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Social Studies of Science https://doi.org/10.1177/0306312721989818
E-Infrastructures and the divergent assetization of public health data: Expectations,
uncertainties, and asymmetries
Paraskevas Vezyridis, Stephen Timmons
Nottingham University Business School, UK
Abstract
In this article, we examine some of the expectations, frictions and uncertainties involved with the
assetization of de-identified NHS patient data by (primary care) research services in UK. Pledges to
Electronic Health Record (EHR) data-driven research attempt to reconfigure public health data as an
asset for realizing multiple values across healthcare, research and finance. We introduce the concept of
‘asymmetrical divergence’ in public health data assetization to study the various practices of
configuring and using this data, both as a continuously generated resource to be extracted and as an
asset to be circulated in the knowledge economy. As data assetization and exploitations grow bigger
and more diverse, the capitalization of these datasets may constitute EHR data-driven research in
healthcare as an attractive technoscientific activity, but one limited to those actors with specific
sociotechnical resources in place to fully exploit them at the required scale.
Keywords
NHS patient data; infrastructure; expectations; assetization; uncertainty
assetization of public health data
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I believe, however, that as we seek to unlock a new age of enterprise, we might need to go further in
exploring ways of unlocking new growth without increasing public spending. As with a business in
a cash crisis, we need to shore up the profit and loss account by reducing waste, as the Government
have so quickly done. Equally, as with real business growth, we need to look creatively at our
balance sheet and think about our assets and our competitive advantage. Everyone in government,
in every Department at every level, should be asking themselves, ‘What can we sell to the rest of the
world, in order to repair our damaged public finances?’ … The third is the National Health Service.
I know from my own experience that we are sitting on billions of pounds worth of patient data. Let
us think about how we can unlock the value of those data around the world. (Freeman, 2010)
It was November 2010, in the aftermath of the last global financial crisis, and the UK House of
Commons was debating the country’s growth policy. George William Freeman MP (Con), who four
years later was appointed Minister for Life Sciences at the Department of Health and the Department
for Business, Innovation and Skills, unwrapped his own vision for the country’s economic growth.
Problematizing the state as a ‘business in a cash crisis’ that needs to enter ‘a new age of enterprise’, the
MP asked public services to start looking for public assets they could sell to potential investors so as to
restore public finances.
While national policy expectations and narratives of wealth creation out of NHS patient data are not
new, they have intensified after the 2008–09 financial crisis as corporate biomedical innovation has
been outsourced to the academic sector (Robinson, 2018) and personal data has emerged in the
economic literature as the world economy’s new asset class (BIGT, 2003; Department of Health, 2011;
HM Government, 2018; World Economic Forum, 2011). At the same time, national health systems are
‘morally obliged’ to become data-driven so as to stop ‘flying blind’ and start ‘saving lives’ and
taxpayers’ money (Du Preez, 2015). They are now expected to become not only more (cost-)effective
in personalized healthcare but also expand their wealth-creating role as investors in biomedical
Research and Development (Department of Health, 2011; Kelsey and Cavendish, 2014).
The UK has been consistently advertised as the home to one of the best and biggest healthcare
datasets in the world (HM Government, 2018). Its universal, largely computerized and centrally
managed public healthcare system (the NHS) has created volumes of linked datasets from longitudinal
Electronic Health Records (EHRs) across all levels of care. In fact, the demand for and availability of
NHS patient data for research into anything from pharmacovigilance, drug prescribing and safety,
standards of care and trial recruitment, continues to grow year after year (NHS Digital, 2018).
Commercial models are currently being debated in the pursuit of lawful and publicly acceptable
contractual arrangements between the NHS and private sectors for creating and realizing the values of
this public asset (Harwich and Lasko-Skinner, 2018; HM Treasury, 2018). Recently, the economic
value of curated NHS patient data was estimated at £9bn per year for both the NHS and the patients
Vezyridis and Timmons 2021
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(Wayman and Hunerlach, 2019). These valuations neglect the investments in technoscientific and
market infrastructures required to reap such financial and epistemic benefits.
Our aim here is to explore how narratives of innovation and normative pledges to the interrelatedness
of health and wealth attempt to mobilize and configure public health data as an asset for realizing
multiple values across scientific research, healthcare and economics (Hogarth, 2017; Welsh and Wynne,
2013). For this, we take as our case study the work of research services in England that aggregate and
release NHS patient data to actors in healthcare and beyond, such as the Clinical Practice Research
Datalink (CPRD), QResearch, The Health Improvement Network (THIN), CALIBER and
ResearchOne. Understanding ‘valuation as a social practice’ (Helgesson and Lee, 2017: 533), we focus
on the intertwined scientific, economic, social and material expectations and logics such research
services translate and enact. These expectations enable them to realize and extract the values of this
public asset for their organizational sustainability and for other actors across society (Birch, 2017a;
Brown and Michael, 2003; Star, 1985). Responding to Birch’s (2019) call for more empirical
investigations of how ‘things are turned into assets’ (Muniesa et al., 2017) and controlled for the
extraction of rents, such as licenses or fees, we show that the assetization of NHS patient data by
research services is a complex and laborious process. It involves the configuration of competing and
complementary frictions, as well as uncertainties around coding practices, regulation, acceptability,
supplies of datasets, technoscientific capital and user demand, among other things.
NHS patient data assetization by these research services serves four main purposes: maintaining
organizational sustainability, developing their capability to continue assetizing data, developing the
epistemic and human capital of this field (e.g. development of new scientific methods and training of
new data scientists) and, consequently, strengthening their role in the valorization, performativity and
financialization of EHR data-driven research. For this, we introduce the concept of asymmetrical
divergence to explore their expectations and valuations as well as their normative, scientific and
economic discourses and practices for assetizing public health data (Birch et al., 2020; Muniesa et al.,
2017). In this way, we elucidate the asymmetries around the sociotechnical and financial infrastructures
that are configured for this assetization, including the control of data flows for research, the public’s
participation in decision-making and the various knowledge assets (e.g. phenotypes, biomarkers, quality
improvement reports, recruitment pools of research participants, clinical risk prediction algorithms and
scientific publications) that are made (im)possible for healthcare, academic and biomedical networks
of actors.
This article draws mostly from 27 interviews conducted in 2016 with seven GPs who were involved
in clinical commissioning, information governance, medical ethics teaching or academic EHR data-
driven research, nine citizens who reported having opted out from and/or had campaigned against
programmes of NHS patient data capitalization (i.e. care.data) and eleven health data researchers. The
last group of participants comprised statisticians, epidemiologists, data architects and research
facilitators who have worked with data from and/or for one of the more established research services in
assetization of public health data
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England that have been collecting and curating de-identified NHS patient data from a network of
contributing GP practices across the country to support EHR data-driven research (Vezyridis and
Timmons, 2016). These research services included:
• QResearch, a partnership between the IT supplier EMIS Health and (since 2019) the University of
Oxford (previously the University of Nottingham), which holds data from 1500 (out of
approximately 9800) GP practices in the UK,
• the CPRD, the oldest research service, with a history dating back to 1987, which extracts and curates
data from GP practices using IMS Health’s computer system and (since 2018) also from EMIS
Health,
• THIN by Cegedim SA that extracts data from over 550 GP practices that use the Vision primary care
software (approximately 6% of the UK population),
• CALIBER at the University College London, which has a license with CPRD since 2012, and
• ResearchOne by TPP and the University of Leeds, which (since 2013) houses data from both primary
and secondary care providers using the company’s SystemOne software.
