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This paper reviews existing literature on digital health innovation ecosystems. It aims to explore the terms digital health, innovation and digital ecosystems to identify components towards presenting a conceptual framework for a digital health innovation ecosystem as part of a larger study. A systematic literature review was conducted on four academic databases: ACM, ScienceDirect, IEEE Xplore and SpringerLink. Due to the dearth in initial search results, the search was broadened to include non-academic publications and practitioner case reports. The study identified components of digital health, components of innovation relevant to the healthcare domain and components of digital ecosystems. It further suggests, within the context, a comprehensive definition of digital health innovation ecosystems. A conceptual framework for digital health innovation ecosystems is proposed. The findings from this study could conceivably be a step towards enabling a common understanding of practitioners, professionals and academics within the digital health domain as well as a basis for further studies on digital health innovation ecosystems.
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Procedia Computer Science 00 (2016) 000000
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1877-0509 © 2016 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of SciKA - Association for Promotion and Dissemination of Scientific Knowledge.
Conference on ENTERprise Information Systems / International Conference on Project
MANagement / Conference on Health and Social Care Information Systems and Technologies,
CENTERIS / ProjMAN / HCist 2016, October 5-7, 2016
Digital health innovation ecosystems: From systematic literature
review to conceptual framework
Gloria Ejehiohen Iyawaa,c,
*
, Marlien Herselmana,b, Adele Bothaa,b
aUniversity of South Africa, Pretoria, 0001, South Africa
bCSIR, Meiring Naude Road, CSIR Campus, Building 43, Pretoria, 0001, South Africa
cUniversity of Namibia, Windhoek, 9000, Namibia
Abstract
This paper reviews existing literature on digital health innovation ecosystems. It aims to explore the terms digital health,
innovation and digital ecosystems to identify components towards presenting a conceptual framework for a digital health
innovation ecosystem as part of a larger study. A systematic literature review was conducted on four academic databases: ACM,
ScienceDirect, IEEE Xplore and SpringerLink. Due to the dearth in initial search results, the search was broadened to include
non-academic publications and practitioner case reports. The study identified components of digital health, components of
innovation relevant to the healthcare domain and components of digital ecosystems. It further suggests, within the context, a
comprehensive definition of digital health innovation ecosystems. A conceptual framework for digital health innovation
ecosystems is proposed. The findings from this study could conceivably be a step towards enabling a common understanding of
practitioners, professionals and academics within the digital health domain as well as a basis for further studies on digital health
innovation ecosystems.
© 2016 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of SciKA - Association for Promotion and Dissemination of Scientific Knowledge.
Keywords: Digital health; innovation; digital ecosystems.
* Corresponding author. Tel.: +264814545413
E-mail address: gloria.iyawa@gmail.com
2 Iyawa et al./ Procedia Computer Science 00 (2016) 000000
1. Introduction
Innovation is described as the ability to create new ideas1-3. Innovation has been applied in different contexts4-5
and the healthcare sector is no exception6-7. Recent trends in healthcare innovation explore user participation in the
healthcare delivery process8-9,55. Digital health is an example of healthcare innovation, as it provides a platform in
which digital technologies facilitate patients’ participation in the healthcare delivery process9. Studies have
identified innovative approaches to improve existing health models, for example, incorporating innovation
ecosystems into providing digital health services10-12,32,33.
Although digital health is a trending topic15, and digital ecosystems are being discussed in academic
literature34,61,62, the term digital health innovation ecosystem is rarely discussed12 and has not been clearly defined in
academic literature. Furthermore, there is limited theoretical research that focuses on the components that constitute
digital health innovation ecosystems. This paper aims to explore the terms digital health, innovation and digital
ecosystems to identify components towards presenting a conceptual framework for a digital health innovation
ecosystem as part of a larger study. Therefore, this study contributes to the emerging body of literature on digital
health innovation ecosystems.
2. Research method
Petticrew and Roberts13 describe a systematic literature review as “literature reviews that adhere closely to a set
of scientific methods that explicitly aim to limit systematic error (bias), mainly by attempting to identify, appraise
and synthesize all relevant studies (of whatever design) in order to answer a particular question (or set of
questions). Furthermore, Okoli14 recommends that studies which aim to make a contribution rather than summarise
existing literature should adopt a systematic literature review approach. As this study aims to explore the terms
digital health, innovation and digital ecosystems and contribute to the emerging body of literature on digital health
innovation ecosystems, a systematic literature review was applied.
A systematic literature review was conducted on the following topics: digital health, innovation and digital
ecosystems. A systematic literature review was conducted on four academic databases: ACM, ScienceDirect, IEEE
Xplore and SpringerLink. In order to broaden the search, non-academic publications and practitioner case reports
were also used. Search keywords include digital health, innovation, and digital ecosystems. The search was
conducted between February and March 2016.
Books, book chapters, journal articles, conference papers, non-academic publications and practitioner case
reports related to digital health, innovation, and digital ecosystems were selected. Only publications written in
English were included. Duplicate publications were excluded from the search. Title and abstracts were first screened
for relevance before full-text documents were screened.
