology [21].
This analysis is conducted by the following question:
RQ: What are the key motivation elements that can be considered in implementation
of a smart health solution?
The search terms and datasets used to search for existing articles are listed below.
Search Terms: “Smart City AND (Health OR Healthcare)”
Datasets: Google Scholar, ScienceDirect, Elsevier, IEEEXplore, ACM and Re-
searchGate
The inclusion criteria were the following: Written in English; Publication date after
2010, Public available papers; and Title and abstract relevance for the research. These
criteria were used to obtain a final selection of 20 relevant articles for this research,
published between 2013 and 2019. The distribution of those articles by venue (i.e.,
Conference, Journals, Technical Reports and Magazines) is shown in Fig. 1, and the
number of selected articles by year is shown in Fig. 2. The list of the selected articles
is shown in Table 1.
3
Fig. 1. Selected articles distribution by document type. Fig. 2. Selected articles by year.
Table 1. Selected papers overview
ID Reference Title Year Type
A Smart Health Application and its Related Privacy Is- 2016 Conference
SP1 [3]
sues
Big Sensed Data Meets Deep Learning for Smarter 2017 Journal
SP2 [4]
Health Care in Smart Cities
SP3 [10] Context-aware recommender for smart health 2015 Conference
Development of Monitoring and Health Service Infor- 2018 Conference
SP4 [11] mation System to Support Smart Health on Android Plat-
form
Effective ways to use Internet of Things in the field of 2016 Conference
SP5 [12]
medical and smart health care
SP6 [13] Internet of Things: Smart Health 2015 Report
Everything You Wanted to Know About Smart Health 2018 Magazine
SP7 [14]
Care
Smart health: A context-aware health paradigm within 2014 Magazine
SP8 [15]
smart cities
Modular and Personalized Smart Health Application 2017 Journal
SP9 [24]
Design in a Smart City Environment
Open data models for smart health interconnected appli- 2016 Journal
SP10 [7]
cations: the example of openEHR
SP11 [8] Personalized medical services using smart cities 2014 Conference
PHR open platform based smart health service using 2016 Journal
SP12 [17]
distributed object group framework
Smart City and Smart-Health Framework, Challenges and 2019 Journal
SP13 [16]
Opportunities
Smart Health: Big Data Enabled Health Paradigm within 2017 Journal
SP14 [18]
Smart Cities
Energy-harvesting based on internet of things and big 2017 Journal
SP15 [19]
data analytics for smart health monitoring
SP16 [6] Smart healthcare: making medical care more intelligent 2019 Journal
Smart healthcare monitoring: A voice pathology detection 2017 Journal
SP17 [25]
paradigm for smart cities
4
SP18 [20] Smart Health and Wellbeing 2013 Journal
Stretching 'Smart': Advancing Health and Wellbeing 2019 Journal
SP19 [9]
Through the Smart City Agenda
Toward a Smart HealthCare Architecture Using WebR 2017 Conference
SP20 [26]
and WoT
From the selected articles, we identified a set of key motivation elements, by search-
ing for motivation elements that occur in two or more of the selected papers. The
inferred conclusions are going to be systematized in next section.
3 Smart Health EAF
This section introduces the ArchiMate language and its motivation elements and then
proposes the Smart Health Enterprise Architecture Framework (SH-EAF), a graphical
view of its motivation elements. The relationships between the motivation elements
are discussed in 3.10. In the end of this section, we present the SH-EAF (see Fig.5).
3.1 ArchiMate and motivation elements
ArchiMate is a popular modelling language for enterprise architecture [21]. It is a
visual language with a set of default iconography for describing, analyzing, and
communicating many concerns of Enterprise Architectures as they change over time
[21]. The ArchiMate Enterprise Architecture modeling language provides a uniform
representation for diagrams that describe Enterprise Architectures [21].
To analyze the key concepts to achieve a successful adoption of a smart health so-
lution, we use the following of ArchiMate motivation elements:
• Stakeholder represents the role of an individual, team, or organization that repre-
sents their interests in the effects of the architecture.
• Driver represents an external or internal condition that motivates an organization to
define its goals and implement the changes necessary to achieve them.
• Assessment represents the result of an analysis of the state of affairs of the enter-
prise with respect to some driver.
