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Intensified and extensive data production and data storage are characteristics of contemporary western societies. Health data sharing is increasing with the growth of Information and Communication Technology (ICT) platforms devoted to the collection of personal health and genomic data. However, the sensitive and personal nature of health data poses ethical challenges when data is disclosed and shared even if for scientific research purposes. With this in mind, the Science and Values Working Group of the COST Action CHIP ME ‘Citizen's Health through public-private Initiatives: Public health, Market and Ethical perspectives’ (IS 1303) identified six core values they considered to be essential for the ethical sharing of health data using ICT platforms. We believe that using this ethical framework will promote respectful scientific practices in order to maintain individuals’ trust in research. We use these values to analyse five ICT platforms and explore how emerging data sharing platforms are reconfiguring the data sharing experience from a range of perspectives. We discuss which types of values, rights and responsibilities they entail and enshrine within their philosophy or outlook on what it means to share personal health information. Through this discussion we address issues of the design and the development process of personal health data and patient-oriented infrastructures, as well as new forms of technologically-mediated empowerment.
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R E S E A R C H Open Access
Ethical sharing of health data in online
platforms which values should be
considered?
Brígida Riso
1*
, Aaro Tupasela
2
, Danya F. Vears
3,4
, Heike Felzmann
5
, Julian Cockbain
6
, Michele Loi
7,8
,
Nana C. H. Kongsholm
9
, Silvia Zullo
10
and Vojin Rakic
11
* Correspondence:
brigida.riso@gmail.com
1
Instituto Universitário de Lisboa
(ISCTE-IUL), Edifício ISCTE, Av. das
Forças Armadas, 1649-026 Lisboa,
Portugal
Full list of author information is
available at the end of the article
Abstract
Intensified and extensive data production and data storage are characteristics of
contemporary western societies. Health data sharing is increasing with the growth of
Information and Communication Technology (ICT) platforms devoted to the
collection of personal health and genomic data. However, the sensitive and personal
nature of health data poses ethical challenges when data is disclosed and shared
even if for scientific research purposes.
With this in mind, the Science and Values Working Group of the COST Action CHIP
ME Citizen's Health through public-private Initiatives: Public health, Market and
Ethical perspectives(IS 1303) identified six core values they considered to be
essential for the ethical sharing of health data using ICT platforms. We believe that
using this ethical framework will promote respectful scientific practices in order to
maintain individualstrust in research.
We use these values to analyse five ICT platforms and explore how emerging data
sharing platforms are reconfiguring the data sharing experience from a range of
perspectives. We discuss which types of values, rights and responsibilities they entail
and enshrine within their philosophy or outlook on what it means to share personal
health information. Through this discussion we address issues of the design and the
development process of personal health data and patient-oriented infrastructures, as
well as new forms of technologically-mediated empowerment.
Keywords: Data sharing, Ethical values, Health data, Health research, Information and
communication technology platforms, Interoperability
Introduction
During the past decade the developments in platforms for data sharing have increased
considerably and calls to share data have intensified (Arzberger et al. 2004). Some have
argued that our societies have become both data rich and data dependent in that the
proliferation of data producing sources has increased exponentially, along with our
need to use and analyse increasing amounts of data (Rodriguez 2013). This has given
rise to the notion of big dataas a major driver of research and development within
the biomedical sector (Leonelli 2014). From a policy perspective, data sharing has be-
come a preoccupation with all major policy actors and supranational organisations
seeking to develop and bolster data generation and sharing strategies (OECD 2009;
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
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indicate if changes were made.
Riso et al. Life Sciences, Society and Policy (2017) 13:12
DOI 10.1186/s40504-017-0060-z
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Laurie 2004; European Society of Human Genetics (ESHG), 2003). In addition, a
massive increase in the uptake of electronic health records has made access to and
analysis of massive data sets a reality (Murdoch and Detsky 2013). Numerous actors,
ranging from national health authorities to businesses, as well as academic researchers,
have been developing new types of information infrastructures that can facilitate the
sharing of personal health data (Bansler and Kensing 2010; Gherardi et al. 2014).
Scandinavian countries in particular have a long-standing policy of utilising their
populations for medical research, capitalising on the pervasive use of social security
numbers to track individuals through a multitude of registries and databases
(Hoeyer 2016; Bauer 2014).
In this respect, the so-called knowledge-based society visions suggested by the European
Union (EU) and the European Commission have come to represent a new form of civil
society (European Commission 2005; Felt and Wynne 2007). At the same time, some
authors have argued that the demand and interest in collecting and analysing more data
from more people has led to a type of data fetishization (Lupton 2014). Models of
knowledge production and use enshrine specific values, which are reflected in the types of
platforms that are developed for sharing data. Some authors, such as Epstein (1995),
Rabeharisoa and Callon (2002) and Novas (2007) have suggested that patient-led activism
has become an important driver within the biomedical research community, accounting
for not only the mobilisation of patients as research participants, but also in raising capital
for research itself (see also Rabeharisoa et al. 2014). In this sense, patient activism is being
viewed as a novel form of social movement, which is, in part, driving the demand, and
creation of new platforms for the generation and distribution of data (Tupasela et al.
2015). Lupton (2014) argues that digital media platforms which elicit lay peoplesexperi-
ences of illness and healthcare are giving rise to the digital patient experience economy
where not only are patients able to express their experiences in more diverse ways, but
are at the same time being exploited using novel strategies, for both research and
commercial purposes. This process is situated within a broader context in which big data
is valorised through a discourse of data sharing not always clear in its intentions
(Van Djick and Poell 2016).
In this article, we seek to examine five data sharing platforms (Taltioni, Healthbank,
MIDATA, ePGA, and PEER Network) that have been developed to facilitate the sharing
of personal health data within a variety of scenarios. We argue that these platforms re-
flect diverse conceptions of data sharing among stakeholders, as well as specific types
of core values associated with data sharing. The relationship between data sharing and
the platforms that facilitate this are therefore a crucial issue in understanding the role
and significance that different data sharing initiatives seek to achieve. We argue
that data sharing is not a value-neutral practice, but rather represents a broad
spectrum of ethical, political and social goals that various actors are seeking to
achieve. By identifying core values, which exist in sharing platforms, we seek to de-
velop a general typology of basic principles of operation that we considered essen-
tial for an ethical sharing of health data.
In the following, we first discuss some of the general challenges related to sharing
personal health data. We then outline and describe in detail the core values we
have identified. Subsequently, we present and discuss five data sharing platforms as
cases studies in order to illustrate whether or not these core values are realised by
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existing platforms before discussing the significance of these cases in relation to
thesecorevalues.
Health data sharing: An interoperability policy in the making
Many public and private funding agencies recognise the value of shared data. However,
if data sharing is to be promoted, reciprocity and societal incentives play a significant
role (Piwowar et al. 2008; Gottweis et al., 2011). Data sharing data is a form of cooper-
ation and it presupposes some notion of mutual advantage. If a platform for data shar-
ing is perceived to only promote the benefits of the platform owners, or their business
partners, it will not invite significant participation from the population. The mutual
benefit provided to platform participants does not have to consist in money or some
other individual advantage. It could also consist - as it does in some of the models we
will explore in what follows - in providing participants with a new opportunity to act
altruistically.Using Amartya Sens distinction between agency and well-being (Sen
1985), it could be said that the benefit provided by the platform to the users may also
consist in an expansion of their agency, even when it does not directly contribute to
their personal well-being.As we shall see in the section on the value analysis, a broader
notion of reciprocity, which considers all participants in society as (indirect) contribu-
tors and beneficiaries to data sharing, is also pertinent.
Data sharing has in some cases taken on values related to notions of the general pub-
lic good, such as with blood donation in which donating to a common good is seen to
produce social rewards (Titmuss 1970). As Prainsack and Buyx (2017: 106) have sug-
gested, contributing to a database can generate social value. Still, data sharing in bio-
medical field has some real obstacles in relation to developing common standards and
platforms for sharing in order to make sharing more meaningful and effective. Society
can, for example, facilitate reciprocity by providing infrastructure as platforms with
proper safeguards, making it easy to access and share data while protecting both scien-
tists and donors. This could be achievable by requiring platforms to demonstrate how
their endeavours contribute to the common good for instance, by allowing anon-
ymised data to be exported to public institutions for public health purposes. Also, by
requiring platforms to provide a high degree of autonomous control of the provided
personal data to the their user, society can promote reciprocity. Since the platform
usersand ownersinterests are not always aligned, enhancing user-centric control is
expected to enable the users to reappropriate at least some benefits from the data.
Platforms can be viewed as a regulated environment enabling developers, users, and
others to interact and share data, services, and applications, while also enabling govern-
ments to support the development of innovative solutions. But platforms are not silos:
they need to be integrated with other platforms and systems, while ensuring their inter-
operability, an effort that may prove more controversial than initially thought.
When discussing data sharing, the term platformrequires explanation. Keating and
Cambrosio (2003: 27) have suggested that the term platform can be seen as a semantic
spectrum where, at one end you have an engineering/physics concept of a bench onto
which other devices can be attached. At the other end of the spectrum, you have the
political platform, which refers to an arrangement of statements and positions in rela-
tion to particular issues, whereby the platform refers more to a way of organising and
arranging activities, as well as material (including data). The spectrum, however, points
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our attention towards the organisation of activities that have very pragmatic goals of
making various activities work. At the same time, platforms represent the enshrinement
of values and expectations of what data sharing will achieve.
While governments and authorities are calling for a new approach to the sharing of
data and are developing policies for data sharing platforms, they have not yet ad-
equately addressed the obstacles that underpin the failure to share data (Pearce and
Smith 2011). Nor are they sufficiently recognising the emergence of diverse systems for
facilitating sharing (Thilakanathan et al. 2014). For example, each European Union
(EU) country has its own rules and codes of practice, and these pose an obstacle to
effective data sharing across national boundaries. Without an integrated model or
agreement on the core values enshrined in sharing, we cannot achieve logistical fluency
(Mascalzoni et al. 2014).