All interviews focused mainly on the opportunities and the technical, social and ethical challenges of
realizing the benefits of NHS patient data-driven research, particularly in English primary care. Health
data researchers and GPs were also asked more specific questions around the challenges of developing
and maintaining such research services, including issues of sociotechnical infrastructure and
information governance, as well as of conducting observational studies with NHS patient data. We
supplemented these interviews with documents, reports and online material from these research
services’ website. The first author also was a non-participant observer of team meetings at some of
these research services, national health data analytics workshops and public consultations on the ethics
of NHS patient data exploitations, and completed university training courses for data researchers related
to the opportunities and challenges of conducting observational studies with EHR primary care data in
the UK.
In the next section, we approach data assetization from the sociology of expectations and science and
techology studies (STS) (Birch, 2019; Birch et al., 2020; Borup et al., 2006). This forms our analytical
approach for studying the performative promises, normativities and sociotechnical practices involved
in these situated processes of NHS patient data capitalization (Muniesa et al., 2017). Following this, we
examine expectations and uncertainties as well as frictions and risks in the process of transforming NHS
patient datasets into research assets. We focus not only on the scientific but also on the economic,
political, social and ethical valuations these research services have to navigate. We conclude by
speculating on the future of EHR data-driven research and argue that asymmetrical divergence may
indeed foster innovation in EHR data-driven research and development (Kleinman and Vallas, 2001).
However, it is also creating unequal configurations of access to knowledge production and public
Vezyridis and Timmons 2021
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scrutiny as these research services compete for data and funding. More centrally controlled flows of
data and assets for the extraction of rents may, in the end, benefit only those networked actors that have
the socio-material resources (including financial capital) and knowledge expertise to capitalize on these
public datasets for specified purposes (Birch, 2019; Muniesa et al., 2017).
Theorizing research expectations, service valuations, and data assetizations
From an STS perspective, expectations are both products and producers of innovation, mobilizing and
institutionalizing people and resources (Borup et al., 2006; Brown, 2003; Brown and Michael, 2003;
Tutton, 2011). Scholars in this field have argued that expectations produce, in their own material-
discursive way, the necessary ‘dynamism and momentum’ (Brown and Michael, 2003) as well as the
‘incentives and obligations’ (Brown, 2003) for human and non-human actors to come together and
‘wishfully enact’ (Tutton, 2011) particular versions of the future.
Research that examined the role of biomedical initiatives in the creation of new forms of scientific,
social, political and economic expectations has demonstrated, for example, how human tissue, medical
data and genomics are assetized for scientific collaborations and circulations in knowledge economies
(Cooper and Waldby, 2014; Dagiral and Peerbaye, 2016; Tarkkala et al., 2018). Geiger and Gross
(2019) have shown how consumer genomics firms mobilize a ‘platform business model’ in order to
assetize genomics information via specific processes of ‘accumulation’, ‘augmentation’ and
‘obscuration’ of related uncertainties. These platforms simultaneously maintain constant flows of data
and values between sellers and customers across different markets. Barrett et al. (2016) have shown
how specific socio-material configurations enacted by digital platforms create multiple epistemic,
ethical and financial expectations from online health communities. Likewise, we have shown (Timmons
and Vezyridis, 2017; Vezyridis and Timmons, 2017) how such expectations (re)configure a range of
socio-material practices and relationships between patients, hospitals, universities and biomedical
industries in the process of valuating and assetizing medical waste and patient data.
A main purpose of this article is to understand how these research services enact NHS patient data
assetizations in order to frame, sustain and expand the ‘liminal space’ between hopes and promises and
concrete products and assets (Brown, 2003; Hogarth, 2017). For this, we conceptualize assetization as
a sociotechnical process whereby data and knowledge are transformed by organizations into closely
governed resources for the extraction of use and exchange values (Birch et al., 2020). We treat any
scientific, ethical and economic values around NHS patient data assetization not as something ‘stable
and predefined, but rather as something grappled with, articulated, and made in concrete practices’
(Dussauge et al., 2015: 2). Values, as Birch (2017a: 462, 466) asserts, are ‘immanent or latent in material
things (e.g. commodity) and/or discursive claims (e.g. hope)’ and they require ‘active, ongoing, and
performative management’. Both conceptually and empirically, as Brown (2003: 5) argues,
expectations and values eventually become both ‘inseparable’ and ‘tradable’, forming ‘the basis of
exchange relationships within “communities of promise”’.
assetization of public health data
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Expectations of EHR data-driven research for speculative valuations of health and wealth have,
therefore, their own important role in how GPs, NHS patients and the data they co-produce are
reimagined and valued for EHR data-driven research within a highstakes biomedical knowledge
economy (Birch et al., 2020; Dussauge et al., 2015; Wienroth et al., 2019). They are fundamental in
mobilizing various state, commercial and academic actors as well as capital for the assetization of NHS
patient data and the enactment of a new health data access market (Birch et al., 2020; Brown, 2003;
Vezyridis and Timmons, 2017).
During our fieldwork, we noted how health data scientists valued the availability of EHR data to
answer different types of research questions. Their anticipatory discourses highlighted the unique
opportunities now available to researchers to identify causes of diseases, complication and management
rates and prescribing practices from the mining of data. They were often excited about the potential for
life sciences in the UK to lead the development of new, EHR data-driven, practices for the prevention
and treatment of diseases or for conducting more ‘pragmatic’ randomized controlled trials to assess the
effectiveness of medications (Powell et al., 2017). And research services appear to have assumed the
technoscientific and economic role of materializing and capitalizing on the multiple scientific,
normative and economic promises and values of EHR data-driven biomedicine (Birch, 2019; Brown
and Michael, 2003; Martin, 2015). They do so by enacting the negotiated and contested infrastructural
work, that is, the situated narratives, heterogeneous relationships and socio-material practices,
necessary to bring this imagined future forward (Brown, 2003; Dagiral and Peerbaye, 2016).
Thus, for the present study we understand the capitalization of these public datasets as a
sociotechnical performance that translates imaginaries and turns resources into assets in order to realize
the expectations of specific actors for value and capital, that is, future earnings whether that is ‘money,
or something comparable’ (Birch, 2019; Birch et al., 2020; Dussauge et al., 2015; Muniesa et al., 2017:
12). Following Muniesa et al. (2017), we treat assetization as a practice that extends organizational
boundaries to include social, technological, regulatory and economic infrastructuring of institutions,
practices and people for the production of those resources and assets deemed appropriate for
capitalization. Research services studied here mediate and act ‘across boundaries between different
scales, levels, times and communities’ (Borup et al., 2006: 293; Dagiral and Peerbaye, 2016), that is,
between patients, GP practices, IT suppliers and researchers, for new, contemporary and future,
‘secondary data uses’.
As we show below, GPs (or other healthcare professionals) and the data they co-produce from
consultations and treatments are disentangled from the local socio-material networks and transformed,
via EHRs, from objects of clinical labour to objects and means of scientific inquiry and economics
(Denis and Goëta, 2017). It is through specific relational socio-material infrastructures and market
arrangements that they are turned into valued tangible (e.g. aggregate databases, risk calculators) and
intangible (e.g. epidemiological and computational expertise) knowledge assets for financialized EHR
Vezyridis and Timmons 2021
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data-driven research and development (Birch et al., 2020; Dagiral and Peerbaye, 2016; Muniesa et al.,
2017; Wienroth et al., 2019).
Research services navigate through various political, economic and epistemological promises and
valuations of NHS patient data-driven research. As they pursue their organizational sustainability and/or
profitability for the capitalization of NHS patient data, they are being hybridized (Kleinman and Vallas,
2001) through various creative configurations of norms and practices (Lilley and Papadopoulos, 2014).
They ‘asymmetrically converge’ their market, academic and public healthcare expectations and logics
in the process of transforming NHS patient data into ‘promissory assets’ for monetary circulations in
knowledge economies (Cooper and Waldby, 2014; Kleinman and Vallas, 2001; Martin, 2015). As they
practice different modi operandi to financialize their operations, which increasingly resemble those of
other digital platforms (Birch et al., 2020; Geiger and Gross, 2019), different economic and non-
economic outcomes are made possible and impossible (Dussauge et al., 2015; Helgesson and Lee,
2017).