• Goal represents a high-level statement of intent, direction, or desired end state for
an organization and its stakeholders.
• Outcome represents an end result of a specific goal.
• Principle represents a statement of intent defining a general property that applies to
any system in a certain context in the architecture.
• Requirement represents a statement of need defining a property that applies to a
specific system as described by the architecture.
• Constraint is a particular requirement that represents a factor that limits the realiza-
tion of goals.
5
3.2 Stakeholders
Regarding the individuals, teams or organizations that have interest in smart health,
we identify 6 key stakeholders: Patient, Healthcare Regulator, Healthcare Provider,
Entrepreneur, Healthcare Professional, and City Government.
Patient (S1) should be provided with a more comprehensive medical care [3]. A
patient relies on receiving better healthcare services, with shorter waiting and treat-
ment times and lower costs. An example of a concern that the Patient may have is the
privacy of his personal data. Healthcare Regulator (S2) plays a role in ensuring
proper standards and procurement processes. Healthcare Provider (S3) is a person or
a company that provides healthcare services to patients. Examples are pharmacy,
blood tests laboratory, hospitals, among others. Entrepreneur (S4) has interest in
regarding the innovation part of technology that fails in most of medical facilities, in
the latest developments in technological innovation to the healthcare challenges [13].
Healthcare Professional (S5) has interest in the uptake of Smart Health as some
of the health monitoring technology solutions would also allow to prevent diseases.
Examples are doctors, nurses, and others. The City Government (S6) has the most
interest in the uptake of smart health solution due to the ageing of the population and
the increase of unhealthy habits amongst the population, which are creating a lot of
pressure in the public healthcare systems [10].
Fig. 3. SH-EAF: Stakeholders
3.3 Drivers
From our analysis the following key drivers are identified: Population increase,
Population ageing, Population quality of life, Quality of healthcare services, and
Healthcare costs.
Population increase (D1) is a driver that comes from the need of smart cites, as
the world population growth will soon be unsustainable as cities will exceed their
capacity [8]. With that, also come the problem of quality of life and the societal chal-
lenge of ageing (D2) [13]. Population quality of life (D3) of those citizens will be
impacted. Several of the selected articles mention the quality of life of the citizens as
a driver for the uptake of smart health and agree on the impact it can have
[1][2][3][12][15][17].
Whilst smart health can significantly improve the quality of life it can also improve
the quality of healthcare services (D4) and help reducing healthcare costs (D5) [8].
The population increase is also deeply connected to the quality, availability, effec-
tiveness, and efficiency of the healthcare system services, as one negligence or im-
proper service may lead to an outbreak of diseases and infections [4]. We can see the
6
example of the COVID-19 pandemic, where countries that did not had a high standard
of healthcare services faced a harder challenge.
Fig. 4. SH-EAF: Drivers
3.4 Assessments
An assessment element defines a quantitative indicator that can help the decision-
makers to monitor and control the performance of their system. For instance, if a city
has: an allocated financial budget lower than a certain value, a certain number of
complaints about its healthcare systems, reached a representation of a certain percent-
age of the city population marked as unhealthy, a certain percentage of the population
that does not have proper access to pharmacies, hospital, and other health facilities,
registered a certain percentage of rise in chronic diseases.
Based on this set of indicators it is possible to discuss if there is a need (or) not to
bring smart healthcare solutions to the table.
For smart health providers, the public sector's financial budget (A1) often poses
a challenge, as there are a lot of bureaucracy behind investments and there are often
late payments [13]. In countries dealing with financial crisis, the public sector's finan-
cial budget is almost close to zero, due to austerity measures [13]. However, regard-
less of that, governments shall invest in smart health solutions to reduce costs and
increase the efficiency, as these solutions can take advantage of the existing smart city
infrastructures [15]. If this investment is made in earlier disease detection and preven-
tion, we will be watching a decrease in hospital visits and treatment numbers [15].