The findings are categorised under different themes: definition of digital health, definition of innovation,
definition of digital ecosystems, components of digital health, components of innovation and components of digital
ecosystems. The components are presented in a tabular format with a description of each component identified. A
comprehensive definition of digital health innovation ecosystems is also presented.
3. Results
In total, 65 publications were included in the current review, with (n=35) publications on digital health, (n=18)
publications on innovation and (n=12) publications on digital ecosystems. The results of the study are provided in
subsequent sections.
3.1. Definition of digital health
Different authors agree that digital health involves the use of different healthcare technologies in administering
healthcare services to enhance patients’ health15-18. In addition, while Sonnier18 and Baumann17 believe that digital
health helps in monitoring patients’ health, Sonnier18 emphasises that digital health not only enhances patients’
health but also enables families to assist in the process by monitoring patients’ health. In contrast to existing
Iyawa et al./ Procedia Computer Science 00 (2016) 000000 3
definitions of digital health, Robinson et al.19 insist that digital health “lacks theoretical definition”. However,
Robinson et al.19 suggest that digital health is the “use of digital media to transform the way healthcare provision is
conceived and delivered.
Furthermore, a proper definition of digital health should include the stakeholders involved in healthcare provision
and delivery processes. In addition to the definitions of digital health provided by Kotskov16, Mellodge and
Vendetti15, Sonnier18, Baumann17, Robinson et al.19 and for the purpose of this study, digital health is defined as: an
improvement in the way healthcare provision is conceived and delivered by healthcare providers through the use of
information and communication technologies to monitor and improve the wellbeing and health of patients and to
empower patients in the management of their health and that of their families.
3.2. Components of digital health
The components that constitute digital health were identified in selected literature. The components of digital
health presented in Table 1 were considered relevant for this study for two reasons:
The components were either stated as components of digital health by the authors or
Descriptions or the purpose of the components were in alignment with the definition of digital health for
this study.
The components of digital health identified in selected literature are described in Table 1.
Table 1. Components of digital health
Components/
Sources
Description
e-health20,18,21,22
E-health refers to the use of internet and web technologies in the provision of healthcare delivery services23.
m-health20,18,21,22
M-health refers to the use of mobile devices in administering healthcare services24.
Health 2.0/Medicine
2.020,18,21,22
Health 2.0/Medicine 2.0 refers to “the integration of Web 2.0 in the utilization of healthcare and medicine to enable
and facilitate specifically social networking, participation, apomediation, collaboration, and openness within and
between these user groups25.
Telemedicine/telecare
20,18,21,22
Telemedicine/telecare refers to the use of different information and communication technologies (ICTs) by
physicians to remotely connect with patients.26
Public health
surveillance 20
Public health surveillance is used in gathering health information of a specific population27 to facilitate “decision
making”28 regarding the health of the population in a particular setting.
Personalized
medicine/patient
engagement18,20
Personalized medicine refers to the provision of unique treatment to patients based on their genetic and genomic
components.36
Health and medical
platforms20
Health and medical platforms include online platforms such as online forums37 that help foster interaction between
patients and experts.
Health promotion
strategies20
Health promotion strategies refer to the process of enabling people to increase control over their health and its
determinants, and thereby improve their health.50
Self-tracking (the
quantified self)18,22,20
Quantified self-tracking enables patients to monitor their health status by adopting a wide range of technologies that
facilitate the process42.
Wireless
health/Wireless
sensors18,40
Wireless sensors refer to the use of different wireless monitoring devices situated in a wireless network used for
monitoring patients’ health by a physician.43
Genomics40
Genomics emphasizes how patients uniquely react to diseases based on their genomic components.44
Imaging/Medical
imaging40,21
Imaging/medical imaging refers to “techniques and processes used to create images of various parts of the human
body for diagnostic and treatment purposes within digital health45.
Information systems40
Information systems in healthcare refer to health information systems. According to Cline and Luiz51, these systems
can significantly improve healthcare delivery services to patients.
4 Iyawa et al./ Procedia Computer Science 00 (2016) 000000
Components/
Sources
Mobile connectivity
and bandwidth40
Internet40
Social networking40
Computing power
and data universe40
Interoperability22
Sensors and
wearables22
Health and wellness
apps22
Gamification18,22
Electronic health
records (EHRs)21-22
Electronic medical
records (EMRs)
Big data21,18, 22
Health information
technology21,18, 22
Health analytics38
Digitized health
systems38
Privacy and security41
Cloud computing18
3.3. Definition of innovation
Discussions on innovation have been recorded in existing literature over a long period of time35,1,2. Therefore, the
concept of innovation is not new. However, innovation has been defined from different perspectives. The
commonality among the different definitions of innovation is that innovation is described as the creation of new
ideas to improve the output of a firm1,2.