The sharing of data can be implemented on different levels: within an organisation,
between organisations, across national borders, or between healthcare institutions and
citizens. Each of these contexts carries with it its own specific challenges. E-Health
systems are adopted by end-users and have the ability to interface with national and
international health systems. This has an impact on the roles and liability of stake-
holders, and on patientsability to give informed consent for personal data processing
(Dunlop 2007). Cross-border and interoperable electronic health-record systems make
confidential data more easily and more rapidly accessible to a wider audience. However,
by enabling greater access to a compilation of personal data concerning ones health
and genetic information from different sources, and spanning a lifetime, they increase
the risk that personal health data could accidentally be disclosed or distributed to un-
authorised parties (Hoffman 2010). There is broad agreement that it is individuals who
should not only control their own data but also have the right to make decisions about
access to their data, and be informed about how they will be used (Kaye et al. 2011;
Brent D Mittelstadt et al. 2012; Solove 2013; Sterckx et al. 2015). Nevertheless, control
over their own data implies also the possibility of withdrawing the information, which
in some cases, could not be guaranteed since data sharing through these platforms tend
not only to combine different kinds of data but also to share them through complex
networks (Shabani and Borry 2015).
Data storage and regulations for processing personal data raise several concerns
about how that data should be used. This should entail a balancing of individual rights
and interests against social benefits. Such an approach presumes value judgment in
favour of individual control over highly beneficial uses of data. Still, that value choice
turns out to be problematic when it comes to balancing the principle of privacy and
data protection against other societal values such as public health, national security, en-
vironmental protection, and economy efficiency.
Tene and Polonetsky (2012) have argued that a coherent framework would be based
on a risk matrix, taking into account the value of different uses of data against
the potential risks to individual autonomy and privacy. If public health is a com-
mon good from which everyone benefits, and is essential to human development,
then in the current debate on building a data sharing environment we need to
address the important issue of how health data can be used for the common
good, while still respecting individual rights and interests, such as the right to
privacy. Yet, we also need to determine which trade offs are acceptable between
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individual rights and the common good, and how the thresholds for such trade
offs can be determined.
It is within this broader context that we are interested in the ways in which emerging
data sharing platforms are reconfiguring the data sharing experience from different per-
spectives. What novel forms of data sharing are made available? What do they allow?
And what types of values, rights and responsibilities do they entail and enshrine within
their philosophy on what it means to share data?
A framework for ethical sharing of health data in online platforms
Moral values are an indispensable component of successful mechanisms of data shar-
ing. Gaining clarity about the values at play is crucial before we can determine which
values need to be retained and which may need to be traded off against each other.
Identifying the core values required for morally responsible data sharing also highlights
how the development of technologies is not value-free, but always reflects and en-
shrines particular beliefs (Langat et al. 2011).
In this respect, we identified six core values that we considered to be essential for the
ethical sharing of data using ICT platforms: scientific value, user protection, facilitating
user agency, trustworthiness, benefit and sustainability. To do this, we utilised the ex-
perience and expertise of members of the Science and Values Working Group of the
COST Action CHIP ME.
1
This was an iterative process where an initial list of aspects
considered important for ethical data sharing more broadly were brainstormed by
members of the working group, many of which have considerable experience with ICT
platforms. Moreover, we acknowledge that listing values entails already a process of
valuation, on our behalf, and constitutes per se a performative process. The discussion
was then shifted to focus on aspects specific to ICT-based platforms and how these as-
pects could be applied in this setting. The final list was refined through discussion,
grouping of concepts to avoid redundancy, and feedback from other working group
members not present at the initial discussion. Before undertaking our exploration, we
were aware that others had already considered the principles and values to be respected
in the sharing of personal health data. For example, Kelty and Panofsky (2014) in their
assessment of participation in online platforms and Prainsack (2014a) in her typology
of citizen science initiatives, for example, have touched upon the issues of data sharing
and both provide critical insights on data sharing and users experience. However, in
our opinion, both scenarios benefit from a proper ethical reflection focused on data
sharing, since that is not their aim. In this sense, the Global Alliance for Genomics and
Health (GA4GH), for example, answers this purpose. GA4GH has set out on its website
the following eight foundational principles (Global Alliance for Genomics and Health
2014): 1) respect for the data sharing and privacy preferences of participants; 2) trans-
parency of governance and operations; 3) accountability to best practices in technology,
ethics, and public outreach; 4) inclusivity by partnering and building trust among stake-
holders; 5) collaboration to share data and information to advance human health; 6)
innovation in order to develop an ecosystem that accelerates progress; 7) agility to en-
sure we act swiftly to benefit those suffering with disease; 8) independence by structure
and governance.
However, to us, these principles are somewhat vague and require further in-depth
consideration of the underlying problems, which surface through the sharing of health
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data. For this reason, in our analysis we set aside the GA4GH principles in an attempt
to develop a more detailed list of core values that we considered to be important fac-
tors for the ethical sharing of data using ICT platforms.
While we recognise that there is some overlap between the values, our intention was
to create ideal categories through which an analysis of various platforms could be con-
ducted. The six core values are described in detail below.
Scientific value
When discussing scientific value in online data sharing platforms, we are referring to
the need for quality, quantity and accessibility of data to be of sufficient standard to
allow morally responsible data sharing. Data quality concerns parameters such as ac-
curacy, reliability, completeness and consistency (OECD 2013, Rippen and Risk 2000).
It also refers to the introduction of bias in the dataset by means of the ways the data
are collected (Vayena and Tasioulas 2013, Leonelli 2014). Data quantity acquisition re-
lates to the ability to collect and connect large datasets. We need a certain amount of
data for them to be useful: a greater data set may support inferences that a smaller data
set does not support (Mayer-Schönberger and Cukier 2014). Still, a trade-off may be
necessary between data quality and data quantity. A platform may enable the collection
of large quantities of data but, due to the large scale, the data quality may be less accur-
ate, reliable, complete, and consistent.
Another contributor to the scientific value of a platform is data accessibility, which
depends on the interoperability of health data sets: the ability of different health data
systems to exchange information accurately and to use the information that has been
exchanged (Heubusch 2006). Open standards and interfaces are useful to enable a
broader utilisation of information, potentially by all stakeholders that may derive a
benefit from them. It is also important to note that there will be a trade off between
data accessibility and user protection, because the greater the opportunities for sharing
data, the greater the opportunities for health data to be misused.
User protection
While the term user protection is often used in relation to privacy, in the context of
morally responsible ICT based health data sharing; we consider that user protection
could also include the protection of users dignity, the protection of users confidential-
ity, data security, and informed consent.
Privacy is related to the value of autonomy. Even if our privacy is being invaded with-
out consequences that we consider to be detrimental to us, the very fact that we have
not been asked infringes our autonomy. As a result, levels of trust in healthcare system
might decrease. The value of privacy is also linked to dignity: the fundamental right of
every person to be valued, respected and be treated in a morally appropriate way
(Global Network 2015). If our privacy is being invaded, our dignity might be
brought into question.
Moreover, privacy as control of information is connected to informed consent, the
[a]greement to a certain course of action, such as treatment or participation in re-
search, on the basis of complete and relevant information by a competent individ-
ual without coercion(Global Network 2015: 29). Informed consent is intended to
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prevent the unauthorised usage of a participants own data in ways that are un-
known, poorly understood, or in conflict with the values and commitments of the
data donor.
Facilitating user agency
Facilitating user agency implies a promotion of autonomy the capacity of an individ-
ual to be self-governing, make decisions, and to act in accordance with them, as well as
with her values, commitments and life goals (Global Network 2015). Enhanced user
agency is often associated with empowerment and is promoted when platforms provide
relevant features to their users. These features include communication, education, and
informed consent. Others should not be authorised to do things to us, for us, or in the
name of us, in ways that are inconsistent with our values, goals, opinions, and life plan.
One relevant question is whether our autonomy is violated if our genetic data or bios-
pecimens are being used without our consent to help others (e.g., to help people suffer-
ing from rare diseases). However, autonomy may not always be the most important
value (Dawson 2010): it might be argued, for instance, that when the physical and priv-
acy risks for participants are negligible, proper safeguards are in place (such as anon-
ymisation), and data are being used to promote public health (or other public goods),
even the re-use of data or biospecimens for purposes other than those for which they
have been initially collected might be considered as morally appropriate (Global
Network 2015). Moreover, various surveys show that the vast majority of people are
willing to make their genetic data and biospecimens available, provided that their per-
mission to do so is sought (Wellcome Trust 2013).
Trustworthiness
Trustworthiness is a feature of human relations that is needed precisely when and be-
cause we lack certainty about othersfuture action: it is redundant when action or out-
comes are guaranteed(ONeill 2002: 13).
It appears that reliance on informed consent and other measures to secure individual
autonomy have failed to secure trust (ONeill 2002; Chadwick and Berg 2001). Some
clumsy approaches within the Care.data scheme in the UK highlighted the importance
of trust and the effort which should be invested in keeping the trust of citizens intact
when the acquisition of their health data is concerned (Sterckx et al. 2015). Hence,
trust is an essential and indispensable value for morally apposite sharing of health data.
It is tightly connected to the values of transparency, the accessibility of information
about management and its decisions, and accountability - the existence of clear proce-
dures and a clear division of responsibility in responding to challenges related to health
data sharing (Global Network 2015).
As platforms are run using information technology, one important aspect of transpar-
ency is associated with the code behind these platforms, which can be inaccessible pro-
prietary software or, more transparently, open source software where the published
code is publicly available. The publication of a platform source code in an easily access-
ible online repository means that any user can, in principle, examine the software to de-
termine if the claims made by platform managers about the way privacy is handled
correspond to the facts. Finally, trust is connected with the capacity of an institution to
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provide some sort of compensation or protection to parties that are harmed (either by
accident or due to negligence) (Prainsack and Buyx 2013).
Benefit
We contend that equity in the distribution of benefits is an important value related to
morally appropriate sharing of health data. We use the concept of benefit as a wide
umbrella term to cover all issues relevant to the augmentation of benefits and their dis-
tribution, or, in other words, to the value of efficiency and distributive justice. We can
make our genetic data or biospecimens available for research and serve a common goal
in that way. But such actions might also serve our own interests, in that we may our-
selves (as individuals) reap the benefits of discoveries in the field of medicine. To en-
sure a fair balance of interests, proper mechanisms for benefit sharing should be put in
place when designing data sharing platforms (Chadwick and Berg 2001).
Data sharing may also entail broader societal goals, which align more with the notion
of reciprocity, where the benefits produced by the data should be shared among all par-
ties involved in the production of that data (platform users and owners). According to
the theory of justice of Rawls (1971), since the production of goods relies on well func-
tioning social institutions the distribution of advantages produced within social institu-
tions is always embedded in relations of reciprocity with all the citizens who are
responsible for upholding and sustaining social institutions, so relationships of reci-
procity are quite broad, and reach out to persons who are not directly involved in the
generation of data.