To ensure there are not many complaints regarding the existing healthcare (A2)
systems, it's necessary to guarantee better services, providing more satisfactory ser-
vices with lower medical costs and more efficient treatments [12][15]. In fact, as the
population increases and ages the quality of life of citizens decreases, leading to a
rapidly increase of the number of chronic diseases patients (A3) [15][17]. Chronic
diseases pose a threat to the quality of services of healthcare organizations, both in
expenses, resources, and medical research [17]. Some patients may also be putting
their quality of life (and life) in risk as some of them may not have proper access to
hospitals and other health facilities (A4) [12]. As the actual healthcare systems are
not able to accommodate everyone's needs (mostly due to population increase),
healthcare costs tend to become unaffordable and unavailable to some [14].
Lastly, we see a trend of unhealthy habits among the population (A5), posing a
challenge for the fostering of healthier habits and therefore for the implementation of
smart health solutions [10].
7
Fig. 5. SH-EAF: Assessments
3.5 Goals
From our analysis seven key goals are defined: Promote healthier lifestyles and im-
prove quality of life, allow citizens to access healthcare services more easily, Provide
more efficient and reliable health services, Monitor and analyze health related data,
Improve and further develop health applications, Introduce Open Data Models, and
Reduce healthcare costs.
The uptake of smart health has the goal to promote a healthier society, where
people can live longer and with better quality of life (G1) [4][9][15][20]. Although,
to improve patient’s quality of life and help reducing healthcare costs, it is needed that
patients have an easy access to the healthcare services (G2) [3]. Another important
goal to take in consideration is to provide a more efficient and reliable health ser-
vice (G3), ensuring the best medical assistance, prompt medical service, more effi-
cient treatments, and the most satisfactory service, to improve the quality and effi-
ciency of the healthcare systems [3][4][8][9][12][14][15][16][20].
Improper health services may lead to disaster situations. See the case of COVID-19
pandemic, where the outbreak of a virus exposed countries healthcare systems fragili-
ties and improper health services. The focus of the smart health solution should be on
improving the efficiency and quality of medical care [6][14].
To ensure that the patients get the required treatments in time, it is needed that their
health-related data is properly managed (G4), allowing remote monitoring of
health conditions and opening the possibility for patients to receive health services in
patients’ homes [4][11][14][15]. By taking proper advantage of the data interoperabil-
ity and analysis within smart city, it is possible to set a goal to improve and further
develop applications (G5), part of innovative smart health solutions. Context-aware
services and applications are especially important here, as they automatically adapt to
discovered context and allow real-time data collection from/by patients, which can
then be combined with the city data [15].
An important goal to ensure transparency on the goal and to enable the efficiency
of data integration and health-related data analysis is the introduction of Open Data
models (G6).
Lastly, the authors of the analyzed articles identify the reduction of healthcare
associated costs (G7) as one of the more important goals. Smart health can help in
cost and wastage reduction, reducing number of unnecessary visits to the hospital, by
providing health services in patients homes, for example [15][16][18][20]. This is
8
very important as the tradition health services are many times not available or afford-
able to everyone [14] and quality smart health services can help patients improve their
quality of life whilst reducing the healthcare costs [3].
Fig. 6. SH-EAF: Goals
3.6 Outcomes
From the SLR analysis seven key outcomes are defined: Improved living standards
and healthier lifestyles, Increased patients’ satisfaction with healthcare services, Re-
duced space and times constraints in medical service, Reduced burden of healthcare
system economics, Provision of remote and context-aware services, Transparency on
medical errors and Increased data integration and processing efficiency.
The motivation behind achieving better living standards and healthier lifestyles
(O1) comes from ensuring the creation of a healthier society [15]. Better healthcare
services help citizens improve their quality of life and therefore prolong their lifetime
[3][4].
Another benefit from its adoption is the increase of patient satisfaction with
healthcare services (O2), as improved services make treatments and health monitor-
ing more comfortable to patients as well as more efficient and affordable [15]. Quality
and efficient healthcare services also tend to imply an experience improvement for the
user [14]. Remote health monitoring comes with a great benefit of reducing space
and time constraints (O3), as by providing services remotely we are eliminating
space constraint by providing services remotely and time constraints, by reducing
treatments time [12][17].
By providing health services by the comfort of patients’ house, expenses decrease,
as the medical cost is lower for the patient and for the healthcare institutions, as it
decreases the unnecessary visits to hospital [15][16]. Patient can then have access to
electronic healthcare records and therapeutic procedures at a lower cost (O4) [3].