Central to our study, therefore, is how, during the process of assetization, such research services
embed specific value assumptions, structure practices and (re-)configure relations of co-production with
GPs and patients. How they order and re-order data flows, legitimize data uses and validate data users
(Dagiral and Peerbaye, 2016; Martin, 2015) for multiple purposes: creating their asset-based incomes
via the licensing of curated datasets (Birch, 2019), supporting the further development of healthcare
knowledge products and services (e.g. scientific publications and quality improvement reports) (Dagiral
and Peerbaye, 2016), and pursuing their own research and other innovation projects (e.g. analytic scripts
and phenotypes for GP practices and the research community). To explore and describe the ‘diverging
registers of value’ (Dussauge et al., 2015) and the (in)commensurate valuations these research services
are making, the asymmetries in knowledge practices (Tsoukas, 1997) and the configurations of
sociotechnical networks involved in the assetization of NHS patient data, we introduce the situated
concept of asymmetrical divergence.
We use the above concept to consider, first, asymmetries between the operational (including
financial) and the scientific and public health logics and valuations that affect assetization (Dussauge et
al., 2015; Kleinman and Vallas, 2001). Second, we use it to consider whether the co-producers and
providers of this public data, i.e. patients and healthcare professionals (GPs), have enough information
and power to participate in the shaping and direction of this assetization (Brown, 2003). Third, we
consider whether data assetization can diverge because some of the processes involved can take place
at different scales between these research services based on resources that are available to them, for
example, funding, access to data, human and technical resources, as well as networks of data providers
and customers. Following that, we reflect on the kind of new or existing asymmetries and inequalities
of access to resources, information and knowledge research services may produce and reproduce as
they assetize NHS patient data (Brown, 2003).
assetization of public health data
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Expectations of networked materialities and alternative epistemologies for divergent
assetizations
Researchers and some GPs shared expectations that collecting and analyzing data from healthcare at a
large scale potentially creates huge opportunities for the improvement of human health. The data
tracking of individual patient events (e.g. treatments), as they navigate the healthcare system, was
portrayed as a new and unique approach to reconstruct medical histories and follow patient journeys
‘from cradle to the grave’. They asserted their determination to study ‘pretty much every disease that
there is out there’ and make sense of the complexity of human health and illness based on the
unprecedented availability of (decontextualised) information stored in EHRs (Tsoukas, 1997). By
mobilizing economies of scale and converging diverse datasets—from the many isolated and small data
repositories across (primary and secondary) care into fewer centralized ones—they anticipated the
reduction of the time, cost and the uncertainties involved with research and the provision of healthcare.
By narrowing temporal, spatial and epistemological asymmetries in the collection and analysis of data,
the speed of knowledge production was expected to increase, as multiple reuses of these datasets from
greater pools of participants and for a variety of purposes were being made possible:
[E]verything in a unified database, everything in one place, so it’s accessible, it is structured, is
very convenient … because you don’t know what might be important later on down the line and to
collect everything from time zero is a more efficient way of collecting data than collecting your data,
then finding out that you need like seven new different measurements, and to go back and collect
that would be very difficult, very costly so collecting everything with the hope that one day it might
be useful to someone is a very bad idea (laughs) but it’s efficient. (Researcher 2)
While for more ‘traditional’ epidemiological studies of rare or long-term conditions and treatments,
researchers have to collect data from hundreds or thousands of individual participants, with routinely
collected EHR data the number of research participants can be millions. It is the size, breadth and
representativeness of the populations covered that constituted, in the eyes of these stakeholders, these
datasets as unique resources for conducting observational studies in a much faster, cheaper and more
‘pragmatic’ way than ever before (Harwich and Lasko-Skinner, 2018; Powell et al., 2017). NHS patient
data was, thus, valuated and valorized by ascertaining the effectiveness of EHR data-driven research in
the modelling of healthcare by other means: a very promising alternative approach to research and
innovation.
The assertion that ‘size is an advantage’ that science and society cannot just ignore constituted a
strong motivation for the EHR data-driven research communities. However, the significant issues of
data quality and the susceptibility of these datasets to many biases were acknowledged by data
researchers (we will return to these issues in the next section). The growing concerns around the
epistemological misconception that bigger is necessarily better, and critiques of the reproducibility of
Vezyridis and Timmons 2021
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EHR data-driven research were accepted (Lipworth et al., 2017). However, the risks and uncertainties
that this asymmetrical quality of EHRs introduce in EHR data-driven research (e.g. research bias) were
considered epistemological challenges (rather than actual barriers to EHR data use) that do not have to
be addressed in full before pursuing the establishment of a research service for the assetization of NHS
patient data.
There is no uniform approach to be followed. For instance, some research services had proceeded
more cautiously in this area and have been trying to recruit GP practices with ‘good’ coding practices,
in order to then carefully curate the extracted data. Others have worried less about data quality and more
about finding a way to gather everything in one place first and deal with issues of data quality later.
Researchers in the field overwhelmingly anticipated the overcoming of such challenges soon enough
and as datafication of healthcare and coding standardization across the NHS continue to grow (Brown,
2003).
[W]e’ve got this wealth of data that we can get hold of; we don’t know quite how to use it. We must
use it because there must be stuff in there that is going to be really valuable basically, and from [the
IT supplier’s] side, they knew that and they want it to be used. (Researcher 9)
The above motivation that there must be a way forward with this data because now we can, which
Fourcade and Healy (2016: 16) aptly assert is ‘the ceremonial aspect of the data imperative’, was based
not only on arguments of size, availability and effectiveness but also on an anticipatory uncertainty, for
example, around (already) problematic numbers participating in research. Welsh and Wynne (2013:
543) have argued that ‘scientific authorities … declare the public meaning of technoscientific
innovations and controversies to be matters of risk or science’. Here, we noted declarations around data
assetization, and by extension around the support of the work of such research services, as a matter of
concern in overcoming risks within the life sciences, that is, low trial recruitment rates (Powell et al.,
2017). The ‘reworking [of] epistemological asymmetries’ (Brown, 2003: 18) was, therefore, framed as
a necessity both for society and the rest of the technoscientific communities ‘because ten years from
now we won’t have any other cohorts remaining’ and the future of population studies will be
compromised. Consequently, for data researchers the development of new methodologies for EHR data-
driven research are materializing their expectations of ‘getting all that we can out of this data’ and
continuing to advance biomedical knowledge and improve healthcare against an uncertain
epistemological future.
At the same time, there are also other stakeholders that come with their own expectations for the
assetization of this public asset. For example, there is the role of, and expectations by, state actors to
drive and facilitate, via investment and regulatory frameworks, the assetization and capitalization of
NHS patient data for the benefit of the national economy (Timmons and Vezyridis, 2017). As one data
researcher noted:
assetization of public health data
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Government Ministers have been quite open about it, wanting to make the UK and UK data sources
world leaders in this kind of research, which usually means they want it to be a money spinner.
(Researcher 5)
For suppliers of primary care computer systems, contributing to a research service can materialize
unique, albeit diversified, expectations. While our participants talked about certain research services
that just want to capitalize EHR data and ‘license just about everything it’s possible to license’, thus
expanding their presence in the biomedical research data market, they also identified opportunities for
the other stakeholders. For example, they discussed how the establishment of ‘knowledge transfer
partnerships’ with academic institutions that can analyse these datasets has allowed them to advance
their portfolio of services they provide to GP practices that purchase their system. GP practices have
now been given a unique opportunity to contribute to epidemiological research and also to the
development of new digital clinical decision support tools to improve healthcare. In exchange, they
offer the research services new (and asymmetrical) advantages for the assetization of NHS patient data,
when compared to smaller research services that rely on bespoke data extractions for research. Through
their arrangements with contributing GP practices and other healthcare organizations, whole networks
of local data providers and infrastructures for data extractions can now be maintained, making the
continuous updating of research databases a relatively seamless and near-real time process. These
databases can then be managed by either the IT supplier, the IT supplier in conjunction with the
academic or governmental partner, or by the academic partner.