It has been argued that every conception of justice relies on a specific interpretation
of the concept of equality: even libertarianism can be considered as a theory advocating
equality of negative rights, and utilitarianism as a theory advocating equality of mar-
ginal utility (Sen 1995). However, with equality understood as equality of welfare, op-
portunity for welfare, or access to advantage (Arneson 1989, Cohen 1989), there will
normally be trade offs between equality and efficiency.
One issue of justice in relation to benefit sharing has to do with access to the benefits
of research by disadvantaged groups and populations. Their members may be put at
risk by participation (e.g. by virtue of privacy loss) while they may not be able to benefit
from the knowledge generated. For example, intellectual property over new drugs and
other technology provides incentives for research and development, but also excludes
disadvantaged members from the benefits due to the price of new drugs and services,
which may not be covered by universal healthcare services and insurance systems
(Global Network 2015). As such, both the amount of benefits and their distribution are
important moral considerations and there is no consensus about the optimal balance
between them. Judgments about the justice of the sharing of benefits generated by a
certain platform should take this into account (Tang et al. 2006). Issues of benefit shar-
ing also emerge in the selection of topics for research (Global Network 2015).
Sustainability
In the context of morally responsible data sharing, sustainability refers to the expect-
ation that the platform will continue to deliver its services for a sufficient period of
time, thus justifying the costs (economic and other) of building it, or that the platform
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terminates operations promptly on failure. Typically, sustainability is achieved by
means of a realistic business plan, specifying where the long term resources that are re-
quired come from and what will happen to the informational resources collected or
generated on termination or transfer of ownership or control of the platform. One as-
pect of sustainability concerns the universality of a platforms business model. Being
more dependent on idiosyncratic cultural or financial circumstances, that cannot be
reproduced in other countries or as political circumstances change, can be viewed as a
weakness. In the world we live in, a sustainable platform should not rely exclusively on
ad hoc public funds or the benevolence of charitable donors but significantly, on the
revenues it generates by providing wider benefits to an entire research and industry
ecosystem (Harris et al. 2012).
Sustainability is closely linked to issues of benefit and fairness and again, there may a
need for trade offs between different desired features. For example, a platform may be
operated as a for profitbusiness entity, have a realistic business plan for generating a
flow of revenues and attract private investment for its initial funding. Yet, because of
the role of private investors, it may have to channel most benefits to, or be controlled
by, its majority shareholders. This could in turn negatively affect transparency, benefit
sharing, and promotion of user agency, since the people who use the platform to share
their data may have little opportunity to voice criticism about and effect the economic
choices of its management. On the other hand, a model for a platform could fulfil the
ideal criteria of transparency, user empowerment and benefit sharing, and then not be
feasible, let alone sustainable, because it is not perceived as an attractive investment by
those entities (public or private) that can fund it.
Translating ethical values into practice: Five examples of online platforms for
health data sharing
In order to assess how well these core values were addressed by online platforms for
health data sharing, purposive sampling was used to identify examples of data sharing
platforms as case studies representing a broad range of potential uses, situations and
challenges.
Selection of platforms was restricted to those orientated for health data sharing. The
specific sampling categories used to identify these data sharing platforms were as fol-
lows: 1) sector (public, private, or public-private initiatives), 2) primary goal (health re-
search or health data sharing), 3) country specificity (national and cross-national), 4)
platform leadership (patient-led or researcher-led approach). The broader working
group was also asked to highlight platforms with the following specific features of
interest: 5) large scale data sharing, 6) platforms which allow sharing of genetic in-
formation, 7) platforms with some participatory features (Web 2.0 or other), and 8)
distinctive/innovative ICT solutions, approaches or functionalities.
In our primary search for platforms we identified numerous examples that sought to
collect and share personal health data and information of different types. We have
listed these examples in Appendix. We subsequently selected five platforms in an effort
to illustrate a range of different perspectives and diverse models of action. Table 1
shows an overview the main characteristics of each selected platform.
Here we present these five case studies to highlight examples of how these six core
values are addressed by online data sharing platforms. Information on these platforms
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Table 1 Features of the selected online platforms for sharing of health data
Platform
entity
Taltioni Healthbank MIDATA ePGA PEER
Scope National (Finland) International (Based in Switzerland). National (Based in Switzerland) International (based in Greece). International (based in United States)
Sector Public-private.
Finnish government;
Member organisations
(public and private).
Private.
Investments of partners and
members/users.
Public private.
Research grants, loans.
Public.
Public grants (EU and Greece).
Public-private.
Public and private funds,
Crowdsourcing.
Primary goal Health data sharing.
Health data safe storage
and management; well-
being apps.
Health data sharing and health
research.
Safe storage; research;
user initiated selling of health data.
Health data sharing and health
research.
Data safe storage and management;
research; data sharing; third-party
services (for-profit) on data made
accessible by members.
Health research.
Genome-based recommendations
about gene-drug-phenotype
interactions open access
pharmacogenomics data.
Health data sharing and health research.
Biospecimen sharing.
Joining communities within the
PEER platform.
Leadership Users and researchers-led. Users and researcher-led. Users and researchers-led. Researcher-led. Patient-led.
Data types Health and lifestyle related
data.
Health and lifestyle related data. All personal data. Established genomic databases;
Genomic profiles and phenotypes
uploaded by users.
Health related data.
Large scale
data sharing
No. Yes. Yes. Yes. Yes.
Participatory
features
Users can decide which
data sets to share and with
whom.
Users decide which data sets to
share and with whom.
The data available is uploaded by
the users.
Users decide which data sets to share.
Users can give specific third- parties
access to run analyses on their data
on the platform.
Data is uploaded by the users. Users decide which data sets to
share and with whom.
ICT
innovative
solutions
Focus on personalisation:
tailor usage according to
individual needs.
Works like a bank: users invest their
data and collect the profit from their
usage in research.
Enables the combination of health
data with other personal data. It is
possible to release all the data to
researchers.
Orientated for supporting health
professionals to increase their
knowledge in genomics and to
support them in providing
appropriate healthcare.
Enrolment on patient networks
while allowing contributions for
accelerating health research.
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was gathered from their websites, published materials on the platforms and, in some
cases, interviews or email contact with persons associated with these platforms. Al-
though these five platforms address the core values in different ways and varying de-
grees, they represent models for data sharing with innovative features in relation to
how data is shared between different stakeholders. Table 2 summarises how the core
values are addressed by each platform.
Taltioni
The Taltioni platform is a national data sharing platform in Finland. The platform is
provided by the Taltioni Cooperative which involves both the public and private sector.
It focuses on health status control, health management, health promotion and well-
being. As a national platform, access to Taltioni and its range of features requires a
Finnish social security number, which allows it to be linked with various personal data
records. Although some sections of the website are available in English, many docu-
ments, such as the data sharing policy are only available in Finnish.
2
The platform is organised as a toolkit, including applications for smartphones and
mobile devices, and allows users to download a selection of free applications (or apps)
and to tailor them to the individuals needs. The users can choose among a wide range
of apps, from blood pressure record, weight management or fitness, to organisation of
the patients medication schedule, management of medical appointments or access to
their own health data. In addition, the platform connects the user and the healthcare
system in both directions, providing practical services for users based on their existing
health information, and also enabling the upload of data about the individuals health
and well-being. Taltioni makes use of an innovative way of sharing personal health data
since it harnesses the potential of mobile devices, allowing it to be accessed from any
location with Internet access.
The Taltioni toolkit structure facilitates user agency: through their ability to utilise applica-
tions for their own purposes, users can store their own data and share with friends and fam-
ily, facilitating the creation of close networks and virtual communities. These features could
also contribute to benefit individual users. In fact, the strong focus on personalisation, par-
ticularly for users to tailor their usage according to their individual needs, is the other dis-
tinctive feature of Taltioni. Still, long-term benefits depend on the type of functionality that
will be offered through the third-party programs that people will have access to.
User protection is addressed through both the security features of the platform and
the privacy policy. Taltioni uses two different types of consent regarding two sets of
data, which are directly related to the platform structure: an umbrella platform that
hosts apps from different providers. In this sense, users have to consent in sharing gen-
eral personal information needed to open a Taltioni account (e.g. name, address, social
security number) and to give an additional consent related to the type of application
the user will share their health data with (the user will be asked to give specific consent
for such use within each subscribed application). As a way to ensure trustworthiness
Taltioni in its Data Protection Agreement disclose the kind of information that is going
to be collected and stored in the platform (name, email address, language, citizenship).
Taltioni also states that information such as identification number and name are re-
moved for statistical purposes, allowing third parties and companies to use anonymised
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Table 2 Overview of how values are addressed by each platform
Platform entity Taltioni Healthbank MIDATA ePGA PEER
Scientific value Data quantity and quality is
determined by the users who
upload data.
Data quantity and quality is
determined by the users who upload
data.
Data quantity and quality is
determined by the users who
upload data.
Established genomic databases;
Genomic profiles and phenotypes
uploaded by users.
Data quantity and quality is
determined by the users data
upload.
User protection Privacy policy.
Require usersconsent to share
general data.
Require usersconsent to share
specific set of data.
Privacy policy.
Require usersconsent for all sharable
data.
Strong encryption and data
access log.
Only users can access their data.
Password protected profile. No
information about security features yet.
Privacy policy.
Users receive a system report
with all the system changes
(edits, data usage, etc.).
Facilitating user
agency
Users are responsible for
choosing apps of interest and
for the data uploaded.
Users decide whether or not to share
their data and to which research
studies.
Members can participate in
cooperative decisions.
Users decide whether or not to
share their data and to which
research studies.
Members can participate in
cooperative decisions.
Features stressing user agency might be
included in the future (the platform is
still underdeveloped).
Users decide whether or not to
share their data and to which
research studies.
Users can also decide who is
going to see the data.
Possibility of joining patient
communities.
Trustworthiness The services have to pass
Taltionis audit.
Disclose which data is going to
be shared and with whom.
Each use of data has to be disclosed
to the users.
Platform IT code is open source.
Every research project will need
ethical committee approval.
Developed within public sector (which
might increase trust).
No other relevant information is
provided.
The familiar environment
promoted by patient
communities ensures trust in
the platform.
Benefit Users can choose apps of their
own interest and receive self-
help and well-being services.
ICT companies can offer paid
services and develop another
apps and services.
Profit (in cash or not) is divided
between participant users. Private
investors could also benefit (in cash or
not).
Upgrade of the services
provided by the platform.