Innovation has been applied in the context of healthcare6,7. Omachonu and Einspruch6 and Thankur, Hsu and
Fontenot7 have provided definitions of innovation in healthcare. Thankur et al.’s7 definition of healthcare innovation
implies that health practices that have proven to have the best approach in healthcare are used in administering
health services to patients. The focus of this study is on healthcare innovation. Adopting the definitions of
Omachonu and Einspruch6 and Thankur et al. and for the purpose of this study, healthcare innovation is defined as:
the adoption of those best-demonstrated practices that have been proven to be successful and implementation of
those practices aimed at improving treatment, diagnosis, education, outreach, prevention and research, and with the
long term goals of improving quality, safety, outcomes, efficiency and costs.
Iyawa et al./ Procedia Computer Science 00 (2016) 000000 5
3.4. Components of innovation
The components that constitute innovation were identified in selected literature. The components of innovation
presented in Table 2 were considered relevant for this study for two reasons:
The components were either stated as relating to innovation by the authors or
Descriptions or the purpose of the components were in alignment with the definition of healthcare
innovation for this study.
Furthermore, these components can be applied within the healthcare context. The components of innovation
identified in selected literature are described in Table 2.
Table 2. Components of innovation
Components/Sources
Description
Process innovation58
Process innovation “entails innovations in the production or delivery method. The customer does not usually pay
directly for process, but the process is required to deliver a product or service and to manage the relationship
with the various stakeholders”58.
Product innovation58
Product innovation is the product that “the customer pays for and typically consists of goods or services”58.
Varkey, Horne and Bennet58 also explain that “clinical procedure innovations belong t o the category of product
innovations58.
Structure innovation58
Varkey et al.58 indicate that “structural innovation usually affects the internal and external infrastructure, and
creates new business models.
Information
technology6
Omachonu and Einspruch6 state that information technology is a component of innovation.
Closed innovation56
Closed innovation refers to a single entity exploring innovative ideas in isolation56. An entity could include a
single company, business or institution.
Open innovation56
Open innovation refers to an entity participating in sharing and gaining ideas from other entities56.
Open innovation 2.059
Open innovation 2.0 is referred to as a “new paradigm based on principles of integrated collaboration, co-created
shared values, cultivated innovation ecosystems, unleashed exponential technologies and extraordinarily rapid
adoption”59.
Innovation networks
ecosystems57
Spruijt57 defines an innovation ecosystem as a dynamic system” which “contains complex feedback loops,
causal links, flows, stocks, delays among the agents”.
Triple Helix system53
The concept of Triple Helix idealizes on universities, industries and government taking centre stage in the
innovation process53. Within the healthcare sector, the Triple Helix system can also be applied to include
stakeholders from universities, industries and the government70.
User Innovation55,54,30
This refers to a process in which users of a product participate in the innovation process55,54,30. User innovation
has been applied within the healthcare domain55.
Intellectual property60
Intellectual property rights can be used to reduce chances of intellectual properties being stolen by others on an
innovation platform. Intellectual property rights can also be applied within the healthcare sector to improve
innovation60.
3.5. Definition of digital ecosystems
Over the years, different definitions of digital ecosystems have emerged. For example, Chang and West34 define
a digital ecosystem as an open, loosely coupled, domain clustered, demand-driven, self-organising agents’
environment, where each species is proactive and responsive for its own benefit or profit. This definition suggests
that each species present in a digital ecosystem participates with the aim of achieving something. Similar definitions
of digital ecosystems by Hadzic and Dillion32 and Serbanatti and Vasilateanu11 imply that interacting components in
a digital ecosystem should be connected. However, Briscoe and De Wilde68 insist that participants in a digital
ecosystem need not be in a specific location to be connected. Kolb63 provides a different perspective to digital
ecosystems as he defines a digital ecosystem as a community of digital devices and their environment functioning
as a whole. Digital devices provide information to the other components in the ecosystem. The digital ecosystem
simulates the actions portrayed by organisms in a natural ecosystem31.
6 Iyawa et al./ Procedia Computer Science 00 (2016) 000000
Furthermore, Hadzic and Dillion32 describe a digital ecosystem as “complex”. Ion et al.69 postulate that the
complexity of digital ecosystems could be attributed to the differences in the objectives of participants who take part
in the activities of the digital ecosystem.
Adopting the definitions of Hadzic and Dillon32 and Serbanatti et al.10 and for the purpose of this study, a digital
ecosystem can thus be defined as: a network of digital communities consisting of interconnected, interrelated and
interdependent digital species, including stakeholders, institutions and digital devices situated in a digital
environment, that interact as a functional unit and are linked together through actions, information and transaction
flows.
3.6. Components of digital ecosystems
The components that constitute digital ecosystems were identified in selected literature. The components of
digital ecosystems presented in Table 3 were considered relevant for this study for two reasons:
The components were either stated as relating to digital ecosystems by the authors or
Descriptions or the purpose of the components were in alignment with the definition of digital
ecosystems for this study.