For instance, CPRD and THIN provide quality improvement reports to collaborating GP practices.
CALIBER has developed a pool of more than 50 computable phenotypes for researchers to identify and
analyse the EHR of patients with particular conditions. QResearch has developed a number of risk
calculators (e.g. for fractures, cancer, stroke, diabetes) (Hippisley-Cox et al., 2017). These and other
research assets can then be provided to the research and GP communities (and some of them to the
public) either free of charge and/or as licensed products via private limited companies (e.g. ClinRisk,
2020). In the process of realizing the values of EHR data-driven research, research studies and
publications that have used research services’ data are also getting assetized and valorized under
specific targets of ‘knowledge transfer’, increasing their reputational and financial value in this
technoscientific market (Barman, 2002; Vezyridis and Timmons, 2016). For instance, QResearch has
supported more than 165 peer-reviewed scientific publications as of March 2019, THIN more than 600
and, CPRD more than 2200, with targets of numbers of supported studies set annually (see MHRA,
2016).
Thus, there is not a single but multiple problematizations (Callon, 1984) for the assetization of NHS
patient data and the divergent assets that can be made possible to fulfil expectations and valuations of
this asset. On the other hand, expectations, no matter how effective they might first appear in mobilizing
stakeholders and capital (e.g. human, technical, financial), they do not necessarily secure stable socio-
Vezyridis and Timmons 2021
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material entanglements across science and/or the economy. While they become indispensable parts of
data valuations and assetizations within particular communities, the gap between hype and reality has
to be maintained by such research services in order to keep capitalizations going (Brown, 2003).
Socio-material frictions and organizational uncertainties
In this section, we look into how research services and researchers attempt to internalize and render
manageable the various uncertainties and asymmetries involved in the production, assetization, and use
of patient data (Dagiral and Peerbaye, 2016; Denis and Goëta, 2017; Powell et al., 2017; Star, 1985).
We explore how processes of assetization reveal and mitigate epistemological, economic, social and
material ‘frictions’ (Edwards, 2010) in data production and analysis. They also shape how medical
phenomena are expected to be coded so as to increase the quality and value of this asset for research
(Petersen et al., 2019).
During our interviews, we were repeatedly exposed to data researchers’ frustration with and, in some
cases, limited understanding of, the way GPs code medical phenomena in EHRs. They often expressed
their surprise, for example, at the fact that the codes GPs use to record even the same disease could vary
substantially from one GP practice to another. They emphasized the impact such inadequate coding
practices have on the overall quality and integrity of the datasets, as well as the time and effort needed
to mitigate discrepancies in preparation for analysis. As one researcher highlighted:
[A] woman with prostate cancer, okay, fine, I have to remove this observation, it’s kind of most
obvious, but … when you’re thinking whether this person has osteoporosis or not, whether they have
prescriptions, maybe these prescriptions are not right, maybe they’re for cancer, not for
osteoporosis …. When it’s definitely bad data, you remove the observation. For example, if it’s 50
people altogether when you use a sample of 50,000, that’s okay, so it doesn’t affect the conclusion.
(Researcher 11)
Researchers and GPs attributed the problem with data inconsistency and redundancy mainly to the fact
that the data is not collected primarily for research purposes. As they explained, data is collected to do
the GP’s clinical and ‘business’ work (Petersen et al., 2019). Coding is a practice highly contingent on
the resources and effort (e.g. staff, time, information technology and skills) that each GP practice
allocates for this purpose (Verheij et al., 2018). This is why research services often aim at recruiting
mostly ‘big’ GP practices with ‘good’ management that can contribute quality patient data . GP coding
practices are also sensitive to the particularities and (unintentional) influence of the particular GP
computer system in use (Verheij et al., 2018). One experienced researcher described this:
I’m sure you’ve seen those patterns of prescribing trends and how they differ nationally when
actually it’s just picked up that different clinical systems order their drugs differently, so obviously
assetization of public health data
12
clinicians pick the drugs at the top of the screen and it’s just picked up a difference in the clinical
systems, rather than GPs’ underlying behaviour, which was the same. (Researcher 10)
Moreover, GP computer systems allow the recording of structured (‘coded’) and unstructured (‘free
text’) patient health and administrative data to diverse levels of granularity but without the use of a
widely-adopted health coding scheme (i.e. ICD-10). Between regions within the UK, different
classification systems or versions of the same system (e.g. Read codes—version 2 and 3) have been
adopted by IT suppliers and, thus, GPs. To overcome this geographical clustering of systems and
datasets (Kontopantelis et al., 2018), ‘simply a side effect of where IT suppliers have been able to sell
their software’, a research service has to go to great lengths to map these coding schemes used by
healthcare providers across the country into more ‘formalized and universal structures’ (Denis and
Goëta, 2017).
Thus extracted datasets are not just clinical artefacts ready for research after technical translations of
variables. And the availability of more EHR data does not necessarily make the NHS more ‘transparent’
to outsiders for research purposes. Our interviews underline the situatedness of coding practices and
how entangled they are with other diverse local sociotechnical practices, normativities and valuations
beyond the clinical aspects of healthcare provision (Denis and Goëta, 2017). To address and mitigate
the uncertainty introduced by such ‘knowledge asymmetries’ (Tsoukas, 1997) between the observer
(i.e. data researcher) and the observed (i.e. healthcare professional), research services also work on the
introduction and mapping of more advanced classification systems (i.e. SNOMED CT). In the future,
greater standardization of coding of even more data is expected to allow the assetization of more
symmetrical and, therefore, more researchable data (Dagiral and Peerbaye, 2016).
Until then, data researchers continue to be uncertain, and often not transparent in their scientific
publications, as to whether any patterns in disease prevalence and trends they observe in the data should
be attributed to the actual disease, demographic shifts, the patient management software, changes of
clinical coding guidelines, preferences for information disclosure and coding during patient
consultations, or the particular pay-forperformance scheme, such as the Quality and Outcomes
Framework (QOF), used to incentivize specific coding practices (Verheij et al., 2018). QOF has, in fact,
been instrumental in improving data coding and collection across GP practices contracted by the NHS.
Introduced in 2004, this point-based reward and incentive national programme for GPs, which currently
constitutes up to 10% of their income, aims at standardizing and improving the care they provide by
having them meet specific quality targets in for example, coronary heart disease, heart failure, stroke,
diabetes mellitus, chronic obstructive pulmonary disease, cancer, dementia, depression and obesity).
However, QOF has not actually achieved its primary purpose (Forbes et al. (2016)), as it conflated high
performance with high quality of the provided care:
Vezyridis and Timmons 2021
13
A few years ago they brought in QOF around depression, so that if you coded somebody as having
depression, you then had to set on a pathway and had to do certain things by certain times, and if
you didn’t, you lost money. So there was a huge drop in anybody being diagnosed, coded as
depression, even though that’s what they may have been treated for. So you can have knock-on
effects. People also might ask not to be coded, because of the stigma, you don’t want something in
your medical record that says something that you think might have an adverse effect on, I don’t
know, insurance. So I think things aren’t ever black and white, are they? (Researcher 9)
It is because of such intricacies of the entangled socio-material and economic practices of data coding
for divergent purposes and often conflicting local assetizations and capitalizations (e.g. QOF
remuneration) that researchers and GPs emphasized the considerable scientific and technical expertise
required to use NHS patient data for research. Consequently, this data assetization is not only about
investments and maintenance of technologies of data storage, curatorial practices and analytical
methods. It is also about investments in interdisciplinary teams of ‘knowledge workers’ (Kleinman and
Vallas, 2001) who are not only experts in computer and data science but also in healthcare in order to
make sense of the knowledge and practice that shape the coding of medical phenomena and, thus, the
local production and use of datasets.