Another aspect to consider in smart healthcare is the provision of remote and con-
text-aware services (O5) that comes from the adoption of smart health solutions as
well as the transparency that this solution can bring to the field (06), especially with
medical errors (O6) [15][20].
Lastly but not less important, smart health applications provide a more efficient
and accurate processing (O7) when taking conclusion, deduction, or predictions
regarding the state of health of an individual [4].
9
Fig. 7. SH-EAF: Outcomes
3.7 Principles
From the SLR analysis two key principles are defined: Ensure uniform technical
standards among medical institutions and utilize available resources to their maxi-
mum potential.
Ensuring uniform technical standards among medical institutions (P1) is a
good principle to adopt when taking in consideration smart health, as the adoption of
healthcare solutions many times require changes in rules and regulations of hospitals
and other healthcare providers [13]. Current smart healthcare lacks macro guidance
and programming documents, which may lead to unclear goals and waste of resources
[6]. Also, regarding data integrity, is fundamental to ensure uniform standards [6].
When talking about reducing healthcare costs with smart health adoption it is also
important to ensure that we are utilizing available resources to their maximum
potential (P2) [6][14].
Fig. 8. SH-EAF: Principles
3.8 Requirements
While the adoption of smart health may be very beneficial to stakeholders, it imposes
several requirements for the architecture of the solution.
One of them is that the smart health shall be user-oriented and personalized
(R1), ensuring and enriched user experience. Redefining traditional healthcare, with
higher quality and efficient services, allows to provide a personal customization of
services with enriched user experience [3][4][14][15].
It also needs to ensure interoperability, compatibility, ample connectivity, reli-
ability, and scalability of the system (R2), addressing problems with compatibility
across different platforms and devices, connectivity issues, ability to interoperate
10
across different platforms and upgrades to newer system versions and technologies
[7][14].
Also, very important is to ensure data confidentiality, integrity, and privacy
(R3), as smart health poses some challenges regarding this, taking in account that
large amounts of information are going to be gathered [16]. The healthcare networks
contain personal information that can be easily manipulated [14]. Therefore, we shall
ensure that the data is only shared with authorized users (confidentiality), that the data
transmitted and received was not altered or compromised (integrity) and that proper
standards regarding personal information and privacy breaches are implemented (pri-
vacy) [6][14].
Then, we shall ensure proper management of health record to store health
monitoring data (R4), because proper management of patient records ensures that
the patient gets required treatment when due and helps in the development of person-
alized medicine applications [4]. This can be achieved with the help of IoT, and it
provides doctors and care givers with new ways to exchange medical records and test
results remotely and instantly [8][12]. This way it is very important to allow data
sharing and communication between systems (R3).
And lastly, the solution shall collect, classify, and analyze data from patients
and combine that with the city data and between systems (R5). There are some
challenges regarding this requirement, as some data remains trapped in EHR (Elec-
tronic Health Record), complicating the exchange of data [7]. But this requirement
also benefits the system because by allowing data integration and exchange, we are
promoting transparency and enabling efficient data integration and reliable analysis
within Smart Health systems [7]. It also helps with the development of new applica-
tions and the maintenance of the existing ones [7]. One of the articles, gives an exam-
ple: an application that collects data from the mobile phones of citizens regarding
traffic lights and pollen concentrations by using wire-less sensors distributed in trees
and streetlights [3]. The application analyses the data and advises citizens to take a
route with low level of pollution or pollen. It would also allow that real time data
could be collected from citizens and combined with city data [15].
An example on how to collect, classify and analyze data from patients between sys-
tems: system where patients report their health condition based on the level of pain
felt at the that time (taking in consideration temperature, heart rate, …). If these health
conditions pass a certain level, system will give notification to the doctor for further
communication and advise patient to check health condition to a health clinic [11].
Fig. 9. SH-EAF: Requirements
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3.9 Constraints
There are several factors that limit the realization of smart health goals, such as:
Funding and economic aspects; Data collection, presentation, and analysis; Data
quantity, variety, velocity, consistency, and storage; Usability and human-computer
interaction; Sensor integration and battery; May require technological developments
in ICT, technology, and connectivity.