Furthermore, these components can be applied within the healthcare context. The components of digital
ecosystems are described in Table 3.
Table 3. Components of digital ecosystems
Components /Sources
Description
Community29
Community in digital ecosystems refers to the entire species available within the digital ecosystem environment.29
Content29
Content in digital ecosystems refers to information or services which are of use to the species available within the
digital ecosystem.29
Practice29
In order for the different species to be comfortable and operate freely, practice is required.29
Technology29
Technology in digital ecosystems refers to hardware and software responsible for the information interchange within
the digital ecosystem. 29
Biological species34
The people who participate in the digital ecosystem.34
Economic species34
The different companies and institutions that participate in the digital ecosystem.34
Digital species34
The digital devices, software and hardware used by people and different companies and institutions that participate in
the digital ecosystem.34
Digital environment32,10
The platform on which digital species interact.32,10
Security61
The protection of resources and species in the digital ecosystem.61
Trust62
The trust that all species in the digital ecosystems are focused on achieving the same goal.62
3.7. Definition of digital health innovation ecosystems
Working definitions of digital health, innovation and digital ecosystems have been provided. A proposed
definition of digital health innovation ecosystems should contain the essence of the definitions for digital health,
innovation and digital ecosystems. Based on the discussions related to digital health, innovation and digital
ecosystems, a digital health innovation ecosystem can be defined as: a network of digital health communities
consisting of interconnected, interrelated and interdependent digital health species, including healthcare
stakeholders, healthcare institutions and digital healthcare devices situated in a digital health environment, who
adopt the best-demonstrated practices that have been proven to be successful, and implementation of those practices
through the use of information and communication technologies to monitor and improve the wellbeing and health of
patients, to empower patients in the management of their health and that of their families.
A conceptual framework for a digital health innovation ecosystem is presented in Fig. 1, showing the underlying
relationships of the different components identified in selected literature.
Iyawa et al./ Procedia Computer Science 00 (2016) 000000 7
4. Preliminary conceptual digital health innovation ecosystem framework
A definition for Digital Health Innovation Ecosystems has been proposed. A preliminary conceptual framework
is presented in Fig 1. The conceptual framework summarises the components that constitute digital health
innovation ecosystems as explained in this study. The conceptual framework will form a basis in which further
studies in Digital Health Innovation Ecosystems are built.
Fig. 1 Conceptual framework for digital health innovation ecosystems
5. Conclusion
This study contributes to the emerging body of literature on digital health innovation ecosystems. A definition of
digital health innovation ecosystems and components of digital health innovation ecosystems is provided within the
academic domain. A conceptual framework for digital health innovation ecosystems is proposed. The findings from
this study could conceivably be a step towards enabling a common understanding of practitioners, professionals and
academics within the digital health domain as well as a basis for further studies on digital health innovation
ecosystems.
The components of digital health, innovation and digital ecosystems were selected based on their descriptions
and purpose, aligned to the definitions of digital health, innovation and digital ecosystems for this study or based on
the authors stating that these components were either related to digital health, innovation and digital ecosystems. As
a result, other relevant components of digital health, innovation and digital ecosystems that did not match our
inclusion criteria might have been excluded and hence, affected the results. However, for further studies, the
inclusion criteria may be broadened to include other relevant components of digital health, innovation and digital
ecosystems. Future work would be to examine how the components of the proposed conceptual framework
presented in this study have been applied in developed and developing countries.
8 Iyawa et al./ Procedia Computer Science 00 (2016) 000000
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... In the unprecedented COVID-19 scenario, the spread rate is quite high and so the reliance on diagnostics and testing is quite evident for making life saving recommendations (Hosseinifard et al., 2021). In healthcare industry, the gradual shift from customer care to customer experience needs to be tapped (Omachonu & Einspruch, 2010;Iyawa et al., 2016). Thus, the customer experience in the health care services, especially in diagnostics is one such lesser explored area and needs to be probed by the research community and institutions. ...
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Purpose: This paper attempts to find out the factors affecting customers' experience in diagnostic centres and to measure the level of overall customers' experience with various health diagnostic centres to identify the major areas of concern for the service providers. Design/methodology/approach: The nature of the research is descriptive. The data is collected from primary sources using structured questionnaires from the individuals who had already received diagnostic services from diagnostic centres. The customer was either the patient himself/herself or his/her attendant in the case of the minor and dependent person. Further, the customer database of at least three months was studied and judgement sampling was undertaken. The SPSS software was used to edit, code and analyse the primary data. Descriptive statistics along with inferential statistical tools was used for analysis. Factor analysis was done to club the components. Findings: Thestudy showed moderately favourable level of customer experience among the customers of the diagnostic centres in Guwahati. The factors affecting customers' experience in a diagnostic centre are availability of requisite infrastructure, comfort of dealing, empathetic treatment to patients, ancillary services and accessibility and availability of services. Originality: The present study opens new vistas from ethical and social angles apart from the managerial points raised. It also underlines how the analyses of customers' experience act as a road map for the improvement of healthcare services. Research limitations/implications:Empirical evidenceof this research work will help the researchers to contribute further after exploring the future scope. Future researches in other organizations can be done for varied results. The customer experience can be tapped for future implications. Practical implications: The findings of the research work will serve as a yardstick and guide for diagnostic centres and others in the same sector for incorporating analytical tools in customer experience management which can help the managers to look carefully into processes to assess evidence and yield proper results. Social implications: A thorough understanding of the customers' experience is presented which will showcase their preferences. The managerial recommendations point towards better resource allocation for the improvement of customer experience. The research will serve for a better healthcare system with increased focus on customer experience. The patients will fare better when these implications are applied.