Patients may benefit from different
health care from the increased
knowledge of health professionals in
genomics.
Users can benefit from
networking engagement.
Researchers benefit from using
data.
Sustainability
(Funding)
Cooperative of Finnish
government and ICT
companies.
Cooperative and associated
commercial company. Investments of
partners and members/users.
Users fees (users and end-users
such researchers); research
grants, citizen loans.
Public national and international grants. Public and private funds and
crowdsourcing.
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data. All other information is shared with service providers under specific user
agreements that might vary among the different apps. Furthermore, data cannot be
transferred outside of the EU. The users decide which information they wish to
share with the provider.
However, the personalisation enabled by Taltioni has also a flipside. The platform is
portrayed as a method for Finnish people to take responsibility for their own health,
both in the media and in the platforms self-representation (cf. Turun Sanomat
2013; Sitra, 2012). This idea corresponds to a general trend toward shifting the
focus of health-related responsibility from government and healthcare systems to
individuals (Prainsack 2014b). Plus, the personalised nature of the features offered
by Taltioni could make the creation of standardised information or a nuclear set
of information difficult which, of course, could compromise the scientific value.
In addition, the diversity of companies developing apps and promoted them
through the umbrella platform could turn the development of data standards
even more complex.
The cooperative nature of Taltioni promotes the sustainability. The platform is run
and maintained by a number of ICT companies who work together to provide other
companies with the possibility to develop sharing platforms through Taltioni. In fact,
the enterprises behind Taltioni could offer some paid services through their free apps
or use the users records for developing new services. As a consequence, ICT compan-
ies would be stimulated to develop services that share common standards and prac-
tices, while ensures broad support from the industry. Whether or not Taltioni
cooperative vision is going to work in practice remains to be seen, since Taltioni plat-
form is not until now, totally functional.
Healthbank
Healthbank is an international platform based in Switzerland. Healthbank combines a
range of different services and features for health and lifestyle data storage (e.g. sleep pat-
tern, fitness tracking) and health research. The platform is open to individuals worldwide,
and can be used by researchers in any location, enabling large scale data sharing.
The platform offers a innovative way of operating. This is not only due to the wide
range of data that is possible to manage and store, but also in that it offers a way for
users to profit from data storage. In fact, Healthbank tries to reproduce the workflow
of a traditional financial institution, as it is emphasised in the platform name. Users
who want to be members pay a fee for depositing their personal information. Data can
be sold to researchers and profits are divided between the participant users. The
process may also be profitable for Healthbank as a whole and its private investors,
attracted through the Healthbank Innovation Company (Healthbank Innovation Ad
3
).
The benefits may or may not be in cash, depending on the research project. These ben-
efits are also tied to the quality and quantity of data provided by users a strategy that
could encourage users to provide more and higher quality data.
One of primary targets of Healthbank is health research. For this reason, data quality
has to meet particularly high standards. Nevertheless, data quantity and quality are still
directly dependent on the amount and the type of data uploaded by the platform users.
There might be doubts as to whether the introduction of economic incentives for data
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compromises data quality. Studies on blood donation have shown that in countries eco-
nomic incentives were introduced, blood quality decreased (Titmuss 1970).
Additionally, the users are responsible for setting which data and to what extent can
be shared, with whom and in which kinds of research. In doing this, Healthbank im-
proves user agency and trustworthiness. Even though, the usersagency could be maxi-
mised in doing this, the scientific value could, again, be compromised.
Furthermore, Healthbank makes use of other tools that enforce the values of user
agency and trustworthiness: users/members are allowed to participate in cooperative
decisions through a general assembly. Users can decide whether or not to share their
data and in which studies they are willing to participate. They also have the option to
share their records with health providers and friends. A consent manager is provided
for all uploaded data and users are required to give consent for their data to be used
for each research project, while each use of their data should be disclosed to them.
Healthbank seems highly committed with user protection. The platform states, in its
privacy and policy statements, the platform is using encryption, highlighting that the
website does not use cookies. The platform stresses that it might be possible for other
companies they work with, such as Youtube or Twitter, to use traceable links and warns
users not to use the links provided in order to maintain a maximum level of privacy.
Regarding sustainability, the platform is managed by a private fund in collabor-
ation with partners from the Information and Technology (IT) sector, innovation
consultants, business partners, universities and research units. Even this aspect is
coherent with their financial-like structure. Although, Healthbank is organised as
a cooperative, member companies can buy economic shares in the cooperative
with no voting rights attached.
4
This allows the Cooperative to attract funds from
private investors that can finance the operations, as well as the development of
the platform.
MIDATA
MIDATA is a Swiss citizen owneddata cooperative. The platform enables the safe
storing and controlled access to membersdata by specific authorised data users, such
as researchers and pharmaceutical companies. This is not limited to health data, but in-
cludes all personal data, such as consumption, fitness, geolocation, and financial data.
Data generated with mobile devices can also be directly imported into the platform.
As the other platforms assessed, scientific value is dependent on the users decisions
of sharing data. Still, MIDATA has the potential to connect data that was collected pre-
viously and has been stored in different silos from different domains while treating the
individual data subject as the only person with the moral right to authorise such link-
age (Hafen et al. 2014), which could be considered a distinctive feature of this platform.
This platform addresses user protection through multiple encryptions of individual
personal data sets and only members possess the key to their own data. This data
architecture promotes data security and privacy, as no one, including members of the
management team and other scientific partners, can access membersdata unless expli-
citly authorised by the user to do so. The security of MIDATA platform has been tested
by three independent companies, including two teams of ethical hackers (Ernst Hafen,
personal communication, 17 May 2016).
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MIDATA is one of the platforms that addresses the privacy and security concerns by
all the possible means. Plus, making available the open code software is a way of
achieving transparency, what is not usually done by the ones in charge of collecting
and distributing data (Van Djick and Poell 2016).
MIDATA is intended to enable any person, independently of their geolocation, to
share sets of their data for scientific research and personalised services. The MIDATA
model allows the construction of regional or national data cooperatives, based on lo-
cally binding cooperative law. Additionally, the widespread similarities between princi-
ples of cooperative governance in different countries and the adoption of similar bylaws
by different national MIDATAs will enable global research projects within a shared
trusted environment (Hafen et al. 2014).
Trustworthiness is addressed through the fact that MIDATA is citizen owned, mean-
ing that the data secure storage and sharing platform is owned by persons who are also
its users, organised as a cooperative of data providers.
5
MIDATA promotes transpar-
ency since the source code of the IT platform is open source. MIDATA emphasises
the role of ethical committees as a way for promoting trust. All researchersre-
quests have to be approved or by a relevant ethics board or by a MIDATA ethics
board, a yet to be implemented cooperative function (Ernst Hafen, personal com-
munication, 17 May 2016).
MIDATA promotes two levels of user agency. One level is individual and relates to
privacy as control of information and individual autonomy. The platform includes soft-
ware tools allowing individual members to make specific subsets of their data accessible
to specific end-users (e.g., doctors, family members, friends, researchers, pharmaceut-
ical companies)
5
(Hafen et al. 2014). A second level is collective and derives from mem-
bership in the cooperative that own the platform for data exchange. The synthesis
between agency as individual freedom in sharing data and agency based on cooperative
membership is achieved through a constitutionalgovernance model: the cooperative
bylaws are approved by the majority of members and establish a framework of rules
binding for all; individual users are permitted to engage with all sorts of data exchanges
that are not explicitly prohibited by these common rules (approved by all members)
and that are authorised by an ethics review board
4
. Members can also decide how
surplus revenues of the cooperative, if any, aretobespent(e.g.,forresearch,infor-
mation, education), although the exact procedures for these options have yet to be
decided
5
(Hafen et al. 2014).
User benefit is achieved for the collective, as the cooperative as a whole can benefit
economically from the service MIDATA provides to researchers and companies who
want to access the data of cooperative members. The cooperative does not distribute
any income to members: the usersbenefit consists in improved services instead, to-
gether with the possibility of controlling the secondary uses of their data
5
. Also, the
members are not permitted to exchange their data for money individually through the
platform. Revenue gained from user fees is employed to cover operational costs, repay
loans, or improve the platform service itself. Private investors cannot invest or buy eco-
nomic shares in the cooperative: only nominal participation shares, which cannot be
sold, and entitling to no economic rewards, are permitted
5
.
MIDATA is currently financed by research grants from a Swiss university, loans by
private citizens, and scientific research grants on a project-by-project basis (Ernst
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Hafen, personal communication, 17 May 2016). Beyond the start up phase, MIDATA
plans to achieve economic sustainability from fees paid for cooperative members for its
personal data secure storage service and fees paid by companies and research institu-
tions accessing the data stored by cooperative members (which members agree to make
accessible) (Ernst Hafen, personal communication, 17 May 2016). As with many other
technological start up companies, whether this economic model is sustainable in the
long run, remains to be seen.
ePGA
ePGA is a Greece-based platform and aims to translate pharmacogenomics information
into a clinically meaningful format. It is targeted at three user groups: health profes-
sionals, biomedical researchers and individuals. As the website states, the aim of the
platform is to provide a “‘one stopweb-based platform, for pharmacogenomics know-
ledge recording, processing, assimilation and sharing.
The data on the platform are comprised of information from established genomic da-
tabases and subsequently uploaded genomic profiles and phenotypes by the three
groups of platform users (researchers, clinicians or interested individuals), creating a
constantly evolving knowledge base. It relies on stakeholders to upload genotype infor-
mation into the database. While researchers can query the database with regard to their
research questions, individual patients and healthcare professionals can also obtain
individualised advice on the basis of the genetic information submitted. The platform
aims to provide practical advice for clinicians who lack training in pharmacogenomics,
with regard to translating patients genomic information into concrete clinical advice,
such as recommending adjustments to standard dosages. Patients or healthy individuals
may also upload their genomic profile and can receive tips regarding the pharmacoge-
nomic features of their profile. However, the platform emphasises that it will require
cliniciansadvice for clinical decisions on the basis of this information.
In relation to scientific value, the development of the platform has been driven by gen-
omic scientists on the basis of well established pharmacogenomics findings and databases
(e.g., PharmGKB) and the platform is designed to meet high standards of scientific accur-
acy. The database incentivises contributions by established genomic researchers through a
system of microattribution, so that the data they have contributed will be acknowledged
in publications that utilise the data (Patrinos et al. 2012). This system aims to achieve a
research-led expansion of the database. It is unclear from the available information which
phenotypes will be included, particularly whether it exclusively relates to metaboliser sta-
tus. If other information is to be included, and if individual users or researchers or clini-
cians are given the opportunity to provide such information, this might lead to variation
in the scientific quality of the data. The current level of accessibility, with no access bar-
riers for researchers, allows for optimal exploitation of this data for research purposes.