As for the funding and economic aspects (C1), although smart health reduces the
cost of healthcare systems [3], there is a limited financial budget for this solution
(especially in the public sector) [13] and the medical services may not be approacha-
ble or affordable to everyone [14]. Another thing to consider, in special when the
smart health solutions are implemented by private entrepreneurs, is the cost of design
of the solution [14], as the technologies implied require funding to be maintained and
upgrade [6]. With the increase of elderly patients and the rise of chronic diseases, so
does the demand for assisted living increases, increasing the healthcare costs and
creating a shortage of healthcare professional [20].
One pillar of smart health application is the proper use of health-related data
(C2), as it is essential for the provision of health services [15]. But, with data is neces-
sary to take in account its quantity, variety, velocity, consistency, and storage (C3).
Many time the information stored in non-uniform, too complicated and too big [6]
[18]. The information collected by sensors is very diverse and smart health demands it
being collected and analyzed almost in real time (to prove useful to patients) and it
also pose a challenge of volume, as the sensor take measurements every few seconds
[15]. Regarding usability and human-computer interaction (C4), it poses a con-
straint as how the citizens interact with the city can lift many problems such as sensor
design, system reliability, among others [15]. Smart health is constrained by the tech-
nological developments in ICT, technology, and connectivity (C5), as smart health
solutions are enabled by specific technologies on which their functionalities rely [13].
Lastly, a constraint that is often forgotten has to do with sensor integration and bat-
tery (C6). Battery life of sensors is limited and the co-existing of heterogeneous sys-
tems represents a challenge [15][19].
Fig. 10. SH-EAF: Constraints
3.10 Relationship between elements
There are many relationships between the motivation elements of SH-EAF, as sug-
gested in Fig.11. The model is divided in 5 different levels: Level 1 - Stakeholder;
Level 2 - Drivers; Level 3 - Assessments; Level 4 - Goals; Level 5 - Outcomes. The
12
stakeholders are concerned with the drivers and the drivers originate from the assess-
ments. In turn, the assessments lead to goals and goals lead to outcomes.
Regarding the relationship between stakeholders and driver:
• The City Government is concerned with all the driver identified in this framework,
as it is the stakeholder that has the most interest in the uptake of smart health.
• Healthcare professional, as being a person who provides healthcare treatment, is
concerned with the quality of life of the population and the quality of healthcare
services that he/she can provide.
• Both Entrepreneur, Patient, Healthcare regulator and Healthcare provider are stake-
holders that only have concern regarding the quality of healthcare services.
Moving on to the relationship between drivers and assessments, in the EAF we on-
ly represent assessment that reveal weaknesses of the healthcare systems. Therefore,
both assessments A5, A1 and A3 are associated with the quality of healthcare ser-
vices. The rise of chronic diseases patients is associated with the ageing of the popula-
tion and lastly, low public sector allocated budget is associated with healthcare costs.
As for the goals, we have goal G4 that is directly connected to the driver of Quality
of healthcare services, as opening data can make the public sector more efficient.
When data produced by cities is made available and accessible (for example, through
open APIs), it can be utilized by both organizations and other parties [5]. All other
goals are associated to the assessments and represent what would be desirable. As for
the smart health implementation we set the goals around trying to solve the weakness-
es of healthcare systems. And so, the represented outcomes are the expected end re-
sult of the goals to each they are connected (Realizes connection).
As we already saw before, if we introduce Open Data models [7], we can achieve
increased data integration and processing efficiency. If we provide more efficient and
reliable health services, we are increasing patients’ satisfaction. If we promote health-
ier lifestyles and improve quality of life, we are improving living standards and
healthier lifestyles. If we manage to reduce healthcare costs, we are reducing the bur-
den of healthcare system economics on the government and therefore redistributing it
to other areas that may be needing it more.
13
Fig. 11. Relationships between Stakeholders, Drivers, Goals, Assessments and
Outcomes.
4 SH-EAF Discussion
Table 2 presents a mapping between each specific ArchiMate motivation element and
the selected papers.