... Digital health uses technology to deliver information and enable communication, with the purpose of monitoring and managing patients and consequently improving their health conditions [28]. Moreover, this approach reduces the burden of health care, allows patients to manage their health even outside traditional hospitals, and individualizes the treatment by implementing behavior change theory or techniques [9]. ...
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With the advent of Digital Therapeutics (DTx), the development of software as a medical device (SaMD) for mobile and wearable devices has gained significant attention in recent years. Existing DTx evaluations, such as randomized clinical trials, mostly focus on verifying the effectiveness of DTx products. To acquire a deeper understanding of DTx engagement and behavioral adherence, beyond efficacy, a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis. In this work, the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets, to investigate contextual patterns associated with DTx usage, and to establish the (causal) relationship of DTx engagement and behavioral adherence. This review of the key components of data-driven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets, which helps to iteratively improve the receptivity of existing DTx.
... Digital health solutions can improve delivery of care through the application of tools such as clinical decision support systems [26]. Electronic clinical decision support systems (eCDSSs) are digital-enabled tools and interventions that are designed to aid directly in clinical decision making. ...
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Background Improvements to the primary prevention of physical health illnesses like diabetes in the general population have not been mirrored to the same extent in people with serious mental illness (SMI). This work evaluates the technical feasibility of implementing an electronic clinical decision support system (eCDSS) for supporting the management of dysglycaemia and diabetes in patients with serious mental illness in a secondary mental healthcare setting. Methods A stepwise approach was taken as an overarching and guiding framework for this work. Participatory methods were employed to design and deploy a monitoring and alerting eCDSS. The eCDSS was evaluated for its technical feasibility. The initial part of the feasibility evaluation was conducted in an outpatient community mental health team. Thereafter, the evaluation of the eCDSS progressed to a more in-depth in silico validation. Results A digital health intervention that enables monitoring and alerting of at-risk patients based on an approved diabetes management guideline was developed. The eCDSS generated alerts according to expected standards and in line with clinical guideline recommendations. Conclusions It is feasible to design and deploy a functional monitoring and alerting eCDSS in secondary mental healthcare. Further work is required in order to fully evaluate the integration of the eCDSS into routine clinical workflows. By describing and sharing the steps that were and will be taken from concept to clinical testing, useful insights could be provided to teams that are interested in building similar digital health interventions.
... Instead, the ecosystems and the platform model are widely used. According to Iyawa et al., a digital ecosystem is "a network of digital communities consisting of interconnected, interrelated and interdependent digital species, including stakeholders, institutions, and digital devices situated in a digital environment, which interact as a functional unit and are linked together through actions, information and transaction flows" (17). The ecosystem metaphor requires that all stakeholders have a common goal, but in health ecosystems, this is not always clear. ...
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A transformed health ecosystem is a multi-stakeholder coalition that collects, stores, and shares personal health information (PHI) for different purposes, such as for personalized care, prevention, health prediction, precise medicine, personal health management, and public health purposes. Those services are data driven, and a lot of PHI is needed not only from received care and treatments, but also from a person’s normal life. Collecting, processing, storing, and sharing of the huge amount of sensitive PHI in the ecosystem cause many security, privacy, and trust challenges to be solved. The authors have studied those challenges from different perspectives using existing literature and found that current security and privacy solutions are insufficient, and for the user it is difficult to know whom to trust, and how much. Furthermore, in today’s widely used privacy approaches, such as privacy as choice or control and belief or perception based trust does not work in digital health ecosystems. The authors state that it is necessary to redefine the way privacy and trust are understood in health, to develop new legislation to support new privacy and approaches, and to force the stakeholders of the health ecosystem to make their privacy and trust practices and features of their information systems available. The authors have also studied some candidate solutions for security, privacy, and trust to be used in future health ecosystems.
... To date, similar to m-Health, there are numerous institutional and individualized definitions of the term digital health. These include, for example, the WHO definitions [21,[28][29][30], the U.S. Food and Drug Administration (FDA) definition [31], the Healthcare Information and Management Systems Society (HIMSS) [32], and many others [33]. A list of these and other widely cited definitions are shown in Table 2. ...