However, unrestricted access might also raise potential ethical concerns. As the platform
is still in its initial stages, it will remain to be seen what level of utilisation it will attract,
which will determine its ultimate scientific utility.
To ensure user protection, individual users create a password-protected profile when
uploading their genomic profile and other information. The exact type and extent of
further security features is still unclear from the information available to the public.
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Potamios et al. (2014) highlight the importance of security and privacy issues for gen-
omics platforms in their conceptual paper, but to date, no dedicated information on the
platforms security features appears to be available on the website. The authors also dis-
cuss the potential integration with personal health data, but again it is unclear whether
and to what extent additional personal and health information may be stored in con-
junction with the genomic information.
The platform aims to achieve a high level of user agency by directing users to access
the platform through one of three services which are tailored for members of three
stakeholder groups, based on their particular pharmacogenomics related interests. It is
evident that the platform is still under development and that the users it primarily tar-
gets currently are researchers and health professionals, rather than non-scientific users.
Still, individuals also have the opportunity of accessing the platform for analysis of their
individual profile (George Patrinos, personal communication, May 5, 2016). Its trans-
parency for these users appears limited and the platform does not offer instructions, ex-
amples, or more easily accessible information required for the informed consent
process, such as lay information on what genomic and phenotypic data can be
uploaded and queried. Nor does it describe what happens to the uploaded information,
any potential privacy and security concerns, or the types secondary uses of the data by
other parties.
One of the core concerns of the public with regard to the assessment of the trust-
worthiness of genomics initiatives has been the role of private partners (Vayena and
Gasser 2016; Dove and Özdemir 2015). In the case of the ePGA, its development and
maintenance have been funded by public grants, while no commercialisation is envis-
aged. This is likely to increase trustworthiness for users, as their personal information
will not be exploited for financial gain. Additional trustworthiness concerns may be
linked to data access modalities, data security and anonymity, on which little informa-
tion is currently available.
The potential benefits to users of this platform are well defined and achievable, provided
the platform proves to be user-friendly in its everyday operation. The platform gives
health professionals instructions on how to adjust their prescriptions to their patients
pharmacogenomic features on the basis of established and evolving pharmacogenomic
knowledge. Similarly, individuals have the opportunity to obtain information on their own
pharmacogenomic characteristics, which they can bring to the attention of their health-
care providers. The availability of this information is likely to translate directly into benefit
to patients, by helping optimise dosage and preventing side-effects resulting from over-
dosing or lack of effectiveness resulting from under-dosing. The open accessibility of a
platform dedicated to pharmacogenomic knowledge is also likely to be of benefit to the
pharmacogenomic research community, especially given the creation of the platform as a
research-led initiative. This has the potential to impact on healthcare practices more
broadly with regard to the use of pharmacogenomic information in the clinic.
The platform has been publicly funded through a number of international and na-
tional grants.
6
Its long-term sustainability is dependent on the continued availability of
grants or the future development of an alternative funding model. The platform coordi-
nators are confident about the viability of the current funding approach for the nearer
future, but aim to find longer term solutions once the platform becomes more estab-
lished (George Patrinos, personal communication, May 11 2016).
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Platform for engaging everyone responsibly (PEER)
PEER is a project run by the Genetic Alliance. Genetic Alliance is a network of more
than 1200 diseases advocacy organisations based in the United States. PEER is based on
the concept of being as user-friendly and personalised as possible. The set-up of sites
for individual groups is simplified and easy to customise and the appearance of the
interface can be edited. Similarly, the services that individual users can access have
many customisation options. In PEER it is possible to share not only health data, but
also bio-specimens through the established network of participants in the platform,
aiming to overcome the physical limits of ordinary biobanks.
In relation to scientific value, PEER users are responsible for the quality and amount
of information they upload. The contribution of data for studies is dependent on user
preferences, since the users choose what kind of data they will share, and researchers
gain access to their information on the basis of these preferences.
User protection is addressed through accountability, which is one of the principles of
PEER. All virtual processes such as edits, searches and usersprivacy settings are re-
corded, allowing PEER to track and investigate any suspicious uses of information or
unauthorised changes. This data will be returned to the individual user in the form of a
system report. Their privacy policy clearly identifies the responsibilities of Privacy
Access and the Global Alliance assuring transparency.
This platform allows user agency through a significant degree of control over the
terms of sharing health data and biological samples. The users have the authority to de-
cide if researchers, relatives or doctors can see their data, and can also decide whether
other users will have access to all, or just components of the data uploaded. Even
though they belong to a community using PEER, all the data is shared according to
usersindividual preferences. Other additional filters regarding the use of data can be
determined by the wider community the user belongs to, while the platform uses a spe-
cific technology and software developed by the Private Access company to combine the
individual and the community settings for sharing data. The data are stored in the
PEER platform and users can edit them anytime. Accordingly, consent here seems to
be similar to a refined broad consent (Kaye et al. 2009): the users have more options
than just to share everything or nothing. However, once consent is given, users cannot
control the destiny of their data unless they withdraw consent. The users agency is also
facilitated through additional strategies, such as through constructing and joining new
communities within the PEER platform, which allows their data to be aggregated with a
pool of individuals with the same or a similar disease. The platform also aims to expand
its reach to other communities by including additional languages, the first of which will
be Spanish (Sharon Terry, personal communication, 2 March, 2016).
Trust is a core guiding concept for PEER. Health data and biological samples can be
interchanged and shared in an environment that provides the look and feel of familiar
trusted communities(PEER website). The extensive possibilities for users to customise
their sharing preferences allow a significant degree of control over what happens to
their data. The software also makes it possible to give feedback to the users on who is
accessing their data, what is likely to increase trust.
PEER is particularly interested in accelerating progress in medical research by con-
necting participants who can share their data on a comprehensive platform across dif-
ferent diseases. The platform focuses on benefit for the common good, rather than
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benefit to individual users. This is the same principle used for the communitys cre-
ation. User benefits consist of possibilities for networking, although scientific or clinical
benefits are mostly defined in future terms, such as advances in medical research en-
abled through the use of information on the platform or through research recruitment
via the platform. Such advances in knowledge have the potential to benefit individuals
in the future by identifying underlying genomic features of diseases and potentially de-
velop new treatments on this basis.
The sustainability of the platform is ensured by funding from a wide range of public
and private sources obtained by the platform. Crowdsourcing is one of the sources of
funding for the platform. PEER has also been involved in a number of major initiatives,
some of which were associated with substantial funding.
7
Comparison of online health data sharing platforms
In this article we have assessed five data sharing platforms with regard to their respect-
ive innovative approaches to the sharing of personal health data. We analysed these
platforms in light of the core values we considered to be critical for the ethical sharing
of data using ICT platforms. It was evident from the analysis that the platforms provide
their users with substantially different ways of engaging with their own health informa-
tion. Implicit in these different modes of engagement is variation in the weighting
attributed to each of the values. While sharing some commonalities, each platform has
its own value profile, with some values emphasised more strongly and other values less
prominently underscored.
Data sharing is generally based on the scientific rationale of openness. Openness here
means that platforms promote the scientific collaborations in an atmosphere of
scientific transparency, which also implies public availability of scientific data (see also
European Comission, 2013). All the platforms reviewed endorse the scientific benefits
of data sharing. However, the scientific rationale is embedded differently in these plat-
forms. For all platforms considered here, scientific values coexist with the realisation of
other values, such as benefits to individual users. What became particularly evident in
the analysis of the platforms was the different understandings of the value of science
implicit in these platforms: while a platform such as ePGA appears to be built on a
traditional understanding of expert-led science, a platform such as PEER highlights a
more participant-led approach to science. For some of these platforms, data sharing for
scientific purposes is not presented as the primary purpose of the platform for its users.
Taltioni provides a wide range of health management functionalities and appears to
emphasize those functionalities more prominently to users than the data sharing aspect
of the platform; it is effectively a platform for other data sharing platforms. Both
Healthbank and MIDATA provide a service that can be used independently of any
engagement in data sharing, making data sharing for scientific purposes optional for
participants. One difference between MIDATA and Healthbank is that the latter is a
health data exchange platform, while MIDATA is designed to be able to host and
provide accessibility to research data of any kind.
All platforms acknowledge the importance of user protection and gaining informed
consent from users for the use of their information. However, the approaches to user
protection and the prominence given to user protection in platform communications
differ between platforms. Not all platforms have an explicit privacy policy. ePGA shows
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the least transparency with regard to privacy, which is probably due to its current
primary focus being on healthcare professionals and researchers, as opposed to individ-
ual users. The common concept of privacy as control of information is used by Health-
bank, MIDATA and PEER, rather than considering that ensuring privacy requires
information to be protected in a type of vault which is externally inaccessible.
MIDATA and Healthbank seem to emphasise the technical side of data security most
explicitly. They employ encryption techniques and any data that is not explicitly
marked as shareable is securely encrypted. MIDATA is in the process of making the
platform software openly available in order to ensure transparency. PEER emphasises
that an audit trail of any activity relating to personal data on the system can be made
transparent to the users so as to identify any unauthorised activities, a feature it also
shares with MIDATA. On the other hand, Healthbank emphasises that they do not
employ cookies to track user activity. For those platforms that make data available to
private sector partners, the privacy policies deserve particular attention. While Taltioni,
MIDATA and Healthbank either share, or allow each individual user to share anon-
ymised data, it is not entirely clear whether particular measures beyond anonymisation
or end-to-end encryption are in place to achieve privacy.
In fact, a complete anonymisation is a target difficult to achieve in the current sce-
nario of online data sharing (Shabani and Borry 2015), what could be acknowledged
and accepted within certain limits (Snell et al. 2012). Indeed, a minimum level of
compliance with data sharing risks is necessary to enable the scientific value of the
platforms. To overcome this eventual limitation, an investment in transparency particu-
larly in providing information to users about the risks of data sharing and respect users
agency and their decision of how and to what extent they want to be involved should
be promoted.