The Popularity metric shows the popularity of the element in face of the SLR and
was calculate dividing the number of articles the element gets mentioned by the total
of articles that were analyzed (20). As we can observe from this table, all the motiva-
tion element from SH-EAF have been referenced at least on two articles, therefore
each element has at least 10% of popularity.To apply this enterprise architecture mod-
el to a city, its elements can be used as guidelines for its implementation. The most
popular elements shall be the priority in an implementation of this framework, fol-
lowed by the remaining. The context can also the adapted to each city reality (for
example, some cities may not have problems with citizens access to health facilities,
there may be already open data models put in place, etc.)
Concluding, this research intends to provide a general mode for city authorities and
to have a reference enterprise architecture when working on their smart health solu-
tions, from a motivation perspective.
Table 2. Mapping between ArchiMate motivation elements and elements relevance
Popularity
Motivation Elements SP1 SP2 SP3 SP4 SP5 SP6 SP7 SP8 SP9 SP10 SP11 SP12 SP13 SP14 SP15 SP16 SP17 SP18 SP19 SP20
(%)
Healthcare professional 25% X X X X X
Stakeholders
Healthcare provider 25% X X X X X
Patient 60% X X X X X X X X X X X X
City Government 30% X X X X X X
Entrepeneur 15% X X X
Healthcare regulator 10% X X
Population increase 20% X X X X
Population ageing 30% X X X X X X
Drivers
Population quality of life 35% X X X X X X X
Quality of healthcare services 30% X X X X X X
Healthcare costs 35% X X X X X X X
Public sector financial allocated budget 10% X X
Number of complaints 15% X X X
Assessment
Citizens with a unhealthy lifestyle 10% X X
Citizens with unproper access to pharmacies, hospitals and other health
10% X X
facilities
Rise of chronic diseases patients 10% X X
Promote healthier lifestyles and improve quality of life 20% X X X X
Reduce healthcare costs 45% X X X X X X X X X
Improve and further delevop health applications 10% X X
Goals
Allow citizens to access healthcare services more easily 10% X X
Introduce Open Data models 10% X X
Monitor and analyse health-related data 15% X X X
Provide more efficient and reliable health services 55% X X X X X X X X X X X
Increased data integration and processing efficiency 10% X X
Provision of remote and context aware-services 10% X X
Outcomes
Improved living standards and healthier lifestyles 30% X X X X X X
Increased patients satisfaction with healthcare services 20% X X X X
Reduced burden of healthcare system economics 40% X X X X X X X X X
Reduced space and time constraints in medical service 10% X X
Transparency on medical errors 10% X X
Ensure uniform technical standards among medical institutions 15% X X X
Principles
Utilize available resources to their maximum potential 10% X X
Shall ensure proper management of medical health record to store health
30% X X X X X X
monitoring data
Requirements
Shall collect, classify and analyze data from patients and combine that
20% X X X X
with the city data and between systems
Shall ensure interoperability, compatibility, ample connectivity,
10% X X
reliability and scalability of the system
Shall ensure data confidentiality, integrity and privacy 30% X X X X X X
Shall be user-oriented and personalized 25% X X X X X
May require techonological developments in ICT, e-location technology
15% X X X
and connnectivity
Constraints
Data quantity, variety, velocity, consistency and storage 15% X X X
Data collection, presentation and analysis 20% X X X X
Sensor integration and battery life 10% X X
Usability and human-computer interaction 5% X
Funding or economic aspects 35% X X X X X X X
14
5 Conclusion and future work
In this work, a SLR was conducted to identify the key motivation elements for smart
health implementations. With the summarized information and the analysis above, we
answer the original research question (RQ) and the propose the SH-EAF.
The enterprise architecture elements set the foundations for a discussion about
smart health implementation. The purpose was mainly to demonstrate how motivation
elements are used to model the motivations, or reasons, that guide the design or
change [21], and we believe these topics should be further addressed in future work to
complement this framework proposal. By identifying the key motivation elements for
smart health implementation, cities are better prepared to guide the design or change
of an Enterprise Architecture [21]. On other hand, our framework is focused on moti-
vation elements, as the goal was to give a reason and a context behind smart health
implementation. Therefore, we believe that a similar approach on business, applica-
tion and technological elements for smart health implementation would extend this
framework proposal.
Lastly, the framework was based on the motivation element which are the most
relevant for smart health.
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