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For nearly two decades, mobile health or (m-Health) was hailed as the most innovative and enabling area for the digital transformation of healthcare globally. However, this profound vision became a fleeting view since the inception and domination of smart phones, and the reorientation of the concept towards the exclusivity of global smart phone application markets and services. The global consumerization of m-Health in numerous disciplines of healthcare, fitness and wellness areas is unprecedented. However, this divergence between ‘mobile health capitalism’ and the ‘science of mobile health’ led to the creation of the ‘m-Health schism’. This schism was sustained by the continued domination of the former on the expense of the latter. This also led to increased global m-Health inequality and divide between the much-perceived health and patient benefits and the markets of m-Health. This divergence was more evident in low and middle income (LMIC) countries compared to the developed world. This powerful yet misguided evolution of the m-Health was driven essentially by complex factors. These are presented in this paper as the ‘known unknowns’ or ‘the obvious but sanctioned facts’ of m-Health. These issues had surreptitiously contributed to this reorientation and the widening schism of m-Health. The collateral damage of this process was the increased shift towards understanding ‘digital health’ as a conjecture term associated with mobile health. However, to date, no clear or scientific views are discussed or analyzed on the actual differences and correlation aspects between digital and mobile health. This particular ‘known unknown’ is presented in detail in order to provide a rapprochement framework of this correlation and valid presentations between the two areas. The framework correlates digital health with the other standard ICT for the healthcare domains of telemedicine, telehealth and e-health. These are also increasingly used in conjunction with digital health, without clear distinctions between these terms and digital health. These critical issues have become timelier and more important to discuss and present, particularly after the world has been caught off guard by the COVID-19 pandemic. The much hyped and the profiteering digital health solutions developed in response of this pandemic provided a modest impact, and the benefits were mostly inadequate in mitigating the massive health, human, and economic impact of this pandemic. This largely commercial reorientation of mobile health was unable not only to predict the severity of the pandemic, but also unable to provide adequate digital tools or effective pre-emptive digital epidemiological shielding and guarding mechanisms against this devastating pandemic. There are many lessons to be learnt from the COVID-19 pandemic from the mobile and digital health perspectives, and lessons must be learnt from the past and to address the critical aspects discussed in this paper for better understanding of mobile health and effective tackling of future global healthcare challenges.
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Currently, researchers and practitioners globally recognize the importance of social innovations to successfully address social, economic, political, and environmental challenges. With the growth of social innovations, ecosystems of social innovations began to emerge, comprising a set of actors from different sectors of society working collaboratively to develop these innovations and meet social needs. As a result of the studies carried out in this research, it was identified that the challenges reported by actors in this domain point to the need for technological support concerning aspects such as collaboration, knowledge sharing, and support for ecosystem management. To fill these gaps, we proposed a new ecosystem category to support social innovation actors, particularly orchestrators, using digital ecosystem concepts, the Social Innovation Digital Ecosystem (SIDE). SIDE is an ecosystem where actors interact and collaborate through the support of a common technological platform and a collaborative, inclusive, and open process to generate social innovations to meet society's challenges. Aiming to characterize this ecosystem, a conceptual model was developed, with elements extracted from systematic mapping studies concerning digital ecosystems and social innovation ecosystems and evaluated by 21 experts in social innovation. Next, we performed studies on mature ecosystems as well as surveys and semi-structured interviews involving professionals and researchers in social innovation ecosystems. These studies aimed to identify elements to build an approach to support SIDE management. As a result of these studies and the conceptual model, a framework was developed, considering three dimensions to support the ecosystem actors (technical, business, and social) and one dimension to help the orchestrator (management). With the generated artifacts, eSIDE was developed, which is a common and central technological platform to support ecosystem actors. The eSIDE features were evaluated through a tool analysis stage and a focus group, in which participants highlighted the relevance of the management dashboard functionalities to support the orchestrators.