Particular importance is placed on the facilitation of user agency in most of these plat-
forms. Interestingly, the platforms focus on quite different aspects of user agency. ePGA
allows users to access pharmacogenomic information about themselves information
that they can use to alert their healthcare professionals to specific pharmacogenomic
features, thereby allowing them an active role in fine-tuning their treatment. Taltioni
provides a high degree of potential customisation through the provision of a wide range
of apps that users have the option of using. PEER has particular strengths regarding its
provision of an accessible, easy to use, customisable and transparent platform. It provides
several helpful resources to support users to understand the platform and make meaning-
ful decisions with regard to how they wish to use it. PEER, MIDATA and Healthbank all
pay significant attention to the control that the user has over their sharing modalities and
provide a variety of possibilities for personal customisation. Healthbank, MIDATA, and
PEER explicitly frame users as owners of their data allowing them to decide what, how,
and with whom to share data and empowering the individual data owner to make fine-
grained decisions about data sharing through an IT interface. In addition, in PEER, users
can make decisions about sharing biological samples. In both Healthbank and MIDATA,
it is the individual who uploads the data who decides what, and with whom, to share data.
In Taltioni, the platform is allowed to use anonymised data if it has been authorised by
the user. Non-anonymised information will only be shared if users have given permission.
In Healthbank and MIDATA, only data explicitly selected for sharing by users/cooperative
members are exported in an anonymised form. While in Healthbank such data can
Riso et al. Life Sciences, Society and Policy (2017) 13:12 Page 20 of 27
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
be made accessible for an economic return to the data owner, this is not the case
in MIDATA.
Additional forms of user agency are evident in the models underlying Healthbank
and MIDATA. In Healthbank, the potential of persons to leverage their personal data
for profit is highlighted and each Healthbank user can sell access to their own data in-
dividually. MIDATA embodies a particularly strong sense of the collective by providing
a basic platform, which enables the user to control data storage, and sharing, while
allowing the community of users to develop platform-compatible software applications
to access the data that individuals have unlocked. In the case of MIDATA, a strong
value-driven cooperative ethos is evident in the set-up, where user agency is
strongly linked to providing opportunities of genuine collective decision-making
and joint ownership. This also leads, however, to limitations of what can be done
with the data by the cooperative bylaws and the requirement of ethics board ap-
proval for any exchanges involving health data. The cooperative bylaws can be
changed by users collectively, but only through collective agency, rather than indi-
vidual autonomy.
The elements of user agency contribute particularly to the perceived trustworthiness
of these platforms. PEERs name, Platform for Engaging Everyone Responsibly clearly ex-
emplifies this self-understanding. Healthbank, MIDATA and PEER present their plat-
form as embodying trustworthiness, by facilitating user control in different ways.
Healthbank focuses specifically on the availability of individualised control and high
levels of data security. MIDATA instead focuses on a collective governance model and
the absence of commercial actors. PEER facilitates the creation of activist communities
to which individuals can affiliate themselves, on the basis of a transparent and custom-
isable interface. In contrast, Taltionis trustworthiness is based on its endorsement by
the Finnish state and its integration with the Finnish healthcare system, as well as users
ability to decide which platforms to use and which data to share. ePGAs presents its
trustworthiness as predominantly scientifically based, given the primary emphasis on its
scientific grounding and the complete absence of private sector interests. A number of
commentators have suggested that the emphasis on agency through user-centredness in
data sharing reflects an ethically desirable shift towards the empowerment of patients and
research participants (Corrigan and Tutton 2006). This move can be seen in the implemen-
tation of platforms where dynamic consent is used to solve the traditional problem of con-
sent (Kaye et al. 2011), as well as in cases where patient organisations take the lead in
implementing data sharing initiatives, such as PEER (Novas 2007). Although Lupton
(2014) has been critical of this transformation, the platforms that we have examined greatly
emphasise the role of users in deciding and controlling (to a large degree) which
information about themselves is shared and how it is done. User-centredness, how-
ever, poses problems for all platforms because if users decide not to contribute
and continue to add data then the possibility of a sustained platform becomes
problematic. Despite the support of the Finnish state, Taltioni is still very reliant
on users to continue to use the services which are provided. It is unclear if it will
succeed in its goal due to the low rates of user interaction and the difficulty that
users may have in integrating it into their daily lives.
Benefits and benefit sharing are conceptualised differently in the five platforms. In
the ePGA the benefit is envisaged as an immediate health benefit arising from
Riso et al. Life Sciences, Society and Policy (2017) 13:12 Page 21 of 27
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
improved prescribing, as well as a more indirect health benefit potentially arising from
the improvement in the pharmacogenomic knowledge base through extensive data
sharing. The features of the platform are immediately useful to different user groups
and can be easily integrated in healthcare practice, not least through translation into
higher quality services from the perspective of healthcare professionals using the plat-
form (Lakiotaki et al. 2014). In Taltioni, the array of health management apps linked to
the platform also constitute an obvious benefit to users, with the promise of achieving
better health through access to new health management opportunities on the platform,
but whether users will continue to use the services is very unclear. In Healthbank, the
benefit foregrounded in the platforms communications is the potential return of their
investment to members in the form of valuable results or monetary benefits from the
sale of their data. The profit would be shared among participants in each venture, de-
pending on the value of the users data. While MIDATA also aims to achieve benefits
for its members, the use of its revenue will be determined by the collective and is
meant to contribute to the common good, particularly through ensuring sustainability
and continuous improvement of the platform. Members are expected to derive a bene-
fit from using the platform itself, for its data security features, the opportunities for
controlled sharing, and also for the additional services that will be successively devel-
oped on the platform. The PEER platform promises benefits particularly in relation to
return of research results and the potential to gain knowledge about their specific
conditions by harnessing a larger collective for research on diseases than usually
possible with traditional methods. Given its organisation in community struc-
tures, it may be argued that community benefit should be considered a real bene-
fit for individuals who are part of these communities. However, where no
immediate tangible benefits arise and any practical impact is merely projected for
the future, one should question how real such benefits are and whether the data
sharing reflects what Lupton (2014) has argued to be a new form of patient
labour (see also Prainsack 2014b).
The platforms show very different approaches to the question of sustainability. As
most of the platforms are fairly recent or still partly under development, it is difficult
to assess their long term sustainability. Taltioni is supported by the Finnish govern-
ment, but also involves private sector parties. How this cooperation will function in the
longer term still remains to be seen. The lack of up-take by users will result in it not
being supported by the State or by companies in the long-term. The PEER platform is
probably the most well-established platform and has received substantial investment
from a variety of sources. Its combination of crowdsourcing and grant funding appears
to be a functioning model. With regard to Healthbank and MIDATA, whether health
data proves to be a sufficiently valuable commodity to make their model economically
sustainable in the long term remains to be seen. The ePGA still needs to develop a long
term funding model.
Implicit in the organisation of these platforms and the values that they represent are
particular understandings of health. The ePGA probably endorses the most traditional
model of health, where the contribution of the platform is to optimise use of existing
pharmacogenomic information, with immediate translation into increasing the accuracy
of professionalsprescription decisions. By focusing on linking research and healthcare
advice, ePGA appears to endorse an evidence-based, knowledge-focused model of
Riso et al. Life Sciences, Society and Policy (2017) 13:12 Page 22 of 27
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
healthcare. However, individuals are also actively encouraged to obtain relevant infor-
mation to achieve higher quality care (ORiordan 2013). In contrast, Taltioni conveys to
the user that health is a personal enterprise, and not merely the result of the passive re-
ceipt of evidence-based professional services. Health is more than routine health exams
and controls; it is also related to well-being and individuals are invited to engage in
healthy lifestyles activities (Lupton and Petersen 1996). But with this invitation also
comes the expectation that users take responsibility for their health, potentially as
an all-encompassing project. Conveying yet another model, Healthbank envisions
health and illness as a matter of economic investment where the health data can
be used to achieve profit: individuals and their records are themselves subjected to
generation of profit.
MIDATA instead places its emphasis on health as a collective endeavour closely
linked to the achievement of citizen empowerment. It appeals to membersaltruism by
presenting the opportunity to benefit society as and of itself as reason to join, in
much the same way as people in many European countries are not allowed to re-
ceive money for their blood donations. Similarly, the PEER platform connects the
construction of health and illness directly with the notion of community. Its
disease-based communities are understood as having the potential to drive signifi-
cant advances for the common good (Novas 2007), outside of traditional models
of healthcare and research, and to shape the future of medical research especially
for the underserved area of rare diseases.
Conclusion
We have illustrated how different approaches to data sharing in innovative online
platforms are underscored by a set of values. The platforms we analysed show substan-
tial differences in how they embody these values. The particular value profile of a plat-
form influences the design and functioning of the platform, including how individuals
can interact with the web interface, which data are shared, and under which circum-
stances. How health information is being shared and distributed in online platforms is
never value neutral, and the identification of ethical considerations requires careful at-
tention to particular features of individual platforms. Further empirical research and
analysis is needed in order to comprehend the impact and the consequences of sharing
health data through online platforms. It should also be noted that data sharing plat-
forms contain many trade offs depending on the nature and scope of the platform.
Nationally based platforms, such as Taltioni, are limited in their scope within the
state, but provide the population with the opportunity to participate in a national
initiative. Other networks, such as the PEER network, are global in scope, but may
suffer due to the lack of consistency or homogeneity of participation, leading to
problems with data reliability.
All platforms, however, share this problem to some extent insofar as they lack
complete coverage of the population and therefore incur data reliability concerns.
Despite political efforts to achieve larger scale data sharing across Europe, there re-
mains a lack of coordination within the European context. Still, there is a signifi-
cant potential for data sharing to be further developed in a socially robust and
morally responsible way across Europe. The data sharing platforms discussed in
Riso et al. Life Sciences, Society and Policy (2017) 13:12 Page 23 of 27
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
this paper illustrate the likely complexity and potential diversity of future innova-
tions in the field.
Endnotes
1
COST Action CHIP ME Citizens Health through public-private initiatives:Public
Health, Market and Ethical Perspectives (COST Action IS 1303) is an interdisciplinary
network of researchers from all over Europe that meet on a regular basis to
discuss issues relating to the intersection between public-private partnerships,
genetics/genomics and ICT.
2
Taltioni Data Sharing Policy; available at: http://taltioni.fi/wp-content/uploads/2014/
06/Taltioni-tietosuojaseloste.pdf
3
Healthbank Innovation AG, Baar. Accessed May 2016 at: http://www.moneyhouse.ch/
u/healthbank_innovation_ag_CH-170.3.039.845-6.htm.
4
Healthbank FAQ section; available at: https://www.healthbank.coop/faq/
5
For more information see MIDATA Cooperative Statute 2017 (valid since 04 03
2017), unofficial English translation, forthcoming in MIDATA website.