Among data researchers there was a wide acknowledgment of ‘the huge shortage of skills across the
board’ to fully realize the values of these datasets. While university training was thought to be
improving, issues around its quality and cost as well as ‘letting the younger generations know that
[health data science] is a career path that’s available’ remain a challenge. They were worried that pitfalls
may occur in the future if training and recruitment are not addressed, particularly as more data providers
are joining these (or developing their own) research services and more researchers ‘with less and less
skill’ from other data-driven (e.g. financial) industries are getting access to these datasets. Other
experienced researchers underlined the asymmetries of capability some (smaller) research services have
been facing as they compete for expertise and resources within this field. As EHR data-driven research
is expanding and datasets collected ‘grow dramatically’ in size and variety (e.g. genomics), the
acquisition and maintenance, for example, of ‘high performance computer systems’ to analyze these
datasets becomes a significant challenge.
The skills required to design and model the systems are the same skills that are required to model
systems in finance. And the salaries of science, they’re much lower, so it’s really difficult to find the
right people to do the job. Plus the resources, a lot of the software, hardware … they’re under
license, for instance … and when you want to use some good software packages, in order to be able
to do your work, in the academic and public sector, they’re not readily available, and that puts a
barrier up of the things that you can do. (Researcher 2)
assetization of public health data
14
The process of assetizing NHS patient data, therefore, reveals and involves several noteworthy
asymmetries of information and practice. There are asymmetries regarding the maintenance of local
socio-material infrastructures and processes, including GP practices, NHS patients and IT suppliers,
capable enough to facilitate the production of datasets of acceptable levels of quality for research. The
mitigation of such asymmetries and uncertainties often involves finding ways to overcome other
processes of assetization and capitalization of data and coding (e.g. QOF, computer systems and their
market distribution, consultation and coding preferences). Lastly, it requires the maintenance of
appropriate and capable research infrastructure, such as resources and skills, to clean, prepare and
guarantee quality datasets for use.
Politics, economics, and societal valuations for asymmetrical asset flows
As we started identifying research services’ own normative understandings of how and by whom these
datasets should be capitalized, we became interested in exploring the way they are gradually
maintaining an essential, mediating, role (Callon, 1984) in the assetization and circulation of data. Here,
our focus is on the particular political, economic, social and professional environment within which
they operate but also enact to normatively increase the responsibility of other stakeholders (e.g. GPs,
patients, regulators) for the disentanglement of NHS patient data from its local sites of production. We
also explore some of the scientific, organizational, societal and ethical asymmetries of this assetization,
produced as research services are re-entangled with data producers and other infrastructures required
for the continuous extraction of data and the management of expectations, and values (Birch et al., 2020;
Brown, 2003).
As we showed previously, there has been considerable interest from all stakeholders to share and
analyse NHS patient data for diverse purposes. However, notions of the perceived usefulness of EHR
data-driven research were not shared uniformly across stakeholders, particularly when compared with
more established types of scientific inquiry. For instance, data researchers were frustrated by the fact
that few people outside their academic communities fully ‘understood’, and prioritized in their work
the production and sharing of good quality data. This was often due to the fact that, as some GPs
explained, the data GP practices provide to research services was not expected to come back to them in
a form ‘useful’ to their practice, for example, clinical guidelines for treating patients. While GPs
generally acknowledged some of the capabilities of EHR datadriven research for improved healthcare,
such as for healthcare planning and identification of public health trends, some of them were also more
cautious about placing this type of research among the so called ‘cutting edge medicine’:
Certainly the capabilities of [EHR data-driven research] and the scope to look at information is
great. I mean I think if you’re looking at genuine treatments for diseases, the bottom line comes
down to the hard graft with looking at molecules and cancer and such. It comes down to laboratory
Vezyridis and Timmons 2021
15
technicians testing on cells and in petri dishes and microscopes … to try and work out and find drugs
that are effective. (GP 3)
Moreover, the competitive environment of expectations and anticipations in which the capitalization of
NHS patient data takes place does not appear to be maintained only between the various groups of
stakeholders that may benefit. The process of assetizing intangible resources, within an increasingly
marketized environment, reveals specific kinds of competition within particular groups of stakeholders.
For instance, even within the data research communities there can be a combination of factors that
makes the sharing of this data a difficult task across their networks. Issues such as personal agendas and
career aspirations, fears of litigation, intellectual property rights and competition for research funds,
(often attributed to the marketization of higher education and public healthcare) were thought to impede
the establishment of a culture of openness in data sharing for the wider benefit of science and society
(Longo and Drazen, 2016).
One particularly problematic situation that stood out during interviews was around the extent to
which a research service could support an increasing number of applications for data access. The
capabilities of research services to support other data researchers, especially non-affiliated ones, with
data and expertise are not identical. Whether it is fees or research outputs, some form of mutually
beneficial collaboration and/or exchange has to take place to compensate for the work involved in
preparing datasets for analyses.
It’s true that people that have access to the databases do tend to set themselves up as gatekeepers
and be a bit choosy about who they work with. Sometimes that’s in furtherance of their own careers,
other times it’s because the data are complicated and they can’t support every person to do a project,
so inevitably you support the ones that you’re interested in. (Researcher 5)
Contrary to some common expectations that any research team with a project approved by the research
services’ own Independent Scientific Advisory Committee for its scientific benefits and information
governance compliance could access data, some of these research services have their own additional
restrictions on eligibility. Various asymmetries of collaboration between research services and the
wider academic and biomedical research communities shape divergent forms of NHS patient data
assetization. For example, CPRD can provide data to any research team from across the world as long
as some of its members have experience working in UK primary care. QResearch, on the other hand,
provides access to data mainly to teams from UK universities, with at least one member registered with
the General Medical Council, while pharmaceutical companies are excluded unless the research project
is about drug safety.
At the same time, the capitalization of NHS patient data under an industrialized environment means
that data researchers and research services are expected to internalize and enact the financial logics of
EHR data-driven research in the UK. Research services operate usually, but not exclusively, on a not-
assetization of public health data
16
for-profit basis. While they do not usually charge directly for the datasets, they do charge for the support
they provide to researchers (e.g. infrastructure, application and project set-up, training, data preparation
and release). They may charge fixed or variable fees for individual projects or for their annual
commercial and non-commercial licenses of data access and support. For instance, on its new pricing
model CPRD charges £75,000 for its non-commercial (i.e. academic, government, charity) multi-study
annual license and more than four times that for the commercial one. CALIBER charges a fixed fee of
£25,000 per approved project. In cases where researchers want to link GP records with hospital records
and mortality data, these research services have to go through the statutory trusted third party
responsible for the handling and linking of NHS patient records, for an additional fee (NHS Digital,
2018).
Public information on how these cost-recovery charges are calculated is limited. This flexibly
interpretive valorization of competitive access to quality data, however, allows a research service to
generate income towards its organizational financial sustainability. It mitigates any asymmetries in
resources and capital available to a research service compared with other research services, such as
direct financial support from research charities and commercial or governmental partners, research
grants, personnel and infrastructure hosting (e.g. by a university), rents for corporate, governmental or
academic office spaces and, any other special financial arrangements with GP practices for the number
of active patients they maintain in their database.