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Background Older adults experience a high risk of adverse events during hospital-to-home transitions. Implementation barriers have prevented widespread clinical uptake of the various digital health technologies that aim to support hospital-to-home transitions. Objective To guide the development of a digital health intervention to support transitions from hospital to home (the Digital Bridge intervention), the specific objectives of this review were to describe the various roles and functions of health care providers supporting hospital-to-home transitions for older adults, allowing future technologies to be more targeted to support their work; describe the types of digital health interventions used to facilitate the transition from hospital to home for older adults and elucidate how these interventions support the roles and functions of providers; describe the lessons learned from the design and implementation of these interventions; and identify opportunities to improve the fit between technology and provider functions within the Digital Bridge intervention and other transition-focused digital health interventions. Methods This 2-phase rapid review involved a selective review of providers’ roles and their functions during hospital-to-home transitions (phase 1) and a structured literature review on digital health interventions used to support older adults’ hospital-to-home transitions (phase 2). During the analysis, the technology functions identified in phase 2 were linked to the provider roles and functions identified in phase 1. Results In phase 1, various provider roles were identified that facilitated hospital-to-home transitions, including navigation-specific roles and the roles of nurses and physicians. The key transition functions performed by providers were related to the 3 categories of continuity of care (ie, informational, management, and relational continuity). Phase 2, included articles (n=142) that reported digital health interventions targeting various medical conditions or groups. Most digital health interventions supported management continuity (eg, follow-up, assessment, and monitoring of patients’ status after hospital discharge), whereas informational and relational continuity were the least supported. The lessons learned from the interventions were categorized into technology- and research-related challenges and opportunities and informed several recommendations to guide the design of transition-focused digital health interventions. Conclusions This review highlights the need for Digital Bridge and other digital health interventions to align the design and delivery of digital health interventions with provider functions, design and test interventions with older adults, and examine multilevel outcomes. International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2020-045596
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Development in digital innovation from customary to shrewd medical care is projected to change medical care frameworks all over the planet. Savvy medical care utilizes computerized innovations to make it simpler to peruse well-being data, associate individuals, assets, and associations, and shrewdly handle and answer well-being-related needs. Patients, medical services experts, associations, and controllers are connected in the shrewd medical care framework. Artificial Intelligence (AI), the Internet of things (IoT), fog processing, cloud computing, block-chain, sensors, 5G innovation, and the Internet of Medical things (IoMT) are instances of remnant advances that are as yet developing. These advances are basic to the improvement of the medical care idea, which is an arising creative idea. The medical services framework, similar to the car business, has gone through ages, from medical care to shrewd medical services, with insurgencies in an assortment of supporting ventures. For instance, because of the absence of advanced innovations, numerous medical services associations utilized paper-based frameworks from 1970 to 1990. Patients and medical services experts physically catch well-being information and clinical solutions on paper during the period of medical care 1.0, which incorporates counsel, testing, and finding. For a long time, this idea has been broadly utilized in healthcare. Patients' records, then again, were helpless against mileage over the long haul, putting patient security and secrecy at risk. Medical services otherwise called e-Health were embraced somewhere in the range of 1991 and 2005 to offer better protection and security of well-being records while additionally improving support and versatility. Digital innovation upset different medical services frameworks by expanding information catch, availability, and sharing productivity. An authoritative target of medical consideration is to give patient-driven clinical consideration to organizations through splendid thought, related care and redid medicine. Notably, medical benefits supporting ventures have embraced the industry as of now progressing toward industry. Such disruption continues to rethink how today's computerized super-advanced firms grow commercial operations and increase effectiveness across the value chain. Medical care delivery, like assembly, is at the start of a paradigm shift to usher in a new era of medical services. This is an exciting time in many ways, including astute infection prevention and discovery, virtual consideration, astute wellness across the board, amazing watching, direction, and clinical research. Regulatory compliance is especially difficult for new digital health devices. As a result, many healthcare systems, particularly in poor countries, rely significantly on paper-based methods to collect, process, and preserve health information. As a result, many healthcare systems, particularly in developing countries, rely significantly on paper-based methods to collect, process, and preserve health data. Despite significant progress in smart and connected healthcare, further research concepts, distribution, and technologies are necessary to wide-open new possibilities and move into health care.
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Life expectancy is increasing, technologies are improving, and medications are spreading at breakneck speed. Benefits, as well as challenges and uncertainties, are evident. The evolution of healthcare is, first and foremost, the evolution of a mindset: health should be viewed as a social and economic investment, a growth driver that generates a circular well-being among those who provide technological equipment (companies), those who use it in emergencies and routine care (hospitals and the medical profession), and those who benefit from it (the general public) (the patients). Only human and economic costs can be used as a starting point: healthcare is only viable if business strategies that improve service quality do not inflate costs to the point where they are no longer available. The technological innovation, more specifically the digital revolution, is deeply changing the way healthcare processes are managed, promoting cooperation of several healthcare players. Healthcare processes strongly rely on both information and knowledge (Lenz et al., 2012; Lenz & Reichert, 2007). Therefore, information management could play an important role and a performing technology supporting processes becomes crucial. At the same time, healthcare organizations, more than others, have to face with growing complexity of care, reducing resources, and increased regulative frameworks. Healthcare providers are trying to increase quality and, at the same time, to reduce costs in order to maximize value. Care for a medical condition often embraces multiple expertise and several interventions. Value for the patient is created by providers' combined efforts over the full cycle of care (Porter, 2010). Brilliant wearable contraptions, sensor-based knowledge gadgets, and shrewd well-being applications can all assist with this. Sensor-based shrewd wearable gadgets, for instance, have been utilized to screen physiological boundaries expected for COVID-19 identification. 64 Remote patient observing has consequently been utilized in an assortment of utilization fields, including cardiovascular checking, blood oxygen immersion checking, temperature checking, breath checking, rest checking, and movement levels observing. Patients can be advised when their physiological changes become concerning, staying away from clinic affirmations generally speaking. For distant clinical consultations, virtual facilities plan advanced correspondence between medical care specialists and patients via phone, a video connect, or other online stages. Virtual facilities can assist with lessening the spread of profoundly infectious sicknesses, patient holding up times, and further develop medical care by diminishing direct understanding contact.