6
ePGA was funded by European Commission (Grant: FP7305444) and Greek
National Secretariat of Research and Technology (Grant: ΠΔΕ11_046).
7
PEER is funded by The White House Initiative on Precision Medicine, the National
Patient-Centered Clinical Research Network (PCORnet), the Patient-Centered Outcomes
Research Institute (PCORI), the FDAs Patient-Focused Drug Development initiative
(PFDD) for which PEER was supported with a grant by the Pharmaceutical Research and
Manufacturers of America (PhRMA) and the Robert Wood Johnson Foundation.
Appendix
Table 3 List of initial online platforms for sharing health data
Platform WWW address
Taltioni http://taltioni.fi/
Healthbank https://www.healthbank.coop/
ePGA http://www.epga.gr/
PEER Network http://peerplatform.org/
PatientsLikeMe https://www.patientslikeme.com/
Genomera Not available
Personal Genome Project http://personalgenomes.org/
DNA Land https://dna.land/
Sundhed.dk https://www.sundhed.dk/
23andMe https://www.23andme.com/
DNA.bits http://socialm1.wix.com/dnabits
Interpretome http://interpretome.com/
MIDATA https://www.midata.coop/
ONCO-i2b2 Not available
TranSMART http://transmartfoundation.org/
FINDbase www.FINDbase.org
KORA http://www.helmholtz-muenchen.de/kora
Riso et al. Life Sciences, Society and Policy (2017) 13:12 Page 24 of 27
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Abbreviations
EU: European Union; GA4GH: Global Alliance for Genomics and Health; ICT: Information and Communication
Technologies; IT: Information and Technology; PEER: Platform for Engaging Everyone Responsibly
Acknowledgements
The authors would like to thank the following platform representatives for providing helpful information on their
platform: Ernst Hafen (MIDATA), George Patrinos (ePGA), and Sharon Terry (PEER).
Funding
This article is based upon work from COST Action IS1303 Citizens Health through public-private Initiatives: Public
health, Market and Ethical perspectives, supported by COST (European Cooperation in Science and Technology)
(http://www.cost.eu).
Brígida Riso is also supported by FCT the Portuguese Foundation for Science and Technology under the PhD grant
SFRH/BD/100779/2014.
Authorscontributions
All the authors contributed for the development and the discussion of the argument presented, and to the writing of
the manuscript. All the authors read and approved the final manuscript.
Authors information
All the authors are part of Science and Values working group of COST Action IS1303 Citizens Health through public-private
Initiatives: Public health, Market and Ethical perspectiveswhich aims to discuss among other issues the wider implications
of the increasing application of genomic research throughout society particularly with regard to the evolution of the
direct- to-consumer genetic testing market, patient-centred initiatives, and the interplay of public and private interests in
the applications of genomic research. Its work reflects on how far the emerging genetic testing practices and opportunities
of applications of genomic research challenge established ethical and societal norms and aims to identify core ethical
demands for responsible innovation in this field, both with regard to general principles and specific cases. (chipme.eu).
Competing interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of
this article.
PublishersNote
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Author details
1
Instituto Universitário de Lisboa (ISCTE-IUL), Edifício ISCTE, Av. das Forças Armadas, 1649-026 Lisboa, Portugal.
2
Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
3
Centre for Biomedical Ethics and
Law, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium.
4
Leuven Institute for Human
Genetics and Society, Leuven, Belgium.
5
Centre of Bioethical Research and Analysis, Philosophy, School of Humanities,
NUI Galway, Galway, Ireland.
6
University of Gent, Ghent, Belgium.
7
Institute of Biomedical Ethics and History of
Medicine, University of Zurich, Zurich, Switzerland.
8
ETH Zürich, Department of Biology, Institute of Molecular Systems
Biology, Zürich, Switzerland.
9
Section of Philosophy, University of Copenhagen, Copenhagen, Denmark.
10
Department
of Legal Studies, CIRSFID, University of Bologna, Bologna, Italy.
11
Centre for the Study of Bioethics, University of
Belgrade, Belgrade, Serbia.
Received: 18 April 2017 Accepted: 8 August 2017
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... DTC companies often build large databases of their customers' data and utilize this data for product research or revenue increase by selling it to clinical research or biopharmaceutical companies (Allyse et al., 2018;Raz et al., 2020). Hence, numerous studies have investigated genetic privacy and sharing of genetic data from ethical (e.g., Lewis et al., 2013;Riso et al., 2017), legal (e.g., Ducournau et al., 2013;Hogarth et al., 2008;Hudson et al., 2007), and social sciences (e.g., Anderson & Agarwal, 2009Thiebes et al., 2017) standpoints. While the DTC genetic testing market is ever-growing (Ugalmugle & Swain, 2020), controversy about service providers' business practices and genetic privacy continues. ...
... While extant research has investigated business models in the healthcare market (e.g., Gleiss et al., 2021;Hwang, 2008), research on business and marketing aspects of DTC genetic testing is still scarce. Literature closest to this study engages with socioeconomic aspects such as research on how marketing strategies of DTC genetic testing services impact consumers (e.g., Ducournau et al., 2013), the impact of consumers' genetic variations on their economic behaviors (e.g., Cesarini et al., 2012;Daviet et al., 2021;Kock, 2009), socioeconomic implications of consumers sharing their genetic data freely (e.g., Riso et al., 2017;Vassilakopoulou et al., 2019), or digital entrepreneurs appropriating value from genetic data (e.g., Jarvenpaa & Markus, 2018;Rothe et al., 2019). A first overview of DTC genetic testing service business models is provided by Thiebes et al. (2020), who analyzed the business models of 277 DTC genetic testing services and developed a comprehensive taxonomy of business models in DTC genetic testing, which consists of 15 dimensions and 41 characteristics and is organized along the four major business model categories introduced above (i.e., strategic choices, value network, create value, and capturing value). ...
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Although consumers and experts often express concerns regarding the questionable business practices of direct-to-consumer (DTC) genetic testing services (e.g., reselling of consumers' genetic data), the DTC genetic testing market keeps expanding rapidly. We employ retail fairness as our theoretical lens to address this seeming paradox and conduct a discrete choice experiment with 16 attributes to better understand consumers' fairness perceptions of DTC genetic testing business models. Our results suggest that, while consumers perceive privacy-preserving DTC genetic testing services fairer, price is the main driver for fairness perception. We contribute to research on consumer perceptions of DTC genetic testing by investigating consumer preferences of DTC genetic testing business models and respective attributes. Further, this research contributes to knowledge about disruptive business models in healthcare and retail fairness by contextualizing the concept of retail fairness in the DTC genetic testing market. We also demonstrate how to utilize discrete choice experiments to elicit perceived fairness. Supplementary information: The online version contains supplementary material available at 10.1007/s12525-022-00571-x.
... In the course of their lives, patients generate data that are stored in central databases as a result of various events at different facilities or via different software structures (Azaria et al., 2016;Chen et al., 2018). The responsibility for the data usually lies with the respective operator of the database and not with the patient, which makes easy access to all data and control over the transfer and use of personal data almost impossible for them (Riso et al., 2017;Ballantyne, 2020). With the current widespread management of medical data, there is no guarantee of the integrity or reliability of patient records and the risk of data loss or data misuse is great (Chen et al., 2018;Lv and Piccialli, 2021). ...
... With the current widespread management of medical data, there is no guarantee of the integrity or reliability of patient records and the risk of data loss or data misuse is great (Chen et al., 2018;Lv and Piccialli, 2021). What is known as blockchain technology offers a promising new framework to enable and support the digital, secure and reliable integration of health information across various applications and stakeholders (Riso et al., 2017;Chen et al., 2018;Qiu et al., 2018;Kassab et al., 2019;Vyas et al., 2020). By setting up a seamless, decentralized data platform, for example information on medical records, records of authorization and proof of utilization of the data, provider directories, information on medicines and their supply chains as well as insurance and damage information can be recorded, tracked and managed securely and digitally (Agbo et al., 2019;Narikimilli et al., 2020). ...
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Blockchain solutions offer efficient approaches for trustworthy data management, especially in the medical field when storing and processing sensitive patient data. Many institutional and industrial facilities have already recognized the importance of the technology for the health sector and have also formulated basic ideas, concepts and main use cases, but concrete implementations and executions are comparatively rare. This mini review examines current research on specific blockchain implementations in healthcare that go beyond the state of concept studies or theoretical implementation ideas and describes the most promising systems based on systematic literature research. The review shows that secure storage and easy access to complete patient data is becoming increasingly important. Blockchain technology can be used as a secure, transparent and digital way to meet these needs. Hybrid solutions consisting of conventional data storage and blockchain-based access management are increasingly being developed and implemented. The automation of blockchain processes through smart contracts is also recommended. The review further reveals ambiguities in the use of permissioned and permissionless blockchain frameworks, machine learning (ML) integration as well as the question of which data should be stored in the blockchain and how this should be viewed legally. Therefore, there is still a need for further research, especially on these aspects, in order to further establish the use of blockchains in healthcare.
... In this study, only 19 out of 102 (19%) submitted proposals stated data collection through digital platforms. Attracted by new avenues of digital technologies offering the researchers for collecting data in realtime especially during public health emergencies, IRBs need thorough scrutiny of proposals for data sharing ethics [27][28][29]. ...
... The IRB to waive the ICF either totally or partly is depending upon the study population and data collection methods and is also supported by Karbwang J, et al. 2018 in their multi-country study launched by Forum for Ethics Review Committees in Asian and the Western Pacific (FERCAP) Regions [30]. Besides, a request for the waiver of the documentation of informed consent was not uncommon especially in research using the online data collection through the Facebook media which was conformed to other studies [28,29]. ...
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... The internet has become a propitious environment for the dissemination of science quickly and simply, such as studies showing the potential of media for health promotion [36-38]. Thus, the Internet has become an environment conducive to the dissemination of science quickly and simply, and, given, the impact of repercussion that the podcast has, its use has propitiated the dissemination of scienti c knowledge, besides allowing the dissemination of health promotion strategies, in uencing the practice of self-care by the population [39]. ...