As demand for up-to-date datasets is constant, data assetization requires not only new circulations of
data to more users but also continuous data production, facilitated by an appropriate regulatory
framework for the lawful handling of sensitive NHS patient data. In England, this framework revolves
around a rather outdated legislative piece of executive power that allows the Secretary of State for
Health to overide the common law duty of confidentiality, and the need for patient consent, for specified
medical purposes (e.g. medical research) (Section 251 of the NHS Act 2006 and Health Service
Regulations 2002). While it has facilitated an unprecedented expansion of the so called ‘secondary use’
of NHS patient data, interview subjects still problematized it as an unavoidable but also necessary
burden within organizational and scientific routines, a bureaucratic activity separate from the wider
societal and historical context of unconsented medical research and part of the costs involved with the
assetization of these datasets.
Some researchers understood information governance in this area to be a ‘hygienic’ practice in ethics
(Vezyridis and Timmons, 2019) of critical importance for minimizing moral frictions and, thus,
maintaining ‘social sustainability’ (Brown and Michael, 2003; Tupasela, 2017) with the public and the
healthcare professionals that co-produce the data. Others, acknowledging their limited understanding
of the complicated legal framework surrounding the use of these datasets, suggested the introduction of
new disciplines within research teams (e.g. information governance experts) to advise researchers on
the legal and ethical legitimacy of research designs. Others directed their frustration towards regulators
who could ‘tighten up on things’ for faster and cheaper access to curated datasets and, therefore, to
Vezyridis and Timmons 2021
17
knowledge production, especially in an industry still operating under a fragmented framework of
multiple public and private providers acting as ‘owners’ of these datasets:
[W]henever we go to for a question to one of these [data providers], they say that they have to do
the linkage for us … so if you go to NHS they say they have to do the linkage, if you go to the GP
data, they say they have to do it, and other sources like clinical registries for cancer they are not
easy to access and even if you [have] a very good high quality protocol then the problem is how
should we link the data, because they will not hand it to you. (Researcher 4)
Intertwined financial and regulatory asymmetries of access become more evident as data researchers
struggle to conduct their work within a competitive technoscientific environment that requires them to
be at the forefront of scientific knowledge production (Brown and Michael, 2003). While they
‘understand’ that research services ‘sell their product’, the ‘whacking costs’ for buying bespoke
extractions and linked datasets, particularly by those not affiliated with these research services, have
created considerable and asymmetrical financial barriers to research (Gilbert et al., 2015). In the
dynamic field of healthcare, where practices and objects of study are constantly in flux (e.g.
prescriptions), data is not only ‘expensive to work with because of all the processes that are involved to
generate it, keep it clean and keep it secure’ but it becomes ‘old’ quickly. Speed of knowledge
production becomes of paramount importance for EHR data-driven research:
It all costs money and if you have money, then it’s easier. … I’ve been doing lots of studies without
funding, because it’s all for hot topics, and then by the time you get funding, it’s gone, the public is
not interested. But then if you have funding you can buy linkages to other sources, like a cancer
registry or other registers. (Researcher 11)
Lastly, and notwithstanding some criticism on issues of medical confidentiality (Brown et al., 2010),
these research services have not attracted the public outcry provoked by other exploitations of these
public datasets, namely NHS England’s defunct care.data (see Freeman, 2016; Vezyridis and Timmons,
2017), and Alphabet’s DeepMind unlawful contractual data arrangements with certain London NHS
Trusts (see Powles and Hodson, 2017). Citizens interviewed were generally supportive of research that
benefits the common good but were increasingly skeptical of NHS patient data exploitations that often
take place away from the public eye, especially after the aforementioned debacles (Skovgaard et al.,
2019; Vezyridis and Timmons, 2019). However, they emphasized the great challenge they face, as NHS
patients, to get access to their EHR in order to understand what is in there that such research services
continuously assetize. They were frustrated that information about such research services is ‘basically
non-existent’, highlighting the NHS’s characteristic information ‘paternalism’ around NHS patient data
capitalizations. This is not to say that such research services do not advocate openness and transparency
about their operations. However, there is limited information to be found about which GP practices
assetization of public health data
18
contribute to them with NHS patient data and/or which research projects they have approved or rejected
for data access. As such, it is only through scientific publications that an interested citizen could
conclude how NHS patient data is used by these research services, while having to rely solely on their
GP practice to let them ‘know whether [they are] on these databases’ and how they could opt-out from
a research service.
Thus, research services’ simultaneous assetization of highly complex data and management of data
assets flows becomes a competitive and contradictory exercise in organizational stability (Denis and
Goëta, 2017; Geiger and Gross, 2019; Tupasela, 2017). In the process, they incorporate new and
existing asymmetries in the capitalization of NHS patient data and healthcare research: prioritization of
EHR data-driven research over other disciplines, competition for funding and scientific career
development, cumbersome information governance frameworks, gated access to data and linkages,
competition for human and technological capital within and beyond the sector, variable distribution of
benefits, limited public awareness and engagement.
Conclusion
In this article, we examined some of the expectations, frictions and uncertainties involved in the
assetization and capitalization of NHS patient data by UK research services. Drawing on the sociology
of expectations and economic STS literature, we brought into focus the way these heterogeneous
assemblages (Callon, 1984) reconfigure practices, responsibilities and accountabilities for materializing
the specific promises of innovative data-driven healthcare research (Brown, 2003; Petty and Heimer,
2011). We showed that the assetization of NHS patient data is both the outcome and the driver of various
competing epistemic and economic expectations and valuations (Birch, 2019; Brown, 2003). Divergent
assets for healthcare and biomedical research are then produced within a specific competitive and
regulated environment increasingly entangled into financial industry expectations, logics and practices
(Martin, 2015; Robinson, 2018). In this way, research services maintain a balance between establishing
and expanding the health data science disciplinary field, securing their organizational and financial
sustainability, supporting biomedical and healthcare innovation, while also maintaining their public
acceptability.
NHS patient data is configured and used both as a continuously generated resource ready for
extraction as well as an asset for circulation in an asset-based biomedical knowledge economy and the
enactment of a range of other ontologies (Dagiral and Peerbaye, 2016; Denis and Goëta, 2017). These
include observational studies, patient recruitment for research, pragmatic trials, clinical risk predictions,
income and so on. As producers, users and brokers of such assets, at the intersection of public
healthcare, academia and the biomedical industry (Timmons and Vezyridis, 2017), these organizational
entities are gradually becoming ‘obligatory passage points’ (Callon, 1984) for how these public
healthcare datasets are (socially, politically, and economically) valuated and EHR data-driven research
and development is conducted (Birch et al., 2020).
Vezyridis and Timmons 2021
19
These research services are constantly attempting to realize and capitalize on their multiple epistemic,
reputational, platform, ethical and financial values out of asymmetrical and divergent data assetizations
(Barrett et al., 2016). They converge and translate expectations and promises of innovative EHR data-
driven research as a pragmatic and more effective alternative to the study of population health for
healthcare improvement. At the same time, they actively participate in the reconfiguration of healthcare
as a datafied technoscientific and ‘transparent’ practice (Tsoukas, 1997), the NHS as both the means
and the subject of data labour, and patients-citizens as scientific, corporate and moral resource for
continuous data co-production (with the NHS) (Brown, 2003). The numbers of contributing GP
practices, sizes of populations covered, data fields, time periods included and speeds of databases
updates with new NHS patient data are valuated and valorized. Information governance, including de-
identification of EHRs, consent and public engagement are mobilized as an epistemic and political
economic apparatus (Birch, 2017b) of institutionalized altruism and volunteerism towards uncontested
data extractions and exploitations.
In effect, research services have assumed the overall role of ‘de-risking’ the costly assetization of
these datasets for actors in the academic and biomedical industries (Brown, 2003; Cooper and Waldby,
2014; Robinson, 2018). They internalize the uncertainties and risks involved—which are externalized
by the NHS (Robinson, 2018) —in valuating, creating, qualifying and mobilizing the data assets, as
well as the capital necessary for shaping renewed promises of health and wealth (Tarkkala et al., 2018).