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Foundational infrastructure requirements in this chapter and lists of the form of studies that can be used to study any national, regional, or organisational digital health infrastructure are identified. Some criteria that may be used to evaluate overall performance are provided. Digital health foundational needs and adaptive complex digital health ecosystems are described. Examples of national digital health strategies adopted by Australia, the United Kingdom, New Zealand, and the United States are provided, noting missing foundational infrastructure components that continue the fragmentation of technical applications and data repositories. There is an urgent need for strong national non-hierarchical leadership promoting and supporting innovation, including greater clinical engagement. This chapter provides the rationale for the adoption of national roadmaps.
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The Global Polio Eradication Initiative, established in 1988, has led to the immunization of 2.5 billion children against the disease. In 2014, only 359 cases of poliomyelitis (polio) were reported worldwide, which is a fraction of the estimated 350 000 children in 125 countries who were paralyzed annually by the poliovirus before 1988. There are only two remaining countries where polio is still endemic – Afghanistan and Pakistan, while circulating vaccine-derived poliovirus is still causing outbreaks in others, such as Guinea, Madagascar and Ukraine. Spread of the poliovirus from the two remaining endemic countries to Iraq, Israel, the Syrian Arab Republic and other vulnerable countries is a continuing threat. Until polio transmission in endemic countries is interrupted, the whole world remains at risk.
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Understanding and improving the health status of communities depends on effective public health surveillance. Adoption of new technologies, standardised case definitions and clinical guidelines for accurate diagnosis, and access to timely and reliable data, remains a challenge for public health surveillance systems however and existing public health surveillance systems are often fragmented, disease specific, inconsistent and of poor quality. We describe the application of an enterprise architecture approach to the design, planning and implementation of a national public health surveillance system in Jordan. This enabled a well planned and collaboratively supported system to be built and implemented using consistent standards for data collection, management, reporting and use. The system is case-based and integrated and employs mobile information technology to aid collection of real-time, standardised data to inform and improve decision-making at different levels of the health system.
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Many scholars are not well trained in conducting a standalone literature review, a scholarly paper that in its entirety summarizes and synthesizes knowledge from a prior body of research. Numerous guides that exist for information systems (IS) research mainly concentrate on only certain parts of the process; few span the entire process. This paper introduces the rigorous, standardized methodology for the systematic literature review (also called systematic review) to IS scholars. This comprehensive guide extends the base methodology from the health sciences and other fields with numerous adaptations to meet the needs of methodologically diverse fields such as IS research, especially those that involve including and synthesizing both quantitative and qualitative studies. Moreover, this guide provides many examples from IS research and provides references to guides with further helpful details for conducting a rigorous and valuable literature review. Although tailored to IS research, it is sufficiently broad to be applicable and valuable to scholars from any social science field. Full text available from http://chitu.okoli.org/pub/okoli-2015-a-guide-to-conducting-a-standalone-systematic-literature-review/
Chapter
‘Digital health’ is an overarching concept that currently lacks theoretical definition and common terminology. For instance, this broad and emerging field includes all of the following terms within its lexicon: mHealth, Wireless Health, Health 2.0, eHealth, e-Patient(s), Healthcare IT/Health IT, Big Data, Health Data, Cloud Computing, Quantified Self, Wearable Computing, Gamification, and Telehealth/Telemedicine [1]. However, whilst a definition is difficult to provide, in this overview it is considered that digital health is the use of digital media to transform the way healthcare provision is conceived and delivered. We consider it does this through three basic features.
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Background: Telemedicine has seen substantial growth in the past 20 years, related to technologic advancements and evolving reimbursement policies. The risks and opportunities of neurosurgical telemedicine are nuanced. Methods: We reviewed general and peer-reviewed literature as it relates to telemedicine and neurosurgery, with particular attention to best practices, relevant state and federal policy conditions, economic evaluations, and prospective clinical studies. Results: Despite technologic development, growing interest, and increasing reimbursement opportunities, telemedicine's utilization remains limited because of concerns regarding an apparent lack of need for telemedicine services, lack of widespread reimbursement, lack of interstate licensure reciprocity, lack of universal access to necessary technology, concerns about maintaining patient confidentiality, and concerns and limited precedent regarding liability issues. The Veterans Health Administration, a component of the U.S. Department of Veterans Affairs, represents a setting in which these concerns can be largely obviated and is a model for telemedicine best practices. Results from the VA demonstrate substantial cost savings and patient satisfaction with remote care for chronic neurologic conditions. Overall, the economic and clinical benefits of telemedicine will likely come from 1) diminished travel times and lost work time for patients; 2) remote consultation of subspecialty experts, such as neurosurgeons; and 3) remote consultation to assist with triage and care in time-sensitive scenarios, including acute stroke care and "teletrauma." Conclusions: Telemedicine is effective in many health care scenarios and will become more relevant to neurosurgical patient care. We favor proceeding with legislation to reduce barriers to telemedicine's growth.