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Purpose Knowing the use in the education of podcasts was occurring in some countries, the present work developed one in Portuguese. The aim was to promote health and combat disinformation in Brazil. Methods Different categories of Podcasts were created: shorter/longer; with/without guests and disease-related issues or other topics about prevention/health education. After that, the audios were edited and submitted on platforms: It was analyzed through the data generated by the published episodes. Results It was observed that duration vs several reproductions did not correlate with (p = 0.2521). Then, the presence/absence of guests (p = 0.1779). However, themes related to infectious and chronic diseases obtained more significant results (p = 0.0466) when compared to the other topics. Also, we noticed that our listeners are primarily men aged between 18 and 27 years old and residing in Brazil. Conclusion Podcasts may be able to disseminate knowledge, however, themes and the type of audience must be considered to choose this kind of approach.
... Public involvement in health data governance entails awareness raising, consultation, partnering with and/or empowering of members of the public to participate in research and governance practices and it can be set in motion through a variety of methods including deliberative polls, citizen juries, participatory appraisals, scenario-based workshops, and focus groups (18). Data holders can also participate via participant-led data cooperatives (e.g., Open Humans, PEER Network, MIDATA) that enable them to share and aggregate their data while keeping control over its uses (19)(20)(21)(22)(23). ...
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Background Data-intensive and needs-driven research can deliver substantial health benefits. However, concerns with privacy loss, undisclosed surveillance, and discrimination are on the rise due to mounting data breaches. This can undermine the trustworthiness of data processing institutions and reduce people's willingness to share their data. Involving the public in health data governance can help to address this problem by imbuing data processing frameworks with societal values. This study assesses public views about involvement in individual-level decisions concerned with health data and their association with trust in science and other institutions.Methods Cross-sectional study with 162 patients and 489 informal carers followed at two reference centers for rare diseases in an academic hospital in Portugal (June 2019–March 2020). Participants rated the importance of involvement in decision-making concerning health data sharing, access, use, and reuse from “not important” to “very important”. Its association with sociodemographic characteristics, interpersonal trust, trust in national and international institutions, and the importance of trust in research teams and host institutions was tested.ResultsMost participants perceived involvement in decision-making about data sharing (85.1%), access (87.1%), use (85%) and reuse (79.9%) to be important or very important. Participants who ascribed a high degree of importance to trust in research host institutions were significantly more likely to value involvement in such decisions. A similar position was expressed by participants who valued trust in research teams for data sharing, access, and use. Participants with low levels of trust in national and international institutions and with lower levels of education attributed less importance to being involved in decisions about data use.Conclusion The high value attributed by participants to involvement in individual-level data governance stresses the need to broaden opportunities for public participation in health data decision-making, namely by introducing a meta consent approach. The important role played by trust in science and in other institutions in shaping participants' views about involvement highlights the relevance of pairing such a meta consent approach with the provision of transparent information about the implications of data sharing, the resources needed to make informed choices and the development of harm mitigation tools and redress.
... Health policy is competing for access to information on the health side, particularly the conditions under which information for research should be accessible when appropriate privacy and security protections are developed (Hamidi, 2019). There are models to establish data-sharing arrangements that promote the appropriate use and attention to ethical standards by information in a safe and secure environment (Riso et al., 2017). ...
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The Malaysian healthcare systems face incredible challenges as technology is being used more and more widely, and citizens' expectations are increasing just as rapidly. The Healthcare industry is adopting big data in daily operations to ensure excellent performance. In this context, Big Data can help providers achieve these objectives in an unparalleled manner. However, the Malaysian government hospitals remain unable to implement big data. Hence, this study examines the mediating role of the use of big data (UBD) on the relationship between hospital performance (HP), data quality (DQ), data integration (DI), and data governance (DG). The study framework is established from theories, namely Resource-Based View (RBV), extending the DeLone and Mclean IS Success Model (D&M ISSM). The data was collected from Malaysian government hospitals. Total questionnaires of 560 were distributed, and 212 were responded. The convenience sampling technique was used. Hypotheses tests were performed via Smart PLS 3.3.2. Results show DQ and DI have significant direct relationships with the UBD. However, DG is not significant with UBD. Findings on the use of big data as a mediating variable reveal DQ and DI have a significant direct relationship with UBD except for DG. Findings provide important insights to government, policymaker, and researchers to further understand the use of big data to enhance hospital performance in Malaysia.
... Furthermore, Switzerland is host to two of the first international examples of data-cooperatives, that is, data-sharing platforms strongly leaning towards the idea of citizens' control of their data. 54 Data-cooperatives are databases 'concerned with the collection, storage, maintenance and analysis of health data' where every citizens can participate by 'pay[ing] a one-time unit price (membership fee), which entitles the person to be a member and owner [of the cooperative] at the same time'. 55 The existence of such initiatives in Switzerland corroborates the idea that patient ownership and control over health data is acknowledged. ...
Article
Full-text available
Objectives The evolution of healthcare and biomedical research into data-rich fields has raised several questions concerning data ownership. In this paper, we aimed to analyse the perspectives of Swiss experts on the topic of health data ownership and control. Design In our qualitative study, we selected participants through purposive and snowball sampling. Interviews were recorded, transcribed verbatim and then analysed thematically. Setting Semi-structured interviews were conducted in person, via phone or online. Participants We interviewed 48 experts (researchers, policy makers and other stakeholders) of the Swiss health-data framework. Results We identified different themes linked to data ownership. These include: (1) the data owner: data-subjects versus data-processors; (2) uncertainty about data ownership; (3) labour as a justification for data ownership and (4) the market value of data. Our results suggest that experts from Switzerland are still divided about who should be the data owner and also about what ownership would exactly mean. There is ambivalence between the willingness to acknowledge patients as the data owners and the fact that the effort made by data-processors (eg, researchers) to collect and manage the data entitles them to assert ownership claims towards the data themselves. Altogether, a tendency to speak about data in market terms also emerged. Conclusions The development of a satisfactory account of data ownership as a concept to organise the relationship between data-subjects, data-processors and data themselves is an important endeavour for Switzerland and other countries who are developing data governance in the healthcare and research domains. Setting clearer rules on who owns data and on what ownership exactly entails would be important. If this proves unfeasible, the idea that health data cannot truly belong to anyone could be promoted. However, this will not be easy, as data are seen as an asset to control and profit from.
... 44 Another novel big data platform is Taltioni, a national data sharing platform in Finland with smartphone and mobile device applications that allow users to customize their "health plans" and access tools for blood pressure control, weight management and fitness, and medication schedule. 45 These innovative data-sharing and patient-engagement platforms are increasingly being recognized as fundamental components of healthcare delivery. ...
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Full-text available
There are huge gaps in evidence-based cardiovascular care at the national, organizational, practice, and provider level that can be attributed to variation in provider attitudes, lack of incentives for positive change and care standardization, and observed uncertainty in clinical decision making. Big data analytics and digital application platforms-such as patient care dashboards, clinical decision support systems, mobile patient engagement applications, and key performance indicators-offer unique opportunities for value-based healthcare delivery and efficient cardiovascular population management. Successful implementation of big data solutions must include a multidisciplinary approach, including investment in big data platforms, harnessing technology to create novel digital applications, developing digital solutions that can inform the actions of clinical and policy decision makers and relevant stakeholders, and optimizing engagement strategies with the public and information-empowered patients.
... IEEE. 41.Riso, B., Tupasela, A., Vears, D. F., Felzmann, H., Cockbain, J., Loi, M., & Rakic, V. (2017). Ethical sharing of health data in online platforms-which values should be considered? ...
Chapter
Rapid economic growth, industrialization, mechanization, sedentary lifestyle, high calorie diets, and processed foods have led to increased incidence of obesity in the United States of America. Prominently affected by the obesity epidemic are the most vulnerable such as the rural poor and those who have less access to nutritious and healthy foods due to barriers such as socioeconomic, infrastructural, and organizational. Wearable technology (WT) and health fitness applications (apps) have the potential to address some of the health disparities associated with obesity. Monitoring health parameters through WT and Apps using remote sensing technology generates personal health data which can be captured, analyzed, and shared with healthcare providers and others in social support network. Because captured data include protected health information, and breaches can occur, the concerns about health data privacy, personal ownership, and portability are addressed in this chapter.
Book
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In times of global economic and political crises, the notion of solidarity is gaining new currency. This book argues that a solidarity-based perspective can help us to find new ways to address pressing problems. Exemplified by three case studies from the field of biomedicine: databases for health and disease research, personalised healthcare, and organ donation, it explores how solidarity can make a difference in how we frame problems, and in the policy solutions that we can offer. Proposes a new understanding of 'solidarity as practice' Offers a systematic overview of the use of the term 'solidarity' in bioethical and related political and social theoretical scholarship Explores how solidarity can be applied to biomedical practice using three case studies: health databases, personalised health care, and organ donation.
Book
Why has autonomy been a leading idea in philosophical writing on bioethics, and why has trust been marginal? In this important book, Onora O'Neill suggests that the conceptions of individual autonomy so widely relied on in bioethics are philosophically and ethically inadequate, and that they undermine rather than support relations of trust. She shows how Kant's non-individualistic view of autonomy provides a stronger basis for an approach to medicine, science and biotechnology, and does not marginalize untrustworthiness, while also explaining why trustworthy individuals and institutions are often undeservingly mistrusted. Her arguments are illustrated with issues raised by practices such as the use of genetic information by the police or insurers, research using human tissues, uses of new reproductive technologies, and media practices for reporting on medicine, science and technology. Autonomy and Trust in Bioethics will appeal to a wide range of readers in ethics, bioethics and related disciplines.
Chapter
Denmark is regularly portrayed in international science journals as ‘the epidemiologist’s dream’: a country where health data on all citizens can be combined with e.g. information about social or financial position, kinship ties, school performance data as well as tissue samples. Moreover, it can all be done without the informed consent of the individual. This chapter describes the practices in Denmark involved in what I call ‘intensified data sourcing’. I define intensified data sourcing as attempts at getting more data, of better quality, on more people – and I point out how intensified data sourcing has emerged as a new way of running the health services. My key point with this chapter is that though research uses of health data receive the most attention, research is not necessarily the main purpose with intensified data sourcing. Nevertheless, ethical debates tend to focus on research and thereby neglect an adequate understanding of the everyday practices of data sourcing and the many competing purposes it serves. Furthermore, I point out how ethical debates often focus on the rights of the individual, though data sourcing operates at the level of the population, and when attending to individual rights there is an unfortunate tendency to conjure concerns about privacy with rights of autonomy. We need new modes of ethical reasoning that take point of departure in an understanding of actual data practices. Since Denmark is in many ways at the forefront of intensified data sourcing, it is a good place from which to begin rethinking the policy challenges associated with intensified data sourcing at both national and European levels.