They are pragmatic in the frictions and challenges they face around datasets’ completeness and integrity,
access delays and costs (Powell et al., 2017), clustered geographical coverage of populations, varied
cultures of collaboration and data sharing or inadequate public engagement. At the same time, however,
they participate in the externalization of other risks and uncertainties: costly datasets and infrastructural
incapacity, inadequate standardization of coding practices and extractions, burdensome information
governance for data extraction and linkage, limited public or professional acceptability, restricted
funding and investments and, inadequate supplies of data scientists (Denis and Goëta, 2017; Kleinman
and Vallas, 2001). In this way, they attempt to commit stakeholders in ‘extensified’ data valuations and
assetizations across society (Borup et al., 2006; Lilley and Papadopoulos, 2014; Robinson, 2018).
Lastly, we found that this assetization of NHS patient data is asymmetrical in several noteworthy
ways. First, although research services attempt to maintain their organizational and financial
sustainability, the rents for these assets are seen as prohibitive for researchers conducting observational
studies (Gilbert et al., 2015). They now have to compete for funding in order to acquire increasingly
diverse and complex datasets, produced out of various asymmetries of knowledge and coding practices
(Barman, 2002; Tsoukas, 1997). There is also limited information and opportunity for GP practices and
the public to participate in the normative shaping of NHS patient data capitalization. Finally, as these
research services embark on their own scientific endeavors and compete for data and customers they
asymmetrically diverge in their assetization of NHS patient data. They operate under different
agreements with IT or data suppliers and experience challenges of access to resources, including human
assetization of public health data
20
capital. In an attempt to differentiate their service for intertwined epistemic and market sustainability
and dominance (Barman, 2002), they not only provide access to carefully curated datasets but also to
other diverse digital assets of objectified knowledge about healthcare (Birch, 2019; Tsoukas, 1997) (e.g.
disease-specific code lists, phenotypes, biomarkers, quality improvement reports, recruitment pools of
research participants, clinical risk prediction algorithms, analytic scripts, data dictionaries, scientific
publications).
Data assetization continues to grow bigger and more diverse in size, complexity and scope (e.g.
genomics, wearables and medical imaging) under a political-economic environment of industrialized
scientific competition and production of expectations, assets and capital (Brown, 2003). The
capitalization of NHS datasets by such hybridized e-infrastructures (Kleinman and Vallas, 2001) is
replacing restrictions of access to dispersed data from the many individual organizational silos (e.g. GP
practices) to fewer and bigger silos where data is now aggregated out of various closed-source,
proprietary clinical systems and converged into licensed databases. Such restrictions around NHS
patient data-driven research may, in the end, stabilize an oligopolistic ‘data acess market’, attractive
only to those actors that have the capital and the socio-material infrastructure to undertake this kind of
research at the required scale and depth (Powles and Hodson, 2017).
We suggest checks and balances regarding research services’ ontological, epistemological and ethical
roles as biomedical knowledge makers and brokers should be considered. Transparent, accountable,
inclusive and equitable research agendas and knowledge-making practices should be elaborated for the
benefit of society at large. This is especially urgent at a time when the role of EHR data-driven research
is gradually moving beyond the concept of ‘secondary use’ and onto the longitudinal surveillance and
management of population health and the contemporary planning of health systems.
Acknowledgements
We are grateful to the study participants for their time and patience. We would also like to thank the
SSS reviewers and the editors Adam Hedgecoe and Sergio Sismondo.
Funding
This study was funded by the European Commission (H2020-MSCA-IF-EF-2014-659478).
References
Barman EA (2002) Asserting difference: The strategic response of non-profit organizations to
competition. Social Forces 80(4): 1191–1222.
Barrett M, Oborn E and Orlikowski W (2016) Creating value in online communities: The sociomaterial
configuring of strategy, platform, and stakeholder engagement. Information Systems Research
27(4): 704–723.
BIGT (2003) Bioscience 2015: Improving national health, increasing national wealth: A report to
government by the Bioscience Innovation and Growth Team. Available at: https://web.
Vezyridis and Timmons 2021
21
archive.org/web/20120107081132/http://www.bioindustry.org/document-
library/bioscience2015/1bia-1103-bioscience-2015.pdf (accessed 15 October 2015).
Birch K (2017a) Rethinking Value in the Bio-economy: Finance, Assetization, and the Management
of Value. Science, Technology, & Human Values 42(3): 460–490.
Birch K (2017b) The problem of bio-concepts: Biopolitics, bio-economy and the political economy of
nothing. Cultural Studies of Science Education 12(4): 915–927.
Birch K (2019) Technoscience rent: Toward a theory of rentiership for technoscientific capitalism.
Science, Technology, & Human Values 45(1): 3–33.
Birch K, Chiappetta M and Artyushina A (2020) The problem of innovation in technoscientific
capitalism: Data rentiership and the policy implications of turning personal digital data into a
private asset. Policy Studies 41(5): 468–487.
Borup M, Brown N, Konrad K, et al. (2006) The sociology of expectations in science and technology.
Technology Analysis & Strategic Management 18(3–4): 285–298.
Brown I, Brown L and Korff D (2010) Using NHS patient data for research without consent. Law,
Innovation and Technology 2(2): 219–258.
Brown N (2003) Hope against hype-accountability in biopasts, presents and futures. Science &
Technology Studies 16(2): 3–21.
Brown N and Michael M (2003) A sociology of expectations: Retrospecting prospects and prospecting
retrospects. Technology Analysis & Strategic Management 15(1): 3–18.
Callon M (1984) Some elements of a sociology of translation: Domestication of the scallops and the
fishermen of St Brieuc Bay. The Sociological Review 32(Suppl. 1): 196–233.
ClinRisk (2020) Welcome. Available at: https://clinrisk.co.uk (accessed 6 November 2020).
Cooper M and Waldby C (2014) Clinical Labor: Tissue Donors and Research Subjects in the Global
Bioeconomy. Durham, NC: Duke University Press.
Dagiral E and Peerbaye A (2016) Making knowledge in boundary infrastructures: Inside and beyond a
database for rare diseases. Science & Technology Studies 29(2): 44–61.
Denis J and Goëta S (2017) Rawification and the careful generation of open government data. Social
Studies of Science 47(5): 604–629.
Department of Health (2011) Innovation health and wealth: Accelerating adoption and diffusion in the
NHS. Available at: https://uk.gsk.com/media/166257/innovation_health_and_wealth.pdf
(accessed 22 October 2020).
Du Preez D (2015) NHS director argues big data in healthcare is a ‘moral obligation’—care. data
relaunched. Available at:
https://web.archive.org/web/20190306022832/https://government.diginomica.com/2015/01/20/
nhs-director-argues-big-data-healthcare-moral-obligationcare-data-relaunched/ (accessed 28
March 2019).
Dussauge I, Helgesson C-F, Lee F, et al. (2015) On the omnipresence, diversity, and elusiveness of
values in the life sciences and medicine. In: Dussauge I, Helgesson C-F and Lee F (eds) Value
Practices in the Life Sciences and Medicine. Oxford: Oxford University Press, 1–28.
Edwards PN (2010) A Vast Machine: Computer Models, Climate Data, and the Politics of Global
Warming. Cambridge: MIT Press.
Forbes L, Marchand C and Peckham S (2016) Review of the quality and outcomes framework in
England: Final report. Available at: https://web.archive.org/web/20190901185357/
http://blogs.lshtm.ac.uk/prucomm/files/2017/02/Review-of-QOF-21st-December-2016.pdf
(accessed 30 December 2018).
Fourcade M and Healy K (2016) Seeing like a market. Socio-Economic Review 15(1): 9–29.
assetization of public health data
22
Freeman G (2010) House of Commons debates: Policy for growth. Available at:
https://web.archive.org/web/20190901190645/https://publications.parliament.uk/pa/cm201011/
cmhansrd/cm101111/debtext/101111-0003.htm (accessed 5 March 2019).
Freeman G (2016) Review of health and care data security and consent. Available at: https://
web.archive.org/web/20190725165037/https://www.gov.uk/government/speeches/reviewof-
health-and-care-data-security-and-consent (accessed 7 July 2016).
Geiger S and Gross N (2019) A tidal wave of inevitable data? Assetization in the consumer genomics
testing industry. Business & Society. DOI: 10.1177/0007650319826307.
Gilbert R, Goldstein H and Hemingway H (2015) The market in healthcare data. BMJ 351: h5897.
Harwich E and Lasko-Skinner R (2018) Making NHS data work for everyone. Available at:
https://web.archive.org/web/20190729101454/https://reform.uk/sites/default/files/2018-12/
NHSdata%20-%20no%20gif%20SP.pdf (accessed 7 March 2019).
Helgesson C-F and Lee F (2017) Valuations as mediators between science and the market: How
economic assumptions shape pharmaceutical trial designs. Science as Culture 26(4): 529–554.
Hippisley-Cox J, Coupland C and Brindle P (2017) Development and validation of QRISK3 risk
prediction algorithms to estimate future risk of cardiovascular disease: Prospective cohort study.
BMJ 357: j2099.
HM Government (2018) Industrial strategy: Life sciences sector deal 2. Available at: https://
web.archive.org/web/20190803200430/https://assets.publishing.service.gov.uk/government/
uploads/system/uploads/attachment_data/file/768570/life-sciences-sector-deal-2-web-
readyupdate.pdf (accessed 10 May 2019).
HM Treasury (2018) Getting smart about intellectual property and other intangibles in the public
sector: Budget 2018. Available at: https://web.archive.org/web/20190724073411/https://
assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/
file/752003/Getting_smart_about_intellectual_property_and_other_intangibles_in_the_public_s
ector_-_Budget_2018.pdf (accessed 8 March 2019).
Hogarth S (2017) Valley of the unicorns: Consumer genomics, venture capital and digital disruption.
New Genetics and Society 36(3): 250–272.
Kelsey T and Cavendish W (2014) Personalised health and care 2020: Using data and technology to
transform outcomes for patients and citizens: A framework for action. Available at: https://
web.archive.org/web/20190802124912/https://assets.publishing.service.gov.uk/government/
uploads/system/uploads/attachment_data/file/384650/NIB_Report.pdf (accessed 28 March
2019).
Kleinman DL and Vallas SP (2001) Science, capitalism, and the rise of the ‘Knowledge Worker’: The
changing structure of knowledge production in the United States. Theory and Society 30(4): 451–
492.
Kontopantelis E, Stevens RJ, Helms PJ, et al. (2018) Spatial distribution of clinical computer systems
in primary care in England in 2016 and implications for primary care electronic medical record
databases: A cross-sectional population study. BMJ Open 8(2): e020738.
Lilley S and Papadopoulos D (2014) Material returns: Cultures of valuation, biofinancialisation and the
autonomy of politics. Sociology 48(5): 972–988.
Lipworth W, Mason PH, Kerridge I, et al. (2017) Ethics and epistemology in big data research. Journal
of Bioethical Inquiry 14(4): 489–500.
Longo DL and Drazen JM (2016) Data sharing. New England Journal of Medicine 374: 276–277.
Martin P (2015) Commercialising neurofutures: Promissory economies, value creation and the making
of a new industry. BioSocieties 10(4): 422–443.
MHRA (Medicines & Healthcare products Regulatory Agency) (2016) Medicines and Healthcare
products Regulatory Agency annual report and accounts 2015/16. Available at: https://
Vezyridis and Timmons 2021
23
web.archive.org/web/20190725154058/https://assets.publishing.service.gov.uk/government/upl
oads/system/uploads/attachment_data/file/539679/MHRA_annual_report_and_
accounts_2015_to_2016.pdf (accessed 25 July 2019).
Muniesa F, Doganova L, Ortiz H, et al. (2017) Capitalization: A Cultural Guide. Paris: Mines
ParisTech.
NHS Digital (2018) Data Access Request Service (DARS) performance reporting. Available at:
https://web.archive.org/web/20190901190130/https://digital.nhs.uk/services/researchadvisory-
group/data-access-request-service-dars-performance-reporting (accessed 8 March 2019).
Petersen I, Welch CA, Nazareth I, et al. (2019) Health indicator recording in UK primary care electronic
health records: Key implications for handling missing data. Clinical Epidemiology 11: 157–167.
Petty J and Heimer CA (2011) Extending the rails: How research reshapes clinics. Social Studies of
Science 41(3): 337–360.
Powell GA, Bonnett LJ, Tudur-Smith C, et al. (2017) Using routinely recorded data in the UK to assess
outcomes in a randomised controlled trial: The Trials of Access. Trials 18(1): 389.
Powles J and Hodson H (2017) Google DeepMind and healthcare in an age of algorithms. Health and
Technology 7(4): 351–367.
Robinson MD (2018) Financializing epistemic norms in contemporary biomedical innovation. Synthese
196: 4391–4407.
Skovgaard LL, Wadmann S and Hoeyer K (2019) A review of attitudes towards the reuse of health data
among people in the European Union: The primacy of purpose and the common good. Health
Policy 123(6): 564–571.
Star SL (1985) Scientific work and uncertainty. Social Studies of Science 15(3):391–427.
Tarkkala H, Helén I and Snell K (2018) From health to wealth: The future of personalized medicine in
the making. Futures 109: 142–152.
Timmons S and Vezyridis P (2017) Market-driven production of biospecimens and the role of NHS
hospital-led biobanks. Sociology of Health & Illness 39(7): 1242–1257.
Tsoukas H (1997) The tyranny of light: The temptations and the paradoxes of the information society.
Futures 29(9): 827–843.
Tupasela A (2017) Data-sharing politics and the logics of competition in biobanking. In: Pavone V and
Goven J (eds) Bioeconomies. Cham: Springer International Publishing, 187–206.
Tutton R (2011) Promising pessimism: Reading the futures to be avoided in biotech. Social Studies of
Science 41(3): 411–429.
Verheij RA, Curcin V, Delaney BC, et al. (2018) Possible sources of bias in primary care electronic
health record data use and reuse. Journal of Medical Internet Research 20(5): e185.
Vezyridis P and Timmons S (2016) Evolution of primary care databases in UK: A scientometric analysis
of research output. BMJ Open 6(10): e012785.
Vezyridis P and Timmons S (2017) Understanding the care.data conundrum: New information flows
for economic growth. Big Data & Society 4(1): 205395171668849.
Vezyridis P and Timmons S (2019) Resisting big data exploitations in public healthcare: Free riding or
distributive justice? Sociology of Health & Illness 41(8): 1585–1599.
Wayman C and Hunerlach N (2019) Realising the value of health care data: A framework for the
future. Available at: https://web.archive.org/web/20190828180130/https://assets.ey.com/
content/dam/ey-sites/ey-com/en_gl/topics/life-sciences/life-sciences-pdfs/ey-value-ofhealth-
care-data-v20-final.pdf (accessed 28 August 2019).
Welsh I and Wynne B (2013) Science, scientism and imaginaries of publics in the UK: Passive objects,
incipient threats. Science as Culture 22(4): 540–566.
Wienroth M, Pearce C and McKevitt C (2019) Research campaigns in the UK National Health Service:
Patient recruitment and questions of valuation. Sociology of Health & Illness 41(7): 1444–1461.
assetization of public health data
24
World Economic Forum (2011) Personal data: The emergence of a new asset class. Available at:
https://web.archive.org/web/20190803001919/http://www3.weforum.org/docs/WEF_ITTC_
PersonalDataNewAsset_Report_2011.pdf (accessed 15 March 2019).
